{smcl}

{com}{sf}{ul off}{txt}{.-}

name: {res}<unnamed>

{txt}log: {res}C:\Users\ecomes\Documents\GitHub\lassopack\lassopack_v141incomplete\cs_cvlasso.smcl

{txt}log type: {res}smcl

{txt}opened on: {res}27 Sep 2020, 20:21:45

{txt}r; t=0.02 20:21:45

{com}. about

{txt}Stata/SE 16.1 for Windows (64-bit x86-64)

Revision 30 Jun 2020

Copyright 1985-2019 StataCorp LLC

Total physical memory: {res} 16.00 GB

{txt}Available physical memory: {res} 5.19 GB

{txt}Stata license: {res}5-user network perpetual

{txt}Serial number: {res}401606228165

{txt}Licensed to: {res}Mark Schaffer

Heriot-Watt University

{txt}r; t=0.01 20:21:45

{com}. which cvlasso

{txt}.\cvlasso.ado

{res}*! cvlasso 1.0.11 27sept2020

*! lassopack package 1.4.1

*! authors aa/ms

{txt}r; t=0.00 20:21:45

{com}. which lasso2

{txt}.\lasso2.ado

{res}*! lasso2 1.0.12 27sept2020

*! lassopack package 1.4.1

*! authors aa/ms

{txt}r; t=0.00 20:21:45

{com}. which lasso2_p

{txt}.\lasso2_p.ado

{res}*! lasso2_p 1.0.06 14oct2019

*! lassopack package 1.4

*! authors aa/ms

{txt}r; t=0.00 20:21:45

{com}. which lassoutils

{txt}.\lassoutils.ado

{res}*! lassoutils 1.2.03 27sept2020

*! lassopack package 1.4.1

*! authors aa/cbh/ms

{txt}r; t=0.01 20:21:45

{com}.

. * data source

. * global prostate prostate.data

. global prostate https://web.stanford.edu/~hastie/ElemStatLearn/datasets/prostate.data

{txt}r; t=0.00 20:21:45

{com}.

. * program to compare two matrices in terms of avg abs deviation

. cap program drop comparemat

{txt}r; t=0.00 20:21:45

{com}. program define comparemat , rclass

{txt} 1{com}. syntax anything [, tol(real 10e-3)]

{txt} 2{com}. local A : word 1 of `0'

{txt} 3{com}. local B : word 2 of `0'

{txt} 4{com}. tempname Amat Bmat

{txt} 5{com}. mat `Amat' = `A'

{txt} 6{com}. mat `Bmat' = `B'

{txt} 7{com}. local diff=mreldif(`Amat',`Bmat')

{txt} 8{com}. di as text "mreldif=`diff'. tolerance = `tol'"

{txt} 9{com}. mat list `Amat'

{txt} 10{com}. mat list `Bmat'

{txt} 11{com}. return scalar mreldif = `diff'

{txt} 12{com}. assert `diff'<`tol'

{txt} 13{com}. end

{txt}r; t=0.00 20:21:45

{com}.

.

. set seed 123456

{txt}r; t=0.00 20:21:45

{com}.

. ********************************************************************************

. *** compare with glmnet ***

. ********************************************************************************

.

. * load example data

. insheet using "$prostate", tab clear

{txt}(11 vars, 97 obs)

r; t=1.50 20:21:47

{com}. drop if _n==97 // to ensure same size for each fold

{txt}(1 observation deleted)

r; t=0.00 20:21:47

{com}.

. global model lpsa lcavol lweight age lbph svi lcp gleason pgg45

{txt}r; t=0.00 20:21:47

{com}.

. * generate fold variable

. gen myfid = 1 if _n<=32

{txt}(64 missing values generated)

r; t=0.00 20:21:47

{com}. replace myfid = 2 if _n>32 & _n<=64

{txt}(32 real changes made)

r; t=0.00 20:21:47

{com}. replace myfid = 3 if _n>64

{txt}(32 real changes made)

r; t=0.00 20:21:47

{com}.

. cvlasso $model, foldvar(myfid) lambda(150 15 1.5)

{txt}Use user-specified fold variable.

K-fold cross-validation with 3 folds. Elastic net with alpha=1.

Fold {res}1 2 3

{txt} {c |} {space 3} {txt} Lambda {space 3} {txt} MSPE {space 3} {txt} st. dev.

{hline 10}{c +}{hline 45}

{txt} 1{c |} {space 3} {res} 150 {space 3} {res} 2.3865184 {space 3} {res} 1.1559279

{txt} 2{c |} {space 3} {res} 15 {space 3} {res} 1.4635023 {space 3} {res} .42894674 *^

{txt} 3{c |} {space 3} {res} 1.5 {space 3} {res} 1.485229 {space 3} {res} .36948736

{txt}* lopt = the lambda that minimizes MSPE.

Run model: {stata cvlasso, lopt}

{p 0 8 2}^ lse = largest lambda for which MSPE is within one standard error of the minimal MSPE.{p_end}

Run model: {stata cvlasso, lse}

r; t=0.77 20:21:48

{com}. mat L = e(mmspe)

{txt}r; t=0.00 20:21:48

{com}. /*

> c<-cv.glmnet(X,y,foldid=fid,lambda=c(150, 15, 1.5)/(2*n),keep=TRUE, intercept=TRUE,standardize=TRUE)

> > c$cvm # mean-squared prediction error

> [1] 2.386518 1.463497 1.484953

> > var(predict(c,newx=X,s="lambda.min"))

> 1

> 1 0.608541

> */

. mat G = ( 2.38651835211576 \ 1.46349718052732 \ 1.48495268875459 )

{txt}r; t=0.00 20:21:48

{com}. comparemat L G // compare coeffs

{txt}mreldif=.0001112103968545. tolerance = .01

{res}

{txt}__000000[3,1]

r1

c1 {res} 2.3865184

{txt}c2 {res} 1.4635023

{txt}c3 {res} 1.485229

{reset}{res}

{txt}__000001[3,1]

c1

r1 {res} 2.3865184

{txt}r2 {res} 1.4634972

{txt}r3 {res} 1.4849527

{reset}r; t=0.04 20:21:48

{com}. cap drop xb

{txt}r; t=0.00 20:21:48

{com}. predict double xb, lopt

{txt}Estimate lasso with lambda=15 (lopt).

{hline 18}{c TT}{hline 32}

{col 10}Selected {c |} Lasso Post-est OLS

{hline 18}{c +}{hline 32}

{space 11}lcavol {c |}{col 21}{res} 0.4705447 0.4939937

{txt}{space 10}lweight {c |}{col 21}{res} 0.4738822 0.5463838

{txt}{space 13}lbph {c |}{col 21}{res} 0.0222266 0.0675109

{txt}{space 14}svi {c |}{col 21}{res} 0.4597209 0.5816563

{txt}{space 12}pgg45 {c |}{col 21}{res} 0.0021287 0.0033429

{txt}{hline 18}{c +}{hline 32}

{col 4}Partialled-out{help lasso2##examples_partialling:*}{c |}

{hline 18}{c +}{hline 32}

{space 12}_cons {c |}{col 21}{res} -0.0467006 -0.4001276

{txt}{hline 18}{c BT}{hline 32}

No xb or residuals options specified. Assume xb (fitted values).

Use e(b) from previous lasso2 estimation (lambda=15).

r; t=0.10 20:21:48

{com}. sum xb

{txt} Variable {c |} Obs Mean Std. Dev. Min Max

{hline 13}{c +}{hline 57}

{space 10}xb {c |}{res} 96 2.446048 .7801003 .9116189 4.111986

{txt}r; t=0.00 20:21:48

{com}. assert reldif(0.608541,r(Var))<0.001 // compare predicted values

{txt}r; t=0.00 20:21:48

{com}.

.

. cvlasso $model, foldvar(myfid) lambda(150 15 1.5) prestd

{txt}Use user-specified fold variable.

K-fold cross-validation with 3 folds. Elastic net with alpha=1.

Fold {res}1 2 3

{txt} {c |} {space 3} {txt} Lambda {space 3} {txt} MSPE {space 3} {txt} st. dev.

