Hello, This is perhaps more of a regression question than R, but I am learning both, so would appreciate your wisdom here. I have some data which reflects power load for an electrical generating system, with some temporal features. The data fields look like this: D,MON,DAY,YR,HR,WDAY,DRYBULB,WETBULB,LOAD 4455 5 13 92 13 4 70 63 1617 4456 3 9 92 13 2 73 57 1397 4457 10 5 92 8 2 58 58 1501 4458 11 24 92 18 3 56 56 1885 4459 9 27 92 8 1 65 65 1402 What R methodology is likely to produce the most accurate load forecast prediction for a given date and temperatures for problems like this? Thank you, Johanus Dagius __________________________________________________ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
Hello, I have received no reply to my previous query, so I will try again. I have tried glm on this problem with the default parameters and it produced a model with mean absolute error of approx 300 MWhrs. (The data is roughly normally distributed with a mean of 1700 MWhrs and SD=500). I know very little about R and so I am not sure what parameter needs to be tweaked from here. Using Cubist (www.rulequest.com) I have created a predictive model whose mean error is around 100 MWhrs. Cubist builds a recursively partitioned tree using piecewise linear regression. Cubist also outputs a nice set of rules which explain the model in terms of feature splits. I think R should give a comparable result. Does R have a method of piecewise approximation like this? I would like to compare R against Cubist. What method(s)in R must I learn to do this? Thank you, Johanus Dagius At 12:13 PM 6/21/02 -0700, I wrote:> Hello, > >This is perhaps more of a regression question than R, >but I am learning both, so would appreciate your >wisdom here. > > >I have some data which reflects power load for an >electrical generating system, with some temporal >features. The data fields look like this: > > >ID,MON,DAY,YR,HR,WDAY,DRYBULB,WETBULB,LOAD >4455 5 13 92 13 4 70 63 1617 >4456 3 9 92 13 2 73 57 1397 >4457 10 5 92 8 2 58 58 1501 >4458 11 24 92 18 3 56 56 1885 >4459 9 27 92 8 1 65 65 1402 > > >What R methodology is likely to produce the most >accurate load forecast prediction for a given dateand>temperatures for problems like this? > > >Thank you, >Johanus Dagius__________________________________________________ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
Daniel Mastropietro wrote:> > Hello, > > I use the option xaxp in the plot function and it is not recognized. > This affects the result of the function grid(), since it takes the default > tick marks used by the plot function, which is not what I want. > > See for example the result of: > > plot(1:10,1:10,xaxp=c(1,10,9)) > grid(9);plot sets par("xaxp") itself. You can change it before calling grid() (and after plot() has set it) as follows: plot(1:10) par(xaxp = c(1, 10, 9)) grid(9) If you want to have tickmarks analogously, you have to create the axis manually: plot(1:10, xaxt = "n") axis(1, 1:10) par(xaxp = c(1, 10, 9)) grid(9) Or just create the grid with abline(): plot(1:10, xaxt = "n") axis(1, 1:10) abline(v = 1:10, h = seq(2, 10, 2), col = "grey")> With grid(9) I want to set a vertical line at each integer value, but this > divides the interval [2,10] in 9 intervals, just because the first and last > tick in the x axis are 2 and 10, not 1 and 10 as I request with the xaxp > option. > > Anybody knows how to go around this problem? > > I am using R1.5.0 under Windows Me.Uwe Liges -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._