I have a vector of observed values and also a vector of values calculated with model:
actual <- c(1411,439,214,100,62,38,29,64)
expected <- c(1425.3,399.5,201.6,116.9,72.2,46.3,30.4,64.8)
Now I'm using the Chi-squared goodness of fit test to see how well my model performs. I wrote the following:
chisq.test(expected,actual)
but it doesn't work. Can you help me with this?
X^2 = 10.2 at 7 degrees of freedom will give you a p ~ 0.18 .
> 1-pchisq(10.2, df = 7)
[1] 0.1775201
You should pass on the expected values under argument p. Make sure you scale your values to sum to 1.
> chisq.test(actual, p = expected/sum(expected))
Chi-squared test for given probabilities
data: actual
X-squared = 10.2581, df = 7, p-value = 0.1744
This about what X^2 test is doing. You give the function a model (expected) and ask - how likely it is that my observed data came from a population that "generated" expected?