R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(14
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+ ,dim=c(5
+ ,162)
+ ,dimnames=list(c('Happiness'
+ ,'Learning'
+ ,'Connected'
+ ,'Depression'
+ ,'Belonging
')
+ ,1:162))
> y <- array(NA,dim=c(5,162),dimnames=list(c('Happiness','Learning','Connected','Depression','Belonging
'),1:162))
> 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 = '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
> 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
Happiness Learning Connected Depression Belonging\r M1 M2 M3 M4 M5 M6 M7 M8
1 14 13 41 12 53 1 0 0 0 0 0 0 0
2 18 16 39 11 86 0 1 0 0 0 0 0 0
3 11 19 30 14 66 0 0 1 0 0 0 0 0
4 12 15 31 12 67 0 0 0 1 0 0 0 0
5 16 14 34 21 76 0 0 0 0 1 0 0 0
6 18 13 35 12 78 0 0 0 0 0 1 0 0
7 14 19 39 22 53 0 0 0 0 0 0 1 0
8 14 15 34 11 80 0 0 0 0 0 0 0 1
9 15 14 36 10 74 0 0 0 0 0 0 0 0
10 15 15 37 13 76 0 0 0 0 0 0 0 0
11 17 16 38 10 79 0 0 0 0 0 0 0 0
12 19 16 36 8 54 0 0 0 0 0 0 0 0
13 10 16 38 15 67 1 0 0 0 0 0 0 0
14 16 16 39 14 54 0 1 0 0 0 0 0 0
15 18 17 33 10 87 0 0 1 0 0 0 0 0
16 14 15 32 14 58 0 0 0 1 0 0 0 0
17 14 15 36 14 75 0 0 0 0 1 0 0 0
18 17 20 38 11 88 0 0 0 0 0 1 0 0
19 14 18 39 10 64 0 0 0 0 0 0 1 0
20 16 16 32 13 57 0 0 0 0 0 0 0 1
21 18 16 32 7 66 0 0 0 0 0 0 0 0
22 11 16 31 14 68 0 0 0 0 0 0 0 0
23 14 19 39 12 54 0 0 0 0 0 0 0 0
24 12 16 37 14 56 0 0 0 0 0 0 0 0
25 17 17 39 11 86 1 0 0 0 0 0 0 0
26 9 17 41 9 80 0 1 0 0 0 0 0 0
27 16 16 36 11 76 0 0 1 0 0 0 0 0
28 14 15 33 15 69 0 0 0 1 0 0 0 0
29 15 16 33 14 78 0 0 0 0 1 0 0 0
30 11 14 34 13 67 0 0 0 0 0 1 0 0
31 16 15 31 9 80 0 0 0 0 0 0 1 0
32 13 12 27 15 54 0 0 0 0 0 0 0 1
33 17 14 37 10 71 0 0 0 0 0 0 0 0
34 15 16 34 11 84 0 0 0 0 0 0 0 0
35 14 14 34 13 74 0 0 0 0 0 0 0 0
36 16 7 32 8 71 0 0 0 0 0 0 0 0
37 9 10 29 20 63 1 0 0 0 0 0 0 0
38 15 14 36 12 71 0 1 0 0 0 0 0 0
39 17 16 29 10 76 0 0 1 0 0 0 0 0
40 13 16 35 10 69 0 0 0 1 0 0 0 0
41 15 16 37 9 74 0 0 0 0 1 0 0 0
42 16 14 34 14 75 0 0 0 0 0 1 0 0
43 16 20 38 8 54 0 0 0 0 0 0 1 0
44 12 14 35 14 52 0 0 0 0 0 0 0 1
45 12 14 38 11 69 0 0 0 0 0 0 0 0
46 11 11 37 13 68 0 0 0 0 0 0 0 0
47 15 14 38 9 65 0 0 0 0 0 0 0 0
48 15 15 33 11 75 0 0 0 0 0 0 0 0
49 17 16 36 15 74 1 0 0 0 0 0 0 0
50 13 14 38 11 75 0 1 0 0 0 0 0 0
51 16 16 32 10 72 0 0 1 0 0 0 0 0
52 14 14 32 14 67 0 0 0 1 0 0 0 0
53 11 12 32 18 63 0 0 0 0 1 0 0 0
54 12 16 34 14 62 0 0 0 0 0 1 0 0
55 12 9 32 11 63 0 0 0 0 0 0 1 0
56 15 14 37 12 76 0 0 0 0 0 0 0 1
57 16 16 39 13 74 0 0 0 0 0 0 0 0
58 15 16 29 9 67 0 0 0 0 0 0 0 0
59 12 15 37 10 73 0 0 0 0 0 0 0 0
60 12 16 35 15 70 0 0 0 0 0 0 0 0
61 8 12 30 20 53 1 0 0 0 0 0 0 0
62 13 16 38 12 77 0 1 0 0 0 0 0 0
63 11 16 34 12 77 0 0 1 0 0 0 0 0
64 14 14 31 14 52 0 0 0 1 0 0 0 0
65 15 16 34 13 54 0 0 0 0 1 0 0 0
66 10 17 35 11 80 0 0 0 0 0 1 0 0
67 11 18 36 17 66 0 0 0 0 0 0 1 0
68 12 18 30 12 73 0 0 0 0 0 0 0 1
69 15 12 39 13 63 0 0 0 0 0 0 0 0
70 15 16 35 14 69 0 0 0 0 0 0 0 0
71 14 10 38 13 67 0 0 0 0 0 0 0 0
72 16 14 31 15 54 0 0 0 0 0 0 0 0
73 15 18 34 13 81 1 0 0 0 0 0 0 0
74 15 18 38 10 69 0 1 0 0 0 0 0 0
75 13 16 34 11 84 0 0 1 0 0 0 0 0
76 12 17 39 19 80 0 0 0 1 0 0 0 0
77 17 16 37 13 70 0 0 0 0 1 0 0 0
78 13 16 34 17 69 0 0 0 0 0 1 0 0
79 15 13 28 13 77 0 0 0 0 0 0 1 0
80 13 16 37 9 54 0 0 0 0 0 0 0 1
81 15 16 33 11 79 0 0 0 0 0 0 0 0
82 16 20 37 10 30 0 0 0 0 0 0 0 0
83 15 16 35 9 71 0 0 0 0 0 0 0 0
84 16 15 37 12 73 0 0 0 0 0 0 0 0
85 15 15 32 12 72 1 0 0 0 0 0 0 0
86 14 16 33 13 77 0 1 0 0 0 0 0 0
87 15 14 38 13 75 0 0 1 0 0 0 0 0
88 14 16 33 12 69 0 0 0 1 0 0 0 0
89 13 16 29 15 54 0 0 0 0 1 0 0 0
90 7 15 33 22 70 0 0 0 0 0 1 0 0
91 17 12 31 13 73 0 0 0 0 0 0 1 0
92 13 17 36 15 54 0 0 0 0 0 0 0 1
93 15 16 35 13 77 0 0 0 0 0 0 0 0
94 14 15 32 15 82 0 0 0 0 0 0 0 0
95 13 13 29 10 80 0 0 0 0 0 0 0 0
96 16 16 39 11 80 0 0 0 0 0 0 0 0
97 12 16 37 16 69 1 0 0 0 0 0 0 0
98 14 16 35 11 78 0 1 0 0 0 0 0 0
99 17 16 37 11 81 0 0 1 0 0 0 0 0
100 15 14 32 10 76 0 0 0 1 0 0 0 0
101 17 16 38 10 76 0 0 0 0 1 0 0 0
102 12 16 37 16 73 0 0 0 0 0 1 0 0
103 16 20 36 12 85 0 0 0 0 0 0 1 0
104 11 15 32 11 66 0 0 0 0 0 0 0 1
105 15 16 33 16 79 0 0 0 0 0 0 0 0
106 9 13 40 19 68 0 0 0 0 0 0 0 0
107 16 17 38 11 76 0 0 0 0 0 0 0 0
108 15 16 41 16 71 0 0 0 0 0 0 0 0
109 10 16 36 15 54 1 0 0 0 0 0 0 0
