iqLearnVar: IQ-Learning: Variance of First-Stage Regression of...
In DynTxRegime: Methods for Estimating Dynamic Treatment Regimes
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
Estimates the variance function of the first-stage regression of
the estimated second-stage contrast by fitting a constant variance function or a
log-linear model to the residuals.
Usage
1
2
Arguments
object
an object of class DynTxRegime:
The value object returned from a previous call to iqLearnFSC()
...
ignored
moMain
For constant variance, NULL.
If a log-linear model is to be used,
an object of class modelObj defining the
regression model and procedures to be used
for the main effects component of this regression step.
The object must be created by a call to
modelObj::buildModelObj().
The method chosen to obtain predictions must return
the prediction on the scale of the response variable.
See ?modelObj for details.
moCont
For constant variance, NULL.
If a log-linear model is to be used,
an object of class modelObj defining the
regression model and procedures to be used
for the contrast component of this regression step.
The object must be created by a call to
modelObj::buildModelObj().
The method chosen to obtain predictions must return
the prediction on the scale of the response variable.
See ?modelObj for details.
data
For constant variance, NULL.
If a log-linear model is to be used,
an object of class data.frame
containing the covariates and treatment history.
Treatment must be an integer (or able to be cast to an integer)
and take values {-1,1}.
iter
For constant variance, NULL.
If a log-linear model is to be used,
an object of class integer.
>=1 if moMain and moCont are to be
fitted separately, iter is the maximum number of iterations.
Assume Y = Ymain + Ycont;
the iterative algorithm is as follows:
(1) hat(Ycont) = 0;
(2) Ymain = Y - hat(Ycont);
(3) fit Ymain ~ moMain;
(4) set Ycont = Y - hat(Ymain);
(5) fit Ycont ~ A*moCont;
(6) Repeat steps (2) - (5) until convergence or
a maximum of iter iterations.
<=0 if the components of the conditional expectation
moMain and moCont will be combined and
fit as a single object.
Note that if iter <= 0, all non-model components of the
moMain and moCont must be
identical. By default, the choices in moMain are used.
suppress
an object of class logical. If TRUE, final
screen prints are suppressed.
Details
There are standard regression analysis tools available for the
object returned by this function. In general, these tools
simply extend the methods defined by the regression function.
If defined, coef() returns the model
parameter estimates; plot() generates the standard x-y plots;
residuals returns model residuals for the combined model; and
summary returns summary information.
Other tools, such as fitted() for the lm
regression function, can be accessed using fitObject().
fitObject() retrieves the standard value object returned by the
regression method, which can be passed as input to other functions.
See ?fitObject for details.
For homoskedastic variance, the standard deviation can be retrieved using
method stdDev().
Value
Returns an object that inherits directly from class DynTxRegime.
Author(s)
Kristin A. Linn, Eric B. Laber, Leonard A. Stefanski, and
Shannon T. Holloway <sthollow@ncsu.edu>
References
Laber, E. B., Linn, K. A., and Stefanski, L. A.
(2014).
Interactive Q-learning.
Biometrika, in press.
See Also
iqLearnFSM, iqLearnFSC, iqLearnSS
Examples
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##########################################################
# Load and process data set
##########################################################
data(bmiData)
