R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(3030.29
+ ,0
+ ,2803.47
+ ,0
+ ,2767.63
+ ,0
+ ,2882.6
+ ,0
+ ,2863.36
+ ,0
+ ,2897.06
+ ,0
+ ,3012.61
+ ,0
+ ,3142.95
+ ,0
+ ,3032.93
+ ,0
+ ,3045.78
+ ,0
+ ,3110.52
+ ,0
+ ,3013.24
+ ,0
+ ,2987.1
+ ,0
+ ,2995.55
+ ,0
+ ,2833.18
+ ,0
+ ,2848.96
+ ,0
+ ,2794.83
+ ,0
+ ,2845.26
+ ,0
+ ,2915.02
+ ,0
+ ,2892.63
+ ,0
+ ,2604.42
+ ,0
+ ,2641.65
+ ,0
+ ,2659.81
+ ,0
+ ,2638.53
+ ,0
+ ,2720.25
+ ,0
+ ,2745.88
+ ,0
+ ,2735.7
+ ,0
+ ,2811.7
+ ,0
+ ,2799.43
+ ,0
+ ,2555.28
+ ,0
+ ,2304.98
+ ,0
+ ,2214.95
+ ,0
+ ,2065.81
+ ,0
+ ,1940.49
+ ,0
+ ,2042
+ ,0
+ ,1995.37
+ ,0
+ ,1946.81
+ ,0
+ ,1765.9
+ ,0
+ ,1635.25
+ ,0
+ ,1833.42
+ ,0
+ ,1910.43
+ ,0
+ ,1959.67
+ ,0
+ ,1969.6
+ ,0
+ ,2061.41
+ ,0
+ ,2093.48
+ ,0
+ ,2120.88
+ ,0
+ ,2174.56
+ ,0
+ ,2196.72
+ ,0
+ ,2350.44
+ ,0
+ ,2440.25
+ ,0
+ ,2408.64
+ ,0
+ ,2472.81
+ ,0
+ ,2407.6
+ ,0
+ ,2454.62
+ ,0
+ ,2448.05
+ ,0
+ ,2497.84
+ ,0
+ ,2645.64
+ ,0
+ ,2756.76
+ ,0
+ ,2849.27
+ ,0
+ ,2921.44
+ ,0
+ ,2981.85
+ ,0
+ ,3080.58
+ ,0
+ ,3106.22
+ ,0
+ ,3119.31
+ ,0
+ ,3061.26
+ ,0
+ ,3097.31
+ ,0
+ ,3161.69
+ ,0
+ ,3257.16
+ ,0
+ ,3277.01
+ ,0
+ ,3295.32
+ ,0
+ ,3363.99
+ ,0
+ ,3494.17
+ ,0
+ ,3667.03
+ ,0
+ ,3813.06
+ ,0
+ ,3917.96
+ ,0
+ ,3895.51
+ ,0
+ ,3801.06
+ ,0
+ ,3570.12
+ ,0
+ ,3701.61
+ ,0
+ ,3862.27
+ ,0
+ ,3970.1
+ ,0
+ ,4138.52
+ ,0
+ ,4199.75
+ ,0
+ ,4290.89
+ ,0
+ ,4443.91
+ ,0
+ ,4502.64
+ ,0
+ ,4356.98
+ ,0
+ ,4591.27
+ ,0
+ ,4696.96
+ ,0
+ ,4621.4
+ ,0
+ ,4562.84
+ ,1
+ ,4202.52
+ ,1
+ ,4296.49
+ ,1
+ ,4435.23
+ ,1
+ ,4105.18
+ ,1
+ ,4116.68
+ ,1
+ ,3844.49
+ ,1
+ ,3720.98
+ ,1
+ ,3674.4
+ ,1
+ ,3857.62
+ ,1
+ ,3801.06
+ ,1
+ ,3504.37
+ ,1
+ ,3032.6
+ ,1
+ ,3047.03
+ ,1
+ ,2962.34
+ ,1
+ ,2197.82
+ ,1
+ ,2014.45
+ ,1)
+ ,dim=c(2
+ ,107)
+ ,dimnames=list(c('BEL20'
+ ,'Wel(1)_geen(0)_financiële_crisis')
+ ,1:107))
> y <- array(NA,dim=c(2,107),dimnames=list(c('BEL20','Wel(1)_geen(0)_financiële_crisis'),1:107))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> 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
BEL20 Wel(1)_geen(0)_financi\353le_crisis M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 3030.29 0 1 0 0 0 0 0 0 0 0 0
2 2803.47 0 0 1 0 0 0 0 0 0 0 0
3 2767.63 0 0 0 1 0 0 0 0 0 0 0
4 2882.60 0 0 0 0 1 0 0 0 0 0 0
5 2863.36 0 0 0 0 0 1 0 0 0 0 0
6 2897.06 0 0 0 0 0 0 1 0 0 0 0
7 3012.61 0 0 0 0 0 0 0 1 0 0 0
8 3142.95 0 0 0 0 0 0 0 0 1 0 0
9 3032.93 0 0 0 0 0 0 0 0 0 1 0
10 3045.78 0 0 0 0 0 0 0 0 0 0 1
11 3110.52 0 0 0 0 0 0 0 0 0 0 0
12 3013.24 0 0 0 0 0 0 0 0 0 0 0
