R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(9
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+ ,dim=c(9
+ ,156)
+ ,dimnames=list(c('month'
+ ,'Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'bestfriend'
+ ,'secondbestfriend'
+ ,'thirdbestfriend')
+ ,1:156))
> y <- array(NA,dim=c(9,156),dimnames=list(c('month','Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','bestfriend','secondbestfriend','thirdbestfriend'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> 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
Popularity month FindingFriends KnowingPeople Liked Celebrity bestfriend
1 13 9 13 14 13 3 1
2 12 9 12 8 13 5 1
3 15 9 10 12 16 6 0
4 12 9 9 7 12 6 2
5 10 9 10 10 11 5 0
6 12 9 12 7 12 3 0
7 15 9 13 16 18 8 1
8 9 9 12 11 11 4 1
9 12 9 12 14 14 4 4
10 11 9 6 6 9 4 0
11 11 9 5 16 14 6 0
12 11 9 12 11 12 6 2
13 15 9 11 16 11 5 0
14 7 9 14 12 12 4 1
15 11 9 14 7 13 6 0
16 11 9 12 13 11 4 0
17 10 9 12 11 12 6 1
18 14 9 11 15 16 6 2
19 10 9 11 7 9 4 1
20 6 9 7 9 11 4 1
21 11 9 9 7 13 2 0
22 15 9 11 14 15 7 1
23 11 9 11 15 10 5 1
24 12 9 12 7 11 4 2
25 14 9 12 15 13 6 1
26 15 9 11 17 16 6 1
27 9 9 11 15 15 7 1
28 13 9 8 14 14 5 2
29 13 9 9 14 14 6 0
30 16 9 12 8 14 4 1
31 13 9 10 8 8 4 0
32 12 9 10 14 13 7 1
33 14 9 12 14 15 7 1
34 11 9 8 8 13 4 0
35 9 9 12 11 11 4 1
36 16 9 11 16 15 6 2
37 12 9 12 10 15 6 1
38 10 9 7 8 9 5 1
39 13 9 11 14 13 6 1
40 16 9 11 16 16 7 1
41 14 9 12 13 13 6 0
42 15 9 9 5 11 3 1
43 5 9 15 8 12 3 1
44 8 9 11 10 12 4 1
45 11 9 11 8 12 6 0
46 16 9 11 13 14 7 2
47 17 9 11 15 14 5 1
48 9 9 15 6 8 4 0
49 9 9 11 12 13 5 0
50 13 9 12 16 16 6 1
51 10 9 12 5 13 6 1
52 6 9 9 15 11 6 0
53 12 9 12 12 14 5 0
54 8 9 12 8 13 4 0
55 14 9 13 13 13 5 0
56 12 9 11 14 13 5 1
57 11 10 9 12 12 4 0
58 16 10 9 16 16 6 0
59 8 10 11 10 15 2 1
60 15 10 11 15 15 8 0
61 7 10 12 8 12 3 0
62 16 10 12 16 14 6 2
63 14 10 9 19 12 6 0
64 16 10 11 14 15 6 0
65 9 10 9 6 12 5 1
66 14 10 12 13 13 5 2
67 11 10 12 15 12 6 3
68 13 10 12 7 12 5 1
69 15 10 12 13 13 6 1
70 5 10 14 4 5 2 2
71 15 10 11 14 13 5 1
72 13 10 12 13 13 5 1
73 11 10 11 11 14 5 2
74 11 10 6 14 17 6 1
75 12 10 10 12 13 6 0
76 12 10 12 15 13 6 1
77 12 10 13 14 12 5 1
78 12 10 8 13 13 5 0
79 14 10 12 8 14 4 2
80 6 10 12 6 11 2 1
81 7 10 12 7 12 4 0
82 14 10 6 13 12 6 3
83 14 10 11 13 16 6 1
84 10 10 10 11 12 5 1
85 13 10 12 5 12 3 3
86 12 10 13 12 12 6 2
87 9 10 11 8 10 4 1
88 12 10 7 11 15 5 0
89 16 10 11 14 15 8 1
90 10 10 11 9 12 4 2
91 14 10 11 10 16 6 1
92 10 10 11 13 15 6 1
93 16 10 12 16 16 7 0
94 15 10 10 16 13 6 2
95 12 10 11 11 12 5 1
96 10 10 12 8 11 4 0
97 8 10 7 4 13 6 0
98 8 10 13 7 10 3 1
99 11 10 8 14 15 5 1
100 13 10 12 11 13 6 1
101 16 10 11 17 16 7 1
102 16 10 12 15 15 7 1
103 14 10 14 17 18 6 0
104 11 10 10 5 13 3 0
105 4 10 10 4 10 2 1
106 14 10 13 10 16 8 2
107 9 10 10 11 13 3 1
108 14 10 11 15 15 8 1
109 8 10 10 10 14 3 0
110 8 10 7 9 15 4 0
111 11 10 10 12 14 5 1
112 12 10 8 15 13 7 1
113 11 10 12 7 13 6 0
114 14 10 12 13 15 6 0
115 15 10 12 12 16 7 2
116 16 10 11 14 14 6 2
117 16 10 12 14 14 6 0
118 11 10 12 8 16 6 1
119 14 10 12 15 14 6 0
120 14 10 11 12 12 4 2
121 12 10 12 12 13 4 1
122 14 10 11 16 12 5 0
123 8 10 11 9 12 4 1
