model_summary {bruceR}R Documentation

Tidy report of regression models.

Description

Tidy report of regression models. Most types of models are supported. This function is an extension (and combination) of 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,
  nsmall = digits,
  file = NULL,
  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? Default is FALSE. Only applicable to linear models and linear mixed models. Not applicable to generalized linear (mixed) models.

digits, nsmall

Number of decimal places of output. Default is 3.

file

File name of MS Word (.doc).

zero

Display "0" before "."? Default is 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

PROCESS

GLM_summary

HLM_summary

med_summary

lavaan_summary

print_table

Examples

## Not run: 

  #### 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

## End(Not run)


[Package bruceR version 0.8.0 Index]