similar to: Principal Component Analysis - Selecting components? + right choice?

I am using nls to fit a non linear function to some data. The non linear function is: y= 1- exp(-(k0+k1*p1+ .... + kn*pn)) I have chosen algorithm "port", with lower boundary is 0 for all of the ki parameters, and I have tried many start values for the parameters ki (including generating them at random). If I fit the non linear function to the same data using an external

Dear list, I have a strange requirement .... I have a vector, for example v<- c(0,0,0,0,1,2,4,6,8,8,8,8). I have a value,for example x<- 4.8. I would like to understand in which sub interval of v is x. In this case, v would be in the sub interval [4,6] that is in the subinterval starting from element j=7 to the element j+1=8. Can we do that with an R command? Regards -- Corrado Topi

Dear friends, what is this error message in gam???? I cannot understand what it means .... is it a bug? gam_bray_scot24_pc_0505<gam(bray~s(PC1,PC2,PC3,PC4,PC5, PC1.1,PC2.1,PC3.1,PC4.1,PC5.1),data=dist_scot24_vector_with_climate) Error in if (length(data) != vl) { : missing value where TRUE/FALSE needed Calls: gam ... smooth.construct -> smooth.construct.tp.smooth.spec -> array In

Dear R-friends, How do you test the goodness of prediction of a model, when you predict on a set of data DIFFERENT from the training set? I explain myself: you train your model M (e.g. glm,gam,regression tree, brt) on a set of data A with a response variable Y. You then predict the value of that same response variable Y on a different set of data B (e.g. predict.glm, predict.gam and so on).

Dear friends, I am testing glm as at page 514/515 of THE R BOOK by M.Crawley, that is on proportion data. I use glm(y~x1+,family=binomial) y is a proportion in (0,1), and x is a real number. I get the error: In eval(expr, envir, enclos) : non-integer #successes in a binomial glm! But that is exactly what was suggested in the book, where there is no mention of a similar warning. Where am I

Dear friends, Do you know how to calculate the CONCAVE hull of a set of points (2- dimensional or n-dimensional)? is that possible in R? (With a "smoothing" parameter of course). Best, -- Corrado Topi Global Climate Change & Biodiversity Indicators Area 18,Department of Biology University of York, York, YO10 5YW, UK Phone: + 44 (0) 1904 328645, E-mail: ct529 at york.ac.uk

Dear friends, I have several sets of points in a transformed environmental space. Each set of points can be represented as a cloud in the environmental space. This space is spanned by n coordinates, corresponding to the first n PCs of 36 PCs of some environmental variables (12 monthly minimum temperatures, 12 monthly maximum temperature, 12 monthly precipitations). I would like to calculate

Hello R-ers, I'm trying to do a weighted principal components analysis. I couldn't find any such option with princomp or prcomp. Does anyone know of a package or way to do this? More specifically, the observations I'm working with are averages from populations of varying sizes. I thus need to weight the observations by sample size. Ideally I could apply these weights at the cell

Dear R list, I just obtained a negative AIC for two models (-221.7E+4 and -230.2E+4). Is that normal? Regards -- Corrado Topi Global Climate Change & Biodiversity Indicators Area 18,Department of Biology University of York, York, YO10 5YW, UK Phone: + 44 (0) 1904 328645, E-mail: ct529 at york.ac.uk

Dear friends, I have to use a very large matrix. Something of the sort of matrix(80000,80000,n) .... where n is something numeric of the sort 0.xxxxxx I have not found a way of doing it. I keep getting the error Error in matrix(nrow = 80000, ncol = 80000, 0.2) : too many elements specified Any suggestions? I have searched the mailing list, but to no avail. Best, -- Corrado Topi Global

Quick informal poll: what is everyone's favorite text editor for working with R? I'd like to hear from people who are using editors that have some level of direct R interface (e.g. Tinn-R, Komodo+SciViews). Thanks! --j -- Jonathan A. Greenberg, PhD Postdoctoral Scholar Center for Spatial Technologies and Remote Sensing (CSTARS) University of California, Davis One Shields Avenue The

note: whole function is below- I am sure I am doing something silly. when I use it like USGS(input="precipitation") it is choking on the precip.1 <- subset(DF, precipitation!="NA") b <- ddply(precip.1$precipitation, .(precip.1$gauge_name), cumsum) DF.precip <- precip.1 DF.precip$precipitation <- b$.data part, but runs fine outside of the function: days=7

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Hello Recently, I've been dealing with a seemingly simple problem. I have a table with two columns filled with values from a test I made (say, amount of precipitation and Months measured). Now, instead of plotting a typical linear graphic with precipitation on the y-axis and the months in the x-axys, I would like to "convert" the values of precipitation in a color gradient,

Hi all, I have gone through some basics of R in eclipse environment. What I need to work is more than basic level so I could not find out the solution by myself. could anyone help me ? My case, I have climate data (precipitation) in nc format with single layer for each time interval in a day. That means for January 1, 2013, I have 8 separate files each containing 3 hrs cumulative precipitation

Dear all, I have a list of precipitation record and a list of time I would like to sum them up per hour, or per day. Does such a function exist ? example: time<-c("2000-10-03 14:00:00","2000-10-03 14:10:00","2000-10-03 14:20:00","2000-10-03 15:30:00","2000-10-03 16:40:00","2000-10-03 16:50:00","2000-10-03

I am performing a precipitation analysis. data is in the form of daily precipitation amounts, e.g. x<- c(4,5,3,0,0,0,2,4,6,4,0,0,0,2,2,0,3,4,1,0,...) I would like to find the length of the "storm", length of storm would be defined as the number of days with continues precipitation. in this case the returned vector would be: (3,4,2,3,...) I would also like the amount of

Dear All, I have to get the value of say 90th percentile of precipitation time series.. The series is of daily precipitation value of 96 years, I have to to get 90the percentile value of daily precipitation each year. If you know the R code or command for this please let me know. I would appreciate your early response. Thanking you, Sincerely, Ajay.

Hi, from what I understand about the canonical correlation function 'cancor', it looks for correlations in two sets of variables, each represented in matrix form. Right? Sounds exactly like what I need. I have tried the following but I am not sure how to interpret the results. AudioPCs <- c(ArTHarF0PCA$x[,2], ArTHarF1PCA$x[,2], ArTHarF2PCA$x[,2], ArTHarF3PCA$x[,2],