iqLearn (version 1.5)

iqResids: IQ-learning: standardized residuals

Description

Creates an object containing the standardized residuals from the contrast mean and variance modeling steps.

Usage

iqResids(object)

Arguments

object

object of type learnIQ1var

Value

Returns object$stdResids from an object of type learnIQ1var in the form of an object of type iqResids.

Details

Creates an object containing the standardized residuals from the contrast mean and variance modeling steps to be used with the plotting function plot.iqResids. The choice of density estimator in the next step of IQ-learning should be based on a QQ-plot of the standardized residuals.

References

Laber, E.B., Linn, K.A., and Stefanski, L.A. (2013). Interactive Q-learning. Submitted.

See Also

learnIQ1var, plot.iqResids

Examples

# NOT RUN {
## load in two-stage BMI data
data (bmiData)
bmiData$A1[which (bmiData$A1=="MR")] = 1
bmiData$A1[which (bmiData$A1=="CD")] = -1
bmiData$A2[which (bmiData$A2=="MR")] = 1
bmiData$A2[which (bmiData$A2=="CD")] = -1
bmiData$A1 = as.numeric (bmiData$A1)
bmiData$A2 = as.numeric (bmiData$A2)
s1vars = bmiData[,1:4]
s2vars = bmiData[,c (1, 3, 5)]
a1 = bmiData[,7]
a2 = bmiData[,8]
## define response y to be the negative 12 month change in BMI from
## baseline 
y = -(bmiData[,6] - bmiData[,4])/bmiData[,4]
## second-stage regression
fitIQ2 = learnIQ2 (y ~ gender + parent_BMI + month4_BMI +
  A2*(parent_BMI + month4_BMI), data=bmiData, "A2", c("parent_BMI",
                                  "month4_BMI"))
## model conditional mean of contrast function
fitIQ1cm = learnIQ1cm (~ gender + race + parent_BMI + baseline_BMI +
  A1*(gender + parent_BMI + baseline_BMI), data=bmiData, "A1", c
  ("gender", "parent_BMI", "baseline_BMI"), fitIQ2)
## variance modeling
fitIQ1var = learnIQ1var (~ gender + race + parent_BMI + baseline_BMI +
  A1*(parent_BMI), data=bmiData, "A1", c ("parent_BMI"), "hetero",
  fitIQ1cm)
## plot standardized residuals
fitResids = iqResids (fitIQ1var)
plot (fitResids)
# }