search for: varid

...ly appreciate if you could help me. I am using R version 2.11.1 (2010-05-31) in a machine with Linux 2.6.26-2-amd64. > library(ncdf) > file_temp<-open.ncdf("File.nc") > temp<-get.var.ncdf(file_temp,"tasmax",verbose=TRUE) [1] "get.var.ncdf: entering. Here is varid:" [1] "tasmax" [1] "checking to see if passed varid is actually a dimvar" [1] "entering vobjtodimname with varid= tasmax" [1] "vobjtodimname: is a character type varid. This file has 3 dims" [1] "vobjtodimname: no cases found, returning FALSE"...

Hello, As I am relatively new to the R environment this question may be either a) Really simple to answer b) Or I am overlooking something relatively simple. I am trying to add a VarIdent structure to my gam model which is fitting smoothing functions to the time variables year and month for a particular species. When I try to add the varIdent weights to variable Month I get this error returned. Error in model.frame.default(formula = Chel ~ 1 + M + YM + M + YM, weights = Vf1, :...

Hi, I would like to fit a model for a factorial design that allows for unequal variances in all groups. If I am not mistaken, this can be done in lm by specifying weights. A function intended to specify weights for unequal variance structures is provided in the nlme library with the varIdent function. Is it apropriate to use these weights with lm? If not, is there another possibility to do factorial designs with heteroscedasticity? When trying to use varIdent I get an errormessage that says that varIndent is not a defined class. The function calls are written in the same way as...

Hello, 1. It appears that "coef<-.varIdent" method does not work properly in some instances. Execution error: Error in `coef<-.varIdent`(`*tmp*`, value = c(11, 12)) : Cannot change the length of the varIdent parameter after initialization occurs when "coef<-.varIdent" is applied to an initialized object...

I was reading the help but just did not get how to specify starting values for varIdent() of the lme() function, although I managed to do it for corSymm(). Do I specify the values just as they are printed out in an output, like c(1, 1.3473, 1.0195). Or do I need to take the residual and multiply it with these like c(0.2235, 0.2235*1.3473, 0.2235*1.0195) or any other form that I...

...t;/home/challar/testunix6.nc",list(var1)) genovals <- c("0.0","3",NaN,NaN,"-4","99") put.var.ncdf(nc,"genotype",genovals,start=c(1,1),count=c(6,1),verbose=TRUE) [1] "put.var.ncdf: entering with filename /home/challar/testunix6.nc and varid:" [1] "genotype" [1] "Checking to see if passed varid is ACTUALLY a dimension" [1] "entering vobjtodimname with varid= genotype" [1] "vobjtodimname: is a character type varid. This file has 2 dims" [1] "vobjtodimname: no cases found, returning FALS...

...am ~ standLRT, data = Exam, random = ~ 1 | school) If I want to model only a few categories of variance, all works fine. For instance, should I (for whatever reason) hypothesize that the variance on the normexam-scores is larger in mixed schools than in boys-schools, I'd use weights = varIdent(form = ~ 1 | type), leading to: heteroscedastic <- lme(fixed = normexam ~ standLRT, data = Exam, weights = varIdent(form = ~ 1 | type), random = ~ 1 | school) This gives me nice and clear output, part of which is shown below: Variance function: Structure: Different standard deviations pe...

.../github.com/downloads/TomRoche/GEIA_to_netCDF/output.1.png + create a netCDF file using the data read from the GEIA file. (At least, after nc_sync(netcdf.file), the file `ncdump -h`s properly.) However, I can only *put* the data to the netCDF file: > ncvar_put( > + nc=netcdf.file, > + varid=emis.var, > + vals=t(global.emis.mx), > + start=c(1, 1, 1), > + count=c(-1,-1, 1)) # -1 -> all data When I try to *pull* the data *from* the netCDF I created, > > target.data <- ncvar_get( > + nc=netcdf.file, > + varid=emis.var, > + # read all the data > +...

...e standard deviations (are patients in group A less variable than those in group B? both within patient and between patient within group). Here is the call from my lme object: > fit3$call lme.formula(fixed = BG ~ group, data = bg, random = list(Encounter = pdDiag(~group - 1)), weights = varIdent(form = ~1 | group)) then I call the intervals function: Approximate 95% confidence intervals Fixed effects: lower est. upper (Intercept) 123.40005 126.88445 130.36885 groupB 17.44991 23.60049 29.75107 attr(,"label") [1] "Fixed effects:" R...

...basic model was something like: model0 <- lme( log(growth) ~ light * species.group , data=data, random=~light|species ) # with 20 odd species divided in 2 groups Following the methods in Pinheiro&Bates I tried to put a variance function in the model: model1 <- update(model0, weights=varIdent(form = ~1|species.group) ) I got no error message or note but when I printed model1 the output did not print any variance structure. It just didn't add any weights or variance structure and model1 = model0 If instead I ran the varFunc sepparately: varf1 <- varIdent(form = ~1|species.gr...

