similar to: Histograms on a log scale

Hi all, I post it on both r-help and r-finance since I don't know where is most appropriate for this topic. Sorry if it bothers you. I did garch fitting on S&P500 monthly returns with garchFit from fSeries. I got same coefficients from all cond.dist except normal. I thought that is probabaly usual for the data. But when I play with it, I got another question. I plot skew normal with

The following behaviour (in R 0.62.3) is disturbing: > (0+0i)^2 [1] NaN+NaNi Is it deliberate?? Laimonis Kavalieris -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To:

The following behaviour (in R 0.62.3) is disturbing: > (0+0i)^2 [1] NaN+NaNi Is it deliberate?? Laimonis Kavalieris -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To:

I have been trying to read in a large data set using read.table, but I've only been able to grab the first 50,871 rows of the total 122,269 rows. > f <- read.table("http://www.cs.odu.edu/~fmccown/R/Tchange_rates_crawled.dat", header=TRUE, nrows=123000, comment.char="", sep="\t") > length(f$change_rate) [1] 50871 From searching the email archives,

Dear friends, After running the lm() model, we can get summary resluts like the following: Coefficients: Estimate Std. Error t value Pr(>|t|) x1 0.11562 0.10994 1.052 0.2957 x2 -0.13879 0.09674 -1.435 0.1548 x3 0.01051 0.09862 0.107 0.9153 x4 0.14183 0.08471 1.674 0.0975 . x5 0.18995 0.10482 1.812 0.0732 . x6 0.24832 0.10059 2.469 0.0154 * x7

Hi. Background - I am working with a dataset involving around 750K observations, where many of the variables (8/11) are unordered factors. The typical model used to model this relationship in the literature has been a simple linear additive model, but this is rejected out of hand by the data. I was asked to model this via kernel methods, but first wanted to play with the parametric

http://www.harding.edu/fmccown/R/#autosdatafile http://www.harding.edu/fmccown/R/#autosdatafile I am tring to get a barchat by factors, following the example in that link above. =========================== x=c(145,40,40,120,180, 140,155,90,160,95, 195,150,205,110,160, 45,40,195,65,145, 195,230,115,235,225, 120,55,50,80,45 ) y2=c( rep(as.character(1),5), rep(as.character(2),5),

Hello, I am new to non-linear growth modelling in R and I am trying to reproduce an analysis that was done (successfully) in S-Plus. I have a simple non-linear growth model, with no nesting. I have attempted to simplify the call as much as possible (by creating another grouped object, instead of using subset= and compacting the fixed and random expressions.) This is a what the grouped

Hi, I have installed R 2.15.2 on windows 7. http://cran.cnr.berkeley.edu/ I tried to run some simple graph code: http://www.harding.edu/fmccown/r/ But, no graphs are presented or poped up. Any help will be appreciated. Thanks [[alternative HTML version deleted]]

Hi there, Here is a minimum working example: ---------------------------------------------------------------- lower = 0 upper = 1 n_bins = 50 interval = (upper - lower) / n_bins bins = vector(mode="numeric", length=n_bins) breaks = seq(from=lower + interval, to=upper, by=interval) for(idx in breaks) { bins[idx / interval] = idx } print(bins)

Hello, I am trying to perform a logistic regression using counts. For example: cedegren <- read.table("http://www.cloudstat.org/index.php?do=/attachment/download/id_95 /", header=T) attach(cedegren) ced.del <- cbind(sDel, sNoDel) ced.logr <- glm(ced.del ~ cat + follows + factor(class), family=binomial("logit")) This works. However, if I change the family to

Is there a simple way to put a legend outside the plot area for a simple plot? I found... (at http://www.harding.edu/fmccown/R/) # Expand right side of clipping rect to make room for the legend *par(xpd=T, mar=par()$mar+c(0,0,0,4))* # Graph autos (transposing the matrix) using heat colors, # put 10% of the space between each bar, and make labels # smaller with horizontal y-axis labels

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

Dears, a way to define x and y positions in plots in relative numbers (e.g in fractions between 0 and 1 referring to relative positions inside the plot region) would really help me. One example I would need this to would be to add text via text() to a plot always at a defined spot, e.g the upper left corner. Until now I always determined maximum x and y values and used those, but defining

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, 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, 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