Description Usage Arguments Details Value Author(s) References See Also Examples
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 |
right-hand side formula containing the linear model to be used for the log-transformed, 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 log-linear variance function; default method is "homo" |
cmObject |
object of type |
object |
object of type |
H1CVar |
matrix or data frame of first-stage covariates to include as main
effects in the log-linear model; default is |
s1sInts |
indices pointing to columns of H1CVar that should be included as
treatment interaction effects in the log-linear 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
log-linear model main effects when |
gammaHat1 |
estimated regression coefficients from the
log-linear 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 Q-Learning 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 Q-learning", Biometrika, 101(4), 831-847.
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 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 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")
## non-constant variance fit
fitIQ1var = learnIQ1var (~ gender + race + parent_BMI + baseline_BMI +
A1*(parent_BMI), data=bmiData, "A1", c ("parent_BMI"),
"hetero", fitIQ1cm)
## non-constant variance fit using formula specification
|
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