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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+ ,dim=c(10
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+ ,dimnames=list(c('G'
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+ ,'KnowingPeople'
+ ,'Knowingpeople*G'
+ ,'Liked'
+ ,'Liked*G'
+ ,'Celebrity'
+ ,'Celebrity*G')
+ ,1:156))
> y <- array(NA,dim=c(10,156),dimnames=list(c('G','Popularity','FindingFriends','Findingfriends*G','KnowingPeople','Knowingpeople*G','Liked','Liked*G','Celebrity','Celebrity*G'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> 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 G FindingFriends Findingfriends*G KnowingPeople Knowingpeople*G
1 13 0 13 0 14 0
2 12 1 12 12 8 8
3 15 1 10 10 12 12
4 12 1 9 9 7 7
5 10 0 10 0 10 0
6 12 0 12 0 7 0
7 15 1 13 13 16 16
8 9 1 12 12 11 11
9 12 1 12 12 14 14
10 11 1 6 6 6 6
11 11 0 5 0 16 0
12 11 1 12 12 11 11
13 15 1 11 11 16 16
14 7 0 14 0 12 0
15 11 0 14 0 7 0
16 11 1 12 12 13 13
17 10 1 12 12 11 11
18 14 0 11 0 15 0
19 10 1 11 11 7 7
20 6 0 7 0 9 0
21 11 1 9 9 7 7
22 15 0 11 0 14 0
23 11 1 11 11 15 15
24 12 0 12 0 7 0
25 14 1 12 12 15 15
26 15 0 11 0 17 0
27 9 0 11 0 15 0
28 13 1 8 8 14 14
29 13 0 9 0 14 0
30 16 1 12 12 8 8
31 13 1 10 10 8 8
32 12 0 10 0 14 0
33 14 1 12 12 14 14
34 11 0 8 0 8 0
35 9 1 12 12 11 11
36 16 0 11 0 16 0
37 12 1 12 12 10 10
38 10 0 7 0 8 0
39 13 1 11 11 14 14
40 16 1 11 11 16 16
41 14 0 12 0 13 0
42 15 1 9 9 5 5
43 5 1 15 15 8 8
44 8 0 11 0 10 0
45 11 1 11 11 8 8
46 16 0 11 0 13 0
47 17 1 11 11 15 15
48 9 0 15 0 6 0
49 9 1 11 11 12 12
50 13 1 12 12 16 16
51 10 1 12 12 5 5
52 6 0 9 0 15 0
53 12 0 12 0 12 0
54 8 0 12 0 8 0
55 14 0 13 0 13 0
56 12 1 11 11 14 14
57 11 1 9 9 12 12
58 16 1 9 9 16 16
59 8 0 11 0 10 0
60 15 1 11 11 15 15
61 7 0 12 0 8 0
62 16 0 12 0 16 0
63 14 1 9 9 19 19
64 16 1 11 11 14 14
65 9 1 9 9 6 6
66 14 1 12 12 13 13
67 11 0 12 0 15 0
68 13 0 12 0 7 0
69 15 1 12 12 13 13
70 5 0 14 0 4 0
71 15 1 11 11 14 14
72 13 1 12 12 13 13
73 11 0 11 0 11 0
74 11 0 6 0 14 0
75 12 1 10 10 12 12
76 12 1 12 12 15 15
77 12 1 13 13 14 14
78 12 1 8 8 13 13
79 14 1 12 12 8 8
80 6 1 12 12 6 6
81 7 0 12 0 7 0
82 14 1 6 6 13 13
83 14 1 11 11 13 13
84 10 1 10 10 11 11
85 13 0 12 0 5 0
86 12 0 13 0 12 0
87 9 0 11 0 8 0
88 12 1 7 7 11 11
89 16 1 11 11 14 14
90 10 0 11 0 9 0
91 14 1 11 11 10 10
92 10 1 11 11 13 13
93 16 1 12 12 16 16
94 15 1 10 10 16 16
95 12 0 11 0 11 0
96 10 1 12 12 8 8
97 8 1 7 7 4 4
98 8 0 13 0 7 0
99 11 0 8 0 14 0
100 13 1 12 12 11 11
101 16 1 11 11 17 17
102 16 1 12 12 15 15
103 14 0 14 0 17 0
104 11 1 10 10 5 5
105 4 0 10 0 4 0
106 14 1 13 13 10 10
107 9 1 10 10 11 11
108 14 1 11 11 15 15
109 8 1 10 10 10 10
110 8 1 7 7 9 9
111 11 1 10 10 12 12
112 12 1 8 8 15 15
113 11 1 12 12 7 7
114 14 1 12 12 13 13
115 15 0 12 0 12 0
116 16 1 11 11 14 14
117 16 1 12 12 14 14
118 11 0 12 0 8 0
119 14 0 12 0 15 0
120 14 0 11 0 12 0
121 12 1 12 12 12 12
122 14 0 11 0 16 0
123 8 0 11 0 9 0
124 13 0 13 0 15 0
125 16 0 12 0 15 0
126 12 1 12 12 6 6
127 16 1 12 12 14 14
128 12 1 12 12 15 15
129 11 1 8 8 10 10
130 4 1 8 8 6 6
131 16 1 12 12 14 14
132 15 1 11 11 12 12
133 10 1 12 12 8 8
134 13 1 13 13 11 11
135 15 0 12 0 13 0
136 12 1 12 12 9 9
137 14 0 11 0 15 0
138 7 1 12 12 13 13
139 19 1 12 12 15 15
140 12 1 10 10 14 14
141 12 0 11 0 16 0
142 13 0 12 0 14 0
