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(11
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+ ,6)
+ ,dim=c(9
+ ,156)
+ ,dimnames=list(c('Maand'
+ ,'Schoolprestaties'
+ ,'Sport'
+ ,'Goingout'
+ ,'Relation'
+ ,'Family'
+ ,'Friends'
+ ,'Coach'
+ ,'Job')
+ ,1:156))
> y <- array(NA,dim=c(9,156),dimnames=list(c('Maand','Schoolprestaties','Sport','Goingout','Relation','Family','Friends','Coach','Job'),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
Schoolprestaties Maand Sport Goingout Relation Family Friends Coach Job t
1 14 11 3 2 3 3 3 7 6 1
2 8 11 5 6 0 7 7 2 7 2
3 12 11 6 6 0 6 8 3 8 3
4 7 11 6 6 6 6 9 8 8 4
5 10 11 7 8 5 5 5 7 9 5
6 9 11 3 1 0 7 7 7 8 6
7 16 11 8 9 8 8 8 9 8 7
8 7 11 4 4 0 2 3 2 7 8
9 14 11 7 7 0 4 8 4 7 9
10 6 11 4 4 9 9 4 4 4 10
11 16 11 6 6 6 6 6 6 6 11
12 11 11 6 5 6 6 4 4 7 12
13 17 11 7 7 5 5 8 9 5 13
14 12 11 4 5 4 4 8 8 8 14
15 7 11 6 6 0 2 2 7 5 15
16 13 11 5 5 0 4 9 4 4 16
17 9 11 0 2 2 2 2 2 9 17
18 15 11 9 9 6 6 8 8 8 18
19 7 11 4 4 0 4 8 4 4 19
20 9 11 4 4 4 4 4 4 6 20
21 7 11 2 5 5 5 5 2 6 21
22 14 11 7 7 7 7 7 9 7 22
23 15 11 5 5 5 5 3 3 3 23
24 7 11 9 9 4 4 4 4 4 24
25 13 11 6 6 6 6 6 6 6 25
26 17 11 6 6 6 6 6 6 6 26
27 15 11 7 3 0 7 9 7 7 27
28 14 11 3 3 1 2 2 2 5 28
29 14 11 6 5 0 6 6 6 8 29
30 8 11 6 5 4 4 4 4 6 30
31 8 11 4 4 4 4 8 2 4 31
32 12 11 7 7 7 7 3 9 9 32
33 14 11 7 6 7 7 7 7 7 33
34 8 11 7 7 0 4 4 4 4 34
35 11 11 4 4 4 4 4 4 6 35
36 16 11 5 5 5 5 8 7 8 36
37 11 11 6 6 0 6 6 6 6 37
38 8 11 5 5 5 5 5 5 5 38
39 14 11 6 0 1 6 6 6 6 39
40 16 11 6 6 2 2 9 2 6 40
41 14 11 6 5 0 6 4 2 4 41
42 5 11 3 3 9 9 7 7 7 42
43 8 11 3 3 3 3 3 3 9 43
44 10 11 3 3 0 4 4 4 8 44
45 8 11 6 7 6 6 6 6 6 45
46 13 11 7 7 1 5 8 5 6 46
47 15 11 5 1 5 5 5 7 5 47
48 6 11 5 5 0 4 4 4 7 48
49 12 11 5 5 0 2 2 2 5 49
50 14 11 6 6 0 6 9 6 8 50
51 5 11 6 2 6 6 6 9 6 51
52 15 11 6 6 7 7 8 8 8 52
53 11 11 5 5 0 5 5 5 5 53
54 8 11 4 2 4 4 4 4 4 54
55 13 11 7 7 5 5 5 2 5 55
56 14 11 5 5 1 5 9 9 6 56
57 12 12 3 3 4 4 4 4 4 57
58 16 12 6 6 9 9 8 6 6 58
59 10 12 2 2 2 2 2 2 9 59
60 15 12 8 8 8 8 8 8 7 60
61 8 12 3 5 3 3 3 3 3 61
62 16 12 0 2 1 6 3 3 6 62
63 19 12 6 6 0 6 6 7 6 63
64 14 12 8 2 6 6 6 2 6 64
65 7 12 4 1 0 5 5 9 5 65
66 13 12 5 5 0 5 5 5 5 66
67 15 12 6 6 6 6 4 4 5 67
68 7 12 5 2 2 2 9 2 9 68
69 13 12 6 6 1 6 6 6 8 69
70 4 12 2 2 5 5 5 5 5 70
71 14 12 6 6 5 5 5 5 6 71
72 13 12 5 5 5 5 3 9 7 72
73 11 12 5 0 5 5 8 2 5 73
74 14 12 6 2 6 6 9 6 6 74
75 12 12 4 4 6 6 6 6 6 75
76 15 12 6 1 0 9 6 6 6 76
77 14 12 5 5 0 5 5 5 6 77
78 13 12 5 5 1 5 3 3 9 78
79 7 12 4 2 7 7 4 2 7 79
80 5 12 2 2 2 2 9 2 9 80
81 7 12 7 7 4 4 4 4 4 81
82 13 12 5 5 0 6 8 8 8 82
83 13 12 6 2 5 5 5 5 5 83
84 11 12 5 5 5 5 5 9 8 84
85 6 12 3 3 3 3 8 2 9 85
86 12 12 6 6 0 6 6 6 6 86
87 8 12 4 1 4 4 9 4 4 87
88 11 12 5 5 9 9 5 5 7 88
89 12 12 7 7 0 8 8 8 8 89
90 9 12 4 2 4 4 3 3 9 90
91 12 12 6 6 2 2 2 2 9 91
92 13 12 8 8 7 7 7 7 7 92
93 16 12 7 7 7 7 7 7 8 93
94 16 12 6 6 6 6 4 9 4 94
95 11 12 7 7 0 5 5 5 6 95
96 8 12 4 4 5 5 9 5 7 96
97 4 12 0 5 6 6 6 2 6 97
98 7 12 3 2 0 3 3 3 7 98
99 14 12 5 5 5 5 5 5 5 99
100 11 12 6 2 9 9 2 2 9 100
101 17 12 5 5 0 7 7 7 7 101
102 15 12 7 7 7 7 7 7 7 102
103 14 12 6 5 1 6 6 6 6 103
104 5 12 8 8 3 3 8 3 6 104
105 4 12 7 2 7 7 9 3 9 105
106 19 12 8 8 8 8 8 2 9 106
107 11 12 3 3 0 3 3 3 8 107
108 15 12 8 2 5 5 5 5 8 108
109 10 12 3 3 3 3 3 3 3 109
110 9 12 4 5 0 4 4 4 6 110
111 12 12 2 2 5 5 5 5 5 111
112 15 12 7 2 7 7 9 7 7 112
113 7 12 6 6 0 6 6 6 6 113
