similar to: fitting a model with the nlme package

Hi  I have a dataframe similar to: >Sample<-c(1,1,1,2,2,2,3,3,3) >Time<-c(1,2,3,1,2,3,1,2,3) >Mass<-c(3,3.1,3.4,4,4.3,4.4,3,3.2,3.5) >mydata<-as.data.frame(cbind(Sample,Time,Mass))   Sample Time Mass 1      1    1  3.0 2      1    2  3.1 3      1    3  3.4 4      2    1  4.0 5      2    2  4.3 6      2    3  4.4 7      3    1  3.0 8      3    2  3.2 9      3    3

I have been using the sp package to deal with gridded data. I would like to know how to extract the vertices (x,y) of polygons that outline areas of like-valued cells in a grid. Here is a simple 3x3 grid: 2 2 1 1 2 1 1 1 3 x <- c(1,1,1,2,2,2,3,3,3) # define a 3 x 3 array of points y <- c(1,2,3,1,2,3,1,2,3) h <- SpatialPoints(cbind(x,y)) # make these an sp object of the points

Dear friends, suppose my dataset is the following data: id<-1:9 x<-c(1,2,3,1,2,3,1,2,3) y<-c(1,1,1,2,2,2,3,3,3) data<-data.frame(id,x,y) id x y 1 1 1 1 2 2 2 1 3 3 3 1 4 4 1 2 5 5 2 2 6 6 3 2 7 7 1 3 8 8 2 3 9 9 3 3 i want to do sampling like this:say the sample size is 3. First: random sampling from x; Next ,random sampling from

Dear users, I'm trying to produce a 3d bar plot but the x and y dimensions have categorical data -- so I only want 3 points on each axis. So I try: require(scatterplot3d) mymat<-data.frame( x=c(1,1,1,2,2,2,3,3,3), y=c(1,2,3,1,2,3,1,2,3), z=c(1,2,3,4,5,6,7,8,9)) scatterplot3d(mymat, type="h", lwd=5, pch=" ", xlab="xlabel",

Dear R-users, I generate a dataset "d", and want to get a subset from it. ** *z<-rnorm(9) coords<-cbind(x=c(1,1,1,2,2,2,3,3,3),y=c(1,2,3,1,2,3,1,2,3)) d<-SpatialPointsDataFrame(coords, data.frame (z=z[1:9]))* The result*/dataset* is coordinates z 1 (1, 1) 1.41173570 2 (1, 2) 0.18546503 3 (1, 3) -0.04369144 4 (2, 1) -0.21591338 5 (2, 2)

Dear all, I have one dependent variable y and two independent variables x1 and x2 which I would like to use to explain y. x1 and x2 are design factors in an experiment and are not correlated with each other. For example assume that: x1 <- rbind(1,1,1,2,2,2,3,3,3) x2 <- rbind(1,2,3,1,2,3,1,2,3) cor(x1,x2) The problem is that I do not only want to analyze the effect of x1 and x2 on y but

Dear R Users, I was hoping for some help with a recurrent error message in lmer. I am trying to model the effect of temperature on metabolic rate in animals (response = int.length) at different temperatures (mean.sst), with repeated measurements on the same individuals (random effect = female). Ideally I would make a random slope and intercept model where the rate can change differently with

Dear R list, I have discovered a seemingly peculiar feature when using a matrix to index itself (yes, this is strange code, which I have now modified to be more reasonable). #this makes sense s <- matrix(1:3,nrow=1) s[s] #all three elements are shown #but when I try s <- matrix(1:2,nrow=1) s[1] #fine, the first element is shown s[2] #fine, the second element is

hi all, I often have a data frame like this example data.frame(sq=c(1,1,1,2,2,3,3,3,3),area=c(1,2,3,1,2,3,1,2,3),habitat=c("garden","garden","pond","field","garden","river","garden","field","field")) for each "sq" I have multiple "habitat"s each with an associated "area". I

On starting an R session, I get the messages: Fatal errir: unable to restore save data in .RData Error in fun(...): couldn't find function "assignInNamespace" Error: .onLoad failed in 'loadNamespace' for 'Matrix' The only object in my .RData is last.warning, thus: > last.warning $"optim or nlminb returned message false convergence (8)"

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

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

Hi guys, I ran into a problem of not being able to create unique labels when creating a factor. Consider an example below: hb <- factor(c(1,1,1,2,2,2,3,3,3), levels=c(1,2,3),labels=c(1,1,2)) > hb [1] 1 1 1 1 1 1 2 2 2 Levels: 1 1 2 > unique(hb) [1] 1 1 2 Levels: 1 1 2 How come there are three unique levels, I thought this would only create one unique level? >

I have the following data: x <- as.factor(c(1,1,1,2,2,2,3,3,3)) y <- as.factor(c(10,10,10,20,20,20,30,30,30)) z <- c(100,100,NA,200,200,200,300,300,300) I could create the cross tab of x and y with Sum of z as its elements using the xtabs function as follows: # X Vs. Y with Sum Z xtabs(z ~ x + y) y x 10 20 30 1 200 0 0 2 0 600 0 3 0 0 900 How do I replace

Hello everybody. I am trying to reproduce a particular matrix in an elegant way. If I have jj1 <- structure(c(1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2, 3,1,2,3,1,2,3,1,2,3,2,3,1,2,3,1,2,3,1,2,3, 1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,3,1,2,3,1, 2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1, 2),.Dim = as.integer(c(9,9))) [ image(jj1) is good here ] then I can get this with

I am basically trying to append a value(vector) to one dataframe using a relational value from another dataframe. Obviously, I can use a loop to accomplish this. However, is there a way to vectorize it? Example: > data <- data.frame(c(1,1,1,2,2,2,3,3,3),rep(2,9)); names(data) <- > c("Sample","Score") > meta <-

Folks, This is so simple is driving me crazy. It's not really an R question is more an Statistics question. I applied a chemical in 3 different events during a growing season. I took 3 samples in each event to estimate mean applied chemical. Thus, I can easily estimate means and st.err by event. I can then estimate the total chemical applied for the whole season by adding the 3 means

Dear all, I am trying to merge two data frames based on a common column but for this common column both data frame do not have the same length and associated information. I checked previous exemples in the list but was not able to apply them in my case... Is someone know how to do that? Below is my code with the expected result: # data frame 1 Id1 <- c(1,1,1,2,2,2,3,3,3) Habit1 <-

I am trying to plot an interaction in a multilevel model. Here is some sample data. In the following example, it is longitudinal (i.e., repeated measures), so the outcome, score (at each of the three time points), is nested within the individual. I am interested in the interaction between gender and happiness predicting score. id <- c(1,1,1,2,2,2,3,3,3) age <-

Hello, I have a data frame with different groups (Time, Target, Conc) and each entry has a triplicate value of the measurements OD and ODnorm. How can I merge the triplicates into a single mean value? I tried the following: ``` df = data.frame(Time=rep(1, 9), Well=paste("A", 1:9, sep=""), OD=c(666, 815, 815, 702, 739, 795, 657, 705, 663),