├── data └── simpleLogistic.csv ├── MEST_writeup.pdf ├── LSA2017.Mixed Models are Sometimes Terrible final.pdf ├── README.md ├── code ├── imbalanced_logistic.stan ├── imbalanced_gaussian.stan ├── simple_logistic_sim.R ├── barretall.R └── complex_imbalanced.R └── LICENSE /data/simpleLogistic.csv: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /MEST_writeup.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jroy042/MixedEffectsAreSometimesTerrible/HEAD/MEST_writeup.pdf -------------------------------------------------------------------------------- /LSA2017.Mixed Models are Sometimes Terrible final.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jroy042/MixedEffectsAreSometimesTerrible/HEAD/LSA2017.Mixed Models are Sometimes Terrible final.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Mixed Effects Are Sometimes Terrible 2 | 3 | Code for simulations and data reported in LSA Poster and Paper: 4 | Eager, Christopher & Joseph Roy. (2017). Mixed Effects are Sometimes Terrible. Linguistics Society of America 2017 Annual Meeting. Austin, Tx (January 5-8). 5 | 6 | -------------------------------------------------------------------------------- /code/imbalanced_logistic.stan: -------------------------------------------------------------------------------- 1 | // Stan code for imbalanced logistic mixed effects simulation 2 | 3 | data { 4 | int N; // number of observations 5 | int S; // number of subjects 6 | 7 | int P; // number of fixed effects 8 | int QS; // number of subject effects 9 | 10 | // sparse model matrix (CSR) 11 | int nz; // number of non-zero elements in x 12 | vector[nz] x_w; // non-zero elements in x 13 | int x_v[nz]; // column indices for x_w 14 | int x_u[N+1]; // row-start indices for x 15 | 16 | int y[N]; // binary response 17 | } 18 | 19 | transformed data { 20 | int K; // number of columns in x 21 | int SF; // first subject effect column in x 22 | int SL; // last subject effect column in x 23 | 24 | K = P + S * QS; 25 | SF = P + 1; 26 | SL = P + S * QS; 27 | } 28 | 29 | parameters { 30 | vector[P] beta; 31 | 32 | matrix[QS,S] gamma_subj_raw; 33 | vector[QS] sigma_subj; // subject effect SDs 34 | cholesky_factor_corr[QS] omega_subj_raw; 35 | } 36 | 37 | transformed parameters { 38 | vector[K] coef; // all coefficients 39 | vector[N] y_hat; // predicted log-odds 40 | 41 | // transform fixed effects 42 | coef[1:P] = 2 * beta; 43 | 44 | // transform subject effects 45 | coef[SF:SL] 46 | = to_vector(rep_matrix(sigma_subj,S) 47 | .* (omega_subj_raw * gamma_subj_raw)); 48 | 49 | // y_hat = x * coef 50 | y_hat = csr_matrix_times_vector(N,K,x_w,x_v,x_u,coef); 51 | } 52 | 53 | model { 54 | beta ~ normal(0,1); 55 | 56 | to_vector(gamma_subj_raw) ~ normal(0,1); 57 | sigma_subj ~ normal(0,1); 58 | omega_subj_raw ~ lkj_corr_cholesky(2); 59 | 60 | y ~ bernoulli_logit(y_hat); // logistic model defined 61 | } 62 | 63 | generated quantities { 64 | real s0; 65 | real s1; 66 | real s2; 67 | real s3; 68 | real r01; 69 | real r02; 70 | real r03; 71 | real r12; 72 | real r13; 73 | real r23; 74 | 75 | s0 = sigma_subj[1]; 76 | s1 = sigma_subj[2]; 77 | s2 = sigma_subj[3]; 78 | s3 = sigma_subj[4]; 79 | { 80 | matrix[QS,QS] omega_subj; // correlation in subject effects 81 | omega_subj = tcrossprod(omega_subj_raw); 82 | r01 = omega_subj[2,1]; 83 | r02 = omega_subj[3,1]; 84 | r03 = omega_subj[4,1]; 85 | r12 = omega_subj[3,2]; 86 | r13 = omega_subj[4,2]; 87 | r23 = omega_subj[4,3]; 88 | } 89 | } 90 | -------------------------------------------------------------------------------- /code/imbalanced_gaussian.stan: -------------------------------------------------------------------------------- 1 | // Stan code for imbalanced gaussian mixed effects simulation 2 | 3 | data { 4 | int N; // number of observations 5 | int S; // number of subjects 6 | 7 | int P; // number of fixed effects 8 | int QS; // number of subject effects 9 | 10 | // sparse model matrix (CSR) 11 | int nz; // number of non-zero elements in x 12 | vector[nz] x_w; // non-zero elements in x 13 | int x_v[nz]; // column indices for x_w 14 | int x_u[N+1]; // row-start indices for x 15 | 16 | vector[N] y; // continuous response 17 | } 18 | 19 | transformed data { 20 | int K; // number of columns in x 21 | int SF; // first subject effect column in x 22 | int SL; // last subject effect column in x 23 | 24 | K = P + S * QS; 25 | SF = P + 1; 26 | SL = P + S * QS; 27 | } 28 | 29 | parameters { 30 | vector[P] beta_raw; 31 | real res_raw; 32 | 33 | matrix[QS,S] gamma_subj_raw; 34 | vector[QS] sigma_subj_raw; 35 | cholesky_factor_corr[QS] omega_subj_raw; 36 | } 37 | 38 | transformed parameters { 39 | vector[K] coef; // all coefficients 40 | real res; // residual standard error 41 | vector[N] y_hat; // predicted log-odds 42 | 43 | // transform fixed effects 44 | coef[1:P] = beta_raw * 2; 45 | res = 0.5 * res_raw; 46 | 47 | // transform subject effects 48 | coef[SF:SL] 49 | = to_vector(rep_matrix(sigma_subj_raw,S) 50 | .