summ.svyglm: Complex survey regression summaries with options

Description

summ prints output for a regression model in a fashion similar to summary, but formatted differently with more options.

Usage

# S3 method for svyglm
summ(model, standardize = FALSE, vifs = FALSE,
  confint = FALSE, ci.width = 0.95, digits = getOption("jtools-digits",
  default = 3), model.info = TRUE, model.fit = TRUE, model.check = FALSE,
  pvals = TRUE, n.sd = 1, center = FALSE, standardize.response = FALSE,
  odds.ratio = FALSE, ...)

Arguments

model

A svyglm object.

standardize

If TRUE, adds a column to output with standardized regression coefficients. Default is FALSE.

vifs

If TRUE, adds a column to output with variance inflation factors (VIF). Default is FALSE.

confint

Show confidence intervals instead of standard errors? Default is FALSE.

ci.width

A number between 0 and 1 that signifies the width of the desired confidence interval. Default is .95, which corresponds to a 95% confidence interval. Ignored if confint = FALSE.

digits

An integer specifying the number of digits past the decimal to report in the output. Default is 3. You can change the default number of digits for all jtools functions with options("jtools-digits" = digits) where digits is the desired number.

model.info

Toggles printing of basic information on sample size, name of DV, and number of predictors.

model.fit

Toggles printing of R-squared, Pseudo-R-squared, and AIC (when applicable).

model.check

Only for linear mdoels. Toggles whether to perform Breusch-Pagan test for heteroskedasticity and print number of high-leverage observations.

pvals

Show p values and significance stars? If FALSE, these are not printed. Default is TRUE, except for merMod objects (see details).

n.sd

If standardize = TRUE, how many standard deviations should predictors be divided by? Default is 1, though some suggest 2.

center

If you want coefficients for mean-centered variables but don't want to standardize, set this to TRUE.

standardize.response

Should standardization apply to response variable? Default is FALSE.

odds.ratio

If TRUE, reports exponentiated coefficients with confidence intervals for exponential models like logit and Poisson models. This quantity is known as an odds ratio for binary outcomes and incidence rate ratio for count models.

...

This just captures extra arguments that may only work for other types of models.

Value

If saved, users can access most of the items that are returned in the output (and without rounding).

coeftable

The outputted table of variables and coefficients

model

The model for which statistics are displayed. This would be most useful in cases in which standardize = TRUE.

Much other information can be accessed as attributes.

Details

By default, this function will print the following items to the console:

The sample size
The name of the outcome variable
The (Pseudo-)R-squared value and AIC.
A table with regression coefficients, standard errors, t-values, and p values.

The standardize and center options are performed via refitting the model with scale_lm and center_lm, respectively. Each of those in turn uses gscale for the mean-centering and scaling. These functions can handle svyglm objects correctly by calling svymean and svyvar to compute means and standard deviations. Weights are not altered. The fact that the model is refit means the runtime will be similar to the original time it took to fit the model.

Examples

Run this code

# NOT RUN {
library(survey)
data(api)
dstrat <- svydesign(id = ~1, strata =~ stype, weights =~ pw, data = apistrat,
 fpc =~ fpc)
regmodel <- svyglm(api00 ~ ell * meals, design = dstrat)
summ(regmodel)
# }