124 13 10 13 15 14 6 1
125 16 10 12 15 14 6 2
126 12 10 12 6 14 5 0
127 16 10 12 14 16 8 2
128 12 10 12 15 13 6 0
129 11 10 8 10 14 5 1
130 4 10 8 6 4 4 0
131 16 10 12 14 16 8 3
132 15 10 11 12 13 6 1
133 10 10 12 8 16 4 0
134 13 10 13 11 15 6 0
135 15 10 12 13 14 6 0
136 12 10 12 9 13 4 0
137 14 10 11 15 14 6 0
138 7 10 12 13 12 3 1
139 19 10 12 15 15 6 1
140 12 10 10 14 14 5 2
141 12 10 11 16 13 4 1
142 13 10 12 14 14 6 0
143 15 10 12 14 16 4 0
144 8 10 10 10 6 4 2
145 12 10 12 10 13 4 1
146 10 10 13 4 13 6 0
147 8 10 12 8 14 5 1
148 10 10 15 15 15 6 2
149 15 10 11 16 14 6 2
150 16 10 12 12 15 8 0
151 13 10 11 12 13 7 1
152 16 10 12 15 16 7 2
153 9 10 11 9 12 4 0
154 14 10 10 12 15 6 1
155 14 10 11 14 12 6 2
156 12 10 11 11 14 2 1
secondbestfriend thirdbestfriend t
1 1 0 1
2 0 0 2
3 0 0 3
4 0 1 4
5 1 2 5
6 0 1 6
7 1 1 7
8 0 0 8
9 0 0 9
10 0 0 10
11 2 1 11
12 0 0 12
13 2 2 13
14 1 1 14
15 1 0 15
16 0 1 16
17 1 0 17
18 0 1 18
19 0 0 19
20 0 0 20
21 1 1 21
22 2 0 22
23 2 1 23
24 0 0 24
25 0 0 25
26 1 0 26
27 1 0 27
28 2 0 28
29 0 2 29
30 1 1 30
31 1 2 31
32 1 1 32
33 2 1 33
34 2 0 34
35 1 0 35
36 2 0 36
37 1 1 37
38 1 2 38
39 0 1 39
40 3 1 40
41 1 2 41
42 0 0 42
43 0 0 43
44 0 0 44
45 1 1 45
46 0 1 46
47 4 4 47
48 0 0 48
49 0 0 49
50 0 1 50
51 1 0 51
52 2 1 52
53 1 0 53
54 1 1 54
55 0 0 55
56 2 2 56
57 0 2 57
58 3 1 58
59 2 0 59
60 0 0 60
61 0 0 61
62 2 0 62
63 1 0 63
64 0 1 64
65 2 1 65
66 0 0 66
67 1 0 67
68 0 0 68
69 2 1 69
70 0 0 70
71 2 2 71
72 3 0 72
73 0 2 73
74 2 1 74
75 3 1 75
76 1 1 76
77 0 2 77
78 1 2 78
79 0 0 79
80 0 0 80
81 1 0 81
82 1 1 82
83 2 1 83
84 1 0 84
85 0 0 85
86 0 0 86
87 1 0 87
88 0 2 88
89 0 1 89
90 0 1 90
91 1 0 91
92 1 1 92
93 3 1 93
94 1 0 94
95 1 1 95
96 0 0 96
97 0 1 97
98 1 0 98
99 1 0 99
100 0 2 100
101 1 2 101
102 1 2 102
103 0 1 103
104 1 1 104
105 0 1 105
106 1 0 106
107 1 1 107
108 1 1 108
109 1 0 109
110 1 0 110
111 0 0 111
112 0 0 112
113 0 0 113
114 1 0 114
115 1 0 115
116 1 0 116
117 0 0 117
118 1 0 118
119 4 1 119
120 0 0 120
121 1 1 121
122 0 3 122
123 2 2 123
124 1 2 124
125 0 2 125
126 0 0 126
127 0 1 127
128 0 0 128
129 1 0 129
130 0 0 130
131 2 1 131
132 0 2 132
133 1 0 133
134 2 4 134
135 2 0 135
136 1 0 136
137 3 0 137
138 0 0 138
139 1 0 139
140 1 1 140
141 0 0 141
142 1 1 142
143 0 0 143
144 1 2 144
145 0 1 145
146 1 0 146
147 0 0 147
148 2 0 148
149 0 1 149
150 0 0 150
151 1 1 151
152 1 0 152
153 0 0 153
154 0 1 154
155 1 2 155
156 1 0 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month FindingFriends KnowingPeople
-0.98336 0.08938 0.10438 0.21162
Liked Celebrity bestfriend secondbestfriend
0.38519 0.59441 0.30765 -0.03240
thirdbestfriend t
0.41155 -0.00181
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.9910 -1.2524 -0.0601 1.3753 6.9294
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.983357 6.011657 -0.164 0.870292
month 0.089381 0.640842 0.139 0.889267
FindingFriends 0.104378 0.098559 1.059 0.291327
KnowingPeople 0.211623 0.064050 3.304 0.001199 **
Liked 0.385194 0.099038 3.889 0.000152 ***
Celebrity 0.594410 0.156961 3.787 0.000222 ***
bestfriend 0.307650 0.211991 1.451 0.148859
secondbestfriend -0.032400 0.202360 -0.160 0.873014
thirdbestfriend 0.411553 0.214612 1.918 0.057107 .
