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)
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(14
+ ,13
+ ,41
+ ,12
+ ,53
+ ,18
+ ,16
+ ,39
+ ,11
+ ,86
+ ,11
+ ,19
+ ,30
+ ,14
+ ,66
+ ,12
+ ,15
+ ,31
+ ,12
+ ,67
+ ,16
+ ,14
+ ,34
+ ,21
+ ,76
+ ,18
+ ,13
+ ,35
+ ,12
+ ,78
+ ,14
+ ,19
+ ,39
+ ,22
+ ,53
+ ,14
+ ,15
+ ,34
+ ,11
+ ,80
+ ,15
+ ,14
+ ,36
+ ,10
+ ,74
+ ,15
+ ,15
+ ,37
+ ,13
+ ,76
+ ,17
+ ,16
+ ,38
+ ,10
+ ,79
+ ,19
+ ,16
+ ,36
+ ,8
+ ,54
+ ,10
+ ,16
+ ,38
+ ,15
+ ,67
+ ,16
+ ,16
+ ,39
+ ,14
+ ,54
+ ,18
+ ,17
+ ,33
+ ,10
+ ,87
+ ,14
+ ,15
+ ,32
+ ,14
+ ,58
+ ,14
+ ,15
+ ,36
+ ,14
+ ,75
+ ,17
+ ,20
+ ,38
+ ,11
+ ,88
+ ,14
+ ,18
+ ,39
+ ,10
+ ,64
+ ,16
+ ,16
+ ,32
+ ,13
+ ,57
+ ,18
+ ,16
+ ,32
+ ,7
+ ,66
+ ,11
+ ,16
+ ,31
+ ,14
+ ,68
+ ,14
+ ,19
+ ,39
+ ,12
+ ,54
+ ,12
+ ,16
+ ,37
+ ,14
+ ,56
+ ,17
+ ,17
+ ,39
+ ,11
+ ,86
+ ,9
+ ,17
+ ,41
+ ,9
+ ,80
+ ,16
+ ,16
+ ,36
+ ,11
+ ,76
+ ,14
+ ,15
+ ,33
+ ,15
+ ,69
+ ,15
+ ,16
+ ,33
+ ,14
+ ,78
+ ,11
+ ,14
+ ,34
+ ,13
+ ,67
+ ,16
+ ,15
+ ,31
+ ,9
+ ,80
+ ,13
+ ,12
+ ,27
+ ,15
+ ,54
+ ,17
+ ,14
+ ,37
+ ,10
+ ,71
+ ,15
+ ,16
+ ,34
+ ,11
+ ,84
+ ,14
+ ,14
+ ,34
+ ,13
+ ,74
+ ,16
+ ,7
+ ,32
+ ,8
+ ,71
+ ,9
+ ,10
+ ,29
+ ,20
+ ,63
+ ,15
+ ,14
+ ,36
+ ,12
+ ,71
+ ,17
+ ,16
+ ,29
+ ,10
+ ,76
+ ,13
+ ,16
+ ,35
+ ,10
+ ,69
+ ,15
+ ,16
+ ,37
+ ,9
+ ,74
+ ,16
+ ,14
+ ,34
+ ,14
+ ,75
+ ,16
+ ,20
+ ,38
+ ,8
+ ,54
+ ,12
+ ,14
+ ,35
+ ,14
+ ,52
+ ,12
+ ,14
+ ,38
+ ,11
+ ,69
+ ,11
+ ,11
+ ,37
+ ,13
+ ,68
+ ,15
+ ,14
+ ,38
+ ,9
+ ,65
+ ,15
+ ,15
+ ,33
+ ,11
+ ,75
+ ,17
+ ,16
+ ,36
+ ,15
+ ,74
+ ,13
+ ,14
+ ,38
+ ,11
+ ,75
+ ,16
+ ,16
+ ,32
+ ,10
+ ,72
+ ,14
+ ,14
+ ,32
+ ,14
+ ,67
+ ,11
+ ,12
+ ,32
+ ,18
+ ,63
+ ,12
+ ,16
+ ,34
+ ,14
+ ,62
+ ,12
+ ,9
+ ,32
+ ,11
+ ,63
+ ,15
+ ,14
+ ,37
+ ,12
+ ,76
+ ,16
+ ,16
+ ,39
+ ,13
+ ,74
+ ,15
+ ,16
+ ,29
+ ,9
+ ,67
+ ,12
+ ,15
+ ,37
+ ,10
+ ,73
+ ,12
+ ,16
+ ,35
+ ,15
+ ,70
+ ,8
+ ,12
+ ,30
+ ,20
+ ,53
+ ,13
+ ,16
+ ,38
+ ,12
+ ,77
+ ,11
+ ,16
+ ,34
+ ,12
+ ,77
+ ,14
+ ,14
+ ,31
+ ,14
+ ,52
+ ,15
+ ,16
+ ,34
+ ,13
+ ,54
+ ,10
+ ,17
+ ,35
+ ,11
+ ,80
+ ,11
+ ,18
+ ,36
+ ,17
+ ,66
+ ,12
+ ,18
+ ,30
+ ,12
+ ,73
+ ,15
+ ,12
+ ,39
+ ,13
+ ,63
+ ,15
+ ,16
+ ,35
+ ,14
+ ,69
+ ,14
+ ,10
+ ,38
+ ,13
+ ,67
+ ,16
+ ,14
+ ,31
+ ,15
+ ,54
+ ,15
+ ,18
+ ,34
+ ,13
+ ,81
+ ,15
+ ,18
+ ,38
+ ,10
+ ,69
+ ,13
+ ,16
+ ,34
+ ,11
+ ,84
+ ,12
+ ,17
+ ,39
+ ,19
+ ,80
+ ,17
+ ,16
+ ,37
+ ,13
+ ,70
+ ,13
+ ,16
+ ,34
+ ,17
+ ,69
+ ,15
+ ,13
+ ,28
+ ,13
+ ,77
+ ,13
+ ,16
+ ,37
+ ,9
+ ,54
+ ,15
+ ,16
+ ,33
+ ,11
+ ,79
+ ,16
+ ,20
+ ,37
+ ,10
+ ,30
+ ,15
+ ,16
+ ,35
+ ,9
+ ,71
+ ,16
+ ,15
+ ,37
+ ,12
+ ,73
+ ,15
+ ,15
+ ,32
+ ,12
+ ,72
+ ,14
+ ,16
+ ,33
+ ,13
+ ,77
+ ,15
+ ,14
+ ,38
+ ,13
+ ,75
+ ,14
+ ,16
+ ,33
+ ,12
+ ,69
+ ,13
+ ,16
+ ,29
+ ,15
+ ,54
+ ,7
+ ,15
+ ,33
+ ,22
+ ,70
+ ,17
+ ,12
+ ,31
+ ,13
+ ,73
+ ,13
+ ,17
+ ,36
+ ,15
+ ,54
+ ,15
+ ,16
+ ,35
+ ,13
+ ,77
+ ,14
+ ,15
+ ,32
+ ,15
+ ,82
+ ,13
+ ,13
+ ,29
+ ,10
+ ,80
+ ,16
+ ,16
+ ,39
+ ,11
+ ,80
+ ,12
+ ,16
+ ,37
+ ,16
+ ,69
+ ,14
+ ,16
+ ,35
+ ,11
+ ,78
+ ,17
+ ,16
+ ,37
+ ,11
+ ,81
+ ,15
+ ,14
+ ,32
+ ,10
+ ,76
+ ,17
+ ,16
+ ,38
+ ,10
+ ,76
+ ,12
+ ,16
+ ,37
+ ,16
+ ,73
+ ,16
+ ,20
+ ,36
+ ,12
+ ,85
+ ,11
+ ,15
+ ,32
+ ,11
+ ,66
+ ,15
+ ,16
+ ,33
+ ,16
+ ,79
+ ,9
+ ,13
+ ,40
+ ,19
+ ,68
+ ,16
+ ,17
+ ,38
+ ,11
+ ,76
+ ,15
+ ,16
+ ,41
+ ,16
+ ,71
+ ,10
+ ,16
+ ,36
+ ,15
+ ,54
+ ,10
+ ,12
+ ,43
+ ,24
+ ,46
+ ,15
+ ,16
+ ,30
+ ,14
+ ,82
+ ,11
+ ,16
+ ,31
+ ,15
+ ,74
+ ,13
+ ,17
+ ,32
+ ,11
+ ,88
+ ,14
+ ,13
+ ,32
+ ,15
+ ,38
+ ,18
+ ,12
+ ,37
+ ,12
+ ,76
+ ,16
+ ,18
+ ,37
+ ,10
+ ,86
+ ,14
+ ,14
+ ,33
+ ,14
+ ,54
+ ,14
+ ,14
+ ,34
+ ,13
+ ,70
+ ,14
+ ,13
+ ,33
+ ,9
+ ,69
+ ,14
+ ,16
+ ,38
+ ,15
+ ,90
+ ,12
+ ,13
+ ,33
+ ,15
+ ,54
+ ,14
+ ,16
+ ,31
+ ,14
+ ,76
+ ,15
+ ,13
+ ,38
+ ,11
+ ,89
+ ,15
+ ,16
+ ,37
+ ,8
+ ,76
+ ,15
+ ,15
+ ,33
+ ,11
+ ,73
+ ,13
+ ,16
+ ,31
+ ,11
+ ,79
+ ,17
+ ,15
+ ,39
+ ,8
+ ,90
+ ,17
+ ,17
+ ,44
+ ,10
+ ,74
+ ,19
+ ,15
+ ,33
+ ,11
+ ,81
+ ,15
+ ,12
+ ,35
+ ,13
+ ,72
+ ,13
+ ,16
+ ,32
+ ,11
+ ,71
+ ,9
+ ,10
+ ,28
+ ,20
+ ,66
+ ,15
+ ,16
+ ,40
+ ,10
+ ,77
+ ,15
+ ,12
+ ,27
+ ,15
+ ,65
+ ,15
+ ,14
+ ,37
+ ,12
+ ,74
+ ,16
+ ,15
+ ,32
+ ,14
+ ,82
+ ,11
