# Libraries
library(glmmPen)
## Warning: package 'glmmPen' was built under R version 4.1.3
## Loading required package: lme4
## Warning: package 'lme4' was built under R version 4.1.2
## Loading required package: Matrix
## Warning: package 'Matrix' was built under R version 4.1.2
## Loading required package: bigmemory
## Warning: package 'bigmemory' was built under R version 4.1.2
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.1.2
library(stringr)
## Warning: package 'stringr' was built under R version 4.1.2
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.1.3
# Code to run an abbreviated version of the glmmPen fit for the basal dataset 
## In Rashid et al. (2020) paper, used 50 covariates. 
## However, this took well over an hour to run. Therefore,
## we instead provide an illustration using 10 covariates to illustrate the process,
## which can finish in under an hour.

# basal data from glmmPen package
data("basal")

# Extract response
y = basal$y
# Select a sampling of 10 TSP covariates from the total 50 covariates
set.seed(1618)
idx = sample(1:50, size = 10, replace = FALSE)
# Selected column index values:
(idx = idx[order(idx)])
##  [1]  3  6 16 18 27 28 29 30 34 41
X = basal$X[,idx]
# Selected predictors:
colnames(X)
##  [1] "GPR160_CD109"  "SPDEF_MFI2"    "CHST6_CAPN9"   "SLC40A1_CDH3" 
##  [5] "PLEK2_HSD17B2" "GPX2_ERO1L"    "CYP3A5_B3GNT5" "LY6D_ATP2C2"  
##  [9] "MYO1A_FGFBP1"  "CTSE_COL17A1"
group = basal$group
# Levels of the grouping variable:
levels(group)
## [1] "UNC_PDAC"     "TCGA_PDAC"    "TCGA_Bladder" "UNC_Breast"
# If have run this code before, this will have resulted in saved posterior samples.
# To completely replicate code, remove these files before running basal variable selection code
if(file.exists("Basal_Posterior_Draws.bin")) file.remove("Basal_Posterior_Draws.bin")
if(file.exists("Basal_Posterior_Draws.desc")) file.remove("Basal_Posterior_Draws.desc")

# Note: In code provided below, output includes iteration-level information
# for each iteration of the MCECM algorithm. In order to suppress this information
# to reduce some of the output, add argument `progress = FALSE` (see glmmPen
# function documentation for more details).

start_basal = proc.time()

