similar to: More words for Allison

So, per the discussion last week and generous donations, we have some new sound files with which to work. The sounds are located in: http://www.loligo.com/asterisk/sounds/ For those of you who just want to download the _new_ sounds, please fetch: http://www.loligo.com/asterisk/sounds/20040117.newsounds.tar All of the sounds in that tarball are also in the main ../sounds/ directory in

John, Pounds Sterling Screw You (purely optional :-) Your Call Is Being Connected Please Wait Sorry, Your Call Will Be Answered Soon Your Call Is Important. (That is why we have not yet bothered to answer it, instead dancing around the office high on tip-ex.) And for the girlfriend filter: Sorry, that number has not been recognized. Please hangup and try again. or This number is no longer in

> > I might as well add to the offtopic thread... why are natuical miles longer than "regular" miles? Andrew A nautical mile is 1 minute of latitude. -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.digium.com/pipermail/asterisk-users/attachments/20040112/6658a905/attachment.htm

Hi, I am a student of Earthquake Engineering, and am new to R. Currently I try to run nonlinear regression analysis by R. My data has three variables: X, Y, and Z. Z is a function of (X, Y). My R script is as below. rm(list=ls()) # read in data Alldata <- read.table("~/Documents/R/Wu_data.dat", header=TRUE) # assign variables Z <- Wu[[1]] # N1,60 X <- Wu[[2]]

Hi, I am new to R. I don't have strong background of statistics. I am a student of Geotechnical Engineering. I tried to run a nonlinear regression for a three-variable function, that is N = f(CSR, ev) # N is a function of CSR and ev, and N = CSR/(A +B*CSR), wherer (A,B) are function of ev. N, CSR and ev are observed in the experiments. Following is my R script. rm(list=ls())

Hola Daniel, si perdona di a responder directamente y no me di cuenta. Ya se donde esta el error, pero queria preguntar si a puede ser que mi R funcione mal o algo porque esta manhana ejecute mi script y funciono perfectamente y ahora volvi a ejecutarlo y me volvio a dar el mismo problema de ayer , despues de reiniciar y demas el tinnR y el R, no se si me vacila o es que tengo algo mal en

Hi, I have a file that looks like this : one,0 two,0.591,0 three,0.356,0.350,0 four,-0.098,0.072,0.380,0 five,0.573,0.408,0.382,0.062,0 six,0.156,0.232,0.517,0.424,0.303,0 seven,0.400,0.414,0.611,0.320,0.401,0.479,0 eight,0.282,0.375,0.512,0.346,0.308,0.463,0.605,0 nine,0.519,0.484,0.467,0.167,0.455,0.311,0.574,0.557,0 I want to create a data matrix out of it, so I tried this :

Hello, I'm trying to use R to plot a series of events (contained in a data.frame) into a single plot. In this case, the data columns represent known time marks. I would like to visually see the timing between those marks, for all events at once. What would be the fastest way to make that? My data.frame is like this event.1 event.2 event.3 ... event.N 1 0 52 87

Does anyone have any recommendations for working with zip code distance ranges? I need to calculate the distances between US zip codes. Thanks! -------------- next part -------------- An HTML attachment was scrubbed... URL: http://wrath.rubyonrails.org/pipermail/rails/attachments/20060711/f133d7de/attachment-0001.html

Dear List, I'm using gam in a multiple imputation framework -- specifying the knot locations, and saving the results of multiple models, each of which is fit with slightly different data (because some of it is predicted when missing). In MI, coefficients from multiple models are averaged, as are variance-covariance matrices. VCV's get an additional correction to account for how

Dear All, I'm interested in creating a function similar to ns() from package splines that can be passed in a model formula. The idea is to produce "safe" predictions from a model using this function. As I have seen, to do this I need to use makepredictcall(). Consider the following toy example: myns <- function (x, df = NULL, knots = NULL, intercept = FALSE, Boundary.knots =

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

Hello. I seem to be having the same problem that Paul had in the thread titled "[Rd] R 2.15.2 make check failure on 32-bit --with-blas="-lgoto2"" from October of last year <https://stat.ethz.ch/pipermail/r-devel/2012-October/065103.html> Unfortunately, that thread ended without an answer to his last question. Briefly, I am trying to compile an Rblas for Windows NT 32bit

Dear, When fitting the following model knots <- 5 lrm.NDWI <- lrm(m.arson ~ rcs(NDWI,knots) I obtain the following result: Logistic Regression Model lrm(formula = m.arson ~ rcs(NDWI, knots)) Frequencies of Responses 0 1 666 35 Obs Max Deriv Model L.R. d.f. P C Dxy Gamma Tau-a R2 Brier 701 5e-07 34.49

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

Hi, I have a question about the usage of eval(). Wonder if any experienced user can help me out of it. I use eval() in the following function: semireg.pwl <- function(coef.s=rnorm(1),coef.a=rnorm(1),knots.pos=knots.x,knots.ini.val=knots.val){ knotn <- length(knots.pos) def.par.env <- sys.frame(1) print(def.par.env) print(environment(coef.s)) tg <- eval( (parse(text=

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

Dear List, I'm using GAMs in a multiple imputation project, and I want to be able to combine the parameter estimates and covariance matrices from each completed dataset's fitted model in the end. In order to do this, I need the knots to be uniform for each model with partially-imputed data. I want to specify these knots based on the quantiles of the unique values of the non-missing

Dear R-help, In the rms package, I have fitted an ols model with a variable represented as a restricted cubic spline, with the knot locations specified as a previously defined vector. When I save the model object and open it in another workspace which does not contain the vector of knot locations, I get an error message if I try to predict with that model. This also happens if only one workspace

Dear Rusers, I am now using R and SAS to fit the piecewise linear functions, and what surprised me is that they have a great differrent result. See below. #R code--Knots for distance are 16.13 and 24, respectively, and Knots for y are -0.4357 and -0.3202 m.glm<-glm(mark~x+poly(elevation,2)+bs(distance,degree=1,knots=c(16.13,24)) +bs(y,degree=1,knots=c(-0.4357,-0.3202