configural (version 0.1.5)

cpa_mat: Conduct criterion profile analysis using a correlation matrix

Description

Conduct criterion profile analysis using a correlation matrix

Usage

cpa_mat(
  formula,
  cov_mat,
  n = NULL,
  se_var_mat = NULL,
  se_beta_method = c("normal", "lm"),
  adjust = c("fisher", "pop", "cv"),
  conf_level = 0.95,
  ...
)

Value

An object of class "cpa" containing the criterion pattern vector and CPA variance decomposition

Arguments

formula

Regression formula with a single outcome variable on the left-hand side and one or more predictor variables on the right-hand side (e.g., Y ~ X1 + X2).

cov_mat

Correlation matrix containing the variables to be used in the regression.

n

Sample size. Used to compute adjusted R-squared and, if se_var_mat is NULL, standard errors. If NULL and se_var_mat is specified, effective sample size is computed based on se_var_mat (cf. Revelle et al., 2017).

se_var_mat

Optional. The sampling error covariance matrix among the unique elements of cov_mat. Used to calculate standard errors. If not supplied, the sampling covariance matrix is calculated using n.

se_beta_method

Method to use to estimate the standard errors of standardized regression (beta) coefficients. Current options include "normal" (use the Jones-Waller, 2015, normal-theory approach) and "lm" (estimate standard errors using conventional regression formulas).

adjust

Method to adjust R-squared for overfitting. See adjust_Rsq() for details.

conf_level

Confidence level to use for confidence intervals.

...

Additional arguments.

References

Jones, J. A., & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF) covariance matrix of standardized regression coefficients: Theoretical extensions and finite sample behavior. Psychometrika, 80(2), 365–378. tools:::Rd_expr_doi("10.1007/s11336-013-9380-y")

Revelle, W., Condon, D. M., Wilt, J., French, J. A., Brown, A., & Elleman, L. G. (2017). Web- and phone-based data collection using planned missing designs. In N. G. Fielding, R. M. Lee, & G. Blank, The SAGE Handbook of Online Research Methods (pp. 578–594). SAGE Publications. tools:::Rd_expr_doi("10.4135/9781473957992.n33")

Wiernik, B. M., Wilmot, M. P., Davison, M. L., & Ones, D. S. (2019). Meta-analytic criterion profile analysis. Psychological Methods tools:::Rd_expr_doi("10.1037/met0000305")

Examples

sevar <- cor_covariance_meta(mindfulness$r, mindfulness$n, mindfulness$sevar_r, mindfulness$source)
cpa_mat(mindfulness ~ ES + A + C + Ex + O,
          cov_mat = mindfulness$r,
          n = NULL,
          se_var_mat = sevar,
          adjust = "pop")