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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> x <- array(list(13
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+ ,0)
+ ,dim=c(8
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'bestfriend'
+ ,'secondbestfriend'
+ ,'thirdbestfriend')
+ ,1:156))
> y <- array(NA,dim=c(8,156),dimnames=list(c('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])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> 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 FindingFriends KnowingPeople Liked Celebrity bestfriend
1 13 13 14 13 3 1
2 12 12 8 13 5 1
3 15 10 12 16 6 0
4 12 9 7 12 6 2
5 10 10 10 11 5 0
6 12 12 7 12 3 0
7 15 13 16 18 8 1
8 9 12 11 11 4 1
9 12 12 14 14 4 4
10 11 6 6 9 4 0
11 11 5 16 14 6 0
12 11 12 11 12 6 2
13 15 11 16 11 5 0
14 7 14 12 12 4 1
15 11 14 7 13 6 0
16 11 12 13 11 4 0
17 10 12 11 12 6 1
18 14 11 15 16 6 2
19 10 11 7 9 4 1
20 6 7 9 11 4 1
21 11 9 7 13 2 0
22 15 11 14 15 7 1
23 11 11 15 10 5 1
24 12 12 7 11 4 2
25 14 12 15 13 6 1
26 15 11 17 16 6 1
27 9 11 15 15 7 1
28 13 8 14 14 5 2
29 13 9 14 14 6 0
30 16 12 8 14 4 1
31 13 10 8 8 4 0
32 12 10 14 13 7 1
33 14 12 14 15 7 1
34 11 8 8 13 4 0
35 9 12 11 11 4 1
36 16 11 16 15 6 2
37 12 12 10 15 6 1
38 10 7 8 9 5 1
39 13 11 14 13 6 1
40 16 11 16 16 7 1
41 14 12 13 13 6 0
42 15 9 5 11 3 1
43 5 15 8 12 3 1
44 8 11 10 12 4 1
45 11 11 8 12 6 0
46 16 11 13 14 7 2
47 17 11 15 14 5 1
48 9 15 6 8 4 0
49 9 11 12 13 5 0
50 13 12 16 16 6 1
51 10 12 5 13 6 1
52 6 9 15 11 6 0
53 12 12 12 14 5 0
54 8 12 8 13 4 0
55 14 13 13 13 5 0
56 12 11 14 13 5 1
57 11 9 12 12 4 0
58 16 9 16 16 6 0
59 8 11 10 15 2 1
60 15 11 15 15 8 0
61 7 12 8 12 3 0
62 16 12 16 14 6 2
63 14 9 19 12 6 0
64 16 11 14 15 6 0
65 9 9 6 12 5 1
66 14 12 13 13 5 2
67 11 12 15 12 6 3
68 13 12 7 12 5 1
69 15 12 13 13 6 1
70 5 14 4 5 2 2
71 15 11 14 13 5 1
72 13 12 13 13 5 1
73 11 11 11 14 5 2
74 11 6 14 17 6 1
75 12 10 12 13 6 0
76 12 12 15 13 6 1
77 12 13 14 12 5 1
78 12 8 13 13 5 0
79 14 12 8 14 4 2
80 6 12 6 11 2 1
81 7 12 7 12 4 0
82 14 6 13 12 6 3
83 14 11 13 16 6 1
84 10 10 11 12 5 1
85 13 12 5 12 3 3
86 12 13 12 12 6 2
87 9 11 8 10 4 1
88 12 7 11 15 5 0
89 16 11 14 15 8 1
90 10 11 9 12 4 2
91 14 11 10 16 6 1
92 10 11 13 15 6 1
93 16 12 16 16 7 0
94 15 10 16 13 6 2
95 12 11 11 12 5 1
96 10 12 8 11 4 0
97 8 7 4 13 6 0
98 8 13 7 10 3 1
99 11 8 14 15 5 1
100 13 12 11 13 6 1
101 16 11 17 16 7 1
102 16 12 15 15 7 1
103 14 14 17 18 6 0
104 11 10 5 13 3 0
105 4 10 4 10 2 1
106 14 13 10 16 8 2
107 9 10 11 13 3 1
108 14 11 15 15 8 1
109 8 10 10 14 3 0
110 8 7 9 15 4 0
111 11 10 12 14 5 1
112 12 8 15 13 7 1
113 11 12 7 13 6 0
114 14 12 13 15 6 0
115 15 12 12 16 7 2
116 16 11 14 14 6 2
117 16 12 14 14 6 0
118 11 12 8 16 6 1
119 14 12 15 14 6 0
120 14 11 12 12 4 2
121 12 12 12 13 4 1
122 14 11 16 12 5 0
123 8 11 9 12 4 1
124 13 13 15 14 6 1
125 16 12 15 14 6 2
126 12 12 6 14 5 0
127 16 12 14 16 8 2
128 12 12 15 13 6 0
129 11 8 10 14 5 1
130 4 8 6 4 4 0
131 16 12 14 16 8 3
132 15 11 12 13 6 1
133 10 12 8 16 4 0
134 13 13 11 15 6 0
135 15 12 13 14 6 0
136 12 12 9 13 4 0
137 14 11 15 14 6 0
138 7 12 13 12 3 1
139 19 12 15 15 6 1
140 12 10 14 14 5 2
141 12 11 16 13 4 1
142 13 12 14 14 6 0
143 15 12 14 16 4 0
144 8 10 10 6 4 2
145 12 12 10 13 4 1
146 10 13 4 13 6 0
147 8 12 8 14 5 1
148 10 15 15 15 6 2
149 15 11 16 14 6 2
150 16 12 12 15 8 0
151 13 11 12 13 7 1
152 16 12 15 16 7 2
153 9 11 9 12 4 0
154 14 10 12 15 6 1
155 14 11 14 12 6 2
