simulation.model: Simulation of the linear model under normality

In agricolae: Statistical Procedures for Agricultural Research

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)

agricolae documentation built on Oct. 23, 2023, 1:06 a.m.