fit_ppl: Estimate HRs using PPL given a known variance component (tau)

In coxmeg: Cox Mixed-Effects Models for Genome-Wide Association Studies

Estimate HRs using PPL given a known variance component (tau)

Description

fit_ppl returns estimates of HRs and their p-values given a known variance component (tau).

Usage

fit_ppl(
  X,
  outcome,
  corr,
  type,
  tau,
  eps = 1e-06,
  order = 1,
  solver = NULL,
  spd = TRUE,
  verbose = TRUE
)

Arguments

X	A matrix of the predictors. Can be quantitative or binary values. Categorical variables need to be converted to dummy variables. Each row is a sample, and the predictors are columns.
outcome	A matrix contains time (first column) and status (second column). The status is a binary variable (1 for failure / 0 for censored).
corr	A relatedness matrix or a List object of matrices if there are multiple relatedness matrices. They can be a matrix or a 'dgCMatrix' class in the Matrix package. The matrix (or the sum if there are multiple) must be symmetric positive definite or symmetric positive semidefinite. The order of subjects must be consistent with that in outcome.
type	A string indicating the sparsity structure of the relatedness matrix. Should be 'bd' (block diagonal), 'sparse', or 'dense'. See details.
tau	A positive scalar or vector. A variance component(s) given by the user. If there are more than one related matrix, this must be a vector, the length of which corresponds to the number of matrices.
eps	An optional positive value indicating the relative convergence tolerance in the optimization algorithm. Default is 1e-6.
order	An optional integer value starting from 0. Only valid when dense=FALSE. It specifies the order of approximation used in the inexact newton method. Default is 1.
solver	An optional binary value that can be either 1 (Cholesky Decomposition using RcppEigen) or 2 (PCG). Default is NULL, which lets the function select a solver. See details.
spd	An optional logical value indicating whether the relatedness matrix is symmetric positive definite. Default is TRUE.
verbose	An optional logical value indicating whether to print additional messages. Default is TRUE.

Value

beta: The estimated coefficient for each predictor in X.

HR: The estimated HR for each predictor in X.

sd_beta: The estimated standard error of beta.

p: The p-value.

iter: The number of iterations until convergence.

ppl: The PPL when the convergence is reached.

About type

Specifying the type of the relatedness matrix (whether it is block-diagonal, general sparse, or dense). In the case of multiple relatedness matrices, it refers to the type of the sum of these matrices.

• "bd" - used for a block-diagonal relatedness matrix, or a sparse matrix the inverse of which is also sparse.

• "sparse" - used for a general sparse relatedness matrix the inverse of which is not sparse.

• "dense" - used for a dense relatedness matrix.

About spd

When spd=TRUE, the relatedness matrix is treated as SPD. If the matrix is SPSD or not sure, use spd=FALSE.

About solver

Specifying which method is used to solve the linear system in the optimization algorithm.

• "1" - Cholesky decompositon (RcppEigen:LDLT) is used to solve the linear system.

• "2" - PCG is used to solve the linear system. When type='dense', it is recommended to set solver=2 to have better computational performance.

Examples

library(Matrix)
library(MASS)
library(coxmeg)

## simulate a block-diagonal relatedness matrix
tau_var <- 0.2
n_f <- 100
mat_list <- list()
size <- rep(10,n_f)
offd <- 0.5
for(i in 1:n_f)
{
  mat_list[[i]] <- matrix(offd,size[i],size[i])
  diag(mat_list[[i]]) <- 1
}
sigma <- as.matrix(bdiag(mat_list))
n <- nrow(sigma)

## simulate random effexts and outcomes
x <- mvrnorm(1, rep(0,n), tau_var*sigma)
myrates <- exp(x-1)
y <- rexp(n, rate = myrates)
cen <- rexp(n, rate = 0.02 )
ycen <- pmin(y, cen)
outcome <- cbind(ycen,as.numeric(y <= cen))

## fit the ppl
re = fit_ppl(x,outcome,sigma,type='bd',tau=0.5,order=1)
re

coxmeg documentation built on Jan. 13, 2023, 5:07 p.m.