Advanced Examples

Coefficients/equations

Exponentiated coefficients (odds ratio, hazard ratio)

To report exponentiated coefficients (aka odds ratio in logistic regression, harzard ratio in the Cox model, incidence rate ratio, relative risk ratio), apply the eform option. Example:

. sysuse auto
(1978 Automobile Data)

. eststo: quietly logit foreign mpg
(est1 stored)

. eststo: quietly logit foreign mpg weight
(est2 stored)

. esttab, eform

--------------------------------------------
                      (1)             (2)   
                  foreign         foreign   
--------------------------------------------
mpg                 1.173**         0.845   
                   (3.04)         (-1.83)   

weight                              0.996***
                                  (-3.86)   
--------------------------------------------
N                      74              74   
--------------------------------------------
Exponentiated coefficients; t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. eststo clear

[do-file]

Note that eform also transforms the standard errors (and confidence intervals), as is illustrated bellow:

. sysuse auto
(1978 Automobile Data)

. quietly logit foreign mpg weight

. eststo raw

. eststo or

. esttab raw or, se mtitles eform(0 1)

--------------------------------------------
                      (1)             (2)   
                      raw              or   
--------------------------------------------
mpg                -0.169           0.845   
                 (0.0919)        (0.0777)   

weight           -0.00391***        0.996***
                (0.00101)       (0.00101)   

_cons               13.71**      898396.7** 
                  (4.519)     (4059591.5)   
--------------------------------------------
N                      74              74   
--------------------------------------------
Standard errors in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. eststo clear

[do-file]

The example also illustrates that, optionally, eform can be applied to selected models only. If you are interested in applying other transformations, see estout's transform() option.

Marginal effects

Since Stata 11, margins is the preferred command to compute marginal effects (example). However, esttab and estout also support Stata's old mfx command for calculating marginal effects and elasticities. To make mfx's results available for tabulation it is essential that the model is stored after applying mfx. In esttab or estout then use the margin option to display the marginal effects. Example:

. sysuse auto
(1978 Automobile Data)

. generate reprec = (rep78 > 3) if rep78<.
(5 missing values generated)

. eststo raw: logit foreign mpg reprec

Iteration 0:   log likelihood = -42.400729
Iteration 1:   log likelihood = -28.036843
Iteration 2:   log likelihood = -27.117187
Iteration 3:   log likelihood =  -27.07727
Iteration 4:   log likelihood =  -27.07712

Logistic regression                               Number of obs   =         69
                                                  LR chi2(2)      =      30.65
                                                  Prob > chi2     =     0.0000
Log likelihood =  -27.07712                       Pseudo R2       =     0.3614

------------------------------------------------------------------------------
     foreign |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         mpg |   .1401865   .0653246     2.15   0.032     .0121527    .2682203
      reprec |   2.649856   .7380623     3.59   0.000     1.203281    4.096432
       _cons |  -5.370321   1.519268    -3.53   0.000    -8.348032   -2.392611
------------------------------------------------------------------------------

. eststo mfx: mfx

Marginal effects after logit
      y  = Pr(foreign) (predict)
         =  .21890427
------------------------------------------------------------------------------
variable |      dy/dx    Std. Err.     z    P>|z|  [    95% C.I.   ]      X
---------+--------------------------------------------------------------------
     mpg |   .0239698       .0121    1.98   0.048    .00026   .04768   21.2899
  reprec*|   .4813761      .11305    4.26   0.000    .25981  .702943    .42029
------------------------------------------------------------------------------
(*) dy/dx is for discrete change of dummy variable from 0 to 1

. esttab, se margin mtitles

--------------------------------------------
                      (1)             (2)   
                      raw             mfx   
--------------------------------------------
mpg                 0.140*         0.0240*  
                 (0.0653)        (0.0121)   

reprec (d)          2.650***        0.481***
                  (0.738)         (0.113)   
--------------------------------------------
N                      69              69   
--------------------------------------------
Marginal effects; Standard errors in parentheses
 (d) for discrete change of dummy variable from 0 to 1
* p<0.05, ** p<0.01, *** p<0.001

. eststo clear

[do-file]

Multiple-equation models

The default in esttab or estout is to arrange the different equations of multiple-equation models in vertical order, as in:

. sysuse auto
(1978 Automobile Data)

. quietly heckman price weight, select(foreign = weight mpg) twostep

. esttab, wide

-----------------------------------------
                      (1)                
                    price                
-----------------------------------------
price                                    
weight              5.284***       (5.89)
_cons             -5925.0***      (-3.50)
-----------------------------------------
foreign                                  
weight           -0.00234***      (-4.13)
mpg                -0.104*        (-2.02)
_cons               8.275**        (3.24)
-----------------------------------------
mills                                    
lambda              104.9          (0.12)
-----------------------------------------
N                      74                
-----------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

[do-file]

However, for models such as mlogit or sureg it is sometimes sensible to arrange the equations horizontally, which can be achieved through the use of the unstack option. Example:

. sysuse auto
(1978 Automobile Data)

. sureg (price foreign weight length) (mpg displ = foreign weight)

Seemingly unrelated regression
----------------------------------------------------------------------
Equation          Obs  Parms        RMSE    "R-sq"       chi2        P
----------------------------------------------------------------------
price              74      3    1967.769    0.5488      89.74   0.0000
mpg                74      2    3.337283    0.6627     145.39   0.0000
displacement       74      2    39.60002    0.8115     318.62   0.0000
----------------------------------------------------------------------

------------------------------------------------------------------------------
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
price        |
     foreign |    3575.26   621.7961     5.75   0.000     2356.562    4793.958
      weight |   5.691462   .9205043     6.18   0.000     3.887307    7.495618
      length |  -88.27114    31.4167    -2.81   0.005    -149.8467   -26.69554
       _cons |   4506.212   3588.044     1.26   0.209    -2526.225    11538.65
-------------+----------------------------------------------------------------
mpg          |
     foreign |  -1.650029   1.053958    -1.57   0.117    -3.715748    .4156902
      weight |  -.0065879   .0006241   -10.56   0.000     -.007811   -.0053647
       _cons |    41.6797   2.121197    19.65   0.000     37.52223    45.83717
-------------+----------------------------------------------------------------
displacement |
     foreign |   -25.6127   12.50621    -2.05   0.041    -50.12441   -1.100984
      weight |   .0967549   .0074051    13.07   0.000     .0822411    .1112686
       _cons |  -87.23548   25.17001    -3.47   0.001    -136.5678   -37.90317
------------------------------------------------------------------------------

. esttab, unstack scalars(r2 chi2 p) noobs nomtitle

------------------------------------------------------------
                      (1)                                   
                    price             mpg    displacement   
------------------------------------------------------------
foreign            3575.3***       -1.650          -25.61*  
                   (5.75)         (-1.57)         (-2.05)   

weight              5.691***     -0.00659***       0.0968***
                   (6.18)        (-10.56)         (13.07)   

length             -88.27**                                 
                  (-2.81)                                   

_cons              4506.2           41.68***       -87.24***
                   (1.26)         (19.65)         (-3.47)   
------------------------------------------------------------
r2                  0.549           0.663           0.812   
chi2                89.74           145.4           318.6   
p                2.50e-19        2.68e-32        6.50e-70   
------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

[do-file]

Table of effects signs, significance stars, or "significant signs"

Tables containing the signs of the coefficients, "significance stars", or "significant signs" (i.e. the signs of the coefficient where each sing is repeated according to significance level) can be produced as follows:

. sysuse auto
(1978 Automobile Data)

. eststo: regress price mpg foreign

      Source |       SS       df       MS              Number of obs =      74
-------------+------------------------------           F(  2,    71) =   14.07
       Model |   180261702     2  90130850.8           Prob > F      =  0.0000
    Residual |   454803695    71  6405685.84           R-squared     =  0.2838
-------------+------------------------------           Adj R-squared =  0.2637
       Total |   635065396    73  8699525.97           Root MSE      =  2530.9

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         mpg |  -294.1955   55.69172    -5.28   0.000    -405.2417   -183.1494
     foreign |   1767.292    700.158     2.52   0.014     371.2169    3163.368
       _cons |   11905.42   1158.634    10.28   0.000     9595.164    14215.67
------------------------------------------------------------------------------
(est1 stored)

. eststo: regress price mpg foreign weight

      Source |       SS       df       MS              Number of obs =      74
-------------+------------------------------           F(  3,    70) =   23.29
       Model |   317252881     3   105750960           Prob > F      =  0.0000
    Residual |   317812515    70  4540178.78           R-squared     =  0.4996
-------------+------------------------------           Adj R-squared =  0.4781
       Total |   635065396    73  8699525.97           Root MSE      =  2130.8

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         mpg |    21.8536   74.22114     0.29   0.769    -126.1758     169.883
     foreign |    3673.06   683.9783     5.37   0.000     2308.909    5037.212
      weight |   3.464706    .630749     5.49   0.000     2.206717    4.722695
       _cons |  -5853.696   3376.987    -1.73   0.087    -12588.88    881.4934
------------------------------------------------------------------------------
(est2 stored)

. esttab, cells(_sign) nogap

--------------------------------------
                      (1)          (2)
                    price        price
                    _sign        _sign
--------------------------------------
mpg                     -            +
foreign                 +            +
weight                               +
_cons                   +            -
--------------------------------------
N                      74           74
--------------------------------------

. esttab, cells(_star) nogap ///
>     starlevels(n.s. 1 * 0.05 ** 0.01)

--------------------------------------
                      (1)          (2)
                    price        price
                    _star        _star
--------------------------------------
mpg                    **         n.s.
foreign                 *           **
weight                              **
_cons                  **         n.s.
--------------------------------------
N                      74           74
--------------------------------------

. esttab, cells(_sigsign) nogap ///
>     starlevels("+/-" 1 "++/--" 0.05 "+++/---" 0.01)

--------------------------------------
                      (1)          (2)
                    price        price
                 _sigsign     _sigsign
--------------------------------------
mpg                   ---            +
foreign                ++          +++
weight                             +++
_cons                 +++            -
--------------------------------------
N                      74           74
--------------------------------------

. eststo clear

[do-file]

Add a hypotheses column

Use the labcol2() option to add a column indicating the expected directions of effects according to theory:

. sysuse auto
(1978 Automobile Data)

. quietly regress price mpg foreign weight displ

. esttab, labcol2(+ ? + -, title("" Hypothesis))

-----------------------------------------
                                   (1)   
              Hypothesis         price   
-----------------------------------------
mpg               +              19.08   
                                (0.26)   

foreign           ?             3930.2***
                                (5.67)   

weight            +              2.458** 
                                (2.82)   

displacement      -              10.22   
                                (1.65)   

_cons                          -4846.8   
                               (-1.43)   
-----------------------------------------
N                                   74   
-----------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

[do-file]