#----------------------------------------------------#
# Recast treatment variables to (-1,1) --- required.
#----------------------------------------------------#
bmiData$A1[which (bmiData$A1=="MR")] <- 1L
bmiData$A1[which (bmiData$A1=="CD")] <- -1L
bmiData$A2[which (bmiData$A2=="MR")] <- 1L
bmiData$A2[which (bmiData$A2=="CD")] <- -1L
bmiData$A1 <- as.integer(bmiData$A1)
bmiData$A2 <- as.integer(bmiData$A2)
#----------------------------------------------------#
# define response y to be the negative 12 month
# change in BMI from baseline
#----------------------------------------------------#
bmiData$y <- -100*(bmiData[,6] - bmiData[,4])/bmiData[,4]
##########################################################
# Second-stage regression
##########################################################
#----------------------------------------------------#
# Create modelObj object for main effect component
#----------------------------------------------------#
moMain <- buildModelObj(model = ~ gender + parentBMI + month4BMI,
solver.method = 'lm')
#----------------------------------------------------#
# Create modelObj object for contrast component
#----------------------------------------------------#
moCont <- buildModelObj(model = ~ parentBMI + month4BMI,
solver.method = 'lm')
iqSS <- iqLearnSS(moMain = moMain,
moCont = moCont,
data = bmiData,
response = bmiData$y,
txName = "A2",
iter = 0)
##########################################################
# Model conditional mean of contrast function
##########################################################
#----------------------------------------------------#
# Create modelObj object for main effect component
#----------------------------------------------------#
moMain <- buildModelObj(model = ~ gender + race + parentBMI + baselineBMI,
solver.method = 'lm')
#----------------------------------------------------#
# Create modelObj object for contrast component
#----------------------------------------------------#
moCont <- buildModelObj(model = ~ gender + parentBMI + month4BMI,
solver.method = 'lm')
iqFSC <- iqLearnFSC(moMain = moMain,
moCont = moCont,
data = bmiData,
response = iqSS,
txName = "A1",
iter = 0)
##########################################################
# Variance Modeling
##########################################################
#----------------------------------------------------#
# homoskedastic variance
#----------------------------------------------------#
iqV1 <- iqLearnFSV(iqFSC)
residuals(iqV1)
#----------------------------------------------------#
# heteroskedastic variance
#----------------------------------------------------#
# Create modelObj object for main effect component
#----------------------------------------------------#
moMain <- buildModelObj(model = ~ gender + race + parentBMI + baselineBMI,
solver.method = 'lm')
#----------------------------------------------------#
# Create modelObj object for contrast component
#----------------------------------------------------#
moCont <- buildModelObj(model = ~ parentBMI + baselineBMI,
solver.method = 'lm')
iqV2 <- iqLearnFSV(object = iqFSC,
moMain = moMain,
moCont = moCont,
data = bmiData,
txName = "A1",
iter = 0)
# Estimated Value functions
vals <- qFuncs(iqV2)
head(vals)
# Residuals
res <- residuals(iqV2)
head(res)
# Model parameter estimates
coef(iqV2)
# Summary information for fit object
#summary(iqV2)
# QQ-plot
plot(iqV2)
# Value objects returned by modeling function
fitObj <- fitObject(iqV2)
fitObj
# All standard lm methods can be applied to the elements of this list.
summary(fitObj[[ "Combined" ]])
coef(fitObj[[ "Combined" ]])
head(residuals(fitObj[[ "Combined" ]]))
head(fitted.values(fitObj[[ "Combined" ]]))
plot(fitObj[[ "Combined"]])
DynTxRegime documentation built on May 2, 2019, 5:21 p.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
Estimates the variance function of the first-stage regression of the estimated second-stage contrast by fitting a constant variance function or a log-linear model to the residuals.
Usage
1 2 |
Arguments
object |
an object of class |
... |
ignored |
moMain |
For constant variance, NULL.
If a log-linear model is to be used,
an object of class |
moCont |
For constant variance, NULL.
If a log-linear model is to be used,
an object of class |
data |
For constant variance, NULL.
If a log-linear model is to be used,
an object of class |
iter |
For constant variance, NULL.
If a log-linear model is to be used,
an object of class >=1 if <=0 if the components of the conditional expectation
|
suppress |
an object of class |
Details
There are standard regression analysis tools available for the
object returned by this function. In general, these tools
simply extend the methods defined by the regression function.
If defined, coef() returns the model
parameter estimates; plot() generates the standard x-y plots;
residuals returns model residuals for the combined model; and
summary returns summary information.
Other tools, such as fitted() for the lm
regression function, can be accessed using fitObject().
fitObject() retrieves the standard value object returned by the
regression method, which can be passed as input to other functions.
See ?fitObject for details.
For homoskedastic variance, the standard deviation can be retrieved using method stdDev().
Value
Returns an object that inherits directly from class DynTxRegime.
