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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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(9
+ ,5.5
+ ,6
+ ,5.33
+ ,12
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+ ,3.5
+ ,4
+ ,5.56
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+ ,4.00
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+ ,8
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+ ,7
+ ,3
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+ ,4
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+ ,10
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+ ,4
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+ ,12
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+ ,7
+ ,4
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+ ,4.5
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+ ,13
+ ,10
+ ,7.5
+ ,3
+ ,4.89
+ ,15
+ ,10
+ ,5.5
+ ,5
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+ ,10
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+ ,6
+ ,6.89
+ ,11
+ ,10
+ ,7
+ ,4.5
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+ ,16
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+ ,9
+ ,6
+ ,6.44
+ ,11
+ ,10
+ ,6
+ ,3.5
+ ,4.22
+ ,11
+ ,10
+ ,6.5
+ ,4
+ ,4.89
+ ,10
+ ,10
+ ,7.5
+ ,5
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+ ,3
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+ ,16
+ ,10
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+ ,5
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+ ,12
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+ ,5
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+ ,5.5
+ ,5
+ ,5.11
+ ,16
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+ ,5.5
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+ ,19
+ ,10
+ ,5
+ ,3.5
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+ ,10
+ ,6.5
+ ,5
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+ ,16
+ ,11
+ ,7.5
+ ,5.5
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+ ,5.5
+ ,2.5
+ ,3.78
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+ ,11
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+ ,12
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+ ,6.5
+ ,4.5
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+ ,6
+ ,6.5
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+ ,11
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+ ,4.5
+ ,6.67
+ ,12
+ ,11
+ ,7.5
+ ,5
+ ,6.67
+ ,10
+ ,11
+ ,12
+ ,10
+ ,5.33
+ ,14
+ ,11
+ ,3.5
+ ,2.5
+ ,4.67
+ ,12
+ ,11
+ ,8.5
+ ,5.5
+ ,4.67
+ ,12
+ ,11
+ ,5.5
+ ,3
+ ,6.44
+ ,11
+ ,11
+ ,8.5
+ ,4.5
+ ,6.89
+ ,12
+ ,11
+ ,5.5
+ ,3.5
+ ,4.44
+ ,13
+ ,11
+ ,6
+ ,4.5
+ ,3.56
+ ,11
+ ,11
+ ,7
+ ,5
+ ,4.89
+ ,19
+ ,11
+ ,5.5
+ ,4.5
+ ,4.44
+ ,12
+ ,11
+ ,8
+ ,4
+ ,6.22
+ ,17
+ ,11
+ ,10.5
+ ,3.5
+ ,8.44
+ ,9
+ ,11
+ ,7
+ ,3
+ ,4.89
+ ,12
+ ,11
+ ,10
+ ,6.5
+ ,4.44
+ ,19
+ ,11
+ ,6.5
+ ,3
+ ,3.78
+ ,18
+ ,11
+ ,5.5
+ ,4
+ ,6.22
+ ,15
+ ,11
+ ,7.5
+ ,5
+ ,4.89
+ ,14
+ ,11
+ ,9.5
+ ,8
+ ,6.89
+ ,11)
+ ,dim=c(5
+ ,159)
+ ,dimnames=list(c('Month'
+ ,'Expect'
+ ,'Criticism'
+ ,'Concerns'
+ ,'Depression')
+ ,1:159))
> y <- array(NA,dim=c(5,159),dimnames=list(c('Month','Expect','Criticism','Concerns','Depression'),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 = '5'
> #'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
Depression Month Expect Criticism Concerns t
1 12 9 5.5 6.0 5.33 1
2 11 9 3.5 4.0 5.56 2
3 14 9 8.5 4.0 3.78 3
4 12 9 5.0 4.0 4.00 4
5 21 9 6.0 4.5 4.00 5
6 12 9 6.0 3.5 3.56 6
7 22 9 5.5 2.0 4.44 7
8 11 9 5.5 5.5 3.56 8
9 10 9 6.0 3.5 4.00 9
10 13 9 6.5 3.5 3.78 10
11 10 9 7.0 6.0 5.11 11
12 8 9 8.0 5.0 6.67 12
13 15 9 5.5 5.0 5.11 13
14 14 9 5.0 4.0 4.00 14
15 10 9 5.5 4.0 3.33 15
16 14 9 7.5 2.0 2.67 16
17 14 9 4.5 4.5 4.67 17
18 11 9 5.5 4.0 3.33 18
19 10 9 8.5 3.5 4.44 19
20 13 9 8.5 5.5 6.89 20
21 7 9 5.5 4.5 6.00 21
22 14 9 9.0 5.5 7.56 22
23 12 9 7.0 6.5 4.67 23
24 14 9 5.0 4.0 6.89 24