110 10 12 43 24 46 0 1 0 0 0 0 0 0
111 15 16 30 14 82 0 0 1 0 0 0 0 0
112 11 16 31 15 74 0 0 0 1 0 0 0 0
113 13 17 32 11 88 0 0 0 0 1 0 0 0
114 14 13 32 15 38 0 0 0 0 0 1 0 0
115 18 12 37 12 76 0 0 0 0 0 0 1 0
116 16 18 37 10 86 0 0 0 0 0 0 0 1
117 14 14 33 14 54 0 0 0 0 0 0 0 0
118 14 14 34 13 70 0 0 0 0 0 0 0 0
119 14 13 33 9 69 0 0 0 0 0 0 0 0
120 14 16 38 15 90 0 0 0 0 0 0 0 0
121 12 13 33 15 54 1 0 0 0 0 0 0 0
122 14 16 31 14 76 0 1 0 0 0 0 0 0
123 15 13 38 11 89 0 0 1 0 0 0 0 0
124 15 16 37 8 76 0 0 0 1 0 0 0 0
125 15 15 33 11 73 0 0 0 0 1 0 0 0
126 13 16 31 11 79 0 0 0 0 0 1 0 0
127 17 15 39 8 90 0 0 0 0 0 0 1 0
128 17 17 44 10 74 0 0 0 0 0 0 0 1
129 19 15 33 11 81 0 0 0 0 0 0 0 0
130 15 12 35 13 72 0 0 0 0 0 0 0 0
131 13 16 32 11 71 0 0 0 0 0 0 0 0
132 9 10 28 20 66 0 0 0 0 0 0 0 0
133 15 16 40 10 77 1 0 0 0 0 0 0 0
134 15 12 27 15 65 0 1 0 0 0 0 0 0
135 15 14 37 12 74 0 0 1 0 0 0 0 0
136 16 15 32 14 82 0 0 0 1 0 0 0 0
137 11 13 28 23 54 0 0 0 0 1 0 0 0
138 14 15 34 14 63 0 0 0 0 0 1 0 0
139 11 11 30 16 54 0 0 0 0 0 0 1 0
140 15 12 35 11 64 0 0 0 0 0 0 0 1
141 13 8 31 12 69 0 0 0 0 0 0 0 0
142 15 16 32 10 54 0 0 0 0 0 0 0 0
143 16 15 30 14 84 0 0 0 0 0 0 0 0
144 14 17 30 12 86 0 0 0 0 0 0 0 0
145 15 16 31 12 77 1 0 0 0 0 0 0 0
146 16 10 40 11 89 0 1 0 0 0 0 0 0
147 16 18 32 12 76 0 0 1 0 0 0 0 0
148 11 13 36 13 60 0 0 0 1 0 0 0 0
149 12 16 32 11 75 0 0 0 0 1 0 0 0
150 9 13 35 19 73 0 0 0 0 0 1 0 0
151 16 10 38 12 85 0 0 0 0 0 0 1 0
152 13 15 42 17 79 0 0 0 0 0 0 0 1
153 16 16 34 9 71 0 0 0 0 0 0 0 0
154 12 16 35 12 72 0 0 0 0 0 0 0 0
155 9 14 35 19 69 0 0 0 0 0 0 0 0
156 13 10 33 18 78 0 0 0 0 0 0 0 0
157 13 17 36 15 54 1 0 0 0 0 0 0 0
158 14 13 32 14 69 0 1 0 0 0 0 0 0
159 19 15 33 11 81 0 0 1 0 0 0 0 0
160 13 16 34 9 84 0 0 0 1 0 0 0 0
161 12 12 32 18 84 0 0 0 0 1 0 0 0
162 13 13 34 16 69 0 0 0 0 0 1 0 0
M9 M10 M11
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
7 0 0 0
8 0 0 0
9 1 0 0
10 0 1 0
11 0 0 1
12 0 0 0
13 0 0 0
14 0 0 0
15 0 0 0
16 0 0 0
17 0 0 0
18 0 0 0
19 0 0 0
20 0 0 0
21 1 0 0
22 0 1 0
23 0 0 1
24 0 0 0
25 0 0 0
26 0 0 0
27 0 0 0
28 0 0 0
29 0 0 0
30 0 0 0
31 0 0 0
32 0 0 0
33 1 0 0
34 0 1 0
35 0 0 1
36 0 0 0
37 0 0 0
38 0 0 0
39 0 0 0
40 0 0 0
41 0 0 0
42 0 0 0
43 0 0 0
44 0 0 0
45 1 0 0
46 0 1 0
47 0 0 1
48 0 0 0
49 0 0 0
50 0 0 0
51 0 0 0
52 0 0 0
53 0 0 0
54 0 0 0
55 0 0 0
56 0 0 0
57 1 0 0
58 0 1 0
59 0 0 1
60 0 0 0
61 0 0 0
62 0 0 0
63 0 0 0
64 0 0 0
65 0 0 0
66 0 0 0
67 0 0 0
68 0 0 0
69 1 0 0
70 0 1 0
71 0 0 1
72 0 0 0
73 0 0 0
74 0 0 0
75 0 0 0
76 0 0 0
77 0 0 0
78 0 0 0
79 0 0 0
80 0 0 0
81 1 0 0
82 0 1 0
83 0 0 1
84 0 0 0
85 0 0 0
86 0 0 0
87 0 0 0
88 0 0 0
89 0 0 0
90 0 0 0
91 0 0 0
92 0 0 0
93 1 0 0
94 0 1 0
95 0 0 1
96 0 0 0
97 0 0 0
98 0 0 0
99 0 0 0
100 0 0 0
101 0 0 0
102 0 0 0
103 0 0 0
104 0 0 0
105 1 0 0
106 0 1 0
107 0 0 1
108 0 0 0
109 0 0 0
110 0 0 0
111 0 0 0
112 0 0 0
113 0 0 0
114 0 0 0
115 0 0 0
116 0 0 0
117 1 0 0
118 0 1 0
119 0 0 1
120 0 0 0
121 0 0 0
122 0 0 0
123 0 0 0
124 0 0 0
125 0 0 0
126 0 0 0
127 0 0 0
128 0 0 0
129 1 0 0
130 0 1 0
131 0 0 1
132 0 0 0
133 0 0 0
134 0 0 0
135 0 0 0
136 0 0 0
137 0 0 0
138 0 0 0
139 0 0 0
140 0 0 0
141 1 0 0
142 0 1 0
143 0 0 1
144 0 0 0
145 0 0 0
146 0 0 0
147 0 0 0
148 0 0 0
149 0 0 0
150 0 0 0
151 0 0 0
152 0 0 0
153 1 0 0
154 0 1 0
155 0 0 1
156 0 0 0
157 0 0 0
158 0 0 0
159 0 0 0
160 0 0 0
161 0 0 0
162 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Learning Connected Depression `Belonging\r`
14.663348 0.062587 0.050153 -0.346010 0.024722
M1 M2 M3 M4 M5
-1.050757 -0.724925 0.003893 -1.170936 -0.062679
M6 M7 M8 M9 M10
-1.211487 0.095805 -0.936190 0.302874 -0.975158
M11
-1.126896
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9224 -1.2659 0.2464 1.0577 4.2052
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.663348 2.380334 6.160 6.7e-09 ***
Learning 0.062587 0.072798 0.860 0.391
Connected 0.050153 0.048297 1.038 0.301
Depression -0.346010 0.054574 -6.340 2.7e-09 ***
`Belonging\r` 0.024722 0.015359 1.610 0.110
M1 -1.050757 0.752902 -1.396 0.165
M2 -0.724925 0.750422 -0.966 0.336
M3 0.003893 0.766342 0.005 0.996
M4 -1.170936 0.752593 -1.556 0.122
M5 -0.062679 0.753656 -0.083 0.934
M6 -1.211487 0.752536 -1.610 0.110
M7 0.095805 0.761292 0.126 0.900
M8 -0.936190 0.768661 -1.218 0.225
M9 0.302874 0.765696 0.396 0.693