Indicate whether groups of control variables are in the model or not

To save space full output is sometimes suppressed for certain control variables and it is only indicated whether the model contains the variables or not. The indicate() in estout supports the construction of such tables:

. sysuse auto
(1978 Automobile Data)

. eststo: quietly regress price mpg foreign
(est1 stored)

. eststo: xi: quietly regress price mpg foreign i.rep78
i.rep78           _Irep78_1-5         (naturally coded; _Irep78_1 omitted)
(est2 stored)

. esttab, indicate(rep dummies = _Irep78*)

--------------------------------------------
                      (1)             (2)   
                    price           price   
--------------------------------------------
mpg                -294.2***       -299.6***
                  (-5.28)         (-4.73)   

foreign            1767.3*         1102.3   
                   (2.52)          (1.22)   

_cons             11905.4***      10856.2***
                  (10.28)          (4.79)   

rep dummies            No             Yes   
--------------------------------------------
N                      74              69   
--------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. eststo clear

[do-file]

indicate() prints its information at the bottom of the main body of the table containing the coefficients. If you want to include the information in the table footer, then use the following approach:

. sysuse auto
(1978 Automobile Data)

. eststo: quietly regress price mpg foreign
(est1 stored)

. estadd local hasrep "No"

added macro:
             e(hasrep) : "No"

. eststo: xi: quietly regress price mpg foreign i.rep78
i.rep78           _Irep78_1-5         (naturally coded; _Irep78_1 omitted)
(est2 stored)

. estadd local hasrep "Yes"

added macro:
             e(hasrep) : "Yes"

. esttab, drop(_Irep78*) scalars("hasrep rep dummies")

--------------------------------------------
                      (1)             (2)   
                    price           price   
--------------------------------------------
mpg                -294.2***       -299.6***
                  (-5.28)         (-4.73)   

foreign            1767.3*         1102.3   
                   (2.52)          (1.22)   

_cons             11905.4***      10856.2***
                  (10.28)          (4.79)   
--------------------------------------------
N                      74              69   
rep dummies            No             Yes   
--------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. eststo clear

[do-file]

Display reference category information

Use the refcat() option to add a table row containing the (omitted) reference category of a categorical variable. Example:

. sysuse cancer
(Patient Survival in Drug Trial)

. stset studytime, failure(died)

     failure event:  died != 0 & died < .
obs. time interval:  (0, studytime]
 exit on or before:  failure

------------------------------------------------------------------------------
       48  total observations
        0  exclusions
------------------------------------------------------------------------------
       48  observations remaining, representing
       31  failures in single-record/single-failure data
      744  total analysis time at risk and under observation
                                              at risk from t =         0
                                   earliest observed entry t =         0
                                        last observed exit t =        39

. xi: stcox age i.drug, nolog
i.drug            _Idrug_1-3          (naturally coded; _Idrug_1 omitted)

         failure _d:  died
   analysis time _t:  studytime

Cox regression -- Breslow method for ties

No. of subjects =           48                     Number of obs   =        48
No. of failures =           31
Time at risk    =          744
                                                   LR chi2(3)      =     36.52
Log likelihood  =   -81.652567                     Prob > chi2     =    0.0000

------------------------------------------------------------------------------
          _t | Haz. Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         age |   1.118334   .0409074     3.06   0.002     1.040963    1.201455
    _Idrug_2 |   .1805839   .0892742    -3.46   0.001     .0685292    .4758636
    _Idrug_3 |   .0520066    .034103    -4.51   0.000     .0143843    .1880305
------------------------------------------------------------------------------

. lab var _Idrug_2 "Tadalafil"

. lab var _Idrug_3 "Sildenafil"

. esttab, eform wide label nostar refcat(_Idrug_2 "Placebo")

----------------------------------------------
                              (1)             
                               _t             
----------------------------------------------
Patient's age at s..        1.118       (3.06)
Placebo                      ref.             
Tadalafil                   0.181      (-3.46)
Sildenafil                 0.0520      (-4.51)
----------------------------------------------
Observations                   48             
----------------------------------------------
Exponentiated coefficients; t statistics in parentheses

. esttab, eform wide label nostar refcat(_Idrug_2 "Placebo", label(1))

----------------------------------------------
                              (1)             
                               _t             
----------------------------------------------
Patient's age at s..        1.118       (3.06)
Placebo                         1             
Tadalafil                   0.181      (-3.46)
Sildenafil                 0.0520      (-4.51)
----------------------------------------------
Observations                   48             
----------------------------------------------
Exponentiated coefficients; t statistics in parentheses

[do-file]

Adding extra rows using the refcat() option

The refcat() option is designed to include information on the (omitted) reference category of a categorical variable (see above), but it can also be used to include extra rows in the table containing subtitles or other information. Example:

. sysuse auto
(1978 Automobile Data)

. regress price weight mpg turn foreign

      Source |       SS       df       MS              Number of obs =      74
-------------+------------------------------           F(  4,    69) =   19.23
       Model |   334771309     4  83692827.3           Prob > F      =  0.0000
    Residual |   300294087    69  4352088.22           R-squared     =  0.5271
-------------+------------------------------           Adj R-squared =  0.4997
       Total |   635065396    73  8699525.97           Root MSE      =  2086.2

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      weight |   4.284532   .7404967     5.79   0.000     2.807282    5.761783
         mpg |  -.4660076   73.51407    -0.01   0.995    -147.1226    146.1905
        turn |  -229.2059   114.2423    -2.01   0.049    -457.1131   -1.298676
     foreign |   3221.415   706.4847     4.56   0.000     1812.017    4630.813
       _cons |   1368.197   4887.597     0.28   0.780    -8382.292    11118.69
------------------------------------------------------------------------------

. foreach v of varlist weight mpg turn foreign {
  2.     label variable `v' `"- `: variable label `v''"'
  3. }

. esttab, refcat(weight "Main effects:" turn "Controls:", nolabel) wide label

-------------------------------------------------
                              (1)                
                            Price                
-------------------------------------------------
Main effects:                                    
- Weight (lbs.)             4.285***       (5.79)
- Mileage (mpg)            -0.466         (-0.01)
Controls:                                        
- Turn Circle (ft.)        -229.2*        (-2.01)
- Car type                 3221.4***       (4.56)
Constant                   1368.2          (0.28)
-------------------------------------------------
Observations                   74                
-------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

[do-file]

Match coefficients across models

Rename coefficients using the rename() option before matching the models and equations to merge different coefficients into the same table row. Example:

. sysuse auto
(1978 Automobile Data)

. set seed 123

. generate altmpg = invnorm(uniform())

. eststo: quietly regress price weight mpg
(est1 stored)

. eststo: quietly regress price weight altmpg
(est2 stored)

. esttab

--------------------------------------------
                      (1)             (2)   
                    price           price   
--------------------------------------------
weight              1.747**         2.037***
                   (2.72)          (5.36)   

mpg                -49.51                   
                  (-0.57)                   

altmpg                             -73.94   
                                  (-0.29)   

_cons              1946.1           6.433   
                   (0.54)          (0.01)   
--------------------------------------------
N                      74              74   
--------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. esttab, rename(altmpg mpg)

--------------------------------------------
                      (1)             (2)   
                    price           price   
--------------------------------------------
weight              1.747**         2.037***
                   (2.72)          (5.36)   

mpg                -49.51          -73.94   
                  (-0.57)         (-0.29)   

_cons              1946.1           6.433   
                   (0.54)          (0.01)   
--------------------------------------------
N                      74              74   
--------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. eststo clear

[do-file]

Model summary statistics

Display summary statistics only (suppress coefficients)

If you want to produce a table that only contains the summary statistics of the models, but no coefficients, add cells(none) to the command:

. sysuse auto
(1978 Automobile Data)

. eststo: quietly regress price weight mpg
(est1 stored)

. eststo: quietly regress price weight mpg foreign
(est2 stored)

. esttab, cells(none) scalars(rank r2 r2_a bic aic) nomtitles

--------------------------------------
                      (1)          (2)
--------------------------------------
N                      74           74
rank                    3            4
r2                  0.293        0.500
r2_a                0.273        0.478
bic                1378.6       1357.4
aic                1371.7       1348.2
--------------------------------------

. eststo clear

[do-file]

Adding likelihood-ratio test statistics

The estadd's lrtest subcommand may be used to add results from likelihood-ratio tests as follows:

. sysuse auto
(1978 Automobile Data)

. eststo A: quietly logit foreign weight

. eststo B: quietly logit foreign weight mpg price

. estadd lrtest A

Likelihood-ratio test                                 LR chi2(2)  =     23.78
(Assumption: A nested in .)                           Prob > chi2 =    0.0000

added scalars:
           e(lrtest_p) =  6.844e-06
        e(lrtest_chi2) =  23.784217
          e(lrtest_df) =  2

. esttab, scalars(lrtest_chi2 lrtest_df lrtest_p)

--------------------------------------------
                      (1)             (2)   
                  foreign         foreign   
--------------------------------------------
weight           -0.00259***     -0.00685***
                  (-4.25)         (-3.43)   

mpg                                -0.121   
                                  (-1.27)   

price                            0.000926** 
                                   (3.01)   

_cons               6.283***        14.42** 
                   (3.92)          (2.66)   
--------------------------------------------
N                      74              74   
lrtest_chi2                         23.78   
lrtest_df                               2   
lrtest_p                       0.00000684   
--------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. eststo clear

[do-file]

Rearranging the summary statistics in the table footer

The default in estout and esttab is to print the scalar summary statistics in the table footer in separate rows beneath one another (in each model's first column). Use the layout() suboption in the stats() option to rearrange the statistics. The option allows you to place the statistics in separate columns beside one another or also to combine multiple statistics in one table cell (see below). Here is an example:

. sysuse auto
(1978 Automobile Data)

. eststo: quietly regress price weight
(est1 stored)

. eststo: quietly regress price weight foreign
(est2 stored)

. esttab, p wide nopar label  ///
>     stats(F p N, layout("@ @" @) fmt(a3 3 a3) ///
>         labels("F statistic" "Observations"))

------------------------------------------------------------------------------
                              (1)                          (2)                
                            Price                        Price                
------------------------------------------------------------------------------
Weight (lbs.)               2.044***        0.000        3.321***        0.000
Car type                                                3637.0***        0.000
Constant                   -6.707           0.995      -4942.8***        0.000
------------------------------------------------------------------------------
F statistic                 29.42           0.000        35.35           0.000
Observations                   74                           74                
------------------------------------------------------------------------------
p-values in second column
* p<0.05, ** p<0.01, *** p<0.001

. eststo clear

[do-file]

In the layout() suboption, the "@" character is used as a placeholder for the statistics, one after another. Statistics to be printed in the same row have to be enclosed in quotes.

Combining multiple summary statistics in one cell

The syntax for combining multiple summary statistics in one table cell is a bit clumsy, as is illustrated in the following example. The cell definition has to be enclosed in double quotes in the example because it contains a blank, and a set of compound double quotes is needed to mark off the row definition.

