Computes Dominance Analysis Statistics and Designations
dominance_analysis( model, sets = NULL, all = NULL, conditional = TRUE, complete = TRUE, quote_args = NULL, contrasts = model$contrasts, ... )
A model object supported by
performance::r2(). See 'Details'.
A (named) list of formula objects with no left hand side/response. If the list has names, the name provided each element will be used as the label for the set. Unnamed list elements will be provided a set number name based on its position among the sets as entered.
Predictors in each formula are bound together as a set in the dominance analysis and dominance statistics and designations are computed for the predictors together. Predictors in
setsmust be present in the model submitted to the
modelargument and cannot be in the
A formula with no left hand side/response.
Predictors in the formula are included in each subset in the dominance analysis and the R2 value associated with them is subtracted from the overall value. Predictors in
allmust be present in the model submitted to the
modelargument and cannot be in the
FALSEthen conditional dominance matrix is not computed.
If conditional dominance is not desired as an importance criterion, avoiding computing the conditional dominance matrix can save computation time.
FALSEthen complete dominance matrix is not computed.
If complete dominance is not desired as an importance criterion, avoiding computing complete dominance designations can save computation time.
A character vector of arguments in the model submitted to
quote()prior to submitting to the dominance analysis. This is necessary for data masked arguments (e.g.,
weights) to prevent them from being evaluated before being applied to the model and causing an error.
A named list of
contrastsused by the model object. This list is required in order for the correct mapping of parameters to predictors in the output when the model creates indicator codes for factor variables using
insight::get_modelmatrix(). By default, the
contrastelement from the model object submitted is used. If the model object does not have a
contrastelement the user can supply this named list.
Not used at current.
Object of class
An object of class
"parameters_da" is a list of
of the following elements:
data.framewhich associates dominance statistics with model parameters. The variables in this
Vector of general dominance statistics. The R2 ascribed to variables in the
allargument are also reported here though they are not general dominance statistics.
Vector of general dominance statistics normalized to sum to 1.
Vector of ranks applied to the general dominance statistics.
Names of the subset to which the parameter belongs in the dominance analysis. Each other
data.framereturned will refer to these subset names.
data.frameof conditional dominance statistics. Each observation represents a subset and each variable represents an the average increment to R2 with a specific number of subsets in the model.
data.frameof complete dominance designations. The subsets in the observations are compared to the subsets referenced in each variable. Whether the subset in each variable dominates the subset in each observation is represented in the logical value.
Computes two decompositions of the model's R2 and returns a matrix of designations from which predictor relative importance determinations can be obtained.
Note in the output that the "constant" subset is associated with a component of the model that does not directly contribute to the R2 such as an intercept. The "all" subset is apportioned a component of the fit statistic but is not considered a part of the dominance analysis and therefore does not receive a rank, conditional dominance statistics, or complete dominance designations.
The input model is parsed using
insight::find_predictors(), does not
yet support interactions, transformations, or offsets applied in the R
formula, and will fail with an error if any such terms are detected.
The model submitted must accept an formula object as a
argument. In addition, the model object must accept the data on which
the model is estimated as a
data argument. Formulas submitted
using object references (i.e.,
lm(mtcars$mpg ~ mtcars$vs)) and
functions that accept data as a non-
design) will fail with an error.
Models that return
TRUE for the
function's values "is_bayesian", "is_mixed", "is_gam",
or "is_hurdle" are not supported at current.
performance::r2() returns multiple values, only the first is used
Azen, R., & Budescu, D. V. (2003). The dominance analysis approach for comparing predictors in multiple regression. Psychological Methods, 8(2), 129-148. doi:10.1037/1082-989X.8.2.129
Budescu, D. V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114(3), 542-551. doi:10.1037/0033-2909.114.3.542
Groemping, U. (2007). Estimators of relative importance in linear regression based on variance decomposition. The American Statistician, 61(2), 139-147. doi:10.1198/000313007X188252
data(mtcars) # Dominance Analysis with Logit Regression model <- glm(vs ~ cyl + carb + mpg, data = mtcars, family = binomial()) performance::r2(model) #> # R2 for Logistic Regression #> Tjur's R2: 0.741 dominance_analysis(model) #> # Dominance Analysis Results #> #> Model R2 Value: 0.741 #> #> General Dominance Statistics #> #> Parameter | General Dominance | Percent | Ranks | Subset #> ------------------------------------------------------------ #> (Intercept) | | | | constant #> cyl | 0.366 | 0.493 | 1 | cyl #> carb | 0.178 | 0.241 | 3 | carb #> mpg | 0.197 | 0.266 | 2 | mpg #> #> Conditional Dominance Statistics #> #> Subset | IVs: 1 | IVs: 2 | IVs: 3 #> --------------------------------- #> cyl | 0.654 | 0.254 | 0.190 #> carb | 0.394 | 0.066 | 0.074 #> mpg | 0.474 | 0.085 | 0.032 #> #> Complete Dominance Designations #> #> Subset | < cyl | < carb | < mpg #> ------------------------------- #> cyl | | FALSE | FALSE #> carb | TRUE | | #> mpg | TRUE | | # Dominance Analysis with Weighted Logit Regression model_wt <- glm(vs ~ cyl + carb + mpg, data = mtcars, weights = wt, family = quasibinomial() ) dominance_analysis(model_wt, quote_args = "weights") #> # Dominance Analysis Results #> #> Model R2 Value: 0.776 #> #> General Dominance Statistics #> #> Parameter | General Dominance | Percent | Ranks | Subset #> ------------------------------------------------------------ #> (Intercept) | | | | constant #> cyl | 0.390 | 0.503 | 1 | cyl #> carb | 0.174 | 0.224 | 3 | carb #> mpg | 0.212 | 0.273 | 2 | mpg #> #> Conditional Dominance Statistics #> #> Subset | IVs: 1 | IVs: 2 | IVs: 3 #> --------------------------------- #> cyl | 0.679 | 0.279 | 0.213 #> carb | 0.376 | 0.062 | 0.083 #> mpg | 0.496 | 0.100 | 0.039 #> #> Complete Dominance Designations #> #> Subset | < cyl | < carb | < mpg #> ------------------------------- #> cyl | | FALSE | FALSE #> carb | TRUE | | #> mpg | TRUE | |