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ezPredict.R
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ezPredict.R
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ezPredict <-
function(
fit
, to_predict = NULL
, numeric_res = 0
, boot = TRUE
, iterations = 1e3
, zero_intercept_variance = FALSE
){
fit_class = class(fit)[1]
if((fit_class=='mer')|(fit_class=='glmerMod')|(fit_class=='lmerMod')){
data = attr(fit,'frame')
vars = as.character(attr(data,'terms'))
dv = vars[2]
vars = gsub('\\(.+?\\) ?\\+','',vars[3])
vars = gsub('\\+ ?\\(.+?\\)','',vars)
# vars = gsub('\\(.+\\)','',vars[3])
vars = unlist(strsplit(vars,'+',fixed=T))
vars = str_replace_all(vars,' ','')
vars = vars[nchar(vars)>0]
these_terms = vars
vars = vars[!str_detect(vars,':')]
vars = unlist(strsplit(vars,'*',fixed=T))
# vars = as.character(attr(attr(data,'terms'),'variables'))
# dv = as.character(vars[2])
# vars = vars[3:length(vars)]
}else{
if(fit_class%in%c('gam','bam')){
data = fit$model
randoms = NULL
for(i in fit$smooth){
if(class(i)[1]=='random.effect'){
randoms = c(randoms,i$term)
}
}
vars = as.character(attr(attr(data,'terms'),'variables'))
dv = as.character(vars[2])
vars = vars[3:length(vars)]
vars = vars[!(vars%in%randoms)]
BY = vars[str_detect(vars,'BY')]
vars = vars[!str_detect(vars,'BY')]
}else{
stop(paste('ezPredict does not know how to handle fits of class "',fit_class,'"',sep=''))
}
}
if(is.null(to_predict)){
if(length(grep('poly(',vars,fixed=TRUE))>0){
stop('Cannot auto-create "to_predict" when the fitted model contains poly(). Please provide a data frame to the "to_return" argument.')
}
data_vars = vars[grep('I(',vars,fixed=T,invert=T)]
temp = list()
for(i in 1:length(data_vars)){
this_fixed_data = data[,names(data)==data_vars[i]]
if(is.numeric(this_fixed_data)&(numeric_res>0)){
temp[[i]] = seq(
min(this_fixed_data)
, max(this_fixed_data)
, length.out=numeric_res
)
}else{
temp[[i]] = sort(unique(this_fixed_data))
if(!is.numeric(this_fixed_data)){
contrasts(temp[[i]]) = contrasts(this_fixed_data)
}
}
}
to_return = data.frame(expand.grid(temp))
names(to_return) = data_vars
}else{
to_return = to_predict
}
data_vars = names(to_return)
if(fit_class%in%c('gam','bam')){
for(i in randoms){
to_return$EZTEMP = data[1,names(data)==i]
names(to_return)[ncol(to_return)] = i
}
for(i in BY){
to_return$EZTEMP = ''
for(j in str_split(i,'BY')[[1]]){
to_return$EZTEMP = paste(to_return$EZTEMP,as.character(to_return[,names(to_return)==j]),sep='')
}
to_return$EZTEMP = ordered(to_return$EZTEMP)
names(to_return)[ncol(to_return)] = i
}
}
to_return$ezDV = 0
names(to_return)[ncol(to_return)] = dv
if((fit_class=='mer')|(fit_class=='glmerMod')|(fit_class=='lmerMod')){
requested_terms = terms(eval(parse(text=paste(
dv
, '~'
, paste(
these_terms#attr(attr(data,'terms'),'term.labels')
, collapse = '+'
)
))))
mm = model.matrix(requested_terms,to_return)
f = lme4::fixef(fit)
v = vcov(fit)
if(zero_intercept_variance){
v[1,] = 0
v[,1] = 0
}
}else{
mm <- predict(fit,to_return,type="lpmatrix") # get a coefficient matrix
for(i in randoms){
mm[,grep(paste('s(',i,')',sep=''),dimnames(mm)[[2]],fixed=T)] = 0 #zero the subject entry
}
f = coef(fit)
for(i in randoms){
f[grep(paste('s(',i,')',sep=''),names(f),fixed=T)] = 0 #zero the subject entry
}
v = vcov(fit)
if(zero_intercept_variance){
v[1,] = 0
v[,1] = 0
}
for(i in randoms){
row = grep(paste('s(',i,')',sep=''),dimnames(v)[[1]],fixed=T)
col = grep(paste('s(',i,')',sep=''),dimnames(v)[[2]],fixed=T)
v[row,] = 0
v[,col] = 0
}
}
value = mm %*% f
to_return$value = as.numeric(value[,1])
tc = Matrix::tcrossprod(v,mm)
to_return$var = Matrix::diag(mm %*% tc)
to_return = to_return[,names(to_return) %in% c(data_vars,'value','var')]
if(boot){
samples = mvrnorm(iterations,f,v)
mat = matrix(NA,nrow=nrow(to_return),ncol=iterations)
for(i in 1:iterations){
mat[,i] <- mm%*%samples[i,]
}
boots = as.data.frame(to_return[,names(to_return) %in% data_vars])
names(boots) = data_vars
boots = cbind(boots,as.data.frame(mat))
boots = melt(
data = boots
, id.vars = names(boots)[1:(ncol(boots)-iterations)]
, variable.name = 'iteration'
)
to_return = list(
cells = to_return
, boots = boots
)
}
return(to_return)
}