Hi,
I need to analyze the influences of several factors on a variable that is a
measure of fecundity, consisting of 73 observations ranging from 0 to 5. The
variable is continuous and highly positive skewed, none of the typical
transformations was able to normalize the data. Thus, I was thinking in
analyzing these data using a generalized linear model where I
can specify a distribution other than normal. I'm thinking it may fit a
gamma or exponential distribution. But I'm not sure if the data meets
the assumptions of those distributions because their definitions are
too complex for my understanding!
I tried to use R to asses the fit to a
particular distribution. I used the fitdistr function from the MASS
package and was able to obtain an estimate for the rate for the
exponential distribution. But I couldn't get the gamma to work. If I don't
provide initial estimates it says "Error in optim (... initial value in 'vmmin'
is not finite)", if I provide some initial values it says "Error in optim (...
non-finite finite-difference value [1]).
I then tried to test the fit of the exponential distribution using the
Kolmogorov-Smirnov goodness of fit test (ks.test), but I got the warning
message "cannot compute correct p-values with ties". This is strange
given that the details for the ks.test says that continuous variables
do not generate ties.
I'll greatly appreciate any ideas on how to proceed with thisThanks,
Andrea
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