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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+ ,21
+ ,6
+ ,13
+ ,9
+ ,108
+ ,18
+ ,17
+ ,13
+ ,18
+ ,16
+ ,16
+ ,4
+ ,12
+ ,18
+ ,94
+ ,20
+ ,18
+ ,17
+ ,20
+ ,16
+ ,18
+ ,4
+ ,13
+ ,16
+ ,105)
+ ,dim=c(10
+ ,162)
+ ,dimnames=list(c('I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A'
+ ,'Happiness'
+ ,'Depression'
+ ,'Motivatie
')
+ ,1:162))
> y <- array(NA,dim=c(10,162),dimnames=list(c('I1','I2','I3','E1','E2','E3','A','Happiness','Depression','Motivatie
'),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 = '10'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '10'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
Motivatie\r I1 I2 I3 E1 E2 E3 A Happiness Depression
1 127 26 21 21 23 17 23 4 14 12
2 108 20 16 15 24 17 20 4 18 11
3 110 19 19 18 22 18 20 6 11 14
4 102 19 18 11 20 21 21 8 12 12
5 104 20 16 8 24 20 24 8 16 21
6 140 25 23 19 27 28 22 4 18 12
7 112 25 17 4 28 19 23 4 14 22
8 115 22 12 20 27 22 20 8 14 11
9 121 26 19 16 24 16 25 5 15 10
10 112 22 16 14 23 18 23 4 15 13
11 118 17 19 10 24 25 27 4 17 10
12 122 22 20 13 27 17 27 4 19 8
13 105 19 13 14 27 14 22 4 10 15
14 111 24 20 8 28 11 24 4 16 14
15 151 26 27 23 27 27 25 4 18 10
16 106 21 17 11 23 20 22 8 14 14
17 100 13 8 9 24 22 28 4 14 14
18 149 26 25 24 28 22 28 4 17 11
19 122 20 26 5 27 21 27 4 14 10
20 115 22 13 15 25 23 25 8 16 13
21 86 14 19 5 19 17 16 4 18 7
22 124 21 15 19 24 24 28 7 11 14
23 69 7 5 6 20 14 21 4 14 12
24 117 23 16 13 28 17 24 4 12 14
25 113 17 14 11 26 23 27 5 17 11
26 123 25 24 17 23 24 14 4 9 9
27 123 25 24 17 23 24 14 4 16 11
28 84 19 9 5 20 8 27 4 14 15
29 97 20 19 9 11 22 20 4 15 14
30 121 23 19 15 24 23 21 4 11 13
31 132 22 25 17 25 25 22 4 16 9
32 119 22 19 17 23 21 21 4 13 15
33 98 21 18 20 18 24 12 15 17 10
34 87 15 15 12 20 15 20 10 15 11
35 101 20 12 7 20 22 24 4 14 13
36 115 22 21 16 24 21 19 8 16 8
37 109 18 12 7 23 25 28 4 9 20
38 109 20 15 14 25 16 23 4 15 12
39 159 28 28 24 28 28 27 4 17 10
40 129 22 25 15 26 23 22 4 13 10
41 119 18 19 15 26 21 27 7 15 9
42 119 23 20 10 23 21 26 4 16 14
43 122 20 24 14 22 26 22 6 16 8
44 131 25 26 18 24 22 21 5 12 14
45 120 26 25 12 21 21 19 4 12 11
46 82 15 12 9 20 18 24 16 11 13
47 86 17 12 9 22 12 19 5 15 9
48 105 23 15 8 20 25 26 12 15 11
49 114 21 17 18 25 17 22 6 17 15
50 100 13 14 10 20 24 28 9 13 11
51 100 18 16 17 22 15 21 9 16 10
52 99 19 11 14 23 13 23 4 14 14
53 132 22 20 16 25 26 28 5 11 18
54 82 16 11 10 23 16 10 4 12 14
55 132 24 22 19 23 24 24 4 12 11
56 107 18 20 10 22 21 21 5 15 12
57 114 20 19 14 24 20 21 4 16 13
58 110 24 17 10 25 14 24 4 15 9
59 105 14 21 4 21 25 24 4 12 10
60 121 22 23 19 12 25 25 5 12 15
61 109 24 18 9 17 20 25 4 8 20
62 106 18 17 12 20 22 23 6 13 12
63 124 21 27 16 23 20 21 4 11 12
64 120 23 25 11 23 26 16 4 14 14
65 91 17 19 18 20 18 17 18 15 13
66 126 22 22 11 28 22 25 4 10 11
67 138 24 24 24 24 24 24 6 11 17
68 118 21 20 17 24 17 23 4 12 12
69 128 22 19 18 24 24 25 4 15 13
70 98 16 11 9 24 20 23 5 15 14
71 133 21 22 19 28 19 28 4 14 13
72 130 23 22 18 25 20 26 4 16 15
73 103 22 16 12 21 15 22 5 15 13
74 124 24 20 23 25 23 19 10 15 10
75 142 24 24 22 25 26 26 5 13 11
76 96 16 16 14 18 22 18 8 12 19
77 93 16 16 14 17 20 18 8 17 13
78 129 21 22 16 26 24 25 5 13 17
79 150 26 24 23 28 26 27 4 15 13
80 88 15 16 7 21 21 12 4 13 9
81 125 25 27 10 27 25 15 4 15 11
82 92 18 11 12 22 13 21 5 16 10
83 0 23 21 12 21 20 23 4 15 9
84 117 20 20 12 25 22 22 4 16 12
85 112 17 20 17 22 23 21 8 15 12
86 144 25 27 21 23 28 24 4 14 13
87 130 24 20 16 26 22 27 5 15 13
88 87 17 12 11 19 20 22 14 14 12
89 92 19 8 14 25 6 28 8 13 15
90 114 20 21 13 21 21 26 8 7 22
91 81 15 18 9 13 20 10 4 17 13
92 127 27 24 19 24 18 19 4 13 15
93 115 22 16 13 25 23 22 6 15 13
94 123 23 18 19 26 20 21 4 14 15
95 115 16 20 13 25 24 24 7 13 10
96 117 19 20 13 25 22 25 7 16 11
97 117 25 19 13 22 21 21 4 12 16
98 103 19 17 14 21 18 20 6 14 11
99 108 19 16 12 23 21 21 4 17 11
100 139 26 26 22 25 23 24 7 15 10
101 113 21 15 11 24 23 23 4 17 10
102 97 20 22 5 21 15 18 4 12 16
103 117 24 17 18 21 21 24 8 16 12
104 133 22 23 19 25 24 24 4 11 11
105 115 20 21 14 22 23 19 4 15 16
