tickTack
tickTack.RmdOverview
tickTack requires in input a CNAqc object with attributes cna, mutations and metadata. The main input for the tool are: * the read counts from somatic mutations such as single-nucleotide variants (SNVs) in the mutation attribute; * allele-specific copy number segments (CNAs) for clonal segments must be encoded in the cna attribute; * a tumor purity estimate in the metadata.
The tool uses chromosome coordinates to map mutations to segments. The conversion of relative to absolute genome coordinates requires to fix a reference genome build; supported reference is GRCh38/hg17 that is also supported in CNAqc.
tickTack can be used to time the genomic segmnents affected by a Copy Number event, identifying the main clocks at which the event can be associated.
The model
The following concepts are used to infer copy number timing.
VAF peaks
The point mutations that are present on the duplicated region are duplicated in copy with the segment. Therefore we can use the proportion of mutations happede before and after the Copy Number event distinguishing between mutations in single copy and double copies.
Therefore, for a single segment the value of the clock associated with the Copy Number event is obtained as a transformation from the proportions of mutations in single and double copy. The following quantities need to be considered:
In the case of a trisomy without LOH, we can consider the fact that, before , 1 chromosome will accumulate mutations that will duplicate, while the other will accumulate mutations that will remain in single copy. On the other hand, after , both chromosomes will accumulate mutations which will remain in single copy. Therefore one can write the system:
Using the first one to obtain and inserting into the second one, the solution for becomes:
$$\begin{equation} N_1 = N_2 + \frac{3N_2}{\tau}(1-\tau) \hspace{2mm} \rightarrow \hspace{2mm} N_1 + 2N_2 = \frac{3N_2}{\tau} \hspace{2mm} \rightarrow \hspace{2mm} \tau = \frac{3N_2}{N_1 + 2N_2} \end{equation}$$
The case of the CNLOH and of the segment doubling can be treated together. In fact, in the first case, before the mutations that will duplicate accumulate on a single chromosome and after the mutations that will remain in a single copy accumulate on two chromosomes. The system therefore becomes:
A very similar things happens in the case of the 2:2, with the only difference that the number of chromosomes accumulating a certain type of mutation will be double, both after and before . Hence, the system becomes:
Therefore, the two system can be solved similarly (you can simply drop a factor of 2 in the second case). The solution for easily becomes:
$$\begin{equation} N_1 = \frac{2N_2(1-\tau)}{\tau} \hspace{2mm} \rightarrow \hspace{2mm} \tau(N_1 + 2N_2) = 2N_2 \hspace{2mm} \rightarrow \hspace{2mm} \tau = \frac{2N_2}{2N_2 + N_1} \end{equation}$$
Clonal CNAs
Consider:
- mutations sitting on a segment ;
- tumour purity ;
- a healthy diploid normal;
Since the proportion of all reads from the tumour is , and from the normal is . Then, muations present in copies of the tumour genome should peak at VAF value
# Load template data
data <- tickTack::pcawg_example
library(tibble)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
data_reduced <- list(mutations = tibble( data$mutations %>% filter(chr %in% paste0("chr",c(1:10)))),
cna = tibble( data$cna %>% filter(chr %in% paste0("chr",c(1:10)))),
metadata = tibble(purity = data$metadata$purity))
data_reduced <- tickTack::fit_h(data_reduced, max_attempts=2, INIT=TRUE, tolerance = 0.1)
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#> init_taus from clustering 0.717296623368948
#> Attempt 1 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.002105 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 21.05 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -10825.886 1.000 1.000
#> 2 -10814.777 0.501 1.000
#> 3 -10808.363 0.334 0.001 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 5.8 seconds.
#> [[1]]
#> [[1]]$w
#> [,1]
#> [1,] 1
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#>
#> [[1]]$tau
#> [1] 0.7172966
#>
#> [[1]]$phi
#> [1] 1
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -10808.4
#> Attempt 2 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.002167 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 21.67 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -10871.395 1.000 1.000
#> 2 -10843.020 0.501 1.000
#> 3 -10820.577 0.335 0.003 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 5.5 seconds.
#> [[1]]
#> [[1]]$w
#> [,1]
#> [1,] 1
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#>
#> [[1]]$tau
#> [1] 0.5895146
#>
#> [[1]]$phi
#> [1] 1
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -10820.6
#> [1] "output_files /tmp/Rtmp11JmwN/timing_mixed_simple-diagnostic-202411271754-1-00cae9.csv\n"
#> init_taus from clustering 0.782061595984112 init_taus from clustering 0.507845095347554
#> Attempt 1 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.00386 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 38.6 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -10962.993 1.000 1.000
#> 2 -10865.013 0.505 1.000
#> 3 -10859.971 0.336 0.009 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 18.5 seconds.
#> [[1]]
#> [[1]]$w
#> [,1] [,2]
#> [1,] 0.972999145 0.0270008551
#> [2,] 0.969321695 0.0306783054
#> [3,] 0.981764906 0.0182350940
#> [4,] 0.999616900 0.0003831003
#> [5,] 0.902747743 0.0972522571
#> [6,] 0.989109653 0.0108903471
#> [7,] 0.999881617 0.0001183833
#> [8,] 0.996135532 0.0038644680
#> [9,] 0.953537574 0.0464624263
#> [10,] 0.927032342 0.0729676581
#> [11,] 0.964453722 0.0355462785
#> [12,] 0.987812071 0.0121879288
#> [13,] 0.813929280 0.1860707203
#> [14,] 0.927071758 0.0729282423
#> [15,] 0.204312715 0.7956872853
#> [16,] 0.998115707 0.0018842928
#> [17,] 0.974913460 0.0250865396
#> [18,] 0.990825863 0.0091741372
#> [19,] 0.997507809 0.0024921912
#> [20,] 0.941312900 0.0586871003
#> [21,] 0.654761345 0.3452386546
#> [22,] 0.997252182 0.0027478181
#> [23,] 0.996135532 0.0038644680
#> [24,] 0.007795247 0.9922047533
#> [25,] 0.999288695 0.0007113054
#> [26,] 0.949709200 0.0502907997
#> [27,] 0.139418013 0.8605819870
#> [28,] 0.006482878 0.9935171217
#> [29,] 0.023104783 0.9768952174
#> [30,] 0.996135532 0.0038644680
#> [31,] 0.955731756 0.0442682445
#> [32,] 0.294303978 0.7056960216
#> [33,] 0.968632302 0.0313676979
#> [34,] 0.955731756 0.0442682445
#> [35,] 0.296588739 0.7034112608
#> [36,] 0.083789627 0.9162103729
#> [37,] 0.654761345 0.3452386546
#> [38,] 0.975677209 0.0243227914
#> [39,] 0.955731756 0.0442682445
#> [40,] 0.955731756 0.0442682445
#> [41,] 0.941312900 0.0586871003
#> [42,] 0.002484797 0.9975152026
#>
#> [[1]]$tau
#> [1] 0.7820616 0.5078451
#>
#> [[1]]$phi
#> [1] 0.5 0.5
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -10860
#> Attempt 2 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.003842 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 38.42 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -11158.401 1.000 1.000
#> 2 -10928.649 0.511 1.000
#> 3 -10894.860 0.341 0.021 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 8.1 seconds.
