similar to: Load prediction

Cubist is a rule-based machine learning model for regression. Parts of the Cubist model are described in: Quinlan. Learning with continuous classes. Proceedings of the 5th Australian Joint Conference On Artificial Intelligence (1992) pp. 343-348 Quinlan. Combining instance-based and model-based learning. Proceedings of the Tenth International Conference on Machine Learning

Cubist is a rule-based machine learning model for regression. Parts of the Cubist model are described in: Quinlan. Learning with continuous classes. Proceedings of the 5th Australian Joint Conference On Artificial Intelligence (1992) pp. 343-348 Quinlan. Combining instance-based and model-based learning. Proceedings of the Tenth International Conference on Machine Learning

I think this is a bug; at least this behavior is not documented in plot or plot.default. plot.default resets xaxp, and leaves xaxp reset when it exits: par(xaxp=c(0,1,4)) print(par("xaxp")) plot(c(0,1),c(0.2,0.3)) print(par("xaxp")) R. Woodrow Setzer, Jr. Phone: (919) 541-0128 Experimental Toxicology Division

R-Helpers; This seems simple to set graphic parameter, but I tried plot a graph with 10 intervals in x axis with par(xaxp = c(0, 150, 10) but to no avail: par(xaxp =c (0, 150, 10)) plot(age, y18, type="n", ylab="Height (m)", xlab=" age (Yrs)", font = 2) lines(age, y6, lty = 4, lwd = 2) lnes(age, y10, lty = 1, lwd =2) lines(age, y14, lty = 2, lwd =2)

Hi there, I guess this must be a standard issue, but I'm starting to go crazy with it. I simply want a plot with the x axis being logarithmic, having labels 1, 10, 100..., and ten unlabelled ticks between each of them - just as they introduce logarithmic axis at school. I've played around a bit with log="x", xlog=T (where exactly is the difference here?), xaxp, and xaxt

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Hello, I'm new to R, and I'm trying to set the number of tickmarks for a plot using xaxp, but R seems to ignore this setting completely. Maybe you've an idea what I'm doing wrong. I'm using R 1.4.1, and here's hat I'm doing: > d[,'seq'] [1] 28913 16323 13922 6237 4257 3881 4100 3781 2694 2064 1769 1550 [13] 1539 480 >

Dear R-help, I am trying to build R-2.2.0-patched (2005-11-07 r36217) on the head node of a Scyld cluster (dual Opteron 250s) using PGI compilers (v6.0). I used the flags suggested by Jennifer Lai on R-devel (taken from R-admin, except that I had to add -L/usr/X11R6/lib64 to LDFLAGS). The build went fine, but make check-all failed when running tests/Examples/graphics-Ex.R, at: > plot(1:2,

Dear friends. As promised ver 1.01 solved the former problem and now dates plot OK in windows. But. In the following code, "cex.axis"=0.5 works OK but "xaxp"=c(14620,14680,4) has seemingly no effect. The limits for xaxp was found from the default plot and inspecting par(). I need to put the date marks at specific points. Could I suppress them altogether and make a new xaxis

Dear R-helpers, I am having difficulty making R plot only integer labels on the x-axis of a simple graph. I want to plot the median values of a score on each of three occasions. Non-integer occasions are impossible. But, R keeps labelling the x-axis with half-occasions, despite my attempts to stop this using the "xaxs" and "xaxp" parameters of 'plot'. p1=c(1,2,3);

Working on the caret package has exposed me to the wide variety of approaches that different authors have taken to creating predictive modeling functions (aka machine learning)(aka pattern recognition). I suspect that many package authors are neophyte R users and are stumbling through the process of writing their first R package (or R code). As such, they may not have been exposed to some of the

Hello, I would like to submit two papers that use LLVM to the Related Publications section. Both papers focus on code isolation applied to perform piecewise compiler optimizations. The code isolation process is performed by CERE, an open source tool based on LLVM. The second paper is an extended version of the first one. 1) Piecewise Holistic Autotuning of Compiler and Runtime Parameters

Hello everybody!!!! Quick question, if you'd like to throw a little tip: does anyone knows a function that runs piecewise regression models with coefficients estimation and inferences ? Thank you [[alternative HTML version deleted]]

To whom it may concern, 1. I would like to know if there is a command in R to choose a function for a given dataset. I am facing a difficult question about how to fit the data [ please attachment ] 2. Are there any function that will allow users to do the piecewise regression? If not, is it going to be provide in the new updated version? What I am using is the piecewise logistic regression,

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Dear Mihail, I've added these two publications to the publications page. Please review it and let me know if I need to make any changes. In particular, if you have URLs to use for the papers, having those would be greatly appreciated. Regards, John Criswell On 11/28/17 12:05 PM, Mihail Popov via llvm-dev wrote: > > Hello, > > I would like to submit two papers that use LLVM

On Fri, 3 May 2002 drew.tyre@csiro.au wrote: > Full_Name: Drew Tyre > Version: 1.4.0 > OS: Win 2000 > Submission from: (NULL) (140.253.74.225) > > > When creating a rather complex plot, the resulting figures 'shrink' when either > copied to the clipboard or saved as a metafile using the GUI menu. Also saving > as postscript or pdf causes the same problem, as

Hi, I am trying to use piecewise linear regression to approximate a nonlinear function. Actually, I don't know how many linear functions I need, therefore, I want build an array of regression models to automate the approximation job. Could you please give me any clue? Attached is ongoing code: rawData = scan("c:/zyang/mass/data/A01/1.PRN", what=list(numeric(),numeric())); len =

Hi, I need to fit a piecewise linear regression. x = c(6.25,6.25,12.50,12.50,18.75,25.00,25.00,25.00,31.25,31.25,37.50,37.50,50.00,50.00,62.50,62.50,75.00,75.00,75.00,100.00,100.00) y = c(0.328,0.395,0.321,0.239,0.282,0.230,0.273,0.347,0.211,0.210,0.259,0.186,0.301,0.270,0.252,0.247,0.277,0.229,0.225,0.168,0.202) there are two change points. so the fitted curve should look like \ \ /\

Dear all, I'm attempting to use a piecewise regression to model the trajectory of reproductive traits with age in a longitudinal data set using a mixed model framework. The aim is to find three slopes and two points- the slope from low performance in early age to a point of high performance in middle age, the slope (may be 0) of the plateau from the start of high performance to the