R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
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+ ,16
+ ,11
+ ,20
+ ,17
+ ,10
+ ,19
+ ,18
+ ,21
+ ,101
+ ,1
+ ,0
+ ,16
+ ,12
+ ,21
+ ,17
+ ,13
+ ,23
+ ,22
+ ,26
+ ,114
+ ,1
+ ,0
+ ,11
+ ,13
+ ,25
+ ,22
+ ,15
+ ,25
+ ,16
+ ,21
+ ,118
+ ,1
+ ,0
+ ,12
+ ,11
+ ,22
+ ,20
+ ,18
+ ,23
+ ,19
+ ,22
+ ,120
+ ,1
+ ,0
+ ,9
+ ,19
+ ,21
+ ,20
+ ,18
+ ,22
+ ,20
+ ,16
+ ,108
+ ,1
+ ,1
+ ,16
+ ,12
+ ,21
+ ,19
+ ,12
+ ,22
+ ,19
+ ,26
+ ,114
+ ,1
+ ,1
+ ,13
+ ,17
+ ,22
+ ,18
+ ,12
+ ,25
+ ,23
+ ,28
+ ,122
+ ,1
+ ,1
+ ,16
+ ,9
+ ,27
+ ,22
+ ,20
+ ,25
+ ,24
+ ,18
+ ,132
+ ,1
+ ,0
+ ,12
+ ,12
+ ,24
+ ,20
+ ,12
+ ,28
+ ,25
+ ,25
+ ,130
+ ,1
+ ,0
+ ,9
+ ,19
+ ,24
+ ,22
+ ,16
+ ,28
+ ,21
+ ,23
+ ,130
+ ,1
+ ,0
+ ,13
+ ,18
+ ,21
+ ,18
+ ,16
+ ,20
+ ,21
+ ,21
+ ,112
+ ,1
+ ,1
+ ,13
+ ,15
+ ,18
+ ,16
+ ,18
+ ,25
+ ,23
+ ,20
+ ,114
+ ,1
+ ,1
+ ,14
+ ,14
+ ,16
+ ,16
+ ,16
+ ,19
+ ,27
+ ,25
+ ,103
+ ,0
+ ,1
+ ,19
+ ,11
+ ,22
+ ,16
+ ,13
+ ,25
+ ,23
+ ,22
+ ,115
+ ,0
+ ,1
+ ,13
+ ,9
+ ,20
+ ,16
+ ,17
+ ,22
+ ,18
+ ,21
+ ,108
+ ,0
+ ,0
+ ,12
+ ,18
+ ,18
+ ,17
+ ,13
+ ,18
+ ,16
+ ,16
+ ,94
+ ,0
+ ,1
+ ,13
+ ,16
+ ,20
+ ,18
+ ,17
+ ,20
+ ,16
+ ,18
+ ,105)
+ ,dim=c(11
+ ,162)
+ ,dimnames=list(c('Pop'
+ ,'Gender'
+ ,'Happiness'
+ ,'Depression'
+ ,'I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'TotaleMotivatie
')
+ ,1:162))
> y <- array(NA,dim=c(11,162),dimnames=list(c('Pop','Gender','Happiness','Depression','I1','I2','I3','E1','E2','E3','TotaleMotivatie
'),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 = '11'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
TotaleMotivatie\r Pop Gender Happiness Depression I1 I2 I3 E1 E2 E3
1 127 1 1 14 12 26 21 21 23 17 23
2 108 1 1 18 11 20 16 15 24 17 20
3 110 1 1 11 14 19 19 18 22 18 20
4 102 1 0 12 12 19 18 11 20 21 21
5 104 1 1 16 21 20 16 8 24 20 24
6 140 1 1 18 12 25 23 19 27 28 22
7 112 1 0 14 22 25 17 4 28 19 23
8 115 1 1 14 11 22 12 20 27 22 20
9 121 1 1 15 10 26 19 16 24 16 25
10 112 1 1 15 13 22 16 14 23 18 23
11 118 1 0 17 10 17 19 10 24 25 27
12 122 1 0 19 8 22 20 13 27 17 27
13 105 1 1 10 15 19 13 14 27 14 22
14 111 1 1 16 14 24 20 8 28 11 24
15 151 1 1 18 10 26 27 23 27 27 25
16 106 1 0 14 14 21 17 11 23 20 22
17 100 1 1 14 14 13 8 9 24 22 28
18 149 1 0 17 11 26 25 24 28 22 28
19 122 1 0 14 10 20 26 5 27 21 27
20 115 1 1 16 13 22 13 15 25 23 25
21 86 1 0 18 7 14 19 5 19 17 16
22 124 1 1 11 14 21 15 19 24 24 28
23 69 1 1 14 12 7 5 6 20 14 21
24 117 1 0 12 14 23 16 13 28 17 24
25 113 1 1 17 11 17 14 11 26 23 27
26 123 1 1 9 9 25 24 17 23 24 14
27 123 1 1 16 11 25 24 17 23 24 14
28 84 1 1 14 15 19 9 5 20 8 27
29 97 1 0 15 14 20 19 9 11 22 20
30 121 1 1 11 13 23 19 15 24 23 21
31 132 1 0 16 9 22 25 17 25 25 22
32 119 1 1 13 15 22 19 17 23 21 21
33 98 1 1 17 10 21 18 20 18 24 12
34 87 1 0 15 11 15 15 12 20 15 20
35 101 1 0 14 13 20 12 7 20 22 24
36 115 1 0 16 8 22 21 16 24 21 19
37 109 1 1 9 20 18 12 7 23 25 28
38 109 1 0 15 12 20 15 14 25 16 23
39 159 1 0 17 10 28 28 24 28 28 27
40 129 1 1 13 10 22 25 15 26 23 22
41 119 1 1 15 9 18 19 15 26 21 27
42 119 1 1 16 14 23 20 10 23 21 26
43 122 1 1 16 8 20 24 14 22 26 22
44 131 1 0 12 14 25 26 18 24 22 21
45 120 1 0 12 11 26 25 12 21 21 19
46 82 1 1 11 13 15 12 9 20 18 24
47 86 1 0 15 9 17 12 9 22 12 19
48 105 1 0 15 11 23 15 8 20 25 26
49 114 1 1 17 15 21 17 18 25 17 22
50 100 1 0 13 11 13 14 10 20 24 28
51 100 1 1 16 10 18 16 17 22 15 21
52 99 1 1 14 14 19 11 14 23 13 23
53 132 1 1 11 18 22 20 16 25 26 28
54 82 1 1 12 14 16 11 10 23 16 10
55 132 1 0 12 11 24 22 19 23 24 24
56 107 1 1 15 12 18 20 10 22 21 21
57 114 1 1 16 13 20 19 14 24 20 21
58 110 1 1 15 9 24 17 10 25 14 24
59 105 1 0 12 10 14 21 4 21 25 24
60 121 1 0 12 15 22 23 19 12 25 25
61 109 1 1 8 20 24 18 9 17 20 25
62 106 1 1 13 12 18 17 12 20 22 23
63 124 1 1 11 12 21 27 16 23 20 21
64 120 1 0 14 14 23 25 11 23 26 16
65 91 1 1 15 13 17 19 18 20 18 17
66 126 1 0 10 11 22 22 11 28 22 25
67 138 1 0 11 17 24 24 24 24 24 24
68 118 1 0 12 12 21 20 17 24 17 23