set.seed(1618)
fitB = glmmPen(formula = y ~ X + (X | group), 
               family = "binomial", covar = "independent", 
               optim_options = optimControl(),
               tuning_options = selectControl(BIC_option = "BICq", pre_screen = T, 
                                              search = "abbrev"),
               BICq_posterior = "Basal_Posterior_Draws")
## recommended starting variance: 0.500000
## Start of stage 1 of abbreviated grid search
## Running prescreening procedure
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        100        100          9         10 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        110        100          9         10 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   3.000000 121.000000   0.023244   9.000000  10.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.000000 133.000000   0.015559   9.000000  10.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.000000 146.000000   0.010719   9.000000  10.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  1.610e+02  7.029e-03  9.000e+00  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  1.770e+02  5.332e-03  9.000e+00  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  1.950e+02  4.058e-03  9.000e+00  1.000e+01 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  2.150e+02  3.852e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  2.370e+02  3.344e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  2.610e+02  3.036e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  2.870e+02  3.165e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  3.160e+02  2.116e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  3.480e+02  2.121e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  3.830e+02  2.176e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  4.210e+02  1.816e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.700e+01  5.000e+02  1.792e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.800e+01  5.000e+02  1.706e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.900e+01  5.000e+02  2.162e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.000e+01  5.000e+02  2.027e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.100e+01  5.000e+02  1.481e-03  9.000e+00  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   2.20e+01   5.00e+02   1.46e-03   9.00e+00   9.00e+00
## The files Basal_Posterior_Draws.bin and Basal_Posterior_Draws.desc do not currently exist.
## Fitting minimal penalty model and saving posterior draws to Basal_Posterior_Draws.bin and Basal_Posterior_Draws.desc
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100         10          9 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100         10          9 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  7.322e-03  1.000e+01  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  3.769e-03  1.000e+01  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  2.974e-03  1.000e+01  9.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  3.563e-03  1.000e+01  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##     7.0000   442.0000     0.0028    10.0000     8.0000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   8.00e+00   4.86e+02   1.66e-03   1.00e+01   8.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.556e-03  1.000e+01  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  1.257e-03  1.000e+01  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  1.543e-03  1.000e+01  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.20e+01   7.12e+02   1.22e-03   1.00e+01   8.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  1.036e-03  1.000e+01  8.000e+00
## Start of sampling from posterior
## Finished sampling from posterior
## ------------------------------------------------------------------ 
## lambda0 0.002753 lambda1 0.002753 
## lambda0 i 1 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100         10          8 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          8 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##     3.0000   302.0000     0.0024     9.0000     8.0000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.505e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.955e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.558e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  1.316e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  1.104e-03  9.000e+00  8.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00275    0.00275         NA         NA  740.04793         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    8.00000   19.00000    8.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.002753 lambda1 0.004593 
## lambda0 i 1 lambda1 j 2 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          8 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          8 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  2.021e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.00e+00   3.32e+02   1.62e-03   9.00e+00   8.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.00e+00   3.65e+02   1.94e-03   9.00e+00   8.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.264e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  1.236e-03  9.000e+00  8.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00275    0.00459         NA         NA  740.08342         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    8.00000   19.00000    7.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.002753 lambda1 0.007661 
## lambda0 i 1 lambda1 j 3 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          8 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          8 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  2.397e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.00e+00   3.32e+02   1.82e-03   9.00e+00   8.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.935e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.645e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  1.639e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  1.696e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.601e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  1.378e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  1.287e-03  9.000e+00  8.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00275    0.00766         NA         NA  740.29352         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    8.00000   19.00000   11.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.002753 lambda1 0.012779 
## lambda0 i 1 lambda1 j 4 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          8 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          8 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.426e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  2.032e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.00e+00   3.65e+02   1.84e-03   9.00e+00   8.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.329e-03  9.000e+00  8.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  1.321e-03  9.000e+00  8.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00275    0.01278         NA         NA  740.41462         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    8.00000   19.00000    7.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.002753 lambda1 0.021317 
## lambda0 i 1 lambda1 j 5 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  2.295e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.025e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.171e-03  9.000e+00  6.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00275    0.02132         NA         NA  733.12540         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    6.00000   17.00000    5.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.002753 lambda1 0.035559 
## lambda0 i 1 lambda1 j 6 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          3 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          3 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   3.00e+00   3.02e+02   4.23e-03   9.00e+00   3.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  2.655e-03  9.000e+00  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  2.213e-03  9.000e+00  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  2.007e-03  9.000e+00  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  1.538e-03  9.000e+00  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   8.00e+00   4.86e+02   8.16e-04   9.00e+00   3.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.192e-03  9.000e+00  3.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00275    0.03556         NA         NA  737.12324         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    3.00000   14.00000    9.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.002753 lambda1 0.059316 
## lambda0 i 1 lambda1 j 7 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          2 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          2 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  4.219e-03  9.000e+00  2.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  2.333e-03  9.000e+00  2.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  2.062e-03  9.000e+00  2.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.016e-03  9.000e+00  2.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  3.735e-03  9.000e+00  2.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   8.00e+00   4.86e+02   2.38e-03   9.00e+00   2.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##     9.0000   535.0000     0.0015     9.0000     2.0000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  2.104e-03  9.000e+00  2.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  1.493e-03  9.000e+00  2.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.20e+01   7.12e+02   1.42e-03   9.00e+00   2.00e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00275    0.05932         NA         NA  751.17673         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    2.00000   13.00000   12.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.002753 lambda1 0.098944 
## lambda0 i 1 lambda1 j 8 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          0 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          0 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   3.000000 302.000000   0.016757   9.000000   0.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  5.194e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  2.807e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  2.483e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  1.711e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##      8.000    486.000      0.001      9.000      0.000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.774e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  1.831e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  1.468e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  1.517e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  2.184e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  1.275e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  9.470e+02  1.162e-03  9.000e+00  0.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00275    0.09894         NA         NA  812.50386         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    0.00000   11.00000   15.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.002753 lambda1 0.165049 
## lambda0 i 1 lambda1 j 9 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100         10          0 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100         10          0 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  2.229e-03  1.000e+01  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.141e-03  1.000e+01  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.042e-03  1.000e+01  0.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00275    0.16505         NA         NA  822.00701         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##   10.00000    0.00000   12.00000    5.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.002753 lambda1 0.275319 
## lambda0 i 1 lambda1 j 10 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100         10          0 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100         10          0 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  2.583e-03  1.000e+01  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  3.369e-03  1.000e+01  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  2.598e-03  1.000e+01  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.916e-03  1.000e+01  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  2.331e-03  1.000e+01  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  1.638e-03  1.000e+01  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   9.00e+00   5.35e+02   8.45e-04   1.00e+01   0.