143 15 1 12 12 14 14
144 8 0 10 0 10 0
145 12 1 12 12 10 10
146 10 1 13 13 4 4
147 8 0 12 0 8 0
148 10 0 15 0 15 0
149 15 0 11 0 16 0
150 16 1 12 12 12 12
151 13 1 11 11 12 12
152 16 1 12 12 15 15
153 9 1 11 11 9 9
154 14 0 10 0 12 0
155 14 0 11 0 14 0
156 12 1 11 11 11 11
Liked Liked*G Celebrity Celebrity*G t
1 13 0 3 0 1
2 13 13 5 5 2
3 16 16 6 6 3
4 12 12 6 6 4
5 11 0 5 0 5
6 12 0 3 0 6
7 18 18 8 8 7
8 11 11 4 4 8
9 14 14 4 4 9
10 9 9 4 4 10
11 14 0 6 0 11
12 12 12 6 6 12
13 11 11 5 5 13
14 12 0 4 0 14
15 13 0 6 0 15
16 11 11 4 4 16
17 12 12 6 6 17
18 16 0 6 0 18
19 9 9 4 4 19
20 11 0 4 0 20
21 13 13 2 2 21
22 15 0 7 0 22
23 10 10 5 5 23
24 11 0 4 0 24
25 13 13 6 6 25
26 16 0 6 0 26
27 15 0 7 0 27
28 14 14 5 5 28
29 14 0 6 0 29
30 14 14 4 4 30
31 8 8 4 4 31
32 13 0 7 0 32
33 15 15 7 7 33
34 13 0 4 0 34
35 11 11 4 4 35
36 15 0 6 0 36
37 15 15 6 6 37
38 9 0 5 0 38
39 13 13 6 6 39
40 16 16 7 7 40
41 13 0 6 0 41
42 11 11 3 3 42
43 12 12 3 3 43
44 12 0 4 0 44
45 12 12 6 6 45
46 14 0 7 0 46
47 14 14 5 5 47
48 8 0 4 0 48
49 13 13 5 5 49
50 16 16 6 6 50
51 13 13 6 6 51
52 11 0 6 0 52
53 14 0 5 0 53
54 13 0 4 0 54
55 13 0 5 0 55
56 13 13 5 5 56
57 12 12 4 4 57
58 16 16 6 6 58
59 15 0 2 0 59
60 15 15 8 8 60
61 12 0 3 0 61
62 14 0 6 0 62
63 12 12 6 6 63
64 15 15 6 6 64
65 12 12 5 5 65
66 13 13 5 5 66
67 12 0 6 0 67
68 12 0 5 0 68
69 13 13 6 6 69
70 5 0 2 0 70
71 13 13 5 5 71
72 13 13 5 5 72
73 14 0 5 0 73
74 17 0 6 0 74
75 13 13 6 6 75
76 13 13 6 6 76
77 12 12 5 5 77
78 13 13 5 5 78
79 14 14 4 4 79
80 11 11 2 2 80
81 12 0 4 0 81
82 12 12 6 6 82
83 16 16 6 6 83
84 12 12 5 5 84
85 12 0 3 0 85
86 12 0 6 0 86
87 10 0 4 0 87
88 15 15 5 5 88
89 15 15 8 8 89
90 12 0 4 0 90
91 16 16 6 6 91
92 15 15 6 6 92
93 16 16 7 7 93
94 13 13 6 6 94
95 12 0 5 0 95
96 11 11 4 4 96
97 13 13 6 6 97
98 10 0 3 0 98
99 15 0 5 0 99
100 13 13 6 6 100
101 16 16 7 7 101
102 15 15 7 7 102
103 18 0 6 0 103
104 13 13 3 3 104
105 10 0 2 0 105
106 16 16 8 8 106
107 13 13 3 3 107
108 15 15 8 8 108
109 14 14 3 3 109
110 15 15 4 4 110
111 14 14 5 5 111
112 13 13 7 7 112
113 13 13 6 6 113
114 15 15 6 6 114
115 16 0 7 0 115
116 14 14 6 6 116
117 14 14 6 6 117
118 16 0 6 0 118
119 14 0 6 0 119
120 12 0 4 0 120
121 13 13 4 4 121
122 12 0 5 0 122
123 12 0 4 0 123
124 14 0 6 0 124
125 14 0 6 0 125
126 14 14 5 5 126
127 16 16 8 8 127
128 13 13 6 6 128
129 14 14 5 5 129
130 4 4 4 4 130
131 16 16 8 8 131
132 13 13 6 6 132
133 16 16 4 4 133
134 15 15 6 6 134
135 14 0 6 0 135
136 13 13 4 4 136
137 14 0 6 0 137
138 12 12 3 3 138
139 15 15 6 6 139
140 14 14 5 5 140
141 13 0 4 0 141
142 14 0 6 0 142
143 16 16 4 4 143
144 6 0 4 0 144
145 13 13 4 4 145
146 13 13 6 6 146
147 14 0 5 0 147
148 15 0 6 0 148
149 14 0 6 0 149
150 15 15 8 8 150
151 13 13 7 7 151
152 16 16 7 7 152
153 12 12 4 4 153
154 15 0 6 0 154
155 12 0 6 0 155
156 14 14 2 2 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) G FindingFriends `Findingfriends*G`
-1.41466 2.84541 0.26236 -0.28719
KnowingPeople `Knowingpeople*G` Liked `Liked*G`
0.24288 0.02506 0.26799 0.13992
Celebrity `Celebrity*G` t
0.73752 -0.19888 -0.00161
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.8791 -1.2988 -0.0423 1.2466 6.4177
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.414657 2.274897 -0.622 0.5350
G 2.845413 2.911505 0.977 0.3300
FindingFriends 0.262357 0.141290 1.857 0.0654 .