69 128 1 1 15 13 22 19 18 24 24 25
70 98 1 1 15 14 16 11 9 24 20 23
71 133 1 1 14 13 21 22 19 28 19 28
72 130 1 0 16 15 23 22 18 25 20 26
73 103 1 0 15 13 22 16 12 21 15 22
74 124 1 1 15 10 24 20 23 25 23 19
75 142 1 1 13 11 24 24 22 25 26 26
76 96 1 1 12 19 16 16 14 18 22 18
77 93 1 1 17 13 16 16 14 17 20 18
78 129 1 0 13 17 21 22 16 26 24 25
79 150 1 0 15 13 26 24 23 28 26 27
80 88 1 0 13 9 15 16 7 21 21 12
81 125 1 0 15 11 25 27 10 27 25 15
82 92 1 1 16 10 18 11 12 22 13 21
83 0 1 0 15 9 23 21 12 21 20 23
84 117 1 1 16 12 20 20 12 25 22 22
85 112 1 0 15 12 17 20 17 22 23 21
86 144 1 0 14 13 25 27 21 23 28 24
87 130 1 1 15 13 24 20 16 26 22 27
88 87 1 1 14 12 17 12 11 19 20 22
89 92 1 1 13 15 19 8 14 25 6 28
90 114 1 1 7 22 20 21 13 21 21 26
91 81 1 1 17 13 15 18 9 13 20 10
92 127 1 0 13 15 27 24 19 24 18 19
93 115 1 1 15 13 22 16 13 25 23 22
94 123 1 1 14 15 23 18 19 26 20 21
95 115 1 1 13 10 16 20 13 25 24 24
96 117 1 1 16 11 19 20 13 25 22 25
97 117 1 0 12 16 25 19 13 22 21 21
98 103 1 1 14 11 19 17 14 21 18 20
99 108 1 0 17 11 19 16 12 23 21 21
100 139 1 0 15 10 26 26 22 25 23 24
101 113 1 1 17 10 21 15 11 24 23 23
102 97 1 0 12 16 20 22 5 21 15 18
103 117 1 1 16 12 24 17 18 21 21 24
104 133 1 1 11 11 22 23 19 25 24 24
105 115 1 0 15 16 20 21 14 22 23 19
106 103 1 1 9 19 18 19 15 20 21 20
107 95 1 0 16 11 18 14 12 20 21 18
108 117 1 1 15 16 24 17 19 23 20 20
109 113 1 1 10 15 24 12 15 28 11 27
110 127 1 1 10 24 22 24 17 23 22 23
111 126 1 1 15 14 23 18 8 28 27 26
112 119 1 1 11 15 22 20 10 24 25 23
113 97 1 1 13 11 20 16 12 18 18 17
114 105 1 1 14 15 18 20 12 20 20 21
115 140 1 1 18 12 25 22 20 28 24 25
116 91 1 0 16 10 18 12 12 21 10 23
117 112 1 1 14 14 16 16 12 21 27 27
118 113 1 1 14 13 20 17 14 25 21 24
119 102 1 0 14 9 19 22 6 19 21 20
120 92 1 1 14 15 15 12 10 18 18 27
121 98 1 1 12 15 19 14 18 21 15 21
122 122 1 1 14 14 19 23 18 22 24 24
123 100 1 1 15 11 16 15 7 24 22 21
124 84 1 1 15 8 17 17 18 15 14 15
125 142 1 1 15 11 28 28 9 28 28 25
126 124 1 0 13 11 23 20 17 26 18 25
127 137 1 1 17 8 25 23 22 23 26 22
128 105 1 1 17 10 20 13 11 26 17 24
129 106 1 0 19 11 17 18 15 20 19 21
130 125 1 0 15 13 23 23 17 22 22 22
131 104 1 1 13 11 16 19 15 20 18 23
132 130 1 0 9 20 23 23 22 23 24 22
133 79 1 0 15 10 11 12 9 22 15 20
134 108 1 0 15 15 18 16 13 24 18 23
135 136 1 0 15 12 24 23 20 23 26 25
136 98 1 1 16 14 23 13 14 22 11 23
137 120 1 1 11 23 21 22 14 26 26 22
138 108 1 0 14 14 16 18 12 23 21 25
139 139 1 0 11 16 24 23 20 27 23 26
140 123 1 1 15 11 23 20 20 23 23 22
141 90 1 1 13 12 18 10 8 21 15 24
142 119 1 1 15 10 20 17 17 26 22 24
143 105 1 1 16 14 9 18 9 23 26 25
144 110 1 0 14 12 24 15 18 21 16 20
145 135 1 1 15 12 25 23 22 27 20 26
146 101 1 1 16 11 20 17 10 19 18 21
147 114 1 0 16 12 21 17 13 23 22 26
148 118 1 0 11 13 25 22 15 25 16 21
149 120 1 0 12 11 22 20 18 23 19 22
150 108 1 0 9 19 21 20 18 22 20 16
151 114 1 1 16 12 21 19 12 22 19 26
152 122 1 1 13 17 22 18 12 25 23 28
153 132 1 1 16 9 27 22 20 25 24 18
154 130 1 0 12 12 24 20 12 28 25 25
155 130 1 0 9 19 24 22 16 28 21 23
156 112 1 0 13 18 21 18 16 20 21 21
157 114 1 1 13 15 18 16 18 25 23 20
158 103 1 1 14 14 16 16 16 19 27 25
159 115 0 1 19 11 22 16 13 25 23 22
160 108 0 1 13 9 20 16 17 22 18 21
161 94 0 0 12 18 18 17 13 18 16 16
162 105 0 1 13 16 20 18 17 20 16 18
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop Gender Happiness Depression I1
-17.46112 -1.70970 0.71523 0.09665 0.33439 0.87259
I2 I3 E1 E2 E3
1.08065 0.96584 1.53094 0.98017 0.83492
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-110.598 -0.721 0.865 2.424 8.260
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -17.46112 11.11299 -1.571 0.118221
Pop -1.70970 4.89002 -0.350 0.727104
Gender 0.71523 1.63364 0.438 0.662145
Happiness 0.09665 0.38682 0.250 0.803029
Depression 0.33439 0.28884 1.158 0.248803
I1 0.87259 0.30114 2.898 0.004320 **
I2 1.08065 0.27197 3.973 0.000109 ***
I3 0.96584 0.19827 4.871 2.77e-06 ***
E1 1.53094 0.29239 5.236 5.41e-07 ***
E2 0.98017 0.22685 4.321 2.81e-05 ***
