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Hi all, I obtained a strage result with LDA (MASS) function in R with NIR data. I tried both CV (leave one out cross validation) and splitting my data in odd (training) and even (prediction) sets. In all the cases the minimum error was near to 0. Due to the strange result, I tried with SPSS IBM software and it give me around 11% of minimum error with and without leave one out cross validation.

Hi all, Is there a R-function that orders Tukey results with conveniant letters, similar to the SPSS output (A, AB, ABC, C, etc.) . [[alternative HTML version deleted]]

Hi, I'm looking for some lines of code that does the following: I have a dataframe with 160 Columns and a number of rows (max 30): Col1 Col2 Col3 ... Col 159 Col 160 Row 1 0 0 LD ... 0 VD Row 2 HD 0 0 0 MD Row 3 0 HD HD 0 LD Row 4 LD HD HD 0 LD ... ... LastRow HD HD LD 0 MD Now I want a dataframe that looks like this. As you see

Hi, Could you please advice some easy way to do the following for a dataframe (header=F) having unequal column- & row- length. 1. Combine/stack/join contents from - a) multiple rows into one column. b) multiple columns into one row. 2. Stack contents from multiple columns (or, rows) into one column (or, row). Thank you. Cheers, Santana [[alternative HTML

Hi list, I am wondering if there is a way to use EM algorithm to handle missing data and get a completed data set in R? I usually do it in SPSS because EM in SPSS kind of "fill in" the estimated value for the missing data, and then the completed dataset can be saved and used for further analysis. But I have not found a way to get the a completed data set like this in R or SAS. With

Dear R users, I want to generating random samples from Inverse Gaussian distribution . How can I do? and what package should I install? Thanks. Shirin [[alternative HTML version deleted]]

I know that I can get a count of histogram bins in base R with plot=FALSE. However, I'd like to do the same thing with lattice. The problem is that I've set up shingles, and I'd like to get the count within each bin within each shingle. plot=FALSE doesn't seem to do it. [[alternative HTML version deleted]]

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 know this is something simple that I cannot do because I do not yet "think" in R. I have a data frame has a variable participation (a factor), and several other factors. I want a chisq test (no contingency tables) for participation vs all of the other factors. In SPSS I would do: CROSSTABS /TABLES= (my other factors) BY participation /FORMAT=NOTABLES /STATISTICS=CHISQ

Dear all, I want to extract rows from a data frame shown here as "test". For example: rows with with sorting PKB123 PKB22 PKB23 PKB32 CTV19 CTV20 PKB11 PKB11 > dput(test) structure(list(Name. = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 19L), .Label = c("CTV10", "CTV11", "CTV12",

I'm trying to read in a tab separated table with read.delim(). I don't particularly care what the row names are. My data file looks like this: start stop Symbol Insert sequence Clone End Pair FISH 203048 67173930 ABC8-43024000D23 TI:993812543 TI:993834585 255176 87869359 ABC8-43034700N15 TI:995224581 TI:995237913 1022033 1060472

Dear all, I have a 2 by 2 matrix and I would like to do a barplot with it. (so 2 bars with each having 2 stacks.). I would like to have one colors per stack, so 4 different colors total. The problem is that R is only given me 2 colors (the same two for the bottom stack and the same two for the top stack). Any idea how I can do to have 4 colors? (without using ggplot2 preferably) Here is my

Hello, Though Bert's and David's answers are what you should do, note that some R functions that need factors will coerce their input variables when necessary. Have you tried to run the code you haven't posted without coercing to factor? It might run... Hope this helps, Rui Barradas On 4/9/2018 6:11 PM, David L Carlson wrote: > Try the help files: > > ?factor >

Take this as an example: > a=data.frame(col1=c(1,2,3,4,5), col2=c ("my","beloved","daughter","son","wife")) > b=data.frame(col1=c(1,2,4), col2=c("my","beloved","son")) > a col1 col2 1 1 my 2 2 beloved 3 3 daughter 4 4 son 5 5 wife > b col1 col2 1 1 my 2

Or add the type column first and then rbind: x <- list(A=data.frame(x=1:2, y=3:4),B=data.frame(x=5:6,y=7:8)) x2 <- do.call(rbind, lapply(names(x), function(z) data.frame(type=z, dat[[z]]))) ---------------------------------------- David L Carlson Department of Anthropology Texas A&M University College Station, TX 77843-4352 -----Original Message----- From: R-help

The rbind on empty and nonempty data frames behaves inconsistently. I am not sure if by design. In the first example, first row is deleted, which may or may not be on purpose: df1 <- data.frame() df2 <- data.frame(foo=c(1, 2), bar=c("a", "b")) rbind(df1, df2) foo bar 2 2 b Now if we continue: df1 <- data.frame(matrix(0, 0, 2)) names(df1) <- names(df2)

Dear all, I have a question concerning the ks.test() function. I tryed to calculate the example given on the German wikipedia page. xi <- c(9.41,9.92,11.55,11.6,11.73,12,12.06,13.3) I get the right results when I calculate: ks.test(xi,pnorm,11,1) Now the question: shouldn't I obtain the same or a very similar result if I commpare the sample and a calculated sample from the distribution?

We often refer requesters to the Posting Guide and chide them for not reading it. Recently I had occasion to re-read the Posting Guide which is for all R lists not just R-help. The word "reproducible" does not appear anywhere in the guide. The closest it comes is the following suggestion: "Sometimes it helps to provide a small example that someone can actually run."

I have Windows XP Professional Version 2002 and the R-Version 2.1.1. I did cluster analysis with the cluster package and the agnes (method = ?ward?). The results are satisfactory. But the dendrogram of agnes is confused to work with the results. Is there a tool, I can get a clear arrangement of the results for the cluster analysis. For example a matrix with different numbers for each group.