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.00e+01   5.88e+02   1.38e-03   1.00e+01   0.00e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00275    0.27532         NA         NA  822.33523         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##   10.00000    0.00000   12.00000   10.00000    1.00000
## End of stage 1 of abbreviated grid search
## Start of stage 2 of abbreviated grid search
## Using saved posterior draws from minimal penalty model for BIC-ICQ calculation:
## file-backed big.matrix stored in Basal_Posterior_Draws.bin and Basal_Posterior_Draws.desc
## Reading in Basal_Posterior_Draws for posterior draws for BICq calculation
## ------------------------------------------------------------------ 
## lambda0 0.002753 lambda1 0.021317 
## lambda0 i 1 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.864e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  2.365e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.688e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.597e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  2.198e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  2.413e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.589e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  1.412e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  1.211e-03  9.000e+00  6.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00275    0.02132         NA         NA  733.37382         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    6.00000   17.00000   11.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.004593 lambda1 0.021317 
## lambda0 i 2 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.671e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.184e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  2.845e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.749e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  1.682e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   8.00e+00   4.86e+02   1.34e-03   9.00e+00   6.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.295e-03  9.000e+00  6.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00459    0.02132         NA         NA  733.31952         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    6.00000   17.00000    9.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.007661 lambda1 0.021317 
## lambda0 i 3 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.424e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.409e-03  9.000e+00  6.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00766    0.02132         NA         NA  733.48834         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    6.00000   17.00000    4.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.012779 lambda1 0.021317 
## lambda0 i 4 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.251e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.449e-03  9.000e+00  6.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.01278    0.02132         NA         NA  733.31993         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    6.00000   17.00000    4.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.021317 lambda1 0.021317 
## lambda0 i 5 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.819e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.658e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.809e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.644e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   7.00e+00   4.42e+02   1.74e-03   9.00e+00   6.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  1.617e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.348e-03  9.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  1.279e-03  9.000e+00  6.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.02132    0.02132         NA         NA  733.50252         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    6.00000   17.00000   10.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.035559 lambda1 0.021317 
## lambda0 i 6 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          8          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          8          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   3.00e+00   3.02e+02   1.26e-03   8.00e+00   6.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  2.145e-03  8.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  2.299e-03  8.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.096e-03  8.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  1.899e-03  8.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  1.497e-03  8.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.493e-03  8.000e+00  6.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.03556    0.02132         NA         NA  730.25011         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    8.00000    6.00000   16.00000    9.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.059316 lambda1 0.021317 
## lambda0 i 7 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          5          5 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          4          5 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   3.000000 302.000000   0.062726   4.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  6.777e-03  4.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.00e+00   3.65e+02   6.81e-03   4.00e+00   5.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   6.000000 402.000000   0.025974   4.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   7.000000 442.000000   0.064217   3.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   8.000000 486.000000   0.045666   3.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  8.363e-03  3.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  7.122e-03  3.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.10e+01   6.47e+02   5.93e-03   3.00e+00   5.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  4.604e-03  3.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  4.299e-03  3.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  3.183e-03  3.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  9.470e+02  2.392e-03  3.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  1.042e+03  2.424e-03  3.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.700e+01  1.250e+03  2.324e-03  3.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.800e+01  1.500e+03  2.538e-03  3.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.900e+01  1.800e+03  1.895e-03  3.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.000e+01  2.160e+03  1.629e-03  3.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.100e+01  2.500e+03  1.216e-03  3.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.200e+01  2.500e+03  1.326e-03  3.000e+00  5.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.05932    0.02132         NA         NA  857.19074         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    3.00000    5.00000   10.00000   22.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.098944 lambda1 0.021317 
## lambda0 i 8 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          1          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          0          5 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   3.000000 302.000000   0.156486   0.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.000000 332.000000   0.040795   0.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.000000 365.000000   0.029902   0.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   6.000000 402.000000   0.026204   0.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   7.000000 442.000000   0.020825   0.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   8.000000 486.000000   0.015879   0.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   9.000000 535.000000   0.012771   0.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  10.000000 588.000000   0.011391   0.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  11.000000 647.000000   0.011511   0.000000   5.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  9.042e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  7.897e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  7.431e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  9.470e+02  7.337e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  1.042e+03  6.769e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.700e+01  1.250e+03  5.717e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.80e+01   1.50e+03   4.36e-03   0.00e+00   5.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.900e+01  1.800e+03  4.412e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.000e+01  2.160e+03  4.821e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.100e+01  2.500e+03  4.864e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.200e+01  2.500e+03  4.139e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.300e+01  2.500e+03  3.797e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.400e+01  2.500e+03  4.101e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.500e+01  2.500e+03  3.436e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.600e+01  2.500e+03  2.878e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.700e+01  2.500e+03  2.785e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.800e+01  2.500e+03  3.052e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.900e+01  2.500e+03  2.234e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+01  2.500e+03  2.057e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.100e+01  2.500e+03  1.364e-03  0.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   3.20e+01   2.50e+03   6.89e-04   0.00e+00   5.00e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.09894    0.02132         NA         NA 1467.65899         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    0.00000    5.00000    7.00000   32.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.165049 lambda1 0.021317 
## lambda0 i 9 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          0          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          0          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   3.000000 302.000000   0.012334   0.000000   6.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  8.985e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  8.456e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   6.00e+00   4.02e+02   5.63e-03   0.00e+00   6.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   7.00e+00   4.42e+02   4.77e-03   0.00e+00   6.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  5.934e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   9.00e+00   5.35e+02   5.13e-03   0.00e+00   6.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  3.888e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  3.419e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.20e+01   7.12e+02   2.35e-03   0.00e+00   6.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  1.722e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  1.886e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.50e+01   9.47e+02   2.52e-03   0.00e+00   6.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  1.042e+03  2.704e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.700e+01  1.250e+03  1.967e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.80e+01   1.50e+03   1.16e-03   0.00e+00   6.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.900e+01  1.800e+03  1.354e-03  0.000e+00  6.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.16505    0.02132         NA         NA 1359.58524         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    0.00000    6.00000    8.00000   19.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.275319 lambda1 0.021317 
## lambda0 i 10 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          0          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          0          6 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.958e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  2.569e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.649e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  2.534e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  1.675e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  2.034e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.446e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##    10.0000   588.0000     0.0021     0.0000     6.0000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  2.696e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  1.721e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  2.103e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  1.709e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  9.470e+02  1.195e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  1.042e+03  1.704e-03  0.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.70e+01   1.25e+03   1.04e-03   0.00e+00   6.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.800e+01  1.500e+03  1.394e-03  0.000e+00  6.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.27532    0.02132         NA         NA 1361.71481         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    0.00000    6.00000    8.00000   18.00000    1.00000
## End of stage 2 of abbreviated grid search
## Start of sampling from posterior
## Finished sampling from posterior
## Pajor Log-Likelihood Calculation
# Save output object for use in "glmmPen_FineSearch()" function used in "_code_extra.Rmd"
save(fitB, file = "fit_basal.RData")