#> [[1]]
#> [[1]]$w
#> [,1] [,2]
#> [1,] 0.972999145 0.0270008551
#> [2,] 0.969321695 0.0306783054
#> [3,] 0.981764906 0.0182350940
#> [4,] 0.999616900 0.0003831003
#> [5,] 0.902747743 0.0972522571
#> [6,] 0.989109653 0.0108903471
#> [7,] 0.999881617 0.0001183833
#> [8,] 0.996135532 0.0038644680
#> [9,] 0.953537574 0.0464624263
#> [10,] 0.927032342 0.0729676581
#> [11,] 0.964453722 0.0355462785
#> [12,] 0.987812071 0.0121879288
#> [13,] 0.813929280 0.1860707203
#> [14,] 0.927071758 0.0729282423
#> [15,] 0.204312715 0.7956872853
#> [16,] 0.998115707 0.0018842928
#> [17,] 0.974913460 0.0250865396
#> [18,] 0.990825863 0.0091741372
#> [19,] 0.997507809 0.0024921912
#> [20,] 0.941312900 0.0586871003
#> [21,] 0.654761345 0.3452386546
#> [22,] 0.997252182 0.0027478181
#> [23,] 0.996135532 0.0038644680
#> [24,] 0.007795247 0.9922047533
#> [25,] 0.999288695 0.0007113054
#> [26,] 0.949709200 0.0502907997
#> [27,] 0.139418013 0.8605819870
#> [28,] 0.006482878 0.9935171217
#> [29,] 0.023104783 0.9768952174
#> [30,] 0.996135532 0.0038644680
#> [31,] 0.955731756 0.0442682445
#> [32,] 0.294303978 0.7056960216
#> [33,] 0.968632302 0.0313676979
#> [34,] 0.955731756 0.0442682445
#> [35,] 0.296588739 0.7034112608
#> [36,] 0.083789627 0.9162103729
#> [37,] 0.654761345 0.3452386546
#> [38,] 0.975677209 0.0243227914
#> [39,] 0.955731756 0.0442682445
#> [40,] 0.955731756 0.0442682445
#> [41,] 0.941312900 0.0586871003
#> [42,] 0.002484797 0.9975152026
#>
#> [[1]]$tau
#> [1] 0.5170100 0.4303675
#>
#> [[1]]$phi
#> [1] 0.5 0.5
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -10894.9
#> [1] "output_files /tmp/Rtmp11JmwN/timing_mixed_simple-diagnostic-202411271754-1-952a77.csv\n"
#> init_taus from clustering 0.821674291112562 init_taus from clustering 0.486384833469723 init_taus from clustering 0.711213122092432
#> Attempt 1 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.00583 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 58.3 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -10968.281 1.000 1.000
#> 2 -10927.013 0.502 1.000
#> 3 -10908.342 0.335 0.004 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 26.7 seconds.
#> [[1]]
#> [[1]]$w
#> [,1] [,2] [,3]
#> [1,] 0.9837855558 0.0019220734 0.0142923707
#> [2,] 0.9746643773 0.0030765663 0.0222590564
#> [3,] 0.9987392242 0.0002078260 0.0010529498
#> [4,] 0.8147338765 0.0111092638 0.1741568597
#> [5,] 0.0009377637 0.0002858036 0.9987764327
#> [6,] 0.3136083667 0.0186116669 0.6677799664
#> [7,] 0.7682562017 0.0129740879 0.2187697104
#> [8,] 0.9394967790 0.0045762383 0.0559269827
#> [9,] 0.0537214233 0.0071355929 0.9391429837
#> [10,] 0.8537874708 0.0239132093 0.1222993199
#> [11,] 0.0916486479 0.0102414561 0.8981098959
#> [12,] 0.2919337278 0.0182505699 0.6898157024
#> [13,] 0.0189241829 0.0073323872 0.9737434299
#> [14,] 0.0117126835 0.0021765845 0.9861107320
#> [15,] 0.1140115366 0.4330905905 0.4528978729
#> [16,] 0.5702225256 0.0182256392 0.4115518352
#> [17,] 0.1516224684 0.0137744401 0.8346030915
#> [18,] 0.9876410591 0.0011508346 0.0112081063
#> [19,] 0.5405575817 0.0186561155 0.4407863028
#> [20,] 0.0282347812 0.0044164319 0.9673487870
#> [21,] 0.0722079817 0.0533503351 0.8744416832
#> [22,] 0.5292600782 0.0187954734 0.4519444484
#> [23,] 0.9394967790 0.0045762383 0.0559269827
#> [24,] 0.0003175796 0.9991054263 0.0005769941
#> [25,] 0.8417150401 0.0099007815 0.1483841784
#> [26,] 0.0442279386 0.0061994829 0.9495725785
#> [27,] 0.0993679692 0.5474747779 0.3531572529
#> [28,] 0.0001519567 0.9996332692 0.0002147742
#> [29,] 0.0375810610 0.8587992681 0.1036196710
#> [30,] 0.9394967790 0.0045762383 0.0559269827
#> [31,] 0.9359077605 0.0086714188 0.0554208206
#> [32,] 0.1221529668 0.3118042785 0.5660427547
#> [33,] 0.9728497049 0.0033143242 0.0238359708
#> [34,] 0.9359077605 0.0086714188 0.0554208206
#> [35,] 0.1927310749 0.5500351778 0.2572337473
#> [36,] 0.0779092616 0.6725230317 0.2495677068
#> [37,] 0.0722079817 0.0533503351 0.8744416832
#> [38,] 0.9896323243 0.0012210181 0.0091466576
#> [39,] 0.9359077605 0.0086714188 0.0554208206
#> [40,] 0.9359077605 0.0086714188 0.0554208206
#> [41,] 0.0282347812 0.0044164319 0.9673487870
#> [42,] 0.0006030386 0.9981530619 0.0012438995
#>
#> [[1]]$tau
#> [1] 0.8216743 0.4863848 0.7112131
#>
#> [[1]]$phi
#> [1] 0.3333333 0.3333333 0.3333333
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -10908.3
#> Attempt 2 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.005776 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 57.76 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -11055.865 1.000 1.000
#> 2 -10940.572 0.505 1.000
#> 3 -10913.694 0.338 0.011 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 26.3 seconds.
#> [[1]]
#> [[1]]$w
#> [,1] [,2] [,3]
#> [1,] 0.9837855558 0.0019220734 0.0142923707
#> [2,] 0.9746643773 0.0030765663 0.0222590564
#> [3,] 0.9987392242 0.0002078260 0.0010529498
#> [4,] 0.8147338765 0.0111092638 0.1741568597
#> [5,] 0.0009377637 0.0002858036 0.9987764327
#> [6,] 0.3136083667 0.0186116669 0.6677799664
#> [7,] 0.7682562017 0.0129740879 0.2187697104
#> [8,] 0.9394967790 0.0045762383 0.0559269827
#> [9,] 0.0537214233 0.0071355929 0.9391429837
#> [10,] 0.8537874708 0.0239132093 0.1222993199
#> [11,] 0.0916486479 0.0102414561 0.8981098959
#> [12,] 0.2919337278 0.0182505699 0.6898157024
#> [13,] 0.0189241829 0.0073323872 0.9737434299
#> [14,] 0.0117126835 0.0021765845 0.9861107320
#> [15,] 0.1140115366 0.4330905905 0.4528978729
#> [16,] 0.5702225256 0.0182256392 0.4115518352
#> [17,] 0.1516224684 0.0137744401 0.8346030915
#> [18,] 0.9876410591 0.0011508346 0.0112081063
#> [19,] 0.5405575817 0.0186561155 0.4407863028
#> [20,] 0.0282347812 0.0044164319 0.9673487870
#> [21,] 0.0722079817 0.0533503351 0.8744416832
#> [22,] 0.5292600782 0.0187954734 0.4519444484
#> [23,] 0.9394967790 0.0045762383 0.0559269827
#> [24,] 0.0003175796 0.9991054263 0.0005769941
#> [25,] 0.8417150401 0.0099007815 0.1483841784
#> [26,] 0.0442279386 0.0061994829 0.9495725785
#> [27,] 0.0993679692 0.5474747779 0.3531572529
#> [28,] 0.0001519567 0.9996332692 0.0002147742
#> [29,] 0.0375810610 0.8587992681 0.1036196710
#> [30,] 0.9394967790 0.0045762383 0.0559269827
#> [31,] 0.9359077605 0.0086714188 0.0554208206
#> [32,] 0.1221529668 0.3118042785 0.5660427547
#> [33,] 0.9728497049 0.0033143242 0.0238359708
#> [34,] 0.9359077605 0.0086714188 0.0554208206
#> [35,] 0.1927310749 0.5500351778 0.2572337473
#> [36,] 0.0779092616 0.6725230317 0.2495677068
#> [37,] 0.0722079817 0.0533503351 0.8744416832
#> [38,] 0.9896323243 0.0012210181 0.0091466576
#> [39,] 0.9359077605 0.0086714188 0.0554208206
#> [40,] 0.9359077605 0.0086714188 0.0554208206
#> [41,] 0.0282347812 0.0044164319 0.9673487870
#> [42,] 0.0006030386 0.9981530619 0.0012438995
#>
#> [[1]]$tau
#> [1] 0.7540726 0.4742881 0.8674985
#>
#> [[1]]$phi
#> [1] 0.3333333 0.3333333 0.3333333
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -10913.7
#> [1] "output_files /tmp/Rtmp11JmwN/timing_mixed_simple-diagnostic-202411271754-1-86958f.csv\n"
#> init_taus from clustering 0.833266358182747 init_taus from clustering 0.000641472811587056 init_taus from clustering 0.542989332490939 init_taus from clustering 0.733629632496324
#> Attempt 1 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.007145 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 71.45 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -11151.518 1.000 1.000
#> 2 -10962.230 0.509 1.000
#> 3 -10941.921 0.340 0.017 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 34.2 seconds.
