# CInp: Construct local confidence intervals from joint empirical... In BSagri: Statistical methods for safety assessment in agricultural field trials

## Description

Construct local confidence intervals for each parameter from the empirical joint distribution of a parameter vector of length P.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## Default S3 method: CInp(x, conf.level = 0.95, alternative = "two.sided", ...) ## S3 method for class 'CCRatio' CInp(x, ...) ## S3 method for class 'CCDiff' CInp(x, ...) ## S3 method for class 'bugs' CInp(x, conf.level = 0.95, alternative = "two.sided", whichp = NULL, ...) ```

## Arguments

 `x` an N-times-P matrix, or an object of class `CCRatio`, `CCDiff`, `bugs`, as can be obtained by calling the functions `CCRatio`, `CCDiff`, or `openbugs` in package `R2WinBUGS` `conf.level` a single numeric value between 0.5 and 1, specifying the local confidence level for each of the P parameters `alternative` a single character string, one of `"two.sided"`, `"less"`, `"greater"`, for two-sided, upper and lower limits `whichp` a single character string, naming an element of the `sims.list` if `x` is a `bugs` object, ignored otherwise `...` currently not used

## Details

Construct simple confidence intervals based on order statistics applied to the marginal empirical distributions in `x`.

## Value

An object of class "CInp", a list with elements

 `conf.int ` a P-times-2 matrix containing the lower and upper confidence limits `estimate ` a numeric vector of length P, containing the medians of the P marginal empirical distributions `x ` the input object `k ` the number of values outside each confidence interval, i.e. conf.level*N `N ` the number of values used to construct each confidence interval `conf.level ` a single numeric value, the nominal confidence level, as input `alternative ` a single character string, as input

The function internally used is `quantile` with its default settings. See `SCSnp` for simultaneous sets.
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```# Assume a 100 times 4 matrix of 4 mutually independent # normal variables: X<-cbind(rnorm(100), rnorm(100), rnorm(100), rnorm(100)) lcits<-CInp(x=X, conf.level=0.95, alternative="two.sided") lcits ci1<-lcits\$conf.int[1,] length( which(X[,1]>=ci1[1] & X[,1]<=ci1[2] ) ) ci2<-lcits\$conf.int[2,] length( which(X[,2]>=ci2[1] & X[,2]<=ci2[2] ) ) ```