t -0.001810 0.006864 -0.264 0.792389
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.103 on 146 degrees of freedom
Multiple R-squared: 0.5171, Adjusted R-squared: 0.4873
F-statistic: 17.37 on 9 and 146 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,] 0.8360018 0.32799642 0.163998208
[2,] 0.9019584 0.19608321 0.098041606
[3,] 0.8792406 0.24151888 0.120759442
[4,] 0.8086168 0.38276641 0.191383206
[5,] 0.7265852 0.54682968 0.273414838
[6,] 0.6667970 0.66640592 0.333202962
[7,] 0.6079103 0.78417931 0.392089653
[8,] 0.7021284 0.59574329 0.297871643
[9,] 0.6941923 0.61161543 0.305807713
[10,] 0.7453161 0.50936776 0.254683880
[11,] 0.6834184 0.63316315 0.316581576
[12,] 0.7077771 0.58444574 0.292222868
[13,] 0.6845358 0.63092845 0.315464225
[14,] 0.6260798 0.74784047 0.373920236
[15,] 0.8061672 0.38766567 0.193832837
[16,] 0.7709757 0.45804869 0.229024347
[17,] 0.7170351 0.56592977 0.282964883
[18,] 0.8186239 0.36275218 0.181376090
[19,] 0.8601566 0.27968676 0.139843380
[20,] 0.8296662 0.34066758 0.170333789
[21,] 0.7873126 0.42537481 0.212687407
[22,] 0.7549054 0.49018918 0.245094589
[23,] 0.7364121 0.52717586 0.263587932
[24,] 0.7567195 0.48656107 0.243280537
[25,] 0.7400780 0.51984393 0.259921964
[26,] 0.6964622 0.60707562 0.303537811
[27,] 0.6463023 0.70739531 0.353697656
[28,] 0.6025817 0.79483663 0.397418313
[29,] 0.5526456 0.89470872 0.447354360
[30,] 0.8431763 0.31364740 0.156823699
[31,] 0.9708149 0.05837016 0.029185082
[32,] 0.9717545 0.05649105 0.028245524
[33,] 0.9631161 0.07376786 0.036883931
[34,] 0.9686958 0.06260831 0.031304154
[35,] 0.9750191 0.04996173 0.024980863
[36,] 0.9715922 0.05681560 0.028407799
[37,] 0.9680591 0.06388174 0.031940869
[38,] 0.9594328 0.08113448 0.040567241
[39,] 0.9527291 0.09454171 0.047270857
[40,] 0.9892727 0.02145455 0.010727273
[41,] 0.9858034 0.02839315 0.014196574
[42,] 0.9891549 0.02169017 0.010845085
[43,] 0.9927679 0.01446422 0.007232109
[44,] 0.9902117 0.01957656 0.009788281
[45,] 0.9865132 0.02697369 0.013486846
[46,] 0.9858478 0.02830434 0.014152169
[47,] 0.9890596 0.02188084 0.010940421
[48,] 0.9878590 0.02428193 0.012140966
[49,] 0.9877983 0.02440334 0.012201668
[50,] 0.9894472 0.02110556 0.010552781
[51,] 0.9878856 0.02422885 0.012114425
[52,] 0.9882874 0.02342512 0.011712559
[53,] 0.9876599 0.02468020 0.012340102
[54,] 0.9856703 0.02865938 0.014329692
[55,] 0.9873147 0.02537057 0.012685284
[56,] 0.9888699 0.02226019 0.011130094
[57,] 0.9882981 0.02340382 0.011701909
[58,] 0.9850824 0.02983529 0.014917645
[59,] 0.9855569 0.02888612 0.014443059
[60,] 0.9821643 0.03567137 0.017835683
[61,] 0.9824988 0.03500242 0.017501208
[62,] 0.9874030 0.02519398 0.012596990
[63,] 0.9833495 0.03330093 0.016650464
[64,] 0.9801562 0.03968752 0.019843758
[65,] 0.9742931 0.05141377 0.025706883
[66,] 0.9666617 0.06667654 0.033338268
[67,] 0.9756401 0.04871974 0.024359872
[68,] 0.9739943 0.05201140 0.026005701
[69,] 0.9749669 0.05006611 0.025033056
[70,] 0.9729835 0.05403303 0.027016517
[71,] 0.9644489 0.07110211 0.035551055
[72,] 0.9552106 0.08957884 0.044789419
[73,] 0.9819981 0.03600385 0.018001926
[74,] 0.9760141 0.04797185 0.023985925
[75,] 0.9687225 0.06255498 0.031277489
[76,] 0.9597490 0.08050210 0.040251050
[77,] 0.9510082 0.09798364 0.048991821
[78,] 0.9384758 0.12304839 0.061524195
[79,] 0.9306312 0.13873764 0.069368822
[80,] 0.9547721 0.09045584 0.045227922
[81,] 0.9451513 0.10969748 0.054848740
[82,] 0.9434308 0.11313849 0.056569244
[83,] 0.9320032 0.13599367 0.067996833
[84,] 0.9197028 0.16059440 0.080297199
[85,] 0.9127614 0.17447721 0.087238604
[86,] 0.8944600 0.21108008 0.105540038
[87,] 0.8789282 0.24214355 0.121071775
[88,] 0.8530227 0.29395459 0.146977293
[89,] 0.8222659 0.35546822 0.177734111
[90,] 0.7957130 0.40857395 0.204286977
[91,] 0.8081799 0.38364027 0.191820133
[92,] 0.8644359 0.27112825 0.135564125
