R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(6.9
+ ,2.28
+ ,6.8
+ ,2.26
+ ,6.7
+ ,2.71
+ ,6.6
+ ,2.77
+ ,6.5
+ ,2.77
+ ,6.5
+ ,2.64
+ ,7.0
+ ,2.56
+ ,7.5
+ ,2.07
+ ,7.6
+ ,2.32
+ ,7.6
+ ,2.16
+ ,7.6
+ ,2.23
+ ,7.8
+ ,2.40
+ ,8.0
+ ,2.84
+ ,8.0
+ ,2.77
+ ,8.0
+ ,2.93
+ ,7.9
+ ,2.91
+ ,7.9
+ ,2.69
+ ,8.0
+ ,2.38
+ ,8.5
+ ,2.58
+ ,9.2
+ ,3.19
+ ,9.4
+ ,2.82
+ ,9.5
+ ,2.72
+ ,9.5
+ ,2.53
+ ,9.6
+ ,2.70
+ ,9.7
+ ,2.42
+ ,9.7
+ ,2.50
+ ,9.6
+ ,2.31
+ ,9.5
+ ,2.41
+ ,9.4
+ ,2.56
+ ,9.3
+ ,2.76
+ ,9.6
+ ,2.71
+ ,10.2
+ ,2.44
+ ,10.2
+ ,2.46
+ ,10.1
+ ,2.12
+ ,9.9
+ ,1.99
+ ,9.8
+ ,1.86
+ ,9.8
+ ,1.88
+ ,9.7
+ ,1.82
+ ,9.5
+ ,1.74
+ ,9.3
+ ,1.71
+ ,9.1
+ ,1.38
+ ,9.0
+ ,1.27
+ ,9.5
+ ,1.19
+ ,10.0
+ ,1.28
+ ,10.2
+ ,1.19
+ ,10.1
+ ,1.22
+ ,10.0
+ ,1.47
+ ,9.9
+ ,1.46
+ ,10.0
+ ,1.96
+ ,9.9
+ ,1.88
+ ,9.7
+ ,2.03
+ ,9.5
+ ,2.04
+ ,9.2
+ ,1.90
+ ,9.0
+ ,1.80
+ ,9.3
+ ,1.92
+ ,9.8
+ ,1.92
+ ,9.8
+ ,1.97
+ ,9.6
+ ,2.46
+ ,9.4
+ ,2.36
+ ,9.3
+ ,2.53
+ ,9.2
+ ,2.31
+ ,9.2
+ ,1.98
+ ,9.0
+ ,1.46
+ ,8.8
+ ,1.26
+ ,8.7
+ ,1.58
+ ,8.7
+ ,1.74
+ ,9.1
+ ,1.89
+ ,9.7
+ ,1.85
+ ,9.8
+ ,1.62
+ ,9.6
+ ,1.30
+ ,9.4
+ ,1.42
+ ,9.4
+ ,1.15
+ ,9.5
+ ,0.42
+ ,9.4
+ ,0.74
+ ,9.3
+ ,1.02
+ ,9.2
+ ,1.51
+ ,9.0
+ ,1.86
+ ,8.9
+ ,1.59
+ ,9.2
+ ,1.03
+ ,9.8
+ ,0.44
+ ,9.9
+ ,0.82
+ ,9.6
+ ,0.86
+ ,9.2
+ ,0.58
+ ,9.1
+ ,0.59
+ ,9.1
+ ,0.95
+ ,9.0
+ ,0.98
+ ,8.9
+ ,1.23
+ ,8.7
+ ,1.17
+ ,8.5
+ ,0.84
+ ,8.3
+ ,0.74
+ ,8.5
+ ,0.65
+ ,8.7
+ ,0.91
+ ,8.4
+ ,1.19
+ ,8.1
+ ,1.30
+ ,7.8
+ ,1.53
+ ,7.7
+ ,1.94
+ ,7.5
+ ,1.79
+ ,7.2
+ ,1.95
+ ,6.8
+ ,2.26
+ ,6.7
+ ,2.04
+ ,6.4
+ ,2.16
+ ,6.3
+ ,2.75
+ ,6.8
+ ,2.79
+ ,7.3
+ ,2.88
+ ,7.1
+ ,3.36
+ ,7.0
+ ,2.97
+ ,6.8
+ ,3.10
+ ,6.6
+ ,2.49
+ ,6.3
+ ,2.20
+ ,6.1
+ ,2.25
+ ,6.1
+ ,2.09
+ ,6.3
+ ,2.79
+ ,6.3
+ ,3.14
+ ,6.0
+ ,2.93
+ ,6.2
+ ,2.65
+ ,6.4
+ ,2.67
+ ,6.8
+ ,2.26
+ ,7.5
+ ,2.35
+ ,7.5
+ ,2.13
+ ,7.6
+ ,2.18
+ ,7.6
+ ,2.90
+ ,7.4
+ ,2.63
+ ,7.3
+ ,2.67
+ ,7.1
+ ,1.81
+ ,6.9
+ ,1.33
+ ,6.8
+ ,0.88
+ ,7.5
+ ,1.28
+ ,7.6
+ ,1.26
+ ,7.8
+ ,1.26
+ ,8.0
+ ,1.29
+ ,8.1
+ ,1.10
+ ,8.2
+ ,1.37
+ ,8.3
+ ,1.21
+ ,8.2
+ ,1.74
+ ,8.0
+ ,1.76
+ ,7.9
+ ,1.48
+ ,7.6
+ ,1.04
+ ,7.6
+ ,1.62
+ ,8.3
+ ,1.49
+ ,8.4
+ ,1.79
+ ,8.4
+ ,1.80
+ ,8.4
+ ,1.58
+ ,8.4
+ ,1.86
+ ,8.6
+ ,1.74
+ ,8.9
+ ,1.59
+ ,8.8
+ ,1.26
+ ,8.3
+ ,1.13
+ ,7.5
+ ,1.92
+ ,7.2
+ ,2.61
+ ,7.4
+ ,2.26
+ ,8.8
+ ,2.41
+ ,9.3
+ ,2.26
+ ,9.3
+ ,2.03
+ ,8.7
+ ,2.86
+ ,8.2
+ ,2.55
+ ,8.3
+ ,2.27
+ ,8.5
+ ,2.26
+ ,8.6
+ ,2.57
+ ,8.5
+ ,3.07
+ ,8.2
+ ,2.76
+ ,8.1
+ ,2.51
+ ,7.9
+ ,2.87
+ ,8.6
+ ,3.14
+ ,8.7
+ ,3.11
+ ,8.7
+ ,3.16
+ ,8.5
+ ,2.47
+ ,8.4
+ ,2.57
+ ,8.5
+ ,2.89
+ ,8.7
+ ,2.63
+ ,8.7
+ ,2.38
+ ,8.6
+ ,1.69
+ ,8.5
+ ,1.96
+ ,8.3
+ ,2.19
+ ,8.0
+ ,1.87
+ ,8.2
+ ,1.6
+ ,8.1
+ ,1.63
+ ,8.1
+ ,1.22
+ ,8.0
+ ,1.21
+ ,7.9
+ ,1.49
+ ,7.9
+ ,1.64)
+ ,dim=c(2
+ ,180)
+ ,dimnames=list(c('Y'
+ ,'X')
+ ,1:180))
> y <- array(NA,dim=c(2,180),dimnames=list(c('Y','X'),1:180))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal 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
Y X
1 6.9 2.28
2 6.8 2.26
3 6.7 2.71
4 6.6 2.77
5 6.5 2.77
6 6.5 2.64
7 7.0 2.56
8 7.5 2.07
9 7.6 2.32
10 7.6 2.16
11 7.6 2.23
12 7.8 2.40
13 8.0 2.84
14 8.0 2.77
15 8.0 2.93
16 7.9 2.91
17 7.9 2.69
18 8.0 2.38
19 8.5 2.58
20 9.2 3.19
21 9.4 2.82
22 9.5 2.72
23 9.5 2.53
24 9.6 2.70
25 9.7 2.42
26 9.7 2.50
27 9.6 2.31
28 9.5 2.41
29 9.4 2.56
30 9.3 2.76
31 9.6 2.71
32 10.2 2.44
33 10.2 2.46
34 10.1 2.12
35 9.9 1.99
36 9.8 1.86
37 9.8 1.88
38 9.7 1.82
39 9.5 1.74
40 9.3 1.71
41 9.1 1.38
42 9.0 1.27
43 9.5 1.19
44 10.0 1.28
45 10.2 1.19
46 10.1 1.22
47 10.0 1.47
48 9.9 1.46
49 10.0 1.96
50 9.9 1.88
51 9.7 2.03
52 9.5 2.04
53 9.2 1.90
54 9.0 1.80
55 9.3 1.92
56 9.8 1.92
57 9.8 1.97
58 9.6 2.46
59 9.4 2.36
60 9.3 2.53
61 9.2 2.31
62 9.2 1.98
63 9.0 1.46
64 8.8 1.26
65 8.7 1.58
66 8.7 1.74
67 9.1 1.89
68 9.7 1.85
69 9.8 1.62
70 9.6 1.30
71 9.4 1.42
72 9.4 1.15
73 9.5 0.42
74 9.4 0.74
75 9.3 1.02
76 9.2 1.51
77 9.0 1.86
78 8.9 1.59
79 9.2 1.03
80 9.8 0.44
81 9.9 0.82
82 9.6 0.86
83 9.2 0.58
84 9.1 0.59
85 9.1 0.95
86 9.0 0.98
87 8.9 1.23
88 8.7 1.17
89 8.5 0.84
90 8.3 0.74
91 8.5 0.65
92 8.7 0.91
93 8.4 1.19
94 8.1 1.30
95 7.8 1.53
96 7.7 1.94
97 7.5 1.79
98 7.2 1.95
99 6.8 2.26
100 6.7 2.04
101 6.4 2.16
102 6.3 2.75
103 6.8 2.79
104 7.3 2.88
105 7.1 3.36
106 7.0 2.97
107 6.8 3.10