+ ,13
+ ,28
+ ,23
+ ,54
+ ,14
+ ,15
+ ,34
+ ,14
+ ,63
+ ,11
+ ,11
+ ,30
+ ,16
+ ,54
+ ,15
+ ,12
+ ,35
+ ,11
+ ,64
+ ,13
+ ,8
+ ,31
+ ,12
+ ,69
+ ,15
+ ,16
+ ,32
+ ,10
+ ,54
+ ,16
+ ,15
+ ,30
+ ,14
+ ,84
+ ,14
+ ,17
+ ,30
+ ,12
+ ,86
+ ,15
+ ,16
+ ,31
+ ,12
+ ,77
+ ,16
+ ,10
+ ,40
+ ,11
+ ,89
+ ,16
+ ,18
+ ,32
+ ,12
+ ,76
+ ,11
+ ,13
+ ,36
+ ,13
+ ,60
+ ,12
+ ,16
+ ,32
+ ,11
+ ,75
+ ,9
+ ,13
+ ,35
+ ,19
+ ,73
+ ,16
+ ,10
+ ,38
+ ,12
+ ,85
+ ,13
+ ,15
+ ,42
+ ,17
+ ,79
+ ,16
+ ,16
+ ,34
+ ,9
+ ,71
+ ,12
+ ,16
+ ,35
+ ,12
+ ,72
+ ,9
+ ,14
+ ,35
+ ,19
+ ,69
+ ,13
+ ,10
+ ,33
+ ,18
+ ,78
+ ,13
+ ,17
+ ,36
+ ,15
+ ,54
+ ,14
+ ,13
+ ,32
+ ,14
+ ,69
+ ,19
+ ,15
+ ,33
+ ,11
+ ,81
+ ,13
+ ,16
+ ,34
+ ,9
+ ,84
+ ,12
+ ,12
+ ,32
+ ,18
+ ,84
+ ,13
+ ,13
+ ,34
+ ,16
+ ,69)
+ ,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 = '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
> 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
1 14 13 41 12 53
2 18 16 39 11 86
3 11 19 30 14 66
4 12 15 31 12 67
5 16 14 34 21 76
6 18 13 35 12 78
7 14 19 39 22 53
8 14 15 34 11 80
9 15 14 36 10 74
10 15 15 37 13 76
11 17 16 38 10 79
12 19 16 36 8 54
13 10 16 38 15 67
14 16 16 39 14 54
15 18 17 33 10 87
16 14 15 32 14 58
17 14 15 36 14 75
18 17 20 38 11 88
19 14 18 39 10 64
20 16 16 32 13 57
21 18 16 32 7 66
22 11 16 31 14 68
23 14 19 39 12 54
24 12 16 37 14 56
25 17 17 39 11 86
26 9 17 41 9 80
27 16 16 36 11 76
28 14 15 33 15 69
29 15 16 33 14 78
30 11 14 34 13 67
31 16 15 31 9 80
32 13 12 27 15 54
33 17 14 37 10 71
34 15 16 34 11 84
35 14 14 34 13 74
36 16 7 32 8 71
37 9 10 29 20 63
38 15 14 36 12 71
39 17 16 29 10 76
40 13 16 35 10 69
41 15 16 37 9 74
42 16 14 34 14 75
43 16 20 38 8 54
44 12 14 35 14 52
45 12 14 38 11 69
46 11 11 37 13 68
47 15 14 38 9 65
48 15 15 33 11 75
49 17 16 36 15 74
50 13 14 38 11 75
51 16 16 32 10 72
52 14 14 32 14 67
53 11 12 32 18 63
54 12 16 34 14 62
55 12 9 32 11 63
56 15 14 37 12 76
57 16 16 39 13 74
58 15 16 29 9 67
59 12 15 37 10 73
60 12 16 35 15 70
61 8 12 30 20 53
62 13 16 38 12 77
63 11 16 34 12 77
64 14 14 31 14 52
65 15 16 34 13 54
66 10 17 35 11 80
67 11 18 36 17 66
68 12 18 30 12 73
69 15 12 39 13 63
70 15 16 35 14 69
71 14 10 38 13 67
72 16 14 31 15 54
73 15 18 34 13 81
74 15 18 38 10 69
75 13 16 34 11 84
76 12 17 39 19 80
77 17 16 37 13 70
78 13 16 34 17 69
79 15 13 28 13 77
80 13 16 37 9 54
81 15 16 33 11 79
82 16 20 37 10 30
83 15 16 35 9 71
84 16 15 37 12 73
85 15 15 32 12 72
86 14 16 33 13 77
87 15 14 38 13 75
88 14 16 33 12 69
89 13 16 29 15 54
90 7 15 33 22 70
91 17 12 31 13 73
92 13 17 36 15 54
93 15 16 35 13 77
94 14 15 32 15 82
95 13 13 29 10 80
96 16 16 39 11 80
97 12 16 37 16 69
98 14 16 35 11 78
99 17 16 37 11 81
100 15 14 32 10 76
101 17 16 38 10 76
102 12 16 37 16 73
103 16 20 36 12 85
104 11 15 32 11 66
105 15 16 33 16 79
106 9 13 40 19 68
107 16 17 38 11 76
108 15 16 41 16 71
109 10 16 36 15 54
110 10 12 43 24 46
111 15 16 30 14 82
112 11 16 31 15 74
113 13 17 32 11 88
114 14 13 32 15 38
115 18 12 37 12 76
116 16 18 37 10 86
117 14 14 33 14 54
118 14 14 34 13 70
119 14 13 33 9 69
120 14 16 38 15 90
121 12 13 33 15 54
122 14 16 31 14 76
123 15 13 38 11 89
124 15 16 37 8 76
125 15 15 33 11 73
126 13 16 31 11 79
127 17 15 39 8 90
128 17 17 44 10 74
129 19 15 33 11 81
130 15 12 35 13 72
131 13 16 32 11 71
132 9 10 28 20 66
133 15 16 40 10 77
134 15 12 27 15 65
135 15 14 37 12 74
136 16 15 32 14 82
137 11 13 28 23 54
138 14 15 34 14 63
139 11 11 30 16 54
140 15 12 35 11 64
141 13 8 31 12 69
142 15 16 32 10 54
143 16 15 30 14 84
144 14 17 30 12 86
145 15 16 31 12 77
146 16 10 40 11 89
147 16 18 32 12 76
148 11 13 36 13 60
149 12 16 32 11 75
150 9 13 35 19 73
151 16 10 38 12 85
152 13 15 42 17 79
153 16 16 34 9 71
154 12 16 35 12 72
155 9 14 35 19 69
156 13 10 33 18 78
157 13 17 36 15 54
158 14 13 32 14 69
159 19 15 33 11 81
160 13 16 34 9 84
161 12 12 32 18 84
162 13 13 34 16 69
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Learning Connected Depression `Belonging\r`
14.29138 0.04476 0.04386 -0.35981 0.03121
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.1087 -1.2283 0.2545 1.1682 4.7752
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.29138 2.29357 6.231 4.08e-09 ***