E3 0.83492 0.23575 3.542 0.000529 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.488 on 151 degrees of freedom
Multiple R-squared: 0.7507, Adjusted R-squared: 0.7342
F-statistic: 45.47 on 10 and 151 DF, p-value: < 2.2e-16
> 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,] 2.396965e-02 4.793931e-02 9.760303e-01
[2,] 5.358602e-03 1.071720e-02 9.946414e-01
[3,] 1.454378e-03 2.908756e-03 9.985456e-01
[4,] 3.429974e-04 6.859948e-04 9.996570e-01
[5,] 6.377881e-05 1.275576e-04 9.999362e-01
[6,] 1.215347e-05 2.430694e-05 9.999878e-01
[7,] 4.493845e-06 8.987691e-06 9.999955e-01
[8,] 1.002328e-06 2.004656e-06 9.999990e-01
[9,] 1.708316e-07 3.416632e-07 9.999998e-01
[10,] 2.979455e-08 5.958911e-08 1.000000e+00
[11,] 6.379679e-09 1.275936e-08 1.000000e+00
[12,] 1.028377e-09 2.056754e-09 1.000000e+00
[13,] 3.016338e-10 6.032676e-10 1.000000e+00
[14,] 5.382493e-11 1.076499e-10 1.000000e+00
[15,] 1.373883e-11 2.747767e-11 1.000000e+00
[16,] 7.898201e-12 1.579640e-11 1.000000e+00
[17,] 1.685704e-12 3.371409e-12 1.000000e+00
[18,] 2.720589e-13 5.441177e-13 1.000000e+00
[19,] 5.202171e-14 1.040434e-13 1.000000e+00
[20,] 1.973524e-11 3.947048e-11 1.000000e+00
[21,] 1.408324e-11 2.816649e-11 1.000000e+00
[22,] 4.515786e-12 9.031572e-12 1.000000e+00
[23,] 1.251525e-12 2.503049e-12 1.000000e+00
[24,] 2.625967e-13 5.251933e-13 1.000000e+00
[25,] 8.729591e-14 1.745918e-13 1.000000e+00
[26,] 1.918352e-14 3.836704e-14 1.000000e+00
[27,] 4.046609e-15 8.093218e-15 1.000000e+00
[28,] 2.163269e-15 4.326538e-15 1.000000e+00
[29,] 4.399776e-16 8.799551e-16 1.000000e+00
[30,] 1.027730e-16 2.055460e-16 1.000000e+00
[31,] 1.982898e-17 3.965796e-17 1.000000e+00
[32,] 3.962797e-18 7.925594e-18 1.000000e+00
[33,] 4.169134e-15 8.338268e-15 1.000000e+00
[34,] 1.091088e-15 2.182177e-15 1.000000e+00
[35,] 2.099457e-15 4.198914e-15 1.000000e+00
[36,] 5.258544e-16 1.051709e-15 1.000000e+00
[37,] 1.505439e-16 3.010877e-16 1.000000e+00
[38,] 5.430522e-17 1.086104e-16 1.000000e+00
[39,] 1.748523e-17 3.497045e-17 1.000000e+00
[40,] 4.070135e-18 8.140271e-18 1.000000e+00
[41,] 1.364610e-18 2.729220e-18 1.000000e+00
[42,] 4.392348e-19 8.784697e-19 1.000000e+00
[43,] 1.003255e-19 2.006509e-19 1.000000e+00
[44,] 2.463696e-20 4.927393e-20 1.000000e+00
[45,] 5.666983e-21 1.133397e-20 1.000000e+00
[46,] 1.498472e-21 2.996944e-21 1.000000e+00
[47,] 7.538597e-22 1.507719e-21 1.000000e+00
[48,] 2.008901e-22 4.017802e-22 1.000000e+00
[49,] 4.481938e-23 8.963876e-23 1.000000e+00
[50,] 9.940086e-24 1.988017e-23 1.000000e+00
[51,] 2.124449e-24 4.248898e-24 1.000000e+00
[52,] 1.305280e-21 2.610559e-21 1.000000e+00
[53,] 3.503427e-22 7.006854e-22 1.000000e+00
[54,] 8.133328e-23 1.626666e-22 1.000000e+00
[55,] 2.155092e-23 4.310185e-23 1.000000e+00
[56,] 6.392701e-24 1.278540e-23 1.000000e+00
[57,] 1.606283e-24 3.212566e-24 1.000000e+00
[58,] 3.569834e-25 7.139668e-25 1.000000e+00
[59,] 8.376950e-26 1.675390e-25 1.000000e+00
[60,] 2.032245e-26 4.064490e-26 1.000000e+00
[61,] 9.152698e-27 1.830540e-26 1.000000e+00
[62,] 2.043866e-27 4.087731e-27 1.000000e+00
[63,] 4.474169e-28 8.948338e-28 1.000000e+00
[64,] 9.471207e-29 1.894241e-28 1.000000e+00
[65,] 1.939863e-29 3.879727e-29 1.000000e+00
[66,] 4.162351e-30 8.324701e-30 1.000000e+00
[67,] 1.392237e-30 2.784474e-30 1.000000e+00
[68,] 2.861622e-31 5.723244e-31 1.000000e+00
[69,] 6.776949e-32 1.355390e-31 1.000000e+00
[70,] 1.000000e+00 4.009727e-37 2.004864e-37
[71,] 1.000000e+00 2.387517e-36 1.193759e-36
[72,] 1.000000e+00 1.254904e-35 6.274518e-36
[73,] 1.000000e+00 6.267700e-35 3.133850e-35
[74,] 1.000000e+00 3.788443e-34 1.894221e-34
[75,] 1.000000e+00 5.572939e-35 2.786469e-35
[76,] 1.000000e+00 3.379743e-34 1.689871e-34
[77,] 1.000000e+00 1.918552e-33 9.592759e-34
[78,] 1.000000e+00 4.862661e-33 2.431331e-33
[79,] 1.000000e+00 3.005976e-32 1.502988e-32
[80,] 1.000000e+00 1.852861e-31 9.264307e-32
[81,] 1.000000e+00 6.724312e-31 3.362156e-31
[82,] 1.000000e+00 4.049955e-30 2.024978e-30
[83,] 1.000000e+00 2.060901e-29 1.030450e-29