{hline 10}{c +}{hline 45}

{txt} 1{c |} {space 3} {res} 150 {space 3} {res} 2.3865184 {space 3} {res} 1.1559279

{txt} 2{c |} {space 3} {res} 15 {space 3} {res} 1.4635023 {space 3} {res} .42894674 *^

{txt} 3{c |} {space 3} {res} 1.5 {space 3} {res} 1.485229 {space 3} {res} .36948736

{txt}* lopt = the lambda that minimizes MSPE.

Run model: {stata cvlasso, lopt}

{p 0 8 2}^ lse = largest lambda for which MSPE is within one standard error of the minimal MSPE.{p_end}

Run model: {stata cvlasso, lse}

r; t=0.03 20:21:48

{com}. mat L = e(mmspe)

{txt}r; t=0.00 20:21:48

{com}. /*

> c<-cv.glmnet(X,y,foldid=fid,lambda=c(150, 15, 1.5)/(2*n),keep=TRUE, intercept=TRUE,standardize=TRUE)

> > c$cvm # mean-squared prediction error

> [1] 2.386518 1.463497 1.484953

> > var(predict(c,newx=X,s="lambda.min"))

> 1

> 1 0.608541

> */

. mat G = ( 2.38651835211576 \ 1.46349718052732 \ 1.48495268875459 )

{txt}r; t=0.00 20:21:48

{com}. comparemat L G // compare coeffs

{txt}mreldif=.0001112103968545. tolerance = .01

{res}

{txt}__000000[3,1]

r1

c1 {res} 2.3865184

{txt}c2 {res} 1.4635023

{txt}c3 {res} 1.485229

{reset}{res}

{txt}__000001[3,1]

c1

r1 {res} 2.3865184

{txt}r2 {res} 1.4634972

{txt}r3 {res} 1.4849527

{reset}r; t=0.00 20:21:48

{com}. cap drop xb

{txt}r; t=0.00 20:21:48

{com}. predict double xb, lopt

{txt}Estimate lasso with lambda=15 (lopt).

{res}

{txt}{hline 18}{c TT}{hline 32}

{col 10}Selected {c |} Lasso Post-est OLS

{hline 18}{c +}{hline 32}

{space 11}lcavol {c |}{col 21}{res} 0.4705447 0.4939937

{txt}{space 10}lweight {c |}{col 21}{res} 0.4738822 0.5463838

{txt}{space 13}lbph {c |}{col 21}{res} 0.0222266 0.0675109

{txt}{space 14}svi {c |}{col 21}{res} 0.4597209 0.5816563

{txt}{space 12}pgg45 {c |}{col 21}{res} 0.0021287 0.0033429

{txt}{hline 18}{c +}{hline 32}

{col 4}Partialled-out{help lasso2##examples_partialling:*}{c |}

{hline 18}{c +}{hline 32}

{space 12}_cons {c |}{col 21}{res} -0.0467006 -0.4001276

{txt}{hline 18}{c BT}{hline 32}

No xb or residuals options specified. Assume xb (fitted values).

Use e(b) from previous lasso2 estimation (lambda=15).

r; t=1.44 20:21:49

{com}. sum xb

{txt} Variable {c |} Obs Mean Std. Dev. Min Max

{hline 13}{c +}{hline 57}

{space 10}xb {c |}{res} 96 2.446048 .7801003 .9116189 4.111986

{txt}r; t=0.00 20:21:49

{com}. assert reldif(0.608541,r(Var))<0.001 // compare predicted values

{txt}r; t=0.00 20:21:49

{com}.

.

. cvlasso $model, foldvar(myfid) lambda(150 15 1.5) unitload

{txt}Use user-specified fold variable.

K-fold cross-validation with 3 folds. Elastic net with alpha=1.

Fold {res}1 2 3

{txt} {c |} {space 3} {txt} Lambda {space 3} {txt} MSPE {space 3} {txt} st. dev.

{hline 10}{c +}{hline 45}

{txt} 1{c |} {space 3} {res} 150 {space 3} {res} 2.1036977 {space 3} {res} .86238904

{txt} 2{c |} {space 3} {res} 15 {space 3} {res} 1.4298598 {space 3} {res} .47710985 ^

{txt} 3{c |} {space 3} {res} 1.5 {space 3} {res} 1.4275375 {space 3} {res} .35365915 *

{txt}* lopt = the lambda that minimizes MSPE.

Run model: {stata cvlasso, lopt}

{p 0 8 2}^ lse = largest lambda for which MSPE is within one standard error of the minimal MSPE.{p_end}

Run model: {stata cvlasso, lse}

{err}Warning: lopt is at the limit of the lambda range.

{txt}r; t=0.03 20:21:49

{com}. mat L = e(mmspe)

{txt}r; t=0.00 20:21:49

{com}. /*

> > # cross-validation with intercept & standardisation

> > c<-cv.glmnet(X,y,foldid=fid,lambda=c(150, 15, 1.5)/(2*n),keep=TRUE, intercept=TRUE,standardize=FALSE)

> > c$cvm # mean-squared prediction error

> [1] 2.103697 1.429934 1.427561

> > var(predict(c,newx=X,s="lambda.min"))

> 1

> 1 0.7840688

> */

. mat G = ( 2.10369705382686 \ 1.42993421234064 \ 1.42756055333919 )

{txt}r; t=0.00 20:21:49

{com}. comparemat L G // compare coeffs

{txt}mreldif=.0000306155184697. tolerance = .01

{res}

{txt}__000000[3,1]

r1

c1 {res} 2.1036977

{txt}c2 {res} 1.4298598

{txt}c3 {res} 1.4275375

{reset}{res}

{txt}__000001[3,1]

c1

r1 {res} 2.1036971

{txt}r2 {res} 1.4299342

{txt}r3 {res} 1.4275606

{reset}r; t=0.00 20:21:49

{com}. cap drop xb

{txt}r; t=0.00 20:21:49

{com}. predict double xb, lopt

{txt}Estimate lasso with lambda=1.5 (lopt).

{hline 18}{c TT}{hline 32}

{col 10}Selected {c |} Lasso Post-est OLS

{hline 18}{c +}{hline 32}

{space 11}lcavol {c |}{col 21}{res} 0.5568426 0.5653571

{txt}{space 10}lweight {c |}{col 21}{res} 0.5707438 0.6139211

{txt}{space 14}age {c |}{col 21}{res} -0.0208973 -0.0224946

{txt}{space 13}lbph {c |}{col 21}{res} 0.0911393 0.0935981

{txt}{space 14}svi {c |}{col 21}{res} 0.6204808 0.7149700

{txt}{space 14}lcp {c |}{col 21}{res} -0.1126800 -0.1447125

{txt}{space 12}pgg45 {c |}{col 21}{res} 0.0068648 0.0071245

{txt}{hline 18}{c +}{hline 32}

{col 4}Partialled-out{help lasso2##examples_partialling:*}{c |}

{hline 18}{c +}{hline 32}

{space 12}_cons {c |}{col 21}{res} 0.6415930 0.5426554

{txt}{hline 18}{c BT}{hline 32}

No xb or residuals options specified. Assume xb (fitted values).

Use e(b) from previous lasso2 estimation (lambda=1.5).

r; t=0.02 20:21:49

{com}. sum xb

{txt} Variable {c |} Obs Mean Std. Dev. Min Max

{hline 13}{c +}{hline 57}

{space 10}xb {c |}{res} 96 2.446048 .8854875 .513909 4.598263

{txt}r; t=0.00 20:21:49

{com}. assert reldif(0.7840688,r(Var))<0.001 // compare predicted values

{txt}r; t=0.00 20:21:49

{com}.

.

. cvlasso $model, foldvar(myfid) lambda(150 15 1.5) nocons unitload

{txt}Use user-specified fold variable.

K-fold cross-validation with 3 folds. Elastic net with alpha=1.

Fold {res}1 2 3

{txt} {c |} {space 3} {txt} Lambda {space 3} {txt} MSPE {space 3} {txt} st. dev.

{hline 10}{c +}{hline 45}

{txt} 1{c |} {space 3} {res} 150 {space 3} {res} 1.9998833 {space 3} {res} .72964224

{txt} 2{c |} {space 3} {res} 15 {space 3} {res} 1.26607 {space 3} {res} .39073554 ^

{txt} 3{c |} {space 3} {res} 1.5 {space 3} {res} 1.246129 {space 3} {res} .28903645 *

{txt}* lopt = the lambda that minimizes MSPE.

