similar to: mixed models

Dear expeRts, there is obviously a general trend to use model comparisons, LRT and AIC instead of Wald-test-based significance, at least in the R community. I personally like this approach. And, when using LME's, it seems to be the preferred way (concluded from postings of Brian Ripley and Douglas Bates' article in R-News 5(2005)1), esp. because of problems with the d.f. approximation.

People I apologise for asking a general stats question, but I'm at a bit of a loss as to what to do following some hostile referees' comments. If I have a fully randomised blocked design, with only three blocks, should I treat block as a random or fixed effect? I have read comments about not treating block as a random effect if the number of blocks is less than 6 or 7: is this

Hello all I'm trying to do a simulation that involves identifying the minimum point between two peaks of a (usually) bimodal distribution. I can do this easily if there are only two peaks: CnBdens<-density(Ys/Xs) #probability density function for ratio of Ys to Xs for(p in 1:512) ifelse(CnBdens$y[p]>CnBdens$y[p-1],peak1<-p,break) #identifies first peak in probability

Hello everyone Not strictly an R question but close... hopefully someone will be able to help. I wish to compare the switchpoints in two switchpoint regressions. The switchpoints were estimated using the segmented library running in R, and I have standard errors for the estimates. I initially thought I could just bootstrap confidence intervals for the difference between the switchpoints,

Hi folks I wonder if anyone can help me. I want to run some simulations to see how big a sample size might be necessary to distinguish a curved bivariate relationship (e.g. something that might be best described by a quadratic model) from a relationship that is two straight lines with a sudden change in slope (e.g. something best described by a breakpoint regression). I am using

Windows 10 64-bit, R-Studio, R version 3.4.3 Several questions relating to groupedData: (1) I am trying to create a groupedData object that can be used to run an analysis that I have been able to urn using lmer. When I include the interaction terms in the groupedData opbject I get an error message stating that + is not meaningful for factors. How do I include factors in my model? See code and

Dear all, I am trying (!!!) to generate pdfs that have 8 plots on one page: df = data.frame( day = c(1,2,3,4), var1 = c(1,2,3,4), var2 = c(100,200,300,4000), var3 = c(10,20,300,40000), var4 = c(100000,20000,30000,4000), var5 = c(10,20,30,40), var6 = c(0.001,0.002,0.003,0.004), var7 = c(123,223,123,412), var8 = c(213,123,234,435), all = as.factor(c(1,1,1,1)))

Dear R-help, I am trying to create groupedData objects using the nlme library. I'm missing something basic, I know: Here is the first example in ch.1 of Pinheiro & Bates (2000): library(nlme) x2=Rail$travel;x1=Rail$Rail;eg1=data.frame(x1,x2);eg1gd=Rail print(eg1gd) x11();print(plot(eg1gd)) femodel=lm(x2~x1-1,data=eg1gd) print(femodel$coefficients) Result: x12 x15 x11

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 >

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 using groupedData from nlme. I set up a groupedData data.frame with outer=~group1. When I try to plot with outer=TRUE, I get "subscript out of bounds." This happens most of the time. When it works, I get spaghetti-type plots for comparing groups. But I don't understand why it doesn't usually work. > longa.mod.1.gd <- groupedData(mod1.logit~time|

Hello, I'm trying to use the groupedData function and R is giving me the message: Error: can not find function "groupedData" The code is coming from a textbook so I think it should be correct: pigs<-data.frame(cbind(pig.time,pig.id,pig.wt)) pig.growth<-groupedData(pig.wt~pig.time|pig.id,data=pigs) I have added the package "nlme" and to confirm that it was installed

Dear John You are only allowed to have the primary covariate on the left hand side of the vertical bar. Other covariates go in inner or outer. Michael On 22/03/2018 12:59, Sorkin, John wrote: > Windows 10 64-bit, R-Studio, R version 3.4.3 > > > Several questions relating to groupedData: > > (1) I am trying to create a groupedData object that can be used to run an analysis

Dear Group: I have been trying to write an R function within which the function groupedData() would be used. The following is a sample program: ####################### sid<-rep(1:6,times=2) time<-c(1:12) trt<-rep(letters[1:3],times=4) tem<-data.frame(sid,time,trt) test1.fun<- function(dat=dat) { groupedData(time ~ trt|sid, data=dat) } test1.fun(dat=tem)

I created the following groupedData object (nlme library): gd <- groupedData(Conc ~ Time | Subj, order.groups=T, FUN = myf, data=mydata) The idea of the myf function is to reverse the order of the grouping factor Subj (or better, reorder from largest to smallest). In the mydata data set, Subj is an integer that gets converted into a factor in the groupedData object. Does anyone

Hello people I am doing some thinking about how to analyse data on dimorphic animals - where different individuals of the same species have rather different morphology. An example of this is that some male beetles have large horns and small wings, and rely on beating the other guys up to get access to mates, whereas others have smaller horns and larger wings, and rely on mobility to

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"      

Sorry - I suspect this is a very basic query. I have a data frame structured "flat" (i.e. separate observations for a given subject ID on separate lines). Using the nlme library I create a groupedData object suitable for further analysis thus: dataset <- groupedData(VARIABLE ~ TIME|ID, data=sfa) The dataset has a large number of VARIABLES of interest so I want to make use of loops

Hi All, I would like to ask a question on fixed and random effecti in lme. I am fiddlying around Mick Crawley dataset "rats" : http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/ The advantage is that most work is already done in Crawley's book (page 361 onwards) so I can check what I am doing. I am tryg to reproduce the nested analysis on page 368:

Hi, I'm trying to work through a Nested ANOVA for the following scenario: 20 males were used to fertilize eggs of 4 females per male, so that female is nested within male (80 females used total). Spine length was measured on 11 offspring per family, resulting in 880 measurements on 80 families. I used the following two commands: summary(aov(Spinelength ~ Male*Female)) and