13 2987.10 0 1 0 0 0 0 0 0 0 0 0
14 2995.55 0 0 1 0 0 0 0 0 0 0 0
15 2833.18 0 0 0 1 0 0 0 0 0 0 0
16 2848.96 0 0 0 0 1 0 0 0 0 0 0
17 2794.83 0 0 0 0 0 1 0 0 0 0 0
18 2845.26 0 0 0 0 0 0 1 0 0 0 0
19 2915.02 0 0 0 0 0 0 0 1 0 0 0
20 2892.63 0 0 0 0 0 0 0 0 1 0 0
21 2604.42 0 0 0 0 0 0 0 0 0 1 0
22 2641.65 0 0 0 0 0 0 0 0 0 0 1
23 2659.81 0 0 0 0 0 0 0 0 0 0 0
24 2638.53 0 0 0 0 0 0 0 0 0 0 0
25 2720.25 0 1 0 0 0 0 0 0 0 0 0
26 2745.88 0 0 1 0 0 0 0 0 0 0 0
27 2735.70 0 0 0 1 0 0 0 0 0 0 0
28 2811.70 0 0 0 0 1 0 0 0 0 0 0
29 2799.43 0 0 0 0 0 1 0 0 0 0 0
30 2555.28 0 0 0 0 0 0 1 0 0 0 0
31 2304.98 0 0 0 0 0 0 0 1 0 0 0
32 2214.95 0 0 0 0 0 0 0 0 1 0 0
33 2065.81 0 0 0 0 0 0 0 0 0 1 0
34 1940.49 0 0 0 0 0 0 0 0 0 0 1
35 2042.00 0 0 0 0 0 0 0 0 0 0 0
36 1995.37 0 0 0 0 0 0 0 0 0 0 0
37 1946.81 0 1 0 0 0 0 0 0 0 0 0
38 1765.90 0 0 1 0 0 0 0 0 0 0 0
39 1635.25 0 0 0 1 0 0 0 0 0 0 0
40 1833.42 0 0 0 0 1 0 0 0 0 0 0
41 1910.43 0 0 0 0 0 1 0 0 0 0 0
42 1959.67 0 0 0 0 0 0 1 0 0 0 0
43 1969.60 0 0 0 0 0 0 0 1 0 0 0
44 2061.41 0 0 0 0 0 0 0 0 1 0 0
45 2093.48 0 0 0 0 0 0 0 0 0 1 0
46 2120.88 0 0 0 0 0 0 0 0 0 0 1
47 2174.56 0 0 0 0 0 0 0 0 0 0 0
48 2196.72 0 0 0 0 0 0 0 0 0 0 0
49 2350.44 0 1 0 0 0 0 0 0 0 0 0
50 2440.25 0 0 1 0 0 0 0 0 0 0 0
51 2408.64 0 0 0 1 0 0 0 0 0 0 0
52 2472.81 0 0 0 0 1 0 0 0 0 0 0
53 2407.60 0 0 0 0 0 1 0 0 0 0 0
54 2454.62 0 0 0 0 0 0 1 0 0 0 0
55 2448.05 0 0 0 0 0 0 0 1 0 0 0
56 2497.84 0 0 0 0 0 0 0 0 1 0 0
57 2645.64 0 0 0 0 0 0 0 0 0 1 0
58 2756.76 0 0 0 0 0 0 0 0 0 0 1
59 2849.27 0 0 0 0 0 0 0 0 0 0 0
60 2921.44 0 0 0 0 0 0 0 0 0 0 0
61 2981.85 0 1 0 0 0 0 0 0 0 0 0
62 3080.58 0 0 1 0 0 0 0 0 0 0 0
63 3106.22 0 0 0 1 0 0 0 0 0 0 0
64 3119.31 0 0 0 0 1 0 0 0 0 0 0
65 3061.26 0 0 0 0 0 1 0 0 0 0 0
66 3097.31 0 0 0 0 0 0 1 0 0 0 0
67 3161.69 0 0 0 0 0 0 0 1 0 0 0
68 3257.16 0 0 0 0 0 0 0 0 1 0 0
69 3277.01 0 0 0 0 0 0 0 0 0 1 0
70 3295.32 0 0 0 0 0 0 0 0 0 0 1
71 3363.99 0 0 0 0 0 0 0 0 0 0 0
72 3494.17 0 0 0 0 0 0 0 0 0 0 0
73 3667.03 0 1 0 0 0 0 0 0 0 0 0
74 3813.06 0 0 1 0 0 0 0 0 0 0 0
75 3917.96 0 0 0 1 0 0 0 0 0 0 0
76 3895.51 0 0 0 0 1 0 0 0 0 0 0
77 3801.06 0 0 0 0 0 1 0 0 0 0 0
78 3570.12 0 0 0 0 0 0 1 0 0 0 0
79 3701.61 0 0 0 0 0 0 0 1 0 0 0
80 3862.27 0 0 0 0 0 0 0 0 1 0 0
81 3970.10 0 0 0 0 0 0 0 0 0 1 0
82 4138.52 0 0 0 0 0 0 0 0 0 0 1
83 4199.75 0 0 0 0 0 0 0 0 0 0 0
84 4290.89 0 0 0 0 0 0 0 0 0 0 0
85 4443.91 0 1 0 0 0 0 0 0 0 0 0
86 4502.64 0 0 1 0 0 0 0 0 0 0 0
87 4356.98 0 0 0 1 0 0 0 0 0 0 0
88 4591.27 0 0 0 0 1 0 0 0 0 0 0
