--------------------------------------------------------------------------------- name: log: C:\Users\tneilands\Box Sync\My Documents\CAPS\Methods Core\Presentat > ions\Missing Data 2015\Part 2\Example 1\Tobacco_Bar_Planned_Missingness_Study_M > I.log log type: text opened on: 26 Feb 2015, 16:48:40 . version 13 . . cd "C:\Users\tneilands\Box Sync\My Documents\CAPS\Methods Core\Presentations\Mi > ssing Data 2015\Part 2\Example 1 C:\Users\tneilands\Box Sync\My Documents\CAPS\Methods Core\Presentations\Missing > Data 2015\Part 2\Example 1 . . use addict_demo3.dta, clear . . // Multiple imputation of previous FIML example . . // Note: ntwrksmk had the most missing data (49%), so I initially set M = 50 . // imputed data sets, following Paul Allison's rule of thumb for setting > M. . // Then I increase M to 75 after reviewing the FMI output from . // -mi estimate- . . mi set flong . mi register imputed smokdays srsmokr extrindx ntwrksmk race lgbtcat (1217 m=0 obs. now marked as incomplete) . mi register regular age male . mi describe Style: flong last mi update 26feb2015 16:48:40, 0 seconds ago Obs.: complete 0 incomplete 1,217 (M = 0 imputations) --------------------- total 1,217 Vars.: imputed: 6; smokdays(72) srsmokr(359) extrindx(416) ntwrksmk(601) race(10) lgbtcat(5) passive: 0 regular: 2; age male system: 3; _mi_m _mi_id _mi_miss (there are 3 unregistered variables; smkdaily venueid addict) . . // The dry run - with default ordering of chained equations . . mi impute chained (pmm) smokdays /// > (logit, iter(50)) srsmokr /// > (ologit, iter(50) ascontinuous) extrindx /// > (regress) ntwrksmk /// > (mlogit, iter(50)) race lgbtcat = age male, / > // > augment dryrun Conditional models: lgbtcat: mlogit lgbtcat i.race smokdays i.srsmokr extrindx ntwrksmk age male , augment iter(50) race: mlogit race i.lgbtcat smokdays i.srsmokr extrindx ntwrksmk age male , augment iter(50) smokdays: pmm smokdays i.lgbtcat i.race i.srsmokr extrindx ntwrksmk age male srsmokr: logit srsmokr i.lgbtcat i.race smokdays extrindx ntwrksmk age male , augment iter(50) extrindx: ologit extrindx i.lgbtcat i.race smokdays i.srsmokr ntwrksmk age male , augment iter(50) ntwrksmk: regress ntwrksmk i.lgbtcat i.race smokdays i.srsmokr extrindx age male . . // The dry run - with ordering of chained equations based on the listing below . . mi impute chained (pmm) smokdays /// > (logit, iter(50)) srsmokr /// > (ologit, iter(50) ascontinuous) extrindx /// > (regress) ntwrksmk /// > (mlogit, iter(50)) race lgbtcat = age male, / > // > augment dryrun orderasis Conditional models: smokdays: pmm smokdays i.srsmokr extrindx ntwrksmk i.race i.lgbtcat age male srsmokr: logit srsmokr smokdays extrindx ntwrksmk i.race i.lgbtcat age male , augment iter(50) extrindx: ologit extrindx smokdays i.srsmokr ntwrksmk i.race i.lgbtcat age male , augment iter(50) ntwrksmk: regress ntwrksmk smokdays i.srsmokr extrindx i.race i.lgbtcat age male race: mlogit race smokdays i.srsmokr extrindx ntwrksmk i.lgbtcat age male , augment iter(50) lgbtcat: mlogit lgbtcat smokdays i.srsmokr extrindx ntwrksmk i.race age male , augment iter(50) . . // Evaluate number of burn-in iterations (here I used the variable with the mos > t . // missing data, ntwrksmk. In an actual application, one should check each . // variable). . . // Syntax drawn from the -mi impute chained- documentation. . . timer on 1 . preserve . . mi impute chained (pmm) smokdays /// > (logit, iter(50)) srsmokr /// > (ologit, iter(50) ascontinuous) extrindx /// > (regress) ntwrksmk /// > (mlogit, iter(50)) race lgbtcat = age male, / > // > rseed(1359) augment chainonly chaindots burn > in(100) /// > savetrace(impstats, replace) orderasis Conditional models: smokdays: pmm smokdays i.srsmokr extrindx ntwrksmk i.race i.lgbtcat age male srsmokr: logit srsmokr smokdays