Title: Create Tables from Different Types of Regression
Version: 1.3.13
Date: 2025-06-30
Description: Create regression tables from generalized linear model(GLM), generalized estimating equation(GEE), generalized linear mixed-effects model(GLMM), Cox proportional hazards model, survey-weighted generalized linear model(svyglm) and survey-weighted Cox model results for publication.
Depends: R (≥ 3.4.0)
License: Apache License 2.0
Encoding: UTF-8
RoxygenNote: 7.3.2
Imports: geepack, lme4, stats, data.table, labelled, tableone, coxme, survival (≥ 3.0.0), survey, methods, dplyr, purrr, magrittr, tibble, rlang, car, lmerTest, nortest
URL: https://github.com/jinseob2kim/jstable
BugReports: https://github.com/jinseob2kim/jstable/issues
Suggests: testthat, knitr, rmarkdown
VignetteBuilder: knitr
LazyData: true
NeedsCompilation: no
Packaged: 2025-07-01 05:55:10 UTC; js
Author: Jinseob Kim ORCID iD [aut, cre], Zarathu [cph, fnd], Yoonkyoung Jeon [aut], Jaehun Shon [aut], Hyojong Myung [aut], Hyungwoo Jo [aut], Sungho Choi [aut]
Maintainer: Jinseob Kim <jinseob2kim@gmail.com>
Repository: CRAN
Date/Publication: 2025-07-01 06:20:02 UTC

ChangeSvyTable: Modify the number n

Description

Replace the number of weights taken into account with the number of n in the original data

Usage

ChangeSvyTable(svy, ori)

Arguments

svy

TableOne object that take weights into account

ori

TableOne object in the original data

Details

DETAILS

Value

A matrix that replaces the n number of weights with the n number of the original data

Examples

# example code
library('survey'); library('tableone')
data(nhanes)
nhanes$SDMVPSU <- as.factor(nhanes$SDMVPSU)
nhanesSvy <- svydesign(
  ids = ~SDMVPSU, strata = ~SDMVSTRA, weights = ~WTMEC2YR,
  nest = TRUE, data = nhanes
)

res <- svyCreateTableOne(
  vars = c("HI_CHOL", "race", "agecat", "RIAGENDR"),
  strata = "RIAGENDR", data = nhanesSvy,
  factorVars = c("HI_CHOL", "race", "RIAGENDR")
)

ori <- CreateTableOne(
  vars = c("HI_CHOL", "race", "agecat", "RIAGENDR"),
  strata = "RIAGENDR", data = nhanes,
  factorVars = c("HI_CHOL", "race", "RIAGENDR")
)
ChangeSvyTable(res, ori)

CreateTableOne2: Modified CreateTableOne function in tableone package

Description

Combine CreateTableOne & print function in tableone package

Usage

CreateTableOne2(
  data,
  strata,
  vars,
  factorVars,
  includeNA = F,
  test = T,
  testApprox = chisq.test,
  argsApprox = list(correct = TRUE),
  testExact = fisher.test,
  argsExact = list(workspace = 2 * 10^5),
  testNormal = oneway.test,
  argsNormal = list(var.equal = F),
  testNonNormal = kruskal.test,
  argsNonNormal = list(NULL),
  showAllLevels = T,
  printToggle = F,
  quote = F,
  smd = F,
  Labels = F,
  exact = NULL,
  nonnormal = NULL,
  catDigits = 1,
  contDigits = 2,
  pDigits = 3,
  labeldata = NULL,
  minMax = F,
  showpm = T,
  addOverall = F,
  pairwise = F,
  pairwise.showtest = F
)

Arguments

data

A data frame in which these variables exist. All variables (both vars and strata) must be in this data frame.

strata

Stratifying (grouping) variable name(s) given as a character vector. If omitted, the overall results are returned.

vars

Variables to be summarized given as a character vector. Factors are handled as categorical variables, whereas numeric variables are handled as continuous variables. If empty, all variables in the data frame specified in the data argument are used.

factorVars

Numerically coded variables that should be handled as categorical variables given as a character vector. Do not include factors, unless you need to relevel them by removing empty levels. If omitted, only factors are considered categorical variables. The variables specified here must also be specified in the vars argument.

includeNA

If TRUE, NA is handled as a regular factor level rather than missing. NA is shown as the last factor level in the table. Only effective for categorical variables., Default: F

test

If TRUE, as in the default and there are more than two groups, groupwise comparisons are performed, Default: T

testApprox

A function used to perform the large sample approximation based tests. The default is chisq.test. This is not recommended when some of the cell have small counts like fewer than 5, Default: chisq.test

argsApprox

A named list of arguments passed to the function specified in testApprox. The default is list(correct = TRUE), which turns on the continuity correction for chisq.test, Default: list(correct = TRUE)

testExact

A function used to perform the exact tests. The default is fisher.test. If the cells have large numbers, it will fail because of memory limitation. In this situation, the large sample approximation based should suffice., Default: fisher.test

argsExact

A named list of arguments passed to the function specified in testExact. The default is list(workspace = 2 * 10^5), which specifies the memory space allocated for fisher.test, Default: list(workspace = 2 * 10^5)

testNormal

A function used to perform the normal assumption based tests. The default is oneway.test. This is equivalent of the t-test when there are only two groups, Default: oneway.test

argsNormal

A named list of arguments passed to the function specified in testNormal. The default is list(var.equal = TRUE), which makes it the ordinary ANOVA that assumes equal variance across groups., Default: list(var.equal = F)

testNonNormal

A function used to perform the nonparametric tests. The default is kruskal.test (Kruskal-Wallis Rank Sum Test). This is equivalent of the wilcox.test (Man-Whitney U test) when there are only two groups, Default: kruskal.test

argsNonNormal

A named list of arguments passed to the function specified in testNonNormal. The default is list(NULL), which is just a placeholder., Default: list(NULL)

showAllLevels

Whether to show all levels. FALSE by default, i.e., for 2-level categorical variables, only the higher level is shown to avoid redundant information., Default: T

printToggle

Whether to print the output. If FALSE, no output is created, and a matrix is invisibly returned., Default: F

quote

Whether to show everything in quotes. The default is FALSE. If TRUE, everything including the row and column names are quoted so that you can copy it to Excel easily, Default: F

smd

If TRUE, as in the default and there are more than two groups, standardized mean differences for all pairwise comparisons are calculated, Default: F