89 4696.96 0 0 0 0 0 1 0 0 0 0 0
90 4621.40 0 0 0 0 0 0 1 0 0 0 0
91 4562.84 1 0 0 0 0 0 0 1 0 0 0
92 4202.52 1 0 0 0 0 0 0 0 1 0 0
93 4296.49 1 0 0 0 0 0 0 0 0 1 0
94 4435.23 1 0 0 0 0 0 0 0 0 0 1
95 4105.18 1 0 0 0 0 0 0 0 0 0 0
96 4116.68 1 0 0 0 0 0 0 0 0 0 0
97 3844.49 1 1 0 0 0 0 0 0 0 0 0
98 3720.98 1 0 1 0 0 0 0 0 0 0 0
99 3674.40 1 0 0 1 0 0 0 0 0 0 0
100 3857.62 1 0 0 0 1 0 0 0 0 0 0
101 3801.06 1 0 0 0 0 1 0 0 0 0 0
102 3504.37 1 0 0 0 0 0 1 0 0 0 0
103 3032.60 1 0 0 0 0 0 0 1 0 0 0
104 3047.03 1 0 0 0 0 0 0 0 1 0 0
105 2962.34 1 0 0 0 0 0 0 0 0 1 0
106 2197.82 1 0 0 0 0 0 0 0 0 0 1
107 2014.45 1 0 0 0 0 0 0 0 0 0 0
M11 t
1 0 1
2 0 2
3 0 3
4 0 4
5 0 5
6 0 6
7 0 7
8 0 8
9 0 9
10 0 10
11 1 11
12 0 12
13 0 13
14 0 14
15 0 15
16 0 16
17 0 17
18 0 18
19 0 19
20 0 20
21 0 21
22 0 22
23 1 23
24 0 24
25 0 25
26 0 26
27 0 27
28 0 28
29 0 29
30 0 30
31 0 31
32 0 32
33 0 33
34 0 34
35 1 35
36 0 36
37 0 37
38 0 38
39 0 39
40 0 40
41 0 41
42 0 42
43 0 43
44 0 44
45 0 45
46 0 46
47 1 47
48 0 48
49 0 49
50 0 50
51 0 51
52 0 52
53 0 53
54 0 54
55 0 55
56 0 56
57 0 57
58 0 58
59 1 59
60 0 60
61 0 61
62 0 62
63 0 63
64 0 64
65 0 65
66 0 66
67 0 67
68 0 68
69 0 69
70 0 70
71 1 71
72 0 72
73 0 73
74 0 74
75 0 75
76 0 76
77 0 77
78 0 78
79 0 79
80 0 80
81 0 81
82 0 82
83 1 83
84 0 84
85 0 85
86 0 86
87 0 87
88 0 88
89 0 89
90 0 90
91 0 91
92 0 92
93 0 93
94 0 94
95 1 95
96 0 96
97 0 97
98 0 98
99 0 99
100 0 100
101 0 101
102 0 102
103 0 103
104 0 104
105 0 105
106 0 106
107 1 107
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Wel(1)_geen(0)_financi\353le_crisis`
2279.778 -106.144
M1 M2
98.801 72.134
M3 M4
8.967 91.311
M5 M6
56.494 -28.733
M7 M8
-76.077 -83.453
M9 M10
-124.195 -181.075
M11 t
-202.082 15.127
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1575.71 -576.40 43.08 451.23 1088.71
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2279.778 267.282 8.529 2.63e-13 ***
`Wel(1)_geen(0)_financi\353le_crisis` -106.144 228.220 -0.465 0.643
M1 98.801 322.030 0.307 0.760
M2 72.134 321.946 0.224 0.823
M3 8.967 321.884 0.028 0.978
M4 91.311 321.845 0.284 0.777
M5 56.494 321.828 0.176 0.861
M6 -28.733 321.833 -0.089 0.929
M7 -76.077 322.476 -0.236 0.814
M8 -83.453 322.393 -0.259 0.796
M9 -124.195 322.333 -0.385 0.701
M10 -181.075 322.295 -0.562 0.576
M11 -202.082 322.279 -0.627 0.532
t 15.127 2.684 5.635 1.86e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 662.3 on 93 degrees of freedom
Multiple R-squared: 0.3431, Adjusted R-squared: 0.2513
F-statistic: 3.736 on 13 and 93 DF, p-value: 8.576e-05
