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misc_code_for_display.R
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# Functions ---------------------------------------------------------------
# ints and Times
dataGen <- function(nclusters,
nwithin,
corr = 0,
balanced = T)
{
# setup
nclus = nclusters # number of groups
clus = factor(rep(1:nclus, each=nwithin)) # cluster variable
n = length(clus) # total n
# parameters
sigma = 1 # residual sd
psi = matrix(c(1,corr,corr,1), 2, 2) # re covar
gamma_ = MASS::mvrnorm(nclus, mu=c(0,0), Sigma=psi, empirical=TRUE) # random effects
e = rnorm(n, mean=0, sd=sigma) # residual error
intercept = 3 # fixed effects
b1 = .75
# data
x = rep(1:nwithin-1, times=nclus) # covariate
y = intercept+gamma_[clus,1] + (b1+gamma_[clus,2])*x + e # target
d = data.frame(time=x, y, clus=clus)
}
# nlme control msMaxIter, msMaxEval, abs.tol set to lavaan defaults
runNLME <- function(data)
{
nlmemod = lme(y ~ time, data=data,
random = ~time|clus,
weights=varIdent(form = ~1|time),
method='ML',
control = list(
maxIter = 1000,
msMaxIter = 10000,
msMaxEval = 20000,
returnObject = T,
abs.tol = 2.220446e-15
))
varRes = coef(nlmemod$modelStruct$varStruct, unconstrained =FALSE,allCoef=T)*nlmemod$sigma
varRE = as.numeric(VarCorr(nlmemod)[1:2,1])
corRE = as.numeric(VarCorr(nlmemod)['time','Corr'])
fixed = fixef(nlmemod)
list(varRes=varRes, varRE=varRE, corRE=corRE, fixed=fixed)
}
runNLME_REML <- function(data) {
nlmemod = lme(y ~ time, data=data,
random = ~time|clus,
weights=varIdent(form = ~1|time),
method='REML',
control = list(
maxIter = 1000,
msMaxIter = 10000,
msMaxEval = 20000,
returnObject = T,
abs.tol = 2.220446e-15
))
varRes = coef(nlmemod$modelStruct$varStruct, unconstrained =FALSE,allCoef=T)*nlmemod$sigma
varRE = as.numeric(VarCorr(nlmemod)[1:2,1])
corRE = as.numeric(VarCorr(nlmemod)['time','Corr'])
fixed = fixef(nlmemod)
list(varRes=varRes, varRE=varRE, corRE=corRE, fixed=fixed)
}
runGAM <- function(data) {
gammod = gam(y ~ time + s(clus, bs='re') + s(time, clus, bs='re'), data=data,
method='REML')
varRes = gammod$sig2
varRE = gam.vcomp(gammod)[1:2,1]^2
# corRE = as.numeric(VarCorr(nlmemod)['time','Corr'])
fixed = coef(gammod)[1:2]
list(varRes=varRes, varRE=varRE, corRE=NULL, fixed=fixed)
}
runBayes_brm = function(data, ...){
# variance heterogeneity coming in next release
bayesmod = brm(y ~ time + (1 + time|clus), data = data,
iter = 6000, warmup=1000, thin=20,
prior = set_prior("normal(0,10)", class = "b"),
...)
vc = VarCorr(bayesmod)
varRes = VarCorr(bayesmod)[[2]][['cov']][['mean']]
varRE = diag(VarCorr(bayesmod)[[1]][['cov']][['mean']])
corRE = VarCorr(bayesmod)[[1]][['cor']][['mean']][2]
fixed = fixef(bayesmod)[,'mean']
list(varRes=varRes, varRE=varRE, corRE=corRE, fixed=fixed)
}
runBayes_rstan = function(compcode, data, ...){
standat = brms::make_standata(y ~ time + (1 + time|clus), data=data)
bayesmod = sampling(sm, data = standat,
iter = 6000, warmup=1000, thin=20,...)