M10 -0.975158 0.764683 -1.275 0.204
M11 -1.126896 0.765763 -1.472 0.143
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.935 on 146 degrees of freedom
Multiple R-squared: 0.3787, Adjusted R-squared: 0.3148
F-statistic: 5.932 on 15 and 146 DF, p-value: 1.646e-09
> 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,] 0.1801475 0.3602949983 8.198525e-01
[2,] 0.8027972 0.3944055654 1.972028e-01
[3,] 0.9466693 0.1066613683 5.333068e-02
[4,] 0.9259315 0.1481369113 7.406846e-02
[5,] 0.8917598 0.2164803150 1.082402e-01
[6,] 0.9715650 0.0568700802 2.843504e-02
[7,] 0.9866795 0.0266409705 1.332049e-02
[8,] 0.9999392 0.0001215184 6.075922e-05
[9,] 0.9998772 0.0002455351 1.227676e-04
[10,] 0.9997823 0.0004354386 2.177193e-04
[11,] 0.9995872 0.0008255508 4.127754e-04
[12,] 0.9999227 0.0001545003 7.725014e-05
[13,] 0.9998480 0.0003040725 1.520362e-04
[14,] 0.9997335 0.0005330139 2.665069e-04
[15,] 0.9995287 0.0009426286 4.713143e-04
[16,] 0.9992063 0.0015874630 7.937315e-04
[17,] 0.9988246 0.0023508216 1.175411e-03
[18,] 0.9981424 0.0037151215 1.857561e-03
[19,] 0.9974456 0.0051088477 2.554424e-03
[20,] 0.9963897 0.0072206485 3.610324e-03
[21,] 0.9958475 0.0083050274 4.152514e-03
[22,] 0.9947466 0.0105068137 5.253407e-03
[23,] 0.9929270 0.0141459151 7.072958e-03
[24,] 0.9930704 0.0138591083 6.929554e-03
[25,] 0.9902810 0.0194380686 9.719034e-03
[26,] 0.9881216 0.0237567106 1.187836e-02
[27,] 0.9951541 0.0096918367 4.845918e-03
[28,] 0.9950957 0.0098085553 4.904278e-03
[29,] 0.9927251 0.0145498024 7.274901e-03
[30,] 0.9902774 0.0194452273 9.722614e-03
[31,] 0.9960105 0.0079789367 3.989468e-03
[32,] 0.9954442 0.0091115598 4.555780e-03
[33,] 0.9934454 0.0131092125 6.554606e-03
[34,] 0.9914493 0.0171013772 8.550689e-03
[35,] 0.9899700 0.0200599748 1.002999e-02
[36,] 0.9882471 0.0235058610 1.175293e-02
[37,] 0.9899576 0.0200847612 1.004238e-02
[38,] 0.9865329 0.0269342765 1.346714e-02
[39,] 0.9816843 0.0366314903 1.831575e-02
[40,] 0.9769898 0.0460204611 2.301023e-02
[41,] 0.9833635 0.0332730832 1.663654e-02
[42,] 0.9864336 0.0271327001 1.356635e-02
[43,] 0.9873788 0.0252424109 1.262121e-02
[44,] 0.9858976 0.0282048743 1.410244e-02
[45,] 0.9953882 0.0092236757 4.611838e-03
[46,] 0.9948128 0.0103744321 5.187216e-03
[47,] 0.9930525 0.0138951000 6.947550e-03
[48,] 0.9988345 0.0023310551 1.165528e-03
[49,] 0.9991374 0.0017252494 8.626247e-04
[50,] 0.9991998 0.0016004640 8.002320e-04
[51,] 0.9987968 0.0024064067 1.203203e-03
[52,] 0.9986622 0.0026756789 1.337839e-03
[53,] 0.9981209 0.0037582439 1.879122e-03
[54,] 0.9986260 0.0027480105 1.374005e-03
[55,] 0.9981743 0.0036514042 1.825702e-03
[56,] 0.9975484 0.0049032829 2.451641e-03
[57,] 0.9984435 0.0031129603 1.556480e-03
[58,] 0.9978309 0.0043382214 2.169111e-03
[59,] 0.9982737 0.0034525610 1.726280e-03
[60,] 0.9977606 0.0044787543 2.239377e-03
[61,] 0.9969029 0.0061942256 3.097113e-03
[62,] 0.9969594 0.0060811669 3.040583e-03
[63,] 0.9960171 0.0079657014 3.982851e-03
[64,] 0.9954726 0.0090548886 4.527444e-03
[65,] 0.9935040 0.0129920713 6.496036e-03
[66,] 0.9914421 0.0171157186 8.557859e-03
[67,] 0.9896637 0.0206726155 1.033631e-02
[68,] 0.9861475 0.0277050288 1.385251e-02
[69,] 0.9821133 0.0357734362 1.788672e-02
[70,] 0.9764162 0.0471675453 2.358377e-02
[71,] 0.9697969 0.0604061495 3.020307e-02
[72,] 0.9806549 0.0386902707 1.934514e-02
[73,] 0.9844104 0.0311791088 1.558955e-02
[74,] 0.9787573 0.0424853422 2.124267e-02
[75,] 0.9718248 0.0563503921 2.817520e-02
[76,] 0.9657456 0.0685088546 3.425443e-02
[77,] 0.9602511 0.0794978514 3.974893e-02
[78,] 0.9482955 0.1034089623 5.170448e-02
[79,] 0.9341011 0.1317978375 6.589892e-02
[80,] 0.9291731 0.1416538297 7.082691e-02
[81,] 0.9155079 0.1689842310 8.449212e-02
[82,] 0.8996304 0.2007391994 1.003696e-01
[83,] 0.8932178 0.2135644955 1.067822e-01
[84,] 0.8702600 0.2594800796 1.297400e-01
[85,] 0.8415718 0.3168563986 1.584282e-01
[86,] 0.9053711 0.1892577152 9.462886e-02
[87,] 0.8841099 0.2317802953 1.158901e-01
[88,] 0.8951600 0.2096799431 1.048400e-01
[89,] 0.8862391 0.2275217085 1.137609e-01
[90,] 0.8906085 0.2187829224 1.093915e-01
[91,] 0.9075312 0.1849376613 9.246883e-02
[92,] 0.8836525 0.2326950716 1.163475e-01
[93,] 0.8570047 0.2859906353 1.429953e-01
[94,] 0.8470084 0.3059832984 1.529916e-01