[84,] 1.000000e+00 1.122460e-28 5.612302e-29
[85,] 1.000000e+00 6.016848e-28 3.008424e-28
[86,] 1.000000e+00 2.839085e-27 1.419542e-27
[87,] 1.000000e+00 7.968541e-27 3.984270e-27
[88,] 1.000000e+00 2.998356e-26 1.499178e-26
[89,] 1.000000e+00 1.527642e-25 7.638211e-26
[90,] 1.000000e+00 7.524107e-25 3.762053e-25
[91,] 1.000000e+00 1.789482e-24 8.947410e-25
[92,] 1.000000e+00 8.426930e-24 4.213465e-24
[93,] 1.000000e+00 3.357065e-23 1.678533e-23
[94,] 1.000000e+00 1.458835e-22 7.294174e-23
[95,] 1.000000e+00 7.574977e-22 3.787488e-22
[96,] 1.000000e+00 2.444229e-21 1.222115e-21
[97,] 1.000000e+00 4.816776e-21 2.408388e-21
[98,] 1.000000e+00 2.462549e-20 1.231275e-20
[99,] 1.000000e+00 1.112118e-19 5.560592e-20
[100,] 1.000000e+00 1.664292e-19 8.321460e-20
[101,] 1.000000e+00 7.414280e-19 3.707140e-19
[102,] 1.000000e+00 3.467291e-18 1.733646e-18
[103,] 1.000000e+00 1.687604e-17 8.438018e-18
[104,] 1.000000e+00 6.109083e-17 3.054542e-17
[105,] 1.000000e+00 2.774765e-16 1.387383e-16
[106,] 1.000000e+00 1.191833e-15 5.959165e-16
[107,] 1.000000e+00 4.747067e-15 2.373534e-15
[108,] 1.000000e+00 2.137528e-14 1.068764e-14
[109,] 1.000000e+00 9.874354e-14 4.937177e-14
[110,] 1.000000e+00 3.430358e-13 1.715179e-13
[111,] 1.000000e+00 3.313974e-13 1.656987e-13
[112,] 1.000000e+00 1.196190e-12 5.980950e-13
[113,] 1.000000e+00 5.377920e-12 2.688960e-12
[114,] 1.000000e+00 1.840591e-11 9.202955e-12
[115,] 1.000000e+00 8.241946e-11 4.120973e-11
[116,] 1.000000e+00 3.212675e-10 1.606338e-10
[117,] 1.000000e+00 1.341899e-09 6.709495e-10
[118,] 1.000000e+00 5.560977e-09 2.780488e-09
[119,] 1.000000e+00 2.184780e-08 1.092390e-08
[120,] 1.000000e+00 1.158053e-08 5.790264e-09
[121,] 1.000000e+00 4.895716e-08 2.447858e-08
[122,] 9.999999e-01 1.537544e-07 7.687719e-08
[123,] 9.999998e-01 3.127290e-07 1.563645e-07
[124,] 9.999998e-01 4.786667e-07 2.393333e-07
[125,] 9.999991e-01 1.725048e-06 8.625240e-07
[126,] 9.999984e-01 3.277365e-06 1.638682e-06
[127,] 9.999928e-01 1.430231e-05 7.151155e-06
[128,] 9.999758e-01 4.832010e-05 2.416005e-05
[129,] 9.999091e-01 1.818618e-04 9.093089e-05
[130,] 9.997904e-01 4.191827e-04 2.095913e-04
[131,] 9.992862e-01 1.427548e-03 7.137741e-04
[132,] 9.974823e-01 5.035456e-03 2.517728e-03
[133,] 9.916013e-01 1.679747e-02 8.398733e-03
[134,] 9.768914e-01 4.621710e-02 2.310855e-02
[135,] 9.773884e-01 4.522323e-02 2.261162e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1f1z31355063178.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/2yryi1355063178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3fqpj1355063178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/44ryx1355063178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5wdq71355063178.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
3.34837143 1.70385549 0.19206142 0.60744617 -4.96602633
6 7 8 9 10
0.53407067 -2.28078042 -2.65496526 2.69042603 2.59170638
11 12 13 14 15
3.36942351 2.75227316 -0.10223169 -0.74050760 1.61968791
16 17 18 19 20
-1.36674072 2.05556346 2.15779037 1.63411146 -1.86137263
21 22 23 24 25
4.40305547 0.18143161 2.90890136 0.84632878 0.65567801
26 27 28 29 30
2.98192142 1.63655794 3.94932930 8.26031791 2.13599076
31 32 33 34 35
2.83632651 1.70607140 -6.72442685 -1.86571717 5.06952902
36 37 38 39 40
-0.58446850 1.36902125 2.36563464 2.45895506 1.43774568
41 42 43 44 45
-0.66113108 2.38357797 1.79149909 1.19305392 4.44216816
46 47 48 49 50
-8.00379972 2.91091893 -1.79425740 -1.58861257 0.58427981
51 52 53 54 55
-1.76765718 2.27584620 -0.02573635 -0.13626205 4.49144535
56 57 58 59 60
1.21750782 1.17684484 2.99069138 4.20179306 7.84362221
61 62 63 64 65
4.40571107 2.13295802 2.07593653 1.37135235 -11.54834986
66 67 68 69 70
1.62742749 0.06027807 3.03297434 2.40459926 0.23409058
71 72 73 74 75
0.43837806 1.79465414 3.07591956 -3.78854752 1.92867677
76 77 78 79 80
-1.98052725 0.03383012 -0.52398017 1.64306088 3.01234819
81 82 83 84 85
0.02856728 2.42516293 -110.59811842 1.03608599 -0.91590638
86 87 88 89 90
3.50113758 0.73907352 -5.39580550 -2.09522698 -2.72021332
