Running examples in 'lime-Ex.R' failed
The error most likely occurred in:

> ### Name: plot_text_explanations
> ### Title: Plot text explanations
> ### Aliases: plot_text_explanations
> 
> ### ** Examples
> 
> # We load a precalculated explanation set based on the procedure in the ?lime
> # examples
> explanations <- .load_text_example()
> 
> # We see that the explanations are in the expected format
> print(explanations)
            model_type case label label_prob  model_r2 model_intercept feature
in      classification    1     1  0.6418385 0.9879563       0.3321225      in
we      classification    1     1  0.6418385 0.9879563       0.3321225      we
in1     classification    2     1  0.8022363 0.8551634       0.3210651      in
We      classification    2     1  0.8022363 0.8551634       0.3210651      We
those   classification    3     0  0.5432571 0.8868849       0.6909923   those
we1     classification    3     0  0.5432571 0.8868849       0.6909923      we
in2     classification    4     1  0.8719526 0.6569822       0.4941383      in
Section classification    4     1  0.8719526 0.6569822       0.4941383 Section
of      classification    5     1  0.7316587 0.3242455       0.3535088      of
our     classification    5     1  0.7316587 0.3242455       0.3535088     our
        feature_value feature_weight feature_desc
in                 in    -0.03694821           in
we                 we     0.34825310           we
in1                in    -0.01236806           in
We                 We     0.42784027           We
those           those     0.07281582        those
we1                we    -0.26484904           we
in2                in     0.01006400           in
Section       Section     0.33433000      Section
of                 of     0.01377475           of
our               our     0.28981386          our
                                                                                                                                                                                                                                                                     data
in                                                                                                                                                                                                                   we validate them in section  and conclude in section
we                                                                                                                                                                                                                   we validate them in section  and conclude in section
in1                                                            We developed a two-level machine learning approach that in the first level considers two different properties important for protein-protein binding derived from structural models of V3 and V3 sequences.
We                                                             We developed a two-level machine learning approach that in the first level considers two different properties important for protein-protein binding derived from structural models of V3 and V3 sequences.
those                                   specifically  we hypothesize that the judgments of those individuals high in this trait will be more influenced by previously presented anchors whereas those individuals low in this trait will be less influenced by the anchor
we1                                     specifically  we hypothesize that the judgments of those individuals high in this trait will be more influenced by previously presented anchors whereas those individuals low in this trait will be less influenced by the anchor
in2                                                                                                                                                                                                      Finally, in Section  we discuss the results and give conclusions
Section                                                                                                                                                                                                  Finally, in Section  we discuss the results and give conclusions
of      We also illustrate the application of our  SYMBOL -mixing generalization bounds to general classes of learning algorithms, including Support Vector Regression (SVR)  CITATION , Kernel Ridge Regression  CITATION , and Support Vector Machines (SVMs)  CITATION
our     We also illustrate the application of our  SYMBOL -mixing generalization bounds to general classes of learning algorithms, including Support Vector Regression (SVR)  CITATION , Kernel Ridge Regression  CITATION , and Support Vector Machines (SVMs)  CITATION
                  prediction
in      0.6418385, 0.3581615
we      0.6418385, 0.3581615
in1     0.8022363, 0.1977637
We      0.8022363, 0.1977637
those   0.4567429, 0.5432571
we1     0.4567429, 0.5432571
in2     0.8719526, 0.1280474
Section 0.8719526, 0.1280474
of      0.7316587, 0.2683413
our     0.7316587, 0.2683413
> 
> # We can now get the explanations in the context of the input text
> plot_text_explanations(explanations)
> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cleanEx()
> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed:  10.92 0.89 11.83 NA NA 
> grDevices::dev.off()
Error in grDevices::dev.off() : write failed
Execution halted