similar to: Help: locfit (local logistic regression)

I have a dataset of pregnancy values for multiple years (and ages, not included) with missing years. I would like to use local polynomial regression to smooth the values and estimate for the missing years. I would also like to use GCV to justify the smoothing parameter selection. When using locfit() with lp() I found that the gcvplot function does not work as it is looking for an alpha value to

As I think it is not spam but helpful, let me repeat my stats.stackexchange.com question here, from http://stats.stackexchange.com/questions/16346/difference-between-lp-or-simply-in-rs-locfit I am not sure I see the difference between different examples for local logistic regression in the documentation of the gold standard locfit package for R:

I've been trying to run locfit on data with 6 inputs and 1 output in R. Whenever I make a prediction for the same exact data that the model was built on though, I get significant discrepancies between the fitted outputs of the prediction and the actual data. I have scaled the inputs, tweaked the alpha parameter, and played around with a lot of the other variables as well. Is their some kind

Hello all! I have recently started using the LOCFIT package, together with Clive Loader's book. I need to implement some method for automatic (plug-in) bandwidth selection in a multivariate kernel regression. From the book, and the LOCFIT documentation, it is not clear whether this is possible. As far as I can see, the only time the various automated procedures, e.g., SJPI, are used is in

Dear all, I am trying to include confidence intervals in a xyplot. This is what I am doing: xyplot(x ~ y|z, alpha = 1,band = "global",panel = panel.locfit) (more specifically, in my case x is a binary response from a logistic regression model) The output plot was fine but it did not include the confidence intervals Anyone knows how to do it? (xYplot did not work either) many thanks

Hi list, This was asked a couple of years ago but I can't find a resolution. Is there any way to get the coefficients from one of the local polynomial fits in locfit. I realize that locfit only constructs polynomials at a handful of intelligently selected points and uses interpolation to predict any other points. I would like to know the terms of the polynomials at these points. It seems

Dear memberships, I'm trying to estimate the following multivariate local regression model using the "locfit" package: BMI=m1(RCC)+m2(WCC) where (m1) and (m2) are unknown smooth functions. My problem is that once I get the regression done I cannot get the fitted values of each of this smooth functions (m1) and (m2). My program is the following: library(locfit)

a closer look to the help on predict.glm will reveal that the function accepts a 'type' argument. In you case 'type = response' will give you the results in probabilities (that it seems to be what you are looking for). There also is an example on use of the 'type' argument at the end of the page. Stefano -----Messaggio originale----- Da: r-help-bounces at r-project.org

The version of locfit on the web site mentioned apparently has been revised by Prof. Loader, and is newer than the CRAN version that I have been maintaining. If Prof. Loader is OK with it, I will take a look and see if I can get the new version into CRAN-conforming form and upload to CRAN. Meanwhile, make sure you're using the package from Prof. Loader's web page, instead of the one on

Below. -- Bert On Tue, Apr 3, 2012 at 1:47 PM, Danielle Duncan <dlduncan2 at alaska.edu> wrote: > Thanks for the response, I should have clarified that the NOEL is the > smallest dose above which there is a statistically significant effect. > This is not a scientifically meaningful nor defensible definition as it is stochastic, depends on the test used, design, level chosen, etc.

Apologies for the coming to this late... 1. By now I hope Somkiat has realized that R-bugs is not the place to report problems in contributed packages. Please direct such reports to the package maintainer. 2. This is really user error. predict() expect the newdata to be a data frame containing variables with the same names as those used in the fitting process. E.g., you fitted the model with

Hi: I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The

?s 12:13 de 13/07/2024, Christofer Bogaso escreveu: > Hi, > > I ran below code > > Dat = read.csv('https://raw.githubusercontent.com/sam16tyagi/Machine-Learning-techniques-in-python/master/logistic%20regression%20dataset-Social_Network_Ads.csv') > head(Dat) > Model = glm(Purchased ~ Gender, data = Dat, family = binomial()) > head(predict(Model,

Dear R-users, One of the main reasons I moved from GAUSS to R (as an econometrician) was because of the existence of the library LOCFIT for local polynomial regression. While doing some checking between my former `GAUSS code' and my new `R code', I came to realize LOCFIT is not quite doing what I want. I wrote the following example script:

I am using logistic regression model (lrm) of package Design. Can some one please tell me how to calculate the average Area Under Curve (AUC) for n-fold cross-validation The help for lrm function says to do cross validation like this f <- lrm( cy ~ x1 + x2, x=TRUE, y=TRUE) val <- validate.lrm(f, method="cross", B=5) Now I dont know what to do with variable "val" to

Hi, I hope you are fine. I am trying to use scan to open a file "prueba" extension txt. I am using the scan command as scan("C:/prueba") and I get the following error: Error in file(file, "r") : cannot open the connection In addition: Warning message: In file(file, "r") : cannot open file 'prueba': No such file or directory I did the following,

Hello, I am trying to shrink the coefficients of a logistic regression for a sparse dataset, I am using the lasso (lasso2) and I am trying to determine the shrinkinage factor by cross-validation. I would like please some of the experts here to tell me whether i'm doing it correctly or not. Below is my dataset and the functions I use w= a b c d e P A 0 0 0 0 0 1 879 1 0 0 0 0 1 3 0 1 0 0 0 7 7

I would like to perform a bootstrap validation of a backward stepwise logistic regression analysis, but I am a beginner with R and I am not sure of how to do it. Is there anyone that can send me a sample file in tab format (that I can modify in Excel by pasting my data) and the pertinent R algorithm? Many thanks Giuseppe -- ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Dr. Giuseppe Biondi

Hi, I am trying to cross-validate a logistic regression model. I am using logistic regression model (lrm) of package Design. f <- lrm( cy ~ x1 + x2, x=TRUE, y=TRUE) val <- validate.lrm(f, method="cross", B=5) My class cy has values 0 and 1. "val" variable will give me indicators like slope and AUC. But, I also need the vector of predicted values of class variable

Hi all, Is there a way to change the loss function in the logistic regression? Or we could provide a customized loss function in the logistic regression so we could use that loss function in the Cross Validation in logistic regression? Thanks a lot!