114 13 12 2 2 0 7 7 7 7 114
115 14 12 7 7 0 9 7 2 7 115
116 14 12 6 6 6 6 6 6 6 116
117 14 12 6 2 0 6 3 9 8 117
118 8 12 6 2 6 6 9 4 9 118
119 15 12 6 5 6 6 6 6 6 119
120 15 12 6 6 2 2 2 2 9 120
121 9 12 4 4 5 5 5 2 5 121
122 16 12 5 5 0 5 5 5 6 122
123 9 12 7 7 4 4 9 4 4 123
124 15 12 6 6 0 7 7 7 7 124
125 15 12 6 6 6 6 6 6 6 125
126 6 12 5 5 5 5 8 7 8 126
127 8 12 8 2 8 8 8 8 8 127
128 15 12 6 6 6 6 6 6 9 128
129 10 12 0 3 5 5 3 3 8 129
130 9 12 4 2 0 4 4 4 4 130
131 14 12 8 8 8 8 9 8 6 131
132 12 12 6 6 0 6 6 9 6 132
133 8 12 4 4 9 9 4 2 7 133
134 11 12 6 6 5 5 5 5 9 134
135 13 12 2 5 0 6 6 6 8 135
136 9 12 4 4 0 4 4 4 4 136
137 15 12 6 2 0 6 6 6 6 137
138 13 12 3 3 3 3 3 3 9 138
139 15 12 6 6 6 6 6 6 6 139
140 14 12 5 5 0 5 5 5 5 140
141 16 12 4 4 4 4 9 8 8 141
142 12 12 6 6 6 6 6 6 6 142
143 14 12 1 1 0 5 9 5 6 143
144 10 12 4 5 4 4 3 3 6 144
145 10 12 4 2 7 7 7 2 7 145
146 4 12 6 6 0 6 6 6 7 146
147 8 12 5 5 5 5 5 5 9 147
148 17 12 9 2 6 6 6 6 6 148
149 16 12 6 6 6 6 9 6 6 149
150 12 12 8 8 8 8 8 9 6 150
151 12 12 7 7 2 2 4 4 4 151
152 15 12 7 7 7 7 7 7 7 152
153 9 12 0 9 0 4 4 4 8 153
154 13 12 6 2 0 6 8 7 7 154
155 14 12 6 6 5 5 5 5 9 155
156 11 12 5 5 0 2 9 2 6 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Maand Sport Goingout Relation Family
-0.9329379 0.6417281 0.4533497 0.1038791 -0.1391977 0.2678751
Friends Coach Job t
-0.0758261 0.3314739 0.0007430 0.0001314
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.2767 -2.2632 0.6849 2.1411 6.7768
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.9329379 10.9870577 -0.085 0.9324
Maand 0.6417281 0.9901771 0.648 0.5179
Sport 0.4533497 0.1769789 2.562 0.0114 *
Goingout 0.1038791 0.1450672 0.716 0.4751
Relation -0.1391977 0.1008198 -1.381 0.1695
Family 0.2678751 0.1913260 1.400 0.1636
Friends -0.0758261 0.1388819 -0.546 0.5859
Coach 0.3314739 0.1398677 2.370 0.0191 *
Job 0.0007430 0.1684111 0.004 0.9965
t 0.0001314 0.0104172 0.013 0.9900
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.232 on 146 degrees of freedom
Multiple R-squared: 0.1939, Adjusted R-squared: 0.1442
F-statistic: 3.902 on 9 and 146 DF, p-value: 0.0001833
> 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,] 0.6598903 0.6802195 0.3401097
[2,] 0.7892466 0.4215068 0.2107534
[3,] 0.9367430 0.1265140 0.0632570
[4,] 0.8937016 0.2125968 0.1062984
[5,] 0.8944589 0.2110822 0.1055411
[6,] 0.8406130 0.3187740 0.1593870
[7,] 0.8534680 0.2930640 0.1465320
[8,] 0.7970175 0.4059650 0.2029825
[9,] 0.7308101 0.5383797 0.2691899
[10,] 0.6622471 0.6755057 0.3377529
[11,] 0.7374199 0.5251603 0.2625801
[12,] 0.8253025 0.3493950 0.1746975
[13,] 0.7766588 0.4466824 0.2233412
[14,] 0.8134024 0.3731953 0.1865976
[15,] 0.7626147 0.4747705 0.2373853
[16,] 0.7960889 0.4078222 0.2039111
[17,] 0.7515427 0.4969146 0.2484573
[18,] 0.7926695 0.4146609 0.2073305
[19,] 0.7816689 0.4366621 0.2183311
[20,] 0.7485475 0.5029049 0.2514525
[21,] 0.6960293 0.6079413 0.3039707
[22,] 0.6891934 0.6216132 0.3108066
[23,] 0.6326437 0.7347127 0.3673563
[24,] 0.6178032 0.7643936 0.3821968
[25,] 0.5628245 0.8743510 0.4371755
[26,] 0.5714231 0.8571539 0.4285769
[27,] 0.5187536 0.9624928 0.4812464
[28,] 0.5522578 0.8954843 0.4477422
[29,] 0.5805929 0.8388143 0.4194071
[30,] 0.7186872 0.5626256 0.2813128
[31,] 0.6885269 0.6229462 0.3114731
[32,] 0.6375896 0.7248209 0.3624104
[33,] 0.6436225 0.7127550 0.3563775
[34,] 0.5927867 0.8144265 0.4072133
[35,] 0.5712497 0.8575006 0.4287503
[36,] 0.6129130 0.7741741 0.3870870
