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5 results for “Parametric bootstrap”
Рис. 1. Calyptra thalictri: 1 — Calyptra thalictri alexander ssp. n., гоΛотип; 2 — Calyptra thalictri alexander ssp. n., паратип; 3 — кΛаΑограмма Calyptra thalictri. Построена метоΑом максимаΛьного схоΑства, параметрическая моΑеΛь Тамура-Неи, 10 000 бутстрапрепΛикаций; 4 — биотоп Calyptra thalictri alexander ssp. n. Fig. 1. Calyptra thalictri: 1 — Calyptra thalictri alexander ssp. n., holotype; 2 — Calyptra thalictri alexander ssp. n., paratype; 3 — cladogram of Calyptra thalictri. Based on the maximum likelihood method, Tamura-Nei parametrical model, 10000 bootstrap replications; 4 — biotope of Calyptra thalictri alexander ssp. n. in A New Subspecies Of (Borkhausen, 1790) (Lepidoptera: Erebidae, Calpinae) From Kyrgyzstan
Рис. 1. Calyptra thalictri: 1 — Calyptra thalictri alexander ssp. n., гоΛотип; 2 — Calyptra thalictri alexander ssp. n., паратип; 3 — кΛаΑограмма Calyptra thalictri. Построена метоΑом максимаΛьного схоΑства, параметрическая моΑеΛь Тамура-Неи, 10 000 бутстрапрепΛикаций; 4 — биотоп Calyptra thalictri alexander ssp. n. Fig. 1. Calyptra thalictri: 1 — Calyptra thalictri alexander ssp. n., holotype; 2 — Calyptra thalictri alexander ssp. n., paratype; 3 — cladogram of Calyptra thalictri. Based on the maximum likelihood method, Tamura-Nei parametrical model, 10000 bootstrap replications; 4 — biotope of Calyptra thalictri alexander ssp. n.
Simulated data for paper "Conditional non-parametric bootstrap for non-linear mixed effect models"
<p>Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4) with a rich (scenarios 1 and 3) and a sparse design (scenarios 2 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder):<br> - scenario 1 - pdemax.rich<br> - scenario 2 - pdemax.sparse<br> - scenario 3 - pdhillhigh.rich<br> - scenario 4 - pdhillhigh.sparse<br> The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse.</p>
Improving Phylogenies Based on Average Nucleotide Identity, Incorporating Saturation Correction and Non-Parametric Bootstrap Support
<p>Whole genome comparisons based on Average Nucleotide Identities (ANI) and the Genome-to-genome distance calculator have risen to prominence in rapidly classifying prokaryotic taxa using whole genome sequences. Some implementations have even been proposed as a new standard in species classification and have become a common technique for papers describing newly sequenced genomes. However, attempts to apply whole genome divergence data to delineation of higher taxonomic units and to phylogenetic inference have had difficulty matching those produced by more complex phylogenetic methods. We present a novel method for generating statistically supported phylogenies of archaeal and bacterial groups using a combined ANI and alignment fraction-based metric. For the test cases to which we applied the developed approach we obtained results comparable with other methodologies up to at least the family-level. The developed method uses non-parametric bootstrapping to gauge support for inferred groups. This method offers the opportunity to make use of whole-genome comparison data, that are already being generated, to quickly produce phylogenies including support for inferred groups. Additionally, the developed ANI methodology can assist classification of higher taxonomic groups.<br> <br> Included herein are supplemental materials, and all whole genome datasets used throughout the construction of this work.</p>
Improving Phylogenies Based on Average Nucleotide Identity, Incorporating Saturation Correction and Non-Parametric Bootstrap Support
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Raw data and results for the paper "Conditional non-parametric bootstrap for non-linear mixed effect models"
<p>*Data* (comets_condBoot_data.zip)</p> <p>Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder):<br> - scenario 1 - pdemax.rich<br> - scenario 2 - pdemax.sparse<br> - scenario 3 - pdhillhigh.rich<br> - scenario 4 - pdhillhigh.sparse<br> The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse.</p> <p>*Results:* (comets_scenarioXXX_results.zip, XXX=1,.. 6)</p> <p>6 simulation scenarios were assessed in the paper. Each file corresponds to 1 of 6 folders, one for each scenario:<br> - scenario 1 - pdemax.rich/results<br> - scenario 2 - pdemax.sparse/results<br> - scenario 3 - pdhillhigh.rich/results<br> - scenario 4 - pdhillhigh.sparse/results<br> - scenario 5 - pdhillhigh.n20rich/results<br> - scenario 6 - pdhillhigh.n20sparse/results</p> <p>In each "results" subfolder, the results for each bootstrap method and each dataset are written to a separate file, eg for simulation 1 in the first scenario:<br> - case bootstrap: scenarioHill1_bootstrapCase_sim1.res <br> - non-parametric bootstrap: scenarioHill1_bootstrapNP_sim1.res<br> - conditional non-parametric bootstrap: scenarioHill1_bootstrapNPc_sim1.res<br> - parametric bootstrap: scenarioHill1_bootstrapPar_sim1.res<br> The folder also contains:<br> - the saemix estimates for the 200 simulations: scenarioHill1_fitOrig.res<br> - tables with the bias and SE for the different bootstraps over the set of simulations, used to evaluate the methods: rbiasSEboot200.res, rbiasSEboot.res, rbiasWRsampleEstimates.res</p> <p> </p>
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OpenNeuro
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