search for: rslt

...quot;)) } xlst_xlst[xlst!=outname] xform_paste(xlst,collapse="+",sep="") if (xform=="") xform_"1" x_lm(formula(paste(outname,"~",xform)),df,na.action=na.omit)$res y_lm(formula(paste(yname,"~",xform)),df,na.action=na.omit)$res rslt_lm(y~x,na.action=na.omit) xlab_paste(outname, "on others residual") ylab_paste(yname, "on all but", outname, "residual") if (!proportional) { if (ares) { plot(rslt$fit, rslt$res) abline(h=0,lty=2) if (!is.null(f)) lines(lowess(rslt$fit,rslt$...

Dear all, I coded a function called u.glm u.glm <- function (x,ahi,age,bmiz,gender) { library(nortest) lil.rslt <- lillie.test(x) if (lil.rslt$p.value >0.05) { cat("Logtrans=0, lillie=",lil.rslt$p.value,"\n") xmodel<-glm(x~ahi+age+bmiz+as.factor(gender)) summary(xmodel) confint(xmodel) } else { cat("Logtrans=1, lillie=",lil.rslt$p.value,"\n&...

Dear list, I have a series of data points which I want to approximate with exactly two linear functions. I would like to choose the intervals so that the total deviation from my fitted lines is minimal. How do I best do this? Thanks! Kamila The information transmitted in this electronic communication...{{dropped}}

...In the initialize method: setMethod( f="initialize", signature=signature(.Object="Test"), definition=function( .Object, path=NULL ){ obj <- callNextMethod(.Object) if(!is.null(path){ rslt <- obj$importImage(path=path) if(!is.logical(rslt)){ obj <- rslt } } return(obj) } Thanks for any comments, Janko

...t to demonstrate the bug in rnorm. # myrnorm <- function(n,mu=0,sigma=1){ mu + sigma*cos(2*pi*runif(n))*sqrt(-2*log(runif(n))) } # If RFUN <- rnorm we get ``wrong'' answers; if RFUN <- myrnorm, # we get ``right'' answers. RFUN <- rnorm NSER <- 1000 set.seed(350734) rslt <- list() for(K in 1:15) { N <- 100*K M <- matrix(RFUN(NSER*N),N,NSER) T2 <- apply(M,2,function(x){max(x**2)}) PV <- 1 - pchisq(T2,1)**N SZ <- sum(PV < 0.05)/NSER rslt[[K]] <- SZ cat(K,"\n") } rslt <- unlist(rslt) plot(100*(1:15),rslt,ylim=c(0,0....

...In the initialize method: setMethod( f="initialize", signature=signature(.Object="Test"), definition=function( .Object, path=NULL ){ obj <- callNextMethod(.Object) if(!is.null(path){ rslt <- obj$importImage(path=path) if(!is.logical(rslt)){ obj <- rslt } } return(obj) } Thanks for any comments, Janko

Rusers, I am trying to append multiple .csv files of different dimensions (but # of columns are the same for all .csv files). I do have .csv files whose names are CA1.csv ~ CA100.csv. CA means california and 1 means the first file. So what I have been doing (after googling how to append by row multiple files) was: cleanup_data<-function(state,count) { out<-matrix() for (i in 1:

...ix-lapack-example.so" ) regression <- function( X, Y ){ m <- nrow( X ) n <- ncol( X ) stopifnot( m >= n , is.vector( Y ), n != length( Y ) ) mn <- min( m, n ) lwork <- mn + max( mn, 1 ) ### to be used with dgels ### http://www.netlib.org/lapack/double/dgels.f rslt <- .C( "reg" , trans=as.character( "N" ) , m=as.integer( m ), n=as.integer( n ) , nrhs=as.integer( 1 ), a=as.double( X ) , lda=as.integer( m ), b=as.double( Y ) , ldb=as.integer( m ) , work=double( lwork )...

Hi everyone, I am quite new to R. my command is t.test(c(1:5,7:11), y=c(1:10),alternative = c("two.sided"), paired = TRUE) The output is Paired t-test data: c(1:5, 7:11) and c(1:10) t = 3, df = 9, p-value = 0.01496 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: 0.1229738 0.8770262 sample estimates: mean of the differences

...8.46 8.19 8.57 8.82 8.89 8.92 8.96 9.00 9.35 9.22 9.30 9.52 I then think the following analysis should be appropriate meta<-factor(scan(file="meta",what="character")) para<-factor(scan(file="para",what="character")) ba<-scan(file="ba") rslt<-lm(ba~meta+para-1) What I wish to obtain is a coefficient for each substituent at each position, as does Kubinyi: H F Cl Br I Me meta 0.00 -0.30 0.21 0.43 0.58 0.45 para 0.00 0.34 0.77 1.02 1.43 1.26 However, I do not get a coefficient for the Br substituent at the para position. I would li...

