similar to: Avaya 4610sw SIP setup problem

Hi All! I am desperately needing some help figuring out how to calculate LD50 with a GLMM (probit link) or, more importantly, the standard error of the LD50. I conducted a cold temperature experiment and am trying to assess after how long 50% of the insects had died (I had 3 different instars (non significant fixed effect) and several different blocks (I did 4 replicates at a time)=

Hello, I am trying to replicate example 4 in the package 'scatterplot3d': s3d.dat_data.frame(cols=as.vector(col(my.model4)), rows=as.vector(row(my.model4)), value=as.vector(my.model4)) scatterplot3d(s3d.dat, type="h", lwd=5, pch=" ", x.ticklabs=colnames(my.model4), y.ticklabs=rownames(my.model4), main="Conditional probabilities, Model 3") Nice! but,

Is there any way to put an argument into an object name. For example, say I have 5 objects, model1, model2, model3, model4 and model5. I would like to make a vector of the r.squares from each model by code such as this: rsq <- summary(model1)$r.squared for(i in 2:5){ rsq <- c(rsq, summary(model%i%)$r.squared) } So I assign the first value to rsq then cycle through models 2 through

Dear R users, I'm trying to use function 'refit' from lme4 and I get this error that I can't understand: > refit(dolo4.model4,cbind(uu,50-uu)) Error in function (classes, fdef, mtable) : unable to find an inherited method for function "refit", for signature "mer", "matrix" if I try: > refit(dolo4.model4,uu) Error in asMethod(object) :

Dear R users, I have used the following function (in blue) aiming to find the linear regression between MOE and XLA and nesting my data by Species. I have obtained the following results (in green). model4<-lme(MOE~XLA, random = ~ XLA|Species, method="ML")summary(model4) Linear mixed-effects model fit by maximum likelihood Data: NULL         AIC     BIC   logLik  -1.040187 8.78533

Hello I am running R 1.8.1 on a Windows platform I am attempting to fit an ordinal logistic regression model, using the polr function, as described in Venables and Ripley. But when I try model4 <- polr(ypsxcat~committed + as.factor(sex) + as.factor(drugusey) + anycsw + as.factor(sex)*committed + as.factor(sex)*as.factor(drugusey)+as.factor(sex)*anycsw, data = duhray) I get a message

Dear R users, Could somebody please help me to find a way of comparing nonlinear, non-nested models in R, where the number of parameters is not necessarily different? Here is a sample (growth rates, y, as a function of internal substrate concentration, x): x <- c(0.52, 1.21, 1.45, 1.64, 1.89, 2.14, 2.47, 3.20, 4.47, 5.31, 6.48) y <- c(0.00, 0.35, 0.41, 0.49, 0.58, 0.61, 0.71, 0.83, 0.98,

hello all, I am trying to use the function getCovariateFormula(nlme) in conjunction with the library lme4. When I load both packages I get the following message and the getCovariateFormula function no longer works: library(nlme) library(lme4) Attaching package 'lme4': The following object(s) are masked from package:nlme : contr.SAS getCovariateFormula

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

Hello list, I have a linear regression: mylm = lm(y~x-1) I've been reading old mail postings as well as the plotmath demo and I came up with a way to print an equation resulting from a linear regression: model = substitute(list("y"==slope%*%"x", R^2==rsq), list(slope=round(mylm$coefficients[[1]],2),rsq=round(summary(mylm)$adj.r.squared, 2))) I have four models and I

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

Dear all, I was wondering if anyone could help solve a problem of a missing interaction effect!! I carried out a 2 x 2 factorial experiment to see if eggs from 2 different locations (Origin = 1 or 2) had different hatching success under 2 different incubation schedules (Treat = 1 or 2). Six eggs were taken from 10 females (random = Female) at each location and split between the treatments,

I am running an lme model with the main effects of four fixed variables (3 continuous and one categorical – see below) and one random variable. The data describe the densities of a mite species – awsm – in relation to four variables: adh31 (temperature related), apsm (another plant feeding mite) awpm (a predatory mite), and orien (sampling location within plant – north or south). I have read

Hello, I am using the following model model1=lmer(PairFrequency~MatingPair+(1|DrugPair)+(1|DrugPair:MatingPair), data=MateChoice, REML=F) 1. After reading around through the R help, I have learned that the above code is the right way to analyze a mixed model with the MatingPair as the fixed effect, DrugPair as the random effect and the interaction between these two as the random effect as well.

Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into

Hi Everyone, So I have been further exploring the integration of our asterisk server and our lucent definity g3si system. I took the suggestion of setting up an isdn-pri line added the two way tie trunk and the signalling group, but can't seem to get the PRI signalling working on the asterisk correctly. I've set pri type to network on the lucent, and pri_cpe in zapata on the asterisk, but

If I have a model line = lm(y~x1) and I want to use a for loop to change the number of explanatory variables, how would I do this? So for example I want to store the model objects in a list. model1 = lm(y~x1) model2 = lm(y~x1+x2) model3 = lm(y~x1+x2+x3) model4 = lm(y~x1+x2+x3+x4) model5 = lm(y~x1+x2+x3+x4+x5)... model10. model_function = function(x){ for(i in 1:x) { } If x =1, then the list

I'd like to start configuring my Avaya 9608G phones for use on Asterisk / FreePBX / PBX-In-a-Flash. I'm using a variety of other phones on my system without major issues. I've read the discussion back in March, May and August of 2016, but unfortunately, my difficulty is much more basic. I think it has to do with DHCP, specifically, what options I'm offering the phone via DHCP.

Dear R-Community, I am relatively new with R, so sorry for things which for you might be obvious... I am trying to automatically update lmer formulae. the variables of the model are: depM= my dependent measure Sb2= a random factor OS = a predictor VR= another predictor So, I am building the first model with random intercept only: model = lmer(depM ~ (1 |Sb2)) then I update the formula

Hello does anyone know how to visualize a response curve based on a regression model with lines rather than dots. Having a large number of parameters the following formula is to time consuming. Perhaps a built in function exists to speed up the process. Model1<-a~b #Setting the scale extent min(area) max(area) avals<-seq(0,10,.1) # generating the plot plot(area,incidence, las=1)