similar to: smoothing line and a pair of confidence intervals

Hello R users & R friends, I just want to ask you if density() can produce a confidence interval, indicating how "certain" the density() line follows the true frequency distribution based on the sample you feed into density(). I've heard of loess.predict(loess(y ~ x), se=TRUE) which gives you a SE estimate of the smoothed scatterplot - but density() kernel smoothing is not the

Hello, In reading the loess description I see: span: the parameter alpha which controls the degree of smoothing. The default seems to be 0.75. Would it be possible to expand on this decription so I can avoid trail and error? Can I increase this pass 'span' > 1? Qualitatively to what degree changing this value affects the smoothing of the data? Thank you. Kevin

Dear all, I'm trying to construct confidence intervals for a LOWESS estimation (by not using bootstrapping). I have checked previous posts and other material online and I understand that the main procedure is: my.count<- seq(...) fit<- loess (y ~ x, data=z) pred<- pred(fit, my.count, se=TRUE) and then the plotting. However, it's not working; as confidence

Hi Folks, I'm looking to do Confidence bands around LOESS smoothing curve. If found the older post about using the Standard error to approximate it https://stat.ethz.ch/pipermail/r-help/2008-August/170011.html Also found this one http://www.r-bloggers.com/sab-r-metrics-basics-of-loess-regression/ But they both seem to be approximations of confidence intervals and I was wonder if there was

Hi all, I?m trying to plot confidence intervals for the fitted values I get with my lme model in R. Is there any way I can plot this in the form of a shaded band, like the output of geom_smooth() in ggplot2 package. ggplot2 seems to use only lm, glm, gam, loess and rlm as smoothing methods. Any advice on the functions I should use to accomplish this will be very helpful. Thank you very much.

Dear all, I want to use kernel estimator to smooth some step funtions such as the empirical distribution and hazard function in survival analysis. Is there a function or package to deal with it in R? How do I use it? Thanks for your help. Jimmy

Hi all, I 've got a question about the usage of loess in the modreg package. The documentation (loess.html) states that the smoothing window is either set by span or enp.target. If span is used, the details section of the docs state... <SNIP> DETAILS Fitting is done locally. That is, for the fit at point x, the fit is made using points in a neighbourhood of x, weighted by their

Hi all, I 've got a question about the usage of loess in the modreg package. The documentation (loess.html) states that the smoothing window is either set by span or enp.target. If span is used, the details section of the docs state... <SNIP> DETAILS Fitting is done locally. That is, for the fit at point x, the fit is made using points in a neighbourhood of x, weighted by their

Dear experts: I am a newbie to R. Recently, I try to make prediction models with R and the Design library. I have read Prof. Harrell's excellent book. But I did not quite understand. I have two problems about the validation and calibration of prediction models: 1. Can someone explain the results outputted by the validate() function? How to get 95% of c-value of validate? 2. How to add 95% ci

Hi there, I'm using R1.0.1 Windows 98. This file contains some inputs and an aov function code. Can someone check it for me? Somehow I got completely different answer when typing them in R and in Splus. Splus gives me this: > summary( Turnip.aov ) Error: Blocks Df Sum of Sq Mean Sq F Value Pr(F) Residuals 3 163.7367 54.57891 Error: Plots %in% Blocks

Dear R-experts! I am working on some meta-analysis using the metafor package. I would like to extract values of the confidence intervals of the effect sizes of the single studies from an rma object. Those values are printed out when plotting a forest plot using the forest function on the rma object, however I was not able to locate them. Many thanks for your help! Jokel [[alternative HTML

R Friends, I have data from which I would like to learn a more general (smoothened) trend by applying data smoothing methods. Data points follow a positive stepwise function. | x x | xxxxxxxx xxxxxxxx | x x |xxxx xxx xxxx | xxxxxxxxxxxxxxxxx | |

Hi, I have a data frame with 6563 observations. I can run a regression with loess using four explanatory variables. If I add a fifth, R crashes. There are no missings in the data, and if I run a regression with any four of the five explanatory variables, it works. Its only when I go from four to five that it crashes. This leads me to believe that it is not an obvious problem with the data,

Hi there, I cant seem to find a way for the lowess smoothing function to handle "NA" values. Can anyone help?? Regards, Wayne Dr Wayne R. Jones Statistician / Research Analyst KSS Group plc St James''s Buildings 79 Oxford Street Manchester M1 6SS Tel: +44(0)161 609 4084 Mob: +44(0)7810 523 713 KSS Ltd A division of Knowledge Support Systems Group plc Seventh Floor St

Colleagues ---------------------------------- System info: R version rw1022 on NT ESS v. 5.1.18 using emacs ver. 20.4 ---------------------------------- I would like to access the residuals from a lowess fit (function lowess() ) as an aid to assessing whether I am over-smoothing or under-smoothing a dataset (as in Cleveland's book "Visualizing data", section 3.3) Unless I

Dear R-Help members, I am using Simon Wood`s mgcv package version1.7-22and R version 3.0.0 (2013-04-03) for fitting a GAM-Model to the LIDAR Data contained in the "SemiPar" package. Here is the code for fitting the model and for plotting the result: data("lidar") attach(lidar) ### # mgcv fitting ### gam_fit <- gam(logratio ~ s(range, k = 40, bs = "cr"), gamma

Dear UseRs: Recently, I have read an article regarding the association between age and lymph node metastases. http://jco.ascopubs.org/content/27/18/2931.long In statistical analysis, the authors stated "Because a nonlinear relationship between age and lymph node involvement was expected based on existing literature, lymph node involvement was also regressed on age using nonparametric

Hi, I'm unable to find a way to smooth data for a persp() graph. Example, suppose that I have data x,y,z like this: x <- 1:10 y <- 1:10 k <- 20 z <- outer(x, y, "*") + matrix( k*runif(100, -1, 1), 10, 10) persp(x, y, z, theta = 35, phi = 25) The graph is not very nice. Is there a way to smooth the z data so that at the end the graph would look more like something

loess() should be able to do robust 2D smoothing. There's no natural ordering in 2D, so defining running medians can be tricky. I seem to recall Prof. Koenker talked about some robust 2D smoothing method at useR! 2004, but can't remember if it's available in some packages. Andy From: Vladislav Petyuk > > Hi, > Are there any multidimenstional versions of runmed() and >

Hi all, I'm having two small problems and was wondering if you could help me out. Problem 1: When I use pch to place a symbol onto a plot it seems to place the point a little off the exact location. I have read the help file for this which says to use "." as the plotting character, which will then produce centered points but I am unclear as to how to do this. Problem 2: How do I