R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(9
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+ ,17)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('maand'
+ ,'O'
+ ,'CM'
+ ,'D'
+ ,'PE'
+ ,'PC'
+ ,'PS
')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('maand','O','CM','D','PE','PC','PS
'),1:159))
> 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'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> 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
O maand CM D PE PC PS\r t
1 26 9 24 14 11 12 24 1
2 23 9 25 11 7 8 25 2
3 25 9 17 6 17 8 30 3
4 23 9 18 12 10 8 19 4
5 19 9 18 8 12 9 22 5
6 29 10 16 10 12 7 22 6
7 25 10 20 10 11 4 25 7
8 21 10 16 11 11 11 23 8
9 22 10 18 16 12 7 17 9
10 25 10 17 11 13 7 21 10
11 24 10 23 13 14 12 19 11
12 18 10 30 12 16 10 19 12
13 22 10 23 8 11 10 15 13
14 15 10 18 12 10 8 16 14
15 22 10 15 11 11 8 23 15
16 28 10 12 4 15 4 27 16
17 20 10 21 9 9 9 22 17
18 12 10 15 8 11 8 14 18
19 24 10 20 8 17 7 22 19
20 20 10 31 14 17 11 23 20
21 21 10 27 15 11 9 23 21
22 20 10 34 16 18 11 21 22
23 21 10 21 9 14 13 19 23
24 23 10 31 14 10 8 18 24
25 28 10 19 11 11 8 20 25
26 24 10 16 8 15 9 23 26
27 24 10 20 9 15 6 25 27
28 24 10 21 9 13 9 19 28
29 23 10 22 9 16 9 24 29
30 23 10 17 9 13 6 22 30
31 29 10 24 10 9 6 25 31
32 24 10 25 16 18 16 26 32
33 18 10 26 11 18 5 29 33
34 25 10 25 8 12 7 32 34
35 21 10 17 9 17 9 25 35
36 26 10 32 16 9 6 29 36
37 22 10 33 11 9 6 28 37
38 22 10 13 16 12 5 17 38
39 22 10 32 12 18 12 28 39
40 23 10 25 12 12 7 29 40
41 30 10 29 14 18 10 26 41
42 23 10 22 9 14 9 25 42
43 17 10 18 10 15 8 14 43
44 23 10 17 9 16 5 25 44
45 23 10 20 10 10 8 26 45
46 25 10 15 12 11 8 20 46
47 24 10 20 14 14 10 18 47
48 24 10 33 14 9 6 32 48
49 23 10 29 10 12 8 25 49
50 21 10 23 14 17 7 25 50
51 24 10 26 16 5 4 23 51
52 24 10 18 9 12 8 21 52
53 28 10 20 10 12 8 20 53
54 16 10 11 6 6 4 15 54
55 20 10 28 8 24 20 30 55
56 29 10 26 13 12 8 24 56
57 27 10 22 10 12 8 26 57
58 22 10 17 8 14 6 24 58
59 28 10 12 7 7 4 22 59
60 16 10 14 15 13 8 14 60
61 25 10 17 9 12 9 24 61
62 24 10 21 10 13 6 24 62
63 28 10 19 12 14 7 24 63
64 24 10 18 13 8 9 24 64
65 23 10 10 10 11 5 19 65
66 30 10 29 11 9 5 31 66
67 24 10 31 8 11 8 22 67
68 21 10 19 9 13 8 27 68
69 25 10 9 13 10 6 19 69
70 25 10 20 11 11 8 25 70
71 22 10 28 8 12 7 20 71
72 23 10 19 9 9 7 21 72
73 26 10 30 9 15 9 27 73
74 23 10 29 15 18 11 23 74
75 25 10 26 9 15 6 25 75
76 21 10 23 10 12 8 20 76
77 25 10 13 14 13 6 21 77
78 24 10 21 12 14 9 22 78
79 29 10 19 12 10 8 23 79
80 22 10 28 11 13 6 25 80
81 27 10 23 14 13 10 25 81
82 26 10 18 6 11 8 17 82
83 22 10 21 12 13 8 19 83
84 24 10 20 8 16 10 25 84
85 27 10 23 14 8 5 19 85
86 24 10 21 11 16 7 20 86
87 24 10 21 10 11 5 26 87
88 29 10 15 14 9 8 23 88
89 22 10 28 12 16 14 27 89
90 21 10 19 10 12 7 17 90
91 24 10 26 14 14 8 17 91
92 24 10 10 5 8 6 19 92
93 23 10 16 11 9 5 17 93
94 20 10 22 10 15 6 22 94
95 27 10 19 9 11 10 21 95
96 26 10 31 10 21 12 32 96
97 25 10 31 16 14 9 21 97
98 21 10 29 13 18 12 21 98
99 21 10 19 9 12 7 18 99
100 19 10 22 10 13 8 18 100
101 21 10 23 10 15 10 23 101
102 21 10 15 7 12 6 19 102
103 16 10 20 9 19 10 20 103
104 22 10 18 8 15 10 21 104
105 29 10 23 14 11 10 20 105
106 15 10 25 14 11 5 17 106
107 17 10 21 8 10 7 18 107
108 15 10 24 9 13 10 19 108
109 21 10 25 14 15 11 22 109
110 21 10 17 14 12 6 15 110
111 19 10 13 8 12 7 14 111
112 24 10 28 8 16 12 18 112
113 20 10 21 8 9 11 24 113
114 17 10 25 7 18 11 35 114
115 23 10 9 6 8 11 29 115
116 24 10 16 8 13 5 21 116
117 14 10 19 6 17 8 25 117
118 19 10 17 11 9 6 20 118
119 24 10 25 14 15 9 22 119
