search for: xerror

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...

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...

Hi, I've searched online, in a few books, and in the archives, but haven't seen this. I believe that xerror is scaled to rel error on the first split. After fitting an rpart object, is it possible with a little math to determine the percentage of true classifications represented by a xerror value? -seth -- View this message in context: http://r.789695.n4.nabble.com/how-is-xerror-calculated-in-rpart-tp...

Full_Name: Gregory R. Warnes Version: R 1.3.1 OS: Solaris 2.8 Submission from: (NULL) (192.77.198.200) rpart library version 3.1-2 Error message: > plotcp(fit.thirds.1,ylim=c(0.7,1.5)); Error in plot.default(ns, xerror, axes = FALSE, xlab = "cp", ylab = "X-val Relative Error", : formal argument "ylim" matched by multiple actual arguments > This can be corrected by adding ylim to the list of formal argument and wrapping the definition of ylim with 'if(missing(ylim))'...

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 f...

...t tree calculated using the 1-se rule. I have included some code that has worked well for me in the past but it was only for selecting the minimum cross-validation error. I include the code for my model, some relevant output and the code for selecting and plotting the frequency histogram of minimum xerror. Here is the output that is being referenced in the code below Regression tree: rpart(formula = chbiomsq ~ HC + BC + POC + RUG + Depth + Exp + DFP + FI + LAT, data = ch, method = "anova", control = rpart.control(minsplit = 10, cp = 0.01, xval = 10)) Variables actually used in t...

...rs for a certain task using several methods, one of them being decision trees. The doubts come 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...

...printcp(fit1) 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...

Dear 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....

...full tree? 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 node...

...ibha I read R-help as a digest so often come late to a discussion. Let me start by being the first to directly answer the question: > fit <- rpart(time ~ age +ph.ecog,lung) > summary(fit) Call: rpart(formula = time ~ age + ph.ecog, data = lung) n= 228 CP nsplit rel error xerror xstd 1 0.03516666 0 1.0000000 1.009949 0.1137819 2 0.01459053 1 0.9648333 1.049636 0.1282259 3 0.01324335 3 0.9356523 1.090562 0.1301632 4 0.01000000 7 0.8810284 1.063609 0.1298557 Node number 1: 228 observations, complexity param=0.03516666 mean=305.2325, MSE=44176.9...

On kde, gtk-window-decorator segfault everytime a minimised "Konqueror download window" close it self... Running with --sync fix the problem so no way to have a backtrace :( Cedric

...rcentages 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 c...

...esults obtained 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...

...d="dist") #directly 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, ch...

...el building. > 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...

...al) then rpart 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, MMat...

...rpart produces 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? 'Ro...

...slope=0,0.1011371,0.1013844,0.2027681,0.2267014,0.32......... 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 (25...

...in the results 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?...