similar to: rename object

Hallo >From a variable "x" that defines, say, four classes, I would like to define the matrix "mat" of dummy variables indicating the classes, i.e. x <- c(1,1,1,1,2,2,2,3,3,3,4,4) mat <- matrix(c(1,0,0,0, 1,0,0,0, 1,0,0,0, 1,0,0,0, 0,1,0,0, 0,1,0,0, 0,1,0,0, 0,0,1,0, 0,0,1,0, 0,0,1,0, 0,0,0,1, 0,0,0,1), ncol=4, byrow=T) Thank you for your help, Serguei

Dear R-users, i try to solve to following linear programm in R 0 * x_1 + 2/3 * x_2 + 1/3 * x_3 + 1/3 * x_4 = 0.3 x_1 + x_2 + x_3 + x_4 = 1 x_1, x_2, x_3, x_4 > 0, x_1, x_2, x_3, x_4 < 1 as you can see i have no objective function here besides that i use the following code. library(lpSolve) f.obj<-c(1,1,1,1) f.con<-matrix(c(0,2/3,1/3,1/3, 1,1,1,1,

# Dear List, # I want to characterize a time series according to its Quarter components. # My data ("a.ts": http://docs.google.com/View?id=dfvvwzr2_478cr9k4cdb)? look like: #???????????????? Qtr1????????? Qtr2????????? Qtr3????????? Qtr4 #?? 1948 -0.0714961837? 0.0101747827? 0.0654816569 -0.0227830729 #?? 1949 -0.1175517556? 0.1151378692? 0.1015777858 -0.1971535900 #?? 1950?

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Dear Help-list, How can I do a multiple comparison test (mct) on selected contrasts from a linear model while using packages lme4 and multcomp? I am running R 2.13.0 under Windows 7. The following linear model and mct produces a global mct of 15 paired contrasts of the combined (Site, Position) factor SitePos of which only 9 are of interest. Model.G = lmer(log10(SrCa) ~ SitePos + (1 | Eel),

Dear list, Does anyone have a suggestion (or better still) code for sampling from the uniform distribution over the convex hull of a set of points? Many thanks and best wishes, Ranjan

Hi R Helpers, I'm still new to R and i experience many difficulties..I'm using vegan package (R version 2.11) trying to calculate checkerboard units for each species pair of a matrix. I've prepared the function: pair.checker=function (dataset) {designdist (dataset, method="c("(A-J)x(B-J)", terms ="binary", abcd=FALSE)} to use with function oecosimu as

Apart from the fact that the statement "such that t1+t2+t3+t4=2970 (as it must)" is not correct, the LP can be implemented as follows: library(lpSolve) LHS <- rbind( c(0,0,0,0, 1, 0, 0,0), c(1,0,0,0,-1, 1, 0,0), c(0,1,0,0, 0,-1, 1,0), c(0,0,1,0, 0, 0,-1,1), cbind(-diag(4),diag(4)), c(0,0,0,0,0,1,0,0), c(0,0,0,0,0,0,1,0), c(0,0,0,0,0,0,0,1) ) RHS <-

Quick question about the usage of glht. I'm working with a data set from an experiment where the response is bounded at 0 whose variance increases with the mean, and is continuous. A Gamma error distribution with a log link seemed like the logical choice, and so I've modeled it as such. However, when I use glht to look for differences between groups, I get significant

Say I have a dataframe like this: df <- data.frame(cbind(c(1,0,0,1),c(0,1,0,0),c(0,0,1,0))) names(df) <- c('a','b','c') I would like to create a factor in a new column, where the factor values are taken from the column names, like this: > df2 a b c f 1 1 0 0 a 2 0 1 0 b 3 0 0 1 c 4 1 0 0 a How would I do this? Thanks, Dan Daniel Nordlund Bothell, WA USA

Question for 'experts' in LP using R (using the lpSolve package, say) -- which does not apply to me for the sort of problem I describe below. I've run any number of LP's using lpSolve in R, but all of them to date have objective and constraint functions that both contain the same variables. This lets you set up a LHS and RHS matrix/vector that are symmetrical. But, for a

Hi, I have a density that I need to get MLEs from, which includes definite integrals both in the denominator and in the numerator of the density function. It looks like the outcome depends on the initial values given. My program is shown below: library(circular) ######################################## 4 parameters ######################################## z<-rvonmises(100,0,1)

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

!urgent! Hi all. I am facing a problem plotting a indicatormatrix to visualize the pattern. Matrix consist from 1's and 0's. for example x <- matrix(c(0,0,1,0, 1,1,1,1, 0,0,0,1, 1,0,1,1), nrow = 4, ncol=4) an i want to have a plot like | . | . . . . | . | . . . |_______> I thinking of somethink like a for loop, which creates a data point whenever

Dear R Development Team, I have encountered the following difficulty in using the function polyroot under either NT4.0 (R version 1.2.1) or linux (R version 0.90.1). In the provided example, the non-zero root of c(0,0,0,1) depends on the results of the previous call of polyroot. R : Copyright 2001, The R Development Core Team Version 1.2.1 (2001-01-15) R is free software and comes with

Dear folks, I have been struggling to create what I fondly imagined would be a straightforward adaptation of the package's example to my own dataset, which looks at incidence of depression following the birth of initial and up to 3 subsequent children (4 children in all, with all subjects having a first child). I've regarded these as equivalent to tooth eruption and subsequent caries in

Hi, I've tried to run the multiple imputation for longitudinal data using Pan in R. The trimmed data consist 10 individuals across 4 time points. Following the example in panex.R, I imputated the model with one y variable and only random intercept. It worked well. The next step for me is to imputate a model with one y variable and both random intercept and slope. The program ran well, but

Dear all, I imagine that this is a trival question, but it has perplexed for most of the day. Any help would be greatly appreciated. Below is an example of what I'm trying to do. Essentially I want to produce all unique 1 x 1 combinations of certain columns from a dataframe, and join these to other columns from the same dataframe. I'm having problems with the nested loop as I can only

I'd just like to report a possible R bug--or rather, confirm an existing one (bug #751). I have had some difficulty using the polyroot() function. For example, in Win 98, R 1.1.1, > polyroot(c(2,1,1)) correctly (per the help index) gives the roots of 1 + (1*x) + (2*x^2) as [1] -0.5+1.322876i -0.5-1.322876i However, > polyroot(c(-100,0,1)) gives the roots of [1] 10+0i -10+0i

Hi, I would like to replicate a sort of Montecarlo experiment: I have to generate a random variable N(0,1) with 100 observations but I have to contaminate this values at certain point in order to obtain different vectors with different samples: tab<-function(N,n,E,L){ for(i in 1:100){ X1<-rnorm(N*(1-a),0,1) X2<-rnorm(N*(a),E,L) X_tab<-sample(c(X1,X2),n,replace=T) } return(X_tab)}