R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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.
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> x <- array(list(31514,27071,29462,26105,22397,23843,21705,18089,20764,25316,17704,15548,28029,29383,36438,32034,22679,24319,18004,17537,20366,22782,19169,13807,29743,25591,29096,26482,22405,27044,17970,18730,19684,19785,18479,10698,31956,29506,34506,27165,26736,23691,18157,17328,18205,20995,17382,9367,31124,26551,30651,25859,25100,25778,20418,18688,20424,24776,19814,12738,31566,30111,30019,31934,25826,26835,20205,17789,20520,22518,15572,11509,25447,24090,27786,26195,20516,22759,19028,16971,20036,22485,18730,14538),dim=c(1,84),dimnames=list(c('y'),1:84))
> y <- array(NA,dim=c(1,84),dimnames=list(c('y'),1:84))
> 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 = '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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 31514 1 0 0 0 0 0 0 0 0 0 0
2 27071 0 1 0 0 0 0 0 0 0 0 0
3 29462 0 0 1 0 0 0 0 0 0 0 0
4 26105 0 0 0 1 0 0 0 0 0 0 0
5 22397 0 0 0 0 1 0 0 0 0 0 0
6 23843 0 0 0 0 0 1 0 0 0 0 0
7 21705 0 0 0 0 0 0 1 0 0 0 0
8 18089 0 0 0 0 0 0 0 1 0 0 0
9 20764 0 0 0 0 0 0 0 0 1 0 0
10 25316 0 0 0 0 0 0 0 0 0 1 0
11 17704 0 0 0 0 0 0 0 0 0 0 1
12 15548 0 0 0 0 0 0 0 0 0 0 0
13 28029 1 0 0 0 0 0 0 0 0 0 0
14 29383 0 1 0 0 0 0 0 0 0 0 0
15 36438 0 0 1 0 0 0 0 0 0 0 0
16 32034 0 0 0 1 0 0 0 0 0 0 0
17 22679 0 0 0 0 1 0 0 0 0 0 0
18 24319 0 0 0 0 0 1 0 0 0 0 0
19 18004 0 0 0 0 0 0 1 0 0 0 0
20 17537 0 0 0 0 0 0 0 1 0 0 0
21 20366 0 0 0 0 0 0 0 0 1 0 0
22 22782 0 0 0 0 0 0 0 0 0 1 0
23 19169 0 0 0 0 0 0 0 0 0 0 1
24 13807 0 0 0 0 0 0 0 0 0 0 0
25 29743 1 0 0 0 0 0 0 0 0 0 0
26 25591 0 1 0 0 0 0 0 0 0 0 0
27 29096 0 0 1 0 0 0 0 0 0 0 0
28 26482 0 0 0 1 0 0 0 0 0 0 0
29 22405 0 0 0 0 1 0 0 0 0 0 0
30 27044 0 0 0 0 0 1 0 0 0 0 0
31 17970 0 0 0 0 0 0 1 0 0 0 0
32 18730 0 0 0 0 0 0 0 1 0 0 0
33 19684 0 0 0 0 0 0 0 0 1 0 0
34 19785 0 0 0 0 0 0 0 0 0 1 0
35 18479 0 0 0 0 0 0 0 0 0 0 1
36 10698 0 0 0 0 0 0 0 0 0 0 0
37 31956 1 0 0 0 0 0 0 0 0 0 0
38 29506 0 1 0 0 0 0 0 0 0 0 0
39 34506 0 0 1 0 0 0 0 0 0 0 0
40 27165 0 0 0 1 0 0 0 0 0 0 0
41 26736 0 0 0 0 1 0 0 0 0 0 0
42 23691 0 0 0 0 0 1 0 0 0 0 0
43 18157 0 0 0 0 0 0 1 0 0 0 0
44 17328 0 0 0 0 0 0 0 1 0 0 0
45 18205 0 0 0 0 0 0 0 0 1 0 0
46 20995 0 0 0 0 0 0 0 0 0 1 0
47 17382 0 0 0 0 0 0 0 0 0 0 1
48 9367 0 0 0 0 0 0 0 0 0 0 0
49 31124 1 0 0 0 0 0 0 0 0 0 0
50 26551 0 1 0 0 0 0 0 0 0 0 0
51 30651 0 0 1 0 0 0 0 0 0 0 0
52 25859 0 0 0 1 0 0 0 0 0 0 0
53 25100 0 0 0 0 1 0 0 0 0 0 0
54 25778 0 0 0 0 0 1 0 0 0 0 0
55 20418 0 0 0 0 0 0 1 0 0 0 0
56 18688 0 0 0 0 0 0 0 1 0 0 0
57 20424 0 0 0 0 0 0 0 0 1 0 0
58 24776 0 0 0 0 0 0 0 0 0 1 0
59 19814 0 0 0 0 0 0 0 0 0 0 1
60 12738 0 0 0 0 0 0 0 0 0 0 0
61 31566 1 0 0 0 0 0 0 0 0 0 0
62 30111 0 1 0 0 0 0 0 0 0 0 0
63 30019 0 0 1 0 0 0 0 0 0 0 0
