Repository Mirror for your Cloud Server and Webhosting

Type:

Package

Title:

Calculates Conditional Mahalanobis Distances

Version:

0.1.4

Description:

Calculates a Mahalanobis distance for every row of a set of outcome variables (Mahalanobis, 1936 <doi:10.1007/s13171-019-00164-5>). The conditional Mahalanobis distance is calculated using a conditional covariance matrix (i.e., a covariance matrix of the outcome variables after controlling for a set of predictors). Plotting the output of the cond_maha() function can help identify which elements of a profile are unusual after controlling for the predictors.

License:

GPL (≥ 3)

URL:

https://github.com/wjschne/unusualprofile, https://wjschne.github.io/unusualprofile/

BugReports:

https://github.com/wjschne/unusualprofile/issues

Depends:

R (≥ 3.1)

Imports:

dplyr, ggnormalviolin, ggplot2, magrittr, purrr, rlang, stats, tibble, tidyr

Suggests:

bookdown, covr, extrafont, forcats, glue, kableExtra, knitr, lavaan, lifecycle, mvtnorm, patchwork, ragg, rmarkdown, roxygen2, scales, simstandard (≥ 0.6.3), stringr, sysfonts, testthat

VignetteBuilder:

knitr

Encoding:

UTF-8

Language:

en-US

LazyData:

TRUE

RoxygenNote:

7.3.1

NeedsCompilation:

Packaged:

2024-02-14 20:09:54 UTC; renee

Author:

W. Joel Schneider

[aut, cre], Feng Ji [aut]

Maintainer:

W. Joel Schneider <w.joel.schneider@gmail.com>

Repository:

CRAN

Date/Publication:

2024-02-14 23:20:03 UTC

unusualprofile: Calculates Conditional Mahalanobis Distances

Description

Calculates a Mahalanobis distance for every row of a set of outcome variables (Mahalanobis, 1936 doi:10.1007/s13171-019-00164-5). The conditional Mahalanobis distance is calculated using a conditional covariance matrix (i.e., a covariance matrix of the outcome variables after controlling for a set of predictors). Plotting the output of the cond_maha() function can help identify which elements of a profile are unusual after controlling for the predictors.

Author(s)

Maintainer: W. Joel Schneider w.joel.schneider@gmail.com (ORCID)

Authors:

Feng Ji fengji@berkeley.edu

An example correlation matrix

Description

A correlation matrix used for demonstration purposes It is the model-implied correlation matrix for this structural model: X =~ 0.7 * X_1 + 0.5 * X_2 + 0.8 * X_3 Y =~ 0.8 * Y_1 + 0.7 * Y_2 + 0.9 * Y_3 Y ~ 0.6 * X

Usage

R_example

Format

A matrix with 8 rows and 8 columns:

X_1: A predictor variable
X_2: A predictor variable
X_3: A predictor variable
Y_1: An outcome variable
Y_2: An outcome variable
Y_3: An outcome variable
X: A latent predictor variable
Y: A latent outcome variable

Calculate the conditional Mahalanobis distance for any variables.

Description

Calculate the conditional Mahalanobis distance for any variables.

Usage

cond_maha(
  data,
  R,
  v_dep,
  v_ind = NULL,
  v_ind_composites = NULL,
  mu = 0,
  sigma = 1,
  use_sample_stats = FALSE,
  label = NA
)

Arguments

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

Value

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
zSEE = Standardized standard error of the estimate for each dependent variable
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

Examples

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"))

An example data.frame

Description

A dataset with 1 row of data for a single case.

Usage

d_example

Format

A data frame with 1 row and 8 variables:

X_1: A predictor variable
X_2: A predictor variable
X_3: A predictor variable
Y_1: An outcome variable
Y_2: An outcome variable
Y_3: An outcome variable
X: A latent predictor variable
Y: A latent outcome variable

Test if matrix is singular

Description

Test if matrix is singular

Usage

is_singular(x)

Arguments

x

matrix

Value

logical

Range label associated with probability

Description

Range label associated with probability

Usage

p2label(p)

Arguments

p

Probability

Value

label string

Plot the variables from the results of the cond_maha function.

Description

Plot the variables from the results of the cond_maha function.

Usage

## S3 method for class 'cond_maha'
plot(
  x,
  ...,
  p_tail = 0,
  family = "sans",
  score_digits = ifelse(min(x$sigma) >= 10, 0, 2)
)

Arguments

x

The results of the cond_maha function.

...

Arguments passed to print function

p_tail

The proportion of the tail to shade

family

Font family.

score_digits

Number of digits to round scores.

Value

A ggplot2-object

Plot objects of the maha class (i.e, the results of the cond_maha function using dependent variables only).

Description

Plot objects of the maha class (i.e, the results of the cond_maha function using dependent variables only).

Usage

## S3 method for class 'maha'
plot(
  x,
  ...,
  p_tail = 0,
  family = "sans",
  score_digits = ifelse(min(x$sigma) >= 10, 0, 2)
)

Arguments

x

The results of the cond_maha function.

...

Arguments passed to print function

p_tail

Proportion in violin tail (defaults to 0).

family

Font family.

score_digits

Number of digits to round scores.

Value

A ggplot2-object

Rounds proportions to significant digits both near 0 and 1, then converts to percentiles

Description

Rounds proportions to significant digits both near 0 and 1, then converts to percentiles

Usage

proportion2percentile(
  p,
  digits = 2,
  remove_leading_zero = TRUE,
  add_percent_character = FALSE
)

Arguments

p

probability

digits

rounding digits. Defaults to 2

remove_leading_zero

Remove leading zero for small percentiles, Defaults to TRUE

add_percent_character

Append percent character. Defaults to FALSE

Value

character vector

Examples

proportion2percentile(0.01111)

Rounds proportions to significant digits both near 0 and 1

Description

Rounds proportions to significant digits both near 0 and 1

Usage

proportion_round(p, digits = 2)

Arguments

p

probability

digits

rounding digits

Value

numeric vector

Examples

proportion_round(0.01111)

unusualprofile: Calculates Conditional Mahalanobis Distances

Description

Author(s)

See Also

An example correlation matrix

Description

Usage

Format

Calculate the conditional Mahalanobis distance for any variables.

Description

Usage

Arguments

Value

Examples

An example data.frame

Description

Usage

Format

Test if matrix is singular

Description

Usage

Arguments

Value

Range label associated with probability

Description

Usage

Arguments

Value

Plot the variables from the results of the cond_maha function.

Description

Usage

Arguments

Value

Plot objects of the maha class (i.e, the results of the cond_maha function using dependent variables only).

Description

Usage

Arguments

Value

Rounds proportions to significant digits both near 0 and 1, then converts to percentiles

Description

Usage

Arguments

Value

Examples

Rounds proportions to significant digits both near 0 and 1

Description

Usage

Arguments

Value

Examples