regress {bruceR}  R Documentation 
Regression analysis.
Description
Regression analysis.
Usage
regress(
formula,
data,
family = NULL,
nsmall = 3,
robust = FALSE,
cluster = NULL,
level2.predictors = "",
vartypes = NULL,
test.rand = FALSE
)
Arguments
formula 
Model formula like y ~ x1 + x2 (for lm, glm ) or y ~ x1 + x2 + (1  group) (for lmer, glmer ).

data 
Data frame.

family 
[optional] The same as in glm and glmer (e.g., family=binomial will fit a logistic model).

nsmall 
Number of decimal places of output. Default is 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 heteroskedasticityrobust standard errors (aka. HuberWhite 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 ]
Clusterrobust standard errors are computed if cluster is set to the name of the input data's cluster variable or is a vector of clusters.
If you specify cluster , you may also specify the type of robust . If you do not specify robust , "HC1" will be set as default.

level2.predictors 
[only for lmer ] [optional] Default is NULL .
If you have predictors at level 2, besides putting them into the formula in the lmer function as usual,
you may also define here the level2 grouping/clustering variables and corresponding level2 predictor variables.
*** Example: level2.predictors="School: W1 + W2; House: 1" ,
where School and House are two grouping variables,
W1 & W2 are schoollevel predictors,
and there is no houselevel predictor.
*** If there is no level2 predictor in the formula of lmer , just leave this parameter blank.

vartypes 
[only for lmer ] Manually setting variable types. Needless in most situations.

test.rand 
[only for lmer ] TRUE or FALSE (default).
Test random effects (i.e., variance components) by using the likelihoodratio test (LRT), which is asymptotically chisquare distributed. For large datasets, it is much timeconsuming.

Value
No return value.
Examples
## 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)
[Package
bruceR version 0.7.0
Index]