rm_uvsum: Output several univariate models nicely in a single table
In reportRmd: Tidy Presentation of Clinical Reporting

rm_uvsum

R Documentation

Output several univariate models nicely in a single table

Description

#'A table with the model parameters from running separate univariate models on each covariate. For factors with more than two levels a Global p-value is returned.

A table with the model parameters from running separate univariate models on each covariate. For factors with more than two levels a Global p-value is returned.

Usage

rm_uvsum(
  response,
  covs,
  data,
  digits = getOption("reportRmd.digits", 2),
  covTitle = "",
  caption = NULL,
  tableOnly = FALSE,
  removeInf = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  whichp = c("levels", "global", "both"),
  chunk_label,
  gee = FALSE,
  id = NULL,
  corstr = NULL,
  family = NULL,
  type = NULL,
  offset = NULL,
  strata = 1,
  nicenames = TRUE,
  showN = TRUE,
  showEvent = TRUE,
  CIwidth = 0.95,
  reflevel = NULL,
  returnModels = FALSE,
  fontsize,
  forceWald = FALSE
)

rm_uvsum(
  response,
  covs,
  data,
  digits = getOption("reportRmd.digits", 2),
  covTitle = "",
  caption = NULL,
  tableOnly = FALSE,
  removeInf = FALSE,
  p.adjust = "none",
  unformattedp = FALSE,
  whichp = c("levels", "global", "both"),
  chunk_label,
  gee = FALSE,
  id = NULL,
  corstr = NULL,
  family = NULL,
  type = NULL,
  offset = NULL,
  strata = 1,
  nicenames = TRUE,
  showN = TRUE,
  showEvent = TRUE,
  CIwidth = 0.95,
  reflevel = NULL,
  returnModels = FALSE,
  fontsize,
  forceWald = FALSE
)

Arguments

`response`	string vector with name of response
`covs`	character vector with the names of columns to fit univariate models to
`data`	dataframe containing data
`digits`	number of digits to round estimates and CI to. Does not affect p-values.
`covTitle`	character with the names of the covariate (predictor) column. The default is to leave this empty for output or, for table only output to use the column name 'Covariate'.
`caption`	character containing table caption (default is no caption)
`tableOnly`	boolean indicating if unformatted table should be returned
`removeInf`	boolean indicating if infinite estimates should be removed from the table
`p.adjust`	p-adjustments to be performed. Uses the p.adjust function from base R
`unformattedp`	boolean indicating if you would like the p-value to be returned unformatted (ie not rounded or prefixed with '<'). Should be used in conjunction with the digits argument.
`whichp`	string indicating whether you want to display p-values for levels within categorical data ("levels"), global p values ("global"), or both ("both"). Irrelevant for continuous predictors.
`chunk_label`	only used if output is to Word to allow cross-referencing
`gee`	boolean indicating if gee models should be fit to account for correlated observations. If TRUE then the id argument must specify the column in the data which indicates the correlated clusters.
`id`	character vector which identifies clusters. Only used for geeglm
`corstr`	character string specifying the correlation structure. Only used for geeglm. The following are permitted: '"independence"', '"exchangeable"', '"ar1"', '"unstructured"' and '"userdefined"'
`family`	description of the error distribution and link function to be used in the model. Only used for geeglm
`type`	string indicating the type of univariate model to fit. The function will try and guess what type you want based on your response. If you want to override this you can manually specify the type. Options include "linear", "logistic", "poisson",coxph", "crr", "boxcox", "ordinal", "geeglm"
`offset`	string specifying the offset term to be used for Poisson or negative binomial regression. Example: offset="log(follow_up)"
`strata`	character vector of covariates to stratify by. Only used for coxph and crr
`nicenames`	boolean indicating if you want to replace . and _ in strings with a space
`showN`	boolean indicating if you want to show sample sizes
`showEvent`	boolean indicating if you want to show number of events. Only available for logistic.
`CIwidth`	width of confidence interval, default is 0.95
`reflevel`	manual specification of the reference level. Only used for ordinal regression This will allow you to see which model is not fitting if the function throws an error
`returnModels`	boolean indicating if a list of fitted models should be returned. If this is TRUE then the models will be returned, but the output will be suppressed. In addition to the model elements a data element will be appended to each model so that the fitted data can be examined, if necessary. See Details
`fontsize`	PDF/HTML output only, manually set the table fontsize
`forceWald`	`forceWald = TRUE` is no longer supported; this function will always use profile likelihoods as per the inclusion of the MASS confidence intervals into base from from R 4.4.0

Details

Global p-values are likelihood ratio tests for lm, glm and polr models. For lme models an attempt is made to re-fit the model using ML and if,successful LRT is used to obtain a global p-value. For coxph models the model is re-run without robust variances with and without each variable and a LRT is presented. If unsuccessful a Wald p-value is returned. For GEE and CRR models Wald global p-values are returned.

As of version 0.1.1 if global p-values are requested they will be included in the p-value column.

The number of decimals places to display the statistics can be changed with digits, but this will not change the display of p-values. If more significant digits are required for p-values then use tableOnly=TRUE and format as desired.

tidyselect can only be used for response and covs variables. Additional arguments must be passed in using characters

Value

A character vector of the table source code, unless tableOnly=TRUE in which case a data frame is returned

Examples

# Examples are for demonstration and are not meaningful
# Coxph model with 90% CI
data("pembrolizumab")
rm_uvsum(response = c('os_time','os_status'),
covs=c('age','sex','baseline_ctdna','l_size','change_ctdna_group'),
data=pembrolizumab,CIwidth=.9)

# Linear model with default 95% CI
rm_uvsum(response = 'baseline_ctdna',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab)

# Logistic model with default 95% CI
rm_uvsum(response = 'os_status',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab,family = binomial)
# Poisson models returned as model list
mList <- rm_uvsum(response = 'baseline_ctdna',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab, returnModels=TRUE)
#'
# GEE on correlated outcomes
data("ctDNA")
rm_uvsum(response = 'size_change',
covs=c('time','ctdna_status'),
gee=TRUE,
id='id', corstr="exchangeable",
family=gaussian("identity"),
data=ctDNA,showN=TRUE)

# Using tidyselect
pembrolizumab |> rm_uvsum(response = sex,
covs = c(age, cohort))
# Examples are for demonstration and are not meaningful
# Coxph model with 90% CI
data("pembrolizumab")
rm_uvsum(response = c('os_time','os_status'),
covs=c('age','sex','baseline_ctdna','l_size','change_ctdna_group'),
data=pembrolizumab,CIwidth=.9)

# Linear model with default 95% CI
rm_uvsum(response = 'baseline_ctdna',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab)

# Logistic model with default 95% CI
rm_uvsum(response = 'os_status',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab,family = binomial)
# Poisson models returned as model list
mList <- rm_uvsum(response = 'baseline_ctdna',
covs=c('age','sex','l_size','pdl1','tmb'),
data=pembrolizumab, returnModels=TRUE)
#'
# GEE on correlated outcomes
data("ctDNA")
rm_uvsum(response = 'size_change',
covs=c('time','ctdna_status'),
gee=TRUE,
id='id', corstr="exchangeable",
family=gaussian("identity"),
data=ctDNA,showN=TRUE)

reportRmd documentation built on April 4, 2025, 2:03 a.m.