`Findingfriends*G` -0.287190 0.195479 -1.469 0.1440
KnowingPeople 0.242881 0.111389 2.180 0.0308 *
`Knowingpeople*G` 0.025064 0.134617 0.186 0.8526
Liked 0.267990 0.151510 1.769 0.0790 .
`Liked*G` 0.139922 0.199783 0.700 0.4848
Celebrity 0.737524 0.295153 2.499 0.0136 *
`Celebrity*G` -0.198880 0.348175 -0.571 0.5687
t -0.001610 0.003841 -0.419 0.6757
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.094 on 145 degrees of freedom
Multiple R-squared: 0.5243, Adjusted R-squared: 0.4915
F-statistic: 15.98 on 10 and 145 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,] 0.26245841 0.52491682 0.737541591
[2,] 0.13387335 0.26774670 0.866126648
[3,] 0.11049029 0.22098058 0.889509711
[4,] 0.06133059 0.12266119 0.938669405
[5,] 0.03753233 0.07506466 0.962467668
[6,] 0.12093874 0.24187748 0.879061259
[7,] 0.08126065 0.16252130 0.918739348
[8,] 0.11173460 0.22346920 0.888265401
[9,] 0.17005886 0.34011773 0.829941137
[10,] 0.11759100 0.23518200 0.882409000
[11,] 0.30580908 0.61161817 0.694190917
[12,] 0.27248934 0.54497868 0.727510658
[13,] 0.21619206 0.43238412 0.783807938
[14,] 0.37065918 0.74131836 0.629340821
[15,] 0.31775051 0.63550103 0.682249485
[16,] 0.31757296 0.63514591 0.682427044
[17,] 0.58500774 0.82998452 0.414992259
[18,] 0.66312079 0.67375843 0.336879214
[19,] 0.65455102 0.69089796 0.345448981
[20,] 0.59919153 0.80161694 0.400808468
[21,] 0.56590743 0.86818515 0.434092574
[22,] 0.59269817 0.81460367 0.407301833
[23,] 0.61120004 0.77759993 0.388799964
[24,] 0.58395712 0.83208576 0.416042880
[25,] 0.60393363 0.79213274 0.396066372
[26,] 0.54739307 0.90521386 0.452606932
[27,] 0.49324322 0.98648643 0.506756783
[28,] 0.46549781 0.93099563 0.534502187
[29,] 0.68385571 0.63228858 0.316144288
[30,] 0.86495197 0.27009606 0.135048030
[31,] 0.89358192 0.21283615 0.106418076
[32,] 0.87442261 0.25115479 0.125577394
[33,] 0.89296007 0.21407987 0.107039933
[34,] 0.92073529 0.15852941 0.079264707
[35,] 0.89912178 0.20175644 0.100878220
[36,] 0.94456988 0.11086023 0.055430115
[37,] 0.94347459 0.11305081 0.056525405
[38,] 0.92960670 0.14078660 0.070393299
[39,] 0.98501016 0.02997967 0.014989837
[40,] 0.98006231 0.03987538 0.019937691
[41,] 0.98583137 0.02833726 0.014168629
[42,] 0.98613460 0.02773079 0.013865397
[43,] 0.98206470 0.03587059 0.017935296
[44,] 0.97927903 0.04144193 0.020720965
[45,] 0.97300020 0.05399960 0.026999801
[46,] 0.97167462 0.05665075 0.028325377
[47,] 0.96447261 0.07105479 0.035527393
[48,] 0.96199721 0.07600557 0.038002785
[49,] 0.96947152 0.06105697 0.030528484
[50,] 0.96009144 0.07981713 0.039908564
[51,] 0.95974472 0.08051055 0.040255276
[52,] 0.95867127 0.08265745 0.041328727
[53,] 0.95665067 0.08669866 0.043349332
[54,] 0.95567529 0.08864942 0.044324709
[55,] 0.96351254 0.07297492 0.036487458
[56,] 0.96604377 0.06791246 0.033956228
[57,] 0.95721737 0.08556526 0.042782632
[58,] 0.95999237 0.08001525 0.040007626
[59,] 0.94947031 0.10105939 0.050529693
[60,] 0.93690634 0.12618732 0.063093660
[61,] 0.94144879 0.11710242 0.058551212
[62,] 0.92853868 0.14292264 0.071461320
[63,] 0.92151186 0.15697628 0.078488140
[64,] 0.90574919 0.18850163 0.094250814
[65,] 0.89444742 0.21110516 0.105552580
[66,] 0.91996289 0.16007422 0.080037112
[67,] 0.92411555 0.15176889 0.075884445
[68,] 0.93042172 0.13915656 0.069578279
[69,] 0.94190013 0.11619973 0.058099866
[70,] 0.92635541 0.14728918 0.073644592
[71,] 0.91601243 0.16797513 0.083987566
[72,] 0.98885202 0.02229596 0.011147981
[73,] 0.98487985 0.03024030 0.015120150
[74,] 0.97958310 0.04083380 0.020416898
[75,] 0.97847743 0.04304514 0.021522572
[76,] 0.97367759 0.05264482 0.026322411
[77,] 0.96649427 0.06701146 0.033505728
[78,] 0.95809325 0.08381349 0.041906747
[79,] 0.98163934 0.03672132 0.018360659
[80,] 0.97678960 0.04642080 0.023210400
[81,] 0.97354310 0.05291380 0.026456902
[82,] 0.96738344 0.06523311 0.032616557
[83,] 0.95697431 0.08605138 0.043025692
[84,] 0.95621585 0.08756831 0.043784155
[85,] 0.95530314 0.08939373 0.044696863
[86,] 0.97989821 0.04020358 0.020101790
[87,] 0.97337406 0.05325189 0.026625944
