similar to: model.frame error with formula=~1 and na.action=na.fail (PR#14066)

I am using SVM to classify categorical data and I would like the probabilities instead of the classification. ?predict.svm says that its only enabled when you train the model with it enabled, so I did that, but it didn't work. I can't even get it to work with iris. The help file shows that probability = TRUE when training the model, but doesn't show an example. Then I try to

Just out of curiosity, I took the default "iris" example in the RF helpfile... but seeing the admonition against using the formula interface for large data sets, I wanted to play around a bit to see how the various options affected the output. Found something interesting I couldn't find documentation for... Just like the example... > set.seed(12) # to be sure I have

Dear R-devel, I've noticed that if the method for formula is not exported in NAMESPACE, then update() would fail. As an example: > library(e1071) > data(iris) > iris.svm <- svm(Species ~ ., data=iris) > update(iris.svm) Error in eval(expr, envir, enclos) : couldn't find function "svm.formula" The same thing happens with randomForest, because

Hello, R-help, does anybody have already a work-around for the problem that the formula version of sunflowerplot() throws an error when provided with a value for xlab (or ylab) different from NULL: > sunflowerplot( Sepal.Length ~ Sepal.Width, data = iris, xlab = "A") Error in model.frame.default(formula = Sepal.Length ~ Sepal.Width, data = iris, : variable lengths differ

I didn't see anything on this in the bug reports, and a search of the archives had lots of false positives when searching on "split" to be helpful. I don't view this as particularly interesting or useful, but wanted to report it because I stumbled on it (and don't remember ever seeing "invalid permissions" as part of a segfault).? Yes, I realize this is a silly

It's in the Venables & Ripley MASS (ed 3) book in the section on principal components. The context is as follows > ir <- rbind(iris[,,1], iris[,,2], iris[,,3]) > ir.species <- factor(c(rep("s",50),rep("c",50),rep("v",50))) (then they use brush(ir) which I guess is not an R function) and then > princomp(log(ir[1:4]),cor=T) (there is no [1:4]

Hello, I am puzzled by the behavior of hist() when generating multiple plots per page on the pdf device. In the following example two pdf files are generated. The first results in 4 plots on one pdf page as expected. However, the second, which swaps one of the plot() calls for hist(), results in a 4 page pdf with one plot per page. How might I get the histogram with 3 other scatter

The following command produces red axis line in a pairs plot: pairs(iris[1:4], main = "Anderson's Iris Data -- 3 species", pch = "+", col = c("red", "green3", "blue")[unclass(iris$Species)]) Trying to fool pairs in the following way produces the same plot as above: pairs(iris[1:4], main = "Anderson's Iris Data -- 3

I have a dataset and I wish to use two different models to predict. Both models are SVM. The reason for two different models is based on the sex of the observation. I wish to be able to make predictions and have the results be in the same order as my original dataset. To illustrate I will use iris: # Take Iris and create a dataframe of just two Species, setosa and versicolor, shuffle them

hi, Does factanal() force the user to use the formula interface if they wish to specify an na.action? v1 <- c(1,1,1,1,1,1,1,1,NA,1,3,3,3,3,3,4,5,6) v2 <- c(1,2,1,1,1,1,2,1,2,1,3,NA,3,3,3,4,6,5) v3 <- c(3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,5,4,6) v4 <- c(3,3,4,NA,3,1,1,2,1,1,1,1,2,NA,1,5,6,4) v5 <- c(1,1,1,1,1,3,3,3,3,3,1,1,1,1,1,6,4,5) v6 <- c(1,1,1,2,1,3,3,3,4,3,1,1,1,2,1,6,5,4) m1

Compiled great, got both my RSA and DSA keys' generated for Protocol 1/2, started up fine ... try to connect and get a bunch of errors: May 11 14:01:47 iris sshd[8578]: error: Couldn't wait for child '/bin/ls -alni' completion: No child processes May 11 14:01:47 iris last message repeated 3 times May 11 14:01:47 iris sshd[8578]: error: Command '/bin/ls -alni': select()

Hi, I am a newbie of R, and playing with the "ifelse" statement. I have the following codes: ## first, for(i in 1:3) { for(j in 2:4) { cor.temp <- cor(iris.allnum[,i], iris.allnum[,j]) if(i==1 & j==2) corr.iris <- cor.temp else corr.iris <- c(corr.iris, cor.temp) } } this code is working fine. I also tried to perform the same thing in another way with "ifelse":

Hi Dieter and R community: I tried both of these three versions with ylim as suggested, none work: I am getting only single (pch = 16) not overlayed (pch =3) everytime. *vs 1* require(lattice) xyplot(Sepal.Length ~ Sepal.Width | Species , data= iris, panel= function(x, y, subscripts) { panel.xyplot(x, y, pch=16, col = "green4", ylim = c(0, 10)) panel.lmline(x, y, lty=4, col =

Hi R People: Several of you pointed out that using "tapply" on a data frame will work on the iris data frame. I'm still having a problem. The iris data frame has 150 rows, 5 variables. The first 4 are numeric, while the last is a factor, which has the Species names. I can use tapply for 1 variable at a time: >tapply(iris[,1],iris[,5],mean) setosa versicolor virginica

Hello all, I have a large data frame and I want to look at the distribution of each variable very quickly by plotting an individual histogram for each variable. I'd like to do so using lattice. Here is a small example using the iris data set: histogram(as.formula(paste("~",paste(colnames(iris[,!sapply(iris,is.factor)]),collapse="+"))),data=iris[,!sapply(iris,is.factor)])

Hi, I tried to use naivebayes in package 'e1071'. when I use following parameter, only one predictor, there is an error. > m<- naiveBayes(iris[,1], iris[,5]) > table(predict(m, iris[,1]), iris[,5]) Error in log(sapply(attribs, function(v) { : Non-numeric argument to mathematical function However, when I use two predictors, there is not error any more. > m<-

Guys, I used Random Forest with a couple of data sets I had to predict for binary response. In all the cases, the AUC of the training set is coming to be 1. Is this always the case with random forests? Can someone please clarify this? I have given a simple example, first using logistic regression and then using random forests to explain the problem. AUC of the random forest is coming out to be

Hi, I tried to use naivebayes in package 'e1071'. when I use following parameter, only one predictor, there is an error. > m <- naiveBayes(iris[,1], iris[,5]) > table(predict(m, iris[,1]), iris[,5]) Error in log(sapply(attribs, function(v) { : Non-numeric argument to mathematical function However, when I use two predictors, there is not error any more. > m <-

Hi, The below very strange: # a) aov function av <- aov(Sepal.Length ~ Species, data=iris) # Error in parse(text = x) : # unexpected symbol in "Sepal(Sepal.Length+Species)Length" av <- aov(iris[, 1] ~ iris[, 5]) # summary(av) # Df Sum Sq Mean Sq F value Pr(>F) # iris[, 5] 2 63.2 31.6 119 <2e-16 *** # Residuals 147 39.0 0.3 # ---

I have a suggestion for the posting guide. One problem with some posts is that they do not provide an example that can be reproduced. I think that many people just do not know how to easily specify some data and some technical assistance should be provided in the posting guide. If the problem depends on specific data they should be made aware, in the posting guide, of: dput(x) since that