search for: subinterval

Dear list, I have a strange requirement .... I have a vector, for example v<- c(0,0,0,0,1,2,4,6,8,8,8,8). I have a value,for example x<- 4.8. I would like to understand in which sub interval of v is x. In this case, v would be in the sub interval [4,6] that is in the subinterval starting from element j=7 to the element j+1=8. Can we do that with an R command? Regards -- Corrado Topi Global Climate Change & Biodiversity Indicators Area 18,Department of Biology University of York, York, YO10 5YW, UK Phone: + 44 (0) 1904 328645, E-mail: ct529 at york.ac.uk

I would like to fit a logistic regression using a smothing spline, where the spline is a piecewise cubic polynomial. Is the knots option used to define the subintervals for each piece of the cubic spline? If yes and there are k knots, then why does the coefficients field in the returned object from gam only list k coefficients? Shouldn't there be 4k -4 coefficients? Sincerely, Bill --------------------------------- [[alternative HTML version dele...

...he value of that same response variable Y on a different set of data B (e.g. predict.glm, predict.gam and so on). Dataset A and dataset B are different in the sense that they contain the same variable, for example temperature, measured in different sites, or on a different interval (e.g. B is a subinterval of A for interpolation, or a different interval for extrapolation). If you have the measured values for Y on the new interval, i.e. B, how do you measure how good is the prediction, that is how well model fits the Y on B (that is, how well does it predict)? In other words: Y~T,data=A for trai...

Hi all, This is probably a blindingly obvious question: Why does it matter in the uniroot function whether the f() values at the end points that you supply are of the same sign? For example: f <- function(x,y) {y-x^2+1} #this gives a warning uniroot(f,interval=c(-5,5),y=0) Error in uniroot(f, interval=c(-5, 5), y = 0) : f() values at end points not of opposite sign #this doesn't give a

Hello, I have three x-y plots whose interesting data occurs at the same time. When I place them in the same plot, they tend to obscure each other. I was hoping to use persp or scatterplot3d to plot them 3-dimensionally, so that the plot is visually useful. My data is simple: x, for all three data sets, is a set of 885 ISOdates, where x <- x0 + seq(1, 885). y is the value of "io",

Hi everyone, I am using the package "spatstat" for ploting kernel maps of my data. It is a marked point pattern, the result of mosquito surveillance in a area in a week. For each trap, the number of individuals captured is the mark of the point. > plot(density(X, weights=X$marks)) makes a nice kernel, but the problem is that I've got several weeks and for each week the density

Dear List-Members, since 3 weeks I have been heavily working on reproducing the results of an economic paper. The method there uses the numerical solution of an integral within nonlinear least squares. Within the integrand there is also some parameter to estimate. Is that in the end possible to implement in R [Originally it was done in GAUSS]? I'm nearly into giving up. I constucted an

Dear list, I'm new to R, please bear with my silly questions. I'm trying to get an understanding of why the results I get from a call to hist() are not as I thought I would get. When I use the parameter freq=FALSE, I think the plot will contain bars that none of them is larger than 1, because they're probabilities. But for my code, the bars exceeded 1. The actual data seems

Hello: The example "integrate(dnorm,0,20000)" says it "fails on many systems". I just got 0 from it, when I should have gotten either an error or something close to 0.5. I got this with R 2.12.0 under both Windows Vista_x64 and Linux (Fedora 13); see the results from Windows below. I thought you might want to know. Thanks for all your work in creating

...ble Y on a different set of data B (e.g. > > predict.glm, predict.gam and so on). Dataset A and dataset B are > > different in the sense that they contain the same variable, for example > > temperature, measured in different sites, or on a different interval > > (e.g. B is a subinterval of A for interpolation, or a different interval > > for extrapolation). If you have the measured values for Y on the new > > interval, i.e. B, how do you measure how good is the prediction, that is > > how well model fits the Y on B (that is, how well does it predict)? > > &...