Last updated on 2021-02-05 15:54:23 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.8.2 | 105.83 | 257.49 | 363.32 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 0.8.2 | 67.76 | 181.80 | 249.56 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.8.2 | 445.02 | NOTE | |||
| r-devel-linux-x86_64-fedora-gcc | 0.8.2 | 403.98 | NOTE | |||
| r-devel-windows-ix86+x86_64 | 0.8.2 | 235.00 | 458.00 | 693.00 | NOTE | |
| r-patched-linux-x86_64 | 0.8.2 | 83.75 | 223.05 | 306.80 | NOTE | |
| r-patched-solaris-x86 | 0.8.2 | 509.10 | NOTE | |||
| r-release-linux-x86_64 | 0.8.2 | 77.56 | 225.45 | 303.01 | NOTE | |
| r-release-macos-x86_64 | 0.8.2 | NOTE | ||||
| r-release-windows-ix86+x86_64 | 0.8.2 | 227.00 | 479.00 | 706.00 | NOTE | |
| r-oldrel-macos-x86_64 | 0.8.2 | NOTE | ||||
| r-oldrel-windows-ix86+x86_64 | 0.8.2 | 142.00 | 360.00 | 502.00 | NOTE |
Version: 0.8.2
Check: for GNU extensions in Makefiles
Result: NOTE
GNU make is a SystemRequirements.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.8.2
Check: examples
Result: ERROR
Running examples in ‘sentometrics-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: peakdates
> ### Title: Extract dates related to sentiment time series peaks
> ### Aliases: peakdates
>
> ### ** Examples
>
> set.seed(505)
>
> data("usnews", package = "sentometrics")
> data("list_lexicons", package = "sentometrics")
> data("list_valence_shifters", package = "sentometrics")
>
> # construct a sento_measures object to start with
> corpus <- sento_corpus(corpusdf = usnews)
> corpusSample <- quanteda::corpus_sample(corpus, size = 500)
> l <- sento_lexicons(list_lexicons[c("LM_en", "HENRY_en")], list_valence_shifters[["en"]])
> ctr <- ctr_agg(howTime = c("equal_weight", "linear"), by = "month", lag = 3)
> sento_measures <- sento_measures(corpusSample, l, ctr)
>
> # extract the peaks
> peaksAbs <- peakdates(sento_measures, n = 5)
Error in xtfrm.data.frame(x) : cannot xtfrm data frames
Calls: peakdates ... lapply -> FUN -> as.vector -> xtfrm -> xtfrm.data.frame
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.8.2
Check: tests
Result: ERROR
Running ‘testthat.R’ [73s/108s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
>
> library("testthat")
> library("sentometrics")
>
> test_check("sentometrics")
Iteration: 1 from 9
alphas run: 0
Iteration: 2 from 9
alphas run: 0
Iteration: 3 from 9
alphas run: 0
Iteration: 4 from 9
alphas run: 0
Iteration: 5 from 9
alphas run: 0
Iteration: 6 from 9
alphas run: 0
Iteration: 7 from 9
alphas run: 0
Iteration: 8 from 9
alphas run: 0
Iteration: 9 from 9
alphas run: 0
This sento_measures object contains 24 textual sentiment time series with 7237 observations each (daily).
Following features are present: wsj wapo economy noneconomy
Following lexicons are used to calculate sentiment: HENRY_en LM_en
Following scheme is applied for aggregation within documents: counts
Following scheme is applied for aggregation across documents: proportional
Following schemes are applied for aggregation across time: linear exponential0.1 exponential0.6
Aggregate average statistics:
mean sd max min meanCorr
-0.02282 0.18258 0.73352 -1.18603 0.19341
A sento_measures object (24 textual sentiment time series, 7237 observations).
alphas run: 0.2, 0.7
alphas run: 0.2, 0.7
alphas run: 0.2, 0.7
Training model... Done.
Training model... Done.
