search for: xmean

I have the following FORTRAN code converted to a DLL: ! my_xmean.f90 ! ! FUNCTIONS/SUBROUTINES exported from my_function.dll: ! my_function - subroutine ! subroutine my_xmean(X,N,XMEAN) ! Expose subroutine my_function to users of this DLL ! !DEC$ ATTRIBUTES DLLEXPORT,C,REFERENCE,ALIAS:'my_xmean_'::my_xmean ! Body of my_function DOUBLE PRECISION X(N)...

...Particularly I am interested in obtaining estimates for mu, and the effects alpha_i I have this data (from the book Applied Linear Statistical Models by Neter et al (1996), page 1020) y<-c(38,43,24,39,38,32,36,38,31,45,27,21,33,34,28) x<-c(21,34,23,26,26,29,22,29,30,28,18,16,19,25,29) xmean<-x-mean(x) grupo<-factor(rep(c(1,2,3),5)) datos<-data.frame(y,xmean,grupo) and I have done the following modelo<-lm(y~xmean+grupo,data=datos) summary(modelo) Call: lm(formula = y ~ xmean + grupo, data = datos) Residuals: Min 1Q Median 3Q Max -2.4348 -1.2739 -0.3...

Hello List: Does anyone know if there's a way to annotate the plots produced by the plot.xmean.ordinaly function in the rms package. I'd like to produce publication quality figures, but first need to annotate the axis labels. Is there a way to do that? I appreciate any advice. Chris [[alternative HTML version deleted]]

...2.8.0 on Mac OS X. I set up LyX 1.6.1 to use Sweave today. I can compile the test file I found on CRAN ( http://cran.r-project.org/contrib/extra/lyx/) without a problem and the output looks very nice. In the test file the following R code is used. <<myFirstChunkInLyX>>= xObs <- 100; xMean <- 10; xVar <- 9 x <- rnorm(n=xObs, mean=xMean, sd=sqrt(xVar)) mean(x) @ that should be the same as: xObs <- 100 xMean <- 10 xVar <- 9 x <- rnorm(n=xObs, mean=xMean, sd=sqrt(xVar)) mean(x) in the R console. My problem is that I want to import data to use in my report. In th...

...m These notes were written by J.R.M. Hosking. Using Fortran routines from R with CVF 1. Take a working Fortran subroutine, and put its name in an ATTRIBUTES DLLEXPORT directive to ensure that the routine name is exported from the DLL that will be built. Example: SUBROUTINE MYSUB(X,N,XMEAN) CDEC$ ATTRIBUTES DLLEXPORT :: MYSUB IMPLICIT DOUBLE PRECISION (A-H,O-Z) DOUBLE PRECISION X(N) XMEAN=0D0 DO 10 J=1,N XMEAN=XMEAN+X(J) 10 CONTINUE XMEAN=XMEAN/N RETURN END 2. Compile and link the routine using the CVF options /assume:underscore and /dll....

Hello all, I have the code below to simulate samples of certain size from a particular distribution (here,beta distribution) and compute some statistics for the samples. betasim2<-function(nsim,n,alpha,beta) { sim<-matrix(rbeta(nsim*n,alpha,beta),ncol=n) xmean<-apply(sim,1,mean) xvar<-apply(sim,1,var) xmedian<-apply(sim,1,median) simset<-data.frame(sampleno=seq1:nsim),means=xmean,vars=xvar,medians=xmedian) return(simset) } I can write a similar coding for any distribution individually. Now, I would like to have a generic code, say if I sp...

...within a C# .Net application equivalent to: source("my_r_code.r") I can get this to run but am not sure how to retrieve R objects defined with script my_r_code.r at runtime. For example, if "my_r_code.r" contains #-- contents of my_r_code.r--------------------- x <- 1:10 xmean <- mean(x) #------------------------------------------------------ My question is how to return the value of xmean to the .Net application? Paul [[alternative HTML version deleted]]

...aced or delete ! tapply(data,var,mean) # makes me problem,because when one value is NA mean is NA,too. Another attempt is this, because the summary does what i want , but is not subsetable to summary[4] if i use tapply,table and the the function below gives me again all means as NA ? xmean <- function (var) { summary(var)[4] } apply(data,2,xmean) Thanks for any suggestion & regards,Christian -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "...

i am using sarima() function as below ___________________________________________________________________________________________ sarima=function(data,p,d,q,P=0,D=0,Q=0,S=-1,tol=.001){ n=length(data) constant=1:n xmean=matrix(1,n,1) if (d>0 & D>0) fitit=arima(data, order=c(p,d,q), seasonal=list(order=c(P,D,Q), period=S), optim.control=list(trace=1,REPORT=1,reltol=tol)) if (d>0 & D==0) fitit=arima(data, order=c(p,d,q), seasonal=list(order=c(P,D,Q), period=S),...