> postscript(file="/var/www/html/rcomp/tmp/1sixv1228655466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25r1n1228655466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3j9w01228655466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45mkk1228655466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5nr581228655466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 107
Frequency = 1
1 2 3 4 5
636.5839441 421.3039441 433.5028330 451.0017219 451.4517219
6 7 8 9 10
555.2517219 703.0179178 825.6068067 741.2023622 795.8045845
11 12 13 14 15
866.4245845 551.9352779 411.8672849 431.8572849 317.5261737
16 17 18 19 20
235.8350626 201.3950626 321.9250626 423.9012585 393.7601474
21 22 23 24 25
131.1657029 210.1479252 234.1879252 -4.3013814 -36.5093744
26 27 28 29 30
0.6606256 38.5195145 17.0484033 24.4684033 -149.5815967
31 32 33 34 35
-367.6654008 -465.4465119 -588.9709563 -672.5387341 -565.1487341
36 37 38 39 40
-828.9880407 -991.4760337 -1160.8460337 -1243.4571448 -1142.7582559
41 42 43 44 45
-1046.0582559 -926.7182559 -884.5720601 -800.5131712 -742.8276156
46 47 48 49 50
-673.6753934 -614.1153934 -809.1647000 -769.3726930 -668.0226930
51 52 53 54 55
-651.5938041 -684.8949152 -730.4149152 -613.2949152 -587.6487194
56 57 58 59 60
-545.6098305 -372.1942749 -219.3220527 -120.9320527 -265.9713593
61 62 63 64 65
-319.4893523 -209.2193523 -135.5404634 -219.9215745 -258.2815745
66 67 68 69 70
-152.1315745 -55.5353787 32.1835102 77.6490658 137.7112880
71 72 73 74 75
212.2612880 125.2319814 184.1639884 341.7339884 494.6728773
76 77 78 79 80
374.7517662 299.9917662 139.1517662 302.8579621 455.7668509
81 82 83 84 85
589.2124065 799.3846287 866.4946287 740.4253221 779.5173291
86 87 88 89 90
849.7873291 752.1662180 888.9851069 1014.3651069 1008.9051069
91 92 93 94 95
1088.7055399 720.6344288 840.2199844 1020.7122066 696.5422066
96 97 98 99 100
490.8329000 104.7149070 -7.2550930 -5.7962041 79.9526847
101 102 103 104 105
43.0826847 -183.5073153 -623.0611194 -616.3822305 -675.4566749
106 107
-1398.2244527 -1575.7144527
> postscript(file="/var/www/html/rcomp/tmp/6su4i1228655466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 107
Frequency = 1
lag(myerror, k = 1) myerror
0 636.5839441 NA
1 421.3039441 636.5839441
2 433.5028330 421.3039441
3 451.0017219 433.5028330
4 451.4517219 451.0017219
5 555.2517219 451.4517219
6 703.0179178 555.2517219
7 825.6068067 703.0179178
8 741.2023622 825.6068067
9 795.8045845 741.2023622
10 866.4245845 795.8045845
11 551.9352779 866.4245845
