time-to-botec/squiggle/node_modules/jstat/doc/md/models.md
NunoSempere b6addc7f05 feat: add the node modules
Necessary in order to clearly see the squiggle hotwiring.
2022-12-03 12:44:49 +00:00

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