Labels

Use Label, Default: F

exact

A character vector to specify the variables for which the p-values should be those of exact tests. By default all p-values are from large sample approximation tests (chisq.test)., Default: NULL

nonnormal

A character vector to specify the variables for which the p-values should be those of nonparametric tests. By default all p-values are from normal assumption-based tests (oneway.test)., Default: NULL

catDigits

Number of digits to print for proportions., Default: 1

contDigits

Number of digits to print for continuous variables. Default 2.

pDigits

Number of digits to print for p-values (also used for standardized mean differences), Default: 3

labeldata

labeldata to use, Default: NULL

minMax

Whether to use [min,max] instead of [p25,p75] for nonnormal variables. The default is FALSE.

showpm

Logical, show normal distributed continuous variables as Mean ± SD. Default: T

addOverall

(optional, only used if strata are supplied) Adds an overall column to the table. Smd and p-value calculations are performed using only the stratifed clolumns. Default: F

pairwise

(optional, only used if strata are supplied) When there are three or more strata, it displays the p-values for pairwise comparisons. Default: F

pairwise.showtest

(optional, only used if strata are supplied) When using pairwise comparison, it displays the test used to calculate p-values for pairwise comparisons. Default: F

Details

DETAILS

Value

A matrix object containing what you see is also invisibly returned. This can be assinged a name and exported via write.csv.

Examples

library(survival)
CreateTableOne2(vars = names(lung), strata = "sex", data = lung)

CreateTableOneJS: Modified CreateTableOne function in tableone package

Description

Combine CreateTableOne & print function in tableone package

Usage

CreateTableOneJS(
  vars,
  strata = NULL,
  strata2 = NULL,
  data,
  factorVars = NULL,
  includeNA = F,
  test = T,
  testApprox = chisq.test,
  argsApprox = list(correct = TRUE),
  testExact = fisher.test,
  argsExact = list(workspace = 2 * 10^5),
  testNormal = oneway.test,
  argsNormal = list(var.equal = F),
  testNonNormal = kruskal.test,
  argsNonNormal = list(NULL),
  showAllLevels = T,
  printToggle = F,
  quote = F,
  smd = F,
  Labels = F,
  exact = NULL,
  nonnormal = NULL,
  catDigits = 1,
  contDigits = 2,
  pDigits = 3,
  labeldata = NULL,
  psub = T,
  minMax = F,
  showpm = T,
  addOverall = F,
  normalityTest = F,
  pairwise = F,
  pairwise.showtest = F
)

Arguments

vars

Variables to be summarized given as a character vector. Factors are handled as categorical variables, whereas numeric variables are handled as continuous variables. If empty, all variables in the data frame specified in the data argument are used.

strata

Stratifying grouping variable name(s) given as a character vector. If omitted, the overall results are returned.

strata2

Stratifying 2nd grouping variable name(s) given as a character vector. If omitted, the 1 group results are returned.

data

A data frame in which these variables exist. All variables (both vars and strata) must be in this data frame.

factorVars

Numerically coded variables that should be handled as categorical variables given as a character vector. Do not include factors, unless you need to relevel them by removing empty levels. If omitted, only factors are considered categorical variables. The variables specified here must also be specified in the vars argument.

includeNA

If TRUE, NA is handled as a regular factor level rather than missing. NA is shown as the last factor level in the table. Only effective for categorical variables., Default: F

test

If TRUE, as in the default and there are more than two groups, groupwise comparisons are performed, Default: T

testApprox

A function used to perform the large sample approximation based tests. The default is chisq.test. This is not recommended when some of the cell have small counts like fewer than 5, Default: chisq.test

argsApprox

A named list of arguments passed to the function specified in testApprox. The default is list(correct = TRUE), which turns on the continuity correction for chisq.test, Default: list(correct = TRUE)

testExact

A function used to perform the exact tests. The default is fisher.test. If the cells have large numbers, it will fail because of memory limitation. In this situation, the large sample approximation based should suffice., Default: fisher.test

argsExact

A named list of arguments passed to the function specified in testExact. The default is list(workspace = 2 * 10^5), which specifies the memory space allocated for fisher.test, Default: list(workspace = 2 * 10^5)

testNormal

A function used to perform the normal assumption based tests. The default is oneway.test. This is equivalent of the t-test when there are only two groups, Default: oneway.test

argsNormal

A named list of arguments passed to the function specified in testNormal. The default is list(var.equal = TRUE), which makes it the ordinary ANOVA that assumes equal variance across groups., Default: list(var.equal = F)

testNonNormal

A function used to perform the nonparametric tests. The default is kruskal.test (Kruskal-Wallis Rank Sum Test). This is equivalent of the wilcox.test (Man-Whitney U test) when there are only two groups, Default: kruskal.test

argsNonNormal

A named list of arguments passed to the function specified in testNonNormal. The default is list(NULL), which is just a placeholder., Default: list(NULL)

showAllLevels

Whether to show all levels. FALSE by default, i.e., for 2-level categorical variables, only the higher level is shown to avoid redundant information., Default: T

printToggle

Whether to print the output. If FALSE, no output is created, and a matrix is invisibly returned., Default: F

quote

Whether to show everything in quotes. The default is FALSE. If TRUE, everything including the row and column names are quoted so that you can copy it to Excel easily, Default: F

smd

If TRUE, as in the default and there are more than two groups, standardized mean differences for all pairwise comparisons are calculated, Default: F

Labels

Use Label, Default: F

exact

A character vector to specify the variables for which the p-values should be those of exact tests. By default all p-values are from large sample approximation tests (chisq.test)., Default: NULL

nonnormal

A character vector to specify the variables for which the p-values should be those of nonparametric tests. By default all p-values are from normal assumption-based tests (oneway.test)., Default: NULL

catDigits

Number of digits to print for proportions. Default: 1

contDigits

Number of digits to print for continuous variables. Default 2.

