Subject index - Stata

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62 Subject index mlabgap() option, [G-3] marker label options mlabposition() option, [G-3] marker label options mlabsize() option, [G-3] marker label options mlabstyle() option, [G-3] marker label options mlabtextstyle() option, [G-3] marker label options mlabvposition() option, [G-3] marker label options mlcolor() option, [G-3] marker options mleval command, [R] ml mlexp command, [R] mlexp, [R] mlexp postestimation mlib add, mata subcommand, [M-3] mata mlib mlib create, mata subcommand, [M-3] mata mlib mlib index, mata subcommand, [M-3] mata mlib .mlib library file, [M-1] how, [M-3] mata describe, [M-3] mata mlib, [M-3] mata set, [M-3] mata which, [M-6] Glossary, [U] 11.6 Filenaming conventions mlib query, mata subcommand, [M-3] mata mlib mlmatbysum command, [R] ml mlmatsum command, [R] ml MLMV, see maximum likelihood with missing values mlmv, see sem option method() mlogit command, [R] mlogit, [R] mlogit postestimation mlogit option, see gsem option mlogit mlong data style, [MI] styles, [MI] Glossary technical description, [MI] technical mlpattern() option, [G-3] marker options mlstyle() option, [G-3] marker options mlsum command, [R] ml mlvecsum command, [R] ml mlwidth() option, [G-3] marker options mm() function, [D] datetime, [D] functions, [M-5] date( ) .mmat matrix file, [M-3] mata matsave, [U] 11.6 Filenaming conventions mmC() function, [D] datetime, [D] functions, [M-5] date( ) MNAR, see missing not at random MNP, see outcomes, multinomial .mo object code file, [M-1] how, [M-3] mata mosave, [M-3] mata which, [M-6] Glossary, [U] 11.6 Filenaming conventions mod() function, [D] functions, [M-5] mod( ) mode(), egen function, [D] egen mode-curvature adaptive Gauss–Hermite quadrature, see quadrature, mode-curvature adaptive Gauss– Hermite model coefficients test, [R] lrtest, [R] test, [R] testnl, [SVY] svy postestimation model identification, [SEM] intro 4, [SEM] intro 12, [SEM] Glossary model simplification test, [SEM] example 8, [SEM] example 10 model specification test, see specification test model, ml subcommand, [R] ml model-implied covariances and correlations, [SEM] example 11 models, maximum number of variables in, [R] matsize modern scaling, [MV] Glossary modification indices, [SEM] estat mindices, [SEM] example 5, [SEM] methods and formulas for sem, [SEM] Glossary modification, file, [D] filefilter modifying data, [D] generate, also see editing data modulus function, [D] functions modulus transformations, [R] boxcox mofd() function, [D] datetime, [D] functions, [M-5] date( ) moments (of a distribution), [SEM] Glossary monadic operator, [M-2] syntax, [M-6] Glossary monotone imputation, see imputation, monotone monotone-missing pattern, [MI] mi impute monotone, [MI] Glossary, [R] misstable monotonicity, see pattern of missingness Monte Carlo error, [MI] mi estimate, [MI] mi estimate using, [MI] Glossary Monte Carlo simulations, [P] postfile, [R] permute, [R] simulate month() function, [D] datetime, [D] functions, [M-5] date( ), [U] 24.5 Extracting components of dates and times monthly() function, [D] datetime, [D] datetime translation, [D] functions, [M-5] date( ) Moore–Penrose inverse, [M-5] pinv( ) moptimize() function, [M-5] moptimize( ) moptimize() function, [M-5] moptimize( ) moptimize ado cleanup() function, [M-5] moptimize( ) moptimize evaluate() function, [M-5] moptimize( ) moptimize evaluate() function, [M-5] moptimize( ) moptimize init() function, [M-5] moptimize( ) moptimize init *() functions, [M-5] moptimize( ) moptimize query() function, [M-5] moptimize( ) moptimize result *() functions, [M-5] moptimize( ) moptimize util *() functions, [M-5] moptimize( ) more command and parameter, [P] macro, [P] more, [R] more, [U] 7 –more– conditions, [U] 16.1.6 Preventing –more– conditions more() function, [M-5] more( ) more, set subcommand, [R] more, [R] set mortality table, see life tables mosave, mata subcommand, [M-3] mata mosave moving average model, [TS] arch, [TS] arfima, [TS] arima, [TS] sspace, [TS] ucm process, [TS] Glossary smoother, [TS] tssmooth, [TS] tssmooth ma mprobit command, [R] mprobit, [R] mprobit postestimation mreldif() function, [D] functions, [M-5] reldif( ), [P] matrix define