25 11 9 5.5 4.0 4.22 25
26 9 9 7.5 4.5 3.56 26
27 11 9 7.5 3.0 4.44 27
28 15 9 6.5 4.5 4.67 28
29 14 9 8.0 4.5 4.89 29
30 13 9 6.5 3.0 3.78 30
31 9 9 4.5 3.0 5.33 31
32 15 9 9.0 8.0 5.56 32
33 10 9 9.0 2.5 5.78 33
34 11 9 6.0 3.5 5.56 34
35 13 9 8.5 4.5 3.78 35
36 8 9 4.5 3.0 7.11 36
37 20 9 4.5 3.0 7.33 37
38 12 9 6.0 2.5 2.89 38
39 10 9 9.0 6.0 7.11 39
40 10 9 6.0 3.5 5.56 40
41 9 9 9.0 5.0 6.44 41
42 14 9 7.0 4.5 4.89 42
43 8 9 7.5 4.0 4.00 43
44 14 9 8.0 2.5 3.78 44
45 11 9 5.0 4.0 4.44 45
46 13 9 5.5 4.0 3.33 46
47 9 9 7.0 5.0 4.44 47
48 11 9 4.5 3.0 7.33 48
49 15 9 6.0 4.0 6.44 49
50 11 9 8.5 3.5 5.11 50
51 10 9 2.5 2.0 5.78 51
52 14 9 6.0 4.0 4.00 52
53 18 9 6.0 4.0 4.44 53
54 14 10 3.0 2.0 2.44 54
55 11 10 12.0 10.0 6.22 55
56 12 10 6.0 4.0 5.78 56
57 13 10 6.0 4.0 4.89 57
58 9 10 7.0 3.0 3.78 58
59 10 10 3.5 2.0 2.67 59
60 15 10 6.5 4.0 3.11 60
61 20 10 6.0 4.5 3.78 61
62 12 10 6.5 3.0 4.67 62
63 12 10 7.0 3.5 4.22 63
64 14 10 4.0 4.5 4.00 64
65 13 10 5.5 2.5 2.22 65
66 11 10 4.5 2.5 6.44 66
67 17 10 5.5 4.0 6.89 67
68 12 10 6.5 4.0 4.22 68
69 13 10 5.0 3.0 2.00 69
70 14 10 5.5 4.0 4.44 70
71 13 10 6.0 3.5 6.22 71
72 15 10 4.5 3.5 4.22 72
73 13 10 7.5 4.5 6.67 73
74 10 10 9.0 5.5 6.44 74
75 11 10 7.5 3.0 5.78 75
76 19 10 6.0 4.0 5.11 76
77 13 10 6.5 3.0 2.89 77
78 17 10 7.0 4.5 4.67 78
79 13 10 5.0 4.0 4.22 79
80 9 10 6.5 3.0 6.22 80
81 11 10 6.5 5.0 5.11 81
82 10 10 5.5 4.0 4.00 82
83 9 10 6.5 4.0 4.67 83
84 12 10 8.0 5.0 4.44 84
85 12 10 4.0 2.5 5.11 85
86 13 10 8.0 3.5 4.67 86
87 13 10 5.5 2.5 4.67 87
88 12 10 4.5 4.0 3.33 88
89 15 10 8.0 7.0 6.22 89
90 22 10 6.0 3.5 4.22 90
91 13 10 7.0 4.0 5.78 91
92 15 10 4.0 3.0 2.22 92
93 13 10 4.5 2.5 3.56 93
94 15 10 7.5 3.0 4.89 94
95 10 10 5.5 5.0 4.22 95
96 11 10 10.5 6.0 6.89 96
97 16 10 7.0 4.5 6.89 97
98 11 10 9.0 6.0 6.44 98
99 11 10 6.0 3.5 4.22 99
100 10 10 6.5 4.0 4.89 100
101 10 10 7.5 5.0 5.11 101
102 16 10 6.0 3.0 3.33 102
103 12 10 9.5 5.0 4.44 103
104 11 10 7.5 5.0 4.00 104
105 16 10 5.5 5.0 5.11 105
106 19 10 5.5 2.5 5.56 106
107 11 10 5.0 3.5 4.67 107
108 16 10 6.5 5.0 5.33 108
109 15 11 7.5 5.5 5.56 109
110 24 11 6.0 3.0 3.78 110
111 14 11 6.0 3.5 2.89 111
112 15 11 8.0 6.0 6.22 112
113 11 11 4.5 5.5 4.67 113
114 15 11 9.0 5.5 5.56 114
115 12 11 4.0 5.5 2.00 115
116 10 11 6.5 2.5 3.56 116
117 14 11 8.5 4.0 4.22 117
118 13 11 4.5 3.0 3.78 118
119 9 11 7.5 4.5 5.56 119
120 15 11 4.0 2.0 4.44 120
121 15 11 3.5 2.0 6.44 121
122 14 11 6.0 3.5 3.11 122
123 11 11 7.0 5.5 4.89 123
124 8 11 3.0 3.0 3.33 124
125 11 11 4.0 3.5 4.22 125
126 11 11 8.5 4.0 4.44 126
127 8 11 5.0 2.0 3.33 127
128 10 11 5.5 4.0 4.44 128
129 11 11 7.0 4.5 4.00 129
130 13 11 5.5 4.0 7.33 130
131 11 11 6.5 5.5 4.89 131
132 20 11 6.0 4.0 3.56 132
133 10 11 5.5 2.5 3.78 133
134 15 11 4.5 2.0 3.56 134
135 12 11 6.0 4.0 4.67 135
136 14 11 10.0 5.0 5.78 136
137 23 11 6.0 3.0 4.00 137
138 14 11 6.5 4.5 4.00 138
139 16 11 6.0 4.5 3.78 139
140 11 11 6.0 6.5 4.89 140
141 12 11 4.5 4.5 6.67 141
142 10 11 7.5 5.0 6.67 142
143 14 11 12.0 10.0 5.33 143
144 12 11 3.5 2.5 4.67 144
145 12 11 8.5 5.5 4.67 145
146 11 11 5.5 3.0 6.44 146
147 12 11 8.5 4.5 6.89 147
148 13 11 5.5 3.5 4.44 148
149 11 11 6.0 4.5 3.56 149
150 19 11 7.0 5.0 4.89 150
151 12 11 5.5 4.5 4.44 151
152 17 11 8.0 4.0 6.22 152
153 9 11 10.5 3.5 8.44 153
154 12 11 7.0 3.0 4.89 154
155 19 11 10.0 6.5 4.44 155
156 18 11 6.5 3.0 3.78 156
157 15 11 5.5 4.0 6.22 157
158 14 11 7.5 5.0 4.89 158
159 11 11 9.5 8.0 6.89 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month Expect Criticism Concerns t
11.049289 0.288174 -0.008486 -0.050277 -0.224886 0.003821
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.8127 -2.1541 -0.5932 1.5255 10.4123
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.049289 8.673509 1.274 0.205
Month 0.288174 0.961714 0.300 0.765
Expect -0.008486 0.186079 -0.046 0.964
Criticism -0.050277 0.234592 -0.214 0.831
Concerns -0.224886 0.216168 -1.040 0.300
t 0.003821 0.016961 0.225 0.822
Residual standard error: 3.154 on 153 degrees of freedom
Multiple R-squared: 0.02637, Adjusted R-squared: -0.005448
F-statistic: 0.8288 on 5 and 153 DF, p-value: 0.5311
> 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,] 0.99471235 0.01057530 0.00528765
[2,] 0.98813593 0.02372813 0.01186407
[3,] 0.97538040 0.04923919 0.02461960
[4,] 0.96379605 0.07240790 0.03620395
[5,] 0.97055746 0.05888508 0.02944254
[6,] 0.95080640 0.09838721 0.04919360
[7,] 0.94397887 0.11204226 0.05602113
[8,] 0.91761417 0.16477167 0.08238583
[9,] 0.89726659 0.20546682 0.10273341
[10,] 0.86137948 0.27724104 0.13862052
[11,] 0.82127730 0.35744539 0.17872270
[12,] 0.82500458 0.34999085 0.17499542
[13,] 0.84974204 0.30051593 0.15025796
[14,] 0.86045155 0.27909689 0.13954845
[15,] 0.84642382 0.30715235 0.15357618
[16,] 0.81726628 0.36546743 0.18273372
[17,] 0.77069238 0.45861524 0.22930762
[18,] 0.73732307 0.52535387 0.26267693
[19,] 0.68632759 0.62734482 0.31367241
[20,] 0.71409560 0.57180880 0.28590440
[21,] 0.69393525 0.61212951 0.30606475
[22,] 0.63774625 0.72450749 0.36225375
[23,] 0.63939675 0.72120650 0.36060325
[24,] 0.69443702 0.61112595 0.30556298
[25,] 0.66384954 0.67230092 0.33615046
[26,] 0.60936855 0.78126290 0.39063145
[27,] 0.55914427 0.88171146 0.44085573
[28,] 0.54250379 0.91499242 0.45749621
[29,] 0.83624951 0.32750098 0.16375049
[30,] 0.80005940 0.39988121 0.19994060
[31,] 0.76699234 0.46601532 0.23300766
[32,] 0.73671692 0.52656616 0.26328308
[33,] 0.71543662 0.56912676 0.28456338
[34,] 0.69682883 0.60634234 0.30317117
[35,] 0.71158032 0.57683936 0.28841968
[36,] 0.68664887 0.62670227 0.31335113
[37,] 0.64296842 0.71406317 0.35703158
[38,] 0.60369463 0.79261073 0.39630537
[39,] 0.58996523 0.82006953 0.41003477
[40,] 0.54208580 0.91582840 0.45791420
[41,] 0.55003640 0.89992720 0.44996360
[42,] 0.50731862 0.98536276 0.49268138
[43,] 0.48214135 0.96428271 0.51785865
[44,] 0.46050878 0.92101756 0.53949122
[45,] 0.57897123 0.84205753 0.42102877
[46,] 0.53026437 0.93947127 0.46973563
[47,] 0.48352016 0.96704032 0.51647984
[48,] 0.43565948 0.87131896 0.56434052
[49,] 0.38795210 0.77590419 0.61204790
[50,] 0.40498972 0.80997944 0.59501028
[51,] 0.39461009 0.78922018 0.60538991
[52,] 0.38129836 0.76259671 0.61870164
[53,] 0.57819015 0.84361970 0.42180985
[54,] 0.53370527 0.93258946 0.46629473
[55,] 0.48932945 0.97865889 0.51067055
[56,] 0.44640033 0.89280067 0.55359967
[57,] 0.40063710 0.80127420 0.59936290
[58,] 0.36435755 0.72871510 0.63564245
[59,] 0.41759279 0.83518558 0.58240721
[60,] 0.37556271 0.75112543 0.62443729
[61,] 0.33292668 0.66585336 0.66707332
[62,] 0.29574808 0.59149617 0.70425192
[63,] 0.25717047 0.51434094 0.74282953
[64,] 0.23441981 0.46883961 0.76558019
[65,] 0.20143239 0.40286478 0.79856761
[66,] 0.18519001 0.37038003 0.81480999
[67,] 0.16279407 0.32558814 0.83720593
[68,] 0.25879509 0.51759017 0.74120491
[69,] 0.22331368 0.44662735 0.77668632
[70,] 0.24795283 0.49590566 0.75204717
[71,] 0.21361933 0.42723866 0.78638067
[72,] 0.22126242 0.44252485 0.77873758
[73,] 0.19758161 0.39516321 0.80241839
[74,] 0.19493201 0.38986402 0.80506799
[75,] 0.21076823 0.42153647 0.78923177
[76,] 0.18204010 0.36408020 0.81795990
[77,] 0.15494124 0.30988247 0.84505876
[78,] 0.13121169 0.26242338 0.86878831
[79,] 0.10873056 0.21746112 0.89126944
[80,] 0.09161664 0.18323328 0.90838336
[81,] 0.08617332 0.17234664 0.91382668
[82,] 0.27770258 0.55540516 0.72229742
[83,] 0.23982413 0.47964825 0.76017587
[84,] 0.21014511 0.42029022 0.78985489
[85,] 0.17806574 0.35613148 0.82193426
[86,] 0.15896515 0.31793030 0.84103485
[87,] 0.15491992 0.30983983 0.84508008
[88,] 0.13366104 0.26732208 0.86633896
[89,] 0.14051693 0.28103385 0.85948307
[90,] 0.11992623 0.23985245 0.88007377
[91,] 0.10822267 0.21644534 0.89177733
[92,] 0.10785112 0.21570224 0.89214888
[93,] 0.10994881 0.21989761 0.89005119
[94,] 0.09777617 0.19555233 0.90222383
