R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(170588
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+ ,0
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+ ,0
+ ,0
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+ ,20
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+ ,185288
+ ,71
+ ,97
+ ,27
+ ,107
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,203
+ ,4
+ ,0
+ ,0
+ ,0
+ ,7199
+ ,5
+ ,7
+ ,0
+ ,0
+ ,46660
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+ ,5
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+ ,0
+ ,0
+ ,105477
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+ ,16
+ ,54)
+ ,dim=c(5
+ ,164)
+ ,dimnames=list(c('Total_Time_spent_in_RFC'
+ ,'Number_of_Logins'
+ ,'Total_Number_of_Blogged_Computations'
+ ,'Total_Number_of_Reviewed_Compendiums'
+ ,'Total_Number_of_submitted_Feedback_Messages_in_PeerReviews')
+ ,1:164))
> y <- array(NA,dim=c(5,164),dimnames=list(c('Total_Time_spent_in_RFC','Number_of_Logins','Total_Number_of_Blogged_Computations','Total_Number_of_Reviewed_Compendiums','Total_Number_of_submitted_Feedback_Messages_in_PeerReviews'),1:164))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Total_Time_spent_in_RFC Number_of_Logins
1 170588 46
2 86621 48
3 113337 37
4 144530 72
5 81530 31
6 35523 17
7 305115 78
8 32750 16
9 115885 37
10 130539 24
11 156990 63
12 128274 74
13 102350 43
14 192887 42
15 129796 55
16 245478 120
17 169569 42
18 185279 100
19 109598 36
20 155012 49
21 154730 46
22 280379 56
23 90938 17
24 101324 31
25 139502 77
26 145120 90
27 161729 80
28 160905 54
29 106888 34
30 187289 38
31 181853 53
32 129340 47
33 196862 63
34 62731 25
35 234863 55
36 167255 37
37 264528 83
38 121976 49
39 80964 26
40 209631 107
41 213310 55
42 115911 40
43 131337 46
44 81106 31
45 93125 49
46 305708 95
47 78800 42
48 157566 54
49 213487 68
50 131108 39
51 128734 53
52 24188 24
53 257662 200
54 65029 17
55 98066 58
56 173587 27
57 179571 58
58 197067 113
59 208823 74
60 134088 50
61 245107 86
62 201409 76
63 141760 61
64 170635 59
65 129100 38
66 108811 34
67 113450 85
68 142286 100
69 143937 49
70 89882 35
71 118807 33
72 69471 28
73 126630 44
74 145908 37
75 96981 33
76 189066 44
77 191467 55
78 106193 58
79 89318 36
80 120362 42
81 98791 30
82 274949 66
83 132798 53
84 128075 57
85 80953 25
86 109237 39
87 96634 35
88 226183 114
89 167226 53
90 117805 70
91 121630 48
92 152193 49
93 112004 42
94 169613 51
95 176577 51
96 130533 27
97 142339 29
98 189764 54
99 201603 92
100 243180 72
101 155931 63
102 182557 40
103 106351 108
104 43287 14
105 127394 44
106 127930 91
107 135306 29
108 175663 63
109 74112 32
110 89059 65
111 166142 41
112 141933 55
113 22938 10
114 125927 53
115 61857 25
116 91185 31
117 236316 64
118 21054 16
119 169093 35
120 31414 19
121 183059 74
122 137544 35
123 75032 45
124 71908 28
125 38214 34
126 90961 25
127 193662 48
128 127185 38
129 242153 49
130 201748 65
131 254599 71
132 139144 23
133 76470 29
134 183260 190
135 280039 113
136 50999 15
137 253056 85
138 98466 48
139 168059 33
140 128768 50
141 75746 72
142 244909 79
143 152366 54
144 173260 63
145 197033 68
146 67507 39
147 139409 49
148 185366 67
149 0 0
150 14688 10
151 98 1
152 455 2
153 0 0
154 0 0
155 128873 57
156 185288 71
157 0 0
158 203 4
159 7199 5
160 46660 20
161 17547 5
162 73567 27
163 969 2
164 105477 33
Total_Number_of_Blogged_Computations Total_Number_of_Reviewed_Compendiums
1 65 26
2 54 20
3 58 24
4 77 25
5 41 15
6 0 16
7 111 20
8 1 18
9 37 19
10 60 20
11 64 26
12 71 37
13 38 23
14 76 36
15 61 28
16 125 35
17 85 20
18 69 22
19 77 19
20 100 28
21 78 27
22 76 25
23 40 15
24 81 26
25 102 27
26 70 24
27 75 21
28 93 27
29 42 21
30 95 30
31 87 25
32 44 33
33 87 30
34 28 20
35 87 27
36 71 25
37 70 30
38 50 20
39 30 8
40 87 24
41 78 22
42 48 25
43 52 20
44 31 21
45 30 21
46 70 26
47 20 26
48 84 30
49 81 26
50 79 30
51 72 18
52 8 4
53 67 31
54 21 18
55 30 14
56 70 20
57 87 35
