similar to: positive log likelihood and BIC values from mCLUST analysis

Hi R-experts, I am using the envfit function over an ordination of floristic data. The problem is that every time that I run it changes the results. Sometimes dramatically, selecting variables that the first time were not significant. I do not get what could be the problem or if is normal given the permutations are different. # the NMDS ordination gap_flor_NMDS_chord <- metaMDS(gaps_flor,

Hello All... I gave a task to my students that involved using mclust to look for clusters in some bivariate data of isotopes vs various mining locations. They discovered something I didn?t expect; the data (called tur) is appended below. p <- qplot(x = dD, y = dCu65, data = tur, color = mine) print(p) # simple bivariate plot of the data; looks fine mod1 <- Mclust(tur[,2:3]) mod1$G mod2

Hello, I can draw a basic stress plot for NMDS with the following code in package Vegan. > stressplot(parth.mds, parth.dis) When I try to specify the line and point types, it gives me error message. > stressplot(parth.mds, parth.dis, pch=1, p.col="gray", lwd=2, l.col="red") Error in plot.xy(xy, type, ...) : invalid plot type In the above code, if I removed line type,

Dear all, I'm analyzing a dataset (A) of 400 cases with 11 binary variables. Unfortunately, several (actually a lot) of cases are identical. NA are also present. I want to to plot distances between cases. For this, I obtained a distance matrix by dist(A, method="binary"). I then analyzed the obtained distance via Principal coordinate analysis with cmdscale(). Results are fine.

Dear list members, I am using Mclust in order to deconvolute a distribution that I believe is a sum of two gaussians. First I can make a model: > my.data.model = Mclust(my.data, modelNames=c("E"), warn=T, G=1:3) But then, when I try to plot the result, I get the following error: > mclust1Dplot(my.data.model, parameters = my.data.model$parameters, what = "density")

I'm running Mclust 3.0 in R-2.7.0 and have run into a situation where the optimal number of clusters, 12, seems to be greater than the number of colors available to Mclust. The code below, which demonstrates errors, does not if the max no of clusters is set at 10. sessionInfo, after a restart of R and loading of packages, follows the output. Is this a known problem with Mclust? Can I

Hi, I'm trying to use the library mclust for gaussian mixture on a numeric vector. The function Mclust(data,G=3) is working fine but the fitting is not optimal and is using modelNames="E". When I'm trying Mclust(data,G=3,modelName="V") I have the following message: Error in if (Sumry$G > 1) ans[c(orderedNames, "z")] else ans[orderedNames] : argument is

Hi, I am trying to run th mclust package on a variable "Tuberculin indurations" recorded as mm. The file has only one variable. When I run the package I get NULL value for mu and sigma. Can anybody say why? This is the program: library("mclust") mc<-Mclust(x.trab,G=1:9,warn=TRUE) mc mc$mu sqrt(mc$sigmasq) and the output I get is > library("mclust")

Hello, I'm trying out flexmix and mclust for the first time on some univariate data which is typically best described as lognormal, but can sometimes be gamma distributed as well. I first tried using EM on mclust assuming the data was lognormally distributed and could only get it to work in "E" mode, i.e. the equal variance mode. I could never get it to work on "V" mode [

Dear All, I am using a Procrustes analysis to compare two NMDS ordinations for the same set of sites. One ordination is based on fish data, the other is based on invertebrate data. Ordinations were derived using metaMDS() from the {vegan} library as follows: fish.mds<-metaMDS(fish.data, distance="bray", k=3, trymax=100, wascores=TRUE, trace=TRUE, zero="add")

Hi, My questions concern the function 'mclustBIC' which compute BIC for a range of clusters of several models on the given data and the other function 'mclustModel' which choose the best model and the best number of cluster accordind to the results of the previous cited function. 1) When trying the following example (see ?mclustModel), I get negative BIC computed by

Hello all (especially MCLUS users). I'm trying to make use of the MCLUST package by C. Fraley and A. Raftery. My problem is trying to figure out how the (model) identifier (e.g, EII, VII, VVI, etc.) relates to the covariance matrix. The parameterization of the covariance matrix makes use of the method of decomposition in Banfield and Rraftery (1993) and Fraley and Raftery (2002) where

Dear R users, I use mclust to fit a mixture of normal distributions to many datasets. Usually the Mclust function finds 1 or two normal distributions, rarely, 3. But I hit a strange case today. my.data <- c(57.96920, 51.79415, 51.20538, 55.53637, 51.64291, 56.61476, 51.28855, 55.56169, 51.85113, 54.03330, 51.37370, 49.48561, 52.41580, 53.51176, 60.49293, 55.77012, 51.59270, 56.29660,

Hi, I'm trying to use the library mclust for gaussian mixture on a numeric vector. The function Mclust(data,G=3) is working fine but the fitting is not optimal and is using modelNames="E". When I'm trying Mclust(data,G=3,modelName="V") I have the following message: Error in if (Sumry$G > 1) ans[c(orderedNames, "z")] else ans[orderedNames] : argument is

Hello everyone! metaMDS(cm, distance = "euclidean", k = 2, trymax = 50, autotransform =TRUE, trace = 1, plot = T) (cm is a similarity matrix, in which values are positive integers or 0) I use this command to run NMDS on my matrix "cm". But the stress is very high after analysis. About 14. Actually, there is no improvment comparing with using isoMDS.

Hi All, I have a n x m matrix. The n rows are individuals, the m columns are variables. The matrix is in itself a collection of 1s (if a variable is observed for an individual), and 0s (is there is no observation). Something like: [,1] [,2] [,3] [,4] [,5] [,6] [1,] 1 0 1 1 0 0 [2,] 1 0 1 1 0 0 [3,] 1 0 1 1 0 0 [4,] 0 1 0

Hello R experts, I've used metaMDS to run NMDS on some fish abundance data, and am also working on correlating environmental data to the NMDS coordinates. I'm fairly new to metaMDS and NMDS in general, so I have what are probably some very basic questions. My fish abundance data consists of 66 sites for which up to 20 species of fish were identified and counted. I ran metaMDS on this data

Hi, I am trying to create 3 mds plots: one with axis 1 vs axis 2, one with axis 2 vs axis 3, and one with axis 1 vs axis 3. When inputting my code, I only end up with one diagram and when inputting mdsg.mds$dims, the program returns 2 for 2 dimensions. How can I create the other two plots? Any help would be greatly appreciated, Calla Carbone The table I use is number of each taxa on each

Hi, all; I got an unusual error while using mclust library 2.1-7 on R 2.0.1. When I tried to run Mclust(rnorm(100)), I got segmentation fault error. Does anyone know what causes this problem? I came across the same problem even when I tried to run the example shown in Mclust help using iris data. Thanks in advance, Tae-Hoon Chung -------------------------------------------------- Tae-Hoon Chung

metaMDS(cm, distance = "euclidean", k = 2, trymax = 50, autotransform =TRUE, trace = 1, plot = T) (cm is a similarity matrix, in which values are positive integers or 0) I use this command to run NMDS on my matrix "cm". But the stress is very high after analysis. About 14. Actually, there is no improvment comparing with using isoMDS.