similar to: understand GEE output for wald test

hi all- I am doing some research, have never used R before until today and need to understand the following program for a project. if some one could PLEASE help me understand this program ASAP i would GREATLY appreciate it (any syntax/ statistic comments would be great) PLEASE PLEASE HELP!! THANKYOU!!! -on a side note, it seems to me that R doesnt include the pv, and it was calculated

I'm attempting to run 250 permutations of a negative binomial GLM model for data on fish counts. Many of the models are fit appropriately, but some issue warnings such as "convergence not reached" or "step size truncated due to divergence." I've attempted using glm.nb from the MASS package and the negbin function from the AOD package, but both still cause some models to

I have tried glm.nb in the MASS package, but many models (I have 250 models with different combinations of predictors for fish counts data) either fail to converge or even diverge. I'm attempting to use the negbin function in the AOD package, but am unsure what to use for the "random" term, which is supposed to provide a right hand formula for the overdispersion parameter.

Hi, I have a data file with columns separated by ";" I read this file without any problem using read.csv2( ) but I had problems trying to read it with read.table( ... sep=";"). So it is not a problem for me, but I wonder if there is a bug here. drt <- read.csv2("t.txt", header=TRUE) # ok dcs <- read.table("t.txt", header=TRUE,

Hi, New to R, having done a wavelet analysis i got a result as list. The problem I have is : how can I acces to a given element of the list. here is what I get from my variable scale_d28 >typeof(scale_d28) [1] "list" > length(scale_d28) [1] 1 and scale_d28 is a list of 100000 double values? Thanks -- Jean Pierre BOTH phone (33) 01.69.08.84.78 (work) email

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> From: "James Bean" <james@hdcs.com.au> > Has anyone every setup an external open/close relay, off say a serial > interface, and have an extension trigger the relay? The following will do the trick. Just add a 5vdc solid state relay ('cause you can't sink too much current out of the RS232C port). Substitute "2", "4" or "6" in the

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Dear all, I'm trying to run a Wald Test for the Penalized Fixed Effects using the rqpd package. Actually, I'd like to test if the coefficients associated to the same variable are statistically different among the quantiles. The usual wald.test, waldtest or anova.rq don't work for rqpd. I would appreciate any help. Thank you. Regards, Fernando Parmagnani, M.A., Economics University

I have a question about the Wald test F-statistics that are calculated when the anova() command is used on a singular gls or lme object. As I recall from my linear models class, the Wald test examines H0: C'B = d0 vs Ha: C'B != d0. Does anybody know how this C matrix is constructed in R? Is there a way to see the C matrix that R is using? In my situation, I'm looking at

Hello, Might anyone know how to conduct Wald-type F-tests of the fixed effects estimated by glmmPQL? I see this implemented in SAS (GLIMMIX), and have seen it recommended in user group discussions, but haven't come across any code to accomplish it. I understand the anova function treats a glmmPQL fit as an lme fit, with the test assumptions based on maximum likelihood, which is inappropriate

Dear R-Helpers, I'm searching for an R package that will produce power analyses for linear Wald tests. I have conducted a thorough analysis and come up with a pile of negative results (I'm the King, baby). I found asypow, which applies to likelihood ratio tests. However, we generally have the power to do Wald tests, I just need to prove it so that we can publish it ;) If this is a

*H*i, I am trying to fit a GEE model with *geeglm* function. The model is the following: Modelo<-geeglm(sqrt ~Tra+ Mes, id=Lugar , data=datos, family=gaussian(identity), corstr="independence") *Tra( is a experimental treatment, 2 levels)*, *Mes* (is the month of take data, 4 levels) and *Lugar* (is the site of study, 3 levels) are categorical variables and *sqrt* (sqrt of Total

Dear R users,    my question concerns my interest in comparing the beta coefficients between two identical regression models in two (actually 3) groups. Disclaimer: I am quite new to R, so I might be missing some terminology that I have not come across.   I am trying to find out if I can easily implement a Wald test in R for this, but the only relevant thing that I came across is this link

Dear R members, I am new in using frailty models in survival analyses and am getting some contrasting results when I compare the Wald and likelihood ratio tests provided by the r output. I am testing the survivorship of different sunflower interspecific crosses using cytoplasm (Cyt), Pollen and the interaction Cyt*Pollen as fixed effects, and sub-block as a random effect. I stratified

hello, I work since a few time on R and i wanted to know how to obtain the Wald chi square value when you make a binary logistic regression. In fact, i have the z value and the signification but is there a script to see what is the value of Wald chi square. You can see my model below, Best regards, S??verine Erhel [Previously saved workspace restored] > m3 = glm(reponse2 ~ form +

Dear all, I am trying to compare the estimated coefficients of a quantile regression model between two different samples. It is a Wald test, but I cannot find one way to do that in R.The samples are collected conditional on a specific characteristic and I would like to test whether such characteristic indeed affect the estimators. The problem in the test anova.rq is that the response variable

I have a doubt about the calculation of tests for fixed effects in mixed-effects models. I have read that, except in well-balanced designs, the F statistic that is usually calculated for ANOVA tables may be far from being distributed as an exact F distribution, and that's the reason why the anova method on "mer" objects (calculated by lmer) do not calculate the denominator df nor a

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