R/glmshow.R

In jstable: Create Tables from Different Types of Regression

#' @title coefNA: make coefficient table with NA
#' @description Make coefficient table with NA
#' @param model glm object (gaussian or binomial)
#' @return coefficient table with NA
#' @details DETAILS
#' @examples
#' coefNA(glm(mpg ~ wt + qsec, data = mtcars))
#' @rdname coefNA
#' @export
#' @importFrom stats coef

coefNA <- function(model) {
  coef.rownames <- merge(coef(summary(model)), model$coefficients, by = 0, all = T)
  coef.matrix <- as.matrix(coef.rownames[, -c(1, ncol(coef.rownames))])
  rownames(coef.matrix) <- coef.rownames[, "Row.names"]
  return(coef.matrix[names(model$coefficients), ])
}


#' @title glmshow.display: Show summary table of glm object.
#' @description Show summary table of glm object(regression, logistic).
#' @param glm.object glm.object
#' @param decimal digits, Default: 2
#' @return table
#' @details DETAILS
#' @examples
#' glmshow.display(glm(mpg ~ wt + qsec, data = mtcars))
#' @seealso
#'  \code{\link[stats]{glm}}
#' @rdname glmshow.display
#' @export
#' @importFrom stats glm cor predict formula

glmshow.display <- function(glm.object, decimal = 2) {
  model <- glm.object
  if (!any(class(model) %in% c("lm", "glm"))) {
    stop("Model not from  GLM")
  }

  xs <- attr(model$terms, "term.labels")
  y <- names(model$model)[1]
  gaussianT <- ifelse(length(grep("gaussian", model$family)) == 1, T, F)
  data <- model$data

  ## table
  if (length(xs) == 0) {
    stop("No independent variable")
  } else if (length(xs) == 1) {
    uni <- data.frame(coefNA(glm.object))[-1, ]
    rn.uni <- lapply(list(uni), rownames)
    if (gaussianT) {
      summ <- paste(round(uni[, 1], decimal), " (", round(uni[, 1] - 1.96 * uni[, 2], decimal), ",", round(uni[, 1] + 1.96 * uni[, 2], decimal), ")", sep = "")
      uni.res <- matrix(cbind(summ, ifelse(uni[, 4] <= 0.001, "< 0.001", as.character(round(uni[, 4], decimal + 1)))), nrow = nrow(uni))
      colnames(uni.res) <- c(paste("Coeff.(", 100 - 100 * 0.05, "%CI)", sep = ""), "P value")
    } else {
      summ <- paste(round(exp(uni[, 1]), decimal), " (", round(exp(uni[, 1] - 1.96 * uni[, 2]), decimal), ",", round(exp(uni[, 1] + 1.96 * uni[, 2]), decimal), ")", sep = "")
      uni.res <- matrix(cbind(summ, ifelse(uni[, 4] <= 0.001, "< 0.001", as.character(round(uni[, 4], decimal + 1)))), nrow = nrow(uni))
      colnames(uni.res) <- c(paste("OR.(", 100 - 100 * 0.05, "%CI)", sep = ""), "P value")
    }
    rownames(uni.res) <- rownames(uni)
    res <- uni.res
  } else {
    basemodel <- stats::update(model, formula(paste(c(". ~ .", xs), collapse = " - ")), data = data)

