This function extracts information, such as the deviations (SD or MAD) from parent variables, that are necessary for post-hoc standardization of parameters. This function gives a window on how standardized are obtained, i.e., by what they are divided. The "basic" method of standardization uses.
standardize_info(model, ...)
# Default S3 method
standardize_info(
model,
robust = FALSE,
two_sd = FALSE,
include_pseudo = FALSE,
verbose = TRUE,
...
)A statistical model.
Arguments passed to or from other methods.
Logical, if TRUE, centering is done by subtracting the
median from the variables and dividing it by the median absolute deviation
(MAD). If FALSE, variables are standardized by subtracting the
mean and dividing it by the standard deviation (SD).
If TRUE, the variables are scaled by two times the deviation
(SD or MAD depending on robust). This method can be useful to obtain
model coefficients of continuous parameters comparable to coefficients
related to binary predictors, when applied to the predictors (not the
outcome) (Gelman, 2008).
(For (G)LMMs) Should Pseudo-standardized information be included?
Toggle warnings and messages on or off.
A data frame with information on each parameter (see
parameters_type()), and various standardization coefficients
for the post-hoc methods (see standardize_parameters()) for the predictor
and the response.
Other standardize:
standardize_parameters()
model <- lm(mpg ~ ., data = mtcars)
standardize_info(model)
#> Parameter Type Link Secondary_Parameter EffectSize_Type
#> 1 (Intercept) intercept Mean <NA> <NA>
#> 2 cyl numeric Association <NA> r
#> 3 disp numeric Association <NA> r
#> 4 hp numeric Association <NA> r
#> 5 drat numeric Association <NA> r
#> 6 wt numeric Association <NA> r
#> 7 qsec numeric Association <NA> r
#> 8 vs numeric Association <NA> r
#> 9 am numeric Association <NA> r
#> 10 gear numeric Association <NA> r
#> 11 carb numeric Association <NA> r
#> Deviation_Response_Basic Deviation_Response_Smart Deviation_Basic
#> 1 6.026948 6.026948 0.0000000
#> 2 6.026948 6.026948 1.7859216
#> 3 6.026948 6.026948 123.9386938
#> 4 6.026948 6.026948 68.5628685
#> 5 6.026948 6.026948 0.5346787
#> 6 6.026948 6.026948 0.9784574
#> 7 6.026948 6.026948 1.7869432
#> 8 6.026948 6.026948 0.5040161
#> 9 6.026948 6.026948 0.4989909
#> 10 6.026948 6.026948 0.7378041
#> 11 6.026948 6.026948 1.6152000
#> Deviation_Smart Deviation_SDy
#> 1 0.0000000 0.13455
#> 2 1.7859216 0.13455
#> 3 123.9386938 0.13455
#> 4 68.5628685 0.13455
#> 5 0.5346787 0.13455
#> 6 0.9784574 0.13455
#> 7 1.7869432 0.13455
#> 8 0.5040161 0.13455
#> 9 0.4989909 0.13455
#> 10 0.7378041 0.13455
#> 11 1.6152000 0.13455
standardize_info(model, robust = TRUE)
#> Parameter Type Link Secondary_Parameter EffectSize_Type
#> 1 (Intercept) intercept Mean <NA> <NA>
#> 2 cyl numeric Association <NA> r
#> 3 disp numeric Association <NA> r
#> 4 hp numeric Association <NA> r
#> 5 drat numeric Association <NA> r
#> 6 wt numeric Association <NA> r
#> 7 qsec numeric Association <NA> r
#> 8 vs numeric Association <NA> r
#> 9 am numeric Association <NA> r
#> 10 gear numeric Association <NA> r
#> 11 carb numeric Association <NA> r
#> Deviation_Response_Basic Deviation_Response_Smart Deviation_Basic
#> 1 5.41149 5.41149 0.0000000
#> 2 5.41149 5.41149 2.9652000
#> 3 5.41149 5.41149 140.4763500
#> 4 5.41149 5.41149 77.0952000
#> 5 5.41149 5.41149 0.7042350
#> 6 5.41149 5.41149 0.7672455
#> 7 5.41149 5.41149 1.4158830
#> 8 5.41149 5.41149 0.0000000
#> 9 5.41149 5.41149 0.0000000
#> 10 5.41149 5.41149 1.4826000
#> 11 5.41149 5.41149 1.4826000
#> Deviation_Smart Deviation_SDy
#> 1 0.0000000 0.13455
#> 2 2.9652000 0.13455
#> 3 140.4763500 0.13455
#> 4 77.0952000 0.13455
#> 5 0.7042350 0.13455
#> 6 0.7672455 0.13455
#> 7 1.4158830 0.13455
#> 8 0.0000000 0.13455
#> 9 0.0000000 0.13455
#> 10 1.4826000 0.13455
#> 11 1.4826000 0.13455
standardize_info(model, two_sd = TRUE)
#> Parameter Type Link Secondary_Parameter EffectSize_Type
#> 1 (Intercept) intercept Mean <NA> <NA>
#> 2 cyl numeric Association <NA> r
#> 3 disp numeric Association <NA> r
#> 4 hp numeric Association <NA> r
#> 5 drat numeric Association <NA> r
#> 6 wt numeric Association <NA> r
#> 7 qsec numeric Association <NA> r
#> 8 vs numeric Association <NA> r
#> 9 am numeric Association <NA> r
#> 10 gear numeric Association <NA> r
#> 11 carb numeric Association <NA> r
#> Deviation_Response_Basic Deviation_Response_Smart Deviation_Basic
#> 1 6.026948 6.026948 0.0000000
#> 2 6.026948 6.026948 3.5718433
#> 3 6.026948 6.026948 247.8773877
#> 4 6.026948 6.026948 137.1257370
#> 5 6.026948 6.026948 1.0693575
#> 6 6.026948 6.026948 1.9569149
#> 7 6.026948 6.026948 3.5738865
#> 8 6.026948 6.026948 1.0080323
#> 9 6.026948 6.026948 0.9979818
#> 10 6.026948 6.026948 1.4756081
#> 11 6.026948 6.026948 3.2304000
#> Deviation_Smart Deviation_SDy
#> 1 0.0000000 0.13455
#> 2 3.5718433 0.13455
#> 3 247.8773877 0.13455
#> 4 137.1257370 0.13455
#> 5 1.0693575 0.13455
#> 6 1.9569149 0.13455
#> 7 3.5738865 0.13455
#> 8 1.0080323 0.13455
#> 9 0.9979818 0.13455
#> 10 1.4756081 0.13455
#> 11 3.2304000 0.13455