Run model: {stata cvlasso, lopt}

{p 0 8 2}^ lse = largest lambda for which MSPE is within one standard error of the minimal MSPE.{p_end}

Run model: {stata cvlasso, lse}

{err}Warning: lopt is at the limit of the lambda range.

{txt}r; t=0.14 20:21:49

{com}. mat L = e(mmspe)

{txt}r; t=0.00 20:21:49

{com}. /*

> > c<-cv.glmnet(X,y,foldid=fid,lambda=c(150, 15, 1.5)/(2*n),keep=TRUE, intercept=FALSE,standardize=FALSE)

> > c$cvm # mean-squared prediction error

> [1] 1.999806 1.266384 1.246805

> > var(predict(c,newx=X,s="lambda.min"))

> 1

> 1 0.7919695

> */

. mat G = ( 1.99980614859113 \ 1.26638436668758 \ 1.24680539174676 )

{txt}r; t=0.00 20:21:49

{com}. comparemat L G // compare coeffs

{txt}mreldif=.0003010647418424. tolerance = .01

{res}

{txt}__000000[3,1]

r1

c1 {res} 1.9998833

{txt}c2 {res} 1.26607

{txt}c3 {res} 1.246129

{reset}{res}

{txt}__000001[3,1]

c1

r1 {res} 1.9998061

{txt}r2 {res} 1.2663844

{txt}r3 {res} 1.2468054

{reset}r; t=0.00 20:21:49

{com}. cap drop xb

{txt}r; t=0.00 20:21:49

{com}. predict double xb, lopt

{txt}Estimate lasso with lambda=1.5 (lopt).

{hline 18}{c TT}{hline 32}

{col 10}Selected {c |} Lasso Post-est OLS

{hline 18}{c +}{hline 32}

{space 11}lcavol {c |}{col 21}{res} 0.5473610 0.5569029

{txt}{space 10}lweight {c |}{col 21}{res} 0.6258035 0.6453516

{txt}{space 14}age {c |}{col 21}{res} -0.0188110 -0.0217852

{txt}{space 13}lbph {c |}{col 21}{res} 0.0801178 0.0880196

{txt}{space 14}svi {c |}{col 21}{res} 0.6260775 0.7250462

{txt}{space 14}lcp {c |}{col 21}{res} -0.1142476 -0.1460891

{txt}{space 10}gleason {c |}{col 21}{res} 0.0505847 0.0620963

{txt}{space 12}pgg45 {c |}{col 21}{res} 0.0059407 0.0060062

{txt}{hline 18}{c BT}{hline 32}

No xb or residuals options specified. Assume xb (fitted values).

Use e(b) from previous lasso2 estimation (lambda=1.5).

r; t=0.03 20:21:49

{com}. sum xb

{txt} Variable {c |} Obs Mean Std. Dev. Min Max

{hline 13}{c +}{hline 57}

{space 10}xb {c |}{res} 96 2.443933 .8905195 .5327895 4.561406

{txt}r; t=0.00 20:21:49

{com}. assert reldif(0.7919695,r(Var))<0.001 // compare predicted values

{txt}r; t=0.00 20:21:49

{com}.

. cvlasso $model, foldvar(myfid) lambda(150 15 1.5) nocons

{txt}Use user-specified fold variable.

K-fold cross-validation with 3 folds. Elastic net with alpha=1.

Fold {res}1 2 3

{txt} {c |} {space 3} {txt} Lambda {space 3} {txt} MSPE {space 3} {txt} st. dev.

{hline 10}{c +}{hline 45}

{txt} 1{c |} {space 3} {res} 150 {space 3} {res} 1.9069161 {space 3} {res} .92525083

{txt} 2{c |} {space 3} {res} 15 {space 3} {res} 1.2339936 {space 3} {res} .35347077 *^

{txt} 3{c |} {space 3} {res} 1.5 {space 3} {res} 1.309495 {space 3} {res} .3270498

{txt}* lopt = the lambda that minimizes MSPE.

Run model: {stata cvlasso, lopt}

{p 0 8 2}^ lse = largest lambda for which MSPE is within one standard error of the minimal MSPE.{p_end}

Run model: {stata cvlasso, lse}

r; t=0.15 20:21:49

{com}. mat L = e(mmspe)

{txt}r; t=0.00 20:21:49

{com}. /*

> c<-cv.glmnet(X,y,foldid=fid,lambda=c(150, 15, 1.5)/(2*n),keep=TRUE, intercept=FALSE,standardize=TRUE)

> > c$cvm # mean-squared prediction error

> [1] 1.906526 1.232220 1.310531

> > var(predict(c,newx=X,s="lambda.min"))

> 1

> 1 0.6062457

> */

. mat G = ( 1.90652583524832 \ 1.23222044017428 \ 1.31053116174191 )

{txt}r; t=0.00 20:21:49

{com}. comparemat L G // compare coeffs

{txt}mreldif=.0007943382600103. tolerance = .01

{res}

{txt}__000000[3,1]

r1

c1 {res} 1.9069161

{txt}c2 {res} 1.2339936

{txt}c3 {res} 1.309495

{reset}{res}

{txt}__000001[3,1]

c1

r1 {res} 1.9065258

{txt}r2 {res} 1.2322204

{txt}r3 {res} 1.3105312

{reset}r; t=0.00 20:21:49

{com}. cap drop xb

{txt}r; t=0.00 20:21:49

{com}. predict double xb, lopt

{txt}Estimate lasso with lambda=15 (lopt).

{hline 18}{c TT}{hline 32}

{col 10}Selected {c |} Lasso Post-est OLS

{hline 18}{c +}{hline 32}

{space 11}lcavol {c |}{col 21}{res} 0.4712765 0.5002634

{txt}{space 10}lweight {c |}{col 21}{res} 0.4608978 0.4351342

{txt}{space 13}lbph {c |}{col 21}{res} 0.0239597 0.0823598

{txt}{space 14}svi {c |}{col 21}{res} 0.4613426 0.5955516

{txt}{space 12}pgg45 {c |}{col 21}{res} 0.0021037 0.0031282

{txt}{hline 18}{c BT}{hline 32}

No xb or residuals options specified. Assume xb (fitted values).

Use e(b) from previous lasso2 estimation (lambda=15).

r; t=0.02 20:21:49

{com}. sum xb

{txt} Variable {c |} Obs Mean Std. Dev. Min Max

{hline 13}{c +}{hline 57}

{space 10}xb {c |}{res} 96 2.446541 .7785216 .9124108 4.1091

{txt}r; t=0.00 20:21:49

{com}. assert reldif(0.6062457,r(Var))<0.001 // compare predicted values

{txt}r; t=0.00 20:21:49

{com}.

. cvlasso $model, foldvar(myfid) lambda(150 15 1.5) nocons prestd

{txt}Use user-specified fold variable.

K-fold cross-validation with 3 folds. Elastic net with alpha=1.

Fold {res}1 2 3

{txt} {c |} {space 3} {txt} Lambda {space 3} {txt} MSPE {space 3} {txt} st. dev.

{hline 10}{c +}{hline 45}

{txt} 1{c |} {space 3} {res} 150 {space 3} {res} 1.9069161 {space 3} {res} .92525083

{txt} 2{c |} {space 3} {res} 15 {space 3} {res} 1.2339936 {space 3} {res} .35347077 *^

{txt} 3{c |} {space 3} {res} 1.5 {space 3} {res} 1.309495 {space 3} {res} .3270498

{txt}* lopt = the lambda that minimizes MSPE.

