clickstream (version 1.3.3)

mcEvaluate: Evaluates the number of occurrences of predicted next clicks

Description

Evaluates the number of occurrences of predicted next clicks vs. total number of starting pattern occurrences in a given clickstream. The predicted next click can be a markov chain of any order.

Usage

mcEvaluate(mc, startPattern, testCLS)

Arguments

mc

a markovchain object (this should have been built from a set of training data)

startPattern

the starting pattern we want to predict next click on, and evaluate observed occurrences in test data.

testCLS

clickstream object with test data

Author

Theo van Kraay

Examples

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

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

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

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

mc <- fitMarkovChain(trainingCLS, order = 1)
startPattern <- new("Pattern", sequence = c("c","c")) 
res <- mcEvaluate(mc, startPattern, testCLS)
res