[R] find parameters for a gamma distribution
Andrew Collier
colliera at ukzn.ac.za
Wed Jan 5 20:58:59 CET 2005
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.
--
Andrew B. Collier
Antarctic Research Fellow tel: +27 31 2601157
Space Physics Research Institute fax: +27 31 2616550
University of KwaZulu-Natal, Durban, 4041, South Africa
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