Subject index - Stata

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64 Subject index multiple-sample, continued test, [PSS] power oneway, [PSS] power twoway, [PSS] power repeated independent samples, [PSS] power oneway, [PSS] power twoway, [PSS] power repeated means, [PSS] power oneway, [PSS] power twoway, [PSS] power repeated multiplication operator, see arithmetic operators multiplicative heteroskedasticity, [TS] arch multistage clustered sampling, [SVY] survey, [SVY] svydescribe, [SVY] svyset multitrait–multimethod data and matrices, [SEM] intro 5, [SEM] example 17 multivalued treatment effect, [TE] teffects aipw, [TE] teffects ipw, [TE] teffects ipwra, [TE] teffects multivalued, [TE] teffects ra, [TE] Glossary multivariable fractional polynomial regression, [R] mfp multivariate analysis, [MV] canon, [MV] hotelling, [MV] mvtest, [U] 26.26 Multivariate and cluster analysis bivariate probit, [R] biprobit three-stage least squares, [R] reg3 Zellner’s seemingly unrelated, [R] nlsur, [R] sureg analysis of covariance, [MV] manova, [MV] Glossary analysis of variance, [MV] manova, [MV] Glossary Behrens–Fisher problem, [MV] mvtest means kurtosis, [MV] mvtest normality normal, [MV] mvtest normality regression, [MV] mvreg, [MV] Glossary skewness, [MV] mvtest normality test, [MV] mvtest multivariate GARCH, [TS] mgarch, [TS] Glossary model, constant conditional correlation, [TS] mgarch ccc diagonal vech, [TS] mgarch dvech dynamic conditional correlation, [TS] mgarch dcc varying conditional correlation, [TS] mgarch vcc postestimation, after ccc model, [TS] mgarch ccc postestimation after dcc model, [TS] mgarch dcc postestimation after dvech model, [TS] mgarch dvech postestimation after vcc model, [TS] mgarch vcc postestimation multivariate imputation, see imputation, multivariate multivariate imputation using chained equations, see imputation, multivariate, chained equations multivariate logistic variable imputation, see imputation, multivariate multivariate normal imputation, see imputation, multivariate normal multivariate normal simulator, [M-5] ghk( ), [M-5] ghkfast( ) multivariate regression, [SEM] example 12, [SEM] Glossary, also see seemingly unrelated regression, see multivariate regression multivariate regression imputation, see imputation, multivariate multivariate time-series estimators, dynamic-factor models, [TS] dfactor MGARCH models, see multivariate GARCH state-space models, [TS] sspace structural vector autoregressive models, [TS] var svar vector autoregressive models, [TS] var, [TS] varbasic vector error-correction models, [TS] vec MVAGH, see quadrature, mean–variance adaptive Gauss–Hermite mvdecode command, [D] mvencode mvencode command, [D] mvencode MVN imputation, see imputation, multivariate normal mvreg command, [MV] mvreg, [MV] mvreg postestimation mvreg, estat subcommand, [MV] procrustes postestimation mvtest, [MV] mvtest correlations command, [MV] mvtest correlations covariances command, [MV] mvtest covariances means command, [MV] mvtest means normality command, [MV] mvtest normality N n and N built-in variables, [U] 13.4 System variables ( variables), [U] 13.7 Explicit subscripting name() option, [G-3] name option nameexternal() function, [M-5] findexternal( ) namelists, [M-3] namelists names, [U] 11.3 Naming conventions conflicts, [P] matrix, [P] matrix define, [P] scalar matrix row and columns, [P] ereturn, [P] matrix define, [P] matrix rownames names, confirm subcommand, [P] confirm namespace and conflicts, matrices and scalars, [P] matrix, [P] matrix define naming convention, [M-1] naming naming groups of variables, [D] rename group naming variables, [D] rename NaN, [M-6] Glossary NARCH, see nonlinear autoregressive conditional heteroskedasticity NARCHK, see nonlinear autoregressive conditional heteroskedasticity with a shift natural log function, [D] functions natural splines, [R] mkspline nbetaden() function, [D] functions, [M-5] normal( ) nbinomial() function, [D] functions, [M-5] normal( ) nbinomialp() function, [D] functions, [M-5] normal( )
