similar to: is AIC always 100% in evaluating a model?

Een ingesloten tekst met niet-gespecificeerde tekenset is van het bericht gescrubt ... Naam: niet beschikbaar Url: https://stat.ethz.ch/pipermail/r-help/attachments/20070803/79b6292b/attachment.pl

Hey, I want to draw several plots sequently, but have to make them dispaly in one figure. So how to achieve this? Thanks. Fred

Hello, I'm trying to understand grofit's estimation of models, and fairly new to growth models generally. The data used by grofit consists of the vector of "experiments", that is the growth values for a vector of individuals measured at different times. Can I understand correctly that the program estimates parameters for the growth model based on a regression(linear or non

Hi, When modeling with negbin from the aod package, parameters for a given count y | lambda~Poisson(lambda) with lambda following a Gamma distribution Gamma(r, theta) are estimated. The intercept is called phi. Some other parameters may be also be estimated from factors in the data: the estimates returned for all these would be in accordance with the Value listing in the negbin entry in the aod

Hi, I have a vector or random variables and I'm estimating the density using "bkde" function in the KernSmooth package. The out put contains two vectors (x and y), and the R documentation calls y as the density estimates, but my y-values are not exact density etstimates (since these are numbers larger than 1)! what is y here? Is it possible to get the true estimated density at each

--- On Mon, 3/12/12, aleksandr shfets <a_shfets at mail.ru> wrote: > From: aleksandr shfets <a_shfets at mail.ru> > Subject: Fwd: Re[2]: [R] B-spline/smooth.basis derivative matrices > To: "Vassily Shvets" <shv736 at yahoo.com> > Received: Monday, March 12, 2012, 5:15 PM > > > > -------- ???????????? ????????? > -------- > ?? ????:

I am trying to calculate a Kullback-Leibler divergence from two vectors with integers but get NA as a result when trying to calulate the measure. Why? x <- cbind(stuff$X, morestuff$X) x[1:5,] [,1] [,2] [1,] 293 938 [2,] 293 942 [3,] 297 949 [4,] 290 956 [5,] 294 959 KLdiv(x) [,1] [,2] [1,] 0 NA [2,] NA 0 Best, Ralf

I am performing some analysis over a large data frame and would like to conduct repeated analysis over grouped-up subsets. How can I do that? Here some example code for clarification: require("flexmix") # for Kullback-Leibler divergence n <- 23 groups <- c(1,2,3) mydata <- data.frame( sequence=c(1:n), data1=c(rnorm(n)), data2=c(rnorm(n)), group=rep(sample(groups, n,

Hi, This is more of a general stat question. I am looking for a easily computable measure of a distance between two empirical distributions. Say I have two samples x and y drawn from X and Y. I want to compute a statistics rho(x,y) which is zero if X = Y and grows as X and Y become less similar. Kullback-Leibler distance is the most "official" choice, however it needs estimation of

Dear R-Users, I have the CDF of a discrete probability distribution. I now observe a change in this CDF at one point. I would like to find a new CDF such that it has the shortest Kullback-Leibler Distance to the original CDF and respects my new observation. Is there an existing package in R which will let me do this ? Google searches based on entropy revealed nothing. Kind regards, Tolga

Hola, ¿qué tal? Tengo una pregunta de esta

Hi, I have come to my wits end again (lately, it's a very short trip). I have been trying to connect to Samba 3.0.21b but Windows keeps throwing up the logon dialog. I'm using "Map network drive" to try to mount this share. It seems the problem is only on the Windows side since I have tested the connection through the Unix account. Both user id and password work fine.

Hello, I'm trying to use AIC to choose between 2 models with positive, continuous response variables and different error distributions (specifically a Gamma GLM with log link and a normal linear model for log(y)). I understand that in some cases it may not be possible (or necessary) to discriminate between these two distributions. However, for the normal linear model I noticed a discrepancy

Dear R Help, I have two nested negative log-likelihood functions that I am optimizing with the spg function [BB package]. I would like to perform model selection on these two objective functions using AIC (and possibly anova() too). However, the spg() function returns a list and I need a fitted model object for AIC(), ICtab() [bbmle package], or anova(). How can I perform AIC-based model

Dear R users, According Brockwell & Davis (1991, Section 9.3, p.304), the penalty term for computing the AIC criteria is "p+q+1" in the context of a zero-mean ARMA(p,q) time series model. They arrived at this criterion (with this particular penalty term) estimating the Kullback-Leibler discrepancy index. In practice, the user usually chooses the model whose estimated index is

Hi there, I'm trying to find the KL divergence measure between a prior and it's posterior distributions, and I'm using the KLdiv method in the flexmix package. plese see the example below: require(flexmix) x=seq(-4,4,length=100) d1=dnorm(x,0,1) d2=dunif(x,-3,3) y=cbind(d1,d2) kl=KLdiv(y) but let say, x1=seq(-5,5,length=100) d3=dunif(x1,-3,3) y1=cbind(d1,d3) kl1=KLdiv(y1) Notice

Hi all, x <- cbind(rnorm(500),rnorm(500)) KLdiv(x, eps=1e-4) KLdiv(x, eps=1e-5) KLdiv(x, eps=1e-6) KLdiv(x, eps=1e-7) KLdiv(x, eps=1e-8) KLdiv(x, eps=1e-9) KLdiv(x, eps=1e-10) ... KLdiv(x, eps=1e-100) ... KLdiv(x, eps=1e-1000) When calling flexmix::KLdiv using the given code I get results with increasing value the smaller I pick the accuracy parameter 'eps' until finally reaching

hello, I'd like know how to sort a data frame in R for example how I should do to sort by Catholic with swiss data frame like below thanks Fertility Agriculture Examination Education Catholic Infant.Mortality Courtelary 80.2 17.0 15 12 9.96 22.2 Delemont 83.1 45.1 6 9 84.84 22.2

Can anyone help me figure out the right way to import a method that is defined as S3 in one package and S4 in another? Specifically: profile() is defined as an S3 method in the stats package: function (fitted, ...) UseMethod("profile") <bytecode: 0xa4cd6e8> <environment: namespace:stats> In stats4 it is defined as an S4 method: stats4:::profile standardGeneric for

Dear R community, I want to estimate coefficients in a (non-linear) system of equations using 'mle2' from the "bbmle" package. Right now the whole data is read in as just one long time series, when it's actually 9 cross sections with 30 observations each. I would like to be able to test and correct for autocorrelation but haven't found a way to do this in this package.