Run model: {stata cvlasso, lopt}

{p 0 8 2}^ lse = largest lambda for which MSPE is within one standard error of the minimal MSPE.{p_end}

Run model: {stata cvlasso, lse}

r; t=0.15 20:21:50

{com}. mat L = e(mmspe)

{txt}r; t=0.00 20:21:50

{com}. /*

> c<-cv.glmnet(X,y,foldid=fid,lambda=c(150, 15, 1.5)/(2*n),keep=TRUE, intercept=FALSE,standardize=TRUE)

> > c$cvm # mean-squared prediction error

> [1] 1.906526 1.232220 1.310531

> > var(predict(c,newx=X,s="lambda.min"))

> 1

> 1 0.6062457

> */

. mat G = ( 1.90652583524832 \ 1.23222044017428 \ 1.31053116174191 )

{txt}r; t=0.00 20:21:50

{com}. comparemat L G // compare coeffs

{txt}mreldif=.0007943382600105. tolerance = .01

{res}

{txt}__000000[3,1]

r1

c1 {res} 1.9069161

{txt}c2 {res} 1.2339936

{txt}c3 {res} 1.309495

{reset}{res}

{txt}__000001[3,1]

c1

r1 {res} 1.9065258

{txt}r2 {res} 1.2322204

{txt}r3 {res} 1.3105312

{reset}r; t=0.00 20:21:50

{com}. cap drop xb

{txt}r; t=0.00 20:21:50

{com}. predict double xb, lopt

{txt}Estimate lasso with lambda=15 (lopt).

{res}

{txt}{hline 18}{c TT}{hline 32}

{col 10}Selected {c |} Lasso Post-est OLS

{hline 18}{c +}{hline 32}

{space 11}lcavol {c |}{col 21}{res} 0.4712765 0.5002634

{txt}{space 10}lweight {c |}{col 21}{res} 0.4608978 0.4351342

{txt}{space 13}lbph {c |}{col 21}{res} 0.0239597 0.0823598

{txt}{space 14}svi {c |}{col 21}{res} 0.4613426 0.5955516

{txt}{space 12}pgg45 {c |}{col 21}{res} 0.0021037 0.0031282

{txt}{hline 18}{c BT}{hline 32}

No xb or residuals options specified. Assume xb (fitted values).

Use e(b) from previous lasso2 estimation (lambda=15).

r; t=0.02 20:21:50

{com}. sum xb

{txt} Variable {c |} Obs Mean Std. Dev. Min Max

{hline 13}{c +}{hline 57}

{space 10}xb {c |}{res} 96 2.446541 .7785216 .9124108 4.1091

{txt}r; t=0.00 20:21:50

{com}. assert reldif(0.6062457,r(Var))<0.001 // compare predicted values

{txt}r; t=0.00 20:21:50

{com}.

.

. ********************************************************************************

. *** validate ***

. ********************************************************************************

.

. * load example data

. insheet using "$prostate", tab clear

{txt}(11 vars, 97 obs)

r; t=0.18 20:21:50

{com}.

. global model lpsa lcavol lweight age lbph svi lcp gleason pgg45

{txt}r; t=0.00 20:21:50

{com}. gen sample = _n<70

{txt}r; t=0.00 20:21:50

{com}.

. foreach type of newlist lopt lse {c -(}

{txt} 2{com}.

. local type lse

{txt} 3{com}.

. // check that right beta is used for predict

. // also validates that "if" works

. cvlasso $model if sample

{txt} 4{com}. local mylopt = e(`type')

{txt} 5{com}. cap drop myxb

{txt} 6{com}. predict double myxb if !sample, xb `type' postres

{txt} 7{com}. mat A = e(b)

{txt} 8{com}.

. lasso2 $model if sample, lambda(`mylopt')

{txt} 9{com}. cap drop myxb2

{txt} 10{com}. predict double myxb2 if !sample, xb

{txt} 11{com}. mat B = e(b)

{txt} 12{com}.

. comparemat A B

{txt} 13{com}. assert myxb2==myxb

{txt} 14{com}.

. // and now with alpha list

. // use lglmnet option

. cvlasso $model if sample, alpha(0 0.3 0.7 1) lglmnet

{txt} 15{com}. local mylopt = e(`type')

{txt} 16{com}. local myalpha = e(alphamin)

{txt} 17{com}. cap drop myr

{txt} 18{com}. predict double myr if !sample, r `type' postres

{txt} 19{com}. mat A = e(b)

{txt} 20{com}.

. lasso2 $model if sample, lambda(`mylopt') alpha(`myalpha') lglmnet

{txt} 21{com}. cap drop myr2

{txt} 22{com}. predict double myr2 if !sample, r

{txt} 23{com}. mat B = e(b)

{txt} 24{com}.

. comparemat A B

{txt} 25{com}. assert myr2==myr

{txt} 26{com}.

. {c )-}

{txt}K-fold cross-validation with 10 folds. Elastic net with alpha=1.

Fold {res}1 2 3 4 5 6 7 8 9 10

{txt} {c |} {space 3} {txt} Lambda {space 3} {txt} MSPE {space 3} {txt} st. dev.

{hline 10}{c +}{hline 45}

{txt} 1{c |} {space 3} {res} 64.86388 {space 3} {res} .70214335 {space 3} {res} .13668767

{txt} 2{c |} {space 3} {res} 59.101552 {space 3} {res} .68166758 {space 3} {res} .13770442

{txt} 3{c |} {space 3} {res} 53.851133 {space 3} {res} .65032545 {space 3} {res} .13589084

{txt} 4{c |} {space 3} {res} 49.067147 {space 3} {res} .61368184 {space 3} {res} .12902249

{txt} 5{c |} {space 3} {res} 44.708157 {space 3} {res} .57648569 {space 3} {res} .11951189

{txt} 6{c |} {space 3} {res} 40.736407 {space 3} {res} .5455564 {space 3} {res} .11125054

{txt} 7{c |} {space 3} {res} 37.117497 {space 3} {res} .52022373 {space 3} {res} .10413738

{txt} 8{c |} {space 3} {res} 33.820081 {space 3} {res} .49994965 {space 3} {res} .09808444

{txt} 9{c |} {space 3} {res} 30.815598 {space 3} {res} .48394209 {space 3} {res} .09299888

{txt} 10{c |} {space 3} {res} 28.078025 {space 3} {res} .4715922 {space 3} {res} .08866583 ^

{txt} 11{c |} {space 3} {res} 25.583651 {space 3} {res} .46159647 {space 3} {res} .08503773

{txt} 12{c |} {space 3} {res} 23.31087 {space 3} {res} .45319845 {space 3} {res} .0819249

{txt} 13{c |} {space 3} {res} 21.239997 {space 3} {res} .44655158 {space 3} {res} .07921285

{txt} 14{c |} {space 3} {res} 19.353094 {space 3} {res} .43989318 {space 3} {res} .07690231

{txt} 15{c |} {space 3} {res} 17.633818 {space 3} {res} .43416143 {space 3} {res} .07503205

{txt} 16{c |} {space 3} {res} 16.067278 {space 3} {res} .42831327 {space 3} {res} .07332298

{txt} 17{c |} {space 3} {res} 14.639906 {space 3} {res} .42328622 {space 3} {res} .07186312

{txt} 18{c |} {space 3} {res} 13.339337 {space 3} {res} .41909404 {space 3} {res} .07059609

{txt} 19{c |} {space 3} {res} 12.154307 {space 3} {res} .41541784 {space 3} {res} .06940436

{txt} 20{c |} {space 3} {res} 11.074552 {space 3} {res} .41239688 {space 3} {res} .06842274

{txt} 21{c |} {space 3} {res} 10.090719 {space 3} {res} .41115051 {space 3} {res} .06762829 *

{txt} 22{c |} {space 3} {res} 9.1942875 {space 3} {res} .41125524 {space 3} {res} .06734084

{txt} 23{c |} {space 3} {res} 8.3774923 {space 3} {res} .41189673 {space 3} {res} .06726297

{txt} 24{c |} {space 3} {res} 7.633259 {space 3} {res} .41299996 {space 3} {res} .06716497

{txt} 25{c |} {space 3} {res} 6.9551413 {space 3} {res} .41535333 {space 3} {res} .06772844

{txt} 26{c |} {space 3} {res} 6.3372657 {space 3} {res} .41747426 {space 3} {res} .06851425

{txt} 27{c |} {space 3} {res} 5.7742805 {space 3} {res} .41770086 {space 3} {res} .06978754

{txt} 28{c |} {space 3} {res} 5.2613093 {space 3} {res} .41811876 {space 3} {res} .07107378

{txt} 29{c |} {space 3} {res} 4.7939091 {space 3} {res} .41867378 {space 3} {res} .07229779

{txt} 30{c |} {space 3} {res} 4.3680314 {space 3} {res} .41856902 {space 3} {res} .07315811

{txt} 31{c |} {space 3} {res} 3.9799876 {space 3} {res} .41856329 {space 3} {res} .07409234

