# 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
################################################################################################