[93,] 0.8618594 0.27628122 0.138140608
[94,] 0.8298873 0.34022549 0.170112744
[95,] 0.7974679 0.40506430 0.202532148
[96,] 0.7836564 0.43268725 0.216343627
[97,] 0.7648546 0.47029075 0.235145374
[98,] 0.7841433 0.43171338 0.215856691
[99,] 0.7690454 0.46190923 0.230954614
[100,] 0.8343243 0.33135149 0.165675744
[101,] 0.7972502 0.40549959 0.202749796
[102,] 0.7663322 0.46733567 0.233667837
[103,] 0.7229761 0.55404774 0.277023870
[104,] 0.7321701 0.53565974 0.267829868
[105,] 0.7443748 0.51125035 0.255625177
[106,] 0.7256230 0.54875406 0.274377029
[107,] 0.6753799 0.64924016 0.324620082
[108,] 0.7713185 0.45736291 0.228681453
[109,] 0.7409584 0.51808329 0.259041646
[110,] 0.6882524 0.62349516 0.311747581
[111,] 0.6743284 0.65134328 0.325671638
[112,] 0.6314835 0.73703293 0.368516463
[113,] 0.6121769 0.77564624 0.387823122
[114,] 0.6125528 0.77489436 0.387447181
[115,] 0.5460357 0.90792866 0.453964329
[116,] 0.5048787 0.99024254 0.495121271
[117,] 0.4829941 0.96598828 0.517005862
[118,] 0.4650093 0.93001850 0.534990750
[119,] 0.3929231 0.78584620 0.607076898
[120,] 0.3714623 0.74292451 0.628537747
[121,] 0.3256673 0.65133467 0.674332666
[122,] 0.2813238 0.56264765 0.718676176
[123,] 0.2375462 0.47509230 0.762453848
[124,] 0.2190692 0.43813830 0.780930849
[125,] 0.1843067 0.36861338 0.815693311
[126,] 0.1975809 0.39516182 0.802419092
[127,] 0.6160697 0.76786051 0.383930255
[128,] 0.5261505 0.94769896 0.473849482
[129,] 0.4055699 0.81113973 0.594430137
[130,] 0.4008517 0.80170336 0.599148318
[131,] 0.2780890 0.55617801 0.721910994
> postscript(file="/var/www/rcomp/tmp/1ezme1321998735.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/www/rcomp/tmp/2pij51321998735.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/35y8m1321998735.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/422r41321998735.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5yzt81321998735.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 156
Frequency = 1
1 2 3 4 5 6
1.79509733 0.94980238 1.87153577 0.54976103 -0.97192460 2.63877668
7 8 9 10 11 12
-1.92686083 -1.30940794 -1.02099786 3.45663378 -2.01494858 -1.18383172
13 14 15 16 17 18
2.70084073 -4.48327347 -0.27816030 0.17792392 -1.83473119 -0.86741282
19 20 21 22 23 24
1.43176030 -3.34255000 2.22067907 0.92623737 -0.58033883 2.25839475
25 26 27 28 29 30
0.91566386 0.47542513 -5.30873535 0.51659678 -0.45298795 4.83054800
31 32 33 34 35 36
4.24837473 -1.62484908 -0.56978303 1.39209830 -1.22813575 1.81509285
37 38 39 40 41 42
-1.15404134 0.28692610 -0.15454691 0.77122638 0.88481507 6.92941668
43 44 45 46 47 48
-4.71510529 -2.31343802 -0.14870526 1.78249253 2.75246916 0.97120603
49 50 51 52 53 54
-2.39958743 -1.81784166 -0.88864535 -5.99104422 0.15048096 -2.43316644
55 56 57 58 59 60
2.19089361 -0.87611803 -0.10923356 1.82524294 -2.27762915 0.34245529
61 62 63 64 65 66
-2.15112060 2.05358897 1.08695261 2.33858579 -1.25072250 1.61050142
67 68 69 70 71 72
-2.29540026 2.57670256 1.98241895 -0.82162780 2.06165169 1.02621245
73 74 75 76 77 78
-2.05750378 -3.13465937 -0.24629106 -1.46055649 -0.81585098 -0.12567040
79 80 81 82 83 84
2.90136299 -2.02152943 -2.46530619 1.36971323 -0.04344332 -0.99967099
85 86 87 88 89 90
3.60423992 -0.45526880 -0.09895259 -0.38273359 0.88736740 -0.82713604
91 92 93 94 95 96
0.98505859 -3.67435902 0.98105057 1.67306147 0.50430862 0.70301568
97 98 99 100 101 102
-2.29755236 -0.48176532 -1.55421352 -0.01457641 0.10428325 0.81015464
103 104 105 106 107 108
-1.69440630 2.00599111 -3.37063442 -0.69301706 -1.56596582 -1.25746386
109 110 111 112 113 114
-2.01671378 -2.46975003 -0.96520980 -1.19313822 -0.01379818 0.98028720
115 116 117 118 119 120
0.59881626 2.64655670 3.12688819 -1.64720197 0.63693340 3.00385049
121 122 123 124 125 126
0.44458568 0.94721013 -2.80650618 -1.27479802 1.49134015 1.43057192
