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Dear R-Helpers: I have tried everything I can think of and hope not to appear too foolish when my error is pointed out to me. I have some real data (18 points) that look linear on a log-log plot so I used them for a comparison of lm() and survreg. There are no suspensions. survreg.df <- data.frame(Cycles=c(2009000, 577000, 145000, 376000, 37000, 979000, 17420000, 71065000, 46397000,

Dear R experts Sorry for this question M1 <- 1:10 lcd1 <- c(11, 22, 33, 44, 11, 22, 33, 33, 22, 11) lcd2 <- c(22, 11, 44, 11, 33, 11, 22, 22, 11, 22) lcd3 <- c(12, 12, 34, 14, 13, 12, 23, 23, 12, 12) #generating variables through sampling pvec <- c("PR1", "PR2", "PR3", "PR4", "PR5", "PR6", "PR7", "PR8", "PR9", "PR10") fun11 <- function(x){ smpool <- c(1,2,3,3) x <- sample(smpool, 10, replace= TRUE) } newd <- sapply (pvec, fun11) #...

...t variables. Specifically, I'd like to be able to change the x-axis title and the colors to black and white. Has anyone had any luck with this? If not, is there a way to override to plotting colors, perhaps in par()? Thanks, Sam fm1 <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD) pr1 <- profile(fm1, alpha = 0.05) opar <- par(mfrow = c(2,2), oma = c(1.1, 0, 1.1, 0), las = 1) plot(pr1, conf = c(95, 90, 80, 50)/100) # works fine plot(pr1, conf = c(95, 90, 80, 50)/100,xlab=expression(theta),col=1) # doesn't change

Hello, I would like to know if there is a clever way to avoid the problem illustrated below within the xyplot function. x <- seq(1:10) y <- seq(1:10) pr1 <- xyplot(x ~ y) u <- seq(1:12) v <- seq(1:12) pr2 <- xyplot(u ~ v, col = "red", more = FALSE) prts <- list(pr1, pr2) for(i in prts) print(i, more = TRUE) I realize that one possibility is to explicitly set the same values for xlim and ylim for the plot, but that...

...', header = T, sep = ",", quote="", dec=".", fill = T, skip=0) attach(d) #function to plot a long time series piece by piece pl <- function(vara, varb, points) { date <- as.POSIXct(strptime(as.character(Date), "%d-%b-%y"), tz = "GMT") pr1 <- vector(mode="numeric") pr2 <- vector(mode="numeric") dat <- vector() for (j in 1:(round(length(Vol)/points)+1)) #number of plots { for (i in ((j-1)*points+1):(j*points)) { pr1[i-points*(j-1)] <- vara[i] pr2[i-points*(j-1)] <- varb[i] dat[i-p...

...mand = /usr/bin/lpq -P%p -L lpq cache time = 4 lprm command = /usr/bin/lprm -P%p %j lppause command = /usr/sbin/lpc hold %p %j lpresume command = /usr/sbin/lpc release %p %j queuepause command = /usr/sbin/lpc stop %p queueresume command = /usr/sbin/lpc start %p Printer Share: [pr1] comment = local printer pr1 on %h browseable = yes printable = yes public = yes create mode = 0700 path = /tmp Relevant Information for printing in printcap: pr1: :done_jobs=0 :rw :sh :lp=/dev/lp0 :sd=/var/spool/lpd/pr1 :fx=flp...

...2],x[5],x[6])+ con3 = rbind(x[3],x[5],x[6])+ con4 = rbind(x[5],x[6])+ rho=exp(x[7])/(1+exp(x[7]))+ ey = exp((yop%*%con1)/rho)+ ed = exp((dan%*%con2)/rho)+ eh = exp((hil%*%con3)/rho)+ ew = exp((wt%*%con4)/rho)+ ev = ey+ed+eh+ew+ den=(ey+ed+eh+ew)+ iv = rho*log(den)+ pp=exp(x[4]+iv)/(1+exp(x[4]+iv))+ pr1 =pp* ey/den+ pr2 =pp* ed/den+ pr3 =pp* eh/den+ pr4 =pp* ew/den+ pnp=1/(1+exp(x[4]+iv))+ likelihood = (pnp*yogurt[,1])+(pr1*yogurt[,2])+(pr2*yogurt[,3])+(pr3*yogurt[,4])+(pr4*yogurt[,4])+ lsum = log(likelihood)+ return(-colSums(lsum))+ }> p = optim(c(0,0,0,0,0.1,-2,-0.2),fr, hessian = TRUE, metho...

