# bootstrap.lvm: Calculate bootstrap estimates of a lvm object In kkholst/lava: Latent Variable Models

 bootstrap.lvm R Documentation

## Calculate bootstrap estimates of a lvm object

### Description

Draws non-parametric bootstrap samples

### Usage

```## 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),...)
```

### 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 `...` 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.

### Author(s)

Klaus K. Holst

`confint.lvmfit`

### 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)
B

```

kkholst/lava documentation built on Nov. 4, 2022, 6:21 a.m.