pDigits

Number of digits to print for p-values (also used for standardized mean differences), Default: 3

labeldata

labeldata to use, Default: NULL

psub

show sub-group p-values, Default: F

minMax

Whether to use [min,max] instead of [p25,p75] for nonnormal variables. The default is FALSE.

showpm

Logical, show normal distributed continuous variables as Mean ± SD. Default: T

addOverall

(optional, only used if strata are supplied) Adds an overall column to the table. Smd and p-value calculations are performed using only the stratifed clolumns. Default: F

normalityTest

Logical, perform the Shapiro test for all variables. Default: F

pairwise

(optional, only used if strata are supplied) When there are three or more strata, it displays the p-values for pairwise comparisons. Default: F#' @return A matrix object containing what you see is also invisibly returned. This can be assinged a name and exported via write.csv.

pairwise.showtest

(optional, only used if strata are supplied) When using pairwise comparison, it displays the test used to calculate p-values for pairwise comparisons. Default: F

Details

DETAILS

Examples

library(survival)
CreateTableOneJS(vars = names(lung), strata = "sex", data = lung)

LabelepiDisplay: Apply label information to epiDisplay object using label data

Description

Apply label information to epiDisplay.object using label data

Usage

LabelepiDisplay(epiDisplay.obj, label = F, ref)

Arguments

epiDisplay.obj

epiDisplay.object or glmshow.object

label

Apply label information, Default: F

ref

Label data made by mk.lev function

Details

DETAILS

Value

epiDisplay.object with label information

Examples

fit <- glm(Sepal.Length ~ Sepal.Width + Species, data = iris)
fit.table <- glmshow.display(fit)
iris.label <- mk.lev(iris)
LabelepiDisplay(fit.table, label = TRUE, ref = iris.label)

LabeljsCox: Apply label information to cox2.display object using label data

Description

Apply label information to cox2.display object using label data

Usage

LabeljsCox(obj, ref)

Arguments

obj

cox2.display object

ref

Label data made by mk.lev function

Details

DETAILS

Value

cox2.display object with label information

Examples

library(survival)
fit <- coxph(Surv(time, status) ~ sex + ph.ecog + ph.karno + cluster(inst),
  data = lung, model = TRUE
)
fit.table <- cox2.display(fit)
lung.label <- mk.lev(lung)
LabeljsCox(fit.table, ref = lung.label)

LabeljsGeeglm: Apply label information to geeglm.display object using label data

Description

Apply label information to geeglm.display object using label data

Usage

LabeljsGeeglm(obj, ref)

Arguments

obj

geeglm.display object

ref

Label data made by mk.lev function

Details

DETAILS

Value

geeglm.display object with label information

Examples

library(geepack)
library(jstable)
data(dietox)
dietox$Cu <- as.factor(dietox$Cu)
gee01 <- geeglm(Weight ~ Time + Cu,
  id = Pig, data = dietox,
  family = gaussian, corstr = "ex"
)
g1 <- geeglm.display(gee01)
LabeljsGeeglm(g1, ref = mk.lev(dietox))

LabeljsMetric: Apply label information to jstable metric object using label data

Description

Apply label information to metric object of jstable using label data

Usage

LabeljsMetric(obj.metric, ref)

Arguments

obj.metric

metric of lmer.display, coxme.display

ref

Label data made by mk.lev function

Details

DETAILS

Value

metric of lmer.display, coxme.display with label information

Examples

library(coxme)
fit <- coxme(Surv(time, status) ~ sex + ph.ecog + ph.karno + (1 | inst) + (1 | sex), lung)
fit.table <- coxme.display(fit)
lung.label <- mk.lev(lung)
LabeljsTable(fit.table$table, ref = lung.label)
LabeljsRanef(fit.table$ranef, ref = lung.label)
LabeljsMetric(fit.table$metric, ref = lung.label)

LabeljsMixed: Apply label information to jstable object using label data

Description

Apply label information to object of jstable using label data

Usage

LabeljsMixed(obj, ref)

Arguments

obj

lmer.display, coxme.display

ref

Label data made by mk.lev function

Details

DETAILS

Value

lmer.display, coxme.display with label information

Examples

library(coxme)
fit <- coxme(Surv(time, status) ~ sex + ph.ecog + ph.karno + (1 | inst) + (1 | sex), lung)
fit.table <- coxme.display(fit)
lung.label <- mk.lev(lung)
LabeljsMixed(fit.table, ref = lung.label)

LabeljsRanef: Apply label information to jstable random effect object using label data

Description

Apply label information to ranef object of jstable using label data

Usage

LabeljsRanef(obj.ranef, ref)

Arguments

obj.ranef

ranef of lmer.display, coxme.display, cox2.display

ref

Label data made by mk.lev function

Details

DETAILS

Value

ranef of lmer.display, coxme.display, cox2.display with label information

Examples

library(coxme)
fit <- coxme(Surv(time, status) ~ sex + ph.ecog + ph.karno + (1 | inst) + (1 | sex), lung)
fit.table <- coxme.display(fit)
lung.label <- mk.lev(lung)
LabeljsTable(fit.table$table, ref = lung.label)
LabeljsRanef(fit.table$ranef, ref = lung.label)

LabeljsTable: Apply label information to jstable object using label data

Description

Apply label information to table of geeglm.display, lmer.display, coxme.display using label data

Usage

LabeljsTable(obj.table, ref)

Arguments

obj.table

table of geeglm.display, lmer.display, coxme.display

ref

Label data made by mk.lev function

Details

DETAILS

Value

table of geeglm.display, lmer.display, coxme.display with label information

Examples

library(coxme)
fit <- coxme(Surv(time, status) ~ sex + ph.ecog + ph.karno + (1 | inst) + (1 | sex), lung)
fit.table <- coxme.display(fit)
lung.label <- mk.lev(lung)
LabeljsTable(fit.table$table, ref = lung.label)

TableSubgroupCox: Sub-group analysis table for Cox/svycox model.

