Running examples in 'outreg-Ex.R' failed
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> ### Name: outreg
> ### Title: Generate Regression Table
> ### Aliases: outreg
> 
> ### ** Examples
> 
> fitlist <- list(lm(mpg ~ cyl, data = mtcars),
+                 lm(mpg ~ cyl + wt + hp, data = mtcars),
+                 lm(mpg ~ cyl + wt + hp + drat, data = mtcars))
> outreg(fitlist)
     .variable    .stat   Model 1   Model 2   Model 3
1  (Intercept) Estimate 37.885*** 38.752*** 34.496***
2               Std Err   [2.074]   [1.787]   [7.441]
5          cyl Estimate -2.876***   -0.942*    -0.762
6               Std Err   [0.322]   [0.551]   [0.635]
9           wt Estimate           -3.167*** -2.973***
10              Std Err             [0.741]   [0.818]
13          hp Estimate              -0.018    -0.021
14              Std Err             [0.012]   [0.013]
17        drat Estimate                         0.818
18              Std Err                       [1.387]
21                    N        32        32        32
22                   R2     0.726     0.843     0.845
23               adj R2     0.717     0.826     0.822
24                  AIC   169.306   155.477   157.067
> 
> # with custom regression names
> outreg(setNames(fitlist, c('small', 'medium', 'large')))
     .variable    .stat     small    medium     large
1  (Intercept) Estimate 37.885*** 38.752*** 34.496***
2               Std Err   [2.074]   [1.787]   [7.441]
5          cyl Estimate -2.876***   -0.942*    -0.762
6               Std Err   [0.322]   [0.551]   [0.635]
9           wt Estimate           -3.167*** -2.973***
10              Std Err             [0.741]   [0.818]
13          hp Estimate              -0.018    -0.021
14              Std Err             [0.012]   [0.013]
17        drat Estimate                         0.818
18              Std Err                       [1.387]
21                    N        32        32        32
22                   R2     0.726     0.843     0.845
23               adj R2     0.717     0.826     0.822
24                  AIC   169.306   155.477   157.067
> 
> # star on standard errors, instead of estimate
> outreg(fitlist, starred = 'se')
     .variable    .stat    Model 1    Model 2    Model 3
1  (Intercept) Estimate     37.885     38.752     34.496
2               Std Err [2.074***] [1.787***] [7.441***]
5          cyl Estimate     -2.876     -0.942     -0.762
6               Std Err [0.322***]   [0.551*]    [0.635]
9           wt Estimate                -3.167     -2.973
10              Std Err            [0.741***] [0.818***]
13          hp Estimate                -0.018     -0.021
14              Std Err               [0.012]    [0.013]
17        drat Estimate                            0.818
18              Std Err                          [1.387]
21                    N         32         32         32
22                   R2      0.726      0.843      0.845
23               adj R2      0.717      0.826      0.822
24                  AIC    169.306    155.477    157.067
> 
> # include other stats
> outreg(fitlist, pv = TRUE, tv = TRUE, se = FALSE)
     .variable    .stat   Model 1   Model 2   Model 3
1  (Intercept) Estimate 37.885*** 38.752*** 34.496***
3               t Value    18.268    21.687     4.636
4               p Value     0.000     0.000     0.000
5          cyl Estimate -2.876***   -0.942*    -0.762
7               t Value    -8.920    -1.709    -1.200
8               p Value     0.000     0.098     0.240
9           wt Estimate           -3.167*** -2.973***
11              t Value              -4.276    -3.634
12              p Value               0.000     0.001
13          hp Estimate              -0.018    -0.021
15              t Value              -1.519    -1.613
16              p Value               0.140     0.118
17        drat Estimate                         0.818
19              t Value                         0.590
20              p Value                         0.560
21                    N        32        32        32
22                   R2     0.726     0.843     0.845
23               adj R2     0.717     0.826     0.822
24                  AIC   169.306   155.477   157.067
> 
> 
> # poisson regression
> counts <- c(18,17,15,20,10,20,25,13,12)
> outcome <- gl(3,1,9)
> treatment <- gl(3,3)
> fitlist2 <- list(glm(counts ~ outcome, family = poisson()),
+                  glm(counts ~ outcome + treatment, family = poisson()))
> outreg(fitlist2)
     .variable    .stat  Model 1  Model 2
1  (Intercept) Estimate 3.045*** 3.045***
2               Std Err  [0.126]  [0.171]
6     outcome2 Estimate -0.454** -0.454**
7               Std Err  [0.202]  [0.202]
11    outcome3 Estimate   -0.293   -0.293
12              Std Err  [0.193]  [0.193]
16  treatment2 Estimate             0.000
17              Std Err           [0.200]
21  treatment3 Estimate             0.000
22              Std Err           [0.200]
26                    N        9        9
27                  AIC   52.761   56.761