127 128 129 130 131 132
0.15892812 -0.67963002 -0.26822600 -1.69832608 -0.07668110 1.93610133
133 134 135 136 137 138
-1.12358065 -1.27845537 2.43589280 1.82580825 1.15304570 -3.37750924
139 140 141 142 143 144
5.29464335 -1.02276607 0.11782665 -0.20701270 2.59238266 -0.90462629
145 146 147 148 149 150
0.87887295 -0.39117521 -3.26231222 -4.27745052 0.83909024 2.03585222
151 152 153 154 155 156
-0.17996427 1.03092037 -0.68624860 0.72146643 0.66443140 2.03911872
> postscript(file="/var/www/rcomp/tmp/6su341321998735.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 1.79509733 NA
1 0.94980238 1.79509733
2 1.87153577 0.94980238
3 0.54976103 1.87153577
4 -0.97192460 0.54976103
5 2.63877668 -0.97192460
6 -1.92686083 2.63877668
7 -1.30940794 -1.92686083
8 -1.02099786 -1.30940794
9 3.45663378 -1.02099786
10 -2.01494858 3.45663378
11 -1.18383172 -2.01494858
12 2.70084073 -1.18383172
13 -4.48327347 2.70084073
14 -0.27816030 -4.48327347
15 0.17792392 -0.27816030
16 -1.83473119 0.17792392
17 -0.86741282 -1.83473119
18 1.43176030 -0.86741282
19 -3.34255000 1.43176030
20 2.22067907 -3.34255000
21 0.92623737 2.22067907
22 -0.58033883 0.92623737
23 2.25839475 -0.58033883
24 0.91566386 2.25839475
25 0.47542513 0.91566386
26 -5.30873535 0.47542513
27 0.51659678 -5.30873535
28 -0.45298795 0.51659678
29 4.83054800 -0.45298795
30 4.24837473 4.83054800
31 -1.62484908 4.24837473
32 -0.56978303 -1.62484908
33 1.39209830 -0.56978303
34 -1.22813575 1.39209830
35 1.81509285 -1.22813575
36 -1.15404134 1.81509285
37 0.28692610 -1.15404134
38 -0.15454691 0.28692610
39 0.77122638 -0.15454691
40 0.88481507 0.77122638
41 6.92941668 0.88481507
42 -4.71510529 6.92941668
43 -2.31343802 -4.71510529
44 -0.14870526 -2.31343802
45 1.78249253 -0.14870526
46 2.75246916 1.78249253
47 0.97120603 2.75246916
48 -2.39958743 0.97120603
49 -1.81784166 -2.39958743
50 -0.88864535 -1.81784166
51 -5.99104422 -0.88864535
52 0.15048096 -5.99104422
53 -2.43316644 0.15048096
54 2.19089361 -2.43316644
55 -0.87611803 2.19089361
56 -0.10923356 -0.87611803
57 1.82524294 -0.10923356
58 -2.27762915 1.82524294
59 0.34245529 -2.27762915
60 -2.15112060 0.34245529
61 2.05358897 -2.15112060
62 1.08695261 2.05358897
63 2.33858579 1.08695261
64 -1.25072250 2.33858579
65 1.61050142 -1.25072250
66 -2.29540026 1.61050142
67 2.57670256 -2.29540026
68 1.98241895 2.57670256
69 -0.82162780 1.98241895
70 2.06165169 -0.82162780
71 1.02621245 2.06165169
72 -2.05750378 1.02621245
73 -3.13465937 -2.05750378
74 -0.24629106 -3.13465937
75 -1.46055649 -0.24629106
76 -0.81585098 -1.46055649
77 -0.12567040 -0.81585098
78 2.90136299 -0.12567040
79 -2.02152943 2.90136299
80 -2.46530619 -2.02152943
81 1.36971323 -2.46530619
82 -0.04344332 1.36971323
83 -0.99967099 -0.04344332
84 3.60423992 -0.99967099
85 -0.45526880 3.60423992
86 -0.09895259 -0.45526880
87 -0.38273359 -0.09895259
88 0.88736740 -0.38273359
89 -0.82713604 0.88736740
90 0.98505859 -0.82713604
91 -3.67435902 0.98505859
92 0.98105057 -3.67435902
93 1.67306147 0.98105057
94 0.50430862 1.67306147
95 0.70301568 0.50430862
96 -2.29755236 0.70301568
97 -0.48176532 -2.29755236
98 -1.55421352 -0.48176532
99 -0.01457641 -1.55421352
100 0.10428325 -0.01457641
101 0.81015464 0.10428325
102 -1.69440630 0.81015464
103 2.00599111 -1.69440630
104 -3.37063442 2.00599111
105 -0.69301706 -3.37063442
106 -1.56596582 -0.69301706
107 -1.25746386 -1.56596582
108 -2.01671378 -1.25746386
109 -2.46975003 -2.01671378
110 -0.96520980 -2.46975003
111 -1.19313822 -0.96520980
112 -0.01379818 -1.19313822
113 0.98028720 -0.01379818
114 0.59881626 0.98028720
115 2.64655670 0.59881626
116 3.12688819 2.64655670
117 -1.64720197 3.12688819
118 0.63693340 -1.64720197
119 3.00385049 0.63693340
120 0.44458568 3.00385049
121 0.94721013 0.44458568
122 -2.80650618 0.94721013
123 -1.27479802 -2.80650618
