fit_ppl {coxmeg}R Documentation

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 = 0.5, FID = NULL, eps = 1e-06,
  order = 1, solver = NULL, spd = TRUE, verbose = TRUE)

Arguments

X

A matrix of the preidctors. 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. Can be a matrix or a 'dgCMatrix' class in the Matrix package. Must be symmetric positive definite or symmetric positive semidefinite.

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. A variance component given by the user. Default is 0.5.

FID

An optional string vector of family ID. If provided, the data will be reordered according to the family ID.

eps

An optional positive value indicating the 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 bianry value that can be either 1 (Cholesky Decomposition using RcppEigen), 2 (PCG) or 3 (Cholesky Decomposition using Matrix). 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

'bd' is used for a block-diagonal relatedness matrix, or a sparse matrix the inverse of which is also sparse. 'sparse' is used for a general sparse relatedness matrix the inverse of which is not sparse.

About solver

When solver=1,3/solver=2, Cholesky decompositon/PCG is used to solve the linear system. When solver=3, the solve function in the Matrix package is used, and when solver=1, it uses RcppEigen:LDLT to solve linear systems.

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

[Package coxmeg version 1.0.12 Index]