Thanks for the answer. Now, only problem is to to get parameter(s) of a
given function. For gamma, I shall try with gammafit() from mhsmm
package. Also, I shall look for others appropriate parameter estimates.
Will use SuppDists too.
Best,
PM
Sunil Suchindran wrote:
#same shape
some_data <- rgamma(500,shape=6,scale=2)
test_data <- rgamma(500,shape=6,scale=2)
plot(sort(some_data),sort(test_data))
# You can also use qqplot(some_data,test_data)
abline(0,1)
# different shape
some_data <- rgamma(500,shape=6,scale=2)
test_data <- rgamma(500,shape=4,scale=2)
plot(sort(some_data),sort(test_data))
abline(0,1)
It is helpful to assess the sampling variability, by
creating repeated sets of test_data, and plotting
all of these along with your observations to create
a confidence "envelope".
The SuppDists provides Inverse Gauss.
On Thu, Sep 17, 2009 at 11:46 AM, Petar Milin <pmi...@ff.uns.ac.rs> wrote:
Hello!
I am trying with this question again:
I would like to test few distributional assumptions for some
behavioral response data. There are few theories about true
distribution of those data, like: normal, lognormal, gamma,
ex-Gaussian (exponential-Gaussian), Wald (inverse Gaussian) etc. The
best way would be via qq-plot, to show to students differences.
First two are trivial:
qqnorm(dat$X)
qqnorm(log(dat$X))
Then, things are getting more "hairy". I am not sure how to make
plots for the rest. I tried gamma with:
qqmath(~ X, data=dat, distribution=function(X)
� qgamma(X, shape, scale))
Which should be the same as:
plot(qgamma(ppoints(dat$X), shape, scale), sort(dat$X))
Shape and scale parameters I got via mhsmm package that has
gammafit() for shape and scale parameters estimation.
Am I on right track? Does anyone know how to plot the rest:
ex-Gaussian (exponential-Gaussian), Wald (inverse Gaussian)?
Thanks,
PM
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