similar to: bug in spline()? (PR#653)

I can't get LaTeX to recognize the output from the pictex device driver. Are these commands for some special latex package which I don't know about? ************************************************ * I.White * * ICAPB, University of Edinburgh * * Ashworth Laboratories, West Mains Road * * Edinburgh EH9 3JT

Hi all, I have thoroughly read the available documentation and I can't seem to find a workaround for my setup... I'm trying to create a phone conference line that users would call using a unique phone number (no matter if they are moderators or just plain users). I use Asterisk 1.2.6 The available conferences are defined as follows: conf => 1000,user pin1, moderator pin1 conf =>

R-1.7.0 built on NetBSD 1.6, but the validation test suite failed: Machinetype: Intel Pentium III (600 MHz); NetBSD 1.6 (GENERIC) Remote gcc version: gcc (GCC) 3.2.2 Remote g++ version: g++ (GCC) 3.2.2 Configure environment: CC=gcc CXX=g++ LDFLAGS=-Wl,-rpath,/usr/local/lib make[5]: Entering directory `/local/build/R-1.7.0/src/library' >>> Building/Updating

Has anyone got a function for smooth monotonic interpolation of a univariate function? I'm after something like the NAG function PCHIM which does monotonic Hermite interpolation. Alternatively, montononic cubic spline interpolation. Please reply directly. Rob Hyndman ___________________________________________________ Rob J Hyndman Associate Professor & Director of Consulting

Dear All, I am trying to simulate data for a spline/piecewise regression model. I am missing something fundamental in my simulation procedure because when I try to fit my simulated data using the Gauss-Newton method in SAS, I am getting some wacky parameter estimates. Can anyone please check my simulation code and tell me what mistake I am making in generating data for spline model? Thank you

Hi r-helpers. Please forgive my ignorance, but I would like to plot a smoothing spline (smooth.spline) from package "stats", and show the knots in the plot, and I can't seem to figure out where smooth.spline has located the knots (when I use nknots). Unfortunately, I don't know a lot about splines, but I know that they provide me an easy way to estimate the location of local

Hello , I have estimated the following model, a sarima: p=9 d=1 q=2 P=0 D=1 Q=1 S=12 In R 2.12.2 Call: arima(x = xdata, order = c(p, d, q), seasonal = list(order = c(P, D, Q), period = S), optim.control = list(reltol = tol)) Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3152 0.8762 -0.4413 0.0152 0.1500 0.0001 -0.0413 -0.1811

Hi, there, I have 5 datasets. I would like to choose a basis spline with same knots in GAM function in order to obtain same basis function for 5 datasets. Moreover, the basis spline is used to for an interaction of two covarites. I used "cr" in one covariate, but it can only smooth w.r.t 1 covariate. Can anyone give me some suggestion about how to choose a proper smoothing spline

Hello everyone,          Dr. wood told me that I can adapting his example to force cubic spline to pass through certain point.          I still have no idea how to achieve this. Suppose we want to force the cubic spline to pass (1,1), how can I achieve this by adapting the following code? # Penalized example: monotonic penalized regression spline ..... # Generate data from a monotonic truth.

Hi, I am trying to fit cubic spline to a data on mortality rate by age and year (1900-2008). The data is noisy and hence I would like to smooth using spline and also extrapolate beyond 2008. Data from 1900 to 1948 are very unreliable while data from 1948 to 2008 are reliable. I would like to have a higher weight for data between 1948 to 2008. I am not sure how to do this. When I smooth data from

All, I am new to using R and know some basics. I wish to use kriging in R to do the following: given data Y =f(X1,X2,X3,.....,Xn) --1000+ irregular measured data set. I would like to be able to get a single value y given sinle input set (x1,x2,x3,...xn) A google search on this takes me lierally to the same example on involving analysis with soil sampling and I cannot figure out how to

Goodmorning, This is a documentation related question about the B-spline function in R. In the help file it is stated that: "df degrees of freedom; one can specify df rather than knots; bs() then chooses df-degree-1 knots at suitable quantiles of x (which will ignore missing values)." So if one were to specify a spline with 6 degrees of freedom (and no intercept) then a basis

Hi R Gurus: I have a tiny data set: > xs [1] 0.7 2.8 0.1 1.9 0.0 1.4 0.2 2.3 0.3 0.2 > and generate the density function for it. I would like to replicate this. Is there a straightforward way to do this, please? According to the help file, the FFT is used. How does this compare to Silverman's version from his page 15 Equation 2.2a, please? Thanks, Edna Bell

Hi all, I have a very easy questions (I hope). I had measure a property of plants, growing in three different substrates (A, B and C). The rest of the conditions remained constant. There was very high variation on the results. I want to do address, whether there is any difference in the response (my measurement) from substrate to substrate?

Dear all, I'm trying to figure out the exact meaning of the B-spline coefficients generated by the R command bs(). After reading a lot of things, I still have no clue... Here's my data. > test time f0 1 1 94.76328 2 2 102.47954 3 3 105.01234 4 4 107.21387 5 5 108.63279 6 6 109.54507 7 7 113.87931 8 8 118.21356 9 9 121.08652 10

windows 7, R 2.12 I am trying to run a piecewise linear regression with a single knot, i.e. a regression composed of two straight lines where the two lines intersect at an x value given by the variable knot. I wish to estimate the slope of both lines, the value of knot, the x value where the two lines intersect, and an intercept. I am using the nls code below, and get the following error

Windows Vista R 2.10 - I know it is old, I will update later today. How might I perform a piece-wise linear regression where two linear segments are separated by a single knot? In addition to estimating the slopes of the two segments (or the slope in one segment and the difference between the slope of the first and second segment), I would like the analysis to select the optimum knot. My first

tmp <- data.frame(x=c(1,1), y=c(1,2)) tmp.lm <- lm(y ~ x, data=tmp) summary(tmp.lm) coef(summary(tmp.lm)) ## I consider this to be a bug. Since summary(tmp.lm) gives ## two rows for the coefficients, I believe the coef() function ## should also give two rows. > summary(tmp.lm) Call: lm(formula = y ~ x, data = tmp) Residuals: 1 2 -0.5 0.5 Coefficients:

Suppose I have two variables, x and y. For a fixed number of knots, I want to create a spline transformation of x such that a loss function is minimized. Presumably, this loss function would be least squares, i.e. sum (f(x)-y)^2. The spline transformations would be linear, quadratic or cubic. I know I can solve this problem using some optimization function in R, but I was wondering if anyone

Hello, I am trying to get the latest wine (Wine 20030219, fake windows) working with the GordianKnot (0.27). Unfortunately, Virtual Dub is not be able to compress a avs-file with the DivX-codec. It crashes if trying to. Also, it would be nice to compress an mpeg-source with Xvid but also it crashes. BTW, VirtualDub does not list it in its codecs window ?f "msvfw32" is set to