similar to: connecting points into a smooth curve

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

Greetings, How might one go about generating a smooth interpolation of a survival curve generated by the survfit function in the survival package? I am able to package my variables by standard methods >x<-(survfit(Surv(time),data) and plot the "step" curve, but would like to obtain a smooth estimation curve for the purpose of approximating survival time between steps on the

Dear all, I have the problem as the title shows. It is time series of measurements. I want to estimate the value at time point when we have no actual measured value (Only in the time range, not for prediction in future time). Hope you could give me some suggestion or hint. Thank you so much. Wang Zhipeng [[alternative HTML version deleted]]

Dear R listers, When using smooth.spline to interpolate data, results are generally good. However, some cases produce totally unreasonable results. The data are values of pressure, temperature, and salinity from a probe that is lowered into the ocean, and the objective is to interpolate temperature and salinity to specified pressures. While smooth.spline provides excellent values at the

Dear All, I've been?searching for?appropriate codes to compute the rate of change and the curvature?of ?nonparametric regression model whish was denoted by a smooth function?but?unfortunately?don't manage to?do?it. I presume that such characteristics from a smooth curve can be determined by the first and second derivative operators. The following are the example of fitting a

I would like to draw smooth curves instead of polygons. I could not find any spline function to do that : given an x and a y vectors, they all take the x in increasing order. Is there a function to draw a smooth curve through a set of points in any order ? with many thanks in advance Alain Guerreau CNRS Paris [[alternate HTML version deleted]]

Hello users, I would like to obtain a survival curve from a Cox model that is smooth and does not have zero differences due to no events for those particular days. I have: > sum((diff(surv))==0) [1] 18 So you can see 18 days where the survival curve did not drop due to no events. Is there a way to ask survfit to fit a nice spline for the survival?? Note: I tried survreg and it did not

Dear list, if I do smooth.spline(tmpSec, tmpT, all.knots=T) with the attached data, I get this error-message: Error in smooth.spline(tmpSec, tmpT, all.knots = T) : smoothing parameter value too small If I do smooth.spline(tmpSec[-single arbitrary number], tmpT[-single arbitrary number], all.knots=T) it works! I just don't see it. It works for hundrets other datasets, but not for

I'm a French developer (so I am sorry if my english is not perfect). I have a problem to smooth the contours of a map. I have a dataset with 3 columns, x, y and z, where x and y are the coordinates of my points and z is evaluate to a qualitative elevation and his representation is a set of colors, which is define by levels. The problem is the curve of my contour is so linear, and I would like

Readers, I have been reading 'the r book' by Crawley and think that the generalised additive model is appropriate for this problem. The package 'gam' was installed using the command (as root) install.package("gam") ... library(gam) > library(gam) Loading required package: splines Loading required package: akima > library(mgcv) This is mgcv 1.3-25 Attaching

Probably a very simple query: When I try to plot a curve from a fitted polynomial, it comes out rather jagged, not smooth like fitted curves in other stats software. Is there a way of getting a smooth curve in R? What I'm doing at the moment (for the sake of example) is: > x <- c(1,2,3,4,5,6,7,8,9,10) > y <- c(10,9,8,7,6,6.5,7,8,9,10) > b <- data.frame(cbind(x,y)) >

Dear All, I'm looking for?a way on computing the derivative of first and second order?of a smoothing curve produced by a nonprametric regression. For instance, if we run the R script below, a smooth nonparametric regression curve is produced. provide.data(trawl) Zone92?? <- (Year == 0 & Zone == 1) Position <- cbind(Longitude - 143, Latitude) dimnames(Position)[[2]][1] <-

Dear R experts, Did someone implemented French Curve yet? Or can anyone point me some papers that I can follow to implement it? thanks in advance for your help. Paul

I simply put, plot(density(XXXX), main="XXXX", + xlab = "XXX"), it says that I have an unexpected "=" in it. -- View this message in context: http://r.789695.n4.nabble.com/how-to-change-the-xlab-name-tp2528733p2528733.html Sent from the R help mailing list archive at Nabble.com.

Hi all, I have this figure: http://docs.google.com/Doc?id=df5zfsj4_103rjt2v4d5 created with the following steps: > x [1] 90.4 57.8 77.0 103.7 55.4 217.5 68.1 85.3 152.0 113.0 97.1 89.9 [13] 68.1 83.7 77.4 34.5 104.9 170.3 88.6 88.1 108.8 77.4 85.6 82.7 [25] 81.3 108.0 49.5 71.0 85.7 99.3 203.5 275.9 51.1 84.8 16.5 72.6 [37] 160.5 158.3 136.7 140.0 98.4 116.1

Hey, R-listers I was recommended to try using smooth.spline function for estimating 2-Dimensinal curve given a data set. So will you please tell me where to get this R function? Or which package provides this function? Thanks for your point. Fred

Hi all, I try to interpolate a data set in the form: time Erg 0.000000 48.650000 1.500000 56.080000 3.000000 38.330000 4.500000 49.650000 6.000000 61.390000 7.500000 51.250000 9.000000 50.450000 10.500000 55.110000 12.000000 61.120000 18.000000 61.260000 24.000000 62.670000 36.000000 63.670000 48.000000 74.880000 I want to get smoothed splines by using the class gam The first way I tried , was

Hi R users, I have a question about fitting a cosine curve. I don't know how to set the approximate starting values. Besides, does the method work for sine curve as well? Thanks. Part of the dataset is in the following: y=c(16.82, 16.72, 16.63, 16.47, 16.84, 16.25, 16.15, 16.83, 17.41, 17.67, 17.62, 17.81, 17.91, 17.85, 17.70, 17.67, 17.45, 17.58, 16.99, 17.10) t=c(7, 37, 58, 79, 96,

Thanks for the tip using a smoothing technique before plotiing in order to get a curve instead of a line connecting the observations. But that's not the solution for my main problem with the "unclean" line plot. In order to show my problem let's take this simple example: > xval <- c(1, 2, 3, 4, 5, 6, 7, 8) > yval <- c(10, 30, 40, 50, 70, 90, 100, 110) > plot

Hi lily, You can get fairly good starting values just by eyeballing the curves: plot(y) lines(supsmu(1:20,y)) lines(0.6*cos((1:20)/3+0.6*pi)+17.2) Jim On Wed, Jun 21, 2017 at 9:17 AM, lily li <chocold12 at gmail.com> wrote: > Hi R users, > > I have a question about fitting a cosine curve. I don't know how to set the > approximate starting values. Besides, does the method