64 31934 0 0 0 1 0 0 0 0 0 0 0
65 25826 0 0 0 0 1 0 0 0 0 0 0
66 26835 0 0 0 0 0 1 0 0 0 0 0
67 20205 0 0 0 0 0 0 1 0 0 0 0
68 17789 0 0 0 0 0 0 0 1 0 0 0
69 20520 0 0 0 0 0 0 0 0 1 0 0
70 22518 0 0 0 0 0 0 0 0 0 1 0
71 15572 0 0 0 0 0 0 0 0 0 0 1
72 11509 0 0 0 0 0 0 0 0 0 0 0
73 25447 1 0 0 0 0 0 0 0 0 0 0
74 24090 0 1 0 0 0 0 0 0 0 0 0
75 27786 0 0 1 0 0 0 0 0 0 0 0
76 26195 0 0 0 1 0 0 0 0 0 0 0
77 20516 0 0 0 0 1 0 0 0 0 0 0
78 22759 0 0 0 0 0 1 0 0 0 0 0
79 19028 0 0 0 0 0 0 1 0 0 0 0
80 16971 0 0 0 0 0 0 0 1 0 0 0
81 20036 0 0 0 0 0 0 0 0 1 0 0
82 22485 0 0 0 0 0 0 0 0 0 1 0
83 18730 0 0 0 0 0 0 0 0 0 0 1
84 14538 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
12601 17311 14871 18536 15367 11065
M6 M7 M8 M9 M10 M11
12295 6755 5275 7399 10065 5521
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4464.3 -1289.3 -174.3 1268.1 5301.1
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12600.7 772.4 16.314 < 2e-16 ***
M1 17310.6 1092.4 15.847 < 2e-16 ***
M2 14871.1 1092.4 13.614 < 2e-16 ***
M3 18536.1 1092.4 16.969 < 2e-16 ***
M4 15367.0 1092.4 14.068 < 2e-16 ***
M5 11064.9 1092.4 10.129 1.71e-15 ***
M6 12294.9 1092.4 11.255 < 2e-16 ***
M7 6754.6 1092.4 6.184 3.40e-08 ***
M8 5275.3 1092.4 4.829 7.48e-06 ***
M9 7399.1 1092.4 6.774 2.88e-09 ***
M10 10064.6 1092.4 9.214 8.37e-14 ***
M11 5520.7 1092.4 5.054 3.17e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2044 on 72 degrees of freedom
Multiple R-squared: 0.8888, Adjusted R-squared: 0.8718
F-statistic: 52.31 on 11 and 72 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/1bds91291913847.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/www/html/rcomp/tmp/2m4ru1291913847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3m4ru1291913847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4m4ru1291913847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xd9x1291913847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 84
Frequency = 1
1 2 3 4 5 6
1602.71429 -400.85714 -1674.85714 -1862.71429 -1268.57143 -1052.57143
7 8 9 10 11 12
2349.71429 213.00000 764.14286 2650.71429 -417.42857 2947.28571
13 14 15 16 17 18
-1882.28571 1911.14286 5301.14286 4066.28571 -986.57143 -576.57143
19 20 21 22 23 24
-1351.28571 -339.00000 366.14286 116.71429 1047.57143 1206.28571
25 26 27 28 29 30
-168.28571 -1880.85714 -2040.85714 -1485.71429 -1260.57143 2148.42857
31 32 33 34 35 36
-1385.28571 854.00000 -315.85714 -2880.28571 357.57143 -1902.71429
37 38 39 40 41 42
2044.71429 2034.14286 3369.14286 -802.71429 3070.42857 -1204.57143
43 44 45 46 47 48
-1198.28571 -548.00000 -1794.85714 -1670.28571 -739.42857 -3233.71429
49 50 51 52 53 54
1212.71429 -920.85714 -485.85714 -2108.71429 1434.42857 882.42857
55 56 57 58 59 60
1062.71429 812.00000 424.14286 2110.71429 1692.57143 137.28571
61 62 63 64 65 66
1654.71429 2639.14286 -1117.85714 3966.28571 2160.42857 1939.42857
