R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(24,25,30,19,22,22,25,23,17,21,19,19,15,16,23,27,22,14,22,23,23,21,19,18,20,23,25,19,24,22,25,26,29,32,25,29,28,17,28,29,26,25,14,25,26,20,18,32,25,25,23,21,20,15,30,24,26,24,22,14,24,24,24,24,19,31,22,27,19,25,20,21,27,23,25,20,21,22,23,25,25,17,19,25,19,20,26,23,27,17,17,19,17,22,21,32,21,21,18,18,23,19,20,21,20,17,18,19,22,15,14,18,24,35,29,21,25,20,22,13,26,17,25,20,19,21,22,24,21,26,24,16,23,18,16,26,19,21,21,22,23,29,21,21,23,27,25,21,10,20,26,24,29,19,24,19,24,22,17),dim=c(1,159),dimnames=list(c('PS'),1:159))
> y <- array(NA,dim=c(1,159),dimnames=list(c('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 = 'Include Monthly Dummies'
> par1 = '1'
> #'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
PS M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 24 1 0 0 0 0 0 0 0 0 0 0 1
2 25 0 1 0 0 0 0 0 0 0 0 0 2
3 30 0 0 1 0 0 0 0 0 0 0 0 3
4 19 0 0 0 1 0 0 0 0 0 0 0 4
5 22 0 0 0 0 1 0 0 0 0 0 0 5
6 22 0 0 0 0 0 1 0 0 0 0 0 6
7 25 0 0 0 0 0 0 1 0 0 0 0 7
8 23 0 0 0 0 0 0 0 1 0 0 0 8
9 17 0 0 0 0 0 0 0 0 1 0 0 9
10 21 0 0 0 0 0 0 0 0 0 1 0 10
11 19 0 0 0 0 0 0 0 0 0 0 1 11
12 19 0 0 0 0 0 0 0 0 0 0 0 12
13 15 1 0 0 0 0 0 0 0 0 0 0 13
14 16 0 1 0 0 0 0 0 0 0 0 0 14
15 23 0 0 1 0 0 0 0 0 0 0 0 15
16 27 0 0 0 1 0 0 0 0 0 0 0 16
17 22 0 0 0 0 1 0 0 0 0 0 0 17
18 14 0 0 0 0 0 1 0 0 0 0 0 18
19 22 0 0 0 0 0 0 1 0 0 0 0 19
20 23 0 0 0 0 0 0 0 1 0 0 0 20
21 23 0 0 0 0 0 0 0 0 1 0 0 21
22 21 0 0 0 0 0 0 0 0 0 1 0 22
23 19 0 0 0 0 0 0 0 0 0 0 1 23
24 18 0 0 0 0 0 0 0 0 0 0 0 24
25 20 1 0 0 0 0 0 0 0 0 0 0 25
26 23 0 1 0 0 0 0 0 0 0 0 0 26
27 25 0 0 1 0 0 0 0 0 0 0 0 27
28 19 0 0 0 1 0 0 0 0 0 0 0 28
29 24 0 0 0 0 1 0 0 0 0 0 0 29
30 22 0 0 0 0 0 1 0 0 0 0 0 30
31 25 0 0 0 0 0 0 1 0 0 0 0 31
32 26 0 0 0 0 0 0 0 1 0 0 0 32
33 29 0 0 0 0 0 0 0 0 1 0 0 33
34 32 0 0 0 0 0 0 0 0 0 1 0 34
35 25 0 0 0 0 0 0 0 0 0 0 1 35
36 29 0 0 0 0 0 0 0 0 0 0 0 36
37 28 1 0 0 0 0 0 0 0 0 0 0 37
38 17 0 1 0 0 0 0 0 0 0 0 0 38
39 28 0 0 1 0 0 0 0 0 0 0 0 39
40 29 0 0 0 1 0 0 0 0 0 0 0 40
41 26 0 0 0 0 1 0 0 0 0 0 0 41
42 25 0 0 0 0 0 1 0 0 0 0 0 42
43 14 0 0 0 0 0 0 1 0 0 0 0 43
44 25 0 0 0 0 0 0 0 1 0 0 0 44
45 26 0 0 0 0 0 0 0 0 1 0 0 45
46 20 0 0 0 0 0 0 0 0 0 1 0 46
47 18 0 0 0 0 0 0 0 0 0 0 1 47
48 32 0 0 0 0 0 0 0 0 0 0 0 48
49 25 1 0 0 0 0 0 0 0 0 0 0 49
50 25 0 1 0 0 0 0 0 0 0 0 0 50
51 23 0 0 1 0 0 0 0 0 0 0 0 51
52 21 0 0 0 1 0 0 0 0 0 0 0 52
53 20 0 0 0 0 1 0 0 0 0 0 0 53
54 15 0 0 0 0 0 1 0 0 0 0 0 54
55 30 0 0 0 0 0 0 1 0 0 0 0 55
56 24 0 0 0 0 0 0 0 1 0 0 0 56
57 26 0 0 0 0 0 0 0 0 1 0 0 57
58 24 0 0 0 0 0 0 0 0 0 1 0 58
59 22 0 0 0 0 0 0 0 0 0 0 1 59
60 14 0 0 0 0 0 0 0 0 0 0 0 60
61 24 1 0 0 0 0 0 0 0 0 0 0 61
62 24 0 1 0 0 0 0 0 0 0 0 0 62
63 24 0 0 1 0 0 0 0 0 0 0 0 63
64 24 0 0 0 1 0 0 0 0 0 0 0 64
65 19 0 0 0 0 1 0 0 0 0 0 0 65
66 31 0 0 0 0 0 1 0 0 0 0 0 66
67 22 0 0 0 0 0 0 1 0 0 0 0 67
68 27 0 0 0 0 0 0 0 1 0 0 0 68
69 19 0 0 0 0 0 0 0 0 1 0 0 69
70 25 0 0 0 0 0 0 0 0 0 1 0 70
71 20 0 0 0 0 0 0 0 0 0 0 1 71
72 21 0 0 0 0 0 0 0 0 0 0 0 72
73 27 1 0 0 0 0 0 0 0 0 0 0 73
74 23 0 1 0 0 0 0 0 0 0 0 0 74
75 25 0 0 1 0 0 0 0 0 0 0 0 75
76 20 0 0 0 1 0 0 0 0 0 0 0 76
77 21 0 0 0 0 1 0 0 0 0 0 0 77
78 22 0 0 0 0 0 1 0 0 0 0 0 78
79 23 0 0 0 0 0 0 1 0 0 0 0 79
80 25 0 0 0 0 0 0 0 1 0 0 0 80
81 25 0 0 0 0 0 0 0 0 1 0 0 81
82 17 0 0 0 0 0 0 0 0 0 1 0 82
83 19 0 0 0 0 0 0 0 0 0 0 1 83
84 25 0 0 0 0 0 0 0 0 0 0 0 84
85 19 1 0 0 0 0 0 0 0 0 0 0 85
86 20 0 1 0 0 0 0 0 0 0 0 0 86
87 26 0 0 1 0 0 0 0 0 0 0 0 87
88 23 0 0 0 1 0 0 0 0 0 0 0 88
89 27 0 0 0 0 1 0 0 0 0 0 0 89
90 17 0 0 0 0 0 1 0 0 0 0 0 90
91 17 0 0 0 0 0 0 1 0 0 0 0 91
92 19 0 0 0 0 0 0 0 1 0 0 0 92
93 17 0 0 0 0 0 0 0 0 1 0 0 93