156 12 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) FindingFriends KnowingPeople Liked
-0.169377 0.102589 0.211592 0.385987
Celebrity bestfriend secondbestfriend thirdbestfriend
0.592858 0.310772 -0.031552 0.410808
t
-0.001014
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.0258 -1.2472 -0.0386 1.3696 6.8955
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.169377 1.437657 -0.118 0.906375
FindingFriends 0.102589 0.097394 1.053 0.293912
KnowingPeople 0.211592 0.063836 3.315 0.001156 **
Liked 0.385987 0.098544 3.917 0.000137 ***
Celebrity 0.592858 0.156043 3.799 0.000212 ***
bestfriend 0.310772 0.210102 1.479 0.141241
secondbestfriend -0.031552 0.201592 -0.157 0.875845
thirdbestfriend 0.410808 0.213829 1.921 0.056642 .
t -0.001014 0.003800 -0.267 0.789984
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.096 on 147 degrees of freedom
Multiple R-squared: 0.517, Adjusted R-squared: 0.4908
F-statistic: 19.67 on 8 and 147 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,] 0.2559410 0.51188208 0.744058961
[2,] 0.7292894 0.54142126 0.270710629
[3,] 0.8391115 0.32177692 0.160888458
[4,] 0.8159234 0.36815327 0.184076635
[5,] 0.7322363 0.53552736 0.267763681
[6,] 0.6421454 0.71570915 0.357854577
[7,] 0.5820957 0.83580859 0.417904297
[8,] 0.5248761 0.95024771 0.475123853
[9,] 0.6323418 0.73531639 0.367658196
[10,] 0.6279704 0.74405924 0.372029618
[11,] 0.6884120 0.62317600 0.311588002
[12,] 0.6235818 0.75283637 0.376418186
[13,] 0.6530681 0.69386390 0.346931950
[14,] 0.6306220 0.73875595 0.369377973
[15,] 0.5710765 0.85784698 0.428923488
[16,] 0.7690074 0.46198521 0.230992607
[17,] 0.7317017 0.53659669 0.268298344
[18,] 0.6747998 0.65040046 0.325200232
[19,] 0.7874671 0.42506579 0.212532893
[20,] 0.8345826 0.33083476 0.165417380
[21,] 0.8019781 0.39604379 0.198021895
[22,] 0.7567824 0.48643518 0.243217590
[23,] 0.7221575 0.55568503 0.277842517
[24,] 0.7040282 0.59194362 0.295971809
[25,] 0.7261190 0.54776204 0.273881022
[26,] 0.7100151 0.57996974 0.289984872
[27,] 0.6642486 0.67150281 0.335751403
[28,] 0.6131344 0.77373124 0.386865621
[29,] 0.5685097 0.86298052 0.431490258
[30,] 0.5175084 0.96498316 0.482491582
[31,] 0.8040062 0.39198757 0.195993785
[32,] 0.9626591 0.07468175 0.037340873
[33,] 0.9653352 0.06932958 0.034664788
[34,] 0.9547856 0.09042880 0.045214401
[35,] 0.9593826 0.08123481 0.040617406
[36,] 0.9609061 0.07818779 0.039093897
[37,] 0.9528571 0.09428582 0.047142908
[38,] 0.9521576 0.09568488 0.047842442
[39,] 0.9455551 0.10888988 0.054444941
[40,] 0.9367443 0.12651141 0.063255703
[41,] 0.9881605 0.02367898 0.011839489
[42,] 0.9844433 0.03111336 0.015556679
[43,] 0.9882169 0.02356618 0.011783092
[44,] 0.9921206 0.01575871 0.007879357
[45,] 0.9895777 0.02084454 0.010422272
[46,] 0.9858210 0.02835806 0.014179028
[47,] 0.9858710 0.02825809 0.014129047
[48,] 0.9886678 0.02266430 0.011332152
[49,] 0.9875718 0.02485647 0.012428237
[50,] 0.9873345 0.02533104 0.012665518
[51,] 0.9897385 0.02052297 0.010261483
[52,] 0.9889609 0.02207818 0.011039088
[53,] 0.9903618 0.01927646 0.009638231
[54,] 0.9886021 0.02279588 0.011397942
[55,] 0.9874775 0.02504495 0.012522476
[56,] 0.9885440 0.02291207 0.011456036
[57,] 0.9906279 0.01874426 0.009372128
[58,] 0.9907689 0.01846225 0.009231125
[59,] 0.9877830 0.02443398 0.012216989
[60,] 0.9886470 0.02270609 0.011353044
[61,] 0.9861893 0.02762131 0.013810655
[62,] 0.9858306 0.02833879 0.014169393
[63,] 0.9896797 0.02064059 0.010320295
[64,] 0.9861522 0.02769551 0.013847755
[65,] 0.9833077 0.03338457 0.016692286
[66,] 0.9781803 0.04363939 0.021819695
