simico_fit_null | R Documentation |
Fit the null model via newton raphson for multiple outcomes interval-censored skat.
simico_fit_null(init_beta, epsilon, xDats, lt_all, rt_all, k, d)
init_beta |
Starting values for NR. |
epsilon |
Stopping criterion for NR. |
xDats |
List of left and right design matrices. |
lt_all |
n x k matrix of left times. |
rt_all |
n x k matrix of right times. |
k |
Total number of outcomes. |
d |
Total number of quadrature nodes. |
beta_fit |
Vector of fitted coefficients. |
iter |
Number of iterations needed for the Newton-Raphson to converge. |
diff |
Difference between the current values of temp_beta and the previous iteration of temp_beta. |
jmat |
Information matrix of the theta parameters. |
grad |
Vector of the first derivaive of the theta parameters. |
# Set number of outcomes k = 2 # Set number of observations n = 100 # Set number of covariates p = 2 # Set number of SNPs q = 50 # Set number of causal SNPs num = 5 # Set number of quadrature nodes d = 100 # Variance of subject-specific random effect tauSq = 1 # Define the effect sizes effectSizes <- c(.03, .15) # Set MAF cutoff for causal SNPs Causal.MAF.Cutoff = 0.1 # the baseline cumulative hazard function bhFunInv <- function(x) {x} set.seed(1) # Generate covariate matrix xMat <- cbind(rnorm(n), rbinom(n=n, size=1, prob=0.5)) # Generate genetic matrix gMat <- matrix(data=rbinom(n=n*q, size=2, prob=0.1), nrow=n) # Get indices to specific select causal variants idx <- Get_CausalSNPs_bynum(gMat, num, Causal.MAF.Cutoff) # Subset the gMat gMatCausal <- gMat[,idx] # Generate the multiple outcomes exampleDat <- simico_gen_dat(bhFunInv = bhFunInv, obsTimes = 1:3, windowHalf = 0.1, n, p, k, tauSq, gMatCausal, xMat, effectSizes) # Set the initial estimate values init_beta <-c (rep(c(0, 0, 0, 1, 0), k), 1) # Run the Newton-Raphson nullFit <- simico_fit_null(init_beta = init_beta, epsilon = 10^-5, xDats = exampleDat$fullDat$xDats, lt_all = exampleDat$leftTimesMat, rt_all = exampleDat$rightTimesMat, k = k, d = d)
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