{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/lab6.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}10 Feb 2015, 08:37:08
{txt}
{com}. use lab6, clear 
{txt}
{com}. 
. * univariate linearity
. lowess bmd age, name(linearity, replace)
{res}{txt}
{com}. graph export linearity.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/linearity.pdf written in PDF format)

{com}. 
. * multivariate linearity
. reg bmd age weight

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}     278
{txt}{hline 13}{char +}{hline 30}           F(  2,   275) ={res}   54.77
    {txt}   Model {char |} {res} 1.48243732     2  .741218662           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 3.72190651   275  .013534205           {txt}R-squared     = {res} 0.2848
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.2796
    {txt}   Total {char |} {res} 5.20434383   277  .018788245           {txt}Root MSE      = {res} .11634

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         bmd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2}-.0049417{col 26}{space 2} .0015401{col 37}{space 1}   -3.21{col 46}{space 3}0.001{col 54}{space 4}-.0079736{col 67}{space 3}-.0019099
{txt}{space 6}weight {c |}{col 14}{res}{space 2} .0047503{col 26}{space 2} .0005353{col 37}{space 1}    8.87{col 46}{space 3}0.000{col 54}{space 4} .0036964{col 67}{space 3} .0058041
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .7925524{col 26}{space 2} .1347116{col 37}{space 1}    5.88{col 46}{space 3}0.000{col 54}{space 4} .5273553{col 67}{space 3} 1.057749
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. cprplot weight, lowess name(cprplot1, replace)
{res}{txt}
{com}. graph export cprplot1.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/cprplot1.pdf written in PDF format)

{com}. quietly adjust age, by(weight) gen(fitted1)
{txt}
{com}. 
. regress bmd age lweight

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}     278
{txt}{hline 13}{char +}{hline 30}           F(  2,   275) ={res}   61.64
    {txt}   Model {char |} {res} 1.61096241     2  .805481207           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 3.59338142   275  .013066842           {txt}R-squared     = {res} 0.3095
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.3045
    {txt}   Total {char |} {res} 5.20434383   277  .018788245           {txt}Root MSE      = {res} .11431

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         bmd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2}-.0047296{col 26}{space 2} .0015137{col 37}{space 1}   -3.12{col 46}{space 3}0.002{col 54}{space 4}-.0077094{col 67}{space 3}-.0017497
{txt}{space 5}lweight {c |}{col 14}{res}{space 2} .3483217{col 26}{space 2} .0364352{col 37}{space 1}    9.56{col 46}{space 3}0.000{col 54}{space 4} .2765944{col 67}{space 3}  .420049
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.3640786{col 26}{space 2} .2156111{col 37}{space 1}   -1.69{col 46}{space 3}0.092{col 54}{space 4}-.7885367{col 67}{space 3} .0603794
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. cprplot lweight, lowess name(cprplot2, replace)
{res}{txt}
{com}. nlcom _b[lweight]*log(1.1)

       {txt}_nl_1:  {res}_b[lweight]*log(1.1)

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         bmd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_nl_1 {c |}{col 14}{res}{space 2} .0331986{col 26}{space 2} .0034726{col 37}{space 1}    9.56{col 46}{space 3}0.000{col 54}{space 4} .0263923{col 67}{space 3} .0400049
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. graph export cprplot2.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/cprplot2.pdf written in PDF format)

{com}. quietly adjust age, by(weight) gen(fitted2)
{txt}
{com}. 
. regress bmd age weight weight2

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}     278
{txt}{hline 13}{char +}{hline 30}           F(  3,   274) ={res}   43.38
    {txt}   Model {char |} {res} 1.67578732     3  .558595772           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 3.52855652   274  .012877943           {txt}R-squared     = {res} 0.3220
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.3146
    {txt}   Total {char |} {res} 5.20434383   277  .018788245           {txt}Root MSE      = {res} .11348

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         bmd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2}-.0047303{col 26}{space 2} .0015033{col 37}{space 1}   -3.15{col 46}{space 3}0.002{col 54}{space 4}-.0076897{col 67}{space 3}-.0017709
{txt}{space 6}weight {c |}{col 14}{res}{space 2} .0182735{col 26}{space 2} .0035289{col 37}{space 1}    5.18{col 46}{space 3}0.000{col 54}{space 4} .0113263{col 67}{space 3} .0252207
{txt}{space 5}weight2 {c |}{col 14}{res}{space 2}-.0000925{col 26}{space 2} .0000239{col 37}{space 1}   -3.87{col 46}{space 3}0.000{col 54}{space 4}-.0001395{col 67}{space 3}-.0000455
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3012021{col 26}{space 2} .1826122{col 37}{space 1}    1.65{col 46}{space 3}0.100{col 54}{space 4}-.0582992{col 67}{space 3} .6607034
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly adjust age, gen(fitted3)
{txt}
{com}. predict residuals2, resid
{txt}
{com}. rvpplot weight, yline(0) addplot(lowess residuals2 weight) name(rvpplot3, replace)
{res}{txt}
{com}. graph export rvpplot3.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/rvpplot3.pdf written in PDF format)

{com}. 
. mkspline wt = weight, cubic
{txt}
{com}. regress bmd age wt1-wt4

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}     278
{txt}{hline 13}{char +}{hline 30}           F(  5,   272) ={res}   27.20
    {txt}   Model {char |} {res} 1.73493728     5  .346987456           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 3.46940655   272  .012755171           {txt}R-squared     = {res} 0.3334
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.3211
    {txt}   Total {char |} {res} 5.20434383   277  .018788245           {txt}Root MSE      = {res} .11294