94 22 0 0 0 0 0 0 0 0 0 1 0 94
95 21 0 0 0 0 0 0 0 0 0 0 1 95
96 32 0 0 0 0 0 0 0 0 0 0 0 96
97 21 1 0 0 0 0 0 0 0 0 0 0 97
98 21 0 1 0 0 0 0 0 0 0 0 0 98
99 18 0 0 1 0 0 0 0 0 0 0 0 99
100 18 0 0 0 1 0 0 0 0 0 0 0 100
101 23 0 0 0 0 1 0 0 0 0 0 0 101
102 19 0 0 0 0 0 1 0 0 0 0 0 102
103 20 0 0 0 0 0 0 1 0 0 0 0 103
104 21 0 0 0 0 0 0 0 1 0 0 0 104
105 20 0 0 0 0 0 0 0 0 1 0 0 105
106 17 0 0 0 0 0 0 0 0 0 1 0 106
107 18 0 0 0 0 0 0 0 0 0 0 1 107
108 19 0 0 0 0 0 0 0 0 0 0 0 108
109 22 1 0 0 0 0 0 0 0 0 0 0 109
110 15 0 1 0 0 0 0 0 0 0 0 0 110
111 14 0 0 1 0 0 0 0 0 0 0 0 111
112 18 0 0 0 1 0 0 0 0 0 0 0 112
113 24 0 0 0 0 1 0 0 0 0 0 0 113
114 35 0 0 0 0 0 1 0 0 0 0 0 114
115 29 0 0 0 0 0 0 1 0 0 0 0 115
116 21 0 0 0 0 0 0 0 1 0 0 0 116
117 25 0 0 0 0 0 0 0 0 1 0 0 117
118 20 0 0 0 0 0 0 0 0 0 1 0 118
119 22 0 0 0 0 0 0 0 0 0 0 1 119
120 13 0 0 0 0 0 0 0 0 0 0 0 120
121 26 1 0 0 0 0 0 0 0 0 0 0 121
122 17 0 1 0 0 0 0 0 0 0 0 0 122
123 25 0 0 1 0 0 0 0 0 0 0 0 123
124 20 0 0 0 1 0 0 0 0 0 0 0 124
125 19 0 0 0 0 1 0 0 0 0 0 0 125
126 21 0 0 0 0 0 1 0 0 0 0 0 126
127 22 0 0 0 0 0 0 1 0 0 0 0 127
128 24 0 0 0 0 0 0 0 1 0 0 0 128
129 21 0 0 0 0 0 0 0 0 1 0 0 129
130 26 0 0 0 0 0 0 0 0 0 1 0 130
131 24 0 0 0 0 0 0 0 0 0 0 1 131
132 16 0 0 0 0 0 0 0 0 0 0 0 132
133 23 1 0 0 0 0 0 0 0 0 0 0 133
134 18 0 1 0 0 0 0 0 0 0 0 0 134
135 16 0 0 1 0 0 0 0 0 0 0 0 135
136 26 0 0 0 1 0 0 0 0 0 0 0 136
137 19 0 0 0 0 1 0 0 0 0 0 0 137
138 21 0 0 0 0 0 1 0 0 0 0 0 138
139 21 0 0 0 0 0 0 1 0 0 0 0 139
140 22 0 0 0 0 0 0 0 1 0 0 0 140
141 23 0 0 0 0 0 0 0 0 1 0 0 141
142 29 0 0 0 0 0 0 0 0 0 1 0 142
143 21 0 0 0 0 0 0 0 0 0 0 1 143
144 21 0 0 0 0 0 0 0 0 0 0 0 144
145 23 1 0 0 0 0 0 0 0 0 0 0 145
146 27 0 1 0 0 0 0 0 0 0 0 0 146
147 25 0 0 1 0 0 0 0 0 0 0 0 147
148 21 0 0 0 1 0 0 0 0 0 0 0 148
149 10 0 0 0 0 1 0 0 0 0 0 0 149
150 20 0 0 0 0 0 1 0 0 0 0 0 150
151 26 0 0 0 0 0 0 1 0 0 0 0 151
152 24 0 0 0 0 0 0 0 1 0 0 0 152
153 29 0 0 0 0 0 0 0 0 1 0 0 153
154 19 0 0 0 0 0 0 0 0 0 1 0 154
155 24 0 0 0 0 0 0 0 0 0 0 1 155
156 19 0 0 0 0 0 0 0 0 0 0 0 156
157 24 1 0 0 0 0 0 0 0 0 0 0 157
158 22 0 1 0 0 0 0 0 0 0 0 0 158
159 17 0 0 1 0 0 0 0 0 0 0 0 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
22.182504 1.496463 -0.494039 1.372603 0.462472 -0.220337
M6 M7 M8 M9 M10 M11
0.404546 1.337122 1.962005 1.663812 1.134849 -0.471037
t
-0.009499
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.5469 -2.5171 -0.2308 2.3304 13.4958
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.182504 1.338124 16.577 <2e-16 ***
M1 1.496463 1.646464 0.909 0.365
M2 -0.494039 1.646315 -0.300 0.765
M3 1.372603 1.646198 0.834 0.406
M4 0.462472 1.677297 0.276 0.783
M5 -0.220337 1.677053 -0.131 0.896
M6 0.404546 1.676841 0.241 0.810
M7 1.337122 1.676662 0.797 0.426
M8 1.962005 1.676515 1.170 0.244
M9 1.663812 1.676401 0.992 0.323
M10 1.134849 1.676319 0.677 0.499
M11 -0.471037 1.676271 -0.281 0.779
t -0.009499 0.007393 -1.285 0.201
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.274 on 146 degrees of freedom
Multiple R-squared: 0.05095, Adjusted R-squared: -0.02706
F-statistic: 0.6531 on 12 and 146 DF, p-value: 0.7934
> 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,] 0.8575615 0.2848769 0.14243847
[2,] 0.7849203 0.4301595 0.21507973
[3,] 0.7358669 0.5282661 0.26413305
[4,] 0.6277461 0.7445078 0.37225390
[5,] 0.5531538 0.8936923 0.44684617
[6,] 0.6513111 0.6973778 0.34868891
[7,] 0.5688122 0.8623755 0.43118776
[8,] 0.4863410 0.9726820 0.51365902
[9,] 0.4051739 0.8103478 0.59482608
[10,] 0.3570576 0.7141151 0.64294244
[11,] 0.3305472 0.6610944 0.66945282
[12,] 0.2572417 0.5144834 0.74275829
[13,] 0.2135992 0.4271983 0.78640083