{txt} 32{c |} {space 3} {res} 3.6264164 {space 3} {res} .418418 {space 3} {res} .07512313

{txt} 33{c |} {space 3} {res} 3.3042556 {space 3} {res} .41815181 {space 3} {res} .0761924

{txt} 34{c |} {space 3} {res} 3.0107146 {space 3} {res} .41800644 {space 3} {res} .07722359

{txt} 35{c |} {space 3} {res} 2.743251 {space 3} {res} .41780862 {space 3} {res} .07813846

{txt} 36{c |} {space 3} {res} 2.4995482 {space 3} {res} .41776606 {space 3} {res} .07897215

{txt} 37{c |} {space 3} {res} 2.2774952 {space 3} {res} .41784915 {space 3} {res} .07973505

{txt} 38{c |} {space 3} {res} 2.0751688 {space 3} {res} .4178434 {space 3} {res} .0803494

{txt} 39{c |} {space 3} {res} 1.8908165 {space 3} {res} .41783315 {space 3} {res} .08086973

{txt} 40{c |} {space 3} {res} 1.7228416 {space 3} {res} .4177931 {space 3} {res} .08133498

{txt} 41{c |} {space 3} {res} 1.5697891 {space 3} {res} .41780825 {space 3} {res} .08175923

{txt} 42{c |} {space 3} {res} 1.4303334 {space 3} {res} .4178703 {space 3} {res} .08214655

{txt} 43{c |} {space 3} {res} 1.3032665 {space 3} {res} .41796691 {space 3} {res} .08250007

{txt} 44{c |} {space 3} {res} 1.1874879 {space 3} {res} .41808819 {space 3} {res} .0828227

{txt} 45{c |} {space 3} {res} 1.0819947 {space 3} {res} .41822631 {space 3} {res} .08311707

{txt} 46{c |} {space 3} {res} .98587331 {space 3} {res} .41837509 {space 3} {res} .08338564

{txt} 47{c |} {space 3} {res} .89829104 {space 3} {res} .41872946 {space 3} {res} .08363953

{txt} 48{c |} {space 3} {res} .81848934 {space 3} {res} .41907617 {space 3} {res} .08387238

{txt} 49{c |} {space 3} {res} .745777 {space 3} {res} .41940754 {space 3} {res} .08408564

{txt} 50{c |} {space 3} {res} .67952423 {space 3} {res} .41972231 {space 3} {res} .08428089

{txt} 51{c |} {space 3} {res} .61915717 {space 3} {res} .42001978 {space 3} {res} .08445959

{txt} 52{c |} {space 3} {res} .56415295 {space 3} {res} .42029967 {space 3} {res} .08462309

{txt} 53{c |} {space 3} {res} .51403516 {space 3} {res} .42056204 {space 3} {res} .08477263

{txt} 54{c |} {space 3} {res} .46836969 {space 3} {res} .4208072 {space 3} {res} .08490936

{txt} 55{c |} {space 3} {res} .42676102 {space 3} {res} .42103565 {space 3} {res} .08503435

{txt} 56{c |} {space 3} {res} .38884875 {space 3} {res} .42124801 {space 3} {res} .08514857

{txt} 57{c |} {space 3} {res} .3543045 {space 3} {res} .42144499 {space 3} {res} .08525293

{txt} 58{c |} {space 3} {res} .32282906 {space 3} {res} .42162738 {space 3} {res} .08534826

{txt} 59{c |} {space 3} {res} .29414982 {space 3} {res} .42179596 {space 3} {res} .08543531

{txt} 60{c |} {space 3} {res} .26801836 {space 3} {res} .42195157 {space 3} {res} .08551481

{txt} 61{c |} {space 3} {res} .24420835 {space 3} {res} .42209501 {space 3} {res} .08558737

{txt} 62{c |} {space 3} {res} .22251355 {space 3} {res} .42222708 {space 3} {res} .08565361

{txt} 63{c |} {space 3} {res} .20274606 {space 3} {res} .42234856 {space 3} {res} .08571407

{txt}* lopt = the lambda that minimizes MSPE.

Run model: {stata cvlasso, lopt}

{p 0 8 2}^ lse = largest lambda for which MSPE is within one standard error of the minimal MSPE.{p_end}

Run model: {stata cvlasso, lse}

Estimate lasso with lambda=28.078 (lse).

{hline 18}{c TT}{hline 32}

{col 10}Selected {c |} Lasso Post-est OLS

{hline 18}{c +}{hline 32}

{space 11}lcavol {c |}{col 21}{res} 0.2177045 0.3782994

{txt}{space 10}lweight {c |}{col 21}{res} 0.4397825 0.8391637

{txt}{hline 18}{c +}{hline 32}

{col 4}Partialled-out{help lasso2##examples_partialling:*}{c |}

{hline 18}{c +}{hline 32}

{space 12}_cons {c |}{col 21}{res} 0.1812277 -1.3919237

{txt}{hline 18}{c BT}{hline 32}

Use e(b) from previous lasso2 estimation (lambda=28.07802540248).

(69 missing values generated)

{hline 18}{c TT}{hline 32}

{col 10}Selected {c |} Lasso Post-est OLS

{hline 18}{c +}{hline 32}

{space 11}lcavol {c |}{col 21}{res} 0.2177045 0.3782994

{txt}{space 10}lweight {c |}{col 21}{res} 0.4397825 0.8391637

{txt}{hline 18}{c +}{hline 32}

{col 4}Partialled-out{help lasso2##examples_partialling:*}{c |}

{hline 18}{c +}{hline 32}

{space 12}_cons {c |}{col 21}{res} 0.1812277 -1.3919237

{txt}{hline 18}{c BT}{hline 32}

Use e(b) from previous lasso2 estimation (lambda=28.07802540248).

(69 missing values generated)

mreldif=0. tolerance = .01

{res}

{txt}__000000[1,3]

lcavol lweight _cons

y1 {res} .21770447 .43978251 .18122774

{reset}{res}

{txt}__000001[1,3]

lcavol lweight _cons

y1 {res} .21770447 .43978251 .18122774

{reset}

K-fold cross-validation with 10 folds. Elastic net with alpha=0.

Fold {res}1 2 3 4 5 6 7 8 9 10

{txt} {c |} {space 3} {txt} Lambda {space 3} {txt} MSPE {space 3} {txt} st. dev.

{hline 10}{c +}{hline 45}

{txt} 1{c |} {space 3} {res} .47002812 {space 3} {res} .41012597 {space 3} {res} .06401609 ^

{txt} 2{c |} {space 3} {res} .42827212 {space 3} {res} .40728618 {space 3} {res} .06307755

{txt} 3{c |} {space 3} {res} .3902256 {space 3} {res} .40470048 {space 3} {res} .06225597

{txt} 4{c |} {space 3} {res} .35555903 {space 3} {res} .40236398 {space 3} {res} .06155117

{txt} 5{c |} {space 3} {res} .32397215 {space 3} {res} .40027036 {space 3} {res} .06096131

{txt} 6{c |} {space 3} {res} .29519136 {space 3} {res} .39841194 {space 3} {res} .06048296

{txt} 7{c |} {space 3} {res} .26896737 {space 3} {res} .39677969 {space 3} {res} .06011112

{txt} 8{c |} {space 3} {res} .24507305 {space 3} {res} .39536326 {space 3} {res} .05983949

{txt} 9{c |} {space 3} {res} .22330144 {space 3} {res} .39415121 {space 3} {res} .05966063

{txt} 10{c |} {space 3} {res} .20346395 {space 3} {res} .39313106 {space 3} {res} .05956626

{txt} 11{c |} {space 3} {res} .18538878 {space 3} {res} .39228957 {space 3} {res} .05954754

{txt} 12{c |} {space 3} {res} .16891935 {space 3} {res} .3916129 {space 3} {res} .05959536

{txt} 13{c |} {space 3} {res} .15391302 {space 3} {res} .39108687 {space 3} {res} .05970059

{txt} 14{c |} {space 3} {res} .14023981 {space 3} {res} .39069715 {space 3} {res} .05985432

{txt} 15{c |} {space 3} {res} .12778129 {space 3} {res} .39042948 {space 3} {res} .06004803

{txt} 16{c |} {space 3} {res} .11642955 {space 3} {res} .3902699 {space 3} {res} .06027379

{txt} 17{c |} {space 3} {res} .10608627 {space 3} {res} .39020487 {space 3} {res} .06052428 *

{txt} 18{c |} {space 3} {res} .09666186 {space 3} {res} .3902215 {space 3} {res} .06079293

{txt} 19{c |} {space 3} {res} .08807469 {space 3} {res} .3903076 {space 3} {res} .0610739

{txt} 20{c |} {space 3} {res} .08025038 {space 3} {res} .39045184 {space 3} {res} .06136209

{txt} 21{c |} {space 3} {res} .07312115 {space 3} {res} .39064375 {space 3} {res} .06165314

{txt} 22{c |} {space 3} {res} .06662527 {space 3} {res} .39087384 {space 3} {res} .06194337

{txt} 23{c |} {space 3} {res} .06070647 {space 3} {res} .39113356 {space 3} {res} .06222972

{txt} 24{c |} {space 3} {res} .05531347 {space 3} {res} .39141529 {space 3} {res} .06250972

{txt} 25{c |} {space 3} {res} .05039957 {space 3} {res} .39171237 {space 3} {res} .0627814

{txt} 26{c |} {space 3} {res} .04592222 {space 3} {res} .39201899 {space 3} {res} .06304327

{txt} 27{c |} {space 3} {res} .04184261 {space 3} {res} .3923302 {space 3} {res} .06329421

{txt} 28{c |} {space 3} {res} .03812543 {space 3} {res} .39264181 {space 3} {res} .06353346

{txt} 29{c |} {space 3} {res} .03473847 {space 3} {res} .39295034 {space 3} {res} .06376053

{txt} 30{c |} {space 3} {res} .0316524 {space 3} {res} .39325295 {space 3} {res} .06397518

{txt} 31{c |} {space 3} {res} .02884049 {space 3} {res} .39354738 {space 3} {res} .06417738

{txt} 32{c |} {space 3} {res} .02627838 {space 3} {res} .39383186 {space 3} {res} .06436723

{txt} 33{c |} {space 3} {res} .02394388 {space 3} {res} .39410506 {space 3} {res} .06454499

{txt} 34{c |} {space 3} {res} .02181677 {space 3} {res} .39436605 {space 3} {res} .064711

{txt} 35{c |} {space 3} {res} .01987863 {space 3} {res} .39461421 {space 3} {res} .06486568

{txt} 36{c |} {space 3} {res} .01811267 {space 3} {res} .39484918 {space 3} {res} .06500951

{txt} 37{c |} {space 3} {res} .01650359 {space 3} {res} .39507085 {space 3} {res} .06514299

{txt} 38{c |} {space 3} {res} .01503746 {space 3} {res} .39527928 {space 3} {res} .06526666

{txt} 39{c |} {space 3} {res} .01370157 {space 3} {res} .39547467 {space 3} {res} .06538108

{txt} 40{c |} {space 3} {res} .01248436 {space 3} {res} .39565737 {space 3} {res} .06548677

{txt} 41{c |} {space 3} {res} .01137528 {space 3} {res} .39582777 {space 3} {res} .0655843

{txt} 42{c |} {space 3} {res} .01036473 {space 3} {res} .39598637 {space 3} {res} .06567418

{txt} 43{c |} {space 3} {res} .00944396 {space 3} {res} .3961337 {space 3} {res} .06575692

{txt} 44{c |} {space 3} {res} .00860498 {space 3} {res} .39627031 {space 3} {res} .06583304

{txt} 45{c |} {space 3} {res} .00784054 {space 3} {res} .39639678 {space 3} {res} .06590299

{txt} 46{c |} {space 3} {res} .00714401 {space 3} {res} .39651369 {space 3} {res} .06596722

{txt} 47{c |} {space 3} {res} .00650936 {space 3} {res} .39662164 {space 3} {res} .06602617

{txt} 48{c |} {space 3} {res} .00593108 {space 3} {res} .39672117 {space 3} {res} .06608023

{txt} 49{c |} {space 3} {res} .00540418 {space 3} {res} .39681286 {space 3} {res} .06612978

{txt} 50{c |} {space 3} {res} .00492409 {space 3} {res} .39689723 {space 3} {res} .06617517

{txt} 51{c |} {space 3} {res} .00448665 {space 3} {res} .39697481 {space 3} {res} .06621673

{txt} 52{c |} {space 3} {res} .00408806 {space 3} {res} .39704608 {space 3} {res} .06625476

{txt} 53{c |} {space 3} {res} .00372489 {space 3} {res} .3971115 {space 3} {res} .06628956

{txt} 54{c |} {space 3} {res} .00339398 {space 3} {res} .39717151 {space 3} {res} .06632139

{txt} 55{c |} {space 3} {res} .00309247 {space 3} {res} .39722654 {space 3} {res} .06635048

{txt} 56{c |} {space 3} {res} .00281774 {space 3} {res} .39727696 {space 3} {res} .06637707

{txt} 57{c |} {space 3} {res} .00256742 {space 3} {res} .39732314 {space 3} {res} .06640137

{txt} 58{c |} {space 3} {res} .00233934 {space 3} {res} .39736541 {space 3} {res} .06642356

{txt} 59{c |} {space 3} {res} .00213152 {space 3} {res} .39740409 {space 3} {res} .06644383

{txt} 60{c |} {space 3} {res} .00194216 {space 3} {res} .39743947 {space 3} {res} .06646234

{txt} 61{c |} {space 3} {res} .00176963 {space 3} {res} .39747182 {space 3} {res} .06647924

{txt} 62{c |} {space 3} {res} .00161242 {space 3} {res} .3975014 {space 3} {res} .06649466

{txt} 63{c |} {space 3} {res} .00146917 {space 3} {res} .39752842 {space 3} {res} .06650873

{txt}* lopt = the lambda that minimizes MSPE.

Run model: {stata cvlasso, lopt}

{p 0 8 2}^ lse = largest lambda for which MSPE is within one standard error of the minimal MSPE.{p_end}

Run model: {stata cvlasso, lse}

{err}Warning: lse is at the limit of the lambda range.

{txt}K-fold cross-validation with 10 folds. Elastic net with alpha=.3.

Fold {res}1 2 3 4 5 6 7 8 9 10

{txt} {c |} {space 3} {txt} Lambda {space 3} {txt} MSPE {space 3} {txt} st. dev.

{hline 10}{c +}{hline 45}

{txt} 1{c |} {space 3} {res} .47002812 {space 3} {res} .44529989 {space 3} {res} .07882048 ^

{txt} 2{c |} {space 3} {res} .42827212 {space 3} {res} .43541694 {space 3} {res} .07554672

{txt} 3{c |} {space 3} {res} .3902256 {space 3} {res} .42687637 {space 3} {res} .07255057

{txt} 4{c |} {space 3} {res} .35555903 {space 3} {res} .41952353 {space 3} {res} .06981929

{txt} 5{c |} {space 3} {res} .32397215 {space 3} {res} .41321631 {space 3} {res} .06733883

{txt} 6{c |} {space 3} {res} .29519136 {space 3} {res} .40782529 {space 3} {res} .06509417

{txt} 7{c |} {space 3} {res} .26896737 {space 3} {res} .40323347 {space 3} {res} .06306964

{txt} 8{c |} {space 3} {res} .24507305 {space 3} {res} .40001487 {space 3} {res} .06128728

{txt} 9{c |} {space 3} {res} .22330144 {space 3} {res} .39808447 {space 3} {res} .05978981

{txt} 10{c |} {space 3} {res} .20346395 {space 3} {res} .39735749 {space 3} {res} .05857717

{txt} 11{c |} {space 3} {res} .18538878 {space 3} {res} .39719435 {space 3} {res} .05757831 *

{txt} 12{c |} {space 3} {res} .16891935 {space 3} {res} .39732921 {space 3} {res} .05677394

{txt} 13{c |} {space 3} {res} .15391302 {space 3} {res} .39811974 {space 3} {res} .05635231

{txt} 14{c |} {space 3} {res} .14023981 {space 3} {res} .39919567 {space 3} {res} .05630205

{txt} 15{c |} {space 3} {res} .12778129 {space 3} {res} .40019299 {space 3} {res} .05646601