end_basal = proc.time()
# Time needed to complete the algorithm
end_basal - start_basal
##    user  system elapsed 
## 1401.67  435.60 1908.02
# Code to examine the output from the glmmPen fit for the basal dataset.
## (a) Illustration that methods such as summary, fixef, ranef ... run and work as expected
## (b) Code to create Figure 1

# Illustration of use of methods
summary(fitB)
## Penalized generalized linear mixed model fit by Monte Carlo Expectation Conditional Minimization (MCECM)
##   algorithm (Stan)  ['pglmmObj'] 
##  Family: binomial  ( logit )
## Formula: y ~ X + (X | group)
## 
## Fixed Effects:
##    (Intercept)   XGPR160_CD109     XSPDEF_MFI2    XCHST6_CAPN9   XSLC40A1_CDH3  
##        -1.1530         -0.7099         -0.7355          0.5082         -0.5831  
## XPLEK2_HSD17B2     XGPX2_ERO1L  XCYP3A5_B3GNT5    XLY6D_ATP2C2   XMYO1A_FGFBP1  
##         0.4337         -0.5895          0.0000          0.4620         -0.7411  
##  XCTSE_COL17A1  
##         0.0000  
## 
## Random Effects:
##  Group Name           Variance Std.Dev.
##  group (Intercept)    0.8193   0.9052  
##  group XGPR160_CD109  0.2036   0.4512  
##  group XSPDEF_MFI2    0.684    0.827   
##  group XCHST6_CAPN9   0        0       
##  group XSLC40A1_CDH3  0        0       
##  group XPLEK2_HSD17B2 0.0804   0.2835  
##  group XGPX2_ERO1L    0.0842   0.2901  
##  group XCYP3A5_B3GNT5 0        0       
##  group XLY6D_ATP2C2   0        0       
##  group XMYO1A_FGFBP1  0.1551   0.3938  
##  group XCTSE_COL17A1  0.7596   0.8715  
## Number Observations: 938,  groups: group, 4 
## 
## Deviance residuals:  
##     Min      1Q  Median      3Q     Max 
## -2.9338 -0.4026 -0.1512  0.3457  2.9630
print(fitB)
## Penalized generalized linear mixed model fit by Monte Carlo Expectation Conditional Minimization (MCECM)
##   algorithm (Stan)  ['pglmmObj'] 
##  Family: binomial  ( logit )
## Formula: y ~ X + (X | group)
## Fixed Effects:
##    (Intercept)   XGPR160_CD109     XSPDEF_MFI2    XCHST6_CAPN9   XSLC40A1_CDH3  
##        -1.1530         -0.7099         -0.7355          0.5082         -0.5831  
## XPLEK2_HSD17B2     XGPX2_ERO1L  XCYP3A5_B3GNT5    XLY6D_ATP2C2   XMYO1A_FGFBP1  
##         0.4337         -0.5895          0.0000          0.4620         -0.7411  
##  XCTSE_COL17A1  
##         0.0000  
## Random Effects:
##  Group Name           Variance
##  group (Intercept)    0.8193  
##  group XGPR160_CD109  0.2036  
##  group XSPDEF_MFI2    0.684   
##  group XCHST6_CAPN9   0       
##  group XSLC40A1_CDH3  0       
##  group XPLEK2_HSD17B2 0.0804  
##  group XGPX2_ERO1L    0.0842  
##  group XCYP3A5_B3GNT5 0       
##  group XLY6D_ATP2C2   0       
##  group XMYO1A_FGFBP1  0.1551  
##  group XCTSE_COL17A1  0.7596  
## Number Observations: 938,  groups: group, 4
## fixed effects coefficients
fixef(fitB)
##    (Intercept)  XGPR160_CD109    XSPDEF_MFI2   XCHST6_CAPN9  XSLC40A1_CDH3 
##     -1.1530236     -0.7098759     -0.7354799      0.5081851     -0.5830644 
## XPLEK2_HSD17B2    XGPX2_ERO1L XCYP3A5_B3GNT5   XLY6D_ATP2C2  XMYO1A_FGFBP1 
##      0.4337248     -0.5894785      0.0000000      0.4620249     -0.7411088 
##  XCTSE_COL17A1 
##      0.0000000
## random effects coefficients for each covariate for each level of the grouping factor
ranef(fitB)
## $group
##              (Intercept) XGPR160_CD109 XSPDEF_MFI2 XCHST6_CAPN9 XSLC40A1_CDH3
## UNC_PDAC       0.6434794     0.2080523  0.65795710            0             0
## TCGA_PDAC      1.0498887     0.1997625 -0.05418621            0             0
## TCGA_Bladder  -0.9066515    -0.6606222  0.66519051            0             0
## UNC_Breast    -0.6584093     0.2931500 -1.25483427            0             0
##              XPLEK2_HSD17B2 XGPX2_ERO1L XCYP3A5_B3GNT5 XLY6D_ATP2C2
## UNC_PDAC         0.04894502 -0.24594772              0            0
## TCGA_PDAC       -0.05659493 -0.03565460              0            0
## TCGA_Bladder     0.30257671 -0.04738065              0            0
## UNC_Breast      -0.30349387  0.34370032              0            0
##              XMYO1A_FGFBP1 XCTSE_COL17A1
## UNC_PDAC         0.1719971   -0.80580524
## TCGA_PDAC       -0.4052438   -0.06311355
## TCGA_Bladder     0.4078796   -0.44303000
## UNC_Breast      -0.1908039    1.31934478
## random effects covariance matrix
sigma(fitB)
## $group
##                (Intercept) XGPR160_CD109 XSPDEF_MFI2 XCHST6_CAPN9 XSLC40A1_CDH3
## (Intercept)      0.8192983     0.0000000   0.0000000            0             0
## XGPR160_CD109    0.0000000     0.2036111   0.0000000            0             0
## XSPDEF_MFI2      0.0000000     0.0000000   0.6839866            0             0
## XCHST6_CAPN9     0.0000000     0.0000000   0.0000000            0             0
## XSLC40A1_CDH3    0.0000000     0.0000000   0.0000000            0             0
## XPLEK2_HSD17B2   0.0000000     0.0000000   0.0000000            0             0
## XGPX2_ERO1L      0.0000000     0.0000000   0.0000000            0             0
## XCYP3A5_B3GNT5   0.0000000     0.0000000   0.0000000            0             0
## XLY6D_ATP2C2     0.0000000     0.0000000   0.0000000            0             0
## XMYO1A_FGFBP1    0.0000000     0.0000000   0.0000000            0             0
## XCTSE_COL17A1    0.0000000     0.0000000   0.0000000            0             0
##                XPLEK2_HSD17B2 XGPX2_ERO1L XCYP3A5_B3GNT5 XLY6D_ATP2C2
## (Intercept)        0.00000000  0.00000000              0            0
## XGPR160_CD109      0.00000000  0.00000000              0            0
## XSPDEF_MFI2        0.00000000  0.00000000              0            0
## XCHST6_CAPN9       0.00000000  0.00000000              0            0
## XSLC40A1_CDH3      0.00000000  0.00000000              0            0
## XPLEK2_HSD17B2     0.08035967  0.00000000              0            0
## XGPX2_ERO1L        0.00000000  0.08416843              0            0
## XCYP3A5_B3GNT5     0.00000000  0.00000000              0            0
## XLY6D_ATP2C2       0.00000000  0.00000000              0            0
## XMYO1A_FGFBP1      0.00000000  0.00000000              0            0
## XCTSE_COL17A1      0.00000000  0.00000000              0            0
##                XMYO1A_FGFBP1 XCTSE_COL17A1
## (Intercept)         0.000000     0.0000000
## XGPR160_CD109       0.000000     0.0000000
## XSPDEF_MFI2         0.000000     0.0000000
## XCHST6_CAPN9        0.000000     0.0000000
## XSLC40A1_CDH3       0.000000     0.0000000
## XPLEK2_HSD17B2      0.000000     0.0000000
## XGPX2_ERO1L         0.000000     0.0000000
## XCYP3A5_B3GNT5      0.000000     0.0000000
## XLY6D_ATP2C2        0.000000     0.0000000
## XMYO1A_FGFBP1       0.155089     0.0000000
## XCTSE_COL17A1       0.000000     0.7595667
summary(residuals(fitB, type = "deviance"))
##      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
## -2.933820 -0.402566 -0.151164 -0.003358  0.345666  2.962965
plot(fitB)

## log-likelihood using corrected arithmetic mean estimator with importance sampling weights (the Pajor method discussed in paper)
logLik(fitB)
## 'log Lik.' -274.1844 (df=16)
## BIC-derived quantities
BIC(fitB)
##     BICh      BIC     BICq  BICNgrp 
## 619.6666 657.8688 730.2501 570.5495
# Figure 1 (sample path plots and autocorrelation)

fitB_covar = sigma(fitB)$group
fitB_var = diag(fitB_covar)[-1]
TSP = names(fitB_var[which(fitB_var != 0)])[1:3]
# TSP = c("XGPR160_CD109", "XSPDEF_MFI2", "XPLEK2_HSD17B2")
plot_diag = plot_mcmc(object = fitB, plots = c("sample.path","autocorr"), 
                      grps = "all", vars = TSP)
# Figure 1a sample path plot
plot_diag$sample_path + theme(axis.text.x = element_text(angle = 270))

ggsave(filename = "Figures/Figure1A Sample Path.pdf",
       width = 7, height = 5, units = "in")
# Figure 1b autocorrelation plot
plot_diag$autocorr

ggsave(filename = "Figures/Figure1B Autocorrelation.pdf",
       width = 7, height = 5, units = "in")

## prediction using fixed effects only
head(predict(object = fitB, newdata = NULL, type = "link", fixed.only = T))
##         1         2         3         4         5         6 
## 1.5902766 0.4899793 3.3632729 0.4424442 3.3632729 2.1353052
## the fitted linear predictor object using fixed effects only
head(fitted(object = fitB, fixed.only = T))
##         1         2         3         4         5         6 
## 0.8306550 0.6201015 0.9665368 0.6088413 0.9665368 0.8942876
# Code to run a single replicate of one of the variable selection simulations (p=10)
## Note: In paper, Tables 3-4 show results from 8 sets of simulations, each with 100 replicates.
## The p=50 simulations especially can take a long time. 
## Therefore, we provide here an example of running a single p=10 simulation, which can finish
## in under an hour.
## The code to run all 100 replicates of all 8 sets of simulations in serial (which would take
## multiple days) is provided in the folder "sims_serial/".
## We ran these simulations on a cluster, allowing us to run all individual replicates in parallel.
## Instructions on running these simulations on a cluster are given in the README file within
## the folder "sims_on_cluster/".