Subject index 63 mreldifre() function, [M-5] reldif( ) mreldifsym() function, [M-5] reldif( ) msize() option, [G-3] marker options, [G-3] rcap options msofhours() function, [D] datetime, [D] functions, [M-5] date( ) msofminutes() function, [D] datetime, [D] functions, [M-5] date( ) msofseconds() function, [D] datetime, [D] functions, [M-5] date( ) mspline, graph twoway subcommand, [G-2] graph twoway mspline mstyle() option, [G-3] marker options msymbol() option, [G-3] marker options MTMM, see multitrait–multimethod data and matrices mtr(), egen function, [D] egen multiarm trial, [ST] stpower, [ST] Glossary multidimensional scaling, [MV] mds, [MV] mds postestimation plots, [MV] mdslong, [MV] mdsmat, [MV] Glossary configuration plot, [MV] Glossary multilevel data, [MI] mi estimate multilevel latent variable, [SEM] intro 2, [SEM] gsem path notation extensions multilevel mixed-effects model, see multilevel model multilevel model, [ME] me, [ME] mecloglog, [ME] meglm, [ME] melogit, [ME] menbreg, [ME] meologit, [ME] meoprobit, [ME] mepoisson, [ME] meprobit, [ME] meqrlogit, [ME] meqrpoisson, [ME] mixed, [R] gllamm, [SEM] intro 5, [SEM] example 30g, [SEM] example 38g, [SEM] example 39g, [SEM] example 40g, [SEM] example 41g, [SEM] example 42g, [SEM] Glossary, [U] 26.19 Multilevel mixedeffects models multinomial logistic regression, [SEM] intro 2, [SEM] intro 5, [SEM] example 37g, [SEM] example 41g, [SEM] Glossary, [SVY] svy estimation logistic regression imputation, see imputation, multinomial logistic regression outcome model, see outcomes, multinomial probit regression, [SVY] svy estimation multiple comparisons, [R] contrast, [R] margins, [R] pwcompare, [R] pwmean, [MV] mvreg, [R] anova postestimation, [R] correlate, [R] oneway, [R] regress postestimation, [R] roccomp, [R] spearman, [R] test, [R] testnl, [R] tetrachoric Bonferroni’s method, [R] contrast, [R] margins, [R] pwcompare, [R] pwmean, [R] anova postestimation, [R] correlate, [R] oneway, [R] regress postestimation, [R] roccomp, [R] spearman, [R] test, [R] testnl, [R] tetrachoric Duncan’s method, [R] pwcompare, [R] pwmean Dunnett’s method, [R] pwcompare, [R] pwmean Holm’s method, [R] anova postestimation, [R] regress postestimation, [R] test, [R] testnl multiple-range method, see Dunnett’s method subentry Scheffé’s method, [R] contrast, [R] margins, [R] pwcompare, [R] pwmean, [R] oneway Šidák’s method, [R] contrast, [R] margins, [R] pwcompare, [R] pwmean, [R] anova postestimation, [R] correlate, [R] oneway, [R] regress postestimation, [R] roccomp, [R] spearman, [R] test, [R] testnl, [R] tetrachoric Studentized-range method, see Tukey’s method subentry Student–Newman–Keuls’ method, [R] pwcompare, [R] pwmean Tukey’s method, [R] pwcompare, [R] pwmean multiple correlation, [SEM] Glossary multiple correspondence analysis, [MV] Glossary multiple imputation, [MI] intro substantive, [MI] intro, [MI] styles, [MI] workflow, [U] 26.25 Multiple imputation analysis step, [MI] intro substantive, [MI] mi estimate, [MI] mi estimate using, [MI] mi estimate postestimation, [MI] mi test estimation, [MI] estimation imputation step, [MI] intro substantive, [MI] mi impute inference, [MI] intro substantive pooling step, [MI] intro substantive, [MI] mi estimate, [MI] mi estimate using prediction, [MI] mi predict theory, [MI] intro substantive multiple indicators and multiple causes model, [SEM] intro 5, [SEM] example 10, [SEM] example 36g, [SEM] Glossary multiple languages, [D] label language multiple regression, see linear regression multiple-failure st data, [ST] stbase, [ST] stci, [ST] stcox, [ST] stcox postestimation, [ST] stcurve, [ST] stdescribe, [ST] stfill, [ST] stgen, [ST] stir, [ST] stptime, [ST] strate, [ST] streg, [ST] streg