...am dynamically loading and calling by means of .Fortran(). The subroutine runs through the vector entry by entry; obviously I want to have it do one thing if y[i] is present and a different thing if it is missing. The way I am thinking of proceeding is along the xlines of: ymiss <- is.na(y) rslt <- .Fortran( "foo", NAOK=TRUE, as.double(y), as.logical(ymiss), etc, etc ) and inside ``foo'' have a logical branch based on the value of xmiss(i). Questions: (1) Is there a sexier way to proceed? E.g. is it possible within (g77) fortran to detect the fact th...

Hi, I am a new user of R using R 2.8.1 in windows 2003. I have a csv file with single column which contain the 30,000 students names. There were typo errors while entering this student names. The actual list of names is < 1000. However we dont have that list for keyword search. I am interested in grouping/cluster these names as those which are similar letter to letter. Are there any

...RbUndumped attr_accessor :result def run(fullScript) myTS = Time.now.strftime("%Y%m%d%H%M%S") msg = "===> Starting #{scriptname} in run #{scriptrun} on #{scriptenv} at #{myTS}" puts msg @result = "***#{scriptname} Launched [#{scriptlaunch}]" rslt = fullScript.run @result << rslt msg = "#{scriptname} Completed [#{Time.now.strftime("%Y%m%d%H%M %S")}]" @result << "***#{msg}" rescue @result << "#{scriptname} Aborted [#{Time.now.strftime("%Y%m%d%H%M %S")}] \n #{$!}...

...t;,"Pr(F)") rownames(ret) <- rownames(sub) ret <- format(ret,digits=1,nsmall=1,scientific=F) if (order.by.p) { ret <- ret[order(ret$'Pr(F)'),]} return(ret) } fscope <- as.formula(paste("dep.sign.up ~ ", paste(names(lr)[2:10], collapse= "+"))) rslt <- add1.coef(lm(dep.sign.up ~ 1, data=lr),fscope,p.value=1,order.by.p=FALSE) Estimate Pr(F) r1.pop.total 0.02 0.09 r1.pop.household 0.05 0.09 r1.pop.female 0.03 0.09 r1.pop.pct.female 14594.39 0.35 r1.pop.male 0.04 0.08 r1.pop.pct.mal...

...ting, web page advertising, url, search engine listings, search engine submissions, search engine registration, search engine secrets, promote submit url internet marketing search engines "> <SCRIPT LANGUAGE="JavaScript"> function newImage(arg) { if (document.images) { rslt = new Image(); rslt.src = arg; return rslt; }} function changeImages() { if (document.images && (preloadFlag == true)) { for (var i=0; i<changeImages.arguments.length; i+=2) {document[changeImages.arguments[i]].src = changeImages.arguments[i+1];}}} var preloadFlag = false; function prelo...

...ting, web page advertising, url, search engine listings, search engine submissions, search engine registration, search engine secrets, promote submit url internet marketing search engines "> <SCRIPT LANGUAGE="JavaScript"> function newImage(arg) { if (document.images) { rslt = new Image(); rslt.src = arg; return rslt; }} function changeImages() { if (document.images && (preloadFlag == true)) { for (var i=0; i<changeImages.arguments.length; i+=2) {document[changeImages.arguments[i]].src = changeImages.arguments[i+1];}}} var preloadFlag = false; function prelo...

...ting, web page advertising, url, search engine listings, search engine submissions, search engine registration, search engine secrets, promote submit url internet marketing search engines "> <SCRIPT LANGUAGE="JavaScript"> function newImage(arg) { if (document.images) { rslt = new Image(); rslt.src = arg; return rslt; }} function changeImages() { if (document.images && (preloadFlag == true)) { for (var i=0; i<changeImages.arguments.length; i+=2) {document[changeImages.arguments[i]].src = changeImages.arguments[i+1];}}} var preloadFlag = false; function prelo...

I was wondering how i can get a list of arguments to evaluate within function call. I'd like to be able do something like this: #mod <- glm(y~1,data=df,family="binomial") opt <- list(family="binomial") mod <- glm(y~1,data=df,opt) Any idea how this can be done properly? TIA -- View this message in context:

I have a data.frame datas which have two columns A and B. I want to filter column A by some values and to get a value list which contain the value in B. Best wishes! [[alternative HTML version deleted]]

Hi all, I just cannot think of how to do it: I want to take the first variable (column) of a data frame and regress it against all other variables. bla <- function (dat) { reg <- lm(whateverthefirstofthevariablenamesis ~., data=dat) return(reg) } What kind of function do I have to take instead of the whateverthefirstofthevariablenamesis, eval(), substitute(), get(), ... to