[37,] 0.5851743 0.8296513 0.4148257
[38,] 0.5520343 0.8959315 0.4479657
[39,] 0.8061561 0.3876878 0.1938439
[40,] 0.7903837 0.4192326 0.2096163
[41,] 0.7546300 0.4907400 0.2453700
[42,] 0.7376604 0.5246791 0.2623396
[43,] 0.7013722 0.5972556 0.2986278
[44,] 0.6608304 0.6783393 0.3391696
[45,] 0.6193117 0.7613767 0.3806883
[46,] 0.5915639 0.8168722 0.4084361
[47,] 0.5559032 0.8881936 0.4440968
[48,] 0.5111545 0.9776910 0.4888455
[49,] 0.4834934 0.9669868 0.5165066
[50,] 0.6502534 0.6994932 0.3497466
[51,] 0.6984977 0.6030046 0.3015023
[52,] 0.6748540 0.6502920 0.3251460
[53,] 0.7891787 0.4216425 0.2108213
[54,] 0.7559643 0.4880714 0.2440357
[55,] 0.7461913 0.5076174 0.2538087
[56,] 0.7932399 0.4135203 0.2067601
[57,] 0.7602051 0.4795898 0.2397949
[58,] 0.8440905 0.3118189 0.1559095
[59,] 0.8248603 0.3502795 0.1751397
[60,] 0.7923626 0.4152748 0.2076374
[61,] 0.7669167 0.4661666 0.2330833
[62,] 0.7501265 0.4997470 0.2498735
[63,] 0.7131297 0.5737407 0.2868703
[64,] 0.6833928 0.6332144 0.3166072
[65,] 0.6600846 0.6798308 0.3399154
[66,] 0.6289411 0.7421179 0.3710589
[67,] 0.6233511 0.7532979 0.3766489
[68,] 0.6271957 0.7456087 0.3728043
[69,] 0.6862878 0.6274244 0.3137122
[70,] 0.6453039 0.7093921 0.3546961
[71,] 0.6102013 0.7795974 0.3897987
[72,] 0.5777347 0.8445306 0.4222653
[73,] 0.5572444 0.8855112 0.4427556
[74,] 0.5132406 0.9735188 0.4867594
[75,] 0.4740238 0.9480476 0.5259762
[76,] 0.4286076 0.8572153 0.5713924
[77,] 0.4052048 0.8104096 0.5947952
[78,] 0.3628480 0.7256959 0.6371520
[79,] 0.3251198 0.6502396 0.6748802
[80,] 0.2835190 0.5670381 0.7164810
[81,] 0.2724003 0.5448006 0.7275997
[82,] 0.2541311 0.5082621 0.7458689
[83,] 0.2264830 0.4529659 0.7735170
[84,] 0.2048544 0.4097089 0.7951456
[85,] 0.2234257 0.4468515 0.7765743
[86,] 0.2151389 0.4302778 0.7848611
[87,] 0.2004048 0.4008095 0.7995952
[88,] 0.1683690 0.3367380 0.8316310
[89,] 0.1874104 0.3748208 0.8125896
[90,] 0.1660081 0.3320163 0.8339919
[91,] 0.1429760 0.2859520 0.8570240
[92,] 0.2363460 0.4726920 0.7636540
[93,] 0.4510654 0.9021309 0.5489346
[94,] 0.6221431 0.7557138 0.3778569
[95,] 0.5747857 0.8504287 0.4252143
[96,] 0.5474903 0.9050194 0.4525097
[97,] 0.4966893 0.9933785 0.5033107
[98,] 0.4664227 0.9328454 0.5335773
[99,] 0.4256033 0.8512066 0.5743967
[100,] 0.3975375 0.7950751 0.6024625
[101,] 0.5107172 0.9785657 0.4892828
[102,] 0.4630764 0.9261527 0.5369236
[103,] 0.4580172 0.9160344 0.5419828
[104,] 0.4165010 0.8330021 0.5834990
[105,] 0.3606962 0.7213924 0.6393038
[106,] 0.3559692 0.7119384 0.6440308
[107,] 0.3375979 0.6751957 0.6624021
[108,] 0.3683109 0.7366218 0.6316891
[109,] 0.3147243 0.6294487 0.6852757
[110,] 0.3861990 0.7723981 0.6138010
[111,] 0.3558559 0.7117118 0.6441441
[112,] 0.3986132 0.7972263 0.6013868
[113,] 0.4129718 0.8259436 0.5870282
[114,] 0.6220655 0.7558689 0.3779345
[115,] 0.8509489 0.2981023 0.1490511
[116,] 0.8393798 0.3212404 0.1606202
[117,] 0.7957505 0.4084990 0.2042495
[118,] 0.7967939 0.4064123 0.2032061
[119,] 0.7396302 0.5207396 0.2603698
[120,] 0.6763630 0.6472740 0.3236370
[121,] 0.6038157 0.7923687 0.3961843
[122,] 0.5187257 0.9625485 0.4812743
[123,] 0.5046787 0.9906426 0.4953213
[124,] 0.4933033 0.9866066 0.5066967
[125,] 0.4928837 0.9857675 0.5071163
[126,] 0.4440435 0.8880869 0.5559565
[127,] 0.4223238 0.8446476 0.5776762
[128,] 0.8188095 0.3623810 0.1811905
[129,] 0.7192843 0.5614313 0.2807157
[130,] 0.6279933 0.7440135 0.3720067
[131,] 0.4905451 0.9810901 0.5094549
