R version 2.6.0 (2007-10-03)
Copyright (C) 2007 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(100.6
+ ,115.9
+ ,59.7
+ ,125
+ ,96.1
+ ,112.9
+ ,58.2
+ ,121.7
+ ,110
+ ,126.3
+ ,75.3
+ ,134.3
+ ,108.2
+ ,116.8
+ ,69
+ ,124.3
+ ,106.9
+ ,112
+ ,66.1
+ ,119.1
+ ,117.2
+ ,129.7
+ ,77.5
+ ,137.8
+ ,105.2
+ ,113.6
+ ,69.3
+ ,120.5
+ ,106.3
+ ,115.7
+ ,70.2
+ ,122.7
+ ,95.9
+ ,119.5
+ ,70.2
+ ,127.2
+ ,107.5
+ ,125.8
+ ,78.2
+ ,133.2
+ ,113
+ ,129.6
+ ,85.4
+ ,136.3
+ ,111.4
+ ,128
+ ,82.4
+ ,134.9
+ ,95.5
+ ,112.8
+ ,61.2
+ ,120.9
+ ,90.3
+ ,101.6
+ ,52.2
+ ,109.4
+ ,110.8
+ ,123.9
+ ,85.3
+ ,129.6
+ ,107.1
+ ,118.8
+ ,79.9
+ ,124.7
+ ,101.4
+ ,109.1
+ ,72.2
+ ,114.6
+ ,112.9
+ ,130.6
+ ,85.7
+ ,137.4
+ ,98.5
+ ,112.4
+ ,75.5
+ ,117.9
+ ,100.1
+ ,111
+ ,69.2
+ ,117.4
+ ,93.4
+ ,116.2
+ ,77.6
+ ,122
+ ,104.4
+ ,119.8
+ ,85.3
+ ,124.8
+ ,101.8
+ ,117.2
+ ,77
+ ,123.3
+ ,107.9
+ ,127.3
+ ,89.9
+ ,132.8
+ ,91.3
+ ,107.7
+ ,60
+ ,115.1
+ ,86.6
+ ,97.5
+ ,54.3
+ ,104.2
+ ,111.4
+ ,120.1
+ ,84
+ ,125.5
+ ,98.4
+ ,110.6
+ ,69.9
+ ,116.8
+ ,102.2
+ ,111.3
+ ,75.1
+ ,116.8
+ ,103
+ ,119.8
+ ,81.7
+ ,125.5
+ ,95.8
+ ,105.5
+ ,69.9
+ ,110.9
+ ,96
+ ,108.7
+ ,68.3
+ ,114.9
+ ,95.7
+ ,128.7
+ ,77.3
+ ,136.4
+ ,106.4
+ ,119.5
+ ,77.4
+ ,125.8
+ ,112
+ ,121.1
+ ,85.3
+ ,126.5
+ ,116.2
+ ,128.4
+ ,91
+ ,134
+ ,93.9
+ ,108.8
+ ,60.6
+ ,116.1
+ ,100.5
+ ,107.5
+ ,57.6
+ ,115
+ ,112.5
+ ,125.6
+ ,93.8
+ ,130.3
+ ,101.2
+ ,102.9
+ ,78.7
+ ,106.5
+ ,107.8
+ ,107.5
+ ,80.3
+ ,111.6
+ ,114.3
+ ,120.4
+ ,89.8
+ ,125
+ ,99.6
+ ,104.3
+ ,77.5
+ ,108.3
+ ,98.6
+ ,100.6
+ ,71.7
+ ,105
+ ,93.6
+ ,121.9
+ ,83.2
+ ,127.4
+ ,99.6
+ ,112.7
+ ,86.2
+ ,116.6
+ ,113.1
+ ,124.9
+ ,100.7
+ ,128.6
+ ,110.7
+ ,123.9
+ ,100.8
+ ,127.5
+ ,88.1
+ ,102.2
+ ,57.1
+ ,108.4
+ ,93.1
+ ,104.9
+ ,62.5
+ ,110.8
+ ,107.4
+ ,109.8
+ ,79.7
+ ,114.2
+ ,99.5
+ ,98.9
+ ,80.3
+ ,101.8
+ ,105.6
+ ,107.3
+ ,92.4
+ ,109.8
+ ,108.3
+ ,112.6
+ ,91.8
+ ,115.9
+ ,99.2
+ ,104
+ ,85.8
+ ,106.9
+ ,99.3
+ ,110.6
+ ,84.2
+ ,114.6
+ ,107.1
+ ,100.8
+ ,93.1
+ ,105.4
+ ,106.9
+ ,103.8
+ ,101.2
+ ,108.1
+ ,115.4
+ ,117
+ ,100.6
+ ,118.4
+ ,99
+ ,108.4
+ ,106.7
+ ,112.7
+ ,100.1
+ ,95.5
+ ,64
+ ,98.4
+ ,96.2
+ ,96.9
+ ,67.5
+ ,99.6
+ ,96.9
+ ,103.9
+ ,101
+ ,103.9
+ ,96.2
+ ,101.1
+ ,95.5
+ ,101.5
+ ,91
+ ,100.6
+ ,97
+ ,100.8
+ ,99
+ ,104.3
+ ,103.8
+ ,104.5
+ ,99
+ ,98
+ ,95.2
+ ,98.2
+ ,107.2
+ ,99.5
+ ,86.7
+ ,99.9
+ ,110.8
+ ,97.4
+ ,93.5
+ ,97.5
+ ,111.1
+ ,105.6
+ ,102.5
+ ,105.7
+ ,104.6
+ ,117.5
+ ,112.3
+ ,117.7
+ ,94.3
+ ,107.4
+ ,105.5
+ ,107.4
+ ,90.7
+ ,97.8
+ ,75.4
+ ,98.4