Hi All, I am illustrating a simple, two-way ANOVA using the following data and I'm having difficulty in expressing the predicted values succinctly in R. X<- data.frame(read.table(textConnection(" Machine.1 Machine.2 Machine.3 53 61 51 47 55 51 46 52 49 50

Dear list, I want to calculate SPE (squared prediction error) in x-space, can someone help? Here are my codes: fit.pls<- plsr(Y~X,data=DAT,ncomp=3,scale=T,method='oscorespls',validation="CV",x= T) actual<-fit.pls$model$X pred<-fit.pls$scores %*% t(fit.pls$loadings) SPE.x<-rowSums((actual-pred)^2) Am I missing something here? Thanks in advance. Stella Sim

Dear all: I am comparing the PLS outputs of R and SAS for the following data set: Y x1 x2 x3 3 6 2 2 3 1 5 5 4 7 4 1 5 6 5 6 2 4 3 2 8 5 0 9 where Y is the dependent variable and x1, x2, x3 are the independent variables. I found several PLS algorithms in R (NIPALS,SIMPLS,KERNEL PLS). SAS has SIMPLS and NIPALS. The following are the NIPALS calculations of

...the breakpoint regression: this package seems to be the one that most people use for this, as far as I can see. Since I want to run some simulations, I'm trying to write functions that use segmented, and it's driving me mad. Here's a simple example: simdata<-function (Ns=200,Xmean=20,Xsd=5,SdYerr=0.5,Yint=0,threshold=20,slopebelow=0.5,slo peabove=1) { Xs<-rnorm(Ns,Xmean,Xsd) Yerr<-rnorm(Ns,0,SdYerr) D<-ifelse(Xs<=threshold,0,1) XminusX0<-Xs-threshold Ys<-Yint+slopebelow*Xs+slopeabove*XminusX0*D+Yerr plot(Xs,Ys) linmod<-lm(Ys~Xs) segment<-s...

...,Glm,Gls,groupkm,Hazard,hazard.ratio.plot,histdensity,"%ia%",ie.setup,interactions.containing,legend.nomabbrev,lm.pfit,lrm,lrtest,lsp,matinv,matrx,Newlabels,Newlevels,nomogram,num.intercepts,ols,ols.influence,oos.loglik,pantext,Penalty.matrix,Penalty.setup,pentrace,perimeter,perlcode,plot.xmean.ordinaly,pol,pphsm,predab.resample,Predict,psm,rcs,related.predictors,reVector,robcov,Rq,sascode,scored,sensuc,setPb,show.influence,specs,strat,Surv,"[.Surv",survdiffplot,survest,Survival,survplot,univarLR,validate,val.prob,val.probg,val.surv,vif,which.influence) importFrom(Hmisc) S3meth...

...invisible(NULL)); } z=as.character(substitute(x)); # must include code here to check that z is the name # of one of our altered functions # if z is an altered function, e.g., "mean" # then concatenating "x" with z gives the overlaid # function -- e.g. xmean is the standard mean #*************************************************************************** # Now we need to get a hold of the ... text * # * w=sys.call...

...tal } wrong <- scan("wrongrawdata.txt", list(x=0)) wrongfit <- fitdistr(wrong$x, "gamma") wrongmean <- mean(wrong$x) wrongshape <- wrongfit[[1]][1] wrongrate <- wrongfit[[1]][2] good <- scan("rawdata.txt", list(x=0)) xmin = 0 newx = good$x xmean = mean(newx) xmax = max(newx)+0.15 goodhist <- hist(newx, br=seq(from=0,to=xmax,by=0.15), probability=T, col="lightyellow") initmean <- (min(newx)+max(newx))/2 totalx <- length(newx) wrongmeanshift <- wrongmean + 0.2 wrongper <- pgamma(wrongmeanshift, wrongshape,...