> postscript(file="/var/wessaorg/rcomp/tmp/1f52u1321985710.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/2zxay1321985710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/39asb1321985710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/411he1321985710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5zje51321985710.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
3.82266058 -3.02884864 0.52915459 -5.21733424 -2.72246966 -2.26139199
7 8 9 10 11 12
1.89928017 -2.33245810 2.17615692 -4.53996015 4.21870166 -0.16699811
13 14 15 16 17 18
3.94786114 0.97345932 -5.17954488 2.36775010 1.88859974 1.03331424
19 20 21 22 23 24
-3.15124136 -0.89937267 -1.48658711 0.61201185 4.67220387 -5.68455592
25 26 27 28 29 30
1.21686258 5.21673122 1.86708773 5.28700096 1.48354417 -2.91126477
31 32 33 34 35 36
-0.93307922 -1.69409238 1.37739375 -4.12820267 1.09865689 4.72001612
37 38 39 40 41 42
-1.61989976 -2.84254815 2.14230952 6.28297595 2.65918326 -5.75610736
43 44 45 46 47 48
-0.96306908 0.09642671 -3.88964374 0.71188817 3.90883797 -5.01781326
49 50 51 52 53 54
2.03058714 1.60438492 -7.36545839 2.57185677 -0.54050692 -1.69459486
55 56 57 58 59 60
2.03518290 1.57496254 2.01275359 3.33641949 1.33465496 0.68668580
61 62 63 64 65 66
-1.81046176 6.77684353 5.40348277 2.40472378 -5.64084113 0.81605725
67 68 69 70 71 72
3.08165578 -2.49579344 -0.12811986 -5.81679358 1.95341697 0.03222340
73 74 75 76 77 78
1.25242149 2.21169233 0.68302385 1.44901897 1.81386924 1.46200205
79 80 81 82 83 84
-3.06492054 -3.13732066 -5.21931419 -0.22309202 1.36809986 -1.81844368
85 86 87 88 89 90
-2.89970981 -1.26806466 -1.85764819 -1.00704016 -2.87421928 -1.08911934
91 92 93 94 95 96
1.10153628 -0.93319261 2.62316178 2.42148241 -2.30295282 -2.63284801
97 98 99 100 101 102
-4.28445068 -2.92425007 2.50771059 -0.38487176 3.76292778 1.62272253
103 104 105 106 107 108
0.97277889 -7.01759442 -7.38221435 6.66800059 0.96994560 2.45588732
109 110 111 112 113 114
0.39099093 -2.21359316 2.17782054 2.29245647 -6.27161146 1.43290636
115 116 117 118 119 120
0.76825057 1.56318044 -0.08000689 -3.13336884 2.66666540 4.09772676
121 122 123 124 125 126
-0.94352887 3.80795791 -2.84570072 1.20267767 2.56199817 -5.93353466
127 128 129 130 131 132
-5.69958419 2.55937504 1.48734288 -1.90309722 -0.24607181 -2.26852911
133 134 135 136 137 138
-2.53712711 -1.05708793 1.64129043 -2.11164351 2.14075206 3.38272332
139 140 141 142 143 144
2.56015909 1.80633639 4.49475332 -0.44023500 4.33741893 -0.40562106
145 146 147 148 149 150
0.15388793 -9.27668945 -3.50156689 3.61444392 3.78632389 -2.65586776
151 152 153 154 155 156
0.02884526 1.61615439 -0.82284517 -0.04204576 1.94015344 0.90484317
> postscript(file="/var/wessaorg/rcomp/tmp/6reo11321985710.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 3.82266058 NA
1 -3.02884864 3.82266058
2 0.52915459 -3.02884864
3 -5.21733424 0.52915459
4 -2.72246966 -5.21733424
5 -2.26139199 -2.72246966
6 1.89928017 -2.26139199
7 -2.33245810 1.89928017
8 2.17615692 -2.33245810
9 -4.53996015 2.17615692
10 4.21870166 -4.53996015
11 -0.16699811 4.21870166
12 3.94786114 -0.16699811
13 0.97345932 3.94786114
14 -5.17954488 0.97345932
15 2.36775010 -5.17954488
16 1.88859974 2.36775010
17 1.03331424 1.88859974
18 -3.15124136 1.03331424
19 -0.89937267 -3.15124136
20 -1.48658711 -0.89937267
21 0.61201185 -1.48658711
22 4.67220387 0.61201185
23 -5.68455592 4.67220387
24 1.21686258 -5.68455592
25 5.21673122 1.21686258