+ ,88.8
+ ,91.5
+ ,70.4
+ ,92
+ ,90.9
+ ,107.7
+ ,108
+ ,107.7
+ ,90.5
+ ,100.1
+ ,100
+ ,100.2
+ ,95.5
+ ,96.6
+ ,93.3
+ ,96.7
+ ,103.1
+ ,106.8
+ ,111.1
+ ,106.8
+ ,100.6
+ ,98
+ ,101.1
+ ,98
+ ,103.1
+ ,98.6
+ ,98.1
+ ,98.6)
+ ,dim=c(4
+ ,80)
+ ,dimnames=list(c('1'
+ ,'2'
+ ,'3'
+ ,'4
')
+ ,1:80))
> y <- array(NA,dim=c(4,80),dimnames=list(c('1','2','3','4
'),1:80))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> 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
1 2 3 4\r t
1 100.6 115.9 59.7 125.0 1
2 96.1 112.9 58.2 121.7 2
3 110.0 126.3 75.3 134.3 3
4 108.2 116.8 69.0 124.3 4
5 106.9 112.0 66.1 119.1 5
6 117.2 129.7 77.5 137.8 6
7 105.2 113.6 69.3 120.5 7
8 106.3 115.7 70.2 122.7 8
9 95.9 119.5 70.2 127.2 9
10 107.5 125.8 78.2 133.2 10
11 113.0 129.6 85.4 136.3 11
12 111.4 128.0 82.4 134.9 12
13 95.5 112.8 61.2 120.9 13
14 90.3 101.6 52.2 109.4 14
15 110.8 123.9 85.3 129.6 15
16 107.1 118.8 79.9 124.7 16
17 101.4 109.1 72.2 114.6 17
18 112.9 130.6 85.7 137.4 18
19 98.5 112.4 75.5 117.9 19
20 100.1 111.0 69.2 117.4 20
21 93.4 116.2 77.6 122.0 21
22 104.4 119.8 85.3 124.8 22
23 101.8 117.2 77.0 123.3 23
24 107.9 127.3 89.9 132.8 24
25 91.3 107.7 60.0 115.1 25
26 86.6 97.5 54.3 104.2 26
27 111.4 120.1 84.0 125.5 27
28 98.4 110.6 69.9 116.8 28
29 102.2 111.3 75.1 116.8 29
30 103.0 119.8 81.7 125.5 30
31 95.8 105.5 69.9 110.9 31
32 96.0 108.7 68.3 114.9 32
33 95.7 128.7 77.3 136.4 33
34 106.4 119.5 77.4 125.8 34
35 112.0 121.1 85.3 126.5 35
36 116.2 128.4 91.0 134.0 36
37 93.9 108.8 60.6 116.1 37
38 100.5 107.5 57.6 115.0 38
39 112.5 125.6 93.8 130.3 39
40 101.2 102.9 78.7 106.5 40
41 107.8 107.5 80.3 111.6 41
42 114.3 120.4 89.8 125.0 42
43 99.6 104.3 77.5 108.3 43
44 98.6 100.6 71.7 105.0 44
45 93.6 121.9 83.2 127.4 45
46 99.6 112.7 86.2 116.6 46
47 113.1 124.9 100.7 128.6 47
48 110.7 123.9 100.8 127.5 48
49 88.1 102.2 57.1 108.4 49
50 93.1 104.9 62.5 110.8 50
51 107.4 109.8 79.7 114.2 51
52 99.5 98.9 80.3 101.8 52
53 105.6 107.3 92.4 109.8 53
54 108.3 112.6 91.8 115.9 54
55 99.2 104.0 85.8 106.9 55
56 99.3 110.6 84.2 114.6 56
57 107.1 100.8 93.1 105.4 57
58 106.9 103.8 101.2 108.1 58
59 115.4 117.0 100.6 118.4 59
60 99.0 108.4 106.7 112.7 60
61 100.1 95.5 64.0 98.4 61
62 96.2 96.9 67.5 99.6 62
63 96.9 103.9 101.0 103.9 63
64 96.2 101.1 95.5 101.5 64
65 91.0 100.6 97.0 100.8 65
66 99.0 104.3 103.8 104.5 66
67 99.0 98.0 95.2 98.2 67
68 107.2 99.5 86.7 99.9 68
69 110.8 97.4 93.5 97.5 69
70 111.1 105.6 102.5 105.7 70
71 104.6 117.5 112.3 117.7 71
72 94.3 107.4 105.5 107.4 72
73 90.7 97.8 75.4 98.4 73
