# bootstrap: Bootstrapping for uncertainty quantification In ouch: Ornstein-Uhlenbeck Models for Phylogenetic Comparative Hypotheses

## Description

Parametric bootstrapping for ouch models.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```## S4 method for signature 'missing' bootstrap(object, ...) ## S4 method for signature 'ANY' bootstrap(object, ...) ## S4 method for signature 'browntree' bootstrap(object, nboot = 200, seed = NULL, ...) ## S4 method for signature 'hansentree' bootstrap(object, nboot = 200, seed = NULL, ...) ```

## Arguments

 `object` A fitted model object. `...` Additional arguments are passed to `update`. `nboot` integer; number of bootstrap replicates. `seed` integer; setting `seed` to a non-`NULL` value allows one to fix the random seed (see simulate).

## Details

`bootstrap` performs a parametric bootstrap for estimation of confidence intervals.

Other methods for ouch trees: `coef()`, `logLik`, `ouch-package`, `paint()`, `plot()`, `print()`, `simulate()`, `summary()`, `update()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39``` ```## Not run: ## Fit BM and a 5-regime OU model to the A. bimaculatus data tree <- with(bimac,ouchtree(node,ancestor,time/max(time),species)) h1 <- brown( data=log(bimac['size']), tree=tree ) h5 <- hansen( data=log(bimac['size']), tree=tree, regimes=bimac['OU.LP'], sqrt.alpha=1, sigma=1, reltol=1e-11, parscale=c(0.1,0.1), hessian=TRUE ) ## What are appropriate AIC.c cutoffs? simdat <- simulate(h1,nsim=100,seed=92759587) b1 <- sapply(simdat,function(x)summary(update(h1,data=x))\$aic.c) tic <- Sys.time() b5 <- sapply(simdat,function(x)summary(update(h5,data=x))\$aic.c) toc <- Sys.time() print(toc-tic) cat("approximate 95% AIC.c cutoff",signif(quantile(b1-b5,0.95),digits=3),"\n") ## Bootstrap confidence intervals boots.h1 <- bootstrap(h1,nboot=200,seed=92759587) cat("bootstrap 95% confidence intervals for h1:\n") print(t(sapply(boots.h1,quantile,probs=c(0.025,0.975))),digits=3) boots.h5 <- bootstrap(h5,nboot=200,seed=92759587) cat("bootstrap 95% confidence intervals for h5:\n") print(t(sapply(boots.h5,quantile,probs=c(0.025,0.975))),digits=3) ## End(Not run) ```