[14,] 0.1850404 0.3700807 0.81495965
[15,] 0.1861452 0.3722905 0.81385475
[16,] 0.1470600 0.2941201 0.85293995
[17,] 0.1251182 0.2502363 0.87488184
[18,] 0.2212021 0.4424043 0.77879786
[19,] 0.4036512 0.8073024 0.59634881
[20,] 0.3866079 0.7732157 0.61339213
[21,] 0.4956002 0.9912003 0.50439983
[22,] 0.4968933 0.9937865 0.50310675
[23,] 0.5589155 0.8821690 0.44108449
[24,] 0.5149858 0.9700285 0.48501425
[25,] 0.5231530 0.9536939 0.47684695
[26,] 0.4777419 0.9554838 0.52225812
[27,] 0.4333847 0.8667695 0.56661527
[28,] 0.7229087 0.5541827 0.27709133
[29,] 0.6753611 0.6492779 0.32463893
[30,] 0.6265930 0.7468139 0.37340696
[31,] 0.6492576 0.7014847 0.35074237
[32,] 0.6419142 0.7161715 0.35808576
[33,] 0.7643672 0.4712656 0.23563280
[34,] 0.7213071 0.5573857 0.27869285
[35,] 0.6891225 0.6217550 0.31087749
[36,] 0.6940524 0.6118952 0.30594762
[37,] 0.6768801 0.6462399 0.32311994
[38,] 0.6686917 0.6626166 0.33130830
[39,] 0.7457025 0.5085949 0.25429746
[40,] 0.7905776 0.4188448 0.20942241
[41,] 0.7550625 0.4898750 0.24493748
[42,] 0.7203035 0.5593931 0.27969654
[43,] 0.6788825 0.6422351 0.32111754
[44,] 0.6314745 0.7370510 0.36852550
[45,] 0.7865279 0.4269443 0.21347214
[46,] 0.7476570 0.5046861 0.25234304
[47,] 0.7155243 0.5689514 0.28447570
[48,] 0.6891037 0.6217925 0.31089625
[49,] 0.6502605 0.6994790 0.34973952
[50,] 0.6347737 0.7304527 0.36522634
[51,] 0.7669333 0.4661335 0.23306673
[52,] 0.7338495 0.5323010 0.26615051
[53,] 0.7131262 0.5737477 0.28687384
[54,] 0.7252771 0.5494459 0.27472293
[55,] 0.6942732 0.6114536 0.30572680
[56,] 0.6539383 0.6921233 0.34606166
[57,] 0.6138054 0.7723893 0.38619465
[58,] 0.5984420 0.8031159 0.40155795
[59,] 0.5613731 0.8772537 0.43862686
[60,] 0.5463730 0.9072539 0.45362695
[61,] 0.5191158 0.9617684 0.48088419
[62,] 0.4767749 0.9535499 0.52322505
[63,] 0.4282642 0.8565285 0.57173577
[64,] 0.3825559 0.7651118 0.61744411
[65,] 0.3511929 0.7023857 0.64880715
[66,] 0.3175681 0.6351362 0.68243189
[67,] 0.3523005 0.7046010 0.64769948
[68,] 0.3160831 0.6321662 0.68391688
[69,] 0.3095111 0.6190222 0.69048888
[70,] 0.3016905 0.6033810 0.69830948
[71,] 0.2654998 0.5309996 0.73450022
[72,] 0.2822098 0.5644195 0.71779025
[73,] 0.2524610 0.5049221 0.74753897
[74,] 0.3155717 0.6311433 0.68442834
[75,] 0.3227082 0.6454165 0.67729177
[76,] 0.3507359 0.7014719 0.64926406
[77,] 0.3441411 0.6882821 0.65585893
[78,] 0.3784554 0.7569107 0.62154463
[79,] 0.3322657 0.6645314 0.66773428
[80,] 0.2884781 0.5769563 0.71152186
[81,] 0.6516606 0.6966787 0.34833937
[82,] 0.6084003 0.7831994 0.39159971
[83,] 0.5696613 0.8606773 0.43033865
[84,] 0.5676712 0.8646576 0.43232881
[85,] 0.5399635 0.9200731 0.46003653
[86,] 0.5506915 0.8986170 0.44930850
[87,] 0.5247884 0.9504232 0.47521159
[88,] 0.4926504 0.9853008 0.50734961
[89,] 0.4461381 0.8922762 0.55386188
[90,] 0.4175508 0.8351015 0.58244924
[91,] 0.4404594 0.8809188 0.55954058
[92,] 0.4205047 0.8410095 0.57949527
[93,] 0.3878113 0.7756227 0.61218866
[94,] 0.3407866 0.6815731 0.65921344
[95,] 0.3683624 0.7367247 0.63163764
[96,] 0.4698826 0.9397651 0.53011743
[97,] 0.4628430 0.9256860 0.53715699
[98,] 0.5104504 0.9790992 0.48954961
[99,] 0.9099942 0.1800116 0.09000582
[100,] 0.9386955 0.1226089 0.06130447
[101,] 0.9207119 0.1585763 0.07928813
[102,] 0.9001952 0.1996097 0.09980483
[103,] 0.8901396 0.2197208 0.10986040
[104,] 0.8596752 0.2806495 0.14032476
[105,] 0.8885992 0.2228015 0.11140076
[106,] 0.8711334 0.2577331 0.12886655
[107,] 0.8778461 0.2443079 0.12215394
[108,] 0.8910250 0.2179500 0.10897502
[109,] 0.8706162 0.2587676 0.12938379
[110,] 0.8669172 0.2661655 0.13308277
[111,] 0.8258561 0.3482878 0.17414392
[112,] 0.7770101 0.4459798 0.22298991