Description

Sub-group analysis table for Cox/svycox model.

Usage

TableSubgroupCox(
  formula,
  var_subgroup = NULL,
  var_cov = NULL,
  data,
  time_eventrate = 3 * 365,
  decimal.hr = 2,
  decimal.percent = 1,
  decimal.pvalue = 3,
  cluster = NULL,
  strata = NULL,
  weights = NULL,
  event = FALSE,
  count_by = NULL,
  labeldata = NULL
)

Arguments

formula

formula with survival analysis.

var_subgroup

1 sub-group variable for analysis, Default: NULL

var_cov

Variables for additional adjust, Default: NULL

data

Data or svydesign in survey package.

time_eventrate

Time for kaplan-meier based event rate calculation, Default = 365 * 3

decimal.hr

Decimal for hazard ratio, Default: 2

decimal.percent

Decimal for percent, Default: 1

decimal.pvalue

Decimal for pvalue, Default: 3

cluster

Cluster variable for coxph, Default: NULL

strata

Strata variable for coxph, Default: NULL

weights

Weights variable for coxph, Default: NULL

event

Show number and rates of event in survival analysis default:F

count_by

Select variables to count by subgroup, Default: NULL

labeldata

Label info, made by 'mk.lev' function, Default: NULL

Details

This result is used to make forestplot.

Value

Sub-group analysis table.

See Also

safely,map,map2 coxph svycoxph confint

Examples

library(survival)
library(dplyr)
lung %>%
  mutate(
    status = as.integer(status == 1),
    sex = factor(sex),
    kk = factor(as.integer(pat.karno >= 70))
  ) -> lung
TableSubgroupCox(Surv(time, status) ~ sex, data = lung, time_eventrate = 100)
TableSubgroupCox(Surv(time, status) ~ sex,
  var_subgroup = "kk", data = lung,
  time_eventrate = 100
)

## survey design
library(survey)
data.design <- svydesign(id = ~1, data = lung)
TableSubgroupCox(Surv(time, status) ~ sex, data = data.design, time_eventrate = 100)
TableSubgroupCox(Surv(time, status) ~ sex,
  var_subgroup = "kk", data = data.design,
  time_eventrate = 100
)

TableSubgroupGLM: Sub-group analysis table for GLM and GLMM(lme4 package).

Description

Sub-group analysis table for GLM.

Usage

TableSubgroupGLM(
  formula,
  var_subgroup = NULL,
  var_cov = NULL,
  data,
  family = "binomial",
  decimal.estimate = 2,
  decimal.percent = 1,
  decimal.pvalue = 3,
  labeldata = NULL,
  count_by = NULL,
  event = FALSE
)

Arguments

formula

formula with survival analysis.

var_subgroup

1 sub-group variable for analysis, Default: NULL

var_cov

Variables for additional adjust, Default: NULL

data

Data or svydesign in survey package.

family

family, "gaussian" or "binomial" or 'poisson' or 'quasipoisson'

decimal.estimate

Decimal for estimate, Default: 2

decimal.percent

Decimal for percent, Default: 1

decimal.pvalue

Decimal for pvalue, Default: 3

labeldata

Label info, made by 'mk.lev' function, Default: NULL

count_by

Variable name to stratify counts by (string). Default: NULL.

event

If 'TRUE', show counts/metrics instead of only model estimates. Default: FALSE.

Details

This result is used to make forestplot.

Value

Sub-group analysis table.

See Also

safely,map,map2 glm svyglm

Examples

library(survival)
library(dplyr)
lung %>%
  mutate(
    status = as.integer(status == 1),
    sex = factor(sex),
    kk = factor(as.integer(pat.karno >= 70))
  ) -> lung
TableSubgroupGLM(status ~ sex, data = lung, family = "binomial")
TableSubgroupGLM(status ~ sex, var_subgroup = "kk", data = lung, family = "binomial")

## survey design
library(survey)
data.design <- svydesign(id = ~1, data = lung)
TableSubgroupGLM(status ~ sex, data = data.design, family = "binomial")
TableSubgroupGLM(status ~ sex, var_subgroup = "kk", data = data.design, family = "binomial")

TableSubgroupMultiCox: Multiple sub-group analysis table for Cox/svycox model.

Description

Multiple sub-group analysis table for Cox/svycox model.

Usage

TableSubgroupMultiCox(
  formula,
  var_subgroups = NULL,
  var_cov = NULL,
  data,
  time_eventrate = 3 * 365,
  decimal.hr = 2,
  decimal.percent = 1,
  decimal.pvalue = 3,
  line = F,
  cluster = NULL,
  strata = NULL,
  weights = NULL,
  event = FALSE,
  count_by = NULL,
  labeldata = NULL
)

Arguments

formula

formula with survival analysis.

var_subgroups

Multiple sub-group variables for analysis, Default: NULL

var_cov

Variables for additional adjust, Default: NULL

data

Data or svydesign in survey package.

time_eventrate

Time for kaplan-meier based event rate calculation, Default = 365 * 3

decimal.hr

Decimal for hazard ratio, Default: 2

decimal.percent

Decimal for percent, Default: 1

decimal.pvalue

Decimal for pvalue, Default: 3

line

Include new-line between sub-group variables, Default: F

cluster

Cluster variable for coxph, Default: NULL

strata

Strata variable for coxph, Default: NULL

weights

Weights variable for coxph, Default: NULL

event

Show number and rates of event in survival analysis default:F

count_by

Select variables to count by subgroup, Default: NULL

labeldata

Label info, made by 'mk.lev' function, Default: NULL

Details

This result is used to make forestplot.

Value

Multiple sub-group analysis table.