106 103 18 19 15 20 21 20 10 9 19
107 95 18 14 12 20 21 18 8 16 11
108 117 24 17 19 23 20 20 6 15 16
109 113 24 12 15 28 11 27 4 10 15
110 127 22 24 17 23 22 23 4 10 24
111 126 23 18 8 28 27 26 4 15 14
112 119 22 20 10 24 25 23 5 11 15
113 97 20 16 12 18 18 17 4 13 11
114 105 18 20 12 20 20 21 6 14 15
115 140 25 22 20 28 24 25 4 18 12
116 91 18 12 12 21 10 23 5 16 10
117 112 16 16 12 21 27 27 7 14 14
118 113 20 17 14 25 21 24 8 14 13
119 102 19 22 6 19 21 20 5 14 9
120 92 15 12 10 18 18 27 8 14 15
121 98 19 14 18 21 15 21 10 12 15
122 122 19 23 18 22 24 24 8 14 14
123 100 16 15 7 24 22 21 5 15 11
124 84 17 17 18 15 14 15 12 15 8
125 142 28 28 9 28 28 25 4 15 11
126 124 23 20 17 26 18 25 5 13 11
127 137 25 23 22 23 26 22 4 17 8
128 105 20 13 11 26 17 24 6 17 10
129 106 17 18 15 20 19 21 4 19 11
130 125 23 23 17 22 22 22 4 15 13
131 104 16 19 15 20 18 23 7 13 11
132 130 23 23 22 23 24 22 7 9 20
133 79 11 12 9 22 15 20 10 15 10
134 108 18 16 13 24 18 23 4 15 15
135 136 24 23 20 23 26 25 5 15 12
136 98 23 13 14 22 11 23 8 16 14
137 120 21 22 14 26 26 22 11 11 23
138 108 16 18 12 23 21 25 7 14 14
139 139 24 23 20 27 23 26 4 11 16
140 123 23 20 20 23 23 22 8 15 11
141 90 18 10 8 21 15 24 6 13 12
142 119 20 17 17 26 22 24 7 15 10
143 105 9 18 9 23 26 25 5 16 14
144 110 24 15 18 21 16 20 4 14 12
145 135 25 23 22 27 20 26 8 15 12
146 101 20 17 10 19 18 21 4 16 11
147 114 21 17 13 23 22 26 8 16 12
148 118 25 22 15 25 16 21 6 11 13
149 120 22 20 18 23 19 22 4 12 11
150 108 21 20 18 22 20 16 9 9 19
151 114 21 19 12 22 19 26 5 16 12
152 122 22 18 12 25 23 28 6 13 17
153 132 27 22 20 25 24 18 4 16 9
154 130 24 20 12 28 25 25 4 12 12
155 130 24 22 16 28 21 23 4 9 19
156 112 21 18 16 20 21 21 5 13 18
157 114 18 16 18 25 23 20 6 13 15
158 103 16 16 16 19 27 25 16 14 14
159 115 22 16 13 25 23 22 6 19 11
160 108 20 16 17 22 18 21 6 13 9
161 94 18 17 13 18 16 16 4 12 18
162 105 20 18 17 20 16 18 4 13 16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) I1 I2 I3 E1 E2
-7.6489 0.6992 0.9000 1.1705 1.3824 1.0698
E3 A Happiness Depression
0.8202 -0.8995 0.0819 0.3579
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-111.522 -0.379 0.699 1.640 5.243
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.6489 10.6852 -0.716 0.475186
I1 0.6992 0.2986 2.341 0.020516 *
I2 0.9000 0.2588 3.478 0.000660 ***
I3 1.1705 0.2001 5.849 2.92e-08 ***
E1 1.3824 0.2884 4.794 3.87e-06 ***
E2 1.0698 0.2208 4.845 3.09e-06 ***
E3 0.8202 0.2277 3.602 0.000426 ***
A -0.8994 0.3166 -2.841 0.005112 **
Happiness 0.0819 0.3754 0.218 0.827619
Depression 0.3579 0.2798 1.279 0.202715
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.225 on 152 degrees of freedom
Multiple R-squared: 0.7628, Adjusted R-squared: 0.7487
F-statistic: 54.3 on 9 and 152 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,] 6.196135e-46 1.239227e-45 1.000000e+00
[2,] 5.145969e-61 1.029194e-60 1.000000e+00
[3,] 5.201914e-76 1.040383e-75 1.000000e+00
[4,] 1.168894e-90 2.337787e-90 1.000000e+00
[5,] 2.391991e-110 4.783982e-110 1.000000e+00
[6,] 1.719720e-126 3.439439e-126 1.000000e+00
[7,] 3.767031e-134 7.534062e-134 1.000000e+00
[8,] 4.588146e-149 9.176293e-149 1.000000e+00
[9,] 4.608599e-168 9.217198e-168 1.000000e+00
[10,] 6.530737e-187 1.306147e-186 1.000000e+00
[11,] 7.156867e-193 1.431373e-192 1.000000e+00
[12,] 3.672731e-209 7.345462e-209 1.000000e+00
[13,] 3.579048e-232 7.158096e-232 1.000000e+00
[14,] 3.192369e-245 6.384737e-245 1.000000e+00
[15,] 1.957809e-259 3.915618e-259 1.000000e+00
[16,] 5.061588e-277 1.012318e-276 1.000000e+00
[17,] 7.063665e-281 1.412733e-280 1.000000e+00
[18,] 3.001685e-310 6.003371e-310 1.000000e+00
[19,] 9.881313e-324 1.976263e-323 1.000000e+00
[20,] 0.000000e+00 0.000000e+00 1.000000e+00
[21,] 0.000000e+00 0.000000e+00 1.000000e+00
[22,] 0.000000e+00 0.000000e+00 1.000000e+00
[23,] 0.000000e+00 0.000000e+00 1.000000e+00
[24,] 0.000000e+00 0.000000e+00 1.000000e+00
[25,] 0.000000e+00 0.000000e+00 1.000000e+00
[26,] 0.000000e+00 0.000000e+00 1.000000e+00
[27,] 0.000000e+00 0.000000e+00 1.000000e+00
[28,] 0.000000e+00 0.000000e+00 1.000000e+00
[29,] 0.000000e+00 0.000000e+00 1.000000e+00