[113,] 0.7234910 0.5530179 0.27650897
[114,] 0.7092004 0.5815992 0.29079961
[115,] 0.6615501 0.6768998 0.33844988
[116,] 0.6043377 0.7913245 0.39566226
[117,] 0.5723129 0.8553742 0.42768711
[118,] 0.4902231 0.9804461 0.50977694
[119,] 0.5498404 0.9003192 0.45015962
[120,] 0.6305122 0.7389756 0.36948780
[121,] 0.5730362 0.8539277 0.42696384
[122,] 0.6336485 0.7327029 0.36635146
[123,] 0.5300585 0.9398830 0.46994150
[124,] 0.5335986 0.9328028 0.46640141
[125,] 0.4606872 0.9213743 0.53931283
[126,] 0.6095060 0.7809880 0.39049401
[127,] 0.7071290 0.5857420 0.29287102
[128,] 0.7435409 0.5129183 0.25645913
> postscript(file="/var/www/html/freestat/rcomp/tmp/10y3g1291023862.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/20y3g1291023862.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/30y3g1291023862.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4b7l11291023862.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5b7l11291023862.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
0.33053221 3.33053221 6.47338936 -3.60698125 0.08532644 -0.53005818
7 8 9 10 11 12
1.54686490 -1.06851972 -6.76082741 -2.22236587 -2.60698125 -3.06851972
13 14 15 16 17 18
-8.55548373 -5.55548373 -0.41262659 4.50700280 0.19931049 -8.41607412
19 20 21 22 23 24
-1.33915105 -0.95453566 -0.64684335 -2.10838181 -2.49299720 -3.95453566
25 26 27 28 29 30
-3.44149968 1.55850032 1.70135747 -3.37901314 2.31329455 -0.30209007
31 32 33 34 35 36
1.77483301 2.15944839 5.46714070 9.00560224 3.62098686 7.15944839
37 38 39 40 41 42
4.67248438 -4.32751562 4.81534152 6.73497091 4.42727860 2.81189399
43 44 45 46 47 48
-9.11118293 1.27343245 2.58112476 -2.88041370 -3.26502909 10.27343245
49 50 51 52 53 54
1.78646843 3.78646843 -0.07067442 -1.15104503 -1.45873734 -7.07412196
55 56 57 58 59 60
7.00280112 0.38741651 2.69510881 1.23357035 0.84895497 -7.61258349
61 62 63 64 65 66
0.90045249 2.90045249 1.04330963 1.96293902 -2.34475329 9.03986210
67 68 69 70 71 72
-0.88321482 3.50140056 -4.19090713 2.34755441 -1.03706098 -0.49859944
73 74 75 76 77 78
4.01443654 2.01443654 2.15729369 -1.92307692 -0.23076923 0.15384615
79 80 81 82 83 84
0.23076923 1.61538462 1.92307692 -5.53846154 -1.92307692 3.61538462
85 86 87 88 89 90
-3.87157940 -0.87157940 3.27127774 1.19090713 5.88321482 -4.73216979
91 92 93 94 95 96
-5.65524671 -4.27063133 -5.96293902 -0.42447748 0.19090713 10.72936867
97 98 99 100 101 102
-1.75759535 0.24240465 -4.61473820 -3.69510881 1.99719888 -2.61818574
103 104 105 106 107 108
-2.54126266 -2.15664727 -2.84895497 -5.31049343 -2.69510881 -2.15664727
109 110 111 112 113 114
-0.64361129 -5.64361129 -8.50075415 -3.58112476 3.11118293 13.49579832
115 116 117 118 119 120
6.57272140 -2.04266322 2.26502909 -2.19650937 1.41887524 -8.04266322
121 122 123 124 125 126
3.47037276 -3.52962724 2.61322991 -1.46714070 -1.77483301 -0.39021763
127 128 129 130 131 132
-0.31329455 1.07132084 -1.62098686 3.91747468 3.53285930 -4.92867916
133 134 135 136 137 138
0.58435682 -2.41564318 -6.27278604 4.64684335 -1.66084895 -0.27623357
139 140 141 142 143 144
-1.19931049 -0.81469511 0.49299720 7.03145874 0.64684335 0.18530489
145 146 147 148 149 150
0.69834087 6.69834087 2.84119802 -0.23917259 -10.54686490 -1.16224952
151 152 153 154 155 156
3.91467356 1.29928895 6.60698125 -2.85455721 3.76082741 -1.70071105
157 158 159
1.81232493 1.81232493 -5.04481793
> postscript(file="/var/www/html/freestat/rcomp/tmp/6b7l11291023862.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 0.33053221 NA
1 3.33053221 0.33053221
2 6.47338936 3.33053221
3 -3.60698125 6.47338936
4 0.08532644 -3.60698125
5 -0.53005818 0.08532644
6 1.54686490 -0.53005818
7 -1.06851972 1.54686490
8 -6.76082741 -1.06851972