extrindx ntwrksmk i.race i.lgbtcat age male , augment iter(50) extrindx: ologit extrindx smokdays i.srsmokr ntwrksmk i.race i.lgbtcat age male , augment iter(50) ntwrksmk: regress ntwrksmk smokdays i.srsmokr extrindx i.race i.lgbtcat age male race: mlogit race smokdays i.srsmokr extrindx ntwrksmk i.lgbtcat age male , augment iter(50) lgbtcat: mlogit lgbtcat smokdays i.srsmokr extrindx ntwrksmk i.race age male , augment iter(50) Performing chained iterations: burn-in 100 .........10.........20.........30.........40.........50.........60. > ........70.........80.........90.........100 done Note: No imputation performed. . . use impstats, clear (Summaries of imputed values from -mi impute chained-) . capture noisiliy erase TracePlot_1.pdf . tsset iter time variable: iter, 0 to 100 delta: 1 unit . tsline ntwrksmk_mean, name(gr1, replace) nodraw . tsline ntwrksmk_sd, name(gr2, replace) nodraw . graph combine gr1 gr2, /// > title(Trace plots of summaries of imputed values) /// > row(2) name(TracePlot_1, replace) . graph export TracePlot_1.pdf, replace (file TracePlot_1.pdf written in PDF format) . . restore . timer off 1 . . // Evaluate multiple chain convergence. . // Syntax drawn from the -mi impute chained- documentation. . . timer on 2 . preserve . . set more off . mi impute chained (pmm) smokdays /// > (logit, iter(50)) srsmokr /// > (ologit, iter(50) ascontinuous) extrindx /// > (regress) ntwrksmk /// > (mlogit, iter(50)) race lgbtcat = age male, / > // > rseed(1359) chaindots augment add(3) burnin(100) savetrace(im > pstats2, replace) orderasis Conditional models: smokdays: pmm smokdays i.srsmokr extrindx ntwrksmk i.race i.lgbtcat age male srsmokr: logit srsmokr smokdays extrindx ntwrksmk i.race i.lgbtcat age male , augment iter(50) extrindx: ologit extrindx smokdays i.srsmokr ntwrksmk i.race i.lgbtcat age male , augment iter(50) ntwrksmk: regress ntwrksmk smokdays i.srsmokr extrindx i.race i.lgbtcat age male race: mlogit race smokdays i.srsmokr extrindx ntwrksmk i.lgbtcat age male , augment iter(50) lgbtcat: mlogit lgbtcat smokdays i.srsmokr extrindx ntwrksmk i.race age male , augment iter(50) Performing chained iterations: imputing m=1: burn-in 100 .........10.........20.........30.........40......... > 50.........60.........70.........80.........90.........100 done imputing m=2: burn-in 100 .........10.........20.........30.........40......... > 50.........60.........70.........80.........90.........100 done imputing m=3: burn-in 100 .........10.........20.........30.........40.........50.........60.........70.........80.........90.........100 done Multivariate imputation Imputations = 3 Chained equations added = 3 Imputed: m=1 through m=3 updated = 0 Initialization: monotone Iterations = 300 burn-in = 100 smokdays: predictive mean matching srsmokr: logistic regression extrindx: ordered logistic regression ntwrksmk: linear regression race: multinomial logistic regression lgbtcat: multinomial logistic regression ------------------------------------------------------------------ | Observations per m |---------------------------------------------- Variable | Complete Incomplete Imputed | Total -------------------+-----------------------------------+---------- smokdays | 1145 72 72 | 1217 srsmokr | 858 359 359 | 1217 extrindx | 801 416 416 | 1217 ntwrksmk | 616 601 601 | 1217 race | 1207 10 10 | 1217 lgbtcat | 1212 5 5 | 1217 ------------------------------------------------------------------ (complete + incomplete = total; imputed is the minimum across m of the number of filled-in observations.) . . use impstats2, clear (Summaries of imputed values from -mi impute chained-) . reshape wide *mean *sd, i(iter) j(m) (note: j = 1 2 3) Data long -> wide ----------------------------------------------------------------------------- Number of obs. 303 -> 101 Number of