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grep rough audit - static analysis tool
v2.8 written by @Wireghoul
=================================[justanotherhacker.com]===
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r-cran-caret-6.0-86+dfsg/tests/testthat/test_resamples.R-16- rs_d <- diff(rs, metric = "RMSE")
r-cran-caret-6.0-86+dfsg/tests/testthat/test_resamples.R:17: t_test <- t.test(rs$values$`lm~RMSE`, rs$values$`rlm~RMSE`, paired = TRUE)
r-cran-caret-6.0-86+dfsg/tests/testthat/test_resamples.R-18- expect_equal(rs_d$statistics$RMSE$lm.diff.rlm$conf.int, t_test$conf.int)
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r-cran-caret-6.0-86+dfsg/vignettes/caret.Rmd-177-
r-cran-caret-6.0-86+dfsg/vignettes/caret.Rmd:178:In this output the grid of results are the average resampled estimates of performance. The note at the bottom tells the user that `r plsFit$bestTune$ncomp` PLS components were found to be optimal. Based on this value, a final PLS model is fit to the whole data set using this specification and this is the model that is used to predict future samples.
r-cran-caret-6.0-86+dfsg/vignettes/caret.Rmd-179-
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r-cran-caret-6.0-86+dfsg/vignettes/caret.Rmd-239-
r-cran-caret-6.0-86+dfsg/vignettes/caret.Rmd:240:Based on this analysis, the difference between the models is `r round(diffs$statistics$ROC[[1]]$estimate, 3)` ROC units (the RDA model is slightly higher) and the two-sided _p_-value for this difference is `r format.pval(diffs$statistics$ROC[[1]]$p.value)`.
r-cran-caret-6.0-86+dfsg/vignettes/caret.Rmd-241-
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r-cran-caret-6.0-86+dfsg/R/bag.R-174-
r-cran-caret-6.0-86+dfsg/R/bag.R:175: `%op%` <- if(bagControl$allowParallel) `%dopar%` else `%do%`
r-cran-caret-6.0-86+dfsg/R/bag.R-176- btFits <- foreach(iter = seq(along = btSamples),
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r-cran-caret-6.0-86+dfsg/R/knn3.R-77- m <- match.call(expand.dots = FALSE)
r-cran-caret-6.0-86+dfsg/R/knn3.R:78: if (is.matrix(eval(m$data, parent.frame())))
r-cran-caret-6.0-86+dfsg/R/knn3.R-79- m$data <- as.data.frame(data, stringsAsFactors = TRUE)
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r-cran-caret-6.0-86+dfsg/R/knnreg.R-76- m <- match.call(expand.dots = FALSE)
r-cran-caret-6.0-86+dfsg/R/knnreg.R:77: if (is.matrix(eval(m$data, parent.frame())))
r-cran-caret-6.0-86+dfsg/R/knnreg.R-78- m$data <- as.data.frame(data, stringsAsFactors = FALSE)
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r-cran-caret-6.0-86+dfsg/inst/doc/caret.Rmd-177-
r-cran-caret-6.0-86+dfsg/inst/doc/caret.Rmd:178:In this output the grid of results are the average resampled estimates of performance. The note at the bottom tells the user that `r plsFit$bestTune$ncomp` PLS components were found to be optimal. Based on this value, a final PLS model is fit to the whole data set using this specification and this is the model that is used to predict future samples.
r-cran-caret-6.0-86+dfsg/inst/doc/caret.Rmd-179-
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r-cran-caret-6.0-86+dfsg/inst/doc/caret.Rmd-239-
r-cran-caret-6.0-86+dfsg/inst/doc/caret.Rmd:240:Based on this analysis, the difference between the models is `r round(diffs$statistics$ROC[[1]]$estimate, 3)` ROC units (the RDA model is slightly higher) and the two-sided _p_-value for this difference is `r format.pval(diffs$statistics$ROC[[1]]$p.value)`.
r-cran-caret-6.0-86+dfsg/inst/doc/caret.Rmd-241-
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r-cran-caret-6.0-86+dfsg/debian/tests/run-unit-test-3-oname=caret
r-cran-caret-6.0-86+dfsg/debian/tests/run-unit-test:4:pkg=r-cran-`echo $oname | tr '[A-Z]' '[a-z]'`
r-cran-caret-6.0-86+dfsg/debian/tests/run-unit-test-5-
r-cran-caret-6.0-86+dfsg/debian/tests/run-unit-test-6-if [ "$AUTOPKGTEST_TMP" = "" ] ; then
r-cran-caret-6.0-86+dfsg/debian/tests/run-unit-test:7: AUTOPKGTEST_TMP=`mktemp -d /tmp/${pkg}-test.XXXXXX`
r-cran-caret-6.0-86+dfsg/debian/tests/run-unit-test-8- trap "rm -rf $AUTOPKGTEST_TMP" 0 INT QUIT ABRT PIPE TERM