Training model... Done.
alphas run: 0.2, 0.7
Iteration: 1 from 16
alphas run: 0, 0.4, 1
Iteration: 2 from 16
alphas run: 0, 0.4, 1
Iteration: 3 from 16
alphas run: 0, 0.4, 1
Iteration: 4 from 16
alphas run: 0, 0.4, 1
Iteration: 5 from 16
alphas run: 0, 0.4, 1
Iteration: 6 from 16
alphas run: 0, 0.4, 1
Iteration: 7 from 16
alphas run: 0, 0.4, 1
Iteration: 8 from 16
alphas run: 0, 0.4, 1
Iteration: 9 from 16
alphas run: 0, 0.4, 1
Iteration: 10 from 16
alphas run: 0, 0.4, 1
Iteration: 11 from 16
alphas run: 0, 0.4, 1
Iteration: 12 from 16
alphas run: 0, 0.4, 1
Iteration: 13 from 16
alphas run: 0, 0.4, 1
Iteration: 14 from 16
alphas run: 0, 0.4, 1
Iteration: 15 from 16
alphas run: 0, 0.4, 1
Iteration: 16 from 16
alphas run: 0, 0.4, 1
Iteration: 1 from 16
alphas run: 0, 0.4, 1
Iteration: 2 from 16
alphas run: 0, 0.4, 1
Iteration: 3 from 16
alphas run: 0, 0.4, 1
Iteration: 4 from 16
alphas run: 0, 0.4, 1
Iteration: 5 from 16
alphas run: 0, 0.4, 1
Iteration: 6 from 16
alphas run: 0, 0.4, 1
Iteration: 7 from 16
alphas run: 0, 0.4, 1
Iteration: 8 from 16
alphas run: 0, 0.4, 1
Iteration: 9 from 16
alphas run: 0, 0.4, 1
Iteration: 10 from 16
alphas run: 0, 0.4, 1
Iteration: 11 from 16
alphas run: 0, 0.4, 1
Iteration: 12 from 16
alphas run: 0, 0.4, 1
Iteration: 13 from 16
alphas run: 0, 0.4, 1
Iteration: 14 from 16
alphas run: 0, 0.4, 1
Iteration: 15 from 16
alphas run: 0, 0.4, 1
Iteration: 16 from 16
alphas run: 0, 0.4, 1
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Number of observations: 226
Optimal elastic net alpha parameter: 0.2
Optimal elastic net lambda parameter: 14.53
Non-zero coefficients
- - - - - - - - - - - - - - - - - - - -
(Intercept) 105.249163
x1 5.404962
x2 -1.393848
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: binomial
Calibration: via cross-validation; ran through 5 samples of size 219, selection based on Accuracy metric
Number of observations: 233
Optimal elastic net alpha parameter: 0.2
Optimal elastic net lambda parameter: 100
Non-zero coefficients
- - - - - - - - - - - - - - - - - - - -
(Intercept) -0.60098780
x1 -0.05540991
x2 0.24513464
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: multinomial
Calibration: via cross-validation; ran through 11 samples of size 213, selection based on Accuracy metric
Number of observations: 229
Optimal elastic net alpha parameter: 0.7
Optimal elastic net lambda parameter: 0.02
Number of non-zero coefficients per level (excl. intercept, incl. non-sentiment variables)
- - - - - - - - - - - - - - - - - - - -
below- 8
below 6
above 8
above+ 6
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Sample size: 216
Total number of iterations/predictions: 16
Optimal average elastic net alpha parameter: 0.89
Optimal average elastic net lambda parameter: 3.67
Out-of-sample performance
- - - - - - - - - - - - - - - - - - - -
Mean directional accuracy: 60 %
Root mean squared prediction error: 60.25
Mean absolute deviation: 45.69
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Sample size: 216
Total number of iterations/predictions: 16
Optimal average elastic net alpha parameter: 0
Optimal average elastic net lambda parameter: 3889.41
Out-of-sample performance
- - - - - - - - - - - - - - - - - - - -
Mean directional accuracy: 26.67 %
Root mean squared prediction error: 44.32
Mean absolute deviation: 29.73
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Number of observations: 226
Optimal elastic net alpha parameter: 0.2
Optimal elastic net lambda parameter: 14.53
Non-zero coefficients
