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.extract_random_parameters <- function(model, ...) {
UseMethod(".extract_random_parameters")
}
.extract_random_parameters.merMod <- function(model,
ci = 0.95,
effects = "random",
...) {
insight::check_if_installed("lme4")
out <- as.data.frame(lme4::ranef(model, condVar = TRUE), stringsAsFactors = FALSE)
colnames(out) <- c("Group", "Parameter", "Level", "Coefficient", "SE")
# coerce to character
out$Parameter <- as.character(out$Parameter)
out$Level <- as.character(out$Level)
out$Group <- as.character(out$Group)
out$Effects <- "random"
if (length(ci) == 1) {
fac <- stats::qnorm((1 + ci) / 2)
out$CI_low <- out$Coefficient - fac * out$SE
out$CI_high <- out$Coefficient + fac * out$SE
ci_cols <- c("CI_low", "CI_high")
} else {
ci_cols <- NULL
for (i in ci) {
fac <- stats::qnorm((1 + i) / 2)
ci_low <- paste0("CI_low_", i)
ci_high <- paste0("CI_high_", i)
out[[ci_low]] <- out$Coefficient - fac * out$SE
out[[ci_high]] <- out$Coefficient + fac * out$SE
ci_cols <- c(ci_cols, ci_low, ci_high)
}
}
stat_column <- gsub("-statistic", "", insight::find_statistic(model), fixed = TRUE)
# to match rbind
out[[stat_column]] <- NA
out$df_error <- NA
out$p <- NA
out <- out[c("Parameter", "Level", "Coefficient", "SE", ci_cols, stat_column, "df_error", "p", "Effects", "Group")]
if (effects == "random") {
out[c(stat_column, "df_error", "p")] <- NULL
}
out
}
.extract_random_parameters.glmmTMB <- function(model,
ci = 0.95,
effects = "random",
component = "conditional",
...) {
insight::check_if_installed("lme4")
out <- as.data.frame(lme4::ranef(model, condVar = TRUE))
colnames(out) <- c("Component", "Group", "Parameter", "Level", "Coefficient", "SE")
# filter component
out <- switch(component,
zi = ,
zero_inflated = out[out$Component == "zi", ],
cond = ,
conditional = out[out$Component == "cond", ],
disp = ,
dispersion = out[out$Component == "disp", ],
out
)
# coerce to character
out$Parameter <- as.character(out$Parameter)
out$Level <- as.character(out$Level)
out$Group <- as.character(out$Group)
out$Effects <- "random"
# rename
out$Component[out$Component == "zi"] <- "zero_inflated"
out$Component[out$Component == "cond"] <- "conditional"
out$Component[out$Component == "disp"] <- "dispersion"
if (length(ci) == 1) {
fac <- stats::qnorm((1 + ci) / 2)
out$CI_low <- out$Coefficient - fac * out$SE
out$CI_high <- out$Coefficient + fac * out$SE
ci_cols <- c("CI_low", "CI_high")
} else {
ci_cols <- NULL
for (i in ci) {
fac <- stats::qnorm((1 + i) / 2)
ci_low <- paste0("CI_low_", i)
ci_high <- paste0("CI_high_", i)
out[[ci_low]] <- out$Coefficient - fac * out$SE
out[[ci_high]] <- out$Coefficient + fac * out$SE
ci_cols <- c(ci_cols, ci_low, ci_high)
}
}
stat_column <- gsub("-statistic", "", insight::find_statistic(model), fixed = TRUE)
# to match rbind
out[[stat_column]] <- NA
out$df_error <- NA
out$p <- NA
out <- out[c(
"Parameter", "Level", "Coefficient", "SE", ci_cols, stat_column,
"df_error", "p", "Component", "Effects", "Group"
)]
if (effects == "random") {
out[c(stat_column, "df_error", "p")] <- NULL
}
out
}
.extract_random_parameters.MixMod <- function(model, ...) {
NULL
}
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