67 68 69 70 71 72
849.71429 -87.00000 520.14286 -147.28571 -2549.42857 -1091.71429
73 74 75 76 77 78
-4464.28571 -3381.85714 -3350.85714 -1772.71429 -3149.57143 -2136.57143
79 80 81 82 83 84
-327.28571 -905.00000 36.14286 -180.28571 608.57143 1937.28571
> postscript(file="/var/www/html/rcomp/tmp/6xd9x1291913847.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 = 84
Frequency = 1
lag(myerror, k = 1) myerror
0 1602.71429 NA
1 -400.85714 1602.71429
2 -1674.85714 -400.85714
3 -1862.71429 -1674.85714
4 -1268.57143 -1862.71429
5 -1052.57143 -1268.57143
6 2349.71429 -1052.57143
7 213.00000 2349.71429
8 764.14286 213.00000
9 2650.71429 764.14286
10 -417.42857 2650.71429
11 2947.28571 -417.42857
12 -1882.28571 2947.28571
13 1911.14286 -1882.28571
14 5301.14286 1911.14286
15 4066.28571 5301.14286
16 -986.57143 4066.28571
17 -576.57143 -986.57143
18 -1351.28571 -576.57143
19 -339.00000 -1351.28571
20 366.14286 -339.00000
21 116.71429 366.14286
22 1047.57143 116.71429
23 1206.28571 1047.57143
24 -168.28571 1206.28571
25 -1880.85714 -168.28571
26 -2040.85714 -1880.85714
27 -1485.71429 -2040.85714
28 -1260.57143 -1485.71429
29 2148.42857 -1260.57143
30 -1385.28571 2148.42857
31 854.00000 -1385.28571
32 -315.85714 854.00000
33 -2880.28571 -315.85714
34 357.57143 -2880.28571
35 -1902.71429 357.57143
36 2044.71429 -1902.71429
37 2034.14286 2044.71429
38 3369.14286 2034.14286
39 -802.71429 3369.14286
40 3070.42857 -802.71429
41 -1204.57143 3070.42857
42 -1198.28571 -1204.57143
43 -548.00000 -1198.28571
44 -1794.85714 -548.00000
45 -1670.28571 -1794.85714
46 -739.42857 -1670.28571
47 -3233.71429 -739.42857
48 1212.71429 -3233.71429
49 -920.85714 1212.71429
50 -485.85714 -920.85714
51 -2108.71429 -485.85714
52 1434.42857 -2108.71429
53 882.42857 1434.42857
54 1062.71429 882.42857
55 812.00000 1062.71429
56 424.14286 812.00000
57 2110.71429 424.14286
58 1692.57143 2110.71429
59 137.28571 1692.57143
60 1654.71429 137.28571
61 2639.14286 1654.71429
62 -1117.85714 2639.14286
63 3966.28571 -1117.85714
64 2160.42857 3966.28571
65 1939.42857 2160.42857
66 849.71429 1939.42857
67 -87.00000 849.71429
68 520.14286 -87.00000
69 -147.28571 520.14286
70 -2549.42857 -147.28571
71 -1091.71429 -2549.42857
72 -4464.28571 -1091.71429
73 -3381.85714 -4464.28571
74 -3350.85714 -3381.85714
75 -1772.71429 -3350.85714
76 -3149.57143 -1772.71429
77 -2136.57143 -3149.57143
78 -327.28571 -2136.57143
79 -905.00000 -327.28571
80 36.14286 -905.00000
81 -180.28571 36.14286
82 608.57143 -180.28571
83 1937.28571 608.57143
84 NA 1937.28571
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -400.85714 1602.71429
[2,] -1674.85714 -400.85714
[3,] -1862.71429 -1674.85714
[4,] -1268.57143 -1862.71429
[5,] -1052.57143 -1268.57143
[6,] 2349.71429 -1052.57143
[7,] 213.00000 2349.71429
[8,] 764.14286 213.00000
[9,] 2650.71429 764.14286
[10,] -417.42857 2650.71429