[67,] 0.9715199 0.05696023 0.028480115
[68,] 0.9795196 0.04096070 0.020480351
[69,] 0.9780895 0.04382098 0.021910492
[70,] 0.9786997 0.04260053 0.021300267
[71,] 0.9768692 0.04626167 0.023130837
[72,] 0.9694181 0.06116371 0.030581853
[73,] 0.9613423 0.07731536 0.038657680
[74,] 0.9848009 0.03039828 0.015199138
[75,] 0.9796444 0.04071117 0.020355583
[76,] 0.9733356 0.05332887 0.026664433
[77,] 0.9655839 0.06883219 0.034416093
[78,] 0.9579387 0.08412264 0.042061318
[79,] 0.9471118 0.10577647 0.052888235
[80,] 0.9401073 0.11978537 0.059892686
[81,] 0.9620340 0.07593192 0.037965959
[82,] 0.9536639 0.09267222 0.046336110
[83,] 0.9514450 0.09710997 0.048554984
[84,] 0.9413241 0.11735172 0.058675860
[85,] 0.9302814 0.13943725 0.069718624
[86,] 0.9245492 0.15090154 0.075450770
[87,] 0.9084677 0.18306466 0.091532328
[88,] 0.8954608 0.20907848 0.104539241
[89,] 0.8721677 0.25566460 0.127832301
[90,] 0.8439056 0.31218873 0.156094366
[91,] 0.8192026 0.36159487 0.180797436
[92,] 0.8316550 0.33668992 0.168344959
[93,] 0.8828964 0.23420726 0.117103630
[94,] 0.8830688 0.23386230 0.116931152
[95,] 0.8548018 0.29039643 0.145198215
[96,] 0.8270985 0.34580297 0.172901485
[97,] 0.8154003 0.36919936 0.184599680
[98,] 0.8003430 0.39931393 0.199656966
[99,] 0.8204454 0.35910917 0.179554585
[100,] 0.8071834 0.38563316 0.192816581
[101,] 0.8657168 0.26856639 0.134283195
[102,] 0.8331083 0.33378334 0.166891671
[103,] 0.8041186 0.39176288 0.195881442
[104,] 0.7637904 0.47241927 0.236209637
[105,] 0.7690478 0.46190439 0.230952195
[106,] 0.7785118 0.44297639 0.221488197
[107,] 0.7626740 0.47465206 0.237326032
[108,] 0.7159169 0.56816629 0.284083145
[109,] 0.8041242 0.39175155 0.195875776
[110,] 0.7768136 0.44637285 0.223186424
[111,] 0.7281007 0.54379853 0.271899266
[112,] 0.7176802 0.56463964 0.282319822
[113,] 0.6788157 0.64236852 0.321184262
[114,] 0.6609098 0.67818032 0.339090161
[115,] 0.6623548 0.67529034 0.337645172
[116,] 0.5992345 0.80153106 0.400765531
[117,] 0.5606050 0.87879005 0.439395023
[118,] 0.5409545 0.91809098 0.459045488
[119,] 0.5260752 0.94784957 0.473924784
[120,] 0.4546111 0.90922224 0.545388878
[121,] 0.4341566 0.86831314 0.565843432
[122,] 0.3887323 0.77746463 0.611267683
[123,] 0.3438531 0.68770621 0.656146894
[124,] 0.2971714 0.59434284 0.702828579
[125,] 0.2791937 0.55838737 0.720806314
[126,] 0.2421386 0.48427717 0.757861413
[127,] 0.2636575 0.52731510 0.736342451
[128,] 0.7119260 0.57614808 0.288074038
[129,] 0.6362846 0.72743074 0.363715369
[130,] 0.5259463 0.94810738 0.474053691
[131,] 0.5377001 0.92459982 0.462299909
[132,] 0.4206675 0.84133498 0.579332512
[133,] 0.2742596 0.54851923 0.725740383
> postscript(file="/var/wessaorg/rcomp/tmp/14qpx1321815556.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/25tu41321815556.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3gyju1321815556.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/47oac1321815556.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5sl1d1321815556.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
1.798817640 0.954701850 1.874479813 0.547638010 -0.967579078 2.642017026
7 8 9 10 11 12
-1.923318605 -1.309155951 -1.033194475 3.449112193 -2.026558622 -1.187576187
13 14 15 16 17 18
2.699945550 -4.485086456 -0.276238722 0.175736317 -1.840182932 -0.880026910
19 20 21 22 23 24
1.422927737 -3.360858801 2.203416904 0.913432661 -0.592299687 2.242661575
25 26 27 28 29 30
0.904023701 0.458033393 -5.324640617 0.488216529 -0.469392888 4.810706963
31 32 33 34 35 36
