reportRmd (version 0.1.1)

m_summary: Output a table for multivariate or univariate regression models

Description

A dataframe corresponding to a univariate or multivariate regression table. If for_plot = TRUE, estimates and confidence interval bounds will also be displayed separately for easy plotting.

Usage

m_summary(
  model,
  CIwidth = 0.95,
  digits = 2,
  vif = FALSE,
  whichp = "levels",
  for_plot = FALSE
)

Arguments

model

model fit

CIwidth

width for confidence intervals, defaults to 0.95

digits

number of digits to round estimates to, does not affect p-values

vif

boolean indicating if the variance inflation factor should be included. See details

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. When for_plot = TRUE, global p values will be displayed in a separate column from p values. If whichp = "levels", global p values will not be included in the outputted table.

for_plot

boolean indicating whether or not the function will be used for plotting. Default is FALSE

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. For negative binomial models a deviance test is used.

If the variance inflation factor is requested (VIF=T) then a generalised VIF will be calculated in the same manner as the car package.

As of R 4.4.0 the likelihood profiles are included in base R.

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.

Examples

if (FALSE) data("pembrolizumab")
uv_lm <- lm(age~sex,data=pembrolizumab)
m_summary(uv_lm, digits = 3, for_plot = FALSE)

mv_binom <- glm(orr~age+sex+cohort,family = 'binomial',data = pembrolizumab)
m_summary(mv_binom, whichp = "both", for_plot = TRUE)