# Situation
# Total predictors: 10
# Covariates: 2 non-zero slopes, one intercept
# Random effects: sd_ranef = 1.0 or 2.0 for intercept and non-zero slopes 

N = 500
sd_ranef = 1.0
K = 5

dat = sim.data(n = N, ptot = 10, pnonzero = 2, nstudies = K,
               sd_raneff = sd_ranef, family = 'binomial',
               seed = 3213, imbalance = 1, 
               pnonzerovar = 0, beta = c(0, 1, 1))

y = dat$y
X = dat$X[,-1]
group = dat$group

# If have run this code before, this will have resulted in saved posterior samples.
# To completely replicate code, remove these files before running simulated data 
# variable selection code
if(file.exists("BICq_Post_SingleSim.bin")) file.remove("BICq_Post_SingleSim.bin")
if(file.exists("BICq_Post_SingleSim.desc")) file.remove("BICq_Post_SingleSim.desc")

start_sim = proc.time()

start1 = proc.time()
set.seed(3213)
fit_glmmPen = glmmPen(formula = y ~ X + (X | group), family = "binomial",
                      covar = "independent", optim_options = optimControl(),
                      tuning_options = selectControl(BIC_option = "BICq", pre_screen = T,
                                                     search = "abbrev", lambda.min.presc = 0.01),
                      BICq_posterior = "BICq_Post_SingleSim")
## recommended starting variance: 0.516134
## Start of stage 1 of abbreviated grid search
## Running prescreening procedure
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        100        100          8         10 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        110        100          8         10 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   3.000000 121.000000   0.036947   8.000000  10.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.000000 133.000000   0.023126   8.000000   9.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.000000 146.000000   0.017773   8.000000   7.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   6.000000 161.000000   0.012678   8.000000   7.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  1.770e+02  9.624e-03  8.000e+00  7.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  1.950e+02  6.524e-03  8.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  2.150e+02  6.363e-03  8.000e+00  6.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  2.370e+02  7.786e-03  8.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  2.610e+02  7.232e-03  8.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  2.870e+02  4.597e-03  8.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  3.160e+02  4.208e-03  8.000e+00  5.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  3.480e+02  4.497e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  3.830e+02  2.198e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  4.210e+02  2.701e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.700e+01  5.000e+02  3.152e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.800e+01  5.000e+02  2.781e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.900e+01  5.000e+02  2.286e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.000e+01  5.000e+02  2.343e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   2.10e+01   5.00e+02   2.27e-03   8.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.200e+01  5.000e+02  2.333e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.300e+01  5.000e+02  2.912e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.400e+01  5.000e+02  2.984e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.500e+01  5.000e+02  1.854e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.600e+01  5.000e+02  1.962e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.700e+01  5.000e+02  2.264e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.800e+01  5.000e+02  2.423e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.900e+01  5.000e+02  2.026e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+01  5.000e+02  1.922e-03  8.000e+00  4.000e+00
## Warning in fit_dat(dat, lambda0 = lam0, lambda1 = lam1, family = family, : glmmPen algorithm did not converge within maxitEM iterations of 30, conv = 0.001922
##  Consider increasing maxitEM iterations or nMC_max in optimControl()
## The files BICq_Post_SingleSim.bin and BICq_Post_SingleSim.desc do not currently exist.
## Fitting minimal penalty model and saving posterior draws to BICq_Post_SingleSim.bin and BICq_Post_SingleSim.desc
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.197e-03  9.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.418e-03  9.000e+00  4.000e+00
## Start of sampling from posterior
## Finished sampling from posterior
## ------------------------------------------------------------------ 
## lambda0 0.001951 lambda1 0.001951 
## lambda0 i 1 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.376e-03  9.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  2.267e-03  9.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.326e-03  9.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.437e-03  9.000e+00  4.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00195    0.00195         NA         NA  600.23719         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    4.00000   15.00000    6.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.001951 lambda1 0.003255 
## lambda0 i 1 lambda1 j 2 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100         10          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100         10          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.571e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.00e+00   3.32e+02   1.68e-03   1.00e+01   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.692e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  3.459e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  4.398e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  1.756e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.882e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  1.096e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  1.963e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  1.498e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  1.487e-03  1.000e+01  4.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00195    0.00325         NA         NA  606.61906         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##   10.00000    4.00000   16.00000   13.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.001951 lambda1 0.005429 
## lambda0 i 1 lambda1 j 3 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100         10          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100         10          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.735e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.729e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.00e+00   3.65e+02   9.16e-04   1.00e+01   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.529e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  1.643e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  1.559e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.414e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  1.005e-03  1.000e+01  4.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00195    0.00543         NA         NA  606.61776         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##   10.00000    4.00000   16.00000   10.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.001951 lambda1 0.009056 
## lambda0 i 1 lambda1 j 4 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100         10          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100         10          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.858e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.333e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.481e-03  1.000e+01  4.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00195    0.00906         NA         NA  606.72151         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##   10.00000    4.00000   16.00000    5.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.001951 lambda1 0.015106 
## lambda0 i 1 lambda1 j 5 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100         10          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100         10          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  4.683e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  8.909e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  5.356e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  2.358e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  2.044e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  1.835e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.933e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  1.464e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.10e+01   6.47e+02   9.63e-04   1.00e+01   3.00e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00195    0.01511         NA         NA  602.60029         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##   10.00000    3.00000   15.00000   11.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.001951 lambda1 0.025199 
## lambda0 i 1 lambda1 j 6 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100         10          3 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100         10          3 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  1.631e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.527e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.601e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  2.031e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   7.00e+00   4.42e+02   7.77e-04   1.00e+01   3.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  1.949e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   9.00e+00   5.35e+02   2.37e-03   1.00e+01   3.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  2.129e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  1.714e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  1.331e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  2.402e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  1.899e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  9.470e+02  1.121e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  1.042e+03  7.430e-04  1.000e+01  3.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00195    0.02520         NA         NA  602.44297         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##   10.00000    3.00000   15.00000   16.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.001951 lambda1 0.042034 
## lambda0 i 1 lambda1 j 7 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100         10          3 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100         10          3 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  2.646e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.913e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.155e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.916e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  2.058e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  2.026e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.815e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  1.527e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  1.116e-03  1.000e+01  3.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  1.325e-03  1.000e+01  3.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00195    0.04203         NA         NA  602.39318         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##   10.00000    3.00000   15.00000   12.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.001951 lambda1 0.070117 
## lambda0 i 1 lambda1 j 8 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          8          1 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          8          1 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  3.448e-03  8.000e+00  1.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.00e+00   3.32e+02   7.26e-04   8.00e+00   1.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.00e+00   3.65e+02   9.56e-04   8.00e+00   1.00e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00195    0.07012         NA         NA  601.94411         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    8.00000    1.00000   11.00000    5.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.001951 lambda1 0.116962 
## lambda0 i 1 lambda1 j 9 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          8          1 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          8          1 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  3.708e-03  8.000e+00  1.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.00e+00   3.32e+02   2.24e-03   8.00e+00   1.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.00e+00   3.65e+02   7.43e-04   8.00e+00   1.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.472e-03  8.000e+00  1.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00195    0.11696         NA         NA  602.12100         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    8.00000    1.00000   11.00000    6.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.001951 lambda1 0.195105 
## lambda0 i 1 lambda1 j 10 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          9          0 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          9          0 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  3.095e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.691e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.00e+00   3.65e+02   8.78e-04   9.00e+00   0.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.781e-03  9.000e+00  0.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   7.00e+00   4.42e+02   7.45e-04   9.00e+00   0.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   8.00e+00   4.86e+02   6.19e-04   9.00e+00   0.00e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00195    0.19510         NA         NA  712.36433         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    9.00000    0.00000   11.00000    8.00000    1.00000
## End of stage 1 of abbreviated grid search
## Start of stage 2 of abbreviated grid search
## Using saved posterior draws from minimal penalty model for BIC-ICQ calculation:
## file-backed big.matrix stored in BICq_Post_SingleSim.bin and BICq_Post_SingleSim.desc
## Reading in BICq_Post_SingleSim for posterior draws for BICq calculation
## ------------------------------------------------------------------ 
## lambda0 0.001951 lambda1 0.001951 
## lambda0 i 1 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100         10          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100         10          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  2.461e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  2.689e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  2.188e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.868e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  1.458e-03  1.000e+01  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   8.00e+00   4.86e+02   9.86e-04   1.00e+01   4.00e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00195    0.00195         NA         NA  606.54040         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##   10.00000    4.00000   16.00000    8.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.003255 lambda1 0.001951 
## lambda0 i 2 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          8          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          8          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  2.342e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  1.589e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  1.394e-03  8.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.347e-03  8.000e+00  4.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00325    0.00195         NA         NA  594.13511         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    8.00000    4.00000   14.00000    6.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.005429 lambda1 0.001951 
## lambda0 i 3 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          7          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          7          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  3.029e-03  7.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  2.313e-03  7.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  2.606e-03  7.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  3.239e-03  7.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  3.461e-03  7.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  1.283e-03  7.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  1.997e-03  7.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.00e+01   5.88e+02   1.94e-03   7.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  1.823e-03  7.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  2.453e-03  7.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  2.113e-03  7.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  1.193e-03  7.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  9.470e+02  1.187e-03  7.000e+00  4.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00543    0.00195         NA         NA  587.91645         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    7.00000    4.00000   13.00000   15.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.009056 lambda1 0.001951 
## lambda0 i 4 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          5          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          5          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  4.042e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  2.131e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  2.174e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  2.705e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   7.00e+00   4.42e+02   2.58e-03   5.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  3.105e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  2.264e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  2.016e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  2.098e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  1.835e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  2.006e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  1.578e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  9.470e+02  2.454e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  1.042e+03  2.307e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.700e+01  1.250e+03  2.488e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.800e+01  1.500e+03  1.332e-03  5.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.900e+01  1.800e+03  1.103e-03  5.000e+00  4.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.00906    0.00195         NA         NA  576.32965         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    5.00000    4.00000   11.00000   19.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.015106 lambda1 0.001951 
## lambda0 i 5 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          5          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          5          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  9.107e-03  4.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  3.953e-03  4.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  3.222e-03  4.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  1.749e-03  4.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  2.172e-03  4.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  2.893e-03  4.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  2.775e-03  4.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  1.819e-03  4.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.10e+01   6.47e+02   1.97e-03   4.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  1.744e-03  4.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  1.292e-03  4.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  1.758e-03  4.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  9.470e+02  1.414e-03  4.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  1.042e+03  1.495e-03  4.000e+00  4.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.01511    0.00195         NA         NA  571.46677         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    4.00000    4.00000   10.00000   16.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.025199 lambda1 0.001951 
## lambda0 i 6 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          4          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          3          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  8.495e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  5.975e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  6.016e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  3.816e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  3.687e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  2.773e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  2.482e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  2.965e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  3.082e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  2.838e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.30e+01   7.83e+02   3.25e-03   3.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  1.643e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  9.470e+02  2.296e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  1.042e+03  2.234e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.700e+01  1.250e+03  1.541e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.800e+01  1.500e+03  1.125e-03  3.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.900e+01  1.800e+03  1.367e-03  3.000e+00  4.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.02520    0.00195         NA         NA  570.58248         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    3.00000    4.00000    9.00000   19.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.042034 lambda1 0.001951 
## lambda0 i 7 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          2          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          2          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  2.048e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  4.155e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  3.905e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  4.181e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  2.384e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  3.816e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  3.087e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  2.665e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  3.161e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  1.916e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  1.329e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  1.759e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  9.470e+02  2.835e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  1.042e+03  2.386e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.700e+01  1.250e+03  1.507e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.800e+01  1.500e+03  2.132e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.900e+01  1.800e+03  1.913e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   2.00e+01   2.16e+03   8.64e-04   2.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.100e+01  2.500e+03  1.048e-03  2.000e+00  4.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.04203    0.00195         NA         NA  565.52014         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    2.00000    4.00000    8.00000   21.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.070117 lambda1 0.001951 
## lambda0 i 8 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          2          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          2          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  2.142e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.00e+00   3.32e+02   2.33e-03   2.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  2.248e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  2.746e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  3.853e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  2.569e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  2.552e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  2.304e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.100e+01  6.470e+02  2.091e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  1.322e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  1.952e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  2.968e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.500e+01  9.470e+02  3.213e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  1.042e+03  2.350e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.70e+01   1.25e+03   1.91e-03   2.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.800e+01  1.500e+03  1.451e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.900e+01  1.800e+03  1.492e-03  2.000e+00  4.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.07012    0.00195         NA         NA  565.54294         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    2.00000    4.00000    8.00000   19.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.116962 lambda1 0.001951 
## lambda0 i 9 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          2          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          2          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+00  3.020e+02  4.067e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.000e+00  3.320e+02  3.399e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  5.000e+00  3.650e+02  2.275e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  6.000e+00  4.020e+02  4.636e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  7.000e+00  4.420e+02  4.748e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  8.000e+00  4.860e+02  2.208e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  9.000e+00  5.350e+02  2.745e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.000e+01  5.880e+02  2.938e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.10e+01   6.47e+02   2.81e-03   2.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.200e+01  7.120e+02  3.031e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.300e+01  7.830e+02  1.239e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.400e+01  8.610e+02  2.359e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   1.50e+01   9.47e+02   2.11e-03   2.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.600e+01  1.042e+03  1.930e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.700e+01  1.250e+03  1.911e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.800e+01  1.500e+03  1.422e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.900e+01  1.800e+03  1.936e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.000e+01  2.160e+03  1.509e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.100e+01  2.500e+03  1.757e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.200e+01  2.500e+03  1.621e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.300e+01  2.500e+03  1.466e-03  2.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.400e+01  2.500e+03  1.188e-03  2.000e+00  4.000e+00 
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.11696    0.00195         NA         NA  565.37556         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    2.00000    4.00000    8.00000   24.00000    1.00000 
## ------------------------------------------------------------------ 
## lambda0 0.195105 lambda1 0.001951 
## lambda0 i 10 lambda1 j 1 
## ------------------------------------------------------------------
## using coef from past model to intialize
## using results from previous model to initialize posterior samples
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          1        250        100          1          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##          2        275        100          0          4 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   3.000000 302.000000   0.156445   0.000000   4.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##    4.00000  332.00000    0.13767    0.00000    4.00000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.000000 365.000000   0.111383   0.000000   4.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   6.000000 402.000000   0.085391   0.000000   4.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   7.000000 442.000000   0.061993   0.000000   4.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   8.000000 486.000000   0.046147   0.000000   4.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   9.000000 535.000000   0.035317   0.000000   4.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  10.000000 588.000000   0.032271   0.000000   4.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  11.000000 647.000000   0.026477   0.000000   4.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   12.00000  712.00000    0.01866    0.00000    4.00000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   13.00000  783.00000    0.01589    0.00000    4.00000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  14.000000 861.000000   0.014388   0.000000   4.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  15.000000 947.000000   0.015228   0.000000   4.000000 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
## 1.6000e+01 1.0420e+03 1.5583e-02 0.0000e+00 4.0000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
## 1.7000e+01 1.2500e+03 1.2382e-02 0.0000e+00 4.0000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  1.800e+01  1.500e+03  1.111e-02  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
## 1.9000e+01 1.8000e+03 1.0431e-02 0.0000e+00 4.0000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.000e+01  2.160e+03  9.146e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.100e+01  2.500e+03  9.168e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.200e+01  2.500e+03  9.004e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.300e+01  2.500e+03  7.536e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.400e+01  2.500e+03  7.757e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.500e+01  2.500e+03  6.959e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.600e+01  2.500e+03  6.058e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.700e+01  2.500e+03  6.925e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.800e+01  2.500e+03  6.428e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  2.900e+01  2.500e+03  5.242e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.000e+01  2.500e+03  5.457e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   3.10e+01   2.50e+03   5.35e-03   0.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.200e+01  2.500e+03  4.543e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.300e+01  2.500e+03  4.794e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.400e+01  2.500e+03  4.204e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.500e+01  2.500e+03  3.082e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.600e+01  2.500e+03  3.501e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.700e+01  2.500e+03  3.821e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.800e+01  2.500e+03  3.632e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  3.900e+01  2.500e+03  3.947e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.00e+01   2.50e+03   3.25e-03   0.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.100e+01  2.500e+03  2.815e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.200e+01  2.500e+03  2.749e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.30e+01   2.50e+03   2.48e-03   0.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.400e+01  2.500e+03  3.264e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.500e+01  2.500e+03  3.967e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.600e+01  2.500e+03  3.187e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   4.70e+01   2.50e+03   2.12e-03   0.00e+00   4.00e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.800e+01  2.500e+03  1.952e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##  4.900e+01  2.500e+03  2.629e-03  0.000e+00  4.000e+00 
##       Iter        nMC    EM conv Non0 Fixef Non0 Ranef 
##   5.00e+01   2.50e+03   1.97e-03   0.00e+00   4.00e+00
## Warning in fit_dat(dat, lambda0 = lambda0_seq[i], lambda1 = lambda1_seq[j], : glmmPen algorithm did not converge within maxitEM iterations of 50, conv = 0.00197
##  Consider increasing maxitEM iterations or nMC_max in optimControl()
##    lambda0    lambda1       BICh        BIC       BICq    BICNgrp     LogLik 
##    0.19510    0.00195         NA         NA  994.67734         NA         NA 
##  Non_0_fef  Non_0_ref Non_0_coef    EM_iter  Converged 
##    0.00000    4.00000    6.00000   50.00000    0.00000
## End of stage 2 of abbreviated grid search
## Start of sampling from posterior
## Finished sampling from posterior
## Pajor Log-Likelihood Calculation
end1 = proc.time()