120 13 10 20 11 8 4 13 120
121 22 10 29 11 7 4 26 121
122 16 10 14 11 12 7 17 122
123 19 10 22 14 14 11 25 123
124 25 10 15 8 6 6 20 124
125 25 10 19 20 8 7 19 125
126 23 10 20 11 17 8 21 126
127 24 10 15 8 10 4 22 127
128 26 10 20 11 11 8 24 128
129 26 10 18 10 14 9 21 129
130 25 10 33 14 11 8 26 130
131 18 10 22 11 13 11 24 131
132 21 10 16 9 12 8 16 132
133 26 10 17 9 11 5 23 133
134 23 10 16 8 9 4 18 134
135 23 10 21 10 12 8 16 135
136 22 10 26 13 20 10 26 136
137 20 10 18 13 12 6 19 137
138 13 10 18 12 13 9 21 138
139 24 10 17 8 12 9 21 139
140 15 10 22 13 12 13 22 140
141 14 10 30 14 9 9 23 141
142 22 10 30 12 15 10 29 142
143 10 10 24 14 24 20 21 143
144 24 10 21 15 7 5 21 144
145 22 10 21 13 17 11 23 145
146 24 10 29 16 11 6 27 146
147 19 10 31 9 17 9 25 147
148 20 10 20 9 11 7 21 148
149 13 10 16 9 12 9 10 149
150 20 10 22 8 14 10 20 150
151 22 10 20 7 11 9 26 151
152 24 10 28 16 16 8 24 152
153 29 10 38 11 21 7 29 153
154 12 10 22 9 14 6 19 154
155 20 10 20 11 20 13 24 155
156 21 10 17 9 13 6 19 156
157 24 10 28 14 11 8 24 157
158 22 10 22 13 15 10 22 158
159 20 10 31 16 19 16 17 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) maand CM D PE PC
6.67822 1.09089 -0.06089 0.21442 -0.14221 -0.24082
`PS\r` t
0.39982 -0.01607
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.2452 -1.8559 0.3678 2.2634 7.4748
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.678224 16.640062 0.401 0.6887
maand 1.090894 1.669545 0.653 0.5145
CM -0.060890 0.062235 -0.978 0.3294
D 0.214417 0.111070 1.930 0.0554 .
PE -0.142213 0.103898 -1.369 0.1731
PC -0.240816 0.129923 -1.854 0.0658 .
`PS\r` 0.399817 0.075551 5.292 4.19e-07 ***
t -0.016068 0.006317 -2.544 0.0120 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.449 on 151 degrees of freedom
Multiple R-squared: 0.2544, Adjusted R-squared: 0.2198
F-statistic: 7.359 on 7 and 151 DF, p-value: 1.347e-07
> 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,] 0.354919789 0.709839577 0.6450802
[2,] 0.471916457 0.943832913 0.5280835
[3,] 0.407737227 0.815474455 0.5922628
[4,] 0.303674341 0.607348681 0.6963257
[5,] 0.500835724 0.998328553 0.4991643
[6,] 0.600440249 0.799119503 0.3995598
[7,] 0.506189591 0.987620817 0.4938104
[8,] 0.595563492 0.808873016 0.4044365
[9,] 0.590404733 0.819190533 0.4095953
[10,] 0.509164409 0.981671181 0.4908356
[11,] 0.450081358 0.900162716 0.5499186
[12,] 0.374747174 0.749494348 0.6252528
[13,] 0.387039000 0.774078000 0.6129610
[14,] 0.487274808 0.974549617 0.5127252
[15,] 0.700172668 0.599654665 0.2998273
[16,] 0.639033132 0.721933736 0.3609669
[17,] 0.573639753 0.852720494 0.4263602
[18,] 0.557957299 0.884085402 0.4420427
[19,] 0.491851753 0.983703506 0.5081482
[20,] 0.427096774 0.854193549 0.5729032
[21,] 0.426368422 0.852736844 0.5736316
[22,] 0.363574902 0.727149804 0.6364251
[23,] 0.618536706 0.762926587 0.3814633
[24,] 0.581391977 0.837216046 0.4186080
[25,] 0.544726822 0.910546355 0.4552732
[26,] 0.488700882 0.977401763 0.5112991
[27,] 0.473974073 0.947948147 0.5260259
[28,] 0.422661114 0.845322227 0.5773389
[29,] 0.372538606 0.745077212 0.6274614
[30,] 0.347706065 0.695412129 0.6522939
[31,] 0.524496841 0.951006318 0.4755032
[32,] 0.469843657 0.939687314 0.5301563
[33,] 0.436007891 0.872015782 0.5639921
[34,] 0.387783054 0.775566109 0.6122169
[35,] 0.347261726 0.694523452 0.6527383
[36,] 0.323095827 0.646191654 0.6769042
[37,] 0.301448151 0.602896302 0.6985518
[38,] 0.296878814 0.593757628 0.7031212
[39,] 0.259055513 0.518111026 0.7409445
[40,] 0.256624034 0.513248068 0.7433760
[41,] 0.236112890 0.472225781 0.7638871
