paretoXY: Computes data needed for a XY Pareto plot.

In iopsych: Methods for Industrial/Organizational Psychology

Description

Computes data needed for a XY Pareto plot.

Usage

paretoXY(r_mat, x_col, y_col, d_vec, gen = 100, pop = 100,
  pred_lower = rep(-2, length(x_col)), pred_upper = rep(2, length(x_col)))

Arguments

r_mat	A correlation matrix.
x_col	A vector of columns representing predictor variables.
y_col	A vector of columns representing criterion variables.
d_vec	A vector of d scores.
gen	The number of iterations used by the algorithim.
pop	The population or number of cases used by the algorithim.
pred_lower	The minimum weight allowed for each predictor.
pred_upper	The maximum weight allowed for each predictor.

Value

• betasA matrix of beta weights for each criteria weight

• mr_dA matrix of multiple correlations or d values corresponding to each row of beta weights.

• pareto_optimalA vector indicating whether each value is pareto optimal

Author(s)

Allen Goebl Jeff Jones

Examples

data(dls2007)
dat <- dls2007
r_mat <- dat[1:6, 2:7]
x_col <- 1:4 
y_col <- 5:6
d_vec <- dat[1:4, 1]

paretoXY(r_mat=r_mat, x_col=1:4, y_col=5, d_vec=d_vec, pred_lower=c(0,0,0,0))
paretoXY(r_mat=r_mat, x_col=1:4, y_col=c(5,6))

iopsych documentation built on May 2, 2019, 2:27 p.m.