end_sim = proc.time()
# Time needed to complete the algorithm
end_sim - start_sim
##    user  system elapsed 
## 1062.94  447.80 1534.56
# From saved RData objects of saved simulation results, extract summary information

# RData object description: List with the following elements:
## Matrix of fixed effects (beta_mat)
## Matrix of variances from the random effect covariance matrices (vars_mat)
## Vector of times to completion (time)
## Matrix of prescreening results (PreSc)

# Code to create rows of Table 3

# file_name: name of saved RData object
Table3_out = function(file_name){
  
  # load results list object
  load(file_name)
  
  # Extract relevant output
  beta_mat = results$beta_mat
  vars_mat = results$vars_mat
  time = results$time
  # PreSc = results$PreSc
  
  # Number of finished simulations
  n = nrow(beta_mat)
  
  # Initializations: 
  ## true positives for fixed effects
  tp_f = 0
  ## false positives for fixed effects
  fp_f = 0
  ## true positives for random effects (non-zero variances in random effect covariance matrix)
  tp_r = 0
  ## false positives for random effects
  fp_r = 0
  ## Fixed effects for predictors 1 and 2 (the truely non-zero fixed effects)
  b1 = 0
  b2 = 0
  
  # Take average of the fixed effects for predictors 1 and 2 (average of the non-zero values)
  b1 = sum(beta_mat[which(beta_mat[,2] != 0),2]) / sum(beta_mat[,2] != 0)
  b2 = sum(beta_mat[which(beta_mat[,3] != 0),3]) / sum(beta_mat[,3] != 0)
  