[42,] 0.206391706 0.412783412 0.7936083
[43,] 0.277768768 0.555537537 0.7222312
[44,] 0.355967741 0.711935483 0.6440323
[45,] 0.339436208 0.678872415 0.6605638
[46,] 0.401634448 0.803268896 0.5983656
[47,] 0.377971364 0.755942729 0.6220286
[48,] 0.346554907 0.693109813 0.6534451
[49,] 0.346166463 0.692332926 0.6538335
[50,] 0.423439818 0.846879635 0.5765602
[51,] 0.379191711 0.758383421 0.6208083
[52,] 0.336952134 0.673904269 0.6630479
[53,] 0.339445418 0.678890835 0.6605546
[54,] 0.305288311 0.610576621 0.6947117
[55,] 0.265987342 0.531974685 0.7340127
[56,] 0.249527995 0.499055989 0.7504720
[57,] 0.221389368 0.442778735 0.7786106
[58,] 0.243638209 0.487276418 0.7563618
[59,] 0.210155768 0.420311536 0.7898442
[60,] 0.177471691 0.354943381 0.8225283
[61,] 0.148776901 0.297553802 0.8512231
[62,] 0.123683108 0.247366217 0.8763169
[63,] 0.107657989 0.215315978 0.8923420
[64,] 0.087542890 0.175085781 0.9124571
[65,] 0.071248545 0.142497091 0.9287515
[66,] 0.059511553 0.119023106 0.9404884
[67,] 0.047141251 0.094282502 0.9528587
[68,] 0.036730119 0.073460237 0.9632699
[69,] 0.041742002 0.083484004 0.9582580
[70,] 0.038241012 0.076482024 0.9617590
[71,] 0.032017335 0.064034670 0.9679827
[72,] 0.042232840 0.084465681 0.9577672
[73,] 0.032952452 0.065904904 0.9670475
[74,] 0.025758911 0.051517821 0.9742411
[75,] 0.023565095 0.047130189 0.9764349
[76,] 0.018594057 0.037188113 0.9814059
[77,] 0.015121100 0.030242200 0.9848789
[78,] 0.015480327 0.030960654 0.9845197
[79,] 0.012987882 0.025975764 0.9870121
[80,] 0.009802252 0.019604504 0.9901977
[81,] 0.008332817 0.016665634 0.9916672
[82,] 0.006784351 0.013568703 0.9932156
[83,] 0.004985707 0.009971414 0.9950143
[84,] 0.004735011 0.009470023 0.9952650
[85,] 0.007017622 0.014035245 0.9929824
[86,] 0.005705681 0.011411361 0.9942943
[87,] 0.004858298 0.009716595 0.9951417
[88,] 0.003709824 0.007419648 0.9962902
[89,] 0.002792435 0.005584870 0.9972076
[90,] 0.002312292 0.004624583 0.9976877
[91,] 0.001812017 0.003624034 0.9981880
[92,] 0.001325474 0.002650948 0.9986745
[93,] 0.001611386 0.003222771 0.9983886
[94,] 0.001252669 0.002505339 0.9987473
[95,] 0.005495308 0.010990616 0.9945047
[96,] 0.013121532 0.026243064 0.9868785
[97,] 0.013742025 0.027484051 0.9862580
[98,] 0.018573570 0.037147140 0.9814264
[99,] 0.014384469 0.028768938 0.9856155
[100,] 0.010391841 0.020783683 0.9896082
[101,] 0.007474240 0.014948479 0.9925258
[102,] 0.021860304 0.043720609 0.9781397
[103,] 0.021958960 0.043917920 0.9780410
[104,] 0.054676085 0.109352170 0.9453239
[105,] 0.046889266 0.093778532 0.9531107
[106,] 0.039358057 0.078716114 0.9606419
[107,] 0.102523949 0.205047897 0.8974761
[108,] 0.096328146 0.192656291 0.9036719
[109,] 0.086020588 0.172041175 0.9139794
[110,] 0.149104401 0.298208802 0.8508956
[111,] 0.146065449 0.292130898 0.8539346
[112,] 0.185090803 0.370181607 0.8149092
[113,] 0.184133922 0.368267844 0.8158661
[114,] 0.174401105 0.348802209 0.8255989
[115,] 0.150958815 0.301917629 0.8490412
[116,] 0.121150888 0.242301775 0.8788491
[117,] 0.094774292 0.189548583 0.9052257
[118,] 0.092426290 0.184852580 0.9075737
[119,] 0.130226845 0.260453690 0.8697732
[120,] 0.112616517 0.225233034 0.8873835
[121,] 0.094197825 0.188395650 0.9058022
[122,] 0.083618782 0.167237564 0.9163812
[123,] 0.081945099 0.163890198 0.9180549
[124,] 0.072953350 0.145906700 0.9270466
[125,] 0.171512039 0.343024078 0.8284880
[126,] 0.136628567 0.273257134 0.8633714
[127,] 0.114239842 0.228479684 0.8857602
[128,] 0.160955314 0.321910628 0.8390447
[129,] 0.391954458 0.783908916 0.6080455
[130,] 0.337750604 0.675501208 0.6622494