. sysuse auto
(1978 Automobile Data)

. eststo: quietly logit foreign weight mpg
(est1 stored)

. eststo: quietly logit foreign weight mpg turn displ
(est2 stored)

. esttab, stats(chi2 df_m r2_p N, layout(`""@ (@)""' @ @))

--------------------------------------------
                      (1)             (2)   
                  foreign         foreign   
--------------------------------------------
weight           -0.00391***      0.00239   
                  (-3.86)          (0.99)   

mpg                -0.169          -0.196*  
                  (-1.83)         (-2.07)   

turn                               -0.502*  
                                  (-2.28)   

displacement                      -0.0769*  
                                  (-2.06)   

_cons               13.71**         26.95** 
                   (3.03)          (3.00)   
--------------------------------------------
chi2 (df_m)     35.72 (2)       55.82 (4)   
r2_p                0.397           0.620   
N                      74              74   
--------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. eststo clear

[do-file]

Note that in this example the layout definition could be simplified to layout(`""@ (@)""') without changing the result.

Specific models

Tabulate results from factor

The factor command does not return e(b) and e(V), which makes tabulation less obvious. For example, the factor loadings are returned in matrix e(L) and the unique variances are returned in e(Psi):

. webuse bg2
(Physician-cost data)

. factor bg2cost1-bg2cost6
(obs=568)

Factor analysis/correlation                        Number of obs    =      568
    Method: principal factors                      Retained factors =        3
    Rotation: (unrotated)                          Number of params =       15

    --------------------------------------------------------------------------
         Factor  |   Eigenvalue   Difference        Proportion   Cumulative
    -------------+------------------------------------------------------------
        Factor1  |      0.85389      0.31282            1.0310       1.0310
        Factor2  |      0.54107      0.51786            0.6533       1.6844
        Factor3  |      0.02321      0.17288            0.0280       1.7124
        Factor4  |     -0.14967      0.03951           -0.1807       1.5317
        Factor5  |     -0.18918      0.06197           -0.2284       1.3033
        Factor6  |     -0.25115            .           -0.3033       1.0000
    --------------------------------------------------------------------------
    LR test: independent vs. saturated:  chi2(15) =  269.07 Prob>chi2 = 0.0000

Factor loadings (pattern matrix) and unique variances

    -----------------------------------------------------------
        Variable |  Factor1   Factor2   Factor3 |   Uniqueness 
    -------------+------------------------------+--------------
        bg2cost1 |   0.2470    0.3670   -0.0446 |      0.8023  
        bg2cost2 |  -0.3374    0.3321   -0.0772 |      0.7699  
        bg2cost3 |  -0.3764    0.3756    0.0204 |      0.7169  
        bg2cost4 |  -0.3221    0.1942    0.1034 |      0.8479  
        bg2cost5 |   0.4550    0.2479    0.0641 |      0.7274  
        bg2cost6 |   0.4760    0.2364   -0.0068 |      0.7175  
    -----------------------------------------------------------

. ereturn list

scalars:
                  e(f) =  3
                  e(N) =  568
               e(df_m) =  15
               e(df_r) =  0
             e(chi2_i) =  269.0736870812582
               e(df_i) =  15
                e(p_i) =  1.43900835150e-48
              e(evsum) =  .8281790835746108

macros:
            e(cmdline) : "factor bg2cost1-bg2cost6"
                e(cmd) : "factor"
       e(marginsnotok) : "_ALL"
         e(properties) : "nob noV eigen"
              e(title) : "Factor analysis"
            e(predict) : "factor_p"
          e(estat_cmd) : "factor_estat"
         e(rotate_cmd) : "factor_rotate"
             e(mtitle) : "principal factors"
             e(method) : "pf"

matrices:
                e(sds) :  1 x 6
              e(means) :  1 x 6
                  e(C) :  6 x 6
                e(Phi) :  3 x 3
                  e(L) :  6 x 3
                e(Psi) :  1 x 6
                 e(Ev) :  1 x 6

functions:
             e(sample)   

. matrix list e(L)

e(L)[6,3]
             Factor1     Factor2     Factor3
bg2cost1   .24704957   .36703122  -.04457883
bg2cost2  -.33741222   .33210838  -.07721559
bg2cost3  -.37640773    .3755668   .02035389
bg2cost4  -.32206954    .1941843   .10341942
bg2cost5   .45501598   .24785063   .06407803
bg2cost6   .47598434   .23638092   -.0067801

. matrix list e(Psi)

e(Psi)[1,6]
             bg2cost1   bg2cost2   bg2cost3   bg2cost4   bg2cost5   bg2cost6
Uniqueness  .80226732  .76989477  .71685252  .84786809  .72742453    .717517

[do-file]

The simplest way to tabulate the factor loadings is to type:

. esttab e(L)

---------------------------------------------------
                     e(L)                          
                  Factor1      Factor2      Factor3
---------------------------------------------------
bg2cost1         .2470496     .3670312    -.0445788
bg2cost2        -.3374122     .3321084    -.0772156
bg2cost3        -.3764077     .3755668     .0203539
bg2cost4        -.3220695     .1941843     .1034194
bg2cost5          .455016     .2478506      .064078
bg2cost6         .4759843     .2363809    -.0067801
---------------------------------------------------

[do-file]

Reproducing the factor loadings table including the unique variances is more involved. The single factors in e(L) have to be addressed individually. For example, type:

. esttab, ///
>     cells("L[1](transpose) L[2](transpose) L[3](transpose) Psi") ///
>     nogap noobs nonumber nomtitle

----------------------------------------------------------------
                     L[1]         L[2]         L[3]          Psi
----------------------------------------------------------------
bg2cost1         .2470496     .3670312    -.0445788     .8022673
bg2cost2        -.3374122     .3321084    -.0772156     .7698948
bg2cost3        -.3764077     .3755668     .0203539     .7168525
bg2cost4        -.3220695     .1941843     .1034194     .8478681
bg2cost5          .455016     .2478506      .064078     .7274245
bg2cost6         .4759843     .2363809    -.0067801      .717517
----------------------------------------------------------------

[do-file]

The transpose suboption is required since the factors are in the columns of e(L) and, by default, e()-matrices are read row-wise (transpose can be abbreviated to t). Hence, L[#](transpose) refers to the #th column of e(L).

The label() suboption can be used to add labels, for example:

. esttab, ///
>     cells("L[1](t label(Factor 1)) L[2](t) L[3](t) Psi") ///
>     nogap noobs nonumber nomtitle

----------------------------------------------------------------
                 Factor 1         L[2]         L[3]          Psi
----------------------------------------------------------------
bg2cost1         .2470496     .3670312    -.0445788     .8022673
bg2cost2        -.3374122     .3321084    -.0772156     .7698948
bg2cost3        -.3764077     .3755668     .0203539     .7168525
bg2cost4        -.3220695     .1941843     .1034194     .8478681
bg2cost5          .455016     .2478506      .064078     .7274245
bg2cost6         .4759843     .2363809    -.0067801      .717517
----------------------------------------------------------------

[do-file]

Alternatively, you can also use syntax el[name], where name refers to the name of the row to be tabulated (or column if transpose is specified) and also sets the label:

. esttab, ///
>     cells("L[Factor1](t) L[Factor2](t) L[Factor3](t) Psi[Uniqueness]") ///
>     nogap noobs nonumber nomtitle

----------------------------------------------------------------
                  Factor1      Factor2      Factor3   Uniqueness
----------------------------------------------------------------
bg2cost1         .2470496     .3670312    -.0445788     .8022673
bg2cost2        -.3374122     .3321084    -.0772156     .7698948
bg2cost3        -.3764077     .3755668     .0203539     .7168525
bg2cost4        -.3220695     .1941843     .1034194     .8478681
bg2cost5          .455016     .2478506      .064078     .7274245
bg2cost6         .4759843     .2363809    -.0067801      .717517
----------------------------------------------------------------

[do-file]

Clean out table after ologit or oprobit

Tables of ologit or oprobit look somewhat complicated in Stata 9 or newer since each cutoff is stored in its own equation. To clean out the table, specify eqlabels(none):

. sysuse auto
(1978 Automobile Data)

. ologit rep mpg foreign

Iteration 0:   log likelihood = -93.692061
Iteration 1:   log likelihood = -78.844995
Iteration 2:   log likelihood = -78.106784
Iteration 3:   log likelihood =  -78.08927
Iteration 4:   log likelihood = -78.089242

Ordered logistic regression                       Number of obs   =         69
                                                  LR chi2(2)      =      31.21
                                                  Prob > chi2     =     0.0000
Log likelihood = -78.089242                       Pseudo R2       =     0.1665

------------------------------------------------------------------------------
       rep78 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         mpg |   .0672774   .0494465     1.36   0.174     -.029636    .1641908
     foreign |   2.599085   .6745627     3.85   0.000     1.276966    3.921204
-------------+----------------------------------------------------------------
       /cut1 |  -1.885212   1.175719                     -4.189579    .4191555
       /cut2 |  -.0922328   .9934139                     -2.039288    1.854823
       /cut3 |   2.524538   1.021289                      .5228488    4.526228
       /cut4 |   4.580877   1.146847                      2.333098    6.828657
------------------------------------------------------------------------------

. esttab, wide

-----------------------------------------
                      (1)                
                    rep78                
-----------------------------------------
rep78                                    
mpg                0.0673          (1.36)
foreign             2.599***       (3.85)
-----------------------------------------
cut1                                     
_cons              -1.885         (-1.60)
-----------------------------------------
cut2                                     
_cons             -0.0922         (-0.09)
-----------------------------------------
cut3                                     
_cons               2.525*         (2.47)
-----------------------------------------
cut4                                     
_cons               4.581***       (3.99)
-----------------------------------------
N                      69                
-----------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. esttab, wide eqlabels(none)

-----------------------------------------
                      (1)                
                    rep78                
-----------------------------------------
mpg                0.0673          (1.36)
foreign             2.599***       (3.85)
cut1               -1.885         (-1.60)
cut2              -0.0922         (-0.09)
cut3                2.525*         (2.47)
cut4                4.581***       (3.99)
-----------------------------------------
N                      69                
-----------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

[do-file]

To print a line between the main part of the table and the cutoffs, type:

. esttab, wide eqlabels(none) ///
>     varlabels(,blist(cut1:_cons "{hline @width}{break}"))

-----------------------------------------
                      (1)                
                    rep78                
-----------------------------------------
mpg                0.0673          (1.36)
foreign             2.599***       (3.85)
-----------------------------------------
cut1               -1.885         (-1.60)
cut2              -0.0922         (-0.09)
cut3                2.525*         (2.47)
cut4                4.581***       (3.99)
-----------------------------------------
N                      69                
-----------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

[do-file]

Furthermore, to suppress significance stars and standard errors for the cutoffs, type:

