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, mc.cores,
                          future.args=list(future.seed=TRUE),
                          ...)

# 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),...)

Arguments

x

lvm-object.

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

mc.cores

Optional number of cores for parallel computing. If omitted future.apply will be used (see future::plan)

future.args

arguments to future.apply::future_lapply

...

Additional arguments, e.g. choice of estimator.

estimator

String definining estimator, e.g. 'gaussian' (see estimator)

weights

Optional weights matrix used by estimator

Value

A bootstrap.lvm object.

See also

confint.lvmfit

Author

Klaus K. Holst

Examples

m <- lvm(y~x)
d <- sim(m,100)
e <- estimate(lvm(y~x), data=d)
 ## Reduce Ex.Timings
B <- bootstrap(e,R=50,mc.cores=1)
B
#> Non-parametric bootstrap statistics (R=50):
#> 
#>      Estimate    Bias        Std.Err     2.5 %       97.5 %     
#> y    -0.02797440 -0.02446408  0.09076098 -0.21276743  0.11109505
#> y~x   0.91271522 -0.02885440  0.06849834  0.75899923  0.99515857
#> y~~y  0.95969487 -0.01217674  0.16545588  0.67932143  1.28333411
#>