[131,] 0.481408141 0.962816281 0.5185919
[132,] 0.390283213 0.780566426 0.6097168
[133,] 0.553431960 0.893136080 0.4465680
[134,] 0.499741685 0.999483371 0.5002583
[135,] 0.403076751 0.806153502 0.5969232
[136,] 0.298613381 0.597226762 0.7013866
[137,] 0.290180448 0.580360897 0.7098196
[138,] 0.172902386 0.345804772 0.8270976
> postscript(file="/var/www/html/rcomp/tmp/1gy5s1290504412.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2r7mc1290504412.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3r7mc1290504412.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4r7mc1290504412.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5jh3x1290504412.ps",horizontal=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 = 159
Frequency = 1
1 2 3 4 5 6
2.83784647 -1.37387683 0.65019871 0.84315302 -2.95732138 4.93560706
7 8 9 10 11 12
-0.86887509 -2.82544196 -1.18182317 1.38838303 2.48688450 -3.05360324
13 14 15 16 17 18
2.28210279 -6.88760942 -2.49630269 2.84433395 -3.31378483 -8.20649339
19 20 21 22 23 24
1.52795215 -2.50924244 -3.28606263 -1.78142153 0.65640298 0.83617674
25 26 27 28 29 30
5.10739036 1.19425461 -0.28261397 2.63126778 0.13577968 -0.50205558
31 32 33 34 35 36
3.95752342 1.03623767 -7.66314239 -1.63581387 -2.32986770 -1.36078372
37 38 39 40 41 42
-3.81192411 -1.50193304 -1.33028330 -3.19762139 6.40835153 -0.33957957
43 44 45 46 47 48
-2.48210275 -1.29073081 -1.83705840 1.98684085 2.58643178 -3.87769508
49 50 51 52 53 54
-0.54053000 -3.27722066 -2.13668580 1.65156635 5.97481536 -5.51691735
55 56 57 58 59 60
-0.47891986 5.14584202 2.76196488 -1.49515528 3.75338906 -4.80901800
61 62 63 64 65 66
1.77665220 0.24163121 4.09011372 -0.54077342 0.09388603 2.97022026
67 68 69 70 71 72
2.35654709 -3.28714496 1.55261972 0.89225579 0.93918033 0.36636205
73 74 75 76 77 78
2.98823041 1.16444666 1.85399304 -0.47294916 1.33731348 1.73418016
79 80 81 82 83 84
5.41898210 -1.65714624 3.37448196 6.23391314 0.63094315 1.95315558
85 86 87 88 89 90
3.92251365 2.67957087 -0.68154448 4.74898629 -0.17342139 0.46707803
91 92 93 94 95 96
3.57695282 2.41398275 1.20992375 -2.09924093 5.74279959 2.78090820
97 98 99 100 101 102
3.19052560 1.01936281 0.42628987 -1.20635930 -0.36242951 0.01913362
103 104 105 106 107 108
-3.53024322 1.60979107 7.47477417 -6.39200328 -3.39339489 -4.65980380
109 110 111 112 113 114
-0.32913924 0.36780951 0.06744995 6.17053415 -1.87484097 -7.51886542
115 116 117 118 119 120
-1.28585411 2.19232271 -7.48807416 -3.28612139 2.34990994 -6.89643830
121 122 123 124 125 126
-2.67219430 -4.53761352 -3.62852259 2.90511704 1.51680359 2.24461307
127 128 129 130 131 132
1.24090839 3.22401841 5.19962933 1.60484357 -3.59912393 1.81431370
133 134 135 136 137 138
3.22789185 1.87133047 3.95255247 0.25098615 -1.52231550 -8.22680526
139 140 141 142 143 144
3.44382653 -5.74429306 -8.24523832 -1.10514520 -6.99664102 0.59248209
145 146 147 148 149 150
1.10477442 -0.69191115 -1.67778470 -1.06715140 -3.27281116 0.85008543
151 152 153 154 155 156
-0.10756862 1.73575529 6.90397483 -7.86350423 0.14185143 0.72196712
157 158 159
1.53386311 1.24912434 3.18278659
> postscript(file="/var/www/html/rcomp/tmp/6jh3x1290504412.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 2.83784647 NA
1 -1.37387683 2.83784647
2 0.65019871 -1.37387683
3 0.84315302 0.65019871
4 -2.95732138 0.84315302
5 4.93560706 -2.95732138
6 -0.86887509 4.93560706
7 -2.82544196 -0.86887509
8 -1.18182317 -2.82544196
9 1.38838303 -1.18182317
10 2.48688450 1.38838303
11 -3.05360324 2.48688450
12 2.28210279 -3.05360324
13 -6.88760942 2.28210279
14 -2.49630269 -6.88760942
