![]() ![]() No return message is given if the command was successful. ![]() If you just want a single permutation at a time, such as when you are using a loop to do your permutation test, then use shuffle () instead of shuffleSet (), e.g.: > set. The PTX clients and topas -C-R command are limited in that the Remote Statistics Interface. R-devel: permute_0.9-7.zip, r-release: permute_0.9-7.zip, r-oldrel: permute_0.9-7.zip The rows of perms are the permutations and the columns refer to the rows of the original data. Restricted permutations using the permute package Vegan (≥ 2.0-0), testthat (≥ 0.5), parallel, knitr, rmarkdown, bookdown, sessioninfo The 'permute' package is modelled after the permutation schemes of 'Canoco 3.1' (and later) by Cajo ter Braak. 'permute' also allows split-plot designs, in which the whole-plots or split-plots or both can be freely-exchangeable or one of the restricted designs. This second bug was fixed via r2838 and r2839 in the SVN sources on R-Forge.Permute: Functions for Generating Restricted Permutations of DataĪ set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. r2839 fixes this particular issue.ĪllPerms() works this way because it does not expect the within-block samples to be located contiguously within the original data series. This requires an operation like id() but on the within-block permutation matrices. r2838 fixes this issue.ĪllPerms() was also not replicating each within-block permutation matrix to match each combination of rows in the other within-block permutation matrices. allPerms() was doing this for every conceivable combination of permutation types except the simple random permutation within blocks case. ![]() Once these permutation indices have been created, the code should have used them to index the row indices of the original data for each block. In the case reported, each block has 3 observations and hence 6 permutations of the indices 1:3. allPerms() generates permutation indices, but internally it works block by block. Version 0.8-3 of permute now happily handles the Plots version of question: R> p dim(p) numPerms(10, control permControl(within Within(type 'series'))) 1 10 shuffle() and shuffleSet() know nothing of these limits, but there are functions in the permute package that can tell you the number of possible permutations (numPerms()) and generate the entire set of permutations for a stated design (allPerms()). I have now implemented fixes for this bug too. The first issue is an obscure bug that I'll need a bit of time to think through a fix for. The examples provided by identify two bugs in allPerms() that were not picked up by the current set of examples or unit tests (that'll be fixed soon too!). A loss of inf means your input sequence is too short to be aligned to your target sequence (ie the data has likelihood 0 given the model - CTC loss is a negative log likelihood after all). I'm interested in where I misunderstand the permute package, not how I can calculate all these permutations myself. batchsize, channels, sequence logits.size () logits logits.view ( (sequence, batchsize, channels)) You almost certainly want permute here and not view. I do have a custom function to obtain the result, using an adaptation of id() with permn() from the combinat package. I tried using the block argument, but it simply returns a matrix that repeats 4 times a matrix with the 6 possible permutations of 3 values: CTRL <- how(within=Within("free"), ![]() Number of rows of matrices must match (see arg 2) However, allPerms(12,control=CTRL) produces the following error: Error in (function (., deparse.level = 1) : Plots=Plots(strata=factor(thedata$judge)), I expect to see 1,296 permutations, as also given by: require(permute) Consider the following example: thedata <- ame(įour judges ranked 3 wines and now I want to calculate every possible permutation within the data for every judge. I want to calculate all permutations of a blocked design suitable for a Friedman test. ![]()
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