mcEvaluate: Evaluates the number of occurrences of predicted next clicks

In clickstream: Analyzes Clickstreams Based on Markov Chains

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(s)

Theo van Kraay [e-mail]

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

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