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         bmd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2}-.0046327{col 26}{space 2} .0014971{col 37}{space 1}   -3.09{col 46}{space 3}0.002{col 54}{space 4}-.0075801{col 67}{space 3}-.0016852
{txt}{space 9}wt1 {c |}{col 14}{res}{space 2} .0127171{col 26}{space 2} .0027237{col 37}{space 1}    4.67{col 46}{space 3}0.000{col 54}{space 4} .0073548{col 67}{space 3} .0180794
{txt}{space 9}wt2 {c |}{col 14}{res}{space 2}-.0380388{col 26}{space 2} .0232925{col 37}{space 1}   -1.63{col 46}{space 3}0.104{col 54}{space 4}-.0838954{col 67}{space 3} .0078177
{txt}{space 9}wt3 {c |}{col 14}{res}{space 2} .1268726{col 26}{space 2}  .099673{col 37}{space 1}    1.27{col 46}{space 3}0.204{col 54}{space 4} -.069356{col 67}{space 3} .3231011
{txt}{space 9}wt4 {c |}{col 14}{res}{space 2}-.1337551{col 26}{space 2} .1235098{col 37}{space 1}   -1.08{col 46}{space 3}0.280{col 54}{space 4}-.3769117{col 67}{space 3} .1094015
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3306817{col 26}{space 2}  .193215{col 37}{space 1}    1.71{col 46}{space 3}0.088{col 54}{space 4}-.0497053{col 67}{space 3} .7110687
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. quietly adjust age, by(weight) gen(fitted4)
{txt}
{com}. 
. twoway (scatter bmd weight, msize(small)) ///
>      (line fitted1 weight, sort lpattern(solid) lwidth(medthick)) ///
>      (line fitted2 weight, sort lpattern(dash_dot) lwidth(thick)) ///
>      (line fitted3 weight, sort lpattern(longdash) lwidth(thick)) ///
>      (line fitted4 weight, sort lpattern(shortdash) lwidth(thick)), ///
>      legend(order(2 "Linear" 3 "Log" 4 "Quadratic" 5 "Cubic spline") rows(2)) ///
>      name(bmdfits, replace)
{res}{txt}
{com}. graph export bmdfits.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/bmdfits.pdf written in PDF format)

{com}. drop fitted*
{txt}
{com}. 
. * normality of residuals
. reg bmd age weight weight2

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}     278
{txt}{hline 13}{char +}{hline 30}           F(  3,   274) ={res}   43.38
    {txt}   Model {char |} {res} 1.67578732     3  .558595772           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 3.52855652   274  .012877943           {txt}R-squared     = {res} 0.3220
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.3146
    {txt}   Total {char |} {res} 5.20434383   277  .018788245           {txt}Root MSE      = {res} .11348

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         bmd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2}-.0047303{col 26}{space 2} .0015033{col 37}{space 1}   -3.15{col 46}{space 3}0.002{col 54}{space 4}-.0076897{col 67}{space 3}-.0017709
{txt}{space 6}weight {c |}{col 14}{res}{space 2} .0182735{col 26}{space 2} .0035289{col 37}{space 1}    5.18{col 46}{space 3}0.000{col 54}{space 4} .0113263{col 67}{space 3} .0252207
{txt}{space 5}weight2 {c |}{col 14}{res}{space 2}-.0000925{col 26}{space 2} .0000239{col 37}{space 1}   -3.87{col 46}{space 3}0.000{col 54}{space 4}-.0001395{col 67}{space 3}-.0000455
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3012021{col 26}{space 2} .1826122{col 37}{space 1}    1.65{col 46}{space 3}0.100{col 54}{space 4}-.0582992{col 67}{space 3} .6607034
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. predict bmdresid, resid 
{txt}
{com}. qnorm bmdresid, name(qqplot1, replace)
{res}{txt}
{com}. graph export qqplot1.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/qqplot1.pdf written in PDF format)

{com}. kdensity bmdresid, normal
{res}{txt}
{com}. graph export kdplot1.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/kdplot1.pdf written in PDF format)

{com}. 
. reg eeu age poorhlth gaitspd

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}     277
{txt}{hline 13}{char +}{hline 30}           F(  3,   273) ={res}    4.22
    {txt}   Model {char |} {res} 98.2991326     3  32.7663775           {txt}Prob > F      = {res} 0.0061
    {txt}Residual {char |} {res} 2118.21339   273   7.7590234           {txt}R-squared     = {res} 0.0443
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.0338
    {txt}   Total {char |} {res} 2216.51252   276  8.03084246           {txt}Root MSE      = {res} 2.7855

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         eeu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2}-.0255973{col 26}{space 2} .0393144{col 37}{space 1}   -0.65{col 46}{space 3}0.516{col 54}{space 4}-.1029952{col 67}{space 3} .0518006
{txt}{space 4}poorhlth {c |}{col 14}{res}{space 2} -.898035{col 26}{space 2} .4189794{col 37}{space 1}   -2.14{col 46}{space 3}0.033{col 54}{space 4}-1.722876{col 67}{space 3}-.0731938
{txt}{space 5}gaitspd {c |}{col 14}{res}{space 2} 1.647421{col 26}{space 2} .8897152{col 37}{space 1}    1.85{col 46}{space 3}0.065{col 54}{space 4}-.1041537{col 67}{space 3} 3.398996
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.414444{col 26}{space 2} 3.538559{col 37}{space 1}    0.96{col 46}{space 3}0.335{col 54}{space 4}-3.551888{col 67}{space 3} 10.38078
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. predict eeuresid, res
{txt}(1 missing value generated)

{com}. qnorm eeuresid, name(qqplot2, replace)
{res}{txt}
{com}. graph export qqplot2.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/qqplot2.pdf written in PDF format)

{com}. 
. replace eeu = eeu+0.01
{txt}(278 real changes made)

{com}. qladder eeu, name(qladder, replace)
{res}{txt}
{com}. graph export qladder.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/qladder.pdf written in PDF format)

{com}. 
. gen ln_eeu = log(eeu)
{txt}
{com}. reg ln_eeu age poorhlth gaitspd

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}     277
{txt}{hline 13}{char +}{hline 30}           F(  3,   273) ={res}    8.27
    {txt}   Model {char |} {res} 25.8655573     3  8.62185242           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 284.570802   273  1.04238389           {txt}R-squared     = {res} 0.0833
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.0732
    {txt}   Total {char |} {res}  310.43636   276  1.12476942           {txt}Root MSE      = {res}  1.021

