simulation.model {agricolae}

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)