See Also

map bind

Examples

library(survival)
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
TableSubgroupMultiCox(Surv(time, status) ~ sex,
  var_subgroups = c("kk", "kk1"),
  data = lung, time_eventrate = 100, line = TRUE
)

## survey design
library(survey)
data.design <- svydesign(id = ~1, data = lung)
TableSubgroupMultiCox(Surv(time, status) ~ sex,
  var_subgroups = c("kk", "kk1"),
  data = data.design, time_eventrate = 100
)

TableSubgroupMultiGLM: Multiple sub-group analysis table for GLM.

Description

Multiple sub-group analysis table for GLM.

Usage

TableSubgroupMultiGLM(
  formula,
  var_subgroups = NULL,
  var_cov = NULL,
  data,
  family = "binomial",
  decimal.estimate = 2,
  decimal.percent = 1,
  decimal.pvalue = 3,
  line = F,
  labeldata = NULL,
  count_by = NULL,
  event = FALSE
)

Arguments

formula

formula with survival analysis.

var_subgroups

Multiple sub-group variables for analysis, Default: NULL

var_cov

Variables for additional adjust, Default: NULL

data

Data or svydesign in survey package.

family

family, "gaussian" or "binomial" or 'poisson' or 'quasipoisson'

decimal.estimate

Decimal for estimate, Default: 2

decimal.percent

Decimal for percent, Default: 1

decimal.pvalue

Decimal for pvalue, Default: 3

line

Include new-line between sub-group variables, Default: F

labeldata

Label info, made by 'mk.lev' function, Default: NULL

count_by

Variable name to stratify counts by (string). Default: NULL.

event

If 'TRUE', show counts/metrics instead of only model estimates. Default: FALSE.

Details

This result is used to make forestplot.

Value

Multiple sub-group analysis table.

See Also

map bind

Examples

library(survival)
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
TableSubgroupMultiGLM(status ~ sex,
  var_subgroups = c("kk", "kk1"),
  data = lung, line = TRUE, family = "binomial"
)

## survey design
library(survey)
data.design <- svydesign(id = ~1, data = lung)
TableSubgroupMultiGLM(status ~ sex,
  var_subgroups = c("kk", "kk1"),
  data = data.design, family = "binomial"
)

coefNA: make coefficient table with NA

Description

Make coefficient table with NA

Usage

coefNA(model)

Arguments

model

glm object (gaussian or binomial)

Details

DETAILS

Value

coefficient table with NA

Examples


coefNA(glm(mpg ~ wt + qsec, data = mtcars))

collapse_counts

Description

#' When a grouping variable count_by has more than two levels, this utility collapses all of the level-specific columns

Usage

collapse_counts(df, count_by)

Arguments

df

A data.frame produced by one of the TableSubgroup functions

count_by

A string giving the name of the variable whose per-level count columns should be collapsed. If NULL, df is returned unmodified.

Details

This helper is intended for internal use by TableSubgroupCox(), TableSubgroupMultiCox(), and their GLM equivalents. It simplifies reporting when you request counts stratified by a factor with more than two levels.

Value

A data.frame identical to 'f except that: All Count(count_by=…) columns are removed. A single column Count(by count_by) remains, containing the first non-missing, non-empty value from the original per-level columns.

count_event_by: funciton to count event, subgroup number inside TableSubgroupCox, TableSubgroupMultiCox

Description

Function to count event, subgroup number

Usage

count_event_by(
  formula,
  data,
  count_by_var = NULL,
  var_subgroup = NULL,
  decimal.percent = 1
)

Arguments

formula

formula with survival analysis

data

same data as in formula

count_by_var

variables to count subgroup for

var_subgroup

1 sub-group variable for analysis,

decimal.percent

decimals to show percent of, Default: 1

Details

This function is used inside TableSubgroupCox, TableSubgroupMultiCox for calculation

Value

Table with event, subgroup number

See Also

group_by, summarise, mutate, bind_rows, arrange

Examples

## Not run: 
if (interactive()) {

}

## End(Not run)

count_event_by_glm: function to count event rates or summary metrics for GLM analysis

Description

Count event and subgroup summary, supports automatic parsing of fixed-effect syntax '(1|subgroup)' and multiple families.

Usage

count_event_by_glm(
  formula,
  data,
  count_by_var = NULL,
  var_subgroup = NULL,
  decimal.percent = 1,
  family = "binomial"
)

Arguments

formula

formula with response (0/1 or count or continuous) and optional fixed-effect '(1|subgroup)' term.

data

data.frame or survey.design

count_by_var

variable name to stratify by (string), default NULL

var_subgroup

subgroup variable name (string); parsed from formula if not provided, default NULL

decimal.percent

decimals for percent or mean/sd, default 1

family

family type: "gaussian", "binomial", "poisson", or "quasipoisson"

Value

tibble with grouping columns and Metric column (rate or mean sd)

cox2.display: table for coxph.object with model option: TRUE - allow "frailty" or "cluster" model

Description

Table for coxph.object with model option: TRUE - allow "frailty" or "cluster" model

Usage

cox2.display(cox.obj.withmodel, dec = 2, msm = NULL, pcut.univariate = NULL)

Arguments

cox.obj.withmodel

coxph.object with model option: TRUE

dec

Decimal point, Default: 2

msm

Multi state model, Default: NULL

pcut.univariate

pcut.univariate, Default: NULL

Details

GEE like - cluster, Mixed effect model like - frailty

Value

Table, cluster/frailty info, metrics, caption

Examples

library(survival)
data(lung)
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)
cox2.display(fit1)
cox2.display(fit2)

coxExp: transform the unit of coefficients in cox model(internal function)

Description

Transform the unit of coefficients to "HR"

Usage

coxExp(cox.coef, dec)

Arguments

cox.coef

cox model coefficients

dec

Decimal point

Details

DETAILS

Value

The transforemed coefficients(95

Examples

library(coxme)
fit <- coxme(Surv(time, status) ~ ph.ecog + age + (1 | inst), lung)
jstable:::coxExp(jstable:::coxmeTable(fit))

coxme.display: table for coxme.object (coxme package)

Description

Make mixed effect model results from coxme.object (coxme package)

Usage

coxme.display(coxme.obj, dec = 2, pcut.univariate = NULL)