variables 14 -> 37 j variable (3 values) m -> (dropped) xij variables: smokdays_mean -> smokdays_mean1 smokdays_mean2 smokdays_mean3 srsmokr_mean -> srsmokr_mean1 srsmokr_mean2 srsmokr_mean3 extrindx_mean -> extrindx_mean1 extrindx_mean2 extrindx_mean3 ntwrksmk_mean -> ntwrksmk_mean1 ntwrksmk_mean2 ntwrksmk_mean3 race_mean -> race_mean1 race_mean2 race_mean3 lgbtcat_mean -> lgbtcat_mean1 lgbtcat_mean2 lgbtcat_mean3 smokdays_sd -> smokdays_sd1 smokdays_sd2 smokdays_sd3 srsmokr_sd -> srsmokr_sd1 srsmokr_sd2 srsmokr_sd3 extrindx_sd -> extrindx_sd1 extrindx_sd2 extrindx_sd3 ntwrksmk_sd -> ntwrksmk_sd1 ntwrksmk_sd2 ntwrksmk_sd3 race_sd -> race_sd1 race_sd2 race_sd3 lgbtcat_sd -> lgbtcat_sd1 lgbtcat_sd2 lgbtcat_sd3 ----------------------------------------------------------------------------- . capture noisiliy erase TracePlot_2.pdf . tsset iter time variable: iter, 0 to 100 delta: 1 unit . . tsline ntwrksmk_mean1 ntwrksmk_mean2 ntwrksmk_mean3, /// > ytitle(Mean of Network Smoking) yline(25.24) /// > legend(rows(1) label(1 "Chain 1") label(2 "Chain 2") label(3 "Chain 3")) /// > title(Overlaid trace plots of summaries of imputed values) /// > name(TracePlot_2, replace) . graph save TracePlot_2, replace (file TracePlot_2.gph saved) . graph export TracePlot_2.pdf, replace (file TracePlot_2.pdf written in PDF format) . . restore . timer off 2 . . // Generate actual imputations used in the analysis - 75 imputed data sets . . timer on 3 . mi extract 0, clear . mi set flong . mi register imputed smokdays srsmokr extrindx ntwrksmk race lgbtcat (1217 m=0 obs. now marked as incomplete) . mi register regular age male . . mi impute chained (pmm) smokdays /// > (logit, iter(50)) srsmokr /// > (ologit, iter(50) ascontinuous) extrindx /// > (regress) ntwrksmk /// > (mlogit, iter(50)) race lgbtcat = age male, /// > chaindots dots add(75) rseed(1349) augment orderasis Conditional models: smokdays: pmm smokdays i.srsmokr extrindx ntwrksmk i.race i.lgbtcat age male srsmokr: logit srsmokr smokdays extrindx ntwrksmk i.race i.lgbtcat age male , augment iter(50) extrindx: ologit extrindx smokdays i.srsmokr ntwrksmk i.race i.lgbtcat age male , augment iter(50) ntwrksmk: regress ntwrksmk smokdays i.srsmokr extrindx i.race i.lgbtcat age male race: mlogit race smokdays i.srsmokr extrindx ntwrksmk i.lgbtcat age male , augment iter(50) lgbtcat: mlogit lgbtcat smokdays i.srsmokr extrindx ntwrksmk i.race age male , augment iter(50) Performing chained iterations: imputing m=1: burn-in 10 .......... done imputing m=2: burn-in 10 .......... done imputing m=3: burn-in 10 .......... done imputing m=4: burn-in 10 .......... done imputing m=5: burn-in 10 .......... done imputing m=6: burn-in 10 .......... done imputing m=7: burn-in 10 .......... done imputing m=8: burn-in 10 .......... done imputing m=9: burn-in 10 .......... done imputing m=10: burn-in 10 .......... done imputing m=11: burn-in 10 .......... done imputing m=12: burn-in 10 .......... done imputing m=13: burn-in 10 .......... done imputing m=14: burn-in 10 .......... done imputing m=15: burn-in 10 .......... done imputing m=16: burn-in 10 .......... done imputing m=17: burn-in 10 .......... done imputing m=18: burn-in 10 .......... done imputing m=19: burn-in 10 .......... done imputing m=20: burn-in 10 .......... done imputing m=21: burn-in 10 .......... done imputing m=22: burn-in 10 .......... done imputing m=23: burn-in 10 .......... done imputing m=24: burn-in 10 .......... done imputing m=25: burn-in 10 .......... done imputing m=26: burn-in 10 .......... done imputing m=27: burn-in 10 .......... done imputing m=28: burn-in 10 .......... done imputing m=29: burn-in 10 .......... done imputing m=30: burn-in 10 .......... done imputing m=31: burn-in 10 .......... done imputing m=32: burn-in 10 .......... done imputing m=33: burn-in 