91 92 93 94 95
4.37742284 1.80278400 -0.57023753 0.27314671 -1.11080547
96 97 98 99 100
-1.22752437 2.96010057 1.46089783 3.06122271 0.48883853
101 102 103 104 105
2.82058707 1.72454908 0.42668423 2.47552509 1.61545631
106 107 108 109 110
-4.39509671 0.28933763 -0.52211389 0.88458707 -1.06677071
111 112 113 114 115
0.03754564 0.45819777 4.79486136 0.42133378 0.53385735
116 117 118 119 120
3.86134584 0.42191886 -2.48439615 4.28977895 0.62872859
121 122 123 124 125
-3.19920084 -1.64118263 0.55584669 -1.46989599 0.76013271
126 127 128 129 130
1.81365264 4.15484218 -0.16130376 4.10740451 3.41743051
131 132 133 134 135
1.07436493 -1.66388676 -2.96333155 1.56343650 3.02534256
136 137 138 139 140
-1.07787174 -9.57615728 -0.53519069 1.05622370 -0.79569443
141 142 143 144 145
2.05621954 -0.98650673 -0.33888098 4.73703632 -2.57183719
146 147 148 149 150
4.48532852 -0.12295415 0.19430940 3.93445039 -4.01786347
151 152 153 154 155
2.43778472 -0.91946839 2.42238864 1.60950255 -0.87546800
156 157 158 159 160
1.93002714 -1.71472836 -7.70975034 -1.99776640 0.46131229
161 162
1.05024059 0.48621352
> postscript(file="/var/wessaorg/rcomp/tmp/6ud5d1355063178.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 3.34837143 NA
1 1.70385549 3.34837143
2 0.19206142 1.70385549
3 0.60744617 0.19206142
4 -4.96602633 0.60744617
5 0.53407067 -4.96602633
6 -2.28078042 0.53407067
7 -2.65496526 -2.28078042
8 2.69042603 -2.65496526
9 2.59170638 2.69042603
10 3.36942351 2.59170638
11 2.75227316 3.36942351
12 -0.10223169 2.75227316
13 -0.74050760 -0.10223169
14 1.61968791 -0.74050760
15 -1.36674072 1.61968791
16 2.05556346 -1.36674072
17 2.15779037 2.05556346
18 1.63411146 2.15779037
19 -1.86137263 1.63411146
20 4.40305547 -1.86137263
21 0.18143161 4.40305547
22 2.90890136 0.18143161
23 0.84632878 2.90890136
24 0.65567801 0.84632878
25 2.98192142 0.65567801
26 1.63655794 2.98192142
27 3.94932930 1.63655794
28 8.26031791 3.94932930
29 2.13599076 8.26031791
30 2.83632651 2.13599076
31 1.70607140 2.83632651
32 -6.72442685 1.70607140
33 -1.86571717 -6.72442685
34 5.06952902 -1.86571717
35 -0.58446850 5.06952902
36 1.36902125 -0.58446850
37 2.36563464 1.36902125
38 2.45895506 2.36563464
39 1.43774568 2.45895506
40 -0.66113108 1.43774568
41 2.38357797 -0.66113108
42 1.79149909 2.38357797
43 1.19305392 1.79149909
44 4.44216816 1.19305392
45 -8.00379972 4.44216816
46 2.91091893 -8.00379972
47 -1.79425740 2.91091893
48 -1.58861257 -1.79425740
49 0.58427981 -1.58861257
50 -1.76765718 0.58427981
51 2.27584620 -1.76765718
52 -0.02573635 2.27584620
53 -0.13626205 -0.02573635
54 4.49144535 -0.13626205
55 1.21750782 4.49144535
56 1.17684484 1.21750782
57 2.99069138 1.17684484
58 4.20179306 2.99069138
59 7.84362221 4.20179306
60 4.40571107 7.84362221
61 2.13295802 4.40571107
62 2.07593653 2.13295802
63 1.37135235 2.07593653
64 -11.54834986 1.37135235
65 1.62742749 -11.54834986
66 0.06027807 1.62742749
67 3.03297434 0.06027807
68 2.40459926 3.03297434
69 0.23409058 2.40459926
70 0.43837806 0.23409058
71 1.79465414 0.43837806
72 3.07591956 1.79465414
73 -3.78854752 3.07591956
74 1.92867677 -3.78854752
75 -1.98052725 1.92867677
76 0.03383012 -1.98052725
77 -0.52398017 0.03383012
78 1.64306088 -0.52398017
79 3.01234819 1.64306088
80 0.02856728 3.01234819
81 2.42516293 0.02856728
82 -110.59811842 2.42516293
83 1.03608599 -110.59811842
84 -0.91590638 1.03608599
85 3.50113758 -0.91590638
86 0.73907352 3.50113758
87 -5.39580550 0.73907352
88 -2.09522698 -5.39580550
89 -2.72021332 -2.09522698
90 4.37742284 -2.72021332
91 1.80278400 4.37742284
92 -0.57023753 1.80278400
93 0.27314671 -0.57023753
94 -1.11080547 0.27314671
95 -1.22752437 -1.11080547
96 2.96010057 -1.22752437
97 1.46089783 2.96010057
98 3.06122271 1.46089783
99 0.48883853 3.06122271
100 2.82058707 0.48883853
101 1.72454908 2.82058707
102 0.42668423 1.72454908
103 2.47552509 0.42668423
104 1.61545631 2.47552509
105 -4.39509671 1.61545631
106 0.28933763 -4.39509671
107 -0.52211389 0.28933763
108 0.88458707 -0.52211389
109 -1.06677071 0.88458707
