Draws non-parametric bootstrap samples

# S3 method for lvm
bootstrap(x,R=100,data,fun=NULL,control=list(),
p, parametric=FALSE, bollenstine=FALSE,
constraints=TRUE,sd=FALSE,
...)

# S3 method for 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. Number of bootstrap samples The data to resample from Optional function of the (bootstrapped) model-fit defining the statistic of interest Options to the optimization routine Parameter vector of the null model for the parametric bootstrap If TRUE a parametric bootstrap is calculated. If FALSE a non-parametric (row-sampling) bootstrap is computed. Bollen-Stine transformation (non-parametric bootstrap) for bootstrap hypothesis testing. Logical indicating whether non-linear parameter constraints should be included in the bootstrap procedure Logical indicating whether standard error estimates should be included in the bootstrap procedure Additional arguments, e.g. choice of estimator. String definining estimator, e.g. 'gaussian' (see estimator) Optional weights matrix used by estimator

## Value

A bootstrap.lvm object.

confint.lvmfit

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,parallel=FALSE)
#> Warning: UNRELIABLE VALUE: One of the ‘future.apply’ iterations (‘future_lapply-1’) unexpectedly generated random numbers without declaring so. There is a risk that those random numbers are not statistically sound and the overall results might be invalid. To fix this, specify 'future.seed=TRUE'. This ensures that proper, parallel-safe random numbers are produced via the L'Ecuyer-CMRG method. To disable this check, use 'future.seed = NULL', or set option 'future.rng.onMisuse' to "ignore".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
#>