[30,] 0.000000e+00 0.000000e+00 1.000000e+00
[31,] 0.000000e+00 0.000000e+00 1.000000e+00
[32,] 0.000000e+00 0.000000e+00 1.000000e+00
[33,] 0.000000e+00 0.000000e+00 1.000000e+00
[34,] 0.000000e+00 0.000000e+00 1.000000e+00
[35,] 0.000000e+00 0.000000e+00 1.000000e+00
[36,] 0.000000e+00 0.000000e+00 1.000000e+00
[37,] 0.000000e+00 0.000000e+00 1.000000e+00
[38,] 0.000000e+00 0.000000e+00 1.000000e+00
[39,] 0.000000e+00 0.000000e+00 1.000000e+00
[40,] 0.000000e+00 0.000000e+00 1.000000e+00
[41,] 0.000000e+00 0.000000e+00 1.000000e+00
[42,] 0.000000e+00 0.000000e+00 1.000000e+00
[43,] 0.000000e+00 0.000000e+00 1.000000e+00
[44,] 0.000000e+00 0.000000e+00 1.000000e+00
[45,] 0.000000e+00 0.000000e+00 1.000000e+00
[46,] 0.000000e+00 0.000000e+00 1.000000e+00
[47,] 0.000000e+00 0.000000e+00 1.000000e+00
[48,] 0.000000e+00 0.000000e+00 1.000000e+00
[49,] 0.000000e+00 0.000000e+00 1.000000e+00
[50,] 0.000000e+00 0.000000e+00 1.000000e+00
[51,] 0.000000e+00 0.000000e+00 1.000000e+00
[52,] 0.000000e+00 0.000000e+00 1.000000e+00
[53,] 0.000000e+00 0.000000e+00 1.000000e+00
[54,] 0.000000e+00 0.000000e+00 1.000000e+00
[55,] 0.000000e+00 0.000000e+00 1.000000e+00
[56,] 0.000000e+00 0.000000e+00 1.000000e+00
[57,] 0.000000e+00 0.000000e+00 1.000000e+00
[58,] 0.000000e+00 0.000000e+00 1.000000e+00
[59,] 0.000000e+00 0.000000e+00 1.000000e+00
[60,] 0.000000e+00 0.000000e+00 1.000000e+00
[61,] 0.000000e+00 0.000000e+00 1.000000e+00
[62,] 0.000000e+00 0.000000e+00 1.000000e+00
[63,] 0.000000e+00 0.000000e+00 1.000000e+00
[64,] 0.000000e+00 0.000000e+00 1.000000e+00
[65,] 0.000000e+00 0.000000e+00 1.000000e+00
[66,] 0.000000e+00 0.000000e+00 1.000000e+00
[67,] 0.000000e+00 0.000000e+00 1.000000e+00
[68,] 0.000000e+00 0.000000e+00 1.000000e+00
[69,] 0.000000e+00 0.000000e+00 1.000000e+00
[70,] 0.000000e+00 0.000000e+00 1.000000e+00
[71,] 1.000000e+00 0.000000e+00 0.000000e+00
[72,] 1.000000e+00 0.000000e+00 0.000000e+00
[73,] 1.000000e+00 0.000000e+00 0.000000e+00
[74,] 1.000000e+00 0.000000e+00 0.000000e+00
[75,] 1.000000e+00 0.000000e+00 0.000000e+00
[76,] 1.000000e+00 0.000000e+00 0.000000e+00
[77,] 1.000000e+00 0.000000e+00 0.000000e+00
[78,] 1.000000e+00 0.000000e+00 0.000000e+00
[79,] 1.000000e+00 0.000000e+00 0.000000e+00
[80,] 1.000000e+00 0.000000e+00 0.000000e+00
[81,] 1.000000e+00 0.000000e+00 0.000000e+00
[82,] 1.000000e+00 0.000000e+00 0.000000e+00
[83,] 1.000000e+00 0.000000e+00 0.000000e+00
[84,] 1.000000e+00 0.000000e+00 0.000000e+00
[85,] 1.000000e+00 0.000000e+00 0.000000e+00
[86,] 1.000000e+00 0.000000e+00 0.000000e+00
[87,] 1.000000e+00 0.000000e+00 0.000000e+00
[88,] 1.000000e+00 0.000000e+00 0.000000e+00
[89,] 1.000000e+00 0.000000e+00 0.000000e+00
[90,] 1.000000e+00 0.000000e+00 0.000000e+00
[91,] 1.000000e+00 0.000000e+00 0.000000e+00
[92,] 1.000000e+00 0.000000e+00 0.000000e+00
[93,] 1.000000e+00 0.000000e+00 0.000000e+00
[94,] 1.000000e+00 0.000000e+00 0.000000e+00
[95,] 1.000000e+00 0.000000e+00 0.000000e+00
[96,] 1.000000e+00 0.000000e+00 0.000000e+00
[97,] 1.000000e+00 0.000000e+00 0.000000e+00
[98,] 1.000000e+00 0.000000e+00 0.000000e+00
[99,] 1.000000e+00 0.000000e+00 0.000000e+00
[100,] 1.000000e+00 0.000000e+00 0.000000e+00
[101,] 1.000000e+00 0.000000e+00 0.000000e+00
[102,] 1.000000e+00 0.000000e+00 0.000000e+00
[103,] 1.000000e+00 0.000000e+00 0.000000e+00
[104,] 1.000000e+00 0.000000e+00 0.000000e+00
[105,] 1.000000e+00 0.000000e+00 0.000000e+00
[106,] 1.000000e+00 0.000000e+00 0.000000e+00
[107,] 1.000000e+00 0.000000e+00 0.000000e+00
[108,] 1.000000e+00 0.000000e+00 0.000000e+00
[109,] 1.000000e+00 0.000000e+00 0.000000e+00
[110,] 1.000000e+00 0.000000e+00 0.000000e+00
[111,] 1.000000e+00 0.000000e+00 0.000000e+00
[112,] 1.000000e+00 0.000000e+00 0.000000e+00
[113,] 1.000000e+00 0.000000e+00 0.000000e+00
[114,] 1.000000e+00 0.000000e+00 0.000000e+00
[115,] 1.000000e+00 0.000000e+00 0.000000e+00
[116,] 1.000000e+00 0.000000e+00 0.000000e+00
[117,] 1.000000e+00 0.000000e+00 0.000000e+00
[118,] 1.000000e+00 0.000000e+00 0.000000e+00
[119,] 1.000000e+00 1.007348e-317 5.036742e-318
[120,] 1.000000e+00 5.905688e-301 2.952844e-301
[121,] 1.000000e+00 1.021373e-279 5.106863e-280