#> [[1]]
#> [[1]]$w
#> [,1] [,2] [,3] [,4]
#> [1,] 0.9984004359 1.173267e-04 0.0002406451 0.0012415923
#> [2,] 0.9933766205 1.904071e-04 0.0008182809 0.0056146914
#> [3,] 0.9910951413 1.949085e-04 0.0009105352 0.0077994150
#> [4,] 0.5534770204 2.037070e-03 0.0202454525 0.4242404574
#> [5,] 0.0254301811 8.035428e-04 0.0123078562 0.9614584199
#> [6,] 0.0776064507 9.079886e-04 0.0102520617 0.9112334991
#> [7,] 0.4843977085 2.078632e-03 0.0210949069 0.4924287524
#> [8,] 0.7877221806 1.462008e-03 0.0132739794 0.1975418321
#> [9,] 0.0002216401 1.022658e-04 0.0001343432 0.9995417509
#> [10,] 0.8631822744 3.485800e-03 0.0224261923 0.1109057330
#> [11,] 0.0021449549 1.341394e-04 0.0005888147 0.9971320910
#> [12,] 0.0668612842 8.237421e-04 0.0092788935 0.9230360803
#> [13,] 0.0715909250 3.015456e-03 0.0619641350 0.8634294838
#> [14,] 0.0108101469 3.546296e-04 0.0042344535 0.9846007700
#> [15,] 0.0479272686 7.339603e-03 0.7988423691 0.1458907596
#> [16,] 0.2582285537 1.790023e-03 0.0193994922 0.7205819308
#> [17,] 0.0139956356 2.992467e-04 0.0028210909 0.9828840268
#> [18,] 0.9174735098 7.873177e-04 0.0063813311 0.0753578413
#> [19,] 0.2317978105 1.706749e-03 0.0186406636 0.7478547771
#> [20,] 0.0036415856 1.752623e-04 0.0012703801 0.9949127720
#> [21,] 0.1103148479 6.991522e-03 0.1998397863 0.6828538438
#> [22,] 0.2221577042 1.673234e-03 0.0183261850 0.7578428765
#> [23,] 0.7877221806 1.462008e-03 0.0132739794 0.1975418321
#> [24,] 0.0277986593 1.494299e-02 0.9032587618 0.0539995903
#> [25,] 0.5973096571 1.981387e-03 0.0194190810 0.3812898744
#> [26,] 0.0008597157 1.149213e-04 0.0003260866 0.9986992764
#> [27,] 0.0277799331 5.009366e-03 0.8892361213 0.0779745798
#> [28,] 0.0260730638 1.366797e-02 0.9093202772 0.0509386883
#> [29,] 0.0001214352 1.060253e-04 0.9996225484 0.0001499911
#> [30,] 0.7877221806 1.462008e-03 0.0132739794 0.1975418321
#> [31,] 0.9575967584 8.450343e-04 0.0056378365 0.0359203708
#> [32,] 0.0738045277 9.352504e-03 0.6663920182 0.2504509498
#> [33,] 0.9921090594 2.098650e-04 0.0009696709 0.0067114047
#> [34,] 0.9575967584 8.450343e-04 0.0056378365 0.0359203708
#> [35,] 0.0001002408 9.996988e-01 0.0001006214 0.0001003223
#> [36,] 0.0114357248 2.483766e-03 0.9564322241 0.0296482850
#> [37,] 0.1103148479 6.991522e-03 0.1998397863 0.6828538438
#> [38,] 0.9996992700 9.997078e-05 0.0001000486 0.0001007107
#> [39,] 0.9575967584 8.450343e-04 0.0056378365 0.0359203708
#> [40,] 0.9575967584 8.450343e-04 0.0056378365 0.0359203708
#> [41,] 0.0036415856 1.752623e-04 0.0012703801 0.9949127720
#> [42,] 0.0196935294 9.388817e-03 0.9315671947 0.0393504589
#>
#> [[1]]$tau
#> [1] 0.8332663582 0.0006414728 0.5429893325 0.7336296325
#>
#> [[1]]$phi
#> [1] 0.25 0.25 0.25 0.25
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -10941.9
#> Attempt 2 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.006949 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 69.49 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -11166.861 1.000 1.000
#> 2 -11006.753 0.507 1.000
#> 3 -10976.026 0.339 0.015 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 34.8 seconds.
#> [[1]]
#> [[1]]$w
#> [,1] [,2] [,3] [,4]
#> [1,] 0.9984004359 1.173267e-04 0.0002406451 0.0012415923
#> [2,] 0.9933766205 1.904071e-04 0.0008182809 0.0056146914
#> [3,] 0.9910951413 1.949085e-04 0.0009105352 0.0077994150
#> [4,] 0.5534770204 2.037070e-03 0.0202454525 0.4242404574
#> [5,] 0.0254301811 8.035428e-04 0.0123078562 0.9614584199
#> [6,] 0.0776064507 9.079886e-04 0.0102520617 0.9112334991
#> [7,] 0.4843977085 2.078632e-03 0.0210949069 0.4924287524
#> [8,] 0.7877221806 1.462008e-03 0.0132739794 0.1975418321
#> [9,] 0.0002216401 1.022658e-04 0.0001343432 0.9995417509
#> [10,] 0.8631822744 3.485800e-03 0.0224261923 0.1109057330
#> [11,] 0.0021449549 1.341394e-04 0.0005888147 0.9971320910
#> [12,] 0.0668612842 8.237421e-04 0.0092788935 0.9230360803
#> [13,] 0.0715909250 3.015456e-03 0.0619641350 0.8634294838
#> [14,] 0.0108101469 3.546296e-04 0.0042344535 0.9846007700
#> [15,] 0.0479272686 7.339603e-03 0.7988423691 0.1458907596
#> [16,] 0.2582285537 1.790023e-03 0.0193994922 0.7205819308
#> [17,] 0.0139956356 2.992467e-04 0.0028210909 0.9828840268
#> [18,] 0.9174735098 7.873177e-04 0.0063813311 0.0753578413
#> [19,] 0.2317978105 1.706749e-03 0.0186406636 0.7478547771
#> [20,] 0.0036415856 1.752623e-04 0.0012703801 0.9949127720
#> [21,] 0.1103148479 6.991522e-03 0.1998397863 0.6828538438
#> [22,] 0.2221577042 1.673234e-03 0.0183261850 0.7578428765
#> [23,] 0.7877221806 1.462008e-03 0.0132739794 0.1975418321
#> [24,] 0.0277986593 1.494299e-02 0.9032587618 0.0539995903
#> [25,] 0.5973096571 1.981387e-03 0.0194190810 0.3812898744
#> [26,] 0.0008597157 1.149213e-04 0.0003260866 0.9986992764
#> [27,] 0.0277799331 5.009366e-03 0.8892361213 0.0779745798
#> [28,] 0.0260730638 1.366797e-02 0.9093202772 0.0509386883
#> [29,] 0.0001214352 1.060253e-04 0.9996225484 0.0001499911
#> [30,] 0.7877221806 1.462008e-03 0.0132739794 0.1975418321
#> [31,] 0.9575967584 8.450343e-04 0.0056378365 0.0359203708
#> [32,] 0.0738045277 9.352504e-03 0.6663920182 0.2504509498
#> [33,] 0.9921090594 2.098650e-04 0.0009696709 0.0067114047
#> [34,] 0.9575967584 8.450343e-04 0.0056378365 0.0359203708
#> [35,] 0.0001002408 9.996988e-01 0.0001006214 0.0001003223
#> [36,] 0.0114357248 2.483766e-03 0.9564322241 0.0296482850
#> [37,] 0.1103148479 6.991522e-03 0.1998397863 0.6828538438
#> [38,] 0.9996992700 9.997078e-05 0.0001000486 0.0001007107
#> [39,] 0.9575967584 8.450343e-04 0.0056378365 0.0359203708
#> [40,] 0.9575967584 8.450343e-04 0.0056378365 0.0359203708
#> [41,] 0.0036415856 1.752623e-04 0.0012703801 0.9949127720
#> [42,] 0.0196935294 9.388817e-03 0.9315671947 0.0393504589
#>
#> [[1]]$tau
#> [1] 0.5078152 0.1310380 0.6253094 0.7107888
#>
#> [[1]]$phi
#> [1] 0.25 0.25 0.25 0.25
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -10976
#> [1] "output_files /tmp/Rtmp11JmwN/timing_mixed_simple-diagnostic-202411271755-1-88e20d.csv\n"
#> init_taus from clustering 0.846922037855411 init_taus from clustering 0.575631127497086 init_taus from clustering 0.716406746962572 init_taus from clustering 0.460516198930884 init_taus from clustering 0.778655945645007
#> Attempt 1 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.008913 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 89.13 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -11050.963 1.000 1.000
#> 2 -10981.225 0.503 1.000
#> 3 -10933.198 0.337 0.006 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 41.7 seconds.