varRes = get_posterior_mean(bayesmod, 'sigma')[,'mean-all chains']
varRE = get_posterior_mean(bayesmod, 'sd_1')[,'mean-all chains']
corRE = get_posterior_mean(bayesmod, 'cor_1')[,'mean-all chains']
fixed = get_posterior_mean(bayesmod, pars=c('b_Intercept', 'b'))[,'mean-all chains']
list(varRes=varRes, varRE=varRE, corRE=corRE, fixed=fixed)
}
runGC <- function(data) {
ntime = unique(data$time)
data$time = factor(data$time)
dataWide = tidyr::spread(data, time, y)
colnames(dataWide)[-1] = paste0('y', colnames(dataWide)[-1])
IModel = paste0('I =~ 1*y0 ', paste0('+ 1*', colnames(dataWide)[-c(1:2)], collapse=''), '\n')
SModel = paste0('S =~ 0*y0 ', paste0('+ ', 1:dplyr::last(ntime), '*', colnames(dataWide)[-c(1:2)], collapse=''), '\n')
CenterY = paste0('y0', paste0(' + ', colnames(dataWide)[-c(1:2)], collapse=''), ' ~ 0*1')
LVmodel = paste0(IModel, SModel, CenterY)
suppressWarnings({semres = growth(LVmodel, data=dataWide)})
varRes = sqrt(coef(semres)[ntime+1])
varRE = coef(semres)[c('I~~I','S~~S')]
covRE = coef(semres)[c('I~~S')]
corRE = covRE/prod(sqrt(varRE))
fixed = coef(semres)[c('I~1', 'S~1')]
list(varRes=varRes, varRE=varRE, corRE=corRE, fixed=fixed)
}
summarize_growth_mixed <- function(results, growth=T, whichpar='fixed', clean=F, settings=grid) {
if (!clean) fail_idx = replicate(45, rep(FALSE, 500), simplify = F)
if (clean) {
if (growth){
fail_idx = sapply(results, function(x)
sapply(x, function(res) any(is.nan(unlist(res)))), simplify=F) # | abs(res$corRE)>1
}
else {
fail_idx = sapply(results, function(x)
sapply(x, function(res) any(res$corRE==1 | res$corRE==-1)), simplify=F)
}
}
if(whichpar=='fixed'){
# fixed effects
resultFE0 = sapply(1:length(results), function(x)
sapply(results[[x]][!fail_idx[[x]]], function(res) res$fixed), simplify=F)
resultFE = lapply(resultFE0, function(res) c(rowMeans(res), c(apply(res, 1, quantile, p=c(.025,.975)))))
resultFE = do.call('rbind', resultFE); colnames(resultFE) = c('Int','Time', 'LL_Int', 'UL_Int', 'LL_Time', 'UL_Time')
resultFE = data.frame(settings, resultFE)
return(resultFE)
}
if(whichpar=='varRes'){
stop('Not wanting to parse residual variance for 5 to 25 scores at present. :P')
# residual variance
# resultvarRes0 = sapply(1:length(results), function(x)
# sapply(results[[x]][!fail_idx[[x]]], function(res) res$varRes), simplify=F)
# resultvarRes = lapply(resultvarRes0, function(res) c(rowMeans(res), c(apply(res, 1, quantile, p=c(.025,.975)))))
# resultvarRes = do.call('rbind', resultvarRes); colnames(resultFE) = c('Int','Time', 'LL_Int', 'UL_Int', 'LL_Time', 'UL_Time')
# resultvarRes = data.frame(settings, round(resultvarRes, 2))
# return(resultvarRes)
}
if (whichpar=='varRE'){
# random effects variance
resultvarRE0 = sapply(1:length(results), function(x)
sapply(results[[x]][!fail_idx[[x]]], function(res) res$varRE), simplify=F)
resultvarRE = lapply(resultvarRE0, function(res) c(rowMeans(res), c(apply(res, 1, quantile, p=c(.025,.975)))))
resultvarRE = do.call('rbind', resultvarRE); colnames(resultvarRE) = c('Int','Time', 'LL_Int', 'UL_Int', 'LL_Time', 'UL_Time')