{txt} 16{c |} {space 3} {res} .11642955 {space 3} {res} .40073679 {space 3} {res} .05669463

{txt} 17{c |} {space 3} {res} .10608627 {space 3} {res} .40091483 {space 3} {res} .05685402

{txt} 18{c |} {space 3} {res} .09666186 {space 3} {res} .40114622 {space 3} {res} .05717764

{txt} 19{c |} {space 3} {res} .08807469 {space 3} {res} .40098742 {space 3} {res} .05776215

{txt} 20{c |} {space 3} {res} .08025038 {space 3} {res} .40099934 {space 3} {res} .05836479

{txt} 21{c |} {space 3} {res} .07312115 {space 3} {res} .40114941 {space 3} {res} .05897052

{txt} 22{c |} {space 3} {res} .06662527 {space 3} {res} .40114799 {space 3} {res} .05948742

{txt} 23{c |} {space 3} {res} .06070647 {space 3} {res} .40109615 {space 3} {res} .05994603

{txt} 24{c |} {space 3} {res} .05531347 {space 3} {res} .40028904 {space 3} {res} .0601798

{txt} 25{c |} {space 3} {res} .05039957 {space 3} {res} .3996653 {space 3} {res} .06044304

{txt} 26{c |} {space 3} {res} .04592222 {space 3} {res} .39919495 {space 3} {res} .06072542

{txt} 27{c |} {space 3} {res} .04184261 {space 3} {res} .39885197 {space 3} {res} .06101861

{txt} 28{c |} {space 3} {res} .03812543 {space 3} {res} .39861389 {space 3} {res} .06131593

{txt} 29{c |} {space 3} {res} .03473847 {space 3} {res} .39846146 {space 3} {res} .06161219

{txt} 30{c |} {space 3} {res} .0316524 {space 3} {res} .39836032 {space 3} {res} .06190805

{txt} 31{c |} {space 3} {res} .02884049 {space 3} {res} .39808305 {space 3} {res} .06224123

{txt} 32{c |} {space 3} {res} .02627838 {space 3} {res} .39784061 {space 3} {res} .06255301

{txt} 33{c |} {space 3} {res} .02394388 {space 3} {res} .39765313 {space 3} {res} .06285056

{txt} 34{c |} {space 3} {res} .02181677 {space 3} {res} .39751086 {space 3} {res} .0631329

{txt} 35{c |} {space 3} {res} .01987863 {space 3} {res} .39740558 {space 3} {res} .06339951

{txt} 36{c |} {space 3} {res} .01811267 {space 3} {res} .39733036 {space 3} {res} .06365023

{txt} 37{c |} {space 3} {res} .01650359 {space 3} {res} .3972794 {space 3} {res} .06388516

{txt} 38{c |} {space 3} {res} .01503746 {space 3} {res} .39724785 {space 3} {res} .06410461

{txt} 39{c |} {space 3} {res} .01370157 {space 3} {res} .3972317 {space 3} {res} .06430905

{txt} 40{c |} {space 3} {res} .01248436 {space 3} {res} .39722762 {space 3} {res} .06449906

{txt} 41{c |} {space 3} {res} .01137528 {space 3} {res} .39723286 {space 3} {res} .06467529

{txt} 42{c |} {space 3} {res} .01036473 {space 3} {res} .39724519 {space 3} {res} .06483845

{txt} 43{c |} {space 3} {res} .00944396 {space 3} {res} .39726277 {space 3} {res} .06498925

{txt} 44{c |} {space 3} {res} .00860498 {space 3} {res} .39728411 {space 3} {res} .06512844

{txt} 45{c |} {space 3} {res} .00784054 {space 3} {res} .39730804 {space 3} {res} .06525674

{txt} 46{c |} {space 3} {res} .00714401 {space 3} {res} .3973336 {space 3} {res} .06537487

{txt} 47{c |} {space 3} {res} .00650936 {space 3} {res} .39736002 {space 3} {res} .06548353

{txt} 48{c |} {space 3} {res} .00593108 {space 3} {res} .39738673 {space 3} {res} .06558339

{txt} 49{c |} {space 3} {res} .00540418 {space 3} {res} .39741327 {space 3} {res} .06567507

{txt} 50{c |} {space 3} {res} .00492409 {space 3} {res} .39743928 {space 3} {res} .0657592

{txt} 51{c |} {space 3} {res} .00448665 {space 3} {res} .39746453 {space 3} {res} .06583634

{txt} 52{c |} {space 3} {res} .00408806 {space 3} {res} .39748881 {space 3} {res} .06590703

{txt} 53{c |} {space 3} {res} .00372489 {space 3} {res} .39751201 {space 3} {res} .06597178

{txt} 54{c |} {space 3} {res} .00339398 {space 3} {res} .39753404 {space 3} {res} .06603105

{txt} 55{c |} {space 3} {res} .00309247 {space 3} {res} .39755485 {space 3} {res} .06608529

{txt} 56{c |} {space 3} {res} .00281774 {space 3} {res} .39757444 {space 3} {res} .0661349

{txt} 57{c |} {space 3} {res} .00256742 {space 3} {res} .39759281 {space 3} {res} .06618027

{txt} 58{c |} {space 3} {res} .00233934 {space 3} {res} .39760997 {space 3} {res} .06622173

{txt} 59{c |} {space 3} {res} .00213152 {space 3} {res} .39762597 {space 3} {res} .06625963

{txt} 60{c |} {space 3} {res} .00194216 {space 3} {res} .39764084 {space 3} {res} .06629424

{txt} 61{c |} {space 3} {res} .00176963 {space 3} {res} .39765464 {space 3} {res} .06632586

{txt} 62{c |} {space 3} {res} .00161242 {space 3} {res} .39766743 {space 3} {res} .06635473

{txt} 63{c |} {space 3} {res} .00146917 {space 3} {res} .39767925 {space 3} {res} .06638109

{txt}* lopt = the lambda that minimizes MSPE.

Run model: {stata cvlasso, lopt}

{p 0 8 2}^ lse = largest lambda for which MSPE is within one standard error of the minimal MSPE.{p_end}

Run model: {stata cvlasso, lse}

{err}Warning: lse is at the limit of the lambda range.

{txt}K-fold cross-validation with 10 folds. Elastic net with alpha=.7.

Fold {res}1 2 3 4 5 6 7 8 9 10

{txt} {c |} {space 3} {txt} Lambda {space 3} {txt} MSPE {space 3} {txt} st. dev.

{hline 10}{c +}{hline 45}

{txt} 1{c |} {space 3} {res} .47002812 {space 3} {res} .56478477 {space 3} {res} .11268272

{txt} 2{c |} {space 3} {res} .42827212 {space 3} {res} .53834409 {space 3} {res} .10494957

{txt} 3{c |} {space 3} {res} .3902256 {space 3} {res} .51647335 {space 3} {res} .09837217

{txt} 4{c |} {space 3} {res} .35555903 {space 3} {res} .49844777 {space 3} {res} .09247087

{txt} 5{c |} {space 3} {res} .32397215 {space 3} {res} .48344329 {space 3} {res} .08707347

{txt} 6{c |} {space 3} {res} .29519136 {space 3} {res} .46920859 {space 3} {res} .08244563

{txt} 7{c |} {space 3} {res} .26896737 {space 3} {res} .45606914 {space 3} {res} .078432

{txt} 8{c |} {space 3} {res} .24507305 {space 3} {res} .44348282 {space 3} {res} .07473117 ^

{txt} 9{c |} {space 3} {res} .22330144 {space 3} {res} .43289941 {space 3} {res} .07144851

{txt} 10{c |} {space 3} {res} .20346395 {space 3} {res} .42377807 {space 3} {res} .06852391

{txt} 11{c |} {space 3} {res} .18538878 {space 3} {res} .416121 {space 3} {res} .0659115

{txt} 12{c |} {space 3} {res} .16891935 {space 3} {res} .40982415 {space 3} {res} .06357773

{txt} 13{c |} {space 3} {res} .15391302 {space 3} {res} .40459551 {space 3} {res} .06151615

{txt} 14{c |} {space 3} {res} .14023981 {space 3} {res} .40024536 {space 3} {res} .05969764

{txt} 15{c |} {space 3} {res} .12778129 {space 3} {res} .39663093 {space 3} {res} .05809528

