search for: corcompsymm

Dear R-devel members, While writing a new correlation structure similar to 'corCompSymm' and intended to be used with 'gnls', I got puzzled with the 'Initialize' method. Using 'Initialize' before 'gnls' may be regarded as a mean to set an initial value for the corStruct parameter. However 'gnls' does not work properly with a 'corComp...

Dear Friends, I am seeking for any help on an error message in lme functions. I use mixed model to analyze a data with compound symmetric correlation structure. But I get an error message: "Error in corMatrix.corCompSymm(object) : NA/NaN/Inf in foreign function call (arg 1)". If I change the correlation structure to corAR1, then no error. I have no clue how to solve this problem. I would highly appreciate any help. Thanks in advance and looking forward to any help. JY I attached my data and codes here:...

We are trying to use the corCompSymm function in nlme The example from the help pages for the corAR1 function gives the following > corAR1(0.2, form = ~ 1 | Mare) Correlation structure of class corAR1 representing Phi 0.2 We are expecting a somewhat similar correlation specification with the help page example for corCompSymm, bu...

dear list, I am wondering if one can find examples and/or more detailed descriptions of modifications needed when going beyond standard corStruct classes (i.e. those already provided for use in lme/nlme)? When I looked at pages 238-239 of Pinheiro/Bates (2000): Mixed-effects models in S and S-plus, I found that I would need a bit more explicit guidance what to do for implementing a new

Dear R Friends, I need help with an error associated with corCompSymm in an lme fit. I am using a mixed effects model to analyze a split-plot with repeated measures and would like to fit with the compound symmetry correlation structure. This problem doesn't occur when using corAR1 or any of the other structures. I would greatly appreciate help on how to solve t...

Hi, I have confirmed temporal correlation problems in my data. Is there a possibility to use corCompSymm for a gamm()? I am an R-beginner. I have very short time series. There are three years and within each year, there are 10 weeks. he 10 weeks are the same every year and have not unique values, I seem not to be able to use AR-1 (I assume that I have too little data for autoregression models of h...

...III F Pr > F GROUP 1 6 2.51 0.1645 TRIAL 3 18 22.34 0.0001 GROUP*TRIAL 3 18 0.58 0.6380 In R, I'm trying the code: results.cs <- lme(DV ~ factor(GROUP)*factor(TRIAL), data=[data set below], random= ~factor(TRIAL)|SUB, correlation=corCompSymm() ) anova(results.cs) which gives the results: numDF denDF F-value p-value (Intercept) 1 18 3383.953 <.0001 factor(GROUP) 1 6 4.887 0.0691 factor(TRIAL) 3 18 239.102 <.0001 factor(GROUP):fa...

Hello list: How to extract the value of "Rho" from a gnls() object. I am using gnls() function similar to res <- gnls(y~SSmicmen(),correlation=corCompSymm(form~1|b),data=dat) Thanks in advance, Mahbub. -- Mahbub Latif School of Mathematical Sciences Queen Mary, University of London United Kingdom [[alternative HTML version deleted]]

...;V22","V23","V24","V25")) colnames(dta1)[1] = "schoolNR" dta2 = dta1[order(dta1$id),] head(dta2) timef = factor(dta2$time) summary(mdl1l <- lme(score~timef-1, dta2, ~timef-1|schoolNR/id,,,,"ML")) summary(mdl1g <- gls(score~timef-1, dta2, corCompSymm(, ~timef|schoolNR/id), varIdent(, ~1|id*timef),,"ML"))

...1 = 0) t.test(mydata[myfactor=='f2',1],mydata[myfactor=='f1',1],paired=TRUE) # yields : t = 3.1379, df = 4, p-value = 0.03492, mean of the differences= 1.6 # lme (f1 as reference level) fit.lme <- lme(dv ~ myfactor, random = ~1|subject,method="REML",correlation=corCompSymm(),data=mydata) summary(fit.lme) # yields identical results as paired t test # f2-f1: t = 3.1379, df = 4, p-value = 0.0349 summary(glht(fit.lme,linfct=mcp(myfactor="Tukey"))) # while test statistic is comparable, p value is different # have noticed cases where the differences betw...

...ot;conLin")$dims$ngrps, lme(distance ~ age + Sex, data = Orthodont, random = ~ 1|Subject) # Error in array(c(rep(1, p), .C(inner_perc_table, as.double(X), as.integer(unlist(grps)), : # object 'inner_perc_table' not found gls(distance ~ age + Sex, data = Orthodont, correlation = corCompSymm( form = ~ 1 | Subject)) # Error in corMatrix.corCompSymm(object) : # object 'compSymm_matList' not found # #Upon debugging, the error is thrown by line 60 of gls code # #glsSt <- Initialize(glsSt, dataMod, glsEstControl) R.version # _ # platform x86_64-w64-mingw32 # arch...

