# resampling.model: Resampling for linear models In agricolae: Statistical Procedures for Agricultural Research

## Description

This process consists of finding the values of P-value by means of a re-sampling (permutation) process along with the values obtained by variance analysis.

## Usage

 `1` ```resampling.model(model,data,k,console=FALSE) ```

## Arguments

 `model` model in R `data` data for the study of the model `k` number of re-samplings `console` logical, print output

## Value

Model solution with resampling.

## Author(s)

Felipe de Mendiburu

## References

Efron, B., Tibshirani, R. (1993) An Introduction to the Boostrap. Chapman and Hall/CRC Phillip I. Good, (2001) Resampling Methods. Birkhauser. Boston . Basel . Berlin

`simulation.model `
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```#example 1 Simple linear regression library(agricolae) data(clay) model<-"ralstonia ~ days" analysis<-resampling.model(model,clay,k=2,console=TRUE) #example 2 Analysis of variance: RCD data(sweetpotato) model<-"yield~virus" analysis<-resampling.model(model,sweetpotato,k=2,console=TRUE) #example 3 Simple linear regression data(Glycoalkaloids) model<-"HPLC ~ spectrophotometer" analysis<-resampling.model(model,Glycoalkaloids,k=2,console=TRUE) #example 4 Factorial in RCD data(potato) potato[,1]<-as.factor(potato[,1]) potato[,2]<-as.factor(potato[,2]) model<-"cutting~variety + date + variety:date" analysis<-resampling.model(model,potato,k=2,console=TRUE) ```