#> [[1]]
#> [[1]]$w
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.9611410146 0.0015572686 0.0069607253 0.0008019210 0.0295390705
#> [2,] 0.9889258723 0.0005446640 0.0021124114 0.0003164180 0.0081006343
#> [3,] 0.7877539658 0.0060697171 0.0315162610 0.0028957119 0.1717643441
#> [4,] 0.0176276902 0.0015234418 0.0129208055 0.0006961054 0.9672319571
#> [5,] 0.0003465144 0.0003253304 0.9980134892 0.0001672492 0.0011474168
#> [6,] 0.0439083222 0.0111849044 0.2289911865 0.0042326087 0.7116829782
#> [7,] 0.0052322810 0.0005827869 0.0047588056 0.0002997682 0.9891263582
#> [8,] 0.1614563216 0.0071481370 0.0508578634 0.0031788531 0.7773588249
#> [9,] 0.0172500024 0.0092195378 0.8648503160 0.0031338748 0.1055462690
#> [10,] 0.8115276488 0.0146706407 0.0481581625 0.0078919594 0.1177515885
#> [11,] 0.0291830846 0.0132092397 0.7445146817 0.0045873090 0.2085056851
#> [12,] 0.0455192979 0.0121279093 0.2626069305 0.0045573829 0.6751884794
#> [13,] 0.0197683393 0.0335499341 0.8741707158 0.0086522198 0.0638587910
#> [14,] 0.0020965149 0.0015531092 0.9859832992 0.0005553492 0.0098117276
#> [15,] 0.0089688726 0.9061021596 0.0400258921 0.0278585421 0.0170445336
#> [16,] 0.0100887468 0.0016118911 0.0194948538 0.0006984501 0.9681060582
#> [17,] 0.0416876135 0.0155343505 0.5672419229 0.0055501626 0.3699859504
#> [18,] 0.4168143270 0.0097552216 0.0598158705 0.0044553341 0.5091592469
#> [19,] 0.0140417392 0.0023428447 0.0302033544 0.0009821179 0.9524299437
#> [20,] 0.0079294139 0.0049692324 0.9428417363 0.0016737782 0.0425858391
#> [21,] 0.0467614736 0.1827733676 0.6136988488 0.0357422170 0.1210240930
#> [22,] 0.0156228625 0.0026550018 0.0350027184 0.0011024308 0.9456169864
#> [23,] 0.1614563216 0.0071481370 0.0508578634 0.0031788531 0.7773588249
#> [24,] 0.0031519757 0.0459952196 0.0074824722 0.9386557876 0.0047145449
#> [25,] 0.0304071756 0.0023317715 0.0193751908 0.0010416597 0.9468442024
#> [26,] 0.0138505149 0.0078000469 0.8946434815 0.0026378439 0.0810681128
#> [27,] 0.0018157530 0.9805087290 0.0069966597 0.0074272118 0.0032516465
#> [28,] 0.0039358709 0.0602243118 0.0094525122 0.9204684972 0.0059188079
#> [29,] 0.0090049374 0.8322357805 0.0276359555 0.1161740625 0.0149492642
#> [30,] 0.1614563216 0.0071481370 0.0508578634 0.0031788531 0.7773588249
#> [31,] 0.9759042659 0.0013902093 0.0052618373 0.0007499612 0.0166937264
#> [32,] 0.0226534864 0.7627831012 0.1186446508 0.0504538324 0.0454649292
#> [33,] 0.9920067618 0.0004215464 0.0015450378 0.0002567866 0.0057698674
#> [34,] 0.9759042659 0.0013902093 0.0052618373 0.0007499612 0.0166937264
#> [35,] 0.1095807390 0.2371461096 0.1531180558 0.3705307539 0.1296243417
#> [36,] 0.0003252731 0.9967746191 0.0009138984 0.0014879710 0.0004982384
#> [37,] 0.0467614736 0.1827733676 0.6136988488 0.0357422170 0.1210240930
#> [38,] 0.9263315552 0.0026675672 0.0125697970 0.0013268102 0.0571042703
#> [39,] 0.9759042659 0.0013902093 0.0052618373 0.0007499612 0.0166937264
#> [40,] 0.9759042659 0.0013902093 0.0052618373 0.0007499612 0.0166937264
#> [41,] 0.0079294139 0.0049692324 0.9428417363 0.0016737782 0.0425858391
#> [42,] 0.0074165300 0.1362098920 0.0185078781 0.8265229097 0.0113427902
#>
#> [[1]]$tau
#> [1] 0.8469220 0.5756311 0.7164067 0.4605162 0.7786559
#>
#> [[1]]$phi
#> [1] 0.2 0.2 0.2 0.2 0.2
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -10933.2
#> Attempt 2 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.008655 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 86.55 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -11275.635 1.000 1.000
#> 2 -10977.809 0.514 1.000
#> 3 -10951.947 0.343 0.027 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 43.2 seconds.
#> [[1]]
#> [[1]]$w
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.9611410146 0.0015572686 0.0069607253 0.0008019210 0.0295390705
#> [2,] 0.9889258723 0.0005446640 0.0021124114 0.0003164180 0.0081006343
#> [3,] 0.7877539658 0.0060697171 0.0315162610 0.0028957119 0.1717643441
#> [4,] 0.0176276902 0.0015234418 0.0129208055 0.0006961054 0.9672319571
#> [5,] 0.0003465144 0.0003253304 0.9980134892 0.0001672492 0.0011474168
#> [6,] 0.0439083222 0.0111849044 0.2289911865 0.0042326087 0.7116829782
#> [7,] 0.0052322810 0.0005827869 0.0047588056 0.0002997682 0.9891263582
#> [8,] 0.1614563216 0.0071481370 0.0508578634 0.0031788531 0.7773588249
#> [9,] 0.0172500024 0.0092195378 0.8648503160 0.0031338748 0.1055462690
#> [10,] 0.8115276488 0.0146706407 0.0481581625 0.0078919594 0.1177515885
#> [11,] 0.0291830846 0.0132092397 0.7445146817 0.0045873090 0.2085056851
#> [12,] 0.0455192979 0.0121279093 0.2626069305 0.0045573829 0.6751884794
#> [13,] 0.0197683393 0.0335499341 0.8741707158 0.0086522198 0.0638587910
#> [14,] 0.0020965149 0.0015531092 0.9859832992 0.0005553492 0.0098117276
#> [15,] 0.0089688726 0.9061021596 0.0400258921 0.0278585421 0.0170445336
#> [16,] 0.0100887468 0.0016118911 0.0194948538 0.0006984501 0.9681060582
#> [17,] 0.0416876135 0.0155343505 0.5672419229 0.0055501626 0.3699859504
#> [18,] 0.4168143270 0.0097552216 0.0598158705 0.0044553341 0.5091592469
#> [19,] 0.0140417392 0.0023428447 0.0302033544 0.0009821179 0.9524299437
#> [20,] 0.0079294139 0.0049692324 0.9428417363 0.0016737782 0.0425858391
#> [21,] 0.0467614736 0.1827733676 0.6136988488 0.0357422170 0.1210240930
#> [22,] 0.0156228625 0.0026550018 0.0350027184 0.0011024308 0.9456169864
#> [23,] 0.1614563216 0.0071481370 0.0508578634 0.0031788531 0.7773588249
#> [24,] 0.0031519757 0.0459952196 0.0074824722 0.9386557876 0.0047145449
#> [25,] 0.0304071756 0.0023317715 0.0193751908 0.0010416597 0.9468442024
#> [26,] 0.0138505149 0.0078000469 0.8946434815 0.0026378439 0.0810681128
#> [27,] 0.0018157530 0.9805087290 0.0069966597 0.0074272118 0.0032516465
#> [28,] 0.0039358709 0.0602243118 0.0094525122 0.9204684972 0.0059188079
#> [29,] 0.0090049374 0.8322357805 0.0276359555 0.1161740625 0.0149492642
#> [30,] 0.1614563216 0.0071481370 0.0508578634 0.0031788531 0.7773588249
#> [31,] 0.9759042659 0.0013902093 0.0052618373 0.0007499612 0.0166937264
#> [32,] 0.0226534864 0.7627831012 0.1186446508 0.0504538324 0.0454649292
#> [33,] 0.9920067618 0.0004215464 0.0015450378 0.0002567866 0.0057698674
#> [34,] 0.9759042659 0.0013902093 0.0052618373 0.0007499612 0.0166937264
#> [35,] 0.1095807390 0.2371461096 0.1531180558 0.3705307539 0.1296243417
#> [36,] 0.0003252731 0.9967746191 0.0009138984 0.0014879710 0.0004982384
#> [37,] 0.0467614736 0.1827733676 0.6136988488 0.0357422170 0.1210240930
#> [38,] 0.9263315552 0.0026675672 0.0125697970 0.0013268102 0.0571042703
#> [39,] 0.9759042659 0.0013902093 0.0052618373 0.0007499612 0.0166937264
#> [40,] 0.9759042659 0.0013902093 0.0052618373 0.0007499612 0.0166937264
#> [41,] 0.0079294139 0.0049692324 0.9428417363 0.0016737782 0.0425858391
#> [42,] 0.0074165300 0.1362098920 0.0185078781 0.8265229097 0.0113427902
#>
#> [[1]]$tau
#> [1] 0.7554940 0.8800000 0.7231075 0.3975749 0.6059676
#>
#> [[1]]$phi
#> [1] 0.2 0.2 0.2 0.2 0.2
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -10951.9
#> [1] "output_files /tmp/Rtmp11JmwN/timing_mixed_simple-diagnostic-202411271756-1-2f802e.csv\n"
#> init_taus from clustering 0.784999645698112 init_taus from clustering 0.000235051279729694 init_taus from clustering 0.490150661526823 init_taus from clustering 0.594054683554349 init_taus from clustering 0.849443376180322 init_taus from clustering 0.724222551723491
#> Attempt 1 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.010015 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 100.15 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -13742.438 1.000 1.000
#> 2 -11202.479 0.613 1.000
#> 3 -11073.432 0.413 0.227
#> 4 -11023.548 0.311 0.227
#> 5 -10995.921 0.249 0.012 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 50.4 seconds.