58 87 24
59 116 26
60 54 20
61 96 31
62 93 21
63 49 31
64 49 26
65 38 19
66 64 15
67 64 19
68 66 28
69 98 20
70 99 17
71 56 25
72 22 20
73 51 25
74 61 24
75 94 22
76 98 25
77 76 20
78 57 23
79 75 22
80 48 25
81 48 18
82 109 30
83 27 22
84 83 25
85 49 8
86 24 21
87 46 22
88 44 24
89 49 30
90 108 27
91 42 21
92 110 25
93 28 21
94 79 24
95 49 20
96 64 20
97 75 20
98 118 24
99 95 40
100 106 22
101 73 31
102 108 26
103 30 20
104 13 19
105 69 15
106 75 21
107 80 22
108 106 24
109 28 19
110 70 20
111 51 23
112 90 27
113 12 1
114 87 24
115 23 11
116 57 27
117 85 22
118 4 0
119 56 17
120 18 8
121 86 23
122 40 31
123 16 23
124 18 17
125 16 8
126 42 22
127 78 33
128 30 33
129 104 31
130 121 33
131 111 35
132 57 21
133 28 20
134 56 24
135 82 29
136 2 20
137 91 27
138 41 24
139 84 26
140 55 26
141 3 12
142 54 21
143 93 24
144 41 21
145 94 30
146 101 32
147 70 24
148 114 29
149 0 0
150 4 0
151 0 0
152 0 0
153 0 0
154 0 0
155 42 20
156 97 27
157 0 0
158 0 0
159 7 0
160 12 5
161 0 1
162 37 23
163 0 0
164 39 16
Total_Number_of_submitted_Feedback_Messages_in_PeerReviews
1 99
2 77
3 90
4 96
5 41
6 64
7 76
8 67
9 72
10 75
11 97
12 139
13 76
14 123
15 106
16 133
17 76
18 83
19 72
20 107
21 99
22 88
23 56
24 104
25 103
26 90
27 78
28 103
29 81
30 114
31 95
32 118
33 113
34 75
35 103
36 93
37 114
38 76
39 27
40 92
41 84
42 92
43 72
44 79
45 57
46 99
47 82
48 113
49 97
50 110
51 78
52 12
53 114
54 67
55 52
56 76
57 134
58 92
59 93
60 75
61 118
62 77
63 122
64 99
65 72
66 58
67 73
68 103
69 76
70 65
71 95
72 76
73 95
74 92
75 84
76 95
77 76
78 87
79 84
80 95
81 69
82 115
83 83
84 47
85 28
86 79
87 83
88 92
89 98
90 103
91 77
92 95
93 78
94 92
95 76
96 76
97 67
98 92
99 151
100 83
101 118
102 98
103 76
104 71
105 57
106 79
107 83
108 92
109 75
110 79
111 88
112 99
113 0
114 91
115 32
116 101
117 84
118 0
119 60
120 25
121 86
122 115
123 88
124 59
125 27
126 83
127 126
128 125
129 119
130 127
131 133
132 79
133 76
134 92
135 109
136 76
137 100
138 87
139 97
140 95
141 48
142 80
143 91
144 79
145 114
146 120
147 89
148 111
149 0
150 0
151 0
152 0
153 0
154 0
155 74
156 107
157 0
158 0
159 0
160 15
161 4
162 82
163 0
164 54
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept)
3514.2
Number_of_Logins
795.6
Total_Number_of_Blogged_Computations
924.1
Total_Number_of_Reviewed_Compendiums
970.7
Total_Number_of_submitted_Feedback_Messages_in_PeerReviews
192.3
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-114512 -17649 -3514 15717 120886
Coefficients:
Estimate Std. Error
(Intercept) 3514.2 7484.4
Number_of_Logins 795.6 113.6
Total_Number_of_Blogged_Computations 924.1 122.5
Total_Number_of_Reviewed_Compendiums 970.7 2014.7
Total_Number_of_submitted_Feedback_Messages_in_PeerReviews 192.3 532.9
t value Pr(>|t|)
(Intercept) 0.470 0.639
Number_of_Logins 7.004 6.63e-11 ***
Total_Number_of_Blogged_Computations 7.545 3.28e-12 ***
Total_Number_of_Reviewed_Compendiums 0.482 0.631
Total_Number_of_submitted_Feedback_Messages_in_PeerReviews 0.361 0.719
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 35210 on 159 degrees of freedom
Multiple R-squared: 0.7411, Adjusted R-squared: 0.7346
F-statistic: 113.8 on 4 and 159 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.7649219 4.701562e-01 2.350781e-01
[2,] 0.6563776 6.872449e-01 3.436224e-01
[3,] 0.6382014 7.235973e-01 3.617986e-01
[4,] 0.5655075 8.689850e-01 4.344925e-01
[5,] 0.4639766 9.279532e-01 5.360234e-01
[6,] 0.4185185 8.370370e-01 5.814815e-01
[7,] 0.4582603 9.165206e-01 5.417397e-01
[8,] 0.3684882 7.369763e-01 6.315118e-01
[9,] 0.2953652 5.907303e-01 7.046348e-01
[10,] 0.2895417 5.790834e-01 7.104583e-01
[11,] 0.2625635 5.251269e-01 7.374365e-01
[12,] 0.4029041 8.058083e-01 5.970959e-01
[13,] 0.4136466 8.272933e-01 5.863534e-01
[14,] 0.3392769 6.785537e-01 6.607231e-01
[15,] 0.8742302 2.515396e-01 1.257698e-01
[16,] 0.8351722 3.296556e-01 1.648278e-01
[17,] 0.8343806 3.312387e-01 1.656194e-01
