Calculate the conditional Mahalanobis distance for any variables.
Calculate the conditional Mahalanobis distance for any variables.
cond_maha( data, R, v_dep, v_ind = NULL, v_ind_composites = NULL, mu = 0, sigma = 1, use_sample_stats = FALSE, label = NA )
data |
Data.frame with the independent and dependent variables. Unless mu and sigma are specified, data are assumed to be z-scores. |
R |
Correlation among all variables. |
v_dep |
Vector of names of the dependent variables in your profile. |
v_ind |
Vector of names of independent variables you would like to control for. |
v_ind_composites |
Vector of names of independent variables that are composites of dependent variables |
mu |
A vector of means. A single value means that all variables have the same mean. |
sigma |
A vector of standard deviations. A single value means that all variables have the same standard deviation |
use_sample_stats |
If TRUE, estimate R, mu, and sigma from data. Only complete cases are used (i.e., no missing values in v_dep, v_ind, v_ind_composites). |
label |
optional tag for labeling output |
a list with the conditional Mahalanobis distance
dCM = Conditional Mahalanobis distance
dCM_df = Degrees of freedom for the conditional Mahalanobis distance
dCM_p = A proportion that indicates how unusual this profile is compared to profiles with the same independent variable values. For example, if dCM_p = 0.88, this profile is more unusual than 88 percent of profiles after controlling for the independent variables.
dM_dep = Mahalanobis distance of just the dependent variables
dM_dep_df = Degrees of freedom for the Mahalanobis distance of the dependent variables
dM_dep_p = Proportion associated with the Mahalanobis distance of the dependent variables
dM_ind = Mahalanobis distance of just the independent variables
dM_ind_df = Degrees of freedom for the Mahalanobis distance of the independent variables
dM_ind_p = Proportion associated with the Mahalanobis distance of the independent variables
v_dep = Dependent variable names
v_ind = Independent variable names
v_ind_singular = Independent variables that can be perfectly predicted from the dependent variables (e.g., composite scores)
v_ind_nonsingular = Independent variables that are not perfectly predicted from the dependent variables
data = data used in the calculations
d_ind = independent variable data
d_inp_p = Assuming normality, cumulative distribution function of the independent variables
d_dep = dependent variable data
d_dep_predicted = predicted values of the dependent variables
d_dep_deviations = d_dep - d_dep_predicted (i.e., residuals of the dependent variables)
d_dep_residuals_z = standardized residuals of the dependent variables
d_dep_cp = conditional proportions associated with standardized residuals
d_dep_p = Assuming normality, cumulative distribution function of the dependent variables
R2 = Proportion of variance in each dependent variable explained by the independent variables
SEE = Standard error of the estimate for each dependent variable
ConditionalCovariance = Covariance matrix of the dependent variables after controlling for the independent variables
distance_reduction = 1 - (dCM / dM_dep) (Degree to which the independent variables decrease the Mahalanobis distance of the dependent variables. Negative reductions mean that the profile is more unusual after controlling for the independent variables. Returns 0 if dM_dep is 0.)
variability_reduction = 1 - sum((X_dep - predicted_dep) ^ 2) / sum((X_dep - mu_dep) ^ 2) (Degree to which the independent variables decrease the variability the dependent variables (X_dep). Negative reductions mean that the profile is more variable after controlling for the independent variables. Returns 0 if X_dep == mu_dep)
mu = Variable means
sigma = Variable standard deviations
d_person = Data frame consisting of Mahalanobis distance data for each person
d_variable = Data frame consisting of variable characteristics
label = label slot
library(unusualprofile)
library(simstandard)
m <- "
Gc =~ 0.85 * Gc1 + 0.68 * Gc2 + 0.8 * Gc3
Gf =~ 0.8 * Gf1 + 0.9 * Gf2 + 0.8 * Gf3
Gs =~ 0.7 * Gs1 + 0.8 * Gs2 + 0.8 * Gs3
Read =~ 0.66 * Read1 + 0.85 * Read2 + 0.91 * Read3
Math =~ 0.4 * Math1 + 0.9 * Math2 + 0.7 * Math3
Gc ~ 0.6 * Gf + 0.1 * Gs
Gf ~ 0.5 * Gs
Read ~ 0.4 * Gc + 0.1 * Gf
Math ~ 0.2 * Gc + 0.3 * Gf + 0.1 * Gs"
# Generate 10 cases
d_demo <- simstandard::sim_standardized(m = m, n = 10)
# Get model-implied correlation matrix
R_all <- simstandard::sim_standardized_matrices(m)$Correlations$R_all
cond_maha(data = d_demo,
R = R_all,
v_dep = c("Math", "Read"),
v_ind = c("Gf", "Gs", "Gc"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.