resultvarRE = data.frame(settings, resultvarRE)
return(resultvarRE)
}
if (whichpar=='corRE'){
# cor random effects
resultcorRE0 = sapply(1:length(results), function(x)
sapply(results[[x]][!fail_idx[[x]]], function(res) res$corRE), simplify=F)
resultcorRE = lapply(resultcorRE0, function(res) c(mean(res, na.rm=T), quantile(res, p=c(.025,.975), na.rm=T)))
resultcorRE = do.call('rbind', resultcorRE); colnames(resultcorRE) = c('corRE_est', 'LL_corRE', 'UL_corRE')
resultcorRE = data.frame(settings, resultcorRE)
return(resultcorRE)
}
}
summary_df <- function(g, m, whichpar='fixed') {
require(dplyr)
if(whichpar=='fixed'){
# fixed effects
res = left_join(g, m, by=c('nClusters', 'nWithinCluster', 'corRE')) %>%
arrange(nClusters, nWithinCluster, corRE) %>%
mutate(widthGrowth_Int = UL_Int.x-LL_Int.x,
widthMixed_Int = UL_Int.y-LL_Int.y,
mixedWidthMinusgrowthWidth_Int = widthMixed_Int-widthGrowth_Int,
widthGrowth_Time = UL_Time.x-LL_Time.x,
widthMixed_Time = UL_Time.y-LL_Time.y,
mixedWidthMinusgrowthWidth_Time = widthMixed_Time-widthGrowth_Time)
return(res)
}
if(whichpar=='varRes'){
stop('Not wanting to parse residual variance for 5 to 25 scores at present. :P')
# residual variance
# growthvarRes0 = sapply(1:length(results), function(x)
# sapply(results[[x]][!fail_idx[[x]]], function(res) res$varRes), simplify=F)
# growthvarRes = lapply(growthvarRes0, function(res) c(rowMeans(res), c(apply(res, 1, quantile, p=c(.025,.975)))))
# growthvarRes = do.call('rbind', growthvarRes); colnames(growthFE) = c('Int','Time', 'LL_Int', 'UL_Int', 'LL_Time', 'UL_Time')
# growthvarRes = data.frame(grid, round(growthvarRes, 2))
# return(growthvarRes)
}
if (whichpar=='varRE'){
# random effects variance
res = left_join(g, m, by=c('nClusters', 'nWithinCluster', 'corRE')) %>%
arrange(nClusters, nWithinCluster, corRE) %>%
mutate(widthGrowth_Int = UL_Int.x-LL_Int.x,
widthMixed_Int = UL_Int.y-LL_Int.y,
mixedWidthMinusgrowthWidth_Int = widthMixed_Int-widthGrowth_Int,
widthGrowth_Time = UL_Time.x-LL_Time.x,
widthMixed_Time = UL_Time.y-LL_Time.y,
mixedWidthMinusgrowthWidth_Time = widthMixed_Time-widthGrowth_Time)
return(res)
}
if (whichpar=='corRE'){
# cor random effects
res = left_join(g, m, by=c('nClusters', 'nWithinCluster', 'corRE')) %>%
arrange(nClusters, nWithinCluster, corRE) %>%
mutate(widthGrowth = UL_corRE.x-LL_corRE.x,
widthMixed = UL_corRE.y-LL_corRE.y,
mixedWidthMinusgrowthWidth = widthMixed-widthGrowth)
return(res)
}
}
bias <- function(res, whichpar='fixed') {
require(dplyr)
if (whichpar=='fixed') {
out = res %>%
arrange(nClusters, nWithinCluster, corRE) %>%
mutate(biasLGC_Int = Int.x-3,
biasLGC_Time = Time.x-.75,
biasMM_Int = Int.y-3,
biasMM_Time = Time.y-.75)
return(out)
}
else if (whichpar=='varRE') {
out = res %>%
arrange(nClusters, nWithinCluster, corRE) %>%
mutate(biasLGC_Int = Int.x-1,
biasLGC_Time = Time.x-1,
biasMM_Int = Int.y-1,
biasMM_Time = Time.y-1)
}
else if (whichpar=='corRE'){
out = corEst %>%
arrange(nClusters, nWithinCluster, corRE) %>%
mutate(biasLGC_corRE = corRE_est.x-corRE,
biasMM_corRE = corRE_est.y-corRE)
}
return(out)
}