Arguments

coxme.obj

coxme.object

dec

Decimal point, Default: 2

pcut.univariate

pcut.univariate, Default: NULL

Details

DETAILS

Value

Fixed effect table, random effect, metrics, caption

Examples

library(coxme)
fit <- coxme(Surv(time, status) ~ ph.ecog + age + (1 | inst), lung)
coxme.display(fit)

coxmeTable: Summary table of coxme.object(internal function)

Description

Extract fixed effect table in coxme.object

Usage

coxmeTable(mod)

Arguments

mod

coxme.object

Details

DETAILS

Value

beta, se, z, p of fixed effects

Examples

library(coxme)
fit <- coxme(Surv(time, status) ~ ph.ecog + age + (1 | inst), lung)
jstable:::coxmeTable(fit)

extractAIC.coxme: Extract AIC from coxme.object

Description

Extract AIC from coxme.object

Usage

## S3 method for class 'coxme'
extractAIC(fit, scale = NULL, k = 2, ...)

Arguments

fit

coxme.object

scale

NULL

k

numeric specifying the 'weight' of the equivalent degrees of freedom (=: edf) part in the AIC formula.

...

further arguments (currently unused in base R).

Details

DETAILS

Value

AIC(Integreted, Penalized)

Examples

library(coxme)
fit <- coxme(Surv(time, status) ~ ph.ecog + age + (1 | inst), lung)
extractAIC(fit)

geeExp: transform the unit of coefficients (internal function)

Description

Transform the unit of coefficients to "Coeff", "OR" or "RR"

Usage

geeExp(gee.coef, family = "binomial", dec)

Arguments

gee.coef

geeUni object.

family

Family: "gaussian", "binomial", "poisson", "quasipoisson", etc..., Default: 'binomial'

dec

Decimal point

Details

DETAILS

Value

The transforemed coefficients(95

Examples

library(geepack)
data(dietox)
dietox$Cu <- as.factor(dietox$Cu)
gee.uni <- geeUni("Weight", c("Time", "Cu"),
  data = dietox, id.vec = dietox$Pig,
  family = "gaussian", cor.type = "exchangeable"
)
gee.exp <- geeExp(gee.uni, "binomial", 2)

geeUni: The coefficient of univariate gee (internal function)

Description

Extract the coefficients of univariate gee using geeglm function (geepack package).

Usage

geeUni(y, x, data, id.vec, family, cor.type = "exchangeable")

Arguments

y

Dependant variable

x

Independent variable

data

Data

id.vec

Vector of id (should be ordered)

family

Family: "gaussian", "binomial", "poisson", "quasipoisson", etc...

cor.type

Correlation structure, Default: 'exchangeable'

Details

DETAILS

Value

coefficient, standard error, p-value

Examples

library(geepack)
data(dietox)
dietox$Cu <- as.factor(dietox$Cu)
gee.uni <- geeUni("Weight", "Time",
  data = dietox, id.vec = dietox$Pig,
  family = "gaussian", cor.type = "exchangeable"
)

geeglm.display

Description

Make gee results from "geeglm" object

Usage

geeglm.display(geeglm.obj, decimal = 2, pcut.univariate = NULL)

Arguments

geeglm.obj

"geeglm" object

decimal

Decimal, Default: 2

pcut.univariate

pcut.univariate, Default: NULL

Details

DETAILS

Value

List: caption, main table, metrics table

See Also

data.table-package complete.cases

Examples

library(geepack)
data(dietox)
dietox$Cu <- as.factor(dietox$Cu)
gee01 <- geeglm(Weight ~ Time + Cu,
  id = Pig, data = dietox,
  family = gaussian, corstr = "ex"
)
geeglm.display(gee01)

glmshow.display: Show summary table of glm object.

Description

Show summary table of glm object(regression, logistic).

Usage

glmshow.display(glm.object, decimal = 2, pcut.univariate = NULL)

Arguments

glm.object

glm.object

decimal

digits, Default: 2

pcut.univariate

pcut.univariate, Default: NULL

Details

DETAILS

Value

table

See Also

glm

Examples

glmshow.display(glm(mpg ~ wt + qsec, data = mtcars))

lmer.display: table for "lmerMod" or "glmerMod" object (lme4 package)

Description

Make mixed effect model results from "lmerMod" or "glmerMod" object (lme4 package)

Usage

lmer.display(lmerMod.obj, dec = 2, ci.ranef = F, pcut.univariate = NULL)

Arguments

lmerMod.obj

"lmerMod" or "glmerMod" object

dec

Decimal, Default: 2

ci.ranef

Show confidence interval of random effects?, Default: F

pcut.univariate

pcut.univariate, Default: NULL

Details

DETAILS

Value

Table: fixed & random effect

Examples

library(geepack)
data(dietox)
dietox$Cu <- as.factor(dietox$Cu)
l1 <- lme4::lmer(Weight ~ Time + Cu + (1 | Pig) + (1 | Evit), data = dietox)
lmer.display(l1)

lmerExp: transform the unit of coefficients (internal function)

Description

Transform the unit of coefficients to "Coeff", "OR" or "RR"

Usage

lmerExp(lmer.coef, family = "binomial", dec)

Arguments

lmer.coef

lmer coefficients.

family

Family: "gaussian", "binomial", "poisson", "quasipoisson", etc..., Default: 'binomial'

dec

Decimal point

Details

DETAILS

Value

The transforemed coefficients(95

Examples

# EXAMPLE1

Export label and level: multiple variable

Description

Export label and level: multiple variable

Usage

mk.lev(data)

Arguments

data

data

Details

DETAILS

Value

default label and level data

Examples

mk.lev(iris)

Export label and level: one variable

Description

Export label and level: one variable

Usage

mk.lev.var(data, vname)

Arguments

data

data

vname

variable to export label and level

Details

DETAILS

Value

if continuous variable - (label, NA), categorical variable - (label, level)

Examples

lapply(names(iris), function(x) {
  jstable::mk.lev.var(iris, x)
})

