R_simulation_code

Dryad2017-01-01 更新2026-04-13 收录

表型实验进化

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http://datadryad.org/resource/doi:10.5061/dryad.bg08n/1 数据链接链接失效反馈

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资源简介：

simulation code for phenotypic experimental evolution and power analysis (written by Thiago Guzella, guzella@biologie.ens.fr)

创建时间：

2017-01-01

Supplement to ``Investigation of methods to enhance the power of high-dimensional tests"

高维统计

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This is a simulation result of the effectiveness improvement methods for high-dimensional data testing (such as mean testing, linear model testing, and independence testing), including images and tabl

科学数据银行2024-10-17 更新60

Statistical power simulations of the gene expression dataset.

差异基因表达分析

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The statistical power (Z; 1- ß) is the probability of obtaining statistical significance in comparing gene expression. Simulations for unpaired two-class comparison statistics are described in the Mat

NIAID Data Ecosystem60

Additional file 3: of Gain of power of the general regression model compared to Cochran-Armitage Trend tests: simulation study and application to bipolar disorder

双相情感障碍遗传关联分析

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Appendix S1. and Appendix S2. reported shell and Pearl scripts which included PLINK commands [19]) used to analyze real dataset and to perform all simulations, computation of type I error and power es

NIAID Data Ecosystem50

Appendix D. Power analysis when varying the number of stations sampled per year.

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灾害监测网络优化

Power analysis when varying the number of stations sampled per year.

NIAID Data Ecosystem60

Post-hoc power analysis to determine statistical power of the stereological quantifications performed in two previous publications from our laboratory.

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体视学量化

*Effect size, f, was determined using one-way ANOVA to compare multiple groups. #Effect size, d, was determined using Student's t-tests to compare between two groups.

NIAID Data Ecosystem60

R_simulation_code

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