108 6.6 2.49
109 6.3 2.20
110 6.1 2.25
111 6.1 2.09
112 6.3 2.79
113 6.3 3.14
114 6.0 2.93
115 6.2 2.65
116 6.4 2.67
117 6.8 2.26
118 7.5 2.35
119 7.5 2.13
120 7.6 2.18
121 7.6 2.90
122 7.4 2.63
123 7.3 2.67
124 7.1 1.81
125 6.9 1.33
126 6.8 0.88
127 7.5 1.28
128 7.6 1.26
129 7.8 1.26
130 8.0 1.29
131 8.1 1.10
132 8.2 1.37
133 8.3 1.21
134 8.2 1.74
135 8.0 1.76
136 7.9 1.48
137 7.6 1.04
138 7.6 1.62
139 8.3 1.49
140 8.4 1.79
141 8.4 1.80
142 8.4 1.58
143 8.4 1.86
144 8.6 1.74
145 8.9 1.59
146 8.8 1.26
147 8.3 1.13
148 7.5 1.92
149 7.2 2.61
150 7.4 2.26
151 8.8 2.41
152 9.3 2.26
153 9.3 2.03
154 8.7 2.86
155 8.2 2.55
156 8.3 2.27
157 8.5 2.26
158 8.6 2.57
159 8.5 3.07
160 8.2 2.76
161 8.1 2.51
162 7.9 2.87
163 8.6 3.14
164 8.7 3.11
165 8.7 3.16
166 8.5 2.47
167 8.4 2.57
168 8.5 2.89
169 8.7 2.63
170 8.7 2.38
171 8.6 1.69
172 8.5 1.96
173 8.3 2.19
174 8.0 1.87
175 8.2 1.60
176 8.1 1.63
177 8.1 1.22
178 8.0 1.21
179 7.9 1.49
180 7.9 1.64
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
9.4065 -0.5093
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.24207 -0.74639 0.04476 0.79500 2.04637
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.4065 0.2384 39.46 < 2e-16 ***
X -0.5093 0.1140 -4.47 1.39e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.024 on 178 degrees of freedom
Multiple R-squared: 0.1009, Adjusted R-squared: 0.09586
F-statistic: 19.98 on 1 and 178 DF, p-value: 1.391e-05
> 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,] 0.0007554383 1.510877e-03 9.992446e-01
[2,] 0.0002000396 4.000792e-04 9.998000e-01
[3,] 0.0003219951 6.439902e-04 9.996780e-01
[4,] 0.0003095261 6.190522e-04 9.996905e-01
[5,] 0.0007738119 1.547624e-03 9.992262e-01
[6,] 0.0003129011 6.258022e-04 9.996871e-01
[7,] 0.0001428450 2.856901e-04 9.998572e-01
[8,] 0.0003908404 7.816808e-04 9.996092e-01
[9,] 0.0079419792 1.588396e-02 9.920580e-01
[10,] 0.0139447554 2.788951e-02 9.860552e-01
[11,] 0.0163473223 3.269464e-02 9.836527e-01
[12,] 0.0127743549 2.554871e-02 9.872256e-01
[13,] 0.0094296741 1.885935e-02 9.905703e-01
[14,] 0.0078988066 1.579761e-02 9.921012e-01
[15,] 0.0125581941 2.511639e-02 9.874418e-01
[16,] 0.0324561305 6.491226e-02 9.675439e-01
[17,] 0.0818637990 1.637276e-01 9.181362e-01
[18,] 0.1673862570 3.347725e-01 8.326137e-01
[19,] 0.2953024440 5.906049e-01 7.046976e-01
[20,] 0.4049536199 8.099072e-01 5.950464e-01
[21,] 0.5714481921 8.571036e-01 4.285518e-01
[22,] 0.6842088559 6.315823e-01 3.157911e-01
[23,] 0.7657877703 4.684245e-01 2.342122e-01
[24,] 0.8050072340 3.899855e-01 1.949928e-01
[25,] 0.8218361525 3.563277e-01 1.781638e-01
[26,] 0.8237782984 3.524434e-01 1.762217e-01
[27,] 0.8472350071 3.055300e-01 1.527650e-01
[28,] 0.9172095687 1.655809e-01 8.279043e-02
[29,] 0.9562854116 8.742918e-02 4.371459e-02
[30,] 0.9747413552 5.051729e-02 2.525864e-02
[31,] 0.9803024912 3.939502e-02 1.969751e-02
[32,] 0.9812736074 3.745279e-02 1.872639e-02
[33,] 0.9813317548 3.733649e-02 1.866825e-02
[34,] 0.9794803831 4.103923e-02 2.051962e-02
[35,] 0.9751335134 4.973297e-02 2.486649e-02
[36,] 0.9687100072 6.257999e-02 3.128999e-02
[37,] 0.9606116153 7.877677e-02 3.938838e-02
[38,] 0.9511702972 9.765941e-02 4.882970e-02
[39,] 0.9397157454 1.205685e-01 6.028425e-02
[40,] 0.9347705435 1.304589e-01 6.522946e-02
[41,] 0.9325465931 1.349068e-01 6.745341e-02
[42,] 0.9282480942 1.435038e-01 7.175191e-02
[43,] 0.9259363558 1.481273e-01 7.406364e-02
[44,] 0.9210846056 1.578308e-01 7.891539e-02
[45,] 0.9315766349 1.368467e-01 6.842337e-02
[46,] 0.9361610809 1.276778e-01 6.383892e-02
[47,] 0.9384397278 1.231205e-01 6.156027e-02
[48,] 0.9361337194 1.277326e-01 6.386628e-02
[49,] 0.9267359899 1.465280e-01 7.326401e-02
[50,] 0.9144748683 1.710503e-01 8.552513e-02
[51,] 0.9054106765 1.891786e-01 9.458932e-02
[52,] 0.9120443955 1.759112e-01 8.795560e-02
[53,] 0.9205989795 1.588020e-01 7.940102e-02
[54,] 0.9361885884 1.276228e-01 6.381141e-02
[55,] 0.9411849683 1.176301e-01 5.881503e-02
[56,] 0.9474224860 1.051550e-01 5.257751e-02
[57,] 0.9473042951 1.053914e-01 5.269570e-02
[58,] 0.9433572152 1.132856e-01 5.664278e-02
[59,] 0.9370653572 1.258693e-01 6.293464e-02
[60,] 0.9336051778 1.327896e-01 6.639482e-02
[61,] 0.9262074390 1.475851e-01 7.379256e-02
[62,] 0.9165369381 1.669261e-01 8.346306e-02
[63,] 0.9089592080 1.820816e-01 9.104079e-02
