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"这是一份关于Stata的教程资料,主要涵盖了Stata 16的使用。该资源可能包括详细的软件操作指南、术语解释以及索引,由StataCorp LLC出版,版权保护自1985年至2019年。"
Stata是一款广泛应用于社会科学、生物统计学、公共卫生和经济学等领域的统计分析软件。它提供了数据管理、图形制作、统计分析和预测等功能。Stata 16是其最新的版本,通常会包含比旧版本更多的新功能和改进。
在学习Stata的过程中,首先需要了解Stata的基本界面和工作流程,包括如何导入数据、查看数据、进行数据清理和预处理。Stata支持多种数据格式,如.dta(Stata自身的格式)、.csv、.txt等,学习如何有效地导入和导出数据是基础技能。
在统计分析方面,Stata可以执行描述性统计、推断性统计(如t检验、卡方检验、ANOVA、回归分析等),以及更复杂的面板数据分析、生存分析和非参数方法。对于初学者,理解基本的统计概念和命令语法是关键,例如`describe`用于查看数据基本信息,`summarize`用于计算统计量,而`regress`或`reg`用于进行线性回归分析。
Stata的图形生成功能强大,可以创建各种类型的图表,如散点图、箱线图、直方图和折线图等。通过`graph`命令系列,用户可以根据需要定制图形的样式和细节。
此外,Stata的编程环境—— Mata,允许用户编写自己的函数和程序,提高效率。学习Mata语言可以帮助你更好地利用Stata进行复杂的数据处理和分析。
索引和词汇表(Glossary and Index)部分通常包含Stata的所有命令和函数的详细解释,是查找特定功能或解决问题的重要参考。熟悉这些内容能帮助用户快速定位所需功能,提高工作效率。
StataCorp提供的这份教程还强调了版权信息,使用者需要遵守版权规定,未经许可不得复制或传播。同时,StataCorp不提供任何明示或暗示的保修,用户需知悉这一事实。
这份Stata教程是学习和掌握Stata软件的强大工具,无论你是初学者还是经验丰富的用户,都能从中受益。通过深入学习和实践,你将能够运用Stata进行高效的数据分析和研究。
Combined subject table of contents 13
Regression diagnostic plots
[
R
] regress postestimation diagnostic plots . . . . . . . . . . . Postestimation plots for regress
ROC analysis
[
R
] estat classification . . . . . . . . . . . . . . . . . . . . . . . . . . . Classification statistics and table
[
R
] estat gof . . . . . . . . . . . . . . . . . . . Pearson or Hosmer–Lemeshow goodness-of-fit test
[
R
] logistic postestimation . . . . . . . . . . . . . . . . . . . . . . . . Postestimation tools for logistic
[
R
] lroc . . . . . . . . . . . . . . . . . . . . . Compute area under ROC curve and graph the curve
[
R
] lsens . . . . . . . . . . . . . . . . Graph sensitivity and specificity versus probability cutoff
[
R
] roccomp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tests of equality of ROC areas
[
R
] rocfit postestimation . . . . . . . . . . . . . . . . . . . . . . . . . . . Postestimation tools for rocfit
[
R
] rocregplot . . . . . . . . . Plot marginal and covariate-specific ROC curves after rocreg
[
R
] roctab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nonparametric ROC analysis
Smoothing and densities
[
R
] kdensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Univariate kernel density estimation
[
R
] lowess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lowess smoothing
[
R
] lpoly . . . . . . . . . . . . . . . . . . . . . . . . . . . Kernel-weighted local polynomial smoothing
Survival-analysis graphs
[
ST
] ltable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life tables for survival data
[
ST
] stci . . . . . . . . . . . . . Confidence intervals for means and percentiles of survival time
[
ST
] stcox PH-assumption tests . . . . . . . . . . . . . Tests of proportional-hazards assumption
[
ST
] stcurve . Plot survivor, hazard, cumulative hazard, or cumulative incidence function
[
ST
] strate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tabulate failure rates and rate ratios
[
ST
] sts graph . . . . . . . . . . . . . Graph the survivor, hazard, or cumulative hazard function
Time-series graphs
[
TS
] corrgram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tabulate and graph autocorrelations
[
TS
] cumsp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graph cumulative spectral distribution
[
TS
] estat acplot . . . . . . . . . Plot parametric autocorrelation and autocovariance functions
[
TS
] estat aroots . . . . . . . . . . . . . . . . . Check the stability condition of ARIMA estimates
[
TS
] estat sbcusum . . . . . . . . . . . . . . . . . . . . . Cumulative sum test for parameter stability
[
TS
] fcast graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graph forecasts after fcast compute
[
TS
] irf cgraph . . Combined graphs of IRFs, dynamic-multiplier functions, and FEVDs
[
TS
] irf graph . . . . . . . . . . . . Graphs of IRFs, dynamic-multiplier functions, and FEVDs
[
TS
] irf ograph . . . . Overlaid graphs of IRFs, dynamic-multiplier functions, and FEVDs
[
TS
] pergram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Periodogram
[
TS
] tsline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Time-series line plots
[
TS
] varstable . . . . . . . . . . . . . Check the stability condition of VAR or SVAR estimates
[
TS
] vecstable . . . . . . . . . . . . . . . . . . . . Check the stability condition of VECM estimates
[
TS
] wntestb . . . . . . . . . . . . . . . . . . . . . Bartlett’s periodogram-based test for white noise
[
TS
] xcorr . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cross-correlogram for bivariate time series
More statistical graphs
[
BAYES
] bayesgraph . . . . . . . . . . . . . . . . . Graphical summaries and convergence diagnostics
[
PSS-3