DATASET_TITLE

Description

DATASET_DESCRIPTION

Usage

mort

Format

A data frame with 17562 rows and 24 variables:

ccode

integer COLUMN_DESCRIPTION

cname

character COLUMN_DESCRIPTION

yy

integer COLUMN_DESCRIPTION

mm

integer COLUMN_DESCRIPTION

dd

integer COLUMN_DESCRIPTION

date

character COLUMN_DESCRIPTION

nonacc

integer COLUMN_DESCRIPTION

cardio

integer COLUMN_DESCRIPTION

respir

integer COLUMN_DESCRIPTION

influenza

integer COLUMN_DESCRIPTION

meanpm10

double COLUMN_DESCRIPTION

meanso2

double COLUMN_DESCRIPTION

meanno2

double COLUMN_DESCRIPTION

meanco

double COLUMN_DESCRIPTION

maxco

double COLUMN_DESCRIPTION

maxo3

double COLUMN_DESCRIPTION

meantemp

double COLUMN_DESCRIPTION

maxtemp

double COLUMN_DESCRIPTION

mintemp

double COLUMN_DESCRIPTION

meanhumi

double COLUMN_DESCRIPTION

meanpress

double COLUMN_DESCRIPTION

season

integer COLUMN_DESCRIPTION

dow

integer COLUMN_DESCRIPTION

sn

integer COLUMN_DESCRIPTION

Details

DETAILS

datable option for data(DT package)

Description

DT::datatable option for data

Usage

opt.data(fname)

Arguments

fname

File name to download

Details

DETAILS

Value

datatable option object

Examples

opt.data("mtcars")

datable option for ROC result(DT package)

Description

DT::datatable option for ROC result

Usage

opt.roc(fname)

Arguments

fname

File name to download

Details

DETAILS

Value

datatable option object

Examples

options <- opt.roc("mtcars")

datable option for simple download(DT package)

Description

Simple download DT::datatable option - No filter, No page

Usage

opt.simpledown(fname)

Arguments

fname

File name to download

Details

DETAILS

Value

datatable option object

Examples

options <- opt.simpledown("mtcars")

datable option for table 1(DT package)

Description

DT::datatable option for table 1

Usage

opt.tb1(fname)

Arguments

fname

File name to download

Details

DETAILS

Value

datatable option object

Examples

options <- opt.tb1("mtcars")

datable option for regression table(DT package)

Description

DT::datatable option for glm, gee(geepack package), lmer/glmer(lme4 package)

Usage

opt.tbreg(fname)

Arguments

fname

File name to download

Details

DETAILS

Value

datatable option object

Examples

options <- opt.tbreg("mtcars")

svyCreateTableOne2: Modified svyCreateTableOne function in tableone package

Description

Combine svyCreateTableOne & print function in tableone package

Usage

svyCreateTableOne2(
  data,
  strata,
  vars,
  factorVars,
  includeNA = F,
  test = T,
  showAllLevels = T,
  printToggle = F,
  quote = F,
  smd = F,
  nonnormal = NULL,
  catDigits = 1,
  contDigits = 2,
  pDigits = 3,
  Labels = F,
  labeldata = NULL,
  minMax = F,
  showpm = T,
  addOverall = F,
  pairwise = F,
  pairwise.showtest = F,
  n_original = T
)

Arguments

data

A data frame in which these variables exist. All variables (both vars and strata) must be in this data frame.

strata

Stratifying (grouping) variable name(s) given as a character vector. If omitted, the overall results are returned.

vars

Variables to be summarized given as a character vector. Factors are handled as categorical variables, whereas numeric variables are handled as continuous variables. If empty, all variables in the data frame specified in the data argument are used.

factorVars

Numerically coded variables that should be handled as categorical variables given as a character vector. Do not include factors, unless you need to relevel them by removing empty levels. If omitted, only factors are considered categorical variables. The variables specified here must also be specified in the vars argument.

includeNA

If TRUE, NA is handled as a regular factor level rather than missing. NA is shown as the last factor level in the table. Only effective for categorical variables., Default: F

test

If TRUE, as in the default and there are more than two groups, groupwise comparisons are performed, Default: T

showAllLevels

Whether to show all levels. FALSE by default, i.e., for 2-level categorical variables, only the higher level is shown to avoid redundant information., Default: T

printToggle

Whether to print the output. If FALSE, no output is created, and a matrix is invisibly returned., Default: F

quote

Whether to show everything in quotes. The default is FALSE. If TRUE, everything including the row and column names are quoted so that you can copy it to Excel easily, Default: F

smd

If TRUE, as in the default and there are more than two groups, standardized mean differences for all pairwise comparisons are calculated, Default: F

nonnormal

A character vector to specify the variables for which the p-values should be those of nonparametric tests. By default all p-values are from normal assumption-based tests (oneway.test)., Default: NULL

catDigits

Number of digits to print for proportions., Default: 1

contDigits

Number of digits to print for continuous variables. Default 2.

pDigits

Number of digits to print for p-values (also used for standardized mean differences), Default: 3

Labels

Use Label, Default: F

labeldata

labeldata to use, Default: NULL

minMax

Whether to use [min,max] instead of [p25,p75] for nonnormal variables. The default is FALSE.

showpm

Logical, show normal distributed continuous variables as Mean ± SD. Default: T

addOverall

(optional, only used if strata are supplied) Adds an overall column to the table. Smd and p-value calculations are performed using only the stratifed clolumns. Default: F

pairwise

(optional, only used if strata are supplied) When there are three or more strata, it displays the p-values for pairwise comparisons. Default: F

pairwise.showtest

(optional, only used if strata are supplied) When using pairwise comparison, it displays the test used to calculate p-values for pairwise comparisons. Default: F

n_original

Replace the number of weighted n with the n in the original data. Default: T

Details

DETAILS

Value

A matrix object containing what you see is also invisibly returned. This can be assinged a name and exported via write.csv.

