[R] find parameters for a gamma distribution

[digitalpenis@bluebottle.com]

hello,

i have just started exploring R as an alternative to matlab for data analysis. so far everything is _very_ promising. i have a question though regarding parameter estimation. i have some data which, from a histogram plot, appears to arise from a gamma distribution. i gather that you can fit the data to the distribution using glm(). i am just not quite sure how this is done in practice... so here is a simple example with artificial data:

d <- rgamma(100000, 20, scale = 2)
h <- hist(d, breaks = c(seq(10, 80, 2), 100))

H <- data.frame(x = h$mids, y = h$density)

g <- glm(y ~ x, data = H, family = Gamma)
summary(g)

Call:
glm(formula = y ~ x, family = Gamma, data = H)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.8654  -2.0887  -0.7685   0.7147   1.4508  

Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  30.4758    26.7258   1.140    0.262
x             1.0394     0.6825   1.523    0.137

(Dispersion parameter for Gamma family taken to be 1.343021)

    Null deviance: 119.51  on 35  degrees of freedom
Residual deviance: 116.28  on 34  degrees of freedom
AIC: -260.49

Number of Fisher Scoring iterations: 7

now i suppose that the estimates parameters are:

	shape = 30.4758
	scale = 1.0394

am i interpreting the output correctly? and, if so, why are these estimates so poor? i would, perhaps naively, expected the parameters from an artificial sample like this to be pretty good.

my apologies if i am doing something stupid here but my statistics capabilties are rather limited!

best regards,
andrew collier.