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testthat
3rd Edition. (0.2.10.2)plotmod()
, labels regarding SDs will no longer be
displayed when w_values
is set. (0.2.10.1)Improved the printout of the summary()
of
std_selected()
and std_selected_boot()
outputs. It now prints the R-squared increase of the highest order term,
as well as the F test for the increase, if the model has one
and only one highest order term (e.g., an interaction term).
(0.2.9.1)
Added the argument w_values
to
cond_effect()
and plolmod()
. Users can specify
the values of the moderator (w
) to be used to compute the
conditional effects. (0.2.9.2)
update.std_selected()
. Though still not
recommended, it should now work more reliably if it needs to be called.
(0.2.9.1)stdmod-package
. (0.2.8.9001)summary()
of
std_selected()
and std_selected_boot()
outputs. Small numbers are rounded to prevent the use of scientific
notation, and small p-values can be printed in formats like
p<.001. Users can also control the number of digits in the
printout. See the help page of print.summary.std_selected()
to learn more about new arguments (0.2.8.9002).dplyr
from the tests and Suggests.
(0.2.7.2)visreg
will be skipped if
visreg
is not installed. (0.2.7.3)cond_effect
-class
object and the summary of a std_selected
-class object. If
one or more variables are standardized but bootstrapping is not
requested, users will be recommended to use
std_selected_boot()
. (0.2.7.4)stdmod_lavaan()
switched to the bootstrapping algorithm
used by lavaan()
. It also updated to allow for partial
standardization. To use the older algorithm, set
use_old_version()
to TRUE
. (0.2.7.5)print()
method of the summary()
output of
std_selected()
. (0.2.6.2)to_standardize
to std_selected()
and
std_selected_boot()
. (0.2.6.3)confint.std_selected()
when
type = "lm"
and bootstrapping is requested. Should not be
an issue because t-based CIs should not be used when
bootstrapping is requested. This option is just for testing.
(0.2.6.4)to_standardize
or mention it as
a shortcut. (0.2.6.5)to_standardize
.
(0.2.6.6)summary()
of
std_selected()
and std_selected_boot()
outputs. (0.2.4.9001).ggplot2
.
(0.2.4.9002)summary()
of std_selected()
. (0.2.4.9003)bibentry()
in CITATION. (0.2.6)std_selected()
: It now works correctly
when a variable in the data frame is a factor. (0.2.0.1)confint()
and coef()
methods for
cond_effect
-class objects. confint()
can
return confidence intervals based on t statistics, which are
appropriate in some situations. (0.2.2)print()
method for cond_effect
-class
objects can print confidence intervals based on t statistics.
(0.2.2)do_boot
to std_selected_boot()
. If
set to FALSE
, it will not do bootstrapping. (0.2.3)cond_effect_boot()
will disable bootstrapping in the
original call if the output is generated by
std_selected_boot()
, to avoid redundant bootstrapping
inside bootstrapping. (0.2.3)do_boot
to cond_effect_boot()
. If
set to FALSE
, it will not do bootstrapping. (0.2.4)confint()
and
vcov()
for std_selected
-class object. If
bootstrap CIs are requested, then bootstrap CIs and VCOV based on
bootstrapping should be returned. (0.2.0.0)(All major changes after 0.1.7.1)
plotmod()
. It now correctly handles more
than two levels when w_method
is set
to"percentile"
. (0.1.7.2, 0.1.7.3)(All major changes after 0.1.5)
plotmod()
for plotting moderation effects. This
function will check whether a variable is standardized. If yes, will
note this in the plot.plotmod()
can also plot a Tumble graph (Bodner, 2016)
if graph_type
is set to "tumble"
.plotmod()
instead of
visreg::visreg()
.cond_effect()
for computing conditional effects.
This function will check which variable(s) is/are standardized. If yes,
will note this in the printout.cond_effect_boot()
, a wrapper of
cond_effect()
that can form nonparametric bootstrap
confidence intervals for the conditional effects, which may be partially
or completely standardized.std_selected()
and std_selected_boot()
.stdmod_lavaan()
now returns an object of the class
stdmod_lavaan
, with methods print, confint, and coef
added.std_selected_boot()
output. Bootstrap confidence intervals
are placed next to parameter estimates.vcov()
method for std_selected()
output. If bootstrapping is used, it can return the variance-covariance
matrix of the bootstrap estimates.confint()
method for std_selected()
output. If bootstrapping is used, it can return the bootstrap percentile
confidence intervals if requested.std_selected()
.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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