R/ConModelTest_cont.R

In LongDat: A Tool for 'Covariate'-Sensitive Longitudinal Analysis on 'omics' Data

#' Covariate model test in longdat_cont() pipeline
#' @param N Internal function argument.
#' @param variables Internal function argument.
#' @param melt_data Internal function argument.
#' @param sel_fac Internal function argument.
#' @param data_type Internal function argument.
#' @param test_var Internal function argument.
#' @param verbose Internal function argument.
#' @importFrom rlang .data
#' @importFrom stats as.formula confint cor.test kruskal.test
#'            na.omit p.adjust wilcox.test
#' @importFrom car Anova
#' @importFrom  MASS polr
#' @importFrom magrittr '%>%'
#' @import dplyr
#' @import bestNormalize
#' @import glmmTMB
#' @import lme4
#' @name ConModelTest_cont
utils::globalVariables(c("value"))

ConModelTest_cont <- function(N, variables, melt_data, sel_fac, data_type,
                              test_var, verbose) {
  Ps_conf_model <- list()
  Ps_inv_conf_model <- list()
  suppressWarnings(
    for (i in 1:N) { # loop through all variables
      aVariable <- variables[i]
      if (verbose == TRUE) {print(i)}
      subdata <- subset(melt_data, variable == aVariable)
      colnames(subdata) <- fix_name_fun(colnames(subdata))
      tryCatch({
        if (data_type %in% c("measurement", "others")) {
          subdata <- subdata %>%
            dplyr::mutate(value_norm =
                            bestNormalize::bestNormalize(value, loo = TRUE)$x.t)
        }}, error=function(e){cat("ERROR :",conditionMessage(e), "\n")})
      ps_lm <- c()
      ps_lm_inv <- c()
      if (length(sel_fac[[i]]) >= 1) {
        #tryCatch skips error in for loop
        tryCatch({
          for (k in seq_len(length(sel_fac[[i]]))) { # Sel_fac exists
            if (data_type == "count") {
              fmla2 <- as.formula(paste("value ~ (1| Individual) +",
                                        fix_name_fun(sel_fac[[i]][k]), "+",
                                        test_var))
              m2 <- glmmTMB::glmmTMB(formula = fmla2, data = subdata,
                                     family = nbinom2,
                                     na.action = na.omit, REML = FALSE)
            } else if (data_type == "proportion") {
              fmla2 <- as.formula(paste("value ~ (1| Individual) +" ,
                                        fix_name_fun(sel_fac[[i]][k]), "+",
                                        test_var))
              m2 <- glmmTMB::glmmTMB(fmla2, data = subdata,
                                     family = beta_family(),
                                     na.action = na.omit, REML = FALSE)
            } else if (data_type %in% c("measurement", "others")) {
              fmla2 <- as.formula(paste("value_norm ~ (1| Individual) +" ,
                                        fix_name_fun(sel_fac[[i]][k]), "+",
                                        test_var))
              m2 <- lme4::lmer(data = subdata, fmla2, REML = FALSE)
            } else if (data_type == "binary") {
              fmla2 <- as.formula(paste("value ~ (1| Individual) +" ,
                                        fix_name_fun(sel_fac[[i]][k]), "+",
                                        test_var))
              m2 <- glmmTMB::glmmTMB(fmla2, data = subdata, family = "binomial",
                                     na.action = na.omit, REML = FALSE)
            } else if (data_type == "ordinal") {
              fmla2 <- as.formula(paste("as.factor(value) ~ (1| Individual) +",
                                        fix_name_fun(sel_fac[[i]][k]), "+",
                                        test_var))
              m2 <- MASS::polr(fmla2, data = subdata, method = "logistic")
            }

            # Wald Chisq test
            p_lm <- car::Anova(m2, type=c("II"),
                               test.statistic=c("Chisq"))$`Pr(>Chisq)`[2]
            names(p_lm) <- paste(sel_fac[[i]][k])
            ps_lm <- c(ps_lm, p_lm)

            p_lm_inv <- car::Anova(m2, type=c("II"),
                                   test.statistic=c("Chisq"))$`Pr(>Chisq)`[1]
            names(p_lm_inv) <- paste(sel_fac[[i]][k])
            ps_lm_inv <- c(ps_lm_inv, p_lm_inv)
          }}, error=function(e){cat("ERROR :",conditionMessage(e), "\n")})
      } else if (length(sel_fac[[i]]) == 0) { # No sel_fac existing
        ps_lm <- "No selected factors for confounding model test"
        ps_lm_inv <- "No selected factors for inverse confounding model test"
      }
      if (is.null(ps_lm)) {
        Ps_conf_model[[i]] <- NA
      } else {
        Ps_conf_model[[i]] <- ps_lm
      }

      if (is.null(ps_lm_inv)) {
        Ps_inv_conf_model[[i]] <- NA
      } else {
        Ps_inv_conf_model[[i]] <- ps_lm_inv
      }
    }
  )
  names(Ps_conf_model) <- variables
  names(Ps_inv_conf_model) <- variables
  return(list(Ps_conf_model, Ps_inv_conf_model))
}