> 
> 
> # logistic regression
> fitlist3 <- list(glm(cbind(ncases, ncontrols) ~ agegp,
+                      data = esoph, family = binomial()),
+                  glm(cbind(ncases, ncontrols) ~ agegp + tobgp + alcgp,
+                      data = esoph, family = binomial()),
+                  glm(cbind(ncases, ncontrols) ~ agegp + tobgp * alcgp,
+                      data = esoph, family = binomial()))
> outreg(fitlist3)
          .variable    .stat   Model 1   Model 2   Model 3
1       (Intercept) Estimate -1.898*** -1.190*** -1.169***
2                    Std Err   [0.191]   [0.207]   [0.208]
6           agegp.L Estimate  3.235***  3.997***  3.971***
7                    Std Err   [0.647]   [0.694]   [0.693]
11          agegp.Q Estimate -1.755*** -1.657***  -1.587**
12                   Std Err   [0.586]   [0.621]   [0.619]
16          agegp.C Estimate     0.044     0.111     0.099
17                   Std Err   [0.446]   [0.468]   [0.473]
21          agegp^4 Estimate     0.257     0.079     0.100
22                   Std Err   [0.305]   [0.325]   [0.328]
26          agegp^5 Estimate    -0.190    -0.262    -0.271
27                   Std Err   [0.193]   [0.213]   [0.215]
31          tobgp.L Estimate            1.117***  1.108***
32                   Std Err             [0.240]   [0.270]
36          tobgp.Q Estimate               0.345     0.266
37                   Std Err             [0.224]   [0.254]
41          tobgp.C Estimate               0.317     0.294
42                   Std Err             [0.211]   [0.240]
46          alcgp.L Estimate            2.539***  2.426***
47                   Std Err             [0.264]   [0.288]
51          alcgp.Q Estimate               0.094     0.130
52                   Std Err             [0.224]   [0.254]
56          alcgp.C Estimate             0.439**     0.366
57                   Std Err             [0.183]   [0.223]
61  tobgp.L:alcgp.L Estimate                        -0.429
62                   Std Err                       [0.586]
66  tobgp.Q:alcgp.L Estimate                         0.337
67                   Std Err                       [0.568]
71  tobgp.C:alcgp.L Estimate                        -0.157
72                   Std Err                       [0.543]
76  tobgp.L:alcgp.Q Estimate                         0.042
77                   Std Err                       [0.530]
81  tobgp.Q:alcgp.Q Estimate                        -0.624
82                   Std Err                       [0.509]
86  tobgp.C:alcgp.Q Estimate                        -0.067
87                   Std Err                       [0.481]
91  tobgp.L:alcgp.C Estimate                        -0.251
92                   Std Err                       [0.472]
96  tobgp.Q:alcgp.C Estimate                         0.023
97                   Std Err                       [0.442]
101 tobgp.C:alcgp.C Estimate                        -0.173
102                  Std Err                       [0.409]
106                        N        88        88        88
107                      AIC   373.964   221.392   233.941
> 
> 
> # survival regression
> library(survival)
> fitlist4 <- list(survreg(Surv(time, status) ~ ph.ecog + age,
+                          data = lung),
+                  survreg(Surv(time, status) ~ ph.ecog + age + strata(sex),
+                          data = lung))
> outreg(fitlist4)
          .variable    .stat   Model 1   Model 2
1       (Intercept) Estimate  6.831***  6.732***
2                    Std Err   [0.429]   [0.424]
6           ph.ecog Estimate -0.326*** -0.324***
7                    Std Err   [0.086]   [0.086]
11              age Estimate    -0.008    -0.006
12                   Std Err   [0.007]   [0.007]
16       Log(scale) Estimate -0.304***          
17                   Std Err   [0.062]          
21 Log(scale) sex=1 Estimate           -0.244***
22                   Std Err             [0.079]
26 Log(scale) sex=2 Estimate           -0.423***
27                   Std Err             [0.107]
31                         N       227       227
32                       AIC  2284.215  2284.504
> 
> 
> # tobit regression
> fitlist5 <- list(survreg(Surv(durable, durable>0, type='left') ~ 1,
+                  data=tobin, dist='gaussian'),
+                  survreg(Surv(durable, durable>0, type='left') ~ age + quant,
+                  data=tobin, dist='gaussian'))
> outreg(fitlist5)
     .variable    .stat  Model 1  Model 2
1  (Intercept) Estimate   -2.227   15.145
2               Std Err  [2.060] [16.079]
6          age Estimate            -0.129
7               Std Err           [0.219]
11       quant Estimate            -0.046
12              Std Err           [0.058]
16  Log(scale) Estimate 1.783*** 1.718***
17              Std Err  [0.309]  [0.310]
21                    N       20       20
22                  AIC   62.984   65.880
> 
> 
> # instrumental variable regression
> library(AER)
Error in library(AER) : there is no package called 'AER'
Execution halted