[122,] 1.000000e+00 9.794071e-283 4.897036e-283
[123,] 1.000000e+00 1.164006e-266 5.820028e-267
[124,] 1.000000e+00 1.900975e-240 9.504876e-241
[125,] 1.000000e+00 2.493130e-223 1.246565e-223
[126,] 1.000000e+00 4.482561e-215 2.241281e-215
[127,] 1.000000e+00 5.917664e-198 2.958832e-198
[128,] 1.000000e+00 4.444409e-179 2.222205e-179
[129,] 1.000000e+00 9.884097e-164 4.942049e-164
[130,] 1.000000e+00 1.169370e-154 5.846851e-155
[131,] 1.000000e+00 4.138498e-142 2.069249e-142
[132,] 1.000000e+00 9.362451e-125 4.681226e-125
[133,] 1.000000e+00 3.031315e-112 1.515657e-112
[134,] 1.000000e+00 7.020670e-91 3.510335e-91
[135,] 1.000000e+00 1.935335e-75 9.676676e-76
[136,] 1.000000e+00 3.896420e-65 1.948210e-65
[137,] 1.000000e+00 1.889290e-46 9.446451e-47
> postscript(file="/var/wessaorg/rcomp/tmp/1ha741353433173.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/25uhy1353433173.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37l4b1353433173.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4si391353433173.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5hm4e1353433173.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
2.298663e+00 1.252871e-01 -3.935719e-01 1.868122e+00 -1.989303e+00
6 7 8 9 10
-2.657715e-01 -1.134950e+00 -2.096341e+00 3.531486e+00 1.279219e+00
11 12 13 14 15
1.515451e+00 2.571051e+00 -1.660087e+00 1.620025e+00 1.078100e+00
16 17 18 19 20
5.923227e-01 -1.414366e+00 9.373305e-01 3.347885e+00 -4.295398e-01
21 22 23 24 25
2.950167e+00 -2.082420e-01 -1.462459e+00 -2.434369e-02 -2.388773e-01
26 27 28 29 30
2.356691e+00 1.067529e+00 3.141364e+00 5.242887e+00 9.462662e-01
31 32 33 34 35
1.584595e+00 -5.324346e-02 -5.245993e-01 3.443739e-01 2.600915e+00
36 37 38 39 40
1.433017e+00 -7.343641e-01 3.102057e-01 1.598498e+00 1.570437e+00
41 42 43 44 45
6.981229e-01 2.552215e+00 2.628823e+00 1.265832e+00 4.521510e+00
46 47 48 49 50
7.406772e-02 1.640775e+00 3.612760e+00 -1.559228e+00 1.058359e+00
51 52 53 54 55
2.858698e-01 -5.043645e-02 -6.485980e-01 -2.653620e+00 2.351125e+00
56 57 58 59 60
1.228890e+00 1.415220e-02 3.788634e+00 2.853405e+00 4.633945e+00
61 62 63 64 65
3.515598e+00 1.705794e+00 1.923872e+00 8.988334e-01 -1.945874e+00
66 67 68 69 70
1.684397e+00 -9.506521e-01 1.157907e+00 4.557406e-01 -1.153709e+00
71 72 73 74 75
-2.751823e-01 3.352254e-01 2.314069e+00 -6.158861e-01 1.312357e+00
76 77 78 79 80
-2.095705e+00 1.644806e-01 -1.337487e+00 -3.160379e-03 5.404707e-01
81 82 83 84 85
1.223171e+00 1.180054e+00 -1.115218e+02 4.708933e-01 -7.065752e-01
86 87 88 89 90
1.652193e+00 1.132068e+00 5.313866e-01 -4.062204e-01 -5.255485e-01
91 92 93 94 95
1.409801e+00 1.077197e+00 -4.489964e-02 -1.352843e+00 -2.291125e-02
96 97 98 99 100
5.952388e-01 1.637570e+00 1.298987e+00 7.009702e-01 1.956821e+00
101 102 103 104 105
1.568627e+00 2.059180e+00 1.908076e+00 1.166521e+00 -5.818042e-01
106 107 108 109 110
-1.655487e+00 4.878098e-01 -8.254445e-01 2.993048e-01 -2.239267e+00
111 112 113 114 115
4.442776e-01 1.001504e+00 2.731731e+00 6.300522e-01 -1.002599e-01
116 117 118 119 120
2.231380e+00 9.364760e-02 -3.371082e-01 4.534950e+00 1.050578e+00
121 122 123 124 125
-9.640798e-01 -1.398459e-01 1.620233e-01 2.078829e+00 3.602196e+00
126 127 128 129 130
1.459971e+00 2.405712e+00 7.002670e-01 9.109187e-01 1.692136e+00
131 132 133 134 135
1.329180e+00 -1.998212e+00 -1.054346e+00 -8.518853e-01 1.616690e+00
136 137 138 139 140
1.308849e+00 -4.262560e+00 -4.121350e-01 -5.276016e-01 7.421423e-01
141 142 143 144 145
1.973510e+00 -2.083088e-01 -2.317943e+00 1.903781e+00 3.435703e-01
146 147 148 149 150
3.263735e+00 1.383020e+00 1.752805e+00 1.709280e+00 -2.478194e+00
151 152 153 154 155
2.646917e+00 1.363039e-01 1.627829e+00 1.184470e+00 -1.637912e+00
156 157 158 159 160
-3.106548e-01 -3.012312e+00 -1.087942e+00 3.434122e-01 1.583455e+00
161 162
-4.043630e-01 -1.560063e-01
> postscript(file="/var/wessaorg/rcomp/tmp/63pc41353433174.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 2.298663e+00 NA
1 1.252871e-01 2.298663e+00
2 -3.935719e-01 1.252871e-01
3 1.868122e+00 -3.935719e-01
4 -1.989303e+00 1.868122e+00
5 -2.657715e-01 -1.989303e+00
6 -1.134950e+00 -2.657715e-01
7 -2.096341e+00 -1.134950e+00