15 2.84433395 -2.49630269
16 -3.31378483 2.84433395
17 -8.20649339 -3.31378483
18 1.52795215 -8.20649339
19 -2.50924244 1.52795215
20 -3.28606263 -2.50924244
21 -1.78142153 -3.28606263
22 0.65640298 -1.78142153
23 0.83617674 0.65640298
24 5.10739036 0.83617674
25 1.19425461 5.10739036
26 -0.28261397 1.19425461
27 2.63126778 -0.28261397
28 0.13577968 2.63126778
29 -0.50205558 0.13577968
30 3.95752342 -0.50205558
31 1.03623767 3.95752342
32 -7.66314239 1.03623767
33 -1.63581387 -7.66314239
34 -2.32986770 -1.63581387
35 -1.36078372 -2.32986770
36 -3.81192411 -1.36078372
37 -1.50193304 -3.81192411
38 -1.33028330 -1.50193304
39 -3.19762139 -1.33028330
40 6.40835153 -3.19762139
41 -0.33957957 6.40835153
42 -2.48210275 -0.33957957
43 -1.29073081 -2.48210275
44 -1.83705840 -1.29073081
45 1.98684085 -1.83705840
46 2.58643178 1.98684085
47 -3.87769508 2.58643178
48 -0.54053000 -3.87769508
49 -3.27722066 -0.54053000
50 -2.13668580 -3.27722066
51 1.65156635 -2.13668580
52 5.97481536 1.65156635
53 -5.51691735 5.97481536
54 -0.47891986 -5.51691735
55 5.14584202 -0.47891986
56 2.76196488 5.14584202
57 -1.49515528 2.76196488
58 3.75338906 -1.49515528
59 -4.80901800 3.75338906
60 1.77665220 -4.80901800
61 0.24163121 1.77665220
62 4.09011372 0.24163121
63 -0.54077342 4.09011372
64 0.09388603 -0.54077342
65 2.97022026 0.09388603
66 2.35654709 2.97022026
67 -3.28714496 2.35654709
68 1.55261972 -3.28714496
69 0.89225579 1.55261972
70 0.93918033 0.89225579
71 0.36636205 0.93918033
72 2.98823041 0.36636205
73 1.16444666 2.98823041
74 1.85399304 1.16444666
75 -0.47294916 1.85399304
76 1.33731348 -0.47294916
77 1.73418016 1.33731348
78 5.41898210 1.73418016
79 -1.65714624 5.41898210
80 3.37448196 -1.65714624
81 6.23391314 3.37448196
82 0.63094315 6.23391314
83 1.95315558 0.63094315
84 3.92251365 1.95315558
85 2.67957087 3.92251365
86 -0.68154448 2.67957087
87 4.74898629 -0.68154448
88 -0.17342139 4.74898629
89 0.46707803 -0.17342139
90 3.57695282 0.46707803
91 2.41398275 3.57695282
92 1.20992375 2.41398275
93 -2.09924093 1.20992375
94 5.74279959 -2.09924093
95 2.78090820 5.74279959
96 3.19052560 2.78090820
97 1.01936281 3.19052560
98 0.42628987 1.01936281
99 -1.20635930 0.42628987
100 -0.36242951 -1.20635930
101 0.01913362 -0.36242951
102 -3.53024322 0.01913362
103 1.60979107 -3.53024322
104 7.47477417 1.60979107
105 -6.39200328 7.47477417
106 -3.39339489 -6.39200328
107 -4.65980380 -3.39339489
108 -0.32913924 -4.65980380
109 0.36780951 -0.32913924
110 0.06744995 0.36780951
111 6.17053415 0.06744995
112 -1.87484097 6.17053415
113 -7.51886542 -1.87484097
114 -1.28585411 -7.51886542
115 2.19232271 -1.28585411
116 -7.48807416 2.19232271
117 -3.28612139 -7.48807416
118 2.34990994 -3.28612139
119 -6.89643830 2.34990994
120 -2.67219430 -6.89643830
121 -4.53761352 -2.67219430
122 -3.62852259 -4.53761352
123 2.90511704 -3.62852259
124 1.51680359 2.90511704
125 2.24461307 1.51680359
126 1.24090839 2.24461307
127 3.22401841 1.24090839
128 5.19962933 3.22401841
129 1.60484357 5.19962933
130 -3.59912393 1.60484357
131 1.81431370 -3.59912393
132 3.22789185 1.81431370
133 1.87133047 3.22789185
134 3.95255247 1.87133047
135 0.25098615 3.95255247
136 -1.52231550 0.25098615
137 -8.22680526 -1.52231550
138 3.44382653 -8.22680526
139 -5.74429306 3.44382653
140 -8.24523832 -5.74429306
141 -1.10514520 -8.24523832
142 -6.99664102 -1.10514520
143 0.59248209 -6.99664102
144 1.10477442 0.59248209
145 -0.69191115 1.10477442
146 -1.67778470 -0.69191115
147 -1.06715140 -1.67778470
148 -3.27281116 -1.06715140
149 0.85008543 -3.27281116
150 -0.10756862 0.85008543
151 1.73575529 -0.10756862
