Calculate bootstrap estimates of a lvm object
Draws non-parametric bootstrap samples
## S3 method for class 'lvm'
bootstrap(x,R=100,data,fun=NULL,control=list(),
p, parametric=FALSE, bollenstine=FALSE,
constraints=TRUE,sd=FALSE,
...)
## S3 method for class 'lvmfit'
bootstrap(x,R=100,data=model.frame(x),
control=list(start=coef(x)),
p=coef(x), parametric=FALSE, bollenstine=FALSE,
estimator=x$estimator,weights=Weights(x),...)x |
|
R |
Number of bootstrap samples |
data |
The data to resample from |
fun |
Optional function of the (bootstrapped) model-fit defining the statistic of interest |
control |
Options to the optimization routine |
p |
Parameter vector of the null model for the parametric bootstrap |
parametric |
If TRUE a parametric bootstrap is calculated. If FALSE a non-parametric (row-sampling) bootstrap is computed. |
bollenstine |
Bollen-Stine transformation (non-parametric bootstrap) for bootstrap hypothesis testing. |
constraints |
Logical indicating whether non-linear parameter constraints should be included in the bootstrap procedure |
sd |
Logical indicating whether standard error estimates should be included in the bootstrap procedure |
... |
Additional arguments, e.g. choice of estimator. |
estimator |
String definining estimator, e.g. 'gaussian' (see
|
weights |
Optional weights matrix used by |
A bootstrap.lvm object.
Klaus K. Holst
m <- lvm(y~x) d <- sim(m,100) e <- estimate(lvm(y~x), data=d) ## Reduce Ex.Timings B <- bootstrap(e,R=50,parallel=FALSE) B
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