Organizational Psychology

Description Usage Arguments Value Author(s) Examples

Computes data needed for a XY Pareto plot.

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

`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.

betas: A matrix of beta weights for each criteria weight
mr_d: A matrix of multiple correlations or d values corresponding to each row of beta weights.
pareto_optimal: A vector indicating whether each value is pareto optimal

Allen Goebl Jeff Jones

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))