Dear UseRs,
I have a dataset in the following form.
> dput(Da)
structure(c(0.0238095238095238, 0.0476190476190476, 0.0714285714285714,
0.0952380952380952, 0.119047619047619, 0.142857142857143, 0.167,
0.19047619047619, 0.214285714285714, 0.238095238095238, 0.261904761904762,
0.285714
Hello.
I am trying to fit my data sample x with different distributions such that
the integral from min(x) to max(x) of the fitted distribution will be one.
Therefore I have wrote my own log-likelihood functions and then I am using
mle {stats4}. So, for example:
ll_gamma <- function(a,b) {
Hello everyone:
I tried to fit a Beta distribution on a right-skewed dataset using:
fitdistr(temp,densfun="beta",start=list(shape1=3,shape2=2))
To assess the fit, I proceeded as follows:
Using distribution parameters from the sample resulting from fitdistr()
function, I generated 1000 samples
On Tue, 19 May 2009 14:04:19 +1000 Kon Knafelman
wrote:
KK> i have the sample variances for 1000 samples, and i want to fit it
KK> to a chi-squared distribution.
KK> can someone please help me fit this to a chi-squared distribution
KK> with n degrees of freedom. Thanks a lot
Dear Kon,
1. ple
Hey Guys,
i have the sample variances for 1000 samples, and i want to fit it to a
chi-squared distribution.
after making a loop for the simulation initially, i have the following code to
compute the variances
samples = replicate(n, rnorm(m, 0, 1), simplify=FALSE)
variances = sapply(samples, va
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