iqResids | R Documentation |
Creates an object containing the standardized residuals from the contrast mean and variance modeling steps.
iqResids(object)
object |
object of type |
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.
Returns object$stdResids
from an object of type
learnIQ1var
in the form of an object of type iqResids
.
Kristin A. Linn <kalinn@ncsu.edu>, Eric B. Laber, Leonard A. Stefanski
Laber, E.B., Linn, K.A., and Stefanski, L.A. (2013). Interactive Q-learning. Submitted.
learnIQ1var
, plot.iqResids
## load in two-stage BMI data data (bmiData) bmiData$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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.