methods: Methods for displaying information about stochastic frontier...
In sfa: Stochastic Frontier Analysis
Description
Usage
Arguments
Examples
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
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 2, 2019, 4:38 a.m.
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Description Usage Arguments Examples
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 |
intercept |
boolean or |
... |
ignored. |
Examples
1 2 3 4 5 6 7 8 9 10 11 |
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
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