search for: hibschman

Johann, Following up on Gabor's reply, the mondate package (new on CRAN last week) will accomplish your needs. >I'm trying to make an integer-backed quarter (as in fraction of year) >class, but I can't quite it to work. I want integer-backed so I don't >have to worry about floating-point effects when doing math, and so that >I can use it as in data.table. mondate

.... Clearly, I'm being bitten by the floating-point representation, but the only "complex" thing I did was to manually lag a time series by assigning date <- date + 1/12, and I was hoping that the yearmon class would apply some magic to normalize the representation. Regards, Johann Hibschman

Is there any way to get the current memory used by R without running gc()? I'd like to include the memory usage in logging output, but calling gc() to get that information takes long enough to be noticeable (~ 6 s with ~ 20 GB of ram in use), which goes against the point of logging. Thanks, Johann

Hi all, I'm trying to plot multiple histograms in one plot (cross-validation values of model parameters), but I cannot seem to reduce the margins enough to fit as many of them in as I would like. I'm using split.screen to divide the window into a 5x4 grid, then plotting with hist. I've tried explicitly reducing the margins with par(mar=c(1,1,1,1)), but it doesn't seem to have

Hi all, I've just started working my way through Mike West and Jeff Harrison's _Bayesian Forecasting and Dynamic Models_, and I was wondering if there were any publically-available packages to handle dynamic linear models, as they describe. I found the "dynlm" package, but either I don't yet understand what's going on or that package uses a different sense of the phrase

Hi all, I'm trying to make an integer-backed quarter (as in fraction of year) class, but I can't quite it to work. I want integer-backed so I don't have to worry about floating-point effects when doing math, and so that I can use it as in data.table. First of all, is there a good reference for this anywhere? All of the S4 tutorials that I've found have been too high-level, and

I'm having trouble calling randomForest::partialPlot programmatically. It tries to use name of the (R) variable as the data column name. Example: library(randomForest) iris.rf <- randomForest(Species ~ ., data=iris, importance=TRUE, proximity=TRUE) partialPlot(iris.rf, iris, Sepal.Width) # works partialPlot(iris.rf, iris, "Sepal.Width") # works (function(var.name)

What is the current best package for manipulating HDF5 data files? I tried "hdf5" a long time ago, but I ran into memory problems. "h5r" is on CRAN now, and "rhdf5" is part of bioconductor. Ideally, I'd like to read simple vectors or tables, either the entire thing or a subset of rows. I don't need much writing support, but it would be nice. Compression is a

I'm using 2.10.0 on Linux (64 bit), and I just noticed that random numbers are occasionally added to the text of names in vectors. It's happened to me in two separate, long-running R sessions, but I can't find a way to reproduce it in a smaller setting. The code I'm using is > diag.gam.2 <- mdl.run.diag(fit.gam.2, ds.valid) > diag.gam.2 rmse mdae.1413751

I'm experimenting with a few model-fitting classes of my own. I'm leaning towards using S4 for my classes, but the R functions I'd want to override (residuals, predict, etc.) are all S3 methods. As I understand it, I could do setGeneric("residuals"), then add S4 specializations to it. However, I don't understand the full consequences of doing this. Are there any

Hi Rusers, I hope to divide the original dataset into several subsets and output these multilple datasets. But errors appeared in my loops. See example. ###### a<-c(1:10) b<-c(rep(1,3),rep(2,3),rep(3,4)) c<-data.frame(a,b) #c is the example data num<-c(unique(b)) # I hope to get the subsets c_1.csv,c_2.csv and c_3.csv #Errors for (i in num) { c_num<-c[c$b==num,]

Is there any package that assists in saving and reconstituting glm and nls fits without bringing along the accompanying data? A quick search on CRAN didn't turn up anything. If not, how do other people deal with saving the coefficients of model fits? For example, I've run a glm fit that has 23 coefficents on data set that had 193,008 rows, by the time the fit was called. When I save

Hi all, I'm trying to fit a nonlinear (logistic-like) regression, and I'd like to get some recommendations for which package to use. The expression I want to fit is something like: y ~ A * exp(X * Beta1) / (1 + exp(-(x + X * Beta2 - xmid)/scal)) Basically, it's a logistic function, but I want to be able to modify the saturation amplitude by a few parameters (Beta1) and shift the

I'd like to access data in my R session from elsewhere via HTTP. My particular use case would be R on Linux, accessing data from Windows, all on the same intranet. Naively, when I say this, I imagine something like: > theData <- big.calculation.returning.data.frame() > startHttpServer(port=8675) (blocks until I C-c it) Elsewhere... http://linuxmachine:8675/htmlDF?theData

When I run predict.Arima in my code, I get warnings like: Warning message: In cbind(intercept = rep(1, n), xreg) : number of rows of result is not a multiple of vector length (arg 1) I think this is because I'm not running predict.Arima in the same environment that I did the fit, so the data object used in the fit is no longer present. Looking at the predict.Arima source,

I'm trying to plot the "marginals" of a fit: the aggregated value of the actual and predicted vs. a cut/bucketed dimension. (The data set is huge, so just plotting the raw points would be unintelligible.) I'd also like to plot the number of points in each bucket (or, rather, the sum of the weights in each bucket), so I can mentally discount crazy behavior at low weights. To do