26 1.86708773 5.21673122
27 5.28700096 1.86708773
28 1.48354417 5.28700096
29 -2.91126477 1.48354417
30 -0.93307922 -2.91126477
31 -1.69409238 -0.93307922
32 1.37739375 -1.69409238
33 -4.12820267 1.37739375
34 1.09865689 -4.12820267
35 4.72001612 1.09865689
36 -1.61989976 4.72001612
37 -2.84254815 -1.61989976
38 2.14230952 -2.84254815
39 6.28297595 2.14230952
40 2.65918326 6.28297595
41 -5.75610736 2.65918326
42 -0.96306908 -5.75610736
43 0.09642671 -0.96306908
44 -3.88964374 0.09642671
45 0.71188817 -3.88964374
46 3.90883797 0.71188817
47 -5.01781326 3.90883797
48 2.03058714 -5.01781326
49 1.60438492 2.03058714
50 -7.36545839 1.60438492
51 2.57185677 -7.36545839
52 -0.54050692 2.57185677
53 -1.69459486 -0.54050692
54 2.03518290 -1.69459486
55 1.57496254 2.03518290
56 2.01275359 1.57496254
57 3.33641949 2.01275359
58 1.33465496 3.33641949
59 0.68668580 1.33465496
60 -1.81046176 0.68668580
61 6.77684353 -1.81046176
62 5.40348277 6.77684353
63 2.40472378 5.40348277
64 -5.64084113 2.40472378
65 0.81605725 -5.64084113
66 3.08165578 0.81605725
67 -2.49579344 3.08165578
68 -0.12811986 -2.49579344
69 -5.81679358 -0.12811986
70 1.95341697 -5.81679358
71 0.03222340 1.95341697
72 1.25242149 0.03222340
73 2.21169233 1.25242149
74 0.68302385 2.21169233
75 1.44901897 0.68302385
76 1.81386924 1.44901897
77 1.46200205 1.81386924
78 -3.06492054 1.46200205
79 -3.13732066 -3.06492054
80 -5.21931419 -3.13732066
81 -0.22309202 -5.21931419
82 1.36809986 -0.22309202
83 -1.81844368 1.36809986
84 -2.89970981 -1.81844368
85 -1.26806466 -2.89970981
86 -1.85764819 -1.26806466
87 -1.00704016 -1.85764819
88 -2.87421928 -1.00704016
89 -1.08911934 -2.87421928
90 1.10153628 -1.08911934
91 -0.93319261 1.10153628
92 2.62316178 -0.93319261
93 2.42148241 2.62316178
94 -2.30295282 2.42148241
95 -2.63284801 -2.30295282
96 -4.28445068 -2.63284801
97 -2.92425007 -4.28445068
98 2.50771059 -2.92425007
99 -0.38487176 2.50771059
100 3.76292778 -0.38487176
101 1.62272253 3.76292778
102 0.97277889 1.62272253
103 -7.01759442 0.97277889
104 -7.38221435 -7.01759442
105 6.66800059 -7.38221435
106 0.96994560 6.66800059
107 2.45588732 0.96994560
108 0.39099093 2.45588732
109 -2.21359316 0.39099093
110 2.17782054 -2.21359316
111 2.29245647 2.17782054
112 -6.27161146 2.29245647
113 1.43290636 -6.27161146
114 0.76825057 1.43290636
115 1.56318044 0.76825057
116 -0.08000689 1.56318044
117 -3.13336884 -0.08000689
118 2.66666540 -3.13336884
119 4.09772676 2.66666540
120 -0.94352887 4.09772676
121 3.80795791 -0.94352887
122 -2.84570072 3.80795791
123 1.20267767 -2.84570072
124 2.56199817 1.20267767
125 -5.93353466 2.56199817
126 -5.69958419 -5.93353466
127 2.55937504 -5.69958419
128 1.48734288 2.55937504
129 -1.90309722 1.48734288
130 -0.24607181 -1.90309722
131 -2.26852911 -0.24607181
132 -2.53712711 -2.26852911
133 -1.05708793 -2.53712711
134 1.64129043 -1.05708793
135 -2.11164351 1.64129043
136 2.14075206 -2.11164351
137 3.38272332 2.14075206
138 2.56015909 3.38272332
139 1.80633639 2.56015909
140 4.49475332 1.80633639
141 -0.44023500 4.49475332
142 4.33741893 -0.44023500
143 -0.40562106 4.33741893
144 0.15388793 -0.40562106
145 -9.27668945 0.15388793
146 -3.50156689 -9.27668945
147 3.61444392 -3.50156689
148 3.78632389 3.61444392
149 -2.65586776 3.78632389
150 0.02884526 -2.65586776
151 1.61615439 0.02884526
152 -0.82284517 1.61615439
153 -0.04204576 -0.82284517
154 1.94015344 -0.04204576
155 0.90484317 1.94015344
156 NA 0.90484317
