search for: superpositioning

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,

Dear R'Helpers and Colleagues, I have looked in the documentation, asked to some colleagues in the lab, but was unable to solve the following difficulty. I am running R on a iMac G5 with OS 10.4. The file below (73 rows x 144 col) shows the values of a climatic index on the globe with a grid of 2,5 ? x 2,5 ? (NA = no value): ? With image() and map() and running the following

I would like to add auxiliary information to the bottom of two strips on each panel that comes from a table look-up using the values of two variables that define the panel. For example I might panel on sex and race, showing 3 randomly chosen time series in each panel and want to add (n=100) in the bottom strip to indicate the 3 curves were sampled from 100. Is there a not-too-hard way to do

here ,I want to plot two lines in one figure.But I have two problems 1) how to move one of the y-axis to be the right ? I tried to the command"axis(2)",But I failed. 2) how to add the axis information correctly.Since I have use the cmommand "axis(1,at=1:6,labels=gradeinfo$gradenam)" but it seems that the correct information that I want is superposition with the old axis

On 2/25/2019 11:15 AM, John Regehr via llvm-dev wrote: > I'd just like to add that the general question here is "where does > poison stop propagating" and this question needs to be definitively > answered by this community. Does a call stop poison? Whatever the decision is may be contradicted after inlining, so what should such a call return? A superposition of poison

Hi, >From the following code (tweaked from another user): variable<-sample(rep(1:2,100)) individual<-rep(1:3, length(variable)) group<-rep(LETTERS[1:2],length(variable)/2) mydata<-data.frame(variable,individual,group) individual<-as.factor(individual) group<-as.factor(group) histogram(~variable|individual+group) I get six panels, one for each of individuals

Dear list, I want to plot a sample depth curve over a barplot. It would be perfect if the argument "inside" in the barplot function would be functional, cause I could just add this curve to the actual barplot, but it seems like it is not (not yet implemented). Argument "inside" would allow not to plot the line dividing two bars and using a null color within these bars would

With a data frame containing some X & Y variables I can get the between set correlations with cor(X,Y): > cor(NLSY[,1:2], NLSY[3:6]) antisoc hyperact income educ math 0.043381307 -0.07581733 0.25487753 0.2876875 read -0.003735785 -0.07555683 0.09114299 0.1884101 Is there somewhere an analog of pairs(X,Y) that will produce the pairwise plots of each X against each

Dear R Users, I recently posted an email on this list about the use of data.frame and overlaying multiple plots. Deepayan kindly indicated to me the panel.superposition command which worked perfectly in the context of the example I gave. I'd like to go a little bit further on this topic using a more complex dataset structure (actually the one I want to work on). >mydata Plot

Dear R-help list members, I have created a package 'mnormt' with facilities for the multivariate normal and t distributions. The core part is simply an interface to Fortran routines by Alan Genz for computing the integral of two densities over rectangular regions, using an adaptive integration method. Other R functions compute densities and generate random numbers. The starting

Hey all.. I had a bad IDE controller that hosed my EXT3 filesystems. A resulting fsck damaged pat of the filesystem and the root inode is gone (on my main drive AND the backup drive). I immediately DD'd the main drive over to an identical drive that I have been working on. But every time.. a fsck destroys all the data (moves everything to lost+found) and nothing that I've found

Duncan - Indeed, this has now been well documented; I have called these constructions "Schr?dinger Facts", since they arise from a superposition of truths in the training data that collapse into an untruth when observed. https://sentientsyllabus.substack.com/p/chatgpts-achilles-heel Now, the curious question is: why can it program. Why is its production of R-syntax less vulnerable

First a bit of background: After doing a search for a flexible polar plot function and coming up empty, I have begun writing one myself. Since I am new to writing mid-level graphics routines, this has required some learning about lattice, grid and related things. I am to the point where I have a workable proof of concept, but still need to make some improvements. My goal is to have

Dear R users, I am pleased to announce that QGA 1.0 is now available on CRAN. QGA implements the Quantum Genetic Algorithm, as proposed by Han and Kim in 2000, and is an R implementation derived from the Python one by Lahoz-Beltra in 2016. Under this approach, each solution is represented as a sequence of (qu)bits. Simulating the quantum paradigm, these qubits are in a superposition state:

Dear R users, I am pleased to announce that QGA 1.0 is now available on CRAN. QGA implements the Quantum Genetic Algorithm, as proposed by Han and Kim in 2000, and is an R implementation derived from the Python one by Lahoz-Beltra in 2016. Under this approach, each solution is represented as a sequence of (qu)bits. Simulating the quantum paradigm, these qubits are in a superposition state:

...rying simultaneously. The output of Predict is suitable for direct use by lattice (e.g., the xyplot function) and ggplot2 if you don't want to use plot.Predict. plot.Predict allows you to specify a lattice formula (less the left hand side) if you don't like plot.Predict's choice of superpositioning and panel variables. The following outlines the most significant change users will need to make (the web page contains the complete list). Note that the convention used for getting predictions over the default range is now predictor=. rather than predictor=NA. require(rms) # instead of Desig...

...rying simultaneously. The output of Predict is suitable for direct use by lattice (e.g., the xyplot function) and ggplot2 if you don't want to use plot.Predict. plot.Predict allows you to specify a lattice formula (less the left hand side) if you don't like plot.Predict's choice of superpositioning and panel variables. The following outlines the most significant change users will need to make (the web page contains the complete list). Note that the convention used for getting predictions over the default range is now predictor=. rather than predictor=NA. require(rms) # instead of Desig...

Dear R community, I am having trouble with a particular plot that I am trying to produce using Hmisc's xYplot function. I've been using primarily lattice and Hmisc packages for my plotting needs for the past few years, with great success. However, what I want to do now with xYplot is plot more than one data trend in the same panel, much as I would use xyplot from package lattice in

https://bugzilla.netfilter.org/bugzilla/show_bug.cgi?id=479 Summary: tunnel0 and br0 Product: iptables Version: 1.2.11 Platform: i386 OS/Version: other Status: NEW Severity: normal Priority: P2 Component: iptables AssignedTo: laforge@netfilter.org ReportedBy: tom@tomdeb.org I have a ipsec

Hello, I've read in a thread from this mailing list ( http://lists.xiph.org/pipermail/vorbis-dev/2009-September/020019.html) that the usage of FLOOR1_fromdB_LOOKUP table is to convert Floor1 values from a dB scale to a linear scale. In a dB scale, given a reference value, the other dB values are obtained from it, normally dB_value = 10log(p1/p0), where p0 is the reference value. Supposing p0