R/carolina.R

In agricolae: Statistical Procedures for Agricultural Research

carolina <-
function(model,data) {
if(model==1) {
# Observe la estructura de los datos carolina1
"set"<-as.factor(data[,1])
"male"<-as.factor(data[,2])
"female"<-as.factor(data[,3])
"progenie"<-as.factor(data[,4])
"replication"<-as.factor(data[,5])
y<-data[,6]
name.y <- names(data)[6]
#analisis
model<-lm(y ~ set+replication%in%set+male%in%set+female%in%male%in%set+replication%in%female%in%male%in%set)
cat("Response(y): ",name.y,"\n\n")
print(anova(model))
cat("\nCV:",cv.model(model), "\tMean:",mean(data[,6]),"\n" )
m<-length(levels(model$model$male))
f<-length(levels(model$model$female))
s<-length(levels(model$model$set))
r<-length(levels(model$model$replication))
n<-length(levels(progenie))
# Componentes de variancia
anva<-as.matrix(anova(model))
anva<-anva[,1:3]
var.m<- (anva["set:male","Mean Sq"] - anva["set:male:female","Mean Sq"])/(f*r*n)
var.f<- (anva["set:male:female","Mean Sq"] - anva["set:replication:male:female","Mean Sq"])/(n*r)
var.A<- 4*var.m
var.D<- 4*var.f-4*var.m
output<-list(model=model,var.m=var.m,var.f=var.f, var.A=var.A , var.D=var.D)
return(output)
}
if(model==2) {
# Observe la estructura de los datos carolina2
"set"<-as.factor(data[,1])
"male"<-as.factor(data[,2])
"female"<-as.factor(data[,3])
"replication"<-as.factor(data[,4])
y<-data[,5]
name.y <- names(data)[5]
# Analisis
model<-lm(y ~ set+replication%in%set+male%in%set+female%in%set+male:female%in%set)
cat("Response(y): ",name.y,"\n\n")
print(anova(model))
cat("\nCV:",cv.model(model), "\tMean:",mean(y),"\n" )
m<-length(levels(model$model$male))
f<-length(levels(model$model$female))
s<-length(levels(model$model$set))
r<-length(levels(model$model$replication))
# Componentes de variancia
anva<-as.matrix(anova(model))
anva<-anva[,1:3]
var.m<- (anva["set:male", "Mean Sq"] - anva["set:male:female","Mean Sq"])/(m*r)
var.f<- (anva["set:female","Mean Sq"] - anva["set:male:female","Mean Sq"])/(f*r)
var.mf<- (anva["set:male:female","Mean Sq"] - anva["Residuals","Mean Sq"])/r
var.Am<- 4*var.m
var.Af<- 4*var.f
var.D <- 4*var.mf
output<-list(model=model,var.m=var.m,var.f=var.f, var.mf=var.mf,var.Am=var.Am ,var.Af=var.Af, var.D=var.D)
return(output)
}

if(model==3) {
# Observe la estructura de los datos carolina3
"set"<-as.factor(data[,1])
"male"<-as.factor(data[,2])
"female"<-as.factor(data[,3])
"replication"<-as.factor(data[,4])
y<-data[,5]
name.y <- names(data)[5]
model<-lm(y~set + replication%in%set + female%in%set + male%in%set+ female:male%in%set)
cat("Response(y): ",name.y,"\n\n")
print(anova(model))
cat("\nCV:",cv.model(model), "\tMean:",mean(y),"\n" )
m<-length(levels(model$model$male))
f<-length(levels(model$model$female))
s<-length(levels(model$model$set))
r<-length(levels(model$model$replication))

# Componentes de variancia
anva<-as.matrix(anova(model))
anva<-anva[,1:3]
var.mi<- (anva[5,3] - anva["Residuals","Mean Sq"])/r
var.m<- (anva["set:male","Mean Sq"] - anva["Residuals","Mean Sq"])/(2*r)
var.A<- 4*var.m
var.D<- 2*var.mi
output<-list(model=model,var.mi=var.mi,var.m=var.m, var.A=var.A, var.D=var.D)
return(output)
}
}