4.232787302 -1.644223544 -0.588811383 1.364239128 -1.250227585 1.786531425
37 38 39 40 41 42
-1.177082534 0.258035278 -0.178408298 0.743256736 0.864137052 6.895494166
43 44 45 46 47 48
-4.739789741 -2.344459982 -0.174464508 1.750663265 2.718764196 0.950326051
49 50 51 52 53 54
-2.430647060 -1.850989194 -0.922146498 -6.025790082 0.116383632 -2.468198778
55 56 57 58 59 60
2.158666508 -0.916017846 -0.060127548 1.870316932 -2.238392829 0.395182001
61 62 63 64 65 66
-2.102998617 2.096292335 1.132266678 2.385737889 -1.212185686 1.650865464
67 68 69 70 71 72
-2.257395123 2.619201902 2.024115685 -0.779459582 2.099191511 1.062375724
73 74 75 76 77 78
-2.023868293 -3.110819536 -0.210706929 -1.423521217 -0.777019087 -0.095130878
79 80 81 82 83 84
2.928875648 -1.992476567 -2.432434799 1.385724961 -0.017061626 -0.974210783
85 86 87 88 89 90
3.623795137 -0.426724217 -0.074150538 -0.362745120 0.914598096 -0.807806615
91 92 93 94 95 96
1.005081380 -3.653500193 1.005179091 1.688353416 0.523545173 0.725618837
97 98 99 100 101 102
-2.282554179 -0.463725717 -1.546559438 0.004820131 0.119605278 0.827200339
103 104 105 106 107 108
-1.675222381 2.015310718 -3.363586838 -0.681376619 -1.561968591 -1.246176734
109 110 111 112 113 114
-2.012756281 -2.471228563 -0.961951690 -1.190263149 -0.003243814 0.987797746
115 116 117 118 119 120
0.600013622 2.645266891 3.133683897 -1.646948017 0.639518113 2.998645736
121 122 123 124 125 126
0.442598247 0.950568229 -2.811279292 -1.274236191 1.487043615 1.428400213
127 128 129 130 131 132
0.153779862 -0.680766771 -0.283787225 -1.704454874 -0.089833207 1.928264537
133 134 135 136 137 138
-1.135249940 -1.283010847 2.426629712 1.814162421 1.139615389 -3.393653560
139 140 141 142 143 144
5.279191650 -1.045758688 0.098357280 -0.220223906 2.573788872 -0.925387827
145 146 147 148 149 150
0.858564772 -0.406046405 -3.284261711 -4.298671108 0.813184618 2.018623441
151 152 153 154 155 156
-0.202968887 1.002755417 -0.711575172 0.691994480 0.635169394 2.002805290
> postscript(file="/var/wessaorg/rcomp/tmp/6xm241321815556.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 1.798817640 NA
1 0.954701850 1.798817640
2 1.874479813 0.954701850
3 0.547638010 1.874479813
4 -0.967579078 0.547638010
5 2.642017026 -0.967579078
6 -1.923318605 2.642017026
7 -1.309155951 -1.923318605
8 -1.033194475 -1.309155951
9 3.449112193 -1.033194475
10 -2.026558622 3.449112193
11 -1.187576187 -2.026558622
12 2.699945550 -1.187576187
13 -4.485086456 2.699945550
14 -0.276238722 -4.485086456
15 0.175736317 -0.276238722
16 -1.840182932 0.175736317
17 -0.880026910 -1.840182932
18 1.422927737 -0.880026910
19 -3.360858801 1.422927737
20 2.203416904 -3.360858801
21 0.913432661 2.203416904
22 -0.592299687 0.913432661
23 2.242661575 -0.592299687
24 0.904023701 2.242661575
25 0.458033393 0.904023701
26 -5.324640617 0.458033393
27 0.488216529 -5.324640617
28 -0.469392888 0.488216529
29 4.810706963 -0.469392888
30 4.232787302 4.810706963
31 -1.644223544 4.232787302
32 -0.588811383 -1.644223544
33 1.364239128 -0.588811383
34 -1.250227585 1.364239128
35 1.786531425 -1.250227585
36 -1.177082534 1.786531425
37 0.258035278 -1.177082534
38 -0.178408298 0.258035278
39 0.743256736 -0.178408298
40 0.864137052 0.743256736
41 6.895494166 0.864137052
42 -4.739789741 6.895494166
43 -2.344459982 -4.739789741
44 -0.174464508 -2.344459982
45 1.750663265 -0.174464508
46 2.718764196 1.750663265
47 0.950326051 2.718764196
48 -2.430647060 0.950326051
49 -1.850989194 -2.430647060
50 -0.922146498 -1.850989194