. esttab, cells("b(fmt(a3) star) se(drop(cut*:))")           ///
>     stardrop(cut*:) eqlabels(none)                         ///
>     varlabels(,blist(cut1:_cons "{hline @width}{break}"))

-----------------------------------------
                      (1)                
                    rep78                
                        b              se
-----------------------------------------
mpg                0.0673          0.0494
foreign             2.599***        0.675
-----------------------------------------
cut1               -1.885                
cut2              -0.0922                
cut3                2.525                
cut4                4.581                
-----------------------------------------
N                      69                
-----------------------------------------

[do-file]

Marginal effects for all outcomes after mlogit

To tabulate the marginal effects for all outcomes after mlogit it is necessary to store several sets of results from margins. Example:

. sysuse auto
(1978 Automobile Data)

. replace price = price / 1000
price was int now float
(74 real changes made)

. replace weight = weight / 1000
weight was int now float
(74 real changes made)

. quietly mlogit rep78 price mpg foreign if rep78>=3, nolog

. eststo mlogit

. foreach o in 3 4 5 {
  2.     quietly margins, dydx(*) predict(outcome(`o')) post
  3.     eststo, title(Outcome `o')
  4.     estimates restore mlogit
  5. }
(est2 stored)
(results mlogit are active now)
(est3 stored)
(results mlogit are active now)
(est4 stored)
(results mlogit are active now)

. eststo drop mlogit
(mlogit dropped)

. esttab, noobs se nostar mtitles nonumbers title(Average Marginal Effects)

Average Marginal Effects
---------------------------------------------------
                Outcome 3    Outcome 4    Outcome 5
---------------------------------------------------
price            -0.00245      -0.0163       0.0188
                 (0.0213)     (0.0251)     (0.0176)

mpg               -0.0118     -0.00802       0.0198
                 (0.0122)     (0.0126)    (0.00770)

foreign            -0.396        0.223        0.173
                 (0.0884)      (0.116)     (0.0754)
---------------------------------------------------
Standard errors in parentheses

. eststo clear

[do-file]

Transforming random-effects parameters of an xtmixed model

Variance parameters are returned by xtmixed as logarithms of standard deviations in e(b). To tabulate the parameters as standard deviations, back-transform them using the transform() option. Example:

. webuse pig
(Longitudinal analysis of pig weights)

. xtmixed weight week || id: week

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log restricted-likelihood = -870.51473  
Iteration 1:   log restricted-likelihood = -870.51473  

Computing standard errors:

Mixed-effects REML regression                   Number of obs      =       432
Group variable: id                              Number of groups   =        48

                                                Obs per group: min =         9
                                                               avg =       9.0
                                                               max =         9


                                                Wald chi2(1)       =   4592.10
Log restricted-likelihood = -870.51473          Prob > chi2        =    0.0000

------------------------------------------------------------------------------
      weight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        week |   6.209896   .0916386    67.77   0.000     6.030287    6.389504
       _cons |   19.35561   .4021142    48.13   0.000     18.56748    20.14374
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
id: Independent              |
                    sd(week) |   .6135471   .0673971      .4947035    .7609409
                   sd(_cons) |   2.630132    .302883      2.098719    3.296105
-----------------------------+------------------------------------------------
                sd(Residual) |    1.26443   .0487971      1.172317    1.363781
------------------------------------------------------------------------------
LR test vs. linear regression:       chi2(2) =   765.92   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. esttab, se wide nostar transform(ln*: exp(@) exp(@))

--------------------------------------
                      (1)             
                   weight             
--------------------------------------
weight                                
week                6.210     (0.0916)
_cons               19.36      (0.402)
--------------------------------------
lns1_1_1                              
_cons               0.614     (0.0674)
--------------------------------------
lns1_1_2                              
_cons               2.630      (0.303)
--------------------------------------
lnsig_e                               
_cons               1.264     (0.0488)
--------------------------------------
N                     432             
--------------------------------------
Standard errors in parentheses

. esttab, se wide nostar transform(ln*: exp(@) exp(@))         ///
>     eqlabels("" "sd(week)" "sd(_cons)" "sd(Residual)", none) ///
>     varlabels(,elist(weight:_cons "{break}{hline @width}"))  ///
>     varwidth(13)

---------------------------------------
                       (1)             
                    weight             
---------------------------------------
week                 6.210     (0.0916)
_cons                19.36      (0.402)
---------------------------------------
sd(week)             0.614     (0.0674)
sd(_cons)            2.630      (0.303)
sd(Residual)         1.264     (0.0488)
---------------------------------------
N                      432             
---------------------------------------
Standard errors in parentheses

[do-file]

(Note that in transform() you also have to include the function's first derivative, which is required for the standard errors. The example above might be confusing because the first derivative of exp(x) is simply exp(x). See below for examples where the two differ.)

Similarly, to display the parameters as variances, type:

. xtmixed, variance

Mixed-effects REML regression                   Number of obs      =       432
Group variable: id                              Number of groups   =        48

                                                Obs per group: min =         9
                                                               avg =       9.0
                                                               max =         9


                                                Wald chi2(1)       =   4592.10
Log restricted-likelihood = -870.51473          Prob > chi2        =    0.0000

------------------------------------------------------------------------------
      weight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        week |   6.209896   .0916386    67.77   0.000     6.030287    6.389504
       _cons |   19.35561   .4021142    48.13   0.000     18.56748    20.14374
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
id: Independent              |
                   var(week) |   .3764401   .0827025      .2447315     .579031
                  var(_cons) |   6.917597   1.593245       4.40462    10.86431
-----------------------------+------------------------------------------------
               var(Residual) |   1.598784   .1234011      1.374328    1.859899
------------------------------------------------------------------------------
LR test vs. linear regression:       chi2(2) =   765.92   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. esttab, se wide nostar transform(ln*: exp(2*@) 2*exp(2*@))   ///
>     eqlabels("" "var(week)" "var(_cons)" "var(Residual)", none) ///
>     varlabels(,elist(weight:_cons "{break}{hline @width}")) ///
>     varwidth(13)

---------------------------------------
                       (1)             
                    weight             
---------------------------------------
week                 6.210     (0.0916)
_cons                19.36      (0.402)
---------------------------------------
var(week)            0.376     (0.0827)
var(_cons)           6.918      (1.593)
var(Residual)        1.599      (0.123)
---------------------------------------
N                      432             
---------------------------------------
Standard errors in parentheses

[do-file]

If the model also has covariance terms, these are returned as arc-hyperbolic tangents of correlations in e(b) and can be back-transformed to correlations using Stata's tanh() function. Example:

. xtmixed weight week || id: week, covariance(unstructured)

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log restricted-likelihood = -870.43562  
Iteration 1:   log restricted-likelihood = -870.43562  

Computing standard errors:

Mixed-effects REML regression                   Number of obs      =       432
Group variable: id                              Number of groups   =        48

                                                Obs per group: min =         9
                                                               avg =       9.0
                                                               max =         9


                                                Wald chi2(1)       =   4552.31
Log restricted-likelihood = -870.43562          Prob > chi2        =    0.0000

------------------------------------------------------------------------------
      weight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        week |   6.209896   .0920382    67.47   0.000     6.029504    6.390287
       _cons |   19.35561   .4038677    47.93   0.000     18.56405    20.14718
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
id: Unstructured             |
                    sd(week) |   .6164379   .0680541      .4964981    .7653519
                   sd(_cons) |   2.643192   .3057584      2.106996    3.315842
            corr(week,_cons) |  -.0634377    .158876     -.3593789    .2440973
-----------------------------+------------------------------------------------
                sd(Residual) |   1.263657   .0487466      1.171638    1.362903
------------------------------------------------------------------------------
LR test vs. linear regression:       chi2(3) =   766.07   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. esttab, se wide nostar ///
>     transform(ln*: exp(@) exp(@) at*: tanh(@) (1-tanh(@)^2)) ///
>     eqlabels("" "sd(week)" "sd(_cons)" "corr(week,_cons)" "sd(Residual)", ///
>         none) ///
>     varlabels(,elist(weight:_cons "{break}{hline @width}"))  ///
>     varwidth(16)

------------------------------------------
                          (1)             
                       weight             
------------------------------------------
week                    6.210     (0.0920)
_cons                   19.36      (0.404)
------------------------------------------
sd(week)                0.616     (0.0681)
sd(_cons)               2.643      (0.306)
corr(week,_cons)      -0.0634      (0.159)
sd(Residual)            1.264     (0.0487)
------------------------------------------
N                         432             
------------------------------------------
Standard errors in parentheses

[do-file]

Unfortunately, it is not possible for transform() to turn such correlations into covariances (requires multiplication by the standard deviations). However, you can use estadd to manually compute the terms in advance and add them in the footer of the table. Example:

. xtmixed, variance

Mixed-effects REML regression                   Number of obs      =       432
Group variable: id                              Number of groups   =        48

                                                Obs per group: min =         9
                                                               avg =       9.0
                                                               max =         9


                                                Wald chi2(1)       =   4552.31
Log restricted-likelihood = -870.43562          Prob > chi2        =    0.0000

------------------------------------------------------------------------------
      weight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        week |   6.209896   .0920382    67.47   0.000     6.029504    6.390287
       _cons |   19.35561   .4038677    47.93   0.000     18.56405    20.14718
------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
id: Unstructured             |
                   var(week) |   .3799957   .0839023      .2465103    .5857635
                  var(_cons) |   6.986465   1.616357      4.439432    10.99481
             cov(week,_cons) |  -.1033632   .2627309     -.6183063      .41158
-----------------------------+------------------------------------------------
               var(Residual) |   1.596829   .1231981      1.372736    1.857506
------------------------------------------------------------------------------
LR test vs. linear regression:       chi2(3) =   766.07   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. mat list e(b)

e(b)[1,6]
         weight:      weight:    lns1_1_1:    lns1_1_2:  atr1_1_1_2:
           week        _cons        _cons        _cons        _cons
y1    6.2098958    19.355613   -.48379761    .97198734   -.06352304

        lnsig_e:
          _cons
y1    .23401004

. estadd scalar v1  = exp(2*[lns1_1_1]_b[_cons])

added scalar:
                 e(v1) =  .37999574

. estadd scalar v2  = exp(2*[lns1_1_2]_b[_cons])

added scalar:
                 e(v2) =  6.9864649

. estadd scalar cov = tanh([atr1_1_1_2]_b[_cons]) ///
>                     * exp([lns1_1_1]_b[_cons])  ///
>                     * exp([lns1_1_2]_b[_cons])

added scalar:
                e(cov) =  -.10336316

. estadd scalar v_e = exp(2*[lnsig_e]_b[_cons])

added scalar:
                e(v_e) =  1.5968295

. esttab, se wide nostar keep(weight:) obslast       ///
>     scalars("v1 var(week)" "v2 var(_cons)"           ///
>         "cov cov(week,_cons)" "v_e var(Residual)") ///
>     eqlabels(none) varwidth(15)

-----------------------------------------
                         (1)             
                      weight             
-----------------------------------------
week                   6.210     (0.0920)
_cons                  19.36      (0.404)
-----------------------------------------
var(week)              0.380             
var(_cons)             6.986             
cov(week,_cons)       -0.103             
var(Residual)          1.597             
N                        432             
-----------------------------------------
Standard errors in parentheses

[do-file]

LaTeX

Advanced LaTeX example: Arrange models in groups

. sysuse auto
(1978 Automobile Data)

. eststo: quietly reg weight mpg
(est1 stored)

. eststo: quietly reg weight mpg foreign
(est2 stored)

. eststo: quietly reg price weight mpg
(est3 stored)

. eststo: quietly reg price weight mpg foreign
(est4 stored)

. esttab using example.tex, booktabs label            ///
>     mgroups(A B, pattern(1 0 1 0)                   ///
>         prefix(\multicolumn{@span}{c}{) suffix(})   ///
>         span erepeat(\cmidrule(lr){@span}))         ///
>     alignment(D{.}{.}{-1}) page(dcolumn) nonumber
(output written to example.tex)

. eststo clear

[do-file]

Result:

advanced501.png

Descriptive tables

Table of descriptives

[estpost supersedes this example. See "Post summary statistics (summarize)" or "Post summary statistics (tabstat)" under "Examples for estpost".]