[11,] 2947.28571 -417.42857
[12,] -1882.28571 2947.28571
[13,] 1911.14286 -1882.28571
[14,] 5301.14286 1911.14286
[15,] 4066.28571 5301.14286
[16,] -986.57143 4066.28571
[17,] -576.57143 -986.57143
[18,] -1351.28571 -576.57143
[19,] -339.00000 -1351.28571
[20,] 366.14286 -339.00000
[21,] 116.71429 366.14286
[22,] 1047.57143 116.71429
[23,] 1206.28571 1047.57143
[24,] -168.28571 1206.28571
[25,] -1880.85714 -168.28571
[26,] -2040.85714 -1880.85714
[27,] -1485.71429 -2040.85714
[28,] -1260.57143 -1485.71429
[29,] 2148.42857 -1260.57143
[30,] -1385.28571 2148.42857
[31,] 854.00000 -1385.28571
[32,] -315.85714 854.00000
[33,] -2880.28571 -315.85714
[34,] 357.57143 -2880.28571
[35,] -1902.71429 357.57143
[36,] 2044.71429 -1902.71429
[37,] 2034.14286 2044.71429
[38,] 3369.14286 2034.14286
[39,] -802.71429 3369.14286
[40,] 3070.42857 -802.71429
[41,] -1204.57143 3070.42857
[42,] -1198.28571 -1204.57143
[43,] -548.00000 -1198.28571
[44,] -1794.85714 -548.00000
[45,] -1670.28571 -1794.85714
[46,] -739.42857 -1670.28571
[47,] -3233.71429 -739.42857
[48,] 1212.71429 -3233.71429
[49,] -920.85714 1212.71429
[50,] -485.85714 -920.85714
[51,] -2108.71429 -485.85714
[52,] 1434.42857 -2108.71429
[53,] 882.42857 1434.42857
[54,] 1062.71429 882.42857
[55,] 812.00000 1062.71429
[56,] 424.14286 812.00000
[57,] 2110.71429 424.14286
[58,] 1692.57143 2110.71429
[59,] 137.28571 1692.57143
[60,] 1654.71429 137.28571
[61,] 2639.14286 1654.71429
[62,] -1117.85714 2639.14286
[63,] 3966.28571 -1117.85714
[64,] 2160.42857 3966.28571
[65,] 1939.42857 2160.42857
[66,] 849.71429 1939.42857
[67,] -87.00000 849.71429
[68,] 520.14286 -87.00000
[69,] -147.28571 520.14286
[70,] -2549.42857 -147.28571
[71,] -1091.71429 -2549.42857
[72,] -4464.28571 -1091.71429
[73,] -3381.85714 -4464.28571
[74,] -3350.85714 -3381.85714
[75,] -1772.71429 -3350.85714
[76,] -3149.57143 -1772.71429
[77,] -2136.57143 -3149.57143
[78,] -327.28571 -2136.57143
[79,] -905.00000 -327.28571
[80,] 36.14286 -905.00000
[81,] -180.28571 36.14286
[82,] 608.57143 -180.28571
[83,] 1937.28571 608.57143
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -400.85714 1602.71429
2 -1674.85714 -400.85714
3 -1862.71429 -1674.85714
4 -1268.57143 -1862.71429
5 -1052.57143 -1268.57143
6 2349.71429 -1052.57143
7 213.00000 2349.71429
8 764.14286 213.00000
9 2650.71429 764.14286
10 -417.42857 2650.71429
11 2947.28571 -417.42857
12 -1882.28571 2947.28571
13 1911.14286 -1882.28571
14 5301.14286 1911.14286
15 4066.28571 5301.14286
16 -986.57143 4066.28571
17 -576.57143 -986.57143
18 -1351.28571 -576.57143
19 -339.00000 -1351.28571
20 366.14286 -339.00000
21 116.71429 366.14286
22 1047.57143 116.71429
23 1206.28571 1047.57143
24 -168.28571 1206.28571
25 -1880.85714 -168.28571
26 -2040.85714 -1880.85714
27 -1485.71429 -2040.85714
28 -1260.57143 -1485.71429
29 2148.42857 -1260.57143
30 -1385.28571 2148.42857