12 411.8672849 551.9352779
13 431.8572849 411.8672849
14 317.5261737 431.8572849
15 235.8350626 317.5261737
16 201.3950626 235.8350626
17 321.9250626 201.3950626
18 423.9012585 321.9250626
19 393.7601474 423.9012585
20 131.1657029 393.7601474
21 210.1479252 131.1657029
22 234.1879252 210.1479252
23 -4.3013814 234.1879252
24 -36.5093744 -4.3013814
25 0.6606256 -36.5093744
26 38.5195145 0.6606256
27 17.0484033 38.5195145
28 24.4684033 17.0484033
29 -149.5815967 24.4684033
30 -367.6654008 -149.5815967
31 -465.4465119 -367.6654008
32 -588.9709563 -465.4465119
33 -672.5387341 -588.9709563
34 -565.1487341 -672.5387341
35 -828.9880407 -565.1487341
36 -991.4760337 -828.9880407
37 -1160.8460337 -991.4760337
38 -1243.4571448 -1160.8460337
39 -1142.7582559 -1243.4571448
40 -1046.0582559 -1142.7582559
41 -926.7182559 -1046.0582559
42 -884.5720601 -926.7182559
43 -800.5131712 -884.5720601
44 -742.8276156 -800.5131712
45 -673.6753934 -742.8276156
46 -614.1153934 -673.6753934
47 -809.1647000 -614.1153934
48 -769.3726930 -809.1647000
49 -668.0226930 -769.3726930
50 -651.5938041 -668.0226930
51 -684.8949152 -651.5938041
52 -730.4149152 -684.8949152
53 -613.2949152 -730.4149152
54 -587.6487194 -613.2949152
55 -545.6098305 -587.6487194
56 -372.1942749 -545.6098305
57 -219.3220527 -372.1942749
58 -120.9320527 -219.3220527
59 -265.9713593 -120.9320527
60 -319.4893523 -265.9713593
61 -209.2193523 -319.4893523
62 -135.5404634 -209.2193523
63 -219.9215745 -135.5404634
64 -258.2815745 -219.9215745
65 -152.1315745 -258.2815745
66 -55.5353787 -152.1315745
67 32.1835102 -55.5353787
68 77.6490658 32.1835102
69 137.7112880 77.6490658
70 212.2612880 137.7112880
71 125.2319814 212.2612880
72 184.1639884 125.2319814
73 341.7339884 184.1639884
74 494.6728773 341.7339884
75 374.7517662 494.6728773
76 299.9917662 374.7517662
77 139.1517662 299.9917662
78 302.8579621 139.1517662
79 455.7668509 302.8579621
80 589.2124065 455.7668509
81 799.3846287 589.2124065
82 866.4946287 799.3846287
83 740.4253221 866.4946287
84 779.5173291 740.4253221
85 849.7873291 779.5173291
86 752.1662180 849.7873291
87 888.9851069 752.1662180
88 1014.3651069 888.9851069
89 1008.9051069 1014.3651069
90 1088.7055399 1008.9051069
91 720.6344288 1088.7055399
92 840.2199844 720.6344288
93 1020.7122066 840.2199844
94 696.5422066 1020.7122066
95 490.8329000 696.5422066
96 104.7149070 490.8329000
97 -7.2550930 104.7149070
98 -5.7962041 -7.2550930
99 79.9526847 -5.7962041
100 43.0826847 79.9526847
101 -183.5073153 43.0826847
102 -623.0611194 -183.5073153
