Chi-squared goodness of fit test in R

Question

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         


        

Answer 1

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?