Research papers usually contain a table displaying the descriptive statistics for all variables in the analysis. The following example illustrates how such a table can be produced using estadd summ and esttab. Assume, your analysis uses price as the dependent variable and weight, mpg, and foreign as independent variables. To create a descriptives table including all four variables, type:

. sysuse auto
(1978 Automobile Data)

. generate y = uniform()

. quietly regress y price weight mpg foreign, noconstant

. estadd summ

added matrices:
                 e(sd) :  1 x 4
                e(max) :  1 x 4
                e(min) :  1 x 4
               e(mean) :  1 x 4

. esttab, cells("mean sd min max") nogap nomtitle nonumber

----------------------------------------------------------------
                     mean           sd          min          max
----------------------------------------------------------------
price            6165.257     2949.496         3291        15906
weight           3019.459     777.1936         1760         4840
mpg               21.2973     5.785503           12           41
foreign          .2972973     .4601885            0            1
----------------------------------------------------------------
N                      74                                       
----------------------------------------------------------------

[do-file]

The trick is to generate a fake variable and regress it on all involved variables, including the dependent variable.

Table of descriptives by subgroups

[estpost supersedes this example. See "Post summary statistics by subgroups (summarize)" or "Post summary statistics by subgroups (tabstat)" under "Examples for estpost".]

A table of descriptive statistics by subgroups can easily be produced using by and eststo:

. sysuse auto
(1978 Automobile Data)

. generate y = uniform()

. by foreign: eststo: quietly regress y price weight mpg, nocons

-------------------------------------------------------------------------------
-> Domestic
(est1 stored)

-------------------------------------------------------------------------------
-> Foreign
(est2 stored)

. estadd summ : *

. esttab, main(mean) aux(sd) label nodepvar nostar nonote

----------------------------------------------
                              (1)          (2)
                         Domestic      Foreign
----------------------------------------------
Price                      6072.4       6384.7
                         (3097.1)     (2621.9)

Weight (lbs.)              3317.1       2315.9
                          (695.4)      (433.0)

Mileage (mpg)               19.83        24.77
                          (4.743)      (6.611)
----------------------------------------------
Observations                   52           22
----------------------------------------------

. eststo clear

[do-file]

Tabulating results from t-Tests

[estpost supersedes this example. See "Post results from two-sample mean-comparison tests (ttest)" under "Examples for estpost".]

Basically anything can be tabulated by estout or esttab once it is posted in e(). Here is an example with t-tests:

. capt prog drop myttests

. *! version 1.0.0  14aug2007  Ben Jann
. program myttests, eclass
  1.     version 8
  2.     syntax varlist [if] [in], by(varname) [ * ]
  3.     marksample touse
  4.     markout `touse' `by'
  5.     tempname mu_1 mu_2 d d_se d_t d_p
  6.     foreach var of local varlist {
  7.         qui ttest `var' if `touse', by(`by') `options'
  8.         mat `mu_1' = nullmat(`mu_1'), r(mu_1)
  9.         mat `mu_2' = nullmat(`mu_2'), r(mu_2)
 10.         mat `d'    = nullmat(`d'   ), r(mu_1)-r(mu_2)
 11.         mat `d_se' = nullmat(`d_se'), r(se)
 12.         mat `d_t'  = nullmat(`d_t' ), r(t)
 13.         mat `d_p'  = nullmat(`d_p' ), r(p)
 14.     }
 15.     foreach mat in mu_1 mu_2 d d_se d_t d_p {
 16.         mat coln ``mat'' = `varlist'
 17.     }
 18.     tempname b V
 19.     mat `b' = `mu_1'*0
 20.     mat `V' = `b''*`b'
 21.     eret post `b' `V'
 22.     eret local cmd "myttests"
 23.     foreach mat in mu_1 mu_2 d d_se d_t d_p {
 24.         eret mat `mat' = ``mat''
 25.     }
 26. end

. sysuse auto
(1978 Automobile Data)

. myttests price weight mpg, by(foreign)

. ereturn list

macros:
                e(cmd) : "myttests"
         e(properties) : "b V"

matrices:
                  e(b) :  1 x 3
                  e(V) :  3 x 3
                e(d_p) :  1 x 3
                e(d_t) :  1 x 3
               e(d_se) :  1 x 3
                  e(d) :  1 x 3
               e(mu_2) :  1 x 3
               e(mu_1) :  1 x 3

. esttab, nomtitle nonumbers noobs ///
>     cells("mu_1(fmt(a3)) mu_2 d(star pvalue(d_p))" ". . d_se(par)")

------------------------------------------------------
                     mu_1         mu_2       d/d_se   
------------------------------------------------------
price              6072.4       6384.7       -312.3   
                                            (754.4)   
weight             3317.1       2315.9       1001.2***
                                            (160.3)   
mpg                 19.83        24.77       -4.946***
                                            (1.362)   
------------------------------------------------------

[do-file]

(An alternative approach would be to save three sets of estimates, one for each group, and one for the differences.)

Frequency tables

[estpost supersedes this example. See "Post a one-way frequency table (tabulate)" and "Post a two-way frequency table (tabulate)" under "Examples for estpost".]

With a little programming you could even do frequency tables in estout. Here is an example for a one-way table:

. capt prog drop e_tabulate

. *! version 1.0.0  24sep2007  Ben Jann
. prog e_tabulate, eclass
  1.     version 8.2
  2.     syntax varname(numeric) [if] [in] [fw aw iw] [, noTOTal * ]
  3.     tempname count percent vals V
  4.     tab `varlist' `if' `in' [`weight'`exp'], matcell(`count') matrow(`vals
> ') `options'
  5.     local N = r(N)
  6.     mat `count' = `count''
  7.     forv r =1/`=rowsof(`vals')' {
  8.         local value: di `vals'[`r',1]
  9.         local label: label (`varlist') `value'
 10.         local values "`values' `value'"
 11.         local labels `"`labels' `value' `"`label'"'"'
 12.     }
 13.     if "`total'"=="" {
 14.         mat `count' = `count', `N'
 15.         local values "`values' total"
 16.         local labels `"`labels' total `"Total"'"'
 17.     }
 18.     mat colname `count' = `values'
 19.     mat `percent' = `count'/`N'*100
 20.     mat `V' = `count''*`count'*0
 21.     eret post `count' `V', depname(`varlist') obs(`N')
 22.     eret local cmd "e_tabulate"
 23.     eret local depvar "`varlist'"
 24.     eret local labels `"`labels'"'
 25.     eret mat percent = `percent'
 26. end

. sysuse auto
(1978 Automobile Data)

. e_tabulate foreign

   Car type |      Freq.     Percent        Cum.
------------+-----------------------------------
   Domestic |         52       70.27       70.27
    Foreign |         22       29.73      100.00
------------+-----------------------------------
      Total |         74      100.00

. ereturn list

scalars:
                  e(N) =  74

macros:
             e(labels) : " 0 `"Domestic"' 1 `"Foreign"' total `"Total"'"
             e(depvar) : "foreign"
                e(cmd) : "e_tabulate"
         e(properties) : "b V"

matrices:
                  e(b) :  1 x 3
                  e(V) :  3 x 3
            e(percent) :  1 x 3

. mat list e(b)

e(b)[1,3]
        0      1  total
y1     52     22     74

. mat list e(percent)

e(percent)[1,3]
           0         1     total
c1  70.27027  29.72973       100

. esttab, cell("b percent") noobs nonumbers nomtitles ///
>     collabels(Freq. Percent, lhs(`:var lab `e(depvar)'')) ///
>     varlabels(`e(labels)', blist(total "{hline @width}{break}"))

--------------------------------------
Car type            Freq.      Percent
--------------------------------------
Domestic               52     70.27027
Foreign                22     29.72973
--------------------------------------
Total                  74          100
--------------------------------------

[do-file]

To construct a twoway table, save the conditional distributions in the table columns as separate estimation sets. Example:

. bys foreign: eststo: e_tabulate rep

-------------------------------------------------------------------------------
-> Domestic

     Repair |
Record 1978 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |          2        4.17        4.17
          2 |          8       16.67       20.83
          3 |         27       56.25       77.08
          4 |          9       18.75       95.83
          5 |          2        4.17      100.00
------------+-----------------------------------
      Total |         48      100.00
(est1 stored)

-------------------------------------------------------------------------------
-> Foreign

     Repair |
Record 1978 |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |          3       14.29       14.29
          4 |          9       42.86       57.14
          5 |          9       42.86      100.00
------------+-----------------------------------
      Total |         21      100.00
(est2 stored)

. esttab, main(percent 2) not nostar mtitles noobs nonote  ///
>     varlab(`e(labels)', blist(total "{hline @width}{break}"))

--------------------------------------
                      (1)          (2)
                 Domestic      Foreign
--------------------------------------
1                    4.17             
2                   16.67             
3                   56.25        14.29
4                   18.75        42.86
5                    4.17        42.86
--------------------------------------
Total              100.00       100.00
--------------------------------------

. eststo clear

[do-file]

Other

Tabulating a Stata matrix

A Stata matrix can be tabulated in estout or esttab by typing matrix(matname) instead of providing a list of names of stored estimation sets. Example:

. matrix A = (11,12,13)\(21,22,23)\(31,32,33)\(41,42,43)

. esttab matrix(A)

---------------------------------------------------
                        A                          
                       c1           c2           c3
---------------------------------------------------
r1                     11           12           13
r2                     21           22           23
r3                     31           32           33
r4                     41           42           43
---------------------------------------------------

