1.0.0 bugfix

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: jstable
 Title: Create Tables from Different Types of Regression
 Version: 1.0.0
-Date: 2020-10-23
+Date: 2020-11-01
 Authors@R: c(person("Jinseob", "Kim", email = "[email protected]", role = c("aut", "cre"),  comment = c(ORCID = "0000-0002-9403-605X")),
              person("Zarathu", role = c("cph", "fnd"))
              )

R/forestglm.R

@@ -103,6 +103,10 @@ TableSubgroupGLM <- function(formula, var_subgroup = NULL, var_cov = NULL, data,
     data.frame(Variable = "Overall", Count = length(model$y), Percent = 100, `Point Estimate` = Point.Estimate, Lower = CI[1], Upper = CI[2]) %>% 
     dplyr::mutate(`P value` = ifelse(pv >= 0.001, pv, "<0.001"), `P for interaction` = NA) -> out
 
+    if (family == "binomial"){
+      names(out)[4] <- "OR"
+    }
+
     return(out)
   } else if (length(var_subgroup) > 1 | any(grepl(var_subgroup, formula))){
     stop("Please input correct subgroup variable.")
@@ -159,8 +163,11 @@ TableSubgroupGLM <- function(formula, var_subgroup = NULL, var_cov = NULL, data,
     model %>% purrr::map(possible_pv) %>% purrr::map_dbl(~round(., decimal.pvalue)) -> pv
 
 
-    data.frame(Variable = paste("  ", label_val) , Count = Count, Percent = round(Count/sum(Count) * 100, decimal.percent), `Point Estimate` = Point.Estimate, Lower = CI[, 1], Upper = CI[, 2]) %>%
+    data.frame(Variable = paste("  ", label_val) , Count = Count, Percent = round(Count/sum(Count) * 100, decimal.percent), "Point Estimate" = Point.Estimate, Lower = CI[, 1], Upper = CI[, 2]) %>%
       dplyr::mutate(`P value` = ifelse(pv >= 0.001, pv, "<0.001"), `P for interaction` = NA) -> out
+    if (family == "binomial"){
+      names(out)[4] <- "OR"
+    }
 
     return(rbind(c(var_subgroup, rep(NA, ncol(out) - 2), ifelse(pv_int >= 0.001, pv_int, "<0.001")), out))
   }
@@ -209,12 +216,12 @@ TableSubgroupGLM <- function(formula, var_subgroup = NULL, var_cov = NULL, data,
 TableSubgroupMultiGLM <- function(formula, var_subgroups = NULL, var_cov = NULL, data, family = "binomial", decimal.estimate = 2, decimal.percent = 1, decimal.pvalue = 3, line = F){
 
   . <- NULL
-  out.all <- TableSubgroupGLM(formula, var_subgroup = NULL, var_cov = var_cov, data = data, decimal.estimate = decimal.estimate, decimal.percent = decimal.percent, decimal.pvalue = decimal.pvalue)
+  out.all <- TableSubgroupGLM(formula, var_subgroup = NULL, var_cov = var_cov, data = data, family = family, decimal.estimate = decimal.estimate, decimal.percent = decimal.percent, decimal.pvalue = decimal.pvalue)
 
   if (is.null(var_subgroups)){
     return(out.all)
   } else {
-    out.list <- purrr::map(var_subgroups, ~TableSubgroupGLM(formula, var_subgroup = ., var_cov = var_cov,  data = data, decimal.estimate = decimal.estimate, decimal.percent = decimal.percent, decimal.pvalue = decimal.pvalue))
+    out.list <- purrr::map(var_subgroups, ~TableSubgroupGLM(formula, var_subgroup = ., var_cov = var_cov, data = data, family = family, decimal.estimate = decimal.estimate, decimal.percent = decimal.percent, decimal.pvalue = decimal.pvalue))
     if (line){
       out.newline <- out.list %>% purrr::map(~rbind(NA, .))
       return(rbind(out.all, out.newline %>% dplyr::bind_rows()))