10 .......... done imputing m=34: burn-in 10 .......... done imputing m=35: burn-in 10 .......... done imputing m=36: burn-in 10 .......... done imputing m=37: burn-in 10 .......... done imputing m=38: burn-in 10 .......... done imputing m=39: burn-in 10 .......... done imputing m=40: burn-in 10 .......... done imputing m=41: burn-in 10 .......... done imputing m=42: burn-in 10 .......... done imputing m=43: burn-in 10 .......... done imputing m=44: burn-in 10 .......... done imputing m=45: burn-in 10 .......... done imputing m=46: burn-in 10 .......... done imputing m=47: burn-in 10 .......... done imputing m=48: burn-in 10 .......... done imputing m=49: burn-in 10 .......... done imputing m=50: burn-in 10 .......... done imputing m=51: burn-in 10 .......... done imputing m=52: burn-in 10 .......... done imputing m=53: burn-in 10 .......... done imputing m=54: burn-in 10 .......... done imputing m=55: burn-in 10 .......... done imputing m=56: burn-in 10 .......... done imputing m=57: burn-in 10 .......... done imputing m=58: burn-in 10 .......... done imputing m=59: burn-in 10 .......... done imputing m=60: burn-in 10 .......... done imputing m=61: burn-in 10 .......... done imputing m=62: burn-in 10 .......... done imputing m=63: burn-in 10 .......... done imputing m=64: burn-in 10 .......... done imputing m=65: burn-in 10 .......... done imputing m=66: burn-in 10 .......... done imputing m=67: burn-in 10 .......... done imputing m=68: burn-in 10 .......... done imputing m=69: burn-in 10 .......... done imputing m=70: burn-in 10 .......... done imputing m=71: burn-in 10 .......... done imputing m=72: burn-in 10 .......... done imputing m=73: burn-in 10 .......... done imputing m=74: burn-in 10 .......... done imputing m=75: burn-in 10 .......... done Multivariate imputation Imputations = 75 Chained equations added = 75 Imputed: m=1 through m=75 updated = 0 Initialization: monotone Iterations = 750 burn-in = 10 smokdays: predictive mean matching srsmokr: logistic regression extrindx: ordered logistic regression ntwrksmk: linear regression race: multinomial logistic regression lgbtcat: multinomial logistic regression ------------------------------------------------------------------ | Observations per m |---------------------------------------------- Variable | Complete Incomplete Imputed | Total -------------------+-----------------------------------+---------- smokdays | 1145 72 72 | 1217 srsmokr | 858 359 359 | 1217 extrindx | 801 416 416 | 1217 ntwrksmk | 616 601 601 | 1217 race | 1207 10 10 | 1217 lgbtcat | 1212 5 5 | 1217 ------------------------------------------------------------------ (complete + incomplete = total; imputed is the minimum across m of the number of filled-in observations.) . timer off 3 . . // Compress and save imputed data to disk for later use . . compress _mi_m was int now byte (92,492 bytes saved) . save addict_demo_imputed.dta, replace file addict_demo_imputed.dta saved . . // Load imputed data . . use addict_demo_imputed.dta, clear . . // Perform post-imputation diagnostics . . timer on 4 . . // The default -midiagplots- is to look at all the variables with any missings, . // and get information about the first imputation. . // tabulate categorical vars; graph kdensity for continuous vars . . // Warning: do not do this for more than a single imptation! . . // Imputed are: smokdays srsmokr extrindx ntwrksmk race lgbtcat . // Of those the categorical vars are: srsmokr race lgbtcat; tables . // The continuous vars are: smokdays extrindx ntwrksmk; kd . . // The following is an example only; it display information for imputation 9 . . midiagplots, m(9) saving(g1,replace) nodraw (M = 75 imputations) (imputed: smokdays srsmokr extrindx ntwrksmk race lgbtcat) Proportions of srsmokr for m=9 Number of observed = 858 Number of imputed = 