test_han2018: Association testing using Han & Lahiri estimating equations...

In ludic: Linkage Using Diagnosis Codes

Description

Association testing using Han & Lahiri estimating equations and jackknife approach

Usage

test_han2018(
  match_prob,
  y,
  x,
  covar_y = NULL,
  covar_x = NULL,
  jackknife_nrep = 100,
  jackknife_blocksize = max(floor(min(length(y), nrow(x))/jackknife_nrep), 1),
  methods = c("F", "M", "M2"),
  dist_family = c("gaussian", "binomial")
)

Arguments

match_prob	matching probabilities matrix (e.g. obtained through recordLink) of dimensions n1 x n2.
y	response variable of length n1. Only binary or gaussian phenotypes are supported at the moment.
x	a matrix or a data.frame of predictors of dimensions n2 x p. An intercept is automatically added within the function.
covar_y	a matrix or a data.frame of predictors of dimensions n1 x q1. An intercept is automatically added within the function.
covar_x	a matrix or a data.frame of predictors of dimensions n2 x q2. An intercept is automatically added within the function.
jackknife_nrep	the number of jackknife repetitions. Default is 100 (from Han et al.).
jackknife_blocksize	the number of observations to remove in each jackknife.
methods	a character vector which must be a subset of ("F", "M", "M2") indicating which estimator from Han et al. 2018 should be computed. Default is all 3.
dist_family	a character string indicating the distribution family for the glm. Currently, only 'gaussian' and 'binomial' are supported. Default is 'gaussian'.

Value

a list containing the following for each estimator in methods:

• beta a vector containing the p estimated coefficients

• varcov the p x p variance-covariance matrix of the beta coefficients

• zscores a vector containing the p Z-scores

• pval the corresponding Gaussian assumption p-values

References

Han, Y., and Lahiri, P. (2019) Statistical Analysis with Linked Data. International Statistical Review, 87: S139– S157. doi: 10.1111/insr.12295.

Examples

# rm(list=ls())
# n_sims <- 500
# res <- pbapply::pblapply(1:n_sims, function(n){
# nx <- 99
# ny <- 103
# x <- matrix(ncol=2, nrow=ny, stats::rnorm(n=ny*2))
# 
# #plot(density(rbeta(n=1000, 1,2)))
# match_prob <- diag(ny)[, 1:nx]#matrix(rbeta(n=ny*nx, 1, 2), nrow=ny, ncol=99)
# 
# covar_y <- matrix(rnorm(n=ny, 1, 0.5), ncol=1)
# covar_x <- matrix(ncol=3, nrow=ny, stats::rnorm(n=ny*3))
# 
# #y <- rnorm(n=ny, mean = x %*% c(2,-3)  + covar_x %*% rep(0.2, ncol(covar_x)) + 0.5*covar_y, 0.5)
# y <- rbinom(n=ny, 1, prob=expit(x %*% c(2,-3)  + covar_x %*% 
#             rep(0.2, ncol(covar_x)) + 0.5*covar_y))
# #glm(y~0+x+covar_y+covar_x, family = "binomial")
# return(
# #test_han2018(match_prob, y, x, jackknife_blocksize = 10, covar_x = NULL, covar_y = NULL)
# test_han2018(match_prob, y[1:ny], x[1:nx, ], dist_family = "binomial", 
#              jackknife_blocksize = 10, covar_x = covar_x[1:nx, ], 
#              covar_y = covar_y[1:ny, , drop=FALSE])
# )
# }, cl=parallel::detectCores()-1)
# pvals_F <- sapply(lapply(res, "[[", "F"), "[[", "beta")
# pvals_M <- sapply(lapply(res, "[[", "M"), "[[", "beta")
# pvals_M2 <- sapply(lapply(res, "[[", "M2"), "[[", "beta")
# quantile(pvals_F)
# quantile(pvals_M)
# quantile(pvals_M2)
# rowMeans(pvals_F<0.05)
# rowMeans(pvals_M<0.05)
# rowMeans(pvals_M2<0.05)

ludic documentation built on Aug. 18, 2021, 5:08 p.m.