[95,] 0.8533326 0.2933347343 1.466674e-01
[96,] 0.8993614 0.2012771355 1.006386e-01
[97,] 0.9355602 0.1288796467 6.443982e-02
[98,] 0.9228440 0.1543119269 7.715596e-02
[99,] 0.8982467 0.2035065162 1.017533e-01
[100,] 0.8680572 0.2638855227 1.319428e-01
[101,] 0.8333571 0.3332858373 1.666429e-01
[102,] 0.7975853 0.4048293708 2.024147e-01
[103,] 0.7499361 0.5001277817 2.500639e-01
[104,] 0.7252966 0.5494068535 2.747034e-01
[105,] 0.7094318 0.5811364188 2.905682e-01
[106,] 0.6549742 0.6900515227 3.450258e-01
[107,] 0.6237031 0.7525938800 3.762969e-01
[108,] 0.6049514 0.7900971859 3.950486e-01
[109,] 0.5356468 0.9287064271 4.643532e-01
[110,] 0.5321637 0.9356726580 4.678363e-01
[111,] 0.6286194 0.7427611026 3.713806e-01
[112,] 0.5909485 0.8181029014 4.090515e-01
[113,] 0.5243808 0.9512384767 4.756192e-01
[114,] 0.5295221 0.9409558888 4.704779e-01
[115,] 0.4491199 0.8982398257 5.508801e-01
[116,] 0.3837642 0.7675284726 6.162358e-01
[117,] 0.3186387 0.6372773527 6.813613e-01
[118,] 0.4689795 0.9379589143 5.310205e-01
[119,] 0.6128179 0.7743641861 3.871821e-01
[120,] 0.5613381 0.8773237631 4.386619e-01
[121,] 0.4883474 0.9766947879 5.116526e-01
[122,] 0.4217108 0.8434216930 5.782892e-01
[123,] 0.7420585 0.5158830240 2.579415e-01
[124,] 0.6134342 0.7731316615 3.865658e-01
[125,] 0.6833927 0.6332146043 3.166073e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1y3df1322158446.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2bsg11322158446.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/32sj11322158446.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4n8tx1322158446.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/58ns71322158446.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 162
Frequency = 1
1 2 3 4 5
0.3593384568 2.7842148127 -3.1485113052 -1.4902304167 4.2052301663
6 7 8 9 10
4.2029382113 2.3976621262 -0.5428335810 -1.0172953207 1.1365823978
11 12 13 14 15
2.0633832874 2.9628258128 -2.9860405193 2.6133502877 1.9229991359
16 17 18 19 20
1.3741345199 -0.3550116614 2.0211388711 -1.9638134559 2.7555135139
21 22 23 24 25
1.2178911188 -2.0812972877 0.1355401995 -2.0607117595 2.0474602071
26 27 28 29 30
-6.9223667862 0.4530781304 1.3980485344 0.6586957127 -2.1915427247
31 32 33 34 35
-0.1163876088 1.0228147239 1.0067173399 0.3346595420 0.5508112697
36 37 38 39 40
0.3064472274 -1.3301995115 0.7766897793 1.4581424753 -1.4948953402
41 42 43 44 45
-1.1730800121 2.9566909059 -0.4836333578 -0.8001528475 -3.6479820460
46 47 48 49 50
-2.4152935498 0.1886557752 -0.3052598028 3.9412121134 -1.7685153604
51 52 53 54 55
0.4065702265 1.2142230256 -1.2859324356 -0.8470963361 -2.6787247780
56 57 58 59 60
0.8141910906 0.7451004031 0.3136817158 -2.6755440372 -1.9605034501
61 62 63 64 65
-2.2583063697 -1.5971232947 -4.1253269605 1.6352071953 0.8558613424
66 67 68 69 70
-4.4428635640 -2.4407271174 -2.0109161099 0.2673905939 1.6933667550
71 72 73 74 75
0.7735993649 2.7608370408 1.0512709051 -0.2165407667 -2.6443912830
76 77 78 79 80
0.0840513171 2.3098481710 1.0178793399 0.6174528032 -1.8051277352
81 82 83 84 85
-0.7696089601 1.9228319165 0.0656100901 0.8895803779 1.2158270387
86 87 88 89 90
-0.0003459062 0.1946870619 0.2974316146 -0.2013512694 -3.1640523095
91 92 93 94 95
2.6284674752 0.2584988195 -0.1284518241 0.9310375092 -1.3221966961
96 97 98 99 100
0.2076221790 -0.6393211342 -0.8173949071 1.2793144220 0.6076846120
101 102 103 104 105
1.0733324175 -0.5774792776 0.2343301663 -3.0964179797 0.9604410381
106 107 108 109 110
-2.6148678271 1.4209725045 1.0598636367 -2.5643469641 0.3209604253
111 112 113 114 115
0.6436967193 -1.6878416615 -2.6389881905 2.3803104887 2.9073704705
116 117 118 119 120
0.6246029466 0.0116460337 0.4979620287 -0.5968780995 -0.6054048060
121 122 123 124 125
-0.2261257682 0.4706930952 -0.7808546623 -0.4602766179 -0.1931371347
126 127 128 129 130
-1.1549406858 -0.1108458920 1.6327800765 3.2435338685 1.5235383004
131 132 133 134 135
-1.0919094773 -2.4049693978 -0.0636179343 2.5396071968 -0.0764474138
136 137 138 139 140
2.7808054129 0.8046429680 1.1907685386 -1.7510432508 0.9903264418
141 142 143 144 145
-1.5753761768 0.8306178818 2.7876277984 -1.2059057189 1.0797833650
146 147 148 149 150
1.0354167216 0.8745281995 -2.0967596414 -3.2550146700 -2.2513815606
151 152 153 154 155
0.7598924192 0.1567206406 -0.3140060681 -2.0728193826 -2.2996719796