359 Number of completed = 1217 ---------------------------------------------- SRSSmokr. | PPT | considers | self a | smoker | (yes/no) | Observed Imputed Completed ----------+----------------------------------- 0. No | 0.696 0.716 0.702 1. Yes | 0.304 0.284 0.298 ---------------------------------------------- Proportions of race for m=9 Number of observed = 1207 Number of imputed = 10 Number of completed = 1217 ---------------------------------------------------------- Race. Race/Ethnicity | (combined Census | categories) | Observed Imputed Completed ----------------------+----------------------------------- 1. Non-Hispanic White | 0.511 0.400 0.510 2. Latino | 0.226 0.200 0.226 3. Non-Hispanic Black | 0.051 0.000 0.050 4. Non-Hispanic A/PI | 0.123 0.200 0.123 5. Non-Hispanic other | 0.089 0.200 0.090 ---------------------------------------------------------- Proportions of lgbtcat for m=9 Number of observed = 1212 Number of imputed = 5 Number of completed = 1217 ------------------------------------------------ LGBTCat. | Sexual | Orientation | Observed Imputed Completed ------------+----------------------------------- 1. Straight | 0.833 0.800 0.833 2. Gay | 0.068 0.200 0.068 3. Bisexual | 0.055 0.000 0.055 4. Other | 0.044 0.000 0.044 ------------------------------------------------ . graph combine g1_9_smokdays.gph g1_9_ntwrksmk.gph g1_9_extrindx.gph, /// > name(MIDiagPlot_9, replace) . graph save MIDiagPlot_9.gph, replace (file MIDiagPlot_9.gph saved) . graph export MIDiagPlot_9.pdf, replace (file MIDiagPlot_9.pdf written in PDF format) . timer off 4 . . // The following is for continuous vars, specified plottype, . // which selected imputations, and displaying the combined graph only . . timer on 5 . . midiagplots ntwrksmk, m(15(15)75) plottype(kdensity, kernel(epan2)) combine (M = 75 imputations) (imputed: smokdays srsmokr extrindx ntwrksmk race lgbtcat) . graph save ntwrksmk_15_75_kd, replace (file ntwrksmk_15_75_kd.gph saved) . graph export ntwrksmk_15_75_kd.pdf, replace (file ntwrksmk_15_75_kd.pdf written in PDF format) . . midiagplots extrindx, m(15(15)75) plottype(kdensity, kernel(epan2)) combine (M = 75 imputations) (imputed: smokdays srsmokr extrindx ntwrksmk race lgbtcat) . graph save extrindx_15_75_kd, replace (file extrindx_15_75_kd.gph saved) . graph export extrindx_15_75_kd.pdf, replace (file extrindx_15_75_kd.pdf written in PDF format) . . midiagplots smokdays, m(15(15)75) plottype(kdensity, kernel(epan2)) combine (M = 75 imputations) (imputed: smokdays srsmokr extrindx ntwrksmk race lgbtcat) . graph save smokdays_15_75_kd, replace (file smokdays_15_75_kd.gph saved) . graph export smokdays_15_75_kd.pdf, replace (file smokdays_15_75_kd.pdf written in PDF format) . . timer off 5 . . // Perform analysis phase . . timer on 6 . mi estimate: regress smokdays age i.race male i.lgbtcat srsmokr ntwrksmk extrindx Multiple-imputation estimates Imputations = 75 Linear regression Number of obs = 1217 Average RVI = 0.5128 Largest FMI = 0.6502 Complete DF = 1204 DF adjustment: Small sample DF: min = 125.46 avg = 506.47 max = 862.03 Model F test: Equal FMI F( 12, 1030.2) = 55.00 Within VCE type: OLS Prob > F = 0.0000 ------------------------------------------------------------------------------ smokdays | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .1089818 .1576032 0.69 0.489 -.2004383 .4184019 | race | 2 | -.9686648 .646838 -1.50 0.135 -2.23891 .3015807 3 | .6931028 1.195103 0.58 0.562 -1.653992 3.040198 4 | -.579261 .823459 -0.70 0.482 -2.196678 1.038156 5 | 1.138385 .9175455 1.24 0.215 -.6632121 2.939982 | male | .2009493 .5427884 0.37 0.711 -.8653246 1.267223 | lgbtcat | 2 | -2.673821 .9781354 -2.73 0.006 -4.593626 -.754015 3 | 3.203147 1.186793 2.70 0.007 .8709034 5.535391 4 | 2.783233 1.354109 2.06 0.040 .1210839 5.445381 | srsmokr | 14.41822 .7666436 18.81 0.000 12.90707 15.92938 ntwrksmk | .4019785 .1545779 2.60 0.010 .0960606 .7078963 extrindx | -.0306593 .2119374 -0.14 0.885 -.4488189 .3875004 _cons | -1.90144 3.882306 -0.49 0.624 -9.525038 5.722158 ------------------------------------------------------------------------------ . mi test 2.race 3.race 4.race 5.race note: assuming equal fractions of missing information ( 1) 2.race = 0 ( 2) 3.race = 0 ( 3) 4.race = 0 ( 4) 5.race = 0 F( 4,1009.7) = 1.41 Prob > F = 0.2290 . mi test 2.lgbtcat 3.lgbtcat 4.lgbtcat note: assuming equal fractions of missing information ( 1) 2.lgbtcat = 0 ( 2) 3.lgbtcat = 0 ( 3) 4.lgbtcat = 0 F( 3, 891.6) = 6.52 Prob > F = 0.0002 . . // Obtain standardized coefficients and r-squares . . mibeta smokdays age i.race male i.lgbtcat srsmokr ntwrksmk extrindx, fisherz Multiple-imputation estimates Imputations = 75 Linear regression Number of obs = 1217 Average RVI = 0.5128 Largest FMI = 0.6491 Complete DF = 1204 DF adjustment: Small sample DF: min = 125.46 avg = 506.47 max = 862.03 Model F test: Equal FMI F( 12, 1030.2) = 55.00 Within VCE type: OLS Prob > F = 0.0000 ------------------------------------------------------------------------------ smokdays | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- age | .1089818 .1576032 0.69 0.489 -.2004383 .4184019 | race | 2 | -.9686648 .646838 -1.50 0.135 -2.23891 .3015807 3 | .6931028 1.195103 0.58 0.562 -1.653992 3.040198 4 | -.579261 .823459 -0.70 0.482 -2.196678 1.038156 5 | 1.138385 .9175455 1.24 0.215 -.6632121 2.939982 | male | .2009493 .5427884 0.37 0.711 -.8653246 1.267223 | lgbtcat | 2 | -2.673821 .9781354 -2.73 0.006 -4.593626 -.754015 3 | 3.203147 1.186793 2.70 0.007 .8709034 5.535391 4 | 2.783233 1.354109 2.06 0.040 .1210839 5.445381 | srsmokr | 14.41822 .7666436 18.81 0.000 12.90707 15.92938 ntwrksmk | .4019785 .1545779 2.60 0.010 .0960606 .7078963 extrindx | -.0306593 .2119374 -0.14 0.885 -.4488189 .3875004 _cons | -1.90144 3.882306 -0.49 0.624 -9.525038 5.722158 ------------------------------------------------------------------------------ Standardized coefficients and R-squared Summary statistics over 75 imputations | mean* min p25 median p75 max -------------+---------------------------------------------------------------- age | .0163798 -.00604 .0091668 .0170676 .0233318 .0383 | race | 2 | -.0380278 -.0604 -.0469406 -.0390896 -.0307228 -.00999 3 | .0142669 -.00898 .0059734 .0158035 .0229622 .0394 4 | -.0178344 -.0484 -.0262066 -.0162919 -.0108741 .00764 5 | .0305105 .00487 .022505 .0312889 .0389716 .051 | male | .0093841 -.0211 -.0014352 .0124287 .01708 .036 | lgbtcat | 2 | -.0629918 -.0827 -.0693064 -.0626806 -.0565511 -.0438 3 | .0687269 .0424 .0591991 .0685244 .0761499 .106 4 | .053442 .0125 .0432162 .0561758 .064656 .0889 | srsmokr | .6205432 .565 .6066126 .6228631 .6359266 .661 ntwrksmk | .1036614 .0167 .0852183 .1025655 .1253399 .173 extrindx | -.004686 -.069 -.0203643 -.0051115 .0120893 .0367 -------------+---------------------------------------------------------------- R-square | .4569659 .389 .4446178 .4603518 .471955 .502 Adj R-square | .4515482 .383 .4390825 .4549732 .4666921 .497 ------------------------------------------------------------------------------ * based on Fisher's z transformation . . timer off 6 . . timer list 1: 21.14 / 1 = 21.1380 2: 58.84 / 1 = 58.8440 3: 159.31 / 1 = 159.3080 4: 282.24 / 1 = 282.2370 5: 319.92 / 1 = 319.9200 6: 4.57 / 1 = 4.5710 . timer clear . . log close name: log: C:\Users\tneilands\Box Sync\My Documents\CAPS\Methods Core\Presentations\Missing Data 2015\Part 2\Example 1\Tobacco_Bar_Planned_Mis > singness_Study_MI.log log type: text closed on: 26 Feb 2015, 17:02:47 ------------------------------------------------------------------------------------------------------------------------------------------------