Description Usage Arguments Details Value Examples
odsmle
provides a maximum semiparametric empirical likelihood estimator
(MSELE) for analyzing the primary outcome Y with respect to expensive exposure
and other covariates in ODS design (Zhou et al. 2002).
1 |
Y |
vector for the primary response |
X |
the design matrix with a column of 1's for the intercept |
beta |
starting parameter values for the regression coefficients that relate Y to X. |
sig |
starting parameter values for the error variance of the regression. |
pis |
starting parameter values for the stratum probabilities (the probability that Y belongs to certain stratum) e.g. pis = c(0.1, 0.8, 0.1). |
a |
vector of cutpoints for the primary response (e.g., a = c(-2.5,2)) |
rs.size |
size of the SRS (simple random sample) |
size |
vector of the stratum sizes of the supplemental samples (e.g. size = c(50,0,50) represents that two supplemental samples each of size 50 are taken from the upper and lower tail of Y.) |
strat |
vector that indicates the stratum numbers (e.g. strat = c(1,2,3) represents that there are three stratums). |
We assume that in the population, the primary outcome variable Y follows the following model:
Y = beta0 + beta1*X + epsilon,
where X are the covariates, and epsilon has variance sig. In ODS design, a simple random sample is taken from the full cohort, then two supplemental samples are taken from two tails of Y, i.e. (-Infty, mu_Y - a*sig_Y) and (mu_Y + a*sig_Y, +Infty). Because ODS data has biased sampling nature, naive regression analysis will yield biased estimates of the population parameters. Zhou et al. (2002) describes a semiparametric empirical likelihood estimator for estimating the parameters in the primary outcome model.
A list which contains the parameter estimates for the primary response model:
Y = beta0 + beta1*X + epsilon,
where epsilon has variance sig. The list contains the following components:
beta |
parameter estimates for beta |
sig |
estimates for sig |
pis |
estimates for the stratum probabilities |
grad |
gradient |
hess |
hessian |
converge |
whether the algorithm converges: True or False |
i |
Number of iterations |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | library(ODS)
# take the example data from the ODS package
# please see the documentation for details about the data set ods_data
Y <- ods_data[,1]
X <- cbind(rep(1,length(Y)), ods_data[,2:5])
# use the simple random sample to get an initial estimate of beta, sig #
# perform an ordinary least squares #
SRS <- ods_data[1:200,]
OLS.srs <- lm(SRS[,1] ~ SRS[,2:5])
OLS.srs.summary <- summary(OLS.srs)
beta <- coefficients(OLS.srs)
sig <- OLS.srs.summary$sigma^2
pis <- c(0.1,0.8,0.1)
# the cut points for this data is Y < 0.162, Y > 2.59.
a <- c(0.162,2.59)
rs.size <- 200
size <- c(100,0,100)
strat <- c(1,2,3)
odsmle(Y,X,beta,sig,pis,a,rs.size,size,strat)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.