51 -6.025790082 -0.922146498
52 0.116383632 -6.025790082
53 -2.468198778 0.116383632
54 2.158666508 -2.468198778
55 -0.916017846 2.158666508
56 -0.060127548 -0.916017846
57 1.870316932 -0.060127548
58 -2.238392829 1.870316932
59 0.395182001 -2.238392829
60 -2.102998617 0.395182001
61 2.096292335 -2.102998617
62 1.132266678 2.096292335
63 2.385737889 1.132266678
64 -1.212185686 2.385737889
65 1.650865464 -1.212185686
66 -2.257395123 1.650865464
67 2.619201902 -2.257395123
68 2.024115685 2.619201902
69 -0.779459582 2.024115685
70 2.099191511 -0.779459582
71 1.062375724 2.099191511
72 -2.023868293 1.062375724
73 -3.110819536 -2.023868293
74 -0.210706929 -3.110819536
75 -1.423521217 -0.210706929
76 -0.777019087 -1.423521217
77 -0.095130878 -0.777019087
78 2.928875648 -0.095130878
79 -1.992476567 2.928875648
80 -2.432434799 -1.992476567
81 1.385724961 -2.432434799
82 -0.017061626 1.385724961
83 -0.974210783 -0.017061626
84 3.623795137 -0.974210783
85 -0.426724217 3.623795137
86 -0.074150538 -0.426724217
87 -0.362745120 -0.074150538
88 0.914598096 -0.362745120
89 -0.807806615 0.914598096
90 1.005081380 -0.807806615
91 -3.653500193 1.005081380
92 1.005179091 -3.653500193
93 1.688353416 1.005179091
94 0.523545173 1.688353416
95 0.725618837 0.523545173
96 -2.282554179 0.725618837
97 -0.463725717 -2.282554179
98 -1.546559438 -0.463725717
99 0.004820131 -1.546559438
100 0.119605278 0.004820131
101 0.827200339 0.119605278
102 -1.675222381 0.827200339
103 2.015310718 -1.675222381
104 -3.363586838 2.015310718
105 -0.681376619 -3.363586838
106 -1.561968591 -0.681376619
107 -1.246176734 -1.561968591
108 -2.012756281 -1.246176734
109 -2.471228563 -2.012756281
110 -0.961951690 -2.471228563
111 -1.190263149 -0.961951690
112 -0.003243814 -1.190263149
113 0.987797746 -0.003243814
114 0.600013622 0.987797746
115 2.645266891 0.600013622
116 3.133683897 2.645266891
117 -1.646948017 3.133683897
118 0.639518113 -1.646948017
119 2.998645736 0.639518113
120 0.442598247 2.998645736
121 0.950568229 0.442598247
122 -2.811279292 0.950568229
123 -1.274236191 -2.811279292
124 1.487043615 -1.274236191
125 1.428400213 1.487043615
126 0.153779862 1.428400213
127 -0.680766771 0.153779862
128 -0.283787225 -0.680766771
129 -1.704454874 -0.283787225
130 -0.089833207 -1.704454874
131 1.928264537 -0.089833207
132 -1.135249940 1.928264537
133 -1.283010847 -1.135249940
134 2.426629712 -1.283010847
135 1.814162421 2.426629712
136 1.139615389 1.814162421
137 -3.393653560 1.139615389
138 5.279191650 -3.393653560
139 -1.045758688 5.279191650
140 0.098357280 -1.045758688
141 -0.220223906 0.098357280
142 2.573788872 -0.220223906
143 -0.925387827 2.573788872
144 0.858564772 -0.925387827
145 -0.406046405 0.858564772
146 -3.284261711 -0.406046405
147 -4.298671108 -3.284261711
148 0.813184618 -4.298671108
149 2.018623441 0.813184618
150 -0.202968887 2.018623441
151 1.002755417 -0.202968887
152 -0.711575172 1.002755417
153 0.691994480 -0.711575172
154 0.635169394 0.691994480
155 2.002805290 0.635169394
156 NA 2.002805290
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.954701850 1.798817640
[2,] 1.874479813 0.954701850
[3,] 0.547638010 1.874479813
[4,] -0.967579078 0.547638010
[5,] 2.642017026 -0.967579078
[6,] -1.923318605 2.642017026
[7,] -1.309155951 -1.923318605
[8,] -1.033194475 -1.309155951
[9,] 3.449112193 -1.033194475
[10,] -2.026558622 3.449112193
[11,] -1.187576187 -2.026558622
[12,] 2.699945550 -1.187576187
[13,] -4.485086456 2.699945550
[14,] -0.276238722 -4.485086456
[15,] 0.175736317 -0.276238722