124 1.49134015 -1.27479802
125 1.43057192 1.49134015
126 0.15892812 1.43057192
127 -0.67963002 0.15892812
128 -0.26822600 -0.67963002
129 -1.69832608 -0.26822600
130 -0.07668110 -1.69832608
131 1.93610133 -0.07668110
132 -1.12358065 1.93610133
133 -1.27845537 -1.12358065
134 2.43589280 -1.27845537
135 1.82580825 2.43589280
136 1.15304570 1.82580825
137 -3.37750924 1.15304570
138 5.29464335 -3.37750924
139 -1.02276607 5.29464335
140 0.11782665 -1.02276607
141 -0.20701270 0.11782665
142 2.59238266 -0.20701270
143 -0.90462629 2.59238266
144 0.87887295 -0.90462629
145 -0.39117521 0.87887295
146 -3.26231222 -0.39117521
147 -4.27745052 -3.26231222
148 0.83909024 -4.27745052
149 2.03585222 0.83909024
150 -0.17996427 2.03585222
151 1.03092037 -0.17996427
152 -0.68624860 1.03092037
153 0.72146643 -0.68624860
154 0.66443140 0.72146643
155 2.03911872 0.66443140
156 NA 2.03911872
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.94980238 1.79509733
[2,] 1.87153577 0.94980238
[3,] 0.54976103 1.87153577
[4,] -0.97192460 0.54976103
[5,] 2.63877668 -0.97192460
[6,] -1.92686083 2.63877668
[7,] -1.30940794 -1.92686083
[8,] -1.02099786 -1.30940794
[9,] 3.45663378 -1.02099786
[10,] -2.01494858 3.45663378
[11,] -1.18383172 -2.01494858
[12,] 2.70084073 -1.18383172
[13,] -4.48327347 2.70084073
[14,] -0.27816030 -4.48327347
[15,] 0.17792392 -0.27816030
[16,] -1.83473119 0.17792392
[17,] -0.86741282 -1.83473119
[18,] 1.43176030 -0.86741282
[19,] -3.34255000 1.43176030
[20,] 2.22067907 -3.34255000
[21,] 0.92623737 2.22067907
[22,] -0.58033883 0.92623737
[23,] 2.25839475 -0.58033883
[24,] 0.91566386 2.25839475
[25,] 0.47542513 0.91566386
[26,] -5.30873535 0.47542513
[27,] 0.51659678 -5.30873535
[28,] -0.45298795 0.51659678
[29,] 4.83054800 -0.45298795
[30,] 4.24837473 4.83054800
[31,] -1.62484908 4.24837473
[32,] -0.56978303 -1.62484908
[33,] 1.39209830 -0.56978303
[34,] -1.22813575 1.39209830
[35,] 1.81509285 -1.22813575
[36,] -1.15404134 1.81509285
[37,] 0.28692610 -1.15404134
[38,] -0.15454691 0.28692610
[39,] 0.77122638 -0.15454691
[40,] 0.88481507 0.77122638
[41,] 6.92941668 0.88481507
[42,] -4.71510529 6.92941668
[43,] -2.31343802 -4.71510529
[44,] -0.14870526 -2.31343802
[45,] 1.78249253 -0.14870526
[46,] 2.75246916 1.78249253
[47,] 0.97120603 2.75246916
[48,] -2.39958743 0.97120603
[49,] -1.81784166 -2.39958743
[50,] -0.88864535 -1.81784166
[51,] -5.99104422 -0.88864535
[52,] 0.15048096 -5.99104422
[53,] -2.43316644 0.15048096
[54,] 2.19089361 -2.43316644
[55,] -0.87611803 2.19089361
[56,] -0.10923356 -0.87611803
[57,] 1.82524294 -0.10923356
[58,] -2.27762915 1.82524294
[59,] 0.34245529 -2.27762915
[60,] -2.15112060 0.34245529
[61,] 2.05358897 -2.15112060
[62,] 1.08695261 2.05358897
[63,] 2.33858579 1.08695261
[64,] -1.25072250 2.33858579
[65,] 1.61050142 -1.25072250
[66,] -2.29540026 1.61050142
[67,] 2.57670256 -2.29540026
[68,] 1.98241895 2.57670256
[69,] -0.82162780 1.98241895
[70,] 2.06165169 -0.82162780
[71,] 1.02621245 2.06165169
[72,] -2.05750378 1.02621245
[73,] -3.13465937 -2.05750378
[74,] -0.24629106 -3.13465937
[75,] -1.46055649 -0.24629106
[76,] -0.81585098 -1.46055649
[77,] -0.12567040 -0.81585098
[78,] 2.90136299 -0.12567040
[79,] -2.02152943 2.90136299
[80,] -2.46530619 -2.02152943
[81,] 1.36971323 -2.46530619
[82,] -0.04344332 1.36971323
[83,] -0.99967099 -0.04344332
[84,] 3.60423992 -0.99967099
[85,] -0.45526880 3.60423992
[86,] -0.09895259 -0.45526880
[87,] -0.38273359 -0.09895259
[88,] 0.88736740 -0.38273359
[89,] -0.82713604 0.88736740
[90,] 0.98505859 -0.82713604
[91,] -3.67435902 0.98505859
[92,] 0.98105057 -3.67435902
[93,] 1.67306147 0.98105057
[94,] 0.50430862 1.67306147
[95,] 0.70301568 0.50430862
[96,] -2.29755236 0.70301568
[97,] -0.48176532 -2.29755236
[98,] -1.55421352 -0.48176532
[99,] -0.01457641 -1.55421352
[100,] 0.10428325 -0.01457641
