similar to: plotting positive predictive values

Hi, I'm trying to fit a smooth line in a plot(y ~ x) graph. x is continuous variable y is a proportion of success in sub-samples, 0 <= y <= 1, from a Monte Carlo simulation. For each x there may be several y-values from different runs. Each run produces several sub-samples, where "0" mean no success in any sub- sample, "0.5" means success in half of the

Hi all How can I change the limits (xlim or ylim) in a plot that has been already created? For example, consider this naive example curve(dbeta(x,2,4)) curve(dbeta(x,8,13),add=T,col=2) When adding the second curve, it goes off the original limits computed by R for the first graph, which are roughly, c(0,2.1) I know two obvious solutions for this, which are: 1) passing a sufficiently large

Dear R-users I have a problem choosing initial points for the function arms() in the package HI I intend to implement a Gibbs sampler and one of my conditional distributions is nonstandard and not logconcave. Therefore I'd like to use arms. But there seem to be a strong influence of the initial point y.start. To show the effect I constructed a demonstration example. It is reproducible

To superimpose two functions plots in the same page. The functions L0 and L1, as defined below, I use the following code: # An accumulative normal distribution function with # several parametres f0 <- function(mu, xm, ds, n) { 1 - pnorm((xm-mu)/(ds/sqrt(n))) } f1 <- function(mu,n) f0(mu, 386.8, 48, n) # Two functions with just the parameter mu L0 <- function(mu)

I'm dealing with a classification problem using ordinal logistic regression. In the case of binary logistic regression with unequal proportions of 0's and 1's, a threshold in the interval [0,1] has to be adapted to transform back the predicted probabilities into 0 and 1. This can be done quite straightforward using e.g. the Kappa statistics as accuracy criterion. With

>>>>> "TL" == Thomas Lumley <thomas@biostat.washington.edu> writes: TL> On Thu, 24 Aug 2000, Troels Ring wrote: >> Dear friends. >> >> Is this as expected ? Is alpha and beta too large simply ? >> >> > dbeta(.1,534,646) >> [1] NaN >> Warning message: >> NaNs produced in:

Hello I am trying to make a graph of 10 different lines built each from 4 different segments and to add a darker line that will represent the average of all graphs - all in the same plot.Actually each line is a ROC plot The code I'm using for plotting one line is as follows: logit.roc.plot <- function(r, title="ROC curve") { old.par <- par(no.readonly = TRUE);

Hi Brian, Your underlying dataset for the ROC curve only has 4 unique values for specificity, even though there are 23 elements in the vector, hence the step function nature of the first plot. The default smoothing in the smooth() function is "binormal". You might try one of the other smoothing options to see the result and whether they make visual sense. In the absence of smoothing,

Hi,   I would like to distinguish my plots using colors but I got error message.  How do I correct that?     plot(ecdf(z), main ="CDF for observed and simulated weighted sum",type="l",lwd=2,col="blue", xlab="Weighted sum (mm)", ylab="Cumulative Percent", xlim=c(0,15), xaxs ='i', yaxs ='i',ylim=c(0,1)) par(new=TRUE)

Hello, I have a question about the ?plot.predict? function in Frank Harrell's rms package. Do you know how to superpose in the same graph the prediction curve of ols and raw data points? Put most simply, I would like to combine these two graphs: > fit_linear <- ols (y4 ~ rcs(x2,c(5,10,15,20,60,80,90)), x=TRUE, y=TRUE) > p <- Predict(fit_linear,x2,conf.int=FALSE) > plot (p,

The answer was (thanks to Mark Leeds) to do with the use of a factor instead of a vector. on [2017-08-05] at 08:57 Myles English writes: > I am having trouble understanding how the 'by' function works. Using > this bit of code: > > i <- data.frame(x=c(1,2,3), y=c(0,0,0), B=c("red","blue","blue")) > j <- data.frame(x=c(1,2,3),

I am having trouble understanding how the 'by' function works. Using this bit of code: i <- data.frame(x=c(1,2,3), y=c(0,0,0), B=c("red","blue","blue")) j <- data.frame(x=c(1,2,3), y=c(1,1,1), B=c('red','blue','green')) plot(0, 0, type="n", xlim=c(0,4), ylim=c(0,1)) by(i, i$B, function(s){ points(s$x, s$y, col=s$B) })

Hi, if I use the xyplot (or levelplot) function (lattice library) with the option axs="i", I have the problem that the tickmarks lie a bit outside the "plot-box". Consider for example: library(lattice) x<-seq(0,1,by=0.01) y<-seq(0,1,by=0.01) xyplot(y~x,type="l",xlim=c(0,1),ylim=c(0,1),scales=list

Hi R User, I was trying to use R for WINBUGS using following model and data (example), but I am new with WINBUGS and don't know how we perform the analysis. I wonder whether I can run the following the example data and Winbugs Model in R. Your help will be highly appreciated. Sincerely, SN PANDIT === library(R2WinBUGS) #Model model{ #likelihood for(i in 1:N){ a1[i] ~ dnorm(a11[i],tau)

Hi R User, I was trying to use R for WINBUGS using following model and data (example), but I am new with WINBUGS and don't know how we perform the analysis. I wonder whether I can run the following the example data and Winbugs Model in R. Your help will be highly appreciated. Sincerely, SN PANDIT === library(R2WinBUGS) #Model model{ #likelihood for(i in 1:N){ a1[i] ~ dnorm(a11[i],tau)

Thomas, Thank you for your reply about the for () loop. The as.character advice worked. Sorry for the delay in getting back to?I had to set the project aside for a few weeks. This didn?t work exactly as is for (patient in as.character(1:n)){ pt <- MRN == patient (rest of the function) } But this did for (patient in as.character(levels(MRN))){ pt <- MRN == patient (rest of the function)

Hi, I have the following problem. I wanted to get a matrix of scatterplots and I used pairs. I wanted to add the line y=x in each plot and I created a panel function for this scope. I used points and abline in the following way: ## put y=x in each plot panel.lin<- function(x, y) { points(x,y, pch=21, bg=par("bg"), col = "black",cex=2)

Someones you need to set up a plot before the data is ready to plot. You know the desired axis limits (xlim and ylim), but don't have the data all in one vector. You would like to set up the coordinate system, perhaps draw the axes and titles, but draw no points. You can do that with plot(x=xlim, y=ylim, type="n", xlab="", ylab="") In R you could use

Hi, I have a large dataframe than I'm writing functions to explore, and to reduce cut and paste I'm trying to write a function that does a subset and then a plot. Firstly, I can write a wrapper around a plot: plotwithfits <- function(formula, data, xylabels=c('','')) { xyplot(formula, data, panel = function(x,y, ...) { panel.xyplot(x,y,

Hola! It is somewhat inconvenient to use plot.function, when add=TRUE. The following (miniscule) change makes it behave better: plot.function <- function (fn, from , to, xlim = NULL, ...) { if (!is.null(xlim)) { if (missing(from)) from <- xlim[1] if (missing(to)) to <- xlim[2] } curve(fn, from, to, xlim = xlim, ...) } The only