Estimates the variance function of the contrast function by fitting a constant variance function or a log linear model to the residuals of the contrast mean fit.
1 2 3 4 5 6 7  learnIQ1var(object, ...)
## S3 method for class 'formula'
learnIQ1var(formula, data, treatName, intNames,
method, cmObject, ...)
## Default S3 method:
learnIQ1var(object, H1CVar, s1sInts, method, ...)

formula 
righthand side formula containing the linear model to be used for the logtransformed, squared residuals from the contrast function mean fit 
data 
data frame containing variables used in 
treatName 
character string indicating the stage 1 treatment name 
intNames 
vector of characters indicating the names of the variables that interact with the stage 1 treatment in the contrast function variance model 
method 
either "homo" for a constant variance function or "hetero" for a loglinear variance function; default method is "homo" 
cmObject 
object of type 
object 
object of type 
H1CVar 
matrix or data frame of firststage covariates to include as main
effects in the loglinear model; default is 
s1sInts 
indices pointing to columns of H1CVar that should be included as
treatment interaction effects in the loglinear model; default is 
... 
additional arguments to be passed to 
If method="homo"
, computes the variance of the residuals from
the contrast function mean fit. If method="hetero"
, fits a
model of the form
E (log e^2  H1, A1) = H10^Tγ0 + A1*H11^Tγ1
where H10 and H11 are summaries of
H1. Though a slight abuse of notation, these summaries are
not required to be the same as H10 and H11 in
the main effect term regression or the contrast mean fit. Also,
e^2 = H21^Tβ21  E(H21^T β21  H1,
A1). For an object of type learnIQ1var
, summary(object)
and
plot(object)
can be used for evaluating model fit.
stdDev 
standard deviation of the residuals from the contrast
function mean fit when 
stdResids 
standardized residuals of the contrast function
after mean and variance modeling, using either 
gammaHat0 
estiamted regression coefficients from the
loglinear model main effects when 
gammaHat1 
estimated regression coefficients from the
loglinear model interaction effects when 
s1VarFit 

homo 
logical variable indicating if 
sigPos 
vector of predicted values when A1=1 for all patients 
sigNeg 
vector of predicted values when A1=1 for all patients 
s1sInts 
indices of variables in 
Kristin A. Linn <kalinn@ncsu.edu>, Eric B. Laber, Leonard A. Stefanski
Linn, K. A., Laber, E. B., Stefanski, L. A. (2015) "iqLearn: Interactive QLearning in R", Journal of Statistical Software, 64(1), 1–25.
Laber, E. B., Linn, K. A., and Stefanski, L. A. (2014) "Interactive model building for Qlearning", Biometrika, 101(4), 831847.
learnIQ1cm
, iqResids
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35  ## load in twostage 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]
## secondstage regression
fitIQ2 = learnIQ2 (y ~ gender + parent_BMI + month4_BMI +
A2*(parent_BMI + month4_BMI), data=bmiData, "A2", c("parent_BMI",
"month4_BMI"))
## model conditional expected value of main effect term
fitIQ1main = learnIQ1main (~ gender + race + parent_BMI + baseline_BMI
+ A1*(gender + parent_BMI), data=bmiData, "A1", c ("gender",
"parent_BMI"), fitIQ2)
## 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 (fitIQ1cm) ## constant variance fit
fitIQ1var = learnIQ1var (fitIQ1cm, s1vars, c (3, 4), method="hetero")
## nonconstant variance fit
fitIQ1var = learnIQ1var (~ gender + race + parent_BMI + baseline_BMI +
A1*(parent_BMI), data=bmiData, "A1", c ("parent_BMI"),
"hetero", fitIQ1cm)
## nonconstant variance fit using formula specification

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