search for: sepals

Hi list, One of my co-workers found this problem with 'cor' in his code and I confirm it too (see below). He's using R 2.2.1 under Win 2K and I'm using R 2.3.0 under Win XP. =========================================== > R.Version() $platform [1] "i386-pc-mingw32" $arch [1] "i386" $os [1] "mingw32" $system [1] "i386, mingw32" $status

I require some help in debugging this code  library(neuralnet) ir<-read.table(file="iris_data.txt",header=TRUE,row.names=NULL) ir1 <- data.frame(ir[1:100,2:6]) ir2 <- data.frame(ifelse(ir1$Species=="setosa",1,ifelse(ir1$Species=="versicolor",0,""))) colnames(ir2)<-("Output") ir3 <- data.frame(rbind(ir1[1:4],ir2))

Dear all When using Sweave, I'm always hitting the same bump: I want to group repetitive calls in a function, but I want both the results and the function calls in the printed output. Let me explain myself. Consider the following computation in an Sweave document: summary(iris[,1:2]) cor(iris[,1:2]) When using these two calls directly, I obtain the following output: > summary(iris[,1:2])

Greetings R experts, I'm having some difficulty recovering lda objects that I've saved within sublists using the save.image() function. I am running a script that exports a variety of different information as a list, included within that list is an lda object. I then take that list and create a list of that with all the different replications I've run. Unfortunately I've been

I am making a battery of levelplots and wireframes for several fitted models. I wrote a function that takes the fitted model object as the sole argument and produces these plots. Various strange behavior ensued, but I have identified one very concrete issue (illustrated below): when my figure-drawing function includes the addition of points/lines to trellis plots, some of the

I have a data set where I want the correlations between 2 variables conditional on a students grade level. This code works just fine. by(tmp[,c('mtsc07', 'DCBASmathscoreSPRING')], tmp$Grade, cor, use='complete', method='pearson') However, this generates an error by(tmp[,c('mtsc07', 'DCBASmathscoreSPRING')], tmp$Grade, cor, use='complete',

Hello, I'll use part of the iris dataset for an example of what I want to do. > data(iris) > iris<-iris[1:10,1:4] > iris Sepal.Length Sepal.Width Petal.Length Petal.Width 1 5.1 3.5 1.4 0.2 2 4.9 3.0 1.4 0.2 3 4.7 3.2 1.3 0.2 4 4.6 3.1 1.5

I would like to create an xyplot with varying y-axis limits and horizontal labels at the y-axis tickmarks. The following does not seem to work, although I think it should, going by the documentation for par. R version 2.5.1, Windows XP Prof. Thanks for a clue. Andreas Krause library(lattice) # axis labels for y-axis are horizontal xyplot(Sepal.Length ~ Sepal.Width | Species, data=iris) #

Hi! The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions written by myself in the workspace either. I've tested it with using the matrix version and the

Dear all This may be obvious, but I cannot get it working. I'm trying to subset & sort a data frame in one go. x <- iris x$Species1 <- as.character(x$Species) ##subsetting alone works fine with(x, x[Sepal.Length==6.7,]) ##sorting alone works fine with(x, x[order(Sepal.Length, rev(sort(Species1))),]) ##gets subsetted, but not sorted as expected with(x, x[(Sepal.Length==6.7) &

Hallo, I'd like to add grid lines to a lattice graph having 2 series of Y data. See these 2 examples: data(iris) [1] xyplot(Sepal.Length + Sepal.Width ~ Petal.Length , data = iris, allow.multiple = TRUE, scales = "same",type="l", ) [2] xyplot(Sepal.Length + Sepal.Width ~ Petal.Length , data = iris, allow.multiple = TRUE, scales =

Dear all Is there a simpler method to achieve the following: When I obtain an empty data.frame after subsetting, I need for it to contain one line of NAs. Here's a dummy example: > (.xb <- iris[ iris$Species=='zz', ]) [1] Sepal.Length Sepal.Width Petal.Length Petal.Width Species <0 rows> (or 0-length row.names) > dim(.xb) [1] 0 5 > (.xa <-

Dear R-devel list members, I've discovered an inconsistency in how lm() and similar functions handle logical predictors as opposed to factor or character predictors. An "lm" object for a model that includes factor or character predictors includes the levels of a factor or unique values of a character predictor in the $xlevels component of the object, but not the FALSE/TRUE values

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

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

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 # ---

Dear Abby, > On Aug 30, 2019, at 8:20 PM, Abby Spurdle <spurdle.a at gmail.com> wrote: > >> I think that it would be better to handle factors, character predictors, and logical predictors consistently. > > "logical predictors" can be regarded as categorical or continuous (i.e. 0 or 1). > And the model matrix should be the same, either way. I think that

Another solution is to only save the parts of the model object that interest you. As long as they don't include the formula (which is what drags along the environment it was created in), you will save space. E.g., tfun2 <- function(subset) { junk <- 1:1e6 list(subset=subset, lm(Sepal.Length ~ Sepal.Width, data=iris, subset=subset)$coef) } saveSize(tfun2(1:4)) #[1] 152 Bill

Dear all The trouble with the current duplicated() function in is that it can report duplicates while searching fromFirst _or_ fromLast, but not both ways. Often users will want to identify and extract all the copies of the item that has duplicates, not only the duplicates themselves. To take the example from the man page: > data(iris) > iris[duplicated(iris), ] ##duplicates while

Hi there: I have a question about generating mean value of a data.frame. Take iris data for example, if I have a data.frame looking like the following: --------------------- Sepal.Length Sepal.Width Petal.Length Petal.Width Species 1 5.1 3.5 1.4 0.2 setosa 2 4.9 3.0 1.4 0.2