] ciwidth, graph . . . . . . . . . . . . . . . . . . . . . . Graph results from the ciwidth command
[
R
] Epitab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tables for epidemiologists
14 Combined subject table of contents
[
R
] fp postestimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Postestimation tools for fp
[
R
] grmeanby . . . . . . . . . . . . . . . . . . Graph means and medians by categorical variables
[
R
] pkexamine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Calculate pharmacokinetic measures
[
R
] pksumm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summarize pharmacokinetic data
[
PSS-2
] power, graph . . . . . . . . . . . . . . . . . . . . . . . . . Graph results from the power command
[
R
] stem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Stem-and-leaf displays
[
TE
] tebalance box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Covariate balance box
[
TE
] teffects overlap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Overlap plots
[
XT
] xtline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Panel-data line plots
Editing
[
G-1
] Graph Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Graph Editor
Graph utilities
[
G-2
] set graphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Set whether graphs are displayed
[
G-2
] set printcolor . . . . . . . . . . . . . . . Set how colors are treated when graphs are printed
[
G-2
] set scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Set default scheme
Graph schemes
[
G-4
] Schemes intro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction to schemes
[
G-4
] Scheme economist . . . . . . . . . . . . . . . . . . . . . . . . . . . Scheme description: economist
[
G-4
] Scheme s1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scheme description: s1 family
[
G-4
] Scheme s2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scheme description: s2 family
[
G-4
] Scheme sj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scheme description: sj
Graph concepts
[
G-4
] Concept: gph files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Using gph files
[
G-4
] Concept: lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Using lines
[
G-4
] Concept: repeated options . . . . . . . . . . . . . . . . . . . Interpretation of repeated options
[
G-4
] text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Text in graphs
Statistics
ANOVA and related
[
U
] Chapter 27 . . . . . . . . . . . . . . . . . . . . . . . . . Overview of Stata estimation commands
[
R
] anova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analysis of variance and covariance
[
R
] contrast . . . . . . . . . . . . . . . . . . Contrasts and linear hypothesis tests after estimation
[
R
] icc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intraclass correlation coefficients
[
R
] loneway . . . . . . . . . . . . . . . . Large one-way ANOVA, random effects, and reliability
[
MV
] manova . . . . . . . . . . . . . . . . . . . . . . Multivariate analysis of variance and covariance
[
ME
] meglm . . . . . . . . . . . . . . . . . . . . . . Multilevel mixed-effects generalized linear model
[
ME
] mixed . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multilevel mixed-effects linear regression
[
R
] oneway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . One-way analysis of variance
[
R
] pkcross . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analyze crossover experiments
[
R
] pkshape . . . . . . . . . . . . . . . . . . . . . . . . . Reshape (pharmacokinetic) Latin-square data
[
R
] pwcompare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pairwise comparisons
[
R
] regress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Linear regression
[
XT
] xtreg Fixed-, between-, and random-effects and population-averaged linear models
Combined subject table of contents 15
Basic statistics
[
R
] anova . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Analysis of variance and covariance
[
R
] bitest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Binomial probability test
[
R
] ci . . . . . . . . . . . . . . . . . . Confidence intervals for means, proportions, and variances
[
R
] correlate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Correlations of variables
[
D
] egen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extensions to generate
[
R
] esize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Effect size based on mean comparison
[
R
] icc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Intraclass correlation coefficients
[
R
] mean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimate means
[
R
] misstable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tabulate missing values
[
MV
] mvtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multivariate tests
[
R
] oneway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . One-way analysis of variance
[
R
] proportion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimate proportions
[
R
] prtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tests of proportions
[
R
] pwmean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pairwise comparisons of means
[
R
] ranksum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Equality tests on unmatched data
[
R
] ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimate ratios
[
R
] regress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Linear regression
[
R
] sdtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Variance-comparison tests
[
R
] signrank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Equality tests on matched data
[
D
] statsby . . . . . . . . . . . . . . . . . . . . . . . Collect statistics for a command across a by list
[
R
] summarize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary statistics
[
R
] table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flexible table of summary statistics
[
R
] tabstat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Compact table of summary statistics
[
R
] tabulate oneway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . One-way table of frequencies
[
R
] tabulate twoway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Two-way table of frequencies
[
R
] tabulate, summarize() . . . . . . . . . . . . One- and two-way tables of summary statistics
[
R
] total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Estimate totals
[
R
] ttest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . t tests (mean-comparison tests)
[
R
] ztest . . . . . . . . . . . . . . . . . . . . . . . . z tests (mean-comparison tests, known variance)
Bayesian analysis
[
U
] Section 27.33 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian analysis
[
BAYES
] Intro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction to Bayesian analysis
[
BAYES
] Bayesian commands . . . . . . . . . . . . Introduction to commands for Bayesian analysis
[
BAYES
] Bayesian estimation . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian estimation commands
[
BAYES
] Bayesian postestimation . . . . Postestimation tools for bayesmh and the bayes prefix
[
BAYES
] bayes . . . . . . . . . . . . . . . . . . . . . . Bayesian regression models using the bayes prefix
[
BAYES
] bayes: betareg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian beta regression
[
BAYES
] bayes: binreg Bayesian generalized linear models: Extensions to the binomial family
[
BAYES
] bayes: biprobit . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian bivariate probit regression
[
BAYES
] bayes: clogit . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian conditional logistic regression
[
BAYES
] bayes: cloglog . . . . . . . . . . . . . . . . . . . . . Bayesian complementary log-log regression
[
BAYES
] bayes: fracreg . . . . . . . . . . . . . . . . . . . . . . . . Bayesian fractional response regression
[
BAYES
] bayes: glm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian generalized linear models
[
BAYES
] bayes: gnbreg . . . . . . . . . . . . . . . Bayesian generalized negative binomial regression
[
BAYES
] bayes: heckman . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian Heckman selection model
[
BAYES
] bayes: heckoprobit . . . . . . . . . Bayesian ordered probit model with sample selection
[
BAYES
] bayes: heckprobit . . . . . . . . . . . . . . . . Bayesian probit model with sample selection
[
BAYES
] bayes: hetoprobit . . . . . . . . . . . . Bayesian heteroskedastic ordered probit regression
[
BAYES
] bayes: hetprobit . . . . . . . . . . . . . . . . . . . . Bayesian heteroskedastic probit regression
16 Combined subject table of contents
[
BAYES
] bayes: hetregress . . . . . . . . . . . . . . . . . . . . Bayesian heteroskedastic linear regression
[
BAYES
] bayes: intreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian interval regression
[
BAYES
] bayes: logistic . . . . . . . . . . . . . . . Bayesian logistic regression, reporting odds ratios
[
BAYES
] bayes: logit . . . . . . . . . . . . . . . . . Bayesian logistic regression, reporting coefficients
[
BAYES
] bayes: mecloglog . . . . . . . . . Bayesian multilevel complementary log-log regression
[
BAYES
] bayes: meglm . . . . . . . . . . . . . . . . . . . . Bayesian multilevel generalized linear model
[
BAYES
] bayes: meintreg . . . . . . . . . . . . . . . . . . . . . . . . Bayesian multilevel interval regression
[
BAYES
] bayes: melogit . . . . . . . . . . . . . . . . . . . . . . . . Bayesian multilevel logistic regression
