View source: R/bruceR-stats_4_regress.R
model_summary | R Documentation |
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()
model_summary( model.list, std = FALSE, digits = 3, nsmall = digits, file = NULL, check = TRUE, zero = ifelse(std, FALSE, TRUE), modify.se = NULL, modify.head = NULL, line = TRUE, bold = 0, ... )
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 |
digits, nsmall |
Number of decimal places of output. Default is |
file |
File name of MS Word ( |
check |
If there is only one model in |
zero |
Display "0" before "."? Default is |
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 |
modify.head |
Replace model names. |
line |
Lines look like true line ( |
bold |
The p-value threshold below which the coefficients will be formatted in bold. |
... |
Other arguments passed to
|
Invisibly return the output (character string).
print_table
(print simple table)
GLM_summary
HLM_summary
med_summary
lavaan_summary
PROCESS
#### 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
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