Last updated: 2019-08-19
Checks: 7 0
Knit directory: polymeRID/
This reproducible R Markdown analysis was created with workflowr (version 1.4.0.9001). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20190729) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rprofile
Ignored: .Rproj.user/
Ignored: analysis/library.bib
Ignored: docs/figure/
Ignored: fun/
Ignored: output/20190810_1538/
Ignored: output/20190810_1546/
Ignored: output/20190810_1609/
Ignored: output/20190813_1044/
Ignored: output/logs/
Ignored: output/natural/
Ignored: output/nnet/
Ignored: output/svm/
Ignored: output/testRunII/
Ignored: output/testRunIII/
Ignored: packrat/lib-R/
Ignored: packrat/lib-ext/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/BH/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/FactoMineR/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/IDPmisc/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/KernSmooth/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/MASS/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/Matrix/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/MatrixModels/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ModelMetrics/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/R6/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/RColorBrewer/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/RCurl/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/Rcpp/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/RcppArmadillo/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/RcppEigen/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/RcppGSL/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/RcppZiggurat/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/Rfast/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/Rgtsvm/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/Rmisc/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/SQUAREM/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/SparseM/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/abind/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/askpass/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/assertthat/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/backports/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/base64enc/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/baseline/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/bit/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/bit64/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/bitops/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/boot/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/callr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/car/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/carData/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/caret/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/cellranger/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/class/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/cli/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/clipr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/cluster/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/codetools/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/colorspace/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/config/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/cowplot/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/crayon/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/crosstalk/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/curl/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/data.table/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/dendextend/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/digest/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/doParallel/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/dplyr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/e1071/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ellipse/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ellipsis/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/evaluate/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/factoextra/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/fansi/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/flashClust/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/forcats/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/foreach/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/foreign/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/fs/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/generics/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/getPass/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ggplot2/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ggpubr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ggrepel/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ggsci/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ggsignif/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/git2r/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/glue/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/gower/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/gridExtra/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/gtable/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/haven/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/hexbin/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/highr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/hms/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/htmltools/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/htmlwidgets/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/httpuv/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/httr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ipred/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/iterators/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/jsonlite/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/keras/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/kerasR/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/knitr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/labeling/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/later/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/lattice/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/lava/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/lazyeval/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/leaps/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/lme4/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/lubridate/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/magrittr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/maptools/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/markdown/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/mgcv/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/mime/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/minqa/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/munsell/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/nlme/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/nloptr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/nnet/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/numDeriv/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/openssl/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/openxlsx/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/packrat/tests/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/pbkrtest/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/pillar/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/pkgconfig/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/plogr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/plotly/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/plyr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/polynom/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/prettyunits/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/processx/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/prodlim/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/progress/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/promises/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/prospectr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/ps/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/purrr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/quantreg/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/randomForest/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/readr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/readxl/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/recipes/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rematch/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/reshape2/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/reticulate/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rio/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rlang/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rmarkdown/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rpart/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rprojroot/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rsconnect/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/rstudioapi/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/scales/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/scatterplot3d/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/shiny/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/sourcetools/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/sp/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/stringi/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/stringr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/survival/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/sys/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/tensorflow/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/tfruns/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/tibble/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/tidyr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/tidyselect/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/timeDate/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/tinytex/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/utf8/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/vctrs/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/viridis/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/viridisLite/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/whisker/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/withr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/workflowr/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/xfun/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/xtable/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/yaml/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/zeallot/
Ignored: packrat/lib/x86_64-pc-linux-gnu/3.6.1/zip/
Ignored: packrat/src/
Ignored: polymeRID.Rproj
Ignored: smp/20190812_1723_NNET/files/
Ignored: smp/20190812_1723_NNET/plots/
Ignored: smp/20190812_1729_NNET/files/
Ignored: smp/20190812_1729_NNET/plots/
Ignored: smp/20190812_1731_NNET/files/
Ignored: smp/20190812_1731_NNET/plots/
Ignored: smp/20190812_1733_NNET/files/
Ignored: smp/20190812_1733_NNET/plots/
Ignored: smp/20190815_1847_FUSION/
Ignored: website/
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 8c2286f | goergen95 | 2019-08-19 | added calibration.html |
| Rmd | d960dc2 | goergen95 | 2019-08-19 | included calibration |
For the calibration we implemented a decision fusion between the best performing models in the exploration stage. Since SVM-based models did not achieve very high accuracies we only included two RF models and two CNN. The RF models yielding to the highest accuracies were trained with with the raw data and the Savitzkiy-Golay smoothed data. For the CNNs, we observed the highest accuracies with with the second derivative of the raw data and with the second derivative of the normalized data. These models are going to be used during calibration. To get an accuracy value for the fusion approach, again we use and cross-validation approach.