74 88.8 91.5 70.4 92.0 74
75 90.9 107.7 108.0 107.7 75
76 90.5 100.1 100.0 100.2 76
77 95.5 96.6 93.3 96.7 77
78 103.1 106.8 111.1 106.8 78
79 100.6 98.0 101.1 98.0 79
80 103.1 98.6 98.1 98.6 80
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `2` `3` `4\r` t
53.2369 0.7611 0.2174 -0.4238 -0.1159
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.45761 -3.46544 -0.08957 3.44238 12.41665
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.23685 11.31065 4.707 1.13e-05 ***
`2` 0.76106 0.84948 0.896 0.3732
`3` 0.21744 0.10491 2.073 0.0416 *
`4\r` -0.42377 0.76957 -0.551 0.5835
t -0.11586 0.07277 -1.592 0.1156
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.582 on 75 degrees of freedom
Multiple R-Squared: 0.479, Adjusted R-squared: 0.4512
F-statistic: 17.24 on 4 and 75 DF, p-value: 4.567e-10
> postscript(file="/var/www/html/rcomp/tmp/1fhg21196595445.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/2vczu1196595445.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3x7qc1196595445.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gexn1196595445.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xeqi1196595445.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 80
Frequency = 1
1 2 3 4 5 6
-0.73812599 -3.91135819 1.52749046 4.20562741 5.10156397 7.49227966
7 8 9 10 11 12
2.31304141 2.66726748 -8.60193978 -0.87769831 1.59421832 1.38683022
13 14 15 16 17 18
-4.15214639 -3.62876145 1.37818894 0.77318458 -0.03442089 1.94508728
19 20 21 22 23 24
-4.53332815 -0.59396974 -11.01281679 -3.12454119 -2.46079137 -2.71086291
25 26 27 28 29 30
-5.27735513 -5.47833518 4.80575379 -1.46914278 0.78326351 -2.51822761
31 32 33 34 35 36
-2.34039704 -2.41694163 -10.66824259 2.63567015 5.71266064 6.41161827
37 38 39 40 41 42
-1.83090467 6.06052016 3.01337398 2.30299812 7.33129353 7.74226478
43 44 45 46 47 48
1.00881046 2.80333094 -11.29956613 -3.41099696 2.85221647 0.84124506
49 50 51 52 53 54
-3.71956104 -0.81571782 7.57171717 2.69792424 3.27995528 4.77765623
55 56 57 58 59 60
-0.17062492 -1.36682653 8.17347676 5.18903153 8.25420534 -5.22673196
61 62 63 64 65 66
9.03179883 3.92964138 -6.04410514 -4.31837936 -9.64479544 -4.25553559
67 68 69 70 71 72
-0.14472101 9.59823841 12.41665185 8.10972671 -4.37675169 -9.76038832
73 74 75 76 77 78
-3.20718976 -1.82155362 -13.45760946 -9.39640793 -1.64315144 -1.28054928
79 80
1.47791384 4.54373401
> postscript(file="/var/www/html/rcomp/tmp/6ucl81196595445.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 = 80
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.73812599 NA
