similar to: Again some questions about multilevelanalysis

Dear list, i am a student of psychology and have to do a multilevelanalysis on some data. About that i have one general and one specific question. This is what i have copied from the help-file on lme: data(bdf) fm <- lme(langPOST ~ IQ.ver.cen + avg.IQ.ver.cen, data = bdf, random = ~ IQ.ver.cen | schoolNR) summary(fm) after summary(fm) i get the following error:

Hello, i have written this little function to draw different normal distributions: n.Plot <- function(x,my,sigma) { e <- exp(1) names(x) <- x f.x <- (1/(sigma*sqrt(2*pi)))*e^(-1*(((x-my)^2)/2*(sigma^2))) plot(f.x,type="l",xlim=c(-5,5)) return(f.x) } if i define x like this: x <- seq(-5,5,0.01) Now n.Plot(x,0,1) DOES draw the correct plot, but the x-axis is labeled

Hello list ! I have a huge data.frame with several variables observed on about 3000 persons. For every person (row) there is variable called GROUP which indices the group the person belongs to. There is also another variable AV for each person. Now i want to create a new variable which holds the group mean of AV as a value for each person. With tapply(AV,GROUP,mean) i get the means for each

Hello R, To analyze multi-level data, I started learning and using lmer. So far so wonderful. I then found some useful functions in package languageR. But then the following problem ocurred: Whenever I load and use the languageR package, then save the workspace - or quit R with saving the workspace - I am unable to reload that workspace in a later session. That is, R doesn't start at all

I am using the 'glmmPQL function in the 'MASS' library to fit a mixed effects logistic regression model to simulated data. I am conducting a series of simulations, and with certain simulated datasets, estimation of the random effects logistic regression model unexpectedly terminates. I receive the following error message from R: Error in lme.formula(fixed=zz + arm.long,random=~1 |

Hi, We have a several AP9612TH environmental cards (they have 2 probe connectors) inserted into our APC UPS devices which monitor temperature and humidity. The apcsmart nut module knows how to query the ambient.temperature and ambient.humidity using the 't' and 'h' commands of the UPS (refer to apcsmart.h). The results of the 't' and 'h' commands are from probe 1.

Hi, I am attempting to set the confidence limits on a ls means plot as follows: mult<-glht(lm(effectModel, data=statdata, na.action = na.omit), linfct=mcp(mainEffect="Means")) meanPlot <- sub(".html", "meanplot.jpg", htmlFile) jpeg(meanPlot) plot(mult, main=NA, xlab=unlist(strsplit(Args[4],"~"))[1]) This produces 95% CIs by default but I would

Hello, 1) What is the difference between a "data frame" (J H Maindonald, Using R, p. 12) and a "vector"? In Using R, the author asks the reader to enter the following data in a data frame, which I will call "mydata": year snow.cover 1970 6.5 1971 12.0 1972 14.9 1973 10.0 1974 10.7 1975 7.9 ... mydata=data.frame(year=c(1970,...),snow.cover=c(6.5,...)) 2) How to

hi, i need help on placing legend/text in a time series plot. here is what i am doing (i am using rw1022 on windoze 2000): #read data file gdpn <- scan("jngdpsa.dat", list(year=0, qtr=0, gdp=0)); gdpr <- scan("jrgdpsa.dat", list(year=0, qtr=0, gdp=0)); #convert to time series object gdpn <- ts(gdpn$gdp, frequency=4, start=c(1955,2)); gdpr <- ts(gdpr$gdp,

Hi List, I have to compare a relationship between y and x for two locations. I found logistic regression fits both datasets well, but I am not sure how to test if relationships for both sites are significantly different. I searched the r site, however no answers exactly match the question. I used Tukey's HSD to compare two means, but the relationship in my study was not simply linear. So I

The subject says it all really. Question 1. Here is some code created to illustrate my problem, can anyone spot where I'm going wrong? Question 2. The reason I'm following a manual specification of models relates to the fact that in reality I am using mgcv::gam, and I'm not aware that dredge is able to separate individual smooth terms out of say s(a,b). Hence an additional request,

As mentioned on the rsync home page, the --files-from=FILE option in rsync version 2.6.0 is a useful option that allows one to "specify a list of files to transfer, and can be much more efficient than a recursive descent using include/exclude statements (if you know in advance what files you want to transfer)". However, --files-from does not help one implement the --rsync-exclude=FILE

Hi there, I have a data frame as listed below: > Ca.P.Biomass.A P Biomass 1 334.5567 0.2870000 2 737.5400 0.5713333 3 894.5300 0.6393333 4 782.3800 0.5836667 5 857.5900 0.6003333 6 829.2700 0.5883333 I have fit the data using logistic, Michaelis?Menten, and linear model, they all give significance. > fm1 <- nls(Biomass~SSlogis(P, phi1, phi2, phi3), data=Ca.P.Biomass.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

Dear all, I am running a panel regression with time and location fixed effects: ### reg1 <- lm(lny ~ factor(id) + factor(year) + x1+ I(x1)^2 + x2+ I(x2)^2 , data=mydata, na.action="na.omit") ### My goal is to use the estimation for prediction. However, I have 8,500 IDs, which is resulting in very slow computation. Ideally, I would like to do the following: ### reg2 <-

[mod: Forwarded by Jeff Uphoff. I tried to mangle the headers that it appears as the original post: with an invalid return address. -- REW] A few months back, a very alpha version of bliss got posted. That shouldn''t have happened, but, it was pretty much ignored so I didn''t worry about it. But now it seems there''s a bit of a fuss about this. I''ll post the

Hi R Users, When I use package lme4 for mixed model analysis, I can't distinguish the significant and insignificant variables from all random independent variables. Here is my data and result: Data: Rice<-data.frame(Yield=c(8,7,4,9,7,6,9,8,8,8,7,5,9,9,5,7,7,8,8,8,4,8,6,4,8,8,9), Variety=rep(rep(c("A1","A2","A3"),each=3),3),

How can I get prediction intervals from a mixed-effects model? Consider the following example: library(nlme) fm3 <- lme(distance ~ age*Sex, data = Orthodont, random = ~ 1) df3.1 <- with(Orthodont, data.frame(age=seq(5, 20, 5), Subject=rep(Subject[1], 4), Sex=rep(Sex[1], 4))) predict(fm3, df3.1, interval='prediction') # M01 M01

I have no direct experience with such horrific models, but your formula is a mess and Google suggests the biglm package with ffdf. Specifically, you should convert your discrete variables to factors before you build the model, particularly since you want to use predict after the fact, for which you will need a new data set with the exact same levels in the factors. Also, your use of I() is

I'm trying to use the FollowMe app with Asterisk 1.4.17. I've followed the WIKI page on setting it up but I can't seem to get it to work. Here is my Asterisk version: pbx1*CLI> core show version Asterisk 1.4.17 built by root @ pbx1 on a i686 running Linux on 2008-01-10 12:08:48 UTC Here is a log of when the FollowMe is being called: NOTE: I've tried to use the AstDB as