similar to: time series interpolation

Greetings list, I am new to programming in R, and am using nnet() function for a project on neural networking. Firstly I wish to ask if there is any pdf explaining the algorithm nnet uses, which could tell me what the objects of the nnet class, like 'conn', 'nconn, 'nsunits', n and 'nunits' mean, and how weights are calculated. The package pdf has little or no

After importing multiple files to data.frames in R, I want to rename all their columns and do other operations with them. The data.frame names are not continuous like 1, 3, 4, 6. I could not find a way of creating a list of the data.frames and loop this and ended up putting them into a list first: # get all objects all.obj = sapply(ls(), get) # get data frames dfrs = all.obj[sapply(all.obj,

Hi In a dataset I have length and age for cod. The age, however, is ony given for 40-100% of the fish. What I need to do is to fill inn the NAs in a correct way, so that age has a value for each length. This is to be done for each sample seperately (there are 324 samples), meaning the NAs for sampleno 1 shall be calculated from the known values from sampleno 1. As for example length 55 cm

In Venables \& Ripley 3rd edition (p. 231) the proportional odds model is described as: logit(p<=k) = zeta_k + eta but polr apparently thinks there is a minus in front of eta, as is apprent below. Is this a bug og a feature I have overlooked? Here is the naked code for reproduction, below the results. ------------------------------------------------------------------------ --- version

Hi, I am trying to test the proportional-odds model using the "polr" function in the MASS library with the dataset of "housing" contained in the MASS book ("Sat" (factor: low, medium, high) is the dependent variable, "Infl" (low, medium, high), "Type" (tower, apartment, atrium, terrace) and "Cont" (low, high) are the predictor variables

Hi r users,   I would like sort of   cdf         seq   rand 0.00E+00    0     0.262123478 1.56E-03    20    0.964293344 1.55E-02    40    0.494827113 5.30E-02    60    0.733726005 1.16E-01    80    0.800408948 1.97E-01    100   0.925748466 2.88E-01    120   0.047578356 3.80E-01    140   0.266060366 4.68E-01    160   0.125522629 5.48E-01    180   0.701193274 6.18E-01    200   0.915799432

----------------------------------------------------------------------------------------------------- Howdy, Gurus I am appying R package for regression analysis as followings. A dependent variable is jhnet that means ratio of dividing internal trip with all trips in a traffic zone. There are many indepentent variables including factor or dummy varibles such as parkfee, ohouse, Devt2, corridor1.

My operating system is Solaris 8 SPARC and it runs Samba 3.0.1 Any suggestions? I have included configuration/logs and traces. This is the smb.conf file: # Samba config file created using SWAT # from 151.184.34.182 (151.184.34.182) # Date: 2003/09/17 20:34:48 # Global parameters [global] netbios name = dali workgroup = DALIUNIX passdb backend = tdbsam os level

Are these 225 compile time regressions real? It sure looks bad! Ciao, Duncan. On 01/12/11 09:39, llvm-testresults at cs.uiuc.edu wrote: > > bwilson__llvm-gcc_PROD__i386 nightly tester results > > URL http://llvm.org/perf/db_default/simple/nts/380/ > Nickname bwilson__llvm-gcc_PROD__i386:4 > Name curlew.apple.com > > Run ID Order Start Time End Time > Current 380

Greetings R Users, I?m new to R but at least managed to read in multiple files: filenames <- list.files(path=getwd()) numfiles <- length(filenames) for (all_temp in c(1:numfiles)) { filenames[all_temp] <- paste(filenames[all_temp],sep="") assign(gsub("[.]ASC$","temp",filenames[all_temp]),read.delim2(filenames[all_temp],