# simult.pvalue: Compute a simultaneous p-value from a sample for a vector... In bayesSurv: Bayesian Survival Regression with Flexible Error and Random Effects Distributions

## Description

The p-value is computed as 1 - the credible level of the credible region which just cover the point (0, 0, ..., 0)'.

The function returns also the simultaneous credible region (rectangle) with a specified credible level.

## Usage

 ```1 2 3``` ```simult.pvalue(sample, precision=0.001, prob=0.95) ## S3 method for class 'simult.pvalue' print(x, ...) ```

## Arguments

 `sample` a data frame or matrix with sampled values (one column = one parameter) `precision` precision with which the p-value is to be computed `prob` probability for which the credible region is to be computed `x` an object of class simult.pvalue `...` who knows

## Value

An object of class 'simult.pvalue'.

## Author(s)

Arnošt Komárek arnost.komarek[AT]mff.cuni.cz

## References

Besag, J., Green, P., Higdon, D. and Mengersen, K. (1995). Bayesian computation and stochastic systems (with Discussion). Statistical Science, 10, 3 - 66. page 30

Held, L. (2004). Simultaneous posterior probability statements from Monte Carlo output. Journal of Computational and Graphical Statistics, 13, 20 - 35.

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```m <- 1000 sample <- data.frame(x1=rnorm(m), x2=rnorm(m), x3=rnorm(m)) simult.pvalue(sample) sample <- data.frame(x1=rnorm(m), x2=rnorm(m), x3=rnorm(m, mean=2)) simult.pvalue(sample) sample <- data.frame(x1=rnorm(m), x2=rnorm(m), x3=rnorm(m, mean=5)) simult.pvalue(sample, prob=0.99, precision=0.0001) ```

bayesSurv documentation built on Jan. 12, 2018, 1:04 a.m.