110 0.03754564 -1.06677071
111 0.45819777 0.03754564
112 4.79486136 0.45819777
113 0.42133378 4.79486136
114 0.53385735 0.42133378
115 3.86134584 0.53385735
116 0.42191886 3.86134584
117 -2.48439615 0.42191886
118 4.28977895 -2.48439615
119 0.62872859 4.28977895
120 -3.19920084 0.62872859
121 -1.64118263 -3.19920084
122 0.55584669 -1.64118263
123 -1.46989599 0.55584669
124 0.76013271 -1.46989599
125 1.81365264 0.76013271
126 4.15484218 1.81365264
127 -0.16130376 4.15484218
128 4.10740451 -0.16130376
129 3.41743051 4.10740451
130 1.07436493 3.41743051
131 -1.66388676 1.07436493
132 -2.96333155 -1.66388676
133 1.56343650 -2.96333155
134 3.02534256 1.56343650
135 -1.07787174 3.02534256
136 -9.57615728 -1.07787174
137 -0.53519069 -9.57615728
138 1.05622370 -0.53519069
139 -0.79569443 1.05622370
140 2.05621954 -0.79569443
141 -0.98650673 2.05621954
142 -0.33888098 -0.98650673
143 4.73703632 -0.33888098
144 -2.57183719 4.73703632
145 4.48532852 -2.57183719
146 -0.12295415 4.48532852
147 0.19430940 -0.12295415
148 3.93445039 0.19430940
149 -4.01786347 3.93445039
150 2.43778472 -4.01786347
151 -0.91946839 2.43778472
152 2.42238864 -0.91946839
153 1.60950255 2.42238864
154 -0.87546800 1.60950255
155 1.93002714 -0.87546800
156 -1.71472836 1.93002714
157 -7.70975034 -1.71472836
158 -1.99776640 -7.70975034
159 0.46131229 -1.99776640
160 1.05024059 0.46131229
161 0.48621352 1.05024059
162 NA 0.48621352
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.70385549 3.34837143
[2,] 0.19206142 1.70385549
[3,] 0.60744617 0.19206142
[4,] -4.96602633 0.60744617
[5,] 0.53407067 -4.96602633
[6,] -2.28078042 0.53407067
[7,] -2.65496526 -2.28078042
[8,] 2.69042603 -2.65496526
[9,] 2.59170638 2.69042603
[10,] 3.36942351 2.59170638
[11,] 2.75227316 3.36942351
[12,] -0.10223169 2.75227316
[13,] -0.74050760 -0.10223169
[14,] 1.61968791 -0.74050760
[15,] -1.36674072 1.61968791
[16,] 2.05556346 -1.36674072
[17,] 2.15779037 2.05556346
[18,] 1.63411146 2.15779037
[19,] -1.86137263 1.63411146
[20,] 4.40305547 -1.86137263
[21,] 0.18143161 4.40305547
[22,] 2.90890136 0.18143161
[23,] 0.84632878 2.90890136
[24,] 0.65567801 0.84632878
[25,] 2.98192142 0.65567801
[26,] 1.63655794 2.98192142
[27,] 3.94932930 1.63655794
[28,] 8.26031791 3.94932930
[29,] 2.13599076 8.26031791
[30,] 2.83632651 2.13599076
[31,] 1.70607140 2.83632651
[32,] -6.72442685 1.70607140
[33,] -1.86571717 -6.72442685
[34,] 5.06952902 -1.86571717
[35,] -0.58446850 5.06952902
[36,] 1.36902125 -0.58446850
[37,] 2.36563464 1.36902125
[38,] 2.45895506 2.36563464
[39,] 1.43774568 2.45895506
[40,] -0.66113108 1.43774568
[41,] 2.38357797 -0.66113108
[42,] 1.79149909 2.38357797
[43,] 1.19305392 1.79149909
[44,] 4.44216816 1.19305392
[45,] -8.00379972 4.44216816
[46,] 2.91091893 -8.00379972
[47,] -1.79425740 2.91091893
[48,] -1.58861257 -1.79425740
[49,] 0.58427981 -1.58861257
[50,] -1.76765718 0.58427981
[51,] 2.27584620 -1.76765718
[52,] -0.02573635 2.27584620
[53,] -0.13626205 -0.02573635
[54,] 4.49144535 -0.13626205
[55,] 1.21750782 4.49144535
[56,] 1.17684484 1.21750782
[57,] 2.99069138 1.17684484
[58,] 4.20179306 2.99069138
[59,] 7.84362221 4.20179306
[60,] 4.40571107 7.84362221
[61,] 2.13295802 4.40571107
[62,] 2.07593653 2.13295802
[63,] 1.37135235 2.07593653
[64,] -11.54834986 1.37135235
[65,] 1.62742749 -11.54834986
[66,] 0.06027807 1.62742749
[67,] 3.03297434 0.06027807
[68,] 2.40459926 3.03297434
[69,] 0.23409058 2.40459926
[70,] 0.43837806 0.23409058
[71,] 1.79465414 0.43837806
[72,] 3.07591956 1.79465414
[73,] -3.78854752 3.07591956
[74,] 1.92867677 -3.78854752
[75,] -1.98052725 1.92867677
[76,] 0.03383012 -1.98052725
[77,] -0.52398017 0.03383012
[78,] 1.64306088 -0.52398017
[79,] 3.01234819 1.64306088
[80,] 0.02856728 3.01234819
[81,] 2.42516293 0.02856728
[82,] -110.59811842 2.42516293
[83,] 1.03608599 -110.59811842
[84,] -0.91590638 1.03608599
[85,] 3.50113758 -0.91590638
[86,] 0.73907352 3.50113758
[87,] -5.39580550 0.73907352
[88,] -2.09522698 -5.39580550
[89,] -2.72021332 -2.09522698
[90,] 4.37742284 -2.72021332