152 6.90397483 1.73575529
153 -7.86350423 6.90397483
154 0.14185143 -7.86350423
155 0.72196712 0.14185143
156 1.53386311 0.72196712
157 1.24912434 1.53386311
158 3.18278659 1.24912434
159 NA 3.18278659
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.37387683 2.83784647
[2,] 0.65019871 -1.37387683
[3,] 0.84315302 0.65019871
[4,] -2.95732138 0.84315302
[5,] 4.93560706 -2.95732138
[6,] -0.86887509 4.93560706
[7,] -2.82544196 -0.86887509
[8,] -1.18182317 -2.82544196
[9,] 1.38838303 -1.18182317
[10,] 2.48688450 1.38838303
[11,] -3.05360324 2.48688450
[12,] 2.28210279 -3.05360324
[13,] -6.88760942 2.28210279
[14,] -2.49630269 -6.88760942
[15,] 2.84433395 -2.49630269
[16,] -3.31378483 2.84433395
[17,] -8.20649339 -3.31378483
[18,] 1.52795215 -8.20649339
[19,] -2.50924244 1.52795215
[20,] -3.28606263 -2.50924244
[21,] -1.78142153 -3.28606263
[22,] 0.65640298 -1.78142153
[23,] 0.83617674 0.65640298
[24,] 5.10739036 0.83617674
[25,] 1.19425461 5.10739036
[26,] -0.28261397 1.19425461
[27,] 2.63126778 -0.28261397
[28,] 0.13577968 2.63126778
[29,] -0.50205558 0.13577968
[30,] 3.95752342 -0.50205558
[31,] 1.03623767 3.95752342
[32,] -7.66314239 1.03623767
[33,] -1.63581387 -7.66314239
[34,] -2.32986770 -1.63581387
[35,] -1.36078372 -2.32986770
[36,] -3.81192411 -1.36078372
[37,] -1.50193304 -3.81192411
[38,] -1.33028330 -1.50193304
[39,] -3.19762139 -1.33028330
[40,] 6.40835153 -3.19762139
[41,] -0.33957957 6.40835153
[42,] -2.48210275 -0.33957957
[43,] -1.29073081 -2.48210275
[44,] -1.83705840 -1.29073081
[45,] 1.98684085 -1.83705840
[46,] 2.58643178 1.98684085
[47,] -3.87769508 2.58643178
[48,] -0.54053000 -3.87769508
[49,] -3.27722066 -0.54053000
[50,] -2.13668580 -3.27722066
[51,] 1.65156635 -2.13668580
[52,] 5.97481536 1.65156635
[53,] -5.51691735 5.97481536
[54,] -0.47891986 -5.51691735
[55,] 5.14584202 -0.47891986
[56,] 2.76196488 5.14584202
[57,] -1.49515528 2.76196488
[58,] 3.75338906 -1.49515528
[59,] -4.80901800 3.75338906
[60,] 1.77665220 -4.80901800
[61,] 0.24163121 1.77665220
[62,] 4.09011372 0.24163121
[63,] -0.54077342 4.09011372
[64,] 0.09388603 -0.54077342
[65,] 2.97022026 0.09388603
[66,] 2.35654709 2.97022026
[67,] -3.28714496 2.35654709
[68,] 1.55261972 -3.28714496
[69,] 0.89225579 1.55261972
[70,] 0.93918033 0.89225579
[71,] 0.36636205 0.93918033
[72,] 2.98823041 0.36636205
[73,] 1.16444666 2.98823041
[74,] 1.85399304 1.16444666
[75,] -0.47294916 1.85399304
[76,] 1.33731348 -0.47294916
[77,] 1.73418016 1.33731348
[78,] 5.41898210 1.73418016
[79,] -1.65714624 5.41898210
[80,] 3.37448196 -1.65714624
[81,] 6.23391314 3.37448196
[82,] 0.63094315 6.23391314
[83,] 1.95315558 0.63094315
[84,] 3.92251365 1.95315558
[85,] 2.67957087 3.92251365
[86,] -0.68154448 2.67957087
[87,] 4.74898629 -0.68154448
[88,] -0.17342139 4.74898629
[89,] 0.46707803 -0.17342139
[90,] 3.57695282 0.46707803
[91,] 2.41398275 3.57695282
[92,] 1.20992375 2.41398275
[93,] -2.09924093 1.20992375
[94,] 5.74279959 -2.09924093
[95,] 2.78090820 5.74279959
[96,] 3.19052560 2.78090820
[97,] 1.01936281 3.19052560
[98,] 0.42628987 1.01936281
[99,] -1.20635930 0.42628987
[100,] -0.36242951 -1.20635930
[101,] 0.01913362 -0.36242951
[102,] -3.53024322 0.01913362
[103,] 1.60979107 -3.53024322
[104,] 7.47477417 1.60979107
[105,] -6.39200328 7.47477417
[106,] -3.39339489 -6.39200328
[107,] -4.65980380 -3.39339489
[108,] -0.32913924 -4.65980380
[109,] 0.36780951 -0.32913924
[110,] 0.06744995 0.36780951
[111,] 6.17053415 0.06744995
[112,] -1.87484097 6.17053415
[113,] -7.51886542 -1.87484097
[114,] -1.28585411 -7.51886542
[115,] 2.19232271 -1.28585411
