Merge pull request #14 from cyk0315/master

Fix `svyglm`
jinseob2kim · Jan 5, 2024 · 77ca86d · 77ca86d
2 parents dc852db + e0bff68
commit 77ca86d
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diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,7 @@
+# jstable 1.1.4
+
+* Fix: confidence interval calculation in `svyglm` ( thanks for `cyk0315`)
+
 # jstable 1.1.3
 
 * Add `addOverall` options to `CreateTableOneJS` and `svyCreateTableOneJS` to add overall column.

diff --git a/R/svyglm.R b/R/svyglm.R
@@ -16,7 +16,7 @@
 #' @rdname svyglm.display
 #' @export
 #' @importFrom survey svyglm
-#' @importFrom stats update
+#' @importFrom stats update confint
 
 
 svyregress.display <- function(svyglm.obj, decimal = 2) {
@@ -25,59 +25,62 @@ svyregress.display <- function(svyglm.obj, decimal = 2) {
   xs <- attr(model$terms, "term.labels")
   y <- names(model$model)[1]
   gaussianT <- ifelse(length(grep("gaussian", model$family)) == 1, T, F)
-
+  
   if (length(xs) == 0) {
     stop("No independent variable")
   } else if (length(xs) == 1) {
-    uni <- data.frame(coefNA(model))[-1, ]
+    uni <- cbind(data.frame(coefNA(model))[-1, ], data.frame(stats::confint(model))[-1, ])
     rn.uni <- lapply(list(uni), rownames)
     # uni <- data.frame(summary(survey::svyglm(as.formula(paste(y, " ~ ", xs)), design = design.model, family = model$family))$coefficients)[-1, ]
     if (gaussianT) {
-      summ <- paste(round(uni[, 1], decimal), " (", round(uni[, 1] - 1.96 * uni[, 2], decimal), ",", round(uni[, 1] + 1.96 * uni[, 2], decimal), ")", sep = "")
+      summ <- paste(round(uni[, 1], decimal), " (", round(uni[,5], decimal), ",", round(uni[,6], decimal), ")", sep = "")
       uni.res <- matrix(cbind(summ, ifelse(uni[, 4] <= 0.001, "< 0.001", as.character(round(uni[, 4], decimal + 1)))), nrow = nrow(uni))
       colnames(uni.res) <- c(paste("Coeff.(", 100 - 100 * 0.05, "%CI)", sep = ""), "P value")
     } else {
-      summ <- paste(round(exp(uni[, 1]), decimal), " (", round(exp(uni[, 1] - 1.96 * uni[, 2]), decimal), ",", round(exp(uni[, 1] + 1.96 * uni[, 2]), decimal), ")", sep = "")
+      summ <- paste(round(exp(uni[, 1]), decimal), " (", round(exp(uni[,5]), decimal), ",", round(exp(uni[,6]), decimal), ")", sep = "")
       uni.res <- matrix(cbind(summ, ifelse(uni[, 4] <= 0.001, "< 0.001", as.character(round(uni[, 4], decimal + 1)))), nrow = nrow(uni))
       colnames(uni.res) <- c(paste("OR.(", 100 - 100 * 0.05, "%CI)", sep = ""), "P value")
     }
     rownames(uni.res) <- rownames(uni)
     res <- uni.res
   } else {
     uni <- lapply(xs, function(v) {
-      data.frame(coefNA(stats::update(model, formula(paste(paste(c(". ~ .", xs), collapse = " - "), " + ", v)), design = design.model)))[-1, ]
+      cbind(
+        data.frame(coefNA(stats::update(model, formula(paste(paste(c(". ~ .", xs), collapse = " - "), " + ", v)), design = design.model)))[-1, ], data.frame(stats::confint(stats::update(model, formula(paste(paste(c(". ~ .", xs), collapse = " - "), " + ", v)), design = design.model)))[-1, ]
+      )
     })
+
     # uni <- lapply(xs, function(v){
     #  summary(survey::svyglm(as.formula(paste(y, " ~ ", v)), design = design.model))$coefficients[-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 = "")
+      summ <- paste(round(uni[, 1], decimal), " (", round(uni[,5], decimal), ",", round(uni[,6], 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 <- summary(model)$coefficients[-1, ]
-      mul <- coefNA(model)[-1, ]