9 -2.22236587 -6.76082741
10 -2.60698125 -2.22236587
11 -3.06851972 -2.60698125
12 -8.55548373 -3.06851972
13 -5.55548373 -8.55548373
14 -0.41262659 -5.55548373
15 4.50700280 -0.41262659
16 0.19931049 4.50700280
17 -8.41607412 0.19931049
18 -1.33915105 -8.41607412
19 -0.95453566 -1.33915105
20 -0.64684335 -0.95453566
21 -2.10838181 -0.64684335
22 -2.49299720 -2.10838181
23 -3.95453566 -2.49299720
24 -3.44149968 -3.95453566
25 1.55850032 -3.44149968
26 1.70135747 1.55850032
27 -3.37901314 1.70135747
28 2.31329455 -3.37901314
29 -0.30209007 2.31329455
30 1.77483301 -0.30209007
31 2.15944839 1.77483301
32 5.46714070 2.15944839
33 9.00560224 5.46714070
34 3.62098686 9.00560224
35 7.15944839 3.62098686
36 4.67248438 7.15944839
37 -4.32751562 4.67248438
38 4.81534152 -4.32751562
39 6.73497091 4.81534152
40 4.42727860 6.73497091
41 2.81189399 4.42727860
42 -9.11118293 2.81189399
43 1.27343245 -9.11118293
44 2.58112476 1.27343245
45 -2.88041370 2.58112476
46 -3.26502909 -2.88041370
47 10.27343245 -3.26502909
48 1.78646843 10.27343245
49 3.78646843 1.78646843
50 -0.07067442 3.78646843
51 -1.15104503 -0.07067442
52 -1.45873734 -1.15104503
53 -7.07412196 -1.45873734
54 7.00280112 -7.07412196
55 0.38741651 7.00280112
56 2.69510881 0.38741651
57 1.23357035 2.69510881
58 0.84895497 1.23357035
59 -7.61258349 0.84895497
60 0.90045249 -7.61258349
61 2.90045249 0.90045249
62 1.04330963 2.90045249
63 1.96293902 1.04330963
64 -2.34475329 1.96293902
65 9.03986210 -2.34475329
66 -0.88321482 9.03986210
67 3.50140056 -0.88321482
68 -4.19090713 3.50140056
69 2.34755441 -4.19090713
70 -1.03706098 2.34755441
71 -0.49859944 -1.03706098
72 4.01443654 -0.49859944
73 2.01443654 4.01443654
74 2.15729369 2.01443654
75 -1.92307692 2.15729369
76 -0.23076923 -1.92307692
77 0.15384615 -0.23076923
78 0.23076923 0.15384615
79 1.61538462 0.23076923
80 1.92307692 1.61538462
81 -5.53846154 1.92307692
82 -1.92307692 -5.53846154
83 3.61538462 -1.92307692
84 -3.87157940 3.61538462
85 -0.87157940 -3.87157940
86 3.27127774 -0.87157940
87 1.19090713 3.27127774
88 5.88321482 1.19090713
89 -4.73216979 5.88321482
90 -5.65524671 -4.73216979
91 -4.27063133 -5.65524671
92 -5.96293902 -4.27063133
93 -0.42447748 -5.96293902
94 0.19090713 -0.42447748
95 10.72936867 0.19090713
96 -1.75759535 10.72936867
97 0.24240465 -1.75759535
98 -4.61473820 0.24240465
99 -3.69510881 -4.61473820
100 1.99719888 -3.69510881
101 -2.61818574 1.99719888
102 -2.54126266 -2.61818574
103 -2.15664727 -2.54126266
104 -2.84895497 -2.15664727
105 -5.31049343 -2.84895497
106 -2.69510881 -5.31049343
107 -2.15664727 -2.69510881
108 -0.64361129 -2.15664727
109 -5.64361129 -0.64361129
110 -8.50075415 -5.64361129
111 -3.58112476 -8.50075415
112 3.11118293 -3.58112476
113 13.49579832 3.11118293
114 6.57272140 13.49579832
115 -2.04266322 6.57272140
116 2.26502909 -2.04266322
117 -2.19650937 2.26502909
118 1.41887524 -2.19650937
119 -8.04266322 1.41887524
120 3.47037276 -8.04266322
121 -3.52962724 3.47037276
122 2.61322991 -3.52962724
123 -1.46714070 2.61322991
124 -1.77483301 -1.46714070
125 -0.39021763 -1.77483301
126 -0.31329455 -0.39021763
127 1.07132084 -0.31329455
128 -1.62098686 1.07132084
129 3.91747468 -1.62098686
130 3.53285930 3.91747468
131 -4.92867916 3.53285930
132 0.58435682 -4.92867916
133 -2.41564318 0.58435682
134 -6.27278604 -2.41564318
135 4.64684335 -6.27278604
136 -1.66084895 4.64684335
137 -0.27623357 -1.66084895
138 -1.19931049 -0.27623357
139 -0.81469511 -1.19931049
140 0.49299720 -0.81469511
141 7.03145874 0.49299720
142 0.64684335 7.03145874
143 0.18530489 0.64684335
144 0.69834087 0.18530489
145 6.69834087 0.69834087
146 2.84119802 6.69834087
147 -0.23917259 2.84119802
