R/print.poLCA.R

In poLCAParallel: Polytomous Variable Latent Class Analysis Parallel

#' @export
print.poLCA <- function(x, ...) {
  R <- length(x$P)
  S <- ifelse(is.na(x$coeff[1]), 1, nrow(x$coeff))
  cat(paste("Conditional item response (column) probabilities,
  \n by outcome variable, for each class (row) \n \n"))
  print(lapply(x$probs, round, 4))
  cat("Estimated class population shares \n", round(x$P, 4), "\n \n")
  cat(
    "Predicted class memberships (by modal posterior prob.) \n",
    round(table(x$predclass) / x$N, 4), "\n \n"
  )
  cat("========================================================= \n")
  cat("Fit for", R, "latent classes: \n")
  cat("========================================================= \n")
  if (S > 1) {
    for (r in 2:R) {
      cat(r, "/ 1 \n")
      disp <- data.frame(
        coeff = round(x$coeff[, (r - 1)], 5),
        se = round(x$coeff.se[, (r - 1)], 5),
        tval = round(x$coeff[, (r - 1)] / x$coeff.se[, (r - 1)], 3),
        pr = round(
          1 - (2 * abs(stats::pt(
            x$coeff[, (r - 1)] / x$coeff.se[, (r - 1)], x$resid.df
          )
          - 0.5)
          ), 3
        )
      )
      colnames(disp) <- c("Coefficient", " Std. error", " t value", " Pr(>|t|)")
      print(disp)
      cat("========================================================= \n")
    }
  }
  cat("number of observations:", x$N, "\n")
  if (x$N != x$Nobs) cat("number of fully observed cases:", x$Nobs, "\n")
  cat("number of estimated parameters:", x$npar, "\n")
  cat("residual degrees of freedom:", x$resid.df, "\n")
  cat("maximum log-likelihood:", x$llik, "\n \n")
  cat("AIC(", R, "): ", x$aic, "\n", sep = "")
  cat("BIC(", R, "): ", x$bic, "\n", sep = "")
  if (S == 1) {
    cat("G^2(", R, "): ", x$Gsq,
      " (Likelihood ratio/deviance statistic) \n",
      sep = ""
    )
  }
  cat("X^2(", R, "): ", x$Chisq, " (Chi-square goodness of fit) \n \n",
    sep = ""
  )
  if (x$numiter == x$maxiter) {
    cat("ALERT: iterations finished, MAXIMUM LIKELIHOOD NOT FOUND \n \n")
  }
  if (!x$probs.start.ok) {
    cat(paste(
      "ALERT: error in user-specified starting values;",
      "new start values generated \n \n"
    ))
  }
  if (x$npar > x$N) {
    cat(
      "ALERT: number of parameters estimated (",
      x$npar, ") exceeds number of observations (", x$N, ") \n \n"
    )
  }
  if (x$resid.df < 0) {
    cat("ALERT: negative degrees of freedom; respecify model \n \n")
  }
  if (x$eflag) {
    cat(paste(
      "ALERT: estimation algorithm automatically restarted with",
      "new initial values \n \n"
    ))
  }
  utils::flush.console()
  invisible(x)
}