similar to: display functions in groupedData and lme

1) I have the following data situation: 96 plots 12 varieties 2 time points 2 technical treatments the experiment is arranged as follows: a single plot has two varieties tested on it. if variety A on plot #1 has treatment T1 applied to it, then variety B on plot #1 has treatment T2 applied to it. across the whole experiment variety A is exposed to treatment T1 the same number of times as

The goal: simulate chi square mixture distributions as a way of simulating likelihood ratio test statistics for some mixed models where the more specific model has some zero variance components (a la Pinheiro and Bates pg. 84-87) The problem: R doesn't have the function ms which is apparently used by simulate.lme In the current version of nlme for R, is there a way around this? Is it

Hi R-experts, I have a problem using nlme. I use the following code to group my data: Parameterg <- groupedData( result ~ time | Batch, data = Batchdata, labels = list( x = "Time", y = "analysis") ) and then uses the nlme function to fit a nonlinear mixed model that includes Process as a fixed covariate: nlme.model001epr <- nlme(result ~ A0 * exp(- ( exp(A1)

Dear list Having just started to use the Windows version, I am very impressed with it's package handling as well as the gui. So I tried to see what was due for update and packages such as Hmisc, Matrix and others came up. But when I had updated them - which took a few goes as something hung between here and Bristol - I noticed that the default packages such as nmle, MASS had disappeared. I

Hi all, I have a very simple question that I can't seem to find the answer to. How do I extract rows that meet a certain criteria from a data frame and group them into a new data frame? For example, if I want to make a new data frame that only includes rows of data for which the p values (given by one of the columns in the data frame) are less than a certain value, how do I do this? It seems

In the non linear mixed effects package a groupedData object can be created to facilitate modeling. The gD object includes a formula of the form 'response variable' ~ 'primary covariate' | 'grouping factor'. In experiments creating response surfaces there are 2 or more primary covariates. Is there any way to use the groupedData() function to include 2 primary

I have the following dataset: 96 plots 12 varieties 2 time points The experiment is arranged as follows: A single plot has two varieties tested on it. With respect to time points, plots come in 3 kinds: (1) varietyA, timepoint#1 vs. variety B, timepoint#1 (2) varietyA timepoint #2 vs. varietyB timepoint #2 (3) varietyA timepoint #1 vs. variety A timepoint#2 - there are 36 of each kind

Full_Name: Ju-Sung Lee Version: 2.2.0 OS: Windows XP Submission from: (NULL) (66.93.61.221) BIC() requires the attribute $nobs from the logLik object but the logLik of a glm(formula,family=binomial()) object does not include $nobs. Adding attr(obj,'nobs') = value, seems to allow BIC() to work. Reproducing the problem: library(nmle); BIC(logLik(glm(1~1,family=binomial())));

Windows Vista R 2.10.1 Does the following use of groupedData and lme produce an analysis with both random intercept and slope, or only random slope? zz<-groupedData(y~time | Subject,data=data.frame(data), labels = list( x = "Time", y = "y" ), units = list( x = "(yr)", y = "(mm)") ) plot(zz)

I am trying to fit a formula to my data, but I just can't find the right way to do it. My experiment consists of manipulating FRUITS and VEGETATION to two levels each(intact or removed) on 12 experimental plots. This leaves me with 4 treatment combinations Fruit intact Vegetation removed Fruit int. Veget int. Fruit rem. Veget rem. Fruit rem. Veget. intac those treatements are distributed

Dear R-users, I'm currently having trouble with the implementation of a groupedData object in the lme() function. Executing the following function > applyScalingSimp <- function(input.population) > { > ## GA is a time value > varInOrder <- c("GA","weight","grouping","sex") > modelVar <-

Dear R users, I have the following code: ---------------------------------------- require(nlme) myfunc <- function(data, n, m, maxIter = 3){ working <- groupedData(formula = y~x|id, data=data) val <- NULL r <- 0 while(r < maxIter){ new.data <- data.frame(x=rnorm(n),y=rnorm(n),id=rep(1:n,each=m)) working <- update(working, data = new.data) # val <- some

Hello I am trying to fit a REML to some soil mineral data which has been collected over the time period 1999 - 2004. I want to know if the 19 different treatments imposed, differ in terms of their soil mineral content. A tree model of the data has shown differences between the treatments can be attributed to the Magnesium, Potassium and organic matter content of the soil, with Magnesium being the

I'm trying to understand better the differences between fixed and random effects by running very simple examples in the nlme package. My first attempt was to try doing a t-test in lme. This is very similar to the Rail example that comes with nlme, but it has two groups instead of five. So I try a1 <- 1:10 a2 <- 7:16 t.test(a2,a1) getting t(18)=4.43, p=.0003224. Then I try to do it

[Sorry, In case this is repeating a message already sent to the list]. I am trying to move a project to R (base or nlme), for which I have a partial solution in BMDP 8V. Here is the 8V control language: /input title='Augenbewegungen'. variables=4. file='latm.dat'. format='11x,f7.0,f7.0,f12.4,f10.0'. /variables names= llat,rlat,vg,diff. /design

I'm using the lme function in nmle to estimate the variance components of a fully nested two-level model: Y_ijk = mu + a_i + b_j(i) + e_k(j(i)) lme computes estimates of the variances for a, b, and e, call them v_a, v_b, and v_e, and I can use the intervals function to get confidence intervals. My understanding is that these intervals are probably not that robust plus I need intervals on the

Hi Everyone, I posted a similar question about a week ago, but haven't gotten any replies -- I'm afraid that's because my previous question was too vague. Let me try again with a more specific question, and I hope someone can help. NOTE, I know I should be using the newer lme4 package, I just haven't had a chance to update my version of R yet, so the question below relates

Why does: > numeric(0.2*25) return [1] 0 0 0 0 0 but > numeric((1-0.8)*25) returns [1] 0 0 0 0 [running version 3.2.0] [Apologies if this has been asked before - it's a hard question to find specific search terms for] Thanks, Scott

help.search("groupedData") says that it's part of the lme4 package, but it appears not to be there (details below). Is this because lme4 is new and (perhaps) still under development? > update.packages() trying URL `http://cran.r-project.org/bin/windows/contrib/1.9/PACKAGES' Content type `text/plain; charset=iso-8859-1' length 19113 bytes opened URL downloaded 18Kb >

Hi,   I have an original data frame with 8 columns of variables, which are stored in 'data1' frame.   data1 <- read.csv("E:\\PHD GLASGOW UNIVERSITY\\Data\\R\\Colin\\Cailness21.csv") attach(data1) names(data1)   [1] "Date"       "d"          "m"          "y"          "Time"      [6] "Depth"      "Temp"