Learning 0.04476 0.07129 0.628 0.5310
Connected 0.04386 0.04673 0.939 0.3494
Depression -0.35981 0.05174 -6.954 8.99e-11 ***
`Belonging\r` 0.03121 0.01490 2.094 0.0379 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.948 on 157 degrees of freedom
Multiple R-squared: 0.3231, Adjusted R-squared: 0.3058
F-statistic: 18.73 on 4 and 157 DF, p-value: 1.316e-12
> 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,] 0.3650860 0.730171930 0.634914035
[2,] 0.2121652 0.424330455 0.787834773
[3,] 0.1544831 0.308966117 0.845516942
[4,] 0.1014805 0.202961061 0.898519469
[5,] 0.8684485 0.263103006 0.131551503
[6,] 0.9819285 0.036142903 0.018071451
[7,] 0.9759132 0.048173519 0.024086760
[8,] 0.9775859 0.044828244 0.022414122
[9,] 0.9661153 0.067769338 0.033884669
[10,] 0.9539835 0.092032933 0.046016466
[11,] 0.9337540 0.132492055 0.066246027
[12,] 0.9231999 0.153600105 0.076800052
[13,] 0.9320464 0.135907209 0.067953604
[14,] 0.9342752 0.131449540 0.065724770
[15,] 0.9523283 0.095343336 0.047671668
[16,] 0.9350379 0.129924255 0.064962127
[17,] 0.9328369 0.134326222 0.067163111
[18,] 0.9125073 0.174985368 0.087492684
[19,] 0.9987961 0.002407896 0.001203948
[20,] 0.9981600 0.003679908 0.001839954
[21,] 0.9971739 0.005652205 0.002826102
[22,] 0.9958080 0.008383966 0.004191983
[23,] 0.9978947 0.004210501 0.002105250
[24,] 0.9967551 0.006489782 0.003244891
[25,] 0.9952062 0.009587621 0.004793811
[26,] 0.9943498 0.011300348 0.005650174
[27,] 0.9918142 0.016371529 0.008185764
[28,] 0.9886425 0.022715087 0.011357544
[29,] 0.9840432 0.031913700 0.015956850
[30,] 0.9880884 0.023823126 0.011911563
[31,] 0.9834720 0.033055965 0.016527982
[32,] 0.9822794 0.035441209 0.017720604
[33,] 0.9828831 0.034233781 0.017116891
[34,] 0.9774220 0.045155995 0.022577997
[35,] 0.9767132 0.046573606 0.023286803
[36,] 0.9694584 0.061083168 0.030541584
[37,] 0.9631799 0.073640156 0.036820078
[38,] 0.9718411 0.056317709 0.028158855
[39,] 0.9787978 0.042404306 0.021202153
[40,] 0.9717989 0.056402171 0.028201086
[41,] 0.9627793 0.074441436 0.037220718
[42,] 0.9758042 0.048391648 0.024195824
[43,] 0.9753948 0.049210401 0.024605201
[44,] 0.9688199 0.062360143 0.031180071
[45,] 0.9598372 0.080325627 0.040162814
[46,] 0.9515485 0.096902982 0.048451491
[47,] 0.9466292 0.106741514 0.053370757
[48,] 0.9465023 0.106995337 0.053497669
[49,] 0.9325982 0.134803542 0.067401771
[50,] 0.9263078 0.147384498 0.073692249
[51,] 0.9083921 0.183215758 0.091607879
[52,] 0.9374185 0.125163015 0.062581507
[53,] 0.9319472 0.136105678 0.068052839
[54,] 0.9438079 0.112384142 0.056192071
[55,] 0.9420697 0.115860563 0.057930282
[56,] 0.9690060 0.061988040 0.030994020
[57,] 0.9632970 0.073406040 0.036703020
[58,] 0.9589951 0.082009784 0.041004892
[59,] 0.9923500 0.015300089 0.007650044
[60,] 0.9916669 0.016666293 0.008333147
[61,] 0.9928974 0.014205217 0.007102608
[62,] 0.9912720 0.017456051 0.008728025
[63,] 0.9896645 0.020670945 0.010335472
[64,] 0.9861432 0.027713659 0.013856830
[65,] 0.9925187 0.014962590 0.007481295
[66,] 0.9900625 0.019874944 0.009937472
[67,] 0.9866827 0.026634669 0.013317334
[68,] 0.9875588 0.024882415 0.012441208
[69,] 0.9835872 0.032825505 0.016412753
[70,] 0.9883081 0.023383797 0.011691899
[71,] 0.9847087 0.030582624 0.015291312
[72,] 0.9816188 0.036762477 0.018381239
[73,] 0.9830524 0.033895184 0.016947592
[74,] 0.9775629 0.044874145 0.022437072
[75,] 0.9770080 0.045984081 0.022992041
[76,] 0.9706576 0.058684813 0.029342407
[77,] 0.9670219 0.065956148 0.032978074
[78,] 0.9588739 0.082252147 0.041126073
[79,] 0.9476152 0.104769638 0.052384819
[80,] 0.9360053 0.127989332 0.063994666
[81,] 0.9203863 0.159227412 0.079613706
[82,] 0.9044140 0.191171956 0.095585978
[83,] 0.9433793 0.113241314 0.056620657
[84,] 0.9629916 0.074016892 0.037008446
[85,] 0.9528086 0.094382801 0.047191400
[86,] 0.9421139 0.115772106 0.057886053
[87,] 0.9281058 0.143788346 0.071894173
[88,] 0.9313141 0.137371750 0.068685875
[89,] 0.9168097 0.166380563 0.083190281
[90,] 0.9018219 0.196356295 0.098178147
[91,] 0.8861737 0.227652637 0.113826319
[92,] 0.8824561 0.235087701 0.117543851
[93,] 0.8572640 0.285471909 0.142735955
[94,] 0.8475905 0.304818935 0.152409468
