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I have a dataframe in which I have values 0-255, I wish to transpose them such that: if value > 127.5 value = 1 if value < 127.5 value = -1 I did something similar using the "binarize" function of the biclust package, this transforms my dataframe to 0 and 1 values, but I wish to use -1 and 1 and looking for a way in R to do this. Brian

I would like to take the values of observations and map them to a new index. I am not sure how to accomplish this. The result would look like so: x[1,2,3,4,5,6,7,8,9,10] becomes y[2,4,6,8,10,12,14,16,18,20] The "newindex" would not necessarily be this sequence, but a sequence I have stored in a vector, so it could be all kinds of values. here is what happens: > x <- rnorm(10)

I am able to split my df into two like so: dataset <- trainset index <- 1:nrow(dataset) testindex <- sample(index, trunc(length(index)*30/100)) trainset <- dataset[-testindex,] testset <- dataset[testindex,-1] So I have the index information, how could I re-combine the data using that back into a single df? I tried what I thought might work, but failed with:

I have a dataframe that has a header like so: class value1 value2 value3 class is a factor the actual values in the columns value1, value2 and value3 are 0-255, I wish to binarize these using biclust. I can do this like so: binarize(dataframe[,-1]) this will return a dataframe, but then I lose my first column class, so I thought I could combine it like so: dataframe <-

I am trying to make it so two columns with similar data use the same internal numbers for same factors, here is the example: > read.csv("test.csv",header =FALSE,sep=",") V1 V2 V3 1 sun moon stars 2 stars moon sun 3 cat dog catdog 4 dog moon sun 5 bird plane superman 6 1000 dog 2000 > data <-

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

I am new to R, so I am sure I am making a simple mistake. I am including complete information in hopes someone can help me. Basically my data in R looks good, I write it to a file, and every value is off by 1. Here is my flow: > str(prediction) Factor w/ 10 levels "0","1","2","3",..: 3 1 10 10 4 8 1 4 1 4 ... - attr(*, "names")= chr

I am used to packages like e1071 where you have a tune step and then pass your tunings to train. It seems with caret, tuning and training are both handled by train. I am using train and trainControl to find my hyper parameters like so: MyTrainControl=trainControl( method = "cv", number=5, returnResamp = "all", classProbs = TRUE ) rbfSVM <- train(label~., data =

I have a rather basic set of data. It is simply a variable that can be 0, 1 or 2 and its value over a series of time t0 - t9 like so: y: 1 1 2 0 1 2 2 1 2 1 x: t0 t1 t2 t3 t4 t5 t6 t7 t8 t9 I need to predict what the value of y will be at t10 through t13. As you can see its rather basic. I am rather new to solving these types of problems so I am looking for some good straight forward things

I have a dataset that has many columns which are NA or constant, and so I remove them like so: same <- sapply(dataset, function(.col){ all(is.na(.col)) || all(.col[1L] == .col) }) dataset <- dataset[!same] This works GREAT (thanks to the r-users list archive I found this) however, then when I do my data sampling like so: testSize <- floor(nrow(x) * 10/100) test <-

I am trying to read in a pipe delimited file that has rows with varying number of columns, here is my sample data: A|B|C|D A|B|C|D|E|F A|B|C|D|E A|B|C|D|E|F|G|H|I A|B|C|D A|B|C|D|E|F|G|H|I|J You can see line 6 has 10 columns. Yet, I can't explain why R does like so: > test <- read.delim("mypaths4.txt", sep="|", quote=NULL, header=F,

I have never been able to get class probabilities to work and I am relatively new to using these tools, and I am looking for some insight as to what may be wrong. I am using caret with kernlab/ksvm. I will simplify my problem to a basic data set which produces the same problem. I have read the caret vignettes as well as documentation for ?train. I appreciate any direction you can give. I

Hi guys, I have 1000 rows of a dataset. In my analysis, I need 70% of the data, run my analysis and then use the remaining 30% to test my model. Could anybody kindly help me on this? Cheers