[64,] 0.9189696021 1.620608e-01 8.103040e-02
[65,] 0.9272092558 1.455815e-01 7.279074e-02
[66,] 0.9250104148 1.499792e-01 7.498959e-02
[67,] 0.9209294822 1.581410e-01 7.907052e-02
[68,] 0.9153375227 1.693250e-01 8.466248e-02
[69,] 0.9107587779 1.784824e-01 8.924122e-02
[70,] 0.9035553908 1.928892e-01 9.644461e-02
[71,] 0.8954979562 2.090041e-01 1.045020e-01
[72,] 0.8891208962 2.217582e-01 1.108791e-01
[73,] 0.8824387732 2.351225e-01 1.175612e-01
[74,] 0.8719955005 2.560090e-01 1.280045e-01
[75,] 0.8625897946 2.748204e-01 1.374102e-01
[76,] 0.8606247694 2.787505e-01 1.393752e-01
[77,] 0.8724691919 2.550616e-01 1.275308e-01
[78,] 0.8752009874 2.495980e-01 1.247990e-01
[79,] 0.8705333297 2.589333e-01 1.294667e-01
[80,] 0.8653208276 2.693583e-01 1.346792e-01
[81,] 0.8595883215 2.808234e-01 1.404117e-01
[82,] 0.8531188730 2.937623e-01 1.468811e-01
[83,] 0.8457691452 3.084617e-01 1.542309e-01
[84,] 0.8371860914 3.256278e-01 1.628139e-01
[85,] 0.8336306937 3.327386e-01 1.663693e-01
[86,] 0.8356036425 3.287927e-01 1.643964e-01
[87,] 0.8303457155 3.393086e-01 1.696543e-01
[88,] 0.8197696263 3.604607e-01 1.802304e-01
[89,] 0.8085121080 3.829758e-01 1.914879e-01
[90,] 0.8016638405 3.966723e-01 1.983362e-01
[91,] 0.8019814216 3.960372e-01 1.980186e-01
[92,] 0.7982485008 4.035030e-01 2.017515e-01
[93,] 0.8072801578 3.854397e-01 1.927198e-01
[94,] 0.8307499511 3.385001e-01 1.692500e-01
[95,] 0.8714064267 2.571871e-01 1.285936e-01
[96,] 0.9136236058 1.727528e-01 8.637639e-02
[97,] 0.9547164766 9.056705e-02 4.528352e-02
[98,] 0.9750939971 4.981201e-02 2.490600e-02
[99,] 0.9789418350 4.211633e-02 2.105816e-02
[100,] 0.9760378987 4.792420e-02 2.396210e-02
[101,] 0.9729269365 5.414613e-02 2.707306e-02
[102,] 0.9725334802 5.493304e-02 2.746652e-02
[103,] 0.9746336898 5.073262e-02 2.536631e-02
[104,] 0.9829019013 3.419620e-02 1.709810e-02
[105,] 0.9928621445 1.427571e-02 7.137855e-03
[106,] 0.9980459805 3.908039e-03 1.954019e-03
[107,] 0.9996082162 7.835676e-04 3.917838e-04
[108,] 0.9998742773 2.514455e-04 1.257227e-04
[109,] 0.9999654040 6.919196e-05 3.459598e-05
[110,] 0.9999976630 4.673989e-06 2.336995e-06
[111,] 0.9999998717 2.565597e-07 1.282798e-07
[112,] 0.9999999935 1.304144e-08 6.520720e-09
[113,] 0.9999999990 2.091612e-09 1.045806e-09
[114,] 0.9999999989 2.180970e-09 1.090485e-09
[115,] 0.9999999988 2.325963e-09 1.162982e-09
[116,] 0.9999999985 2.913643e-09 1.456821e-09
[117,] 0.9999999987 2.674483e-09 1.337242e-09
[118,] 0.9999999992 1.518469e-09 7.592347e-10
[119,] 0.9999999998 4.828066e-10 2.414033e-10
[120,] 0.9999999999 1.248658e-10 6.243292e-11
[121,] 1.0000000000 1.541704e-11 7.708522e-12
[122,] 1.0000000000 9.875654e-13 4.937827e-13
[123,] 1.0000000000 8.636249e-13 4.318124e-13
[124,] 1.0000000000 9.735060e-13 4.867530e-13
[125,] 1.0000000000 1.728242e-12 8.641209e-13
[126,] 1.0000000000 4.078235e-12 2.039118e-12
[127,] 1.0000000000 1.012666e-11 5.063331e-12
[128,] 1.0000000000 2.635943e-11 1.317972e-11
[129,] 1.0000000000 6.462314e-11 3.231157e-11
[130,] 0.9999999999 1.684083e-10 8.420417e-11
[131,] 0.9999999998 3.724309e-10 1.862154e-10
[132,] 0.9999999996 7.323884e-10 3.661942e-10
[133,] 0.9999999996 7.760264e-10 3.880132e-10
[134,] 0.9999999997 6.431304e-10 3.215652e-10
[135,] 0.9999999992 1.681396e-09 8.406979e-10
[136,] 0.9999999978 4.339685e-09 2.169842e-09
[137,] 0.9999999945 1.102513e-08 5.512566e-09
[138,] 0.9999999864 2.721303e-08 1.360651e-08
[139,] 0.9999999665 6.702549e-08 3.351275e-08
[140,] 0.9999999303 1.394875e-07 6.974375e-08
[141,] 0.9999999220 1.559379e-07 7.796897e-08
[142,] 0.9999999157 1.686665e-07 8.433327e-08
[143,] 0.9999998170 3.659183e-07 1.829591e-07
[144,] 0.9999998648 2.704076e-07 1.352038e-07
[145,] 0.9999999923 1.535951e-08 7.679753e-09
[146,] 0.9999999992 1.661163e-09 8.305813e-10
[147,] 0.9999999984 3.227206e-09 1.613603e-09
[148,] 0.9999999999 2.652837e-10 1.326418e-10
[149,] 1.0000000000 1.182156e-12 5.910778e-13
[150,] 1.0000000000 3.646326e-12 1.823163e-12
[151,] 1.0000000000 1.200092e-11 6.000461e-12
[152,] 1.0000000000 5.605864e-11 2.802932e-11
[153,] 0.9999999999 2.038746e-10 1.019373e-10
[154,] 0.9999999997 6.730435e-10 3.365218e-10
[155,] 0.9999999985 2.917623e-09 1.458812e-09
[156,] 0.9999999962 7.586170e-09 3.793085e-09
[157,] 0.9999999922 1.567108e-08 7.835542e-09