    uni <- lapply(xs, function(v) {
      data.frame(coefNA(stats::update(basemodel, formula(paste0(". ~ . +", v)), data = data)))
      # data.frame(coefNA(stats::glm(as.formula(paste(y, " ~ ", v)), data = data, family = model$family)))[-1, ]
    })
    rn.uni <- lapply(uni, rownames)
    uni <- Reduce(rbind, uni)
    if (gaussianT) {
      summ <- paste(round(uni[, 1], decimal), " (", round(uni[, 1] - 1.96 * uni[, 2], decimal), ",", round(uni[, 1] + 1.96 * uni[, 2], decimal), ")", sep = "")
      uni.res <- t(rbind(summ, ifelse(uni[, 4] <= 0.001, "< 0.001", as.character(round(uni[, 4], decimal + 1)))))
      colnames(uni.res) <- c(paste("crude coeff.(", 100 - 100 * 0.05, "%CI)", sep = ""), "crude P value")
      rownames(uni.res) <- rownames(uni)
      mul <- coefNA(model)[-1, ]
      mul.summ <- paste(round(mul[, 1], decimal), " (", round(mul[, 1] - 1.96 * mul[, 2], decimal), ",", round(mul[, 1] + 1.96 * mul[, 2], decimal), ")", sep = "")
      mul.res <- t(rbind(mul.summ, ifelse(mul[, 4] <= 0.001, "< 0.001", as.character(round(mul[, 4], decimal + 1)))))
      colnames(mul.res) <- c(paste("adj. coeff.(", 100 - 100 * 0.05, "%CI)", sep = ""), "adj. P value")
    } else {
      summ <- paste(round(exp(uni[, 1]), decimal), " (", round(exp(uni[, 1] - 1.96 * uni[, 2]), decimal), ",", round(exp(uni[, 1] + 1.96 * uni[, 2]), decimal), ")", sep = "")
      uni.res <- t(rbind(summ, ifelse(uni[, 4] <= 0.001, "< 0.001", as.character(round(uni[, 4], decimal + 1)))))
      colnames(uni.res) <- c(paste("crude OR.(", 100 - 100 * 0.05, "%CI)", sep = ""), "crude P value")
      rownames(uni.res) <- rownames(uni)
      mul <- coefNA(model)[-1, ]
      mul.summ <- paste(round(exp(mul[, 1]), decimal), " (", round(exp(mul[, 1] - 1.96 * mul[, 2]), decimal), ",", round(exp(mul[, 1] + 1.96 * mul[, 2]), decimal), ")", sep = "")
      mul.res <- t(rbind(mul.summ, ifelse(mul[, 4] <= 0.001, "< 0.001", as.character(round(mul[, 4], decimal + 1)))))
      colnames(mul.res) <- c(paste("adj. OR.(", 100 - 100 * 0.05, "%CI)", sep = ""), "adj. P value")
    }

    res <- cbind(uni.res[rownames(uni.res) %in% rownames(mul.res), ], mul.res)
    rownames(res) <- rownames(mul)
  }

  ## label
  fix.all <- res

  ## rownames
  fix.all.list <- lapply(1:length(xs), function(x) {
    fix.all[rownames(fix.all) %in% rn.uni[[x]], ]
  })
  varnum.mfac <- which(lapply(fix.all.list, length) > ncol(fix.all))
  lapply(varnum.mfac, function(x) {
    fix.all.list[[x]] <<- rbind(rep(NA, ncol(fix.all)), fix.all.list[[x]])
  })
  fix.all.unlist <- Reduce(rbind, fix.all.list)

  rn.list <- lapply(1:length(xs), function(x) {
    rownames(fix.all)[rownames(fix.all) %in% rn.uni[[x]]]
  })
  varnum.2fac <- which(xs %in% names(model$xlevels)[lapply(model$xlevels, length) == 2])
  lapply(varnum.2fac, function(x) {
    rn.list[[x]] <<- paste(xs[x], ": ", model$xlevels[[xs[x]]][2], " vs ", model$xlevels[[xs[x]]][1], sep = "")
  })
  lapply(varnum.mfac, function(x) {
    rn.list[[x]] <<- c(paste(xs[x], ": ref.=", model$xlevels[[xs[x]]][1], sep = ""), gsub(xs[x], "   ", rn.list[[x]]))
  })
  if (class(fix.all.unlist)[1] == "character") {
    fix.all.unlist <- t(data.frame(fix.all.unlist))
  }
  rownames(fix.all.unlist) <- unlist(rn.list)

  # pv.colnum = which(colnames(fix.all.unlist) %in% c("P value", "crude P value", "adj. P value"))
  # for (i in pv.colnum){
  #  fix.all.unlist[, i] = ifelse(as.numeric(fix.all.unlist[, i]) < 0.001, "< 0.001", round(as.numeric(fix.all.unlist[, i]), decimal + 1))
  # }


  outcome.name <- y


  if (gaussianT) {
    first.line <- paste("Linear regression predicting ", outcome.name, sep = "", "\n")
    last.lines <- paste("No. of observations = ",
      length(model$y), "\n", "R-squared = ", round(cor(model$y, predict(model))^2, decimal + 2), "\n",
      "AIC value = ", round(model$aic, decimal + 2), "\n", "\n",
      sep = ""
    )
  } else {
    first.line <- paste("Logistic regression predicting ", outcome.name, sep = "", "\n")
    last.lines <- paste("No. of observations = ",
      length(model$y), "\n",
      "AIC value = ", round(model$aic, decimal + 2), "\n", "\n",
      sep = ""
    )
  }

  results <- list(
    first.line = first.line, table = fix.all.unlist,
    last.lines = last.lines
  )
  class(results) <- c("display", "list")
  return(results)
}