The cross-validation of the decision fusion was conducted on 10 folds and repeated 5 times. The complete code can be found here. For every fold, 4 models are trained and evaluated against a 50% test split. The final decision is then achieved by combining the probability output of each model and assigning the class with the highest overall probability.
classRFRaw = as.character(predict(rfModRaw, pcaRaw_testing))
propRFRaw = predict(rfModRaw, pcaRaw_testing, type = "prob")
classRFSG = as.character(predict(rfModSG, pcaSG_testing))
propRFSG = predict(rfModSG, pcaSG_testing, type = "prob")
classCNND2 = as.character(classes[keras::predict_classes(cnnD2, x_testD2)+1])
propCNND2 = keras::predict_proba(cnnD2, x_testD2)
classCNNND2 = as.character(classes[keras::predict_classes(cnnND2, x_testND2)+1])
propCNNND2 = keras::predict_proba(cnnND2, x_testND2)
# probability
probs = (propRFRaw + propRFSG + propCNND2 + propCNNND2) / 4
pred = lapply(1:nrow(probs), function(x){
which.max(probs[x,])
})
predVals = lapply(1:nrow(probs), function(x){
probs[x,unlist(pred)[x]]
})
predVals = unlist(predVals)
pred= names(unlist(pred))
obsv = as.character(testingRaw$class)
pred[which(pred %in% c("FIBRE","FUR","WOOD"))] = "OTHER"
obsv[which(obsv %in% c("FIBRE","FUR","WOOD"))] = "OTHER"
obsv = as.factor(obsv)
pred = as.factor(pred)
cfMat = caret::confusionMatrix(pred,obsv)Note, that we combine the classes which are not synthetic polymers to a class called OTHER since we are only interested in the correct classification of plastic polymers. If a particle is correctly identified as non-plastic the main goal of the analysis is achieved, no matter if the different models disagree on exact non-plastic class. By using the caret::confusionMatrix() function we easily get overall accuracy values as well as class specific metrics.
By calculating the average across all folds and all repeats we end up with our final accuracy results per class and in general.
| value | |
|---|---|
| Accuracy | 0.914 |
| Kappa | 0.894 |
| AccuracyLower | 0.824 |
| AccuracyUpper | 0.966 |
| AccuracyNull | 0.371 |
We achieved an overall accuracy of 91.4% with a Kappa coefficient of 0.89 (Tab. 1). This is accuracy is substantially higher than compared to the single model accuracies of RF and CNN. By the decision fusion and combining the non-synthetic classes we were able to rise the accuracy about 5%.