[95,] 0.09070906 0.18141813 0.90929094
[96,] 0.10089427 0.20178853 0.89910573
[97,] 0.08915290 0.17830580 0.91084710
[98,] 0.12264888 0.24529776 0.87735112
[99,] 0.12158898 0.24317796 0.87841102
[100,] 0.10556367 0.21112735 0.89443633
[101,] 0.09348760 0.18697520 0.90651240
[102,] 0.45982133 0.91964267 0.54017867
[103,] 0.41510479 0.83020958 0.58489521
[104,] 0.42544510 0.85089020 0.57455490
[105,] 0.39751751 0.79503501 0.60248249
[106,] 0.40379334 0.80758669 0.59620666
[107,] 0.36425445 0.72850891 0.63574555
[108,] 0.36850703 0.73701406 0.63149297
[109,] 0.32720750 0.65441500 0.67279250
[110,] 0.28571360 0.57142720 0.71428640
[111,] 0.28087993 0.56175985 0.71912007
[112,] 0.26965171 0.53930342 0.73034829
[113,] 0.34110092 0.68220184 0.65889908
[114,] 0.29741974 0.59483947 0.70258026
[115,] 0.25694631 0.51389261 0.74305369
[116,] 0.30430852 0.60861704 0.69569148
[117,] 0.26415170 0.52830340 0.73584830
[118,] 0.23326326 0.46652651 0.76673674
[119,] 0.35603054 0.71206108 0.64396946
[120,] 0.35307531 0.70615061 0.64692469
[121,] 0.35998982 0.71997964 0.64001018
[122,] 0.35877497 0.71754994 0.64122503
[123,] 0.31912314 0.63824628 0.68087686
[124,] 0.43194296 0.86388592 0.56805704
[125,] 0.52145202 0.95709596 0.47854798
[126,] 0.45695534 0.91391068 0.54304466
[127,] 0.41503373 0.83006746 0.58496627
[128,] 0.34961532 0.69923065 0.65038468
[129,] 0.77997870 0.44004261 0.22002130
[130,] 0.71742895 0.56514209 0.28257105
[131,] 0.69885044 0.60229913 0.30114956
[132,] 0.63364337 0.73271325 0.36635663
[133,] 0.59769047 0.80461905 0.40230953
[134,] 0.51462576 0.97074848 0.48537424
[135,] 0.42980512 0.85961024 0.57019488
[136,] 0.33913291 0.67826581 0.66086709
[137,] 0.27485471 0.54970942 0.72514529
[138,] 0.19646160 0.39292319 0.80353840
[139,] 0.13015438 0.26030875 0.86984562
[140,] 0.07959580 0.15919160 0.92040420
[141,] 0.15867508 0.31735016 0.84132492
[142,] 0.15399511 0.30799022 0.84600489
> postscript(file="/var/www/html/rcomp/tmp/1ivhz1290768265.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ivhz1290768265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3a4y21290768265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4a4y21290768265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5a4y21290768265.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
-0.09969808 -1.16932246 1.46899085 -0.51505799 8.51474549 -0.63830280
7 8 9 10 11 12
9.47611688 -1.54963483 -2.55081714 0.40012971 -2.17465752 -3.86944722
13 14 15 16 17 18
2.75469308 1.44672792 -2.70352398 1.06064859 1.60683267 -1.71498821
19 20 21 22 23 24
-2.46886536 1.17883815 -5.10086806 2.32611221 -0.29432581 2.05843465
25 26 27 28 29 30
-1.54158944 -3.65172439 -1.53306152 2.58176996 1.64015309 0.29856303
31 32 33 34 35 36
-3.37365771 2.96381841 -2.26705151 -1.29553005 0.37184426 -3.99246751
37 38 39 40 41 42
8.05318603 -0.96153856 -1.81491199 -2.31845851 -3.02350548 1.58198838
43 44 45 46 47 48
-4.64287696 1.23265440 -1.57278588 0.17801234 -3.51317892 -0.98884947
49 50 51 52 53 54
2.87018707 -1.43665535 -2.41613693 1.31000077 5.40512925 0.53734832
55 56 57 58 59 60
-1.13781018 -0.59316178 0.20286819 -4.09236738 -3.42579173 1.79534991
61 62 63 64 65 66
6.96309748 -0.91174759 -0.98738605 0.98413541 -0.50780764 -1.57109612
67 68 69 70 71 72
4.61018310 -0.98559780 -0.55167292 1.04774793 0.42332848 1.95700528
73 74 75 76 77 78
0.57989098 -2.41264765 -1.70331595 6.17973636 -0.36936598 4.10676855
79 80 81 82 83 84
-0.04036289 -3.63195950 -1.78485050 -3.09705886 -3.94172019 -0.93425882
85 86 87 88 89 90
-0.94704459 0.03440668 -0.04090770 -1.27914739 2.54748537 8.90094951
91 92 93 94 95 96
0.28157531 1.40142322 -0.32194612 2.02392866 -3.04698524 -1.35765181
97 98 99 100 101 102
3.53340891 -1.47922296 -2.13344317 -2.95720920 -2.85279228 2.62980549
103 104 105 106 107 108
-0.99413600 -2.11388007 3.11494930 6.08663415 -2.07130209 3.16144640
109 110 111 112 113 114
1.95479952 10.41225879 0.23342725 2.12114181 -2.28609392 1.94842207