[18,] 0.9126133 1.747735e-01 8.738673e-02
[19,] 0.9025763 1.948474e-01 9.742372e-02
[20,] 0.8766348 2.467304e-01 1.233652e-01
[21,] 0.8468267 3.063467e-01 1.531733e-01
[22,] 0.8145681 3.708639e-01 1.854319e-01
[23,] 0.7844334 4.311331e-01 2.155666e-01
[24,] 0.7434014 5.131972e-01 2.565986e-01
[25,] 0.7016141 5.967717e-01 2.983859e-01
[26,] 0.6666645 6.666710e-01 3.333355e-01
[27,] 0.6201416 7.597167e-01 3.798584e-01
[28,] 0.7315688 5.368624e-01 2.684312e-01
[29,] 0.7019566 5.960868e-01 2.980434e-01
[30,] 0.9085407 1.829186e-01 9.145930e-02
[31,] 0.8840456 2.319087e-01 1.159544e-01
[32,] 0.8575944 2.848112e-01 1.424056e-01
[33,] 0.8250756 3.498488e-01 1.749244e-01
[34,] 0.8590071 2.819857e-01 1.409929e-01
[35,] 0.8277423 3.445153e-01 1.722577e-01
[36,] 0.7932401 4.135197e-01 2.067599e-01
[37,] 0.7557862 4.884276e-01 2.442138e-01
[38,] 0.7417313 5.165373e-01 2.582687e-01
[39,] 0.9699482 6.010370e-02 3.005185e-02
[40,] 0.9625550 7.488994e-02 3.744497e-02
[41,] 0.9539403 9.211938e-02 4.605969e-02
[42,] 0.9519410 9.611805e-02 4.805902e-02
[43,] 0.9454373 1.091253e-01 5.456265e-02
[44,] 0.9350271 1.299458e-01 6.497289e-02
[45,] 0.9236206 1.527587e-01 7.637937e-02
[46,] 0.9118273 1.763455e-01 8.817273e-02
[47,] 0.8915613 2.168773e-01 1.084387e-01
[48,] 0.8676755 2.646491e-01 1.323245e-01
[49,] 0.8821175 2.357649e-01 1.178825e-01
[50,] 0.8578901 2.842198e-01 1.421099e-01
[51,] 0.8412162 3.175677e-01 1.587838e-01
[52,] 0.8242305 3.515390e-01 1.757695e-01
[53,] 0.7928447 4.143106e-01 2.071553e-01
[54,] 0.7853303 4.293394e-01 2.146697e-01
[55,] 0.7573274 4.853453e-01 2.426726e-01
[56,] 0.7231844 5.536312e-01 2.768156e-01
[57,] 0.7202490 5.595020e-01 2.797510e-01
[58,] 0.7051857 5.896287e-01 2.948143e-01
[59,] 0.6711664 6.576673e-01 3.288336e-01
[60,] 0.7130229 5.739543e-01 2.869771e-01
[61,] 0.7412999 5.174002e-01 2.587001e-01
[62,] 0.7325702 5.348596e-01 2.674298e-01
[63,] 0.8188132 3.623736e-01 1.811868e-01
[64,] 0.7874464 4.251072e-01 2.125536e-01
[65,] 0.7553645 4.892711e-01 2.446355e-01
[66,] 0.7179531 5.640938e-01 2.820469e-01
[67,] 0.6858093 6.283814e-01 3.141907e-01
[68,] 0.7504670 4.990660e-01 2.495330e-01
[69,] 0.7213781 5.572439e-01 2.786219e-01
[70,] 0.7304441 5.391119e-01 2.695559e-01
[71,] 0.7282879 5.434241e-01 2.717121e-01
[72,] 0.7627762 4.744475e-01 2.372238e-01
[73,] 0.7265807 5.468386e-01 2.734193e-01
[74,] 0.6876314 6.247372e-01 3.123686e-01
[75,] 0.7816648 4.366703e-01 2.183352e-01
[76,] 0.7656099 4.687802e-01 2.343901e-01
[77,] 0.7719893 4.560214e-01 2.280107e-01
[78,] 0.7359129 5.281742e-01 2.640871e-01
[79,] 0.7066936 5.866127e-01 2.933064e-01
[80,] 0.6723497 6.553007e-01 3.276503e-01
[81,] 0.7187376 5.625248e-01 2.812624e-01
[82,] 0.6944402 6.111196e-01 3.055598e-01
[83,] 0.8576291 2.847418e-01 1.423709e-01
[84,] 0.8306295 3.387410e-01 1.693705e-01
[85,] 0.8294177 3.411647e-01 1.705823e-01
[86,] 0.8036689 3.926622e-01 1.963311e-01
[87,] 0.7739664 4.520672e-01 2.260336e-01
[88,] 0.8188586 3.622828e-01 1.811414e-01
[89,] 0.7907022 4.185956e-01 2.092978e-01
[90,] 0.7587161 4.825679e-01 2.412839e-01
[91,] 0.7222644 5.554711e-01 2.777356e-01
[92,] 0.7079319 5.841361e-01 2.920681e-01
[93,] 0.7390475 5.219051e-01 2.609525e-01
[94,] 0.7076026 5.847948e-01 2.923974e-01
[95,] 0.6659260 6.681479e-01 3.340740e-01
[96,] 0.6868246 6.263508e-01 3.131754e-01
[97,] 0.6528772 6.942456e-01 3.471228e-01
[98,] 0.6078950 7.842100e-01 3.921050e-01
[99,] 0.6605360 6.789281e-01 3.394640e-01
[100,] 0.6151486 7.697029e-01 3.848514e-01
[101,] 0.5774978 8.450044e-01 4.225022e-01
[102,] 0.5350765 9.298469e-01 4.649235e-01
[103,] 0.6508635 6.982729e-01 3.491365e-01
[104,] 0.6714222 6.571556e-01 3.285778e-01
[105,] 0.6733251 6.533498e-01 3.266749e-01
[106,] 0.6269288 7.461423e-01 3.730712e-01
[107,] 0.6535537 6.928926e-01 3.464463e-01
[108,] 0.6060009 7.879982e-01 3.939991e-01
[109,] 0.6104094 7.791813e-01 3.895906e-01
[110,] 0.7107010 5.785981e-01 2.892990e-01
