summ prints output for a regression model in a fashion similar to
summary, but formatted differently with more options.
# S3 method for lm
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),
pvals = TRUE, n.sd = 1, center = FALSE, standardize.response = FALSE,
part.corr = FALSE, model.info = TRUE, model.fit = TRUE,
model.check = FALSE, ...)A lm object.
If TRUE, adds a column to output with standardized regression
coefficients. Default is FALSE.
If TRUE, adds a column to output with variance inflation
factors (VIF). Default is FALSE.
Show confidence intervals instead of standard errors? Default
is FALSE.
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.
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.
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.
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.
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.
Show p values and significance stars? If FALSE, these
are not printed. Default is TRUE, except for merMod objects (see
details).
If standardize = TRUE, how many standard deviations should
predictors be divided by? Default is 1, though some suggest 2.
If you want coefficients for mean-centered variables but don't
want to standardize, set this to TRUE.
Should standardization apply to response variable?
Default is FALSE.
Print partial (labeled "partial.r") and semipartial (labeled
"part.r") correlations with the table?
Default is FALSE. See details about these quantities when robust
standard errors are used.
Toggles printing of basic information on sample size, name of DV, and number of predictors.
Toggles printing of R-squared and adjusted R-squared.
Toggles whether to perform Breusch-Pagan test for heteroskedasticity and print number of high-leverage observations. See details for more info.
This just captures extra arguments that may only work for other types of models.
If saved, users can access most of the items that are returned in the output (and without rounding).
The outputted table of variables and coefficients
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.
By default, this function will print the following items to the console:
The sample size
The name of the outcome variable
The R-squared value plus adjusted R-squared
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.
If using part.corr = TRUE, then you will get these two common
effect size metrics on the far right two columns of the output table.
However, it should be noted that these do not go hand in hand with
robust standard error estimators. The standard error of the coefficient
doesn't change the point estimate, just the uncertainty. However,
this function uses t-statistics in its calculation of the
partial and semipartial correlation. This provides what amounts to a
heteroskedasticity-adjusted set of estimates, but I am unaware of any
statistical publication that validates this type of use. Please
use these as a heuristic when used alongside robust standard errors; do
not report the "robust" partial and semipartial correlations in
publications.
There are two pieces of information given for model.check, provided that
the model is an lm object. First, a Breusch-Pagan test is performed with
ncvTest. This is a
hypothesis test for which the alternative hypothesis is heteroskedastic errors.
The test becomes much more likely to be statistically significant as the sample
size increases; however, the homoskedasticity assumption becomes less important
to inference as sample size increases (Lumley, Diehr, Emerson, & Lu, 2002).
Take the result of the test as a cue to check graphical checks rather than a
definitive decision. Note that the use of robust standard errors can account
for heteroskedasticity, though some oppose this approach (see King & Roberts,
2015).
The second piece of information provided by setting model.check to
TRUE is the number of high leverage observations. There are no hard
and fast rules for determining high leverage either, but in this case it is
based on Cook's Distance. All Cook's Distance values greater than (4/N) are
included in the count. Again, this is not a recommendation to locate and
remove such observations, but rather to look more closely with graphical and
other methods.
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
scale_lm can simply perform the standardization if
preferred.
gscale does the heavy lifting for mean-centering and scaling
behind the scenes.
# 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)
# }
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