cran 1.1.7

DESCRIPTION

@@ -10,7 +10,7 @@ Description: Create regression tables from generalized linear model(GLM), genera
 Depends: R (>= 3.4.0)
 License: Apache License 2.0 
 Encoding: UTF-8
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Imports: geepack, lme4, stats, data.table, labelled, tableone, coxme, survival (>= 3.0.0), survey, methods, dplyr, purrr, magrittr, tibble
 URL: https://github.com/jinseob2kim/jstable
 BugReports: https://github.com/jinseob2kim/jstable/issues

NAMESPACE

@@ -73,8 +73,11 @@ importFrom(stats,glm)
 importFrom(stats,kruskal.test)
 importFrom(stats,oneway.test)
 importFrom(stats,pnorm)
+importFrom(stats,poisson)
 importFrom(stats,predict)
+importFrom(stats,qnorm)
 importFrom(stats,quasibinomial)
+importFrom(stats,quasipoisson)
 importFrom(stats,update)
 importFrom(survey,regTermTest)
 importFrom(survey,svycoxph)

R/coxme.R

@@ -41,10 +41,10 @@ coxmeTable <- function(mod) {
 #' fit <- coxme(Surv(time, status) ~ ph.ecog + age + (1 | inst), lung)
 #' jstable:::coxExp(jstable:::coxmeTable(fit))
 #' @rdname coxExp
-#' @importFrom stats pnorm
+#' @importFrom stats pnorm qnorm
 
 coxExp <- function(cox.coef, dec) {
-  HR <- paste(round(exp(cox.coef[, 1]), dec), " (", round(exp(cox.coef[, 1] - 1.96 * cox.coef[, 2]), dec), ",", round(exp(cox.coef[, 1] + 1.96 * cox.coef[, 2]), dec), ")", sep = "")
+  HR <- paste(round(exp(cox.coef[, 1]), dec), " (", round(exp(cox.coef[, 1] - stats::qnorm(0.975) * cox.coef[, 2]), dec), ",", round(exp(cox.coef[, 1] + stats::qnorm(0.975) * cox.coef[, 2]), dec), ")", sep = "")
   pv <- cox.coef[, "p"]
   # pv = 2*(1-pnorm(abs(cox.coef[, "z"])))
   return(cbind(HR, pv))

R/forestglm.R

@@ -37,7 +37,7 @@
 #' @importFrom dplyr group_split select filter mutate bind_cols
 #' @importFrom magrittr %>%
 #' @importFrom survey svyglm
-#' @importFrom stats glm coefficients anova gaussian quasibinomial
+#' @importFrom stats glm coefficients anova gaussian quasibinomial poisson quasipoisson qnorm
 #' @importFrom utils tail
 
 TableSubgroupGLM <- function(formula, var_subgroup = NULL, var_cov = NULL, data, family = "binomial", decimal.estimate = 2, decimal.percent = 1, decimal.pvalue = 3) {
@@ -158,7 +158,7 @@ TableSubgroupGLM <- function(formula, var_subgroup = NULL, var_cov = NULL, data,
         purrr::map(~ possible_glm(formula, data = ., x = T, family = family)) -> model
       data %>%
         subset(!is.na(get(var_subgroup))) %>%
-        select(all_of(var_subgroup)) %>%
+        select(dplyr::all_of(var_subgroup)) %>%
         table() %>%
         names() -> label_val
       xlev <- stats::glm(formula, data = data)$xlevels
@@ -186,7 +186,7 @@ TableSubgroupGLM <- function(formula, var_subgroup = NULL, var_cov = NULL, data,
     CI0 <- model %>%
       purrr::map(function(model) {
         cc0 <- summary(model)$coefficients
-        c(cc0[2, 1] - qnorm(0.975) * cc0[2, 2], cc0[2, 1] + qnorm(0.975) * cc0[2, 2])
+        c(cc0[2, 1] - stats::qnorm(0.975) * cc0[2, 2], cc0[2, 1] + stats::qnorm(0.975) * cc0[2, 2])
       }) %>%
       Reduce(rbind, .)
     Point.Estimate <- round(Estimate, decimal.estimate)

tests/testthat/test-forestglm.R

@@ -13,7 +13,7 @@ test_that("Run TableSubgroupMultiGLM", {
     ) -> lung
 
   expect_is(TableSubgroupMultiGLM(status ~ sex, data = lung, family = "binomial"), "data.frame")
-  expect_warning(TableSubgroupMultiGLM(status ~ sex, var_subgroups = c("kk", "kk1"), data = lung, family = "binomial"))
+  expect_is(TableSubgroupMultiGLM(status ~ sex, var_subgroups = c("kk", "kk1"), data = lung, family = "binomial"), "data.frame")
   expect_is(TableSubgroupMultiGLM(pat.karno ~ sex, var_subgroups = c("kk", "kk1"), data = lung, family = "gaussian", line = TRUE), "data.frame")
 
   ## Survey data

tests/testthat/test-regtable.R

@@ -10,7 +10,7 @@ test_that("Run glmshow.display", {
 
 test_that("Run cox2.display", {
   library(survival)
-  data(lung)
+  #data(lung)
   fit0 <- coxph(Surv(time, status) ~ ph.ecog + age, data = lung, model = TRUE)
   fit1 <- coxph(Surv(time, status) ~ ph.ecog + age + cluster(inst), data = lung, model = TRUE)
   fit2 <- coxph(Surv(time, status) ~ ph.ecog + age + frailty(inst), data = lung, model = TRUE)