156 157 158 159 160
0.3555786605 0.3730661151 0.7813547028 3.5425152540 -2.1615825540
161 162
-0.8050954043 0.8596301028
> postscript(file="/var/wessaorg/rcomp/tmp/6xqpl1322158446.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 0.3593384568 NA
1 2.7842148127 0.3593384568
2 -3.1485113052 2.7842148127
3 -1.4902304167 -3.1485113052
4 4.2052301663 -1.4902304167
5 4.2029382113 4.2052301663
6 2.3976621262 4.2029382113
7 -0.5428335810 2.3976621262
8 -1.0172953207 -0.5428335810
9 1.1365823978 -1.0172953207
10 2.0633832874 1.1365823978
11 2.9628258128 2.0633832874
12 -2.9860405193 2.9628258128
13 2.6133502877 -2.9860405193
14 1.9229991359 2.6133502877
15 1.3741345199 1.9229991359
16 -0.3550116614 1.3741345199
17 2.0211388711 -0.3550116614
18 -1.9638134559 2.0211388711
19 2.7555135139 -1.9638134559
20 1.2178911188 2.7555135139
21 -2.0812972877 1.2178911188
22 0.1355401995 -2.0812972877
23 -2.0607117595 0.1355401995
24 2.0474602071 -2.0607117595
25 -6.9223667862 2.0474602071
26 0.4530781304 -6.9223667862
27 1.3980485344 0.4530781304
28 0.6586957127 1.3980485344
29 -2.1915427247 0.6586957127
30 -0.1163876088 -2.1915427247
31 1.0228147239 -0.1163876088
32 1.0067173399 1.0228147239
33 0.3346595420 1.0067173399
34 0.5508112697 0.3346595420
35 0.3064472274 0.5508112697
36 -1.3301995115 0.3064472274
37 0.7766897793 -1.3301995115
38 1.4581424753 0.7766897793
39 -1.4948953402 1.4581424753
40 -1.1730800121 -1.4948953402
41 2.9566909059 -1.1730800121
42 -0.4836333578 2.9566909059
43 -0.8001528475 -0.4836333578
44 -3.6479820460 -0.8001528475
45 -2.4152935498 -3.6479820460
46 0.1886557752 -2.4152935498
47 -0.3052598028 0.1886557752
48 3.9412121134 -0.3052598028
49 -1.7685153604 3.9412121134
50 0.4065702265 -1.7685153604
51 1.2142230256 0.4065702265
52 -1.2859324356 1.2142230256
53 -0.8470963361 -1.2859324356
54 -2.6787247780 -0.8470963361
55 0.8141910906 -2.6787247780
56 0.7451004031 0.8141910906
57 0.3136817158 0.7451004031
58 -2.6755440372 0.3136817158
59 -1.9605034501 -2.6755440372
60 -2.2583063697 -1.9605034501
61 -1.5971232947 -2.2583063697
62 -4.1253269605 -1.5971232947
63 1.6352071953 -4.1253269605
64 0.8558613424 1.6352071953
65 -4.4428635640 0.8558613424
66 -2.4407271174 -4.4428635640
67 -2.0109161099 -2.4407271174
68 0.2673905939 -2.0109161099
69 1.6933667550 0.2673905939
70 0.7735993649 1.6933667550
71 2.7608370408 0.7735993649
72 1.0512709051 2.7608370408
73 -0.2165407667 1.0512709051
74 -2.6443912830 -0.2165407667
75 0.0840513171 -2.6443912830
76 2.3098481710 0.0840513171
77 1.0178793399 2.3098481710
78 0.6174528032 1.0178793399
79 -1.8051277352 0.6174528032
80 -0.7696089601 -1.8051277352
81 1.9228319165 -0.7696089601
82 0.0656100901 1.9228319165
83 0.8895803779 0.0656100901
84 1.2158270387 0.8895803779
85 -0.0003459062 1.2158270387
86 0.1946870619 -0.0003459062
87 0.2974316146 0.1946870619
88 -0.2013512694 0.2974316146
89 -3.1640523095 -0.2013512694
90 2.6284674752 -3.1640523095
91 0.2584988195 2.6284674752
92 -0.1284518241 0.2584988195
93 0.9310375092 -0.1284518241
94 -1.3221966961 0.9310375092
95 0.2076221790 -1.3221966961
96 -0.6393211342 0.2076221790
97 -0.8173949071 -0.6393211342
98 1.2793144220 -0.8173949071
99 0.6076846120 1.2793144220
100 1.0733324175 0.6076846120
101 -0.5774792776 1.0733324175
102 0.2343301663 -0.5774792776
103 -3.0964179797 0.2343301663
104 0.9604410381 -3.0964179797
105 -2.6148678271 0.9604410381
106 1.4209725045 -2.6148678271
107 1.0598636367 1.4209725045
108 -2.5643469641 1.0598636367
109 0.3209604253 -2.5643469641
110 0.6436967193 0.3209604253
111 -1.6878416615 0.6436967193
112 -2.6389881905 -1.6878416615
113 2.3803104887 -2.6389881905
114 2.9073704705 2.3803104887
115 0.6246029466 2.9073704705
116 0.0116460337 0.6246029466
117 0.4979620287 0.0116460337
118 -0.5968780995 0.4979620287
119 -0.6054048060 -0.5968780995
120 -0.2261257682 -0.6054048060
121 0.4706930952 -0.2261257682
122 -0.7808546623 0.4706930952
123 -0.4602766179 -0.7808546623
124 -0.1931371347 -0.4602766179
125 -1.1549406858 -0.1931371347
126 -0.1108458920 -1.1549406858
127 1.6327800765 -0.1108458920
128 3.2435338685 1.6327800765
129 1.5235383004 3.2435338685
130 -1.0919094773 1.5235383004
131 -2.4049693978 -1.0919094773
132 -0.0636179343 -2.4049693978
133 2.5396071968 -0.0636179343