[111,] 0.6647662 6.704676e-01 3.352338e-01
[112,] 0.7353547 5.292905e-01 2.646453e-01
[113,] 0.6996398 6.007203e-01 3.003602e-01
[114,] 0.6515211 6.969577e-01 3.484789e-01
[115,] 0.6098538 7.802924e-01 3.901462e-01
[116,] 0.5746075 8.507850e-01 4.253925e-01
[117,] 0.5204179 9.591642e-01 4.795821e-01
[118,] 0.4829533 9.659067e-01 5.170467e-01
[119,] 0.4327594 8.655188e-01 5.672406e-01
[120,] 0.3969824 7.939648e-01 6.030176e-01
[121,] 0.3460896 6.921792e-01 6.539104e-01
[122,] 0.4079592 8.159183e-01 5.920408e-01
[123,] 0.3653387 7.306775e-01 6.346613e-01
[124,] 0.3652544 7.305088e-01 6.347456e-01
[125,] 0.3594835 7.189670e-01 6.405165e-01
[126,] 0.3060172 6.120344e-01 6.939828e-01
[127,] 0.9427315 1.145369e-01 5.726847e-02
[128,] 0.9315842 1.368316e-01 6.841581e-02
[129,] 0.9593747 8.125058e-02 4.062529e-02
[130,] 0.9471113 1.057775e-01 5.288874e-02
[131,] 0.9319932 1.360137e-01 6.800683e-02
[132,] 0.9819628 3.607430e-02 1.803715e-02
[133,] 0.9721649 5.567018e-02 2.783509e-02
[134,] 0.9999945 1.101023e-05 5.505115e-06
[135,] 0.9999963 7.328345e-06 3.664173e-06
[136,] 0.9999903 1.935540e-05 9.677701e-06
[137,] 0.9999751 4.989957e-05 2.494978e-05
[138,] 0.9999764 4.729050e-05 2.364525e-05
[139,] 1.0000000 6.198889e-09 3.099445e-09
[140,] 1.0000000 3.171276e-08 1.585638e-08
[141,] 1.0000000 6.181656e-08 3.090828e-08
[142,] 0.9999998 4.069455e-07 2.034728e-07
[143,] 0.9999990 1.947420e-06 9.737102e-07
[144,] 0.9999936 1.279422e-05 6.397112e-06
[145,] 0.9999614 7.729664e-05 3.864832e-05
[146,] 0.9997804 4.391680e-04 2.195840e-04
[147,] 0.9988423 2.315498e-03 1.157749e-03
[148,] 0.9942908 1.141848e-02 5.709239e-03
[149,] 0.9747849 5.043012e-02 2.521506e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1hzx51321832087.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/22urp1321832087.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3w3bq1321832087.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4y2ec1321832087.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5wc5c1321832087.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 164
Frequency = 1
1 2 3 4 5 6
26130.8666 -39207.1657 -13818.5256 -30155.3462 -6982.6431 -9354.7213
7 8 9 10 11 12
102934.4760 -14774.4937 16451.2273 18645.4240 316.0938 -62374.7122
13 14 15 16 17 18
-7433.5239 27124.8369 -21412.5782 -28577.6918 20058.4376 1121.3933
19 20 21 22 23 24
-26005.8408 -27656.7113 -2711.6565 120886.0885 11603.6638 -46946.6288
25 26 27 28 29 30
-65552.4398 -35293.0111 -10128.9171 -17532.9352 1548.3257 14705.3557
31 32 33 34 35 36
13235.2829 -6954.1834 11973.3306 -20385.6451 61174.2993 26538.0525
37 38 39 40 41 42
79245.0782 -758.9159 16081.9321 -402.5413 56445.6459 -5745.2336
43 44 45 46 47 48
9909.8536 -11296.6559 -8444.0552 117644.8502 -17619.1688 -17389.6766
49 50 51 52 53 54
37124.5846 -26715.7897 -15957.7668 -12004.5095 -18906.5326 -1773.9554
55 56 57 58 59 60
-2907.3989 49872.8473 -10231.2433 -17740.2522 -3888.9037 7053.1965
61 62 63 64 65 66
31669.6531 16291.2974 -9121.4015 30621.1010 27946.4667 -6612.8615
67 68 69 70 71 72
-49317.5978 -48769.2218 -23156.7399 -61969.9029 -5249.9403 -10679.9472
73 74 75 76 77 78
-1558.0328 15595.4487 -57166.0222 17443.2854 39930.6769 -35198.7978
79 80 81 82 83 84
-49657.1765 -3462.3693 -3691.4334 66957.7483 24848.4329 -30798.2416
85 86 87 88 89 90
-884.4501 16938.3914 -14553.1356 50318.2424 28295.4117 -87224.9634
91 92 93 94 95 96
5920.8653 -34497.5130 13814.2669 11527.2309 53174.9893 12363.7003
97 98 99 100 101 102
14143.5959 -6750.1690 -30765.8769 47106.4506 -17951.8520 3327.7640
103 104 105 106 107 108
-44842.5630 -15475.8360 -414.4576 -52872.0200 -2528.2584 -16922.0293
109 110 111 112 113 114
-13603.2996 -65465.5537 43628.2697 -33758.9288 -592.7994 -40950.8437
115 116 117 118 119 120
366.0229 -35299.9930 65822.0844 1113.3020 57940.7082 -16424.4333
121 122 123 124 125 126
2329.4833 17012.0557 -18319.2284 1634.7395 -20095.0254 -8573.3821
