similar to: Making R lazy

Dear All, Apologies for not providing a reproducible example, but if I could, then I would be able to answer myself my question. Essentially, I am trying to fit a very complicated custom probability distribution to some data. Fitdistrplus does in principle everything which I need, but if require me to specify not only the density function d, but also the cumulative p and and inverse cumulative

Dear All, Say you want to plot, on the same figure two quantities, concentration and temperature, both as function of the same variable. I'd like to be able to put a certain label and scale on the y axis on the left of the figure (referring to the temperature) and another label and scale for the concentration on the right. Any suggestion about what to do? I am sure it is trivial, but I could

Why not define your own functions based on d? e.g. myCumDist <- function(x) { integrate(d, lower=-Inf, upper=x)$value } myQuantile <- function(x) { uniroot(f=function(y) { h(y) - x }, interval=c(-5,5)) } # limits -5,5 should be replaced by your own which might require some fiddling e.g. d <- function(x) { exp(-x^2/2)/(sqrt(2*pi)) } # just an example for you to test with; use your own

Hi Ralf, I tried the following > install.packages("RCurl") which went OK, but then same story when I tried to install tseries. > sessionInfo() R version 3.6.1 (2019-07-05) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Debian GNU/Linux 10 (buster) Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.8.0 LAPACK:

Dear All, I am far from being a guru about parallel programming. Most of the time, I rely or randomForest for data mining large datasets. I would like to give a try also to the gradient boosted methods in GBM, but I have a need for parallelization. I normally rely on gbm.fit for speed reasons, and I usually call it this way gbm_model <- gbm.fit(trainRF,prices_train, offset = NULL, misc =

Dear All, Probably a one liner, but I am banging my head against the floor. Consider the following DF <- data.frame( x=1:10, y=10:1, z=rep(5,10), a=11:20 ) mn<-names(DF) but then I cannot retrieve a column by doing e.g, DF$mn[2] I tried to play with the quotes and so on, but so far with no avail. Any suggestion is welcome. Cheers Lorenzo

Dear All, I would like to be able to plot histograms/densities on a semi-log or log-log scale. I found several suggestions online http://tolstoy.newcastle.edu.au/R/help/05/09/12044.html https://stat.ethz.ch/pipermail/r-help/2002-June/022295.html http://www.harding.edu/fmccown/R/#histograms Now, consider the code snippet taken from http://www.harding.edu/fmccown/R/#histograms # Get a random

Dear All, I am struggling with the following problem: I am given a NxN symmetric matrix P ( P[i,i]=0, i=1...N and P[i,j]>0 for i!=j) which stands for the relative distances of N points. I would like use it to get the coordinates of the N points in a 2D plane. Of course, the solution is not unique (given one solution, I can translate or rotate all the points by the same amount and generate

Apologies for the previous mail (I sent it off too early by mistake). This is the correct example: rm(list=ls()) D_mean<-seq(-5,5,length=100) y<-exp(-D_mean^2/5) pdf("my.pdf") plot(D_mean,y,type="l",yaxt="n",lty=2,lwd=2,col="black", ylab = list(expression(paste(dN/dlogD[agg]," ["*cm^-3*"]"))), xlab = expression(paste(D[agg],"

Dear All, The institute I work for is organizing an internal workshop for High Performance Computing (HPC). I am planning to attend it and talk a bit about fluid dynamics, but there is also quite a lot of interest devoted to data post-processing and management of huge data sets. A lot of people are interested in image processing/pattern recognition and statistic applied to geography/ecology, but I

Dear All, I am given some data to analyze. The data is in the form of a Stata database (.dta file). What is the best way to import it into an R dataframe? Is there any particular caveat I should be aware of? Many thanks Lorenzo

Dear All, I implemented an algorithm for (uniform) random rotations. In order to test it, I can apply it to a unit vector (0,0,1) in Cartesian coordinates. The result is supposed to be a set of random, uniformly distributed, points on a sphere (not the point of the algorithm, but a way to test it). This is what the points look like when I plot them, but other then eyeballing them, can anyone

Dear All, I would like to use a randomForest algorithm on a dataset. The set is not particularly large/difficult to handle, but it has some missing values (both factors and numerical values). According to what I found https://stat.ethz.ch/pipermail/r-help/2005-September/078880.html https://stat.ethz.ch/pipermail/r-help/2007-January/123117.html the randomForest package has a problem with missing

Dear All, Probably something very easy, but I am looking for the most efficient ways to achieve this. Consider the following snippet y<-c('a','b','c','d','e','f','g') x<-rnorm(length(y)) df<-data.frame(y,x) leading to > df$y [1] a b c d e f g Levels: a b c d e f g Now, I would like to replace levels

Dear All, I have never had this problem before. I run debian testing on my box and I have recently update my R environment. Now, see what happens when I try the most trivial of all plots > plot(seq(22)) Error in (function (display = "", width, height, pointsize, gamma, bg, : X11 module cannot be loaded In addition: Warning message: In (function (display = "", width,

Dear All, Consider a matrix (N x N) where each entry is either zero or one (can hardly get any simpler). Now, I would like to plot it as a 'chessboard' where every matrix entry is a black (1) or white (0) square. Whatever tool I use to plot it, it should not try to interpolate the data at all. I found some online references http://www.phaget4.org/R/image_matrix.html but probably I can

Dear All, I am given a set of files names as: velocity1.txt velocity2.txt and so on. I am sure there must be a way to read them automatically in R. It is really taking me longer to read them than to analyze them. Anybody has a suggestion to help me out with this? Many thanks Lorenzo

Dear All, I have just upgraded to Debian stable 10 and rebuilt most of the R packages. I use the R backported packages from here https://cran.r-project.org/bin/linux/debian/#debian-buster-testing for the core system. I encounter some issues when updating quantmod, tseries and forecast. For instance, see the following > install.packages("tseries") which finally fails with the

Dear All, I have just upgraded to Debian stable 10 and rebuilt most of the R packages. I use the R backported packages from here https://cran.r-project.org/bin/linux/debian/#debian-buster-testing for the core system. I encounter some issues when updating quantmod, tseries and forecast. For instance, see the following > install.packages("tseries") which finally fails with the

Dear All, For a data mining project, I am relying heavily on the RandomForest and Party packages. Due to the large size of the data set, I have often memory problems (in particular with the Party package; RandomForest seems to use less memory). I really have two questions at this point 1) Please see how I am using the Party and RandomForest packages. Any comment is welcome and useful.