On Nov 26, 2009, at 9:48 AM, Steve Murray wrote:
Dear all,
I am trying to validate a model by comparing simulated output values
against observed values. I have produced a simple X-y scatter plot
with a 1:1 line, so that the closer the points fall to this line,
the better the 'fit' between the modelled data and the observation
data.
I am now attempting to quantify the strength of this fit by using a
statistical test in R. I am no statistics guru, but from my limited
understanding, I suspect that I need to use the Chi Squared test (I
am more than happy to be corrected on this though!).
However, this results in the following:
chisq.test(data$Simulation,data$Observation)
Pearson's Chi-squared test
data: data$Simulation and data$Observation
X-squared = 567, df = 550, p-value = 0.2989
Warning message:
In chisq.test(data$Simulation, data$Observation) :
Chi-squared approximation may be incorrect
The ?chisq.test document suggests that the objects should be of
vector or matrix format, so I tried the following, but still receive
a warning message (and different results):
chisq.test(as.matrix(data[,4:5]))
Pearson's Chi-squared test
data: as.matrix(data[, 4:5])
X-squared = 130.8284, df = 26, p-value = 6.095e-16
When you look at your "data" you see only 27 cases, so it would be
implausible that your first invocation with a degree of freedom = 550
would be giving you something meaningful. The second one might have
been more meaningful goodness of fit. I cannot explain why code # 1
did not give the same results since I would have thought that the
positional matching of R would have resulted in the same results for
both calls. What happens if you try:
chisq.test(data$Simulation, y=data$Observation) # ?
All of that being said, chisq.test is primarily intended for
contingency tables. Testing association between two paired continuous
variables is usually approached with regression and correlation tests.
E.g.:
?cor
?lm
Also may want to look at the Q-Q plot.
?qqplot
--
David Winsemius
Warning message:
In chisq.test(as.matrix(data[, 4:5])) :
Chi-squared approximation may be incorrect
What am I doing wrong and how can I successfully measure how well
the simulated values fit the observed values?
If it's of any help, here are how my data are structured - note that
I am only using columns 4 and 5 (Observation and Simulation).
str(data)
'data.frame': 27 obs. of 5 variables:
$ Location : Factor w/ 27 levels "Australia","Brazil",..: 8
2 13 19 22 14 16 23 6 7 ...
$ Vegetation : Factor w/ 21 levels "Beech","Broadleaf
evergreen laurel",..: 17 21 2 16 15 16 9 16 3 4 ...
$ Vegetation.Class: Factor w/ 4 levels "Boreal and Temperate
Evergreen",..: 3 3 4 1 1 1 4 1 4 1 ...
$ Observation : num 24 8.9 14.7 26.7 42.4 31.7 30.8 7.5 14
22 ...
$ Simulation : num 33.9 7.8 9.74 7.6 11.8 10.7 12 28.1 1.7
1.7 ...
I hope someone is able to point me in the right direction.
Many thanks,
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