-      mul.summ <- paste(round(mul[, 1], decimal), " (", round(mul[, 1] - 1.96 * mul[, 2], decimal), ",", round(mul[, 1] + 1.96 * mul[, 2], decimal), ")", sep = "")
+      mul <- cbind(coefNA(model)[-1, ], stats::confint(model)[-1, ])
+      mul.summ <- paste(round(mul[, 1], decimal), " (", round(mul[,5], decimal), ",", round(mul[,6], decimal), ")", sep = "")
       mul.res <- t(rbind(mul.summ, ifelse(mul[, 4] <= 0.001, "< 0.001", as.character(round(mul[, 4], decimal + 1)))))
       colnames(mul.res) <- c(paste("adj. coeff.(", 100 - 100 * 0.05, "%CI)", sep = ""), "adj. P value")
     } else {
-      summ <- paste(round(exp(uni[, 1]), decimal), " (", round(exp(uni[, 1] - 1.96 * uni[, 2]), decimal), ",", round(exp(uni[, 1] + 1.96 * uni[, 2]), decimal), ")", sep = "")
+      summ <- paste(round(exp(uni[, 1]), decimal), " (", round(exp(uni[,5]), decimal), ",", round(exp(uni[,6]), decimal), ")", sep = "")
       uni.res <- t(rbind(summ, ifelse(uni[, 4] <= 0.001, "< 0.001", as.character(round(uni[, 4], decimal + 1)))))
       colnames(uni.res) <- c(paste("crude OR.(", 100 - 100 * 0.05, "%CI)", sep = ""), "crude P value")
       rownames(uni.res) <- rownames(uni)
       # mul <- summary(model)$coefficients[-1, ]
-      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 <- cbind(coefNA(model)[-1, ], stats::confint(model)[-1, ])
+      mul.summ <- paste(round(exp(mul[, 1]), decimal), " (", round(exp(mul[,5]), decimal), ",", round(exp(mul[,6]), 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)
   }
-
+  
   fix.all <- res
   mdata <- model$model
   ## rownames
@@ -90,7 +93,7 @@ svyregress.display <- function(svyglm.obj, decimal = 2) {
     fix.all.list[[x]] <<- rbind(rep(NA, ncol(fix.all)), fix.all.list[[x]])
   })
   fix.all.unlist <- Reduce(rbind, fix.all.list)
-
+  
   # rn.list = lapply(xs, function(x){rownames(fix.all)[grepl(x, rownames(fix.all))]})
   rn.list <- lapply(1:length(xs), function(x) {
     rownames(fix.all)[rownames(fix.all) %in% rn.uni[[x]]]
@@ -106,37 +109,37 @@ svyregress.display <- function(svyglm.obj, decimal = 2) {
     fix.all.unlist <- t(data.frame(fix.all.unlist))
   }
   rownames(fix.all.unlist) <- unlist(rn.list)
-
+  
   # pv.colnum = which(colnames(fix.all.unlist) %in% c("P value", "crude P value", "adj. P value"))
   # for (i in pv.colnum){
   #  fix.all.unlist[, i] = ifelse(as.numeric(fix.all.unlist[, i]) < 0.001, "< 0.001", round(as.numeric(fix.all.unlist[, i]), decimal + 1))
   # }
-
-
+  
+  
   outcome.name <- names(model$model)[1]
-
-
+  
+  
   if (gaussianT) {
     first.line <- paste("Linear regression predicting ", outcome.name, sep = "", "- weighted data\n")
     last.lines <- paste("No. of observations = ",
-      length(model$y), "\n", "AIC value = ", round(
-        model$aic,
-        decimal + 2
-      ), "\n", "\n",
-      sep = ""
+                        length(model$y), "\n", "AIC value = ", round(
+                          model$aic,
+                          decimal + 2
+                        ), "\n", "\n",
+                        sep = ""
     )
   } else {
     first.line <- paste("Logistic regression predicting ", outcome.name, sep = "", "- weighted data\n")
     last.lines <- paste("No. of observations = ", nrow(model$model),
-      "\n", "\n",
-      sep = ""
+                        "\n", "\n",
+                        sep = ""
     )
   }
-
+  
   results <- list(
     first.line = first.line, table = fix.all.unlist,
     last.lines = last.lines
   )
   class(results) <- c("display", "list")
   return(results)
-}
+}