similar to: Installing archived package alongisde current one

Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]

Let's say I have the following string: str <- "P = 0.0, T = 0.0, Q = 0.0" I'd like to find a function that generates the following object from 'str'. list(P = 0.0, T = 0.0, Q = 0.0) Thanks! -- http://mutualism.williams.edu

Hello list members, I'm looking for a way to force the results of a crosstabulation to be square - that is, to include 0 values. For example: table(letters[1:4],letters[c(1:3,3)]) yields: a b c a 1 0 0 b 0 1 0 c 0 0 1 d 0 0 1 I would like to return: a b c d a 1 0 0 0 b 0 1 0 0 c 0 0 1 0 d 0 0 1 0 Any suggestions? Thanks! -- Manuel A. Morales

Dear R users, I have now tried out several options of obtaining p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and single-term deletions with subsequent chi-square tests (although I am aware that the latter may be problematic). However, I encountered several problems that can be classified as (1) the quasipoisson lmer model does not give p-values when

Dear list, This is more of a stats question than an R question per se. First, I realize there has been a lot of discussion about the problems with estimating P-values from F-ratios for mixed-effects models in lme4. Using mcmcsamp() seems like a great alternative for evaluating the significance of individual coefficients, but not for groups of coefficients as might occur in an experimental design

Hello list, I'm trying to fit a model like beta[trt]/(1+alpha*x) where the data include some grouping factor. The problem is that the estimate for alpha is undefined for some of the treatments - any value greater than 20 is equally good and a step function would suffice. Ignoring the grouping structure, I can fit this using nls with the port algorithm by restricting the upper value of alpha

Dear list, A while ago, I posted a question asking how to use data or subset arguments in a user-defined function. Duncan Murdoch suggested the following solution in the context of a data argument: data <- data.frame(a=c(1:10),b=c(1:10)) eg.fn <- function(expr, data) { x <- eval(substitute(expr), envir=data) return(mean(x)) } eg.fn(a,data) I've

Hola Manuel lo he tratado de hacer pero me sale Error: unexpected string constante in: "anova(a,as,test=Chisq") no tengo ni idea de por qué... Me resulta alucinante no poder contar ya con pvals.fnc. ¿Será imposible hacerse con ello? Saludos, Miguel -------------------------------------------- El vie, 13/6/14, Manuel Azcárate <mazcarategarcia en gmail.com> escribió:

I tried to submit this in R, but not sure if it worked. When running R as sudo, using ctrl-c dumps me to the command line. Hitting exit to exit the terminal window results in R taking 100% of resources. I am using R-2.1.0 on Fedora Core 3. Thanks. Manuel

Hello list, Is anyone aware of a non-lattice-based alternative to xyplot()? Thanks! Manuel -- http://mutualism.williams.edu

Hello list, I'm doing a bootstrap analysis where some models occasionally fail to converge. I'd like to automate the process of restricting AIC to the models that do converge. A contrived example of what I'd like to do is below: resp <- c(1,1,2) pred <- c(1,2,3) m1 <- lm(resp~pred) m2 <- lm(resp~poly(pred,2)) m3 <- lm(resp~poly(pred,3)) # Fails, obviously ## Some

Existe discusión sobre el uso de los p-valores en modelos mixtos. Como se ha dicho antes, para mi lo más adecuado es comparar modelos mediante la función anova. Por Internet se puede encontrar un buen libro de Douglas Bates y en español, busca modelos mixtos con R de Luis Cayuela, enfocado hacia ecología, pero está muy bien El 13/06/2014 14:00, "Jorge I Velez"

Estimados integrantes de la lista. Disculpas por posteo cruzado. Estoy ajustando un modelo con lmer (lm4). La variable respuesta es un índice (ADI) que se midió en 3 áreas diferentes en 4 estaciones climáticas diferentes, así mis efectos fijos son area y estaciones climáticas. Cada área tiene 12 puntos de muestreo (Punto). En cada punto de muestreo el índice se midió repetidamente a

Hola a todos, quería preguntaros un medio para obtener los valores p usando lmer. He tratado con pvals.fnc, que es lo que me habían recomendado, pero por algún motivo no está ya disponible etc. Ésta es la función que tengo, pero da las "t", sin los valores p. Aunque Baayen indica que valores por encima de 2 son significativos necesito saber las p. resultado = lmer(rt_ln ~ (fre_ln *

Hola quisiera saber como parameterizar el siguiente diseño con lme4. Tengo 4 tratamientos, con 4 parcelas cada uno, divididas a su vez en 4 subparcelas, y se han tomado 5 medidas repetidas (5 ocasiones) y se quiere ver el cambio en la variable respuesta producido por los tratamientos a través de las 5 ocasiones. Mi intento con lmer es el siguiente: mod <- lmer(variable respuesta ~ ocasión *

Dear R-Helpers, I want to compare the results of outputs from glmmPQL and lmer analyses. I could do this if I could extract the coefficients and standard errors from the summaries of the lmer models. This is easy to do for the glmmPQL summaries, using > glmm.fit <- try(glmmPQL(score ~ x*type, random = ~ 1 | subject, data = df, family = binomial), TRUE) > summary(glmmPQL.fit)$tTable

Hola Olivier, Adjunto los datos. Manuel El 31 de octubre de 2016, 3:29, Olivier Nuñez <onunez en unex.es> escribió: > Manuel, > > no estoy seguro de entender el diseño. > Cada parcela recibe los 4 tratamientos (un tratamiento por subparcela de > una misma parcela)? > Las "ocasiones" (supongo que instantes) son los mismos para cada > parcela*tratamiento?

This concerns whether p-values from lmer can be trusted. From simulations, it seems that lmer can produce very small, and probably spurious, p-values. I realize that lmer is not yet a finished product. Is it likely that the problem will be fixed in a future release of the lme4 package? Using simulated data for a quite standard mixed-model anova (a balanced two-way design; see code for the

I'm trying to fit a generalized mixed effects model to a data set where each subject has paired categorical responses y (so I'm trying to use a binomial logit link). There are about 183 observations and one explanatory factor x. I'm trying to fit something like: (lmer(y~x+(1|subject))) I also tried fitting the same type of model using glmmPQL from MASS. In both cases, I get a

Dear R Helpers, I have noticed that when I use lmer to analyse data, the summary function gives different values for the AIC, BIC and log-likelihood compared with the anova function. Here is a sample program #make some data set.seed(1); datx=data.frame(array(runif(720),c(240,3),dimnames=list(NULL,c('x1','x2','y' )))) id=rep(1:120,2); datx=cbind(id,datx) #give x1 a