Folks,
I am holding a dataset where firms are observed for a fixed (and
small) set of years. The data is in "long" format - one record for one
firm for one point in time. A state variable is observed (a factor).
I wish to make a markov transition matrix about the time-series
evolution of that state variable. The code below does this. But it's
hardcoded to the specific years that I observe. How might one
generalise this and make a general function which does this? :-)
-ans.
set.seed(1001)
# Raw data in long format --
raw <- data.frame(name=c("f1","f1","f1","f1","f2","f2","f2","f2"),
year=c(83, 84, 85, 86, 83, 84, 85, 86),
state=sample(1:3, 8, replace=TRUE)
)
# Shift to wide format --
fixedup <- reshape(raw, timevar="year", idvar="name", v.names="state",
direction="wide")
# Now tediously build up records for an intermediate data structure
try <- rbind(
data.frame(prev=fixedup$state.83, new=fixedup$state.84),
data.frame(prev=fixedup$state.84, new=fixedup$state.85),
data.frame(prev=fixedup$state.85, new=fixedup$state.86)
)
# This is a bad method because it is hardcoded to the specific values
# of "year".
markov <- table(destination$prev.state, destination$new.state)
--
Ajay Shah http://www.mayin.org/ajayshah
[EMAIL PROTECTED] http://ajayshahblog.blogspot.com
<*(:-? - wizard who doesn't know the answer.
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