# methods: Methods for displaying information about stochastic frontier... In sfa: Stochastic Frontier Analysis

## Description

`coef.sfa` is used to display the fitted coefficients. `print.sfa` is used to display some information about the fitted SFA. `predict.sfa` is used to predict (new) data with the fitted SFA model. `fitted.sfa` is used to predict the original data with the fitted SFA model. `logLik.sfa` is used to display the value of the log likelihood function. `residuals.sfa` is used to return the residuals of the fitted SFA model. `summary.sfa` is used to calculate the summary result of the SFA. `print.summary.sfa` is used display the summary result of the SFA. `eff.sfa` is used to return the efficiencies of the SFA.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```## S3 method for class 'sfa' coef(object, ...) ## S3 method for class 'sfa' print(x, ...) ## S3 method for class 'sfa' predict(object, newdata = NULL, intercept = NULL, ...) ## S3 method for class 'sfa' fitted(object, ...) ## S3 method for class 'sfa' logLik(object, ...) ## S3 method for class 'sfa' residuals(object, ...) ## S3 method for class 'sfa' summary(object, ...) ## S3 method for class 'sfa' print.summary(x, ...) ## S3 method for class 'sfa' eff(object, ...) ```

## Arguments

 `x` an object of class sfa `object` an object of class sfa `newdata` a data frame. If `newdata = NULL` then original data will be used. `intercept` boolean or `NULL`. If `intercept = NULL` then the function uses the same intercept options as specified in sfa. `...` ignored.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```set.seed(225) daten <- dgp(n = 100, b = c(1, 2), sc = -1) test <- sfa(y ~ x, data = daten) coef(test) print(test) predict(test) fitted(test) logLik(test) residuals(test) summary(test) eff(test) ```

