similar to: goodfit par estimates

Try changing your method to "ML" and try again. I tried the run the first example from the documentation and it failed with the same error. Changing the estimation method to ML worked. @List: Can anyone else verify the error I got? I literally ran the following two lines interactively from the example for goodfit: dummy <- rnbinom(200, size = 1.5, prob = 0.8) gf <- goodfit(dummy,

Hello, Sample code: library("vcd") dummy <- rnbinom(200, size=1.5, prob=0.8) gf <- goodfit(dummy, type="nbinomial", method="MinChisq") plot(gf) I would like to: 1. add a lgened stating the bars show the actual counts and the red dots - the fit. 2. show the goodness-of-fit values calculated somewhere on an empty white space ob the plot. But... the legend

Hi, I am hoping some one can help with this. I am using nlme to fit a random coefficients model. It ran for hours before returning Error: Singularity in backsolve at level 0, block 1 The model is > plavix.nlme<-nlme(PLX_NRX~loglike(PLX_NRX,PD4_42D,GAT_34D,VIS_42D,MSL_42D,SPE_ROL,XM2_DUM,THX_DUM,b0,b1,b2,b3,b4,b5,b6,b7,alpha), + data=data, + fixed=list(b0 +

Dear All, I run the goodness of fit test using goodfit() in vcd package. The result is as follow: Goodness-of-fit test for poisson distribution X^2 df P(> X^2) Pearson 1.053348 2 0.5905661 Warning message: In summary.goodfit(gf) : Chi-squared approximation may be incorrect I want to save the the test statistics(X^2), df, and p-value. How can I save the result.

Hello, I am a student and for project I need R. I have one problem regarding function goodfit(). Does goodfit() require frequency count of numbers or numbers themselves? For example suppose I have data say 150 readings.Do I need to use goodfit() directly on data or should I make suitable no of bins and then apply goodfit()? Aswad [[alternative HTML version deleted]]

I have troubles understanding how goodfit() function in the vcd package computes the Pearson coefficient. Can anybody provide more information on the computation? In particular, for HorseKicks data in vcd package, goodfit() yields > oo <- goodfit(HorseKicks,type="poisson",method="MinChisq") > summary(oo) Goodness-of-fit test for poisson distribution

Dear R-Help: As you can see, Prof. Friendly refers me to your site for an executable version of vcd. I don't mean to be obtuse, but 15 minutes spent exploring your site failed to locate a downloadable version of the vcd package to which he referred. I know plainly what this application can do. What I need to know is how to obtain the application itself. My thanks in advance for any

Hey, Can anyone answer this question. I am working with really large datasets and most of the programs I have been running take quite some time. I heard that R may be faster in Unix. I sthis true and if so can anyone reccomend which system and requirements may allow things to go faster for? Thanks!! Elizabeth Lawson --------------------------------- [[alternative

Hey eveyone! I hope someone can help wiht this question. I have a matirux of all zeros and ones and I would like to indentify all unique patterns in the rows andthe number of times the pattern occurs. I changed all zeros to NA tried to use prelim.norm to identify all patterns of missing data in the rows. I got the message Warning message: NAs introduced by coercion Any ideas of how

Hello all, I would like to fit a mixed effects model, but my response is of the negative binomial (or overdispersed poisson) family. The only (?) package that looks like it can do this is glmm.ADMB (but it cannot run on Mac OS X - please correct me if I am wrong!) [1] I think that glmmML {glmmML}, lmer {Matrix}, and glmmPQL {MASS} do not provide this "family" (i.e. nbinom, or

I'm trying to enter a frequency table manually so that I can run a goodness of fit test (I only have the frequencies, I don't have the raw data). So for example, let's say I want to re-create the HorseKicks table: library(vcd) data(HorseKicks) str(HorseKicks) 'table' int [1:5(1d)] 109 65 22 3 1 - attr(*, "dimnames")=List of 1 ..$ nDeaths: chr [1:5]

Hi All, I have a dataset consisting of abundance counts of a fish and I want to test if my data are poisson in distribution or normal. My first question is whether it is more appropriate to model my data according to a poisson distribution (if my test says it conforms) or use transformed data to normalise the data distribution? I have been using the vcd package gf<-goodfit(Y,type=

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Dear all, I am analyzing counts of seabirds made from line transects at sea. I have been fitting Poisson and negative binomial distributions to the data using the goodfit function from the vcd library. I would also like to evaluate how well a quasi-poisson distribution fits the data. However, none of the potentially suitable functions I have identified (goodfit(vcd), fitdistr(MASS),

I realized that I may not have answered the question you were asking and that no one else has responded. I can across a similar problem and may have an answer to your question now. If you have both the wavelet coefficients and the scaling coefficients then create a fake sequence of the same length as the original and decompose that sequence using wd form wavethersh with the same wavelet family

Estimados miembros de la lista. Estoy usando 2 funciones del paquete vcd y obtengo resultados que parecen discrepar entre ambas funciones. > dput(peces) c(26L, 63L, 1L, 21L, 20L, 50L, 0L, 104L, 19L, 0L, 8L, 32L, 0L, 24L, 5L, 2L, 0L, 36L, 0L, 5L, 27L, 1L, 13L, 24L, 15L, 25L, 23L, 5L, 37L, 26L, 29L, 14L, 15L) La función Ord_plot me dice que la mejor distribución para los datos es binomial

Hi, is there any method for goodness of fit testing of an (as general as possible) univariate distribution with parameters estimated, for normal, exponential, gamma distributions, say (e.g. the corrected p-values for the Kolmogorov-Smirnov or Chi-squared with corresponding ML estimation method)? It seems that neither ks.test nor chisq.test handle estimated parameters. I am aware of function

Dear r list,   I am using glm.nb in the MASS package to fit negative binomial models to data on manta ray abundance, and AICctab in the bbmle package to compare model IC.  However, I need to test for the goodness of fit of the full model, and have not been able to find a Pearson's Chi Squared statistic in any of the output.  Am I missing it somewhere?  Is there a way to run the test using

Dear R-list members, I have been a user of SPSS for a few years and quite new to R. I read the documentation and tried samples but I have some problems to obtain results for a logistic regression under R. The following SPSS script LOGISTIC REGRESSION vir /METHOD = FSTEP(LR) d007 d008 d009 d010 d011 d012 d013 d014 d015 d016 d017 d018 d069 d072 d073 /SAVE = PRED COOK SRESID

I have seen several queries about parameterizing the negative binomial scale parameter. This is called the heterogeneous negative binomial. I have written a function called "nbinomial" which is in the msme package on CRAN. Type ?nbinomial to see the help file. The default model is a negative binomial for which the dispersion parameter is directly related to mu, which is how Stata,