paretoXY {iopsych}

Computes data needed for a XY Pareto plot.

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))