iNEXT: An R package for interpolation and extrapolation in measuring species ...Johnson Hsieh
This document discusses species diversity and the iNEXT R package for interpolating and extrapolating species diversity measures. It begins with definitions of species diversity and effective number of species. It explains why interpolation and extrapolation of diversity curves is needed when sample sizes are limited. The document demonstrates use of the iNEXT package functions on sample data and introduces the shiny package for building interactive web apps in R.
This document discusses association and attributes in statistics. It provides three methods for determining the association between two attributes/variables: the frequency method, proportion method, and Yule's coefficient of association. The frequency method compares observed and expected values, the proportion method compares ratios, and Yule's coefficient ranges from -1 to 1 to measure the intensity of association. Formulas are provided for the proportion method and Yule's coefficient to calculate the nature and strength of association between two attributes.
有人用勞力做公益,也有人用財力做公益,如果用「資料力」來做公益,會擦出怎樣的火花?D4SG 資料公益計畫(Data for Social Good)結合公益與實務,透過專業的資料人與公益組織合作,攜手解決因資料而生、也能因資料而解的問題。
在這場演講你將可以知道資料科學是什麼、它有什麼迷人的特質、在地的真實案例、資料的極限以及執行資料專案所面對的挑戰。
This document provides an overview of simple linear regression and correlation analysis. It defines regression as estimating the relationship between two variables and correlation as measuring the strength and direction of that relationship. The key points covered include:
- Regression finds an estimating equation to relate known and unknown variables. Correlation determines how well that equation fits the data.
- Pearson's correlation coefficient r measures the linear relationship between two variables on a scale from -1 to 1.
- The coefficient of determination r2 indicates what percentage of variation in the dependent variable is explained by the independent variable.
- Statistical tests can evaluate whether a correlation is statistically significant or could be due to chance.
iNEXT: An R package for interpolation and extrapolation in measuring species ...Johnson Hsieh
This document discusses species diversity and the iNEXT R package for interpolating and extrapolating species diversity measures. It begins with definitions of species diversity and effective number of species. It explains why interpolation and extrapolation of diversity curves is needed when sample sizes are limited. The document demonstrates use of the iNEXT package functions on sample data and introduces the shiny package for building interactive web apps in R.
This document discusses association and attributes in statistics. It provides three methods for determining the association between two attributes/variables: the frequency method, proportion method, and Yule's coefficient of association. The frequency method compares observed and expected values, the proportion method compares ratios, and Yule's coefficient ranges from -1 to 1 to measure the intensity of association. Formulas are provided for the proportion method and Yule's coefficient to calculate the nature and strength of association between two attributes.
有人用勞力做公益,也有人用財力做公益,如果用「資料力」來做公益,會擦出怎樣的火花?D4SG 資料公益計畫(Data for Social Good)結合公益與實務,透過專業的資料人與公益組織合作,攜手解決因資料而生、也能因資料而解的問題。
在這場演講你將可以知道資料科學是什麼、它有什麼迷人的特質、在地的真實案例、資料的極限以及執行資料專案所面對的挑戰。
This document provides an overview of simple linear regression and correlation analysis. It defines regression as estimating the relationship between two variables and correlation as measuring the strength and direction of that relationship. The key points covered include:
- Regression finds an estimating equation to relate known and unknown variables. Correlation determines how well that equation fits the data.
- Pearson's correlation coefficient r measures the linear relationship between two variables on a scale from -1 to 1.
- The coefficient of determination r2 indicates what percentage of variation in the dependent variable is explained by the independent variable.
- Statistical tests can evaluate whether a correlation is statistically significant or could be due to chance.
This document contains a collection of links to examples of data visualization on various topics such as popular baby names, American whaling, user demographics by city and gender, dengue fever maps of Tainan, human vs bot classification, and funny data visualizations. It also includes examples comparing line charts to area charts and discussions around correlation vs causation. Recommended visualization tools mentioned include ggplot2, Plotly, and CartoDB. The document calls for data science talks and shares information about an upcoming data value competition platform in Taiwan.