127 128 129 130 131 132
23612.9677 9643.2761 50568.1000 -21756.9592 32466.6041 29078.5957
133 134 135 136 137 138
-10021.4187 -64161.4682 61729.9907 -326.0636 52379.0750 -21154.0133
139 140 141 142 143 144
16770.8049 -8860.9873 -8703.6709 92869.0876 -20852.3152 46156.1311
145 146 147 148 149 150
1504.9434 -114512.2632 -8191.3538 -26301.7077 -3514.2337 -478.9871
151 152 153 154 155 156
-4211.8522 -4650.4706 -3514.2337 -3514.2337 7550.8727 -11141.2165
157 158 159 160 161 162
-3514.2337 -6493.7076 -6762.3212 8405.8220 8314.8006 -23716.2836
163 164
-4136.4706 13750.8345
> postscript(file="/var/wessaorg/rcomp/tmp/6fm3j1321832087.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 26130.8666 NA
1 -39207.1657 26130.8666
2 -13818.5256 -39207.1657
3 -30155.3462 -13818.5256
4 -6982.6431 -30155.3462
5 -9354.7213 -6982.6431
6 102934.4760 -9354.7213
7 -14774.4937 102934.4760
8 16451.2273 -14774.4937
9 18645.4240 16451.2273
10 316.0938 18645.4240
11 -62374.7122 316.0938
12 -7433.5239 -62374.7122
13 27124.8369 -7433.5239
14 -21412.5782 27124.8369
15 -28577.6918 -21412.5782
16 20058.4376 -28577.6918
17 1121.3933 20058.4376
18 -26005.8408 1121.3933
19 -27656.7113 -26005.8408
20 -2711.6565 -27656.7113
21 120886.0885 -2711.6565
22 11603.6638 120886.0885
23 -46946.6288 11603.6638
24 -65552.4398 -46946.6288
25 -35293.0111 -65552.4398
26 -10128.9171 -35293.0111
27 -17532.9352 -10128.9171
28 1548.3257 -17532.9352
29 14705.3557 1548.3257
30 13235.2829 14705.3557
31 -6954.1834 13235.2829
32 11973.3306 -6954.1834
33 -20385.6451 11973.3306
34 61174.2993 -20385.6451
35 26538.0525 61174.2993
36 79245.0782 26538.0525
37 -758.9159 79245.0782
38 16081.9321 -758.9159
39 -402.5413 16081.9321
40 56445.6459 -402.5413
41 -5745.2336 56445.6459
42 9909.8536 -5745.2336
43 -11296.6559 9909.8536
44 -8444.0552 -11296.6559
45 117644.8502 -8444.0552
46 -17619.1688 117644.8502
47 -17389.6766 -17619.1688
48 37124.5846 -17389.6766
49 -26715.7897 37124.5846
50 -15957.7668 -26715.7897
51 -12004.5095 -15957.7668
52 -18906.5326 -12004.5095
53 -1773.9554 -18906.5326
54 -2907.3989 -1773.9554
55 49872.8473 -2907.3989
56 -10231.2433 49872.8473
57 -17740.2522 -10231.2433
58 -3888.9037 -17740.2522
59 7053.1965 -3888.9037
60 31669.6531 7053.1965
61 16291.2974 31669.6531
62 -9121.4015 16291.2974
63 30621.1010 -9121.4015
64 27946.4667 30621.1010
65 -6612.8615 27946.4667
66 -49317.5978 -6612.8615
67 -48769.2218 -49317.5978
68 -23156.7399 -48769.2218
69 -61969.9029 -23156.7399
70 -5249.9403 -61969.9029
71 -10679.9472 -5249.9403
72 -1558.0328 -10679.9472
73 15595.4487 -1558.0328
74 -57166.0222 15595.4487
75 17443.2854 -57166.0222
76 39930.6769 17443.2854
77 -35198.7978 39930.6769
78 -49657.1765 -35198.7978
79 -3462.3693 -49657.1765
80 -3691.4334 -3462.3693
81 66957.7483 -3691.4334
82 24848.4329 66957.7483
83 -30798.2416 24848.4329
84 -884.4501 -30798.2416
85 16938.3914 -884.4501
86 -14553.1356 16938.3914
87 50318.2424 -14553.1356
88 28295.4117 50318.2424
89 -87224.9634 28295.4117
90 5920.8653 -87224.9634
91 -34497.5130 5920.8653
92 13814.2669 -34497.5130
93 11527.2309 13814.2669
94 53174.9893 11527.2309
95 12363.7003 53174.9893
96 14143.5959 12363.7003
97 -6750.1690 14143.5959
98 -30765.8769 -6750.1690
99 47106.4506 -30765.8769
100 -17951.8520 47106.4506
101 3327.7640 -17951.8520
102 -44842.5630 3327.7640
103 -15475.8360 -44842.5630
104 -414.4576 -15475.8360
105 -52872.0200 -414.4576
106 -2528.2584 -52872.0200
107 -16922.0293 -2528.2584
108 -13603.2996 -16922.0293
109 -65465.5537 -13603.2996
110 43628.2697 -65465.5537
111 -33758.9288 43628.2697
112 -592.7994 -33758.9288
113 -40950.8437 -592.7994
114 366.0229 -40950.8437
115 -35299.9930 366.0229
116 65822.0844 -35299.9930
117 1113.3020 65822.0844
118 57940.7082 1113.3020
119 -16424.4333 57940.7082
120 2329.4833 -16424.4333
121 17012.0557 2329.4833
122 -18319.2284 17012.0557
123 1634.7395 -18319.2284
124 -20095.0254 1634.7395
