resid_boot {CLME} | R Documentation |
Generates bootstrap samples of the data vector.
resid_boot( formula, data, gfix = NULL, eps = NULL, xi = NULL, null.resids = TRUE, theta = NULL, ssq = NULL, tsq = NULL, cov.theta = NULL, seed = NULL, nsim = 1000, mySolver = "LS", ... )
formula |
a formula expression. The constrained effect(s) must come before any unconstrained covariates on the right-hand side of the expression. The first |
data |
data frame containing the variables in the model. |
gfix |
optional vector of group levels for residual variances. Data should be sorted by this value. |
eps |
estimates of residuals. |
xi |
estimates of random effects. |
null.resids |
logical indicating if residuals should be computed under the null hypothesis. |
theta |
estimates of fixed effects coefficients. Estimated if not submitted. |
ssq |
estimates of residual variance components. Estimated if not submitted. |
tsq |
estimates of random effects variance components. Estimated if not submitted. |
cov.theta |
covariance matrix of fixed effects coefficients. Estimated if not submitted. |
seed |
set the seed for the RNG. |
nsim |
number of bootstrap samples to use for significance testing. |
mySolver |
solver to use, passed to |
... |
space for additional arguments. |
If any of the parameters theta
, ssq
, tsq
, eps
, or xi
are provided, the function will use those values in generating the bootstrap samples. They will be estimated if not submitted. Ifnull.resids=TRUE
, then theta
will be projected onto the space of the null hypothesis ( Ho: theta_1 = theta_2 = ... = theta_p1) regardless of whether it is provided or estimated. To generate bootstraps with a specific theta
, set null.residuals=FALSE
.
Output is N x nsim matrix, where each column is a bootstrap sample of the response data Y
.
This function is primarily designed to be called by clme
.
By default, homogeneous variances are assumed for the residuals and (if included) random effects. Heterogeneity can be induced using the arguments Nks
and Qs
, which refer to the vectors (n1, n2 ,... , nk) and (c1, c2 ,... , cq), respectively. See clme_em
for further explanation of these values.
data( rat.blood ) boot_sample <- resid_boot(mcv ~ time + temp + sex + (1|id), nsim = 10, data = rat.blood, null.resids = TRUE )