similar to: do.call with Vectorial Argument

Dear All, I'd like to know if it is possible to create animations with R. To be specific, I attach a code I am using for my research to plot some analytical results in 3D using the lattice package. It is not necessary to go through the code. Simply, it plots some 3D density profiles at two different times selected by the user. I wonder if it is possible to use the data generated for different

I'm not aware of any routine that those the job, although I think that it could be relatively easily done by multiplication the manifest variable vector with the estimates for the specific effect. To make an example: v1; v2; v3; v4 are manifest variables that loads on one y latent variablein a data frame called "A" the code for the model should be like: model <-specifymodel( y

Hi all, I am new to R. I made this script that plots the deviation of a skellam-distributed random variable with respect to the skellam distribution. I would expect to get random errors, but the plot sistematically shows a non-random pattern (first a peak and then a low). I don't know how to explain this. Can somebody enlighten me?? require(skellam) n <- 5000 lam1 <- 5.45 lam2 <-

Today I tried to install the R-2.5.0 (currently running R-2.4.1) on Mandriva Linux. The ./configure and make commands seem to run fine, but "make check" gives the following messages: running regression tests make[3]: Entering directory `/home/ethomp04/R-2.5.0/tests' running code in 'reg-tests-1.R' ...make[3]: *** [reg-tests-1.Rout] Error 1 make[3]: Leaving directory

Hi there, as a student sociology, I'm starting to learn about SEM. The course I follow is based on LISREL, but I want to use the SEM-package on R parallel to it. Using LISREL, I found it to be very usable to be able to see the total direct and total indirect effects (standardized and unstandardized) in the output. Can I create these effects using R? I know how to calculate them

I am using a binomial mixture model to estimate abundance (N) and detection probability (p) using simulated count data: -Each site has a simulated abundance that follow a Poisson distribution with lambda = 5 -There are 200 simulated sampled sites -3 repeated counts at each site - only 50 percent of the animals are counted during each count (i.e, detection probability p =0.5, see codes) We removed

I like to bootstrap regression models, saving the entire set of bootstrapped regression coefficients for later use so that I can get confidence limits for a whole set of contrasts derived from the coefficients. I'm finding that ordinary bootstrap percentile confidence limits can provide poor coverage for odds ratios for binary logistic models with small N. So I'm exploring BCa confidence

I'm trying to build a SEM using the sem package. I'll attach my model specification below. When I turn debug=TRUE, it seems as if I'm getting to convergence because I get this message: Successive iterates within tolerance. Current iterate is probably solution. However, at the end of the process I get this message: Warning message: In sem.default(ram = ram, S = S, N = N,

I wonder if someone could explain why, when I perform confirmatory factor-analysis model using polychoric correlations why I do not get an estimated confidence interval for the RMSEA. My experience with these type models is that I would obtain a confidence interval estimate. I did not get any warning messages with the output. RESULTS: Model Chisquare = 1374 Df = 185 Pr(>Chisq) = 0

Dear users, i'm using the sem package in R, because i need to improve a confermative factor analisys. I have so many questions in my survey, and i suppose, for example, that Question 1 (Q1) Q2 and Q3 explain the same thing (factor F1), Q4,Q5 and Q6 explain F2 and Q7 and Q8 explain F3... For check that what i supposed is true, i run this code to see if the values of loadings are big or not.

Dear R-core Team, As Thomas Lumley pointed out in one of his e-mails one can use commandArgs() to get a copy of the command line arguments supplied when R session was invoked and then use grep to extract parameters of interest. His solution works very well if the custom options are passed by names, e.g. --my-option=value, but what if one wants to pass parameters by their positions. Then it's

Hi, I created the model below, which returns me the following warning message: In sem.default(ram = ram, S = S, N = N, param.names = pars, var.names = vars, : Could not compute QR decomposition of Hessian. Optimization probably did not converge. ######### Model ######## mDPDF = data.frame(mj1,mj2,mj3,mj4,mj5,eL1,eL2,eL3,eL4,eL5,aC1,aC2,aC3,aC4,disR1,disR2,disR3,disR4,disR5,

Well, the benefit lies in the ability to pass along arguments via `...` to more than one recipient that use *identical argument names* and/or when these recipients are not necessarily located on the same calling stack layer. I'm *not* after a *general* change in the way arguments are dispatched/functions are called as I'm actually a big friend of keepings things quite explicit (thus

Harold: As a general rule, if you are using eval(parse(...)) you are doing it poorly in R; cf library("fortunes") fortune(106) Why is something like this not suitable: fun1 <- function(a1,a2,a3 = c("hi","by")) { cat(a3,a1+a2,"\n") } > fun1 (1,2) hi by 3 > fun1(1,2, a3 = "whoopee") whoopee 3 ... or, if you want to include the

I'm making two plots, one on top of the other. On the upper plot, I do not print the x-label or the x-tick-label. To reduce space, I'd like to keep the white space between the two figures at a minimum. However, I can't figure out how to methodically reduce the space while maintaining the same figure dimensions for both plots. I could add margin space below the lower plot and reduce

Hi Gabriel, and thanks for answering. I'm basically just trying to find a way to use the power of `...` in more complex scenarios and I'm well aware that this might not be the best approach ;-) Regarding your actual question: "Are you suggesting methods be dispatched based on the *contents* of ... [...]?" Yes, I guess currently I kind of do - but not on the argument *names*

Hi! I want to construct a path analysis model that can account for measurement error in totally aggregated parcels, which refer to parcels where all of the items in a scale are summed or averaged. If I am not mistaken, Bollen (1989) advocates the following formula for computing the error variance of each parcel: (1−α(parcel))×variance(parcel), such that α refers to Cronbach's alpha, which

I would like to preserve the values of all the arguments to a function in a results object. foo <- function(a, b=1) .... foo(x, 3) match.call() looks promising, but it records that a is x, while I want the value of x (in the calling frame). Also, if the invocation is foo(x), then match.call doesn't record that b is 1. So I tried this (inside the function definition): myargs <-

R-1.7.0 built on NetBSD 1.6, but the validation test suite failed: Machinetype: Intel Pentium III (600 MHz); NetBSD 1.6 (GENERIC) Remote gcc version: gcc (GCC) 3.2.2 Remote g++ version: g++ (GCC) 3.2.2 Configure environment: CC=gcc CXX=g++ LDFLAGS=-Wl,-rpath,/usr/local/lib make[5]: Entering directory `/local/build/R-1.7.0/src/library' >>> Building/Updating

I was trying to ByteCompile a package that I made. The package compiles successfully with byte compile set to FALSE. When I set ByteCompile to TRUE, I receive the following error message while doing R CMD INSTALL /usr/lib/R/bin/INSTALL: line 34: 9964 Done echo 'tools:::.install_packages()' 9965 Segmentation fault | R_DEFAULT_PACKAGES= LC_COLLATE=C "${R_HOME}/bin/R" $myArgs