search for: xstd

Dear all, After a thorough research, I still find myself unable to find a function that does linear regression of 2 vectors of data using the "total least squares", also called "orthogonal regression" (see : http://en.wikipedia.org/wiki/Total_least_squares) instead of the "ordinary least squares" method. Indeed, the "lm" function has a

...ow. -Greg ---PATCH STARTS HERE--- diff -r rpart.orig//R/plotcp.s rpart/R/plotcp.s 5c5 < upper=c("size", "splits", "none"), ...) --- > upper=c("size", "splits", "none"), ylim, ...) 18c18,19 < ylim <- c(min(xerror - xstd) - 0.1, max(xerror + xstd) + 0.1) --- > if(missing(ylim)) > ylim <- c(min(xerror - xstd) - 0.1, max(xerror + xstd) + 0.1) -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-devel mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Sen...

Hello, I'm using mvpart option xv="1se" to compute a regression tree of good size with the 1-SE rule. To better understand 1-SE rule, I took a look on its coding in mvpart, which is : Let z be a rpart object , xerror <- z$cptable[, 4] xstd <- z$cptable[, 5] splt <- min(seq(along = xerror)[xerror <= min(xerror) + xvse * xstd]) I interprete this as following: the simplest tree with xerror under min(xerror) + its own xstd Neverthless, in some article I read the following rule: the simplest tree with xerror under min(xerror)...

Hi All, I have some questions on using library rpart. Given my data below, the plotcp gives me increasing 'xerrors' across different cp's with huge xstd (plot attached). What causes the problem or it's not a problem at all? I am thinking 'xerror's should be decreasing when 'cp' gets smaller. Also what the 'xstd' really tells us? If the error bars for each xerror overlap for different cp's, does that mean we don...

I am running some models (for the first time) using rpart and am getting results I don't know how to interpret. I'm using cross-validation to prune the tree and the results look like: Root node error: 172.71/292 = 0.59148 n= 292 CP nsplit rel error xerror xstd 1 0.124662 0 1.00000 1.00731 0.093701 2 0.064634 1 0.87534 1.08076 0.092337 3 0.057300 2 0.81070 1.07684 0.095582 4 0.038462 4 0.69610 0.99104 0.091659 5 0.036200 5 0.65764 1.01596 0.094635 6 0.029228 7 0.58524 1.00058 0.095440 7 0.028779 8 0....

...s of each class rather then counts. I don't see an option for this so would like to modify the text.rpart. However, I can't find the source since it is a method that's "hidden". How can I find the source? (2) printcp prints a table with columns cp, nsplit, rel error, xerror, xstd. I am guessing that cp is complexity, nsplit is the number of the split, rel error is the error on test set, xerror is cross-validation error and xstd is standard deviation of error across the cross-validation sets. Is there any documentation on this? For instance, how exactly is complexity compute...

...with a tree, say A, obtained with "class" method with the one, say B, I get with my custom functions (init, split and eval). I should compare the cp tables for the two fitted rpart object. For tree B I only have the "rel error" column and I need to obtain the xerror and the xstd columns as for tree A. To this aim I should know how this values are computed. I guess they depend on the xval value (in rpart.control) which is set to 10 by default. Does this mean that the observations are divided into 10 groups and, as before, the xerror is computed by averaging the erorrs o...

...rectly from ?rpart summary(fit3) ...which returned the following: Error in apply(formatg(yval, digits - 3), 1, paste, collapse = ",", sep = "") : dim(X) must have a positive length This seems to be a problem with the cross-validation, since the "xerror" and "xstd" columns are missing from the summary table as well. Using the mpart() wrapper results in the same error: fit4<-mvpart(gdist(spider[,1:12],meth="bray",full=TRUE,sq=TRUE)~water+twigs+reft+herbs+moss+sand,spider,method="dist") summary(fit4) Note, changing the 'metho...

...? If so, it's up to me to choose a subtree by using the printcp method. In the technical report from Atkinson and Therneau "An Introduction to recursive partitioning using the rpart routines" from 2000, one can see the following table on page 15: CP nsplit relerror xerror xstd 1 0.105 0 1.00000 1.0000 0.108 2 0.056 3 0.68519 1.1852 0.111 3 0.028 4 0.62963 1.0556 0.109 4 0.574 6 0.57407 1.0556 0.109 5 0.100 7 0.55556 1.0556 0.109 Some lines below it says "We see that the best tree has 5 terminal nodes (4 sp...

.... > printcp(fit.dimer) Classification tree: rpart(formula = outcome ~ ., data = p_df, method = "class") Variables actually used in tree construction: [1] CT DP DY FC NE NW QT SK TA WC WD WG WW YG Root node error: 608/1743 = 0.34882 n= 1743 CP nsplit rel error xerror xstd 1 0.185033 0 1.00000 1.00000 0.032726 2 0.052632 2 0.62993 0.65132 0.028773 3 0.026316 4 0.52467 0.58059 0.027596 4 0.025768 5 0.49836 0.57072 0.027419 5 0.019737 8 0.42105 0.56086 0.027239 6 0.018092 9 0.40132 0.55263 0.027087 7 0.013158 12 0.34704 0...

...+ Depth + Exp + DFP + FI + LAT, data = ch, method = "anova", control = rpart.control(minsplit = 10, cp = 0.01, xval = 10)) Variables actually used in tree construction: [1] BC Depth DFP Exp Root node error: 47456/99 = 479.35 n= 99 CP nsplit rel error xerror xstd 1 0.344626 0 1.00000 1.02074 0.139585 2 0.179054 1 0.65537 0.76522 0.107470 3 0.072037 2 0.47632 0.68115 0.092627 4 0.063469 3 0.40428 0.67320 0.094830 5 0.036190 4 0.34081 0.58516 0.096726 6 0.034677 5 0.30462 0.56747 0.074953 7 0.018219 6 0....