README.Rmd

@@ -51,7 +51,7 @@ glmshow.display(glm_binomial, decimal = 2)
 library(geepack)  ## for dietox data
 data(dietox)
 dietox$Cu <- as.factor(dietox$Cu)
-dietox$ddn = as.numeric(rnorm(nrow(dietox)) > 0)
+dietox$ddn <- as.numeric(rnorm(nrow(dietox)) > 0)
 gee01 <- geeglm (Weight ~ Time + Cu , id = Pig, data = dietox, family = gaussian, corstr = "ex")
 geeglm.display(gee01)
 
@@ -94,10 +94,10 @@ coxme.display(fit)
 ```{r}
 library(survey)
 data(api)
-apistrat$tt = c(rep(1, 20), rep(0, nrow(apistrat) -20))
-apistrat$tt2 = factor(c(rep(0, 40), rep(1, nrow(apistrat) -40)))
+apistrat$tt <- c(rep(1, 20), rep(0, nrow(apistrat) -20))
+apistrat$tt2 <- factor(c(rep(0, 40), rep(1, nrow(apistrat) -40)))
 
-dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
+dstrat <-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)
 ds <- svyglm(api00~ell+meals+mobility + tt2, design=dstrat)
 ds2 <- svyglm(tt~ell+meals+mobility + tt2, design=dstrat, family = quasibinomial())
 svyregress.display(ds)
@@ -108,13 +108,13 @@ svyregress.display(ds2)
 
 ```{r}
 data(pbc, package="survival")
-pbc$sex = factor(pbc$sex)
-pbc$stage = factor(pbc$stage)
-pbc$randomized<-with(pbc, !is.na(trt) & trt>0)
-biasmodel<-glm(randomized~age*edema,data=pbc,family=binomial)
-pbc$randprob<-fitted(biasmodel)
+pbc$sex <- factor(pbc$sex)
+pbc$stage <- factor(pbc$stage)
+pbc$randomized <- with(pbc, !is.na(trt) & trt>0)
+biasmodel <- glm(randomized~age*edema,data=pbc,family=binomial)
+pbc$randprob <- fitted(biasmodel)
 
-if (is.null(pbc$albumin)) pbc$albumin<-pbc$alb ##pre2.9.0
+if (is.null(pbc$albumin)) pbc$albumin <- pbc$alb ##pre2.9.0
 
 dpbc <- svydesign(id=~1, prob=~randprob, strata=~edema, data=subset(pbc,randomized))
 
@@ -124,18 +124,26 @@ svycox.display(model)
 
 ## Sub-group analysis for Cox/svycox model
 ```{r}
-library(survival); library(dplyr)
+library(dplyr)
 lung %>% 
-mutate(status = as.integer(status == 1),
-       sex = factor(sex),
-       kk = factor(as.integer(pat.karno >= 70)),
-       kk1 = factor(as.integer(pat.karno >= 60))) -> lung
+  mutate(status = as.integer(status == 1),
+         sex = factor(sex),
+         kk = factor(as.integer(pat.karno >= 70)),
+         kk1 = factor(as.integer(pat.karno >= 60))) -> lung
 
-#TableSubgroupMultiCox(Surv(time, status) ~ sex, var_subgroups = c("kk", "kk1"), data=lung, line = TRUE)
+TableSubgroupMultiCox(Surv(time, status) ~ sex, var_subgroups = c("kk", "kk1"), data = lung, line = TRUE)
 
 ## Survey data
 library(survey)
 data.design <- svydesign(id = ~1, data = lung)
-#TableSubgroupMultiCox(Surv(time, status) ~ sex, var_subgroups = c("kk", "kk1"), data = data.design, line = FALSE)
+TableSubgroupMultiCox(Surv(time, status) ~ sex, var_subgroups = c("kk", "kk1"), data = data.design, line = FALSE)
 ```
 
+
+## Sub-group analysis for GLM
+```{r}
+TableSubgroupMultiGLM(status ~ sex, var_subgroups = c("kk", "kk1"), data = lung, family = "binomial")
+
+## Survey data
+TableSubgroupMultiGLM(pat.karno ~ sex, var_subgroups = c("kk", "kk1"), data = data.design, family = "gaussian", line = TRUE)
+```