model_summary: Tidy report of regression models.

In bruceR: Broadly Useful Convenient and Efficient R Functions

Tidy report of regression models.

Description

Tidy report of regression models (most model types are supported). This function uses:

• texreg::screenreg()

• texreg::htmlreg()

• MuMIn::std.coef()

• MuMIn::r.squaredGLMM()

• performance::r2_mcfadden()

• performance::r2_nagelkerke()

Usage

model_summary(
  model.list,
  std = FALSE,
  digits = 3,
  file = NULL,
  check = TRUE,
  zero = ifelse(std, FALSE, TRUE),
  modify.se = NULL,
  modify.head = NULL,
  line = TRUE,
  bold = 0,
  ...
)

Arguments

model.list	A single model or a list of (various types of) models. Most types of regression models are supported!
std	Standardized coefficients? Defaults to FALSE. Only applicable to linear models and linear mixed models. Not applicable to generalized linear (mixed) models.
digits	Number of decimal places of output. Defaults to 3.
file	File name of MS Word (.doc).
check	If there is only one model in model.list, it checks for multicollinearity using performance::check_collinearity(). You may turn it off by setting check=FALSE.
zero	Display "0" before "."? Defaults to TRUE.
modify.se	Replace standard errors. Useful if you need to replace raw SEs with robust SEs. New SEs should be provided as a list of numeric vectors. See usage in texreg::screenreg().
modify.head	Replace model names.
line	Lines look like true line (TRUE) or === --- === (FALSE). Only relevant to R Console output.
bold	The p-value threshold below which the coefficients will be formatted in bold.
...	Other arguments passed to texreg::screenreg() or texreg::htmlreg().

Value

Invisibly return the output (character string).

See Also

print_table (print simple table)

GLM_summary

HLM_summary

med_summary

lavaan_summary

PROCESS

Examples

#### Example 1: Linear Model ####
lm1 = lm(Temp ~ Month + Day, data=airquality)
lm2 = lm(Temp ~ Month + Day + Wind + Solar.R, data=airquality)
model_summary(lm1)
model_summary(lm2)
model_summary(list(lm1, lm2))
model_summary(list(lm1, lm2), std=TRUE, digits=2)
model_summary(list(lm1, lm2), file="OLS Models.doc")
unlink("OLS Models.doc")  # delete file for code check

#### Example 2: Generalized Linear Model ####
glm1 = glm(case ~ age + parity,
           data=infert, family=binomial)
glm2 = glm(case ~ age + parity + education + spontaneous + induced,
           data=infert, family=binomial)
model_summary(list(glm1, glm2))  # "std" is not applicable to glm
model_summary(list(glm1, glm2), file="GLM Models.doc")
unlink("GLM Models.doc")  # delete file for code check

#### Example 3: Linear Mixed Model ####
library(lmerTest)
hlm1 = lmer(Reaction ~ (1 | Subject), data=sleepstudy)
hlm2 = lmer(Reaction ~ Days + (1 | Subject), data=sleepstudy)
hlm3 = lmer(Reaction ~ Days + (Days | Subject), data=sleepstudy)
model_summary(list(hlm1, hlm2, hlm3))
model_summary(list(hlm1, hlm2, hlm3), std=TRUE)
model_summary(list(hlm1, hlm2, hlm3), file="HLM Models.doc")
unlink("HLM Models.doc")  # delete file for code check

#### Example 4: Generalized Linear Mixed Model ####
library(lmerTest)
data.glmm = MASS::bacteria
glmm1 = glmer(y ~ trt + week + (1 | ID), data=data.glmm, family=binomial)
glmm2 = glmer(y ~ trt + week + hilo + (1 | ID), data=data.glmm, family=binomial)
model_summary(list(glmm1, glmm2))  # "std" is not applicable to glmm
model_summary(list(glmm1, glmm2), file="GLMM Models.doc")
unlink("GLMM Models.doc")  # delete file for code check

#### Example 5: Multinomial Logistic Model ####
library(nnet)
d = airquality
d$Month = as.factor(d$Month)  # Factor levels: 5, 6, 7, 8, 9
mn1 = multinom(Month ~ Temp, data=d, Hess=TRUE)
mn2 = multinom(Month ~ Temp + Wind + Ozone, data=d, Hess=TRUE)
model_summary(mn1)
model_summary(mn2)
model_summary(mn2, file="Multinomial Logistic Model.doc")
unlink("Multinomial Logistic Model.doc")  # delete file for code check

bruceR documentation built on June 22, 2024, 12:26 p.m.