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Fit y ~ 3 - 2 x + 0.05 * sin(x) model using 20 points (sin(x) ~ noise)
trend coefficients = [3.01168,-2.00025]
Fit y ~ 1 + 0.1 x + 10 x^2 model using 100 points
result =
LinearModelResult
- input dimension=2
- basis size=3
- design matrix=100 x 3
- coefficients=3
- formula=Basis( [[v0,v1]->[1],[v0,v1]->[v0],[v0,v1]->[v1]] )
- coefficients names=[[v0,v1]->[1],[v0,v1]->[v0],[v0,v1]->[v1]]
- residuals size=100
- standard residuals size=100
- inverse Gram diagonal=[0.0864939,0.0184756,0.000172994]
- leverages size=100
- Cook's distances size=100
- residuals variance=0.00991604
- has intercept=true
- is model selection=false
trend coefficients = [0.978992,0.110565,9.99924]
LinearModelResult
- input dimension=2
- basis size=3
- design matrix=100 x 3
- coefficients=3
- formula=Basis( [[X0,X1]->[1],[X0,X1]->[X0],[X0,X1]->[X1]] )
- coefficients names=[[X0,X1]->[1],[X0,X1]->[X0],[X0,X1]->[X1]]
- residuals size=100
- standard residuals size=100
- inverse Gram diagonal=[0.0864939,0.0184756,0.000172994]
- leverages size=100
- Cook's distances size=100
- residuals variance=0.00991604
- has intercept=true
- is model selection=false
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