similar to: Create matrix with subset from unlist

Hello, I am trying to construct two matrices, F and V, composed of partial derivatives and then find the eigenvalues of F*Inverse(V). I have the following equations in ryacas notation: > library(Ryacas) > FIh <- Expr("betah*Sh*Iv") > FIv <- Expr("betav*Sv*Ih") > VIh <- Expr("(muh + gamma)*Ih") > VIv <- Expr("muv*Iv") I

Hi, I'm having a problem using sample() within a function. Basically I get an error reading: Error in sample(v, 1, prob = h) : non-positive probability Can anyone advise me as to the possible origin of this error? Here is my code #Discretised Gillespie algorithm function (From SMfSB, D.J. Wilkinson) gillespied=function (N, T=100, dt=1, ...) { tt=0 n=T%/%dt x=N$M

I'm trying to apply a function defined in the VW R docs, that attemps to convert a data.table object to Vowpal Wabbit format. In the process i'm getting the error in printf mentioned in the subject. The original function is here: https://github.com/JohnLangford/vowpal_wabbit/blob/master/R/dt2vw.R Below there is a small example that reproduces the error. The function works great with

Hi all, I have some trouble reading data from a csv file. I used command "read.delim("clipboard") to read in the data. > aalpha.data <- read.delim("clipboard") > class(aalpha.data) [1] "data.frame" > dim(aalpha.data) [1] 8 25 > colnames(aalpha.data) [1] "X" "V1" "V2" "V3" "V4"

Wouldn't it make sense to have this in the man page? The 8192-byte limitation for 'fmt' is mentioned but not this one. Thanks, H. On 08/25/2015 02:08 AM, Prof Brian Ripley wrote: > From the sources: > > #define MAXNARGS 100 > /* ^^^ not entirely arbitrary, but strongly linked to > allowing %$1 to %$99 !*/ > > > > On 22/08/2015 04:21, Martin

Dear R community, I have trouble implementing a nested variance-covariance matrix in the lme function. The model has two fixed effects called End and logpgc, the response variable is the logarithm to base 2 of Intensity ( log2(Intensity) ) and the random effects are called Probe and ProbeNo. The model has the following nesting structure: A Pixel is nested within the ProbeNo,the ProbeNo is

HI all, I might add some more information in order to possibly solve my problem. I'm really stuck and no obvious solutions do the trick. I'm using R 1.7.1 on Windows 2000 with the packages regurarly updated. I'm using hypothetical data constructed as a pseudo population conforming to a certain Var-Cov structure. I might add that just > predict(level2) works. But when I add the

Hi. I have a simple (I think) question My dataset have these variables: names(data) [1] "v1" "v2" "v3" "v4" "v5" "v6" "v7" "v8" "v9" "v10" "v11" "v12" "v13" "v14" "v15" "v16" "v17"

Dear All I want to chose just spacial columns in R. (read table) data1<- read.table("/home/Documents/data.txt",header=F,sep = "\t", as.is =F) data.2<-data1[sub.data[,1],] The dimension of data.2 is correct but my data are transformed NA head(data.2) V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 NA <NA> NA NA NA

Hi All I was wondering how to get the same results with gls and lme. In my lme, the design matrix for the random effects is (should be) a identity matrix and therefore G should add up with R to produce the R matrix that gls would report (V=ZGZ'+R). Added complexity is that I have 3 levels, so I have R, G and say H (V=WHW'+ZGZ'+R). The lme is giving me the correct results, I am

Hello R I can not figure out, how to add text string or number to lines like on this image: http://r.789695.n4.nabble.com/file/n3350452/graph.png I have some data series in text file. My script: require(graphics) require(stats) graf=read.table("file",header =FALSE,sep = "", dec = ",",fileEncoding = "", encoding = "unknown", skip=19,nrows=400)

Hello, I am trying to select the following headers from a data frame but when I try and run the command it executes halfway through and give me an error at V188 and V359. Temp <- data.frame(V4, V5, V6, V7, V8, V9, V10, V11, V12, V13, V14, V15, V16, V17, V18, V19, V20, V21, V22, V23, V24, V25, V26, V27, V28, V29, V30, V31, V32, V33, V34, V35, V36, V37, V38, V39, V40, V41, V42, V43, V44, V45,

Dear R gurus, Thank you all for continuous support and guidance -- learning without you would not be efficient. I have a question regarding LD analysis and how to best code it up in R. I have a file of (V52 and 671 time points across all columns) and another file of phonetic features (each vowel is aligned with a distinct binary sequence, i.e. E 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0

Hello R-Users, its again me with a question. I´m using R 2.5.0 on Mac Power Book running Mac OS X 10.4.10 I try to calculate distances betweeen two data tables looking like this V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24 V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36 1 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 1 0 0

Dear all, I'm trying to make an R code for the drought severity index (DSI) developed by Philips and McGregor (1998). You can refer to the description of the algorithm on page 19 from http://dissertations.port.ac.uk/229/01/MaloneS.pdf The code is given in Excel as the following and can be found on page 60 from the same link. C7 = IF(C6<0,IF(@SUM(A6:A1)<0,C6+A6,"0"),

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While I don't know anything about Box's M test, I googled around and found a Matlab M-file that computes it. Below is my straight-forward translation of the code, without knowing Matlab or the formula (and done in a few minutes). I hope this demonstrates one of Prof. Ripley's point: If you really want to shoot yourself in the foot, you can probably program R to do that for you. [BTW:

From the sources: #define MAXNARGS 100 /* ^^^ not entirely arbitrary, but strongly linked to allowing %$1 to %$99 !*/ On 22/08/2015 04:21, Martin Bel wrote: > I'm trying to apply a function defined in the VW R docs, that attemps to > convert a data.table object to Vowpal Wabbit format. In the process i'm > getting the error in printf mentioned in the subject.

Dear Community, I have been using two types of survival programs to analyse a data set. The first one is an R function called aftreg. The second one an STATA function called streg. Both of them include the same analyisis with a weibull distribution. Yet, results are very different. Shouldn't the results be the same? Kind regards, J -- View this message in context:

Hi, R users I am trying to use EM algorithmto estimate a latent class model of discrete choice. The basic model is a logit model which has two variables X=(X1,X2) and the utility is defined as v = Beta*X. There are 3 classes of individuals each has its own Beta values, so Beta is a 3*2 matrix. For each individual, (there are 1000), he make a choice between two randomly generated choice