similar to: cannot calculate standard estimate with predict on 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 <-

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

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

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

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 +

Hi, I am trying to plot densities given on a two dimensional grid. My data is in the an external file, and is arranged in three columns: x, y, density how may i get a plot of this? i would like to get (1) a three dimensional plot and (2) a color coded two dimensional plot. I have tried using image(x, y, density) but i am asked to put the data in ascending order. i am not sure how i may

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

Hi, I need some suggestion on how I could modify the color on some rectangle that I have created using "image". In other words, I have a 5x5 matrix, say, m. m <- matrix(rnorm(25), nrow=5) I create a grid of rectangles by: image(m) Now I want to change the color of rectangle (3,3) to blue. I don't know how this could be done, and searching the web has

hi, i am need to plot two or more sets of data in a 3d scatterplot, each set with different color. i tried Rcmdr, and the 3d scatterplot facility, based on rgl. that is what i need. but i cannot seem to code different sets of data differently. any help will be very helpful. i tried scatterplot3d, but it is difficult to get the right angle in it. i need to be able to rotate the axes, and

Dear R community, I am trying to understand how the predict function, specifically, the predict.loess function works. I understand that the loess function calculates regression parameters at each data point in 'data'. lo <- loess ( y~x, data) p <- predict (lo, newdata) I understand that the predict function predicts values for 'newdata' according to the loess regression

Hi, I was wondering if anyone knows of a way by which one can estimate values from a contour plot created by using the loess function? I am hoping to use the loess contour plot as a means of interpolation to identify the loess created values at points at pre-defined (x,y) locations. Could anyone point me in the right direction please? Thanks. Laura Quinn Institute of Atmospheric Science School

Hi! In a current project, I am fitting loess models to subsets of data in order to use the loess predicitons for normalization (similar to what is done in many microarray analyses). While working on this I ran into a problem when I tried to predict from the loess models and the data contained NAs or NaNs. I tracked down the problem to the fact that predict.loess will not return a value at all

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

hi all, i am trying to make multi-color plots. that is, i generally use, plot(x, y, type="n") text(x, y, labels=class) here, the vector class denotes the class of each point. there are usually 3-4 classes of points. how may i display the different classes in different colors? thanks for any help. -- saurav

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

Hello all I have been working to fix this for weeks now, It should be simple to fix. Please help Let me explain what I am doing, I have a data set for 65 countries over a period of 9 years (2000-2008). Each country has on an average say 2000 interviews, so that the total set has roughly 65*9*2000 data points/observations (of course there are missing vales as well). Now let me explain how are the

Hi I have a stata data set (.dta file) of size 600 MB, I need to import it in R and do a 2SLS multilevel analysis on the data set. I would eb grateful if help is provided for the first part of how to import this big file from Stata to R and then how to open the imported Stata file in R? Kindly help Thanks in advance -- Dr.Saurav Pathak PhD, Univ.of.Florida Mechanical Engineering Doctoral

Full_Name: Benjamin Tyner Version: 2.1.0, 4/18/2005 OS: i686-redhat-linux-gnu Submission from: (NULL) (4.64.8.220) # Just run my.test() below in a newly opened R session. Once too many models have been fit (~20 on my system), the computed standard error jumps to a different value. This is (superficially) due to a different residual sum of squares, not a different one.delta. No other aspect of

Dear altogether, I tried local regression with the following data. These data are a part of a bigger dataset for which loess is no problem. However, the plot shows extreme values and by looking into the fits, it reveals very extreme values (up to 20000 !) although the original data are > summary(cbind(x,y)) x y Min. :1.800 Min. :2.000 1st Qu.:2.550

Hello, I'm attempting to evaluate the accuracy of the probability predictions for my model. As previously discussed here, the AUC is not a good measure as I'm not concerned with classification accuracy but probability accurcy. It was suggested to me that the loess function would be a good measure to look at. I can see some libraries (Design) will plot the loess function as a curve