summ prints output for a regression model in a fashion similar to
summary, but formatted differently with more options.
# S3 method for merMod
summ(model, standardize = FALSE, confint = FALSE,
ci.width = 0.95, digits = getOption("jtools-digits", default = 3),
model.info = TRUE, model.fit = TRUE, r.squared = FALSE, pvals = NULL,
n.sd = 1, center = FALSE, standardize.response = FALSE,
odds.ratio = FALSE, t.df = NULL, ...)A merMod object.
If TRUE, adds a column to output with standardized regression
coefficients. 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.
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.
Toggles printing of basic information on sample size, name of DV, and number of predictors.
Toggles printing of AIC/BIC (when applicable).
Calculate an r-squared model fit statistic? Default is
FALSE because it seems to have convergence problems too often.
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.
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.
For lmerMod models only. User may set the degrees of
freedom used in conducting t-tests. See details for options.
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 (Pseudo-)R-squared value and AIC/BIC.
A table with regression coefficients, standard errors, and t-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.
merMod models are a bit different than the others. The lme4
package developers have, for instance, made a decision not to report or
compute p values for lmer models. There are good reasons for this,
most notably that the t-values produced are not "accurate" in the sense of
the Type I error rate. For certain large, balanced samples with many
groups, this is no big deal. What's
a "big" or "small" sample? How much balance is necessary? What type of
random effects structure is okay? Good luck getting a statistician to
give you any clear guidelines on this.
Some simulation studies have been done on fewer than 100 observations, so
for sure if your sample is around 100 or fewer you should not interpret
the t-values. A large number of groups is also crucial for avoiding bias
using t-values. If groups are nested or crossed in a linear model,
it is best to just get the pbkrtest package.
By default, this function follows lme4's lead and does not report
the p values for lmer models. If the user has pbkrtest
installed, however, p values are reported using the Kenward-Roger
d.f. approximation unless pvals = FALSE or t.df is
set to something other than NULL.
See pvalues from the lme4 for more details.
If you're looking for a simple test with no extra packages installed,
it is better to use the confidence
intervals and check to see if they exclude zero than use the t-test.
For users of glmer, see some of the advice there as well. While
lme4 and by association summ does as well, they are
still imperfect.
You have some options to customize the output in this regard with the
t.df argument. If NULL, the default, the
degrees of freedom used depends on whether the user has pbkrtest
installed. If installed, the Kenward-Roger approximation is used. If not,
and user sets pvals = TRUE, then the residual degrees of freedom
is used. If t.df = "residual", then the residual d.f. is used
without a message. If the user prefers to use some other method to
determine the d.f., then any number provided as the argument will be
used.
Johnson, P. C. D. (2014). Extension of Nakagawa & Schielzeth<U+2019>s $R^2_GLMM$ to random slopes models. Methods in Ecology and Evolution, 5, 944<U+2013>946. https://doi.org/10.1111/2041-210X.12225
Kenward, M. G., & Roger, J. H. (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics, 53, 983. https://doi.org/10.2307/2533558
Luke, S. G. (2017). Evaluating significance in linear mixed-effects models in R. Behavior Research Methods, 49, 1494<U+2013>1502. https://doi.org/10.3758/s13428-016-0809-y
Nakagawa, S., & Schielzeth, H. (2013). A general and simple method for obtaining $R^2$ from generalized linear mixed-effects models. Methods in Ecology and Evolution, 4, 133<U+2013>142. https://doi.org/10.1111/j.2041-210x.2012.00261.x
scale_lm can simply perform the standardization if
preferred.
gscale does the heavy lifting for mean-centering and scaling
behind the scenes.
get_ddf_Lb gets the Kenward-Roger degrees of
freedom if you have pbkrtest installed.
A tweaked version of r.squaredGLMM is used to
generate the pseudo-R-squared estimates for linear models.
# NOT RUN {
library(lme4, quietly = TRUE)
data(sleepstudy)
mv <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
summ(mv) # Note lack of p values if you don't have pbkrtest
# Without pbkrtest, you'll get message about Type 1 errors
summ(mv, pvals = TRUE)
# To suppress message, manually specify t.df argument
summ(mv, t.df = "residual")
# }
# NOT RUN {
# Confidence intervals may be better alternative in absence of pbkrtest
summ(mv, confint = TRUE)
# }
# NOT RUN {
# }
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