similar to: package lme4

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,

This is an example from chapter 11 of the 6th edition of Devore's engineering statistics text. It happens to be a balanced data set in two factors but the calculations will also work for unbalanced data. I create a factor called 'cell' from the text representation of the Variety level and the Density level using '/' as the separator character. The coefficients for the linear

Hello All: I am looking for you help. I am trying to replicate the results of an example found in Alan Agresti's "Categorical Data Analysis" on pages 267-269. The example is one of a 2 x 2 cross-classification table of ordinal counts: job satisfaction and income. I am able to get Agresti's results for the independence model (G^2 = 12.03 with df = 9) assuming as he does that

Greetings, 1) Is there a nice way of extracting the variance estimates from an lme fit? They don't seem to be part of the lme object. 2) In a series of simulations, I am finding that with ML fitting one of my random effect variances is sometimes being estimated as essentially zero with massive CI instead of the finite value it should have, whilst using REML I get the expected value. I guess

Hello, I wanted to fit a linear mixed model to a data that is similar in terms of design to the 'Machines' data in 'nlme' package except that each worker (with triplicates) only operates one machine. I created a subset of observations from 'Machines' data such that it looks the same as the data I wanted to fit the model with (see code below). I fitted a model in

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

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)

This question comes up periodically, probably enough to give it a proper thread and maybe point to this thread for reference (similar to the 'conservative anova' thread not too long ago). Moving from lme syntax, which is the function found in the nlme package, to lmer syntax (found in lme4) is not too difficult. It is probably useful to first explain what the differences are between the

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

Hello, In the following I tried 3 versions of an example in R help. Only the two first predict command work. After : library(splines) require(stats) 1) fm1 <- lm(weight ~ bs(height, df = 5), data = women) ht1 <- seq(57, 73, len = 200) ph1 <- predict(fm1, data.frame(height=ht1)) # OK plot(women, xlab = "Height (in)", ylab = "Weight (lb)") lines(ht1, ph1) 2)

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

Hello everyone I am currently trying to conduct analysis of my graduate thesis data using a mixed effects model and I have reached an impass. When I try to conduct a multiple comparison, I get an error (See below): > fm3<- lme(abovegroundbiomass.m.2~medium*amelioration*fertilizer*treatment, random=~1|block/medium/amelioration/fertilizer) > tukeytest<-glht(fm3,

Hi, this site: http://www.uow.edu.au/~andrewm/linux/ext3/ says "Forbidden" when I try to access .... , where can I download the ext3 patch for 2.4.15 ? Thank you Nikolai

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi I would like to create a Namespace during runtime, and not during loading a package. My reasoning is that I want to be able to store a number of variables and to "protect them from the user" (might be even myself). The code will not be in a package, but will be source()ed. Next step: pass variables from org mode / emacs to R and to

hey, folks, I have two very simple questions. I am not quite sure if I can do this using R. so, I am analyzing a large data frame with thousands of variables. For example: Dependent variables: d1, d2, d3 (i.e., there are three dependent variables) Independent variables: s1, s2, s3, ......s1000 (i.e., there are 1000 independent variables) now I want to run simple linear regression analyses of

Dear list, I asked some questions about multilevelanalysis a couple of months ago. In the meantime I did some reading about the subject. Now I'd like to check, if I understood it all correctly. If you think my questions are not appropriate for this list, please tell me so and i will immediatly stop asking them. I have a dataset with one predicted variable (y), two explanatory variables

Hello: I am using R mainly on windows XP, version 2.5. I?m a biologist, with a medium level statistics background. I have a problem stating a two-way factorial design where both factors are random. I?m using the lmer() function implemented in the Matrix package version 0.99. My design is as follows: Two species were randomly selected from a total of 4 species. This species are present

I am thinking about a making a web based directory that dials a number with one click. >From an overview picture does the below look like the correct way to go about it: web app sends something like the below call file to asterisk Action: Originate Channel: SIP/1010 Context: demo Exten: 1234 Priority: 1 Callerid: 1212 The main problem is the actual phone must be set to auto answer

I'm puzzled by the following problem, which appears when attempting to run an analysis on part of a dataset: If I try: csubset <- dat$Diagnosis==0 cont <- lme(fixed=cform, random = ~1|StudyName, data=dat,subset=csubset,na.action=na.omit) Then I get: Error in eval(expr, envir, enclos) : Object "csubset" not found But if I do

Hi I am struggling with hardware choices to get started with. My options are narrowed down to SIP phones - Polycom IP500, IP600 and Cisco 7940G. of importance is: - functionality / integration with asterisk - headset functionality and use - voice quality - build quality Is there much of a difference between Polycom and Cisco? Scanning the group it looks like there may be slightly more