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.02884864 3.82266058
[2,] 0.52915459 -3.02884864
[3,] -5.21733424 0.52915459
[4,] -2.72246966 -5.21733424
[5,] -2.26139199 -2.72246966
[6,] 1.89928017 -2.26139199
[7,] -2.33245810 1.89928017
[8,] 2.17615692 -2.33245810
[9,] -4.53996015 2.17615692
[10,] 4.21870166 -4.53996015
[11,] -0.16699811 4.21870166
[12,] 3.94786114 -0.16699811
[13,] 0.97345932 3.94786114
[14,] -5.17954488 0.97345932
[15,] 2.36775010 -5.17954488
[16,] 1.88859974 2.36775010
[17,] 1.03331424 1.88859974
[18,] -3.15124136 1.03331424
[19,] -0.89937267 -3.15124136
[20,] -1.48658711 -0.89937267
[21,] 0.61201185 -1.48658711
[22,] 4.67220387 0.61201185
[23,] -5.68455592 4.67220387
[24,] 1.21686258 -5.68455592
[25,] 5.21673122 1.21686258
[26,] 1.86708773 5.21673122
[27,] 5.28700096 1.86708773
[28,] 1.48354417 5.28700096
[29,] -2.91126477 1.48354417
[30,] -0.93307922 -2.91126477
[31,] -1.69409238 -0.93307922
[32,] 1.37739375 -1.69409238
[33,] -4.12820267 1.37739375
[34,] 1.09865689 -4.12820267
[35,] 4.72001612 1.09865689
[36,] -1.61989976 4.72001612
[37,] -2.84254815 -1.61989976
[38,] 2.14230952 -2.84254815
[39,] 6.28297595 2.14230952
[40,] 2.65918326 6.28297595
[41,] -5.75610736 2.65918326
[42,] -0.96306908 -5.75610736
[43,] 0.09642671 -0.96306908
[44,] -3.88964374 0.09642671
[45,] 0.71188817 -3.88964374
[46,] 3.90883797 0.71188817
[47,] -5.01781326 3.90883797
[48,] 2.03058714 -5.01781326
[49,] 1.60438492 2.03058714
[50,] -7.36545839 1.60438492
[51,] 2.57185677 -7.36545839
[52,] -0.54050692 2.57185677
[53,] -1.69459486 -0.54050692
[54,] 2.03518290 -1.69459486
[55,] 1.57496254 2.03518290
[56,] 2.01275359 1.57496254
[57,] 3.33641949 2.01275359
[58,] 1.33465496 3.33641949
[59,] 0.68668580 1.33465496
[60,] -1.81046176 0.68668580
[61,] 6.77684353 -1.81046176
[62,] 5.40348277 6.77684353
[63,] 2.40472378 5.40348277
[64,] -5.64084113 2.40472378
[65,] 0.81605725 -5.64084113
[66,] 3.08165578 0.81605725
[67,] -2.49579344 3.08165578
[68,] -0.12811986 -2.49579344
[69,] -5.81679358 -0.12811986
[70,] 1.95341697 -5.81679358
[71,] 0.03222340 1.95341697
[72,] 1.25242149 0.03222340
[73,] 2.21169233 1.25242149
[74,] 0.68302385 2.21169233
[75,] 1.44901897 0.68302385
[76,] 1.81386924 1.44901897
[77,] 1.46200205 1.81386924
[78,] -3.06492054 1.46200205
[79,] -3.13732066 -3.06492054
[80,] -5.21931419 -3.13732066
[81,] -0.22309202 -5.21931419
[82,] 1.36809986 -0.22309202
[83,] -1.81844368 1.36809986
[84,] -2.89970981 -1.81844368
[85,] -1.26806466 -2.89970981
[86,] -1.85764819 -1.26806466
[87,] -1.00704016 -1.85764819
[88,] -2.87421928 -1.00704016
[89,] -1.08911934 -2.87421928
[90,] 1.10153628 -1.08911934
[91,] -0.93319261 1.10153628
[92,] 2.62316178 -0.93319261
[93,] 2.42148241 2.62316178
[94,] -2.30295282 2.42148241
[95,] -2.63284801 -2.30295282
[96,] -4.28445068 -2.63284801
[97,] -2.92425007 -4.28445068
[98,] 2.50771059 -2.92425007
[99,] -0.38487176 2.50771059
[100,] 3.76292778 -0.38487176
[101,] 1.62272253 3.76292778
[102,] 0.97277889 1.62272253
[103,] -7.01759442 0.97277889
[104,] -7.38221435 -7.01759442
[105,] 6.66800059 -7.38221435
[106,] 0.96994560 6.66800059
[107,] 2.45588732 0.96994560
[108,] 0.39099093 2.45588732
[109,] -2.21359316 0.39099093
[110,] 2.17782054 -2.21359316
[111,] 2.29245647 2.17782054
[112,] -6.27161146 2.29245647
[113,] 1.43290636 -6.27161146
[114,] 0.76825057 1.43290636
[115,] 1.56318044 0.76825057
[116,] -0.08000689 1.56318044
[117,] -3.13336884 -0.08000689
[118,] 2.66666540 -3.13336884