#> [[1]]
#> [[1]]$w
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 5.588028e-02 0.0003134501 1.343609e-03 2.636433e-03 9.279279e-01
#> [2,] 2.051510e-02 0.0001913822 6.245529e-04 1.158167e-03 9.726909e-01
#> [3,] 2.528938e-01 0.0007000903 3.738425e-03 7.744867e-03 6.947623e-01
#> [4,] 9.978077e-01 0.0001049137 1.349134e-04 1.827964e-04 8.828834e-04
#> [5,] 1.906607e-02 0.0002857353 1.985219e-03 6.649905e-03 5.289041e-03
#> [6,] 5.101068e-01 0.0008439895 6.109371e-03 1.628446e-02 4.892186e-02
#> [7,] 9.977874e-01 0.0001049606 1.357724e-04 1.859394e-04 8.023099e-04
#> [8,] 8.769691e-01 0.0004093794 2.161319e-03 4.765688e-03 8.114597e-02
#> [9,] 2.422680e-02 0.0002238648 1.231214e-03 3.566085e-03 4.960635e-03
#> [10,] 1.171570e-01 0.0017001043 8.047599e-03 1.463610e-02 8.117613e-01
#> [11,] 7.650713e-02 0.0004107803 2.854944e-03 8.270238e-03 1.370766e-02
#> [12,] 4.683384e-01 0.0008526818 6.220758e-03 1.669527e-02 4.788772e-02
#> [13,] 8.902851e-02 0.0016539050 1.817736e-02 7.578652e-02 3.075078e-02
#> [14,] 1.312239e-03 0.0001091676 1.893522e-04 3.934865e-04 3.960558e-04
#> [15,] 1.144905e-03 0.0001992830 3.058926e-03 9.925040e-01 6.746272e-04
#> [16,] 8.830679e-01 0.0003939017 2.314621e-03 5.680706e-03 2.843060e-02
#> [17,] 1.847509e-01 0.0006547589 4.857444e-03 1.371905e-02 2.783556e-02
#> [18,] 6.348185e-01 0.0007567033 4.292331e-03 9.267563e-03 2.935316e-01
#> [19,] 8.525695e-01 0.0004570770 2.811184e-03 6.980507e-03 3.295006e-02
#> [20,] 2.945405e-03 0.0001178557 2.683301e-04 6.333183e-04 7.348332e-04
#> [21,] 1.139230e-01 0.0036854126 5.118909e-02 3.017945e-01 4.779627e-02
#> [22,] 8.400734e-01 0.0004814672 3.004774e-03 7.491799e-03 3.458660e-02
#> [23,] 8.769691e-01 0.0004093794 2.161319e-03 4.765688e-03 8.114597e-02
#> [24,] 9.296136e-04 0.0004303843 9.904555e-01 6.123149e-03 6.645268e-04
#> [25,] 9.906913e-01 0.0001256278 2.788719e-04 5.204162e-04 4.476953e-03
#> [26,] 1.453283e-02 0.0001793674 8.319385e-04 2.366907e-03 3.111764e-03
#> [27,] 1.522110e-03 0.0002629218 6.131698e-03 9.881183e-01 9.103786e-04
#> [28,] 5.220301e-04 0.0002634356 9.948102e-01 3.255423e-03 3.863069e-04
#> [29,] 2.147013e-02 0.0042812152 4.218067e-01 5.006966e-01 1.339620e-02
#> [30,] 8.769691e-01 0.0004093794 2.161319e-03 4.765688e-03 8.114597e-02
#> [31,] 1.134448e-02 0.0001796742 5.346273e-04 9.457497e-04 9.835824e-01
#> [32,] 1.463836e-02 0.0012043657 2.672924e-02 9.142733e-01 7.733391e-03
#> [33,] 1.616205e-02 0.0001738885 5.230176e-04 9.516542e-04 9.783189e-01
#> [34,] 1.134448e-02 0.0001796742 5.346273e-04 9.457497e-04 9.835824e-01
#> [35,] 9.994203e-05 0.9995002852 9.994516e-05 9.994353e-05 9.994174e-05
#> [36,] 1.008352e-02 0.0014945820 6.888963e-02 8.939315e-01 5.990788e-03
#> [37,] 1.139230e-01 0.0036854126 5.118909e-02 3.017945e-01 4.779627e-02
#> [38,] 9.721663e-02 0.0004270775 2.027657e-03 4.065135e-03 8.770978e-01
#> [39,] 1.134448e-02 0.0001796742 5.346273e-04 9.457497e-04 9.835824e-01
#> [40,] 1.134448e-02 0.0001796742 5.346273e-04 9.457497e-04 9.835824e-01
#> [41,] 2.945405e-03 0.0001178557 2.683301e-04 6.333183e-04 7.348332e-04
#> [42,] 2.520505e-04 0.0001529189 9.976679e-01 1.382363e-03 2.019259e-04
#> [,6]
#> [1,] 1.189837e-02
#> [2,] 4.819857e-03
#> [3,] 4.016052e-02
#> [4,] 8.867562e-04
#> [5,] 9.667240e-01
#> [6,] 4.177335e-01
#> [7,] 9.835841e-04
#> [8,] 3.454852e-02
#> [9,] 9.657914e-01
#> [10,] 4.669792e-02
#> [11,] 8.982492e-01
#> [12,] 4.600051e-01
#> [13,] 7.846029e-01
#> [14,] 9.975997e-01
#> [15,] 2.418248e-03
#> [16,] 8.011226e-02
#> [17,] 7.681823e-01
#> [18,] 5.733330e-02
#> [19,] 1.042317e-01
#> [20,] 9.953003e-01
#> [21,] 4.816117e-01
#> [22,] 1.143620e-01
#> [23,] 3.454852e-02
#> [24,] 1.396835e-03
#> [25,] 3.906851e-03
#> [26,] 9.789772e-01
#> [27,] 3.054606e-03
#> [28,] 7.625721e-04
#> [29,] 3.834919e-02
#> [30,] 3.454852e-02
#> [31,] 3.413080e-03
#> [32,] 3.542131e-02
#> [33,] 3.870479e-03
#> [34,] 3.413080e-03
#> [35,] 9.994238e-05
#> [36,] 1.960997e-02
#> [37,] 4.816117e-01
#> [38,] 1.916565e-02
#> [39,] 3.413080e-03
#> [40,] 3.413080e-03
#> [41,] 9.953003e-01
#> [42,] 3.428522e-04
#>
#> [[1]]$tau
#> [1] 0.7849996457 0.0002350513 0.4901506615 0.5940546836 0.8494433762
#> [6] 0.7242225517
#>
#> [[1]]$phi
#> [1] 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -11073.4
#> Attempt 2 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.010325 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 103.25 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -11426.474 1.000 1.000
#> 2 -11197.379 0.510 1.000
#> 3 -11087.234 0.343 0.020 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 22.0 seconds.