[do-file]

Numeric formats can be set by adding a fmt() suboption in the matrix() argument. Examples:

. esttab matrix(A, fmt(1 2 3))

---------------------------------------------------
                        A                          
                       c1           c2           c3
---------------------------------------------------
r1                   11.0        12.00       13.000
r2                   21.0        22.00       23.000
r3                   31.0        32.00       33.000
r4                   41.0        42.00       43.000
---------------------------------------------------

. esttab matrix(A, fmt("1 2 3 4" "4 3 2 1"))

---------------------------------------------------
                        A                          
                       c1           c2           c3
---------------------------------------------------
r1                   11.0      12.0000         13.0
r2                  21.00       22.000        23.00
r3                 31.000        32.00       33.000
r4                41.0000         42.0      43.0000
---------------------------------------------------

[do-file]

Examples for tabulating a matrix that also contains equation names:

. mat rownames A = "eq1:row1" "eq1:row2" "eq2:row1" "eq2:row2"

. esttab matrix(A)

---------------------------------------------------
                        A                          
                       c1           c2           c3
---------------------------------------------------
eq1                                                
row1                   11           12           13
row2                   21           22           23
---------------------------------------------------
eq2                                                
row1                   31           32           33
row2                   41           42           43
---------------------------------------------------

. esttab matrix(A), unstack compress

----------------------------------------------------------------------
                   A                                                  
                 eq1                           eq2                    
                  c1        c2        c3        c1        c2        c3
----------------------------------------------------------------------
row1              11        12        13        31        32        33
row2              21        22        23        41        42        43
----------------------------------------------------------------------

. set seed 123

. matrix A = matuniform(4,4)

. mat coleq A = eq1 eq1 eq2 eq2

. mat roweq A = eq1 eq1 eq2 eq2

. esttab matrix(A), eqlabels(,merge)

----------------------------------------------------------------
                        A                                       
                   eq1:c1       eq1:c2       eq2:c3       eq2:c4
----------------------------------------------------------------
eq1:r1            .912044     .0075452     .2808588     .4602787
eq1:r2           .5601059     .6731906     .6177612     .8656876
eq2:r3           9.57e-06     .4090917     .7234821     .4862948
eq2:r4           .9899684     .3205308     .1244845     .3839803
----------------------------------------------------------------

[do-file]

More on correlation coefficients

(See "Post correlation coefficients (correlate)" under "Examples for estpost" for some basic examples on tabulating correlation coefficients.)

Kelvin Tan asked on statalist (see here): "I would like to know if I can stack two correlation matrix tables into one big correlation matrix ((foreign=1) in lower diagonal and (foreign=0) in upper diagonal of the big correlation matrix table)." Maarten Buis suggested a solution that works for the coefficients but does not provide significance stars or p-values (see here). Here are some examples for combining correlation coefficients while preserving the p-values. If you just want to stack two correlation matrices, you could code:

. eststo clear

. sysuse auto
(1978 Automobile Data)

. local vlist price mpg weight

. local rest `vlist'

. foreach v of local vlist {
  2.     estpost correlate `v' `rest' if foreign==0
  3.     foreach m in b rho p count {
  4.         matrix tmp = e(`m')
  5.         matrix coleq tmp = "foreign=0"
  6.         matrix `m' = tmp
  7.     }
  8.     estpost correlate `v' `rest' if foreign==1
  9.     foreach m in b rho p count {
 10.         matrix tmp = e(`m')
 11.         matrix coleq tmp = "foreign=1"
 12.         matrix `m' = `m', tmp
 13.     }
 14.     ereturn post b
 15.     foreach m in rho p count {
 16.         quietly estadd matrix `m' = `m'
 17.     }
 18.     eststo `v'
 19.     local rest: list rest - v
 20. }

       price |      e(b)     e(rho)       e(p)   e(count) 
-------------+--------------------------------------------
       price |         1          1                    52 
         mpg | -.5042629  -.5042629   .0001381         52 
      weight |  .6723974   .6723974   4.79e-08         52 

       price |      e(b)     e(rho)       e(p)   e(count) 
-------------+--------------------------------------------
       price |         1          1                    22 
         mpg | -.6313026  -.6313026    .001628         22 
      weight |   .885529    .885529   4.31e-08         22 

         mpg |      e(b)     e(rho)       e(p)   e(count) 
-------------+--------------------------------------------
         mpg |         1          1                    52 
      weight | -.8759427  -.8759427   1.89e-17         52 

         mpg |      e(b)     e(rho)       e(p)   e(count) 
-------------+--------------------------------------------
         mpg |         1          1                    22 
      weight |  -.682854   -.682854   .0004617         22 

      weight |      e(b)     e(rho)       e(p)   e(count) 
-------------+--------------------------------------------
      weight |         1          1                    52 

      weight |      e(b)     e(rho)       e(p)   e(count) 
-------------+--------------------------------------------
      weight |         1          1                    22 

. esttab, nonumbers mtitles noobs not

------------------------------------------------------------
                    price             mpg          weight   
------------------------------------------------------------
foreign=0                                                   
price                   1                                   
mpg                -0.504***            1                   
weight              0.672***       -0.876***            1   
------------------------------------------------------------
foreign=1                                                   
price                   1                                   
mpg                -0.631**             1                   
weight              0.886***       -0.683***            1   
------------------------------------------------------------
* p<0.05, ** p<0.01, *** p<0.001

[do-file]

The trick is to save a separate estimation set for each column of the correlation matrix and use equation names for the two groups.

Kelvin's upper/lower triangle layout can be achieved using a similar approach:

. eststo clear

. sysuse auto
(1978 Automobile Data)

. local vlist price mpg weight

. local upper

. local lower `vlist'

. foreach v of local vlist {
  2.     estpost correlate `v' `lower' if foreign==1
  3.     foreach m in b rho p count {
  4.         matrix `m' = e(`m')
  5.     }
  6.     if "`upper'"!="" {
  7.         estpost correlate `v' `upper' if foreign==0
  8.         foreach m in b rho p count {
  9.             matrix `m' = e(`m'), `m'
 10.         }
 11.     }
 12.     ereturn post b
 13.     foreach m in rho p count {
 14.         quietly estadd matrix `m' = `m'
 15.     }
 16.     eststo `v'
 17.     local lower: list lower - v
 18.     local upper `upper' `v'
 19. }

       price |      e(b)     e(rho)       e(p)   e(count) 
-------------+--------------------------------------------
       price |         1          1                    22 
         mpg | -.6313026  -.6313026    .001628         22 
      weight |   .885529    .885529   4.31e-08         22 

         mpg |      e(b)     e(rho)       e(p)   e(count) 
-------------+--------------------------------------------
         mpg |         1          1                    22 
      weight |  -.682854   -.682854   .0004617         22 

         mpg |      e(b)     e(rho)       e(p)   e(count) 
-------------+--------------------------------------------
       price | -.5042629  -.5042629   .0001381         52 

      weight |      e(b)     e(rho)       e(p)   e(count) 
-------------+--------------------------------------------
      weight |         1          1                    22 

      weight |      e(b)     e(rho)       e(p)   e(count) 
-------------+--------------------------------------------
       price |  .6723974   .6723974   4.79e-08         52 
         mpg | -.8759427  -.8759427   1.89e-17         52 

. esttab, nonumbers mtitles noobs not

------------------------------------------------------------
                    price             mpg          weight   
------------------------------------------------------------
price                   1          -0.504***        0.672***
mpg                -0.631**             1          -0.876***
weight              0.886***       -0.683***            1   
------------------------------------------------------------
* p<0.05, ** p<0.01, *** p<0.001

[do-file]

Flip models and coefficients (place models in rows instead of in columns)

esttab and estout place different models in separate columns. Sometimes it is desirable, however, to arrange a table so that the models are placed in separate rows. Here are two approaches to construct such a table.

Approach 1: esttab and estout return a matrix r(coefs) that contains the tabulated results. You can run esttab or estout and then run it again in matrix mode to transpose and tabulate r(coefs). This approach is simple but the possibilities for formatting the table are somewhat limited. Example:

. sysuse auto
(1978 Automobile Data)

. eststo model1: quietly reg price weight

. eststo model2: quietly reg price weight mpg

. esttab, se nostar

--------------------------------------
                      (1)          (2)
                    price        price
--------------------------------------
weight              2.044        1.747
                  (0.377)      (0.641)

mpg                             -49.51
                               (86.16)

_cons              -6.707       1946.1
                 (1174.4)     (3597.0)
--------------------------------------
N                      74           74
--------------------------------------
Standard errors in parentheses

. mat list r(coefs)

r(coefs)[3,4]
            model1:     model1:     model2:     model2:
                 b          se           b          se
weight   2.0440626   .37683413   1.7465592   .64135379
   mpg          .z          .z  -49.512221   86.156039
 _cons  -6.7073534   1174.4296   1946.0687   3597.0496

. esttab r(coefs, transpose)

---------------------------------------------------
                 r(coefs)                          
                   weight          mpg        _cons
---------------------------------------------------
model1                                             
b                2.044063                 -6.707353
se               .3768341                   1174.43
---------------------------------------------------
model2                                             
b                1.746559    -49.51222     1946.069
se               .6413538     86.15604      3597.05
---------------------------------------------------

. eststo clear

[do-file]

Approach 2: Again run esttab or estout to compile r(coefs) but then, for each coefficient, collect the results and post them in e() (i.e. post one "model" per coefficient). This approach requires some programming but gives you full flexibility. Example:

. sysuse auto
(1978 Automobile Data)

. eststo model1: quietly reg price weight

. eststo model2: quietly reg price weight mpg

. esttab, se nostar

--------------------------------------
                      (1)          (2)
                    price        price
--------------------------------------
weight              2.044        1.747
                  (0.377)      (0.641)

mpg                             -49.51
                               (86.16)

_cons              -6.707       1946.1
                 (1174.4)     (3597.0)
--------------------------------------
N                      74           74
--------------------------------------
Standard errors in parentheses

. matrix C = r(coefs)

. eststo clear

. local rnames : rownames C

. local models : coleq C

. local models : list uniq models

. local i 0

. foreach name of local rnames {
  2.     local ++i
  3.     local j 0
  4.     capture matrix drop b
  5.     capture matrix drop se
  6.     foreach model of local models {
  7.         local ++j
  8.         matrix tmp = C[`i', 2*`j'-1]
  9.         if tmp[1,1]<. {
 10.             matrix colnames tmp = `model'
 11.             matrix b = nullmat(b), tmp
 12.             matrix tmp[1,1] = C[`i', 2*`j']
 13.             matrix se = nullmat(se), tmp
 14.         }
 15.     }
 16.     ereturn post b
 17.     quietly estadd matrix se
 18.     eststo `name'
 19. }