148 -10.54686490 -0.23917259
149 -1.16224952 -10.54686490
150 3.91467356 -1.16224952
151 1.29928895 3.91467356
152 6.60698125 1.29928895
153 -2.85455721 6.60698125
154 3.76082741 -2.85455721
155 -1.70071105 3.76082741
156 1.81232493 -1.70071105
157 1.81232493 1.81232493
158 -5.04481793 1.81232493
159 NA -5.04481793
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.33053221 0.33053221
[2,] 6.47338936 3.33053221
[3,] -3.60698125 6.47338936
[4,] 0.08532644 -3.60698125
[5,] -0.53005818 0.08532644
[6,] 1.54686490 -0.53005818
[7,] -1.06851972 1.54686490
[8,] -6.76082741 -1.06851972
[9,] -2.22236587 -6.76082741
[10,] -2.60698125 -2.22236587
[11,] -3.06851972 -2.60698125
[12,] -8.55548373 -3.06851972
[13,] -5.55548373 -8.55548373
[14,] -0.41262659 -5.55548373
[15,] 4.50700280 -0.41262659
[16,] 0.19931049 4.50700280
[17,] -8.41607412 0.19931049
[18,] -1.33915105 -8.41607412
[19,] -0.95453566 -1.33915105
[20,] -0.64684335 -0.95453566
[21,] -2.10838181 -0.64684335
[22,] -2.49299720 -2.10838181
[23,] -3.95453566 -2.49299720
[24,] -3.44149968 -3.95453566
[25,] 1.55850032 -3.44149968
[26,] 1.70135747 1.55850032
[27,] -3.37901314 1.70135747
[28,] 2.31329455 -3.37901314
[29,] -0.30209007 2.31329455
[30,] 1.77483301 -0.30209007
[31,] 2.15944839 1.77483301
[32,] 5.46714070 2.15944839
[33,] 9.00560224 5.46714070
[34,] 3.62098686 9.00560224
[35,] 7.15944839 3.62098686
[36,] 4.67248438 7.15944839
[37,] -4.32751562 4.67248438
[38,] 4.81534152 -4.32751562
[39,] 6.73497091 4.81534152
[40,] 4.42727860 6.73497091
[41,] 2.81189399 4.42727860
[42,] -9.11118293 2.81189399
[43,] 1.27343245 -9.11118293
[44,] 2.58112476 1.27343245
[45,] -2.88041370 2.58112476
[46,] -3.26502909 -2.88041370
[47,] 10.27343245 -3.26502909
[48,] 1.78646843 10.27343245
[49,] 3.78646843 1.78646843
[50,] -0.07067442 3.78646843
[51,] -1.15104503 -0.07067442
[52,] -1.45873734 -1.15104503
[53,] -7.07412196 -1.45873734
[54,] 7.00280112 -7.07412196
[55,] 0.38741651 7.00280112
[56,] 2.69510881 0.38741651
[57,] 1.23357035 2.69510881
[58,] 0.84895497 1.23357035
[59,] -7.61258349 0.84895497
[60,] 0.90045249 -7.61258349
[61,] 2.90045249 0.90045249
[62,] 1.04330963 2.90045249
[63,] 1.96293902 1.04330963
[64,] -2.34475329 1.96293902
[65,] 9.03986210 -2.34475329
[66,] -0.88321482 9.03986210
[67,] 3.50140056 -0.88321482
[68,] -4.19090713 3.50140056
[69,] 2.34755441 -4.19090713
[70,] -1.03706098 2.34755441
[71,] -0.49859944 -1.03706098
[72,] 4.01443654 -0.49859944
[73,] 2.01443654 4.01443654
[74,] 2.15729369 2.01443654
[75,] -1.92307692 2.15729369
[76,] -0.23076923 -1.92307692
[77,] 0.15384615 -0.23076923
[78,] 0.23076923 0.15384615
[79,] 1.61538462 0.23076923
[80,] 1.92307692 1.61538462
[81,] -5.53846154 1.92307692
[82,] -1.92307692 -5.53846154
[83,] 3.61538462 -1.92307692
[84,] -3.87157940 3.61538462
[85,] -0.87157940 -3.87157940
[86,] 3.27127774 -0.87157940
[87,] 1.19090713 3.27127774
[88,] 5.88321482 1.19090713
[89,] -4.73216979 5.88321482
[90,] -5.65524671 -4.73216979
[91,] -4.27063133 -5.65524671
[92,] -5.96293902 -4.27063133
[93,] -0.42447748 -5.96293902
[94,] 0.19090713 -0.42447748
[95,] 10.72936867 0.19090713
[96,] -1.75759535 10.72936867
[97,] 0.24240465 -1.75759535
[98,] -4.61473820 0.24240465
[99,] -3.69510881 -4.61473820
[100,] 1.99719888 -3.69510881
[101,] -2.61818574 1.99719888
[102,] -2.54126266 -2.61818574
[103,] -2.15664727 -2.54126266
[104,] -2.84895497 -2.15664727
[105,] -5.31049343 -2.84895497
[106,] -2.69510881 -5.31049343
[107,] -2.15664727 -2.69510881
[108,] -0.64361129 -2.15664727
[109,] -5.64361129 -0.64361129
[110,] -8.50075415 -5.64361129
[111,] -3.58112476 -8.50075415
[112,] 3.11118293 -3.58112476
[113,] 13.49579832 3.11118293
[114,] 6.57272140 13.49579832
