similar to: Ctree Question

Hello All, I am using library "party" and I have found a curious/strange behaviour when trying to save the output of a ctree in a file via jpeg/png command. If you use: ################ library(party) airq <- subset(airquality, !is.na(Ozone)) airct <- ctree(Ozone ~ ., data = airq) plot(airct, terminal_panel = node_boxplot, drop_terminal = FALSE) ############### you get a

Hello All, I am using library "party" and I have found a curious/strange behaviour when trying to save the output of a ctree in a file via jpeg/png command. If you use: ################ library(party) airq <- subset(airquality, !is.na(Ozone)) airct <- ctree(Ozone ~ ., data = airq) plot(airct, terminal_panel = node_boxplot, drop_terminal = FALSE) ############### you get a

Hello, This is a re-submittal of question I submitted last week, but haven't rec'd any responses. I need to extract the probabilities used to construct the barplots displayed as part of the graph produced by plot("ctree"). For example, library(party) iris.ct <- ctree(Species ~ . , data = iris) plot(iris.ct) Instead of a simple example with only 4 terminal nodes, my

I'm trying to understand how to set graphical parameters for trees created with the party package.  For example take the following code:   library(party)     data(airquality)     airq <- subset(airquality, !is.na(Ozone))     airct <- ctree(Ozone ~ ., data = airq,                    controls = ctree_control(maxsurrogate = 3))     plot(airct)   My problem is, I've got a ctree that has

I have run the ctree function, and my dependent variable is broken into 3 categories: low cost, moderate cost and high cost. When i plot the results (eg. using plot(test.ct)), the plot shows, at the very bottom of each node, the probability of falling into each cost category. When i print the actual results (eg. using print(test.ct)), i get all of the backup

Hi, I am trying to get the terminal nodes of a plot of a ctree object to look nice. Using the iris data I have: library(party) mtree <- ctree(Species ~ ., data=iris) plot(mtree,terminal_panel=node_barplot(mtree)) The terminal nodes don't display the species names because the names are displayed horizontally. ?I would like to reduce the size of the labels and make the terminal nodes

Dear R.Users, using ctree() (from "party" library) on a data.frame, I want to append a column with the references for the groups/segments detected. While these nodes are easy readable in output, I need a vector for my obs. Hints? Cheers -- Daniele Medri

Hello, I plotted a nice tree with "ctree" . It shows 3 nodes with the prediction of my 2 groups. (see picture) Unfortunately I need a larger scale to read the exact prediction of my groups to get the specificity and sensitivity. I tried to change the scale with "axis" but it didn't work, my guess because it's not a normal graph with x and y axis. Has someone an idea

I know this has been addressed before, but I'm still confused by the solution. When running TINN - R (ver 2.1.1.6) with R (2.8.0) I have lost the functionality of TINN-R It returns an error source(.trPaths[5], echo=TRUE, max.deparse.length=150) I modified the RProfile.site file as follows: options(pager="interal") .trPaths=c( 'C:/Documents and

Hello I have a Linux machine (Ubuntu 8.04 hardy, Gcc version 4.2.4 (i486-linux-gnu) currently running R 2.6.2. I'd like to upgrade to 2.10. First Question): What is the appropriate way to remove the old version of R? Part 2. After downloading r-base_2.10.0.orig.tar.gz and opening the archive. I ran the ./configure routine. It failed claiming that it could not find the F77 compiler. My

Hi, I need to create a plot (type = "h") and then overlay a skewed-normal curve on this distribution, but I'm not finding a procedure to accomplish this. I want to use the plot function here in order to control the bin distributions. I have explored the sn library and found the dsn function. dsn uses known location, scaling and shape parameters associated with a given input

I am using windows XP with R 2.8.1 I am generating a lattice plot of annual rain patterns using the following function: > xyplot(rain.stats$min+ rain.stats$max + rain.stats$ave ~ rain.stats$month |rain.stats$year, lty = 1, data = rain.stats, type = c("l","l", "l"), col = c("red", "blue", "green"), distribute.type =

Hello I've been asked to help evaluate a vegetation data set, specifically to examine it for community similarity. The initial problem I see is that the data is ordinal. At best this only captures a relative ranking of abundance and ordinal ranks are assigned after data collection. I've been trying to find a procedure in R that can handle ordinal based classification and so far have

Hello, I've been getting the following error when using the mlogit function from the mlogit package This is one of the examples provided in the Package "mlogit" January 27, 2010 description data("Fishing", package="mlogit") Fish <- mlogit.data(Fishing, varying = c(4:11), shape="wide", choice="mode") summary(mlogit(mode ~ pr + ca - 1,

Hello, I've tried to install randomForest on a Ubuntu 8.04 Hardy Heron system. I've repeatedly rec'd the error: > install.packages("randomForest", dependencies = TRUE) ERROR: compiliation failed for package 'randomForest' ** Removing '/home/admuser/R/i486-pc-linux-gnu-library/2.6/randomForest' The downloaded packages are in

I'd like to annotate an xyplot with an R^2 value, but can not find the syntax to define the superscript format for the text. I'd appreciate suggestions, xyplot(SharkSloughEggs.df$Sharkeggs.rel + SharkSloughEggs.df$SharkHatched.rel + SharkSloughEggs.df$SharkFlooded.rel + HSI.shark$MEAN ~ Year, pch=c(1,9,5,4), lty=c(1,6,3,4), lwd = c(2), col = c("black",

My data contains a variable "observation_date" and it contains values as: 1985-09-02 1985-09-15 1985-07-31 1985-09-02 I need to process data annually rather than daily, therefore I'm trying to 1) either extract the first 4 digits from this field and use them as a new variable "year" or 2) keep the variable as it is and process the analysis using the first 4 digits of

Thanks for the gracious assistance in advance I'm working on a non-metric scaling problem and am calculating the distance for input to isoMDS Here is the code library(MASS) vegdata <- tapply(Percent, list(PRIMARY_VE, MASTERID), sum) x.mat <- matrix(x, nrow=40, ncol=2750, byrow=TRUE, dimnames = c(dimnames(x)[1], dimnames(x)[2])) vegdata.dd <- dist(x) vegdata.dd <-

Hello everyone I'm working with R 2.8.1 on a windows machine I have a question regarding time series analysis The first question is how does R expect the input file to be structured? I'm working with a *.txt file similar to the abbreviated one here: Date,stage 4/2/1953,7.56 4/3/1953,7.56 4/4/1953,7.54 4/5/1953,7.53 4/6/1953,7.5 4/7/1953,7.47 4/8/1953,7.44 4/9/1953,7.41 4/10/1953,7.37

If someone can explain this odd behavior I'd appreciate it. I have a data.frame Cell.ave (attached and created via dput(Cell.ave, "Cell.ave") which contains three columns of parameters year, month and AveRain. I need to subset the data.frame by months such that DrySeaonMonths are 1,2,3,4, 11, and 12 or Jan - April, November and December and WetSeasonMonths are 5:10 or