{txt} 16{c |} {space 3} {res} .11642955 {space 3} {res} .39475363 {space 3} {res} .05678376

{txt} 17{c |} {space 3} {res} .10608627 {space 3} {res} .39420819 {space 3} {res} .05582604 *

{txt} 18{c |} {space 3} {res} .09666186 {space 3} {res} .39475825 {space 3} {res} .05516119

{txt} 19{c |} {space 3} {res} .08807469 {space 3} {res} .39566183 {space 3} {res} .05469839

{txt} 20{c |} {space 3} {res} .08025038 {space 3} {res} .39662863 {space 3} {res} .05440645

{txt} 21{c |} {space 3} {res} .07312115 {space 3} {res} .39855638 {space 3} {res} .05466686

{txt} 22{c |} {space 3} {res} .06662527 {space 3} {res} .40034315 {space 3} {res} .05515023

{txt} 23{c |} {space 3} {res} .06070647 {space 3} {res} .40171035 {space 3} {res} .05578078

{txt} 24{c |} {space 3} {res} .05531347 {space 3} {res} .4027468 {space 3} {res} .0563652

{txt} 25{c |} {space 3} {res} .05039957 {space 3} {res} .40351716 {space 3} {res} .0568747

{txt} 26{c |} {space 3} {res} .04592222 {space 3} {res} .40373416 {space 3} {res} .05759446

{txt} 27{c |} {space 3} {res} .04184261 {space 3} {res} .40383878 {space 3} {res} .05838472

{txt} 28{c |} {space 3} {res} .03812543 {space 3} {res} .40410598 {space 3} {res} .05913399

{txt} 29{c |} {space 3} {res} .03473847 {space 3} {res} .40426995 {space 3} {res} .05972735

{txt} 30{c |} {space 3} {res} .0316524 {space 3} {res} .40421342 {space 3} {res} .06016101

{txt} 31{c |} {space 3} {res} .02884049 {space 3} {res} .40343107 {space 3} {res} .06040336

{txt} 32{c |} {space 3} {res} .02627838 {space 3} {res} .4026502 {space 3} {res} .0607131

{txt} 33{c |} {space 3} {res} .02394388 {space 3} {res} .40203757 {space 3} {res} .0610298

{txt} 34{c |} {space 3} {res} .02181677 {space 3} {res} .40155988 {space 3} {res} .06134707

{txt} 35{c |} {space 3} {res} .01987863 {space 3} {res} .40115794 {space 3} {res} .06166821

{txt} 36{c |} {space 3} {res} .01811267 {space 3} {res} .4008511 {space 3} {res} .06197901

{txt} 37{c |} {space 3} {res} .01650359 {space 3} {res} .40062127 {space 3} {res} .06227702

{txt} 38{c |} {space 3} {res} .01503746 {space 3} {res} .40028311 {space 3} {res} .0626034

{txt} 39{c |} {space 3} {res} .01370157 {space 3} {res} .39994508 {space 3} {res} .06292698

{txt} 40{c |} {space 3} {res} .01248436 {space 3} {res} .39966671 {space 3} {res} .06322981

{txt} 41{c |} {space 3} {res} .01137528 {space 3} {res} .39943782 {space 3} {res} .06351225

{txt} 42{c |} {space 3} {res} .01036473 {space 3} {res} .39924995 {space 3} {res} .06377493

{txt} 43{c |} {space 3} {res} .00944396 {space 3} {res} .39908768 {space 3} {res} .06401694

{txt} 44{c |} {space 3} {res} .00860498 {space 3} {res} .39890069 {space 3} {res} .06423062

{txt} 45{c |} {space 3} {res} .00784054 {space 3} {res} .39874262 {space 3} {res} .06442897

{txt} 46{c |} {space 3} {res} .00714401 {space 3} {res} .39860883 {space 3} {res} .06461269

{txt} 47{c |} {space 3} {res} .00650936 {space 3} {res} .39849548 {space 3} {res} .06478256

{txt} 48{c |} {space 3} {res} .00593108 {space 3} {res} .39839932 {space 3} {res} .06493935

{txt} 49{c |} {space 3} {res} .00540418 {space 3} {res} .39831763 {space 3} {res} .06508388

{txt} 50{c |} {space 3} {res} .00492409 {space 3} {res} .39824813 {space 3} {res} .06521693

{txt} 51{c |} {space 3} {res} .00448665 {space 3} {res} .39818892 {space 3} {res} .06533929

{txt} 52{c |} {space 3} {res} .00408806 {space 3} {res} .39813838 {space 3} {res} .0654517

{txt} 53{c |} {space 3} {res} .00372489 {space 3} {res} .39809518 {space 3} {res} .0655549

{txt} 54{c |} {space 3} {res} .00339398 {space 3} {res} .39805819 {space 3} {res} .06564956

{txt} 55{c |} {space 3} {res} .00309247 {space 3} {res} .39802645 {space 3} {res} .06573633

{txt} 56{c |} {space 3} {res} .00281774 {space 3} {res} .39799916 {space 3} {res} .06581582

{txt} 57{c |} {space 3} {res} .00256742 {space 3} {res} .39797566 {space 3} {res} .0658886

{txt} 58{c |} {space 3} {res} .00233934 {space 3} {res} .39795539 {space 3} {res} .06595521

{txt} 59{c |} {space 3} {res} .00213152 {space 3} {res} .39793785 {space 3} {res} .06601615

{txt} 60{c |} {space 3} {res} .00194216 {space 3} {res} .39792265 {space 3} {res} .06607188

{txt} 61{c |} {space 3} {res} .00176963 {space 3} {res} .39790945 {space 3} {res} .06612282

{txt} 62{c |} {space 3} {res} .00161242 {space 3} {res} .39789797 {space 3} {res} .06616937

{txt} 63{c |} {space 3} {res} .00146917 {space 3} {res} .39788795 {space 3} {res} .0662119

{txt}* lopt = the lambda that minimizes MSPE.

Run model: {stata cvlasso, lopt}

{p 0 8 2}^ lse = largest lambda for which MSPE is within one standard error of the minimal MSPE.{p_end}

Run model: {stata cvlasso, lse}

K-fold cross-validation with 10 folds. Elastic net with alpha=1.

Fold {res}1 2 3 4 5 6 7 8 9 10

{txt} {c |} {space 3} {txt} Lambda {space 3} {txt} MSPE {space 3} {txt} st. dev.

{hline 10}{c +}{hline 45}

{txt} 1{c |} {space 3} {res} .47002812 {space 3} {res} .68765443 {space 3} {res} .1373409

{txt} 2{c |} {space 3} {res} .42827212 {space 3} {res} .65513813 {space 3} {res} .13755403

{txt} 3{c |} {space 3} {res} .3902256 {space 3} {res} .61330573 {space 3} {res} .12760865

{txt} 4{c |} {space 3} {res} .35555903 {space 3} {res} .57588694 {space 3} {res} .11700443

{txt} 5{c |} {space 3} {res} .32397215 {space 3} {res} .54476261 {space 3} {res} .10768145

{txt} 6{c |} {space 3} {res} .29519136 {space 3} {res} .51886926 {space 3} {res} .09948626

{txt} 7{c |} {space 3} {res} .26896737 {space 3} {res} .49790373 {space 3} {res} .09268422

{txt} 8{c |} {space 3} {res} .24507305 {space 3} {res} .48106487 {space 3} {res} .08704268

{txt} 9{c |} {space 3} {res} .22330144 {space 3} {res} .46757902 {space 3} {res} .08192997

{txt} 10{c |} {space 3} {res} .20346395 {space 3} {res} .4562232 {space 3} {res} .07754346

{txt} 11{c |} {space 3} {res} .18538878 {space 3} {res} .44550619 {space 3} {res} .07376981 ^

{txt} 12{c |} {space 3} {res} .16891935 {space 3} {res} .43595363 {space 3} {res} .07036464

{txt} 13{c |} {space 3} {res} .15391302 {space 3} {res} .42657658 {space 3} {res} .06740889

{txt} 14{c |} {space 3} {res} .14023981 {space 3} {res} .41860881 {space 3} {res} .06487241

{txt} 15{c |} {space 3} {res} .12778129 {space 3} {res} .41202072 {space 3} {res} .06265271