[116,] -7.48807416 2.19232271
[117,] -3.28612139 -7.48807416
[118,] 2.34990994 -3.28612139
[119,] -6.89643830 2.34990994
[120,] -2.67219430 -6.89643830
[121,] -4.53761352 -2.67219430
[122,] -3.62852259 -4.53761352
[123,] 2.90511704 -3.62852259
[124,] 1.51680359 2.90511704
[125,] 2.24461307 1.51680359
[126,] 1.24090839 2.24461307
[127,] 3.22401841 1.24090839
[128,] 5.19962933 3.22401841
[129,] 1.60484357 5.19962933
[130,] -3.59912393 1.60484357
[131,] 1.81431370 -3.59912393
[132,] 3.22789185 1.81431370
[133,] 1.87133047 3.22789185
[134,] 3.95255247 1.87133047
[135,] 0.25098615 3.95255247
[136,] -1.52231550 0.25098615
[137,] -8.22680526 -1.52231550
[138,] 3.44382653 -8.22680526
[139,] -5.74429306 3.44382653
[140,] -8.24523832 -5.74429306
[141,] -1.10514520 -8.24523832
[142,] -6.99664102 -1.10514520
[143,] 0.59248209 -6.99664102
[144,] 1.10477442 0.59248209
[145,] -0.69191115 1.10477442
[146,] -1.67778470 -0.69191115
[147,] -1.06715140 -1.67778470
[148,] -3.27281116 -1.06715140
[149,] 0.85008543 -3.27281116
[150,] -0.10756862 0.85008543
[151,] 1.73575529 -0.10756862
[152,] 6.90397483 1.73575529
[153,] -7.86350423 6.90397483
[154,] 0.14185143 -7.86350423
[155,] 0.72196712 0.14185143
[156,] 1.53386311 0.72196712
[157,] 1.24912434 1.53386311
[158,] 3.18278659 1.24912434
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.37387683 2.83784647
2 0.65019871 -1.37387683
3 0.84315302 0.65019871
4 -2.95732138 0.84315302
5 4.93560706 -2.95732138
6 -0.86887509 4.93560706
7 -2.82544196 -0.86887509
8 -1.18182317 -2.82544196
9 1.38838303 -1.18182317
10 2.48688450 1.38838303
11 -3.05360324 2.48688450
12 2.28210279 -3.05360324
13 -6.88760942 2.28210279
14 -2.49630269 -6.88760942
15 2.84433395 -2.49630269
16 -3.31378483 2.84433395
17 -8.20649339 -3.31378483
18 1.52795215 -8.20649339
19 -2.50924244 1.52795215
20 -3.28606263 -2.50924244
21 -1.78142153 -3.28606263
22 0.65640298 -1.78142153
23 0.83617674 0.65640298
24 5.10739036 0.83617674
25 1.19425461 5.10739036
26 -0.28261397 1.19425461
27 2.63126778 -0.28261397
28 0.13577968 2.63126778
29 -0.50205558 0.13577968
30 3.95752342 -0.50205558
31 1.03623767 3.95752342
32 -7.66314239 1.03623767
33 -1.63581387 -7.66314239
34 -2.32986770 -1.63581387
35 -1.36078372 -2.32986770
36 -3.81192411 -1.36078372
37 -1.50193304 -3.81192411
38 -1.33028330 -1.50193304
39 -3.19762139 -1.33028330
40 6.40835153 -3.19762139
41 -0.33957957 6.40835153
42 -2.48210275 -0.33957957
43 -1.29073081 -2.48210275
44 -1.83705840 -1.29073081
45 1.98684085 -1.83705840
46 2.58643178 1.98684085
47 -3.87769508 2.58643178
48 -0.54053000 -3.87769508
49 -3.27722066 -0.54053000
50 -2.13668580 -3.27722066
51 1.65156635 -2.13668580
52 5.97481536 1.65156635
53 -5.51691735 5.97481536
54 -0.47891986 -5.51691735
55 5.14584202 -0.47891986
56 2.76196488 5.14584202
57 -1.49515528 2.76196488
58 3.75338906 -1.49515528
59 -4.80901800 3.75338906
60 1.77665220 -4.80901800
61 0.24163121 1.77665220
62 4.09011372 0.24163121
63 -0.54077342 4.09011372
64 0.09388603 -0.54077342
65 2.97022026 0.09388603
66 2.35654709 2.97022026
67 -3.28714496 2.35654709
68 1.55261972 -3.28714496
69 0.89225579 1.55261972
70 0.93918033 0.89225579
71 0.36636205 0.93918033
72 2.98823041 0.36636205
73 1.16444666 2.98823041
74 1.85399304 1.16444666
75 -0.47294916 1.85399304
76 1.33731348 -0.47294916
77 1.73418016 1.33731348
78 5.41898210 1.73418016
79 -1.65714624 5.41898210
80 3.37448196 -1.65714624
81 6.23391314 3.37448196
82 0.63094315 6.23391314
83 1.95315558 0.63094315
84 3.92251365 1.95315558
85 2.67957087 3.92251365
86 -0.68154448 2.67957087
87 4.74898629 -0.68154448