Hi I Have a very large dataset that I would like to conduct ANOVA tests on. Im not a very strong programmer so any help would be appreciated. the format is Identifier             A1       A2        B1      B2       C1   C2      Norm1         Norm2 1234                  1        1            NA     NA      4       3        NA               NA 4567                  2        2              4     

...re response-specific, e.g. a constant for cost (cons.1) and one for effect (cons.2) , a treatment term for cost (treat.1) and one for effect (treat.2). The syntax I used is this: lme(yl~ -1+cons.1+treat.1+cons.2+treat.2, random=~-1+cons.1+cons.2|cidl, weights=varIdent(form=~1|cons.1), corr=corCompSymm(form=~1|cidl/indl), data=datalong) where cidl = cluster id, indl = individual id and datalong = data (also data had been stacked before model fitting) I have been trying to extend this model to allow individual level standard deviations and correlations to differ by cluster. In my data, there...

...;m attempting to fit a generalized linear mixed-effects model using lmer (R v 2.6.0, lmer 0.99875-9, Mac OS X 10.4.10) using the call: vidusLMER1 <- lmer(jail ~ visit + gender + house + cokefreq + cracfreq + herofreq + borcur + comc + (1 | code), data = vidusGD, family = binomial, correlation = corCompSymm(form = 1 | ID), method = "ML") Although the model fits, the summary indicates the model is a "Generalized linear mixed model fit using Laplace". I've tried any number of permutations; is only Laplace supported in lmer, despite the text of the help file? Thanks for any guid...

...lme(distance ~ age + Sex, data = Orthodont, random = ~ 1|Subject) > > # Error in array(c(rep(1, p), .C(inner_perc_table, as.double(X), as.integer(unlist(grps)), : > # object 'inner_perc_table' not found > > gls(distance ~ age + Sex, data = Orthodont, > correlation = corCompSymm( form = ~ 1 | Subject)) > > # Error in corMatrix.corCompSymm(object) : > # object 'compSymm_matList' not found > # > #Upon debugging, the error is thrown by line 60 of gls code > # > #glsSt <- Initialize(glsSt, dataMod, glsEstControl) > > R.version > > #...

...ix. See vignette('troubleshooting') I also tried Ben Bolker?s suggestion (posted on Nabble; see link above) to use glmmPQL, but I got an error message: m3 <- glmmPQL(Diract ~ Dir*Rec + offset(LnScan) + Year, random = ~ 1 | ID, family = negative.binomial(1), data = Direct, correlation=corCompSymm(form=~1|ID)) Error in glmmPQL(Diract ~ DirPar * RecPar + offset(LnScan) + Year, random = ~1 | : could not find function "corCompSymm" If anyone has tips for a) a GEE with a negative binomial family and/or b) making sure I am specifying the random effect in a GLMM correctly to account...

...4",..: 2 2 2 2 2 2 7 7 7 7 ... $ sex_ratio: Factor w/ 3 levels "3m:9f","6m:0f",..: 3 3 3 3 3 3 3 3 3 3 ... $ response : num 62.6 72.2 68.9 81.3 54.8 ... 1> ###Now the first model 1> library(nlme) 1> cs1<-gls(response~rep * sex_ratio, data=tmp.data, 1+ corr=corCompSymm(, form=~ 1 | tank), method="ML") 1> summary(cs1) Generalized least squares fit by maximum likelihood Model: response ~ rep * sex_ratio Data: tmp.data AIC BIC logLik 223.8389 236.5071 -103.9195 Correlation Structure: Compound symmetry Formula: ~1 | tank Paramete...

  Hola,   Por favor, ¿podríais indicarme qué recursos (librerías o ideas) pueden resultar de utilidad para crear un gráfico del estilo del de la figura 3.8 del siguiente link?   http://www.tsc.uvigo.es/BIO/Bioing/ChrLDoc3.html#3.5   Actualmente estoy utilizando funciones muy básicas y la verdad es que no me encuentro muy satisfecha con el resultado.   Muchas gracias.   Eva [[alternative HTML

...me obvious lines): NonAdditive model model vr = A B A*B; random suj A*suj B*suj; repeated / type=cs subj=suj; Additive model; model vr = A B A*B /ddfm=satterth; repeated / type=cs subj=suj; Using LME I do not find any problems to fit the additive model with lme(vr~A*B, random=~1|suj, cor=corCompSymm()) but I have found some difficulties fitting the nonadditive model. Can anyone help me? Thanks in advance. Manuel Ato Dpto. Psic.B?sica y Metodolog?a Apartado 4021 30080 MURCIA (Spain) e-mail: matogar at um.es

...mission binary outcome (Y/N) and total volume of their most responsible physician as the covariate. Since any physician can have multiple patients, the data contains clustering structure which I am trying to account for. My original formula is a <- gamm(readmission~s(volume,fx=F), correlation=corCompSymm(0.1,form=~1|physician), family=binomial) During the first iteration, I received the warning " Incompatible formulas for groups in "random" and "correlation"". Frankly I don't understand what it means. So I looked up the archive and saw one posted by Prof. Thomas...