...t1) Regression tree: rpart(formula = CHAB ~ ., data = chabun, method = "anova", control = rpart.control(minsplit = 10, cp = 0.01, xval = 10)) Variables actually used in tree construction: [1] EXP LAT POC RUG Root node error: 35904/33 = 1088 n= 33 CP nsplit rel error xerror xstd 1 0.539806 0 1.00000 1.0337 0.41238 2 0.050516 1 0.46019 1.2149 0.38787 3 0.016788 2 0.40968 1.2719 0.41280 4 0.010221 3 0.39289 1.1852 0.38300 5 0.010000 4 0.38267 1.1740 0.38333 Each time I re-run the model I will get a slightly different output. I want to extr...

...r R-list members, I am doing a CART analysis in R using the rpart function in the rpart package: Phrag.rpart=rpart(PhragDiff~., data = Phrag, method="anova", xval=10). I used the xerror values in the CP table to prune the tree to 4 nsplits: CP nsplit rel error xerror xstd 1 0.098172 0 1.00000 1.02867 0.12768 2 0.055991 3 0.70548 1.00823 0.12911 3 0.029306 4 0.64949 0.83275 0.12074 4 0.018943 5 0.62019 0.86994 0.12467 5 0.010503 6 0.60124 0.86975 0.12080 6 0.010000 7 0.59074 0.87944 0.11757 I would like to get R^2 values for the m...

...part no longer cross-validates the resulting tree to return errors. A simple test is to use the usersplits.R function to get a simple, custom rpart function, and then change fit1 and fit2 so that the both have xvals of 10. The problem occurs in that the cptable for fit1 doesn't have xerror or xstd, despite the fact that the cross-validation is set to 10-fold. I guess I just need conformation that cross-validation doesn't work with custom functions, and if someone could explain to me why that is the case it would be greatly appreciated. Thanks, Sam Stewart -- Sam Stewart, MMath Resear...

...me when I want to estimate the cross-validation error of the generated tree: tree <- rpart(y~., data=data.frame(xsel, y), cp=0.00001) ptree <- prune(tree, cp=tree$cptable[which.min(tree$cptable[,"xerror"]),"CP"]) ptree$cptable CP nsplit rel error xerror xstd 1 0.33120000 0 1.0000 1.0000 0.02856022 2 0.08640000 1 0.6688 0.6704 0.02683544 3 0.02986667 2 0.5824 0.5856 0.02584564 4 0.02880000 5 0.4928 0.5760 0.02571738 5 0.01920000 6 0.4640 0.5168 0.02484761 6 0.01440000 8 0.4256 0.5056 0.02466708 7 0....

...duces output like this :- > printcp(rpart.LFM) Classification tree: rpart(formula = Sex ~ LFM, data = Brides2) Variables actually used in tree construction: LFM Root node error: 44/104 = 0.42308 n= 104 CP nsplit rel error xerror xstd 1 0.227273 0 1.00000 1.00000 0.11451 2 0.113636 1 0.77273 0.95455 0.11372 3 0.022727 2 0.65909 0.95455 0.11372 4 0.010000 5 0.59091 0.95455 0.11372 > Presumably 'root node error' and 'rel error' are something to do with error but what? 'Root node...

...1371,0.1013844,0.2027681,0.2267014,0.32......... MISSING 2392 3 random (0.001254181 0.998745819) * This is not like other trees I have run! And: summary(tree) > summary(tree) Call: rpart(formula = class ~ curvegrid + slope + tci10) n= 4966 CP nsplit rel error xerror xstd 1 0.9609344 0 1.00000000 1.0322191 0.01418310 2 0.0100000 1 0.03906565 0.7635924 0.01378822 Node number 1: 4966 observations, complexity param=0.9609344 predicted class=dw expected loss=0.5 class counts: 2483 2483 probabilities: 0.500 0.500 left son=2 (2574 obs) rig...

...lts of the 2 functions (summary and plot) or it is not always the same? Look forward to your reply, Carol -------------------------------------------- ?summary(rpart.res) Call: rpart(formula = mydata$class ~ ., data = as.data.frame(t(mydata))) ? n= 62 ???????? CP nsplit rel error??? xerror????? xstd 1 0.6363636????? 0 1.0000000 1.0000000 0.1712469 2 0.1363636????? 1 0.3636364 0.6818182 0.1532767 3 0.0100000????? 2 0.2272727 0.7727273 0.1596659 Variable importance ? Hsa.627?? Hsa.692 Hsa.692.2? Hsa.3306?? Hsa.601?? Hsa.831? Hsa.1832? Hsa.2456 ?????? 19??????? 13??????? 11??????? 10??????? 10?...

...ond 2 1 1 0 braz 2 3 2 1 czarne 0 2 2 0 rude 1 1 1 1 model=rpart(y~.,dane,method="class",control=rpart.control(xval=3,cp=0)) > plot(model) > text(model) model$cptable CP nsplit rel error xerror xstd 1 0.05691057 0 1.0000000 1.097561 0.08180737 2 0.02439024 3 0.8292683 1.219512 0.07040857 3 0.00000000 4 0.8048780 1.195122 0.07310295 npt=which.min(model$table[,4]) > npt integer(0) I need to describe this subject, but i don't know what R is saying to me. This subject is a...

Hi all, I have a package that is specific to a task I was repetitively using a few years ago. I now needed to run it again with new data. However I am told it was built with an older version or R and will not work. How can I tweak the package so it will run on 11.1? It was a one-off product and has not been maintained. Is there a way to "unpackage" it and repackage it to work? I