125 -8573.3821 -20095.0254
126 23612.9677 -8573.3821
127 9643.2761 23612.9677
128 50568.1000 9643.2761
129 -21756.9592 50568.1000
130 32466.6041 -21756.9592
131 29078.5957 32466.6041
132 -10021.4187 29078.5957
133 -64161.4682 -10021.4187
134 61729.9907 -64161.4682
135 -326.0636 61729.9907
136 52379.0750 -326.0636
137 -21154.0133 52379.0750
138 16770.8049 -21154.0133
139 -8860.9873 16770.8049
140 -8703.6709 -8860.9873
141 92869.0876 -8703.6709
142 -20852.3152 92869.0876
143 46156.1311 -20852.3152
144 1504.9434 46156.1311
145 -114512.2632 1504.9434
146 -8191.3538 -114512.2632
147 -26301.7077 -8191.3538
148 -3514.2337 -26301.7077
149 -478.9871 -3514.2337
150 -4211.8522 -478.9871
151 -4650.4706 -4211.8522
152 -3514.2337 -4650.4706
153 -3514.2337 -3514.2337
154 7550.8727 -3514.2337
155 -11141.2165 7550.8727
156 -3514.2337 -11141.2165
157 -6493.7076 -3514.2337
158 -6762.3212 -6493.7076
159 8405.8220 -6762.3212
160 8314.8006 8405.8220
161 -23716.2836 8314.8006
162 -4136.4706 -23716.2836
163 13750.8345 -4136.4706
164 NA 13750.8345
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -39207.1657 26130.8666
[2,] -13818.5256 -39207.1657
[3,] -30155.3462 -13818.5256
[4,] -6982.6431 -30155.3462
[5,] -9354.7213 -6982.6431
[6,] 102934.4760 -9354.7213
[7,] -14774.4937 102934.4760
[8,] 16451.2273 -14774.4937
[9,] 18645.4240 16451.2273
[10,] 316.0938 18645.4240
[11,] -62374.7122 316.0938
[12,] -7433.5239 -62374.7122
[13,] 27124.8369 -7433.5239
[14,] -21412.5782 27124.8369
[15,] -28577.6918 -21412.5782
[16,] 20058.4376 -28577.6918
[17,] 1121.3933 20058.4376
[18,] -26005.8408 1121.3933
[19,] -27656.7113 -26005.8408
[20,] -2711.6565 -27656.7113
[21,] 120886.0885 -2711.6565
[22,] 11603.6638 120886.0885
[23,] -46946.6288 11603.6638
[24,] -65552.4398 -46946.6288
[25,] -35293.0111 -65552.4398
[26,] -10128.9171 -35293.0111
[27,] -17532.9352 -10128.9171
[28,] 1548.3257 -17532.9352
[29,] 14705.3557 1548.3257
[30,] 13235.2829 14705.3557
[31,] -6954.1834 13235.2829
[32,] 11973.3306 -6954.1834
[33,] -20385.6451 11973.3306
[34,] 61174.2993 -20385.6451
[35,] 26538.0525 61174.2993
[36,] 79245.0782 26538.0525
[37,] -758.9159 79245.0782
[38,] 16081.9321 -758.9159
[39,] -402.5413 16081.9321
[40,] 56445.6459 -402.5413
[41,] -5745.2336 56445.6459
[42,] 9909.8536 -5745.2336
[43,] -11296.6559 9909.8536
[44,] -8444.0552 -11296.6559
[45,] 117644.8502 -8444.0552
[46,] -17619.1688 117644.8502
[47,] -17389.6766 -17619.1688
[48,] 37124.5846 -17389.6766
[49,] -26715.7897 37124.5846
[50,] -15957.7668 -26715.7897
[51,] -12004.5095 -15957.7668
[52,] -18906.5326 -12004.5095
[53,] -1773.9554 -18906.5326
[54,] -2907.3989 -1773.9554
[55,] 49872.8473 -2907.3989
[56,] -10231.2433 49872.8473
[57,] -17740.2522 -10231.2433
[58,] -3888.9037 -17740.2522
[59,] 7053.1965 -3888.9037
[60,] 31669.6531 7053.1965
[61,] 16291.2974 31669.6531
[62,] -9121.4015 16291.2974
[63,] 30621.1010 -9121.4015
[64,] 27946.4667 30621.1010
[65,] -6612.8615 27946.4667
[66,] -49317.5978 -6612.8615
[67,] -48769.2218 -49317.5978
[68,] -23156.7399 -48769.2218
[69,] -61969.9029 -23156.7399
[70,] -5249.9403 -61969.9029
[71,] -10679.9472 -5249.9403
[72,] -1558.0328 -10679.9472
[73,] 15595.4487 -1558.0328
[74,] -57166.0222 15595.4487
[75,] 17443.2854 -57166.0222
[76,] 39930.6769 17443.2854
[77,] -35198.7978 39930.6769
[78,] -49657.1765 -35198.7978
[79,] -3462.3693 -49657.1765
[80,] -3691.4334 -3462.3693
[81,] 66957.7483 -3691.4334
[82,] 24848.4329 66957.7483
[83,] -30798.2416 24848.4329
[84,] -884.4501 -30798.2416
[85,] 16938.3914 -884.4501
[86,] -14553.1356 16938.3914
[87,] 50318.2424 -14553.1356
[88,] 28295.4117 50318.2424
[89,] -87224.9634 28295.4117
[90,] 5920.8653 -87224.9634
[91,] -34497.5130 5920.8653
[92,] 13814.2669 -34497.5130
[93,] 11527.2309 13814.2669
[94,] 53174.9893 11527.2309
[95,] 12363.7003 53174.9893
[96,] 14143.5959 12363.7003
[97,] -6750.1690 14143.5959
[98,] -30765.8769 -6750.1690
[99,] 47106.4506 -30765.8769
