#' @rdname APA2
#' @description
#' Ausgabe von Regressions Tabelle nach der APA-Style vorgabe. Die Funktion
#' ist eine Kopie von texreg aggregate.matrix.
#' @param custom.model.names Namen ner Modelle
#' @param include.custom liste mit Statistiken für Gofs also zB F-Tests
#' @param include.se,include.ci,include.odds SE, 95-Ci, OR noch nicht fertig
#' @param include.ftest,include.loglik noch nicht fertig
#' @param include.r,include.pseudo pseudo R
#' @param include.aic,include.bic geht nur zusammen
#' @param ci.level Ci default 95 Prozent
#' @param rgroup Zwischen Beschriftung
#'
#' @return invisible data.frame und Output mit html/knit oder Text.
#' @export
#'
#' @examples
#'
#' library(lmerTest)
#'
#' fit1 <- lm(chol0 ~ ak + rrs0 + med + g, hyper)
#' fit2 <- glm(chol0 ~ med + ak + g + rrs0 , hyper, family = poisson())
#' fit3 <- lmerTest::lmer(chol0 ~ rrs0 + med + ak + (1|g) , hyper )
#' fits <- list(fit1, fit2, fit3)
#'
#' APA2(fits,
#' custom.model.names=c("lm", "glm", "lmer"),
#' digits= list(c(1,2,3,4,5,6,7),
#' c(1,2,3,4,5,6,7),
#' c(1,2,3,4,5,6)),
#' include.custom=list(
#' Wald=c("F(1)=245", "F(2)=245","F(3)=245"),
#' Chi=c("X(4)=2.45", "X(5)=24.5","X(6)=24.5")))
#'
#'
APA2.list <- function(...) regression_table(...)
#APA2.aov()
#' @rdname APA2
#' @param anova_type bei lme: "F" F-werte (wie SPSS) oder Chi (car::Anova)
#' @export
#'
#'
APA2.lme <- function(...){
APA2.lmerMod(...)
}
#' @rdname APA2
#' @export
APA2.lmerMod <- function(x,
caption = NULL,
note = NULL,
output = stp25output::which_output(),
col_names = NULL,
include.b = TRUE,
include.se = TRUE,
include.ci = FALSE,
include.odds = FALSE,
include.odds.ci = include.ci,
include.statistic = TRUE,
include.p = TRUE,
include.stars = FALSE,
include.r = TRUE,
include.pseudo = include.r,
include.test = FALSE,
ci.level = .95,
conf.method = "Wald",
conf.style.1 =TRUE,
digits = 2,
...) {
info <- model_info(x)
AV <-
ifelse(is.na(info$labels[info$y]), info$y, info$labels[info$y])
if (is.null(note)) {
note <- paste0("Model: ", info$family[1])
if (include.test)
note <- paste(note, APA(x, FALSE))
if (include.r) {
r2 <- R2(x)
note <- paste(note, "\npseudo r-squared:", rndr_r2pseudo(r2))
}
}
if (is.null(caption))
caption <- paste0("AV: ", AV, " Obs: ", info$N)
coefs <- extract_param(
x,
include.b = include.b,
include.se = include.se,
include.beta = FALSE,
include.ci = include.ci,
include.odds = include.odds,
include.odds.ci = include.odds.ci,
include.statistic = include.statistic,
include.p = include.p,
include.stars = include.stars,
ci.level = ci.level,
conf.method = conf.method,
fix_format = TRUE, conf.style.1 = conf.style.1
)
res <- prepare_output(coefs,
caption, note, info$N, info$labels)
if (!is.logical(output))
Output(res, output = output, col_names = col_names)
invisible(res)
}
GOF_LMER <- function(x){
res <- lmerTest::summary(x)
# goodnes <- cbind(
# Obs = res$devcomp$dims["N"],
# round(r.squared.merMod(x)[, 4:6], 2),
# # BIC = round(res$BIC,2),
# logLik = round(c(as.numeric(res$logLik)), 2),
# REML = round(res$devcomp$cmp["REML"], 2)
# )
# goodnes <- prepare_output(goodnes,
# caption = paste("Goodness-of-fit", caption),
# note = "R-Quadrat entspricht Marginal und Conditional")
res
}
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