[158,] 0.9999999996 7.590355e-10 3.795177e-10
[159,] 0.9999999982 3.575386e-09 1.787693e-09
[160,] 0.9999999902 1.956371e-08 9.781855e-09
[161,] 0.9999999493 1.014925e-07 5.074626e-08
[162,] 0.9999997317 5.366887e-07 2.683444e-07
[163,] 0.9999988790 2.242024e-06 1.121012e-06
[164,] 0.9999968920 6.216083e-06 3.108041e-06
[165,] 0.9999845940 3.081202e-05 1.540601e-05
[166,] 0.9999503583 9.928338e-05 4.964169e-05
[167,] 0.9999751521 4.969573e-05 2.484786e-05
[168,] 0.9999783150 4.337000e-05 2.168500e-05
[169,] 0.9999343760 1.312480e-04 6.562400e-05
[170,] 0.9994191198 1.161760e-03 5.808802e-04
[171,] 0.9983794804 3.241039e-03 1.620520e-03
> postscript(file="/var/www/html/rcomp/tmp/13ijd1258721717.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2no7n1258721717.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b8ax1258721717.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wb171258721717.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5e7q81258721717.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 = 180
Frequency = 1
1 2 3 4 5 6
-1.345304808 -1.455491113 -1.326299261 -1.395740347 -1.495740347 -1.561951327
7 8 9 10 11 12
-1.102696545 -0.852261006 -0.624932199 -0.706422635 -0.670770570 -0.384186981
13 14 15 16 17 18
0.039911719 0.004259653 0.085750089 -0.024436216 -0.136485565 -0.194373286
19 20 21 22 23 24
0.407489760 1.418172048 1.429725414 1.478793891 1.382023998 1.568607587
25 26 27 28 29 30
1.525999323 1.566744542 1.369974649 1.320906171 1.297303455 1.299166500
31 32 33 34 35 36
1.573700739 2.036185628 2.046371932 1.773204755 1.506993776 1.340782797
37 38 39 40 41 42
1.350969101 1.220410188 0.979664969 0.764385513 0.396311488 0.240286813
43 44 45 46 47 48
0.699541595 1.245379965 1.399541595 1.314821052 1.342149858 1.237056706
49 50 51 52 53 54
1.591714319 1.450969101 1.327366385 1.132459537 0.761155406 0.510223883
55 56 57 58 59 60
0.871341710 1.371341710 1.396807472 1.446371932 1.195440410 1.182023998
61 62 63 64 65 66
0.969974649 0.801900624 0.337056706 0.035193661 0.098174533 0.179664969
67 68 69 70 71 72
0.656062253 1.235689644 1.218547142 0.855566270 0.716684097 0.579168986
73 74 75 76 77 78
0.307368870 0.370349743 0.412958006 0.562522467 0.540782797 0.303267685
79 80 81 82 83 84
0.318051159 0.617555175 0.911094961 0.631467570 0.088859307 -0.006047541
85 86 87 88 89 90
0.177305940 0.092585397 0.119914204 -0.110644710 -0.478718734 -0.729650257
91 92 93 94 95 96
-0.575488627 -0.243066669 -0.400458405 -0.644433730 -0.827291228 -0.718471985
97 98 99 100 101 102
-0.994869269 -1.213378833 -1.455491113 -1.667540463 -1.906422635 -1.705926652
103 104 105 106 107 108
-1.185554043 -0.639715672 -0.595244364 -0.893877302 -1.027666323 -1.538348611
109 110 111 112 113 114
-1.986050026 -2.160584265 -2.242074701 -1.685554043 -1.507293714 -1.914249911
115 116 117 118 119 120
-1.856858174 -1.646671870 -1.455491113 -0.709652742 -0.821702092 -0.696236331
121 122 123 124 125 126
-0.329529368 -0.667044479 -0.746671870 -1.384682965 -1.829154273 -2.158346125
127 128 129 130 131 132
-1.254620035 -1.164806339 -0.964806339 -0.749526882 -0.746296776 -0.508781664
133 134 135 136 137 138
-0.490272101 -0.320335031 -0.510148726 -0.752756989 -1.276855689 -0.981452858
139 140 141 142 143 144
-0.347663837 -0.094869269 -0.089776117 -0.201825467 -0.059217203 0.079664969
145 146 147 148 149 150
0.303267685 0.035193661 -0.531017319 -0.928658290 -0.877230784 -0.855491113
151 152 153 154 155 156
0.620906171 1.044508887 0.927366385 0.750098023 0.092210303 0.049602039
157 158 159 160 161 162
0.244508887 0.502396607 0.657054221 0.199166500 -0.028162306 -0.044808825
163 164 165 166 167 168
0.792706286 0.877426830 0.902892591 0.351465085 0.302396607 0.565377480
169 170 171 172 173 174
0.632955521 0.505626714 0.054199208 0.091714319 0.008856821 -0.454124051
175 176 177 178 179 180
-0.391639162 -0.476359705 -0.685178948 -0.790272101 -0.747663837 -0.671266553
> postscript(file="/var/www/html/rcomp/tmp/6maz01258721717.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 = 180
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.345304808 NA
1 -1.455491113 -1.345304808
2 -1.326299261 -1.455491113
3 -1.395740347 -1.326299261
4 -1.495740347 -1.395740347
5 -1.561951327 -1.495740347
6 -1.102696545 -1.561951327