[95,] 0.8267111 0.346577872 0.173288936
[96,] 0.8033342 0.393331508 0.196665754
[97,] 0.8680553 0.263889423 0.131944712
[98,] 0.8688860 0.262228092 0.131114046
[99,] 0.8977371 0.204525775 0.102262888
[100,] 0.8796251 0.240749793 0.120374896
[101,] 0.8796682 0.240663682 0.120331841
[102,] 0.9029447 0.194110615 0.097055307
[103,] 0.8824847 0.235030593 0.117515296
[104,] 0.8677758 0.264448378 0.132224189
[105,] 0.8746510 0.250698035 0.125349018
[106,] 0.8859676 0.228064773 0.114032387
[107,] 0.8934959 0.213008179 0.106504090
[108,] 0.9404251 0.119149873 0.059574936
[109,] 0.9229200 0.154160096 0.077080048
[110,] 0.9129969 0.174006199 0.087003100
[111,] 0.8896799 0.220640133 0.110320067
[112,] 0.8756405 0.248719070 0.124359535
[113,] 0.8452658 0.309468480 0.154734240
[114,] 0.8110144 0.377971214 0.188985607
[115,] 0.7714149 0.457170157 0.228585079
[116,] 0.7317761 0.536447894 0.268223947
[117,] 0.7034663 0.593067407 0.296533703
[118,] 0.6525668 0.694866400 0.347433200
[119,] 0.6737199 0.652560228 0.326280114
[120,] 0.6210554 0.757889205 0.378944602
[121,] 0.6158418 0.768316495 0.384158247
[122,] 0.7557242 0.488551513 0.244275757
[123,] 0.7246183 0.550763311 0.275381655
[124,] 0.7179552 0.564089550 0.282044775
[125,] 0.7324634 0.535073149 0.267536574
[126,] 0.6784443 0.643111381 0.321555691
[127,] 0.6713830 0.657234085 0.328617042
[128,] 0.6196648 0.760670337 0.380335169
[129,] 0.6155972 0.768805687 0.384402844
[130,] 0.6310064 0.737987130 0.368993565
[131,] 0.5914425 0.817115080 0.408557540
[132,] 0.5241312 0.951737609 0.475868804
[133,] 0.4656818 0.931363506 0.534318247
[134,] 0.4254351 0.850870205 0.574564897
[135,] 0.3729526 0.745905153 0.627047424
[136,] 0.3695721 0.739144290 0.630427855
[137,] 0.3081524 0.616304875 0.691847562
[138,] 0.2456000 0.491199920 0.754400040
[139,] 0.1841589 0.368317810 0.815841095
[140,] 0.1965979 0.393195842 0.803402079
[141,] 0.2682849 0.536569827 0.731715086
[142,] 0.2723082 0.544616445 0.727691778
[143,] 0.2660208 0.532041530 0.733979235
[144,] 0.2006217 0.401243377 0.799378312
[145,] 0.2423069 0.484613769 0.757693116
[146,] 0.1503314 0.300662856 0.849668572
[147,] 0.1036909 0.207381858 0.896309071
> postscript(file="/var/wessaorg/rcomp/tmp/1vfjl1322149723.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/27y7s1322149723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3nwmw1322149723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4e8kj1322149723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/59rez1322149723.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 6
-0.007710717 2.556138131 -2.479829427 -2.095487863 4.775169277 3.475323294
7 8 9 10 11 12
3.409602857 -0.992559766 -0.208105726 0.720306000 1.458623763 3.606855483
13 14 15 16 17 18
-3.367836560 2.634158243 2.383529693 0.861129189 0.155188144 1.358550590
19 20 21 22 23 24
-1.206674923 2.487760328 2.048022395 -2.451823483 -0.219750085 -1.340528944
25 26 27 28 29 30
1.511378519 -7.108741799 0.999778801 0.833821573 1.148397750 -2.822499213
31 32 33 34 35 36
0.419396453 0.699354270 1.841648860 -0.162141363 -0.040937687 0.654646174
37 38 39 40 41 42
-1.780626052 0.605140252 1.946997374 -2.097735568 -0.701301598 2.287671562
43 44 45 46 47 48
0.340093227 -1.038463929 -2.779987305 -2.851011579 -0.374794810 0.207329618
49 50 51 52 53 54
3.501448772 -1.967220282 0.940233651 0.625039336 -0.721360475 -1.396176214
55 56 57 58 59 60
-2.105784848 0.405250869 1.650233575 -0.131967905 -3.265521745 -1.329867341
61 62 63 64 65 66
-2.601952219 -1.759335756 -3.583888146 1.136983682 1.493653011 -5.125940894
67 68 69 70 71 72
-1.618796996 -2.373137776 1.172532484 1.341523410 0.181091628 3.434387434
73 74 75 76 77 78
0.561585389 -0.318840501 -2.162141363 -0.422870548 2.862779365 0.464829547
79 80 81 82 83 84
1.173376853 -2.077191675 0.037748020 1.852516527 -0.519961305 1.454107744
85 86 87 88 89 90
0.704622753 -0.180211498 0.752409207 -0.290382274 0.432592013 -3.678680711
91 92 93 94 95 96
3.211372743 0.080799082 0.732064696 0.472012025 -2.043545772 0.743371109
97 98 99 100 101 102
-1.026570906 -1.018770289 1.799889417 -0.095069108 1.552240251 -1.151392890
103 104 105 106 107 108