[115,] -2.04266322 6.57272140
[116,] 2.26502909 -2.04266322
[117,] -2.19650937 2.26502909
[118,] 1.41887524 -2.19650937
[119,] -8.04266322 1.41887524
[120,] 3.47037276 -8.04266322
[121,] -3.52962724 3.47037276
[122,] 2.61322991 -3.52962724
[123,] -1.46714070 2.61322991
[124,] -1.77483301 -1.46714070
[125,] -0.39021763 -1.77483301
[126,] -0.31329455 -0.39021763
[127,] 1.07132084 -0.31329455
[128,] -1.62098686 1.07132084
[129,] 3.91747468 -1.62098686
[130,] 3.53285930 3.91747468
[131,] -4.92867916 3.53285930
[132,] 0.58435682 -4.92867916
[133,] -2.41564318 0.58435682
[134,] -6.27278604 -2.41564318
[135,] 4.64684335 -6.27278604
[136,] -1.66084895 4.64684335
[137,] -0.27623357 -1.66084895
[138,] -1.19931049 -0.27623357
[139,] -0.81469511 -1.19931049
[140,] 0.49299720 -0.81469511
[141,] 7.03145874 0.49299720
[142,] 0.64684335 7.03145874
[143,] 0.18530489 0.64684335
[144,] 0.69834087 0.18530489
[145,] 6.69834087 0.69834087
[146,] 2.84119802 6.69834087
[147,] -0.23917259 2.84119802
[148,] -10.54686490 -0.23917259
[149,] -1.16224952 -10.54686490
[150,] 3.91467356 -1.16224952
[151,] 1.29928895 3.91467356
[152,] 6.60698125 1.29928895
[153,] -2.85455721 6.60698125
[154,] 3.76082741 -2.85455721
[155,] -1.70071105 3.76082741
[156,] 1.81232493 -1.70071105
[157,] 1.81232493 1.81232493
[158,] -5.04481793 1.81232493
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.33053221 0.33053221
2 6.47338936 3.33053221
3 -3.60698125 6.47338936
4 0.08532644 -3.60698125
5 -0.53005818 0.08532644
6 1.54686490 -0.53005818
7 -1.06851972 1.54686490
8 -6.76082741 -1.06851972
9 -2.22236587 -6.76082741
10 -2.60698125 -2.22236587
11 -3.06851972 -2.60698125
12 -8.55548373 -3.06851972
13 -5.55548373 -8.55548373
14 -0.41262659 -5.55548373
15 4.50700280 -0.41262659
16 0.19931049 4.50700280
17 -8.41607412 0.19931049
18 -1.33915105 -8.41607412
19 -0.95453566 -1.33915105
20 -0.64684335 -0.95453566
21 -2.10838181 -0.64684335
22 -2.49299720 -2.10838181
23 -3.95453566 -2.49299720
24 -3.44149968 -3.95453566
25 1.55850032 -3.44149968
26 1.70135747 1.55850032
27 -3.37901314 1.70135747
28 2.31329455 -3.37901314
29 -0.30209007 2.31329455
30 1.77483301 -0.30209007
31 2.15944839 1.77483301
32 5.46714070 2.15944839
33 9.00560224 5.46714070
34 3.62098686 9.00560224
35 7.15944839 3.62098686
36 4.67248438 7.15944839
37 -4.32751562 4.67248438
38 4.81534152 -4.32751562
39 6.73497091 4.81534152
40 4.42727860 6.73497091
41 2.81189399 4.42727860
42 -9.11118293 2.81189399
43 1.27343245 -9.11118293
44 2.58112476 1.27343245
45 -2.88041370 2.58112476
46 -3.26502909 -2.88041370
47 10.27343245 -3.26502909
48 1.78646843 10.27343245
49 3.78646843 1.78646843
50 -0.07067442 3.78646843
51 -1.15104503 -0.07067442
52 -1.45873734 -1.15104503
53 -7.07412196 -1.45873734
54 7.00280112 -7.07412196
55 0.38741651 7.00280112
56 2.69510881 0.38741651
57 1.23357035 2.69510881
58 0.84895497 1.23357035
59 -7.61258349 0.84895497
60 0.90045249 -7.61258349
61 2.90045249 0.90045249
62 1.04330963 2.90045249
63 1.96293902 1.04330963
64 -2.34475329 1.96293902
65 9.03986210 -2.34475329
66 -0.88321482 9.03986210
67 3.50140056 -0.88321482
68 -4.19090713 3.50140056
69 2.34755441 -4.19090713
70 -1.03706098 2.34755441
71 -0.49859944 -1.03706098
72 4.01443654 -0.49859944
73 2.01443654 4.01443654
74 2.15729369 2.01443654
75 -1.92307692 2.15729369
76 -0.23076923 -1.92307692
77 0.15384615 -0.23076923
78 0.23076923 0.15384615
79 1.61538462 0.23076923
80 1.92307692 1.61538462
81 -5.53846154 1.92307692
82 -1.92307692 -5.53846154
83 3.61538462 -1.92307692
84 -3.87157940 3.61538462
85 -0.87157940 -3.87157940
86 3.27127774 -0.87157940
87 1.19090713 3.27127774
88 5.88321482 1.19090713
89 -4.73216979 5.88321482
90 -5.65524671 -4.73216979