[
BAYES
] bayes: menbreg . . . . . . . . . . . . . . . Bayesian multilevel negative binomial regression
[
BAYES
] bayes: meologit . . . . . . . . . . . . . . . . Bayesian multilevel ordered logistic regression
[
BAYES
] bayes: meoprobit . . . . . . . . . . . . . . . . Bayesian multilevel ordered probit regression
[
BAYES
] bayes: mepoisson . . . . . . . . . . . . . . . . . . . . . . Bayesian multilevel Poisson regression
[
BAYES
] bayes: meprobit . . . . . . . . . . . . . . . . . . . . . . . . Bayesian multilevel probit regression
[
BAYES
] bayes: mestreg . . . . . . . . . . . . . . . . . Bayesian multilevel parametric survival models
[
BAYES
] bayes: metobit . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian multilevel tobit regression
[
BAYES
] bayes: mixed . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian multilevel linear regression
[
BAYES
] bayes: mlogit . . . . . . . . . . . . . . . . . . . . . . . Bayesian multinomial logistic regression
[
BAYES
] bayes: mprobit . . . . . . . . . . . . . . . . . . . . . . . Bayesian multinomial probit regression
[
BAYES
] bayes: mvreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian multivariate regression
[
BAYES
] bayes: nbreg . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian negative binomial regression
[
BAYES
] bayes: ologit . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian ordered logistic regression
[
BAYES
] bayes: oprobit . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian ordered probit regression
[
BAYES
] bayes: poisson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian Poisson regression
[
BAYES
] bayes: probit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian probit regression
[
BAYES
] bayes: regress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian linear regression
[
BAYES
] bayes: streg . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian parametric survival models
[
BAYES
] bayes: tnbreg . . . . . . . . . . . . . . . . . . Bayesian truncated negative binomial regression
[
BAYES
] bayes: tobit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian tobit regression
[
BAYES
] bayes: tpoisson . . . . . . . . . . . . . . . . . . . . . . . . Bayesian truncated Poisson regression
[
BAYES
] bayes: truncreg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian truncated regression
[
BAYES
] bayes: zinb . . . . . . . . . . . . . . . . Bayesian zero-inflated negative binomial regression
[
BAYES
] bayes: zioprobit . . . . . . . . . . . . . . . Bayesian zero-inflated ordered probit regression
[
BAYES
] bayes: zip . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian zero-inflated Poisson regression
[
BAYES
] bayesgraph . . . . . . . . . . . . . . . . . Graphical summaries and convergence diagnostics
[
BAYES
] bayesmh . . . . . . . . . . . . . . . . Bayesian models using Metropolis–Hastings algorithm
[
BAYES
] bayesmh evaluators . . . . . . . . . . . . . . . . . . . . User-defined evaluators with bayesmh
[
BAYES
] bayespredict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian predictions
[
BAYES
] bayesstats . . . . . . . . . . . . . . . . . . . . . . . Bayesian statistics after Bayesian estimation
[
BAYES
] bayesstats ess . . . . . . . . . . . . . . . . . . . . . Effective sample sizes and related statistics
[
BAYES
] bayesstats grubin . . . . . . . . . . . . . . . . . . . . . Gelman–Rubin convergence diagnostics
[
BAYES
] bayesstats ic . . . . . . . . . . . . . . . . . . Bayesian information criteria and Bayes factors
[
BAYES
] bayesstats ppvalues . . . Bayesian predictive p-values and other predictive summaries
[
BAYES
] bayesstats summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian summary statistics
[
BAYES
] bayestest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian hypothesis testing
[
BAYES
] bayestest interval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Interval hypothesis testing
[
BAYES
] bayestest model . . . . . . . . . . Hypothesis testing using model posterior probabilities
Binary outcomes
[
U
] Chapter 20 . . . . . . . . . . . . . . . . . . . . . . . . . Estimation and postestimation commands
[
U
] Section 27.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Binary outcomes
[
BAYES
] Bayesian estimation . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian estimation commands
Combined subject table of contents 17
[
R
] binreg . . . . . . . . . . . . Generalized linear models: Extensions to the binomial family
[
R
] biprobit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bivariate probit regression
[
R
] cloglog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Complementary log-log regression
[
LASSO