7 -0.852261006 -1.102696545
8 -0.624932199 -0.852261006
9 -0.706422635 -0.624932199
10 -0.670770570 -0.706422635
11 -0.384186981 -0.670770570
12 0.039911719 -0.384186981
13 0.004259653 0.039911719
14 0.085750089 0.004259653
15 -0.024436216 0.085750089
16 -0.136485565 -0.024436216
17 -0.194373286 -0.136485565
18 0.407489760 -0.194373286
19 1.418172048 0.407489760
20 1.429725414 1.418172048
21 1.478793891 1.429725414
22 1.382023998 1.478793891
23 1.568607587 1.382023998
24 1.525999323 1.568607587
25 1.566744542 1.525999323
26 1.369974649 1.566744542
27 1.320906171 1.369974649
28 1.297303455 1.320906171
29 1.299166500 1.297303455
30 1.573700739 1.299166500
31 2.036185628 1.573700739
32 2.046371932 2.036185628
33 1.773204755 2.046371932
34 1.506993776 1.773204755
35 1.340782797 1.506993776
36 1.350969101 1.340782797
37 1.220410188 1.350969101
38 0.979664969 1.220410188
39 0.764385513 0.979664969
40 0.396311488 0.764385513
41 0.240286813 0.396311488
42 0.699541595 0.240286813
43 1.245379965 0.699541595
44 1.399541595 1.245379965
45 1.314821052 1.399541595
46 1.342149858 1.314821052
47 1.237056706 1.342149858
48 1.591714319 1.237056706
49 1.450969101 1.591714319
50 1.327366385 1.450969101
51 1.132459537 1.327366385
52 0.761155406 1.132459537
53 0.510223883 0.761155406
54 0.871341710 0.510223883
55 1.371341710 0.871341710
56 1.396807472 1.371341710
57 1.446371932 1.396807472
58 1.195440410 1.446371932
59 1.182023998 1.195440410
60 0.969974649 1.182023998
61 0.801900624 0.969974649
62 0.337056706 0.801900624
63 0.035193661 0.337056706
64 0.098174533 0.035193661
65 0.179664969 0.098174533
66 0.656062253 0.179664969
67 1.235689644 0.656062253
68 1.218547142 1.235689644
69 0.855566270 1.218547142
70 0.716684097 0.855566270
71 0.579168986 0.716684097
72 0.307368870 0.579168986
73 0.370349743 0.307368870
74 0.412958006 0.370349743
75 0.562522467 0.412958006
76 0.540782797 0.562522467
77 0.303267685 0.540782797
78 0.318051159 0.303267685
79 0.617555175 0.318051159
80 0.911094961 0.617555175
81 0.631467570 0.911094961
82 0.088859307 0.631467570
83 -0.006047541 0.088859307
84 0.177305940 -0.006047541
85 0.092585397 0.177305940
86 0.119914204 0.092585397
87 -0.110644710 0.119914204
88 -0.478718734 -0.110644710
89 -0.729650257 -0.478718734
90 -0.575488627 -0.729650257
91 -0.243066669 -0.575488627
92 -0.400458405 -0.243066669
93 -0.644433730 -0.400458405
94 -0.827291228 -0.644433730
95 -0.718471985 -0.827291228
96 -0.994869269 -0.718471985
97 -1.213378833 -0.994869269
98 -1.455491113 -1.213378833
99 -1.667540463 -1.455491113
100 -1.906422635 -1.667540463
101 -1.705926652 -1.906422635
102 -1.185554043 -1.705926652
103 -0.639715672 -1.185554043
104 -0.595244364 -0.639715672
105 -0.893877302 -0.595244364
106 -1.027666323 -0.893877302
107 -1.538348611 -1.027666323
108 -1.986050026 -1.538348611
109 -2.160584265 -1.986050026
110 -2.242074701 -2.160584265
111 -1.685554043 -2.242074701
112 -1.507293714 -1.685554043
113 -1.914249911 -1.507293714
114 -1.856858174 -1.914249911
115 -1.646671870 -1.856858174
116 -1.455491113 -1.646671870
117 -0.709652742 -1.455491113
118 -0.821702092 -0.709652742
119 -0.696236331 -0.821702092
120 -0.329529368 -0.696236331
121 -0.667044479 -0.329529368
122 -0.746671870 -0.667044479
123 -1.384682965 -0.746671870
124 -1.829154273 -1.384682965
125 -2.158346125 -1.829154273
126 -1.254620035 -2.158346125
127 -1.164806339 -1.254620035
128 -0.964806339 -1.164806339
129 -0.749526882 -0.964806339
130 -0.746296776 -0.749526882
131 -0.508781664 -0.746296776
132 -0.490272101 -0.508781664
133 -0.320335031 -0.490272101
134 -0.510148726 -0.320335031
135 -0.752756989 -0.510148726
136 -1.276855689 -0.752756989
137 -0.981452858 -1.276855689
138 -0.347663837 -0.981452858
139 -0.094869269 -0.347663837
140 -0.089776117 -0.094869269
141 -0.201825467 -0.089776117
142 -0.059217203 -0.201825467
143 0.079664969 -0.059217203
144 0.303267685 0.079664969
145 0.035193661 0.303267685
146 -0.531017319 0.035193661
147 -0.928658290 -0.531017319
148 -0.877230784 -0.928658290
149 -0.855491113 -0.877230784
150 0.620906171 -0.855491113
151 1.044508887 0.620906171
152 0.927366385 1.044508887
153 0.750098023 0.927366385
154 0.092210303 0.750098023
155 0.049602039 0.092210303
156 0.244508887 0.049602039
157 0.502396607 0.244508887
158 0.657054221 0.502396607