[91,] 1.80278400 4.37742284
[92,] -0.57023753 1.80278400
[93,] 0.27314671 -0.57023753
[94,] -1.11080547 0.27314671
[95,] -1.22752437 -1.11080547
[96,] 2.96010057 -1.22752437
[97,] 1.46089783 2.96010057
[98,] 3.06122271 1.46089783
[99,] 0.48883853 3.06122271
[100,] 2.82058707 0.48883853
[101,] 1.72454908 2.82058707
[102,] 0.42668423 1.72454908
[103,] 2.47552509 0.42668423
[104,] 1.61545631 2.47552509
[105,] -4.39509671 1.61545631
[106,] 0.28933763 -4.39509671
[107,] -0.52211389 0.28933763
[108,] 0.88458707 -0.52211389
[109,] -1.06677071 0.88458707
[110,] 0.03754564 -1.06677071
[111,] 0.45819777 0.03754564
[112,] 4.79486136 0.45819777
[113,] 0.42133378 4.79486136
[114,] 0.53385735 0.42133378
[115,] 3.86134584 0.53385735
[116,] 0.42191886 3.86134584
[117,] -2.48439615 0.42191886
[118,] 4.28977895 -2.48439615
[119,] 0.62872859 4.28977895
[120,] -3.19920084 0.62872859
[121,] -1.64118263 -3.19920084
[122,] 0.55584669 -1.64118263
[123,] -1.46989599 0.55584669
[124,] 0.76013271 -1.46989599
[125,] 1.81365264 0.76013271
[126,] 4.15484218 1.81365264
[127,] -0.16130376 4.15484218
[128,] 4.10740451 -0.16130376
[129,] 3.41743051 4.10740451
[130,] 1.07436493 3.41743051
[131,] -1.66388676 1.07436493
[132,] -2.96333155 -1.66388676
[133,] 1.56343650 -2.96333155
[134,] 3.02534256 1.56343650
[135,] -1.07787174 3.02534256
[136,] -9.57615728 -1.07787174
[137,] -0.53519069 -9.57615728
[138,] 1.05622370 -0.53519069
[139,] -0.79569443 1.05622370
[140,] 2.05621954 -0.79569443
[141,] -0.98650673 2.05621954
[142,] -0.33888098 -0.98650673
[143,] 4.73703632 -0.33888098
[144,] -2.57183719 4.73703632
[145,] 4.48532852 -2.57183719
[146,] -0.12295415 4.48532852
[147,] 0.19430940 -0.12295415
[148,] 3.93445039 0.19430940
[149,] -4.01786347 3.93445039
[150,] 2.43778472 -4.01786347
[151,] -0.91946839 2.43778472
[152,] 2.42238864 -0.91946839
[153,] 1.60950255 2.42238864
[154,] -0.87546800 1.60950255
[155,] 1.93002714 -0.87546800
[156,] -1.71472836 1.93002714
[157,] -7.70975034 -1.71472836
[158,] -1.99776640 -7.70975034
[159,] 0.46131229 -1.99776640
[160,] 1.05024059 0.46131229
[161,] 0.48621352 1.05024059
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.70385549 3.34837143
2 0.19206142 1.70385549
3 0.60744617 0.19206142
4 -4.96602633 0.60744617
5 0.53407067 -4.96602633
6 -2.28078042 0.53407067
7 -2.65496526 -2.28078042
8 2.69042603 -2.65496526
9 2.59170638 2.69042603
10 3.36942351 2.59170638
11 2.75227316 3.36942351
12 -0.10223169 2.75227316
13 -0.74050760 -0.10223169
14 1.61968791 -0.74050760
15 -1.36674072 1.61968791
16 2.05556346 -1.36674072
17 2.15779037 2.05556346
18 1.63411146 2.15779037
19 -1.86137263 1.63411146
20 4.40305547 -1.86137263
21 0.18143161 4.40305547
22 2.90890136 0.18143161
23 0.84632878 2.90890136
24 0.65567801 0.84632878
25 2.98192142 0.65567801
26 1.63655794 2.98192142
27 3.94932930 1.63655794
28 8.26031791 3.94932930
29 2.13599076 8.26031791
30 2.83632651 2.13599076
31 1.70607140 2.83632651
32 -6.72442685 1.70607140
33 -1.86571717 -6.72442685
34 5.06952902 -1.86571717
35 -0.58446850 5.06952902
36 1.36902125 -0.58446850
37 2.36563464 1.36902125
38 2.45895506 2.36563464
39 1.43774568 2.45895506
40 -0.66113108 1.43774568
41 2.38357797 -0.66113108
42 1.79149909 2.38357797
43 1.19305392 1.79149909
44 4.44216816 1.19305392
45 -8.00379972 4.44216816
46 2.91091893 -8.00379972
47 -1.79425740 2.91091893
48 -1.58861257 -1.79425740
49 0.58427981 -1.58861257
50 -1.76765718 0.58427981
51 2.27584620 -1.76765718
52 -0.02573635 2.27584620
53 -0.13626205 -0.02573635
54 4.49144535 -0.13626205
55 1.21750782 4.49144535
56 1.17684484 1.21750782
57 2.99069138 1.17684484
58 4.20179306 2.99069138
59 7.84362221 4.20179306
60 4.40571107 7.84362221
61 2.13295802 4.40571107
62 2.07593653 2.13295802
63 1.37135235 2.07593653
64 -11.54834986 1.37135235
65 1.62742749 -11.54834986
66 0.06027807 1.62742749
67 3.03297434 0.06027807
68 2.40459926 3.03297434
69 0.23409058 2.40459926
70 0.43837806 0.23409058
71 1.79465414 0.43837806
72 3.07591956 1.79465414
73 -3.78854752 3.07591956