31 854.00000 -1385.28571
32 -315.85714 854.00000
33 -2880.28571 -315.85714
34 357.57143 -2880.28571
35 -1902.71429 357.57143
36 2044.71429 -1902.71429
37 2034.14286 2044.71429
38 3369.14286 2034.14286
39 -802.71429 3369.14286
40 3070.42857 -802.71429
41 -1204.57143 3070.42857
42 -1198.28571 -1204.57143
43 -548.00000 -1198.28571
44 -1794.85714 -548.00000
45 -1670.28571 -1794.85714
46 -739.42857 -1670.28571
47 -3233.71429 -739.42857
48 1212.71429 -3233.71429
49 -920.85714 1212.71429
50 -485.85714 -920.85714
51 -2108.71429 -485.85714
52 1434.42857 -2108.71429
53 882.42857 1434.42857
54 1062.71429 882.42857
55 812.00000 1062.71429
56 424.14286 812.00000
57 2110.71429 424.14286
58 1692.57143 2110.71429
59 137.28571 1692.57143
60 1654.71429 137.28571
61 2639.14286 1654.71429
62 -1117.85714 2639.14286
63 3966.28571 -1117.85714
64 2160.42857 3966.28571
65 1939.42857 2160.42857
66 849.71429 1939.42857
67 -87.00000 849.71429
68 520.14286 -87.00000
69 -147.28571 520.14286
70 -2549.42857 -147.28571
71 -1091.71429 -2549.42857
72 -4464.28571 -1091.71429
73 -3381.85714 -4464.28571
74 -3350.85714 -3381.85714
75 -1772.71429 -3350.85714
76 -3149.57143 -1772.71429
77 -2136.57143 -3149.57143
78 -327.28571 -2136.57143
79 -905.00000 -327.28571
80 36.14286 -905.00000
81 -180.28571 36.14286
82 608.57143 -180.28571
83 1937.28571 608.57143
> 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/7748i1291913847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8748i1291913847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9748i1291913847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
hat values (leverages) are all = 0.1428571
and there are no factor predictors; no plot no. 5
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/104wo91291913847.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/11e55u1291913847.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/12lokn1291913847.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/13op0t1291913847.tab")
>
> try(system("convert tmp/1bds91291913847.ps tmp/1bds91291913847.png",intern=TRUE))
character(0)
> try(system("convert tmp/2m4ru1291913847.ps tmp/2m4ru1291913847.png",intern=TRUE))
character(0)
> try(system("convert tmp/3m4ru1291913847.ps tmp/3m4ru1291913847.png",intern=TRUE))
character(0)
> try(system("convert tmp/4m4ru1291913847.ps tmp/4m4ru1291913847.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xd9x1291913847.ps tmp/5xd9x1291913847.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xd9x1291913847.ps tmp/6xd9x1291913847.png",intern=TRUE))
character(0)
> try(system("convert tmp/7748i1291913847.ps tmp/7748i1291913847.png",intern=TRUE))
character(0)
> try(system("convert tmp/8748i1291913847.ps tmp/8748i1291913847.png",intern=TRUE))
character(0)
> try(system("convert tmp/9748i1291913847.ps tmp/9748i1291913847.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.030 1.475 6.276