mcEvaluateAllClusters: Evaluates all next page clicks in a clickstream training data...

In clickstream: Analyzes Clickstreams Based on Markov Chains

Evaluates all next page clicks in a clickstream training data set against a test data

Description

Evaluates all next page clicks in a clickstream training data set against a test data on the basis of a set of pre-computed Markov chains and corresponding clusters. Handles higher order by cycling through every possible pattern permutation. Produces and produces a report of observed and expected values in a matrix

Usage

mcEvaluateAllClusters(
  markovchains,
  clusters,
  testCLS,
  trainingCLS,
  includeChiSquare = TRUE,
  returnChiSquareOnly = FALSE
)

Arguments

markovchains	A list of MarkovChain-objects.
clusters	The list of clusters.
testCLS	Clickstream object with test data.
trainingCLS	Clickstream object with training data (this should be the data used to build the markov chain object).
includeChiSquare	Should the result include the chi-square value?
returnChiSquareOnly	Should the result only consist of the chi-square value?

Author(s)

Theo van Kraay [e-mail]

See Also

mcEvaluateAll

Examples

training <- c("User1,h,c,c,p,c,h,c,h,o,p,p,c,p,p,o",
              "User2,i,c,i,c,c,c,o,o,o,i,d",
              "User3,h,i,c,i,c,o,i,p,c,c,p,c,c,i,d",
              "User4,c,c,p,c,d,o,i,h,o,o")

test <- c("User1,h,c,c,p,p,h,o,i,c,p,p,c,p,p,o",
          "User2,i,c,i,c,c,c,d",
          "User4,c,c,c,c,d")

csf <- tempfile()
writeLines(training, csf)
trainingCLS <- readClickstreams(csf, header = TRUE)
unlink(csf)

csf <- tempfile()
writeLines(test, csf)
testCLS <- readClickstreams(csf, header = TRUE)
unlink(csf)

clusters <- clusterClickstreams(trainingCLS, centers = 2, order = 1)
markovchains <- fitMarkovChains(clusters, order = 2)
mcEvaluateAllClusters(markovchains, clusters, testCLS, trainingCLS)

clickstream documentation built on Sept. 27, 2023, 5:06 p.m.