0.899705628 -3.467959014 1.836821743 -2.913228045 0.867295383 1.735570492
109 110 111 112 113 114
-2.874441305 0.485540499 1.155161473 -2.279241716 -2.243999156 1.934573083
115 116 117 118 119 120
3.494770094 0.194527966 0.986848884 0.083884298 -1.235547669 -0.085562972
121 122 123 124 125 126
-0.608576759 0.298532547 -0.359337616 -1.123527335 0.269740610 -1.874528175
127 128 129 130 131 132
0.396631526 1.306720215 4.020096641 1.067130629 -1.668746108 -1.830380638
133 134 135 136 137 138
-0.566689050 2.356093812 0.467661861 2.112197281 1.489250711 0.617377903
139 140 141 142 143 144
-1.027657080 0.597145109 -0.844581565 0.501932583 2.137510094 -0.734049614
145 146 147 148 149 150
0.547697562 0.687217418 1.445521930 -2.647024932 -2.793568093 -2.849946013
151 152 153 154 155 156
1.259577952 -0.153361023 0.523900598 -2.471722567 -2.769883641 0.856214406
157 158 159 160 161 162
0.080799082 0.607387957 4.020096641 -2.881770852 -0.376675895 0.239293641
> postscript(file="/var/wessaorg/rcomp/tmp/6ewp21322149723.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 -0.007710717 NA
1 2.556138131 -0.007710717
2 -2.479829427 2.556138131
3 -2.095487863 -2.479829427
4 4.775169277 -2.095487863
5 3.475323294 4.775169277
6 3.409602857 3.475323294
7 -0.992559766 3.409602857
8 -0.208105726 -0.992559766
9 0.720306000 -0.208105726
10 1.458623763 0.720306000
11 3.606855483 1.458623763
12 -3.367836560 3.606855483
13 2.634158243 -3.367836560
14 2.383529693 2.634158243
15 0.861129189 2.383529693
16 0.155188144 0.861129189
17 1.358550590 0.155188144
18 -1.206674923 1.358550590
19 2.487760328 -1.206674923
20 2.048022395 2.487760328
21 -2.451823483 2.048022395
22 -0.219750085 -2.451823483
23 -1.340528944 -0.219750085
24 1.511378519 -1.340528944
25 -7.108741799 1.511378519
26 0.999778801 -7.108741799
27 0.833821573 0.999778801
28 1.148397750 0.833821573
29 -2.822499213 1.148397750
30 0.419396453 -2.822499213
31 0.699354270 0.419396453
32 1.841648860 0.699354270
33 -0.162141363 1.841648860
34 -0.040937687 -0.162141363
35 0.654646174 -0.040937687
36 -1.780626052 0.654646174
37 0.605140252 -1.780626052
38 1.946997374 0.605140252
39 -2.097735568 1.946997374
40 -0.701301598 -2.097735568
41 2.287671562 -0.701301598
42 0.340093227 2.287671562
43 -1.038463929 0.340093227
44 -2.779987305 -1.038463929
45 -2.851011579 -2.779987305
46 -0.374794810 -2.851011579
47 0.207329618 -0.374794810
48 3.501448772 0.207329618
49 -1.967220282 3.501448772
50 0.940233651 -1.967220282
51 0.625039336 0.940233651
52 -0.721360475 0.625039336
53 -1.396176214 -0.721360475
54 -2.105784848 -1.396176214
55 0.405250869 -2.105784848
56 1.650233575 0.405250869
57 -0.131967905 1.650233575
58 -3.265521745 -0.131967905
59 -1.329867341 -3.265521745
60 -2.601952219 -1.329867341
61 -1.759335756 -2.601952219
62 -3.583888146 -1.759335756
63 1.136983682 -3.583888146
64 1.493653011 1.136983682
65 -5.125940894 1.493653011
66 -1.618796996 -5.125940894
67 -2.373137776 -1.618796996
68 1.172532484 -2.373137776
69 1.341523410 1.172532484
70 0.181091628 1.341523410
71 3.434387434 0.181091628
72 0.561585389 3.434387434
73 -0.318840501 0.561585389
74 -2.162141363 -0.318840501
75 -0.422870548 -2.162141363
76 2.862779365 -0.422870548
77 0.464829547 2.862779365
78 1.173376853 0.464829547
79 -2.077191675 1.173376853
80 0.037748020 -2.077191675
81 1.852516527 0.037748020
82 -0.519961305 1.852516527
83 1.454107744 -0.519961305
84 0.704622753 1.454107744
85 -0.180211498 0.704622753
86 0.752409207 -0.180211498
87 -0.290382274 0.752409207
88 0.432592013 -0.290382274
89 -3.678680711 0.432592013
90 3.211372743 -3.678680711
91 0.080799082 3.211372743
92 0.732064696 0.080799082
93 0.472012025 0.732064696
94 -2.043545772 0.472012025
95 0.743371109 -2.043545772
96 -1.026570906 0.743371109
97 -1.018770289 -1.026570906
98 1.799889417 -1.018770289
99 -0.095069108 1.799889417
100 1.552240251 -0.095069108
101 -1.151392890 1.552240251
102 0.899705628 -1.151392890
103 -3.467959014 0.899705628
104 1.836821743 -3.467959014
105 -2.913228045 1.836821743
106 0.867295383 -2.913228045
107 1.735570492 0.867295383
108 -2.874441305 1.735570492
109 0.485540499 -2.874441305
110 1.155161473 0.485540499
111 -2.279241716 1.155161473