159 0.199166500 0.657054221
160 -0.028162306 0.199166500
161 -0.044808825 -0.028162306
162 0.792706286 -0.044808825
163 0.877426830 0.792706286
164 0.902892591 0.877426830
165 0.351465085 0.902892591
166 0.302396607 0.351465085
167 0.565377480 0.302396607
168 0.632955521 0.565377480
169 0.505626714 0.632955521
170 0.054199208 0.505626714
171 0.091714319 0.054199208
172 0.008856821 0.091714319
173 -0.454124051 0.008856821
174 -0.391639162 -0.454124051
175 -0.476359705 -0.391639162
176 -0.685178948 -0.476359705
177 -0.790272101 -0.685178948
178 -0.747663837 -0.790272101
179 -0.671266553 -0.747663837
180 NA -0.671266553
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.455491113 -1.345304808
[2,] -1.326299261 -1.455491113
[3,] -1.395740347 -1.326299261
[4,] -1.495740347 -1.395740347
[5,] -1.561951327 -1.495740347
[6,] -1.102696545 -1.561951327
[7,] -0.852261006 -1.102696545
[8,] -0.624932199 -0.852261006
[9,] -0.706422635 -0.624932199
[10,] -0.670770570 -0.706422635
[11,] -0.384186981 -0.670770570
[12,] 0.039911719 -0.384186981
[13,] 0.004259653 0.039911719
[14,] 0.085750089 0.004259653
[15,] -0.024436216 0.085750089
[16,] -0.136485565 -0.024436216
[17,] -0.194373286 -0.136485565
[18,] 0.407489760 -0.194373286
[19,] 1.418172048 0.407489760
[20,] 1.429725414 1.418172048
[21,] 1.478793891 1.429725414
[22,] 1.382023998 1.478793891
[23,] 1.568607587 1.382023998
[24,] 1.525999323 1.568607587
[25,] 1.566744542 1.525999323
[26,] 1.369974649 1.566744542
[27,] 1.320906171 1.369974649
[28,] 1.297303455 1.320906171
[29,] 1.299166500 1.297303455
[30,] 1.573700739 1.299166500
[31,] 2.036185628 1.573700739
[32,] 2.046371932 2.036185628
[33,] 1.773204755 2.046371932
[34,] 1.506993776 1.773204755
[35,] 1.340782797 1.506993776
[36,] 1.350969101 1.340782797
[37,] 1.220410188 1.350969101
[38,] 0.979664969 1.220410188
[39,] 0.764385513 0.979664969
[40,] 0.396311488 0.764385513
[41,] 0.240286813 0.396311488
[42,] 0.699541595 0.240286813
[43,] 1.245379965 0.699541595
[44,] 1.399541595 1.245379965
[45,] 1.314821052 1.399541595
[46,] 1.342149858 1.314821052
[47,] 1.237056706 1.342149858
[48,] 1.591714319 1.237056706
[49,] 1.450969101 1.591714319
[50,] 1.327366385 1.450969101
[51,] 1.132459537 1.327366385
[52,] 0.761155406 1.132459537
[53,] 0.510223883 0.761155406
[54,] 0.871341710 0.510223883
[55,] 1.371341710 0.871341710
[56,] 1.396807472 1.371341710
[57,] 1.446371932 1.396807472
[58,] 1.195440410 1.446371932
[59,] 1.182023998 1.195440410
[60,] 0.969974649 1.182023998
[61,] 0.801900624 0.969974649
[62,] 0.337056706 0.801900624
[63,] 0.035193661 0.337056706
[64,] 0.098174533 0.035193661
[65,] 0.179664969 0.098174533
[66,] 0.656062253 0.179664969
[67,] 1.235689644 0.656062253
[68,] 1.218547142 1.235689644
[69,] 0.855566270 1.218547142
[70,] 0.716684097 0.855566270
[71,] 0.579168986 0.716684097
[72,] 0.307368870 0.579168986
[73,] 0.370349743 0.307368870
[74,] 0.412958006 0.370349743
[75,] 0.562522467 0.412958006
[76,] 0.540782797 0.562522467
[77,] 0.303267685 0.540782797
[78,] 0.318051159 0.303267685
[79,] 0.617555175 0.318051159
[80,] 0.911094961 0.617555175
[81,] 0.631467570 0.911094961
[82,] 0.088859307 0.631467570
[83,] -0.006047541 0.088859307
[84,] 0.177305940 -0.006047541
[85,] 0.092585397 0.177305940
[86,] 0.119914204 0.092585397
[87,] -0.110644710 0.119914204
[88,] -0.478718734 -0.110644710
[89,] -0.729650257 -0.478718734
[90,] -0.575488627 -0.729650257
[91,] -0.243066669 -0.575488627
[92,] -0.400458405 -0.243066669
[93,] -0.644433730 -0.400458405
[94,] -0.827291228 -0.644433730
[95,] -0.718471985 -0.827291228
[96,] -0.994869269 -0.718471985
[97,] -1.213378833 -0.994869269
[98,] -1.455491113 -1.213378833
[99,] -1.667540463 -1.455491113
[100,] -1.906422635 -1.667540463
[101,] -1.705926652 -1.906422635
[102,] -1.185554043 -1.705926652
[103,] -0.639715672 -1.185554043
[104,] -0.595244364 -0.639715672
[105,] -0.893877302 -0.595244364
[106,] -1.027666323 -0.893877302
[107,] -1.538348611 -1.027666323
[108,] -1.986050026 -1.538348611
[109,] -2.160584265 -1.986050026
[110,] -2.242074701 -2.160584265
[111,] -1.685554043 -2.242074701
[112,] -1.507293714 -1.685554043
[113,] -1.914249911 -1.507293714
[114,] -1.856858174 -1.914249911
[115,] -1.646671870 -1.856858174
[116,] -1.455491113 -1.646671870
[117,] -0.709652742 -1.455491113