Author(s)
Kristin A. Linn, Eric B. Laber, Leonard A. Stefanski, and Shannon T. Holloway <sthollow@ncsu.edu>
References
Laber, E. B., Linn, K. A., and Stefanski, L. A. (2014). Interactive Q-learning. Biometrika, in press.
See Also
iqLearnFSM, iqLearnFSC, iqLearnSS
Examples
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 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 | ##########################################################
# Load and process data set
##########################################################
data(bmiData)
#----------------------------------------------------#
# Recast treatment variables to (-1,1) --- required.
#----------------------------------------------------#
bmiData$A1[which (bmiData$A1=="MR")] <- 1L
bmiData$A1[which (bmiData$A1=="CD")] <- -1L
bmiData$A2[which (bmiData$A2=="MR")] <- 1L
bmiData$A2[which (bmiData$A2=="CD")] <- -1L
bmiData$A1 <- as.integer(bmiData$A1)
bmiData$A2 <- as.integer(bmiData$A2)
#----------------------------------------------------#
# define response y to be the negative 12 month
# change in BMI from baseline
#----------------------------------------------------#
bmiData$y <- -100*(bmiData[,6] - bmiData[,4])/bmiData[,4]
##########################################################
# Second-stage regression
##########################################################
#----------------------------------------------------#
# Create modelObj object for main effect component
#----------------------------------------------------#
moMain <- buildModelObj(model = ~ gender + parentBMI + month4BMI,
solver.method = 'lm')
#----------------------------------------------------#
# Create modelObj object for contrast component
#----------------------------------------------------#
moCont <- buildModelObj(model = ~ parentBMI + month4BMI,
solver.method = 'lm')
iqSS <- iqLearnSS(moMain = moMain,
moCont = moCont,
data = bmiData,
response = bmiData$y,
txName = "A2",
iter = 0)
##########################################################
# Model conditional mean of contrast function
##########################################################
#----------------------------------------------------#
# Create modelObj object for main effect component
#----------------------------------------------------#
moMain <- buildModelObj(model = ~ gender + race + parentBMI + baselineBMI,
solver.method = 'lm')
#----------------------------------------------------#
# Create modelObj object for contrast component
#----------------------------------------------------#
moCont <- buildModelObj(model = ~ gender + parentBMI + month4BMI,
solver.method = 'lm')
iqFSC <- iqLearnFSC(moMain = moMain,
moCont = moCont,
data = bmiData,
response = iqSS,
txName = "A1",
iter = 0)
##########################################################
# Variance Modeling
##########################################################
#----------------------------------------------------#
# homoskedastic variance
#----------------------------------------------------#
iqV1 <- iqLearnFSV(iqFSC)
residuals(iqV1)
#----------------------------------------------------#
# heteroskedastic variance
#----------------------------------------------------#
# Create modelObj object for main effect component
#----------------------------------------------------#
moMain <- buildModelObj(model = ~ gender + race + parentBMI + baselineBMI,
solver.method = 'lm')
#----------------------------------------------------#
# Create modelObj object for contrast component
#----------------------------------------------------#
moCont <- buildModelObj(model = ~ parentBMI + baselineBMI,
solver.method = 'lm')
iqV2 <- iqLearnFSV(object = iqFSC,
moMain = moMain,
moCont = moCont,
data = bmiData,
txName = "A1",
iter = 0)
# Estimated Value functions
vals <- qFuncs(iqV2)
head(vals)
# Residuals
res <- residuals(iqV2)
head(res)
# Model parameter estimates
coef(iqV2)
# Summary information for fit object
#summary(iqV2)
# QQ-plot
plot(iqV2)
# Value objects returned by modeling function
fitObj <- fitObject(iqV2)
fitObj
# All standard lm methods can be applied to the elements of this list.
summary(fitObj[[ "Combined" ]])
coef(fitObj[[ "Combined" ]])
head(residuals(fitObj[[ "Combined" ]]))
head(fitted.values(fitObj[[ "Combined" ]]))
plot(fitObj[[ "Combined"]])
|
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