similar to: mob(party) formula question

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

DeaR all, I am using mob() for model based partitioning, with a dichotomous variable (participant's correct/incorrect response to a test item) regressed onto a continuous predictor related to a given property of the test item. Although this variable is continuous, the value of this variable for many items in this particular analysis is 0. The partitioning criterion is self-reported ability in

Dear useRs: I try to use mob from the party package (thanks Achim and Co.!) to model based recursive partition a data set. The model is a logistic regression specified with model=glinearModel and family=binomial(). Running mob results in a few warnings of the type: In glm.fit ... algorithm did not converge. As I speculate that this may be due to an insufficient number of iterations I am

Hi, from what I understand about the canonical correlation function 'cancor', it looks for correlations in two sets of variables, each represented in matrix form. Right? Sounds exactly like what I need. I have tried the following but I am not sure how to interpret the results. AudioPCs <- c(ArTHarF0PCA$x[,2], ArTHarF1PCA$x[,2], ArTHarF2PCA$x[,2], ArTHarF3PCA$x[,2],

Dear all, I didn't find any graphical solution in the package "mva" to plot the canonical scores from a CCA (canonical correlation analysis). Does anybody knows how to plot or has anybody already programmed : - the map of the canonical scores, - the graph of the canonical weights, - the correlation circle i.e. the canonical loadings ? Thank you for help ...

Hi, Given that cdplot() is used to produce the conditional density of a categorical y along a numerical x, it seems natural that it could be used with a date or time x (such as 'POSIXct'). Is this desirable? If so, I've created a patch that would allow this, by coercing the POSIXct x variable to produce the density, but use the original POSIXct x to draw the x axis. Index:

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

Hello, I wanted to create a histogram, but somehow I got stuck... The interval limits are: x = 1, 2, 3, 3.5, 4.5, 5, 5.5 The interval widths are therefore: 1, 1, 0.5, 1, 0.5, 0.5 Nothing I tried worked... Can anyone help me please? Thanks Tobias -- View this message in context: http://www.nabble.com/Histogram-tf4315900.html#a12288850 Sent from the R help mailing list archive at Nabble.com.

Hello: I am using rpart and would like more background on how the splits are made and how to interpret results - also how to properly use text(.rpart). I have looked through Venables and Ripley and through the rpart help and still have some questions. If there is a source (say, Breiman et al) on decision trees that would clear this all up, please let me know. The questions below pertain to a

Dear all, I am quite new with R and I have a problem with plotting a binomial regression line in a plot. This is what I type in: > model<-glm(Para~Size,binomial) > par(mfrow=c(1,1)) > xv<-seq(3.2,4.5,0.01) > yv<-predict(model,list(area=xv),type="response") > plot(Size,Para) > lines(xv,yv) The error message that I get is: > Error in xy.coords(x, y) :

Hi Mark, I don't know wether you recived a sufficient reply or not, so here are my comments to your problem. Supressing the constant term in a regression model will probably lead to a violation of the classical assumptions for this model. From the OLS normal equations (in matrix notation) (1) (X'X)b=X'y and the definition of the OLS residuals (2) e = y-Xb you get - by

R Users - Tomorrow night, we are leading a group of R programmers to a site called Stack Overflow, Stackoverflow is a collaborative question and answer site for programmers, currently lacks much R content. to populate some of the most oft-asked and reluctantly-answered questions about R. The R-help mailing list is an indispensable resource to members of the R community. Perhaps owing to its

Dear useRs: I successfully model-based partitioned several datasets through the use of mob from the party package (thanks Achim et al. once again !!!). At times, however, the partitioning leads to terminal nodes in which the parameter estimates of the model are not significant (although the split points and in general the proposed segmentation both seem reasonable). As I do not seem to be able

Dear r-help list, I'm trying to use multiple imputation for my MSc thesis. Having good exemples using the Hmisc package, I tried the aregImpute function. But with my own dataset, I have the following error : Erreur dans matxv(X, xcof) : columns in a (51) must be <= length of b (50) De plus : Warning message: In f$xcoef[, 1] * f$xcenter :   la taille d'un objet plus long n'est pas

Hello, I am using the party module to estimate the relationship between the probability of being a student and number of siblings (alive). However, I need to include a number of relevant covariates. My code is below: fm3 <- mob(Student ~ age + alive + sex2 + cwa + cha + cym | Religion+Servant + Literacy, control = ctrl, data = samp2, model = glinearModel, family =binomial()) plot(fm3, tp_args

Hi Prof. Williams, thanks for your suggestion. The updated code is below. It turns out it was a matter of displaying the second column in yval to get the number of N and subtracting it from the n column in the frame to get the number of Y remaining in a binary example. once this is added now the function returns the rules along with Y and N count affected by the resulting rule. I am ccing

I've got the graphic with this: a=c(120,40,75,85,55,75,55,90,90,55,155) plot(ecdf(a)) How can I draw lines to show the lower quatile and upper quartil? I only know there is the argument lines(), but I don't know how to use it -- View this message in context: http://r.789695.n4.nabble.com/How-can-I-draw-a-line-in-empirical-distribution-function-tp3090675p3090675.html Sent from the R

Hi List, I am unsuccessfully trying to beautify barplot outputs from ctree. For example I would like to rotate x-axis lables and resize/change font/type. mtree <- ctree(ME ~ ., data = mammoexp) plot(mtree,terminal_panel=node_barplot(mtree,col="black",fill=NULL, beside=TRUE, ylines=NULL, widths=1,gap=NULL, reverse=FALSE,id=FALSE))

Hi, I want to plot grouped bars to compare 95% confidence interval estimates from two models. Each bar represents a 95% confidence interval estimate of a coefficient from one of the two models. Each group represents confidence interval estimates of the same coefficient from the two models. I think such a bar plot will nicely present whether 95% confidence interval estimates of the same

Hi to all, I'm sorry for posting this question, I am sure I am missing something important but after reading the documentation I cannot find where the problem is. I want to add a legend to a figure. If I use a simple example drawn from the R Reference Manual such as, for instance: x <- seq(-pi, pi, len = 65) plot(x, sin(x), type="l", col = 2) legend(x = -3, y = .9,