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.
Arguments
- model
A statistical model.
- robust
Logical, if
TRUE, centering is done by subtracting the median from the variables and dividing it by the median absolute deviation (MAD). IfFALSE, variables are standardized by subtracting the mean and dividing it by the standard deviation (SD).- two_sd
If
TRUE, the variables are scaled by two times the deviation (SD or MAD depending onrobust). 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).- include_pseudo
(For (G)LMMs) Should Pseudo-standardized information be included?
- ...
Arguments passed to or from other methods.
Value
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.
See also
Other standardize:
standardize_parameters()
Examples
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
#> 1 0.0000000
#> 2 1.7859216
#> 3 123.9386938
#> 4 68.5628685
#> 5 0.5346787
#> 6 0.9784574
#> 7 1.7869432
#> 8 0.5040161
#> 9 0.4989909
#> 10 0.7378041
#> 11 1.6152000
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
#> 1 0.0000000
#> 2 2.9652000
#> 3 140.4763500
#> 4 77.0952000
#> 5 0.7042350
#> 6 0.7672455
#> 7 1.4158830
#> 8 0.0000000
#> 9 0.0000000
#> 10 1.4826000
#> 11 1.4826000
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
#> 1 0.0000000
#> 2 3.5718433
#> 3 247.8773877
#> 4 137.1257370
#> 5 1.0693575
#> 6 1.9569149
#> 7 3.5738865
#> 8 1.0080323
#> 9 0.9979818
#> 10 1.4756081
#> 11 3.2304000