115 116 117 118 119 120
-1.89842581 -3.68103990 0.55595179 -0.63104207 -4.13369156 1.45521975
121 122 123 124 125 126
1.89692734 0.24086670 -2.25361709 -5.76789886 -2.53794675 -2.42896595
127 128 129 130 131 132
-5.81266729 -3.46206795 -2.52697116 0.18021005 -2.28843155 6.32898985
133 134 135 136 137 138
-3.70501530 1.20806346 -1.43285081 0.89717391 9.35855569 0.43439297
139 140 141 142 143 144
2.37685342 -2.27679044 -0.99359817 -2.94682190 1.03758305 -1.56387496
145 146 147 148 149 150
-1.37443343 -2.13135811 -0.93310611 -0.56363462 -2.71083561 5.61806638
151 152 153 154 155 156
-1.52482186 3.86773147 -3.64076531 -1.49777324 5.59863526 4.24071718
157 158 159
1.82740844 0.59173830 -1.79450715
> postscript(file="/var/www/html/rcomp/tmp/63wyn1290768265.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 -0.09969808 NA
1 -1.16932246 -0.09969808
2 1.46899085 -1.16932246
3 -0.51505799 1.46899085
4 8.51474549 -0.51505799
5 -0.63830280 8.51474549
6 9.47611688 -0.63830280
7 -1.54963483 9.47611688
8 -2.55081714 -1.54963483
9 0.40012971 -2.55081714
10 -2.17465752 0.40012971
11 -3.86944722 -2.17465752
12 2.75469308 -3.86944722
13 1.44672792 2.75469308
14 -2.70352398 1.44672792
15 1.06064859 -2.70352398
16 1.60683267 1.06064859
17 -1.71498821 1.60683267
18 -2.46886536 -1.71498821
19 1.17883815 -2.46886536
20 -5.10086806 1.17883815
21 2.32611221 -5.10086806
22 -0.29432581 2.32611221
23 2.05843465 -0.29432581
24 -1.54158944 2.05843465
25 -3.65172439 -1.54158944
26 -1.53306152 -3.65172439
27 2.58176996 -1.53306152
28 1.64015309 2.58176996
29 0.29856303 1.64015309
30 -3.37365771 0.29856303
31 2.96381841 -3.37365771
32 -2.26705151 2.96381841
33 -1.29553005 -2.26705151
34 0.37184426 -1.29553005
35 -3.99246751 0.37184426
36 8.05318603 -3.99246751
37 -0.96153856 8.05318603
38 -1.81491199 -0.96153856
39 -2.31845851 -1.81491199
40 -3.02350548 -2.31845851
41 1.58198838 -3.02350548
42 -4.64287696 1.58198838
43 1.23265440 -4.64287696
44 -1.57278588 1.23265440
45 0.17801234 -1.57278588
46 -3.51317892 0.17801234
47 -0.98884947 -3.51317892
48 2.87018707 -0.98884947
49 -1.43665535 2.87018707
50 -2.41613693 -1.43665535
51 1.31000077 -2.41613693
52 5.40512925 1.31000077
53 0.53734832 5.40512925
54 -1.13781018 0.53734832
55 -0.59316178 -1.13781018
56 0.20286819 -0.59316178
57 -4.09236738 0.20286819
58 -3.42579173 -4.09236738
59 1.79534991 -3.42579173
60 6.96309748 1.79534991
61 -0.91174759 6.96309748
62 -0.98738605 -0.91174759
63 0.98413541 -0.98738605
64 -0.50780764 0.98413541
65 -1.57109612 -0.50780764
66 4.61018310 -1.57109612
67 -0.98559780 4.61018310
68 -0.55167292 -0.98559780
69 1.04774793 -0.55167292
70 0.42332848 1.04774793
71 1.95700528 0.42332848
72 0.57989098 1.95700528
73 -2.41264765 0.57989098
74 -1.70331595 -2.41264765
75 6.17973636 -1.70331595
76 -0.36936598 6.17973636
77 4.10676855 -0.36936598
78 -0.04036289 4.10676855
79 -3.63195950 -0.04036289
80 -1.78485050 -3.63195950
81 -3.09705886 -1.78485050
82 -3.94172019 -3.09705886
83 -0.93425882 -3.94172019
84 -0.94704459 -0.93425882
85 0.03440668 -0.94704459
86 -0.04090770 0.03440668
87 -1.27914739 -0.04090770
88 2.54748537 -1.27914739
89 8.90094951 2.54748537
90 0.28157531 8.90094951
91 1.40142322 0.28157531
92 -0.32194612 1.40142322
93 2.02392866 -0.32194612
94 -3.04698524 2.02392866
95 -1.35765181 -3.04698524
96 3.53340891 -1.35765181
97 -1.47922296 3.53340891
98 -2.13344317 -1.47922296
99 -2.95720920 -2.13344317
100 -2.85279228 -2.95720920
101 2.62980549 -2.85279228
102 -0.99413600 2.62980549
103 -2.11388007 -0.99413600
104 3.11494930 -2.11388007
105 6.08663415 3.11494930
106 -2.07130209 6.08663415
107 3.16144640 -2.07130209
108 1.95479952 3.16144640
109 10.41225879 1.95479952
110 0.23342725 10.41225879
111 2.12114181 0.23342725
112 -2.28609392 2.12114181
113 1.94842207 -2.28609392
114 -1.89842581 1.94842207
115 -3.68103990 -1.89842581
116 0.55595179 -3.68103990
117 -0.63104207 0.55595179
