calcStat {gRapHD} | R Documentation |
Calculates pairwise statistics (-2*log-LR, AIC, or BIC) for each variable pair (edge) in the dataset.
calcStat(dataset,homog=TRUE,forbEdges=NULL,stat="LR")
dataset |
matrix or data frame (nrow(dataset) observations and
ncol(dataset) variables). |
homog |
TRUE for homogeneous covariance structure, FALSE
for heterogeneous. This is only meaningful with mixed models.
Default is homogeneous (TRUE ). |
forbEdges |
list with edges that should not be considered. Matrix with 2
columns, each row representing one edge, and each column one
of the vertices in the edge. Default is NULL . |
stat |
measure to be minimized: LR (-2*log-likelihood), AIC, or BIC.
Default is LR (the result is a tree). It can also be a user
defined function with format: FUN(newEdge,varType,numCat,
dataset) ; where the parameters varType and numCat
are as defined in the Value section; newEdge is a vector
with length two; and dataset is a matrix (n by p). |
Calculates pairwise statistics (-2*log-LR, AIC, or BIC) for all possible edges, returning these sorted in descending order.
A matrix with p(p-1)/2
lines and 4
columns, where each line
refers to a possible edge, and the columns are: vertex 1, vertex 2, value of
the statistic, and number of estimated parameters (degrees of freedom) associated with the edge.
Gabriel Coelho Goncalves de Abreu (Gabriel.Abreu@agrsci.dk)
Rodrigo Labouriau (Rodrigo.Labouriau@agrsci.dk)
David Edwards (David.Edwards@agrsci.dk)
set.seed(7,kind="Mersenne-Twister") dataset <- matrix(rnorm(1000),nrow=100,ncol=10) m <- calcStat(dataset,stat="BIC") data(dsCont) # m1 <- calcStat(dataset,varType=0,homog=TRUE,forbEdges=NULL,stat="LR") # 1. in this case, there is no use for homog # 2. no forbidden edges # 3. the measure used is the LR (the result is a tree) v <- calcStat(dsCont,homog=TRUE,forbEdges=NULL,stat="LR") # result head(v) # column 1: first vertex of the edge # column 2: second vertex of the edge # column 3: in this case, -LR # column 4: number of parameters for the edge # [,1] [,2] [,3] [,4] # [1,] 17 27 393.0072 1 # [2,] 21 27 343.5780 1 # [3,] 22 25 306.0097 1 # [4,] 17 21 302.9414 1 # [5,] 27 32 300.0275 1 # [6,] 21 32 289.4179 1