[16,] -1.840182932 0.175736317
[17,] -0.880026910 -1.840182932
[18,] 1.422927737 -0.880026910
[19,] -3.360858801 1.422927737
[20,] 2.203416904 -3.360858801
[21,] 0.913432661 2.203416904
[22,] -0.592299687 0.913432661
[23,] 2.242661575 -0.592299687
[24,] 0.904023701 2.242661575
[25,] 0.458033393 0.904023701
[26,] -5.324640617 0.458033393
[27,] 0.488216529 -5.324640617
[28,] -0.469392888 0.488216529
[29,] 4.810706963 -0.469392888
[30,] 4.232787302 4.810706963
[31,] -1.644223544 4.232787302
[32,] -0.588811383 -1.644223544
[33,] 1.364239128 -0.588811383
[34,] -1.250227585 1.364239128
[35,] 1.786531425 -1.250227585
[36,] -1.177082534 1.786531425
[37,] 0.258035278 -1.177082534
[38,] -0.178408298 0.258035278
[39,] 0.743256736 -0.178408298
[40,] 0.864137052 0.743256736
[41,] 6.895494166 0.864137052
[42,] -4.739789741 6.895494166
[43,] -2.344459982 -4.739789741
[44,] -0.174464508 -2.344459982
[45,] 1.750663265 -0.174464508
[46,] 2.718764196 1.750663265
[47,] 0.950326051 2.718764196
[48,] -2.430647060 0.950326051
[49,] -1.850989194 -2.430647060
[50,] -0.922146498 -1.850989194
[51,] -6.025790082 -0.922146498
[52,] 0.116383632 -6.025790082
[53,] -2.468198778 0.116383632
[54,] 2.158666508 -2.468198778
[55,] -0.916017846 2.158666508
[56,] -0.060127548 -0.916017846
[57,] 1.870316932 -0.060127548
[58,] -2.238392829 1.870316932
[59,] 0.395182001 -2.238392829
[60,] -2.102998617 0.395182001
[61,] 2.096292335 -2.102998617
[62,] 1.132266678 2.096292335
[63,] 2.385737889 1.132266678
[64,] -1.212185686 2.385737889
[65,] 1.650865464 -1.212185686
[66,] -2.257395123 1.650865464
[67,] 2.619201902 -2.257395123
[68,] 2.024115685 2.619201902
[69,] -0.779459582 2.024115685
[70,] 2.099191511 -0.779459582
[71,] 1.062375724 2.099191511
[72,] -2.023868293 1.062375724
[73,] -3.110819536 -2.023868293
[74,] -0.210706929 -3.110819536
[75,] -1.423521217 -0.210706929
[76,] -0.777019087 -1.423521217
[77,] -0.095130878 -0.777019087
[78,] 2.928875648 -0.095130878
[79,] -1.992476567 2.928875648
[80,] -2.432434799 -1.992476567
[81,] 1.385724961 -2.432434799
[82,] -0.017061626 1.385724961
[83,] -0.974210783 -0.017061626
[84,] 3.623795137 -0.974210783
[85,] -0.426724217 3.623795137
[86,] -0.074150538 -0.426724217
[87,] -0.362745120 -0.074150538
[88,] 0.914598096 -0.362745120
[89,] -0.807806615 0.914598096
[90,] 1.005081380 -0.807806615
[91,] -3.653500193 1.005081380
[92,] 1.005179091 -3.653500193
[93,] 1.688353416 1.005179091
[94,] 0.523545173 1.688353416
[95,] 0.725618837 0.523545173
[96,] -2.282554179 0.725618837
[97,] -0.463725717 -2.282554179
[98,] -1.546559438 -0.463725717
[99,] 0.004820131 -1.546559438
[100,] 0.119605278 0.004820131
[101,] 0.827200339 0.119605278
[102,] -1.675222381 0.827200339
[103,] 2.015310718 -1.675222381
[104,] -3.363586838 2.015310718
[105,] -0.681376619 -3.363586838
[106,] -1.561968591 -0.681376619
[107,] -1.246176734 -1.561968591
[108,] -2.012756281 -1.246176734
[109,] -2.471228563 -2.012756281
[110,] -0.961951690 -2.471228563
[111,] -1.190263149 -0.961951690
[112,] -0.003243814 -1.190263149
[113,] 0.987797746 -0.003243814
[114,] 0.600013622 0.987797746
[115,] 2.645266891 0.600013622
[116,] 3.133683897 2.645266891
[117,] -1.646948017 3.133683897
[118,] 0.639518113 -1.646948017
[119,] 2.998645736 0.639518113
[120,] 0.442598247 2.998645736
[121,] 0.950568229 0.442598247
[122,] -2.811279292 0.950568229
[123,] -1.274236191 -2.811279292
[124,] 1.487043615 -1.274236191
[125,] 1.428400213 1.487043615
[126,] 0.153779862 1.428400213
[127,] -0.680766771 0.153779862
