DA_FDN2M1M2: Data Analysis for Combining (N2,M1) + (N2,M2)

In SPPcomb: Combining Different Spatial Datasets in Cancer Risk Estimation

Description

The main function to solve the estimating equations constructed by combining pair (N2,M1) and (N2,M2). Since there is just one case data, no selection bias needed.

Usage

DA_FDN2M1M2(realdata_covariates, realdata_alpha, subset_2, subset_4, p, beta0)

Arguments

realdata_covariates	a list contains the following data matrics: CASEZ_2, CASEZhat_2, CASEZhat_22, CONTZ_1, CONTZhat_1, CONTZhat_2, CONTZhat_22
realdata_alpha	a list contains the following data matrics: prob_case_22, prob_cont_1, prob_cont_2, pwt_cont_2
subset_2	A vector of 1:(p-2), which is the subset of \hat{Z}_{21}, i.e. \hat{Z}_{21}^\star in equation (10) of Huang(2014). \hat{Z}_l may be highly correlated with Z_d, so it is removed in the estimation. For the view of including more information, you can use the whole dataset.
subset_4	A vector of 1:(p-2), which is the subset of \hat{Z}_{22}.
p	number of parameters.
beta0	an initial parameter for solver "nleqslv".

Details

The function solves estimating equation based on (N2,M1) and (N2,M2), see Huang(2014).

Value

A list of estimator and its standard deviation.

References

Huang, H., Ma, X., Waagepetersen, R., Holford, T.R. , Wang, R., Risch, H., Mueller, L. & Guan, Y. (2014). A New Estimation Approach for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical Association, 109:505, 11-23.

Examples

 #p <- 8
 #subset_2 <- 1:p
 #subset_4 <- 1:p
 #beta0=c(-5.4163,0.7790,-0.1289,0.2773,-0.5510,0.1568,0.4353,-0.6895)
 #DA_FDN2M1M2(realdata_covariates,realdata_alpha,subset_2,subset_4,p=p,beta0=beta0)

SPPcomb documentation built on May 2, 2019, 3:29 p.m.