Eric Turkheimer 写道:
I am wondering if it is possible to perform the following two basic
functions with primitive R functions. I know I could write functions for
either, but it seems as though they are probably built-in somewhere.
1) Fill out a vector to a desired length with missing values or
mclust is not doing the hierarchicial clustering, if I understand your
question correctly. Presumably you define certain distance measure,
hclust and cut funtions should do the job. On the other hand, if the
purpose is to extract the classification labels from mclust package, it
should be strai
First, simulate a uniform r.v on [0,1] and then cast it to binary label
according to your underlying mixing probability;
Second, simulate a Gaussian r.v. in above selected component.
Of course, you can vecterize the two steps to simply your code.
X
Peng Jiang 写道:
Hi,
Is there any package
Hi Jenny,
A simple solution is to add your line to the function, re-load/source
the modified function to the console (e.g. by copy and paste). Then the
new function with the same name as the original one will be called next
time. If you don't want to use modified function any longer, just use
stop('Different number of assets! \n\n')
X
Bill Cunliffe 写道:
For example, based on a certain condition, I may want to exit my code early:
# Are there the same number of assets in "prices" and
"positions"?
if (nAssetPositions != nAssetPrices) {
binom.test(x=12, n=50, p=12/50, conf.level = 0.90)$estimate
Gundala Viswanath 写道:
With this line:
binom.test(x=12, n=50, p=12/50, conf.level = 0.90)
I get this output:
Exact binomial test
data: 12 and 50
number of successes = 12, number of trials = 50, p-value = 1
alte
Andrew Robinson 写道:
On Wed, May 28, 2008 at 03:47:49PM -0700, Xiaohui Chen wrote:
Frank E Harrell Jr ??:
Xiaohui Chen wrote:
step or stepAIC functions do the job. You can opt to use BIC by
changing the mulplication of penalty.
I think AIC and BIC are not only limited to
Frank E Harrell Jr 写道:
Xiaohui Chen wrote:
step or stepAIC functions do the job. You can opt to use BIC by
changing the mulplication of penalty.
I think AIC and BIC are not only limited to compare two pre-defined
models, they can be used as model search criteria. You could
enumerate the
step or stepAIC functions do the job. You can opt to use BIC by changing
the mulplication of penalty.
I think AIC and BIC are not only limited to compare two pre-defined
models, they can be used as model search criteria. You could enumerate
the information criteria for all possible models if t
Note that the scale of x-axis and y-axis is different in your plot. One
simple way to avoid this is to keep the data unit in the x direction is
equal that in the y direction, by setting asp=1 in calling plot function.
X
Dr. Christoph Scherber 写道:
Dear all,
I stumbled over a problem recently
were you meaning Gaussian mixture model? If so, try the mclust package.
But you might want to try to learn how to search using R website
resourse, try
RSiteSearch('GMM')
X
Correia 写道:
> Hello there!!!
>
> Sorry to bother you all with such question and difficulties that I have been
> facing on.
Hi Mark,
As I said in earlier emails, you do NOT need to explicitly code the
likelihood function for gamma distn. I just code you example as below:
vsamples<- c(14.7, 18.8, 14, 15.9, 9.7, 12.8)
gamma.nll <- function(par,data)
-sum(dgamma(data,shape=par[1],scale=par[2],log=T))
optim(c(1,1),ga
By the scale of log-likelihood, I did not mean the scale parameter of
the gamma density...
Generally, as you get more and more data, the log-likelihood will get
more and more negative. Hence, what I mean by scale is how negative of
the values of loglik.
So the 10 values returned from your dgamma
The scale of log-likelihood depends on the number of your data samples,
you should sum over the log-densities from individual points:
sum(llgm)
Xiaohui
Edward Wijaya 写道:
Dear all,
How can I compute the log likelihood of a gamma
distributions of a vector.
I tried the following. But it doesn'
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