1 -3.91135819 -0.73812599
2 1.52749046 -3.91135819
3 4.20562741 1.52749046
4 5.10156397 4.20562741
5 7.49227966 5.10156397
6 2.31304141 7.49227966
7 2.66726748 2.31304141
8 -8.60193978 2.66726748
9 -0.87769831 -8.60193978
10 1.59421832 -0.87769831
11 1.38683022 1.59421832
12 -4.15214639 1.38683022
13 -3.62876145 -4.15214639
14 1.37818894 -3.62876145
15 0.77318458 1.37818894
16 -0.03442089 0.77318458
17 1.94508728 -0.03442089
18 -4.53332815 1.94508728
19 -0.59396974 -4.53332815
20 -11.01281679 -0.59396974
21 -3.12454119 -11.01281679
22 -2.46079137 -3.12454119
23 -2.71086291 -2.46079137
24 -5.27735513 -2.71086291
25 -5.47833518 -5.27735513
26 4.80575379 -5.47833518
27 -1.46914278 4.80575379
28 0.78326351 -1.46914278
29 -2.51822761 0.78326351
30 -2.34039704 -2.51822761
31 -2.41694163 -2.34039704
32 -10.66824259 -2.41694163
33 2.63567015 -10.66824259
34 5.71266064 2.63567015
35 6.41161827 5.71266064
36 -1.83090467 6.41161827
37 6.06052016 -1.83090467
38 3.01337398 6.06052016
39 2.30299812 3.01337398
40 7.33129353 2.30299812
41 7.74226478 7.33129353
42 1.00881046 7.74226478
43 2.80333094 1.00881046
44 -11.29956613 2.80333094
45 -3.41099696 -11.29956613
46 2.85221647 -3.41099696
47 0.84124506 2.85221647
48 -3.71956104 0.84124506
49 -0.81571782 -3.71956104
50 7.57171717 -0.81571782
51 2.69792424 7.57171717
52 3.27995528 2.69792424
53 4.77765623 3.27995528
54 -0.17062492 4.77765623
55 -1.36682653 -0.17062492
56 8.17347676 -1.36682653
57 5.18903153 8.17347676
58 8.25420534 5.18903153
59 -5.22673196 8.25420534
60 9.03179883 -5.22673196
61 3.92964138 9.03179883
62 -6.04410514 3.92964138
63 -4.31837936 -6.04410514
64 -9.64479544 -4.31837936
65 -4.25553559 -9.64479544
66 -0.14472101 -4.25553559
67 9.59823841 -0.14472101
68 12.41665185 9.59823841
69 8.10972671 12.41665185
70 -4.37675169 8.10972671
71 -9.76038832 -4.37675169
72 -3.20718976 -9.76038832
73 -1.82155362 -3.20718976
74 -13.45760946 -1.82155362
75 -9.39640793 -13.45760946
76 -1.64315144 -9.39640793
77 -1.28054928 -1.64315144
78 1.47791384 -1.28054928
79 4.54373401 1.47791384
80 NA 4.54373401
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.91135819 -0.73812599
[2,] 1.52749046 -3.91135819
[3,] 4.20562741 1.52749046
[4,] 5.10156397 4.20562741
[5,] 7.49227966 5.10156397
[6,] 2.31304141 7.49227966
[7,] 2.66726748 2.31304141
[8,] -8.60193978 2.66726748
[9,] -0.87769831 -8.60193978
[10,] 1.59421832 -0.87769831
[11,] 1.38683022 1.59421832
[12,] -4.15214639 1.38683022
[13,] -3.62876145 -4.15214639
[14,] 1.37818894 -3.62876145
[15,] 0.77318458 1.37818894
[16,] -0.03442089 0.77318458