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}      ln_eeu{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2} -.008435{col 26}{space 2} .0144099{col 37}{space 1}   -0.59{col 46}{space 3}0.559{col 54}{space 4}-.0368037{col 67}{space 3} .0199337
{txt}{space 4}poorhlth {c |}{col 14}{res}{space 2}-.3544198{col 26}{space 2} .1535688{col 37}{space 1}   -2.31{col 46}{space 3}0.022{col 54}{space 4}-.6567494{col 67}{space 3}-.0520902
{txt}{space 5}gaitspd {c |}{col 14}{res}{space 2}  1.06321{col 26}{space 2} .3261079{col 37}{space 1}    3.26{col 46}{space 3}0.001{col 54}{space 4} .4212045{col 67}{space 3} 1.705216
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .3000781{col 26}{space 2}  1.29699{col 37}{space 1}    0.23{col 46}{space 3}0.817{col 54}{space 4}-2.253296{col 67}{space 3} 2.853452
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. predict ln_eeuresid, res
{txt}(1 missing value generated)

{com}. qnorm ln_eeuresid, name(qqplot3, replace)
{res}{txt}
{com}. graph export qqplot3.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/qqplot3.pdf written in PDF format)

{com}. 
. * constant variance
. rvfplot, name(rvfplot1, replace)
{res}{txt}
{com}. graph export rvfplot1.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/rvfplot1.pdf written in PDF format)

{com}. rvpplot age, name(rvpplot1, replace)
{res}{txt}
{com}. graph export rvpplot1.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/rvpplot1.pdf written in PDF format)

{com}. rvpplot gaitspd, name(rvpplot2, replace)
{res}{txt}
{com}. graph export rvpplot2.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/rvpplot2.pdf written in PDF format)

{com}. table poorhlth, c(n ln_eeuresid sd ln_eeuresid)

{txt}{hline 10}{c TT}{hline 27}
poor      {c |}
health by {c |}
self-repo {c |}
rt        {c |}  N(ln_eeu~d)  sd(ln_eeu~d)
{hline 10}{c +}{hline 27}
       no {c |}          {res}217      .9795251
      {txt}yes {c |}           {res}60      1.144814
{txt}{hline 10}{c BT}{hline 27}

{com}. 
. * leverage and influence
. reg bmd age lweight

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}     278
{txt}{hline 13}{char +}{hline 30}           F(  2,   275) ={res}   61.64
    {txt}   Model {char |} {res} 1.61096241     2  .805481207           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 3.59338142   275  .013066842           {txt}R-squared     = {res} 0.3095
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.3045
    {txt}   Total {char |} {res} 5.20434383   277  .018788245           {txt}Root MSE      = {res} .11431

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         bmd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2}-.0047296{col 26}{space 2} .0015137{col 37}{space 1}   -3.12{col 46}{space 3}0.002{col 54}{space 4}-.0077094{col 67}{space 3}-.0017497
{txt}{space 5}lweight {c |}{col 14}{res}{space 2} .3483217{col 26}{space 2} .0364352{col 37}{space 1}    9.56{col 46}{space 3}0.000{col 54}{space 4} .2765944{col 67}{space 3}  .420049
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.3640786{col 26}{space 2} .2156111{col 37}{space 1}   -1.69{col 46}{space 3}0.092{col 54}{space 4}-.7885367{col 67}{space 3} .0603794
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. dfbeta
                       {txt}_dfbeta_1: dfbeta(age)
                       _dfbeta_2: dfbeta(lweight)

{com}. graph box _dfbeta_1 _dfbeta_2, name(boxplot1, replace)
{res}{txt}
{com}. graph export boxplot1.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/boxplot1.pdf written in PDF format)

{com}. list bmd age lweight _dfbeta_1 if abs(_dfbeta_1) > 0.2 & ~missing(_dfbeta_1)
{txt}
     {c TLC}{hline 6}{c -}{hline 5}{c -}{hline 10}{c -}{hline 11}{c TRC}
     {c |} {res} bmd   age    lweight   _dfbeta_1 {txt}{c |}
     {c LT}{hline 6}{c -}{hline 5}{c -}{hline 10}{c -}{hline 11}{c RT}
140. {c |} {res}.458    88   4.234107   -.2716317 {txt}{c |}
151. {c |} {res}.914    90   4.023564    .3781947 {txt}{c |}
200. {c |} {res} .91    87   4.219508    .2188265 {txt}{c |}
     {c BLC}{hline 6}{c -}{hline 5}{c -}{hline 10}{c -}{hline 11}{c BRC}

{com}. list bmd age lweight _dfbeta_2 if abs(_dfbeta_2) > 0.2 & ~missing(_dfbeta_2)
{txt}
     {c TLC}{hline 7}{c -}{hline 5}{c -}{hline 10}{c -}{hline 10}{c TRC}
     {c |} {res}  bmd   age    lweight   _dfbet~2 {txt}{c |}
     {c LT}{hline 7}{c -}{hline 5}{c -}{hline 10}{c -}{hline 10}{c RT}
 94. {c |} {res} 1.18    78   4.435567   .2476206 {txt}{c |}
108. {c |} {res} .366    82   3.720862   .2242683 {txt}{c |}
231. {c |} {res} .441    73   3.642836   .2110856 {txt}{c |}
262. {c |} {res}1.193    78   4.390738   .2162125 {txt}{c |}
     {c BLC}{hline 7}{c -}{hline 5}{c -}{hline 10}{c -}{hline 10}{c BRC}

{com}. reg bmd age lweight if abs(_dfbeta_1) <= .2 & abs(_dfbeta_2) <= 0.2

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}     271
{txt}{hline 13}{char +}{hline 30}           F(  2,   268) ={res}   60.60
    {txt}   Model {char |} {res} 1.37868504     2  .689342521           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res}  3.0485926   268  .011375346           {txt}R-squared     = {res} 0.3114
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.3063
    {txt}   Total {char |} {res} 4.42727765   270  .016397325           {txt}Root MSE      = {res} .10666