[101,] 0.81015464 0.10428325
[102,] -1.69440630 0.81015464
[103,] 2.00599111 -1.69440630
[104,] -3.37063442 2.00599111
[105,] -0.69301706 -3.37063442
[106,] -1.56596582 -0.69301706
[107,] -1.25746386 -1.56596582
[108,] -2.01671378 -1.25746386
[109,] -2.46975003 -2.01671378
[110,] -0.96520980 -2.46975003
[111,] -1.19313822 -0.96520980
[112,] -0.01379818 -1.19313822
[113,] 0.98028720 -0.01379818
[114,] 0.59881626 0.98028720
[115,] 2.64655670 0.59881626
[116,] 3.12688819 2.64655670
[117,] -1.64720197 3.12688819
[118,] 0.63693340 -1.64720197
[119,] 3.00385049 0.63693340
[120,] 0.44458568 3.00385049
[121,] 0.94721013 0.44458568
[122,] -2.80650618 0.94721013
[123,] -1.27479802 -2.80650618
[124,] 1.49134015 -1.27479802
[125,] 1.43057192 1.49134015
[126,] 0.15892812 1.43057192
[127,] -0.67963002 0.15892812
[128,] -0.26822600 -0.67963002
[129,] -1.69832608 -0.26822600
[130,] -0.07668110 -1.69832608
[131,] 1.93610133 -0.07668110
[132,] -1.12358065 1.93610133
[133,] -1.27845537 -1.12358065
[134,] 2.43589280 -1.27845537
[135,] 1.82580825 2.43589280
[136,] 1.15304570 1.82580825
[137,] -3.37750924 1.15304570
[138,] 5.29464335 -3.37750924
[139,] -1.02276607 5.29464335
[140,] 0.11782665 -1.02276607
[141,] -0.20701270 0.11782665
[142,] 2.59238266 -0.20701270
[143,] -0.90462629 2.59238266
[144,] 0.87887295 -0.90462629
[145,] -0.39117521 0.87887295
[146,] -3.26231222 -0.39117521
[147,] -4.27745052 -3.26231222
[148,] 0.83909024 -4.27745052
[149,] 2.03585222 0.83909024
[150,] -0.17996427 2.03585222
[151,] 1.03092037 -0.17996427
[152,] -0.68624860 1.03092037
[153,] 0.72146643 -0.68624860
[154,] 0.66443140 0.72146643
[155,] 2.03911872 0.66443140
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.94980238 1.79509733
2 1.87153577 0.94980238
3 0.54976103 1.87153577
4 -0.97192460 0.54976103
5 2.63877668 -0.97192460
6 -1.92686083 2.63877668
7 -1.30940794 -1.92686083
8 -1.02099786 -1.30940794
9 3.45663378 -1.02099786
10 -2.01494858 3.45663378
11 -1.18383172 -2.01494858
12 2.70084073 -1.18383172
13 -4.48327347 2.70084073
14 -0.27816030 -4.48327347
15 0.17792392 -0.27816030
16 -1.83473119 0.17792392
17 -0.86741282 -1.83473119
18 1.43176030 -0.86741282
19 -3.34255000 1.43176030
20 2.22067907 -3.34255000
21 0.92623737 2.22067907
22 -0.58033883 0.92623737
23 2.25839475 -0.58033883
24 0.91566386 2.25839475
25 0.47542513 0.91566386
26 -5.30873535 0.47542513
27 0.51659678 -5.30873535
28 -0.45298795 0.51659678
29 4.83054800 -0.45298795
30 4.24837473 4.83054800
31 -1.62484908 4.24837473
32 -0.56978303 -1.62484908
33 1.39209830 -0.56978303
34 -1.22813575 1.39209830
35 1.81509285 -1.22813575
36 -1.15404134 1.81509285
37 0.28692610 -1.15404134
38 -0.15454691 0.28692610
39 0.77122638 -0.15454691
40 0.88481507 0.77122638
41 6.92941668 0.88481507
42 -4.71510529 6.92941668
43 -2.31343802 -4.71510529
44 -0.14870526 -2.31343802
45 1.78249253 -0.14870526
46 2.75246916 1.78249253
47 0.97120603 2.75246916
48 -2.39958743 0.97120603
49 -1.81784166 -2.39958743
50 -0.88864535 -1.81784166
51 -5.99104422 -0.88864535
52 0.15048096 -5.99104422
53 -2.43316644 0.15048096
54 2.19089361 -2.43316644
55 -0.87611803 2.19089361
56 -0.10923356 -0.87611803
57 1.82524294 -0.10923356
58 -2.27762915 1.82524294
59 0.34245529 -2.27762915
60 -2.15112060 0.34245529
61 2.05358897 -2.15112060
62 1.08695261 2.05358897
63 2.33858579 1.08695261
64 -1.25072250 2.33858579
65 1.61050142 -1.25072250
66 -2.29540026 1.61050142
67 2.57670256 -2.29540026
68 1.98241895 2.57670256
69 -0.82162780 1.98241895
70 2.06165169 -0.82162780
71 1.02621245 2.06165169
72 -2.05750378 1.02621245
73 -3.13465937 -2.05750378
74 -0.24629106 -3.13465937
75 -1.46055649 -0.24629106
76 -0.81585098 -1.46055649
77 -0.12567040 -0.81585098
78 2.90136299 -0.12567040
79 -2.02152943 2.90136299
80 -2.46530619 -2.02152943
81 1.36971323 -2.46530619