[119,] 4.09772676 2.66666540
[120,] -0.94352887 4.09772676
[121,] 3.80795791 -0.94352887
[122,] -2.84570072 3.80795791
[123,] 1.20267767 -2.84570072
[124,] 2.56199817 1.20267767
[125,] -5.93353466 2.56199817
[126,] -5.69958419 -5.93353466
[127,] 2.55937504 -5.69958419
[128,] 1.48734288 2.55937504
[129,] -1.90309722 1.48734288
[130,] -0.24607181 -1.90309722
[131,] -2.26852911 -0.24607181
[132,] -2.53712711 -2.26852911
[133,] -1.05708793 -2.53712711
[134,] 1.64129043 -1.05708793
[135,] -2.11164351 1.64129043
[136,] 2.14075206 -2.11164351
[137,] 3.38272332 2.14075206
[138,] 2.56015909 3.38272332
[139,] 1.80633639 2.56015909
[140,] 4.49475332 1.80633639
[141,] -0.44023500 4.49475332
[142,] 4.33741893 -0.44023500
[143,] -0.40562106 4.33741893
[144,] 0.15388793 -0.40562106
[145,] -9.27668945 0.15388793
[146,] -3.50156689 -9.27668945
[147,] 3.61444392 -3.50156689
[148,] 3.78632389 3.61444392
[149,] -2.65586776 3.78632389
[150,] 0.02884526 -2.65586776
[151,] 1.61615439 0.02884526
[152,] -0.82284517 1.61615439
[153,] -0.04204576 -0.82284517
[154,] 1.94015344 -0.04204576
[155,] 0.90484317 1.94015344
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.02884864 3.82266058
2 0.52915459 -3.02884864
3 -5.21733424 0.52915459
4 -2.72246966 -5.21733424
5 -2.26139199 -2.72246966
6 1.89928017 -2.26139199
7 -2.33245810 1.89928017
8 2.17615692 -2.33245810
9 -4.53996015 2.17615692
10 4.21870166 -4.53996015
11 -0.16699811 4.21870166
12 3.94786114 -0.16699811
13 0.97345932 3.94786114
14 -5.17954488 0.97345932
15 2.36775010 -5.17954488
16 1.88859974 2.36775010
17 1.03331424 1.88859974
18 -3.15124136 1.03331424
19 -0.89937267 -3.15124136
20 -1.48658711 -0.89937267
21 0.61201185 -1.48658711
22 4.67220387 0.61201185
23 -5.68455592 4.67220387
24 1.21686258 -5.68455592
25 5.21673122 1.21686258
26 1.86708773 5.21673122
27 5.28700096 1.86708773
28 1.48354417 5.28700096
29 -2.91126477 1.48354417
30 -0.93307922 -2.91126477
31 -1.69409238 -0.93307922
32 1.37739375 -1.69409238
33 -4.12820267 1.37739375
34 1.09865689 -4.12820267
35 4.72001612 1.09865689
36 -1.61989976 4.72001612
37 -2.84254815 -1.61989976
38 2.14230952 -2.84254815
39 6.28297595 2.14230952
40 2.65918326 6.28297595
41 -5.75610736 2.65918326
42 -0.96306908 -5.75610736
43 0.09642671 -0.96306908
44 -3.88964374 0.09642671
45 0.71188817 -3.88964374
46 3.90883797 0.71188817
47 -5.01781326 3.90883797
48 2.03058714 -5.01781326
49 1.60438492 2.03058714
50 -7.36545839 1.60438492
51 2.57185677 -7.36545839
52 -0.54050692 2.57185677
53 -1.69459486 -0.54050692
54 2.03518290 -1.69459486
55 1.57496254 2.03518290
56 2.01275359 1.57496254
57 3.33641949 2.01275359
58 1.33465496 3.33641949
59 0.68668580 1.33465496
60 -1.81046176 0.68668580
61 6.77684353 -1.81046176
62 5.40348277 6.77684353
63 2.40472378 5.40348277
64 -5.64084113 2.40472378
65 0.81605725 -5.64084113
66 3.08165578 0.81605725
67 -2.49579344 3.08165578
68 -0.12811986 -2.49579344
69 -5.81679358 -0.12811986
70 1.95341697 -5.81679358
71 0.03222340 1.95341697
72 1.25242149 0.03222340
73 2.21169233 1.25242149
74 0.68302385 2.21169233
75 1.44901897 0.68302385
76 1.81386924 1.44901897
77 1.46200205 1.81386924
78 -3.06492054 1.46200205
79 -3.13732066 -3.06492054
80 -5.21931419 -3.13732066
81 -0.22309202 -5.21931419
82 1.36809986 -0.22309202
83 -1.81844368 1.36809986
84 -2.89970981 -1.81844368
85 -1.26806466 -2.89970981
86 -1.85764819 -1.26806466
87 -1.00704016 -1.85764819
88 -2.87421928 -1.00704016
89 -1.08911934 -2.87421928
90 1.10153628 -1.08911934
