jtools (version 0.7.3)

summ.glm: Generalized linear 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 glm
summ(model, standardize = FALSE, vifs = FALSE,
  confint = FALSE, ci.width = 0.95, robust = FALSE, robust.type = "HC3",
  cluster = NULL, digits = getOption("jtools-digits", default = 3),
  odds.ratio = FALSE, model.info = TRUE, model.fit = TRUE, pvals = TRUE,
  n.sd = 1, center = FALSE, standardize.response = FALSE, ...)

Arguments

model

A glm 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.

robust

If TRUE, reports heteroskedasticity-robust standard errors instead of conventional SEs. These are also known as Huber-White standard errors.

Default is FALSE.

This requires the sandwich and lmtest packages to compute the standard errors.

robust.type

Only used if robust=TRUE. Specifies the type of robust standard errors to be used by sandwich. By default, set to "HC3". See details for more on options.

cluster

For clustered standard errors, provide the column name of the cluster variable in the input data frame (as a string). Alternately, provide a vector of clusters.

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.

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.

model.info

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

model.fit

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

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.

...

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/BIC.

• A table with regression coefficients, standard errors, t-values, and p values.

There are several options available for robust.type. The heavy lifting is done by vcovHC, where those are better described. Put simply, you may choose from "HC0" to "HC5". Based on the recommendation of the developers of sandwich, the default is set to "HC3". Stata's default is "HC1", so that choice may be better if the goal is to replicate Stata's output. Any option that is understood by vcovHC will be accepted. 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.

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.

References

King, G., & Roberts, M. E. (2015). How robust standard errors expose methodological problems they do not fix, and what to do about it. Political Analysis, 23(2), 159<U+2013>179. https://doi.org/10.1093/pan/mpu015

Lumley, T., Diehr, P., Emerson, S., & Chen, L. (2002). The Importance of the Normality Assumption in Large Public Health Data Sets. Annual Review of Public Health, 23, 151<U+2013>169. https://doi.org/10.1146/annurev.publhealth.23.100901.140546

See Also

scale_lm can simply perform the standardization if preferred.

gscale does the heavy lifting for mean-centering and scaling behind the scenes.

Examples

# NOT RUN {
# Create lm object
fit <- lm(Income ~ Frost + Illiteracy + Murder,
 data = as.data.frame(state.x77))

# Print the output with standardized coefficients and 2 digits past the decimal
summ(fit, standardize = TRUE, digits = 2)


# }