49 lines
1.3 KiB
Markdown
49 lines
1.3 KiB
Markdown
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## Regression Models
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## Instance Functionality
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### ols( endog, exog )
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What's the `endog`, `exog`?
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Please see:
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http://statsmodels.sourceforge.net/stable/endog_exog.html
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`ols` use ordinary least square(OLS) method to estimate linear model and return
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a `model`object.
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`model` object attribute is vrey like to `statsmodels` result object attribute
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(nobs,coef,...).
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The following example is compared by `statsmodels`. They take same result
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exactly.
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var A=[[1,2,3],
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[1,1,0],
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[1,-2,3],
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[1,3,4],
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[1,-10,2],
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[1,4,4],
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[1,10,2],
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[1,3,2],
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[1,4,-1]];
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var b=[1,-2,3,4,-5,6,7,-8,9];
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var model=jStat.models.ols(b,A);
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// coefficient estimated
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model.coef // -> [0.662197222856431, 0.5855663255775336, 0.013512111085743017]
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// R2
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model.R2 // -> 0.309
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// t test P-value
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model.t.p // -> [0.8377444317889267, 0.15296736158442314, 0.9909627983826583]
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// f test P-value
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model.f.pvalue // -> 0.3306363671859872
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The adjusted R^2 provided by jStat is the formula variously called the 'Wherry Formula',
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'Ezekiel Formula', 'Wherry/McNemar Formula', or the 'Cohen/Cohen Formula', and is the same
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as the adjusted R^2 value provided by R's `summary.lm` method on a linear model.
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