Nothing
dc.glm = function(model, values = NULL, sim.count = 1000, conf.int = 0.95, sigma = NULL, set.seed = NULL, values1 = NULL, values2 = NULL,
type = c("any", "simulation", "bootstrap"), summary = TRUE){
# check inputs
if(is.null(values) && (is.null(values1) || is.null(values2))){
stop("Either values1 and values2 or values has to be specified!")
}
if(!is.null(values)){
l = length(values)
values1 = values[1 : (l/2)]
values2 = values[(l/2 + 1) : l]
}
if(sum("glm" %in% class(model)) == 0){
stop("model has to be of type glm()")
}
if(length(values1) != length(coef(model))){
stop("the length of values1 is not identical to the number of coefficient of the model")
}
if(length(values2) != length(coef(model))){
stop("the length of values2 is not identical to the number of coefficient of the model")
}
if(!is.numeric(sim.count) | round(sim.count) != sim.count){
stop("sim.count has to be whole number")
}
if(!is.numeric(conf.int)){
stop("conf.int has to be numeric")
}
if(!is.null(set.seed) & !is.numeric(set.seed)){
stop("set.seed must be numeric")
}
type = match.arg(type)
if(type == "bootstrap" && "svyglm" %in% class(model)){
warning("Boostrap not supported for survey()-models, using simulations instead.")
type = "simulation"
}
# model type
model.type = family(model)
link = model.type[2]
if(type == "any"){
if("svyglm" %in% class(model)){
type = "simulation"
}else if(nrow(model$data) < 500){
type = "bootstrap"
message("Type not specified: Using bootstrap as n < 500")
}else{
type = "simulation"
message("Type not specified: Using simulation as n >= 500")
}
}
if(type == "simulation"){
if(is.null(sigma)){
sigma = stats::vcov(model)
}
if(nrow(sigma) != length(values1)){
warning("sigma and values do not match, ignoring the specified sigma")
sigma = vcov(model)
}
if(!is.null(set.seed)){
set.seed(set.seed)
}
betas_sim = MASS::mvrnorm(sim.count, coef(model), sigma)
pred1 = calculate_glm_pred(betas_sim, values1, link)
pred2 = calculate_glm_pred(betas_sim, values2, link)
}else{ # bootstrap
boot = function(x, model){
data = model$data
sample_data = data[sample(seq_len(nrow(data)), replace = TRUE), ]
coef(update(model, data = sample_data))
}
betas_boot = do.call('rbind', lapply(seq_len(sim.count), boot, model))
pred1 = calculate_glm_pred(betas_boot, values1, link)
pred2 = calculate_glm_pred(betas_boot, values2, link)
}
diff = pred1 - pred2
all = cbind(pred1, pred2, diff)
# return all simulated / bootstrapped values if summary is FALSE
if(!summary){
return(all)
}
confint_lower = (1 - conf.int) / 2
result = apply(all, 2, quantile, probs = c(confint_lower, 1 - confint_lower))
result = t(rbind(apply(all, 2, mean), result))
colnames(result) = c("Mean",
paste0(100 * confint_lower,"%"),
paste0(100 * (1 - confint_lower),"%"))
rownames(result) = c("Case 1", "Case 2", "Difference")
result
}
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