[88,] 0.96444560 0.07110880 0.035554399
[89,] 0.95600078 0.08799844 0.043999218
[90,] 0.94416820 0.11166360 0.055831800
[91,] 0.96520643 0.06958715 0.034793575
[92,] 0.95762288 0.08475425 0.042377123
[93,] 0.95166306 0.09667387 0.048336936
[94,] 0.94261683 0.11476634 0.057383171
[95,] 0.94442084 0.11115833 0.055579165
[96,] 0.95071006 0.09857988 0.049289941
[97,] 0.95635502 0.08728996 0.043644978
[98,] 0.95264990 0.09470019 0.047350097
[99,] 0.95858197 0.08283605 0.041418027
[100,] 0.94638158 0.10723684 0.053618420
[101,] 0.93680029 0.12639943 0.063199715
[102,] 0.91611723 0.16776554 0.083882770
[103,] 0.91029571 0.17940858 0.089704289
[104,] 0.90508209 0.18983583 0.094917913
[105,] 0.88217113 0.23565773 0.117828865
[106,] 0.85264072 0.29471857 0.147359284
[107,] 0.93803393 0.12393213 0.061966065
[108,] 0.91556853 0.16886293 0.084431467
[109,] 0.89463026 0.21073948 0.105369739
[110,] 0.86247479 0.27505042 0.137525212
[111,] 0.82033572 0.35932855 0.179664277
[112,] 0.83498770 0.33002460 0.165012298
[113,] 0.81134594 0.37730811 0.188654056
[114,] 0.76214480 0.47571041 0.237855203
[115,] 0.76365738 0.47268524 0.236342619
[116,] 0.70882529 0.58234942 0.291174711
[117,] 0.65547653 0.68904694 0.344523468
[118,] 0.61634329 0.76731341 0.383656706
[119,] 0.63523563 0.72952875 0.364764373
[120,] 0.75848651 0.48302697 0.241513487
[121,] 0.77222990 0.45554020 0.227770098
[122,] 0.79530388 0.40939225 0.204696124
[123,] 0.76807797 0.46384405 0.231922027
[124,] 0.68172595 0.63654810 0.318274049
[125,] 0.95652933 0.08694134 0.043470669
[126,] 0.99278821 0.01442359 0.007211794
[127,] 0.98389264 0.03221473 0.016107363
[128,] 0.95917747 0.08164506 0.040822528
[129,] 0.88890016 0.22219968 0.111099838
> postscript(file="/var/wessaorg/rcomp/tmp/106901321989532.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/27p2g1321989532.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3gn8q1321989532.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4b0al1321989532.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5abl91321989532.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
1.908852760 0.730821137 0.848605352 0.796756364 -0.265179493 3.147416780
7 8 9 10 11 12
-2.035344422 -1.708880095 -0.734841470 2.300890305 -0.942512098 -1.187638423
13 14 15 16 17 18
2.395973065 -4.316339909 -0.843364694 -0.231885207 -2.179586013 0.201520475
19 20 21 22 23 24
1.171605652 -2.473547475 1.570796672 0.981309581 -0.912064827 2.706872012
25 26 27 28 29 30
0.353607104 0.728643083 -5.253518633 -0.342218430 0.522810877 4.906645565
31 32 33 34 35 36
4.306066655 -1.204247862 -0.720033883 1.993542165 -1.665397076 2.255619028
37 38 39 40 41 42
-1.103170287 1.596778880 -0.380734759 0.322604623 1.265937571 6.417687641
43 44 45 46 47 48
-4.643449542 -1.995193979 -0.355492156 2.530832276 3.494935541 0.005305489
49 50 51 52 53 54
-3.290097239 -2.097813729 -0.925075511 -5.879057162 -0.002322571 -2.023675145
55 56 57 58 59 60
1.763651529 -0.814712834 -0.380323050 0.840570833 -1.299960351 -0.507972176
61 62 63 64 65 66
-2.006887326 2.303125324 -0.323559007 1.843701694 -1.298416012 1.494169571
67 68 69 70 71 72
-1.909960643 2.772218782 1.960357289 -0.932126528 2.209444399 0.503832464
73 74 75 76 77 78
-0.464875565 -1.421618706 -0.811701530 -1.564258305 -0.323313711 -0.585837037
79 80 81 82 83 84
2.985559191 -2.175915499 -2.469321004 1.240207768 -0.265667461 -1.582706176
85 86 87 88 89 90
4.760406120 -0.413076347 0.095799000 -0.874501945 0.806676291 0.321768825
91 92 93 94 95 96
0.551049850 -3.843260321 0.432793276 1.147119692 1.106536040 0.236675790
97 98 99 100 101 102
-2.707214338 -0.430794602 -0.632565186 0.546171206 0.152899550 1.123144901
103 104 105 106 107 108
-1.470401003 1.726545032 -2.166285192 -0.452414177 -1.876290431 -1.430669033
109 110 111 112 113 114
-3.013037783 -3.764538640 -1.622993244 -2.144257069 -0.361114587 0.217003385
115 116 117 118 119 120
1.086498434 2.335359567 2.361803147 -1.199623675 0.637803432 3.641441416
121 122 123 124 125 126