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         bmd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}age {c |}{col 14}{res}{space 2}-.0055429{col 26}{space 2} .0014588{col 37}{space 1}   -3.80{col 46}{space 3}0.000{col 54}{space 4} -.008415{col 67}{space 3}-.0026708
{txt}{space 5}lweight {c |}{col 14}{res}{space 2} .3168664{col 26}{space 2} .0352548{col 37}{space 1}    8.99{col 46}{space 3}0.000{col 54}{space 4} .2474548{col 67}{space 3} .3862781
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1709832{col 26}{space 2} .2094359{col 37}{space 1}   -0.82{col 46}{space 3}0.415{col 54}{space 4}-.5833321{col 67}{space 3} .2413656
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. * checking overlap
. recode estrogen 1=0 2=1, gen(ht_current)
{txt}(124 differences between estrogen and ht_current)

{com}. label values ht_current yesno
{txt}
{com}. tab estrogen ht_current

{txt}current or {c |}  RECODE of estrogen
      past {c |}   (current or past
  estrogen {c |}     estrogen use)
       use {c |}        no        yes {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     never {c |}{res}       154          0 {txt}{c |}{res}       154 
{txt}      past {c |}{res}        74          0 {txt}{c |}{res}        74 
{txt}   current {c |}{res}         0         50 {txt}{c |}{res}        50 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       228         50 {txt}{c |}{res}       278 

{txt}
{com}. foreach x in age bmi gaitspd has10 {c -(}
{txt}  2{com}.         table ht_current, c(mean `x' p5 `x' min `x' p95 `x' max `x')
{txt}  3{com}.         {c )-}

{txt}{hline 10}{c TT}{hline 59}
RECODE of {c |}
estrogen  {c |}
(current  {c |}
or past   {c |}
estrogen  {c |}
use)      {c |}  mean(age)     p5(age)    min(age)    p95(age)    max(age)
{hline 10}{c +}{hline 59}
       no {c |}   {res}78.91666          73          72          89          95
      {txt}yes {c |}      {res}77.04          73          73          83          87
{txt}{hline 10}{c BT}{hline 59}

{hline 10}{c TT}{hline 59}
RECODE of {c |}
estrogen  {c |}
(current  {c |}
or past   {c |}
estrogen  {c |}
use)      {c |}  mean(bmi)     p5(bmi)    min(bmi)    p95(bmi)    max(bmi)
{hline 10}{c +}{hline 59}
       no {c |}    {res}27.0989      20.153      14.875      35.466      44.894
      {txt}yes {c |}   {res}27.14962      20.587      20.134      33.566      37.608
{txt}{hline 10}{c BT}{hline 59}

{hline 10}{c TT}{hline 69}
RECODE of {c |}
estrogen  {c |}
(current  {c |}
or past   {c |}
estrogen  {c |}
use)      {c |} mean(gait~d)   p5(gaitspd)  min(gaitspd)  p95(gaitspd)  max(gaitspd)
{hline 10}{c +}{hline 69}
       no {c |}      {res}.930329          .588          .299          1.25         1.429
      {txt}yes {c |}     {res}.9680612          .606          .401         1.304           1.5
{txt}{hline 10}{c BT}{hline 69}

{hline 10}{c TT}{hline 64}
RECODE of {c |}
estrogen  {c |}
(current  {c |}
or past   {c |}
estrogen  {c |}
use)      {c |} mean(has10)    p5(has10)   min(has10)   p95(has10)   max(has10)
{hline 10}{c +}{hline 64}
       no {c |}    {res}.9177778           .5           .3         1.45         1.85
      {txt}yes {c |}        {res}.986          .65          .45         1.45         1.65
{txt}{hline 10}{c BT}{hline 64}

{com}. foreach x in usearms poorhlth calsupp etid nsfx2 {c -(}
{txt}  2{com}.         tab ht_current `x', row
{txt}  3{com}.         {c )-}
{txt}
{c TLC}{hline 16}{c TRC}
{c |} Key{col 18}{c |}
{c LT}{hline 16}{c RT}
{c |}{space 3}{it:frequency}{col 18}{c |}
{c |}{space 1}{it:row percentage}{col 18}{c |}
{c BLC}{hline 16}{c BRC}

 RECODE of {c |}
  estrogen {c |}
  (current {c |}
   or past {c |}
  estrogen {c |} use arms to stand up
      use) {c |}        no        yes {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
        no {c |}{res}       204         24 {txt}{c |}{res}       228 
           {txt}{c |}{res}     89.47      10.53 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
       yes {c |}{res}        43          5 {txt}{c |}{res}        48 
           {txt}{c |}{res}     89.58      10.42 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       247         29 {txt}{c |}{res}       276 
           {txt}{c |}{res}     89.49      10.51 {txt}{c |}{res}    100.00 

{txt}
{c TLC}{hline 16}{c TRC}
{c |} Key{col 18}{c |}
{c LT}{hline 16}{c RT}
{c |}{space 3}{it:frequency}{col 18}{c |}
{c |}{space 1}{it:row percentage}{col 18}{c |}
{c BLC}{hline 16}{c BRC}

 RECODE of {c |}
  estrogen {c |}
  (current {c |}
   or past {c |}    poor health by
  estrogen {c |}      self-report
      use) {c |}        no        yes {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
        no {c |}{res}       179         49 {txt}{c |}{res}       228 
           {txt}{c |}{res}     78.51      21.49 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
       yes {c |}{res}        38         12 {txt}{c |}{res}        50 
           {txt}{c |}{res}     76.00      24.00 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       217         61 {txt}{c |}{res}       278 
           {txt}{c |}{res}     78.06      21.94 {txt}{c |}{res}    100.00 

{txt}
{c TLC}{hline 16}{c TRC}
{c |} Key{col 18}{c |}
{c LT}{hline 16}{c RT}
{c |}{space 3}{it:frequency}{col 18}{c |}
{c |}{space 1}{it:row percentage}{col 18}{c |}
{c BLC}{hline 16}{c BRC}

 RECODE of {c |}
  estrogen {c |}
  (current {c |}
   or past {c |}  last 30 days: taken
  estrogen {c |}  calcium supplements
      use) {c |}        no        yes {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
        no {c |}{res}       156         72 {txt}{c |}{res}       228 
           {txt}{c |}{res}     68.42      31.58 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
       yes {c |}{res}        22         28 {txt}{c |}{res}        50 
           {txt}{c |}{res}     44.00      56.00 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       178        100 {txt}{c |}{res}       278 
           {txt}{c |}{res}     64.03      35.97 {txt}{c |}{res}    100.00 

