similar to: extracting the names of the dataframe and variables in aov or lm

Dear Help, Suppose I have a character vector. x <- c("Bob", "loves", "Sally") I want to combine it into a single string: "Bob loves Sally" . paste(x) yields: paste(x) [1] "Bob" "loves" "Sally" The following function combines the character vector into a string in the way that I want, but it seems somewhat inelegant.

Dear R Help, I am trying to create a boolean vector that is TRUE whenever a particular value occurs in a numeric vector, and FALSE otherwise. For example, suppose that > y <- c(5, 2, 4, 3, 1) > y [1] 5 2 4 3 1 and suppose that I want to find where 3 occurs in y. Then, the following yields the solution: > y == 3 [1] FALSE FALSE FALSE TRUE FALSE My problem arises when the

I have run into a general problem with subsetting of which the following is a simple example. Suppose that x <- c(5, NA, 7, 5, NA, 3) y <- c(1, 2, 3, 4, 5, 6) I want to extract the values of y for which x = 5, but y[x==5] yields > y[x==5] [1] 1 NA 4 NA I find that y[!is.na(x) & x==5] yields the desired result: > y[!is.na(x) & x==5] [1] 1 4 but I am wondering whether

Dear R-Help, Suppose I have a character matrix, e.g., (ch.mat <- matrix(c('a','s','*','f','w','*','k','*','*','f','i','o'), ncol=3)) When I convert 'ch.mat' to a dataframe, the columns are converted to factors: (d1 <- data.frame(ch.mat)) mode(d1[,1]) is.factor(d1[,1]) To prevent

Dear R-Help, I'm using R version 1.6.0 on a Windows computer. I am trying to create a function that, among other things, constructs strings that refer to Windows files, e.g., I might want to construct a string like 'c:\work\part1.txt'. I have found that the following does not work. > paste("c:", "\", "work", "\", "part1.txt",

A student of mine wants to use R to do some nonmetric multidimensional scaling. According to the R FAQ, there's a package called pcurve that computes multidimensional scaling solutions, but I was not able to locate it the contrib page (I am a Windows user with R version 1.4.1). Can anyone tell me whether it is possible to do nonmetric multidimensional scaling with R, and if so, how? John

Dear R-users, I am learning to use R 1.3.1 on a Pentium running Windows '98. I'm puzzled that several statistical procedures, t.test and chisq.test, do not appear to be available on R version 1.3.1. For example, if I type "t.test", I get the reply, "Object "t.test" not found". Is there a package that I have to load in order to have access to these

Greetings, I have a question pertaining to the concept of "environment variables" that is mentioned in the R documentation for "Startup" and also in the discussion of the Windows configuration of R in the recent book "An Introduction to R" authored by Venables, Smith, and the R Development Core Team (referred to as VS in this message). The Startup documentation and

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I sent a query to R-Help about the availability of nonmetric multidimensional scaling (MDS) algorithms in R. I would like to thank Tony Rossini, Jonathan Baron, Sundar Dorai-Raj, and Brian Ripley for helpful replies. The gist of the replies is that isoMDS in the MASS library provides Kruskal's method for nonmetric MDS, sammon in the MASS library provides Sammon's nonlinear mapping method

Dear R-Help, I am using the studres function from the MASS package to compute studentized deleted residuals in a oneway anova. I'm having trouble interpreting the results in situations where a factor level has only one observation. Sometimes studres yields an NaN and sometimes it produces a numeric value for cases where a factor level has only one observation. I would think it should

I hope someone can point me, a newcomer to building R as well as 64-bit R, in a useful direction. I have a dual Opteron 275 box running RedHat EL4. I ran configure successfully, and, then, get the following error while running make: make[4]: Entering directory `/tmp/R-2.2.0/src/modules/lapack' g77 -fPIC -g -O2 -ffloat-store -c dlamc.f -o dlamc.lo g77 -fPIC -g -O2 -c dlapack0.f -o

I am trying to compile R on an Itanium machine running Red Hat 7.2 and gcc version 2.96. The build fails with the following symptoms: g77 -fPIC -g -O2 -c cmplx.f -o cmplx.lo g77 -fPIC -g -O2 -c cmplxblas.f -o cmplxblas.lo gcc -shared -L/usr/local/lib -o libRlapack.so dlapack0.lo dlapack1.lo dlapack2.lo dlapack3.lo cmplx.lo cmplxblas.lo -L/usr/local/lib

We are trying to install R2.2.1 on a IBM P655 Cluster, SuSE LE 9.1 We are using gcc v 3.3.3 and we are getting this error on make g77 -fPIC -g -O2 -ffloat-store -c dlamc.f -o dlamc.lo g77 -fPIC -g -O2 -c dlapack0.f -o dlapack0.lo g77 -fPIC -g -O2 -c dlapack1.f -o dlapack1.lo g77 -fPIC -g -O2 -c dlapack2.f -o dlapack2.lo g77 -fPIC -g -O2 -c dlapack3.f -o dlapack3.lo g77 -fPIC -g

Hi everyone, I have just tried to compile R 1.3.1 on the following platform: R is now configured for mips-sgi-irix6.5 Source directory: . Installation directory: /usr/local C compiler: gcc -g -O2 C++ compiler: g++ -g -O2 FORTRAN compiler: g77 -g -O2 X11 support: yes Gnome support: no Tcl/Tk support:

I am having a problem compiling R on an HP-UX 10.20 system using gcc/g77 compiler. It appears to compile without problems until it gets to the lapack module and I get the following error: gcc -I. -I../../../src/include -I../../../src/include -I/usr/local/include -DHAVE_CONFIG_H -D_HPUX_SOURCE -fPIC -O2 -c Lapack.c -o Lapack.lo g77 -fPIC -O2 -c double.f -o double.lo g77 -fPIC -O2 -c cmplx.f

On Sun, Oct 19, 2014 at 6:45 AM, Joerg Sonnenberger <joerg at britannica.bec.de > wrote: > On Sun, Oct 19, 2014 at 06:31:24AM +0700, C Bergström wrote: > > I apologize that I haven't been able to follow this thread entirely, but > if > > someone gives me a Fortran testcase I can check what Fortran+llvm would > do > > currently and maybe give more feedback.

Hello R-users, is it possible to navigate from one workspace to the other from within an R session or does one has to close R and restart it from the directory where resides the desired workspace? For example from Splus I have this little function, see at the end, which I used all the time to navigate between valid Splus directories. I find this particularly usefull when I develop new

Hello R experts I have two linear regressions for sexes (Male, Female, Unknown). All have a good correlation between body length (response variable) and head length (explanatory variable). I know it is not recommended, but for a good practical reason (the purpose of study is to find a single conversion factor from head length to body length), the regressions need to go through the origin (0

Dear list members, I would like to know the difference in outputs and calculation processes between residuals.glm(object, type="response") and residuals.lm(object). For above-ground biomass estimation of trees, I estimated parameters of an allometric equation (ln y = b0 + b1*ln x) using glm as follows: fm <- glm(Ws~log(Wb), family=quasi(link="log",