134 -0.0764474138 2.5396071968
135 2.7808054129 -0.0764474138
136 0.8046429680 2.7808054129
137 1.1907685386 0.8046429680
138 -1.7510432508 1.1907685386
139 0.9903264418 -1.7510432508
140 -1.5753761768 0.9903264418
141 0.8306178818 -1.5753761768
142 2.7876277984 0.8306178818
143 -1.2059057189 2.7876277984
144 1.0797833650 -1.2059057189
145 1.0354167216 1.0797833650
146 0.8745281995 1.0354167216
147 -2.0967596414 0.8745281995
148 -3.2550146700 -2.0967596414
149 -2.2513815606 -3.2550146700
150 0.7598924192 -2.2513815606
151 0.1567206406 0.7598924192
152 -0.3140060681 0.1567206406
153 -2.0728193826 -0.3140060681
154 -2.2996719796 -2.0728193826
155 0.3555786605 -2.2996719796
156 0.3730661151 0.3555786605
157 0.7813547028 0.3730661151
158 3.5425152540 0.7813547028
159 -2.1615825540 3.5425152540
160 -0.8050954043 -2.1615825540
161 0.8596301028 -0.8050954043
162 NA 0.8596301028
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.7842148127 0.3593384568
[2,] -3.1485113052 2.7842148127
[3,] -1.4902304167 -3.1485113052
[4,] 4.2052301663 -1.4902304167
[5,] 4.2029382113 4.2052301663
[6,] 2.3976621262 4.2029382113
[7,] -0.5428335810 2.3976621262
[8,] -1.0172953207 -0.5428335810
[9,] 1.1365823978 -1.0172953207
[10,] 2.0633832874 1.1365823978
[11,] 2.9628258128 2.0633832874
[12,] -2.9860405193 2.9628258128
[13,] 2.6133502877 -2.9860405193
[14,] 1.9229991359 2.6133502877
[15,] 1.3741345199 1.9229991359
[16,] -0.3550116614 1.3741345199
[17,] 2.0211388711 -0.3550116614
[18,] -1.9638134559 2.0211388711
[19,] 2.7555135139 -1.9638134559
[20,] 1.2178911188 2.7555135139
[21,] -2.0812972877 1.2178911188
[22,] 0.1355401995 -2.0812972877
[23,] -2.0607117595 0.1355401995
[24,] 2.0474602071 -2.0607117595
[25,] -6.9223667862 2.0474602071
[26,] 0.4530781304 -6.9223667862
[27,] 1.3980485344 0.4530781304
[28,] 0.6586957127 1.3980485344
[29,] -2.1915427247 0.6586957127
[30,] -0.1163876088 -2.1915427247
[31,] 1.0228147239 -0.1163876088
[32,] 1.0067173399 1.0228147239
[33,] 0.3346595420 1.0067173399
[34,] 0.5508112697 0.3346595420
[35,] 0.3064472274 0.5508112697
[36,] -1.3301995115 0.3064472274
[37,] 0.7766897793 -1.3301995115
[38,] 1.4581424753 0.7766897793
[39,] -1.4948953402 1.4581424753
[40,] -1.1730800121 -1.4948953402
[41,] 2.9566909059 -1.1730800121
[42,] -0.4836333578 2.9566909059
[43,] -0.8001528475 -0.4836333578
[44,] -3.6479820460 -0.8001528475
[45,] -2.4152935498 -3.6479820460
[46,] 0.1886557752 -2.4152935498
[47,] -0.3052598028 0.1886557752
[48,] 3.9412121134 -0.3052598028
[49,] -1.7685153604 3.9412121134
[50,] 0.4065702265 -1.7685153604
[51,] 1.2142230256 0.4065702265
[52,] -1.2859324356 1.2142230256
[53,] -0.8470963361 -1.2859324356
[54,] -2.6787247780 -0.8470963361
[55,] 0.8141910906 -2.6787247780
[56,] 0.7451004031 0.8141910906
[57,] 0.3136817158 0.7451004031
[58,] -2.6755440372 0.3136817158
[59,] -1.9605034501 -2.6755440372
[60,] -2.2583063697 -1.9605034501
[61,] -1.5971232947 -2.2583063697
[62,] -4.1253269605 -1.5971232947
[63,] 1.6352071953 -4.1253269605
[64,] 0.8558613424 1.6352071953
[65,] -4.4428635640 0.8558613424
[66,] -2.4407271174 -4.4428635640
[67,] -2.0109161099 -2.4407271174
[68,] 0.2673905939 -2.0109161099
[69,] 1.6933667550 0.2673905939
[70,] 0.7735993649 1.6933667550
[71,] 2.7608370408 0.7735993649
[72,] 1.0512709051 2.7608370408
[73,] -0.2165407667 1.0512709051
[74,] -2.6443912830 -0.2165407667
[75,] 0.0840513171 -2.6443912830
[76,] 2.3098481710 0.0840513171
[77,] 1.0178793399 2.3098481710
[78,] 0.6174528032 1.0178793399
[79,] -1.8051277352 0.6174528032
[80,] -0.7696089601 -1.8051277352
[81,] 1.9228319165 -0.7696089601
[82,] 0.0656100901 1.9228319165
[83,] 0.8895803779 0.0656100901
[84,] 1.2158270387 0.8895803779
[85,] -0.0003459062 1.2158270387
[86,] 0.1946870619 -0.0003459062
[87,] 0.2974316146 0.1946870619
[88,] -0.2013512694 0.2974316146
[89,] -3.1640523095 -0.2013512694
[90,] 2.6284674752 -3.1640523095
[91,] 0.2584988195 2.6284674752
[92,] -0.1284518241 0.2584988195
[93,] 0.9310375092 -0.1284518241
[94,] -1.3221966961 0.9310375092
[95,] 0.2076221790 -1.3221966961
[96,] -0.6393211342 0.2076221790
[97,] -0.8173949071 -0.6393211342
[98,] 1.2793144220 -0.8173949071
[99,] 0.6076846120 1.2793144220
[100,] 1.0733324175 0.6076846120
[101,] -0.5774792776 1.0733324175
[102,] 0.2343301663 -0.5774792776
[103,] -3.0964179797 0.2343301663
[104,] 0.9604410381 -3.0964179797
[105,] -2.6148678271 0.9604410381
[106,] 1.4209725045 -2.6148678271
[107,] 1.0598636367 1.4209725045
[108,] -2.5643469641 1.0598636367
[109,] 0.3209604253 -2.5643469641
[110,] 0.6436967193 0.3209604253
[111,] -1.6878416615 0.6436967193
[112,] -2.6389881905 -1.6878416615
[113,] 2.3803104887 -2.6389881905
[114,] 2.9073704705 2.3803104887
[115,] 0.6246029466 2.9073704705
[116,] 0.0116460337 0.6246029466
[117,] 0.4979620287 0.0116460337
[118,] -0.5968780995 0.4979620287
[119,] -0.6054048060 -0.5968780995
[120,] -0.2261257682 -0.6054048060
[121,] 0.4706930952 -0.2261257682
[122,] -0.7808546623 0.4706930952
[123,] -0.4602766179 -0.7808546623
[124,] -0.1931371347 -0.4602766179
[125,] -1.1549406858 -0.1931371347
[126,] -0.1108458920 -1.1549406858
[127,] 1.6327800765 -0.1108458920
[128,] 3.2435338685 1.6327800765
[129,] 1.5235383004 3.2435338685
[130,] -1.0919094773 1.5235383004
[131,] -2.4049693978 -1.0919094773
[132,] -0.0636179343 -2.4049693978
[133,] 2.5396071968 -0.0636179343
[134,] -0.0764474138 2.5396071968
[135,] 2.7808054129 -0.0764474138
[136,] 0.8046429680 2.7808054129
[137,] 1.1907685386 0.8046429680
[138,] -1.7510432508 1.1907685386
[139,] 0.9903264418 -1.7510432508
[140,] -1.5753761768 0.9903264418
[141,] 0.8306178818 -1.5753761768
[142,] 2.7876277984 0.8306178818
[143,] -1.2059057189 2.7876277984
[144,] 1.0797833650 -1.2059057189
[145,] 1.0354167216 1.0797833650
[146,] 0.8745281995 1.0354167216
[147,] -2.0967596414 0.8745281995
[148,] -3.2550146700 -2.0967596414
[149,] -2.2513815606 -3.2550146700
[150,] 0.7598924192 -2.2513815606
[151,] 0.1567206406 0.7598924192
[152,] -0.3140060681 0.1567206406
[153,] -2.0728193826 -0.3140060681
[154,] -2.2996719796 -2.0728193826
[155,] 0.3555786605 -2.2996719796
[156,] 0.3730661151 0.3555786605
[157,] 0.7813547028 0.3730661151
[158,] 3.5425152540 0.7813547028
[159,] -2.1615825540 3.5425152540
[160,] -0.8050954043 -2.1615825540
[161,] 0.8596301028 -0.8050954043
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.7842148127 0.3593384568
2 -3.1485113052 2.7842148127
3 -1.4902304167 -3.1485113052
4 4.2052301663 -1.4902304167
5 4.2029382113 4.2052301663
6 2.3976621262 4.2029382113
7 -0.5428335810 2.3976621262
8 -1.0172953207 -0.5428335810
9 1.1365823978 -1.0172953207
10 2.0633832874 1.1365823978
11 2.9628258128 2.0633832874
12 -2.9860405193 2.9628258128
13 2.6133502877 -2.9860405193
14 1.9229991359 2.6133502877
15 1.3741345199 1.9229991359
16 -0.3550116614 1.3741345199
17 2.0211388711 -0.3550116614
18 -1.9638134559 2.0211388711
19 2.7555135139 -1.9638134559
20 1.2178911188 2.7555135139
21 -2.0812972877 1.2178911188
22 0.1355401995 -2.0812972877
23 -2.0607117595 0.1355401995
24 2.0474602071 -2.0607117595
25 -6.9223667862 2.0474602071
26 0.4530781304 -6.9223667862
27 1.3980485344 0.4530781304
28 0.6586957127 1.3980485344
29 -2.1915427247 0.6586957127
30 -0.1163876088 -2.1915427247
31 1.0228147239 -0.1163876088
32 1.0067173399 1.0228147239
33 0.3346595420 1.0067173399
34 0.5508112697 0.3346595420
35 0.3064472274 0.5508112697
36 -1.3301995115 0.3064472274
37 0.7766897793 -1.3301995115
38 1.4581424753 0.7766897793
39 -1.4948953402 1.4581424753
40 -1.1730800121 -1.4948953402
41 2.9566909059 -1.1730800121
42 -0.4836333578 2.9566909059
43 -0.8001528475 -0.4836333578
44 -3.6479820460 -0.8001528475
45 -2.4152935498 -3.6479820460
46 0.1886557752 -2.4152935498
47 -0.3052598028 0.1886557752
48 3.9412121134 -0.3052598028
49 -1.7685153604 3.9412121134
50 0.4065702265 -1.7685153604
51 1.2142230256 0.4065702265
52 -1.2859324356 1.2142230256
53 -0.8470963361 -1.2859324356
54 -2.6787247780 -0.8470963361
55 0.8141910906 -2.6787247780
56 0.7451004031 0.8141910906
57 0.3136817158 0.7451004031
58 -2.6755440372 0.3136817158
59 -1.9605034501 -2.6755440372
60 -2.2583063697 -1.9605034501
61 -1.5971232947 -2.2583063697
62 -4.1253269605 -1.5971232947
63 1.6352071953 -4.1253269605
64 0.8558613424 1.6352071953
65 -4.4428635640 0.8558613424
66 -2.4407271174 -4.4428635640
67 -2.0109161099 -2.4407271174
68 0.2673905939 -2.0109161099
69 1.6933667550 0.2673905939
70 0.7735993649 1.6933667550
71 2.7608370408 0.7735993649
72 1.0512709051 2.7608370408
73 -0.2165407667 1.0512709051
74 -2.6443912830 -0.2165407667
75 0.0840513171 -2.6443912830
76 2.3098481710 0.0840513171
