similar to: monotonic GAM with more than one term

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 all, I am trying to do an inversion of electromagnetic data with non-negative least squares method (Tikhonov regularisation) and have got it programmed in S-Plus. However I am trying to move all my scripts from S-Plus to R. Is there an equivalent to nnls.fit in R? I think this can be done with pcls? Right? S-Plus script: A, L and data are matrices, lambda is a vector of possible lambda

Hi R-users,   I wrote a code to evaluate double sum as follows:   ff2 <- function(bb,eta,z,k) { r <- length(z) for (i in 1:r) { sm1 <- sum((z[i]*bb/2)*(psigamma((0:k)+eta+1,deriv=0)/(factorial(0:k)*gamma((0:k)+eta+1))))  sm2 <- sum((besselI(z[i]*bb,eta)*log(z[i]*bb/2) - sm1)/besselI(z[i]*bb,eta))  sm2 } ff2(bb,eta,z,10)     but it gave me the following message:   >

Hi, I have a least square fitting problem with linear inequality constraints. pcls seems capable of solving it so I tried it, unfortunately, it is stuck with the following error: > M <- list() > M$y = Dmat[,1] > M$X = Cmat > M$Ain = as.matrix(Amat) > M$bin = rep(0, dim(Amat)[1]) > M$p=qr.solve(as.matrix(Cmat), Dmat[,1]) > M$w = rep(1, length(M$y)) > M$C = matrix(0,0,0)

R-Helpers, I'm sorry to have to ask this -- I've not used R very much in the last 8 or 10 months, and I've gotten rusty. I have the following (ff2 is a subset of a much, much larger dataset): > ff2 hostName user sys idle obsTime 10142 fred 0.4 0.5 98.0 2007-11-01 02:02:18 16886 barney 0.5 0.2 94.6 2007-10-25 19:12:12 8795 fred 0.0 0.1 99.8

Dear R help list, This is a long post so apologies in advance. I am estimating a model with the mgcv package, which has several covariates both linear and smooth terms. For 1 or 2 of these smooth terms, I "know" that the truth is monotonic and downward sloping. I am aware that a new package "scam" exists for this kind of thing, but I am in the unfortunate situation that I am

Dear List, I am having trouble with some R code I have written to perform Redundancy Analysis (RDA) on a matrix of species abundance data (Y) and a matrix of environmental data (X). RDA is a constrained form of PCA and can be thought of as a PCA of the fitted values of a regression of each variable in Y on all variables in X. For info, the first use of RDA is in: Rao, C.R, 1964. The use and

I'm trying to do a Hosmer-Lemeshow 'goodness of fit' test on my logistic regression model. I found some code here: http://sas-and-r.blogspot.com/2010/09/example-87-hosmer-and-lemeshow-goodness.html The R code is above is a little complicated for me but I'm having trouble with my answer: Hosmer-Lemeshow: p=0.6163585 le Cessie and Houwelingen test (Design library): p=0.2843620

Hi, See below I reply your message for <https://stat.ethz.ch/pipermail/r-help/2008-April/160966.html>[R] predict.glm & newdata posted on Fri Apr 4 21:02:24 CEST 2008 You say it ##works fine but it does not: if you look at the length of yhat2, you will find 100 and not 200 as expected. In fact predict(reg1, data=x2) gives the same results as predict(reg1). So I am still looking for

I have a question about passing arguments to the function f that nlm minimizes. I have no problems if I do this: x<-seq(0,1,.1) y<-1.1*x + (1-1.1) + rnorm(length(x),0,.1) fn<-function(p) { yhat<-p*x+(1-p) sum((y-yhat)^2) } out<-nlm(fn,p=1.5,hessian=TRUE) But I would like to define fn<-function(x,y,p) { yhat<-p*x+(1-p) sum((y-yhat)^2) } so

I wish to fit a dynamical model in R and I am running in a problem that requires some of your wisdom to solve. For SAS users I am searching for the equivalent of the */retain/ *statement. For people that want to read complicated explanations to help me: I have a system of two equations written as difference equations here. To boil it down. I have a dataframe with three variables y, X1, X2

Hello, I am trying to learn how to perform Multiple Regression Analysis in R. I decided to take a simple example given in this PDF: http://www.utdallas.edu/~herve/abdi-prc-pretty.pdf I created a small CSV called, students.csv that contains the following data: s1 14 4 1 s2 23 4 2 s3 30 7 2 s4 50 7 4 s5 39 10 3 s6 67 10 6 Col headers: Student id, Memory span(Y), age(X1), speech rate(X2) Now

Dear R - help, I am working on the Credit scorecard model. I am using the Logistic regression to arrive at the regression coefficients model. I want to use the Hosmer - Lemeshow test . In order to understand the use of R - language, I had referred the following URL       http://www.stat.sc.edu/~hitchcock/diseaseoutbreakRexample704.txt The related data 'diseaseoutbreak' is available

I am trying to fit a nonlinear regression model to data. There are several predictor variables and 8 parameters. I will write the model as Y ~ Yhat(theta1,...,theta8) OK, I can do this using nls() - but "only just" as there are not as many observations as might be desired. Now the problem is that we have a factor "Site" and I want to include a corresponding error

Hi to everybody. I use the following routine (i found it in the internet) to compute the Hosmer-Lemeshow test in the framework of logistic regression. hosmerlemeshow = function(obj, g=10) { # first, check to see if we fed in the right kind of object stopifnot(family(obj)$family=="binomial" && family(obj)$link=="logit") y = obj$model[[1]] # the double bracket

I know that there are two method to apply the Hosmer and Lemeshow?s. One of them is calculated based on the fixed and pre-determined cut-off points of the estimated probability of success. One of them is calculated based on the percentiles of estimated probabilities. In the previous post,i find that the Hosmer and Lemeshow?s test how to use in R. hosmerlem <- function (y, yhat, g = 10) {

Hello, I have the following function that receives a "function pointer" formal parameter name "fnc": loocv <- function(data, fnc) { n <- length(data.x) score <- 0 for (i in 1:n) { x_i <- data.x[-i] y_i <- data.y[-i] yhat <- fnc(x=x_i,y=y_i) score <- score + (y_i - yhat)^2 } score <- score/n

Hi, I am fitting a weighted least square regression and trying to compute SSE,SST and SSReg but I am not getting SST = SSReg + SSE and I dont know what I am coding wrong. Can you help please? xnam <-colnames(X) # colnames Design Matrix fmla1 <- as.formula(paste("Y ~",paste(xnam, collapse=

I am trying to test goodness of fit for my legalistic regression using several options as shown below.  Hosmer-Lemeshow test (whose function I borrowed from a previous post), Hosmer–le Cessie omnibus lack of fit test (also borrowed from a previous post), Pearson chi-square test, and deviance test.  All the tests, except the deviance tests, produced p-values well above 0.05.  Would anyone please

Dear list # I have a function ff <- function(a,b=2,c=4){a+b+c} # which I programmatically want to modify to a more specialized function in which a is replaced by 1 ff1 <- function(b=2,c=4){1+b+c} # I do as follows: vals <- list(a=1) (expr1 <- as.expression(body(ff))) expression({ a + b + c }) (expr2 <- do.call("substitute", list(expr1[[1]], vals))) { 1 +