74 1.92867677 -3.78854752
75 -1.98052725 1.92867677
76 0.03383012 -1.98052725
77 -0.52398017 0.03383012
78 1.64306088 -0.52398017
79 3.01234819 1.64306088
80 0.02856728 3.01234819
81 2.42516293 0.02856728
82 -110.59811842 2.42516293
83 1.03608599 -110.59811842
84 -0.91590638 1.03608599
85 3.50113758 -0.91590638
86 0.73907352 3.50113758
87 -5.39580550 0.73907352
88 -2.09522698 -5.39580550
89 -2.72021332 -2.09522698
90 4.37742284 -2.72021332
91 1.80278400 4.37742284
92 -0.57023753 1.80278400
93 0.27314671 -0.57023753
94 -1.11080547 0.27314671
95 -1.22752437 -1.11080547
96 2.96010057 -1.22752437
97 1.46089783 2.96010057
98 3.06122271 1.46089783
99 0.48883853 3.06122271
100 2.82058707 0.48883853
101 1.72454908 2.82058707
102 0.42668423 1.72454908
103 2.47552509 0.42668423
104 1.61545631 2.47552509
105 -4.39509671 1.61545631
106 0.28933763 -4.39509671
107 -0.52211389 0.28933763
108 0.88458707 -0.52211389
109 -1.06677071 0.88458707
110 0.03754564 -1.06677071
111 0.45819777 0.03754564
112 4.79486136 0.45819777
113 0.42133378 4.79486136
114 0.53385735 0.42133378
115 3.86134584 0.53385735
116 0.42191886 3.86134584
117 -2.48439615 0.42191886
118 4.28977895 -2.48439615
119 0.62872859 4.28977895
120 -3.19920084 0.62872859
121 -1.64118263 -3.19920084
122 0.55584669 -1.64118263
123 -1.46989599 0.55584669
124 0.76013271 -1.46989599
125 1.81365264 0.76013271
126 4.15484218 1.81365264
127 -0.16130376 4.15484218
128 4.10740451 -0.16130376
129 3.41743051 4.10740451
130 1.07436493 3.41743051
131 -1.66388676 1.07436493
132 -2.96333155 -1.66388676
133 1.56343650 -2.96333155
134 3.02534256 1.56343650
135 -1.07787174 3.02534256
136 -9.57615728 -1.07787174
137 -0.53519069 -9.57615728
138 1.05622370 -0.53519069
139 -0.79569443 1.05622370
140 2.05621954 -0.79569443
141 -0.98650673 2.05621954
142 -0.33888098 -0.98650673
143 4.73703632 -0.33888098
144 -2.57183719 4.73703632
145 4.48532852 -2.57183719
146 -0.12295415 4.48532852
147 0.19430940 -0.12295415
148 3.93445039 0.19430940
149 -4.01786347 3.93445039
150 2.43778472 -4.01786347
151 -0.91946839 2.43778472
152 2.42238864 -0.91946839
153 1.60950255 2.42238864
154 -0.87546800 1.60950255
155 1.93002714 -0.87546800
156 -1.71472836 1.93002714
157 -7.70975034 -1.71472836
158 -1.99776640 -7.70975034
159 0.46131229 -1.99776640
160 1.05024059 0.46131229
161 0.48621352 1.05024059
> 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/745vp1355063178.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/8pgje1355063178.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/9g9u91355063178.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/10daj81355063178.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11rshp1355063178.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12uurj1355063178.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13pc3f1355063178.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14evqf1355063178.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/15oe8r1355063178.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/166lbj1355063178.tab")
+ }
>
> try(system("convert tmp/1f1z31355063178.ps tmp/1f1z31355063178.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yryi1355063178.ps tmp/2yryi1355063178.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fqpj1355063178.ps tmp/3fqpj1355063178.png",intern=TRUE))
character(0)
> try(system("convert tmp/44ryx1355063178.ps tmp/44ryx1355063178.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wdq71355063178.ps tmp/5wdq71355063178.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ud5d1355063178.ps tmp/6ud5d1355063178.png",intern=TRUE))
character(0)
> try(system("convert tmp/745vp1355063178.ps tmp/745vp1355063178.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pgje1355063178.ps tmp/8pgje1355063178.png",intern=TRUE))
character(0)
> try(system("convert tmp/9g9u91355063178.ps tmp/9g9u91355063178.png",intern=TRUE))
character(0)
> try(system("convert tmp/10daj81355063178.ps tmp/10daj81355063178.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.623 1.003 9.628