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.
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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(97.3,0,101,0,113.2,0,101,0,105.7,0,113.9,0,86.4,0,96.5,0,103.3,0,114.9,0,105.8,0,94.2,0,98.4,0,99.4,0,108.8,0,112.6,0,104.4,0,112.2,0,81.1,0,97.1,0,112.6,0,113.8,0,107.8,0,103.2,0,103.3,0,101.2,0,107.7,0,110.4,0,101.9,0,115.9,0,89.9,0,88.6,0,117.2,0,123.9,0,100,1,103.6,1,94.1,1,98.7,1,119.5,1,112.7,1,104.4,1,124.7,1,89.1,1,97,1,121.6,1,118.8,1,114,1,111.5,1,97.2,1,102.5,1,113.4,1,109.8,1,104.9,1,126.1,1,80,1,96.8,1,117.2,1,112.3,1,117.3,1,111.1,1,102.2,1,104.3,1,122.9,1,107.6,1,121.3,1,131.5,1,89,1,104.4,1,128.9,1,135.9,1,133.3,1,121.3,1,120.5,1,120.4,1,137.9,1,126.1,1,133.2,1,146.6,1,103.4,1,117.2,1),dim=c(2,80),dimnames=list(c('y','x'),1:80))
> y <- array(NA,dim=c(2,80),dimnames=list(c('y','x'),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 = '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
y x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 97.3 0 1 0 0 0 0 0 0 0 0 0 0 1
2 101.0 0 0 1 0 0 0 0 0 0 0 0 0 2
3 113.2 0 0 0 1 0 0 0 0 0 0 0 0 3
4 101.0 0 0 0 0 1 0 0 0 0 0 0 0 4
5 105.7 0 0 0 0 0 1 0 0 0 0 0 0 5
6 113.9 0 0 0 0 0 0 1 0 0 0 0 0 6
7 86.4 0 0 0 0 0 0 0 1 0 0 0 0 7
8 96.5 0 0 0 0 0 0 0 0 1 0 0 0 8
9 103.3 0 0 0 0 0 0 0 0 0 1 0 0 9
10 114.9 0 0 0 0 0 0 0 0 0 0 1 0 10
11 105.8 0 0 0 0 0 0 0 0 0 0 0 1 11
12 94.2 0 0 0 0 0 0 0 0 0 0 0 0 12
13 98.4 0 1 0 0 0 0 0 0 0 0 0 0 13
14 99.4 0 0 1 0 0 0 0 0 0 0 0 0 14
15 108.8 0 0 0 1 0 0 0 0 0 0 0 0 15
16 112.6 0 0 0 0 1 0 0 0 0 0 0 0 16
17 104.4 0 0 0 0 0 1 0 0 0 0 0 0 17
18 112.2 0 0 0 0 0 0 1 0 0 0 0 0 18
19 81.1 0 0 0 0 0 0 0 1 0 0 0 0 19
20 97.1 0 0 0 0 0 0 0 0 1 0 0 0 20
21 112.6 0 0 0 0 0 0 0 0 0 1 0 0 21
22 113.8 0 0 0 0 0 0 0 0 0 0 1 0 22
23 107.8 0 0 0 0 0 0 0 0 0 0 0 1 23
24 103.2 0 0 0 0 0 0 0 0 0 0 0 0 24
25 103.3 0 1 0 0 0 0 0 0 0 0 0 0 25
26 101.2 0 0 1 0 0 0 0 0 0 0 0 0 26
27 107.7 0 0 0 1 0 0 0 0 0 0 0 0 27
28 110.4 0 0 0 0 1 0 0 0 0 0 0 0 28
29 101.9 0 0 0 0 0 1 0 0 0 0 0 0 29
30 115.9 0 0 0 0 0 0 1 0 0 0 0 0 30
31 89.9 0 0 0 0 0 0 0 1 0 0 0 0 31
32 88.6 0 0 0 0 0 0 0 0 1 0 0 0 32
33 117.2 0 0 0 0 0 0 0 0 0 1 0 0 33
34 123.9 0 0 0 0 0 0 0 0 0 0 1 0 34
35 100.0 1 0 0 0 0 0 0 0 0 0 0 1 35
36 103.6 1 0 0 0 0 0 0 0 0 0 0 0 36
37 94.1 1 1 0 0 0 0 0 0 0 0 0 0 37