103 -616.3822305 -623.0611194
104 -675.4566749 -616.3822305
105 -1398.2244527 -675.4566749
106 -1575.7144527 -1398.2244527
107 NA -1575.7144527
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 421.3039441 636.5839441
[2,] 433.5028330 421.3039441
[3,] 451.0017219 433.5028330
[4,] 451.4517219 451.0017219
[5,] 555.2517219 451.4517219
[6,] 703.0179178 555.2517219
[7,] 825.6068067 703.0179178
[8,] 741.2023622 825.6068067
[9,] 795.8045845 741.2023622
[10,] 866.4245845 795.8045845
[11,] 551.9352779 866.4245845
[12,] 411.8672849 551.9352779
[13,] 431.8572849 411.8672849
[14,] 317.5261737 431.8572849
[15,] 235.8350626 317.5261737
[16,] 201.3950626 235.8350626
[17,] 321.9250626 201.3950626
[18,] 423.9012585 321.9250626
[19,] 393.7601474 423.9012585
[20,] 131.1657029 393.7601474
[21,] 210.1479252 131.1657029
[22,] 234.1879252 210.1479252
[23,] -4.3013814 234.1879252
[24,] -36.5093744 -4.3013814
[25,] 0.6606256 -36.5093744
[26,] 38.5195145 0.6606256
[27,] 17.0484033 38.5195145
[28,] 24.4684033 17.0484033
[29,] -149.5815967 24.4684033
[30,] -367.6654008 -149.5815967
[31,] -465.4465119 -367.6654008
[32,] -588.9709563 -465.4465119
[33,] -672.5387341 -588.9709563
[34,] -565.1487341 -672.5387341
[35,] -828.9880407 -565.1487341
[36,] -991.4760337 -828.9880407
[37,] -1160.8460337 -991.4760337
[38,] -1243.4571448 -1160.8460337
[39,] -1142.7582559 -1243.4571448
[40,] -1046.0582559 -1142.7582559
[41,] -926.7182559 -1046.0582559
[42,] -884.5720601 -926.7182559
[43,] -800.5131712 -884.5720601
[44,] -742.8276156 -800.5131712
[45,] -673.6753934 -742.8276156
[46,] -614.1153934 -673.6753934
[47,] -809.1647000 -614.1153934
[48,] -769.3726930 -809.1647000
[49,] -668.0226930 -769.3726930
[50,] -651.5938041 -668.0226930
[51,] -684.8949152 -651.5938041
[52,] -730.4149152 -684.8949152
[53,] -613.2949152 -730.4149152
[54,] -587.6487194 -613.2949152
[55,] -545.6098305 -587.6487194
[56,] -372.1942749 -545.6098305
[57,] -219.3220527 -372.1942749
[58,] -120.9320527 -219.3220527
[59,] -265.9713593 -120.9320527
[60,] -319.4893523 -265.9713593
[61,] -209.2193523 -319.4893523
[62,] -135.5404634 -209.2193523
[63,] -219.9215745 -135.5404634
[64,] -258.2815745 -219.9215745
[65,] -152.1315745 -258.2815745
[66,] -55.5353787 -152.1315745
[67,] 32.1835102 -55.5353787
[68,] 77.6490658 32.1835102
[69,] 137.7112880 77.6490658
[70,] 212.2612880 137.7112880
[71,] 125.2319814 212.2612880
[72,] 184.1639884 125.2319814
[73,] 341.7339884 184.1639884
[74,] 494.6728773 341.7339884
[75,] 374.7517662 494.6728773
[76,] 299.9917662 374.7517662