118 -4.13369156 -0.63104207
119 1.45521975 -4.13369156
120 1.89692734 1.45521975
121 0.24086670 1.89692734
122 -2.25361709 0.24086670
123 -5.76789886 -2.25361709
124 -2.53794675 -5.76789886
125 -2.42896595 -2.53794675
126 -5.81266729 -2.42896595
127 -3.46206795 -5.81266729
128 -2.52697116 -3.46206795
129 0.18021005 -2.52697116
130 -2.28843155 0.18021005
131 6.32898985 -2.28843155
132 -3.70501530 6.32898985
133 1.20806346 -3.70501530
134 -1.43285081 1.20806346
135 0.89717391 -1.43285081
136 9.35855569 0.89717391
137 0.43439297 9.35855569
138 2.37685342 0.43439297
139 -2.27679044 2.37685342
140 -0.99359817 -2.27679044
141 -2.94682190 -0.99359817
142 1.03758305 -2.94682190
143 -1.56387496 1.03758305
144 -1.37443343 -1.56387496
145 -2.13135811 -1.37443343
146 -0.93310611 -2.13135811
147 -0.56363462 -0.93310611
148 -2.71083561 -0.56363462
149 5.61806638 -2.71083561
150 -1.52482186 5.61806638
151 3.86773147 -1.52482186
152 -3.64076531 3.86773147
153 -1.49777324 -3.64076531
154 5.59863526 -1.49777324
155 4.24071718 5.59863526
156 1.82740844 4.24071718
157 0.59173830 1.82740844
158 -1.79450715 0.59173830
159 NA -1.79450715
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.16932246 -0.09969808
[2,] 1.46899085 -1.16932246
[3,] -0.51505799 1.46899085
[4,] 8.51474549 -0.51505799
[5,] -0.63830280 8.51474549
[6,] 9.47611688 -0.63830280
[7,] -1.54963483 9.47611688
[8,] -2.55081714 -1.54963483
[9,] 0.40012971 -2.55081714
[10,] -2.17465752 0.40012971
[11,] -3.86944722 -2.17465752
[12,] 2.75469308 -3.86944722
[13,] 1.44672792 2.75469308
[14,] -2.70352398 1.44672792
[15,] 1.06064859 -2.70352398
[16,] 1.60683267 1.06064859
[17,] -1.71498821 1.60683267
[18,] -2.46886536 -1.71498821
[19,] 1.17883815 -2.46886536
[20,] -5.10086806 1.17883815
[21,] 2.32611221 -5.10086806
[22,] -0.29432581 2.32611221
[23,] 2.05843465 -0.29432581
[24,] -1.54158944 2.05843465
[25,] -3.65172439 -1.54158944
[26,] -1.53306152 -3.65172439
[27,] 2.58176996 -1.53306152
[28,] 1.64015309 2.58176996
[29,] 0.29856303 1.64015309
[30,] -3.37365771 0.29856303
[31,] 2.96381841 -3.37365771
[32,] -2.26705151 2.96381841
[33,] -1.29553005 -2.26705151
[34,] 0.37184426 -1.29553005
[35,] -3.99246751 0.37184426
[36,] 8.05318603 -3.99246751
[37,] -0.96153856 8.05318603
[38,] -1.81491199 -0.96153856
[39,] -2.31845851 -1.81491199
[40,] -3.02350548 -2.31845851
[41,] 1.58198838 -3.02350548
[42,] -4.64287696 1.58198838
[43,] 1.23265440 -4.64287696
[44,] -1.57278588 1.23265440
[45,] 0.17801234 -1.57278588
[46,] -3.51317892 0.17801234
[47,] -0.98884947 -3.51317892
[48,] 2.87018707 -0.98884947
[49,] -1.43665535 2.87018707
[50,] -2.41613693 -1.43665535
[51,] 1.31000077 -2.41613693
[52,] 5.40512925 1.31000077
[53,] 0.53734832 5.40512925
[54,] -1.13781018 0.53734832
[55,] -0.59316178 -1.13781018
[56,] 0.20286819 -0.59316178
[57,] -4.09236738 0.20286819
[58,] -3.42579173 -4.09236738
[59,] 1.79534991 -3.42579173
[60,] 6.96309748 1.79534991
[61,] -0.91174759 6.96309748
[62,] -0.98738605 -0.91174759
[63,] 0.98413541 -0.98738605
[64,] -0.50780764 0.98413541
[65,] -1.57109612 -0.50780764
[66,] 4.61018310 -1.57109612
[67,] -0.98559780 4.61018310
[68,] -0.55167292 -0.98559780
[69,] 1.04774793 -0.55167292
[70,] 0.42332848 1.04774793
[71,] 1.95700528 0.42332848
[72,] 0.57989098 1.95700528
[73,] -2.41264765 0.57989098
[74,] -1.70331595 -2.41264765
[75,] 6.17973636 -1.70331595
[76,] -0.36936598 6.17973636
[77,] 4.10676855 -0.36936598
[78,] -0.04036289 4.10676855
[79,] -3.63195950 -0.04036289
[80,] -1.78485050 -3.63195950
[81,] -3.09705886 -1.78485050
[82,] -3.94172019 -3.09705886
[83,] -0.93425882 -3.94172019
[84,] -0.94704459 -0.93425882
[85,] 0.03440668 -0.94704459
[86,] -0.04090770 0.03440668
[87,] -1.27914739 -0.04090770
[88,] 2.54748537 -1.27914739
[89,] 8.90094951 2.54748537
[90,] 0.28157531 8.90094951
[91,] 1.40142322 0.28157531
[92,] -0.32194612 1.40142322
[93,] 2.02392866 -0.32194612
[94,] -3.04698524 2.02392866
[95,] -1.35765181 -3.04698524