* (omega_subj_raw * gamma_subj_raw)); 51 | 52 | // y_hat = x * coef 53 | y_hat = csr_matrix_times_vector(N,K,x_w,x_v,x_u,coef); 54 | } 55 | 56 | model { 57 | beta_raw ~ normal(0,1); 58 | res_raw ~ normal(0,1); 59 | 60 | to_vector(gamma_subj_raw) ~ normal(0,1); 61 | sigma_subj_raw ~ normal(0,1); 62 | omega_subj_raw ~ lkj_corr_cholesky(2); 63 | 64 | y ~ normal(y_hat,res); // linear model defined 65 | } 66 | 67 | generated quantities { 68 | real s0; 69 | real s1; 70 | real s2; 71 | real s3; 72 | real r01; 73 | real r02; 74 | real r03; 75 | real r12; 76 | real r13; 77 | real r23; 78 | 79 | s0 = sigma_subj_raw[1]; 80 | s1 = sigma_subj_raw[2]; 81 | s2 = sigma_subj_raw[3]; 82 | s3 = sigma_subj_raw[4]; 83 | { 84 | matrix[QS,QS] omega_subj; // correlation in subject effects 85 | omega_subj = tcrossprod(omega_subj_raw); 86 | r01 = omega_subj[2,1]; 87 | r02 = omega_subj[3,1]; 88 | r03 = omega_subj[4,1]; 89 | r12 = omega_subj[3,2]; 90 | r13 = omega_subj[4,2]; 91 | r23 = omega_subj[4,3]; 92 | } 93 | } 94 | -------------------------------------------------------------------------------- /code/simple_logistic_sim.R: -------------------------------------------------------------------------------- 1 | #### compare_lme4_stan 2 | #### simple logistic model 3 | 4 | compare_lme4_stan <- function(B=10000,threads=16){ 5 | # B is the number of models to run for each of the 8 model types 6 | # threads is the number of threads to split this task by; set to 1 to not parallelize 7 | # in which case the individual stan models will be parallelized 8 | 9 | single_thread <- function(pardat){ 10 | require(lme4) 11 | require(rstan) 12 | 13 | ### create data frame without response 14 | S <- 50 15 | I <- 30 16 | N <- S * I 17 | slist <- rep(c(1,2),length.out=S) 18 | ilist <- list( 19 | rep(c(1,-1),length.out=I), 20 | rep(c(-1,1),length.out=I)) 21 | data <- data.frame( 22 | y = rep(0,N), 23 | subject = sort(rep(1:S,I)), 24 | item = rep(1:I,S), 25 | condition = unlist(ilist[slist])) 26 | formula <- y ~ condition + (1 + condition | subject) + (1 + condition | item) 27 | x <- model.matrix(nobars(formula),data) 28 | z <- mkReTrms(findbars(formula),data) 29 | xz <- cbind(x,t(z$Ztlist[[1]]),t(z$Ztlist[[2]])) 30 | xzsparse <- extract_sparse_parts(xz) 31 | standata <- list(N = N, S = S, I = I, P = 2, QS = 2, QI = 2, y = 0, 32 | nz = length(xzsparse$w), x_w = xzsparse$w, x_v = xzsparse$v, 33 | x_u = xzsparse$u) 34 | keep <- c("s0","s1","rs","i0","i1","ri","coef") 35 | 36 | ### Generate model 37 | generate_model <- function(H0){ 38 | b0 <- runif(1,-1,1) 39 | b1 <- ifelse(H0,0,0.8) 40 | 41 | ss <- runif(1,0,1.5) 42 | ss[2] <- runif(1,0,0.75) 43 | 44 | si <- runif(1,0,1) 45 | si[2] <- runif(1,0,0.5) 46 | 47 | rs <- runif(1,-0.9,0.9) 48 | Ls <- t(chol(matrix(c(1,rs,rs,1),2,2))) 49 | 50 | ri <- runif(1,-0.9,0.9) 51 | Li <- t(chol(matrix(c(1,ri,ri,1),2,2))) 52 | 53 | gs <- Ls %*% matrix(rnorm(S*2),2,S) 54 | gs[1,] <- gs[1,] * ss[1] 55 | gs[2,] <- gs[2,] * ss[2] 56 | 57 | gi <- Li %*% matrix(rnorm(I*2),2,I) 58 | gi[1,] <- gi[1,] * si[1] 59 | gi[2,] <- gi[2,] * si[2] 60 | 61 | bg <- c(b0,b1,as.vector(gs),as.vector(gi)) 62 | lo <- as.vector(xz %*% bg) 63 | pext <- mean(abs(lo)>5) 64 | y <- rbinom(N,1,plogis(lo)) 65 | 66 | return(list(y = y, s0 = ss[1], s1 = ss[2], 67 | rs = rs, i0 = si[1], i1 = si[2], ri = ri, 68 | pext = pext, b0 = b0, b1 = b1)) 69 | } 70 | 71 | ### Fit single model with both lme4 and stan 72 | single_model <- function(H0){ 73 | mod <- generate_model(H0) 74 | y <- data$y <- standata$y <- mod$y 75 | 76 | m_lme4 <- summary(glmer(formula, data, family = binomial)) 77 | p_H0_lme4 <- m_lme4$coef[2,4] 78 | mess_lme4 <- m_lme4$optinfo$conv$lme4 79 | if(!is.null(names(mess_lme4))){ 80 | mess_lme4 <- paste(mess_lme4$messages,collapse="; ") 81 | mess_lme4 <- str_replace_all(mess_lme4,",","") 82 | mess_lme4 <- str_replace_all(mess_lme4,"\n","") 83 | } else { 84 | mess_lme4 <- "" 85 | } 86 | vc <- data.frame(m_lme4$varcor) 87 | 88 | m_stan <- sampling(object = stanmod, data = standata, pars = keep, chains = 3) 89 | samp <- data.frame(do.call(rbind,args=get_sampler_params(m_stan,inc_warmup=FALSE))) 90 | nmtd <- sum(samp$treedepth__ > 10) 91 | ndiv <- sum(samp$divergent__) 92 | p_HA_stan <- mean(extract(m_stan,pars="coef")$coef[,2]>0) 93 | m_stan <- summary(m_stan,probs=c(.025,.975))$summary 94 | ci_0_stan <- m_stan["coef[2]",4]<0 & m_stan["coef[2]",5]>0 95 | rhat <- quantile(m_stan[,"Rhat"],c(0,.25,.5,.75,1)) 96 | neff <- quantile(m_stan[,"n_eff"],c(0,.25,.5,.75,1)) 97 | m_stan <- m_stan[,1] 98 | 99 | if(mess_lme4 != "" | ndiv > 0 | as.numeric(rhat[5]) >= 1.1){ 100 | save(y, file = paste(getwd(),"/unconverged_thread_",thread,"_iteration_",b,"_H0_",H0,".rda",sep="")) 101 | } 102 | 103 | return(data.frame( 104 | H0, pext = mod$pext, 105 | b0 = mod$b0, b0_lme4 = m_lme4$coef[1,1], b0_stan = m_stan["coef[1]"], 106 | b1 = mod$b1, b1_lme4 = m_lme4$coef[2,1], b1_stan = m_stan["coef[2]"], 107 | s0 = mod$s0, s0_lme4 = vc[1,5], s0_stan = m_stan["s0"], 108 | s1 = mod$s1, s1_lme4 = vc[2,5], s1_stan = m_stan["s1"], 109 | rs = mod$rs, rs_lme4 = vc[3,5], rs_stan = m_stan["rs"], 110 | i0 = mod$i0, i0_lme4 = vc[4,5], i0_stan = m_stan["i0"], 111 | i1 = mod$i1, i1_lme4 = vc[5,5], i1_stan = m_stan["i1"], 112 | ri = mod$ri, ri_lme4 = vc[6,5], ri_stan = m_stan["ri"], 113 | p_H0_lme4, p_HA_stan, ci_0_stan, 114 | mess_lme4, nmtd, ndiv, 115 | rhat_min = rhat[1], rhat_q25 = rhat[2], rhat_med = rhat[3], rhat_q75 = rhat[4], rhat_max = rhat[5], 116 | neff_min = neff[1], neff_q25 = neff[2], neff_med = neff[3], neff_q75 = neff[4], neff_max = neff[5])) 117 | } 118 | 119 | ## unpack arguments 120 | B <- pardat[[1]] 121 | thread <- pardat[[2]] 122 | stanmod <- pardat[[3]] 123 | 124 | ### run B iterations of each of the 2 models based on H0 and output progress to thread-specific file 125 | results <- NULL 126 | progfile <- paste(getwd(),"/","simple_logistic_sim_thread_",thread,".progress",sep="") 127 | csvfile <- paste(getwd(),"/","simple_logistic_sim_thread_",thread,".csv",sep="") 128 | cat(paste(Sys.time(),"Starting Thread",thread),file=progfile,sep="\n",append=FALSE) 129 | 130 | for(b in 1:B){ # Each iteration has 2 calls to single_model, one for each H0 condition 131 | cat("\n",paste(Sys.time(),"Iteration",b,"/",B),file=progfile,sep="",append=TRUE) 132 | results <- rbind(results,single_model(TRUE)) 133 | cat(" . ",file=progfile,sep="",append=TRUE) 134 | results <- rbind(results,single_model(FALSE)) 135 | cat(" . ",file=progfile,sep="",append=TRUE) 136 | write.csv(results,row.names=F,quote=F,file=csvfile) 137 | } 138 | cat("\n",paste(Sys.time(),"Finished Thread",thread),file=progfile,sep="",append=TRUE) 139 | 140 | ### return results 141 | return(results) 142 | } 143 | 144 | {### Stan code 145 | stancode <- "// Stan code for simple logistic regression simulation 146 | // adapted from Kimball, Shantz, Eager, and Roy (2016) 147 | 148 | data { 149 | int N; // number of observations 150 | int S; // number of subjects 151 | int I; // number of items 152 | 153 | int P; // number of fixed effects 154 | int QS; // number of subject effects 155 | int QI; // number of item effects 156 | 157 | int y[N]; // binary response 158 | 159 | // sparse model matrix (CSR) 160 | int nz; // number of non-zero elements in x 161 | vector[nz] x_w; // non-zero elements in x 162 | int x_v[nz]; // column indices for x_w 163 | int x_u[N+1]; // row-start indices for x 164 | } 165 | 166 | transformed data { 167 | int K; // number of columns in x 168 | int SF; // first subject effect column in x 169 | int SL; // last subject effect column in x 170 | int IF; // first item effect column in x 171 | int IL; // last item effect column in x 172 | 173 | K = P + S * QS + I * QI; 174 | SF = P + 1; 175 | SL = P + S * QS; 176 | IF = SL + 1; 177 | IL = SL + I * QI; 178 | } 179 | 180 | parameters { 181 | vector[P] beta; 182 | 183 | matrix[QS,S] gamma_subj_raw; 184 | vector[QS] sigma_subj; // subject effect SDs 185 | cholesky_factor_corr[QS] omega_subj_raw; 186 | 187 | matrix[QI,I] gamma_item_raw; 188 | vector[QI] sigma_item; // item effect SDs 189 | cholesky_factor_corr[QI] omega_item_raw; 190 | } 191 | 192 | transformed parameters { 193 | vector[K] coef; // all coefficients 194 | vector[N] y_hat; // predicted log-odds 195 | 196 | // transform fixed effects 197 | coef[1:P] = beta; 198 | 199 | // transform subject effects 200 | coef[SF:SL] 201 | = to_vector(rep_matrix(sigma_subj,S) 202 | .* (omega_subj_raw * gamma_subj_raw)); 203 | 204 | // transform item effects 205 | coef[IF:IL] 206 | = to_vector(rep_matrix(sigma_item,I) 207 | .* (omega_item_raw * gamma_item_raw)); 208 | 209 | // y_hat = x * coef 210 | y_hat = csr_matrix_times_vector(N,K,x_w,x_v,x_u,coef); 211 | } 212 | 213 | model { 214 | beta ~ normal(0,1); 215 | 216 | to_vector(gamma_subj_raw) ~ normal(0,1); 217 | sigma_subj ~ normal(0,1); 218 | omega_subj_raw ~ lkj_corr_cholesky(2); 219 | 220 | to_vector(gamma_item_raw) ~ normal(0,1); 221 | sigma_item ~ normal(0,1); 222 | omega_item_raw ~ lkj_corr_cholesky(2); 223 | 224 | y ~ bernoulli_logit(y_hat); // logistic model defined 225 | } 226 | 227 | generated quantities { 228 | real s0; 229 | real s1; 230 | real i0; 231 | real i1; 232 | real rs; 233 | real ri; 234 | 235 | s0 = sigma_subj[1]; 236 | s1 = sigma_subj[2]; 237 | i0 = sigma_item[1]; 238 | i1 = sigma_item[2]; 239 | { 240 | matrix[QS,QS] omega_subj; // correlation in subject effects 241 | matrix[QI,QI] omega_item; // correlation in item effects 242 | omega_subj = tcrossprod(omega_subj_raw); 243 | omega_item = tcrossprod(omega_item_raw); 244 | rs = omega_subj[1,2]; 245 | ri = omega_item[1,2]; 246 | } 247 | } 248 | " 249 | } 250 | 251 | require(rstan) 252 | stanmod <- stan_model(model_name = "simple_logistic_sim", model_code = stancode, save_dso = TRUE) 253 | 254 | if(threads==1){ 255 | options(mc.cores = parallel::detectCores()) 256 | results <- single_thread(list(B,threads,stanmod)) 257 | } else { 258 | require(parallel) 259 | cl = makeCluster(rep("localhost", threads)) 260 | 261 | # simulations per thread and thread number 262 | left <- B %% threads 263 | B <- rep(floor(B/threads),threads) 264 | if(left>0) B[1:left] <- B[1:left] + 1 265 | pardat <- list() 266 | for(i in 1:threads) pardat[[i]] <- list(B[i],i,stanmod) 267 | 268 | results <- parLapply(cl,pardat,single_thread) 269 | 270 | stopCluster(cl) 271 | 272 | results <- do.call(rbind,args=results) 273 | } 274 | 275 | return(results) 276 | } 277 | 278 | results <- compare_lme4_stan() 279 | -------------------------------------------------------------------------------- /code/barretall.R: -------------------------------------------------------------------------------- 1 | #### replicate Barr et al (2013) 2 | 3 | compare_lmer_stan_barretal <- function(B=10000,threads=8){ 4 | # B is the number of models to run for each of the 8 model types 5 | # threads is the number of threads to split this task by; set to 1 to not parallelize 6 | # in which case the individual stan models will be parallelized 7 | 8 | loop_lmer_stan_barretal <- function(pardat){ 9 | require(afex) 10 | require(rstan) 11 | 12 | {### create data frames without response 13 | 14 | ## within-item, 24 items 15 | S <- 24 # number of subjects 16 | I <- 24 # number of items 17 | 18 | wi24 <- expand.grid(subject=1:S,item=1:I) 