- - - - - - - - - - - - - - - - - - - -
(Intercept) 105.249163
x1 5.404962
x2 -1.393848
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: binomial
Calibration: via cross-validation; ran through 5 samples of size 219, selection based on Accuracy metric
Number of observations: 233
Optimal elastic net alpha parameter: 0.2
Optimal elastic net lambda parameter: 100
Non-zero coefficients
- - - - - - - - - - - - - - - - - - - -
(Intercept) -0.60098780
x1 -0.05540991
x2 0.24513464
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: multinomial
Calibration: via cross-validation; ran through 11 samples of size 213, selection based on Accuracy metric
Number of observations: 229
Optimal elastic net alpha parameter: 0.7
Optimal elastic net lambda parameter: 0.02
Number of non-zero coefficients per level (excl. intercept, incl. non-sentiment variables)
- - - - - - - - - - - - - - - - - - - -
below- 8
below 6
above 8
above+ 6
Model specification
- - - - - - - - - - - - - - - - - - - -
Model type: gaussian
Calibration: via Cp information criterion
Sample size: 216
Total number of iterations/predictions: 16
Optimal average elastic net alpha parameter: 0.89
Optimal average elastic net lambda parameter: 3.67
Out-of-sample performance
- - - - - - - - - - - - - - - - - - - -
Mean directional accuracy: 60 %
Root mean squared prediction error: 60.25
Mean absolute deviation: 45.69
A sento_model object.
A sento_model object.
A sento_model object.
A sento_modelIter object.
══ Failed tests ════════════════════════════════════════════════════════════════
── Error (test_aggregation.R:81:3): Output for peak documents extraction in line with input ──
Error: cannot xtfrm data frames
Backtrace:
█
1. ├─testthat::expect_length(peakdocs(s1, n = 7, type = "both"), 7) test_aggregation.R:81:2
2. │ └─testthat::quasi_label(enquo(object), arg = "object")
3. │ └─rlang::eval_bare(expr, quo_get_env(quo))
4. └─sentometrics::peakdocs(s1, n = 7, type = "both")
5. └─base::order(s, decreasing = ifelse(type == "neg", FALSE, TRUE))
6. └─base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
7. └─base:::FUN(X[[i]], ...)
8. ├─base::as.vector(xtfrm(x))
9. ├─base::xtfrm(x)
10. └─base::xtfrm.data.frame(x)
── Error (test_aggregation.R:89:3): Output for peak dates extraction in line with input ──
Error: cannot xtfrm data frames
Backtrace:
█
1. ├─testthat::expect_length(...) test_aggregation.R:89:2
2. │ └─testthat::quasi_label(enquo(object), arg = "object")
3. │ └─rlang::eval_bare(expr, quo_get_env(quo))
4. └─sentometrics::peakdates(sentMeas1, n = 15, type = "both")
5. └─base::order(...)
6. └─base::lapply(z, function(x) if (is.object(x)) as.vector(xtfrm(x)) else x)
7. └─base:::FUN(X[[i]], ...)
8. ├─base::as.vector(xtfrm(x))
9. ├─base::xtfrm(x)
10. └─base::xtfrm.data.frame(x)
[ FAIL 2 | WARN 0 | SKIP 0 | PASS 209 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.8.2
Check: installed package size
Result: NOTE
installed size is 8.6Mb
sub-directories of 1Mb or more:
data 2.3Mb
libs 5.2Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-windows-ix86+x86_64, r-release-macos-x86_64, r-release-windows-ix86+x86_64, r-oldrel-macos-x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.8.2
Check: Rd cross-references
Result: NOTE
Undeclared packages ‘MCS’, ‘stopwords’, ‘lexicon’ in Rd xrefs
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.8.2
Check: data for non-ASCII characters
Result: NOTE
Note: found 4436 marked UTF-8 strings
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-macos-x86_64, r-oldrel-macos-x86_64