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Related Concept Videos

Frequency-dependent Selection01:21

Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Determination of Expected Frequency01:08

Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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Construction of Frequency Distribution01:15

Construction of Frequency Distribution

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A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is...
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Random Sampling Method01:09

Random Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Demographic model selection using random forests and the site frequency spectrum.

Megan L Smith1, Megan Ruffley2,3, Anahí Espíndola2,3

  • 1Department of Evolution, Ecology & Organismal Biology, The Ohio State University, Columbus, OH, USA.

Molecular Ecology
|July 1, 2017
PubMed
Summary
This summary is machine-generated.

New statistical methods leverage Random Forest (RF) and binned site frequency spectra to analyze large phylogeographic datasets, overcoming limitations of traditional approximate Bayesian computation (ABC). This approach improves demographic history inference and model selection accuracy.

Keywords:
RADseqmachine learningmodel selectionphylogeography

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Phylogeographic datasets have rapidly expanded, exceeding the capacity of current statistical methods.
  • Traditional approximate Bayesian computation (ABC) struggles with high-dimensional next-generation sequencing (NGS) data due to the curse of dimensionality and simulation challenges.

Purpose of the Study:

  • To develop and evaluate improved statistical methods for analyzing large phylogeographic datasets.
  • To overcome the limitations of traditional ABC in handling high-dimensional NGS data.
  • To enhance the accuracy and efficiency of demographic history inference and model selection.

Main Methods:

  • Implemented a Random Forest (RF) classifier for dimensionality reduction in model selection.
  • Utilized a binned multidimensional site frequency spectrum (mSFS) to manage large SNP datasets.
  • Evaluated method performance using simulations and applied the approach to Haplotrema vancouverense data.

Main Results:

  • Achieved low overall error rates (~7%) in demographic model comparison through simulations.
  • Supported a recent coastal-to-inland rainforest dispersal model for Haplotrema vancouverense.
  • Demonstrated that RF and binned mSFS are effective strategies for large-scale phylogeographic analysis.

Conclusions:

  • The combined strategies of RF and binned mSFS significantly enhance the analysis of large phylogeographic datasets.
  • This approach offers statistical power comparable to traditional ABC methods while handling greater data complexity.
  • The developed methods enable the evaluation of extensive sets of demographic models, including those with varying population numbers.