38 98.7 1 0 1 0 0 0 0 0 0 0 0 0 38
39 119.5 1 0 0 1 0 0 0 0 0 0 0 0 39
40 112.7 1 0 0 0 1 0 0 0 0 0 0 0 40
41 104.4 1 0 0 0 0 1 0 0 0 0 0 0 41
42 124.7 1 0 0 0 0 0 1 0 0 0 0 0 42
43 89.1 1 0 0 0 0 0 0 1 0 0 0 0 43
44 97.0 1 0 0 0 0 0 0 0 1 0 0 0 44
45 121.6 1 0 0 0 0 0 0 0 0 1 0 0 45
46 118.8 1 0 0 0 0 0 0 0 0 0 1 0 46
47 114.0 1 0 0 0 0 0 0 0 0 0 0 1 47
48 111.5 1 0 0 0 0 0 0 0 0 0 0 0 48
49 97.2 1 1 0 0 0 0 0 0 0 0 0 0 49
50 102.5 1 0 1 0 0 0 0 0 0 0 0 0 50
51 113.4 1 0 0 1 0 0 0 0 0 0 0 0 51
52 109.8 1 0 0 0 1 0 0 0 0 0 0 0 52
53 104.9 1 0 0 0 0 1 0 0 0 0 0 0 53
54 126.1 1 0 0 0 0 0 1 0 0 0 0 0 54
55 80.0 1 0 0 0 0 0 0 1 0 0 0 0 55
56 96.8 1 0 0 0 0 0 0 0 1 0 0 0 56
57 117.2 1 0 0 0 0 0 0 0 0 1 0 0 57
58 112.3 1 0 0 0 0 0 0 0 0 0 1 0 58
59 117.3 1 0 0 0 0 0 0 0 0 0 0 1 59
60 111.1 1 0 0 0 0 0 0 0 0 0 0 0 60
61 102.2 1 1 0 0 0 0 0 0 0 0 0 0 61
62 104.3 1 0 1 0 0 0 0 0 0 0 0 0 62
63 122.9 1 0 0 1 0 0 0 0 0 0 0 0 63
64 107.6 1 0 0 0 1 0 0 0 0 0 0 0 64
65 121.3 1 0 0 0 0 1 0 0 0 0 0 0 65
66 131.5 1 0 0 0 0 0 1 0 0 0 0 0 66
67 89.0 1 0 0 0 0 0 0 1 0 0 0 0 67
68 104.4 1 0 0 0 0 0 0 0 1 0 0 0 68
69 128.9 1 0 0 0 0 0 0 0 0 1 0 0 69
70 135.9 1 0 0 0 0 0 0 0 0 0 1 0 70
71 133.3 1 0 0 0 0 0 0 0 0 0 0 1 71
72 121.3 1 0 0 0 0 0 0 0 0 0 0 0 72
73 120.5 1 1 0 0 0 0 0 0 0 0 0 0 73
74 120.4 1 0 1 0 0 0 0 0 0 0 0 0 74
75 137.9 1 0 0 1 0 0 0 0 0 0 0 0 75
76 126.1 1 0 0 0 1 0 0 0 0 0 0 0 76
77 133.2 1 0 0 0 0 1 0 0 0 0 0 0 77
78 146.6 1 0 0 0 0 0 1 0 0 0 0 0 78
79 103.4 1 0 0 0 0 0 0 1 0 0 0 0 79
80 117.2 1 0 0 0 0 0 0 0 1 0 0 0 80
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
94.4684 -8.6451 -4.2140 -2.5897 10.6632 4.0447
M5 M6 M7 M8 M9 M10
2.9690 16.1076 -20.3395 -9.5437 9.2171 11.9034
M11 t
5.9971 0.4471
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.3586 -3.3193 0.2383 4.0631 9.9807
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 94.46838 2.73592 34.529 < 2e-16 ***
x -8.64511 2.66331 -3.246 0.00184 **
M1 -4.21402 3.31980 -1.269 0.20877
M2 -2.58969 3.31915 -0.780 0.43805
M3 10.66321 3.31947 3.212 0.00204 **
M4 4.04467 3.32077 1.218 0.22756
M5 2.96900 3.32306 0.893 0.37486
M6 16.10761 3.32631 4.842 8.08e-06 ***
M7 -20.33950 3.33054 -6.107 6.04e-08 ***
M8 -9.54374 3.33573 -2.861 0.00565 **
M9 9.21713 3.45568 2.667 0.00961 **
M10 11.90336 3.45963 3.441 0.00101 **
M11 5.99710 3.44230 1.742 0.08614 .