8 3.531486e+00 -2.096341e+00
9 1.279219e+00 3.531486e+00
10 1.515451e+00 1.279219e+00
11 2.571051e+00 1.515451e+00
12 -1.660087e+00 2.571051e+00
13 1.620025e+00 -1.660087e+00
14 1.078100e+00 1.620025e+00
15 5.923227e-01 1.078100e+00
16 -1.414366e+00 5.923227e-01
17 9.373305e-01 -1.414366e+00
18 3.347885e+00 9.373305e-01
19 -4.295398e-01 3.347885e+00
20 2.950167e+00 -4.295398e-01
21 -2.082420e-01 2.950167e+00
22 -1.462459e+00 -2.082420e-01
23 -2.434369e-02 -1.462459e+00
24 -2.388773e-01 -2.434369e-02
25 2.356691e+00 -2.388773e-01
26 1.067529e+00 2.356691e+00
27 3.141364e+00 1.067529e+00
28 5.242887e+00 3.141364e+00
29 9.462662e-01 5.242887e+00
30 1.584595e+00 9.462662e-01
31 -5.324346e-02 1.584595e+00
32 -5.245993e-01 -5.324346e-02
33 3.443739e-01 -5.245993e-01
34 2.600915e+00 3.443739e-01
35 1.433017e+00 2.600915e+00
36 -7.343641e-01 1.433017e+00
37 3.102057e-01 -7.343641e-01
38 1.598498e+00 3.102057e-01
39 1.570437e+00 1.598498e+00
40 6.981229e-01 1.570437e+00
41 2.552215e+00 6.981229e-01
42 2.628823e+00 2.552215e+00
43 1.265832e+00 2.628823e+00
44 4.521510e+00 1.265832e+00
45 7.406772e-02 4.521510e+00
46 1.640775e+00 7.406772e-02
47 3.612760e+00 1.640775e+00
48 -1.559228e+00 3.612760e+00
49 1.058359e+00 -1.559228e+00
50 2.858698e-01 1.058359e+00
51 -5.043645e-02 2.858698e-01
52 -6.485980e-01 -5.043645e-02
53 -2.653620e+00 -6.485980e-01
54 2.351125e+00 -2.653620e+00
55 1.228890e+00 2.351125e+00
56 1.415220e-02 1.228890e+00
57 3.788634e+00 1.415220e-02
58 2.853405e+00 3.788634e+00
59 4.633945e+00 2.853405e+00
60 3.515598e+00 4.633945e+00
61 1.705794e+00 3.515598e+00
62 1.923872e+00 1.705794e+00
63 8.988334e-01 1.923872e+00
64 -1.945874e+00 8.988334e-01
65 1.684397e+00 -1.945874e+00
66 -9.506521e-01 1.684397e+00
67 1.157907e+00 -9.506521e-01
68 4.557406e-01 1.157907e+00
69 -1.153709e+00 4.557406e-01
70 -2.751823e-01 -1.153709e+00
71 3.352254e-01 -2.751823e-01
72 2.314069e+00 3.352254e-01
73 -6.158861e-01 2.314069e+00
74 1.312357e+00 -6.158861e-01
75 -2.095705e+00 1.312357e+00
76 1.644806e-01 -2.095705e+00
77 -1.337487e+00 1.644806e-01
78 -3.160379e-03 -1.337487e+00
79 5.404707e-01 -3.160379e-03
80 1.223171e+00 5.404707e-01
81 1.180054e+00 1.223171e+00
82 -1.115218e+02 1.180054e+00
83 4.708933e-01 -1.115218e+02
84 -7.065752e-01 4.708933e-01
85 1.652193e+00 -7.065752e-01
86 1.132068e+00 1.652193e+00
87 5.313866e-01 1.132068e+00
88 -4.062204e-01 5.313866e-01
89 -5.255485e-01 -4.062204e-01
90 1.409801e+00 -5.255485e-01
91 1.077197e+00 1.409801e+00
92 -4.489964e-02 1.077197e+00
93 -1.352843e+00 -4.489964e-02
94 -2.291125e-02 -1.352843e+00
95 5.952388e-01 -2.291125e-02
96 1.637570e+00 5.952388e-01
97 1.298987e+00 1.637570e+00
98 7.009702e-01 1.298987e+00
99 1.956821e+00 7.009702e-01
100 1.568627e+00 1.956821e+00
101 2.059180e+00 1.568627e+00
102 1.908076e+00 2.059180e+00
103 1.166521e+00 1.908076e+00
104 -5.818042e-01 1.166521e+00
105 -1.655487e+00 -5.818042e-01
106 4.878098e-01 -1.655487e+00
107 -8.254445e-01 4.878098e-01
108 2.993048e-01 -8.254445e-01
109 -2.239267e+00 2.993048e-01
110 4.442776e-01 -2.239267e+00
111 1.001504e+00 4.442776e-01
112 2.731731e+00 1.001504e+00
113 6.300522e-01 2.731731e+00
114 -1.002599e-01 6.300522e-01
115 2.231380e+00 -1.002599e-01
116 9.364760e-02 2.231380e+00
117 -3.371082e-01 9.364760e-02
118 4.534950e+00 -3.371082e-01
119 1.050578e+00 4.534950e+00
120 -9.640798e-01 1.050578e+00
121 -1.398459e-01 -9.640798e-01
122 1.620233e-01 -1.398459e-01
123 2.078829e+00 1.620233e-01
124 3.602196e+00 2.078829e+00
125 1.459971e+00 3.602196e+00
126 2.405712e+00 1.459971e+00
127 7.002670e-01 2.405712e+00
128 9.109187e-01 7.002670e-01
129 1.692136e+00 9.109187e-01
130 1.329180e+00 1.692136e+00
131 -1.998212e+00 1.329180e+00
132 -1.054346e+00 -1.998212e+00
133 -8.518853e-01 -1.054346e+00
134 1.616690e+00 -8.518853e-01
135 1.308849e+00 1.616690e+00
136 -4.262560e+00 1.308849e+00
137 -4.121350e-01 -4.262560e+00
138 -5.276016e-01 -4.121350e-01
139 7.421423e-01 -5.276016e-01
140 1.973510e+00 7.421423e-01
141 -2.083088e-01 1.973510e+00
142 -2.317943e+00 -2.083088e-01
143 1.903781e+00 -2.317943e+00
144 3.435703e-01 1.903781e+00
145 3.263735e+00 3.435703e-01
