Regression analysis.

NOTE: model_summary is preferred.

Usage

regress(
  formula,
  data,
  family = NULL,
  digits = 3,
  robust = FALSE,
  cluster = NULL,
  test.rand = FALSE
)

Arguments

formula

Model formula.

data

Data frame.

family

[Optional] The same as in glm and glmer (e.g., family=binomial fits a logistic regression model).

digits

Number of decimal places of output. Defaults to 3.

robust

[Only for lm and glm] FALSE (default), TRUE (then the default is "HC1"), "HC0", "HC1", "HC2", "HC3", "HC4", "HC4m", or "HC5". It will add a table with heteroskedasticity-robust standard errors (aka. Huber-White standard errors). For details, see ?sandwich::vcovHC and ?jtools::summ.lm.

*** "HC1" is the default of Stata, whereas "HC3" is the default suggested by the sandwich package.

cluster

[Only for lm and glm] Cluster-robust standard errors are computed if cluster is set to the name of the input data's cluster variable or is a vector of clusters.

test.rand

[Only for lmer and glmer] TRUE or FALSE (default). Test random effects (i.e., variance components) by using the likelihood-ratio test (LRT), which is asymptotically chi-square distributed. For large datasets, it is much time-consuming.

Value

No return value.

See also

print_table (print simple table)

model_summary (highly suggested)

GLM_summary

HLM_summary

Examples

if (FALSE) {

  ## lm
  regress(Temp ~ Month + Day + Wind + Solar.R, data=airquality, robust=TRUE)

  ## glm
  regress(case ~ age + parity + education + spontaneous + induced,
          data=infert, family=binomial, robust="HC1", cluster="stratum")

  ## lmer
  library(lmerTest)
  regress(Reaction ~ Days + (Days | Subject), data=sleepstudy)
  regress(Preference ~ Sweetness + Gender + Age + Frequency +
            (1 | Consumer), data=carrots)

  ## glmer
  library(lmerTest)
  data.glmm = MASS::bacteria
  regress(y ~ trt + week + (1 | ID), data=data.glmm, family=binomial)
  regress(y ~ trt + week + hilo + (1 | ID), data=data.glmm, family=binomial)
}