Creates or adjusts a two-phase survey design object using a logistic regression model for second-phase sampling probability. This function should be particularly useful in reweighting to account for missing data.
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twophase design object or data frame
Predictors for estimating weights
Model fitted to complete (ie phase 1) data
Subset of data frame with complete data (ie phase 1).
Stratification (if any) of phase 2 sampling
for future expansion
data is a data frame,
estWeights first creates a
two-phase design object. The
strata argument is used only to
compute finite population corrections, the same variables must be
formula to compute stratified sampling probabilities.
With a two-phase design object,
estWeights estimates the sampling
probabilities using logistic regression as described by Robins et al
(1994) and adds information to the object to enable correct sandwich
standard errors to be computed.
An alternative to specifying
formula is to specify
working.model. The estimating functions from this model will be
used as predictors of the sampling probabilities, which will increase
efficiency to the extent that the working model and the model of
interest estimate the same parameters (Kulich \& Lin 2004).
The effect on a two-phase design object is very similar to
calibrate, and is identical when
specifies a saturated model.
A two-phase survey design object.
Breslow NE, Lumley T, Ballantyne CM, Chambless LE, Kulich M. (2009) Using the Whole Cohort in the Analysis of Case-Cohort Data. Am J Epidemiol. 2009 Jun 1;169(11):1398-405.
Robins JM, Rotnitzky A, Zhao LP. (1994) Estimation of regression coefficients when some regressors are not always observed. Journal of the American Statistical Association, 89, 846-866.
Kulich M, Lin DY (2004). Improving the Efficiency of Relative-Risk Estimation in Case-Cohort Studies. Journal of the American Statistical Association, Vol. 99, pp.832-844
Lumley T, Shaw PA, Dai JY (2011) "Connections between survey calibration estimators and semiparametric models for incomplete data" International Statistical Review. 79:200-220. (with discussion 79:221-232)
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data(airquality) ## ignoring missingness, using model-based standard error summary(lm(log(Ozone)~Temp+Wind, data=airquality)) ## Without covariates to predict missingness we get ## same point estimates, but different (sandwich) standard errors daq<-estWeights(airquality, formula=~1,subset=~I(!is.na(Ozone))) summary(svyglm(log(Ozone)~Temp+Wind,design=daq)) ## Reweighting based on weather, month d2aq<-estWeights(airquality, formula=~Temp+Wind+Month, subset=~I(!is.na(Ozone))) summary(svyglm(log(Ozone)~Temp+Wind,design=d2aq))