### Example output

```There were 18 warnings (use warnings() to see them)
Intercept         x   sigmau2   sigmav2
0.9333911 1.9901077 2.0304447 0.6786540

Stochastic frontier analysis model

Coefficients:
Intercept         x   sigmau2   sigmav2
0.9333911 1.9901077 2.0304447 0.6786540

[,1]
1     6.265555
2    19.050184
3    64.971702
4     5.476038
5    18.898189
6    40.010547
7    58.565511
8    45.309766
9    19.560933
10   30.986510
11   79.604453
12   89.136422
13   90.099547
14   77.913940
15   77.718683
16   80.940833
17   28.318482
18   41.079757
19   27.413883
20   50.882761
21   49.051178
22   98.598381
23   51.178551
24   47.867717
25   35.931096
26   30.247336
27   42.040052
28   97.314690
29   28.043977
30   80.293050
31   71.536103
32   58.961352
33   54.022444
34   36.191599
35   97.737349
36   47.102466
37   26.470245
38   19.682335
39   92.705484
40   94.616339
41   23.514950
42   29.617983
43   97.859187
44   71.682954
45   51.497053
46   82.903269
47   39.336008
48   46.163907
49   86.807837
50   97.898996
51   89.957655
52   57.861780
53   60.558011
54   93.959992
55   70.777263
56   80.717358
57   91.872665
58   93.664440
59   14.292941
60   10.914579
61    9.095285
62   68.843846
63   64.242202
64   80.586940
65   92.881167
66   80.856221
67   51.230771
68   38.507042
69   65.081380
70   84.028407
71   63.931997
72   16.039294
73   40.418355
74   78.887921
75   99.374825
76    2.992972
77   11.312937
78   60.173197
79   10.033566
80   45.440724
81   47.242133
82    9.371454
83   78.836031
84   20.589887
85    1.271332
86   84.069687
87   39.038814
88   81.517180
89  100.144191
90   45.760450
91   95.384826
92   96.629710
93   47.499488
94   54.410977
95   92.352756
96    2.976017
97   53.246066
98   85.544268
99   33.753821
100  35.001619
[,1]
1     6.265555
2    19.050184
3    64.971702
4     5.476038
5    18.898189
6    40.010547
7    58.565511
8    45.309766
9    19.560933
10   30.986510
11   79.604453
12   89.136422
13   90.099547
14   77.913940
15   77.718683
16   80.940833
17   28.318482
18   41.079757
19   27.413883
20   50.882761
21   49.051178
22   98.598381
23   51.178551
24   47.867717
25   35.931096
26   30.247336
27   42.040052
28   97.314690
29   28.043977
30   80.293050
31   71.536103
32   58.961352
33   54.022444
34   36.191599
35   97.737349
36   47.102466
37   26.470245
38   19.682335
39   92.705484
40   94.616339
41   23.514950
42   29.617983
43   97.859187
44   71.682954
45   51.497053
46   82.903269
47   39.336008
48   46.163907
49   86.807837
50   97.898996
51   89.957655
52   57.861780
53   60.558011
54   93.959992
55   70.777263
56   80.717358
57   91.872665
58   93.664440
59   14.292941
60   10.914579
61    9.095285
62   68.843846
63   64.242202
64   80.586940
65   92.881167
66   80.856221
67   51.230771
68   38.507042
69   65.081380
70   84.028407
71   63.931997
72   16.039294
73   40.418355
74   78.887921
75   99.374825
76    2.992972
77   11.312937
78   60.173197
79   10.033566
80   45.440724
81   47.242133
82    9.371454
83   78.836031
84   20.589887
85    1.271332
86   84.069687
87   39.038814
88   81.517180
89  100.144191
90   45.760450
91   95.384826
92   96.629710
93   47.499488
94   54.410977
95   92.352756
96    2.976017
97   53.246066
98   85.544268
99   33.753821
100  35.001619
Log-Lik normal/half-normal distribution
-158.2432
[,1]
1    1.51311930
2    2.91147595
3    2.46843679
4    1.91451921
5    1.09476700
6    0.92185716
7    3.00590723
8   -0.42076360
9    0.72307799
10   0.69777516
11   1.63444348
12   0.29485529
13  -0.04676440
14   2.53514766
15   0.08922776
16  -0.76073327
17   3.60725078
18   0.36387617
19   1.92970519
20   1.06342951
21   2.66249793
22   0.77354901
23   2.41068381
24   0.60663569
25   1.61403472
26   4.04478952
27   0.23308003
28  -0.06690501
29   1.45376608
30   1.50120272
31   2.98252315
32   1.89942054
33   1.63693924
34   1.09615052
35   0.57526237
36   0.24134683
37   2.28838824
38   0.32057811
39   2.35924147
40   0.36849464
41   0.08240825
42  -1.33908738
43   0.54814901
44   1.26450840
45   3.05998829
46   0.76077967
47  -0.19247870
48   0.04125106
49   1.45426643
50   1.45512529
51   2.03521349
52  -1.10291694
53   1.46555957
54  -0.11699404
55   0.60325599
56   0.47471384
57   1.57514703
58   0.14862053
59  -0.36920537
60  -0.53922770
61   2.85388188
62   1.65332431
63   0.18281133
64  -0.84470195
65   1.87463260
66   2.45694171
67   0.52068148
68   1.24703090
69   3.30725096
70  -0.04966281
71   0.32956820
72   0.30877827
73   0.48832590
74   1.08923167
75   1.52683518
76   0.36552411
77   2.54598952
78   1.73544063
79   0.26760648
80   0.72275088
81   2.30621982
82   1.28549405
83   0.51064968
84   2.87179501
85   2.20880942
86  -0.48518530
87   1.64201575
88   0.29197088
89   3.45915497
90   1.72604273
91   3.46894723
92   2.18528837
93  -0.31264503
94   1.24646581
95  -0.30384219
96  -1.35653348
97   0.17819799
98   1.88978693
99   2.25879634
100  0.87537961
Stochastic frontier analysis model

Estimate  Std. Error    t value
Intercept 0.9333911 0.515903963   1.809234
x         1.9901077 0.008102162 245.626751
sigmau2   2.0304447 1.405877986   1.444254
sigmav2   0.6786540 0.444241664   1.527669

LR-test: sigmau2 = 0 (inefficiency has no influence to the model)
H0: sigmau2 = 0 (beta_sfa = beta_ols)

value Log-Lik
sfa     -158.2432
ols     -158.7202

value LR-Test: 0.954 on 3 degrees of freedom p-value 0.18762

mean efficiency
0.9614833
[,1]
1   0.8371099
2   0.8971146
3   0.9721695
4   0.7879876
5   0.9503042
6   0.9778959
7   0.9629262
8   0.9897663
9   0.9599298
10  0.9746482
11  0.9840167
12  0.9926808
13  0.9938615
14  0.9760933
15  0.9924145
16  0.9950242
17  0.9128428
18  0.9837101
19  0.9486635
20  0.9812448
21  0.9608009
22  0.9915728
23  0.9656896
24  0.9841836
25  0.9655915
26  0.9089042
27  0.9850650
28  0.9943678
29  0.9594863
30  0.9851286
31  0.9696711
32  0.9757828
33  0.9765976
34  0.9734008
35  0.9923139
36  0.9865948
37  0.9386184
38  0.9672846
39  0.9811458
40  0.9928451
41  0.9754395
42  0.9892116
43  0.9924279
44  0.9851936
45  0.9573348
46  0.9900581
47  0.9869606
48  0.9875829
49  0.9865394
50  0.9880411
51  0.9830106
52  0.9939370
53  0.9807115
54  0.9943033
55  0.9892665
56  0.9911669
57  0.9865072
58  0.9935140
59  0.9675472
60  0.9608513
61  0.8093751
62  0.9814003
63  0.9904138
64  0.9951714
65  0.9846609
66  0.9776121
67  0.9858314
68  0.9730170
69  0.9633007
70  0.9934299
71  0.9896489
72  0.9603745
73  0.9823951
74  0.9876620
75  0.9877995
76  0.8146798
77  0.8551555
78  0.9779532
79  0.9393015
80  0.9823510
81  0.9644041
82  0.8959123
83  0.9907933
84  0.9052382
85  0.4315089
86  0.9946131
87  0.9678280
88  0.9920136
89  0.9747609
90  0.9713327
91  0.9734622
92  0.9831267
93  0.9897522
94  0.9807575
95  0.9946818
96  0.9026483
97  0.9884827
98  0.9832539
99  0.9517791
100 0.9753927
```

sfa documentation built on May 29, 2017, 5:51 p.m.