91 -0.93319261 1.10153628
92 2.62316178 -0.93319261
93 2.42148241 2.62316178
94 -2.30295282 2.42148241
95 -2.63284801 -2.30295282
96 -4.28445068 -2.63284801
97 -2.92425007 -4.28445068
98 2.50771059 -2.92425007
99 -0.38487176 2.50771059
100 3.76292778 -0.38487176
101 1.62272253 3.76292778
102 0.97277889 1.62272253
103 -7.01759442 0.97277889
104 -7.38221435 -7.01759442
105 6.66800059 -7.38221435
106 0.96994560 6.66800059
107 2.45588732 0.96994560
108 0.39099093 2.45588732
109 -2.21359316 0.39099093
110 2.17782054 -2.21359316
111 2.29245647 2.17782054
112 -6.27161146 2.29245647
113 1.43290636 -6.27161146
114 0.76825057 1.43290636
115 1.56318044 0.76825057
116 -0.08000689 1.56318044
117 -3.13336884 -0.08000689
118 2.66666540 -3.13336884
119 4.09772676 2.66666540
120 -0.94352887 4.09772676
121 3.80795791 -0.94352887
122 -2.84570072 3.80795791
123 1.20267767 -2.84570072
124 2.56199817 1.20267767
125 -5.93353466 2.56199817
126 -5.69958419 -5.93353466
127 2.55937504 -5.69958419
128 1.48734288 2.55937504
129 -1.90309722 1.48734288
130 -0.24607181 -1.90309722
131 -2.26852911 -0.24607181
132 -2.53712711 -2.26852911
133 -1.05708793 -2.53712711
134 1.64129043 -1.05708793
135 -2.11164351 1.64129043
136 2.14075206 -2.11164351
137 3.38272332 2.14075206
138 2.56015909 3.38272332
139 1.80633639 2.56015909
140 4.49475332 1.80633639
141 -0.44023500 4.49475332
142 4.33741893 -0.44023500
143 -0.40562106 4.33741893
144 0.15388793 -0.40562106
145 -9.27668945 0.15388793
146 -3.50156689 -9.27668945
147 3.61444392 -3.50156689
148 3.78632389 3.61444392
149 -2.65586776 3.78632389
150 0.02884526 -2.65586776
151 1.61615439 0.02884526
152 -0.82284517 1.61615439
153 -0.04204576 -0.82284517
154 1.94015344 -0.04204576
155 0.90484317 1.94015344
> 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/7852x1321985710.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/8y00e1321985710.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/962951321985710.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/10zk491321985710.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11uwlw1321985710.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12y2ax1321985710.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13mo3e1321985710.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/143rfc1321985710.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/15xiaw1321985710.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/16iaao1321985710.tab")
+ }
>
> try(system("convert tmp/1f52u1321985710.ps tmp/1f52u1321985710.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zxay1321985710.ps tmp/2zxay1321985710.png",intern=TRUE))
character(0)
> try(system("convert tmp/39asb1321985710.ps tmp/39asb1321985710.png",intern=TRUE))
character(0)
> try(system("convert tmp/411he1321985710.ps tmp/411he1321985710.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zje51321985710.ps tmp/5zje51321985710.png",intern=TRUE))
character(0)
> try(system("convert tmp/6reo11321985710.ps tmp/6reo11321985710.png",intern=TRUE))
character(0)
> try(system("convert tmp/7852x1321985710.ps tmp/7852x1321985710.png",intern=TRUE))
character(0)
> try(system("convert tmp/8y00e1321985710.ps tmp/8y00e1321985710.png",intern=TRUE))
character(0)
> try(system("convert tmp/962951321985710.ps tmp/962951321985710.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zk491321985710.ps tmp/10zk491321985710.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.329 0.645 6.043