statistics - How to calculate P values in ridge regression in R? -


following example ?lm.ridge of mass package: 

> data(longley) # not same s-plus dataset > names(longley)[1] <- "y" > lm.ridge(y ~ ., longley)                         gnp    unemployed  armed.forces    population          year      employed  2946.85636017    0.26352725    0.03648291    0.01116105   -1.73702984   -1.41879853    0.23128785  > plot(lm.ridge(y ~ ., longley, +               lambda = seq(0,0.1,0.001))) > select(lm.ridge(y ~ ., longley, +                lambda = seq(0,0.1,0.0001))) modified hkb estimator 0.006836982  modified l-w estimator 0.05267247  smallest value of gcv  @ 0.0057

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how can calculate p values or confidence intervals can in usual linear regression summary.

as far concerned mass::lm.ridge not calculate p-values coefficients. use linearridge function ridge package, however, does. see following example:

data(longley)  names(longley)[1] <- "y"  library(ridge) mymod <- linearridge(y ~ ., longley)  > summary(mymod)  call: linearridge(formula = y ~ ., data = longley)   coefficients:                estimate scaled estimate std. error (scaled) t value (scaled) pr(>|t|)     (intercept)  -1.247e+03              na                  na               na       na     gnp           4.338e-02       1.670e+01           3.689e+00            4.526  6.0e-06 *** unemployed    1.184e-02       4.286e+00           2.507e+00            1.710   0.0873 .   armed.forces  1.381e-02       3.721e+00           1.905e+00            1.953   0.0508 .   population   -2.831e-02      -7.627e-01           5.285e+00            0.144   0.8853     year          6.566e-01       1.211e+01           2.691e+00            4.500  6.8e-06 *** employed      6.745e-01       9.175e+00           4.996e+00            1.836   0.0663 .   --- signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1  ridge parameter: 0.01046912, chosen automatically, computed using 2 pcs  degrees of freedom: model 3.67 , variance 3.218 , residual 4.123

and using summary familiar looking table p-values , significance!


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