[100,] -17951.8520 47106.4506
[101,] 3327.7640 -17951.8520
[102,] -44842.5630 3327.7640
[103,] -15475.8360 -44842.5630
[104,] -414.4576 -15475.8360
[105,] -52872.0200 -414.4576
[106,] -2528.2584 -52872.0200
[107,] -16922.0293 -2528.2584
[108,] -13603.2996 -16922.0293
[109,] -65465.5537 -13603.2996
[110,] 43628.2697 -65465.5537
[111,] -33758.9288 43628.2697
[112,] -592.7994 -33758.9288
[113,] -40950.8437 -592.7994
[114,] 366.0229 -40950.8437
[115,] -35299.9930 366.0229
[116,] 65822.0844 -35299.9930
[117,] 1113.3020 65822.0844
[118,] 57940.7082 1113.3020
[119,] -16424.4333 57940.7082
[120,] 2329.4833 -16424.4333
[121,] 17012.0557 2329.4833
[122,] -18319.2284 17012.0557
[123,] 1634.7395 -18319.2284
[124,] -20095.0254 1634.7395
[125,] -8573.3821 -20095.0254
[126,] 23612.9677 -8573.3821
[127,] 9643.2761 23612.9677
[128,] 50568.1000 9643.2761
[129,] -21756.9592 50568.1000
[130,] 32466.6041 -21756.9592
[131,] 29078.5957 32466.6041
[132,] -10021.4187 29078.5957
[133,] -64161.4682 -10021.4187
[134,] 61729.9907 -64161.4682
[135,] -326.0636 61729.9907
[136,] 52379.0750 -326.0636
[137,] -21154.0133 52379.0750
[138,] 16770.8049 -21154.0133
[139,] -8860.9873 16770.8049
[140,] -8703.6709 -8860.9873
[141,] 92869.0876 -8703.6709
[142,] -20852.3152 92869.0876
[143,] 46156.1311 -20852.3152
[144,] 1504.9434 46156.1311
[145,] -114512.2632 1504.9434
[146,] -8191.3538 -114512.2632
[147,] -26301.7077 -8191.3538
[148,] -3514.2337 -26301.7077
[149,] -478.9871 -3514.2337
[150,] -4211.8522 -478.9871
[151,] -4650.4706 -4211.8522
[152,] -3514.2337 -4650.4706
[153,] -3514.2337 -3514.2337
[154,] 7550.8727 -3514.2337
[155,] -11141.2165 7550.8727
[156,] -3514.2337 -11141.2165
[157,] -6493.7076 -3514.2337
[158,] -6762.3212 -6493.7076
[159,] 8405.8220 -6762.3212
[160,] 8314.8006 8405.8220
[161,] -23716.2836 8314.8006
[162,] -4136.4706 -23716.2836
[163,] 13750.8345 -4136.4706
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -39207.1657 26130.8666
2 -13818.5256 -39207.1657
3 -30155.3462 -13818.5256
4 -6982.6431 -30155.3462
5 -9354.7213 -6982.6431
6 102934.4760 -9354.7213
7 -14774.4937 102934.4760
8 16451.2273 -14774.4937
9 18645.4240 16451.2273
10 316.0938 18645.4240
11 -62374.7122 316.0938
12 -7433.5239 -62374.7122
13 27124.8369 -7433.5239
14 -21412.5782 27124.8369
15 -28577.6918 -21412.5782
16 20058.4376 -28577.6918
17 1121.3933 20058.4376
18 -26005.8408 1121.3933
19 -27656.7113 -26005.8408
20 -2711.6565 -27656.7113
21 120886.0885 -2711.6565
22 11603.6638 120886.0885
23 -46946.6288 11603.6638
24 -65552.4398 -46946.6288
25 -35293.0111 -65552.4398
26 -10128.9171 -35293.0111
27 -17532.9352 -10128.9171
28 1548.3257 -17532.9352
29 14705.3557 1548.3257
30 13235.2829 14705.3557
31 -6954.1834 13235.2829
32 11973.3306 -6954.1834
33 -20385.6451 11973.3306
34 61174.2993 -20385.6451
35 26538.0525 61174.2993
36 79245.0782 26538.0525
37 -758.9159 79245.0782
38 16081.9321 -758.9159
39 -402.5413 16081.9321
40 56445.6459 -402.5413
41 -5745.2336 56445.6459
42 9909.8536 -5745.2336
43 -11296.6559 9909.8536
44 -8444.0552 -11296.6559
45 117644.8502 -8444.0552
46 -17619.1688 117644.8502
47 -17389.6766 -17619.1688
48 37124.5846 -17389.6766
49 -26715.7897 37124.5846
50 -15957.7668 -26715.7897
51 -12004.5095 -15957.7668
52 -18906.5326 -12004.5095
53 -1773.9554 -18906.5326
54 -2907.3989 -1773.9554
55 49872.8473 -2907.3989
56 -10231.2433 49872.8473
57 -17740.2522 -10231.2433
58 -3888.9037 -17740.2522
59 7053.1965 -3888.9037
60 31669.6531 7053.1965
61 16291.2974 31669.6531
62 -9121.4015 16291.2974
63 30621.1010 -9121.4015
64 27946.4667 30621.1010
65 -6612.8615 27946.4667
66 -49317.5978 -6612.8615
67 -48769.2218 -49317.5978
68 -23156.7399 -48769.2218
69 -61969.9029 -23156.7399
70 -5249.9403 -61969.9029
71 -10679.9472 -5249.9403
72 -1558.0328 -10679.9472
73 15595.4487 -1558.0328
74 -57166.0222 15595.4487
75 17443.2854 -57166.0222
76 39930.6769 17443.2854
77 -35198.7978 39930.6769
78 -49657.1765 -35198.7978