...------------- # Set working directory and load library setwd('C:/Users/steinber/Documents/DATA/ENSEMBLES/') library(ncdf) library(rgdal) library(chron) library(fields) # Read netCDF file tg.ncdf = open.ncdf('tg_0.25deg_CRU_version1.0.nc') tg.ncdf lonmat = get.var.ncdf(nc=tg.ncdf,varid="longitude") # reads entire matrix latmat = get.var.ncdf(nc=tg.ncdf,varid="latitude") # ditto timearr = get.var.ncdf(nc=tg.ncdf,varid="time") # reads entire time array targettime = julian(x=1, d=1, y=2002, origin=c(month = 1, day = 1, year = 1950)) inds...

Hello R users- I am new to R, and tried searching the archives and literature for an answer to this - please be patient if I missed something obvious. I am fitting a logistic regression model, and would like to include variance functions (specifically the varIdent function). I cannot figure out how to do this either in glmmPQL (or something similar) for the model with random effects, or in glm for the model without random effects. Is it possible to fit a varIdent function in a generalized linear model? If so, what are the appropriate packages/functions...

...tmp = gfedmly[*,j-1] gfedmly[*,j-1] = gfedmly[*,nlat-j] gfedmly[*,nlat-j] = tmp endfor undefine, tmp ; Then, you can read daily fire fractions from the netcdf file. file1_in = string('fraction_emissions_20040121.nc') file1_in=strcompress(file1_in, /REMOVE_ALL) fid=NCDF_OPEN(file1_in) varid=NCDF_VARID(fid,'Fraction_of_Emissions') NCDF_VARGET, fid, varid, DATA NCDF_CLOSE, fid gfeddly=gfedmly*DATA ;++++++++++++++++++++ the end for daily emissions ++++++++++++++++++ ;++++++++++++++++++++ idl code to generate 3-hourly emissions ++++++ nlon=720 ; number of grid points by longitude...

...1:length(thesenames[,1])){ data <- nc_open(paste(INDIR, thesenames[i,c("wholename")], sep=""), write=F) d.vars <- names(data$var) d.size <- (data$var[[length(d.vars)]])$size # Obtaining longitude and latitude values d.lon <- as.vector(ncvar_get(data, varid="lon", start=c(1,1), count=c(d.size[1],d.size[2]))) d.lat <- as.vector(ncvar_get(data, varid="lat", start=c(1,1), count=c(d.size[1],d.size[2]))) # Obtaining climate data values df.clim <- data.frame(rn=seq(1:length(d.lon))) for(y in 1:d.size[3]){ df.clim[...

...ommended) nlme package (R version 2.3.1 on a Linux platform). When trying to reproduce an example from Jose Pinheiro & Douglas Bates (2000, p 210) I get the following error message (code to produce message pasted as well): library("nlme") data("Orthodont") vf1Ident <- varIdent( c(Female = 0.5), form = ~ 1 | Sex ) vf1Ident <- initialize(vf1Ident, Orthodont) Error in getClass(Class) : c("\"varIdent\" is not a defined class", "\"varFunc\" is not a defined class") In addition: Warning message: the condition has length > 1 and...

I've got a gls formula that's a mix of continuous and ordered variables. I wanted to use gls because I wanted to use the varIdent structure. Anyway, attempts to use "predict.gls" choke with the error that the levels I use are not allowed for one of them -- the first one alphabetically, so I'd guess the second would have the same problem. So I have three linked questions -- answering any of the three would s...

Greetings, I have a meta-analysis problem in which I have fixed effects regression coefficients (and estimated standard errors) from identical models fit to different data sets. I would like to use these results to create pooled estimated regression coefficients and estimated standard errors for these pooled coefficients. In particular, I would like to estimate the model \beta_{i} = \mu +

...come from separate normal distributions with possibly unequal variances. There must be some straightforward obvious way to fit the larger model, but I do not see it. Pinheiro and Bates, chapter 5.2, show how to model unequal *residual* ("within-group") variances for the two groups using varIdent. They also tantalizingly say, "The single-level variance function model (5.10) can be generalized to multilevel models" (page 206), which seems to suggest that a solution to the current problem might exist. The pdMat classes provide a way to *constrain* the dispersion matrix of the r...

...# ---------------- nc <- open.ncdf(gcfilename) nc.ori <- open.ncdf(gcfilename.ori) print ("fininshed open file") print(nc) print("Reading TFLAG") # error occurs on any of the following "get.var.ncdf" calls: tflag<- get.var.ncdf(nc,varid="TFLAG") # So this would produce the failure isop <- get.var.ncdf(nc,varid="ISOPRENE") isop.ori <- get.var.ncdf(nc.ori,varid="ISOPRENE") meoh <- get.var.ncdf(nc,varid="MEOH") meoh.ori <- get.var.ncdf(nc.ori,varid="MEOH")...

...Birds~Rain+ID+ s(Time,by=as.numeric(ID=="Stilt.Oahu"))+ s(Time,by=as.numeric(ID=="Stilt.Maui"))+ s(Time,by=as.numeric(ID=="Coot.Oahu"))+ s(Time,by=as.numeric(ID=="Coot.Maui")), correlation=corAR1(form=~Time |ID ), weights=varIdent(form=~1|ID)) However, in the current version of R, this does not work anymore, and should be changed into BM2<-gamm(Birds~Rain+ID+ s(Time,by=ID), correlation=corAR1(form=~Time |ID ), weights=varIdent(form=~1|ID)) It turns out that 2 of the 4 smoothers have estimated de...