similar to: Question on partial effect

I have a general question about using thin plate splines in the surf and ordisurf routines. My rudimentary knowledge of a gam is that with each predictive variable there is a different smooth for each one and then they are added together with no real interaction term (because they don't handle this well?). Now, If I have two variables that have a high D^2 score and a low GCV score (I am

Hello mailing list! I would like to know, how I can introduce a covariate in a glm, I've two factors and a covariate. Thank you very much! _______________________________________________________________________________________________ Michelangelo La Spina Equipo de Protección de cultivos - Control Biológico Departamento de Biotecnología y Protección de Cultivos Instituto Murciano de

Greetings! I am looking for advice regarding the best way to compare GAMMs. I know other model outputs return enough information for R's AIC, ANOVA, etc. commands to function, but this is not the case with GAMM unless one specifies the gam or lme portion. I know these parts of the gamm contain items that will facilitate comparisons between gamms. Is it correct to simply use these values

Dear List, I need to add a subscript and a superscript to some of the ions in the labels on some plots. I have got to here but now I'm stuck: plot(1:10, xlab = expression(paste("nm SO"[4], " ", mu, "eq cm"^{-2}, " yr"^{-1}))) Which gives almost what I require. No matter what I tried, however, I could not get bot a sub script *and* a superscript

Hi All, I was wondering if someone could help me to solve this issue with lmer. In order to understand the best mixed effects model to fit my data, I compared the following options according to the procedures specified in many papers (i.e. Baayen <http://www.google.it/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0CDsQFjAA

Hi All, I'm conducting a meta-analysis and have taken a data.frame with multiple rows per study (for each effect size) and performed a weighted average of effect size for each study. This results in a reduced # of rows. I am particularly interested in simply reducing the additional variables in the data.frame to the first row of the corresponding id variable. For example:

I have the following questions about the variance of the random effects in the survreg() function in the survival package: 1) How can I extract the variance of the random effects after fitting a model? For example: set.seed(1007) x <- runif(100) m <- rnorm(10, mean = 1, sd =2) mu <- rep(m, rep(10,10)) test1 <- data.frame(Time = qsurvreg(x, mean = mu, scale= 0.5, distribution =

Hi I'm having a problem with model.frame, encapsulated in this example: y1 <- matrix(c(3,1,0,1,0,1,1,0,0,0,1,0,0,0,1,1,0,1,1,1), nrow = 5, byrow = TRUE) y1 <- as.data.frame(y1) rownames(y1) <- paste("site", 1:5, sep = "") colnames(y1) <- paste("spp", 1:4, sep = "") y1 model.frame(~ y1) Error in model.frame(formula, rownames,

Dear List, I often have to merge two or more data frames containing unique row names but with some columns (names) common to the two data frames and some columns not common. This toy example will explain the kind of setup I am talking about: mat1 <- as.data.frame(matrix(rnorm(20), nrow = 5)) mat2 <- as.data.frame(matrix(rnorm(20), nrow = 4)) rownames(mat1) <- paste("site",

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Dear List, I have the following function: coca <- function(x, ...) { if(is.null(class(x))) class(x) <- data.class(x) UseMethod("coca", x) } and a default method coca.default <- function(x, y, method = c("predictive", "symmetric"), reg.method = c("simpls", "eigen"), weights = NULL,

##data y1 <- matrix(c(3,1,0,1,0,1,1,0,0,0,1,0,0,0,1,1,0,1,1,1), nrow = 5, byrow = TRUE) y2 <- matrix(c (3,0,10,3,3,0,0,1,1,0,0,0,0,0,1,0,1,0,0,2,1,0,1,1,0,2,1,1,4,1), nrow = 5, byrow = TRUE) y1 <- as.data.frame(y1) y2 <- as.data.frame(y2) rownames(y1) <- rownames(y2) <- paste("site", 1:5, sep = "") colnames(y1) <-

Sorry to disturb you, but can you help me to solve one little problem? I want to draw a graphic after another with R but I cannot find the first one after that. Do you know the command to hold the graphic with R? I remember with Matlab you may use "hold on". Thanks. -- Baohua Yang Email:yangbaohua@gmail.com [[alternative HTML version deleted]]

Hi All, I am interested in creating a function that will take x number of lm objects and automate the comparison of each model (using anova). Here is a simple example (the actual function will involve more than what Im presenting but is irrelevant for the example): # sample data: id<-rep(1:20) n<-c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20)

Dear List, I have the following problem with axis.Date, here is an artificial example: dates <- scan(what = "character") "25/03/2000" "26/03/2000" "27/03/2000" "28/03/2000" "29/03/2000" "30/03/2000" "31/03/2000" "01/04/2000" "02/04/2000" "03/04/2000" "04/04/2000"

When I fit a multivariate linear model, and the formula is defined outside the call to lm(), the method summary.mlm() fails. This works well: > y <- matrix(rnorm(20),nrow=10) > x <- matrix(rnorm(10)) > mod1 <- lm(y~x) > summary(mod1) ... But this does not: > f <- y~x > mod2 <- lm(f) > summary(mod2) Error en object$call$formula[[2L]] <- object$terms[[2L]]

something like "sprintf" in C? so I can do: print(sprintf("the correct result is %3.4f\n", myresult)); ------- Also, I am desperately looking for a "clear console screen" function in R... thanks a lot! [[alternative HTML version deleted]]

Dear list, given this formula: > fmla <- formula(y1 ~ spp1 + spp2 + spp3 + spp5) > fmla[[3]] spp1 + spp2 + spp3 + spp5 is this the intended behaviour of as.character: > as.character(fmla[[3]]) [1] "+" "spp1 + spp2 + spp3" "spp5" ? Where does the extra "+" come from? > as.character(fmla) [1] "~"

Hi All, I am interested in aggregating a data frame based on 2 categories--mean effect size (r) for each 'id's' 'mod1'. The 'with' function works well when aggregating on one category (e.g., based on 'id' below) but doesnt work if I try 2 categories. How can this be accomplished? # sample data id<-c(1,1,1,rep(4:12)) n<-c(10,20,13,22,28,12,12,36,19,12,

Hi In trying to fit a linear model , I use the leaps() function to determine wich predictors I should include in my model. I would like to plot the Mallow's Cp criteria against p with the indexes of selected model variates as points labels Is there already such a function? (I could not find it) Thanks Anne [[alternative HTML version deleted]]