[77,] 139.1517662 299.9917662
[78,] 302.8579621 139.1517662
[79,] 455.7668509 302.8579621
[80,] 589.2124065 455.7668509
[81,] 799.3846287 589.2124065
[82,] 866.4946287 799.3846287
[83,] 740.4253221 866.4946287
[84,] 779.5173291 740.4253221
[85,] 849.7873291 779.5173291
[86,] 752.1662180 849.7873291
[87,] 888.9851069 752.1662180
[88,] 1014.3651069 888.9851069
[89,] 1008.9051069 1014.3651069
[90,] 1088.7055399 1008.9051069
[91,] 720.6344288 1088.7055399
[92,] 840.2199844 720.6344288
[93,] 1020.7122066 840.2199844
[94,] 696.5422066 1020.7122066
[95,] 490.8329000 696.5422066
[96,] 104.7149070 490.8329000
[97,] -7.2550930 104.7149070
[98,] -5.7962041 -7.2550930
[99,] 79.9526847 -5.7962041
[100,] 43.0826847 79.9526847
[101,] -183.5073153 43.0826847
[102,] -623.0611194 -183.5073153
[103,] -616.3822305 -623.0611194
[104,] -675.4566749 -616.3822305
[105,] -1398.2244527 -675.4566749
[106,] -1575.7144527 -1398.2244527
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 421.3039441 636.5839441
2 433.5028330 421.3039441
3 451.0017219 433.5028330
4 451.4517219 451.0017219
5 555.2517219 451.4517219
6 703.0179178 555.2517219
7 825.6068067 703.0179178
8 741.2023622 825.6068067
9 795.8045845 741.2023622
10 866.4245845 795.8045845
11 551.9352779 866.4245845
12 411.8672849 551.9352779
13 431.8572849 411.8672849
14 317.5261737 431.8572849
15 235.8350626 317.5261737
16 201.3950626 235.8350626
17 321.9250626 201.3950626
18 423.9012585 321.9250626
19 393.7601474 423.9012585
20 131.1657029 393.7601474
21 210.1479252 131.1657029
22 234.1879252 210.1479252
23 -4.3013814 234.1879252
24 -36.5093744 -4.3013814
25 0.6606256 -36.5093744
26 38.5195145 0.6606256
27 17.0484033 38.5195145
28 24.4684033 17.0484033
29 -149.5815967 24.4684033
30 -367.6654008 -149.5815967
31 -465.4465119 -367.6654008
32 -588.9709563 -465.4465119
33 -672.5387341 -588.9709563
34 -565.1487341 -672.5387341
35 -828.9880407 -565.1487341
36 -991.4760337 -828.9880407
37 -1160.8460337 -991.4760337
38 -1243.4571448 -1160.8460337
39 -1142.7582559 -1243.4571448
40 -1046.0582559 -1142.7582559
41 -926.7182559 -1046.0582559
42 -884.5720601 -926.7182559
43 -800.5131712 -884.5720601
44 -742.8276156 -800.5131712
45 -673.6753934 -742.8276156
46 -614.1153934 -673.6753934
47 -809.1647000 -614.1153934
48 -769.3726930 -809.1647000
49 -668.0226930 -769.3726930
50 -651.5938041 -668.0226930
51 -684.8949152 -651.5938041
52 -730.4149152 -684.8949152
53 -613.2949152 -730.4149152
54 -587.6487194 -613.2949152
55 -545.6098305 -587.6487194
56 -372.1942749 -545.6098305