. esttab, se mtitle noobs

------------------------------------------------------------
                      (1)             (2)             (3)   
                   weight             mpg           _cons   
------------------------------------------------------------
model1              2.044***                       -6.707   
                  (0.377)                        (1174.4)   

model2              1.747**        -49.51          1946.1   
                  (0.641)         (86.16)        (3597.0)   
------------------------------------------------------------
Standard errors in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. eststo clear

[do-file]

Approach 2 with summary statistics:

. sysuse auto
(1978 Automobile Data)

. eststo model1: quietly reg price weight

. eststo model2: quietly reg price weight mpg

. esttab, se nostar r2

--------------------------------------
                      (1)          (2)
                    price        price
--------------------------------------
weight              2.044        1.747
                  (0.377)      (0.641)

mpg                             -49.51
                               (86.16)

_cons              -6.707       1946.1
                 (1174.4)     (3597.0)
--------------------------------------
N                      74           74
R-sq                0.290        0.293
--------------------------------------
Standard errors in parentheses

. matrix C = r(coefs)

. matrix S = r(stats)

. eststo clear

. local rnames : rownames C

. local models : coleq C

. local models : list uniq models

. local i 0

. foreach name of local rnames {
  2.     local ++i
  3.     local j 0
  4.     capture matrix drop b
  5.     capture matrix drop se
  6.     foreach model of local models {
  7.         local ++j
  8.         matrix tmp = C[`i', 2*`j'-1]
  9.         if tmp[1,1]<. {
 10.             matrix colnames tmp = `model'
 11.             matrix b = nullmat(b), tmp
 12.             matrix tmp[1,1] = C[`i', 2*`j']
 13.             matrix se = nullmat(se), tmp
 14.         }
 15.     }
 16.     ereturn post b
 17.     quietly estadd matrix se
 18.     eststo `name'
 19. }

. local snames : rownames S

. local i 0

. foreach name of local snames {
  2.     local ++i
  3.     local j 0
  4.     capture matrix drop b
  5.     foreach model of local models {
  6.         local ++j
  7.         matrix tmp = S[`i', `j']
  8.         matrix colnames tmp = `model'
  9.         matrix b = nullmat(b), tmp
 10.     }
 11.     ereturn post b
 12.     eststo `name'
 13. }

. esttab, se mtitle noobs compress nonumb

---------------------------------------------------------------------------
              weight          mpg        _cons            N           r2   
---------------------------------------------------------------------------
model1         2.044***                 -6.707           74        0.290   
             (0.377)                  (1174.4)                             

model2         1.747**     -49.51       1946.1           74        0.293   
             (0.641)      (86.16)     (3597.0)                             
---------------------------------------------------------------------------
Standard errors in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. eststo clear

[do-file]

Tabulating results from an r-class program

Many Stata commands and user programs return results in r(). To tabulate such results in estout or esttab you can collect them in a matrix and tabulate the matrix (Approach 1) or post the results as one or more vectors in e() and tabulate them from there (Approach 2). Approach 2 is more flexible than Approach 1.

Approach 1: collect results in a matrix and tabulate the matrix
In the following example the ineqrbd command by Carlo V. Fiorio and Stephen P. Jenkins is used (see http://ideas.repec.org/c/boc/bocode/s456960.html). ineqrbd happens to return results in a series of r()-macros. We can construct a matrix from these macros (and also compute some additional results using the formulas provided in ineqrbd's output) and then tabulate the matrix as follows:

. capture which ineqrbd      // check whether -ineqrbd- is installed

. if _rc ssc install ineqrbd // and get it if not

. sysuse auto
(1978 Automobile Data)

. ineqrbd price trunk weight length foreign, noregression
 
Regression-based decomposition of inequality in  price
---------------------------------------------------------------------------
Decomp.  |     100*s_f        S_f     100*m_f/m      CV_f   CV_f/CV(total)
---------+-----------------------------------------------------------------
residual |     45.1031      0.2158      0.0000    1.79e+15    3.74e+15
trunk    |     -0.4687     -0.0022     -2.2943     -0.3109     -0.6499
weight   |     81.8711      0.3917    282.5215      0.2574      0.5380
length   |    -29.2268     -0.1398   -273.2854     -0.1185     -0.2477
foreign  |      2.7213      0.0130     17.2635      1.5479      3.2356
---------+-----------------------------------------------------------------
Total    |    100.0000      0.4784    100.0000      0.4784      1.0000
---------------------------------------------------------------------------
Note: proportionate contribution of composite var f to inequality of Total,
      s_f = rho_f*sd(f)/sd(Total). S_f = s_f*CV(Total).
      m_f = mean(f). sd(f) = std.dev. of f. CV_f = sd(f)/m_f.
      Total = price

. return list

macros:
              r(sf_Z4) : ".0272132003266729"
              r(cv_Z4) : "1.547906632830336"
              r(sd_Z4) : "1647.497942176796"
            r(mean_Z4) : "1064.339351763328"
              r(sf_Z3) : "-.2922677469661964"
              r(cv_Z3) : "-.1184805603472781"
              r(sd_Z3) : "1996.248597028214"
            r(mean_Z3) : "-16848.7437194508"
              r(sf_Z2) : ".8187108270390759"
              r(cv_Z2) : ".2573949336205026"
              r(sd_Z2) : "4483.350954895487"
            r(mean_Z2) : "17418.17871794493"
              r(sf_Z1) : "-.004687100101882"
              r(cv_Z1) : "-.3109311493112151"
              r(sd_Z1) : "43.98088549818675"
            r(mean_Z1) : "-141.4489529132564"
              r(sf_Z0) : ".4510308197023296"
              r(cv_Z0) : "1790770538833059"
              r(sd_Z0) : "1980.84687222313"
            r(mean_Z0) : "1.10614220486e-12"
             r(cv_tot) : ".4784060098610566"
             r(sd_tot) : "2949.495884768919"
           r(mean_tot) : "6165.256756756757"
              r(total) : " price"
              r(xvars) : "trunk weight length foreign"
               r(yvar) : "price"
            r(varlist) : "price trunk weight length foreign"

. // Step 1: collect results from r(sf_Z#), r(mean_Z#), and r(cv_Z#)
. local xvars "`r(xvars)'"

. local nx : list sizeof xvars

. foreach s in sf mean cv {
  2.    tempname `s'
  3.    matrix ``s'' = J(`nx'+2, 1, .z)
  4.    matrix rownames ``s'' = residual `r(xvars)' Total
  5.    forv i = 0/`nx' {
  6.        matrix ``s''[`i'+1, 1] = `r(`s'_Z`i')'
  7.    }
  8. }

. matrix `sf'[rowsof(`sf'), 1]     = 1

. matrix `mean'[rowsof(`mean'), 1] = `r(mean_tot)'

. matrix `cv'[rowsof(`sf'), 1]     = `r(cv_tot)'

. // Step 2: build matrix that mirrors -ineqrbd-'s output
. matrix ineqrbd = ///
>    `sf' * 100 ,                    /// column 1: 100*s_f
>    `sf' * `r(cv_tot)' ,            /// column 2: S_f
>    `mean' / `r(mean_tot)' * 100,   /// column 3: 100*m_f/m
>    `cv',                           /// column 4: CV_f
>    `cv' / `r(cv_tot)'              //  column 5: CV_f/CV(total)

. matrix colnames ineqrbd = 100*s_f S_f 100*m_f/m CV_f CV_f/CV(total)

. // Step 3: tabulate the matrix
. esttab matrix(ineqrbd)

-----------------------------------------------------------------------------
                  ineqrbd                                                    
                  100*s_f          S_f    100*m_f/m         CV_f CV_f/CV(to~)
-----------------------------------------------------------------------------
residual         45.10308     .2157759     1.79e-14     1.79e+15     3.74e+15
trunk             -.46871    -.0022423    -2.294291    -.3109311    -.6499315
weight           81.87108     .3916762     282.5215     .2573949     .5380261
length          -29.22677    -.1398226    -273.2854    -.1184806    -.2476569
foreign           2.72132      .013019      17.2635     1.547907      3.23555
Total                 100      .478406          100      .478406            1
-----------------------------------------------------------------------------

[do-file]

Approach 2: post results as vectors in e()
Instead of directly tabulating the matrix you can post the matrix columns as vectors in e() and then tabulate these vectors. This gives you some additional flexibility for formatting the columns. Here is an example (Stata 9 is required):

. capture which ineqrbd      // check whether -ineqrbd- is installed

. if _rc ssc install ineqrbd // and get it if not

. sysuse auto
(1978 Automobile Data)

. ineqrbd price trunk weight length foreign, noregression
 
Regression-based decomposition of inequality in  price
---------------------------------------------------------------------------
Decomp.  |     100*s_f        S_f     100*m_f/m      CV_f   CV_f/CV(total)
---------+-----------------------------------------------------------------
residual |     45.1031      0.2158      0.0000    1.79e+15    3.74e+15
trunk    |     -0.4687     -0.0022     -2.2943     -0.3109     -0.6499
weight   |     81.8711      0.3917    282.5215      0.2574      0.5380
length   |    -29.2268     -0.1398   -273.2854     -0.1185     -0.2477
foreign  |      2.7213      0.0130     17.2635      1.5479      3.2356
---------+-----------------------------------------------------------------
Total    |    100.0000      0.4784    100.0000      0.4784      1.0000
---------------------------------------------------------------------------
Note: proportionate contribution of composite var f to inequality of Total,
      s_f = rho_f*sd(f)/sd(Total). S_f = s_f*CV(Total).
      m_f = mean(f). sd(f) = std.dev. of f. CV_f = sd(f)/m_f.
      Total = price

. // Step 1: collect results from r(sf_Z#), r(mean_Z#), and r(cv_Z#)
. local xvars "`r(xvars)'"

. local nx : list sizeof xvars

. foreach s in sf mean cv {
  2.    tempname `s'
  3.    matrix ``s'' = J(`nx'+2, 1, .z)
  4.    matrix rownames ``s'' = residual `r(xvars)' Total
  5.    forv i = 0/`nx' {
  6.        matrix ``s''[`i'+1, 1] = `r(`s'_Z`i')'
  7.    }
  8. }

. matrix `sf'[rowsof(`sf'), 1]     = 1

. matrix `mean'[rowsof(`mean'), 1] = `r(mean_tot)'

. matrix `cv'[rowsof(`sf'), 1]     = `r(cv_tot)'

. // Step 2: build matrix that mirrors -ineqrbd-'s output
. matrix ineqrbd = ///
>    `sf' * 100 ,                    /// column 1: 100*s_f
>    `sf' * `r(cv_tot)' ,            /// column 2: S_f
>    `mean' / `r(mean_tot)' * 100,   /// column 3: 100*m_f/m
>    `cv',                           /// column 4: CV_f
>    `cv' / `r(cv_tot)'              //  column 5: CV_f/CV(total)

. matrix colnames ineqrbd = 100*s_f S_f 100*m_f/m CV_f CV_f/CV(total)

