「Regression model」の版間の差分
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*'''Multivariable† multinominal logistic regression''' | *'''Multivariable† multinominal logistic regression''' | ||
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+ | Maximum likelihood estimation method | ||
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!Ordinal | !Ordinal | ||
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*'''Multivariable† ordinal logistic regression''' | *'''Multivariable† ordinal logistic regression''' | ||
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+ | Maximum likelihood estimation method | ||
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!Rate ratio | !Rate ratio | ||
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::<math>\log Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math><br>where <math>Y</math> is '''rate ratio''' <math>\frac{events_1/person \cdot time}{events_2/person \cdot time}</math> | ::<math>\log Y = a + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math><br>where <math>Y</math> is '''rate ratio''' <math>\frac{events_1/person \cdot time}{events_2/person \cdot time}</math> | ||
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+ | Maximum likelihood estimation method | ||
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!Survival time | !Survival time | ||
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::<math>\log h(T) = \log h_0(T) + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math><br>where <math>h(T)</math> is the hazard at time <math>T</math><br>and <math>h_0(T)</math> is the baseline hazard at time <math>T</math> | ::<math>\log h(T) = \log h_0(T) + b_1X_1 + b_2X_2 + b_3X_3 + \cdots</math><br>where <math>h(T)</math> is the hazard at time <math>T</math><br>and <math>h_0(T)</math> is the baseline hazard at time <math>T</math> | ||
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+ | Maximum likelihood estimation method | ||
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2023年2月4日 (土) 17:10時点における版
目次
Classification of Regression models
Independent variable (exposure) | ||||
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Univariable (single variable) | Multivariable (multiple variables) | How to derive coefficients [math]\displaystyle{ b_i }[/math] | ||
Dependent variable (outcome) |
Continuous |
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Least squares method |
Binary |
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Maximum likelihood estimation method | |
Multinominal ≥ 3 |
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Maximum likelihood estimation method | |
Ordinal |
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Maximum likelihood estimation method | |
Rate ratio |
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Maximum likelihood estimation method | ||
Survival time |
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Maximum likelihood estimation method |
†'Multivariable' can be rephrased as 'Multiple'; Multivariable is NOT equal to 'Multivariate'!!
Generalized linear model
Penalized multivariable logistic regression model
- Penalized Logistic Regression Essentials in R: Ridge, Lasso and Elastic Net
- 罰則付き・正則化回帰モデルについて(About penalized/regularized regression model)