[118,] -0.821702092 -0.709652742
[119,] -0.696236331 -0.821702092
[120,] -0.329529368 -0.696236331
[121,] -0.667044479 -0.329529368
[122,] -0.746671870 -0.667044479
[123,] -1.384682965 -0.746671870
[124,] -1.829154273 -1.384682965
[125,] -2.158346125 -1.829154273
[126,] -1.254620035 -2.158346125
[127,] -1.164806339 -1.254620035
[128,] -0.964806339 -1.164806339
[129,] -0.749526882 -0.964806339
[130,] -0.746296776 -0.749526882
[131,] -0.508781664 -0.746296776
[132,] -0.490272101 -0.508781664
[133,] -0.320335031 -0.490272101
[134,] -0.510148726 -0.320335031
[135,] -0.752756989 -0.510148726
[136,] -1.276855689 -0.752756989
[137,] -0.981452858 -1.276855689
[138,] -0.347663837 -0.981452858
[139,] -0.094869269 -0.347663837
[140,] -0.089776117 -0.094869269
[141,] -0.201825467 -0.089776117
[142,] -0.059217203 -0.201825467
[143,] 0.079664969 -0.059217203
[144,] 0.303267685 0.079664969
[145,] 0.035193661 0.303267685
[146,] -0.531017319 0.035193661
[147,] -0.928658290 -0.531017319
[148,] -0.877230784 -0.928658290
[149,] -0.855491113 -0.877230784
[150,] 0.620906171 -0.855491113
[151,] 1.044508887 0.620906171
[152,] 0.927366385 1.044508887
[153,] 0.750098023 0.927366385
[154,] 0.092210303 0.750098023
[155,] 0.049602039 0.092210303
[156,] 0.244508887 0.049602039
[157,] 0.502396607 0.244508887
[158,] 0.657054221 0.502396607
[159,] 0.199166500 0.657054221
[160,] -0.028162306 0.199166500
[161,] -0.044808825 -0.028162306
[162,] 0.792706286 -0.044808825
[163,] 0.877426830 0.792706286
[164,] 0.902892591 0.877426830
[165,] 0.351465085 0.902892591
[166,] 0.302396607 0.351465085
[167,] 0.565377480 0.302396607
[168,] 0.632955521 0.565377480
[169,] 0.505626714 0.632955521
[170,] 0.054199208 0.505626714
[171,] 0.091714319 0.054199208
[172,] 0.008856821 0.091714319
[173,] -0.454124051 0.008856821
[174,] -0.391639162 -0.454124051
[175,] -0.476359705 -0.391639162
[176,] -0.685178948 -0.476359705
[177,] -0.790272101 -0.685178948
[178,] -0.747663837 -0.790272101
[179,] -0.671266553 -0.747663837
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.455491113 -1.345304808
2 -1.326299261 -1.455491113
3 -1.395740347 -1.326299261
4 -1.495740347 -1.395740347
5 -1.561951327 -1.495740347
6 -1.102696545 -1.561951327
7 -0.852261006 -1.102696545
8 -0.624932199 -0.852261006
9 -0.706422635 -0.624932199
10 -0.670770570 -0.706422635
11 -0.384186981 -0.670770570
12 0.039911719 -0.384186981
13 0.004259653 0.039911719
14 0.085750089 0.004259653
15 -0.024436216 0.085750089
16 -0.136485565 -0.024436216
17 -0.194373286 -0.136485565
18 0.407489760 -0.194373286
19 1.418172048 0.407489760
20 1.429725414 1.418172048
21 1.478793891 1.429725414
22 1.382023998 1.478793891
23 1.568607587 1.382023998
24 1.525999323 1.568607587
25 1.566744542 1.525999323
26 1.369974649 1.566744542
27 1.320906171 1.369974649
28 1.297303455 1.320906171
29 1.299166500 1.297303455
30 1.573700739 1.299166500
31 2.036185628 1.573700739
32 2.046371932 2.036185628
33 1.773204755 2.046371932
34 1.506993776 1.773204755
35 1.340782797 1.506993776
36 1.350969101 1.340782797
37 1.220410188 1.350969101
38 0.979664969 1.220410188
39 0.764385513 0.979664969
40 0.396311488 0.764385513
41 0.240286813 0.396311488
42 0.699541595 0.240286813
43 1.245379965 0.699541595
44 1.399541595 1.245379965
45 1.314821052 1.399541595
46 1.342149858 1.314821052
47 1.237056706 1.342149858
48 1.591714319 1.237056706
49 1.450969101 1.591714319
50 1.327366385 1.450969101
51 1.132459537 1.327366385
52 0.761155406 1.132459537
53 0.510223883 0.761155406
54 0.871341710 0.510223883
55 1.371341710 0.871341710
56 1.396807472 1.371341710
57 1.446371932 1.396807472
58 1.195440410 1.446371932
59 1.182023998 1.195440410
60 0.969974649 1.182023998
61 0.801900624 0.969974649
62 0.337056706 0.801900624
63 0.035193661 0.337056706
64 0.098174533 0.035193661
65 0.179664969 0.098174533
66 0.656062253 0.179664969
67 1.235689644 0.656062253
68 1.218547142 1.235689644
69 0.855566270 1.218547142
70 0.716684097 0.855566270
71 0.579168986 0.716684097
72 0.307368870 0.579168986
73 0.370349743 0.307368870
74 0.412958006 0.370349743
75 0.562522467 0.412958006
76 0.540782797 0.562522467
77 0.303267685 0.540782797
78 0.318051159 0.303267685
79 0.617555175 0.318051159
80 0.911094961 0.617555175
81 0.631467570 0.911094961
82 0.088859307 0.631467570
