similar to: Error when using cobs library

Hi, how to suppress a "loading required package:... " message? Kind regards Jaci --

Hi, how is it possible to retrieve the corresponding tau value for each observed data pair (x(t) y(t), t=1,...,n) when doing a quantile regression like rq.fit <- rq(y~x,tau=-1). Thank you for your help. Jaci --

Hello, I have been trying to take the derivative of a quadratic B-spline obtained by using the COBS library. What I would like to do is similar to what one can do by using fit<-smooth.spline(cdf) xx<-seq(-10,10,.1) predict(fit, xx, deriv = 1) The goal is to fit the spline to data that is approximating a cumulative distribution function (e.g. in my example, cdf is a 2-column matrix with x

Hi all, I'm looking for smooth monotone estimation packages, preferably using splines. I downloaded the 'cobs' package and intend to use it, but since it offers only quadratic splines based on L1 minimization, I'd like to compare its performance to that of a more 'mainstream' cubic-spline, L2-norm minimizing spline. Preferably a smoothing spline. Does anyone know of such

Hi, I am approximation a value function (in dynamic programming) V: R^n->R. In different versions of the problem, n is between 2 and 5. I would like to use splines to use this, I checked the mailing list and CRAN but couldn't find anything that would help me (Tps in fields is too slow, cobs would be really nice but seems to be one-dimensional). Is there any package that would at least

Hello everone,            Anyone who knows how to force a cubic smoothing spline to pass through a particular point?            I found on website  someone said that we can use "cobs package" to force the spline pass through certain points or impose shape           constraints (increasing, decreasing). However,  this package is using  B-spline and can only do linear and quadratic

Hello everyone, I have a function here that I wrote but doesn't seem to work quite right. Attached is the code. In the calib funcion under the for loop Bt[i+2]<-(1-m)*Bt[i+1]+Rt[i]*Rerr-Ct[i+1] returns NA's for everything after years 1983 and 1984. However the code works when it reads Bt[i+2]<-(1-m)*Bt[i+1]+Rt[i]*Rerr-Ct[i]. I don't quite understand why since it should be

Hello everyone, I have a function here that I wrote but doesn't seem to work quite right. Attached is the code. In the calib funcion under the for loop Bt[i+2]<-(1-m)*Bt[i+1]+Rt[i]*Rerr-Ct[i+1] returns NA's for everything after years 1983 and 1984. However the code works when it reads Bt[i+2]<-(1-m)*Bt[i+1]+Rt[i]*Rerr-Ct[i]. I don't quite understand why since it should be

I don't understand what is happening! I have a (large) object sim1, an matrix list with dim c(101,101) where each element is an 3*3 matrix. I am subsetting that with a matrix coo, of dim c(100,2), of unique indices, but the resulting object has length 99, not 100 as expected. Code reproducing the problem follows: library(RandomFields) set.seed(123) sim0 <- GaussRF(x=seq(0, 100, by=1),

Dear R-users, within the rgl-package, I would have a question about the usage of persp3d in combination of rgl.viewpoint. I am not able to figure out how to let a 3D plot rotating around likewise the example in ?rgl.viewpoint. It seems that when I use persp3d(...) I see something on my screen, which is different from what I get when it's rotating. Is there any different behavior between

Dear all, I'm training an SVM with default settings on a matrix csr (SparseM package). I realized that if I train the SVM with the (hopefully) equivalent matrix (Matrix package) representation, the returned models and predictions sometimes differ. I expected both representations of the same data to lead to the same results though. It could be that it is a scaling problem, because unscaled

I'm trying to find a bootstrap based confidence band for a linear model. I have created a data set with X and Y X=runif(n,-1.25,1.25) e=rnorm(n,0,1) Y=exp(3*X)+5*sin((30*X)/(2*pi))+2*e fit=lm(Y~X) summary(fit)   I define a bootstrap function named PairedBootstrap which is not listed here. Than I try many ways to find the confidence band. One way is to predict Y using the model I get above for

To my understanding, a confidence interval typically covers a single valued parameter. In contrast, a confidence band covers an entire line with a band. In regression, it is quite common to construct confidence and prediction bands. I have found that many people are connecting individual confidence/prediction interval values produced with predict(object,sd.fit=T,type="conf/pred") and

Dear List: I'm working to construct a very large sparse matrix and have found relief using the SparseM package. I have encountered an issue that is confusing to me and wonder if anyone may be able to suggest a smarter solution. The matrix I'm creating is a covariance matrix for a larger research problem that is subsequently used in a simulation. Below is the latex form of the matrix if

Hi all. I hope I have my terminology right here... For a simple lm, one can add ?pointwise confidence bounds? to a fitted line using something like >predict(results.lm, newdata = something, interval = "confidence") (I'm following DAAG page 154-155 for this) I would like to do the same thing for a glm of the logistic regression type, for instance, the example in MASS pg

Is there a painless way to find the names of all packages on CRAN that "Depend" on a specified package? url: www.econ.uiuc.edu/~roger Roger Koenker email rkoenker at uiuc.edu Department of Economics vox: 217-333-4558 University of Illinois fax: 217-244-6678 Champaign, IL 61820

Dear all, Is there a way to plot / calculate pointwise confidence bands or interval values for a non parametric regression like sm.regression? Thank you in advance. Regards, Martin

Just trying to create returns from prices, and do something like: returns.z = tail(prices.z,-1)/head(prices.z,-1) - 1 # should be equivalent to returns = exp(diff(log(prices.z))) - 1 Curiously, I get a zoo object back with zeros everywhere and also with the index having one fewer element than it should. Does anyone know how to pointwise divide zoo objects, and what exactly "/" is

Hi all, I just uploaded a new version of the bpcp package. It calculates confidence intervals for a survival distribution for right-censored data using the newly developed beta product confidence procedure. Previously developed methods can have substantial error rate inflation for the lower limit, especially at the right end of the curves when there are small numbers of events. The bpcp method

Hi, I have just installed R 2.4.0 and when I try to load SpareseM, I get the following error message library(SparseM) Package SparseM (0.71) loaded. To cite, see citation("SparseM") Error in loadNamespace(package, c(which.lib.loc, lib.loc), keep.source = keep.source) : in 'SparseM' methods specified for export, but none defined: as.matrix.csr, as.matrix.csc,