Stata : liste de modules utiles

ssc install stripplot

Voir aussi les autres commandes graphiques développées par Nick Cox

La commande fitstat, développée par les auteurs de Regression Models for Categorical Dependent Variables Using Stata [1] fournit des indicateurs additionnels de qualité d’ajustement d’une large variété de modèles de régression. Elle est compatible avec

Exemple :

webuse lbw
tabulate race, gen(irace)
logit low lwt irace2 irace3 ui, or nolog
fitstat

set more off
webuse lbw
(Hosmer & Lemeshow data)
tabulate race, gen(irace)

       race |      Freq.     Percent        Cum.
------------+-----------------------------------
      white |         96       50.79       50.79
      black |         26       13.76       64.55
      other |         67       35.45      100.00
------------+-----------------------------------
      Total |        189      100.00
logit low lwt irace2 irace3 ui, or nolog

Logistic regression                               Number of obs   =        189
                                                  LR chi2(4)      =      15.15
                                                  Prob > chi2     =     0.0044
Log likelihood = -109.76147                       Pseudo R2       =     0.0646

------------------------------------------------------------------------------
         low | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         lwt |   .9862315   .0063834    -2.14   0.032     .9737992    .9988226
      irace2 |   3.032503   1.485612     2.26   0.024     1.160916    7.921398
      irace3 |   1.616586   .5835255     1.33   0.183     .7967967    3.279819
          ui |   2.299748   .9819923     1.95   0.051     .9959037    5.310596
       _cons |   1.628981   1.399932     0.57   0.570      .302274    8.778724
------------------------------------------------------------------------------
fitstat

Measures of Fit for logit of low

Log-Lik Intercept Only:       -117.336   Log-Lik Full Model:           -109.761
D(184):                        219.523   LR(4):                          15.149
                                         Prob > LR:                       0.004
McFadden's R2:                   0.065   McFadden's Adj R2:               0.022
ML (Cox-Snell) R2:               0.077   Cragg-Uhler(Nagelkerke) R2:      0.108
McKelvey & Zavoina's R2:         0.115   Efron's R2:                      0.078
Variance of y*:                  3.718   Variance of error:               3.290
Count R2:                        0.683   Adj Count R2:                   -0.017
AIC:                             1.214   AIC*n:                         229.523
BIC:                          -744.959   BIC':                            5.818
BIC used by Stata:             245.732   AIC used by Stata:             229.523

Une commande de post-estimation similaire est prvalue

Attention, pour installer cette commande correctement il faut bien choisir le package spost9 et non l’entrée correspondant à un Stata Journal.

quietly: summarize lwt, detail
display r(p50)
prvalue, x(lwt=121 irace2=1 irace3=0 ui=1)

quietly: summarize lwt, detail
display r(p50)
121
prvalue, x(lwt=121 irace2=1 irace3=0 ui=1)

logit: Predictions for low

Confidence intervals by delta method

                                95% Conf. Interval
  Pr(y=1|x):          0.6797   [ 0.4394,    0.9201]
  Pr(y=0|x):          0.3203   [ 0.0799,    0.5606]

       lwt  irace2  irace3      ui
x=     121       1       0       1