similar to: loess question

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

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

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

Hi All, I am trying to trace the origin of the current loess implementation in R. The reference mentions that Prof Ripley based it on the 1998 version of dloess. When I look at dloess in http://www.netlib.org/a, the file "changes" mentions dloess was made available in 1992 and that a memory leak was plugged in 1996 with no mention of 1998. Is there another version available?

hi, i have just begun using R, so please bear with me. i am trying to use cmdscale and display the result. i read the data using read.table(), calculate the proximity matrix using dist() and the display the result using the cmdscale(). this is very fine. in addition, i want the display to distinguish between two classes of records in my data. i have my data records marked as "1" or

I'm updating the loess routines to allow for, among other things, arbitrary local polynomial degree and number of predictors. For now, I've given the updated package its own namespace. The trouble is, panel.loess still calls the original code in package:stats instead of the new loess package, regardless of whether package:loess or package:lattice comes first in the search list. If I

Thanks, John. However, loess.smooth() is producing a very different curve compared to the one that results from applying predict() on a loess(). I am guessing they are using different defaults. Correct? On Thu, 23 Mar 2023 at 20:20, John Fox <jfox at mcmaster.ca> wrote: > Dear Anupam Tyagi, > > You didn't include your data, so it's not possible to see exactly what >

I have a question about running an optimization function on an existing LOESS function defined in R. I have a very large dataset (1 million observations) and have run a LOESS regression. Now, I want to run a Newton-Raphson optimization to determine the point at which the slope change is the greatest. I am relatively new to R and have tried several permutations of the maxNR and nlm functions with

Dear Anupam Tyagi, You didn't include your data, so it's not possible to see exactly what happened, but I think that you misunderstand the object that loess() returns. It returns a "loess" object with several components, including the original data in x and y. So if pass the object to lines(), you'll simply connect the points, and if x isn't sorted, the points

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