#> [[1]]
#> [[1]]$w
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 5.588028e-02 0.0003134501 1.343609e-03 2.636433e-03 9.279279e-01
#> [2,] 2.051510e-02 0.0001913822 6.245529e-04 1.158167e-03 9.726909e-01
#> [3,] 2.528938e-01 0.0007000903 3.738425e-03 7.744867e-03 6.947623e-01
#> [4,] 9.978077e-01 0.0001049137 1.349134e-04 1.827964e-04 8.828834e-04
#> [5,] 1.906607e-02 0.0002857353 1.985219e-03 6.649905e-03 5.289041e-03
#> [6,] 5.101068e-01 0.0008439895 6.109371e-03 1.628446e-02 4.892186e-02
#> [7,] 9.977874e-01 0.0001049606 1.357724e-04 1.859394e-04 8.023099e-04
#> [8,] 8.769691e-01 0.0004093794 2.161319e-03 4.765688e-03 8.114597e-02
#> [9,] 2.422680e-02 0.0002238648 1.231214e-03 3.566085e-03 4.960635e-03
#> [10,] 1.171570e-01 0.0017001043 8.047599e-03 1.463610e-02 8.117613e-01
#> [11,] 7.650713e-02 0.0004107803 2.854944e-03 8.270238e-03 1.370766e-02
#> [12,] 4.683384e-01 0.0008526818 6.220758e-03 1.669527e-02 4.788772e-02
#> [13,] 8.902851e-02 0.0016539050 1.817736e-02 7.578652e-02 3.075078e-02
#> [14,] 1.312239e-03 0.0001091676 1.893522e-04 3.934865e-04 3.960558e-04
#> [15,] 1.144905e-03 0.0001992830 3.058926e-03 9.925040e-01 6.746272e-04
#> [16,] 8.830679e-01 0.0003939017 2.314621e-03 5.680706e-03 2.843060e-02
#> [17,] 1.847509e-01 0.0006547589 4.857444e-03 1.371905e-02 2.783556e-02
#> [18,] 6.348185e-01 0.0007567033 4.292331e-03 9.267563e-03 2.935316e-01
#> [19,] 8.525695e-01 0.0004570770 2.811184e-03 6.980507e-03 3.295006e-02
#> [20,] 2.945405e-03 0.0001178557 2.683301e-04 6.333183e-04 7.348332e-04
#> [21,] 1.139230e-01 0.0036854126 5.118909e-02 3.017945e-01 4.779627e-02
#> [22,] 8.400734e-01 0.0004814672 3.004774e-03 7.491799e-03 3.458660e-02
#> [23,] 8.769691e-01 0.0004093794 2.161319e-03 4.765688e-03 8.114597e-02
#> [24,] 9.296136e-04 0.0004303843 9.904555e-01 6.123149e-03 6.645268e-04
#> [25,] 9.906913e-01 0.0001256278 2.788719e-04 5.204162e-04 4.476953e-03
#> [26,] 1.453283e-02 0.0001793674 8.319385e-04 2.366907e-03 3.111764e-03
#> [27,] 1.522110e-03 0.0002629218 6.131698e-03 9.881183e-01 9.103786e-04
#> [28,] 5.220301e-04 0.0002634356 9.948102e-01 3.255423e-03 3.863069e-04
#> [29,] 2.147013e-02 0.0042812152 4.218067e-01 5.006966e-01 1.339620e-02
#> [30,] 8.769691e-01 0.0004093794 2.161319e-03 4.765688e-03 8.114597e-02
#> [31,] 1.134448e-02 0.0001796742 5.346273e-04 9.457497e-04 9.835824e-01
#> [32,] 1.463836e-02 0.0012043657 2.672924e-02 9.142733e-01 7.733391e-03
#> [33,] 1.616205e-02 0.0001738885 5.230176e-04 9.516542e-04 9.783189e-01
#> [34,] 1.134448e-02 0.0001796742 5.346273e-04 9.457497e-04 9.835824e-01
#> [35,] 9.994203e-05 0.9995002852 9.994516e-05 9.994353e-05 9.994174e-05
#> [36,] 1.008352e-02 0.0014945820 6.888963e-02 8.939315e-01 5.990788e-03
#> [37,] 1.139230e-01 0.0036854126 5.118909e-02 3.017945e-01 4.779627e-02
#> [38,] 9.721663e-02 0.0004270775 2.027657e-03 4.065135e-03 8.770978e-01
#> [39,] 1.134448e-02 0.0001796742 5.346273e-04 9.457497e-04 9.835824e-01
#> [40,] 1.134448e-02 0.0001796742 5.346273e-04 9.457497e-04 9.835824e-01
#> [41,] 2.945405e-03 0.0001178557 2.683301e-04 6.333183e-04 7.348332e-04
#> [42,] 2.520505e-04 0.0001529189 9.976679e-01 1.382363e-03 2.019259e-04
#> [,6]
#> [1,] 1.189837e-02
#> [2,] 4.819857e-03
#> [3,] 4.016052e-02
#> [4,] 8.867562e-04
#> [5,] 9.667240e-01
#> [6,] 4.177335e-01
#> [7,] 9.835841e-04
#> [8,] 3.454852e-02
#> [9,] 9.657914e-01
#> [10,] 4.669792e-02
#> [11,] 8.982492e-01
#> [12,] 4.600051e-01
#> [13,] 7.846029e-01
#> [14,] 9.975997e-01
#> [15,] 2.418248e-03
#> [16,] 8.011226e-02
#> [17,] 7.681823e-01
#> [18,] 5.733330e-02
#> [19,] 1.042317e-01
#> [20,] 9.953003e-01
#> [21,] 4.816117e-01
#> [22,] 1.143620e-01
#> [23,] 3.454852e-02
#> [24,] 1.396835e-03
#> [25,] 3.906851e-03
#> [26,] 9.789772e-01
#> [27,] 3.054606e-03
#> [28,] 7.625721e-04
#> [29,] 3.834919e-02
#> [30,] 3.454852e-02
#> [31,] 3.413080e-03
#> [32,] 3.542131e-02
#> [33,] 3.870479e-03
#> [34,] 3.413080e-03
#> [35,] 9.994238e-05
#> [36,] 1.960997e-02
#> [37,] 4.816117e-01
#> [38,] 1.916565e-02
#> [39,] 3.413080e-03
#> [40,] 3.413080e-03
#> [41,] 9.953003e-01
#> [42,] 3.428522e-04
#>
#> [[1]]$tau
#> [1] 0.52131387 0.79565966 0.43018417 0.88000000 0.01915634 0.74911746
#>
#> [[1]]$phi
#> [1] 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667 0.1666667
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -11087.2
#> [1] "output_files /tmp/Rtmp11JmwN/timing_mixed_simple-diagnostic-202411271758-1-0c566a.csv\n"
#> init_taus from clustering 0.733321922674837 init_taus from clustering 0.672000532427463 init_taus from clustering 0.850142517825187 init_taus from clustering 0.586603212865031 init_taus from clustering 0.488400959695255 init_taus from clustering 0.789336995435509 init_taus from clustering 0.000212310102096115
#> Attempt 1 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.012119 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 121.19 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Success! Found best value [eta = 1] earlier than expected.
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -11298.291 1.000 1.000
#> 2 -11074.348 0.510 1.000
#> 3 -11021.152 0.342 0.020 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 23.2 seconds.