Examples

library(survey)
data(nhanes)
nhanes$SDMVPSU <- as.factor(nhanes$SDMVPSU)
nhanesSvy <- svydesign(
  ids = ~SDMVPSU, strata = ~SDMVSTRA, weights = ~WTMEC2YR,
  nest = TRUE, data = nhanes
)
svyCreateTableOne2(
  vars = c("HI_CHOL", "race", "agecat", "RIAGENDR"),
  strata = "RIAGENDR", data = nhanesSvy,
  factorVars = c("HI_CHOL", "race", "RIAGENDR")
)

svyCreateTableOneJS: Modified CreateTableOne function in tableone package

Description

Combine svyCreateTableOne & print function in tableone package

Usage

svyCreateTableOneJS(
  vars,
  strata = NULL,
  strata2 = NULL,
  data,
  factorVars = NULL,
  includeNA = F,
  test = T,
  showAllLevels = T,
  printToggle = F,
  quote = F,
  smd = F,
  Labels = F,
  nonnormal = NULL,
  catDigits = 1,
  contDigits = 2,
  pDigits = 3,
  labeldata = NULL,
  psub = T,
  minMax = F,
  showpm = T,
  addOverall = F,
  pairwise = F,
  pairwise.showtest = F,
  n_original = T
)

Arguments

vars

Variables to be summarized given as a character vector. Factors are handled as categorical variables, whereas numeric variables are handled as continuous variables. If empty, all variables in the data frame specified in the data argument are used.

strata

Stratifying grouping variable name(s) given as a character vector. If omitted, the overall results are returned.

strata2

Stratifying 2nd grouping variable name(s) given as a character vector. If omitted, the 1 group results are returned.

data

A data frame in which these variables exist. All variables (both vars and strata) must be in this data frame.

factorVars

Numerically coded variables that should be handled as categorical variables given as a character vector. Do not include factors, unless you need to relevel them by removing empty levels. If omitted, only factors are considered categorical variables. The variables specified here must also be specified in the vars argument.

includeNA

If TRUE, NA is handled as a regular factor level rather than missing. NA is shown as the last factor level in the table. Only effective for categorical variables., Default: F

test

If TRUE, as in the default and there are more than two groups, groupwise comparisons are performed, Default: T

showAllLevels

Whether to show all levels. FALSE by default, i.e., for 2-level categorical variables, only the higher level is shown to avoid redundant information., Default: T

printToggle

Whether to print the output. If FALSE, no output is created, and a matrix is invisibly returned., Default: F

quote

Whether to show everything in quotes. The default is FALSE. If TRUE, everything including the row and column names are quoted so that you can copy it to Excel easily, Default: F

smd

If TRUE, as in the default and there are more than two groups, standardized mean differences for all pairwise comparisons are calculated, Default: F

Labels

Use Label, Default: F

nonnormal

A character vector to specify the variables for which the p-values should be those of nonparametric tests. By default all p-values are from normal assumption-based tests (oneway.test)., Default: NULL

catDigits

Number of digits to print for proportions., Default: 1

contDigits

Number of digits to print for continuous variables. Default 2.

pDigits

Number of digits to print for p-values (also used for standardized mean differences), Default: 3

labeldata

labeldata to use, Default: NULL

psub

show sub-group p-values, Default: F

minMax

Whether to use [min,max] instead of [p25,p75] for nonnormal variables. The default is FALSE.

showpm

Logical, show normal distributed continuous variables as Mean ± SD. Default: T

addOverall

(optional, only used if strata are supplied) Adds an overall column to the table. Smd and p-value calculations are performed using only the stratifed clolumns. Default: F

pairwise

(optional, only used if strata are supplied) When there are three or more strata, it displays the p-values for pairwise comparisons. Default: F

pairwise.showtest

(optional, only used if strata are supplied) When using pairwise comparison, it displays the test used to calculate p-values for pairwise comparisons. Default: F

n_original

Replace the number of weighted n with the n in the original data. Default: T

Details

DETAILS

Value

A matrix object containing what you see is also invisibly returned. This can be assinged a name and exported via write.csv.

Examples

library(survey)
data(nhanes)
nhanes$SDMVPSU <- as.factor(nhanes$SDMVPSU)
nhanesSvy <- svydesign(
  ids = ~SDMVPSU, strata = ~SDMVSTRA, weights = ~WTMEC2YR,
  nest = TRUE, data = nhanes
)
svyCreateTableOneJS(
  vars = c("HI_CHOL", "race", "agecat", "RIAGENDR"),
  strata = "RIAGENDR", data = nhanesSvy,
  factorVars = c("HI_CHOL", "race", "RIAGENDR")
)

svycoxph.display: table for svycoxph.object in survey package.

Description

Table for complex design cox model.

Usage

svycox.display(svycoxph.obj, decimal = 2, pcut.univariate = NULL)

Arguments

svycoxph.obj

svycoxph.object

decimal

digit, Default: 2

pcut.univariate

pcut.univariate, Default: NULL

Details

DETAILS

Value

List including table, metric, caption

See Also

svycoxph AIC

Examples

library(survival)
data(pbc)
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

dpbc <- survey::svydesign(
  id = ~1, prob = ~randprob, strata = ~edema,
  data = subset(pbc, randomized)
)

model <- survey::svycoxph(Surv(time, status > 0) ~ sex + protime + albumin + stage,
  design = dpbc
)
svycox.display(model)

svyregress.display: table for svyglm.object

Description

table for svyglm.object (survey package).

Usage

svyregress.display(svyglm.obj, decimal = 2, pcut.univariate = NULL)

Arguments

svyglm.obj

svyglm.object

decimal

digit, Default: 2

pcut.univariate

pcut.univariate, Default: NULL

Details

DETAILS

Value

table

Examples

library(survey)
data(api)
apistrat$tt <- c(rep(1, 20), rep(0, nrow(apistrat) - 20))
dstrat <- svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, fpc = ~fpc)
ds <- svyglm(api00 ~ ell + meals + cname + mobility, design = dstrat)
ds2 <- svyglm(tt ~ ell + meals + cname + mobility, design = dstrat, family = quasibinomial())
svyregress.display(ds)
svyregress.display(ds2)