77 1.0178793399 2.3098481710
78 0.6174528032 1.0178793399
79 -1.8051277352 0.6174528032
80 -0.7696089601 -1.8051277352
81 1.9228319165 -0.7696089601
82 0.0656100901 1.9228319165
83 0.8895803779 0.0656100901
84 1.2158270387 0.8895803779
85 -0.0003459062 1.2158270387
86 0.1946870619 -0.0003459062
87 0.2974316146 0.1946870619
88 -0.2013512694 0.2974316146
89 -3.1640523095 -0.2013512694
90 2.6284674752 -3.1640523095
91 0.2584988195 2.6284674752
92 -0.1284518241 0.2584988195
93 0.9310375092 -0.1284518241
94 -1.3221966961 0.9310375092
95 0.2076221790 -1.3221966961
96 -0.6393211342 0.2076221790
97 -0.8173949071 -0.6393211342
98 1.2793144220 -0.8173949071
99 0.6076846120 1.2793144220
100 1.0733324175 0.6076846120
101 -0.5774792776 1.0733324175
102 0.2343301663 -0.5774792776
103 -3.0964179797 0.2343301663
104 0.9604410381 -3.0964179797
105 -2.6148678271 0.9604410381
106 1.4209725045 -2.6148678271
107 1.0598636367 1.4209725045
108 -2.5643469641 1.0598636367
109 0.3209604253 -2.5643469641
110 0.6436967193 0.3209604253
111 -1.6878416615 0.6436967193
112 -2.6389881905 -1.6878416615
113 2.3803104887 -2.6389881905
114 2.9073704705 2.3803104887
115 0.6246029466 2.9073704705
116 0.0116460337 0.6246029466
117 0.4979620287 0.0116460337
118 -0.5968780995 0.4979620287
119 -0.6054048060 -0.5968780995
120 -0.2261257682 -0.6054048060
121 0.4706930952 -0.2261257682
122 -0.7808546623 0.4706930952
123 -0.4602766179 -0.7808546623
124 -0.1931371347 -0.4602766179
125 -1.1549406858 -0.1931371347
126 -0.1108458920 -1.1549406858
127 1.6327800765 -0.1108458920
128 3.2435338685 1.6327800765
129 1.5235383004 3.2435338685
130 -1.0919094773 1.5235383004
131 -2.4049693978 -1.0919094773
132 -0.0636179343 -2.4049693978
133 2.5396071968 -0.0636179343
134 -0.0764474138 2.5396071968
135 2.7808054129 -0.0764474138
136 0.8046429680 2.7808054129
137 1.1907685386 0.8046429680
138 -1.7510432508 1.1907685386
139 0.9903264418 -1.7510432508
140 -1.5753761768 0.9903264418
141 0.8306178818 -1.5753761768
142 2.7876277984 0.8306178818
143 -1.2059057189 2.7876277984
144 1.0797833650 -1.2059057189
145 1.0354167216 1.0797833650
146 0.8745281995 1.0354167216
147 -2.0967596414 0.8745281995
148 -3.2550146700 -2.0967596414
149 -2.2513815606 -3.2550146700
150 0.7598924192 -2.2513815606
151 0.1567206406 0.7598924192
152 -0.3140060681 0.1567206406
153 -2.0728193826 -0.3140060681
154 -2.2996719796 -2.0728193826
155 0.3555786605 -2.2996719796
156 0.3730661151 0.3555786605
157 0.7813547028 0.3730661151
158 3.5425152540 0.7813547028
159 -2.1615825540 3.5425152540
160 -0.8050954043 -2.1615825540
161 0.8596301028 -0.8050954043
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7jv0x1322158446.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8mwrl1322158446.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9z50b1322158446.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10xhdz1322158446.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11aw1x1322158446.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/124uvd1322158446.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13inlf1322158446.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14ea1x1322158446.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/1561us1322158446.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16ymey1322158446.tab")
+ }
>
> try(system("convert tmp/1y3df1322158446.ps tmp/1y3df1322158446.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bsg11322158446.ps tmp/2bsg11322158446.png",intern=TRUE))
character(0)
> try(system("convert tmp/32sj11322158446.ps tmp/32sj11322158446.png",intern=TRUE))
character(0)
> try(system("convert tmp/4n8tx1322158446.ps tmp/4n8tx1322158446.png",intern=TRUE))
character(0)
> try(system("convert tmp/58ns71322158446.ps tmp/58ns71322158446.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xqpl1322158446.ps tmp/6xqpl1322158446.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jv0x1322158446.ps tmp/7jv0x1322158446.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mwrl1322158446.ps tmp/8mwrl1322158446.png",intern=TRUE))
character(0)
> try(system("convert tmp/9z50b1322158446.ps tmp/9z50b1322158446.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xhdz1322158446.ps tmp/10xhdz1322158446.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.988 0.512 5.619