] dslogit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Double-selection lasso logistic regression
[
ERM
] eprobit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Extended probit regression
[
TE
] eteffects . . . . . . . . . . . . . . . . . . . . . . . . . . . . Endogenous treatment-effects estimation
[
R
] exlogistic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exact logistic regression
[
FMM
] fmm estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fitting finite mixture models
[
R
] glm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Generalized linear models
[
R
] heckprobit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Probit model with sample selection
[
R
] hetprobit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Heteroskedastic probit model
[
IRT
] irt 1pl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . One-parameter logistic model
[
IRT
] irt 2pl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Two-parameter logistic model
[
IRT
] irt 3pl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Three-parameter logistic model
[
IRT
] irt hybrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hybrid IRT models
[
R
] ivprobit . . . . . . . . . . . . . . . . . . Probit model with continuous endogenous covariates
[
R
] logistic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logistic regression, reporting odds ratios
[
R
] logit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logistic regression, reporting coefficients
[
ME
] mecloglog . . . . . . . . . . . Multilevel mixed-effects complementary log-log regression
[
ME
] melogit . . . . . . . . . . . . . . . . . . . . . . . . . . Multilevel mixed-effects logistic regression
[
ME
] meprobit . . . . . . . . . . . . . . . . . . . . . . . . . . Multilevel mixed-effects probit regression
[
LASSO
] pologit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Partialing-out lasso logistic regression
[
R
] probit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Probit regression
[
R
] rocfit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Parametric ROC models
[
R
] rocreg . . . . . . . . . . . . . . . . . . . . . Receiver operating characteristic (ROC) regression
[
R
] scobit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Skewed logistic regression
[
TE
] teffects aipw . . . . . . . . . . . . . . . . . . . . . . . . Augmented inverse-probability weighting
[
TE
] teffects ipw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inverse-probability weighting
[
TE
] teffects ipwra . . . . . . . . . . . . . . . Inverse-probability-weighted regression adjustment
[
TE
] teffects nnmatch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nearest-neighbor matching
[
TE
] teffects psmatch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Propensity-score matching
[
TE
] teffects ra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Regression adjustment
[
LASSO
] xpologit . . . . . . . . . . . . . . . . . . . . . . . Cross-fit partialing-out lasso logistic regression
[
XT
] xtcloglog . . . . . . . . . . . . . . Random-effects and population-averaged cloglog models
[
XT
] xteprobit . . . . . . . . . . . . . . . . . . . . . . . . . . Extended random-effects probit regression
[
XT
] xtlogit . . . . . . . Fixed-effects, random-effects, and population-averaged logit models
[
XT
] xtprobit . . . . . . . . . . . . . . . . Random-effects and population-averaged probit models
Categorical outcomes
[
U
] Chapter 20 . . . . . . . . . . . . . . . . . . . . . . . . . Estimation and postestimation commands
[
U
] Section 27.6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ordinal outcomes
[
U
] Section 27.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Categorical outcomes
[
BAYES
] Bayesian estimation . . . . . . . . . . . . . . . . . . . . . . . . . . Bayesian estimation commands
[
R
] clogit . . . . . . . . . . . . . . . . . . . . . . . . . . . Conditional (fixed-effects) logistic regression
[
CM
] cmclogit . . . . . . . . . . . . . . . . . . . . . . . Conditional logit (McFadden’s) choice model
[
CM
] cmmixlogit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mixed logit choice model
[
CM
] cmmprobit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multinomial probit choice model
[
CM
] cmxtmixlogit . . . . . . . . . . . . . . . . . . . . . . . . . . . Panel-data mixed logit choice model
[
FMM
] fmm estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fitting finite mixture models
[
IRT
] irt nrm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nominal response model
[
R
] mlogit . . . . . . . . . . . . . . . . . . . . . . . . . . Multinomial (polytomous) logistic regression
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