0.389334303 1.935615935 -1.625085264 -0.616500896 2.647466325 1.058497094
127 128 129 130 131 132
0.484794911 -1.480513233 -1.107781793 -2.416621634 0.491236839 2.304929052
133 134 135 136 137 138
-1.743300374 -0.190064904 2.149332396 1.217324990 0.929148849 -3.904675456
139 140 141 142 143 144
4.721376469 -1.112178231 0.435748544 -0.082274854 1.665137538 0.036153967
145 146 147 148 149 150
0.963874844 -0.479302122 -2.879414749 -4.370553299 1.705594010 1.465637771
151 152 153 154 155 156
-0.203115515 0.795756208 -1.372217111 1.679535160 1.736999150 1.358187221
> postscript(file="/var/wessaorg/rcomp/tmp/6j22q1321989532.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 1.908852760 NA
1 0.730821137 1.908852760
2 0.848605352 0.730821137
3 0.796756364 0.848605352
4 -0.265179493 0.796756364
5 3.147416780 -0.265179493
6 -2.035344422 3.147416780
7 -1.708880095 -2.035344422
8 -0.734841470 -1.708880095
9 2.300890305 -0.734841470
10 -0.942512098 2.300890305
11 -1.187638423 -0.942512098
12 2.395973065 -1.187638423
13 -4.316339909 2.395973065
14 -0.843364694 -4.316339909
15 -0.231885207 -0.843364694
16 -2.179586013 -0.231885207
17 0.201520475 -2.179586013
18 1.171605652 0.201520475
19 -2.473547475 1.171605652
20 1.570796672 -2.473547475
21 0.981309581 1.570796672
22 -0.912064827 0.981309581
23 2.706872012 -0.912064827
24 0.353607104 2.706872012
25 0.728643083 0.353607104
26 -5.253518633 0.728643083
27 -0.342218430 -5.253518633
28 0.522810877 -0.342218430
29 4.906645565 0.522810877
30 4.306066655 4.906645565
31 -1.204247862 4.306066655
32 -0.720033883 -1.204247862
33 1.993542165 -0.720033883
34 -1.665397076 1.993542165
35 2.255619028 -1.665397076
36 -1.103170287 2.255619028
37 1.596778880 -1.103170287
38 -0.380734759 1.596778880
39 0.322604623 -0.380734759
40 1.265937571 0.322604623
41 6.417687641 1.265937571
42 -4.643449542 6.417687641
43 -1.995193979 -4.643449542
44 -0.355492156 -1.995193979
45 2.530832276 -0.355492156
46 3.494935541 2.530832276
47 0.005305489 3.494935541
48 -3.290097239 0.005305489
49 -2.097813729 -3.290097239
50 -0.925075511 -2.097813729
51 -5.879057162 -0.925075511
52 -0.002322571 -5.879057162
53 -2.023675145 -0.002322571
54 1.763651529 -2.023675145
55 -0.814712834 1.763651529
56 -0.380323050 -0.814712834
57 0.840570833 -0.380323050
58 -1.299960351 0.840570833
59 -0.507972176 -1.299960351
60 -2.006887326 -0.507972176
61 2.303125324 -2.006887326
62 -0.323559007 2.303125324
63 1.843701694 -0.323559007
64 -1.298416012 1.843701694
65 1.494169571 -1.298416012
66 -1.909960643 1.494169571
67 2.772218782 -1.909960643
68 1.960357289 2.772218782
69 -0.932126528 1.960357289
70 2.209444399 -0.932126528
71 0.503832464 2.209444399
72 -0.464875565 0.503832464
73 -1.421618706 -0.464875565
74 -0.811701530 -1.421618706
75 -1.564258305 -0.811701530
76 -0.323313711 -1.564258305
77 -0.585837037 -0.323313711
78 2.985559191 -0.585837037
79 -2.175915499 2.985559191
80 -2.469321004 -2.175915499
81 1.240207768 -2.469321004
82 -0.265667461 1.240207768
83 -1.582706176 -0.265667461
84 4.760406120 -1.582706176
85 -0.413076347 4.760406120
86 0.095799000 -0.413076347
87 -0.874501945 0.095799000
88 0.806676291 -0.874501945
89 0.321768825 0.806676291
90 0.551049850 0.321768825
91 -3.843260321 0.551049850
92 0.432793276 -3.843260321
93 1.147119692 0.432793276
94 1.106536040 1.147119692
95 0.236675790 1.106536040
96 -2.707214338 0.236675790
97 -0.430794602 -2.707214338
98 -0.632565186 -0.430794602
99 0.546171206 -0.632565186
100 0.152899550 0.546171206
101 1.123144901 0.152899550
102 -1.470401003 1.123144901
103 1.726545032 -1.470401003
104 -2.166285192 1.726545032
105 -0.452414177 -2.166285192
106 -1.876290431 -0.452414177
107 -1.430669033 -1.876290431
108 -3.013037783 -1.430669033
109 -3.764538640 -3.013037783
110 -1.622993244 -3.764538640
111 -2.144257069 -1.622993244
112 -0.361114587 -2.144257069
113 0.217003385 -0.361114587
114 1.086498434 0.217003385
115 2.335359567 1.086498434
116 2.361803147 2.335359567
117 -1.199623675 2.361803147
118 0.637803432 -1.199623675
119 3.641441416 0.637803432
120 0.389334303 3.641441416