postestimation, [ST] sts, [ST] sts generate, [ST] sts graph, [ST] sts list, [ST] sts test, [ST] stset, [ST] stsplit, [ST] stsum multiple-range multiple-comparison adjustment, see multiple comparisons, Dunnett’s method multiple-record st data, [ST] stbase, [ST] stci, [ST] stcox, [ST] stcox postestimation, [ST] stcrreg, [ST] stcrreg postestimation, [ST] stcurve, [ST] stdescribe, [ST] stfill, [ST] stgen, [ST] stir, [ST] stptime, [ST] strate, [ST] streg, [ST] streg postestimation, [ST] sts, [ST] sts generate, [ST] sts graph, [ST] sts list, [ST] sts test, [ST] stset, [ST] stsplit, [ST] stsum, [ST] stvary, [ST] Glossary multiple-sample means, see means, multiple-sample study, [PSS] power oneway, [PSS] power twoway, [PSS] power repeated
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Subject index Symbols ! (not), see
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Subject index 3 alternative, contin
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Subject index 5 autoregressive, con
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Subject index 7 biquartimax rotatio
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Subject index 9 c(maxstrvarlen) c-c
Page 11 and 12: Subject index 11 char, continued ma
Page 13 and 14: Subject index 13 cluster analysis,
Page 15 and 16: Subject index 15 colnames, matrix s
Page 17 and 18: Subject index 17 confidence interva
Page 19 and 20: Subject index 19 convergence, [SEM]
Page 21 and 22: Subject index 21 cross-sectional da
Page 23 and 24: Subject index 23 defective matrix,
Page 25 and 26: Subject index 25 direction of an ef
Page 27 and 28: Subject index 27 dsign() function,
Page 29 and 30: Subject index 29 endogenous, contin
Page 31 and 32: Subject index 31 estat, continued m
Page 33 and 34: Subject index 33 experimental data,
Page 35 and 36: Subject index 35 files, continued c
Page 37 and 38: Subject index 37 frequencies, creat
Page 39 and 40: Subject index 39 ghessenbergd la()
Page 41 and 42: Subject index 41 graphs, continued
Page 43 and 44: Subject index 43 gsem option, conti
Page 45 and 46: Subject index 45 httpproxy, set sub
Page 47 and 48: Subject index 47 incidence-rate rat
Page 49 and 50: Subject index 49 invpoissontail() f
Page 51 and 52: Subject index 51 label values, [P]
Page 53 and 54: Subject index 53 lines, continued l
Page 55 and 56: Subject index 55 lower-triangular m
Page 57 and 58: Subject index 57 matmissing() funct
Page 59 and 60: Subject index 59 means, continued g
Page 61: Subject index 61 mindices, estat su
Page 65 and 66: Subject index 65 nbinomialtail() fu
Page 67 and 68: Subject index 67 nonparametric anal
Page 69 and 70: Subject index 69 ocloglog option, s
Page 71 and 72: Subject index 71 outcomes, continue
Page 73 and 74: Subject index 73 pcorr command, [R]
Page 75 and 76: Subject index 75 postestimation com
Page 77 and 78: Subject index 77 probability of a t
Page 79 and 80: Subject index 79 quad precision, [M
Page 81 and 82: Subject index 81 rcap, graph twoway
Page 83 and 84: Subject index 83 reset, mi subcomma
Page 85 and 86: Subject index 85 robust, Huber/Whit
Page 87 and 88: Subject index 87 sample size, conti
Page 89 and 90: Subject index 89 sem command, [SEM]
Page 91 and 92: Subject index 91 set, cluster subco
Page 93 and 94: Subject index 93 snapshot, [D] snap
Page 95 and 96: Subject index 95 stability, [TS] va
Page 97 and 98: Subject index 97 stbase command, [S
Page 99 and 100: Subject index 99 Student’s t, dis
Page 101 and 102: Subject index 101 svy: mean command
Page 103 and 104: Subject index 103 tempfile command,
Page 105 and 106: Subject index 105 timer, continued
Page 107 and 108: Subject index 107 Tukey’s Student
Page 109 and 110: Subject index 109 use command, [D]
Page 111 and 112: Subject index 111 verifying data, [
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Subject index 113 xtcloglog command
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