Nothing
BANOVA.BernNormal <-
function(l1_formula = 'NA', l2_formula = 'NA', data, id, l2_hyper, burnin, sample, thin, adapt, conv_speedup, jags){
cat('Model initializing...\n')
if (l1_formula == 'NA'){
stop("Formula in level 1 is missing or not correct!")
}else{
mf1 <- model.frame(formula = l1_formula, data = data)
# check y, if it is integers
y <- model.response(mf1)
if (!inherits(y, 'integer')){
warning("The response variable must be integers (data class also must be 'integer')..")
y <- as.integer(as.character(y))
warning("The response variable has been converted to integers..")
}
}
if (l2_formula == 'NA'){
stop("The level 2 formula is not specified, please check BANOVA.run for single level models.")
# # check each column in the dataframe should have the class 'factor' or 'numeric', no other classes such as 'matrix'...
# for (i in 1:ncol(data)){
# if(class(data[,i]) != 'factor' && class(data[,i]) != 'numeric' && class(data[,i]) != 'integer') stop("data class must be 'factor', 'numeric' or 'integer'")
# # checking numerical predictors, converted to categorical variables if the number of levels is <= 3
# if ((class(data[,i]) == 'numeric' | class(data[,i]) == 'integer') & length(unique(data[,i])) <= 3){
# data[,i] <- as.factor(data[,i])
# warning("Variables(levels <= 3) have been converted to factors")
# }
# }
# n <- nrow(data)
# uni_id <- unique(id)
# num_id <- length(uni_id)
# new_id <- rep(0, length(id)) # store the new id from 1,2,3,...
# for (i in 1:length(id))
# new_id[i] <- which(uni_id == id[i])
# id <- new_id
# dMatrice <- design.matrix(l1_formula, l2_formula, data = data, id = id)
# JAGS.model <- JAGSgen.bernNormal(dMatrice$X, dMatrice$Z, l2_hyper, conv_speedup)
# JAGS.data <- dump.format(list(n = n, id = id, M = num_id, y = y, X = dMatrice$X, Z = dMatrice$Z))
# result <- run.jags (model = JAGS.model$sModel, data = JAGS.data, inits = JAGS.model$inits, n.chains = 1,
# monitor = c(JAGS.model$monitorl1.parameters, JAGS.model$monitorl2.parameters),
# burnin = burnin, sample = sample, thin = thin, adapt = adapt, jags = jags, summarise = FALSE,
# method="rjags")
# samples <- result$mcmc[[1]]
# # find the correct samples, in case the order of monitors is shuffled by JAGS
# n_p_l1 <- length(JAGS.model$monitorl1.parameters)
# index_l1_param<- array(0,dim = c(n_p_l1,1))
# for (i in 1:n_p_l1)
# index_l1_param[i] <- which(colnames(result$mcmc[[1]]) == JAGS.model$monitorl1.parameters[i])
# if (length(index_l1_param) > 1)
# samples_l1_param <- result$mcmc[[1]][,index_l1_param]
# else
# samples_l1_param <- matrix(result$mcmc[[1]][,index_l1_param], ncol = 1)
# colnames(samples_l1_param) <- colnames(result$mcmc[[1]])[index_l1_param]
#
# #anova.table <- table.ANOVA(samples_l1_param, dMatrice$X, dMatrice$Z)
# cat('Constructing ANOVA/ANCOVA tables...\n')
# # anova.table <- table.ANCOVA(samples_l1_param, dMatrice$X, dMatrice$Z, samples_l2_param) # for ancova models
# # coef.tables <- table.coefficients(samples_l2_param, JAGS.model$monitorl2.parameters, colnames(dMatrice$X), colnames(dMatrice$Z),
# # attr(dMatrice$X, 'assign') + 1, attr(dMatrice$Z, 'assign') + 1)
# # pvalue.table <- table.pvalue(coef.tables$coeff_table, coef.tables$row_indices, l1_names = attr(dMatrice$X, 'varNames'),
# # l2_names = attr(dMatrice$Z, 'varNames'))
# # conv <- conv.geweke.heidel(samples_l2_param, colnames(dMatrice$X), colnames(dMatrice$Z))
# # class(conv) <- 'conv.diag'
# # cat('Done...\n')
}else{
mf2 <- model.frame(formula = l2_formula, data = data)
