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Hi all, I have some difficulties with the lme function and so this is my problem. Supoose i have the following model y_(ijk)=beta_j + e_i + epsilon_(ijk) where beta_j are fixed effects, e_i is a random effect and epsilon_(ijk) is the error. If i want to estimate a such model, i execute >lme(y~vec.J , random~1 |vec .I ) where y is the vector of my data, vec.J is a factor object and vec.I is the vector for the...

...atments, but stratifies by study. This means that the cut-off points associated with the distribution of the underlying latent variable for determining the response category are allowed to vary from study to study but are the same for both treatment groups within a study. It is given by log(Q_{ijk}/(1-Q_{ijk}) = \alpha_{ik} + \beta*x_{ij} (k=1,...,m-1) This model can be considered as arising from a latent continuos variable . Assume that the response of the j-th subject in study i is truly equal to G_{ij} although this latent reponse will never be observerd. G_{ij} has a logistic distrib...

...'? The desired end result can be achieved by logically combining the results of a grep using 'rex1' with the results of a grep on 'rex2', illustrated by the following example: ## Given character vector X (below), and two regular exdpressions ## rex1="abc", rex2="ijk", to return the elements of X which match ## rex1 AND do not match rex1: X <- c( "abcdefg", # Yes "abchijk", # No "mnopqrs", # No "ijkpqrs", # No "abcpqrs" ) # Yes rex1 <- "abc" rex2 <-...

...judge the variability of the parameter estimates produces some strange results. The posterior sample is constant for the fixed effects, and the estimates of the variance components are way out in the tails of their posterior samples. The model I'm using says (for l = 1, ..., L - 1) logit P(X[ijk] = l | X[ijk] >= l, U[i], V[j], W[k]) = U[i] + V[j] + W[k] + a[l] where X[ijk] is the ordinal response to question k for individual j in area i. The U, V, and W are random effects and the a's are fixed effects. Here's a function to simulate data which mimics this setup (with a sequence...

Dear R users: I've a problem with lme function, when I want to model an unbalanced two-way anova, with 2 random factors say t and b. My two models are: model1- y(ijk) = beta+b(i)+t(j)+epsilon(ijk) model2- y(ijk)= beta+b(i)+t(j)+b:t(ij)+epsilon(ijk) beta overall mean effect The data.frame is X t b med celda 1 1 10 1 1 1 12 1 1 1 11 1 1 2 13 2 1 2 15 2 1 3 21 3 1 3 19 3 2 1 16 4 2...

Dear all I need to run an anova from a factorial model y_{ijk}=\alpha_i+\beta_j+(\alpha\beta)_{ij}+e_{ijk} and calculate type II and III sums of square, but I have an empty cells, so anova function from package car fail. (I believe) y<-c(7,13,6,10,8,11,8,3,7,5,65) a<-as.factor(c(1,1,2,2,3,3,3,1,1,1,2)) b<-as.factor( c(rep(1,7),rep(2,4)) ) table...

Hello everyone, I wanted to ask if there is an R-package to fit the following Poisson regression model log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k} i=1,\cdots,N (subjects) j=0,1 (two levels) k=0,1 (two levels) treating the \phi_{i} as nuinsance parameters. Thank you very much -- -Tony [[alternative HTML version deleted]]

Dear All, I wonder if there is an efficient way to fit the generalized linear mixed model for multivariate outcomes. More specifically, Suppose that for a given subject i and at a given time j we observe a multivariate outcome Yij = (Y_ij1, Y_ij2, ..., Y_ijK). where Y_ijk is a binomial(n_ijk, p_ijk). One way to jointly model the data is to use the following specification: g(p_ijk) = beta_0k + b_0ik + (beta_1k + b_1ik)*x_ijk with k = 1,2 ...., K , g is a specified link function and (b_0ik,b_1ik) k=1,...K are random effects ... I my case, the gl...

...(i.e., instead of the error EMS). Here is the example: Expected Mean Square df Mi ?2+18?2M 1 Ij ?2+6?2MI+12?(I) 2 MIij ?2+6?2MI 2 ?(ijk)l ?2 30 The clear test for Ij is EMS(I) / EMS(MI) - F(2,2) However, observe the following example carried out in R, M <- rep(c("M1", "M2"), each = 18) I <- as.ordered(rep(rep(c(5,10,15), each = 6), 2)) y <- c...

HI, I would like to extract the variance components estimation in lme function like a.fit<-lme(distance~age, data=aaa, random=~day/subject) There should be three variances \sigma_day, \sigma_{day %in% subject } and \sigma_e. I can extract the \sigma_e using something like a.fit$var. However, I cannot manage to extract the first two variance components. I can only see the results in

...ormation about discrete multivariate analysis. This chapter is restricted to an introduction to log-linear models for analysis of multidimensional contingency tables. For example, in the log-linear model for the 3-way table e can test several Hypothesis: Note: MLE, maximum likelihood estimator Mijk: counts in the cell i, j, k Hypothesis # Hypothesis df MLE of Mijk 1 (ABC) (a-1)(b-1)(c-1) ---- 2 (AB)(ABC) (a-1)(b-1)c mi+k m+jk/m++k 3 (AC)(ABC) (a-1)(c-1)b mij+m+jk/m+j+ ..... 8 (AB)(AC)(BC)(ABC) abc-a-b-c+2 mi++m+j+m++k/n? .......... 18 for better understanding it...

I have the following: layout: <%= javascript_include_tag :defaults %> [...] <div id="menu"> <div id="myrandom"></div> <%= render :partial => ''menu'' %> </div> menu partial: <%= link_to_remote "Generate Text", :update => ''myrandom'', :url => { :action =>

Hello, First of all, let me apologize that my statistics background is modest at best. I am doing some extreme value analysis on model output (WRF) which have the following dimensions: speed(time,lat,lon) I am trying to fit the GPD (gpd.fit) to each point (time,lat,lon) to get a return level plot with values at each grid point. (Map with return level by location.) Here is some code I

Hola. Hay alguna función que haga un Friedman test (digamos 4 tratamientos o tiempos relacionados/dependientes) y que después haga una comparación de un tratamiento contra el resto, digamos el primero, como un contratase simple, o un Dunnett? o simplemente ¿como hago un Dunnett para unos tratamientos relacionados? -- Antonio M [[alternative HTML version deleted]]

...lang-check-mac-libcxx-abspath.cpp.tmp/mock-libcxx/include/c++ 12K build/tools/clang/test/Tooling/Output/clang-check-chdir.cpp.tmp 12K build/tools/clang/test/Tooling/Output/clang-check-builtin-headers.cpp.tmp 12K build/tools/clang/test/Tooling/Output/auto-detect-from-source-parent.cpp.tmp/abc/def/ijk 12K build/tools/clang/test/Tooling/Output/auto-detect-from-source-parent-of-cwd.cpp.tmp/abc/def/ijk 12K build/tools/clang/test/Profile/Output/gcc-flag-compatibility.c.tmp.dir/some 12K build/tools/clang/test/Preprocessor/Output/iwithprefix.c.tmp.tmps 12K build/tools/clang/test/PCH/Output/verify_...

...il.com> To: r-help at r-project.org Subject: [R] Poisson Regression Message-ID: <AANLkTikXc5tvziGaxuV1GqM3CgNyPPpay-FQCC6uzQWE at mail.gmail.com> Content-Type: text/plain Hello everyone, I wanted to ask if there is an R-package to fit the following Poisson regression model log(\lambda_{ijk}) = \phi_{i} + \alpha_{j} + \beta_{k} i=1,\cdots,N (subjects) j=0,1 (two levels) k=0,1 (two levels) treating the \phi_{i} as nuinsance parameters. Thank you very much -- -Tony [[alternative HTML version deleted]] ------------------------------ Message: 109 Date: Wed, 13 Oct 2010 14:54:39 -...

I have a layout which contains multiple divs, (header, menu, content, footer), content contains <%= @content_for_layout %>. I have switched all link_to and form_tag to link_to_remote and form_remote_tag and they are supposed update the content div. It is working, however instead of updating just the div content, it is re-rendering the entire layout within the div. Hopefully someone can