On Feb 20, 2012, at 5:57 AM, Louise Mair wrote:
Hello,
I am trying to fit gamma, negative exponential and inverse power
functions
to a dataset, and then test whether the fit of each curve is good.
To do
this I have been advised to calculate predicted values for bins of
data (I
have grouped a continuous range of distances into 1km bins), and
then apply
a chi-squared test. Example:
data <- data.frame(distance=c(1,2,3,4,5,6,7),
observed=c(43,13,10,6,2,1),
predicted=c(28, 18, 10, 5 ,3, 1, 1))
There's an error with that code.
chisq.test(data$observed, data$predicted)
Which gives:
Pearson's Chi-squared test
data: data$observed and data$predicted
X-squared = 35, df = 25, p-value = 0.0882
Warning message:
In chisq.test(data$observed, data$predicted) :
Chi-squared approximation may be incorrect
I understand this is due to having observed/predicted values of less
than
five, however I am interested to know firstly why R uses such a large
number of degrees of freedom (when by my understanding there should
only be
4 df), and secondly whether using the following manual calculation is
therefore inappropriate -
Read the help page Details section .... end of second paragraph.
You probably wanted:
chisq.test(cbind(data$observed, data$predicted))
X2 <- sum(((data$observed - data$predicted)^2)/data$predicted)
1-pchisq(X2,4)
[1] 0.04114223
If chi-squared is unsuitable, what other test can I use to determine
whether my observed and predicted data come from the same
distribution? The
frequently recommended fisher's test doesn't seem to be any more
appropriate as it requires values of greater than 5 for contingency
tables
larger than 2 x 2.
Thanks for your help.
Louise
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Plain text is requested as the mail format.
David Winsemius, MD
West Hartford, CT
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