{txt}
{c TLC}{hline 16}{c TRC}
{c |} Key{col 18}{c |}
{c LT}{hline 16}{c RT}
{c |}{space 3}{it:frequency}{col 18}{c |}
{c |}{space 1}{it:row percentage}{col 18}{c |}
{c BLC}{hline 16}{c BRC}

 RECODE of {c |}
  estrogen {c |}
  (current {c |}
   or past {c |}  last 2 years: used
  estrogen {c |}      Etidronate
      use) {c |}        no        yes {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
        no {c |}{res}       225          3 {txt}{c |}{res}       228 
           {txt}{c |}{res}     98.68       1.32 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
       yes {c |}{res}        49          1 {txt}{c |}{res}        50 
           {txt}{c |}{res}     98.00       2.00 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       274          4 {txt}{c |}{res}       278 
           {txt}{c |}{res}     98.56       1.44 {txt}{c |}{res}    100.00 

{txt}
{c TLC}{hline 16}{c TRC}
{c |} Key{col 18}{c |}
{c LT}{hline 16}{c RT}
{c |}{space 3}{it:frequency}{col 18}{c |}
{c |}{space 1}{it:row percentage}{col 18}{c |}
{c BLC}{hline 16}{c BRC}

 RECODE of {c |}
  estrogen {c |}
  (current {c |}
   or past {c |} non-spine fracture in
  estrogen {c |}     last 2 years
      use) {c |}        no        yes {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
        no {c |}{res}       209         19 {txt}{c |}{res}       228 
           {txt}{c |}{res}     91.67       8.33 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
       yes {c |}{res}        46          4 {txt}{c |}{res}        50 
           {txt}{c |}{res}     92.00       8.00 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       255         23 {txt}{c |}{res}       278 
           {txt}{c |}{res}     91.73       8.27 {txt}{c |}{res}    100.00 

{txt}
{com}. mkspline agesp = age, cubic
{txt}
{com}. mkspline bmisp = bmi, cubic
{txt}
{com}. mkspline gaitspdsp = gaitspd, cubic
{txt}
{com}. mkspline has10sp = has10, cubic
{txt}
{com}. sw, pr(.2): logistic ht_current (agesp*) (bmisp*) (has10*) nsfx2 calsupp etid (gaitspdsp*)
{txt}note: has10sp1 dropped because of collinearity
{p2colset 0 23 32 2}{...}
{p2col :}begin with full model{p_end}
{p2col :p = {res:0.9631} >= 0.2000}removing {res:etid}{p_end}
{p2col :p = {res:0.6373} >= 0.2000}removing {res:nsfx2}{p_end}
{p2col :p = {res:0.5327} >= 0.2000}removing {res:gaitspdsp1 gaitspdsp2 gaitspdsp3 gaitspdsp4}{p_end}
{p2col :p = {res:0.3872} >= 0.2000}removing {res:bmisp1 bmisp2 bmisp3 bmisp4}{p_end}
{p2col :p = {res:0.4245} >= 0.2000}removing {res:has10 has10sp2 has10sp3 has10sp4}{p_end}
{p2colreset}{...}

Logistic regression{col 51}Number of obs{col 67}= {res}       274
{txt}{col 51}LR chi2({res}5{txt}){col 67}= {res}     17.76
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0033
{txt}Log likelihood = {res}-119.79665{txt}{col 51}Pseudo R2{col 67}= {res}    0.0690

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  ht_current{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}agesp1 {c |}{col 14}{res}{space 2} .6577646{col 26}{space 2} .2254138{col 37}{space 1}   -1.22{col 46}{space 3}0.222{col 54}{space 4} .3360211{col 67}{space 3} 1.287581
{txt}{space 6}agesp2 {c |}{col 14}{res}{space 2} 491.5358{col 26}{space 2} 2705.423{col 37}{space 1}    1.13{col 46}{space 3}0.260{col 54}{space 4} .0101513{col 67}{space 3} 2.38e+07
{txt}{space 6}agesp3 {c |}{col 14}{res}{space 2} 1.01e-06{col 26}{space 2} .0000127{col 37}{space 1}   -1.10{col 46}{space 3}0.273{col 54}{space 4} 1.93e-17{col 67}{space 3} 52520.76
{txt}{space 6}agesp4 {c |}{col 14}{res}{space 2} 12798.96{col 26}{space 2} 128111.7{col 37}{space 1}    0.94{col 46}{space 3}0.345{col 54}{space 4} .0000386{col 67}{space 3} 4.24e+12
{txt}{space 5}calsupp {c |}{col 14}{res}{space 2} 2.493148{col 26}{space 2} .8247382{col 37}{space 1}    2.76{col 46}{space 3}0.006{col 54}{space 4} 1.303674{col 67}{space 3} 4.767901
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.08e+12{col 26}{space 2} 1.28e+14{col 37}{space 1}    1.16{col 46}{space 3}0.247{col 54}{space 4} 1.55e-09{col 67}{space 3} 1.67e+34
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logistic ht_current agesp* calsupp
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       278
{txt}{col 51}LR chi2({res}5{txt}){col 67}= {res}     19.18
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0018
{txt}Log likelihood = {res}-121.39847{txt}{col 51}Pseudo R2{col 67}= {res}    0.0732

