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I am trying to decipher, via post hoc test (Tukey), which of my sites differ from eachother. I have 4 sites, 2 sets of In vs Out (MPA) in separate Regions. Therefore my Mixed Effects Model code has 2 fixed effects: CB.lme <- lme(AsinCB~ In_Out*Region, random = (~1| site.trans/Quadrat) , data = Subsampled_props, control = lmeControl(maxIter = 500, msMaxIter = 500, msMaxEval = 500))

I am unsure what is being returned, and what is supposed to be returned, when using 'predict' with "type='expected'" for an aftreg survival model. The code below first generates a weibull model, then uses predict to create a vector of the linear predictors, then attempts to create the 'expected' vector, which is empty. The final two steps in the code generate a

Hi, I am trying to fit the following model: sr.reg.s4.nore <- survreg(Surv(age_sym4,sym4), as.factor(lifedxm), data=bip.surv) Where age_sym4 is the age that a subject develops clinical thought problems; sym4 is whether they develop clinical thoughts problems (0 or 1); and lifedxm is mother's diagnosis: BIPOLAR, MAJOR DEPRESSION, or CONTROL. I am interested in whether or not

Hi, I have searched the archives and can't quite confirm the answer to this. I appreciate your time... I have 4 treatments (fixed) and I would like to know if there is a significant difference in metal volume (metal) between the treatments. The experiment has 5 blocks (random) in each treatment and no block is repeated across treatments. Within each plot there are varying numbers of

Hi guys, I'm very new to R and have been teaching myself over the past few months - it's a great tool and I'm hoping to use it to analyse my PhD data.As I'm a bit of a newb, I'd really appreciate any feedback and/or guidance with regards to the following questions that relate to generalized linearmixed modelling (or, at least, I think they do!)(if there is a 'better',

Hello, I need some results from the survival analysis of my data that I do not know whether exist in Survival Package or how to obtain if they do: 1. The Mean survival time 2. The standard error of the mean 3. Point and 95% Lower & Upper Confidence Intervals estimates Any help will be greatly appreciated. Cem [[alternative HTML version

I was wondering if somebody could explain why I get different results here: >treats[,2]<-as.factor(treats[,2]) >treats[,5]<-as.factor(treats[,5]) >treats[,4]<-as.factor(treats[,4]) #there are 'c' on more days than I have 'h2o2', where treats[,4] is the day. I only want 'c' that correspond to the same days that I have a 'h2o2' also.

R gurus: I'm trying to get another round of rconifers out and I need some advice/help crushing differences in the examples test. I'm trying to make sure the max sdi values are being respected. I've added a tests/rconifers-Ex.Rout.save (from windows i386-pc-mingw32) and when I ran R CMD check (both R-2.13.0), I got the following results: * using log directory

Hello, I'm attempting to analyze a split-split plot model, currently using lmer. My design is similar to that used in Casella's Experimental Designs Ozone Example (example 5.7, p 197), but I have been unable to find any coding help for split-split plot models through searches of several textbooks and the R list serve. I have three treatments of interest (A,B,C), each with 2 treatment

Full_Name: Adrian Custer Version: 0.90.0 OS: Linux on Thinkpad (pentium) and desktop (K6) Submission from: (NULL) (128.32.251.234) When I create a boxplot, and then try to overlay a lowess fit or just the points, the points do not appear in the highest level and the lowess curve does not reach the highest level. However, if I add one to each of the models, the problem is solved. I tried this

https://bugs.freedesktop.org/show_bug.cgi?id=89730 Bug ID: 89730 Summary: NV50: LightDM GTK+ Greeter Background - inconsistent display Product: xorg Version: git Hardware: Other OS: Linux (All) Status: NEW Severity: normal Priority: medium Component: Driver/nouveau

Hi, I'm new to R and using it for Cox survival analysis. Thanks to this great forum I learned how to compute the HR with its confidence interval. My question would be: Is there any way to get the p-value for a hazard ratio in addition to the confidence interval? Thanks, Thierry -- Thierry Panje Visiting Student Researcher Department of Psychology Stanford

Hello, I need to perform a mixed-model (with nesting) MANCOVA, using Type III sums of squares. I know how to perform each of these types of tests individually, but I am not sure if performing a mixed-model MANCOVA is possible. Please let me know. Erika <>< <>< <>< <>< <>< <>< <>< Erika Crispo, PhD candidate

Dear all, Does any one knows why should I get the following error message, when trying to do a simple data.frame?? DataF<-data.frame(Subject,BiomR,Spp,Capas,Litter,Herbs,LitterD,MaxCanH,DDifS p,DSSp,Slope, CanDens,NearestSp) Erro em data.frame(Subject, BiomR, Spp, Capas, Litter, Herbs, LitterD, : arguments imply differing number of rows: 202, 0 The data I am using

I have a dataframe called "data" with 5 records (in rows) each of which has been scored on each of many variables (in columns). Five of the variables are named var1, var2, var3, var4, var5 using headers. The other variables are named using other conventions. I can create a new variable called var6 with the value 15 for each record with this code: > var6=var1+var2+var3+var4+var5