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I'm trying to do a linear regression between the columns of matrices. In example below I want to regress column 1 of matrix xdat with column1 of ydat and do a separate regression between the column 2s of each matrix. But the output I get seems to give correct slopes but incorrect intercepts and another set of slopes with value NA. How do I do this correctly? I'm after the slope and intercept of each columns regression...

Can anyone explain to me what this error message means, why I'm getting it, and how to fix it? lines(lowess(xdat, ydat, f=.5), col=3) Error: NAs in foreign function call (arg 1) ______________________________________________________________________ Stuart Luppescu -=-=- University of Chicago ºÍʸ ¤ÈÃÒÆàÈþ¤ÎÉã(EUC) -=-=- s-luppescu at uchicago.edu http://www.consortium-chicago.org/people/sl/sl.html...

i am trying to replicate the following graph using xyplot : attach(x) plot ( jitter(type), mortality, pch=16, xlim = c(0.25, 3.75)) lines ( c(1-0.375,1.375) , c ( median(mortality[type==1]), median(mortality[type==1])), lwd=5,col=2) lines ( c(2-0.375,2.375) , c ( median(mortality[type==2]), median(mortality[type==2])), lwd=5,col=2) lines ( c(3-0.375,3.375) , c ( median(mortality[type==3]),

hi all: Here's a dataframe(dat) and a vector(z): dat: x1 x2 x3 0.2 1.2 2.5 0.5 2 5 0.8 3 6.2 > z [1] 10 100 100 I wanna do the following: 10*x1,100*x2,1000*x3 My solution is using the loop for z and dat(since the length of z is the same as ncol of dat),which is tedious. I wanna an efficient solution to do it . Any help? Many thanks! My best

...6) - (sum(.expr9 * .expr5)/.expr10 - sum(.expr9) * (.expr10 * .expr2)/.expr19) .grad[, "b"] <- -(.n*.expr23 - .n*.expr4 * .expr23 - sum(.expr9) * (b^.expr4 * a)/.expr19) attr(.value, "gradient") <- .grad .value } > set.seed(99) > xdat <-rweibull(42,shape=1,scale=20) > sum(dweibull(xdat,1,20,log=T)) [1] -174.6415 3. Use the fllwfuncHnew function on nlm: > source("fnlmw") > fllwfuncHnew(1,20,xdat) [1] -174.6415 attr(,"gradient") a b [1,] 30.88661 0.3410358 > nlm(fllw...

...statistic T = ?rank(Xi). For n1=106 and n2=192, determine by simulation the ?=.05 critical point for testing H0: ?=0, H1:?<0. We can do this as follows: For m=10000 (no wimpy m=200 or 500 as in the book), draw m=10000 subsets of size 106 from the integers 1:298 using repeatedly the command xdat = sample(1:298, size=106). For each such subset, the value of the Wilcoxon is sum(xdat). Be sure to answer the following question: why is it unnecessary to calculate ranks? Any help would be greatly appreciately at this time. -- View this message in context: http://r.789695.n4.nabble.com/Wilcox...

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Hi! I'd like to plot things with axes going from the highest to the lowest value, so that e.g. high values on the xaxis are plotted to the left and low values to the right. Could anyone tell me how this is done? I couldn't find anything in the documentation. Thanks a lot! Silli

I can't get identify to work, using R 2.0.1 under windows xp pro, service pack 2. Here's what I enter, and the result: > plot((our.frame2$c1),(our.frame2$c9)) # Produces desired plot > identify(our.frame2$c1) # Plot comes to forefront, so I select a point warning: no point with 0.25 inches numeric(0) Is my call to identify correct? The help page for indentify (from

...ible, but i couldn't manage so far; could you help ? For instance, #Simulation n <- 200 x1 <- runif(n, min=-1, max=1) x2 <- runif(n, min=-1, max=1) y <- ifelse(x1 + x2 + rnorm(n) > 0, 1, 0) #Klein and Spady estimation, letting np compute the bandwidth npindexbw(bws=c(1,1,.5),xdat=cbind(x1,x2),ydat=y,bandwidth.compute=TRUE,method="kleinspady") #Works fine! #Klein and Spady estimation, attempting to constrain the bandwidth to .5 npindexbw(bws=c(1,1,.5),xdat=cbind(x1,x2),ydat=y,bandwidth.compute=FALSE,method="kleinspady") #Returns a result with the startin...