...data) { # define the parameter matrix used in following part vbar2<-matrix(0,n,nt) vbar3<-matrix(0,n,nt) v8 <-matrix(0,n,nt) regw<-matrix(0,n,nt) wden<-matrix(0,n,nt) lia<-matrix(0,n,nt) ccl<-matrix(1,n,ns) eta<-c(0,0) # setup the parts for loglikelihood q1<-exp(par[1]) pr1<-q1/(1+q1) pr2<-1-pr1 eta[2]<-par[2] a<-par[3:6] b<-par[7:11] w<-par[12:npar] for(m in 1:ns){ regw<-exp(w[1]+w[2]*eta[m])*actr+exp(w[3]+ w[4]*eta[m])*acwrk vbar2=a[1]+ eta[m]+regw*a[2]+acwrk*a[3]+actr*a[4] vbar3=b[1]+b[2]*eta[m]+regw*b[3]+acwrk*b[4]+ac...

...data) { # define the parameter matrix used in following part vbar2<-matrix(0,n,nt) vbar3<-matrix(0,n,nt) v8 <-matrix(0,n,nt) regw<-matrix(0,n,nt) wden<-matrix(0,n,nt) lia<-matrix(0,n,nt) ccl<-matrix(1,n,ns) eta<-c(0,0) # setup the parts for loglikelihood q1<-exp(par[1]) pr1<-q1/(1+q1) pr2<-1-pr1 eta[2]<-par[2] a<-par[3:6] b<-par[7:11] w<-par[12:npar] for(m in 1:ns){ ?for(i in 1:nt){ ?? regw[,i]<-w[1]+ w[2]*eta[m]+exp(w[3]+w[4]*eta[m])*actr[,i]+w[5]*acwrk[,i] ??? vbar2[,i]=a[1]+???? eta[m]+regw[,i]*a[2]+acwrk[,i]*a[3]+actr[,i]*a[4] ??? vbar3[,i]=...

...= rbind(x[2],x[5],x[6]) con3 = rbind(x[3],x[5],x[6]) con4 = rbind(x[5],x[6]) rho = exp(x[7])/(1+exp(x[7])) ey = exp((yop%*%con1)/rho) ed = exp((dan%*%con2)/rho) eh = exp((hil%*%con3)/rho) ew = exp((wt%*%con4)/rho) ev = ey+ed+eh+ew den=(ey+ed+eh+ew) iv = rho*log(den) pp=exp(x[4]+iv)/(1+exp(x[4]+iv)) pr1 =pp*(ey/den) pr2 =pp*(ed/den) pr3 =pp*(eh/den) pr4 =pp*(ew/den) pnp=1/(1+exp(x[4]+iv)) likelihood = (pnp*yogurt[,1])+(pr1*yogurt[,2])+(pr2*yogurt[,3])+(pr3*yogurt[,4])+(pr4*yogurt[,4]) lsum = log(likelihood) return(colSums(lsum)) } p = summary(maxLik(fr,start=c(0,0,0,1,0,-25,-0.2))) p _________...

...1.3 14 a 1.3 4 m 1.4 15 f 1.4 12 a 1.4 3 However, after having performed the analysis, it appears this is not correct as the 3 outputs per experiment are not linked... library(MASS) dt.plr <- polr(output ~ factor, data=dt, weights=n) > dt.pr1<-predict(dt.plr, , type="probs") > dt.pr1 a f m 1 0.09987167 0.4578184 0.4423099 2 0.09987167 0.4578184 0.4423099 3 0.09987167 0.4578184 0.4423099 4 0.09437078 0.4477902 0.4578390 5 0.09437078 0.4477902 0.4578390 6 0.09437078 0.4477902 0.4578390 7 0.0891...

...t, family = binomial(link=logit))  and I would like to compare these two models based on the prediction that I get from each model: pred1 = predict(model1, test.data, type = "response")pred2 = predict(model2, test.data, type = "response") I have used ROCR package to compare them:pr1 = prediction(pred1,test.y)pf1 = performance(pr1, measure = "prec", x.measure = "rec")  plot(pf1) which cutoff this plot is based on? pr2 = prediction(pred2,test.y)pf2 = performance(pr2, measure = "prec", x.measure = "rec")pf2_roc  = performance(pr2,measure=&q...