. // Step 3: post matrix columns in e()
. ereturn post

. tempname tmp

. local i 0

. foreach col in s_f100 S_f m_f100 CV_f CV_ftot {
  2.    local ++i
  3.    matrix `tmp' = ineqrbd[1...,`i']'
  4.    quietly estadd matrix `col' = `tmp'
  5. }

. ereturn list

matrices:
            e(CV_ftot) :  1 x 6
               e(CV_f) :  1 x 6
             e(m_f100) :  1 x 6
                e(S_f) :  1 x 6
             e(s_f100) :  1 x 6

. // Step 4: tabulate
. esttab, cell("s_f100 S_f m_f100 CV_f CV_ftot") noobs

-----------------------------------------------------------------------------
                      (1)                                                    
                                                                             
                   s_f100          S_f       m_f100         CV_f      CV_ftot
-----------------------------------------------------------------------------
residual         45.10308     .2157759     1.79e-14     1.79e+15     3.74e+15
trunk             -.46871    -.0022423    -2.294291    -.3109311    -.6499315
weight           81.87108     .3916762     282.5215     .2573949     .5380261
length          -29.22677    -.1398226    -273.2854    -.1184806    -.2476569
foreign           2.72132      .013019      17.2635     1.547907      3.23555
Total                 100      .478406          100      .478406            1
-----------------------------------------------------------------------------

. esttab, cell((S_f s_f100(fmt(1) par("" "%")))) noobs

--------------------------------------
                      (1)             
                                      
                      S_f       s_f100
--------------------------------------
residual         .2157759        45.1%
trunk           -.0022423        -0.5%
weight           .3916762        81.9%
length          -.1398226       -29.2%
foreign           .013019         2.7%
Total             .478406       100.0%
--------------------------------------

[do-file]

Including a column containing bivariate effects (stack models)

estout cannot stack models. A solution is to stack the models in advance and save the result in e(). Here is an example where the goal is to include a column containing the bivariate effects of the regressors:

. capt prog drop appendmodels

. *! version 1.0.0  14aug2007  Ben Jann
. program appendmodels, eclass
  1.     // using first equation of model
.     version 8
  2.     syntax namelist
  3.     tempname b V tmp
  4.     foreach name of local namelist {
  5.         qui est restore `name'
  6.         mat `tmp' = e(b)
  7.         local eq1: coleq `tmp'
  8.         gettoken eq1 : eq1
  9.         mat `tmp' = `tmp'[1,"`eq1':"]
 10.         local cons = colnumb(`tmp',"_cons")
 11.         if `cons'<. & `cons'>1 {
 12.             mat `tmp' = `tmp'[1,1..`cons'-1]
 13.         }
 14.         mat `b' = nullmat(`b') , `tmp'
 15.         mat `tmp' = e(V)
 16.         mat `tmp' = `tmp'["`eq1':","`eq1':"]
 17.         if `cons'<. & `cons'>1 {
 18.             mat `tmp' = `tmp'[1..`cons'-1,1..`cons'-1]
 19.         }
 20.         capt confirm matrix `V'
 21.         if _rc {
 22.             mat `V' = `tmp'
 23.         }
 24.         else {
 25.             mat `V' = ///
>             ( `V' , J(rowsof(`V'),colsof(`tmp'),0) ) \ ///
>             ( J(rowsof(`tmp'),colsof(`V'),0) , `tmp' )
 26.         }
 27.     }
 28.     local names: colfullnames `b'
 29.     mat coln `V' = `names'
 30.     mat rown `V' = `names'
 31.     eret post `b' `V'
 32.     eret local cmd "whatever"
 33. end

. sysuse auto
(1978 Automobile Data)

. eststo b1: quietly regress price weight

. eststo b2: quietly regress price mpg

. eststo b3: quietly regress price foreign

. eststo bivar: appendmodels b1 b2 b3

. eststo multi: quietly regress price weight mpg foreign

. esttab b1 b2 b3 bivar, mtitles

----------------------------------------------------------------------------
                      (1)             (2)             (3)             (4)   
                       b1              b2              b3           bivar   
----------------------------------------------------------------------------
weight              2.044***                                        2.044***
                   (5.42)                                          (5.42)   

mpg                                -238.9***                       -238.9***
                                  (-4.50)                         (-4.50)   

foreign                                             312.3           312.3   
                                                   (0.41)          (0.41)   

_cons              -6.707         11253.1***       6072.4***                
                  (-0.01)          (9.61)         (14.76)                   
----------------------------------------------------------------------------
N                      74              74              74                   
----------------------------------------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. esttab multi bivar, mtitles

--------------------------------------------
                      (1)             (2)   
                    multi           bivar   
--------------------------------------------
weight              3.465***        2.044***
                   (5.49)          (5.42)   

mpg                 21.85          -238.9***
                   (0.29)         (-4.50)   

foreign            3673.1***        312.3   
                   (5.37)          (0.41)   

_cons             -5853.7                   
                  (-1.73)                   
--------------------------------------------
N                      74                   
--------------------------------------------
t statistics in parentheses
* p<0.05, ** p<0.01, *** p<0.001

. eststo clear

[do-file]

Displaying group sizes for categorical regressors

Assume you are including a categorical variable in a regression model (e.g. using the xi command) and want to report the group sizes. You could proceed as follows:

. sysuse auto
(1978 Automobile Data)

. xi: reg price weight mpg i.rep
i.rep78           _Irep78_1-5         (naturally coded; _Irep78_1 omitted)

      Source |       SS       df       MS              Number of obs =      69
-------------+------------------------------           F(  6,    62) =    6.03
       Model |   212481723     6  35413620.6           Prob > F      =  0.0001
    Residual |   364315236    62  5876052.19           R-squared     =  0.3684
-------------+------------------------------           Adj R-squared =  0.3073
       Total |   576796959    68  8482308.22           Root MSE      =  2424.1

------------------------------------------------------------------------------
       price |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      weight |   2.093066    .636901     3.29   0.002     .8199193    3.366213
         mpg |   -63.0971   87.45276    -0.72   0.473    -237.9127    111.7185
   _Irep78_2 |   753.7024   1919.763     0.39   0.696    -3083.849    4591.254
   _Irep78_3 |   1349.361   1772.706     0.76   0.449    -2194.228     4892.95
   _Irep78_4 |    2030.47    1810.09     1.12   0.266    -1587.848    5648.788
   _Irep78_5 |    3376.91    1900.17     1.78   0.080    -421.4749    7175.296
       _cons |  -598.9665   3960.904    -0.15   0.880    -8516.701    7318.768
------------------------------------------------------------------------------

. capt matrix drop nobs

. foreach cat of varlist _Irep* {
  2.     count if `cat'==1 & e(sample)
  3.     matrix nobs = nullmat(nobs), r(N)
  4.     local collab "`collab'`cat' "
  5. }
    8
   30
   18
   11

. matrix colname nobs = `collab'

. estadd matrix nobs

added matrix:
               e(nobs) :  1 x 4

. esttab, cells("b(fmt(a3)) t(fmt(2)) nobs") nogap

---------------------------------------------------
                      (1)                          
                    price                          
                        b            t         nobs
---------------------------------------------------
weight              2.093         3.29             
mpg                -63.10        -0.72             
_Irep78_2           753.7         0.39            8
_Irep78_3          1349.4         0.76           30
_Irep78_4          2030.5         1.12           18
_Irep78_5          3376.9         1.78           11
_cons              -599.0        -0.15             
---------------------------------------------------
N                      69                          
---------------------------------------------------

[do-file]

Tabulating results from stci

[estpost supersedes this example. See "Post confidence intervals for survival time (stci)" under "Examples for estpost".]

The stci command to compute confidence intervals for survival time estimates returns its results in r(), or not at all if used with the by() option. The following example therefore provides a wrapper for stci that collects the results and posts them in e(), so that they can be tabulated using estout or esttab:

. capt prog drop e_stci

. *! version 1.0.0  16sep2008  Ben Jann
. prog e_stci, eclass
  1.     version 9.2
  2.     syntax [if] [in] [ , by(varname) Median Rmean Emean p(str) * ]
  3.     local stat "p50"
  4.     if `"`p'"'!=""          local stat `"p`p'"'
  5.     else if "`rmean'"!=""   local stat "rmean"
  6.     else if "`emean'"!=""   local stat "emean"
  7.     tempname b V N_sub lb ub
  8.     marksample touse
  9.     if "`by'"!="" {
 10.         markout `touse' `by', strok
 11.         qui levelsof `by' if `touse', local(levels)
 12.     }
 13.     local levels `"`levels' "total""'
 14.     gettoken l rest : levels, quotes
 15.     while (`"`l'"'!="") {
 16.         if `"`rest'"'=="" local lcond
 17.         else              local lcond `" & `by'==`l'"'
 18.         qui stci if `touse'`lcond', `median' `rmean' `emean' `p' `options'
 19.         mat `b' = nullmat(`b'), r(`stat')
 20.         mat `V' = nullmat(`V'), r(se)^2
 21.         mat `N_sub' = nullmat(`N_sub'), r(N_sub)
 22.         mat `lb' = nullmat(`lb'), r(lb)
 23.         mat `ub' = nullmat(`ub'), r(ub)
 24.         gettoken l rest : rest
 25.     }
 26.     foreach m in b V N_sub lb ub {
 27.         mat coln ``m'' = `levels'
 28.     }
 29.     if matmissing(`V') {
 30.         mat `V' = `b'' * `b' * 0  // set V to zero
 31.     }
 32.     else {
 33.         mat `V' = diag(`V')
 34.     }
 35.     eret post `b' `V'
 36.     eret matrix N_sub = `N_sub'
 37.     eret matrix lb = `lb'
 38.     eret matrix ub = `ub'
 39.     eret local cmd "e_stci"
 40. end

. webuse page2

. stci, by(group)

         failure _d:  dead
   analysis time _t:  time

             |    no. of 
group        |  subjects         50%     Std. Err.     [95% Conf. Interval]
-------------+-------------------------------------------------------------
           1 |        19         216      7.661029          190        234
           2 |        21         233      3.081611          232        280
-------------+-------------------------------------------------------------
       total |        40         232      2.562933          213        239

. e_stci, by(group)

. ereturn list

macros:
                e(cmd) : "e_stci"
         e(properties) : "b V"

matrices:
                  e(b) :  1 x 3
                  e(V) :  3 x 3
                 e(ub) :  1 x 3
                 e(lb) :  1 x 3
              e(N_sub) :  1 x 3

. estout, cell("N_sub b(label(50%)) se lb ub")

-----------------------------------------------------------------------------
                        .                                                    
                    N_sub          50%           se           lb           ub
-----------------------------------------------------------------------------
1                      19          216     7.661029          190          234
2                      21          233     3.081611          232          280
total                  40          232     2.562933          213          239
-----------------------------------------------------------------------------

[do-file]