similar to: the c implementation of loess

Hi All, I am trying to use loess to smooth a 2D image, and also obtain the standard error for every pixel. I see that the standard error does not make sense. For example, running the following: library(stats) x <- array(c(1:100), dim=c(100,100)) y <- t(x) v <- exp(-((x-50)^2+(y-50)^2)/30^2) s <- v*0.02 g_noise <- rnorm(10000, mean = 0, sd = s) f <- v + g_noise f.loess <-

Hi, For some reason I have been unable to use the predict function when I desire the standard error to be calculated too. For example, when I try the following: l<- loess(d~x+y, span=span, se=TRUE) p<- predict(l, se=TRUE) I get the following error message: Error in vector("double", length) : vector size cannot be NA In addition: Warning message: In N * M1 : NAs produced by

Hi, First time on this list. Here it goes I am trying to find Loess. I could not find it on the windows version. Is there one in either Unix version of R or some C program somewhere? I tried one from http://netlib.bell-labs.com/netlib/a/, but I cannot get it to compile due to problem with missing lib (I think): cc -o gas.x gas.o loessc.o loess.o predict.o misc.o loessf.o -llinpack -lcor -lm

Hi, We have been trying to use loess on 2D data (basically a matrix) in the following way: x <- 1:256 y <- 1:256 z <- data # input from data z.loess = loess(z ~ x + y) We get a 256 x 1 vector of fitted values with a 256 x 256 array of residuals, but not a 256 x 256 array of fitted values. Why would this be? I think we are using loess incorrectly but can't figure out what is

Hi, Our firm is looking to see if it is possible to wrap some of more advanced lib in R like modreg.dll and specifically Loess function in there for both regression and prediction into MSVC++. We will probably take it to either VB or Python for implementation issues. Given that locfit has many utitlities built in for data transformation, this would be a good thing. If that's not

Hello, I was hoping that someone familiar with the implementation details of the loess algorithm might be able to help me resolve some difficulties I am having. I am attempting to reproduce some of the functionality of the loess() function in C++. My primary motivation is that I would like to understand the algorithm in detail. So far I have managed to create a working port in C++ for the

Hi, I am segfaulting when using predict.loess() (checked with r62092). I've traced the source with the help of valgrind (output pasted below) and it appears that this is due to int overflow when allocating an int work array in loess_workspace(): liv = 50 + ((int)pow((double)2, (double)D) + 4) * nvmax + 2 * N; where liv is an (global) int. For D=1 (one x variable), this overflows at

I am typing the following on the command prompt: >variab = read.csv(file.choose(), header=T) >variab It lists 900,000 ( this is the total number of observations in "variab" ) minus 797124 observations and prompts the following message [ reached getOption("max.print") -- omitted 797124 entries ]] Is there a way to see the entire set of data, ie all of 900,000 obs, and

Hello: I recall something like this being discuss recently, but I can't seem to locate an example in the archives. I have data like the following: df <- expand.grid(1:4, 1992:2002) names(df) <- c("MSA", "YEAR") df$IDUPREV <- runif(44) df$VALIDAT <- rnorm(44) I want to create an xyplot() with separate loess lines for each series (IDUPREV and VALIDAT) in

I have so far used the following command glm(formula = s ~ age + gender + gemedu + gemhinc + es_gdppc + imf_pop + estbbo_m, family = binomial(link = "probit")) My question is 1. How do i discard the non significant selection variables (one out of the seven variables above is non-significant) and calculate the Inverse Mills Ratio of the significant variables 2. I need the inverse

Please do as we ask (repeatedly) and study the help page before posting. 'family' is a separate argument, not part of loess.control, as the help page correctly documents. If you use cars.lo2 <- loess(dist ~ speed, cars, family = "symmetric", control = loess.control(surface = "direct", iterations = 20)) cars.lo2$pars$iterations it prints *20*, as it is

Dear R users I have a data set which has five variables. One depenedent variable y, and 4 Independent variables (education-level, householdincome, countrygdp and countrygdpsquare). The first two are data corresponding to the individual and the next two coorespond to the country to which the individual belongs to. My data set does not make this distinction between individual level and country

Hello, I need help with the details of loess prediction algorithm. I would like to get it implemented as a part of a measurement system programmed in LabView. My job is provide a detailed description of the algorithm. This is a simple one-dimensional problem - smoothing an (x, y) data set. I found quite a detailed description of the fitting procedure in the "white book". It is also

Hello, I am interested in performing a 2D loess smooth on microarray data, i.e. log2 ratios on a 2D grid, using different spans in the horizontal and vertical directions (the immediate reason being that replicate spots are laid out in the horizontal direction). Is it possible to do this in R? Functions like loess(stats) seem to apply the same span for all predictors, which carries over to

Dear R experts, I have a problem that is a related to the question raised in this earlier post ??? https://stat.ethz.ch/pipermail/r-help/2007-January/124064.html My situation is different in that I have only 2 predictors (coordinates x,y) for local regression but a number of global ("parametric") offsets that I need to consider. Essentially, I have a spatial distortion overlaid over a

Dear R developers! Considering I got no response or comments in the general r-help forum so far, perhaps my question is actually better suited for this list? I have added some more hopefully relevant technical details to my original post (edited below). Any comments gratefully received! Best regards, David Kreil. ---------- Dear R experts, I have a problem that is a related to the question

Hi I am using Stata 10 and I need to import a data set in stata 10 to R, I have saved the dataset in lower versions of Stata as well by using saveold command in Stata. My RAM is 4gb and the stata file is 600MB, I am getting an error message which says : "Error: cannot allocate vector of size 3.4 Mb In addition: There were 50 or more warnings (use warnings() to see the first 50)" Thus

Dear John, > I suggest that you look at the abilities of the mgcv package. > There are notes of mine at > > http://www.maths.anu.edu.au/%7Ejohnm/r-book/xtras/autosmooth.pdf > > that may help you get started. Thank?you very much for the suggestion and the link to your write-up, it was indeed very helpful! I have experimented with this library for a while now and am really happy

Dear R experts, I have a problem that is a related to the question raised in this earlier post https://stat.ethz.ch/pipermail/r-help/2007-January/124064.html My situation is different in that I have only 2 predictors (coordinates x,y) for local regression but a number of global ("parametric") offsets that I need to consider. Essentially, I have a spatial distortion overlaid over a

Hi Saurav! On Sun, Jul 12, 2009 at 6:06 PM, Pathak, Saurav<s.pathak08 at imperial.ac.uk> wrote: > I am new to R, I have to do a 2 step Heckman model, my selection equation is > below which I was successful in running but I am unable to proceed further, > > > > I have so far used the following command > > glm(formula = s ~ age + gender + gemedu + gemhinc + es_gdppc +