cnSamples-method {catnet} | R Documentation |
Generates samples from of a catNetwork
object.
cnSamples(object, numsamples = 1, perturbations = NULL, output="frame", as.index=FALSE, naRate=0)
object |
a |
numsamples |
an |
perturbations |
a |
output |
a |
as.index |
a |
naRate |
a |
If the output format is "matrix" then the resulting sample matrix is in row-node format - the rows correspond to the object's nodes while
the individual samples are represented by columns.
If the output format is "frame", which is by default,
the result is a data frame with columns representing the nodes
and levels the set of categories of the respected nodes.
If as.index
is set to TRUE, the output sample consists of categorical indices, otherwise, and this is by default, of characters specifying the categories.
A perturbed sample is a sample having nodes with predefined, thus fixed, values.
Non-perturbed nodes, the nodes which values have to be set, are designated with zeros in the perturbation
vector and their values are generated conditional on the values of their parents.
While the non-zero values in the perturbation vector are carried on unchanged to the output.
If naRate
is positive, then floor(numnodes*naRate)
NA values are randomly placed in each sample instance.
A matrix
or data.frame
of node categories as integers or characters
N. Balov
cnet <- cnRandomCatnet(numnodes=10, maxParents=3, numCategories=3) ## generate a sample of size 100 from cnet psamples <- cnSamples(object=cnet, numsamples=100, output="frame", as.index=FALSE) ## perturbed sample nsamples <- 20 perturbations <- rbinom(10, 2, 0.4) ## generate a perturbed sample of size 100 from cnet psamples <- cnSamples(object=cnet, numsamples=nsamples, perturbations, as.index=TRUE)