#> [[1]]
#> [[1]]$w
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 1.521660e-02 6.057874e-03 9.022491e-01 2.684339e-03 1.432609e-03
#> [2,] 6.559477e-03 2.727340e-03 9.605426e-01 1.260768e-03 7.045836e-04
#> [3,] 4.610475e-02 1.664601e-02 6.240375e-01 6.921290e-03 3.523297e-03
#> [4,] 2.420908e-03 6.031509e-04 1.749254e-03 2.653475e-04 1.743754e-04
#> [5,] 7.599297e-01 1.532330e-01 1.496859e-02 1.691455e-02 5.473768e-03
#> [6,] 6.130894e-01 4.514795e-02 3.617104e-02 1.119383e-02 4.547944e-03
#> [7,] 1.458976e-02 3.017971e-03 8.168376e-03 1.033808e-03 5.153186e-04
#> [8,] 2.380897e-02 6.535893e-03 4.209480e-02 2.415827e-03 1.186190e-03
#> [9,] 9.989352e-01 2.762016e-04 1.465904e-04 1.302512e-04 1.107630e-04
#> [10,] 4.881014e-02 2.516112e-02 7.849468e-01 1.300181e-02 7.450449e-03
#> [11,] 9.836328e-01 4.936828e-03 1.770981e-03 1.018300e-03 4.377181e-04
#> [12,] 6.555118e-01 4.404153e-02 3.316193e-02 1.074175e-02 4.341104e-03
#> [13,] 2.034929e-01 7.106782e-01 1.330730e-02 2.857321e-02 7.825785e-03
#> [14,] 9.115034e-01 4.655747e-02 7.183592e-03 6.439626e-03 2.230111e-03
#> [15,] 9.633642e-03 3.281174e-02 2.807126e-03 9.356698e-01 1.368130e-02
#> [16,] 1.781917e-01 2.584288e-02 4.026654e-02 7.515803e-03 3.256569e-03
#> [17,] 9.098140e-01 2.080955e-02 9.726400e-03 4.447673e-03 1.750368e-03
#> [18,] 5.485452e-02 1.721573e-02 2.171052e-01 6.670242e-03 3.279659e-03
#> [19,] 2.175784e-01 2.979261e-02 4.303256e-02 8.518496e-03 3.660599e-03
#> [20,] 9.739304e-01 1.161647e-02 2.434656e-03 1.879301e-03 7.171287e-04
#> [21,] 6.312299e-03 9.853114e-01 9.191477e-04 4.395256e-03 9.668812e-04
#> [22,] 2.335164e-01 3.125721e-02 4.387260e-02 8.879653e-03 3.804300e-03
#> [23,] 2.380897e-02 6.535893e-03 4.209480e-02 2.415827e-03 1.186190e-03
#> [24,] 8.738368e-04 1.451786e-03 4.611863e-04 4.535820e-03 9.917473e-01
#> [25,] 1.330447e-04 1.074317e-04 1.274210e-04 1.024371e-04 1.010667e-04
#> [26,] 9.948683e-01 1.924739e-03 5.429759e-04 4.033769e-04 2.073142e-04
#> [27,] 1.002165e-04 1.007751e-04 1.000189e-04 9.993984e-01 1.005713e-04
#> [28,] 4.173766e-04 6.581632e-04 2.471435e-04 1.973270e-03 9.962082e-01
#> [29,] 2.809580e-02 6.144732e-02 1.079226e-02 5.596360e-01 3.197931e-01
#> [30,] 2.380897e-02 6.535893e-03 4.209480e-02 2.415827e-03 1.186190e-03
#> [31,] 3.256157e-03 1.511761e-03 9.832266e-01 7.611662e-04 4.558787e-04
#> [32,] 4.232289e-02 1.834998e-01 1.075154e-02 7.058151e-01 3.645785e-02
#> [33,] 5.358264e-03 2.250694e-03 9.681132e-01 1.053314e-03 5.974303e-04
#> [34,] 3.256157e-03 1.511761e-03 9.832266e-01 7.611662e-04 4.558787e-04
#> [35,] 9.993033e-05 9.993039e-05 9.993026e-05 9.993049e-05 9.993069e-05
#> [36,] 8.223454e-03 2.115948e-02 2.839910e-03 9.315256e-01 3.091682e-02
#> [37,] 6.312299e-03 9.853114e-01 9.191477e-04 4.395256e-03 9.668812e-04
#> [38,] 2.371959e-02 9.177958e-03 8.385041e-01 3.981690e-03 2.086138e-03
#> [39,] 3.256157e-03 1.511761e-03 9.832266e-01 7.611662e-04 4.558787e-04
#> [40,] 3.256157e-03 1.511761e-03 9.832266e-01 7.611662e-04 4.558787e-04
#> [41,] 9.739304e-01 1.161647e-02 2.434656e-03 1.879301e-03 7.171287e-04
#> [42,] 5.948908e-04 9.940121e-04 3.235903e-04 3.394755e-03 9.940511e-01
#> [,6] [,7]
#> [1,] 7.202850e-02 3.310242e-04
#> [2,] 2.799881e-02 2.063709e-04
#> [3,] 3.020967e-01 6.704794e-04
#> [4,] 9.946763e-01 1.106417e-04
#> [5,] 4.884252e-02 6.377901e-04
#> [6,] 2.891919e-01 6.578908e-04
#> [7,] 9.725159e-01 1.588247e-04
#> [8,] 9.236935e-01 2.648287e-04
#> [9,] 2.998960e-04 1.011339e-04
#> [10,] 1.190369e-01 1.592807e-03
#> [11,] 8.064816e-03 1.385796e-04
#> [12,] 2.515735e-01 6.284011e-04
#> [13,] 3.534711e-02 7.754981e-04
#> [14,] 2.576270e-02 3.230858e-04
#> [15,] 4.825849e-03 5.704999e-04
#> [16,] 7.444024e-01 5.241338e-04
#> [17,] 5.315698e-02 2.950519e-04
#> [18,] 7.002712e-01 6.034718e-04
#> [19,] 6.968425e-01 5.748279e-04
#> [20,] 9.255458e-03 1.665644e-04
#> [21,] 1.933014e-03 1.619809e-04
#> [22,] 6.780773e-01 5.925740e-04
#> [23,] 9.236935e-01 2.648287e-04
#> [24,] 6.179190e-04 3.121519e-04
#> [25,] 9.993285e-01 1.000951e-04
#> [26,] 1.941566e-03 1.117514e-04
#> [27,] 1.000801e-04 9.994801e-05
#> [28,] 3.116029e-04 1.842927e-04
#> [29,] 1.675801e-02 3.477492e-03
#> [30,] 9.236935e-01 2.648287e-04
#> [31,] 1.062263e-02 1.658418e-04
#> [32,] 1.950271e-02 1.650138e-03
#> [33,] 2.243938e-02 1.877486e-04
#> [34,] 1.062263e-02 1.658418e-04
#> [35,] 9.993029e-05 9.994004e-01
#> [36,] 4.582943e-03 7.518283e-04
#> [37,] 1.933014e-03 1.619809e-04
#> [38,] 1.220901e-01 4.404206e-04
#> [39,] 1.062263e-02 1.658418e-04
#> [40,] 1.062263e-02 1.658418e-04
#> [41,] 9.255458e-03 1.665644e-04
#> [42,] 4.250727e-04 2.165620e-04
#>
#> [[1]]$tau
#> [1] 0.7333219227 0.6720005324 0.8501425178 0.5866032129 0.4884009597
#> [6] 0.7893369954 0.0002123101
#>
#> [[1]]$phi
#> [1] 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -11021.2
#> Attempt 2 of 2
#> ------------------------------------------------------------
#> EXPERIMENTAL ALGORITHM:
#> This procedure has not been thoroughly tested and may be unstable
#> or buggy. The interface is subject to change.
#> ------------------------------------------------------------
#> Gradient evaluation took 0.011687 seconds
#> 1000 transitions using 10 leapfrog steps per transition would take 116.87 seconds.
#> Adjust your expectations accordingly!
#> Begin eta adaptation.
#> Iteration: 1 / 250 [ 0%] (Adaptation)
#> Iteration: 50 / 250 [ 20%] (Adaptation)
#> Iteration: 100 / 250 [ 40%] (Adaptation)
#> Iteration: 150 / 250 [ 60%] (Adaptation)
#> Iteration: 200 / 250 [ 80%] (Adaptation)
#> Iteration: 250 / 250 [100%] (Adaptation)
#> Success! Found best value [eta = 0.1].
#> Begin stochastic gradient ascent.
#> iter ELBO delta_ELBO_mean delta_ELBO_med notes
#> 1 -12539.504 1.000 1.000
#> 2 -12542.237 0.500 1.000
#> 3 -12170.449 0.344 0.031 MEDIAN ELBO CONVERGED
#> Drawing a sample of size 1000 from the approximate posterior...
#> COMPLETED.
#> Finished in 36.8 seconds.