| Sensitivity | Specificity | Pos Pred Value | Neg Pred Value | Precision | Recall | F1 | Prevalence | Detection Rate | Detection Prevalence | Balanced Accuracy | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Class: HDPE | 0.860 | 0.994 | 0.932 | 0.989 | 0.932 | 0.860 | 0.896 | 0.071 | 0.061 | 0.067 | 0.927 |
| Class: LDPE | 0.960 | 0.990 | 0.895 | 0.997 | 0.895 | 0.960 | 0.919 | 0.071 | 0.069 | 0.078 | 0.975 |
| Class: OTHER | 0.965 | 0.978 | 0.964 | 0.980 | 0.964 | 0.965 | 0.964 | 0.371 | 0.359 | 0.373 | 0.972 |
| Class: PA | 0.946 | 0.991 | 0.928 | 0.994 | 0.928 | 0.946 | 0.934 | 0.100 | 0.095 | 0.103 | 0.968 |
| Class: PE | 0.895 | 0.996 | 0.948 | 0.994 | 0.948 | 0.895 | 0.912 | 0.057 | 0.051 | 0.055 | 0.946 |
| Class: PES | 0.691 | 0.980 | 0.832 | 0.967 | 0.832 | 0.691 | 0.734 | 0.100 | 0.069 | 0.087 | 0.836 |
| Class: PET | 0.755 | 0.974 | 0.658 | 0.985 | 0.658 | 0.755 | 0.688 | 0.057 | 0.043 | 0.067 | 0.865 |
| Class: PP | 1.000 | 0.999 | 0.989 | 1.000 | 0.989 | 1.000 | 0.994 | 0.086 | 0.086 | 0.087 | 0.999 |
| Class: PS | 0.987 | 0.999 | 0.980 | 0.999 | 0.980 | 0.987 | 0.981 | 0.043 | 0.042 | 0.043 | 0.993 |
| Class: PUR | 0.920 | 0.999 | 0.978 | 0.997 | 0.978 | 0.920 | 0.960 | 0.043 | 0.039 | 0.041 | 0.959 |
When we analyse the class specific accuracy metrics (Tab. 2) we observe that the lowest sensitivity is 0.69 for PES. That means that PES is most likely to not be identified correctly. PET shows a similar low sensitivity value of 0.76. PP shows the highest sensitivity value of 1, which means that all samples classified as PP actually represent that class. Concerning the specificity, across all classes we observe similar values of about 0.99. In general, we can state that we achieved a very good distinction between non-synthetic polymers and microplastic polymers. However, some microplastic polymers, such as PES and PET achieve only poor accuracies (0.84 and 0.86 of balanced accuracy respectively). These shortcomings might be compensated for with future extensions of the training data base which could include more samples, especially for these two classes as well as others. In the end, the machine learning algorithms can only learn from the data base which is presented to them. Therefore, adding reference samples could prove beneficial when it comes to accuracy.
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Linux Mint 19.1
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] tensorflow_1.14.0 abind_1.4-5
[3] e1071_1.7-2 keras_2.2.4.1
[5] workflowr_1.4.0.9001 baseline_1.2-1
[7] gridExtra_2.3 stringr_1.4.0
[9] prospectr_0.1.3 RcppArmadillo_0.9.600.4.0
[11] openxlsx_4.1.0.1 magrittr_1.5
[13] ggplot2_3.2.0 reshape2_1.4.3
[15] dplyr_0.8.3
loaded via a namespace (and not attached):
[1] reticulate_1.13 tidyselect_0.2.5 xfun_0.8 purrr_0.3.2
[5] lattice_0.20-38 colorspace_1.4-1 generics_0.0.2 htmltools_0.3.6
[9] yaml_2.2.0 base64enc_0.1-3 rlang_0.4.0 pillar_1.4.2
[13] glue_1.3.1 withr_2.1.2 foreach_1.4.7 plyr_1.8.4
[17] munsell_0.5.0 gtable_0.3.0 zip_2.0.3 codetools_0.2-16
[21] evaluate_0.14 knitr_1.24 SparseM_1.77 tfruns_1.4
[25] class_7.3-15 highr_0.8 Rcpp_1.0.2 scales_1.0.0
[29] backports_1.1.4 jsonlite_1.6 fs_1.3.1 digest_0.6.20
[33] stringi_1.4.3 grid_3.6.1 rprojroot_1.3-2 tools_3.6.1
[37] lazyeval_0.2.2 tibble_2.1.3 crayon_1.3.4 whisker_0.3-2
[41] pkgconfig_2.0.2 zeallot_0.1.0 Matrix_1.2-17 assertthat_0.2.1
[45] rmarkdown_1.14 iterators_1.0.12 R6_2.4.0 git2r_0.26.1
[49] compiler_3.6.1