146 1.383020e+00 3.263735e+00
147 1.752805e+00 1.383020e+00
148 1.709280e+00 1.752805e+00
149 -2.478194e+00 1.709280e+00
150 2.646917e+00 -2.478194e+00
151 1.363039e-01 2.646917e+00
152 1.627829e+00 1.363039e-01
153 1.184470e+00 1.627829e+00
154 -1.637912e+00 1.184470e+00
155 -3.106548e-01 -1.637912e+00
156 -3.012312e+00 -3.106548e-01
157 -1.087942e+00 -3.012312e+00
158 3.434122e-01 -1.087942e+00
159 1.583455e+00 3.434122e-01
160 -4.043630e-01 1.583455e+00
161 -1.560063e-01 -4.043630e-01
162 NA -1.560063e-01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.252871e-01 2.298663e+00
[2,] -3.935719e-01 1.252871e-01
[3,] 1.868122e+00 -3.935719e-01
[4,] -1.989303e+00 1.868122e+00
[5,] -2.657715e-01 -1.989303e+00
[6,] -1.134950e+00 -2.657715e-01
[7,] -2.096341e+00 -1.134950e+00
[8,] 3.531486e+00 -2.096341e+00
[9,] 1.279219e+00 3.531486e+00
[10,] 1.515451e+00 1.279219e+00
[11,] 2.571051e+00 1.515451e+00
[12,] -1.660087e+00 2.571051e+00
[13,] 1.620025e+00 -1.660087e+00
[14,] 1.078100e+00 1.620025e+00
[15,] 5.923227e-01 1.078100e+00
[16,] -1.414366e+00 5.923227e-01
[17,] 9.373305e-01 -1.414366e+00
[18,] 3.347885e+00 9.373305e-01
[19,] -4.295398e-01 3.347885e+00
[20,] 2.950167e+00 -4.295398e-01
[21,] -2.082420e-01 2.950167e+00
[22,] -1.462459e+00 -2.082420e-01
[23,] -2.434369e-02 -1.462459e+00
[24,] -2.388773e-01 -2.434369e-02
[25,] 2.356691e+00 -2.388773e-01
[26,] 1.067529e+00 2.356691e+00
[27,] 3.141364e+00 1.067529e+00
[28,] 5.242887e+00 3.141364e+00
[29,] 9.462662e-01 5.242887e+00
[30,] 1.584595e+00 9.462662e-01
[31,] -5.324346e-02 1.584595e+00
[32,] -5.245993e-01 -5.324346e-02
[33,] 3.443739e-01 -5.245993e-01
[34,] 2.600915e+00 3.443739e-01
[35,] 1.433017e+00 2.600915e+00
[36,] -7.343641e-01 1.433017e+00
[37,] 3.102057e-01 -7.343641e-01
[38,] 1.598498e+00 3.102057e-01
[39,] 1.570437e+00 1.598498e+00
[40,] 6.981229e-01 1.570437e+00
[41,] 2.552215e+00 6.981229e-01
[42,] 2.628823e+00 2.552215e+00
[43,] 1.265832e+00 2.628823e+00
[44,] 4.521510e+00 1.265832e+00
[45,] 7.406772e-02 4.521510e+00
[46,] 1.640775e+00 7.406772e-02
[47,] 3.612760e+00 1.640775e+00
[48,] -1.559228e+00 3.612760e+00
[49,] 1.058359e+00 -1.559228e+00
[50,] 2.858698e-01 1.058359e+00
[51,] -5.043645e-02 2.858698e-01
[52,] -6.485980e-01 -5.043645e-02
[53,] -2.653620e+00 -6.485980e-01
[54,] 2.351125e+00 -2.653620e+00
[55,] 1.228890e+00 2.351125e+00
[56,] 1.415220e-02 1.228890e+00
[57,] 3.788634e+00 1.415220e-02
[58,] 2.853405e+00 3.788634e+00
[59,] 4.633945e+00 2.853405e+00
[60,] 3.515598e+00 4.633945e+00
[61,] 1.705794e+00 3.515598e+00
[62,] 1.923872e+00 1.705794e+00
[63,] 8.988334e-01 1.923872e+00
[64,] -1.945874e+00 8.988334e-01
[65,] 1.684397e+00 -1.945874e+00
[66,] -9.506521e-01 1.684397e+00
[67,] 1.157907e+00 -9.506521e-01
[68,] 4.557406e-01 1.157907e+00
[69,] -1.153709e+00 4.557406e-01
[70,] -2.751823e-01 -1.153709e+00
[71,] 3.352254e-01 -2.751823e-01
[72,] 2.314069e+00 3.352254e-01
[73,] -6.158861e-01 2.314069e+00
[74,] 1.312357e+00 -6.158861e-01
[75,] -2.095705e+00 1.312357e+00
[76,] 1.644806e-01 -2.095705e+00
[77,] -1.337487e+00 1.644806e-01
[78,] -3.160379e-03 -1.337487e+00
[79,] 5.404707e-01 -3.160379e-03
[80,] 1.223171e+00 5.404707e-01
[81,] 1.180054e+00 1.223171e+00
[82,] -1.115218e+02 1.180054e+00
[83,] 4.708933e-01 -1.115218e+02
[84,] -7.065752e-01 4.708933e-01
[85,] 1.652193e+00 -7.065752e-01
[86,] 1.132068e+00 1.652193e+00
[87,] 5.313866e-01 1.132068e+00
[88,] -4.062204e-01 5.313866e-01
[89,] -5.255485e-01 -4.062204e-01
[90,] 1.409801e+00 -5.255485e-01
[91,] 1.077197e+00 1.409801e+00
[92,] -4.489964e-02 1.077197e+00
[93,] -1.352843e+00 -4.489964e-02
[94,] -2.291125e-02 -1.352843e+00
[95,] 5.952388e-01 -2.291125e-02
[96,] 1.637570e+00 5.952388e-01
[97,] 1.298987e+00 1.637570e+00
[98,] 7.009702e-01 1.298987e+00
[99,] 1.956821e+00 7.009702e-01
[100,] 1.568627e+00 1.956821e+00
[101,] 2.059180e+00 1.568627e+00
[102,] 1.908076e+00 2.059180e+00
[103,] 1.166521e+00 1.908076e+00
[104,] -5.818042e-01 1.166521e+00
[105,] -1.655487e+00 -5.818042e-01
[106,] 4.878098e-01 -1.655487e+00
[107,] -8.254445e-01 4.878098e-01
[108,] 2.993048e-01 -8.254445e-01
[109,] -2.239267e+00 2.993048e-01
[110,] 4.442776e-01 -2.239267e+00