121 1.935615935 0.389334303
122 -1.625085264 1.935615935
123 -0.616500896 -1.625085264
124 2.647466325 -0.616500896
125 1.058497094 2.647466325
126 0.484794911 1.058497094
127 -1.480513233 0.484794911
128 -1.107781793 -1.480513233
129 -2.416621634 -1.107781793
130 0.491236839 -2.416621634
131 2.304929052 0.491236839
132 -1.743300374 2.304929052
133 -0.190064904 -1.743300374
134 2.149332396 -0.190064904
135 1.217324990 2.149332396
136 0.929148849 1.217324990
137 -3.904675456 0.929148849
138 4.721376469 -3.904675456
139 -1.112178231 4.721376469
140 0.435748544 -1.112178231
141 -0.082274854 0.435748544
142 1.665137538 -0.082274854
143 0.036153967 1.665137538
144 0.963874844 0.036153967
145 -0.479302122 0.963874844
146 -2.879414749 -0.479302122
147 -4.370553299 -2.879414749
148 1.705594010 -4.370553299
149 1.465637771 1.705594010
150 -0.203115515 1.465637771
151 0.795756208 -0.203115515
152 -1.372217111 0.795756208
153 1.679535160 -1.372217111
154 1.736999150 1.679535160
155 1.358187221 1.736999150
156 NA 1.358187221
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.730821137 1.908852760
[2,] 0.848605352 0.730821137
[3,] 0.796756364 0.848605352
[4,] -0.265179493 0.796756364
[5,] 3.147416780 -0.265179493
[6,] -2.035344422 3.147416780
[7,] -1.708880095 -2.035344422
[8,] -0.734841470 -1.708880095
[9,] 2.300890305 -0.734841470
[10,] -0.942512098 2.300890305
[11,] -1.187638423 -0.942512098
[12,] 2.395973065 -1.187638423
[13,] -4.316339909 2.395973065
[14,] -0.843364694 -4.316339909
[15,] -0.231885207 -0.843364694
[16,] -2.179586013 -0.231885207
[17,] 0.201520475 -2.179586013
[18,] 1.171605652 0.201520475
[19,] -2.473547475 1.171605652
[20,] 1.570796672 -2.473547475
[21,] 0.981309581 1.570796672
[22,] -0.912064827 0.981309581
[23,] 2.706872012 -0.912064827
[24,] 0.353607104 2.706872012
[25,] 0.728643083 0.353607104
[26,] -5.253518633 0.728643083
[27,] -0.342218430 -5.253518633
[28,] 0.522810877 -0.342218430
[29,] 4.906645565 0.522810877
[30,] 4.306066655 4.906645565
[31,] -1.204247862 4.306066655
[32,] -0.720033883 -1.204247862
[33,] 1.993542165 -0.720033883
[34,] -1.665397076 1.993542165
[35,] 2.255619028 -1.665397076
[36,] -1.103170287 2.255619028
[37,] 1.596778880 -1.103170287
[38,] -0.380734759 1.596778880
[39,] 0.322604623 -0.380734759
[40,] 1.265937571 0.322604623
[41,] 6.417687641 1.265937571
[42,] -4.643449542 6.417687641
[43,] -1.995193979 -4.643449542
[44,] -0.355492156 -1.995193979
[45,] 2.530832276 -0.355492156
[46,] 3.494935541 2.530832276
[47,] 0.005305489 3.494935541
[48,] -3.290097239 0.005305489
[49,] -2.097813729 -3.290097239
[50,] -0.925075511 -2.097813729
[51,] -5.879057162 -0.925075511
[52,] -0.002322571 -5.879057162
[53,] -2.023675145 -0.002322571
[54,] 1.763651529 -2.023675145
[55,] -0.814712834 1.763651529
[56,] -0.380323050 -0.814712834
[57,] 0.840570833 -0.380323050
[58,] -1.299960351 0.840570833
[59,] -0.507972176 -1.299960351
[60,] -2.006887326 -0.507972176
[61,] 2.303125324 -2.006887326
[62,] -0.323559007 2.303125324
[63,] 1.843701694 -0.323559007
[64,] -1.298416012 1.843701694
[65,] 1.494169571 -1.298416012
[66,] -1.909960643 1.494169571
[67,] 2.772218782 -1.909960643
[68,] 1.960357289 2.772218782
[69,] -0.932126528 1.960357289
[70,] 2.209444399 -0.932126528
[71,] 0.503832464 2.209444399
[72,] -0.464875565 0.503832464
[73,] -1.421618706 -0.464875565
[74,] -0.811701530 -1.421618706
[75,] -1.564258305 -0.811701530
[76,] -0.323313711 -1.564258305
[77,] -0.585837037 -0.323313711
[78,] 2.985559191 -0.585837037
[79,] -2.175915499 2.985559191
[80,] -2.469321004 -2.175915499
[81,] 1.240207768 -2.469321004
[82,] -0.265667461 1.240207768
[83,] -1.582706176 -0.265667461
[84,] 4.760406120 -1.582706176
[85,] -0.413076347 4.760406120
[86,] 0.095799000 -0.413076347
[87,] -0.874501945 0.095799000
[88,] 0.806676291 -0.874501945
[89,] 0.321768825 0.806676291
[90,] 0.551049850 0.321768825
[91,] -3.843260321 0.551049850
[92,] 0.432793276 -3.843260321
[93,] 1.147119692 0.432793276
[94,] 1.106536040 1.147119692
[95,] 0.236675790 1.106536040