112 -2.243999156 -2.279241716
113 1.934573083 -2.243999156
114 3.494770094 1.934573083
115 0.194527966 3.494770094
116 0.986848884 0.194527966
117 0.083884298 0.986848884
118 -1.235547669 0.083884298
119 -0.085562972 -1.235547669
120 -0.608576759 -0.085562972
121 0.298532547 -0.608576759
122 -0.359337616 0.298532547
123 -1.123527335 -0.359337616
124 0.269740610 -1.123527335
125 -1.874528175 0.269740610
126 0.396631526 -1.874528175
127 1.306720215 0.396631526
128 4.020096641 1.306720215
129 1.067130629 4.020096641
130 -1.668746108 1.067130629
131 -1.830380638 -1.668746108
132 -0.566689050 -1.830380638
133 2.356093812 -0.566689050
134 0.467661861 2.356093812
135 2.112197281 0.467661861
136 1.489250711 2.112197281
137 0.617377903 1.489250711
138 -1.027657080 0.617377903
139 0.597145109 -1.027657080
140 -0.844581565 0.597145109
141 0.501932583 -0.844581565
142 2.137510094 0.501932583
143 -0.734049614 2.137510094
144 0.547697562 -0.734049614
145 0.687217418 0.547697562
146 1.445521930 0.687217418
147 -2.647024932 1.445521930
148 -2.793568093 -2.647024932
149 -2.849946013 -2.793568093
150 1.259577952 -2.849946013
151 -0.153361023 1.259577952
152 0.523900598 -0.153361023
153 -2.471722567 0.523900598
154 -2.769883641 -2.471722567
155 0.856214406 -2.769883641
156 0.080799082 0.856214406
157 0.607387957 0.080799082
158 4.020096641 0.607387957
159 -2.881770852 4.020096641
160 -0.376675895 -2.881770852
161 0.239293641 -0.376675895
162 NA 0.239293641
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.55613813 -0.007710717
[2,] -2.47982943 2.556138131
[3,] -2.09548786 -2.479829427
[4,] 4.77516928 -2.095487863
[5,] 3.47532329 4.775169277
[6,] 3.40960286 3.475323294
[7,] -0.99255977 3.409602857
[8,] -0.20810573 -0.992559766
[9,] 0.72030600 -0.208105726
[10,] 1.45862376 0.720306000
[11,] 3.60685548 1.458623763
[12,] -3.36783656 3.606855483
[13,] 2.63415824 -3.367836560
[14,] 2.38352969 2.634158243
[15,] 0.86112919 2.383529693
[16,] 0.15518814 0.861129189
[17,] 1.35855059 0.155188144
[18,] -1.20667492 1.358550590
[19,] 2.48776033 -1.206674923
[20,] 2.04802239 2.487760328
[21,] -2.45182348 2.048022395
[22,] -0.21975008 -2.451823483
[23,] -1.34052894 -0.219750085
[24,] 1.51137852 -1.340528944
[25,] -7.10874180 1.511378519
[26,] 0.99977880 -7.108741799
[27,] 0.83382157 0.999778801
[28,] 1.14839775 0.833821573
[29,] -2.82249921 1.148397750
[30,] 0.41939645 -2.822499213
[31,] 0.69935427 0.419396453
[32,] 1.84164886 0.699354270
[33,] -0.16214136 1.841648860
[34,] -0.04093769 -0.162141363
[35,] 0.65464617 -0.040937687
[36,] -1.78062605 0.654646174
[37,] 0.60514025 -1.780626052
[38,] 1.94699737 0.605140252
[39,] -2.09773557 1.946997374
[40,] -0.70130160 -2.097735568
[41,] 2.28767156 -0.701301598
[42,] 0.34009323 2.287671562
[43,] -1.03846393 0.340093227
[44,] -2.77998731 -1.038463929
[45,] -2.85101158 -2.779987305
[46,] -0.37479481 -2.851011579
[47,] 0.20732962 -0.374794810
[48,] 3.50144877 0.207329618
[49,] -1.96722028 3.501448772
[50,] 0.94023365 -1.967220282
[51,] 0.62503934 0.940233651
[52,] -0.72136047 0.625039336
[53,] -1.39617621 -0.721360475
[54,] -2.10578485 -1.396176214
[55,] 0.40525087 -2.105784848
[56,] 1.65023357 0.405250869
[57,] -0.13196790 1.650233575
[58,] -3.26552174 -0.131967905
[59,] -1.32986734 -3.265521745
[60,] -2.60195222 -1.329867341
[61,] -1.75933576 -2.601952219
[62,] -3.58388815 -1.759335756
[63,] 1.13698368 -3.583888146
[64,] 1.49365301 1.136983682
[65,] -5.12594089 1.493653011
[66,] -1.61879700 -5.125940894
[67,] -2.37313778 -1.618796996
[68,] 1.17253248 -2.373137776
[69,] 1.34152341 1.172532484
[70,] 0.18109163 1.341523410
[71,] 3.43438743 0.181091628
[72,] 0.56158539 3.434387434
[73,] -0.31884050 0.561585389
[74,] -2.16214136 -0.318840501
[75,] -0.42287055 -2.162141363
[76,] 2.86277936 -0.422870548
[77,] 0.46482955 2.862779365
[78,] 1.17337685 0.464829547
[79,] -2.07719167 1.173376853
[80,] 0.03774802 -2.077191675
[81,] 1.85251653 0.037748020
[82,] -0.51996130 1.852516527
[83,] 1.45410774 -0.519961305
[84,] 0.70462275 1.454107744
[85,] -0.18021150 0.704622753
[86,] 0.75240921 -0.180211498
[87,] -0.29038227 0.752409207
[88,] 0.43259201 -0.290382274
[89,] -3.67868071 0.432592013