91 -4.27063133 -5.65524671
92 -5.96293902 -4.27063133
93 -0.42447748 -5.96293902
94 0.19090713 -0.42447748
95 10.72936867 0.19090713
96 -1.75759535 10.72936867
97 0.24240465 -1.75759535
98 -4.61473820 0.24240465
99 -3.69510881 -4.61473820
100 1.99719888 -3.69510881
101 -2.61818574 1.99719888
102 -2.54126266 -2.61818574
103 -2.15664727 -2.54126266
104 -2.84895497 -2.15664727
105 -5.31049343 -2.84895497
106 -2.69510881 -5.31049343
107 -2.15664727 -2.69510881
108 -0.64361129 -2.15664727
109 -5.64361129 -0.64361129
110 -8.50075415 -5.64361129
111 -3.58112476 -8.50075415
112 3.11118293 -3.58112476
113 13.49579832 3.11118293
114 6.57272140 13.49579832
115 -2.04266322 6.57272140
116 2.26502909 -2.04266322
117 -2.19650937 2.26502909
118 1.41887524 -2.19650937
119 -8.04266322 1.41887524
120 3.47037276 -8.04266322
121 -3.52962724 3.47037276
122 2.61322991 -3.52962724
123 -1.46714070 2.61322991
124 -1.77483301 -1.46714070
125 -0.39021763 -1.77483301
126 -0.31329455 -0.39021763
127 1.07132084 -0.31329455
128 -1.62098686 1.07132084
129 3.91747468 -1.62098686
130 3.53285930 3.91747468
131 -4.92867916 3.53285930
132 0.58435682 -4.92867916
133 -2.41564318 0.58435682
134 -6.27278604 -2.41564318
135 4.64684335 -6.27278604
136 -1.66084895 4.64684335
137 -0.27623357 -1.66084895
138 -1.19931049 -0.27623357
139 -0.81469511 -1.19931049
140 0.49299720 -0.81469511
141 7.03145874 0.49299720
142 0.64684335 7.03145874
143 0.18530489 0.64684335
144 0.69834087 0.18530489
145 6.69834087 0.69834087
146 2.84119802 6.69834087
147 -0.23917259 2.84119802
148 -10.54686490 -0.23917259
149 -1.16224952 -10.54686490
150 3.91467356 -1.16224952
151 1.29928895 3.91467356
152 6.60698125 1.29928895
153 -2.85455721 6.60698125
154 3.76082741 -2.85455721
155 -1.70071105 3.76082741
156 1.81232493 -1.70071105
157 1.81232493 1.81232493
158 -5.04481793 1.81232493
> 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/freestat/rcomp/tmp/73y241291023862.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/83y241291023862.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9ep171291023862.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10ep171291023862.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11iqzv1291023862.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/freestat/rcomp/tmp/1239gj1291023862.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/freestat/rcomp/tmp/13hie91291023862.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/freestat/rcomp/tmp/1431ux1291023862.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/freestat/rcomp/tmp/15o1b31291023862.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/freestat/rcomp/tmp/162b8u1291023862.tab")
+ }
> try(system("convert tmp/10y3g1291023862.ps tmp/10y3g1291023862.png",intern=TRUE))
character(0)
> try(system("convert tmp/20y3g1291023862.ps tmp/20y3g1291023862.png",intern=TRUE))
character(0)
> try(system("convert tmp/30y3g1291023862.ps tmp/30y3g1291023862.png",intern=TRUE))
character(0)
> try(system("convert tmp/4b7l11291023862.ps tmp/4b7l11291023862.png",intern=TRUE))
character(0)
> try(system("convert tmp/5b7l11291023862.ps tmp/5b7l11291023862.png",intern=TRUE))
character(0)
> try(system("convert tmp/6b7l11291023862.ps tmp/6b7l11291023862.png",intern=TRUE))
character(0)
> try(system("convert tmp/73y241291023862.ps tmp/73y241291023862.png",intern=TRUE))
character(0)
> try(system("convert tmp/83y241291023862.ps tmp/83y241291023862.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ep171291023862.ps tmp/9ep171291023862.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ep171291023862.ps tmp/10ep171291023862.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.577 2.679 5.889