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(k, file, model, categorical = NULL)
Arguments
k |
Number of simulations. |
file |
Data for the study of the model. |
model |
Model in R. |
categorical |
position of the columns of the data that correspond
to categorical variables. |
Value
k |
constant numeric. |
file |
data frame |
model |
Model |
categorical |
Numeric |
Author(s)
Felipe de Mendiburu
See Also
resampling.model
Examples
library(agricolae)
#example 1
data(clay)
model<-"ralstonia ~ days"
simulation.model(100,clay,model)
#example 2
data(sweetpotato)
model<-"yield~virus"
simulation.model(100,sweetpotato,model,categorical=1)
#example 3
data(Glycoalkaloids)
model<-"HPLC ~ spectrophotometer"
simulation.model(100,Glycoalkaloids,model)
#example 4
data(potato)
model<-"cutting~date+variety"
simulation.model(100,potato,model,categorical=c(1,2,3))
[Package
agricolae version 1.0-6
Index]