83 -0.006047541 0.088859307
84 0.177305940 -0.006047541
85 0.092585397 0.177305940
86 0.119914204 0.092585397
87 -0.110644710 0.119914204
88 -0.478718734 -0.110644710
89 -0.729650257 -0.478718734
90 -0.575488627 -0.729650257
91 -0.243066669 -0.575488627
92 -0.400458405 -0.243066669
93 -0.644433730 -0.400458405
94 -0.827291228 -0.644433730
95 -0.718471985 -0.827291228
96 -0.994869269 -0.718471985
97 -1.213378833 -0.994869269
98 -1.455491113 -1.213378833
99 -1.667540463 -1.455491113
100 -1.906422635 -1.667540463
101 -1.705926652 -1.906422635
102 -1.185554043 -1.705926652
103 -0.639715672 -1.185554043
104 -0.595244364 -0.639715672
105 -0.893877302 -0.595244364
106 -1.027666323 -0.893877302
107 -1.538348611 -1.027666323
108 -1.986050026 -1.538348611
109 -2.160584265 -1.986050026
110 -2.242074701 -2.160584265
111 -1.685554043 -2.242074701
112 -1.507293714 -1.685554043
113 -1.914249911 -1.507293714
114 -1.856858174 -1.914249911
115 -1.646671870 -1.856858174
116 -1.455491113 -1.646671870
117 -0.709652742 -1.455491113
118 -0.821702092 -0.709652742
119 -0.696236331 -0.821702092
120 -0.329529368 -0.696236331
121 -0.667044479 -0.329529368
122 -0.746671870 -0.667044479
123 -1.384682965 -0.746671870
124 -1.829154273 -1.384682965
125 -2.158346125 -1.829154273
126 -1.254620035 -2.158346125
127 -1.164806339 -1.254620035
128 -0.964806339 -1.164806339
129 -0.749526882 -0.964806339
130 -0.746296776 -0.749526882
131 -0.508781664 -0.746296776
132 -0.490272101 -0.508781664
133 -0.320335031 -0.490272101
134 -0.510148726 -0.320335031
135 -0.752756989 -0.510148726
136 -1.276855689 -0.752756989
137 -0.981452858 -1.276855689
138 -0.347663837 -0.981452858
139 -0.094869269 -0.347663837
140 -0.089776117 -0.094869269
141 -0.201825467 -0.089776117
142 -0.059217203 -0.201825467
143 0.079664969 -0.059217203
144 0.303267685 0.079664969
145 0.035193661 0.303267685
146 -0.531017319 0.035193661
147 -0.928658290 -0.531017319
148 -0.877230784 -0.928658290
149 -0.855491113 -0.877230784
150 0.620906171 -0.855491113
151 1.044508887 0.620906171
152 0.927366385 1.044508887
153 0.750098023 0.927366385
154 0.092210303 0.750098023
155 0.049602039 0.092210303
156 0.244508887 0.049602039
157 0.502396607 0.244508887
158 0.657054221 0.502396607
159 0.199166500 0.657054221
160 -0.028162306 0.199166500
161 -0.044808825 -0.028162306
162 0.792706286 -0.044808825
163 0.877426830 0.792706286
164 0.902892591 0.877426830
165 0.351465085 0.902892591
166 0.302396607 0.351465085
167 0.565377480 0.302396607
168 0.632955521 0.565377480
169 0.505626714 0.632955521
170 0.054199208 0.505626714
171 0.091714319 0.054199208
172 0.008856821 0.091714319
173 -0.454124051 0.008856821
174 -0.391639162 -0.454124051
175 -0.476359705 -0.391639162
176 -0.685178948 -0.476359705
177 -0.790272101 -0.685178948
178 -0.747663837 -0.790272101
179 -0.671266553 -0.747663837
> 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/7akbk1258721717.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80sud1258721717.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ab0r1258721717.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10pzms1258721717.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11k4mi1258721718.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/12egjr1258721718.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/132rjp1258721718.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/14u9kc1258721718.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/159k341258721718.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/16ahx71258721718.tab")
+ }
> system("convert tmp/13ijd1258721717.ps tmp/13ijd1258721717.png")
> system("convert tmp/2no7n1258721717.ps tmp/2no7n1258721717.png")
> system("convert tmp/3b8ax1258721717.ps tmp/3b8ax1258721717.png")
> system("convert tmp/4wb171258721717.ps tmp/4wb171258721717.png")
> system("convert tmp/5e7q81258721717.ps tmp/5e7q81258721717.png")
> system("convert tmp/6maz01258721717.ps tmp/6maz01258721717.png")
> system("convert tmp/7akbk1258721717.ps tmp/7akbk1258721717.png")
> system("convert tmp/80sud1258721717.ps tmp/80sud1258721717.png")
> system("convert tmp/9ab0r1258721717.ps tmp/9ab0r1258721717.png")
> system("convert tmp/10pzms1258721717.ps tmp/10pzms1258721717.png")
>
>
> proc.time()
user system elapsed
4.222 1.712 9.229