similar to: PREDICTIONS from a PIECEWISE LINEAR (mixed) MODEL: THEY AIN'T LINEAR BETWEEN BREAK POINTS!!

Hi folks... check this out.. > GLU<-lme(gluc~rt*cd4+sex+age+rf+nadir+pharmac+factor(hcv)+factor(hbs)+ + haartd+hivdur+factor(arv), + random= ~rt|id, na.action=na.omit) > intervals(GLU)$fixed lower est. upper (Intercept) 67.3467070345 7.362307e+01 7.989944e+01 rt *0.0148050160* 6.249304e-02 1.101811e-01 cd4

Hi, I am bring SPEEX up on Analog Device's Blackfin (using Visual DSP++ tool set). The floating point was too slow on this fixed-point processor so I am going for the integer version. The floating point library build was very clean, but when I build with FIXED_POINT defined, the compiler exits when it find this discrepancy in \libspeex\cb_search.c : ".\libspeex\cb_search.c",

hello list, Can someone help me figure out why the following code doesn't work? I'm trying to but both Greek letters and subscripts into a tcltk menu. The code creates all the mu's, and the 1 and 2 subscripts, but it won't create the 0. Is there a certain set of characters that R won't recognize the unicode for? Or am I input the \u2080 incorrectly? library(tcltk) m

Dear R-Help, I am using the LIMMA User's Guide 5 January 2007 PDF version. For the example show in Section 7.4 DIRECT TWO-COLOR DESIGNS (pgs. 33-34), I could not grasp the rationale in developing the contrast.matrix with these R statements (">" indicates the R command prompt): > contrast.matrix <-

I have a list of dataframes, and want to apply a function to subsets of the rows of each dataframe. It seemed natural to write a function that takes a dataframe as an argument, and uses 'by() within it to apply the function to the dataframe subsets. However, I cannot get it to work. The problem seems to be passing the data argument of by() as a function argument. Is this bug, or am I

Hi dears, I do > CHOL<-lme(chol~rt*cd4+sex+age+rf+nadir+pharmac+factor(hcv)+factor(hbs)+ haartd+hivdur+factor(arv), random= ~rt|id, na.action=na.omit) ...runs sweet,..then ....try a multicomparisons approach for the categorical rf > summary(glht(CHOL, linfct=mcp(rf="Tukey"))) * Error in model.frame.default(object, data, xlev = xlev) : l'oggetto non è una matrice

Hi, I'm trying to do a multiple events model using coxph. I need to choose which covariates stay in the final model and i'm using anova but it seems that it doesn't work with robust variances as a error message appears when I try to do it: /PWPfit<-coxph(Surv(ini,fim,status)~Sexo+Transmissao+Regime+HAART+Drogas+Psi+ + CD4+CV+Neo+IO+cluster(idPARTICIPANTE)+strata(Estrato),data=PWP)

Hi all,I have some questions about the covariants of regression. My target: To explore the trend of CD4 level through a period of time. Response variable: CD4 count Explanatory variable:time Also, the demology information is available,such as gender,occupation,income level... Q1,Are these variables of demology information called covariant? Q2,How can I correct the impact of

Hi, I am an R newbie and one thing I am having trouble with binding variables that I have created within one data frame into a new data frame when using na.omit(). To illustrate this problem I will give the example I am working on and the approah I have been using:- data.frame1<-filepath.... attach(data.frame1) #create a new variable using a function new.variable<-rep(1,length(weight3))

Hi dears while modeling an interaction random effect in lmer i receive the instantaneous error message > ldlM4<-lmer(ldl~rt*cd4+age+rf+pharmac+factor(hcv)+ + hivdur+(rt:cd4|id),na.action=na.omit,REML=F) *Warning message: In mer_finalize(ans) : false convergence (8) * I think the matter lies in syntax, 'cause i sistematically receive the same message even when changing response... PS:

Thomas, Thank you for your reply about the for () loop. The as.character advice worked. Sorry for the delay in getting back to?I had to set the project aside for a few weeks. This didn?t work exactly as is for (patient in as.character(1:n)){ pt <- MRN == patient (rest of the function) } But this did for (patient in as.character(levels(MRN))){ pt <- MRN == patient (rest of the function)

Hi all: There's a question about glht function. My data:data_ori,which inclue CD4, GROUP,time. f_GROUP<-factor(data_ori$GROUP) f_GROUP is a factor of 3 levels(0,1,2,3) result <- lme(sqrt(CD4) ~ f_GROUP*time ,random = ~time|ID,data=data_ori) glht(result, linfct = mcp(f_GROUP="Tukey") ) Error in `[.data.frame`(mf, nhypo[checknm]) : undefined columns selected I can't

Hello, I am working from a linux 64 machine on a server with R-2.12 (I can't update to 2.13). I am iterating through many linear mixed models for longitudinal data and I occasionally receive the following convergence error: > BI.lme <- lme(cd4 ~ time + genBI + genBI:time + C1 + C2 + C11 + C12, random =~ 1 + time | IID, data = d) Error in lme.formula(cd4 ~ time + genBI + genBI:time +

Hello! I need some help with creating plots for each study subject. I have tried the 'for' command as described in 'An Introduction to R', but I wasn't successful. Here's what I want to do: -Create and save the following overlayed scatterplots for each subject (1 to n). -The data frame is in the form of one line per visit per subject (so more than one line per

I need to remove certain interactions and keep only the one between the second level of the factor and the continuous var t2 bin4 <- glm(resp2~ t*t2+c5.vrm,data=dfa,family="quasibinomial") > summary(bin4) Call: glm(formula = resp2 ~ t * t2 + c5.vrm, family = "quasibinomial", data = dfa) Deviance Residuals: Min 1Q Median 3Q Max -6.5464

Hello everyone, I would like to perform a 2-way manova test, but I'm having some issues. I implemented like this Y<-cbind(Resp1,Resp2,Resp3,....,Respn) model<-manova(Y "tilda" FactorA*FactorB) summary.aov(model) 1. I don't know at what level I have to do the Type I error correction. Is it on p-values returned by "summary.aov(model)? Or is it when I compare each

Dear MCMCglmm users, I am currently struggling with the specification of a proper prior and model formula for a multi-response MCMCglmm with two of the three response variables being Gaussian and the third being za-poisson. The model includes several fixed effects and three nested random effects. In general, I would prefer to fit a model with a fixed effect of trait and suppressed intercept for

Hi I am using the flowClust package from BioConductor, which is largely implemented in c. For some of my data, the package occasionally (and quite stochastically) encounters a particular condition which halts its operation. At this point, it calls the error() function defined by Rcpp, and halts. What I would like to be able to do is to catch the error thrown, and retry the operation a few

Hi I have two data frames that contains the same sort of data and I want do add them together to get one big data frame, anyone know how to do that? ex: data.frame1 A B C 1 2 3 4 5 6 7 8 9 and data.frame2 A B C 9 8 7 6 5 4 3 2 1 Would then become one big set: data.frame3 A B C 1 2 3 4 5 6 7 8 9 9 8 7 6 5 4 3 2 1 Thx for your help //Joel -- View this message in context:

Hi Is there any way to fit a bivaraite longitudinal mixed model using R. I have a data set with col names resp1 (Y_ij1), resp2 (Y_ij2), timepts (t_ij), unit(i) j=1,2,..,m and i=1,2,..n. I want to fit the following two models Model 1 Y_ij1, Y_ij2 | U_i = u_i ~ N(alpha + u_i + beta1*t_ij, Sigma) U_i ~ iid N(0, sigu^2) Sigma = bivariate AR structure alpha and beta are vectors of order 2.