19 | wi24$x <- 0.5 20 | wi24[wi24$subject %in% 1:(S/2) & wi24$item %in% 1:(I/2),"x"] <- -0.5 21 | wi24[wi24$subject %in% (S/2+1):S & wi24$item %in% (I/2+1):I,"x"] <- -0.5 22 | 23 | ## within-item, 12 items 24 | S <- 24 # number of subjects 25 | I <- 12 # number of items 26 | 27 | wi12 <- expand.grid(subject=1:S,item=1:I) 28 | wi12$x <- 0.5 29 | wi12[wi12$subject %in% 1:(S/2) & wi12$item %in% 1:(I/2),"x"] <- -0.5 30 | wi12[wi12$subject %in% (S/2+1):S & wi12$item %in% (I/2+1):I,"x"] <- -0.5 31 | 32 | ## between-item, 24 items 33 | S <- 24 # number of subjects 34 | I <- 24 # number of items 35 | 36 | bi24 <- expand.grid(subject=1:S,item=1:I) 37 | bi24$x <- 0.5 38 | bi24[bi24$item %in% 1:(I/2),"x"] <- -0.5 39 | 40 | ## between-item, 12 items 41 | S <- 24 # number of subjects 42 | I <- 12 # number of items 43 | 44 | bi12 <- expand.grid(subject=1:S,item=1:I) 45 | bi12$x <- 0.5 46 | bi12[bi12$item %in% 1:(I/2),"x"] <- -0.5 47 | } 48 | 49 | ### Generate data frame with y and missing data 50 | generate_linear_data <- function(btwn,I,H0){ 51 | # I should be 12 or 24 52 | # btwn should be TRUE for between-item, FALSE for within-item 53 | # H0 should be TRUE if the treatment effect is 0 and FALSE if it is 0.8 54 | 55 | require(MASS) 56 | 57 | S <- 24 58 | 59 | if(btwn){ 60 | if(I==12){ 61 | dat <- bi12 62 | } else dat <- bi24 63 | } else if(I==12){ 64 | dat <- wi12 65 | } else dat <- wi24 66 | N <- nrow(dat) 67 | 68 | # Barr et al (2013) Table 2 69 | b0 <- runif(1,-3,3) 70 | b1 <- ifelse(H0,0,0.8) 71 | sigma_subj <- sqrt(runif(2,0,3)) 72 | rho_subj <- runif(1,-0.8,0.8) 73 | if(!btwn){ 74 | sigma_item <- sqrt(runif(2,0,3)) 75 | rho_item <- runif(1,-0.8,0.8) 76 | } else { 77 | sigma_item <- sqrt(runif(1,0,3)) 78 | rho_item <- NA 79 | } 80 | sigma_res <- sqrt(runif(1,0,3)) 81 | p_missing <- runif(1,0,0.05) 82 | 83 | gamma_subj <- mvrnorm(n = S, mu = c(0,0), 84 | Sigma = diag(sigma_subj) %*% matrix(c(1,rho_subj,rho_subj,1),2,2) %*% diag(sigma_subj)) 85 | 86 | if(btwn){ 87 | gamma_item <- cbind(rnorm(I,0,sigma_item),0) 88 | } else { 89 | gamma_item <- mvrnorm(n = I, mu = c(0,0), 90 | Sigma = diag(sigma_item) %*% matrix(c(1,rho_item,rho_item,1),2,2) %*% diag(sigma_item)) 91 | } 92 | 93 | dat$y <- 0 94 | for(n in 1:N){ 95 | dat[n,"y"] <- sum(c(1,dat[n,"x"]) * 96 | (c(b0,b1) + gamma_subj[dat[n,"subject"],] + gamma_item[dat[n,"item"],])) 97 | } 98 | dat$y <- dat$y + rnorm(N,0,sigma_res) 99 | dat <- dat[-sample(1:N,ceiling(N*p_missing),replace=FALSE),] 100 | 101 | return(list(dat = dat, pars = data.frame(b0, b1, s0=sigma_subj[1], s1=sigma_subj[2], rho_subj, 102 | i0 = sigma_item[1], i1 = ifelse(btwn,NA,sigma_item[2]), rho_item, sigma_res, p_missing))) 103 | } 104 | 105 | ### Fit single model with both lmer and stan 106 | model_lmer_stan_barretal <- function(btwn,I,H0){ 107 | dat <- generate_linear_data(btwn,I,H0) 108 | data <- dat$dat 109 | dat <- dat$pars 110 | y_mu <- mean(data$y) 111 | y_sd <- sd(data$y) 112 | stan_data <- list(N = nrow(data), S = max(data$subject), I = max(data$item), 113 | y = data$y, y_mu = y_mu, y_sd = y_sd, 114 | x = data$x, subj = data$subject, item = data$item) 115 | 116 | stan_pars <- c("b0","b1","sigma_e","sigma_subj","sigma_item", 117 | "gamma_subj","gamma_item","rho_subj","y_hat") 118 | 119 | if(btwn){ 120 | formula <- y ~ x + (1 + x | subject) + (1 | item) 121 | stan_model <- stan_model_bi 122 | } else { 123 | formula <- y ~ x + (1 + x | subject) + (1 + x | item) 124 | stan_model <- stan_model_wi 125 | stan_pars <- c(stan_pars,"rho_item") 126 | } 127 | 128 | data$y <- scale(data$y)[,1] 129 | 130 | m_lmer <- mixed(formula, data) 131 | p_data_H0_lmer <- m_lmer$anova$P 132 | conv_rest <- summary(m_lmer$restricted$x)$optinfo$conv$lme4 133 | m_lmer <- summary(m_lmer$full) 134 | conv_lmer <- m_lmer$optinfo$conv$lme4 135 | t_lmer <- m_lmer$coef[2,3] 136 | if(!is.null(names(conv_rest))){ 137 | conv_rest <- paste(conv_rest$messages,collapse="; ") 138 | conv_rest <- str_replace_all(conv_rest,",","") 139 | conv_rest <- str_replace_all(conv_rest,"\n","") 140 | } else conv_rest <- "" 141 | if(!is.null(names(conv_lmer))){ 142 | conv_lmer <- paste(conv_lmer$messages,collapse="; ") 143 | conv_lmer <- str_replace_all(conv_lmer,",","") 144 | conv_lmer <- str_replace_all(conv_lmer,"\n","") 145 | } else conv_lmer <- "" 146 | 147 | m_stan <- sampling(object = stan_model, data = stan_data, pars = stan_pars, 148 | chains = 3, iter = 13000, warmup = 3000, thin = 10) 149 | samp <- data.frame(do.call(rbind,args=get_sampler_params(m_stan,inc_warmup=FALSE))) 150 | nmtd <- sum(samp$treedepth__>10) 151 | ndiv <- sum(samp$divergent__) 152 | p_HA_data_stan <- mean(extract(m_stan,pars="b1")$b1>0) 153 | m_stan <- summary(m_stan)$summary 154 | ci_0_stan <- m_stan["b1",4]<0 & m_stan["b1",8]>0 155 | rhat <- quantile(m_stan[,"Rhat"],c(0,.25,.5,.75,1)) 156 | neff <- quantile(m_stan[,"n_eff"],c(0,.25,.5,.75,1)) 157 | m_stan <- m_stan[,1] 158 | 159 | vc <- data.frame(m_lmer$varcor) 160 | if(btwn){ 161 | i1_lmer <- ri_lmer <- i1_stan <- ri_stan <- NA 162 | i0_stan <- m_stan["sigma_item"] 163 | res_lmer <- vc[5,5] * y_sd 164 | } else { 165 | i1_lmer <- vc[5,5] * y_sd 166 | ri_lmer <- vc[6,5] 167 | res_lmer <- vc[7,5] * y_sd 168 | i0_stan <- m_stan["sigma_item[1]"] 169 | i1_stan <- m_stan["sigma_item[2]"] 170 | ri_stan <- m_stan["rho_item"] 171 | } 172 | 173 | return(data.frame( 174 | btwn, I, H0, pmiss = dat$p_missing, 175 | b0 = dat$b0, b0_lmer = m_lmer$coef[1,1]*y_sd+y_mu, b0_stan = m_stan["b0"], 176 | b1 = dat$b1, b1_lmer = m_lmer$coef[2,1]*y_sd, b1_stan = m_stan["b1"], 177 | s0 = dat$s0, s0_lmer = vc[1,5]*y_sd, s0_stan = m_stan["sigma_subj[1]"], 178 | s1 = dat$s1, s1_lmer = vc[2,5]*y_sd, s1_stan = m_stan["sigma_subj[2]"], 179 | rs = dat$rho_subj, rs_lmer = vc[3,5], rs_stan = m_stan["rho_subj"], 180 | i0 = dat$i0, i0_lmer = vc[4,5]*y_sd, i0_stan, 181 | i1 = dat$i1, i1_lmer, i1_stan, 182 | ri = dat$rho_item, ri_lmer, ri_stan, 183 | res = dat$sigma_res, res_lmer, res_stan = m_stan["sigma_e"], 184 | t_lmer, p_data_H0_lmer, p_HA_data_stan, ci_0_stan, 185 | conv_lmer, conv_rest, nmtd, ndiv, 186 | rhat_min = rhat[1], rhat_q25 = rhat[2], rhat_med = rhat[3], rhat_q75 = rhat[4], rhat_max = rhat[5], 187 | neff_min = neff[1], neff_q25 = neff[2], neff_med = neff[3], neff_q75 = neff[4], neff_max = neff[5])) 188 | } 189 | 190 | ## unpack arguments 191 | B <- pardat[[1]] 192 | thread <- pardat[[2]] 193 | stan_model_wi <- pardat[[3]] 194 | stan_model_bi <- pardat[[4]] 195 | 196 | ### run B iterations of each of the 8 models and output progress to thread-specific file 197 | barretal_comparison <- NULL 198 | tfile <- paste(getwd(),"/","barretal_thread_",thread,".progress",sep="") 199 | tcsvfile <- paste(getwd(),"/","barretal_thread_",thread,".csv",sep="") 200 | cat(paste(Sys.time(),"Starting Thread",thread),file=tfile,sep="\n",append=FALSE) 201 | for(b in 1:B){ # Each iteration has 8 calls to model_lmer_stan_barretal, one for each combination 202 | cat("\n",paste(Sys.time(),"Iteration",b,"/",B),file=tfile,sep="",append=TRUE) 203 | barretal_comparison <- rbind(barretal_comparison,model_lmer_stan_barretal(TRUE,12,TRUE)) 204 | cat(" . ",file=tfile,sep="",append=TRUE) 205 | barretal_comparison <- rbind(barretal_comparison,model_lmer_stan_barretal(TRUE,12,FALSE)) 206 | cat(" . ",file=tfile,sep="",append=TRUE) 207 | barretal_comparison <- rbind(barretal_comparison,model_lmer_stan_barretal(FALSE,12,TRUE)) 208 | cat(" . ",file=tfile,sep="",append=TRUE) 209 | barretal_comparison <- rbind(barretal_comparison,model_lmer_stan_barretal(FALSE,12,FALSE)) 210 | cat(" . ",file=tfile,sep="",append=TRUE) 211 | barretal_comparison <- rbind(barretal_comparison,model_lmer_stan_barretal(TRUE,24,TRUE)) 212 | cat(" . ",file=tfile,sep="",append=TRUE) 213 | barretal_comparison <- rbind(barretal_comparison,model_lmer_stan_barretal(TRUE,24,FALSE)) 214 | cat(" . ",file=tfile,sep="",append=TRUE) 215 | barretal_comparison <- rbind(barretal_comparison,model_lmer_stan_barretal(FALSE,24,TRUE)) 216 | cat(" . ",file=tfile,sep="",append=TRUE) 217 | barretal_comparison <- rbind(barretal_comparison,model_lmer_stan_barretal(FALSE,24,FALSE)) 218 | cat(" . ",file=tfile,sep="",append=TRUE) 219 | write.csv(barretal_comparison,file=tcsvfile) 220 | } 221 | cat("\n",paste(Sys.time(),"Finished Thread",thread),file=tfile,sep="",append=TRUE) 222 | 223 | ### return results 224 | return(barretal_comparison) 225 | } 226 | 227 | {### Stan code 228 | 229 | {## code for within-item 230 | stan_code_wi <- "// stan code for within-item Barr et al (2013) replication 231 | data { 232 | int N; 233 | int S; 234 | int I; 235 | 236 | vector[N] y; 237 | real y_mu; 238 | real y_sd; 239 | 240 | vector[N] x; 241 | int subj[N]; 242 | int item[N]; 243 | } 244 | 245 | parameters { 246 | real b0_raw; 247 | real b1_raw; 248 | real sigma_e_raw; 249 | 250 | vector[2] sigma_subj_raw; 251 | cholesky_factor_corr[2] L_subj; 252 | vector[2] gamma_subj_raw[S]; 253 | 254 | vector[2] sigma_item_raw; 255 | cholesky_factor_corr[2] L_item; 256 | vector[2] gamma_item_raw[I]; 257 | } 258 | 259 | transformed parameters { 260 | real b0; 261 | real b1; 262 | real sigma_e; 263 | 264 | vector[2] sigma_subj; 265 | vector[2] gamma_subj[S]; 266 | 267 | vector[2] sigma_item; 268 | vector[2] gamma_item[I]; 269 | 270 | vector[N] y_hat; 271 | 272 | b0 = y_mu + 0.25 * y_sd * b0_raw; 273 | b1 = y_sd * b1_raw; 274 | sigma_e = 0.5 * y_sd * sigma_e_raw; 275 | 276 | sigma_subj = y_sd * sigma_subj_raw; 277 | for(s in 1:S){ 278 | gamma_subj[s] = sigma_subj .* (L_subj * gamma_subj_raw[s]); 279 | } 280 | 281 | sigma_item = y_sd * sigma_item_raw; 282 | for(i in 1:I){ 283 | gamma_item[i] = sigma_item .* (L_item * gamma_item_raw[i]); 284 | } 285 | 286 | for(n in 1:N){ 287 | y_hat[n] = b0 + gamma_subj[subj[n],1] + gamma_item[item[n],1] 288 | + x[n] * (b1 + gamma_subj[subj[n],2] + gamma_item[item[n],2]); 289 | } 290 | } 291 | 292 | model { 293 | b0_raw ~ normal(0,1); 294 | b1_raw ~ normal(0,1); 295 | sigma_e_raw ~ normal(0,1); 296 | 297 | sigma_subj_raw ~ normal(0,1); 298 | L_subj ~ lkj_corr_cholesky(2); 299 | for(s in 1:S) gamma_subj_raw[s] ~ normal(0,1); 300 | 301 | sigma_item_raw ~ normal(0,1); 302 | L_item ~ lkj_corr_cholesky(2); 303 | for(i in 1:I) gamma_item_raw[i] ~ normal(0,1); 304 | 305 | y ~ normal(y_hat,sigma_e); 306 | } 307 | 308 | generated quantities { 309 | real rho_subj; 310 | real rho_item; 311 | { 312 | matrix[2,2] omega_subj; 313 | matrix[2,2] omega_item; 314 | 315 | omega_subj = tcrossprod(L_subj); 316 | rho_subj = omega_subj[2,1]; 317 | omega_item = tcrossprod(L_item); 318 | rho_item = omega_item[2,1]; 319 | } 320 | } 321 | " 322 | } 323 | 324 | {## code for between-item 325 | stan_code_bi <- "// stan code for between-item Barr et al (2013) replication 326 | data { 327 | int N; 328 | int S; 329 | int I; 330 | 331 | vector[N] y; 332 | real y_mu; 