[128,] -0.283787225 -0.680766771
[129,] -1.704454874 -0.283787225
[130,] -0.089833207 -1.704454874
[131,] 1.928264537 -0.089833207
[132,] -1.135249940 1.928264537
[133,] -1.283010847 -1.135249940
[134,] 2.426629712 -1.283010847
[135,] 1.814162421 2.426629712
[136,] 1.139615389 1.814162421
[137,] -3.393653560 1.139615389
[138,] 5.279191650 -3.393653560
[139,] -1.045758688 5.279191650
[140,] 0.098357280 -1.045758688
[141,] -0.220223906 0.098357280
[142,] 2.573788872 -0.220223906
[143,] -0.925387827 2.573788872
[144,] 0.858564772 -0.925387827
[145,] -0.406046405 0.858564772
[146,] -3.284261711 -0.406046405
[147,] -4.298671108 -3.284261711
[148,] 0.813184618 -4.298671108
[149,] 2.018623441 0.813184618
[150,] -0.202968887 2.018623441
[151,] 1.002755417 -0.202968887
[152,] -0.711575172 1.002755417
[153,] 0.691994480 -0.711575172
[154,] 0.635169394 0.691994480
[155,] 2.002805290 0.635169394
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.954701850 1.798817640
2 1.874479813 0.954701850
3 0.547638010 1.874479813
4 -0.967579078 0.547638010
5 2.642017026 -0.967579078
6 -1.923318605 2.642017026
7 -1.309155951 -1.923318605
8 -1.033194475 -1.309155951
9 3.449112193 -1.033194475
10 -2.026558622 3.449112193
11 -1.187576187 -2.026558622
12 2.699945550 -1.187576187
13 -4.485086456 2.699945550
14 -0.276238722 -4.485086456
15 0.175736317 -0.276238722
16 -1.840182932 0.175736317
17 -0.880026910 -1.840182932
18 1.422927737 -0.880026910
19 -3.360858801 1.422927737
20 2.203416904 -3.360858801
21 0.913432661 2.203416904
22 -0.592299687 0.913432661
23 2.242661575 -0.592299687
24 0.904023701 2.242661575
25 0.458033393 0.904023701
26 -5.324640617 0.458033393
27 0.488216529 -5.324640617
28 -0.469392888 0.488216529
29 4.810706963 -0.469392888
30 4.232787302 4.810706963
31 -1.644223544 4.232787302
32 -0.588811383 -1.644223544
33 1.364239128 -0.588811383
34 -1.250227585 1.364239128
35 1.786531425 -1.250227585
36 -1.177082534 1.786531425
37 0.258035278 -1.177082534
38 -0.178408298 0.258035278
39 0.743256736 -0.178408298
40 0.864137052 0.743256736
41 6.895494166 0.864137052
42 -4.739789741 6.895494166
43 -2.344459982 -4.739789741
44 -0.174464508 -2.344459982
45 1.750663265 -0.174464508
46 2.718764196 1.750663265
47 0.950326051 2.718764196
48 -2.430647060 0.950326051
49 -1.850989194 -2.430647060
50 -0.922146498 -1.850989194
51 -6.025790082 -0.922146498
52 0.116383632 -6.025790082
53 -2.468198778 0.116383632
54 2.158666508 -2.468198778
55 -0.916017846 2.158666508
56 -0.060127548 -0.916017846
57 1.870316932 -0.060127548
58 -2.238392829 1.870316932
59 0.395182001 -2.238392829
60 -2.102998617 0.395182001
61 2.096292335 -2.102998617
62 1.132266678 2.096292335
63 2.385737889 1.132266678
64 -1.212185686 2.385737889
65 1.650865464 -1.212185686
66 -2.257395123 1.650865464
67 2.619201902 -2.257395123
68 2.024115685 2.619201902
69 -0.779459582 2.024115685
70 2.099191511 -0.779459582
71 1.062375724 2.099191511
72 -2.023868293 1.062375724
73 -3.110819536 -2.023868293
74 -0.210706929 -3.110819536
75 -1.423521217 -0.210706929
76 -0.777019087 -1.423521217
77 -0.095130878 -0.777019087
78 2.928875648 -0.095130878
79 -1.992476567 2.928875648
80 -2.432434799 -1.992476567
81 1.385724961 -2.432434799
82 -0.017061626 1.385724961
83 -0.974210783 -0.017061626
84 3.623795137 -0.974210783
85 -0.426724217 3.623795137
86 -0.074150538 -0.426724217
87 -0.362745120 -0.074150538
88 0.914598096 -0.362745120
89 -0.807806615 0.914598096
90 1.005081380 -0.807806615
91 -3.653500193 1.005081380
92 1.005179091 -3.653500193