...t) <- c('gN', 'MnmN', 'OptN', 'DIs', 'beta', 'eta1', 'eta2') simparj.fm <- nls(Y.simulated ~ Y.model(gN, MnmN, OptN, DIs, beta, eta1, eta2, Popn, Dmax, AWC, SumEp, PotYield3, Nsupply), start = simparj.st, trace =T) pr1<-profile(simparj.fm) # define a new profile function pr2<- function (fitted, which = 1:npar, maxpts = 100, alphamax = 0.01, delta.t = cutoff/5) { f.summary <- summary(fitted) std.err <- f.summary$parameters[, "Std. Error"] npar <- length(std.err) n...

...;wins service = yes wins support = Yes path = /smb/Delorme local master = yes [joec] comment = Personal network share path = /smb/joec read only = No [peggyc] comment = Personal network share path = /smb/peggyc read only = No [pr1] printable = yes printcap name = /etc/printcap print command = /usr/bin/lpr -P%p -r %s ;printer driver = dj1 printer = dj1 printing = BSD path = /var/tmp valid users = joec peggyc root administrator min print space = 2000 I ca...

...for (j in 1:m){ UserItem[,j] =factor(UserItem[,j],levels=1:5) } occ=factor(c(rep(1,dim(M1)[1]),rep(2,dim(M2)[1]), ...,rep(21,dim(M21)[1])),levels=1:21) Z <- array(rep(0,m*21*5),c(m,21,5), list(paste("item",1:m,sep=""),paste("Occ",1:21,sep=""),c("pr1","pr2","pr3","pr4","pr5"))) for ( i in 1:m){ as.matrix(table(occ, UserItem[,2])) Z[i,,]=table(occ, UserItem[,i]) } Z.CAND <- CANPARA(Z,dim=7) I have implemented this code but I have one error in correspondance of: for ( i in 1:m){ Z[i...

Hi, I am using the lda function from the MASS library. I would to find the decision boundaries of each class and subsequently plot them. I wonder if anybody can offer any help on this topic? Below I applied the lda function on a small dataset of mine. Any help will be much appreciated. > library(MASS) >

> Will you be able to start numbering in github at a number larger than the largest bug in bugzilla? It would be annoying to have overlapping bug numbers. Bug numbers exist in code comments, list archives, etc., etc. If someone reads 'clang bug #1234' somewhere it will be ambiguous, which would be a real shame. This won't work in general, unfortunately as there are already a

...Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 Residual standard error: 0.1542 on 66 degrees of freedom Correlation of Parameter Estimates: theta1 theta2 theta3 theta2 -0.02168 theta3 -0.02029 0.997736 theta4 -0.97182 -0.008054 -0.008952 > pr1 <- profile(hyp.res) 1.825584 : 0.3894059947 -4.8975821760 0.0004141343 1.58426 : 0.373691474 -4.909091289 0.000824045 1.583673 : 0.4176596873 -4.8774106487 0.0008176545 1.583670 : 0.4196944963 -4.8760375504 0.0008187918 1.583670 : 0.4199010211 -4.8758854269 0.0008188162 1.624899 :...

Dear all R-users, I am a new user of R and I am trying to build a discrete choice model (with more than two alternatives A, B, C and D) using logistic regression. I have data that describes the observed choice probabilities and some background information. An example below describes the data: Sex Age pr(A) pr(B) pr(C) pr(D) ... 1 11 0.5 0.5 0 0 1 40 1 0 0 0 0 34 0 0 0 1 0 64 0.1 0.5 0.2 0.2 ...

...Error t value Low|Medium -0.4961 0.1248 -3.9740 Medium|High 0.6907 0.1255 5.5049 Residual Deviance: 3479.149 AIC: 3495.149 I also tried to predict the probabilities of the 3 categories of "Sat" using the predict function: > hnames<-lapply(housing[,-5],levels) > house.pr1<-predict(house.plr,expand.grid(hnames[-1]),type="probs")> cbind(expand.grid(hnames[-1]),round(house.pr1,2)) Infl Type Cont Low Medium High 1 Low Tower Low 0.38 0.29 0.33 2 Medium Tower Low 0.26 0.27 0.47 3 High Tower Low 0.14 0.21 0.65 4 L...