similar to: negative binomial regression

I would like to know if it is possible to perform negative binomial regression with rate data (incidence density) using the glm.nb (in MASS) function. I used the poisson regression glm call to assess the count of injuries across census tracts. The glm request was adjusted to handle the data as rates using the offset parameter since the population of census tracts can vary by a factor of

I am not certain how nlme works so I followed an example from the web ( http://www.menne-biomed.de/gastempt/gastempt1.html). I was able to successfully reproduce the example. However, when I modified my the example to use my data and with my formula, I get a set of errors having to do with the log() function. I get 10 of them (all exactly the same) and there are 10 levels in my factor variable.

libswfdec/Makefile.am | 2 libswfdec/swfdec_as_strings.c | 17 + libswfdec/swfdec_as_types.c | 16 + libswfdec/swfdec_as_types.h | 2 libswfdec/swfdec_gradient_pattern.c | 129 +++++++++++++++ libswfdec/swfdec_gradient_pattern.h | 67 +++++++

Hi, I was hoping to get some advice on how to derive estimates of slopes from four parameter logistic models fit with SSfpl. I fit the model using: model<-nls(temp~SSfpl(time,a,b,c,d)) summary(model) I am interested in the values of the lower and upper asymptotes (parameters a and b), but also in the gradient of the line at the inflection point (c) which I assume tells me my rate of

Hi there, I don't have much experience with fitting at all and I'd like to get some advice how to use the "weights"-argument with nls correctly. I have created some data with a sigmoidal curve shape. Each y-Value was generated by the mean of three values. A standard deviation was calculated too. Now, I'd like to weight the data points respective to its standard

Hello, I have a problem concerning logistic regressions. When I add a quadratic term to my linear model, I cannot draw the line through my scatterplot anymore, which is no problem without the quadratic term. In this example my binary response variable is "incidence", the explanatory variable is "sun": > model0<-glm(incidence~1,binomial) >

Using R 1.8.1, and the negative binomial glm implemented in MASS, the default when using anova and a chi-square test is to divide the deviance by the estimated dispersion. Using my UNIX version of S-plus (v 3.4), and the same MASS functions, the deviances are *not* divided by the estimated dispersion. Firstly, I'm wondering if anyone can enlighten about the correct procedure (I thought

Dear R Users/Experts, I am using a function called logitreg() originally described in MASS (the book 4th Ed.) by Venebles & Ripley, p445. I used the code as provided but made couple of changes to run a 'constrained' logistic regression, I set the method = "L-BFGS-B", set lower/upper values for the variables. Here is the function, logitregVR <- function(x, y, wt =

Sorry, I deleted my old post. Pasting the new query below. Dear R Users/Experts, I am using a function called logitreg() originally described in MASS (the book 4th Ed.) by Venebles & Ripley, p445. I used the code as provided but made couple of changes to run a 'constrained' logistic regression, I set the method = "L-BFGS-B", set lower/upper values for the variables. Here

Hi all I am not an experienced user of R. I am trying to perform DIF analysis using Lordif and I get the follow error: > GroupDIF <- lordif(resp.data=Resp, group=Group, criterion="R2", pseudo.R2="McFadden", R2.change=0.02) Iteration: 500, Log-Lik: -137340.437, Max-Change: 0.00119 EM cycles terminated after 500 iterations. (mirt) | Iteration: 1, 14 items flagged for DIF

I postulate the following model AC <- glmer(Accuracy ~ RT*Group + (1+RT|Group:subject) + (1+RT|Group:Trial), data = da, family = binomial, verbose = T) Here I predict Accuracy from RT, Group (which has values 0 or 1) and the interaction of Group and RT (those are the fixed effects). I also estimate the random effects for both intercepts and slopes for subjects and different trials.

Hello, I am trying to embed R-code inside VB for Excel (probably a perverse endeavour anyway) and I am running into difficulties, especially when passing vectors back and forth between the two environments. (1) I am using the RExcel package. (2) An example of error that I often get and that I can't seem to be able to work myself around of is the following VB message: ------ Run-time

Dear R-helpers, this mail is intended to mention a rather trange result and generate potential useful comments on it. I am not aware of another posts on this issue ( RSiteSearch("quasipoisson lmer version dispersion")). MUsing the exemple in the reference of the lmer function (in lme4 library) and turning it into a quasi-poisson or quasi-binomial analysis, we get different results,

Dear forum members, How can I force a negative slope in a linear regression even though the slope might be positive? I will need it for the purpose of determining the trend due reasons other than biological because the biological (genetic) trend is not positive for these data. Thanks. Julia Example of the data: [1] 1.254 1.235 1.261 0.952 1.202 1.152 0.801 0.424 0.330 0.251 0.229

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

I have two related variables, each with 16 points (x and Y). I am given variance and the y-intercept. I know how to create a regression line and find the residuals, but here is my problem. I have to make a loop that uses the seq() function, so that it changes the slope value of the y=mx + B equation ranging from 0-5 in increments of 0.01. The loop also needs to calculate the negative log

On 15 Jun 2004 at 13:52, Vito Muggeo wrote: > Dear Petr, > Probably I don't understand exactly what you are looking for. > > However your "plot(x,c(y,z))" suggests a broken-line model for the > response "c(y,x)" versus the variables x. Therefore you could estimate > a segmented model to obtain (different) slope (and breakpoint) > estimates. See

Hello, I am having a problem with the zero-inflated negative binomial (package pscl). I have 6 sites with plant populations, and I am trying to model the number of seeds produced as a function of their size and their site. There are a lot of zero's because many of my plants get eaten before flowering, thereby producing 0 seeds, and that varies by site. Because of that and because the

Hello All I am currently looking at spatial data - Chorophyll A concentration in sea water over a wide geographic area. These data are used to determine the location of ocean fronts and hence where tuna are located. A front is identified by a steep gradient in the change in chloroA concentration. I have been looking at these data qualitatively using persp, contour, and image but would like to

Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree