similar to: confidence interval in local polynomial regression

Dear R users, The package 'mfp' that fits fractional polynomial terms to predictors. Example: data(GBSG) f <- mfp(Surv(rfst, cens) ~ fp(age, df = 4, select = 0.05) + fp(prm, df = 4, select = 0.05), family = cox, data = GBSG) print(f) To describe the association between the original predictor, eg. age and risk for different values of age I can plot it the polynomials

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

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

I'm trying to find a bootstrap based confidence band for a linear model. I have created a data set with X and Y X=runif(n,-1.25,1.25) e=rnorm(n,0,1) Y=exp(3*X)+5*sin((30*X)/(2*pi))+2*e fit=lm(Y~X) summary(fit)   I define a bootstrap function named PairedBootstrap which is not listed here. Than I try many ways to find the confidence band. One way is to predict Y using the model I get above for

Dear all, Is there a way to plot / calculate pointwise confidence bands or interval values for a non parametric regression like sm.regression? Thank you in advance. Regards, Martin

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:

>From: Sundar Dorai-Raj <sundar.dorai-raj at PDF.COM> >Reply-To: sundar.dorai-raj at PDF.COM >To: Zhen Pang <nusbj at hotmail.com> >CC: r-help at stat.math.ethz.ch >Subject: Vectorising and loop (was Re: [R] optim "a log-likelihood >function") >Date: Wed, 29 Sep 2004 18:21:17 -0700 > > > >Zhen Pang wrote: > >> >>I also use

To my understanding, a confidence interval typically covers a single valued parameter. In contrast, a confidence band covers an entire line with a band. In regression, it is quite common to construct confidence and prediction bands. I have found that many people are connecting individual confidence/prediction interval values produced with predict(object,sd.fit=T,type="conf/pred") and

Hi all. I hope I have my terminology right here... For a simple lm, one can add ?pointwise confidence bounds? to a fitted line using something like >predict(results.lm, newdata = something, interval = "confidence") (I'm following DAAG page 154-155 for this) I would like to do the same thing for a glm of the logistic regression type, for instance, the example in MASS pg

Hello All, Is there a local bandwidth selection routine for local polynomial regression (locpoly) ? Thanks Chinthaka Kuruwita

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

Dear all, Our department uses the linux system and we are not allowed to submit job directly. We must make a batch to submit through "qmon". so, I make a foo.sh file, which only contains one line: nohup R --vanilla < foo.txt > foo.results foo is all my codes. It is a simulation of 200 times. I set the seed at the beginning. It is to estimate the success probability, which is

Hello, I am fitting data using different methods e.g. Local Polynomial and Smoothing splines. The data is generated out of a true function model with added normally distributed noise. I would like to know "how often the confidence band for all points simultaneously contain all true values". I can answer the question for one point in the following way: e.g. #

I am trying to understand what is the difference between linear and polynomial kernel: linear: u'*v polynomial: (gamma*u'*v + coef0)^degree It would seem that polynomial kernel with gamma = 1; coef0 = 0 and degree = 1 should be identical to linear kernel, however it gives me significantly different results for very simple data set, with linear kernel

Hi everybody! I'm looking some way to do in R a polynomial fit, say like polyfit function of Octave/MATLAB. For who don't know, c = polyfit(x,y,m) finds the coefficients of a polynomial p(x) of degree m that fits the data, p(x[i]) to y[i], in a least squares sense. The result c is a vector of length m+1 containing the polynomial coefficients in descending powers: p(x) = c[1]*x^n +

Hi, I would like to know whether there exist algorithms to compute the coefficients or, at least, the degree of the minimal polynomial of a square matrix A (over the field of complex numbers)? I don't know whether this would require symbolic computation. If not, has any of the algorithms been implemented in R? Thanks very much, Ravi. P.S. Just for the sake of completeness, a

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

Dear all, I am doing 200 times simulation. For each time, I generate a matrix and define some function on this matrix to get a 6 dimension vector as my results. As the loop should be slow, I generate 200 matrice first, and save them into a list named ma, then I define zz<-sapply(ma, myfunction) To my surprise, It almost costs me the same time to get my results if I directly use a loop

Dear all, I am doing 200 times simulation. For each time, I generate a matrix and define some function on this matrix to get a 6 dimension vector as my results. As the loop should be slow, I generate 200 matrice first, and save them into a list named ma, then I define zz<-sapply(ma, myfunction) To my surprise, It almost costs me the same time to get my results if I directly use a loop

Hello All First - a warning. I'm not very R or programming savvy. I am trying to do something without much luck, and have scoured help-pages, but nothing has come up. Here it is: I have a matrix (m) of approx 40,000 rows and 3 columns, filled with numbers. I would like to convert the contents of this matrix into another matrix (m_p), where the numbers of (m) have been coerced into a