93 1.688353416 1.005179091
94 0.523545173 1.688353416
95 0.725618837 0.523545173
96 -2.282554179 0.725618837
97 -0.463725717 -2.282554179
98 -1.546559438 -0.463725717
99 0.004820131 -1.546559438
100 0.119605278 0.004820131
101 0.827200339 0.119605278
102 -1.675222381 0.827200339
103 2.015310718 -1.675222381
104 -3.363586838 2.015310718
105 -0.681376619 -3.363586838
106 -1.561968591 -0.681376619
107 -1.246176734 -1.561968591
108 -2.012756281 -1.246176734
109 -2.471228563 -2.012756281
110 -0.961951690 -2.471228563
111 -1.190263149 -0.961951690
112 -0.003243814 -1.190263149
113 0.987797746 -0.003243814
114 0.600013622 0.987797746
115 2.645266891 0.600013622
116 3.133683897 2.645266891
117 -1.646948017 3.133683897
118 0.639518113 -1.646948017
119 2.998645736 0.639518113
120 0.442598247 2.998645736
121 0.950568229 0.442598247
122 -2.811279292 0.950568229
123 -1.274236191 -2.811279292
124 1.487043615 -1.274236191
125 1.428400213 1.487043615
126 0.153779862 1.428400213
127 -0.680766771 0.153779862
128 -0.283787225 -0.680766771
129 -1.704454874 -0.283787225
130 -0.089833207 -1.704454874
131 1.928264537 -0.089833207
132 -1.135249940 1.928264537
133 -1.283010847 -1.135249940
134 2.426629712 -1.283010847
135 1.814162421 2.426629712
136 1.139615389 1.814162421
137 -3.393653560 1.139615389
138 5.279191650 -3.393653560
139 -1.045758688 5.279191650
140 0.098357280 -1.045758688
141 -0.220223906 0.098357280
142 2.573788872 -0.220223906
143 -0.925387827 2.573788872
144 0.858564772 -0.925387827
145 -0.406046405 0.858564772
146 -3.284261711 -0.406046405
147 -4.298671108 -3.284261711
148 0.813184618 -4.298671108
149 2.018623441 0.813184618
150 -0.202968887 2.018623441
151 1.002755417 -0.202968887
152 -0.711575172 1.002755417
153 0.691994480 -0.711575172
154 0.635169394 0.691994480
155 2.002805290 0.635169394
> 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/7kvi41321815556.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/8srsw1321815556.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/9hhum1321815556.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/10bprs1321815556.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11obmv1321815556.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/120x8s1321815556.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13ja5g1321815556.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14x8kh1321815556.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/1533mw1321815556.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/16uetc1321815556.tab")
+ }
>
> try(system("convert tmp/14qpx1321815556.ps tmp/14qpx1321815556.png",intern=TRUE))
character(0)
> try(system("convert tmp/25tu41321815556.ps tmp/25tu41321815556.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gyju1321815556.ps tmp/3gyju1321815556.png",intern=TRUE))
character(0)
> try(system("convert tmp/47oac1321815556.ps tmp/47oac1321815556.png",intern=TRUE))
character(0)
> try(system("convert tmp/5sl1d1321815556.ps tmp/5sl1d1321815556.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xm241321815556.ps tmp/6xm241321815556.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kvi41321815556.ps tmp/7kvi41321815556.png",intern=TRUE))
character(0)
> try(system("convert tmp/8srsw1321815556.ps tmp/8srsw1321815556.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hhum1321815556.ps tmp/9hhum1321815556.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bprs1321815556.ps tmp/10bprs1321815556.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.010 0.602 5.697