  # Calculate true positives and false positives
  for(f in 1:n){
    
    beta = beta_mat[f,]
    vars = vars_mat[f,]
    
    tp_f = tp_f + sum(beta[2:3] != 0)
    fp_f = fp_f + sum(beta[-c(1:3)] != 0)
    
    tp_r = tp_r + sum(vars[2:3] != 0)
    fp_r = fp_r + sum(vars[-c(1:3)] != 0)
    
  }
  
  # Note: need to calculate percentages for true positive and false positive values
  # out = round(c(b1, b2, c(tp_f, fp_f, tp_r, fp_r) / n, median(time)), 2)
  out = c(round(c(b1, b2), 2), round(c(tp_f/(2*n)*100, fp_f/((length(beta)-3)*n)*100, 
                                       tp_r/(2*n)*100, fp_r/((length(vars)-3)*n)*100), 1),
          round(median(time), 2))
  names(out) = c("Beta 1","Beta 2","TP % Fixef","FP % Fixef","TP % Ranef","FP % Ranef","Median Time (hrs)")
  
  return(out)
}

# Code to create rows of Table 4

Table4_out = function(file_name){
  
  # load RData list object
  load(file_name)
  
  # Extract pre-screening information
  PreSc = results$PreSc
  
  # Find true and false positives
  pre_pos = numeric(2)
  names(pre_pos) = c("TP %","FP %")
  
  pre_pos[1] = mean(rowSums(PreSc[,c(2,3)])) / 2 * 100
  pre_pos[2] =  mean(rowSums(PreSc[,-c(1:3)])) / (ncol(PreSc) - 3) * 100
  
  
  return(round(pre_pos, 1))
}


# Table output

N = 500
K = rep(rep(c(5,10), each=2), times = 2)
sigma = rep(rep(c(1,sqrt(2)), times=2), times = 2)

# file names of simulation output for Tables 3 and 4
output_T34 = list.files(pattern = "Tables 3 and 4", full.names = T)

# Create Table 3 - coefficient and timing results

Table3 = NULL
for(i in 1:length(output_T34)){
  
  # run Table3_out function sourced from "sim_output.R"
  row = Table3_out(output_T34[i])
  if(i == 1){
    Table3 = row
  }else{
    Table3 = rbind(Table3, row)
  }
}

(Table3 = cbind(N, K, sigma, Table3))
##          N  K    sigma Beta 1 Beta 2 TP % Fixef FP % Fixef TP % Ranef
## Table3 500  5 1.000000   1.02   1.12       89.0        2.1       90.5
## row    500  5 1.414214   1.12   1.18       83.0        1.4       96.0
## row    500 10 1.000000   0.99   1.04       99.0        3.0       95.0
## row    500 10 1.414214   1.02   1.11       91.0        1.8       99.5
## row    500  5 1.000000   1.18   1.14       84.5        1.2       83.5
## row    500  5 1.414214   1.42   1.43       75.5        2.5       89.0
## row    500 10 1.000000   1.12   1.11       95.0        1.8       93.0
## row    500 10 1.414214   1.33   1.31       84.5        2.4       95.5
##        FP % Ranef Median Time (hrs)
## Table3        3.5              0.20
## row           3.6              0.26
## row           4.8              0.24
## row           7.0              0.32
## row           2.2              8.07
## row           2.5             12.20
## row           3.9             10.67
## row           6.2             15.75
# Create Table 4 - pre-screening results

Table4 = NULL
for(i in 1:length(output_T34)){
  
  # run Table4_out function sourced from "sim_output.R"
  row = Table4_out(output_T34[i])
  if(i == 1){
    Table4 = row
  }else{
    Table4 = rbind(Table4, row)
  }
}

(Table4 = cbind(N, K, sigma, Table4))
##          N  K    sigma  TP % FP %
## Table4 500  5 1.000000  98.0 25.8
## row    500  5 1.414214 100.0 26.1
## row    500 10 1.000000 100.0 33.0
## row    500 10 1.414214 100.0 32.2
## row    500  5 1.000000  96.0 24.6
## row    500  5 1.414214  96.5 25.7
## row    500 10 1.000000  97.5 25.9
## row    500 10 1.414214  98.5 27.3
sessionInfo()
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.1252 
## [2] LC_CTYPE=English_United States.1252   
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggplot2_3.4.2    stringr_1.4.0    glmmPen_1.5.3.4  Rcpp_1.0.7      
## [5] bigmemory_4.5.36 lme4_1.1-27.1    Matrix_1.3-4    
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.4           jsonlite_1.7.2       splines_4.1.1       
##  [4] bslib_0.4.2          RcppParallel_5.1.4   StanHeaders_2.21.0-7
##  [7] stats4_4.1.1         yaml_2.2.1           pillar_1.6.4        
## [10] lattice_0.20-44      glue_1.6.2           ncvreg_3.13.0       
## [13] digest_0.6.29        minqa_1.2.4          colorspace_2.0-2    
## [16] htmltools_0.5.4      plyr_1.8.6           pkgconfig_2.0.3     
## [19] rstan_2.21.2         purrr_0.3.4          mvtnorm_1.1-3       
## [22] scales_1.2.1         processx_3.5.2       tibble_3.1.6        
## [25] farver_2.1.0         generics_0.1.2       ellipsis_0.3.2      
## [28] cachem_1.0.6         withr_2.5.0          cli_3.3.0           
## [31] survival_3.5-5       magrittr_2.0.1       crayon_1.4.2        
## [34] evaluate_0.14        ps_1.6.0             fansi_0.5.0         
## [37] nlme_3.1-152         MASS_7.3-54          pkgbuild_1.3.0      
## [40] tools_4.1.1          coxme_2.2-18.1       loo_2.4.1           
## [43] prettyunits_1.1.1    lifecycle_1.0.3      matrixStats_0.61.0  
## [46] V8_3.6.0             munsell_0.5.0        callr_3.7.0         
## [49] compiler_4.1.1       jquerylib_0.1.4      rlang_1.1.0         
## [52] grid_4.1.1           nloptr_1.2.2.3       rstudioapi_0.13     
## [55] bigmemory.sri_0.1.3  labeling_0.4.2       rmarkdown_2.11      
## [58] boot_1.3-28          gtable_0.3.0         codetools_0.2-18    
## [61] inline_0.3.19        curl_4.3.2           reshape2_1.4.4      
## [64] R6_2.5.1             gridExtra_2.3        rstantools_2.1.1    
## [67] knitr_1.36           dplyr_1.0.9          bdsmatrix_1.3-6     
## [70] fastmap_1.1.0        utf8_1.2.2           stringi_1.7.6       
## [73] parallel_4.1.1       vctrs_0.6.2          tidyselect_1.1.1    
## [76] xfun_0.28
################################################################################################