Hello, I have a double vector, x. I can access the i'th element as REAL(x)[i] Is there a function for this? I know i can write my own, but was seeing if one already exists. I did check Rinternals.h but didn't see one. Cheers Saptarshi

...xt. Can somebody help me to solve it.i'll very appreciate... ##GEV simulation(Non-stationary) dsim<-function(n, alpha, beta,sca,sha){ t <- 1:n location <- alpha + beta*t inv.df<-function(x) location + -(sca/sha)+(sca/sha)*(-log(x))^(-sha) u<-runif(n) y<-inv.df(u) return(y) } xdat<-c(3.4,7.8,10.1,11.7,13.9,14.1,14.2,14.6,14.6,14.6,15,15,16.1,18) ##GEV function(Non-stationary) GEV.lik<-function(a,x)#a=(location,scale,shape) { alpha <- a[1] beta <- a[2] scale<-a[3] shape<-a[4] n <- length(x) t <- 1:n location<-100/(1+alpha*exp(beta*x)) if(scale&lt...

...to estimate the standard errors of Klein and Spady's estimator for that I am using: library(np) N<-100 X<-matrix(c(rnorm(N,1,1), rnorm(N,0,1)), ncol=2) BETA <-matrix(1,2,1) Z<-X%*%BETA L<-rlogis(N,location=0, scale=1) Y <-as.vector(X%*%BETA+L>=0)*1 KS <- npindexbw (xdat=X, ydat=Y, bandwidth.compute=TRUE, method="kleinspady", ckertype="epanechnikov" ) KSi <- npindex(KS, errors=TRUE) se(KSi) But then I get as a result a vector Nx1, which I do not understand what it is, and if I let errors=FALSE then I get a NA as a result. So, how can I get...

Dear all, I am using np package in order to estimate a model with Klein and Spady estimator. To estimate the model I use KS <- npindexbw (xdat=X, ydat=Y, bandwidth.compute=TRUE, method="kleinspady", optim.maxit=10^3, ckertype="epanechnikov", ckerorder=2) and to estimate beta hats standard errors I use KSi <- npindex(KS, gradients=T, boot.num=300) vcov(KSi) This is fine so far, but if I want to estimate the bootst...

...? (I already tried all the possible optimization methods) The code that I run is: library(np) N<-250 q<-2 BETA<-matrix(1,3,1) X<-matrix(c(rnorm(N,0,1), rnorm(N,1,1)), ncol=q) X<-cbind(X,1) L<-rlogis(N,location=0, scale=1) Y <-as.vector(X%*%BETA+L>=0)*1 KS <- npindexbw(xdat=X, ydat=Y, bandwidth.compute=TRUE, method="kleinspady", optim.maxit=10^3, ckertype="epanechnikov", ckerorder=2) Thank you Dimitris -- View this message in context: http://r.789695.n4.nabble.com/error-in-optimization-when-I-include-constant-term-in-Klein-and-Spady-np-package-...

Dear list, I am using the np package. With the npindex function I estimate a semiparametric single index model using the method of Klein-Spady. P(Z=1|X) = G(X?b) I don?t have any problems to calculated the fitted values and standard errors X?b: bw = npindexbw(xdat=x, ydat=y_bi, method="kleinspady", nmulti=2) model = npindex(bws= bw3, gradients= TRUE, residuals = TRUE, boot.num = 50) x_fit = predict(model, se.fit = TRUE) x_fit_bi= x_fit$fit x_fit_bi_se = x_fit$se.fit However, I also would like to obtain an estimate of G(X?b). For example, after est...