[96,] -2.707214338 0.236675790
[97,] -0.430794602 -2.707214338
[98,] -0.632565186 -0.430794602
[99,] 0.546171206 -0.632565186
[100,] 0.152899550 0.546171206
[101,] 1.123144901 0.152899550
[102,] -1.470401003 1.123144901
[103,] 1.726545032 -1.470401003
[104,] -2.166285192 1.726545032
[105,] -0.452414177 -2.166285192
[106,] -1.876290431 -0.452414177
[107,] -1.430669033 -1.876290431
[108,] -3.013037783 -1.430669033
[109,] -3.764538640 -3.013037783
[110,] -1.622993244 -3.764538640
[111,] -2.144257069 -1.622993244
[112,] -0.361114587 -2.144257069
[113,] 0.217003385 -0.361114587
[114,] 1.086498434 0.217003385
[115,] 2.335359567 1.086498434
[116,] 2.361803147 2.335359567
[117,] -1.199623675 2.361803147
[118,] 0.637803432 -1.199623675
[119,] 3.641441416 0.637803432
[120,] 0.389334303 3.641441416
[121,] 1.935615935 0.389334303
[122,] -1.625085264 1.935615935
[123,] -0.616500896 -1.625085264
[124,] 2.647466325 -0.616500896
[125,] 1.058497094 2.647466325
[126,] 0.484794911 1.058497094
[127,] -1.480513233 0.484794911
[128,] -1.107781793 -1.480513233
[129,] -2.416621634 -1.107781793
[130,] 0.491236839 -2.416621634
[131,] 2.304929052 0.491236839
[132,] -1.743300374 2.304929052
[133,] -0.190064904 -1.743300374
[134,] 2.149332396 -0.190064904
[135,] 1.217324990 2.149332396
[136,] 0.929148849 1.217324990
[137,] -3.904675456 0.929148849
[138,] 4.721376469 -3.904675456
[139,] -1.112178231 4.721376469
[140,] 0.435748544 -1.112178231
[141,] -0.082274854 0.435748544
[142,] 1.665137538 -0.082274854
[143,] 0.036153967 1.665137538
[144,] 0.963874844 0.036153967
[145,] -0.479302122 0.963874844
[146,] -2.879414749 -0.479302122
[147,] -4.370553299 -2.879414749
[148,] 1.705594010 -4.370553299
[149,] 1.465637771 1.705594010
[150,] -0.203115515 1.465637771
[151,] 0.795756208 -0.203115515
[152,] -1.372217111 0.795756208
[153,] 1.679535160 -1.372217111
[154,] 1.736999150 1.679535160
[155,] 1.358187221 1.736999150
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.730821137 1.908852760
2 0.848605352 0.730821137
3 0.796756364 0.848605352
4 -0.265179493 0.796756364
5 3.147416780 -0.265179493
6 -2.035344422 3.147416780
7 -1.708880095 -2.035344422
8 -0.734841470 -1.708880095
9 2.300890305 -0.734841470
10 -0.942512098 2.300890305
11 -1.187638423 -0.942512098
12 2.395973065 -1.187638423
13 -4.316339909 2.395973065
14 -0.843364694 -4.316339909
15 -0.231885207 -0.843364694
16 -2.179586013 -0.231885207
17 0.201520475 -2.179586013
18 1.171605652 0.201520475
19 -2.473547475 1.171605652
20 1.570796672 -2.473547475
21 0.981309581 1.570796672
22 -0.912064827 0.981309581
23 2.706872012 -0.912064827
24 0.353607104 2.706872012
25 0.728643083 0.353607104
26 -5.253518633 0.728643083
27 -0.342218430 -5.253518633
28 0.522810877 -0.342218430
29 4.906645565 0.522810877
30 4.306066655 4.906645565
31 -1.204247862 4.306066655
32 -0.720033883 -1.204247862
33 1.993542165 -0.720033883
34 -1.665397076 1.993542165
35 2.255619028 -1.665397076
36 -1.103170287 2.255619028
37 1.596778880 -1.103170287
38 -0.380734759 1.596778880
39 0.322604623 -0.380734759
40 1.265937571 0.322604623
41 6.417687641 1.265937571
42 -4.643449542 6.417687641
43 -1.995193979 -4.643449542
44 -0.355492156 -1.995193979
45 2.530832276 -0.355492156
46 3.494935541 2.530832276
47 0.005305489 3.494935541
48 -3.290097239 0.005305489
49 -2.097813729 -3.290097239
50 -0.925075511 -2.097813729
51 -5.879057162 -0.925075511
52 -0.002322571 -5.879057162
53 -2.023675145 -0.002322571
54 1.763651529 -2.023675145
55 -0.814712834 1.763651529
56 -0.380323050 -0.814712834
57 0.840570833 -0.380323050
58 -1.299960351 0.840570833
59 -0.507972176 -1.299960351
60 -2.006887326 -0.507972176
61 2.303125324 -2.006887326
62 -0.323559007 2.303125324
63 1.843701694 -0.323559007
64 -1.298416012 1.843701694
65 1.494169571 -1.298416012
66 -1.909960643 1.494169571
67 2.772218782 -1.909960643
68 1.960357289 2.772218782
69 -0.932126528 1.960357289
70 2.209444399 -0.932126528
71 0.503832464 2.209444399
72 -0.464875565 0.503832464
73 -1.421618706 -0.464875565
74 -0.811701530 -1.421618706