t 0.44710 0.05698 7.846 4.99e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.961 on 66 degrees of freedom
Multiple R-Squared: 0.8297, Adjusted R-squared: 0.7962
F-statistic: 24.74 on 13 and 66 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/11uvh1195467347.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/22uqi1195467347.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3zz451195467347.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4yim11195467347.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58b151195467347.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
6.59853070 8.22710212 6.72710212 0.69853070 6.02710212 0.64138784
7 8 9 10 11 12
9.14138784 7.99853070 -4.40944137 4.05722529 0.41637329 -5.63362671
13 14 15 16 17 18
2.33328484 1.26185627 -3.03814373 6.93328484 -0.63814373 -6.42385802
19 20 21 22 23 24
-1.52385802 3.23328484 -0.47468723 -2.40802056 -2.94887256 -1.99887256
25 26 27 28 29 30
1.86803899 -2.30338958 -9.50338958 -0.63196101 -8.50338958 -8.08910387
31 32 33 34 35 36
1.91089613 -10.63196101 -1.23993308 2.32673359 -7.46900640 1.68099360
37 38 39 40 41 42
-4.05209485 -1.52352342 5.57647658 4.94790515 -2.72352342 3.99076229
43 44 45 46 47 48
4.39076229 1.04790515 6.43993308 0.50659975 1.16574775 4.21574775
49 50 51 52 53 54
-6.31734070 -3.08876928 -5.88876928 -3.31734070 -7.58876928 0.02551644
55 56 57 58 59 60
-10.07448356 -4.51734070 -3.32531277 -11.35864611 -0.89949811 -1.54949811
61 62 63 64 65 66
-6.68258656 -6.65401513 -1.75401513 -10.88258656 3.44598487 0.06027058
67 68 69 70 71 72
-6.43972942 -2.28258656 3.00944137 6.87610804 9.73525604 3.28525604
73 74 75 76 77 78
6.25216759 4.08073902 7.88073902 2.25216759 9.98073902 9.79502473
79 80
2.59502473 5.15216759
> postscript(file="/var/www/html/rcomp/tmp/6md1b1195467347.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 6.59853070 NA
1 8.22710212 6.59853070
2 6.72710212 8.22710212
3 0.69853070 6.72710212
4 6.02710212 0.69853070
5 0.64138784 6.02710212
6 9.14138784 0.64138784
7 7.99853070 9.14138784
8 -4.40944137 7.99853070
9 4.05722529 -4.40944137
10 0.41637329 4.05722529
11 -5.63362671 0.41637329
12 2.33328484 -5.63362671
13 1.26185627 2.33328484
14 -3.03814373 1.26185627
15 6.93328484 -3.03814373
16 -0.63814373 6.93328484
17 -6.42385802 -0.63814373
18 -1.52385802 -6.42385802
19 3.23328484 -1.52385802
20 -0.47468723 3.23328484
21 -2.40802056 -0.47468723
22 -2.94887256 -2.40802056
23 -1.99887256 -2.94887256
24 1.86803899 -1.99887256
25 -2.30338958 1.86803899
26 -9.50338958 -2.30338958
27 -0.63196101 -9.50338958
28 -8.50338958 -0.63196101
29 -8.08910387 -8.50338958
30 1.91089613 -8.08910387
31 -10.63196101 1.91089613
32 -1.23993308 -10.63196101
33 2.32673359 -1.23993308
34 -7.46900640 2.32673359
35 1.68099360 -7.46900640
36 -4.05209485 1.68099360
37 -1.52352342 -4.05209485
38 5.57647658 -1.52352342
39 4.94790515 5.57647658
40 -2.72352342 4.94790515
41 3.99076229 -2.72352342
42 4.39076229 3.99076229
43 1.04790515 4.39076229
44 6.43993308 1.04790515
45 0.50659975 6.43993308
46 1.16574775 0.50659975
47 4.21574775 1.16574775
48 -6.31734070 4.21574775
49 -3.08876928 -6.31734070
50 -5.88876928 -3.08876928
51 -3.31734070 -5.88876928
