similar to: summary nls output

Hi all, I am having issues comparing models with lmer. As an example, when I run the code below the model summaries (AIC, BIC, loglik) differ between the summary() and anova() commands. Can anyone clear up what's wrong? Thank you! Darren Ward library(lme4) data(sleepstudy) fm1<-lmer(Reaction ~ Days + (1|Subject), sleepstudy) summary(fm1) fm2<-lmer(Reaction ~ Days +

My question concerns the logic behind hypothesis tests for fixed-effect terms in models fitted with lme. Suppose the levels of Subj indicate a grouping structure (k subjects) and Trt is a two-level factor (two treatments) for which there are several (n) responses y from each treatment and subject combination. If one suspects a subject by treatment interaction, either of the following models seem

Dear R users: I have a problem about catch the value from function. I have following two functions (part): fbolus1 <- function() {......... par<-data.frame(Parameter=c("kel","Vd"),Initial=c(0)) check(par) .....} check<-function(par) { if (par[ ,2] <= 0){ cat("\nEnter again (y/n) ?\n\n") ans<-readline() if (ans == "n" ){

The subject says it all really. Question 1. Here is some code created to illustrate my problem, can anyone spot where I'm going wrong? Question 2. The reason I'm following a manual specification of models relates to the fact that in reality I am using mgcv::gam, and I'm not aware that dredge is able to separate individual smooth terms out of say s(a,b). Hence an additional request,

Dear All -- I am trying to use within a little table producing code an anova comparison of two gls fitted objects, contained in a list of such object, obtained using nlme function gls. The anova procedure fails to locate the second of the objects. The following code, borrowed from the help page of anova.gls, exemplifies: --------------- start example code --------------- library(nlme) ##

Hello: Will someone PLEASE help me with this problem. This is the third time I've posted it. When I appply anova() to two equations estimated using glmmPQL, I get a complaint, > anova(fm1, fm2) Error in anova.lme(fm1, fm2) : Objects must inherit from classes "gls", "gnls" "lm","lmList", "lme","nlme","nlsList", or

Paul: I may have found the issue (which is similar to your conclusion). I checked using egsingle in the mlmRev package as these individuals are strictly nested in this case: library(mlmRev) library(nlme) fm1 <- lme(math ~ year, random=~1|schoolid/childid, egsingle) fm2 <- lmer(math ~ year +(1|schoolid:childid) + (1|schoolid), egsingle) Checking the summary of both models, the output is

Hi there, I have a data frame as listed below: > Ca.P.Biomass.A P Biomass 1 334.5567 0.2870000 2 737.5400 0.5713333 3 894.5300 0.6393333 4 782.3800 0.5836667 5 857.5900 0.6003333 6 829.2700 0.5883333 I have fit the data using logistic, Michaelis?Menten, and linear model, they all give significance. > fm1 <- nls(Biomass~SSlogis(P, phi1, phi2, phi3), data=Ca.P.Biomass.A)

Hello, I wanted to fit a linear mixed model to a data that is similar in terms of design to the 'Machines' data in 'nlme' package except that each worker (with triplicates) only operates one machine. I created a subset of observations from 'Machines' data such that it looks the same as the data I wanted to fit the model with (see code below). I fitted a model in

Hello: Let me rephrase my question to attract interest in the problem I'm having. When I appply anova() to two equations estimated using glmmPQL, I get a complaint, > anova(fm1, fm2) Error in anova.lme(fm1, fm2) : Objects must inherit from classes "gls", "gnls" "lm","lmList", "lme","nlme","nlsList", or "nls"

Dear people of the help list I am drying to analyze my data using the 'lmer' function and I keep having problems. This is the model: > fm1<-lmer(dbh~spec+scheme+(1|Plot),data=d, REML=FALSE). I analyse tree size (dbh) of 3 different species (spec) and 3 planting schemes (scheme). I have 5 plots, which I hope to model as a random factor. (However, the subsequent output is based on

Dear all, It looks like the parameters var-cov matrix returned by gee() is not adjusted for the scale parameter: > fm1 <- gee(nbtrp ~ strate * saison + offset(log(surf)), family = poisson, data = Eff2001, + id = loc, tol = 1e-10, corstr = "exchangeable") [1] "Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27" [1] "running glm to get initial

>>>>> Anupam Tyagi >>>>> on Tue, 9 Jul 2024 16:16:43 +0530 writes: > How can I do automatic knot selection while fitting piecewise linear > splines to two variables x and y? Which package to use to do it simply? I > also want to visualize the splines (and the scatter plot) with a graph. > Anupam NB: linear splines, i.e. piecewise

Hi there fellow R-users, I am trying to follow an example of modelling a serial correlation structure in the textbook "Mixed Effects Model in S and Splus". However, I am getting some very odd results. Here is what I am trying to run: library(nlme) data(Ovary) fm1<-lme(follicles~sin(2*pi*Time)+cos(2*pi*Time),data=Ovary,random=pdDiag(~s in(2*pi*Time))) ### The example is fine up

Hello, I'm a newcomer to R so please forgive me if this is a silly question. It's that I have a linear regression: fm <- lm (x ~ y) and I want to test whether the slope of the regression is significantly less than 1. How can I do this in R? I'm also interested in comparing the slopes of two regressions: fm1 <- lm (x ~ y) fm2 <- lm (a ~ b) and asking if the slope of fm1 is

I'm trying to use the FollowMe app with Asterisk 1.4.17. I've followed the WIKI page on setting it up but I can't seem to get it to work. Here is my Asterisk version: pbx1*CLI> core show version Asterisk 1.4.17 built by root @ pbx1 on a i686 running Linux on 2008-01-10 12:08:48 UTC Here is a log of when the FollowMe is being called: NOTE: I've tried to use the AstDB as

dear all, I apologize for my delay in replying you. Here my contribution, maybe just for completeness: Similar to "earth", "segmented" also fits piecewise linear relationships with the number of breakpoints being selected by the AIC or BIC (recommended). #code (example and code from Martin Maechler previous email) library(segmented) o<-selgmented(y, ~x, Kmax=20,

I have the following code: ----------------------------------------------------------------------------------------------------- result <- matrix(NA, nrow=1, ncol=5) for(i in 1:(nsnp-1)) { for(j in (i+1):nsnp){ tempsnp1 <- data.lme[,i] tempsnp2 <- data.lme[,j] fm1 <- lme(trait~sex+age+rmtemp.b+fc+tempsnp1+tempsnp2+tempsnp1*tempsnp2, random=~1|famid, na.action=na.omit) fm2 <-

Hello, In the following I tried 3 versions of an example in R help. Only the two first predict command work. After : library(splines) require(stats) 1) fm1 <- lm(weight ~ bs(height, df = 5), data = women) ht1 <- seq(57, 73, len = 200) ph1 <- predict(fm1, data.frame(height=ht1)) # OK plot(women, xlab = "Height (in)", ylab = "Weight (lb)") lines(ht1, ph1) 2)

Dear R users: I have two set of data, as follow: x<-c(0,0,0.28,0.55,1.2,2,1.95,1.85, 1.6,0.86,0.78,0.6,0.21,0.18) y<-c(0,0,0,0.53,1.34,1.79,2.07,1.88, 1.52,0.92,0.71,0.55,0.32,0.19) i<-1:length(x) I want to sum each (x[i]-y[i])^2/x[i] together, like: >Sum <-sum((x[i]-y[i])^2/x[i]) >Sum [1] NaN Because the denominator shoud not be zero. So I want to overlook those