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  ht_current{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}agesp1 {c |}{col 14}{res}{space 2} .6542074{col 26}{space 2} .2239392{col 37}{space 1}   -1.24{col 46}{space 3}0.215{col 54}{space 4} .3344598{col 67}{space 3} 1.279638
{txt}{space 6}agesp2 {c |}{col 14}{res}{space 2} 744.7702{col 26}{space 2} 4079.485{col 37}{space 1}    1.21{col 46}{space 3}0.227{col 54}{space 4} .0162016{col 67}{space 3} 3.42e+07
{txt}{space 6}agesp3 {c |}{col 14}{res}{space 2} 3.17e-07{col 26}{space 2} 3.97e-06{col 37}{space 1}   -1.20{col 46}{space 3}0.231{col 54}{space 4} 7.18e-18{col 67}{space 3} 14012.11
{txt}{space 6}agesp4 {c |}{col 14}{res}{space 2} 39901.15{col 26}{space 2} 395380.3{col 37}{space 1}    1.07{col 46}{space 3}0.285{col 54}{space 4} .0001467{col 67}{space 3} 1.09e+13
{txt}{space 5}calsupp {c |}{col 14}{res}{space 2} 2.638898{col 26}{space 2} .8679393{col 37}{space 1}    2.95{col 46}{space 3}0.003{col 54}{space 4} 1.385034{col 67}{space 3} 5.027877
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 7.19e+12{col 26}{space 2} 1.82e+14{col 37}{space 1}    1.17{col 46}{space 3}0.241{col 54}{space 4} 2.33e-09{col 67}{space 3} 2.22e+34
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. testparm agesp*

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[ht_current]agesp1 = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} [ht_current]agesp2 = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} [ht_current]agesp3 = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} [ht_current]agesp4 = 0{p_end}

{txt}{col 12}chi2(  4) ={res}    6.39
{txt}{col 10}Prob > chi2 =  {res}  0.1719
{txt}
{com}. predict logit_pscore, xb
{txt}
{com}. tab ht_current, sum(logit_pscore)

  {txt}RECODE of {c |}
   estrogen {c |}
(current or {c |}
       past {c |}  Summary of Linear prediction (log
   estrogen {c |}                odds)
       use) {c |}        Mean   Std. Dev.       Freq.
{hline 12}{c +}{hline 36}
         no {c |}  {res}-1.8217386   .90527908         228
  {txt}      yes {c |}  {res}  -1.28434   .59299373          50
{txt}{hline 12}{c +}{hline 36}
      Total {c |}  {res}-1.7250842   .88122527         278
{txt}
{com}. twoway (kdensity logit_pscore if estrogen==1, area(1) lpattern(solid)) ///
>         (kdensity logit_pscore if estrogen==0, area(1) lpattern(longdash)), ///
>         ytitle("Density") xtitle("Logit Propensity Score") ///
>         legend(order(1 "Current estrogen users" 2 "Past and never users")) ///
>         name(pscores, replace)
{res}{txt}
{com}. graph export pscores.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/pscores.pdf written in PDF format)

{com}.         
. * optional section on splines
. capture drop bmicat
{txt}
{com}. capture drop fitted*
{txt}
{com}. recode bmi min/18.5=1 18.5001/25=2 25.0001/30=3 30.00001/35=4 35.00001/max=5, gen(bmicat)
{txt}(278 differences between bmi and bmicat)

{com}. xi: reg bmd i.bmicat age i.estrogen
{txt}i.bmicat{col 19}_Ibmicat_1-5{col 39}(naturally coded; _Ibmicat_1 omitted)
i.estrogen{col 19}_Iestrogen_0-2{col 39}(naturally coded; _Iestrogen_0 omitted)

      Source {c |}       SS       df       MS              Number of obs ={res}     278
{txt}{hline 13}{char +}{hline 30}           F(  7,   270) ={res}   16.67
    {txt}   Model {char |} {res} 1.57067458     7  .224382083           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 3.63366925   270  .013458034           {txt}R-squared     = {res} 0.3018
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.2837
    {txt}   Total {char |} {res} 5.20434383   277  .018788245           {txt}Root MSE      = {res} .11601

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         bmd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}_Ibmicat_2 {c |}{col 14}{res}{space 2} .1473241{col 26}{space 2} .0494168{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} .0500329{col 67}{space 3} .2446154
{txt}{space 2}_Ibmicat_3 {c |}{col 14}{res}{space 2} .2002839{col 26}{space 2}  .049321{col 37}{space 1}    4.06{col 46}{space 3}0.000{col 54}{space 4} .1031813{col 67}{space 3} .2973864
{txt}{space 2}_Ibmicat_4 {c |}{col 14}{res}{space 2} .2845589{col 26}{space 2} .0510954{col 37}{space 1}    5.57{col 46}{space 3}0.000{col 54}{space 4} .1839629{col 67}{space 3} .3851549
{txt}{space 2}_Ibmicat_5 {c |}{col 14}{res}{space 2} .2922039{col 26}{space 2} .0563304{col 37}{space 1}    5.19{col 46}{space 3}0.000{col 54}{space 4} .1813013{col 67}{space 3} .4031065
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0044677{col 26}{space 2} .0015618{col 37}{space 1}   -2.86{col 46}{space 3}0.005{col 54}{space 4}-.0075425{col 67}{space 3}-.0013929
{txt}_Iestrogen_1 {c |}{col 14}{res}{space 2} .0386094{col 26}{space 2} .0167944{col 37}{space 1}    2.30{col 46}{space 3}0.022{col 54}{space 4} .0055448{col 67}{space 3}  .071674
{txt}_Iestrogen_2 {c |}{col 14}{res}{space 2} .0692431{col 26}{space 2} .0193894{col 37}{space 1}    3.57{col 46}{space 3}0.000{col 54}{space 4} .0310694{col 67}{space 3} .1074168
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  .854929{col 26}{space 2} .1399065{col 37}{space 1}    6.11{col 46}{space 3}0.000{col 54}{space 4} .5794826{col 67}{space 3} 1.130375
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. qui adjust age _Iestrogen_*, gen(fitted1) xb
{txt}
{com}. * rerun using Version 13 notation to test for trend, departure from linearity
. reg bmd i.bmicat age i.estrogen

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}     278
{txt}{hline 13}{char +}{hline 30}           F(  7,   270) ={res}   16.67
    {txt}   Model {char |} {res} 1.57067458     7  .224382083           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 3.63366925   270  .013458034           {txt}R-squared     = {res} 0.3018
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.2837
    {txt}   Total {char |} {res} 5.20434383   277  .018788245           {txt}Root MSE      = {res} .11601