82 -0.04344332 1.36971323
83 -0.99967099 -0.04344332
84 3.60423992 -0.99967099
85 -0.45526880 3.60423992
86 -0.09895259 -0.45526880
87 -0.38273359 -0.09895259
88 0.88736740 -0.38273359
89 -0.82713604 0.88736740
90 0.98505859 -0.82713604
91 -3.67435902 0.98505859
92 0.98105057 -3.67435902
93 1.67306147 0.98105057
94 0.50430862 1.67306147
95 0.70301568 0.50430862
96 -2.29755236 0.70301568
97 -0.48176532 -2.29755236
98 -1.55421352 -0.48176532
99 -0.01457641 -1.55421352
100 0.10428325 -0.01457641
101 0.81015464 0.10428325
102 -1.69440630 0.81015464
103 2.00599111 -1.69440630
104 -3.37063442 2.00599111
105 -0.69301706 -3.37063442
106 -1.56596582 -0.69301706
107 -1.25746386 -1.56596582
108 -2.01671378 -1.25746386
109 -2.46975003 -2.01671378
110 -0.96520980 -2.46975003
111 -1.19313822 -0.96520980
112 -0.01379818 -1.19313822
113 0.98028720 -0.01379818
114 0.59881626 0.98028720
115 2.64655670 0.59881626
116 3.12688819 2.64655670
117 -1.64720197 3.12688819
118 0.63693340 -1.64720197
119 3.00385049 0.63693340
120 0.44458568 3.00385049
121 0.94721013 0.44458568
122 -2.80650618 0.94721013
123 -1.27479802 -2.80650618
124 1.49134015 -1.27479802
125 1.43057192 1.49134015
126 0.15892812 1.43057192
127 -0.67963002 0.15892812
128 -0.26822600 -0.67963002
129 -1.69832608 -0.26822600
130 -0.07668110 -1.69832608
131 1.93610133 -0.07668110
132 -1.12358065 1.93610133
133 -1.27845537 -1.12358065
134 2.43589280 -1.27845537
135 1.82580825 2.43589280
136 1.15304570 1.82580825
137 -3.37750924 1.15304570
138 5.29464335 -3.37750924
139 -1.02276607 5.29464335
140 0.11782665 -1.02276607
141 -0.20701270 0.11782665
142 2.59238266 -0.20701270
143 -0.90462629 2.59238266
144 0.87887295 -0.90462629
145 -0.39117521 0.87887295
146 -3.26231222 -0.39117521
147 -4.27745052 -3.26231222
148 0.83909024 -4.27745052
149 2.03585222 0.83909024
150 -0.17996427 2.03585222
151 1.03092037 -0.17996427
152 -0.68624860 1.03092037
153 0.72146643 -0.68624860
154 0.66443140 0.72146643
155 2.03911872 0.66443140
> 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/www/rcomp/tmp/7crmv1321998735.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/www/rcomp/tmp/8jvmx1321998735.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/www/rcomp/tmp/9u8ck1321998735.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/www/rcomp/tmp/10rwqw1321998735.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/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11vziy1321998735.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/1219201321998735.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/139w811321998735.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14qc631321998735.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/www/rcomp/tmp/153syn1321998735.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/www/rcomp/tmp/16nlli1321998735.tab")
+ }
>
> try(system("convert tmp/1ezme1321998735.ps tmp/1ezme1321998735.png",intern=TRUE))
character(0)
> try(system("convert tmp/2pij51321998735.ps tmp/2pij51321998735.png",intern=TRUE))
character(0)
> try(system("convert tmp/35y8m1321998735.ps tmp/35y8m1321998735.png",intern=TRUE))
character(0)
> try(system("convert tmp/422r41321998735.ps tmp/422r41321998735.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yzt81321998735.ps tmp/5yzt81321998735.png",intern=TRUE))
character(0)
> try(system("convert tmp/6su341321998735.ps tmp/6su341321998735.png",intern=TRUE))
character(0)
> try(system("convert tmp/7crmv1321998735.ps tmp/7crmv1321998735.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jvmx1321998735.ps tmp/8jvmx1321998735.png",intern=TRUE))
character(0)
> try(system("convert tmp/9u8ck1321998735.ps tmp/9u8ck1321998735.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rwqw1321998735.ps tmp/10rwqw1321998735.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.070 0.400 6.455