75 -1.564258305 -0.811701530
76 -0.323313711 -1.564258305
77 -0.585837037 -0.323313711
78 2.985559191 -0.585837037
79 -2.175915499 2.985559191
80 -2.469321004 -2.175915499
81 1.240207768 -2.469321004
82 -0.265667461 1.240207768
83 -1.582706176 -0.265667461
84 4.760406120 -1.582706176
85 -0.413076347 4.760406120
86 0.095799000 -0.413076347
87 -0.874501945 0.095799000
88 0.806676291 -0.874501945
89 0.321768825 0.806676291
90 0.551049850 0.321768825
91 -3.843260321 0.551049850
92 0.432793276 -3.843260321
93 1.147119692 0.432793276
94 1.106536040 1.147119692
95 0.236675790 1.106536040
96 -2.707214338 0.236675790
97 -0.430794602 -2.707214338
98 -0.632565186 -0.430794602
99 0.546171206 -0.632565186
100 0.152899550 0.546171206
101 1.123144901 0.152899550
102 -1.470401003 1.123144901
103 1.726545032 -1.470401003
104 -2.166285192 1.726545032
105 -0.452414177 -2.166285192
106 -1.876290431 -0.452414177
107 -1.430669033 -1.876290431
108 -3.013037783 -1.430669033
109 -3.764538640 -3.013037783
110 -1.622993244 -3.764538640
111 -2.144257069 -1.622993244
112 -0.361114587 -2.144257069
113 0.217003385 -0.361114587
114 1.086498434 0.217003385
115 2.335359567 1.086498434
116 2.361803147 2.335359567
117 -1.199623675 2.361803147
118 0.637803432 -1.199623675
119 3.641441416 0.637803432
120 0.389334303 3.641441416
121 1.935615935 0.389334303
122 -1.625085264 1.935615935
123 -0.616500896 -1.625085264
124 2.647466325 -0.616500896
125 1.058497094 2.647466325
126 0.484794911 1.058497094
127 -1.480513233 0.484794911
128 -1.107781793 -1.480513233
129 -2.416621634 -1.107781793
130 0.491236839 -2.416621634
131 2.304929052 0.491236839
132 -1.743300374 2.304929052
133 -0.190064904 -1.743300374
134 2.149332396 -0.190064904
135 1.217324990 2.149332396
136 0.929148849 1.217324990
137 -3.904675456 0.929148849
138 4.721376469 -3.904675456
139 -1.112178231 4.721376469
140 0.435748544 -1.112178231
141 -0.082274854 0.435748544
142 1.665137538 -0.082274854
143 0.036153967 1.665137538
144 0.963874844 0.036153967
145 -0.479302122 0.963874844
146 -2.879414749 -0.479302122
147 -4.370553299 -2.879414749
148 1.705594010 -4.370553299
149 1.465637771 1.705594010
150 -0.203115515 1.465637771
151 0.795756208 -0.203115515
152 -1.372217111 0.795756208
153 1.679535160 -1.372217111
154 1.736999150 1.679535160
155 1.358187221 1.736999150
> 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/7akpp1321989532.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/83la31321989532.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/9sj3z1321989532.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/1096gq1321989532.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11tph51321989532.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12qrr01321989532.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/132dam1321989532.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14gccr1321989532.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/15w1gz1321989532.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/16nq3f1321989532.tab")
+ }
>
> try(system("convert tmp/106901321989532.ps tmp/106901321989532.png",intern=TRUE))
character(0)
> try(system("convert tmp/27p2g1321989532.ps tmp/27p2g1321989532.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gn8q1321989532.ps tmp/3gn8q1321989532.png",intern=TRUE))
character(0)
> try(system("convert tmp/4b0al1321989532.ps tmp/4b0al1321989532.png",intern=TRUE))
character(0)
> try(system("convert tmp/5abl91321989532.ps tmp/5abl91321989532.png",intern=TRUE))
character(0)
> try(system("convert tmp/6j22q1321989532.ps tmp/6j22q1321989532.png",intern=TRUE))
character(0)
> try(system("convert tmp/7akpp1321989532.ps tmp/7akpp1321989532.png",intern=TRUE))
character(0)
> try(system("convert tmp/83la31321989532.ps tmp/83la31321989532.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sj3z1321989532.ps tmp/9sj3z1321989532.png",intern=TRUE))
character(0)
> try(system("convert tmp/1096gq1321989532.ps tmp/1096gq1321989532.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.194 0.515 5.785