[96,] 3.53340891 -1.35765181
[97,] -1.47922296 3.53340891
[98,] -2.13344317 -1.47922296
[99,] -2.95720920 -2.13344317
[100,] -2.85279228 -2.95720920
[101,] 2.62980549 -2.85279228
[102,] -0.99413600 2.62980549
[103,] -2.11388007 -0.99413600
[104,] 3.11494930 -2.11388007
[105,] 6.08663415 3.11494930
[106,] -2.07130209 6.08663415
[107,] 3.16144640 -2.07130209
[108,] 1.95479952 3.16144640
[109,] 10.41225879 1.95479952
[110,] 0.23342725 10.41225879
[111,] 2.12114181 0.23342725
[112,] -2.28609392 2.12114181
[113,] 1.94842207 -2.28609392
[114,] -1.89842581 1.94842207
[115,] -3.68103990 -1.89842581
[116,] 0.55595179 -3.68103990
[117,] -0.63104207 0.55595179
[118,] -4.13369156 -0.63104207
[119,] 1.45521975 -4.13369156
[120,] 1.89692734 1.45521975
[121,] 0.24086670 1.89692734
[122,] -2.25361709 0.24086670
[123,] -5.76789886 -2.25361709
[124,] -2.53794675 -5.76789886
[125,] -2.42896595 -2.53794675
[126,] -5.81266729 -2.42896595
[127,] -3.46206795 -5.81266729
[128,] -2.52697116 -3.46206795
[129,] 0.18021005 -2.52697116
[130,] -2.28843155 0.18021005
[131,] 6.32898985 -2.28843155
[132,] -3.70501530 6.32898985
[133,] 1.20806346 -3.70501530
[134,] -1.43285081 1.20806346
[135,] 0.89717391 -1.43285081
[136,] 9.35855569 0.89717391
[137,] 0.43439297 9.35855569
[138,] 2.37685342 0.43439297
[139,] -2.27679044 2.37685342
[140,] -0.99359817 -2.27679044
[141,] -2.94682190 -0.99359817
[142,] 1.03758305 -2.94682190
[143,] -1.56387496 1.03758305
[144,] -1.37443343 -1.56387496
[145,] -2.13135811 -1.37443343
[146,] -0.93310611 -2.13135811
[147,] -0.56363462 -0.93310611
[148,] -2.71083561 -0.56363462
[149,] 5.61806638 -2.71083561
[150,] -1.52482186 5.61806638
[151,] 3.86773147 -1.52482186
[152,] -3.64076531 3.86773147
[153,] -1.49777324 -3.64076531
[154,] 5.59863526 -1.49777324
[155,] 4.24071718 5.59863526
[156,] 1.82740844 4.24071718
[157,] 0.59173830 1.82740844
[158,] -1.79450715 0.59173830
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.16932246 -0.09969808
2 1.46899085 -1.16932246
3 -0.51505799 1.46899085
4 8.51474549 -0.51505799
5 -0.63830280 8.51474549
6 9.47611688 -0.63830280
7 -1.54963483 9.47611688
8 -2.55081714 -1.54963483
9 0.40012971 -2.55081714
10 -2.17465752 0.40012971
11 -3.86944722 -2.17465752
12 2.75469308 -3.86944722
13 1.44672792 2.75469308
14 -2.70352398 1.44672792
15 1.06064859 -2.70352398
16 1.60683267 1.06064859
17 -1.71498821 1.60683267
18 -2.46886536 -1.71498821
19 1.17883815 -2.46886536
20 -5.10086806 1.17883815
21 2.32611221 -5.10086806
22 -0.29432581 2.32611221
23 2.05843465 -0.29432581
24 -1.54158944 2.05843465
25 -3.65172439 -1.54158944
26 -1.53306152 -3.65172439
27 2.58176996 -1.53306152
28 1.64015309 2.58176996
29 0.29856303 1.64015309
30 -3.37365771 0.29856303
31 2.96381841 -3.37365771
32 -2.26705151 2.96381841
33 -1.29553005 -2.26705151
34 0.37184426 -1.29553005
35 -3.99246751 0.37184426
36 8.05318603 -3.99246751
37 -0.96153856 8.05318603
38 -1.81491199 -0.96153856
39 -2.31845851 -1.81491199
40 -3.02350548 -2.31845851
41 1.58198838 -3.02350548
42 -4.64287696 1.58198838
43 1.23265440 -4.64287696
44 -1.57278588 1.23265440
45 0.17801234 -1.57278588
46 -3.51317892 0.17801234
47 -0.98884947 -3.51317892
48 2.87018707 -0.98884947
49 -1.43665535 2.87018707
50 -2.41613693 -1.43665535
51 1.31000077 -2.41613693
52 5.40512925 1.31000077
53 0.53734832 5.40512925
54 -1.13781018 0.53734832
55 -0.59316178 -1.13781018
56 0.20286819 -0.59316178
57 -4.09236738 0.20286819
58 -3.42579173 -4.09236738
59 1.79534991 -3.42579173
60 6.96309748 1.79534991
61 -0.91174759 6.96309748
62 -0.98738605 -0.91174759
63 0.98413541 -0.98738605
64 -0.50780764 0.98413541
65 -1.57109612 -0.50780764
66 4.61018310 -1.57109612
67 -0.98559780 4.61018310
68 -0.55167292 -0.98559780
69 1.04774793 -0.55167292
70 0.42332848 1.04774793
71 1.95700528 0.42332848
72 0.57989098 1.95700528
73 -2.41264765 0.57989098
74 -1.70331595 -2.41264765
75 6.17973636 -1.70331595
76 -0.36936598 6.17973636
