corHMM (version 1.22)

rate.mat.maker: Rate matrix maker

Description

Generates and manipulates the index of the rate parameters to be optimized

Usage

rate.mat.maker(rate.cat, hrm=TRUE, ntraits=NULL, nstates=NULL, 
model=c("ER", "SYM", "ARD"))
rate.par.drop(rate.mat.index=NULL,drop.par=NULL)
rate.par.eq(rate.mat.index=NULL, eq.par=NULL)

Arguments

rate.cat

specifies the number of rate categories in the HRM.

hrm

a logical indicating whether the underlying model is the hidden rates model (HRM). The default is FALSE.

ntraits

specifies the number of traits in the data file if the underlying model is not the HRM.

nstates

specifies the number of characters in the data file used in rayDISC.

model

if the model is not HRM, specifies the underlying model.

rate.mat.index

A user-supplied rate matrix index to be manipulated.

drop.par

a vector of transitions to be dropped from the model. Use rate.mat.index to see what correspond to which transition.

eq.par

a vector of transitions pairs to be set equal. Use rate.mat.index to see what correspond to which transition.

Value

Returns a rate matrix index

Details

Outputs the full index of the rate parameters that are to be optimized. The intention is that a user might want to see how the matrix is designed prior to an analysis and perhaps drops a few parameters beforehand due to some hypothesis that he or she might have. The resulting matrix can then be plugged directly into corHMM, corDISC, or rayDISC.

Examples

# NOT RUN {
#Generate a matrix for two binary traits:
rate.mat<-rate.mat.maker(hrm=FALSE,ntraits=2,model="ARD")
#Drop parameter 8 from the model
rate.mat<-rate.par.drop(rate.mat, drop.par=c(8))
#Set parameters 1 and 2 equal to one another:
rate.mat<-rate.par.eq(rate.mat, eq.par=c(1,2))

#Precursor model. There are many ways to do this, but here is one way 
rate.mat<-rate.mat.maker(hrm=TRUE,rate.cat=2)
rate.mat<-rate.par.drop(rate.mat,c(1,3,4,6,7,8))
rate.mat<-rate.par.eq(rate.mat,c(1,2))
#Now add in a couple more connections:
rate.mat[3,2]<-1
rate.mat[2,3]<-1
#Now just use this matrix when using the corHMM function

#Here is a one way of doing a more complicated precursor:
rate.mat[3,2]<-2
rate.mat[1,3]<-3
rate.mat[2,3]<-4
#Again, just use this matrix when using the corHMM function

#Finally, here is an easier way of doing the precursor:
rate.mat<-rate.mat.maker(hrm=TRUE,rate.cat=2)
rate.mat<-rate.par.drop(rate.mat,c(1,3,4,7))
rate.mat[!is.na(rate.mat)]<-1

#Not run
# pp<-corHMM(primates$tree,primates$trait,rate.cat=2,rate.mat=rate.mat,
# node.states="marginal",diagn=FALSE)

# }