Subject index 65 nbinomialtail() function, [D] functions, [M-5] normal( ) nbreg command, [R] nbreg, [R] nbreg postestimation nbreg option, see gsem option nbreg nchi2() function, [D] functions, [M-5] normal( ) nchi2den() function, [D] functions, [M-5] normal( ) nchi2tail() function, [D] functions, [M-5] normal( ) n-class command, [P] program, [P] return ndots() option, [G-2] graph twoway dot nearest neighbor, [MI] mi impute pmm, [MV] discrim knn, [MV] Glossary nearest-neighbor matching, [TE] teffects intro, [TE] teffects intro advanced, [TE] teffects nnmatch, [TE] Glossary needle plot, [R] spikeplot negate() function, [M-5] negate( ) negation matrix, [M-5] negate( ) negation operator, see arithmetic operators negative binomial, [SEM] example 39g distribution, cumulative, [D] functions inverse cumulative, [D] functions inverse reverse cumulative, [D] functions reverse cumulative, [D] functions probability mass function, [D] functions regression, [R] nbreg, [SEM] Glossary, [SVY] svy estimation, [XT] Glossary fixed-effects, [XT] xtnbreg generalized linear models, [R] glm population-averaged, [XT] xtgee, [XT] xtnbreg random-effects, [XT] xtnbreg truncated, [R] tnbreg zero-inflated, [R] zinb regression, mixed-effects, [ME] menbreg negative effect size, [PSS] intro, [PSS] power, [PSS] power onemean, [PSS] power twomeans, [PSS] power pairedmeans, [PSS] power oneproportion, [PSS] power twoproportions, [PSS] power pairedproportions, [PSS] power onevariance, [PSS] power twovariances, [PSS] power onecorrelation, [PSS] power twocorrelations, [PSS] Glossary Nelder–Mead algorithm, [M-5] moptimize( ), [M-5] optimize( ) Nelson–Aalen cumulative hazard, [ST] sts, [ST] sts generate, [ST] sts graph, [ST] sts list nested case–control data, [ST] sttocc designs, [MV] manova, [R] anova effects, [MV] manova, [R] anova logit, [R] nlogit model statistics, [R] nestreg number list, [PSS] intro, [PSS] power, [PSS] power, graph, [PSS] power, table, [PSS] power onemean, [PSS] power twomeans, [PSS] power pairedmeans, [PSS] power oneproportion, [PSS] power twoproportions, [PSS] power pairedproportions, [PSS] power onevariance, nested number list, continued [PSS] power twovariances, [PSS] power onecorrelation, [PSS] power twocorrelations, [PSS] power oneway, [PSS] power twoway, [PSS] power repeated random effects, [ME] mecloglog, [ME] meglm, [ME] melogit, [ME] menbreg, [ME] meologit, [ME] meoprobit, [ME] mepoisson, [ME] meprobit, [ME] meqrlogit, [ME] meqrpoisson, [ME] mixed, [ME] Glossary regression, [R] nestreg nested, misstable subcommand, [R] misstable nested-effects model, [SEM] Glossary nestreg prefix command, [R] nestreg net cd command, [R] net describe command, [R] net from command, [R] net get command, [R] net install command, [R] net link command, [R] net query command, [R] net search command, [R] net search set ado command, [R] net set other command, [R] net sj command, [R] net stb command, [R] net net, view subcommand, [R] view NetCourseNow, [U] 3.7.2 NetCourses NetCourses, [U] 3.7.2 NetCourses net d, view subcommand, [R] view network, query subcommand, [R] query .new built-in class function, [P] class new lines, data without, [D] infile (fixed format) new() function, [M-2] class new, ssc subcommand, [R] ssc newey command, [TS] newey, [TS] newey postestimation Newey–West covariance matrix, [TS] Glossary postestimation, [TS] newey postestimation regression, [TS] newey standard errors, [P] matrix accum, [R] glm newline(#), display directive, [P] display news command, [R] news news, view subcommand, [R] view newsletter, [U] 3 Resources for learning and using Stata Newton–Raphson algorithm, [M-5] moptimize( ), [M-5] optimize( ), [R] ml Newton–Raphson method, [M-5] solvenl( ) Newton’s method, see iteration, Newton’s method Neyman allocation, [SVY] estat nF() function, [D] functions, [M-5] normal( ) nFden() function, [D] functions, [M-5] normal( ) nFtail() function, [D] functions, [M-5] normal( ) nibeta() function, [D] functions, [M-5] normal( ) niceness, set subcommand, [D] memory, [R] set
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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
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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 and 62: Subject index 61 mindices, estat su
Page 63: Subject index 63 mreldifre() functi
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, [
Page 113 and 114: Subject index 113 xtcloglog command
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