[111,] 1.001504e+00 4.442776e-01
[112,] 2.731731e+00 1.001504e+00
[113,] 6.300522e-01 2.731731e+00
[114,] -1.002599e-01 6.300522e-01
[115,] 2.231380e+00 -1.002599e-01
[116,] 9.364760e-02 2.231380e+00
[117,] -3.371082e-01 9.364760e-02
[118,] 4.534950e+00 -3.371082e-01
[119,] 1.050578e+00 4.534950e+00
[120,] -9.640798e-01 1.050578e+00
[121,] -1.398459e-01 -9.640798e-01
[122,] 1.620233e-01 -1.398459e-01
[123,] 2.078829e+00 1.620233e-01
[124,] 3.602196e+00 2.078829e+00
[125,] 1.459971e+00 3.602196e+00
[126,] 2.405712e+00 1.459971e+00
[127,] 7.002670e-01 2.405712e+00
[128,] 9.109187e-01 7.002670e-01
[129,] 1.692136e+00 9.109187e-01
[130,] 1.329180e+00 1.692136e+00
[131,] -1.998212e+00 1.329180e+00
[132,] -1.054346e+00 -1.998212e+00
[133,] -8.518853e-01 -1.054346e+00
[134,] 1.616690e+00 -8.518853e-01
[135,] 1.308849e+00 1.616690e+00
[136,] -4.262560e+00 1.308849e+00
[137,] -4.121350e-01 -4.262560e+00
[138,] -5.276016e-01 -4.121350e-01
[139,] 7.421423e-01 -5.276016e-01
[140,] 1.973510e+00 7.421423e-01
[141,] -2.083088e-01 1.973510e+00
[142,] -2.317943e+00 -2.083088e-01
[143,] 1.903781e+00 -2.317943e+00
[144,] 3.435703e-01 1.903781e+00
[145,] 3.263735e+00 3.435703e-01
[146,] 1.383020e+00 3.263735e+00
[147,] 1.752805e+00 1.383020e+00
[148,] 1.709280e+00 1.752805e+00
[149,] -2.478194e+00 1.709280e+00
[150,] 2.646917e+00 -2.478194e+00
[151,] 1.363039e-01 2.646917e+00
[152,] 1.627829e+00 1.363039e-01
[153,] 1.184470e+00 1.627829e+00
[154,] -1.637912e+00 1.184470e+00
[155,] -3.106548e-01 -1.637912e+00
[156,] -3.012312e+00 -3.106548e-01
[157,] -1.087942e+00 -3.012312e+00
[158,] 3.434122e-01 -1.087942e+00
[159,] 1.583455e+00 3.434122e-01
[160,] -4.043630e-01 1.583455e+00
[161,] -1.560063e-01 -4.043630e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.252871e-01 2.298663e+00
2 -3.935719e-01 1.252871e-01
3 1.868122e+00 -3.935719e-01
4 -1.989303e+00 1.868122e+00
5 -2.657715e-01 -1.989303e+00
6 -1.134950e+00 -2.657715e-01
7 -2.096341e+00 -1.134950e+00
8 3.531486e+00 -2.096341e+00
9 1.279219e+00 3.531486e+00
10 1.515451e+00 1.279219e+00
11 2.571051e+00 1.515451e+00
12 -1.660087e+00 2.571051e+00
13 1.620025e+00 -1.660087e+00
14 1.078100e+00 1.620025e+00
15 5.923227e-01 1.078100e+00
16 -1.414366e+00 5.923227e-01
17 9.373305e-01 -1.414366e+00
18 3.347885e+00 9.373305e-01
19 -4.295398e-01 3.347885e+00
20 2.950167e+00 -4.295398e-01
21 -2.082420e-01 2.950167e+00
22 -1.462459e+00 -2.082420e-01
23 -2.434369e-02 -1.462459e+00
24 -2.388773e-01 -2.434369e-02
25 2.356691e+00 -2.388773e-01
26 1.067529e+00 2.356691e+00
27 3.141364e+00 1.067529e+00
28 5.242887e+00 3.141364e+00
29 9.462662e-01 5.242887e+00
30 1.584595e+00 9.462662e-01
31 -5.324346e-02 1.584595e+00
32 -5.245993e-01 -5.324346e-02
33 3.443739e-01 -5.245993e-01
34 2.600915e+00 3.443739e-01
35 1.433017e+00 2.600915e+00
36 -7.343641e-01 1.433017e+00
37 3.102057e-01 -7.343641e-01
38 1.598498e+00 3.102057e-01
39 1.570437e+00 1.598498e+00
40 6.981229e-01 1.570437e+00
41 2.552215e+00 6.981229e-01
42 2.628823e+00 2.552215e+00
43 1.265832e+00 2.628823e+00
44 4.521510e+00 1.265832e+00
45 7.406772e-02 4.521510e+00
46 1.640775e+00 7.406772e-02
47 3.612760e+00 1.640775e+00
48 -1.559228e+00 3.612760e+00
49 1.058359e+00 -1.559228e+00
50 2.858698e-01 1.058359e+00
51 -5.043645e-02 2.858698e-01
52 -6.485980e-01 -5.043645e-02
53 -2.653620e+00 -6.485980e-01
54 2.351125e+00 -2.653620e+00
55 1.228890e+00 2.351125e+00
56 1.415220e-02 1.228890e+00
57 3.788634e+00 1.415220e-02
58 2.853405e+00 3.788634e+00
59 4.633945e+00 2.853405e+00
60 3.515598e+00 4.633945e+00
61 1.705794e+00 3.515598e+00
62 1.923872e+00 1.705794e+00
63 8.988334e-01 1.923872e+00
64 -1.945874e+00 8.988334e-01
65 1.684397e+00 -1.945874e+00
66 -9.506521e-01 1.684397e+00
67 1.157907e+00 -9.506521e-01
68 4.557406e-01 1.157907e+00
69 -1.153709e+00 4.557406e-01
70 -2.751823e-01 -1.153709e+00
71 3.352254e-01 -2.751823e-01
72 2.314069e+00 3.352254e-01
73 -6.158861e-01 2.314069e+00
74 1.312357e+00 -6.158861e-01
75 -2.095705e+00 1.312357e+00
76 1.644806e-01 -2.095705e+00
77 -1.337487e+00 1.644806e-01
78 -3.160379e-03 -1.337487e+00
79 5.404707e-01 -3.160379e-03
80 1.223171e+00 5.404707e-01
81 1.180054e+00 1.223171e+00
