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.

## 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)
}
```