[90,] 3.21137274 -3.678680711
[91,] 0.08079908 3.211372743
[92,] 0.73206470 0.080799082
[93,] 0.47201203 0.732064696
[94,] -2.04354577 0.472012025
[95,] 0.74337111 -2.043545772
[96,] -1.02657091 0.743371109
[97,] -1.01877029 -1.026570906
[98,] 1.79988942 -1.018770289
[99,] -0.09506911 1.799889417
[100,] 1.55224025 -0.095069108
[101,] -1.15139289 1.552240251
[102,] 0.89970563 -1.151392890
[103,] -3.46795901 0.899705628
[104,] 1.83682174 -3.467959014
[105,] -2.91322804 1.836821743
[106,] 0.86729538 -2.913228045
[107,] 1.73557049 0.867295383
[108,] -2.87444130 1.735570492
[109,] 0.48554050 -2.874441305
[110,] 1.15516147 0.485540499
[111,] -2.27924172 1.155161473
[112,] -2.24399916 -2.279241716
[113,] 1.93457308 -2.243999156
[114,] 3.49477009 1.934573083
[115,] 0.19452797 3.494770094
[116,] 0.98684888 0.194527966
[117,] 0.08388430 0.986848884
[118,] -1.23554767 0.083884298
[119,] -0.08556297 -1.235547669
[120,] -0.60857676 -0.085562972
[121,] 0.29853255 -0.608576759
[122,] -0.35933762 0.298532547
[123,] -1.12352734 -0.359337616
[124,] 0.26974061 -1.123527335
[125,] -1.87452817 0.269740610
[126,] 0.39663153 -1.874528175
[127,] 1.30672022 0.396631526
[128,] 4.02009664 1.306720215
[129,] 1.06713063 4.020096641
[130,] -1.66874611 1.067130629
[131,] -1.83038064 -1.668746108
[132,] -0.56668905 -1.830380638
[133,] 2.35609381 -0.566689050
[134,] 0.46766186 2.356093812
[135,] 2.11219728 0.467661861
[136,] 1.48925071 2.112197281
[137,] 0.61737790 1.489250711
[138,] -1.02765708 0.617377903
[139,] 0.59714511 -1.027657080
[140,] -0.84458157 0.597145109
[141,] 0.50193258 -0.844581565
[142,] 2.13751009 0.501932583
[143,] -0.73404961 2.137510094
[144,] 0.54769756 -0.734049614
[145,] 0.68721742 0.547697562
[146,] 1.44552193 0.687217418
[147,] -2.64702493 1.445521930
[148,] -2.79356809 -2.647024932
[149,] -2.84994601 -2.793568093
[150,] 1.25957795 -2.849946013
[151,] -0.15336102 1.259577952
[152,] 0.52390060 -0.153361023
[153,] -2.47172257 0.523900598
[154,] -2.76988364 -2.471722567
[155,] 0.85621441 -2.769883641
[156,] 0.08079908 0.856214406
[157,] 0.60738796 0.080799082
[158,] 4.02009664 0.607387957
[159,] -2.88177085 4.020096641
[160,] -0.37667589 -2.881770852
[161,] 0.23929364 -0.376675895
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.55613813 -0.007710717
2 -2.47982943 2.556138131
3 -2.09548786 -2.479829427
4 4.77516928 -2.095487863
5 3.47532329 4.775169277
6 3.40960286 3.475323294
7 -0.99255977 3.409602857
8 -0.20810573 -0.992559766
9 0.72030600 -0.208105726
10 1.45862376 0.720306000
11 3.60685548 1.458623763
12 -3.36783656 3.606855483
13 2.63415824 -3.367836560
14 2.38352969 2.634158243
15 0.86112919 2.383529693
16 0.15518814 0.861129189
17 1.35855059 0.155188144
18 -1.20667492 1.358550590
19 2.48776033 -1.206674923
20 2.04802239 2.487760328
21 -2.45182348 2.048022395
22 -0.21975008 -2.451823483
23 -1.34052894 -0.219750085
24 1.51137852 -1.340528944
25 -7.10874180 1.511378519
26 0.99977880 -7.108741799
27 0.83382157 0.999778801
28 1.14839775 0.833821573
29 -2.82249921 1.148397750
30 0.41939645 -2.822499213
31 0.69935427 0.419396453
32 1.84164886 0.699354270
33 -0.16214136 1.841648860
34 -0.04093769 -0.162141363
35 0.65464617 -0.040937687
36 -1.78062605 0.654646174
37 0.60514025 -1.780626052
38 1.94699737 0.605140252
39 -2.09773557 1.946997374
40 -0.70130160 -2.097735568
41 2.28767156 -0.701301598
42 0.34009323 2.287671562
43 -1.03846393 0.340093227
44 -2.77998731 -1.038463929
45 -2.85101158 -2.779987305
46 -0.37479481 -2.851011579
47 0.20732962 -0.374794810
48 3.50144877 0.207329618
49 -1.96722028 3.501448772
50 0.94023365 -1.967220282
51 0.62503934 0.940233651
52 -0.72136047 0.625039336
53 -1.39617621 -0.721360475
54 -2.10578485 -1.396176214
55 0.40525087 -2.105784848
56 1.65023357 0.405250869
57 -0.13196790 1.650233575
58 -3.26552174 -0.131967905
59 -1.32986734 -3.265521745
60 -2.60195222 -1.329867341
61 -1.75933576 -2.601952219
62 -3.58388815 -1.759335756
63 1.13698368 -3.583888146
64 1.49365301 1.136983682
65 -5.12594089 1.493653011
66 -1.61879700 -5.125940894
67 -2.37313778 -1.618796996
68 1.17253248 -2.373137776
69 1.34152341 1.172532484
70 0.18109163 1.341523410
71 3.43438743 0.181091628