82 -1.115218e+02 1.180054e+00
83 4.708933e-01 -1.115218e+02
84 -7.065752e-01 4.708933e-01
85 1.652193e+00 -7.065752e-01
86 1.132068e+00 1.652193e+00
87 5.313866e-01 1.132068e+00
88 -4.062204e-01 5.313866e-01
89 -5.255485e-01 -4.062204e-01
90 1.409801e+00 -5.255485e-01
91 1.077197e+00 1.409801e+00
92 -4.489964e-02 1.077197e+00
93 -1.352843e+00 -4.489964e-02
94 -2.291125e-02 -1.352843e+00
95 5.952388e-01 -2.291125e-02
96 1.637570e+00 5.952388e-01
97 1.298987e+00 1.637570e+00
98 7.009702e-01 1.298987e+00
99 1.956821e+00 7.009702e-01
100 1.568627e+00 1.956821e+00
101 2.059180e+00 1.568627e+00
102 1.908076e+00 2.059180e+00
103 1.166521e+00 1.908076e+00
104 -5.818042e-01 1.166521e+00
105 -1.655487e+00 -5.818042e-01
106 4.878098e-01 -1.655487e+00
107 -8.254445e-01 4.878098e-01
108 2.993048e-01 -8.254445e-01
109 -2.239267e+00 2.993048e-01
110 4.442776e-01 -2.239267e+00
111 1.001504e+00 4.442776e-01
112 2.731731e+00 1.001504e+00
113 6.300522e-01 2.731731e+00
114 -1.002599e-01 6.300522e-01
115 2.231380e+00 -1.002599e-01
116 9.364760e-02 2.231380e+00
117 -3.371082e-01 9.364760e-02
118 4.534950e+00 -3.371082e-01
119 1.050578e+00 4.534950e+00
120 -9.640798e-01 1.050578e+00
121 -1.398459e-01 -9.640798e-01
122 1.620233e-01 -1.398459e-01
123 2.078829e+00 1.620233e-01
124 3.602196e+00 2.078829e+00
125 1.459971e+00 3.602196e+00
126 2.405712e+00 1.459971e+00
127 7.002670e-01 2.405712e+00
128 9.109187e-01 7.002670e-01
129 1.692136e+00 9.109187e-01
130 1.329180e+00 1.692136e+00
131 -1.998212e+00 1.329180e+00
132 -1.054346e+00 -1.998212e+00
133 -8.518853e-01 -1.054346e+00
134 1.616690e+00 -8.518853e-01
135 1.308849e+00 1.616690e+00
136 -4.262560e+00 1.308849e+00
137 -4.121350e-01 -4.262560e+00
138 -5.276016e-01 -4.121350e-01
139 7.421423e-01 -5.276016e-01
140 1.973510e+00 7.421423e-01
141 -2.083088e-01 1.973510e+00
142 -2.317943e+00 -2.083088e-01
143 1.903781e+00 -2.317943e+00
144 3.435703e-01 1.903781e+00
145 3.263735e+00 3.435703e-01
146 1.383020e+00 3.263735e+00
147 1.752805e+00 1.383020e+00
148 1.709280e+00 1.752805e+00
149 -2.478194e+00 1.709280e+00
150 2.646917e+00 -2.478194e+00
151 1.363039e-01 2.646917e+00
152 1.627829e+00 1.363039e-01
153 1.184470e+00 1.627829e+00
154 -1.637912e+00 1.184470e+00
155 -3.106548e-01 -1.637912e+00
156 -3.012312e+00 -3.106548e-01
157 -1.087942e+00 -3.012312e+00
158 3.434122e-01 -1.087942e+00
159 1.583455e+00 3.434122e-01
160 -4.043630e-01 1.583455e+00
161 -1.560063e-01 -4.043630e-01
> 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/7jkwd1353433174.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8grjf1353433174.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9t6gv1353433174.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10sax91353433174.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116s5w1353433174.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/12b7c81353433174.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/13yt5y1353433174.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/14rv1b1353433174.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/15rr5g1353433174.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/16u2g71353433174.tab")
+ }
>
> try(system("convert tmp/1ha741353433173.ps tmp/1ha741353433173.png",intern=TRUE))
character(0)
> try(system("convert tmp/25uhy1353433173.ps tmp/25uhy1353433173.png",intern=TRUE))
character(0)
> try(system("convert tmp/37l4b1353433173.ps tmp/37l4b1353433173.png",intern=TRUE))
character(0)
> try(system("convert tmp/4si391353433173.ps tmp/4si391353433173.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hm4e1353433173.ps tmp/5hm4e1353433173.png",intern=TRUE))
character(0)
> try(system("convert tmp/63pc41353433174.ps tmp/63pc41353433174.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jkwd1353433174.ps tmp/7jkwd1353433174.png",intern=TRUE))
character(0)
> try(system("convert tmp/8grjf1353433174.ps tmp/8grjf1353433174.png",intern=TRUE))
character(0)
> try(system("convert tmp/9t6gv1353433174.ps tmp/9t6gv1353433174.png",intern=TRUE))
character(0)
> try(system("convert tmp/10sax91353433174.ps tmp/10sax91353433174.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.132 1.070 10.236