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Hello everybody, I am using glmmADMB and when I run some models, I recieve the following message: Erro em glmmadmb(eumencells ~ 1 + (1 | owners), data = pred3, family = "nbinom", : The function maximizer failed (couldn't find STD file) Furthermore: Lost warning messages: Command execution 'C:\Windows\system32\cmd.exe /c "C:/Users/helenametal/Documents/R/win-library/2.15/glmmADMB/bin/windows32/glmmadmb.exe" -maxfn 500 -maxph 5...

...ns a linear model and returns r2. But, the number of predictor variables passed to the function changes from 1 to 3. How can I change the formula inside the function depending on the number of variables passed in? An example: get.model.fit <- function(response.dat, pred1.dat, pred2.dat = NULL, pred3.dat = NULL) { res <- lm(response.dat ~ pred1.dat + pred2.dat + pred3.dat) summary(res)$r.squared # other stuff happens here... } y <- rnorm(10) x1 <- y + runif(10) x2 <- y + runif(10) x3 <- y + runif(10) get.model.fit(y, x1, x2, x3) get.model.fit(y, x1, x2) get.model.fit...

...t) 0.12965 0.36007 Xr.1 s(hours24) 1291.42444 35.93639 Number of obs: 97920, groups: vpnr, 114; Xr.1, 8 Fixed effects: Estimate Std. Error z value Pr(>|z|) X(Intercept) 0.3713345 0.0644469 5.762 8.32e-09 *** Xpred2 -0.0575848 0.0865231 -0.666 0.506 Xpred3 0.0003748 0.0869543 0.004 0.997 … Parametric coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.3713345 0.0149858 24.779 < 2e-16 *** pred2 -0.0575848 0.0197488 -2.916 0.00355 ** pred3 0.0003748 0.0198803 0.019 0.98496...

..." model matrix for predictions > p <- poly(Orthodont$age, 3) > mm2 <- model.matrix(~ poly(age, 3, coefs = attr(p, "coefs")) + Sex, data = Newdata) > > data.frame(pred1 = predict(fm, level = 0, newdata = Newdata), + pred2 = mm1 %*% fixef(fm), + pred3 = head(predict(fm, level = 0)), + pred4 = mm2 %*% fixef(fm)) pred1 pred2 pred3 pred4 1 18.61469 18.61469 23.13079 23.13079 2 23.23968 23.23968 24.11227 24.11227 3 29.90620 29.90620 25.59375 25.59375 4 36.19756 36.19756 27.03819 27.03819 5 18.61469 18.61469 23.13079 23.13079...

...l=0) summary(grd.lme0) # Gives true, all.equal(grd$pred1,grd$pred0) # Everything as expected without treat grd.lme2 = lme(newbone~t,data=grd,random=~1|subject) grd$pred2 = predict(grd.lme2,level=0) summary(grd.lme2) # Forced intercept = 0 grd.lme3 = lme(newbone~t-1,data=grd,random=~1|subject) grd$pred3 = predict(grd.lme3,level=0) summary(grd.lme3) # As expected: not equal all.equal(grd$pred2,grd$pred3) #------------------------------------------------------------------- R version 2.9.0 Under development (unstable) (2009-03-13 r48127) i386-pc-mingw32 locale: LC_COLLATE=German_Germany.1252;LC_C...

Hi, I am just wondering if there is a test available for testing if a linear fit of an independent variable in a Cox regression is enough? Thanks for any suggestions. John Zhang ____________________________________________________________________________________ [[elided Yahoo spam]]

...When my sample size is reduced from 300 to 150, the function complains (length of dimnames[1] not equal to array) and does not produce any results. There are no missing values in the data. Any suggestions for a work-around? Thank you in Advance. > f.total=cph(Surv(fu,censor)~predictor+pred2+pred3,data=data,x=T,y=T,surv=T) > set.seed(6) > val.step=validate(f.total,B=155,bw=T) Backwards Step-down - Original Model No Factors Deleted Factors in Final Model [1] pred2 .Random.seed: 1 -1021164091 1170333634 in .GlobalEnv Iteration: 1 2 3 4 5 6 7 Error in fit(NULL, y[trai...

...ut that. As it is easy to understand I'm not a statistician so be patient please. I want to estimate the possible effects of some predictors on my response variable that is nº of males and nº of females (cbind(males,females)), so, it would be: fullmodel<-glm(cbind(males,females)~pred1+pred2+pred3, binomial) I have n= 11 (ecological data, small sample size is a a frequent problem!). Someone told me that I have to check for normality of the predictors (and in case transform to reach normality) but I am in doubt about the fact that a normality test can be very informative with such a small s...

...<- model.matrix(~ poly(age, 3, coefs =3D attr(p, "coefs")) + Sex,= data =3D > >> Newdata) > >>> > >>> data.frame(pred1 =3D predict(fm, level =3D 0, newdata =3D Newdata), > >> + pred2 =3D mm1 %*% fixef(fm), > >> + pred3 =3D head(predict(fm, level =3D 0)), > >> + pred4 =3D mm2 %*% fixef(fm)) > >> pred1 pred2 pred3 pred4 > >> 1 18.61469 18.61469 23.13079 23.13079 > >> 2 23.23968 23.23968 24.11227 24.11227 > >> 3 29.90620 29.90620 25.59375 25.59375...

...x(dat$popc100))] <- max(dat$popc100) > dat3$popc100 <- dat$popc100 - 1000 > dat3$popc100[which(dat3$popc100 < min(dat$popc100))] <- min(dat$popc100) > pred1 <- predict(mod, type="response") > pred2 <- predict(mod, newdata=dat2, type="response") > pred3 <- predict(mod, newdata=dat3, type="response") > pop.group <- cut(dat$popc100kpc, breaks=quantile(dat$popc100kpc, > seq(0,1,by=.3)), include.lowest=T) > means <- by(cbind(pred1, pred2, pred3), list(pop.group), apply, 2, mean) > means <- do.call(rbind, means) >...

Dear R-users, Half a year ago we put out the R package "glmmADMB" for fitting overdispersed count data. http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html Several people who used this package have requested additional features. We now have a new version ready. The major new feature is that glmmADMB allows Bernoulli responses with logistic and probit links. In addition there

....AIA) ,family=binomial(link="logit"),data=Attrition_data_2) summary(newlogit3) to predict I have a have used the code Test_data_1011 <- read.csv(file="TEST DATA_10_11.csv", header = TRUE, sep = ",", row.names = "employee_id", stringsAsFactors = TRUE) pred3 = predict(newlogit3,Test_data_1011 ,type = "response") Warm regards, Dwaipayan American Express made the following annotations on Tue Jun 12 2012 05:44:05 ****************************************************************************** "This message and any attachments are sol...