# check each column in the dataframe should have the class 'factor' or 'numeric', no other classes such as 'matrix'...
for (i in 1:ncol(data)){
if(!inherits(data[,i], 'factor') && !inherits(data[,i], 'numeric') && !inherits(data[,i], 'integer')) stop("data class must be 'factor', 'numeric' or 'integer'")
#response_name <- attr(mf1,"names")[attr(attr(mf1, "terms"),"response")]
# checking missing predictors, already checked in design matrix
# if(i != which(colnames(data) == response_name) & sum(is.na(data[,i])) > 0) stop("Data type error, NAs/missing values included in independent variables")
#if(i != which(colnames(data) == response_name) & class(data[,i]) == 'numeric')
# data[,i] = data[,i] - mean(data[,i])
# checking numerical predictors, converted to categorical variables if the number of levels is <= 3
if ((inherits(data[,i], 'numeric') | inherits(data[,i], 'integer')) & length(unique(data[,i])) <= 3){
data[,i] <- as.factor(data[,i])
warning("Variables(levels <= 3) have been converted to factors")
}
}
n <- nrow(data)
uni_id <- unique(id)
num_id <- length(uni_id)
new_id <- rep(0, length(id)) # store the new id from 1,2,3,...
for (i in 1:length(id))
new_id[i] <- which(uni_id == id[i])
id <- new_id
dMatrice <- design.matrix(l1_formula, l2_formula, data = data, id = id)
JAGS.model <- JAGSgen.bernNormal(dMatrice$X, dMatrice$Z, l2_hyper, conv_speedup)
JAGS.data <- dump.format(list(n = n, id = id, M = num_id, y = y, X = dMatrice$X, Z = dMatrice$Z))
result <- run.jags (model = JAGS.model$sModel, data = JAGS.data, inits = JAGS.model$inits, n.chains = 1,
monitor = c(JAGS.model$monitorl1.parameters, JAGS.model$monitorl2.parameters),
burnin = burnin, sample = sample, thin = thin, adapt = adapt, jags = jags, summarise = FALSE,
method="rjags")
samples <- result$mcmc[[1]]
# find the correct samples, in case the order of monitors is shuffled by JAGS
n_p_l2 <- length(JAGS.model$monitorl2.parameters)
index_l2_param<- array(0,dim = c(n_p_l2,1))
for (i in 1:n_p_l2)
index_l2_param[i] <- which(colnames(result$mcmc[[1]]) == JAGS.model$monitorl2.parameters[i])
if (length(index_l2_param) > 1)
samples_l2_param <- result$mcmc[[1]][,index_l2_param]
else
samples_l2_param <- matrix(result$mcmc[[1]][,index_l2_param], ncol = 1)
colnames(samples_l2_param) <- colnames(result$mcmc[[1]])[index_l2_param]
n_p_l1 <- length(JAGS.model$monitorl1.parameters)
index_l1_param<- array(0,dim = c(n_p_l1,1))
for (i in 1:n_p_l1)
index_l1_param[i] <- which(colnames(result$mcmc[[1]]) == JAGS.model$monitorl1.parameters[i])
if (length(index_l1_param) > 1)
samples_l1_param <- result$mcmc[[1]][,index_l1_param]
else
samples_l1_param <- matrix(result$mcmc[[1]][,index_l1_param], ncol = 1)
colnames(samples_l1_param) <- colnames(result$mcmc[[1]])[index_l1_param]
#anova.table <- table.ANOVA(samples_l1_param, dMatrice$X, dMatrice$Z)
cat('Constructing ANOVA/ANCOVA tables...\n')
anova.table <- table.ANCOVA(samples_l1_param, dMatrice$X, dMatrice$Z, samples_l2_param) # for ancova models
coef.tables <- table.coefficients(samples_l2_param, JAGS.model$monitorl2.parameters, colnames(dMatrice$X), colnames(dMatrice$Z),
attr(dMatrice$X, 'assign') + 1, attr(dMatrice$Z, 'assign') + 1)
pvalue.table <- table.pvalue(coef.tables$coeff_table, coef.tables$row_indices, l1_names = attr(dMatrice$X, 'varNames'),
l2_names = attr(dMatrice$Z, 'varNames'))
conv <- conv.geweke.heidel(samples_l2_param, colnames(dMatrice$X), colnames(dMatrice$Z))
class(conv) <- 'conv.diag'
cat('Done...\n')
}
return(list(anova.table = anova.table,
coef.tables = coef.tables,
pvalue.table = pvalue.table,
conv = conv,
dMatrice = dMatrice, samples_l2_param = samples_l2_param, data = data, num_trials = 1,
mf1 = mf1, mf2 = mf2, JAGSmodel = JAGS.model$sModel, single_level = F, model_name = "BANOVA.Bernoulli"))
}
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