models.md (1287B)
1 ## Regression Models 2 3 ## Instance Functionality 4 5 ### ols( endog, exog ) 6 7 What's the `endog`, `exog`? 8 9 Please see: 10 11 http://statsmodels.sourceforge.net/stable/endog_exog.html 12 13 `ols` use ordinary least square(OLS) method to estimate linear model and return 14 a `model`object. 15 16 `model` object attribute is vrey like to `statsmodels` result object attribute 17 (nobs,coef,...). 18 19 The following example is compared by `statsmodels`. They take same result 20 exactly. 21 22 var A=[[1,2,3], 23 [1,1,0], 24 [1,-2,3], 25 [1,3,4], 26 [1,-10,2], 27 [1,4,4], 28 [1,10,2], 29 [1,3,2], 30 [1,4,-1]]; 31 var b=[1,-2,3,4,-5,6,7,-8,9]; 32 var model=jStat.models.ols(b,A); 33 34 // coefficient estimated 35 model.coef // -> [0.662197222856431, 0.5855663255775336, 0.013512111085743017] 36 37 // R2 38 model.R2 // -> 0.309 39 40 // t test P-value 41 model.t.p // -> [0.8377444317889267, 0.15296736158442314, 0.9909627983826583] 42 43 // f test P-value 44 model.f.pvalue // -> 0.3306363671859872 45 46 The adjusted R^2 provided by jStat is the formula variously called the 'Wherry Formula', 47 'Ezekiel Formula', 'Wherry/McNemar Formula', or the 'Cohen/Cohen Formula', and is the same 48 as the adjusted R^2 value provided by R's `summary.lm` method on a linear model.