79 -3462.3693 -49657.1765
80 -3691.4334 -3462.3693
81 66957.7483 -3691.4334
82 24848.4329 66957.7483
83 -30798.2416 24848.4329
84 -884.4501 -30798.2416
85 16938.3914 -884.4501
86 -14553.1356 16938.3914
87 50318.2424 -14553.1356
88 28295.4117 50318.2424
89 -87224.9634 28295.4117
90 5920.8653 -87224.9634
91 -34497.5130 5920.8653
92 13814.2669 -34497.5130
93 11527.2309 13814.2669
94 53174.9893 11527.2309
95 12363.7003 53174.9893
96 14143.5959 12363.7003
97 -6750.1690 14143.5959
98 -30765.8769 -6750.1690
99 47106.4506 -30765.8769
100 -17951.8520 47106.4506
101 3327.7640 -17951.8520
102 -44842.5630 3327.7640
103 -15475.8360 -44842.5630
104 -414.4576 -15475.8360
105 -52872.0200 -414.4576
106 -2528.2584 -52872.0200
107 -16922.0293 -2528.2584
108 -13603.2996 -16922.0293
109 -65465.5537 -13603.2996
110 43628.2697 -65465.5537
111 -33758.9288 43628.2697
112 -592.7994 -33758.9288
113 -40950.8437 -592.7994
114 366.0229 -40950.8437
115 -35299.9930 366.0229
116 65822.0844 -35299.9930
117 1113.3020 65822.0844
118 57940.7082 1113.3020
119 -16424.4333 57940.7082
120 2329.4833 -16424.4333
121 17012.0557 2329.4833
122 -18319.2284 17012.0557
123 1634.7395 -18319.2284
124 -20095.0254 1634.7395
125 -8573.3821 -20095.0254
126 23612.9677 -8573.3821
127 9643.2761 23612.9677
128 50568.1000 9643.2761
129 -21756.9592 50568.1000
130 32466.6041 -21756.9592
131 29078.5957 32466.6041
132 -10021.4187 29078.5957
133 -64161.4682 -10021.4187
134 61729.9907 -64161.4682
135 -326.0636 61729.9907
136 52379.0750 -326.0636
137 -21154.0133 52379.0750
138 16770.8049 -21154.0133
139 -8860.9873 16770.8049
140 -8703.6709 -8860.9873
141 92869.0876 -8703.6709
142 -20852.3152 92869.0876
143 46156.1311 -20852.3152
144 1504.9434 46156.1311
145 -114512.2632 1504.9434
146 -8191.3538 -114512.2632
147 -26301.7077 -8191.3538
148 -3514.2337 -26301.7077
149 -478.9871 -3514.2337
150 -4211.8522 -478.9871
151 -4650.4706 -4211.8522
152 -3514.2337 -4650.4706
153 -3514.2337 -3514.2337
154 7550.8727 -3514.2337
155 -11141.2165 7550.8727
156 -3514.2337 -11141.2165
157 -6493.7076 -3514.2337
158 -6762.3212 -6493.7076
159 8405.8220 -6762.3212
160 8314.8006 8405.8220
161 -23716.2836 8314.8006
162 -4136.4706 -23716.2836
163 13750.8345 -4136.4706
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7ymks1321832087.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8jlc01321832087.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ka6n1321832087.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/105xd21321832087.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/111osz1321832087.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12r6bn1321832087.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13jh0z1321832087.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1434i21321832087.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15q3va1321832087.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16o7b91321832088.tab")
+ }
>
> try(system("convert tmp/1hzx51321832087.ps tmp/1hzx51321832087.png",intern=TRUE))
character(0)
> try(system("convert tmp/22urp1321832087.ps tmp/22urp1321832087.png",intern=TRUE))
character(0)
> try(system("convert tmp/3w3bq1321832087.ps tmp/3w3bq1321832087.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y2ec1321832087.ps tmp/4y2ec1321832087.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wc5c1321832087.ps tmp/5wc5c1321832087.png",intern=TRUE))
character(0)
> try(system("convert tmp/6fm3j1321832087.ps tmp/6fm3j1321832087.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ymks1321832087.ps tmp/7ymks1321832087.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jlc01321832087.ps tmp/8jlc01321832087.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ka6n1321832087.ps tmp/9ka6n1321832087.png",intern=TRUE))
character(0)
> try(system("convert tmp/105xd21321832087.ps tmp/105xd21321832087.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.629 0.567 5.279