# S3 method for lm summ( model, scale = FALSE, confint = getOption("summ-confint", FALSE), ci.width = getOption("summ-ci.width", 0.95), robust = getOption("summ-robust", FALSE), cluster = NULL, vifs = getOption("summ-vifs", FALSE), digits = getOption("jtools-digits", 2), pvals = getOption("summ-pvals", TRUE), n.sd = 1, center = FALSE, transform.response = FALSE, data = NULL, part.corr = FALSE, model.info = getOption("summ-model.info", TRUE), model.fit = getOption("summ-model.fit", TRUE), which.cols = NULL, vcov = NULL, ... )
Show confidence intervals instead of standard errors? Default
A number between 0 and 1 that signifies the width of the
desired confidence interval. Default is
This requires the
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 2. You can change the default number of
digits for all jtools functions with
Show p values? If
If you want coefficients for mean-centered variables but don't
want to standardize, set this to
Should scaling/centering apply to response
variable? Default is
If you provide the data used to fit the model here, that data
frame is used to re-fit the model (if
Print partial (labeled "partial.r") and
semipartial (labeled "part.r") correlations with the table?
Toggles printing of basic information on sample size, name of DV, and number of predictors.
Toggles printing of model fit statistics.
Developmental feature. By providing columns by name, you can add/remove/reorder requested columns in the output. Not fully supported, for now.
You may provide your own variance-covariance matrix for the
regression coefficients if you want to calculate standard errors in
some way not accommodated by the
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
scale = TRUE.
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. The heavy
lifting is done by
sandwich::vcovHC(), where those are better
Put simply, you may choose from
"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
vcovHC() will be accepted. Cluster-robust standard errors are
cluster is set to the name of the input data's cluster
variable or is a vector of clusters.
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
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), 159–179. 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, 151–169. https://doi.org/10.1146/annurev.publhealth.23.100901.140546
scale_mod() can simply perform the standardization if
gscale() does the heavy lifting for mean-centering and scaling
behind the scenes.
# Create lm object fit <- lm(Income ~ Frost + Illiteracy + Murder, data = as.data.frame(state.x77)) # Print the output with standardized coefficients and 3 digits summ(fit, scale = TRUE, digits = 3)#> MODEL INFO: #> Observations: 50 #> Dependent Variable: Income #> Type: OLS linear regression #> #> MODEL FIT: #> F(3,46) = 4.049, p = 0.012 #> R² = 0.209 #> Adj. R² = 0.157 #> #> Standard errors: OLS #> ------------------------------------------------------- #> Est. S.E. t val. p #> ----------------- ---------- --------- -------- ------- #> (Intercept) 4435.800 79.773 55.605 0.000 #> Frost -65.188 109.686 -0.594 0.555 #> Illiteracy -372.251 129.914 -2.865 0.006 #> Murder 85.179 114.217 0.746 0.460 #> ------------------------------------------------------- #> #> Continuous predictors are mean-centered and scaled by 1 s.d.