88 -0.17342139 4.74898629
89 0.46707803 -0.17342139
90 3.57695282 0.46707803
91 2.41398275 3.57695282
92 1.20992375 2.41398275
93 -2.09924093 1.20992375
94 5.74279959 -2.09924093
95 2.78090820 5.74279959
96 3.19052560 2.78090820
97 1.01936281 3.19052560
98 0.42628987 1.01936281
99 -1.20635930 0.42628987
100 -0.36242951 -1.20635930
101 0.01913362 -0.36242951
102 -3.53024322 0.01913362
103 1.60979107 -3.53024322
104 7.47477417 1.60979107
105 -6.39200328 7.47477417
106 -3.39339489 -6.39200328
107 -4.65980380 -3.39339489
108 -0.32913924 -4.65980380
109 0.36780951 -0.32913924
110 0.06744995 0.36780951
111 6.17053415 0.06744995
112 -1.87484097 6.17053415
113 -7.51886542 -1.87484097
114 -1.28585411 -7.51886542
115 2.19232271 -1.28585411
116 -7.48807416 2.19232271
117 -3.28612139 -7.48807416
118 2.34990994 -3.28612139
119 -6.89643830 2.34990994
120 -2.67219430 -6.89643830
121 -4.53761352 -2.67219430
122 -3.62852259 -4.53761352
123 2.90511704 -3.62852259
124 1.51680359 2.90511704
125 2.24461307 1.51680359
126 1.24090839 2.24461307
127 3.22401841 1.24090839
128 5.19962933 3.22401841
129 1.60484357 5.19962933
130 -3.59912393 1.60484357
131 1.81431370 -3.59912393
132 3.22789185 1.81431370
133 1.87133047 3.22789185
134 3.95255247 1.87133047
135 0.25098615 3.95255247
136 -1.52231550 0.25098615
137 -8.22680526 -1.52231550
138 3.44382653 -8.22680526
139 -5.74429306 3.44382653
140 -8.24523832 -5.74429306
141 -1.10514520 -8.24523832
142 -6.99664102 -1.10514520
143 0.59248209 -6.99664102
144 1.10477442 0.59248209
145 -0.69191115 1.10477442
146 -1.67778470 -0.69191115
147 -1.06715140 -1.67778470
148 -3.27281116 -1.06715140
149 0.85008543 -3.27281116
150 -0.10756862 0.85008543
151 1.73575529 -0.10756862
152 6.90397483 1.73575529
153 -7.86350423 6.90397483
154 0.14185143 -7.86350423
155 0.72196712 0.14185143
156 1.53386311 0.72196712
157 1.24912434 1.53386311
158 3.18278659 1.24912434
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7cq201290504412.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cq201290504412.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9nzj31290504412.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10nzj31290504412.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/118z0r1290504412.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12uihf1290504412.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/130jv91290504412.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14bsvt1290504412.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15ebbz1290504412.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16alr81290504412.tab")
+ }
>
> try(system("convert tmp/1gy5s1290504412.ps tmp/1gy5s1290504412.png",intern=TRUE))
character(0)
> try(system("convert tmp/2r7mc1290504412.ps tmp/2r7mc1290504412.png",intern=TRUE))
character(0)
> try(system("convert tmp/3r7mc1290504412.ps tmp/3r7mc1290504412.png",intern=TRUE))
character(0)
> try(system("convert tmp/4r7mc1290504412.ps tmp/4r7mc1290504412.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jh3x1290504412.ps tmp/5jh3x1290504412.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jh3x1290504412.ps tmp/6jh3x1290504412.png",intern=TRUE))
character(0)
> try(system("convert tmp/7cq201290504412.ps tmp/7cq201290504412.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cq201290504412.ps tmp/8cq201290504412.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nzj31290504412.ps tmp/9nzj31290504412.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nzj31290504412.ps tmp/10nzj31290504412.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.167 1.745 27.612