Example R run for NCAA Data

> read.table("ncaa.data2.txt",header=T)->ncaa2

> nams(x=ncaa2[,1:19],y=ncaa2[,20],B=4000,seed=231)->out
   var   pval  pvmax
1    2 0.0000 0.0000
2    3 0.0001 0.0001
3    5 0.0116 0.0116
4    4 0.0053 0.0116
5    7 0.0025 0.0116
6   17 0.0433 0.0433
7   15 0.0527 0.0527
8    6 0.1056 0.1056
9    9 0.0826 0.1056
10   8 0.0536 0.1056
11  12 0.2350 0.2350
12  10 0.2864 0.2864
13  13 0.3163 0.3163
14  18 0.2697 0.3163
15  11 0.4953 0.4953
16   1 0.6326 0.6326
17  14 0.7056 0.7056
18  19 0.8605 0.8605
19  16 0.9032 0.9032
   m  n    B seed      tau ng   alphahat
1 19 94 4000  231 6.847734 74 0.04488502

Variables Selected = x2 x3 x5 x4 x7 x17

> summary(out$mod)

Call:
lm(formula = y ~ x)

Residuals:
    Min      1Q  Median      3Q     Max 
-13.453  -5.368  -1.075   5.562  13.801 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -25.32221   12.04056  -2.103 0.038347 *  
xx2           3.04148    0.50466   6.027 3.93e-08 ***
xx3           0.20950    0.07563   2.770 0.006853 ** 
xx5           0.28602    0.07652   3.738 0.000331 ***
xx4           0.78783    0.19885   3.962 0.000152 ***
xx7          -2.72718    0.81983  -3.327 0.001290 ** 
xx17         -0.12016    0.05860  -2.051 0.043315 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Residual standard error: 7.075 on 87 degrees of freedom
Multiple R-Squared: 0.8197,     Adjusted R-squared: 0.8072 
F-statistic: 65.91 on 6 and 87 DF,  p-value: < 2.2e-16