[17,] 1.94508728 -0.03442089
[18,] -4.53332815 1.94508728
[19,] -0.59396974 -4.53332815
[20,] -11.01281679 -0.59396974
[21,] -3.12454119 -11.01281679
[22,] -2.46079137 -3.12454119
[23,] -2.71086291 -2.46079137
[24,] -5.27735513 -2.71086291
[25,] -5.47833518 -5.27735513
[26,] 4.80575379 -5.47833518
[27,] -1.46914278 4.80575379
[28,] 0.78326351 -1.46914278
[29,] -2.51822761 0.78326351
[30,] -2.34039704 -2.51822761
[31,] -2.41694163 -2.34039704
[32,] -10.66824259 -2.41694163
[33,] 2.63567015 -10.66824259
[34,] 5.71266064 2.63567015
[35,] 6.41161827 5.71266064
[36,] -1.83090467 6.41161827
[37,] 6.06052016 -1.83090467
[38,] 3.01337398 6.06052016
[39,] 2.30299812 3.01337398
[40,] 7.33129353 2.30299812
[41,] 7.74226478 7.33129353
[42,] 1.00881046 7.74226478
[43,] 2.80333094 1.00881046
[44,] -11.29956613 2.80333094
[45,] -3.41099696 -11.29956613
[46,] 2.85221647 -3.41099696
[47,] 0.84124506 2.85221647
[48,] -3.71956104 0.84124506
[49,] -0.81571782 -3.71956104
[50,] 7.57171717 -0.81571782
[51,] 2.69792424 7.57171717
[52,] 3.27995528 2.69792424
[53,] 4.77765623 3.27995528
[54,] -0.17062492 4.77765623
[55,] -1.36682653 -0.17062492
[56,] 8.17347676 -1.36682653
[57,] 5.18903153 8.17347676
[58,] 8.25420534 5.18903153
[59,] -5.22673196 8.25420534
[60,] 9.03179883 -5.22673196
[61,] 3.92964138 9.03179883
[62,] -6.04410514 3.92964138
[63,] -4.31837936 -6.04410514
[64,] -9.64479544 -4.31837936
[65,] -4.25553559 -9.64479544
[66,] -0.14472101 -4.25553559
[67,] 9.59823841 -0.14472101
[68,] 12.41665185 9.59823841
[69,] 8.10972671 12.41665185
[70,] -4.37675169 8.10972671
[71,] -9.76038832 -4.37675169
[72,] -3.20718976 -9.76038832
[73,] -1.82155362 -3.20718976
[74,] -13.45760946 -1.82155362
[75,] -9.39640793 -13.45760946
[76,] -1.64315144 -9.39640793
[77,] -1.28054928 -1.64315144
[78,] 1.47791384 -1.28054928
[79,] 4.54373401 1.47791384
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.91135819 -0.73812599
2 1.52749046 -3.91135819
3 4.20562741 1.52749046
4 5.10156397 4.20562741
5 7.49227966 5.10156397
6 2.31304141 7.49227966
7 2.66726748 2.31304141
8 -8.60193978 2.66726748
9 -0.87769831 -8.60193978
10 1.59421832 -0.87769831
11 1.38683022 1.59421832
12 -4.15214639 1.38683022
13 -3.62876145 -4.15214639
14 1.37818894 -3.62876145
15 0.77318458 1.37818894
16 -0.03442089 0.77318458
17 1.94508728 -0.03442089
18 -4.53332815 1.94508728
19 -0.59396974 -4.53332815
20 -11.01281679 -0.59396974
21 -3.12454119 -11.01281679
22 -2.46079137 -3.12454119
23 -2.71086291 -2.46079137
24 -5.27735513 -2.71086291
25 -5.47833518 -5.27735513
