similar to: Problem loading .Rdata file

Dear all, I'm not getting what I'm doing wrong. The line below from my read.fsa.bin function throws an error when just loading my AFLP package and disappears when I load the zoo package as well. #the line that throws the error Index <- which(Peak == rollmax(Peak, k = 1 + 2 * floor((min(diff(SizeStandard)) * Fs - 1) / 2), fill = -Inf)) #the error Error in UseMethod("rollmax")

Dear all, I have a list of n matrices which all have the same dimension (r x s). What would be a fast/elegant way to calculate the element wise average? So result[1, 1] <- mean(c(raw[[1]][1, 1] , raw[[2]][1, 1], raw[[...]][1, 1], raw[[n]][1, 1])) Here is my attempt. #create a dummy dataset n <- 3 r <- 5 s <- 6 raw <- lapply(seq_len(n), function(i){ matrix(rnorm(r * s), ncol =

Dear all, I get a bug in the examples of my AFLP package on R-forge (https://r-forge.r-project.org/R/?group_id=1027) but only on the Linux version. The windows version compiles. The Mac version skips the examples and compiles. The strange thing is that the packages compiles on my Ubuntu 10.10 machine with R 2.15.0. Therefore I can't reproduce the error. I have traced the problem at

Dear all, I'm documenting an elaborate analysis as a private package. The package writes all required output to a database. I use a Sweave file to transform all the results from the database into a report. Now I would like to add this Sweave to my package. I'm a bit puzzled on how to do that. It is not a vignette: the data is not available when the package is build and I need to

Dear all, I'm trying to connect to an MSAccess database (ArcGIS personal geodatabase). I keep getting an error about the channel when using sqlQuery(). However, sqlTables() does not complain about the channel and lists all tables in the database. If I try sqlFetch(), then R crashes. I'm happy to hear suggestions on how to solve this. Best regards, Thierry > MDB <-

Dear all, My package has Depends: lme4.0 in the DESCRIPTION. I need to extract the fixed effect of a model and their standard errors. I use coef(summary(model)) inside a function to do that. Model is the output of a call to glmer() from the lme4.0 package. coef(summary(model)) throws an error: $ operator is invalid for atomic vectors I have tracked it down to a problem with summary(model)

Dear all, I'm puzzled by the error I get from R CMD check one of my packages. I'm running R CMD check with the --as-cran flag and it get the error both from running it from the command line as from within Rstudio. On the same machine R CMD check on my GRTS package (https://r-forge.r-project.org/R/?group_id=1027) works fine. Any suggestions on how to fix this? I can send the source code

Dear all, One of the functions that I wrote (ggsave.latex) extents the functionality of a function (ggsave) in another package (ggplot2). Instead of copying all the information I would like to create a link in the helpfile of ggsave.latex to the helpfile of ggsave. I tried \code{\link{ggsave}} and \code{\link{ggplot2::ggsave}}, but neither worked. Both cases gave a 'missing link' warning.

Dear all, I'm plotting some points on a graph where both axes need to have the same scale. See the example below. Coord_equal does that trick but in this case it wastes a lot of space on the y-axis. Setting the limits of the y-axis myself was no avail. Any suggestions to solve this problem? library(ggplot2) ds <- data.frame(x = runif(1000, min = 0, max = 300000), y = runif(1000, min =

Dear all, Could someone point me to a function or algorithm to generate random bivariate binomial data? Some details about what I'm trying to do. I have a dataset of trees who were categorised as not damaged or damaged. Each tree is measured twice (once in two consecutive years). The trees can recover from the damage but the data is clearly correlated. As a (un)damaged tree is more likely

Hi all, I have managed to create a figure on the screen with multiple plots in it. Something like the example below. When I save that with ggsave(), only the last plot gets saved (pPath in the example) instead of the entire figure. Any suggestions how I can save this kind of figures automated? Thanks, Thierry library(ggplot2) pPoint <- qplot(unemploy/pop, psavert, data=economics) pPath

Dear all, Is it possible to incorporate a nested design in GEE? I have measurements on trees that where measured in two years. The trees are nested in plots. Each plot contains 24 trees. The number of plots is 72. Hence we would expect 2 * 24 * 72 = 3456 data points. A few are missing, so we end up wih 3431 data points. This is what I have tried until now. #assuming independence between trees

Dear all, I'm trying to use a Markdown vignette with UTF-8 encoding. It compiles well when knitting the vignette in RStudio, but it fails to recognize the UTF-8 settings when building the source package. Can someone point out what I'm doing wrong? I tried to put the relevant information below. Best regards, Thierry Details: Using 64-bit R 3.1.2 with encoding = "native.enc"

Dear Duncan, The UTF-8 characters aren't properly rendered in the pdf version of the vignette. $?? ????? ?????? ?????? ????? ?? ?? is rendered as $????? ?????????? ???????????? ????? ?????? ? ???????? ???????? The same problem occurs when I use render("vignette.md", output_format = "mypackage::mystyle"), instead of render("vignette.md", output_format =

Dear all, I'm using zeroinfl() from the pscl-package for zero inflated Poisson regression. I would like to calculate (aproximate) prediction intervals for the fitted values. The package itself does not provide them. Can this be calculated analyticaly? Or do I have to use bootstrap? What I tried until now is to use bootstrap to estimate these intervals. Any comments on the code are welcome.

That is strange. Another function in the same package (INBOmd::inbo_rapport) uses the same trick. I actually started by copying the post_processor() from that function. INBOmd::inbo_rapport() works both with and without BibTex. Working examples are source/inbo_rapport and source/inbo_rapport_basic from https://github.com/inbo/inbomd_examples. Note that you need some extra work after installing

On 07/09/2017 2:04 PM, Duncan Murdoch wrote: > On 07/09/2017 10:11 AM, Thierry Onkelinx wrote: >> Dear Duncan, >> >> Thanks for chiming in. Could you explain how you set debug() on >> post_processor()? I've tried adding debug(post_processor) to >> rsos_article() or adding debug(post_processor) when after post_processor >> was defined in the debugger.

Dear Duncan, Thanks for chiming in. Could you explain how you set debug() on post_processor()? I've tried adding debug(post_processor) to rsos_article() or adding debug(post_processor) when after post_processor was defined in the debugger. Neither work for me. All supporting files are available within the package. The code below should be reproducible on your machine.

Dear Thierry, thank you for the quick reply. I have only one question about the approach you proposed. As you suggested, imagine that the model we end up after the model selection procedure is: mod2.1 <- lmer(dT_purs ~ T + Z + (1 +T+Z| subject), data =x, REML= FALSE) According to the common procedures specified in many manuals and recent papers, if I want to compute the p_values relative to

On 07/09/2017 10:11 AM, Thierry Onkelinx wrote: > Dear Duncan, > > Thanks for chiming in. Could you explain how you set debug() on > post_processor()? I've tried adding debug(post_processor) to > rsos_article() or adding debug(post_processor) when after post_processor > was defined in the debugger. Neither work for me. Not working for me either right now for some reason