# ICSKATO_bootstrap.R The version of ICSKATO to run when bootstrapping to match kurtosis. ```r ICSKATO_bootstrap( icskatOut, B, intervalProbs, allVisits, quant_r, seed = NULL, null_fit, gMat, xMat, fitAgain, checkpoint = FALSE, downsample = 1, rhoVec ) ``` ## Arguments - `icskatOut`: The output list from ICSKAT(). - `B`: Number of bootstrap replications. - `intervalProbs`: n*(s+1) matrix where n is number of subjects and s is the number of visits possible, probability of falling in each interval. - `allVisits`: n*s matrix with all the visit times for each subject. - `quant_r`: Quantiles of time from make_IC_dmats, to keep them constant through bootstrapping. - `seed`: Seed to start the bootstrapping. - `null_fit`: The null fit output from ICSKAT_fit_null. - `gMat`: Genotype matrix used in original test. - `xMat`: n*p matrix of non-genetic covariates. - `fitAgain`: Boolean, whether to fit the null model again in each bootstrap. - `checkpoint`: Boolean, whether to print every time 100 bootstraps finish. - `downsample`: A number in (0, 1], will use this fraction of the bootstrap iterations to try running the test with fewer bootstraps. - `rhoVec`: Vector of rhos to search over in SKATO. ## Returns A list with the elements: - **kurtQvec**: Vector of bootstrapped excess kurtosis of each Qrho. - **varQvec**: Vector of bootstrapped variance of each Qrho. - **meanQvec**: Vector of bootstrapped mean of each Qrho. - **kurtKappa**: Bootstrapped kurtosis of kappa term without zeta. - **kurtKappaAll**: Bootstrapped kurtosis of full kappa term with zeta. - **varKappaAll**: Bootstrapped variance of full kappa term with zeta. - **meanKappaAll**: Bootstrapped mean of full kappa term with zeta. - **bootDF**: Matrix with B rows containing all the bootstrapped quantities over all iterations. - **QrhoBoot**: Matrix with B rows containing all the bootstrapped Qrho values, one column for each rho. - **listDS**: A list containing all of the other elements in this return list, except using the downsampled iterations. - **nonNA**: Number of bootstraps that did not result in NA (and thus were not removed). ## Examples ```r set.seed(2) gMat <- matrix(data=rbinom(n=2000, size=2, prob=0.3), nrow=100) xMat <- matrix(data=rnorm(200), nrow=100) bhFunInv <- function(x) {x} obsTimes <- 1:5 etaVec <- rep(0, 100) outcomeDat <- gen_IC_data(bhFunInv = bhFunInv, obsTimes = obsTimes, windowHalf = 0.1, probMiss = 0.1, etaVec = etaVec) lt <- outcomeDat$leftTimes rt <- outcomeDat$rightTimes tpos_ind <- as.numeric(lt > 0) obs_ind <- as.numeric(rt != Inf) dmats <- make_IC_dmat(xMat, lt, rt, obs_ind, tpos_ind) nullFit <- ICSKAT_fit_null(init_beta = rep(0, 5), left_dmat = dmats$left_dmat, right_dmat=dmats$right_dmat, obs_ind = obs_ind, tpos_ind = tpos_ind, lt = lt, rt = rt) icskatOut <- ICskat(left_dmat = dmats$left_dmat, right_dmat=dmats$right_dmat, lt = lt, rt = rt, obs_ind = obs_ind, tpos_ind = tpos_ind, gMat = gMat, null_beta = nullFit$beta_fit, Itt = nullFit$Itt) intervalProbOutput <- construct_interval_probs(allTimes = outcomeDat$allVisits, dmats = dmats, nullBeta = nullFit$beta_fit, p = ncol(xMat), nKnots=1) ICSKATO_bootstrap(icskatOut = icSkatOut, B = 100, intervalProbs = intervalProbOutput$probMat, allVisits = intervalProbOutput$allTimesFilled, quant_r = dmats$quant_r, seed = 0, null_fit = nullFit, gMat = gMat, xMat, fitAgain = TRUE, rhoVec=c(0, 0.01, 0.04, 0.09, 0.25, 0.5, 1)) ```

ICSKATO_bootstrap function