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         bmd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}bmicat {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .1473241{col 26}{space 2} .0494168{col 37}{space 1}    2.98{col 46}{space 3}0.003{col 54}{space 4} .0500329{col 67}{space 3} .2446154
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2002839{col 26}{space 2}  .049321{col 37}{space 1}    4.06{col 46}{space 3}0.000{col 54}{space 4} .1031813{col 67}{space 3} .2973864
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2845589{col 26}{space 2} .0510954{col 37}{space 1}    5.57{col 46}{space 3}0.000{col 54}{space 4} .1839629{col 67}{space 3} .3851549
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2922039{col 26}{space 2} .0563304{col 37}{space 1}    5.19{col 46}{space 3}0.000{col 54}{space 4} .1813013{col 67}{space 3} .4031065
{txt}{space 12} {c |}
{space 9}age {c |}{col 14}{res}{space 2}-.0044677{col 26}{space 2} .0015618{col 37}{space 1}   -2.86{col 46}{space 3}0.005{col 54}{space 4}-.0075425{col 67}{space 3}-.0013929
{txt}{space 12} {c |}
{space 4}estrogen {c |}
{space 7}past  {c |}{col 14}{res}{space 2} .0386094{col 26}{space 2} .0167944{col 37}{space 1}    2.30{col 46}{space 3}0.022{col 54}{space 4} .0055448{col 67}{space 3}  .071674
{txt}{space 4}current  {c |}{col 14}{res}{space 2} .0692431{col 26}{space 2} .0193894{col 37}{space 1}    3.57{col 46}{space 3}0.000{col 54}{space 4} .0310694{col 67}{space 3} .1074168
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}  .854929{col 26}{space 2} .1399065{col 37}{space 1}    6.11{col 46}{space 3}0.000{col 54}{space 4} .5794826{col 67}{space 3} 1.130375
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. * test for linear  trend across categories
. contrast q(1).bmicat
{res}
{txt}Contrasts of marginal linear predictions

{txt}{p2colset 1 14 16 2}{...}
{p2col:Margins}:{space 1}{res:asbalanced}{p_end}
{p2colreset}{...}

{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}           F{col 38}        P>F
{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 6}bmicat {col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}    39.46{col 38}{space 2}   0.0000
{col 14}{text}{c |}
{col 1}{text} Denominator{col 14}{c |}{result}{space 2}      270
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 6}bmicat {c |}
{space 3}(linear)  {c |}{col 14}{res}{space 2} .1020557{col 26}{space 2} .0162455{col 37}{space 5} .0700717{col 51}{space 3} .1340396
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. * test for departure from linearity
. contrast q(2/4).bmicat
{res}
{txt}Contrasts of marginal linear predictions

{txt}{p2colset 1 14 16 2}{...}
{p2col:Margins}:{space 1}{res:asbalanced}{p_end}
{p2colreset}{...}

{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}           F{col 38}        P>F
{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 6}bmicat {c |}
(quadratic)  {col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     4.65{col 38}{space 2}   0.0320
{txt}    (cubic)  {col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     0.06{col 38}{space 2}   0.7993
{txt}  (quartic)  {col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     3.85{col 38}{space 2}   0.0509
{col 1}{text}      Joint {col 14}{c |}{result}{space 2}        3{col 26}{space 3}     1.97{col 38}{space 2}   0.1186
{col 14}{text}{c |}
{col 1}{text} Denominator{col 14}{c |}{result}{space 2}      270
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 6}bmicat {c |}
(quadratic)  {c |}{col 14}{res}{space 2}-.0296468{col 26}{space 2} .0137502{col 37}{space 5} -.056718{col 51}{space 3}-.0025756
{txt}    (cubic)  {c |}{col 14}{res}{space 2}  .002508{col 26}{space 2} .0098528{col 37}{space 5}-.0168901{col 51}{space 3} .0219061
{txt}  (quartic)  {c |}{col 14}{res}{space 2}-.0124878{col 26}{space 2} .0063676{col 37}{space 5}-.0250242{col 51}{space 3} .0000486
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. * linear splines
. capture drop bmi1 bmi2 bmi3 bmi4 bmi5
{txt}
{com}. mkspline bmi1 18.5 bmi2 25 bmi3 30 bmi4 35 bmi5 = bmi
{txt}
{com}. xi: reg bmd bmi1-bmi5 age i.estrogen 
{txt}i.estrogen{col 19}_Iestrogen_0-2{col 39}(naturally coded; _Iestrogen_0 omitted)

      Source {c |}       SS       df       MS              Number of obs ={res}     278
{txt}{hline 13}{char +}{hline 30}           F(  8,   269) ={res}   16.65
    {txt}   Model {char |} {res}   1.723868     8    .2154835           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 3.48047583   269  .012938572           {txt}R-squared     = {res} 0.3312
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.3113
    {txt}   Total {char |} {res} 5.20434383   277  .018788245           {txt}Root MSE      = {res} .11375

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         bmd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}bmi1 {c |}{col 14}{res}{space 2} .0479726{col 26}{space 2} .0288431{col 37}{space 1}    1.66{col 46}{space 3}0.097{col 54}{space 4}-.0088143{col 67}{space 3} .1047595
{txt}{space 8}bmi2 {c |}{col 14}{res}{space 2} .0142805{col 26}{space 2} .0058679{col 37}{space 1}    2.43{col 46}{space 3}0.016{col 54}{space 4} .0027277{col 67}{space 3} .0258333
{txt}{space 8}bmi3 {c |}{col 14}{res}{space 2}  .018217{col 26}{space 2} .0051934{col 37}{space 1}    3.51{col 46}{space 3}0.001{col 54}{space 4} .0079922{col 67}{space 3} .0284418
{txt}{space 8}bmi4 {c |}{col 14}{res}{space 2} .0014476{col 26}{space 2} .0067401{col 37}{space 1}    0.21{col 46}{space 3}0.830{col 54}{space 4}-.0118225{col 67}{space 3} .0147178
{txt}{space 8}bmi5 {c |}{col 14}{res}{space 2} .0115979{col 26}{space 2} .0073531{col 37}{space 1}    1.58{col 46}{space 3}0.116{col 54}{space 4}-.0028792{col 67}{space 3} .0260749
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0039816{col 26}{space 2} .0015351{col 37}{space 1}   -2.59{col 46}{space 3}0.010{col 54}{space 4} -.007004{col 67}{space 3}-.0009592
{txt}_Iestrogen_1 {c |}{col 14}{res}{space 2} .0427419{col 26}{space 2} .0165707{col 37}{space 1}    2.58{col 46}{space 3}0.010{col 54}{space 4} .0101172{col 67}{space 3} .0753666
{txt}_Iestrogen_2 {c |}{col 14}{res}{space 2} .0735367{col 26}{space 2} .0190421{col 37}{space 1}    3.86{col 46}{space 3}0.000{col 54}{space 4} .0360462{col 67}{space 3} .1110273
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0061371{col 26}{space 2} .5363318{col 37}{space 1}    0.01{col 46}{space 3}0.991{col 54}{space 4}-1.049805{col 67}{space 3} 1.062079
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. qui adjust age _Iestrogen_*, gen(fitted2) xb
{txt}
{com}. * test for departure from linear trend
. testparm bmi*, equal

