similar to: `bivariate apply'

Dear all, I would like to write a function like: myfun<-function(x,fn) {xx<-exp(x); x*fn(xx)} where fn is a symbolic description of any function with its argument to be specified. Therefore myfun(5,"2+0.3*y^2") should return 5*(2+0.3*exp(5)^2), myfun(5,"log(y)") should return 5*log(exp(5)) and so on. I tried with "expression" and others, but without success.

Dear all, Is there *any* reason explaining what I describe below? I have the following line myfun(x) If I type them directly in R (or copy/past), it works.. However if I type in R 2.6.1 > source("code.R") ##code.R includes the above line Error in inherits(x, "data.frame") : object "d" not found namely myfun() does not work correctly. In particular the

Hi all, Could anybody recommend some easy-to-understand and example based notes/tutorials on how to use cubic splines to do filtering on multi-dimension data? I am confused by the 1-dimensional case, and more confused by multi-dimensional case. I found all the books suddenly become very abstract when it comes to this subject. They don't provide examples in R or Splus at all. Specifically,

Hi, I have a function myFun which I want to call multiple times, using different argument lists. myFun("v1", "2009", 1) myFun("v2", "2008", 1) myFun("q", "2001") How can I easily do this in R? Should I use mapply? I unsuccessfully tried something like: x <- list(c("v1", "2009", 1), c("v2",

Dear all, I am having a problem with mapply. I guess the reason is that mapply is not "vectorized". But could you please take a look at my code below and help me to find a solution (either a better way to use mapply or a different function to call). Thanks a lot! ##beginning of my code myfun <- function(threshold, all.data) { ##Just a demostration of a function that takes a

Hi all, In a simulation context, I'm applying some my function, "myfun" say, to a list of glm obj, "list.glm": >length(list.glm) #number of samples simulated [1] 1000 >class(list.glm[[324]]) #any component of the list [1] "glm" "lm" >length(list.glm[[290]]$y) #sample size [1] 1000 Because length(list.glm) and the sample size are rather large,

I have estimated a multiple nonparametric regression using the loess command in R. I have also estimated an additive version of the model using the gam function. Is there a way of using the output of these two models to test the restrictions imposed by the additive model?

Dear all, The help file for the generic function vcov states "Classes with methods for this function include: 'lm', 'glm', 'nls', 'lme', 'gls', 'coxph' and 'survreg' (the last two in package 'survival')." Since, I am not able to use vcov.coxph(), I am wondering whether I am missing something (as I suspect..) regards, vito

Dear R-users, I am trying to understand how the 'segmented'-package works to determine breakpoints and slopes of regression lines in broken-line regression models. However, I am not able to repeat the example on the "plant"-dataset, which was reported in the accompanying paper of the package. (V.M.R Muggeo, "Segmented: an R package to fit regression models with

Hi all, I am experiencing the following quite strange (at least in my knowledge) problem in a simple function like the following: fn<-function(y,v=2){ n<-length(y) y<-y[(v+1):(n-v)] plot(y,type="l",lty=3,xlab="Time",ylab=deparse(substitute(y))) } fn(rnorm(50)) #look at the plot!!! The plot appears with numbers on the left side! If I delete the

Hi all, I'd like to fit model where the terms both are in the data.frame, mydata say, and are vectors *not in the data.frame*. >obj<-glm(y~x, data=mydata) #works >Z<-pmax(mydata$x-20,0) >(length(Z)==length(obj$y)) >[1] TRUE >update(obj,.~.+Z) #works However for some subset it doesn't works: >obj<-glm(y~x, data=mydata, subset=f==1) #works

Dear all, I'm looking for someone that help me to write an R function to simulate survival data under complex situations, namely time-varying hazard ratio, marginal distribution of survival times and covariates. The algorithm is described in the reference below and it should be not very difficult to implement it. However I tried but without success....;-( Below there the code that I used; it

Am trying to do some study on bivariate binomial distribution. Anyone knows if there is package in R that I can use to calculate the density function of bivariate binomial distribution and to generate random samples of it. Thanks, -- View this message in context: http://n4.nabble.com/How-to-calculate-density-function-of-Bivariate-binomial-distribution-tp992002p992002.html Sent from the R help

Hello all!!! 1) I am wondering is there a way to generate random numbers in R for Bivariate Uniform distribution? 2) Does R have  built-in function for generating random numbers for any given bivariate distribution. Any help would be greatly appreciated !! Good day! Haneef Anver [[alternative HTML version deleted]]

dear all, This is a probably a silly question. If I type > grep("x",c("a.x" ,"b.x","a.xx"),value=TRUE) [1] "a.x" "b.x" "a.xx" Instead, I would like to obtain only "a.x" "b.x" How is it possible to get this result with grep()? many thanks for your attention, best, vito --

Hi: I am using the package "KernSmooth" to do the local polynomial regression. However, it seems the function "locpoly" can only deal with univariate covaraite. I wonder is there any kernel smoothing package in R can deal with bivariate covariates? I also checked the package "lcofit" in which function "lcofit" can indeed deal with bivariate case. The

Dear list members I need a function that calculates the bivariate normal distribution for each observation. It is part of a likelihood function and I have 1000's of cases. As I understand it I cannot use packages like "mvtnorm" because it requres a covariance matrix of the same dimension as the number of observations. Basically what I need is a function that takes as arguments a

Any one know is there any package or function to generate bivariate exponential distribution? I gusee there should be three parameters, two rate parameters and one correlation parameter. I just did not find any function available on R. Any suggestion is appreciated. -- View this message in context:

Dear list I have a vector of values that allegedly have a bivariate normal distribution. I want to create a plot that shows the values I have obtained, and the bivariate normal distribution curve for the data. Is there a way of doing this in R? Many thanks for your help, Sarah. --------------------------------- [[alternative HTML version deleted]]

Dear all, I have a bivariate dataset from a preliminary study. I want to do two things: (1) estimate the probability density of this bivariate distribution using some nonparametric method (kernel, spline etc); (2) sample a big dataset from this bivariate distribution for a simulation study. Is there any good method or package I can use in R for my work? I don?t want parametric models like