52 -7.58876928 -3.31734070
53 0.02551644 -7.58876928
54 -10.07448356 0.02551644
55 -4.51734070 -10.07448356
56 -3.32531277 -4.51734070
57 -11.35864611 -3.32531277
58 -0.89949811 -11.35864611
59 -1.54949811 -0.89949811
60 -6.68258656 -1.54949811
61 -6.65401513 -6.68258656
62 -1.75401513 -6.65401513
63 -10.88258656 -1.75401513
64 3.44598487 -10.88258656
65 0.06027058 3.44598487
66 -6.43972942 0.06027058
67 -2.28258656 -6.43972942
68 3.00944137 -2.28258656
69 6.87610804 3.00944137
70 9.73525604 6.87610804
71 3.28525604 9.73525604
72 6.25216759 3.28525604
73 4.08073902 6.25216759
74 7.88073902 4.08073902
75 2.25216759 7.88073902
76 9.98073902 2.25216759
77 9.79502473 9.98073902
78 2.59502473 9.79502473
79 5.15216759 2.59502473
80 NA 5.15216759
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 8.22710212 6.59853070
[2,] 6.72710212 8.22710212
[3,] 0.69853070 6.72710212
[4,] 6.02710212 0.69853070
[5,] 0.64138784 6.02710212
[6,] 9.14138784 0.64138784
[7,] 7.99853070 9.14138784
[8,] -4.40944137 7.99853070
[9,] 4.05722529 -4.40944137
[10,] 0.41637329 4.05722529
[11,] -5.63362671 0.41637329
[12,] 2.33328484 -5.63362671
[13,] 1.26185627 2.33328484
[14,] -3.03814373 1.26185627
[15,] 6.93328484 -3.03814373
[16,] -0.63814373 6.93328484
[17,] -6.42385802 -0.63814373
[18,] -1.52385802 -6.42385802
[19,] 3.23328484 -1.52385802
[20,] -0.47468723 3.23328484
[21,] -2.40802056 -0.47468723
[22,] -2.94887256 -2.40802056
[23,] -1.99887256 -2.94887256
[24,] 1.86803899 -1.99887256
[25,] -2.30338958 1.86803899
[26,] -9.50338958 -2.30338958
[27,] -0.63196101 -9.50338958
[28,] -8.50338958 -0.63196101
[29,] -8.08910387 -8.50338958
[30,] 1.91089613 -8.08910387
[31,] -10.63196101 1.91089613
[32,] -1.23993308 -10.63196101
[33,] 2.32673359 -1.23993308
[34,] -7.46900640 2.32673359
[35,] 1.68099360 -7.46900640
[36,] -4.05209485 1.68099360
[37,] -1.52352342 -4.05209485
[38,] 5.57647658 -1.52352342
[39,] 4.94790515 5.57647658
[40,] -2.72352342 4.94790515
[41,] 3.99076229 -2.72352342
[42,] 4.39076229 3.99076229
[43,] 1.04790515 4.39076229
[44,] 6.43993308 1.04790515
[45,] 0.50659975 6.43993308
[46,] 1.16574775 0.50659975
[47,] 4.21574775 1.16574775
[48,] -6.31734070 4.21574775
[49,] -3.08876928 -6.31734070
[50,] -5.88876928 -3.08876928
[51,] -3.31734070 -5.88876928
[52,] -7.58876928 -3.31734070
[53,] 0.02551644 -7.58876928
[54,] -10.07448356 0.02551644
[55,] -4.51734070 -10.07448356
[56,] -3.32531277 -4.51734070
[57,] -11.35864611 -3.32531277
[58,] -0.89949811 -11.35864611
[59,] -1.54949811 -0.89949811
[60,] -6.68258656 -1.54949811
[61,] -6.65401513 -6.68258656
[62,] -1.75401513 -6.65401513
[63,] -10.88258656 -1.75401513
[64,] 3.44598487 -10.88258656
[65,] 0.06027058 3.44598487
[66,] -6.43972942 0.06027058
[67,] -2.28258656 -6.43972942
[68,] 3.00944137 -2.28258656
[69,] 6.87610804 3.00944137
[70,] 9.73525604 6.87610804
[71,] 3.28525604 9.73525604
[72,] 6.25216759 3.28525604
[73,] 4.08073902 6.25216759
[74,] 7.88073902 4.08073902
[75,] 2.25216759 7.88073902
[76,] 9.98073902 2.25216759
[77,] 9.79502473 9.98073902
[78,] 2.59502473 9.79502473
[79,] 5.15216759 2.59502473
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 8.22710212 6.59853070