72 0.56158539 3.434387434
73 -0.31884050 0.561585389
74 -2.16214136 -0.318840501
75 -0.42287055 -2.162141363
76 2.86277936 -0.422870548
77 0.46482955 2.862779365
78 1.17337685 0.464829547
79 -2.07719167 1.173376853
80 0.03774802 -2.077191675
81 1.85251653 0.037748020
82 -0.51996130 1.852516527
83 1.45410774 -0.519961305
84 0.70462275 1.454107744
85 -0.18021150 0.704622753
86 0.75240921 -0.180211498
87 -0.29038227 0.752409207
88 0.43259201 -0.290382274
89 -3.67868071 0.432592013
90 3.21137274 -3.678680711
91 0.08079908 3.211372743
92 0.73206470 0.080799082
93 0.47201203 0.732064696
94 -2.04354577 0.472012025
95 0.74337111 -2.043545772
96 -1.02657091 0.743371109
97 -1.01877029 -1.026570906
98 1.79988942 -1.018770289
99 -0.09506911 1.799889417
100 1.55224025 -0.095069108
101 -1.15139289 1.552240251
102 0.89970563 -1.151392890
103 -3.46795901 0.899705628
104 1.83682174 -3.467959014
105 -2.91322804 1.836821743
106 0.86729538 -2.913228045
107 1.73557049 0.867295383
108 -2.87444130 1.735570492
109 0.48554050 -2.874441305
110 1.15516147 0.485540499
111 -2.27924172 1.155161473
112 -2.24399916 -2.279241716
113 1.93457308 -2.243999156
114 3.49477009 1.934573083
115 0.19452797 3.494770094
116 0.98684888 0.194527966
117 0.08388430 0.986848884
118 -1.23554767 0.083884298
119 -0.08556297 -1.235547669
120 -0.60857676 -0.085562972
121 0.29853255 -0.608576759
122 -0.35933762 0.298532547
123 -1.12352734 -0.359337616
124 0.26974061 -1.123527335
125 -1.87452817 0.269740610
126 0.39663153 -1.874528175
127 1.30672022 0.396631526
128 4.02009664 1.306720215
129 1.06713063 4.020096641
130 -1.66874611 1.067130629
131 -1.83038064 -1.668746108
132 -0.56668905 -1.830380638
133 2.35609381 -0.566689050
134 0.46766186 2.356093812
135 2.11219728 0.467661861
136 1.48925071 2.112197281
137 0.61737790 1.489250711
138 -1.02765708 0.617377903
139 0.59714511 -1.027657080
140 -0.84458157 0.597145109
141 0.50193258 -0.844581565
142 2.13751009 0.501932583
143 -0.73404961 2.137510094
144 0.54769756 -0.734049614
145 0.68721742 0.547697562
146 1.44552193 0.687217418
147 -2.64702493 1.445521930
148 -2.79356809 -2.647024932
149 -2.84994601 -2.793568093
150 1.25957795 -2.849946013
151 -0.15336102 1.259577952
152 0.52390060 -0.153361023
153 -2.47172257 0.523900598
154 -2.76988364 -2.471722567
155 0.85621441 -2.769883641
156 0.08079908 0.856214406
157 0.60738796 0.080799082
158 4.02009664 0.607387957
159 -2.88177085 4.020096641
160 -0.37667589 -2.881770852
161 0.23929364 -0.376675895
> 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/7jifc1322149723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8g3tb1322149723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9jdxq1322149723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10b4jz1322149723.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1105vf1322149723.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/12dgzm1322149723.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/138dmm1322149723.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/14abot1322149723.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/15t3691322149723.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/1670lq1322149723.tab")
+ }
>
> try(system("convert tmp/1vfjl1322149723.ps tmp/1vfjl1322149723.png",intern=TRUE))
character(0)
> try(system("convert tmp/27y7s1322149723.ps tmp/27y7s1322149723.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nwmw1322149723.ps tmp/3nwmw1322149723.png",intern=TRUE))
character(0)
> try(system("convert tmp/4e8kj1322149723.ps tmp/4e8kj1322149723.png",intern=TRUE))
character(0)
> try(system("convert tmp/59rez1322149723.ps tmp/59rez1322149723.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ewp21322149723.ps tmp/6ewp21322149723.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jifc1322149723.ps tmp/7jifc1322149723.png",intern=TRUE))
character(0)
> try(system("convert tmp/8g3tb1322149723.ps tmp/8g3tb1322149723.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jdxq1322149723.ps tmp/9jdxq1322149723.png",intern=TRUE))
character(0)
> try(system("convert tmp/10b4jz1322149723.ps tmp/10b4jz1322149723.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.797 0.709 5.914