Confidence interval for a ratio of mean absolute prediction errors in a 2-group design

Source: R/statpsych2.R

Computes a confidence interval for a ratio of population mean absolute prediction errors from a general linear model in two independent groups. The number of predictor variables can differ across groups and the two models can be non-nested. This function requires a vector of estimated residuals from each group. This function does not assume zero excess kurtosis but does assume symmetry in the population prediction errors for the two models.

ci.ratio.mape2(alpha, res1, res2, s1, s2)

Arguments

alpha

alpha level for 1-alpha confidence

res1

vector of residuals from group 1

res2

vector of residuals from group 2

s1

number of predictor variables used in group 1

s2

number of predictor variables used in group 2

Value

Returns a 1-row matrix. The columns are:

  • MAPE1 - bias adjusted mean absolute prediction error for group 1

  • MAPE2 - bias adjusted mean absolute prediction error for group 2

  • MAPE1/MAPE2 - ratio of bias adjusted mean absolute prediction errors

  • LL - lower limit of the confidence interval

  • UL - upper limit of the confidence interval

Examples

res1 <- c(-2.70, -2.69, -1.32, 1.02, 1.23, -1.46, 2.21, -2.10, 2.56, -3.02
        -1.55, 1.46, 4.02, 2.34)
res2 <- c(-0.71, -0.89, 0.72, -0.35, 0.33 -0.92, 2.37, 0.51, 0.68, -0.85,
        -0.15, 0.77, -1.52, 0.89, -0.29, -0.23, -0.94, 0.93, -0.31 -0.04)
ci.ratio.mape2(.05, res1, res2, 1, 1)
#>    MAPE1     MAPE2 MAPE1/MAPE2       LL       UL
#>  2.58087 0.8327273    3.099298 1.917003 5.010761

# Should return:
#   MAPE1     MAPE2 MAPE1/MAPE2       LL       UL
# 2.58087 0.8327273    3.099298 1.917003 5.010761