logistic_estimation_commands.hlp

是一个经济学管理应用软件 很难找的 但是经济学学生又必须用到

{smcl}
{* 07apr2005}{...}
{cmd:help logistic estimation commands}
{hline}

{title:Description}

{pstd}Stata has a variety of commands for performing estimation when the 
dependent variable is dichotomous or polychotomous.  Here is a list of some
estimation commands that may be of interest.  See {help estimation commands}
for a complete list of all of Stata's estimation commands.

{p2colset 5 41 43 2}{...}
{p2col :command{space 8}manual entry}description{p_end}
{p2line}
{p2col :{helpb asmprobit}{space 6}{bf:[R] asmprobit}}Alternative-specific multinomial probit regression{p_end}
{p2col :{helpb binreg}{space 9}{bf:[R] binreg}}GLM models for the binomial family{p_end}
{p2col :{helpb biprobit}{space 7}{bf:[R] biprobit}}Bivariate probit regression{p_end}
{p2col :{helpb blogit}{space 9}{bf:[R] glogit}}Logit regression for grouped data{p_end}
{p2col :{helpb bprobit}{space 8}{bf:[R] glogit}}Probit regression for grouped data{p_end}
{p2col :{helpb clogit}{space 9}{bf:[R] clogit}}Conditional (fixed-effects) logistic regression{p_end}
{p2col :{helpb cloglog}{space 8}{bf:[R] cloglog}}Complementary log-log regression{p_end}
{p2col :{helpb glm}{space 12}{bf:[R] glm}}Generalized linear models{p_end}
{p2col :{helpb glogit}{space 9}{bf:[R] glogit}}Weighted least-squares logistic regression for grouped data{p_end}
{p2col :{helpb gprobit}{space 8}{bf:[R] glogit}}Weighted least-squares probit regression for grouped data{p_end}
{p2col :{helpb heckprob}{space 7}{bf:[R] heckprob}}Probit model with selection{p_end}
{p2col :{helpb hetprob}{space 8}{bf:[R] hetprob}}Heteroskedastic probit model{p_end}
{p2col :{helpb ivprobit}{space 7}{bf:[R] ivprobit}}Probit model with endogenous regressors{p_end}
{p2col :{helpb logit}{space 10}{bf:[R] logit}}Logistic regression, reporting coefficients{p_end}
{p2col :{helpb mlogit}{space 9}{bf:[R] mlogit}}Multinomial (polytomous) logistic regression{p_end}
{p2col :{helpb mprobit}{space 8}{bf:[R] mprobit}}Multinomial probit regression{p_end}
{p2col :{helpb nlogit}{space 9}{bf:[R] nlogit}}Nested logit regression{p_end}
{p2col :{helpb ologit}{space 9}{bf:[R] ologit}}Ordered logistic regression{p_end}
{p2col :{helpb oprobit}{space 8}{bf:[R] oprobit}}Ordered probit regression{p_end}
{p2col :{helpb probit}{space 9}{bf:[R] probit}}Probit regression{p_end}
{p2col :{helpb rologit}{space 8}{bf:[R] rologit}}Rank-ordered logistic regression{p_end}
{p2col :{helpb scobit}{space 9}{bf:[R] scobit}}Skewed logistic regression{p_end}
{p2col :{helpb slogit}{space 9}{bf:[R] slogit}}Stereotype logistic regression{p_end}
{p2col :{helpb "svy: heckprob"}{space 2}{bf:[SVY] svy: heckprob}}Survey version of {cmd:heckprob}{p_end}
{p2col :{helpb "svy: logit"}{space 5}{bf:[SVY] svy: logit}}Survey version of {cmd:logit}{p_end}
{p2col :{helpb "svy: logistic"}{space 2}{bf:[SVY] svy: logistic}}Survey version of {cmd:logistic}{p_end}
{p2col :{helpb "svy: mlogit"}{space 4}{bf:[SVY] svy: mlogit}}Survey version of {cmd:mlogit}{p_end}
{p2col :{helpb "svy: ologit"}{space 4}{bf:[SVY] svy: ologit}}Survey version of {cmd:ologit}{p_end}
{p2col :{helpb "svy: oprobit"}{space 3}{bf:[SVY] svy: oprobit}}Survey version of {cmd:oprobit}{p_end}
{p2col :{helpb "svy: probit"}{space 4}{bf:[SVY] svy: probit}}Survey version of {cmd:probit}{p_end}
{p2col :{helpb xtcloglog}{space 6}{bf:[XT] xtcloglog}}Random-effects and population-averaged cloglog models{p_end}
{p2col :{helpb xtgee}{space 10}{bf:[XT] xtgee}}GEE population-averaged generalized linear models{p_end}
{p2col :{helpb xtlogit}{space 8}{bf:[XT] xtlogit}}Fixed-effects, random-effects, and population-averaged logit models{p_end}
{p2col :{helpb xtprobit}{space 7}{bf:[XT] xtprobit}}Random-effects and population-averaged probit models{p_end}
{p2line}
{p2colreset}{...}