similar to: calibration/inverse regression?

Hello, Is there a better way to use paste such as: a = paste(colnames(list.indep)[1],colnames(list.indep)[2],colnames(list.indep)[3],colnames(list.indep)[4],colnames(list.indep)[5],sep="+") > a [1] "aa+dummy1+dummy2+bb+cc" I tried a = paste(colnames(list.indep)[1:5],sep="+") > a [1] "aa" "dummy1" "dummy2"

Hi, how to estimate a the following model in R: y(t)=beta0+beta1*x1(t)+beta2*x2(t)+...+beta5*x5(t)+beta6*y(t-1)+beta7*y(t-2)+beta8*y(t-3) 1) using "lm" : dates <- as.Date(data.df[,1]) selection<-which(dates>=as.Date("1986-1-1") & dates<=as.Date("2007-12-31")) dep <- ts(data.df[selection,c("dep")]) indep.ret1

Hi useRs, I'm experiencing something quite weird with glm() and weights, and maybe someone can explain what I'm doing wrong. I have a dataset where each row represents a single case, and I run glm(...,family="binomial") and get my coefficients. However, some of my cases have the exact same values for predictor variables, so I should be able to aggregate up my data frame and

Hi all, I am trying to construct a pairwise coocurrence matrix for certain terms appearing in a number of documents. For example I have the following table with binary values showing the presence or absence of a certain term in a document: term1 term2 term3 term4 term5 doc1 1 1 0 0 1 doc2 1 1 0 1 1 doc3 1 0 0 0 1 And I want to have a matrix with the number of the pairwise coocurrencies. So,

I am having a problem with any terms which contain hyphens in them. Is there a particular reason why this wouldn''t work? i''m using aaf, and have tried with the the previous version (pre 0.10.0) and the newest versions of both ferret and aaf. Basically, the problem is that term1-term2 is something my users want to search for, and it appears in the titles of publications,

Hello R users, I am new to R and am wondering if anyone can help me out with the following issue: I wrote a function to build ts models using different inputs, but when R displays the call for a model, I cannot tell which variables it is using because it shows the arguments instead of the real variables passed to the function. (e.g Call: lm(formula = dyn(dep ~ lag(dep, -1) + indep)) --->

Hello R community, I have a question about the logistic regression function. Specifically, when the predictor variable has not just 0's and 1's, but also fractional values (between zero and one). I get a warning when I use the "glm(formula = ... , family = binomial(link = "logit"))" which says: "In eval(expr, envir, enclos) : non-integer #successes in a binomial

In a function, say foo.glm for glm objects I want to use the name of the object as a label for some output, but *only* if a glm object was passed as an argument, not a call to glm() producing that object. How can I distinguish these two cases? For example, I can use the following to get the name of the argument: foo.glm <- function(object) { oname <- as.character(sys.call())[2]

Hi, I'm compiling lldiv.c from the NetBSD standard library. It works on ARM, Mips, Microblaze,ppc, ppc64, and x86_64. On i386 a very strange thing happens. Here's the source: #include <stdlib.h> #define __weak_alias(sym) __attribute__ ((weak, alias (#sym))) lldiv_t lldiv(long long int num, long long int denom) __weak_alias(_lldiv); lldiv_t _lldiv(long long num, long

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I am using R on a Windows XP professional platform. The following code is part of a bigger one CODE press=function(y,x){ library(qpcR) models.press=numeric(0) cat("\n") dep=y print(dep) indep=log(x) print(indep) yfit=dep-PRESS(lm(dep~indep))[[2]] cat("\n yfit\n") print(yfit) yfit.orig=yfit presid=y-yfit.orig press=sum(presid^2)

Dear R-experts, I am running a large simulation exercise where the enough complicated integration is required. The integral is computed within a C-function called Denom by use of function qags from the gsl library. Here is a piece of R-code: denom<-try(.C("Denom",as.double(x),as.integer(n), as.integer(p), as.double(param),

Could anyone tell me what am I doing wrong? > pro<-function(indep,dep){ + d<-data.frame(indep) + form<-formula(lm(dep~.,data=d)) + forward<-step(lm(dep~X1,data=d),scope=form,trace=0,direction='f') + return(forward) + } > pro(m,q) Error in inherits(x, "data.frame") : Object "d" not found Where q is a vector with the dependent variable's

The following plot is a first approximation to what I need: *********************************** mu1 <- 0 mu2 <- 5 s <- 1 x <- seq(-2.5, 7.5, length = 41) y <- seq(-2.5, 2.5, length = 41) f <- function(x,y){ term1 <- 1/(2*pi*sqrt(s*s)) term2 <- -1/2 term3 <- (x - mu1)^2/s term4 <- (y - mu1)^2/s term5 <- (x - mu2)^2/s term1*(.5 * exp(term2*(term3 + term4)) + .5 *

Hello R users, I have been trying to output all my results (text, plots, etc) into the same postscript file as one document, but have been unable to...Can anyone help me improve my code below so that I can accomplish this? Currently I have to output them separately then piece them back together into one document.. Thanks in Advance for any help! options (scipen=999, digits=7)

HI >i have a problem please help me to solve it: http://r.789695.n4.nabble.com/file/n4639434/aj.pdf aj.pdf >i want to calculate the vecteur a[j] where j: 1...8 >this is the code in R: >aj.fun <- function(j, i, X, z, E, beta0, beta1){ + n <- length(X) + iX <- order(X) + iz <- order(z) + e1 <- -(beta)*z[ iz[1:(i - 1)] ] + numer <- E[j] - sum( X[ iX[1:(i - 1)] ]

Dear list members, I want to draw surfaces of Dirichlet distributions with different parameter settings. My code is the following: #<begin code> a1 <- a2 <- a3 <- 2 #a2 <- .5 #a3 <- .5 x1 <- x2 <- seq(0.01, .99, by=.01) f <- function(x1, x2){ term1 <- gamma(a1+a2+a3)/(gamma(a1)*gamma(a2)*gamma(a3)) term2 <- x1^(a1-1)*x2^(a2-1)*(1-x1-x2)^(a3-1)

Hello, I am interested in fitting a segmented model with unknown joint points in nls and perhaps eventually in nlme. I can fit this model in sas (see below, joint points to be estimated are a41 and a41), but am unsure how to specify this in the nlm function. I would really appreciate any suggestions or example code. Thanks a lot. -andy proc nlin data=Stems.Trees; params b41=-3 b42=1.5

I found a core dump: term1$ nbdkit -fv --filter=xz null xz-max-depth=$((0x4000000000000000)) term1... term2$ qemu-nbd --list term1... nbdkit: debug: null: open readonly=1 nbdkit: error: calloc: Cannot allocate memory nbdkit: debug: xz: close Segmentation fault (core dumped) (Note that the use of --run or daemonizing nbdkit makes it a bit harder to see the core dump, but it is still happening.)

Dear members, I am trying to use boot () to compute the distributions of a statistic of a data set. The statistic is defined in the following code: eds<-function(x) { r<-cor(x) paren<-1-abs(r)/2 denom<-sum(sum(paren)+0.5) desvio<-sd(x) media<-mean(x) a<-desvio/media