#> [[1]]
#> [[1]]$w
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 1.521660e-02 6.057874e-03 9.022491e-01 2.684339e-03 1.432609e-03
#> [2,] 6.559477e-03 2.727340e-03 9.605426e-01 1.260768e-03 7.045836e-04
#> [3,] 4.610475e-02 1.664601e-02 6.240375e-01 6.921290e-03 3.523297e-03
#> [4,] 2.420908e-03 6.031509e-04 1.749254e-03 2.653475e-04 1.743754e-04
#> [5,] 7.599297e-01 1.532330e-01 1.496859e-02 1.691455e-02 5.473768e-03
#> [6,] 6.130894e-01 4.514795e-02 3.617104e-02 1.119383e-02 4.547944e-03
#> [7,] 1.458976e-02 3.017971e-03 8.168376e-03 1.033808e-03 5.153186e-04
#> [8,] 2.380897e-02 6.535893e-03 4.209480e-02 2.415827e-03 1.186190e-03
#> [9,] 9.989352e-01 2.762016e-04 1.465904e-04 1.302512e-04 1.107630e-04
#> [10,] 4.881014e-02 2.516112e-02 7.849468e-01 1.300181e-02 7.450449e-03
#> [11,] 9.836328e-01 4.936828e-03 1.770981e-03 1.018300e-03 4.377181e-04
#> [12,] 6.555118e-01 4.404153e-02 3.316193e-02 1.074175e-02 4.341104e-03
#> [13,] 2.034929e-01 7.106782e-01 1.330730e-02 2.857321e-02 7.825785e-03
#> [14,] 9.115034e-01 4.655747e-02 7.183592e-03 6.439626e-03 2.230111e-03
#> [15,] 9.633642e-03 3.281174e-02 2.807126e-03 9.356698e-01 1.368130e-02
#> [16,] 1.781917e-01 2.584288e-02 4.026654e-02 7.515803e-03 3.256569e-03
#> [17,] 9.098140e-01 2.080955e-02 9.726400e-03 4.447673e-03 1.750368e-03
#> [18,] 5.485452e-02 1.721573e-02 2.171052e-01 6.670242e-03 3.279659e-03
#> [19,] 2.175784e-01 2.979261e-02 4.303256e-02 8.518496e-03 3.660599e-03
#> [20,] 9.739304e-01 1.161647e-02 2.434656e-03 1.879301e-03 7.171287e-04
#> [21,] 6.312299e-03 9.853114e-01 9.191477e-04 4.395256e-03 9.668812e-04
#> [22,] 2.335164e-01 3.125721e-02 4.387260e-02 8.879653e-03 3.804300e-03
#> [23,] 2.380897e-02 6.535893e-03 4.209480e-02 2.415827e-03 1.186190e-03
#> [24,] 8.738368e-04 1.451786e-03 4.611863e-04 4.535820e-03 9.917473e-01
#> [25,] 1.330447e-04 1.074317e-04 1.274210e-04 1.024371e-04 1.010667e-04
#> [26,] 9.948683e-01 1.924739e-03 5.429759e-04 4.033769e-04 2.073142e-04
#> [27,] 1.002165e-04 1.007751e-04 1.000189e-04 9.993984e-01 1.005713e-04
#> [28,] 4.173766e-04 6.581632e-04 2.471435e-04 1.973270e-03 9.962082e-01
#> [29,] 2.809580e-02 6.144732e-02 1.079226e-02 5.596360e-01 3.197931e-01
#> [30,] 2.380897e-02 6.535893e-03 4.209480e-02 2.415827e-03 1.186190e-03
#> [31,] 3.256157e-03 1.511761e-03 9.832266e-01 7.611662e-04 4.558787e-04
#> [32,] 4.232289e-02 1.834998e-01 1.075154e-02 7.058151e-01 3.645785e-02
#> [33,] 5.358264e-03 2.250694e-03 9.681132e-01 1.053314e-03 5.974303e-04
#> [34,] 3.256157e-03 1.511761e-03 9.832266e-01 7.611662e-04 4.558787e-04
#> [35,] 9.993033e-05 9.993039e-05 9.993026e-05 9.993049e-05 9.993069e-05
#> [36,] 8.223454e-03 2.115948e-02 2.839910e-03 9.315256e-01 3.091682e-02
#> [37,] 6.312299e-03 9.853114e-01 9.191477e-04 4.395256e-03 9.668812e-04
#> [38,] 2.371959e-02 9.177958e-03 8.385041e-01 3.981690e-03 2.086138e-03
#> [39,] 3.256157e-03 1.511761e-03 9.832266e-01 7.611662e-04 4.558787e-04
#> [40,] 3.256157e-03 1.511761e-03 9.832266e-01 7.611662e-04 4.558787e-04
#> [41,] 9.739304e-01 1.161647e-02 2.434656e-03 1.879301e-03 7.171287e-04
#> [42,] 5.948908e-04 9.940121e-04 3.235903e-04 3.394755e-03 9.940511e-01
#> [,6] [,7]
#> [1,] 7.202850e-02 3.310242e-04
#> [2,] 2.799881e-02 2.063709e-04
#> [3,] 3.020967e-01 6.704794e-04
#> [4,] 9.946763e-01 1.106417e-04
#> [5,] 4.884252e-02 6.377901e-04
#> [6,] 2.891919e-01 6.578908e-04
#> [7,] 9.725159e-01 1.588247e-04
#> [8,] 9.236935e-01 2.648287e-04
#> [9,] 2.998960e-04 1.011339e-04
#> [10,] 1.190369e-01 1.592807e-03
#> [11,] 8.064816e-03 1.385796e-04
#> [12,] 2.515735e-01 6.284011e-04
#> [13,] 3.534711e-02 7.754981e-04
#> [14,] 2.576270e-02 3.230858e-04
#> [15,] 4.825849e-03 5.704999e-04
#> [16,] 7.444024e-01 5.241338e-04
#> [17,] 5.315698e-02 2.950519e-04
#> [18,] 7.002712e-01 6.034718e-04
#> [19,] 6.968425e-01 5.748279e-04
#> [20,] 9.255458e-03 1.665644e-04
#> [21,] 1.933014e-03 1.619809e-04
#> [22,] 6.780773e-01 5.925740e-04
#> [23,] 9.236935e-01 2.648287e-04
#> [24,] 6.179190e-04 3.121519e-04
#> [25,] 9.993285e-01 1.000951e-04
#> [26,] 1.941566e-03 1.117514e-04
#> [27,] 1.000801e-04 9.994801e-05
#> [28,] 3.116029e-04 1.842927e-04
#> [29,] 1.675801e-02 3.477492e-03
#> [30,] 9.236935e-01 2.648287e-04
#> [31,] 1.062263e-02 1.658418e-04
#> [32,] 1.950271e-02 1.650138e-03
#> [33,] 2.243938e-02 1.877486e-04
#> [34,] 1.062263e-02 1.658418e-04
#> [35,] 9.993029e-05 9.994004e-01
#> [36,] 4.582943e-03 7.518283e-04
#> [37,] 1.933014e-03 1.619809e-04
#> [38,] 1.220901e-01 4.404206e-04
#> [39,] 1.062263e-02 1.658418e-04
#> [40,] 1.062263e-02 1.658418e-04
#> [41,] 9.255458e-03 1.665644e-04
#> [42,] 4.250727e-04 2.165620e-04
#>
#> [[1]]$tau
#> [1] 0.3965669 0.6194093 0.8699277 0.0700000 0.7828862 0.6621517 0.4771371
#>
#> [[1]]$phi
#> [1] 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571 0.1428571
#>
#> [[1]]$kappa
#> [1] 5
#> ELBO for this run: -12170.4
#> [1] "output_files /tmp/Rtmp11JmwN/timing_mixed_simple-diagnostic-202411271759-1-439c4c.csv\n"
results <- data_reduced$results_timing
input_data <- results$data$input_data
draws_and_summary <- results$draws_and_summary
log_lik_matrix_list <- results$log_lik_matrix_list
elbo_iterations <- results$elbo_iterations
results_model_selection <- model_selection_h(input_data, draws_and_summary, log_lik_matrix_list, elbo_iterations)
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> [1] "entropy per segment: "
#> [1] -0.003462998
#> [1] "entropy per segment normalized: "
#> [1] -9.999995e-07
#> [1] "entropy per segment: "
#> [1] 153.3267
#> [1] "entropy per segment normalized: "
#> [1] 0.0442757
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> [1] "entropy per segment: "
#> [1] 148.632
#> [1] "entropy per segment normalized: "
#> [1] 0.04292002
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> [1] "entropy per segment: "
#> [1] 14.57779
#> [1] "entropy per segment normalized: "
#> [1] 0.004209585
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> [1] "entropy per segment: "
#> [1] 414.8372
#> [1] "entropy per segment normalized: "
#> [1] 0.1197913
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> [1] "entropy per segment: "
#> [1] 1200.246
#> [1] "entropy per segment normalized: "
#> [1] 0.3465915
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> [1] "entropy per segment: "
#> [1] 451.0137
#> [1] "entropy per segment normalized: "
#> [1] 0.1302379
best_K <- results_model_selection$best_K
model_selection_tibble <- results_model_selection$model_selection_tibble
entropy <- results_model_selection$entropy_list
p <- plot_inference_h(results, best_K)
p