similar to: problem with anova() and syntax in lmer

Displaying 19 results from an estimated 19 matches similar to: "problem with anova() and syntax in lmer"

2009 Dec 11
1
how to simulate brown, white and pink noise time series
Dear List, Is it possible to simulate a time-series in R based on 1/f noise? Many Thanks Enrico Crema --------------------------------------- Enrico R. Crema PhD Candidate Institute of Archaeology, UCL AHRC Centre for the Evolution of Cultural Diversity, UCL Centre for Advanced Spatial Analysis, UCL Personal Webpages: http://www.cecd.ucl.ac.uk/people/?go1=91
2005 Aug 26
1
Memory leakage/violation?
Hi, I've spotted a possible memory leakage/violation in the latest R v2.1.1 patched and R v2.2.0dev on Windows XP Pro SP2 Eng. I first caught it deep down in a nested svd algorithm when subtracting a double 'c' from a integer vector 'a' where both had finite values but when assigning 'a <- a - c' would report NaNs whereas (a-c) alone would not. Different runs
2008 Nov 25
4
glm or transformation of the response?
Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into
2006 Sep 12
4
variables in object names
Is there any way to put an argument into an object name. For example, say I have 5 objects, model1, model2, model3, model4 and model5. I would like to make a vector of the r.squares from each model by code such as this: rsq <- summary(model1)$r.squared for(i in 2:5){ rsq <- c(rsq, summary(model%i%)$r.squared) } So I assign the first value to rsq then cycle through models 2 through
2010 Sep 29
1
Understanding linear contrasts in Anova using R
#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to
2005 Mar 01
3
packages masking other objects
hello all, I am trying to use the function getCovariateFormula(nlme) in conjunction with the library lme4. When I load both packages I get the following message and the getCovariateFormula function no longer works: library(nlme) library(lme4) Attaching package 'lme4': The following object(s) are masked from package:nlme : contr.SAS getCovariateFormula
2009 Mar 09
1
lme anova() and model simplification
I am running an lme model with the main effects of four fixed variables (3 continuous and one categorical – see below) and one random variable. The data describe the densities of a mite species – awsm – in relation to four variables: adh31 (temperature related), apsm (another plant feeding mite) awpm (a predatory mite), and orien (sampling location within plant – north or south). I have read
2005 Jul 15
1
nlme and spatially correlated errors
Dear R users, I am using lme and nlme to account for spatially correlated errors as random effects. My basic question is about being able to correct F, p, R2 and parameters of models that do not take into account the nature of such errors using gls, glm or nlm and replace them for new F, p, R2 and parameters using lme and nlme as random effects. I am studying distribution patterns of 50 tree
2010 Aug 03
2
Specifying interactions in rms package... error
I am encountering an error I do not know how to debug. The error arises when I try to add an interaction term involving two continuous variables (defined using rcs functions) to an existing (and working) model. The new model reads: model5 <- lrm( B_fainting ~ gender+ rcs(exactage, 7) + rcs(DW_nadler_bv, 7) + rcs(drawtimefrom8am, 7)+ DW_firsttime+ DW_race_eth +
2010 Oct 03
5
How to iterate through different arguments?
If I have a model line = lm(y~x1) and I want to use a for loop to change the number of explanatory variables, how would I do this? So for example I want to store the model objects in a list. model1 = lm(y~x1) model2 = lm(y~x1+x2) model3 = lm(y~x1+x2+x3) model4 = lm(y~x1+x2+x3+x4) model5 = lm(y~x1+x2+x3+x4+x5)... model10. model_function = function(x){ for(i in 1:x) { } If x =1, then the list
2010 Mar 28
1
keeping track of who did what
hello everybody. i need to log "who did what" on some models. i was reading recipe 59 of rails recipes and i created something like that: class LogSweeper < ActionController::Caching::Sweeper observe :model1, :model2, :model3, :model4, :model5, ... after_save(model) save_log(model, "save") end after_destroy(model) save_log(model, "destroy) end
2011 Sep 28
2
GAMs in R : How to put the new data into the model?
I have 5 GAMs ( model1, model2, model3, model4 and model5) Before I use some data X(predictor -January to June data) to form a equation and calculate the expected value of Y (predictand -January to June). After variable selection, GAMs (Model 1)were bulit up! R-square :0.40 NOW, I want to use new X'( predictor -July - December data) and put into Model 1, then get the expected value of Y'
2009 Feb 09
1
gee with auto-regressive correlation structure (AR-M)
Dear all, I need to fit a gee model with an auto-regressive correlation structure and I faced some problems. I attach a simple example: ####################################################### library(gee) library(geepack) # I SIMULATE DATA FROM POISSON DISTRIBUTION, 10 OBS FOR EACH OF 50 GROUPS set.seed(1) y <- rpois(500,50) x <- rnorm(500) id <- rep(1:50,each=10) # EXAMPLES FOR
2006 Apr 16
0
[S] Problems with lme and 2 levels of nesting:Summary
I have taken the liberty of including the R-help mailing list on this reply as that is the appropriate place to discuss lmer results. On 4/5/06, Andreas Svensson <andreas.svensson at bio.ntnu.no> wrote: > Hello again > I have now recieved some helpful hints in this matter and will summarize them but first let me reiterate the problem: > > I had two treatments: 2 types of food
2013 Jul 05
1
kruskal.test followed by kruskalmc
Hi all, After running kruskal.test I have got results (p<0,005) pointing to reject the hypothesis that the samples were draw from the same population. Howerver when I run the kruskalmc there are no significant differences in any of the multiple comparisons. Is that possible? Some clarification? Thanks, Humber <https://sites.google.com/site/humberandrade> [[alternative HTML version
2007 Jun 01
2
lguest problem on boot of guest kernel
Hi ! Kenrel 2.6.21 (kernel.org) Patch lguest-2.6.21-254.patch Distro Slackware 11.0 GCC 3.4.6 GLIBC 2.3.6 HW model name : AMD Duron(tm) procu{s{ Module Size Used by tun 7680 0 lg 54600 0 just started playing with lguest - patching, compiling and booting the host-kernel goes ok - compiling lguest is ok as well after
2007 Jun 01
2
lguest problem on boot of guest kernel
Hi ! Kenrel 2.6.21 (kernel.org) Patch lguest-2.6.21-254.patch Distro Slackware 11.0 GCC 3.4.6 GLIBC 2.3.6 HW model name : AMD Duron(tm) procu{s{ Module Size Used by tun 7680 0 lg 54600 0 just started playing with lguest - patching, compiling and booting the host-kernel goes ok - compiling lguest is ok as well after
2009 Jul 30
0
randomized block design analysis PROBLEM
Dear All user, Hello, I'm a student and I have some trouble with the experimental (columns-experiments) design of my project. I use a randomized block design with 4 treatments including a control. For each treatment, I use 3 replicates and 3 blocks. The treatments are: -T1 = COD (300 mg/Lit) COD=chemical oxygen demand -T2 = COD (200 mg/Lit) -T3 = COD (100 mg/Lit) -T4 = COD (0 mg/Lit) as
2011 Nov 11
2
Estimating IRT models by using nlme() function
Hi, I have a question about estimating IRT models by using nlme, not just rasch model, but also other models. Behavior Research Methods <http://www.springerlink.com/content/1554-351x/> Volume 37, Number 2 <http://www.springerlink.com/content/1554-351x/37/2/>, 202-218, DOI: 10.3758/BF03192688 Using SAS PROC NLMIXED to fit item response theory models (2005). Ching-Fan