The document discusses using the EM algorithm to estimate parameters in a generalized linear finite mixture model. It introduces the model and describes how the likelihood equation can be split into two terms, allowing the estimators to be separated into an iterative M-step that fits two standard non-mixture problems using weights and multinomial or Poisson data. An example application fits Weibull distributions to mutant and control mouse data.
This document contains a collection of links to examples of data visualization on various topics such as popular baby names, American whaling, user demographics by city and gender, dengue fever maps of Tainan, human vs bot classification, and funny data visualizations. It also includes examples comparing line charts to area charts and discussions around correlation vs causation. Recommended visualization tools mentioned include ggplot2, Plotly, and CartoDB. The document calls for data science talks and shares information about an upcoming data value competition platform in Taiwan.
The document discusses using the EM algorithm to estimate parameters in a generalized linear finite mixture model. It introduces the model and describes how the likelihood equation can be split into two terms, allowing the estimators to be separated into an iterative M-step that fits two standard non-mixture problems using weights and multinomial or Poisson data. An example application fits Weibull distributions to mutant and control mouse data.
iNEXT: an r package for interpolation and extrapolation species diversity
1. iNEXT : an R package for interpolation and
extrapolation species diversity
種類數的稀釋與預測方法
謝宗震 (Johnson)
2. About me
· 清華統計所
- 研究領域:Statistics,
ecology
and
genetics
· Taiwan R User Group Officer
· Data Science Program 籌備委員
· R 相關作品:
- R package: CARE1 [主要作者], iNEXT
[主要作者], ChaoEntropy, ChaoSpecies
- Shiny app: iNEXT-Online [主要作者], LoL
Champion
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7. 統計方法 — 種類數的稀釋與預測
時,
)m (S
資料中出現的種類數
m
利用樣本數對種類數的稀釋與預測曲線 (rarefaction and extrapolation curve),來描述樣本數為
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8. 8/15
1
n > m fi ])
2
fn
f2
)
n ≤ m fi
)
− 1( − 1 [ 0f +
ˆ
m
n
(
x− n
sb o
S = )m ( S
ˆ
0> i x
∑ −
m
i
sb o S
(
= )m ( S
ˆ
統計學家 (Smith and Grassle 1977, Shen et al. 2003) 得到估計量
1= i
]
) ip − 1 ( − 1[∑ = )m (S
m
S
稀釋與預測函數的期望值 (Good 1953)
統計方法 — 種類數的稀釋與預測(續)
9. R套件 iNEXT
R package iNEXT = mehtod of iNTerpolation and EXTrapolation curve
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irr(NX)
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15. 參考文獻
1. Chao, A. 1984. Nonparametric estimation of the number of classes in a population. Scandinavian
Journal of Statistics 11:265-270.
2. Colwell, R. K., A. Chao, N. J. Gotelli, S. Y. Lin, C. X. Mao, R. L. Chazdon, and J. T. Longino.
2012. Models and estimators linking individual-based and sample-based rarefaction,
extrapolation and comparison of assemblages. Journal of Plant Ecology 5:3-21.
3. Hsieh, T. C., K. H. Ma, and A. Chao. 2013. iNEXT online: interpolation and extrapolation
(Version 1.3.0) [Software]. Available from http://chao.stat.nthu.edu.tw/blog/software-download/.
4. Hsieh, T. C., K. H. Ma, and A. Chao. 2013. iNEXT: an R package for interpolation and
extrapolation species diversity. http://johnsonhsieh.github.io/iNEXT/
5. Ramnath V. 2012. slidify: Generate
http://ramnathv.github.com/slidify/
reproducible
html5
slides
from
R
markdown.
6. Taiwan R User Group. 2013. R topic - estimation and prediction of richness. Programmer
magazine 12:48-53. http://programmermagazine.github.io/201312/htm/article6.html
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