similar to: Question about KLdiv and large datasets

I am receiving the following error while executing KLdiv method: kl<-KLdiv(y) Error in function (classes, fdef, mtable) : unable to find an inherited method for function "KLdiv", for signature "data.frame" the code is as follows: require(flexmix) KLdiv(y) the object y (snippet, actual object contains 188 entries) is as follows: s1p5 s2p5 1 0.6 0.8 2 0.8

Hi all, I wonder why KLdiv does not work with data.frames: n <- 50 mydata <- data.frame( sequence=c(1:n), data1=c(rnorm(n)), data2=c(rnorm(n)) ) # does NOT work KLdiv(mydata) # works fine dataOnly <- cbind(mydata$data1, mydata$data2, mydata$group) KLdiv(dataOnly) Any ideas? Is there a better implementation that can deal with data.frame or is there a simpler way of converting? 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,

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

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

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, I have a data frame with column over which I would like to run repeated functions for data analysis. Currently I am only running recursively over two columns where I column 1 has two states over which I split and column two has 3 states. The function therefore runs 2 x 3 = 6 times as shown when running the following code: mydata <- data.frame(userid = c(5, 6, 5, 6, 5, 6), taskid =

Dear list, I have been comparing the outputs of two packages for latent class regression, namely 'flexmix', and 'mmlcr'. What I have noticed is that the flexmix package appears to come up with a much better fit than the mmlcr package (based on logLik, AIC, BIC, and visual inspection). Has anyone else observed such behaviour? Has anyone else been successful in using the mmlcr

Greetings I'm a relatively new R user and I'm trying to build a latent class model. I've used the 'R Site Search' and it appears there's not much dialogue on these packages On mmlcr, I've gotten it working, but not sure if I'm using it correctly. On flexmix, I can only seem to get results for one class. I'm attaching my code below - if anyone

Problem on flexmix when trying to apply signature developed in one model to a new sample. Dear R Users, R Core Team, I have a problem when trying to know the classification of the tested cases using two variables with the function of flexmix: After importing the database and creating a matrix: BM<-cbind(Data$var1,Data$var2) I see that the best model has 2 groups and use: ex2

Hi, I was going through the examples in smartpred. It seems there's an unstated dependency on the fitted() function in package flexmix. n = 20 set.seed(86) x = sort(runif(n)) y = sort(runif(n)) library(splines) fit = lm(y ~ ns(x, df=5)) plot(x, y) lines(x, fitted(fit)) # won't work w/o prior loading of the flexmix package. newx = seq(0, 1, len=n) points(newx, predict(fit,

Hi, I am trying to understand how to use the flexmix package, I have read the Leisch paper but am very unclear what is needed for the M-step driver. I am just fitting a simple linear regression model. The documentation is far from clear what the FLXmclust function does, but, it in principle could do all I need, however, I do not get sentible results as if I try the following the result is poor:

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

Dear list: I would like to recreate how the artificial data set BregFix was generated in package flexmix (thanks Bettina and Friedrich). The data set is thoroughly described in Grun and Leisch's Computational Statistics & Data Analysis 51(11) :5247-5252 but references to the appropriate seed number(s) are missing (providing these details was certainly beyond the scope of the authors'

Hello all, I am trying to fit a truncated mixture model and I wrote a driver for flexmix following the example in the vignette, but it doesn't work for me: it assigns all data points to one component only, e.g.: > > source('bugged.R') > > Call: > flexmix(formula = x ~ 1, k = 2, model = truncatedmodel(lower = -4, > upper = 4)) > > prior size

Dear R-help, A colleague of mine was running some code on two of our boxes, and noticed a rather large difference in running time. We've so far isolated the problem to the difference between R 1.7.1 and 1.8.0, but not more than that. The exact same code took 933.5 seconds in 1.7.1, and 3594.4 seconds in 1.8.1, on the same box. Basically, the code calls boot() to bootstrap fitting mixture

Dear R-helpers & Friedrich Leisch, I'm trying to fit a mixture of regression models on attached data set by doing the following: agl <- read.table("agl.txt") mod1 <- flexmix(resp~gng+csc|Subject,data=agl, model=FLXglm(family="binomial"),k=2) The result is a (varying) number of the following warnings: Warning messages: 1: non-integer #successes in a

Hi, Before I write code to do it does anyone know of code for fitting mixtures of multivariate-t distributions. I can't use McLachan's EMMIX code because the license is "For non commercial use only". I checked, mclust and flexmix but both only do Gaussian. Thanks Nicholas

G'day Timothy, On Tue, 19 Dec 2017 18:28:00 -0600 Timothy Axberg <axbergtimothy at gmail.com> wrote: > Should I repost the question with reply-all? Nope, we got all from Jeff's post. :) > On Tue, Dec 19, 2017 at 6:13 PM, Jeff Newmiller > <jdnewmil at dcn.davis.ca.us> wrote: > > > You also need to reply-all so the mailing list stays in the loop. > >

You also need to reply-all so the mailing list stays in the loop. -- Sent from my phone. Please excuse my brevity. On December 19, 2017 4:00:29 PM PST, Timothy Axberg <axbergtimothy at gmail.com> wrote: >Sorry about that. Here is the code typed directly on the email. > >qe = (Qmax * Kl * ce) / (1 + Kl * ce) > >##The data >ce <- c(15.17, 42.15, 69.12, 237.7, 419.77)