2 6.72710212 8.22710212
3 0.69853070 6.72710212
4 6.02710212 0.69853070
5 0.64138784 6.02710212
6 9.14138784 0.64138784
7 7.99853070 9.14138784
8 -4.40944137 7.99853070
9 4.05722529 -4.40944137
10 0.41637329 4.05722529
11 -5.63362671 0.41637329
12 2.33328484 -5.63362671
13 1.26185627 2.33328484
14 -3.03814373 1.26185627
15 6.93328484 -3.03814373
16 -0.63814373 6.93328484
17 -6.42385802 -0.63814373
18 -1.52385802 -6.42385802
19 3.23328484 -1.52385802
20 -0.47468723 3.23328484
21 -2.40802056 -0.47468723
22 -2.94887256 -2.40802056
23 -1.99887256 -2.94887256
24 1.86803899 -1.99887256
25 -2.30338958 1.86803899
26 -9.50338958 -2.30338958
27 -0.63196101 -9.50338958
28 -8.50338958 -0.63196101
29 -8.08910387 -8.50338958
30 1.91089613 -8.08910387
31 -10.63196101 1.91089613
32 -1.23993308 -10.63196101
33 2.32673359 -1.23993308
34 -7.46900640 2.32673359
35 1.68099360 -7.46900640
36 -4.05209485 1.68099360
37 -1.52352342 -4.05209485
38 5.57647658 -1.52352342
39 4.94790515 5.57647658
40 -2.72352342 4.94790515
41 3.99076229 -2.72352342
42 4.39076229 3.99076229
43 1.04790515 4.39076229
44 6.43993308 1.04790515
45 0.50659975 6.43993308
46 1.16574775 0.50659975
47 4.21574775 1.16574775
48 -6.31734070 4.21574775
49 -3.08876928 -6.31734070
50 -5.88876928 -3.08876928
51 -3.31734070 -5.88876928
52 -7.58876928 -3.31734070
53 0.02551644 -7.58876928
54 -10.07448356 0.02551644
55 -4.51734070 -10.07448356
56 -3.32531277 -4.51734070
57 -11.35864611 -3.32531277
58 -0.89949811 -11.35864611
59 -1.54949811 -0.89949811
60 -6.68258656 -1.54949811
61 -6.65401513 -6.68258656
62 -1.75401513 -6.65401513
63 -10.88258656 -1.75401513
64 3.44598487 -10.88258656
65 0.06027058 3.44598487
66 -6.43972942 0.06027058
67 -2.28258656 -6.43972942
68 3.00944137 -2.28258656
69 6.87610804 3.00944137
70 9.73525604 6.87610804
71 3.28525604 9.73525604
72 6.25216759 3.28525604
73 4.08073902 6.25216759
74 7.88073902 4.08073902
75 2.25216759 7.88073902
76 9.98073902 2.25216759
77 9.79502473 9.98073902
78 2.59502473 9.79502473
79 5.15216759 2.59502473
> 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/742iv1195467347.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8mq551195467347.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rgsv1195467347.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10221u1195467348.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/11wj2s1195467348.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/12atw11195467348.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/13466k1195467348.tab")
>
> system("convert tmp/11uvh1195467347.ps tmp/11uvh1195467347.png")
> system("convert tmp/22uqi1195467347.ps tmp/22uqi1195467347.png")
> system("convert tmp/3zz451195467347.ps tmp/3zz451195467347.png")
> system("convert tmp/4yim11195467347.ps tmp/4yim11195467347.png")
> system("convert tmp/58b151195467347.ps tmp/58b151195467347.png")
> system("convert tmp/6md1b1195467347.ps tmp/6md1b1195467347.png")
> system("convert tmp/742iv1195467347.ps tmp/742iv1195467347.png")
> system("convert tmp/8mq551195467347.ps tmp/8mq551195467347.png")
> system("convert tmp/9rgsv1195467347.ps tmp/9rgsv1195467347.png")
>
>
> proc.time()
user system elapsed
2.441 1.534 2.872