333 | real y_sd; 334 | 335 | vector[N] x; 336 | int subj[N]; 337 | int item[N]; 338 | } 339 | 340 | parameters { 341 | real b0_raw; 342 | real b1_raw; 343 | real sigma_e_raw; 344 | 345 | vector[2] sigma_subj_raw; 346 | cholesky_factor_corr[2] L_subj; 347 | vector[2] gamma_subj_raw[S]; 348 | 349 | real sigma_item_raw; 350 | vector[I] gamma_item_raw; 351 | } 352 | 353 | transformed parameters { 354 | real b0; 355 | real b1; 356 | real sigma_e; 357 | 358 | vector[2] sigma_subj; 359 | vector[2] gamma_subj[S]; 360 | 361 | real sigma_item; 362 | vector[I] gamma_item; 363 | 364 | vector[N] y_hat; 365 | 366 | b0 = y_mu + 0.25 * y_sd * b0_raw; 367 | b1 = y_sd * b1_raw; 368 | sigma_e = 0.5 * y_sd * sigma_e_raw; 369 | 370 | sigma_subj = y_sd * sigma_subj_raw; 371 | for(s in 1:S){ 372 | gamma_subj[s] = sigma_subj .* (L_subj * gamma_subj_raw[s]); 373 | } 374 | 375 | sigma_item = y_sd * sigma_item_raw; 376 | gamma_item = sigma_item * gamma_item_raw; 377 | 378 | for(n in 1:N){ 379 | y_hat[n] = b0 + gamma_subj[subj[n],1] + gamma_item[item[n]] 380 | + x[n] * (b1 + gamma_subj[subj[n],2]); 381 | } 382 | } 383 | 384 | model { 385 | b0_raw ~ normal(0,1); 386 | b1_raw ~ normal(0,1); 387 | sigma_e_raw ~ normal(0,1); 388 | 389 | sigma_subj_raw ~ normal(0,1); 390 | L_subj ~ lkj_corr_cholesky(2); 391 | for(s in 1:S) gamma_subj_raw[s] ~ normal(0,1); 392 | 393 | sigma_item_raw ~ normal(0,1); 394 | gamma_item_raw ~ normal(0,1); 395 | 396 | y ~ normal(y_hat,sigma_e); 397 | } 398 | 399 | generated quantities { 400 | real rho_subj; 401 | { 402 | matrix[2,2] omega_subj; 403 | 404 | omega_subj = tcrossprod(L_subj); 405 | rho_subj = omega_subj[2,1]; 406 | } 407 | } 408 | " 409 | } 410 | } 411 | 412 | require(rstan) 413 | stan_model_wi <- stan_model(model_name = "barretal_within_item", model_code = stan_code_wi, save_dso = TRUE) 414 | stan_model_bi <- stan_model(model_name = "barretal_between_item", model_code = stan_code_bi, save_dso = TRUE) 415 | 416 | if(threads==1){ 417 | options(mc.cores = parallel::detectCores()) 418 | barretal_comparison <- loop_lmer_stan_barretal(list(B,threads,stan_model_wi,stan_model_bi)) 419 | } else { 420 | require(parallel) 421 | cl = makeCluster(rep("localhost", threads)) 422 | 423 | # simulations per thread and thread number 424 | left <- B %% threads 425 | B <- rep(floor(B/threads),threads) 426 | if(left>0) B[1:left] <- B[1:left] + 1 427 | pardat <- list() 428 | for(i in 1:threads) pardat[[i]] <- list(B[i],i,stan_model_wi,stan_model_bi) 429 | 430 | barretal_comparison <- parLapply(cl,pardat,loop_lmer_stan_barretal) 431 | barretal_comparison <- do.call(rbind,args=barretal_comparison) 432 | } 433 | 434 | return(barretal_comparison) 435 | } 436 | -------------------------------------------------------------------------------- /code/complex_imbalanced.R: -------------------------------------------------------------------------------- 1 | 2 | compare_lme4_stan_imbalanced_sims <- function(B = 10000, threads = 16, dir = getwd(), 3 | gau_file = "imbalanced_gaussian.stan", bin_file = "imbalanced_logistic.stan"){ 4 | # B is the number of models to run for each of the 2 model types. 5 | # threads is the number of threads to split this task by. 6 | # set threads to 1 to not parallelize the iterations, 7 | # in which case the individual Stan models will be parallelized 8 | 9 | single_thread <- function(pardat){ 10 | require(lme4) 11 | require(rstan) 12 | require(stringr) 13 | require(gtools) 14 | require(clusterGeneration) 15 | options(contrasts=c("contr.sum","contr.poly")) 16 | 17 | ### Parameters of interest and lme4 formula 18 | keep <- c("coef","s0","s1","s2","s3", 19 | "r01","r02","r03","r12","r13","r23") 20 | formula <- y ~ x1 + x2 + (1 + x1 + x2 | subject) 21 | 22 | ### Generate dataset and model 23 | generate_model <- function(isgau){ 24 | balance <- function(dat){ 25 | nobs <- nrow(dat) 26 | counts <- as.data.frame(xtabs(~.,dat)) 27 | cells <- nrow(counts) 28 | non_zero_counts <- subset(counts, Freq > 0) 29 | non_empty_cells <- nrow(non_zero_counts) 30 | pct_non_empty_cells <- non_empty_cells / cells 31 | expected <- nobs / non_empty_cells 32 | freq <- non_zero_counts$Freq 33 | ratio <- freq / expected 34 | ratio[ratio < 1] <- 1 / ratio[ratio < 1] 35 | bal <- mean(ratio) / pct_non_empty_cells 36 | bal <- 2 * (1 - bal / (1 + bal)) 37 | return(bal) 38 | } 39 | 40 | mod <- list(fam = ifelse(isgau,"Gaussian","Logistic")) 41 | 42 | # S is the total number of subjects 43 | # L is the mean observations per subject 44 | # NS is a vector of the number of observations for each subject 45 | mod$S <- S <- sample(30:60,1) 46 | mod$L <- L <- runif(1,20,30) 47 | NS <- rpois(S,L) 48 | 49 | # Ensure all subjects have observations 50 | # This loop should rarely execute 51 | while(any(NS==0)){ 52 | NS <- rpois(S,L) 53 | } 54 | 55 | # Generate data 56 | subject <- NULL 57 | for(s in 1:S) subject <- c(subject,rep(s,NS[s])) 58 | x1 = sample(c("a","b"), sum(NS), 59 | prob=rdirichlet(1,c(1,1)), replace=T) 60 | x2 = sample(c("a","b","c"), sum(NS), 61 | prob=rdirichlet(1,c(1,1,1)), replace=T) 62 | 63 | # Ensure all levels have observations, which should 64 | # almost never be required 65 | x1[sample(1:sum(NS),2,replace=F)] <- c("a","b") 66 | x2[sample(1:sum(NS),3,replace=F)] <- c("a","b","c") 67 | 68 | # Create data frame and compute balance 69 | mod$frame <- data.frame(x1,x2,subject) 70 | mod$N <- N <- nrow(mod$frame) 71 | mod$balance <- balance(mod$frame) 72 | mod$frame$y <- 0 73 | 74 | # Create model matrix 75 | x <- model.matrix(nobars(formula),mod$frame) 76 | z <- mkReTrms(findbars(formula),mod$frame) 77 | mod$mat <- cbind(x,t(z$Ztlist[[1]])) 78 | sparse <- extract_sparse_parts(mod$mat) 79 | mod$stan <- list(N = N, S = S, P = 4, QS = 4, 80 | nz = length(sparse$w), x_w = sparse$w, x_v = sparse$v, 81 | x_u = sparse$u) 82 | 83 | # Generate model 84 | mod$b <- b <- c(runif(1,-2,2),runif(3,-1,1)) 85 | mod$ss <- ss <- c(runif(1,0,1),runif(3,0,0.5)) 86 | mod$os <- os <- rcorrmatrix(4) 87 | cholfail <- tryCatch(Ls <- t(chol(os)), error = function(e) e) 88 | while(inherits(cholfail, "error")){ 89 | mod$os <- os <- rcorrmatrix(4) 90 | cholfail <- tryCatch(Ls <- t(chol(os)), error = function(e) e) 91 | } 92 | gs <- Ls %*% matrix(rnorm(S*4),4,S) 93 | for(i in 1:4) gs[i,] <- gs[i,] * ss[i] 94 | 95 | y <- as.vector(mod$mat %*% c(b,as.vector(gs))) 96 | 97 | if(isgau){ 98 | mod$se <- se <- runif(1,0,1) 99 | y <- y + rnorm(N,0,se) 100 | mod$pext <- NA 101 | mod$frame$y <- mod$stan$y <- scale(y)[,1] 102 | mod$b[1] <- mod$b[1] - mean(y) 103 | mod$b <- mod$b / sd(y) 104 | mod$ss <- mod$ss / sd(y) 105 | mod$se <- mod$se / sd(y) 106 | } else { 107 | mod$se <- NA 108 | mod$pext <- mean(abs(y)>5) 109 | mod$frame$y <- mod$stan$y <- rbinom(N,1,plogis(y)) 110 | } 111 | 112 | return(mod) 113 | } 114 | 115 | ### function to perform rank-deficiency test from Bates et al. 2015 116 | my.repca <- function(theta, cutoff = 1){ 117 | m <- matrix(0,4,4) 118 | m[lower.tri(m,diag=T)] <- theta 119 | vv <- svd(m,nv=0L) 120 | names(vv) <- c("sdev","rotation") 121 | vv$center <- FALSE 122 | vv$scale <- FALSE 123 | class(vv) <- "prcomp" 124 | return(1 * (which(summary(vv)$importance[3,] >= cutoff)[1] < 4)) 125 | } 126 | 127 | ### Fit single model with both lme4 and stan 128 | single_model <- function(linear){ 129 | # generate_model shouldn't throw errors, but this is included in case 130 | genmod_fails <- 0 131 | possfail_genmod <- tryCatch(mod <- generate_model(linear), error = function(e) e) 132 | while(inherits(possfail_genmod, "error")){ 133 | genmod_fails <- genmod_fails + 1 134 | possfail_genmod <- tryCatch(mod <- generate_model(linear), error = function(e) e) 135 | } 136 | 137 | if(linear){ 138 | possfail_lme4 <- tryCatch(m_lme4 <- summary(lmer(formula, mod$frame)), error = function(e) e) 139 | possfail_stan <- tryCatch(m_stan <- sampling(object = gaustan, data = mod$stan, chains = 3, 140 | pars = c(keep,"res"), control = list(adapt_delta = 0.99)), error = function(e) e) 141 | } else { 142 | possfail_lme4 <- tryCatch(m_lme4 <- summary(glmer(formula, mod$frame, family = binomial)), error = function(e) e) 143 | possfail_stan <- tryCatch(m_stan <- sampling(object = binstan, data = mod$stan, chains = 3, 144 | pars = keep, control = list(adapt_delta = 0.99)), error = function(e) e) 145 | } 146 | 147 | if(inherits(possfail_lme4, "error") | inherits(possfail_stan, "error")){ 148 | save(mod, possfail_lme4, possfail_stan, file = paste(getwd(),"/Imbalanced_", 149 | threadchar,"_",bchar,"_",mod$fam,"_Error.rda",sep="")) 150 | return(NULL) 151 | 152 | } else { 153 | ## assess lme4 convergence 154 | mess_lme4 <- m_lme4$optinfo$conv$lme4 155 | if(!is.null(names(mess_lme4))){ 156 | mess_lme4 <- paste(mess_lme4$messages,collapse="; ") 157 | mess_lme4 <- str_replace_all(mess_lme4,",","") 158 | mess_lme4 <- str_replace_all(mess_lme4,"\n","") 159 | conv_lme4 <- 1 * (nrow(str_locate_all(mess_lme4,"negative")[[1]])==0 160 | & nrow(str_locate_all(mess_lme4,"ratio")[[1]])==0 161 | & nrow(str_locate_all(mess_lme4,"unable")[[1]])==0) 162 | if(nrow(str_locate_all(mess_lme4,"converge with max")[[1]])>0){ 163 | j <- str_split(mess_lme4,"converge with max")[[1]][2] 164 | j <- substr(j,10,nchar(j)) 165 | grad_lme4 <- as.numeric(str_split(j," ")[[1]][1]) 166 | conv_lme4 <- conv_lme4 * (grad_lme4 < .01) 167 | } else grad_lme4 <- .002 168 | } else { 169 | mess_lme4 <- "" 170 | conv_lme4 <- 1 171 | grad_lme4 <- .002 172 | } 173 | 174 | ## get ranef cov and chol from lme4 175 | vc <- data.frame(m_lme4$varcor) 176 | theta <- m_lme4$optinfo$val 177 | 178 | ## assess stan convergence and get parameter estimates 179 | samp <- data.frame(do.call(rbind, 180 | args=get_sampler_params(m_stan,inc_warmup=FALSE))) 181 | ndiv <- sum(samp$divergent__) 182 | nmtd <- sum(samp$treedepth__ > 10) 183 | m_stan <- summary(m_stan,probs=c(.025,.975))$summary 184 | rhat <- quantile(m_stan[,"Rhat"],c(0,.25,.5,.75,1)) 185 | neff <- quantile(m_stan[,"n_eff"],c(0,.25,.5,.75,1)) 186 | m_stan <- m_stan[,1] 187 | conv_stan <- 1 * (ndiv == 0 & as.numeric(rhat[5]) < 1.1) 188 | 189 | if(conv_stan==0 | conv_lme4==0){ 190 | save(mod, file = paste(getwd(),"/Imbalanced_", 191 | threadchar,"_",bchar,"_",mod$fam,"_Unconverged.rda",sep="")) 192 | } 193 | 194 | ## assemble results 195 | resblme4 <- data.frame( 196 | thread = thread, iteration = b, fam = mod$fam, reg = "lme4", minvar = min(mod$ss), 197 | pext = mod$pext, N = mod$N, S = mod$S, L = mod$L, balance = mod$balance, 198 | b0 = mod$b[1], b0_pred = m_lme4$coef[1,1], 199 | b1 = mod$b[2], b1_pred = m_lme4$coef[2,1], 200 | b2 = mod$b[3], b2_pred = m_lme4$coef[3,1], 201 | b3 = mod$b[4], b3_pred = m_lme4$coef[4,1], 202 | s0 = mod$ss[1], s0_pred = vc[1,5], 203 | s1 = mod$ss[2], s1_pred = vc[2,5], 204 | s2 = mod$ss[3], s2_pred = vc[3,5], 205 | s3 = mod$ss[4], s3_pred = vc[4,5], 206 | r01 = mod$os[2,1], r01_pred = vc[5,5], 207 | r02 = mod$os[3,1], r02_pred = vc[6,5], 208 | r03 = mod$os[4,1], r03_pred = vc[7,5], 209 | r12 = mod$os[3,2], r12_pred = vc[8,5], 210 | r13 = mod$os[4,2], r13_pred = vc[9,5], 211 | r23 = mod$os[4,3], r23_pred = vc[10,5], 212 | sres = mod$se, sres_pred = ifelse(linear,vc[11,5],NA), 213 | conv = conv_lme4, grad = grad_lme4, mess = mess_lme4, ndiv = NA, nmtd = NA, 214 | rhat_min = NA, rhat_q25 = NA, rhat_med = NA, rhat_q75 = NA, rhat_max = NA, 215 | neff_min = NA, neff_q25 = NA, neff_med = NA, neff_q75 = NA, neff_max = NA, 216 | genmod_fails, stringsAsFactors = FALSE, row.names = NULL) 217 | resblme4$repca <- my.repca(theta) 218 | resblme4[,paste("theta",str_pad(1:10,width=2,pad="0",side="left"),sep="_")] <- theta 219 | 220 | resbstan <- data.frame( 221 | thread = thread, iteration = b, fam = mod$fam, reg = "Stan", minvar = min(mod$ss), 222 | pext = mod$pext, N = mod$N, S = mod$S, L = mod$L, balance = mod$balance, 223 | b0 = mod$b[1], b0_pred = m_stan["coef[1]"], 224 | b1 = mod$b[2], b1_pred = m_stan["coef[2]"], 225 | b2 = mod$b[3], b2_pred = m_stan["coef[3]"], 226 | b3 = mod$b[4], b3_pred = m_stan["coef[4]"], 227 | s0 = mod$ss[1], s0_pred = m_stan["s0"], 228 | s1 = mod$ss[2], s1_pred = m_stan["s1"], 229 | s2 = mod$ss[3], s2_pred = m_stan["s2"], 230 | s3 = mod$ss[4], s3_pred = m_stan["s3"], 231 | r01 = mod$os[2,1], r01_pred = m_stan["r01"], 232 | r02 = mod$os[3,1], r02_pred = m_stan["r02"], 233 | r03 = mod$os[4,1], r03_pred = m_stan["r03"], 234 | r12 = mod$os[3,2], r12_pred = m_stan["r12"], 235 | r13 = mod$os[4,2], r13_pred = m_stan["r13"], 236 | r23 = mod$os[4,3], r23_pred = m_stan["r23"], 237 | sres = mod$se, sres_pred = ifelse(linear,m_stan["res"],NA), 238 | conv = conv_stan, grad = NA, mess = NA, ndiv, nmtd, 239 | rhat_min = rhat[1], rhat_q25 = rhat[2], rhat_med = rhat[3], rhat_q75 = rhat[4], rhat_max = rhat[5], 240 | neff_min = neff[1], neff_q25 = neff[2], neff_med = neff[3], neff_q75 = neff[4], neff_max = neff[5], 241 | genmod_fails, stringsAsFactors = FALSE, row.names = NULL) 242 | resbstan$repca <- NA 243 | resbstan[,paste("theta",str_pad(1:10,width=2,pad="0",side="left"),sep="_")] <- NA 244 | 245 | return(rbind(resblme4,resbstan)) 246 | } 247 | } 248 | 249 | ## unpack arguments 250 | B <- pardat[[1]] 251 | thread <- pardat[[2]] 252 | gaucode <- pardat[[3]] 253 | bincode <- pardat[[4]] 254 | threadchar <- str_pad(paste(thread),width=nchar(paste(pardat[[5]])),side="left",pad="0") 255 | 256 | gaustan <- stan_model(model_name = "linear_sim", model_code = gaucode, save_dso = TRUE) 257 | binstan <- stan_model(model_name = "logistic_sim", model_code = bincode, save_dso = TRUE) 258 | 259 | ### run B iterations of each of the 2 models and output progress to thread-specific file 260 | results <- NULL 261 | progfile <- paste(getwd(),"/","Imbalanced_",threadchar,".progress",sep="") 262 | csvfile <- paste(getwd(),"/","Imbalanced_",threadchar,".csv",sep="") 263 | 264 | cat(paste(Sys.time(),"Started Thread",threadchar),file=progfile,sep="\n",append=FALSE) 265 | for(b in 1:B){ # Each iteration has 2 calls to single_model, one for each model family 266 | bchar <- str_pad(paste(b),width=nchar(paste(B)),side="left",pad="0") 267 | cat("\n",paste(Sys.time(),"Iteration",bchar,"/",B),file=progfile,sep="",append=TRUE) 268 | 269 | temp <- single_model(TRUE) 270 | if(is.null(temp)){ 271 | results[nrow(results)+1,1:2] <- c(thread,b) 272 | results[nrow(results),3] <- "Gaussian" 273 | results[nrow(results),4] <- "lme4" 274 | results[nrow(results)+1,1:2] <- c(thread,b) 275 | results[nrow(results),3] <- "Gaussian" 276 | results[nrow(results),4] <- "Stan" 277 | cat(" ERROR ",file=progfile,sep="",append=TRUE) 278 | } else { 279 | results <- rbind(results, temp) 280 | cat(" . ",file=progfile,sep="",append=TRUE) 281 | } 282 | 283 | temp <- single_model(FALSE) 284 | if(is.null(temp)){ 285 | results[nrow(results)+1,1:2] <- c(thread,b) 286 | results[nrow(results),3] <- "Logistic" 287 | results[nrow(results),4] <- "lme4" 288 | results[nrow(results)+1,1:2] <- c(thread,b) 289 | results[nrow(results),3] <- "Logistic" 290 | results[nrow(results),4] <- "Stan" 291 | cat(" ERROR ",file=progfile,sep="",append=TRUE) 292 | } else { 293 | results <- rbind(results, temp) 294 | cat(" . ",file=progfile,sep="",append=TRUE) 295 | } 296 | 297 | write.csv(results,row.names=F,quote=F,file=csvfile) 298 | } 299 | cat("\n\n",paste(Sys.time(),"Finished Thread",threadchar),file=progfile,sep="",append=TRUE) 300 | 301 | ### return results 302 | return(results) 303 | } 304 | 305 | setwd(dir) 306 | gaucode <- paste(readLines(gau_file),collapse="\n") 307 | bincode <- paste(readLines(bin_file),collapse="\n") 308 | 309 | if(threads==1){ 310 | options(mc.cores = parallel::detectCores()) 311 | results <- single_thread(list(B,threads,gaucode,bincode,threads)) 312 | } else { 313 | require(parallel) 314 | cl = makeCluster(rep("localhost", threads)) 315 | 316 | # simulations per thread and thread number 317 | left <- B %% threads 318 | B <- rep(floor(B/threads),threads) 319 | if(left>0) B[1:left] <- B[1:left] + 1 320 | pardat <- list() 321 | for(i in 1:threads) pardat[[i]] <- list(B[i],i,gaucode,bincode,threads) 322 | 323 | results <- parLapply(cl,pardat,single_thread) 324 | 325 | stopCluster(cl) 326 | 327 | results <- do.call(rbind,args=results) 328 | } 329 | 330 | return(results) 331 | } 332 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------