57 -219.3220527 -372.1942749
58 -120.9320527 -219.3220527
59 -265.9713593 -120.9320527
60 -319.4893523 -265.9713593
61 -209.2193523 -319.4893523
62 -135.5404634 -209.2193523
63 -219.9215745 -135.5404634
64 -258.2815745 -219.9215745
65 -152.1315745 -258.2815745
66 -55.5353787 -152.1315745
67 32.1835102 -55.5353787
68 77.6490658 32.1835102
69 137.7112880 77.6490658
70 212.2612880 137.7112880
71 125.2319814 212.2612880
72 184.1639884 125.2319814
73 341.7339884 184.1639884
74 494.6728773 341.7339884
75 374.7517662 494.6728773
76 299.9917662 374.7517662
77 139.1517662 299.9917662
78 302.8579621 139.1517662
79 455.7668509 302.8579621
80 589.2124065 455.7668509
81 799.3846287 589.2124065
82 866.4946287 799.3846287
83 740.4253221 866.4946287
84 779.5173291 740.4253221
85 849.7873291 779.5173291
86 752.1662180 849.7873291
87 888.9851069 752.1662180
88 1014.3651069 888.9851069
89 1008.9051069 1014.3651069
90 1088.7055399 1008.9051069
91 720.6344288 1088.7055399
92 840.2199844 720.6344288
93 1020.7122066 840.2199844
94 696.5422066 1020.7122066
95 490.8329000 696.5422066
96 104.7149070 490.8329000
97 -7.2550930 104.7149070
98 -5.7962041 -7.2550930
99 79.9526847 -5.7962041
100 43.0826847 79.9526847
101 -183.5073153 43.0826847
102 -623.0611194 -183.5073153
103 -616.3822305 -623.0611194
104 -675.4566749 -616.3822305
105 -1398.2244527 -675.4566749
106 -1575.7144527 -1398.2244527
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7hs8z1228655466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8mt3r1228655466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9azg01228655466.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/10813w1228655467.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11btm61228655467.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12lvea1228655467.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13jcsj1228655467.tab")
>
> system("convert tmp/1sixv1228655466.ps tmp/1sixv1228655466.png")
> system("convert tmp/25r1n1228655466.ps tmp/25r1n1228655466.png")
> system("convert tmp/3j9w01228655466.ps tmp/3j9w01228655466.png")
> system("convert tmp/45mkk1228655466.ps tmp/45mkk1228655466.png")
> system("convert tmp/5nr581228655466.ps tmp/5nr581228655466.png")
> system("convert tmp/6su4i1228655466.ps tmp/6su4i1228655466.png")
> system("convert tmp/7hs8z1228655466.ps tmp/7hs8z1228655466.png")
> system("convert tmp/8mt3r1228655466.ps tmp/8mt3r1228655466.png")
> system("convert tmp/9azg01228655466.ps tmp/9azg01228655466.png")
>
>
> proc.time()
user system elapsed
2.153 1.500 2.876