77 4.10676855 -0.36936598
78 -0.04036289 4.10676855
79 -3.63195950 -0.04036289
80 -1.78485050 -3.63195950
81 -3.09705886 -1.78485050
82 -3.94172019 -3.09705886
83 -0.93425882 -3.94172019
84 -0.94704459 -0.93425882
85 0.03440668 -0.94704459
86 -0.04090770 0.03440668
87 -1.27914739 -0.04090770
88 2.54748537 -1.27914739
89 8.90094951 2.54748537
90 0.28157531 8.90094951
91 1.40142322 0.28157531
92 -0.32194612 1.40142322
93 2.02392866 -0.32194612
94 -3.04698524 2.02392866
95 -1.35765181 -3.04698524
96 3.53340891 -1.35765181
97 -1.47922296 3.53340891
98 -2.13344317 -1.47922296
99 -2.95720920 -2.13344317
100 -2.85279228 -2.95720920
101 2.62980549 -2.85279228
102 -0.99413600 2.62980549
103 -2.11388007 -0.99413600
104 3.11494930 -2.11388007
105 6.08663415 3.11494930
106 -2.07130209 6.08663415
107 3.16144640 -2.07130209
108 1.95479952 3.16144640
109 10.41225879 1.95479952
110 0.23342725 10.41225879
111 2.12114181 0.23342725
112 -2.28609392 2.12114181
113 1.94842207 -2.28609392
114 -1.89842581 1.94842207
115 -3.68103990 -1.89842581
116 0.55595179 -3.68103990
117 -0.63104207 0.55595179
118 -4.13369156 -0.63104207
119 1.45521975 -4.13369156
120 1.89692734 1.45521975
121 0.24086670 1.89692734
122 -2.25361709 0.24086670
123 -5.76789886 -2.25361709
124 -2.53794675 -5.76789886
125 -2.42896595 -2.53794675
126 -5.81266729 -2.42896595
127 -3.46206795 -5.81266729
128 -2.52697116 -3.46206795
129 0.18021005 -2.52697116
130 -2.28843155 0.18021005
131 6.32898985 -2.28843155
132 -3.70501530 6.32898985
133 1.20806346 -3.70501530
134 -1.43285081 1.20806346
135 0.89717391 -1.43285081
136 9.35855569 0.89717391
137 0.43439297 9.35855569
138 2.37685342 0.43439297
139 -2.27679044 2.37685342
140 -0.99359817 -2.27679044
141 -2.94682190 -0.99359817
142 1.03758305 -2.94682190
143 -1.56387496 1.03758305
144 -1.37443343 -1.56387496
145 -2.13135811 -1.37443343
146 -0.93310611 -2.13135811
147 -0.56363462 -0.93310611
148 -2.71083561 -0.56363462
149 5.61806638 -2.71083561
150 -1.52482186 5.61806638
151 3.86773147 -1.52482186
152 -3.64076531 3.86773147
153 -1.49777324 -3.64076531
154 5.59863526 -1.49777324
155 4.24071718 5.59863526
156 1.82740844 4.24071718
157 0.59173830 1.82740844
158 -1.79450715 0.59173830
> 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/7e5f81290768265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8e5f81290768265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9e5f81290768265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107wwt1290768265.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11axcz1290768265.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/12dxtm1290768265.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/13979d1290768265.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/14vp711290768265.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/15yq6p1290768265.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/161qmv1290768265.tab")
+ }
>
> try(system("convert tmp/1ivhz1290768265.ps tmp/1ivhz1290768265.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ivhz1290768265.ps tmp/2ivhz1290768265.png",intern=TRUE))
character(0)
> try(system("convert tmp/3a4y21290768265.ps tmp/3a4y21290768265.png",intern=TRUE))
character(0)
> try(system("convert tmp/4a4y21290768265.ps tmp/4a4y21290768265.png",intern=TRUE))
character(0)
> try(system("convert tmp/5a4y21290768265.ps tmp/5a4y21290768265.png",intern=TRUE))
character(0)
> try(system("convert tmp/63wyn1290768265.ps tmp/63wyn1290768265.png",intern=TRUE))
character(0)
> try(system("convert tmp/7e5f81290768265.ps tmp/7e5f81290768265.png",intern=TRUE))
character(0)
> try(system("convert tmp/8e5f81290768265.ps tmp/8e5f81290768265.png",intern=TRUE))
character(0)
> try(system("convert tmp/9e5f81290768265.ps tmp/9e5f81290768265.png",intern=TRUE))
character(0)
> try(system("convert tmp/107wwt1290768265.ps tmp/107wwt1290768265.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.936 1.741 8.840