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- bmi1 + bmi2 = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1}{space 1}{res}- bmi1 + bmi3 = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1}{space 1}{res}- bmi1 + bmi4 = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1}{space 1}{res}- bmi1 + bmi5 = 0{p_end}

{txt}       F(  4,   269) ={res}    1.69
{txt}{col 13}Prob > F ={res}    0.1524
{txt}
{com}. 
. * cubic splines
. capture drop bmisp*
{txt}
{com}. mkspline bmisp = bmi, cubic
{txt}
{com}. xi: reg bmd bmisp* age i.estrogen 
{txt}i.estrogen{col 19}_Iestrogen_0-2{col 39}(naturally coded; _Iestrogen_0 omitted)

      Source {c |}       SS       df       MS              Number of obs ={res}     278
{txt}{hline 13}{char +}{hline 30}           F(  7,   270) ={res}   19.68
    {txt}   Model {char |} {res} 1.75848253     7   .25121179           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 3.44586131   270  .012762449           {txt}R-squared     = {res} 0.3379
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.3207
    {txt}   Total {char |} {res} 5.20434383   277  .018788245           {txt}Root MSE      = {res} .11297

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         bmd{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}bmisp1 {c |}{col 14}{res}{space 2} .0314653{col 26}{space 2} .0076344{col 37}{space 1}    4.12{col 46}{space 3}0.000{col 54}{space 4} .0164347{col 67}{space 3} .0464959
{txt}{space 6}bmisp2 {c |}{col 14}{res}{space 2}-.1377472{col 26}{space 2}  .058356{col 37}{space 1}   -2.36{col 46}{space 3}0.019{col 54}{space 4}-.2526379{col 67}{space 3}-.0228566
{txt}{space 6}bmisp3 {c |}{col 14}{res}{space 2} .6172104{col 26}{space 2} .2652112{col 37}{space 1}    2.33{col 46}{space 3}0.021{col 54}{space 4} .0950654{col 67}{space 3} 1.139355
{txt}{space 6}bmisp4 {c |}{col 14}{res}{space 2}-.7514937{col 26}{space 2}  .328523{col 37}{space 1}   -2.29{col 46}{space 3}0.023{col 54}{space 4}-1.398286{col 67}{space 3}-.1047012
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0038331{col 26}{space 2} .0015263{col 37}{space 1}   -2.51{col 46}{space 3}0.013{col 54}{space 4} -.006838{col 67}{space 3}-.0008281
{txt}_Iestrogen_1 {c |}{col 14}{res}{space 2} .0361593{col 26}{space 2} .0162605{col 37}{space 1}    2.22{col 46}{space 3}0.027{col 54}{space 4} .0041458{col 67}{space 3} .0681729
{txt}_Iestrogen_2 {c |}{col 14}{res}{space 2} .0732025{col 26}{space 2} .0189018{col 37}{space 1}    3.87{col 46}{space 3}0.000{col 54}{space 4} .0359889{col 67}{space 3} .1104161
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .2581214{col 26}{space 2} .2166109{col 37}{space 1}    1.19{col 46}{space 3}0.234{col 54}{space 4}-.1683398{col 67}{space 3} .6845826
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. qui adjust age _Iestrogen_*, gen(fitted3) xb
{txt}
{com}. * test for overall BMI effect
. testparm bmisp*

{p 0 7}{space 1}{text:( 1)}{space 1} {res}bmisp1 = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} bmisp2 = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} bmisp3 = 0{p_end}
{p 0 7}{space 1}{text:( 4)}{space 1} bmisp4 = 0{p_end}

{txt}       F(  4,   270) ={res}   22.07
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. * test for departure from linearity
. testparm bmisp2 bmisp3 bmisp4

{p 0 7}{space 1}{text:( 1)}{space 1} {res}bmisp2 = 0{p_end}
{p 0 7}{space 1}{text:( 2)}{space 1} bmisp3 = 0{p_end}
{p 0 7}{space 1}{text:( 3)}{space 1} bmisp4 = 0{p_end}

{txt}       F(  3,   270) ={res}    3.19
{txt}{col 13}Prob > F ={res}    0.0242
{txt}
{com}. 
. twoway ///
>         (scatter bmd bmi, msize(vtiny)) ///
>         (line fitted1 bmi, sort c(J) lp(longdash) lcol(red) lw(medthick)) ///
>         (line fitted2 bmi, sort lp(shortdash) lcol(green) lw(medthick)) ///
>         (line fitted3 bmi, sort lp(shortdash) lcol(black) lw(medthick)) ///
>         (lowess bmd bmi, lp(solid) lcol(blue) lw(medthick)), /// 
>         plotregion(style(none)) scheme(s1color) ytitle("BMD (gm/cm^2)") ///
>         legend(order(2 "Categorical" 3 "Linear Spline" 4 "Cubic Spline" 5 "Lowess") ///
>                 rows(2)) caption(Adjusted for age and estrogen use) name(splinefit, replace)
{res}{txt}
{com}. graph export splinefit.pdf, replace
{txt}(file /Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/splinefit.pdf written in PDF format)

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/steve/Documents/teaching/c2015/biostat208/labs/lab6/lab6.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}10 Feb 2015, 08:37:30
{txt}{.-}
{smcl}
{txt}{sf}{ul off}