search for: fm0

Windows 2000 (v.5.00.2195), R 1.5.1 I have an lme object, fm0, which I examine with anova() and intervals(). The anova output indicates one of the interaction terms is significant, but the intervals output shows that the single parameter for that term includes 0.0 in its 95% CI. I believe that the anova is a conditional (sequential) test; is intervals marg...

I'm having trouble using binomial(link="cloglog") with GLMM in lme4, Version: 0.5-2, Date: 2004/03/11. The example in the Help file works fine, even simplified as follows: fm0 <- GLMM(immun~1, data=guImmun, family=binomial, random=~1|comm) However, for another application, I need binomial(link="cloglog"), and this generates an error for me: > fm0. <- GLMM(immun~1, data=guImmun, family=binomial(link="cloglog"), random=~1|comm) Erro...

...S package. I haven't been able to reproduce the plot either in R ( version 1.5 ) and S. It makes the axes and it puts the labels, but it doesn't shows the contour lines. Could you help me resolving this problem? This is the code that appears in the book and I've been testing. > fm0 <- lm(Wt*Time ~ Viscosity + Time -1, data= stormer) > b0 <- coef(fm0); names(b0) <- c("b1", "b2") > storm.fm <- nls(Time ~ b1*Viscosity/(Wt-b2), data = stormer, start=b0, trace = T) > bc <- coef(storm.fm) > se <- sqrt(diag(vcov(storm.fm))) > dv...

...rf, x, dummy) suggestion rm(fm, fm1, lrf, x, y, w, dummy) The next section will look at data from the classical experiment of Michaelson and Morley to measure the speed of light. file.show("morley.tab") mm <- read.table("morley.tab") suggestion mm <- data(morley) rm(fm, fm0) suggestion rm(fm, fm0, mm) objects(); rm(x, y, f, fa) suggestion objects(); rm(x, y, f, fa, oldpar) It might also be usefull to use # for comments, since it is easier to copy&paste to R terminal from manual. -- Lep pozdrav / With regards, Gregor GORJANC --------------------------------...

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive defin...

...m trying to use stepAIC for an lmer object but it doesn't work. Here is an example: x1 <- gl(4,100) x2 <- gl(2,200) time <- rep(1:4,100) ID <- rep(1:100, each=4) Y <- runif(400) <=.5 levels(Y) <- c(1,0) dfr <- as.data.frame(cbind(ID,Y,time,x1,x2)) fm0.lmer <- lmer(Y ~ time+x1+x2 + (1|ID), data = dfr, family = binomial) fm.lmer <- stepAIC(fm0.lmer,scope = list(upper = ~I(time-6)*SF_0_N4*SEX, lower = ~1,trace = FALSE)) I obtain the following error: Error in terms.default(object) : no terms component I would be very gr...

...say s(a,b). Hence an additional request, if anyone has example code for using gam in a multimodel inference framework, especially with bivariate smooths, I'd be most grateful. Cheers and Thanks in Advance Mike require(MuMIn) data(Cement) # option 1, create model.selection object using dredge fm0 <- lm(y ~ ., data = Cement) print(dd <- dredge(fm0)) fm1 <- lm(formula = y ~ X1 + X2, data = Cement) fm2 <- lm(formula = y ~ X1 + X2 + X4, data = Cement) fm3 <- lm(formula = y ~ X1 + X2 + X3, data = Cement) fm4 <- lm(formula = y ~ X1 + X4, data = Cement) fm5 <- lm(formula = y ~...

Dear All, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

Hi, I am wondering how I can specify no intercept in a mixed model using lmer(). Here is an example dataset attached ("test.txt"). There are 3 workers, in 5 days, measured a response variable "y" on independent variable "x". I want to use a quadratic term (x2 in the dataset) to model the relationship between y and x.

Sorry I made a mistake for the variables in the model: fm0.lmer <- lmer(Y ~ time+x1+x2 + (1|ID), data = dfr, family = binomial) fm.lmer <- stepAIC(fm0.lmer,scope = list(upper = ~ time*x1*x2, lower = ~1,trace = FALSE)) I obtain the following error: Error in terms.default(object) : no terms component I would be very grateful for...

Hello R-users! I tried to find a package to run a CHOW TEST. As a reference package I found the STRUCCHANGE package. Do you know if it works well otherwise can you recommend a different one? Thanks, Bernd -- View this message in context: http://www.nabble.com/Chow-Test-tf3878416.html#a10990270 Sent from the R help mailing list archive at Nabble.com.

Dear All: I need some guidance in calculating a goodness-of-fit statistic for a Tobit Regression model. To develop the Tobit regression, I used the tobit() method from the AER package, which is basically a simpler interface to the survreg() method. I've read about pseudo R2 and C-index and was wondering if there is a package that calculates this for me. Also, is there a reason to select

Hi, I would like to compare to regression models - each model has a different dependent variable. The first model uses a number that represents the learning curve for reward. The second model uses a number that represents the learning curve from punishment stimuli. The first model is significant and the second isn't. I want to compare those two models and show that they are significantly

k=lm(y~x) summary(k) returns R^2=0.9994 lm(y~x) is supposed to find coef. a anb b in y=a*x+b l=lm(y~x+0) summary(l) returns R^2=0.9998 lm(y~x+0) is supposed to find coef. a in y=a*x+b while setting b=0 The question is why do I get better R^2, when it should be otherwise? Im sorry to use the word "MS exel" here, but I verified it in exel and it gives: R^2=0.9994 when y=a*x+b is used

consider p as random effect with 5 levels, what is difference between these two models? > p5.random.p <- lmer(Y ~p+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) > p5.random.p1 <- lmer(Y ~1+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) thanks, Aimin Yan

...space intricacies) can >>> suggest something. >>> >>> >>>> Tahira Jamil wrote: >>>>> Hi >>>>> I can extract the AIC value of a model like this >>>>> >>>>> AIC(logLik(fm0) >>>>> >>>>> How can I extract the BIC value if I need! >>>>> >>>>> Cheers >>>>> Tahira >>>>> Biometris >>>>> Wageningen University >>>>> &g...

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