26 4.80575379 -5.47833518
27 -1.46914278 4.80575379
28 0.78326351 -1.46914278
29 -2.51822761 0.78326351
30 -2.34039704 -2.51822761
31 -2.41694163 -2.34039704
32 -10.66824259 -2.41694163
33 2.63567015 -10.66824259
34 5.71266064 2.63567015
35 6.41161827 5.71266064
36 -1.83090467 6.41161827
37 6.06052016 -1.83090467
38 3.01337398 6.06052016
39 2.30299812 3.01337398
40 7.33129353 2.30299812
41 7.74226478 7.33129353
42 1.00881046 7.74226478
43 2.80333094 1.00881046
44 -11.29956613 2.80333094
45 -3.41099696 -11.29956613
46 2.85221647 -3.41099696
47 0.84124506 2.85221647
48 -3.71956104 0.84124506
49 -0.81571782 -3.71956104
50 7.57171717 -0.81571782
51 2.69792424 7.57171717
52 3.27995528 2.69792424
53 4.77765623 3.27995528
54 -0.17062492 4.77765623
55 -1.36682653 -0.17062492
56 8.17347676 -1.36682653
57 5.18903153 8.17347676
58 8.25420534 5.18903153
59 -5.22673196 8.25420534
60 9.03179883 -5.22673196
61 3.92964138 9.03179883
62 -6.04410514 3.92964138
63 -4.31837936 -6.04410514
64 -9.64479544 -4.31837936
65 -4.25553559 -9.64479544
66 -0.14472101 -4.25553559
67 9.59823841 -0.14472101
68 12.41665185 9.59823841
69 8.10972671 12.41665185
70 -4.37675169 8.10972671
71 -9.76038832 -4.37675169
72 -3.20718976 -9.76038832
73 -1.82155362 -3.20718976
74 -13.45760946 -1.82155362
75 -9.39640793 -13.45760946
76 -1.64315144 -9.39640793
77 -1.28054928 -1.64315144
78 1.47791384 -1.28054928
79 4.54373401 1.47791384
> 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/7uogl1196595445.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80e2x1196595445.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9o1sg1196595445.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10mxcd1196595445.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/11mrsp1196595445.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/1294ir1196595446.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/13c7s51196595446.tab")
>
> system("convert tmp/1fhg21196595445.ps tmp/1fhg21196595445.png")
> system("convert tmp/2vczu1196595445.ps tmp/2vczu1196595445.png")
> system("convert tmp/3x7qc1196595445.ps tmp/3x7qc1196595445.png")
> system("convert tmp/4gexn1196595445.ps tmp/4gexn1196595445.png")
> system("convert tmp/5xeqi1196595445.ps tmp/5xeqi1196595445.png")
> system("convert tmp/6ucl81196595445.ps tmp/6ucl81196595445.png")
> system("convert tmp/7uogl1196595445.ps tmp/7uogl1196595445.png")
> system("convert tmp/80e2x1196595445.ps tmp/80e2x1196595445.png")
> system("convert tmp/9o1sg1196595445.ps tmp/9o1sg1196595445.png")
>
>
> proc.time()
user system elapsed
2.382 1.454 2.875