fit_ppl: Estimate HRs using PPL given a known variance component (tau)
In coxmeg: Cox Mixed-Effects Models for Genome-Wide Association Studies

Description Usage Arguments Value About type About solver Examples

View source: R/fit_ppl.R

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

1 2	fit_ppl(X, outcome, corr, type, tau = 0.5, FID = NULL, eps = 1e-06, order = 1, solver = NULL, spd = TRUE, verbose = TRUE)

`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.

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.

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