R: Simulation of the linear model under normality

simulation.model {agricolae} | R Documentation

## Simulation of the linear model under normality

### Description

This process consists of validating the variance analysis results using a simulation process of the experiment. The validation consists of comparing the calculated values of each source of variation of the simulated data with respect to the calculated values of the original data. If in more than 50 percent of the cases they are higher than the real one, then it is considered favorable and the probability reported by the ANOVA is accepted, since the P-Value is the probability of (F > F.value).

### Usage

    simulation.model(model,file, categorical = NULL,k,console=FALSE)

### Arguments

`model` | Model in R
`file` | Data for the study of the model
`categorical` | position of the columns of the data that correspond to categorical variables
`k` | Number of simulations
`console` | logical, print output

### Value

`model` | ouput linear model, lm
`simulation` | anova simulation

### Author(s)

Felipe de Mendiburu

### See Also

`resampling.model`

### Examples

    library(agricolae)
    #example 1
    data(clay)
    model<-"ralstonia ~ days"
    simulation.model(model,clay,k=15,console=TRUE)
    #example 2
    data(sweetpotato)
    model<-"yield~virus"
    simulation.model(model,sweetpotato,categorical=1,k=15,console=TRUE)
    #example 3
    data(Glycoalkaloids)
    model<-"HPLC ~ spectrophotometer"
    simulation.model(model,Glycoalkaloids,k=15,console=TRUE)
    #example 4
    data(potato)
    model<-"cutting~date+variety"
    simulation.model(model,potato,categorical=c(1,2,3),k=15,console=TRUE)

[Package agricolae version 1.2-8 Index]