search for: assames

I have this assignment to do and after ten hours of constant trying my eyes ache and i give up.. all i'm able to get is this plot please help me these are the commands i have used till now read.table(file.choose(), sep=";", header=T) read.table(file.choose(), sep=";", header=T)->areas melt(areas,id=c("Year","State"),m=c("Rice"))->

I'm havinf a problem with a simple file i have the following data State 1960 1970 1980 1990 1 All India 35988.70 37346.00 39707.30 42321.00 2 Andhra Pradesh 3431.03 3163.27 3687.23 3695.63 3 Assam 1902.93 2001.60 2278.47 2525.33 4 Bihar 5277.07 5133.80 5138.70 4662.57 5 Gujarat 538.13 456.10 484.23 590.47 6

Dear R Users I have difficulties accessing the variance components for an lme fit when the variance covariance matrix of the random effects is not positive definite. Can anyone inform me on how to get by this ? Thanks in advance Pryseley --------------------------------- [[alternative HTML version deleted]]

Hi Experts, As i am a new in this forum, let me introduce myself:- i am Debaranjan, from India, Assam. I have heard many things about R and its statistical Magic feature. i am really have nothing knowledge about R language nor have ever used before. i am only a nursery in R. please help me in learning R. I have gone through many study material for R but have gain nothing

Hello R-users I am new to R and trying to write some functions. I have problems writing functions that takes a data set as an arguement and uses variables in the data. I illustrate my problem with a small example below: sample data #------------------ visual24<-rnorm(30,3,5) visual52<-rt(30,7) dats<- data.frame(cbind(visual24,visual52)) remove(visual24, visual52)

Good day R-Users, I have a problem accessing some values in the output from the summary of an lme fit. The structure of my data is as shown below (I have attached a copy of the full data). id trials endp Z.sas ST 1 1 -1 -1 42.42884 1 1 1 -1 48.12007 2 1 -1 -1 43.42878 2 1 1 -1

Dear R-Users, Is it possible to get the covariance matrix from an lme model that did not converge ? I am doing a simulation which entails fitting linear mixed models, using a "for loop". Within each loop, i generate a new data set and analyze it using a mixed model. The loop stops When the "lme function" does not converge for a simulated dataset. I want to

As far as I know from experience if your motherboard has a bios update that says it supports presler microcode then you''re set. One gotcha is you may need something simple like a celeron d chip to get your motherboard to boot to get the bios update installed. (As the 3 montherboards I''ve seen won''t boot presler chips without bios update. Adam Wendt IPCoast, Inc. On

Dear R-users Can anyone inform me of a library or more specifically functions that can maximise (or calculate) a Pairwsie likelihood from a data. Better still, i would like to know if there is a function (library) that fits regression models based on pairwise likelihoods. Thanks Pryseley --------------------------------- [[alternative HTML version deleted]]

Hello R-users Is it possible to fit a three level linear mixed effect model in R? If anyone has an idea or sample code, i will appreciate it very much if i can receive it. I am reading the book by Pinheiro and Bates but have not come across that yet! Kind regards Pryseley --------------------------------- [[alternative HTML version deleted]]

Dear R-Users I used the nlme library to fit a linear mixed model (lme). The random effect standard errors and correlation reported are based on a Log-Cholesky parametrization. Can anyone tell me how to get the Covariance matrix of the random effects, given the above mentioned parameters based on the Log-Cholesky parametrization?? Thanks in advance Pryseley

Dear R users, Can someone inform me about a library/function in R that fits a Plackett-Dale model ? Thanks in advance Pryseley --------------------------------- [[alternative HTML version deleted]]

Wow. dada spam. Antranig Alfaro wrote: > humans bouts scrupulously taxied annette methacrylate exert crouching > exclude hubris meteorite aides acronym exogenous boater plead savoring > tapestries courtrooms taunter bodyweight poultry pokes brandishes > poems ama mightiness assam countless actinolite merges crave technic > accomplishers bombast creation bonaparte

Hello All, Having some issue with local machine, I need to locate myself on Amazon for running R and Hadoop with Amazon instance. After searching a lot, I can't able to take a decision for choosing Image for Amazon instance. Can any one using R + Hadoop on Amazon. Thanks [[alternative HTML version deleted]]

Dear R-Users I have a problem accessing some values in the output from the summary of an lme fit. I fit the model below: ggg <- lme (ST~ -1 + as.factor(endp):Z.sas + as.factor(endp), data=dat4a, random=~-1 + as.factor(endp) + as.factor(endp):Z.sas|as.factor(trials), correlation = corSymm(form=~1|as.factor(trials)/as.factor(id)), weights=varIdent(form=~1|endp)) hh

Dear R-users I have problems using lme The model i want to fit can be viewed as a two-level bivariate model Two-level bivariate: bivariate (S coded as -1,T coded as 1) endpoint within trial OR It can equivalently be considered as a three-level model.Three-level: endpoint within patient, patient within trial. My code tries to model the levels through a RANDOM statement and a

Dear R-users I intend to create a function which calls some smaller other functions in return. Some of these smaller functions all call some functions. I do not know a good way to do this. I tried using the source() function to include the smaller functions within the main functions before they are called. This does not work, or maybe i am not doing the right thing. For example: the

Dear R-users I am relatively new to R, i hope my many novice questions are welcome. I have problems accessing some objects (specifically the random effects, correlation structure and variance function) from an object of class gls and lme. I used the following models: yah <- gls (outcome~ -1 + as.factor(Trial):as.factor(endpoint)+

Dear R - Users I have some problems fitting a linear mixed effects model using the lme function (nlme library). A sample data is as shown at the bottom of this mail. I fit my linear mixed model using the following R code: bmr <-lme (outcome~ -1 + as.factor(endpoint)+ as.factor(endpoint):trt, data=datt, random=~-1 + as.factor(endpoint) + as.factor(endpoint):trt|as.factor(Trial),

Dear R-helpers, We marked 6000 leaves from 5 SPECIES - 10 individuals/species - in two different TREATMENTs: a control and a dry-plot from which 50% of incoming precipitation was excluded. We followed those leaves for 42 months and noted the presence and absence at each visit. I then carried out a Cox Harzard model to see differences in leaf mortality between parcels and among species over time: