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.
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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(113.6
+ ,123.06
+ ,83.4
+ ,79.8
+ ,112.9
+ ,123.39
+ ,113.6
+ ,83.4
+ ,104
+ ,120.28
+ ,112.9
+ ,113.6
+ ,109.9
+ ,115.33
+ ,104
+ ,112.9
+ ,99
+ ,110.4
+ ,109.9
+ ,104
+ ,106.3
+ ,114.49
+ ,99
+ ,109.9
+ ,128.9
+ ,132.03
+ ,106.3
+ ,99
+ ,111.1
+ ,123.16
+ ,128.9
+ ,106.3
+ ,102.9
+ ,118.82
+ ,111.1
+ ,128.9
+ ,130
+ ,128.32
+ ,102.9
+ ,111.1
+ ,87
+ ,112.24
+ ,130
+ ,102.9
+ ,87.5
+ ,104.53
+ ,87
+ ,130
+ ,117.6
+ ,132.57
+ ,87.5
+ ,87
+ ,103.4
+ ,122.52
+ ,117.6
+ ,87.5
+ ,110.8
+ ,131.8
+ ,103.4
+ ,117.6
+ ,112.6
+ ,124.55
+ ,110.8
+ ,103.4
+ ,102.5
+ ,120.96
+ ,112.6
+ ,110.8
+ ,112.4
+ ,122.6
+ ,102.5
+ ,112.6
+ ,135.6
+ ,145.52
+ ,112.4
+ ,102.5
+ ,105.1
+ ,118.57
+ ,135.6
+ ,112.4
+ ,127.7
+ ,134.25
+ ,105.1
+ ,135.6
+ ,137
+ ,136.7
+ ,127.7
+ ,105.1
+ ,91
+ ,121.37
+ ,137
+ ,127.7
+ ,90.5
+ ,111.63
+ ,91
+ ,137
+ ,122.4
+ ,134.42
+ ,90.5
+ ,91
+ ,123.3
+ ,137.65
+ ,122.4
+ ,90.5
+ ,124.3
+ ,137.86
+ ,123.3
+ ,122.4
+ ,120
+ ,119.77
+ ,124.3
+ ,123.3
+ ,118.1
+ ,130.69
+ ,120
+ ,124.3
+ ,119
+ ,128.28
+ ,118.1
+ ,120
+ ,142.7
+ ,147.45
+ ,119
+ ,118.1
+ ,123.6
+ ,128.42
+ ,142.7
+ ,119
+ ,129.6
+ ,136.9
+ ,123.6
+ ,142.7
+ ,151.6
+ ,143.95
+ ,129.6
+ ,123.6
+ ,110.4
+ ,135.64
+ ,151.6
+ ,129.6
+ ,99.2
+ ,122.48
+ ,110.4
+ ,151.6
+ ,130.5
+ ,136.83
+ ,99.2
+ ,110.4
+ ,136.2
+ ,153.04
+ ,130.5
+ ,99.2
+ ,129.7
+ ,142.71
+ ,136.2
+ ,130.5
+ ,128
+ ,123.46
+ ,129.7
+ ,136.2
+ ,121.6
+ ,144.37
+ ,128
+ ,129.7
+ ,135.8
+ ,146.15
+ ,121.6
+ ,128
+ ,143.8
+ ,147.61
+ ,135.8
+ ,121.6
+ ,147.5
+ ,158.51
+ ,143.8
+ ,135.8
+ ,136.2
+ ,147.4
+ ,147.5
+ ,143.8
+ ,156.6
+ ,165.05
+ ,136.2
+ ,147.5
+ ,123.3
+ ,154.64
+ ,156.6
+ ,136.2
+ ,104.5
+ ,126.2
+ ,123.3
+ ,156.6
+ ,139.8
+ ,157.36
+ ,104.5
+ ,123.3
+ ,136.5
+ ,154.15
+ ,139.8
+ ,104.5
+ ,112.1
+ ,123.21
+ ,136.5
+ ,139.8
+ ,118.5
+ ,113.07
+ ,112.1
+ ,136.5
+ ,94.4
+ ,110.45
+ ,118.5
+ ,112.1
+ ,102.3
+ ,113.57
+ ,94.4
+ ,118.5
+ ,111.4
+ ,122.44
+ ,102.3
+ ,94.4
+ ,99.2
+ ,114.93
+ ,111.4
+ ,102.3
+ ,87.8
+ ,111.85
+ ,99.2
+ ,111.4
+ ,115.8
+ ,126.04
+ ,87.8
+ ,99.2)
+ ,dim=c(4
+ ,58)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2')
+ ,1:58))
> y <- array(NA,dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> 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)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 113.6 123.06 83.4 79.8 1 0 0 0 0 0 0 0 0 0 0 1
2 112.9 123.39 113.6 83.4 0 1 0 0 0 0 0 0 0 0 0 2
3 104.0 120.28 112.9 113.6 0 0 1 0 0 0 0 0 0 0 0 3
4 109.9 115.33 104.0 112.9 0 0 0 1 0 0 0 0 0 0 0 4
5 99.0 110.40 109.9 104.0 0 0 0 0 1 0 0 0 0 0 0 5
6 106.3 114.49 99.0 109.9 0 0 0 0 0 1 0 0 0 0 0 6
7 128.9 132.03 106.3 99.0 0 0 0 0 0 0 1 0 0 0 0 7
8 111.1 123.16 128.9 106.3 0 0 0 0 0 0 0 1 0 0 0 8
9 102.9 118.82 111.1 128.9 0 0 0 0 0 0 0 0 1 0 0 9
10 130.0 128.32 102.9 111.1 0 0 0 0 0 0 0 0 0 1 0 10
11 87.0 112.24 130.0 102.9 0 0 0 0 0 0 0 0 0 0 1 11
12 87.5 104.53 87.0 130.0 0 0 0 0 0 0 0 0 0 0 0 12
13 117.6 132.57 87.5 87.0 1 0 0 0 0 0 0 0 0 0 0 13
14 103.4 122.52 117.6 87.5 0 1 0 0 0 0 0 0 0 0 0 14
15 110.8 131.80 103.4 117.6 0 0 1 0 0 0 0 0 0 0 0 15
16 112.6 124.55 110.8 103.4 0 0 0 1 0 0 0 0 0 0 0 16
17 102.5 120.96 112.6 110.8 0 0 0 0 1 0 0 0 0 0 0 17
18 112.4 122.60 102.5 112.6 0 0 0 0 0 1 0 0 0 0 0 18
19 135.6 145.52 112.4 102.5 0 0 0 0 0 0 1 0 0 0 0 19
20 105.1 118.57 135.6 112.4 0 0 0 0 0 0 0 1 0 0 0 20
21 127.7 134.25 105.1 135.6 0 0 0 0 0 0 0 0 1 0 0 21
22 137.0 136.70 127.7 105.1 0 0 0 0 0 0 0 0 0 1 0 22
23 91.0 121.37 137.0 127.7 0 0 0 0 0 0 0 0 0 0 1 23
24 90.5 111.63 91.0 137.0 0 0 0 0 0 0 0 0 0 0 0 24
25 122.4 134.42 90.5 91.0 1 0 0 0 0 0 0 0 0 0 0 25
26 123.3 137.65 122.4 90.5 0 1 0 0 0 0 0 0 0 0 0 26
27 124.3 137.86 123.3 122.4 0 0 1 0 0 0 0 0 0 0 0 27
28 120.0 119.77 124.3 123.3 0 0 0 1 0 0 0 0 0 0 0 28
29 118.1 130.69 120.0 124.3 0 0 0 0 1 0 0 0 0 0 0 29
30 119.0 128.28 118.1 120.0 0 0 0 0 0 1 0 0 0 0 0 30
31 142.7 147.45 119.0 118.1 0 0 0 0 0 0 1 0 0 0 0 31
32 123.6 128.42 142.7 119.0 0 0 0 0 0 0 0 1 0 0 0 32
33 129.6 136.90 123.6 142.7 0 0 0 0 0 0 0 0 1 0 0 33
34 151.6 143.95 129.6 123.6 0 0 0 0 0 0 0 0 0 1 0 34
35 110.4 135.64 151.6 129.6 0 0 0 0 0 0 0 0 0 0 1 35
36 99.2 122.48 110.4 151.6 0 0 0 0 0 0 0 0 0 0 0 36
37 130.5 136.83 99.2 110.4 1 0 0 0 0 0 0 0 0 0 0 37
38 136.2 153.04 130.5 99.2 0 1 0 0 0 0 0 0 0 0 0 38
39 129.7 142.71 136.2 130.5 0 0 1 0 0 0 0 0 0 0 0 39
40 128.0 123.46 129.7 136.2 0 0 0 1 0 0 0 0 0 0 0 40
41 121.6 144.37 128.0 129.7 0 0 0 0 1 0 0 0 0 0 0 41
42 135.8 146.15 121.6 128.0 0 0 0 0 0 1 0 0 0 0 0 42
43 143.8 147.61 135.8 121.6 0 0 0 0 0 0 1 0 0 0 0 43
44 147.5 158.51 143.8 135.8 0 0 0 0 0 0 0 1 0 0 0 44
45 136.2 147.40 147.5 143.8 0 0 0 0 0 0 0 0 1 0 0 45
46 156.6 165.05 136.2 147.5 0 0 0 0 0 0 0 0 0 1 0 46
47 123.3 154.64 156.6 136.2 0 0 0 0 0 0 0 0 0 0 1 47
48 104.5 126.20 123.3 156.6 0 0 0 0 0 0 0 0 0 0 0 48
49 139.8 157.36 104.5 123.3 1 0 0 0 0 0 0 0 0 0 0 49
50 136.5 154.15 139.8 104.5 0 1 0 0 0 0 0 0 0 0 0 50
51 112.1 123.21 136.5 139.8 0 0 1 0 0 0 0 0 0 0 0 51
52 118.5 113.07 112.1 136.5 0 0 0 1 0 0 0 0 0 0 0 52
53 94.4 110.45 118.5 112.1 0 0 0 0 1 0 0 0 0 0 0 53
54 102.3 113.57 94.4 118.5 0 0 0 0 0 1 0 0 0 0 0 54
55 111.4 122.44 102.3 94.4 0 0 0 0 0 0 1 0 0 0 0 55
56 99.2 114.93 111.4 102.3 0 0 0 0 0 0 0 1 0 0 0 56
57 87.8 111.85 99.2 111.4 0 0 0 0 0 0 0 0 1 0 0 57
58 115.8 126.04 87.8 99.2 0 0 0 0 0 0 0 0 0 1 0 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
-37.2941 0.7390 0.1593 0.2349 25.8124 18.8217
M3 M4 M5 M6 M7 M8
10.7187 22.8169 10.4075 18.6652 26.9841 14.8733
M9 M10 M11 t
12.0425 29.6389 -5.4063 -0.1115
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.9452 -2.1081 0.2833 2.5902 7.4766
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -37.29407 7.49873 -4.973 1.16e-05 ***
X 0.73903 0.07510 9.840 1.81e-12 ***
Y1 0.15933 0.07519 2.119 0.04004 *
Y2 0.23492 0.08837 2.658 0.01106 *
M1 25.81245 5.43906 4.746 2.42e-05 ***
M2 18.82170 6.63192 2.838 0.00696 **
M3 10.71869 4.04241 2.652 0.01125 *
M4 22.81695 3.72863 6.119 2.67e-07 ***
M5 10.40753 4.25972 2.443 0.01884 *
M6 18.66518 3.88995 4.798 2.04e-05 ***
M7 26.98406 5.33037 5.062 8.69e-06 ***
M8 14.87327 5.06927 2.934 0.00540 **
M9 12.04245 3.45053 3.490 0.00115 **
M10 29.63889 4.71255 6.289 1.52e-07 ***
M11 -5.40628 5.11822 -1.056 0.29688
t -0.11151 0.03800 -2.935 0.00539 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.066 on 42 degrees of freedom
Multiple R-squared: 0.9572, Adjusted R-squared: 0.9419
F-statistic: 62.59 on 15 and 42 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.1195946 0.2391892 0.8804054
[2,] 0.1730436 0.3460873 0.8269564
[3,] 0.7164517 0.5670965 0.2835483
[4,] 0.7139729 0.5720542 0.2860271
[5,] 0.7532405 0.4935190 0.2467595
[6,] 0.6464945 0.7070110 0.3535055
[7,] 0.6426185 0.7147629 0.3573815
[8,] 0.5970153 0.8059694 0.4029847
[9,] 0.6696892 0.6606216 0.3303108
[10,] 0.8462186 0.3075628 0.1537814
[11,] 0.7860719 0.4278562 0.2139281
[12,] 0.8135224 0.3729553 0.1864776
[13,] 0.7273208 0.5453584 0.2726792
[14,] 0.7934810 0.4130379 0.2065190
[15,] 0.8256172 0.3487655 0.1743828
[16,] 0.8380069 0.3239861 0.1619931
[17,] 0.7465448 0.5069104 0.2534552
[18,] 0.6911528 0.6176944 0.3088472
[19,] 0.6511024 0.6977952 0.3488976
[20,] 0.6469607 0.7060787 0.3530393
[21,] 0.5384010 0.9231981 0.4615990
> postscript(file="/var/www/html/rcomp/tmp/1jl9e1258715376.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gj351258715376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3x9qu1258715376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4u04b1258715376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ec131258715376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 58
Frequency = 1
1 2 3 4 5 6
2.212382022 2.713169157 -2.657054277 -3.503064862 2.912042972 -0.606049564
7 8 9 10 11 12
2.221448580 -2.116895916 -6.640275619 1.442138520 3.090909033 4.479029055
13 14 15 16 17 18
-1.822402764 -6.406251481 -2.458607256 -5.130520550 -2.081691117 -0.353434560
19 20 21 22 23 24
-1.504143489 -5.887157232 7.476590467 1.045236926 -5.259750824 1.288233498
25 26 27 28 29 30
1.530830682 2.180736156 3.602618270 0.314232122 3.315122473 -0.837040940
31 32 33 34 35 36
0.791262381 3.989739394 4.140665745 6.976575166 2.159751687 -3.213088806
37 38 39 40 41 42
3.244193350 1.710714473 2.798164659 3.034430350 -4.499973060 1.657514134
43 44 45 46 47 48
-0.387879303 2.868382505 0.252461288 -8.945154676 0.009090105 -2.554173747
49 50 51 52 53 54
-5.165003290 -0.198368305 -1.285121397 5.284922940 0.354498732 0.139010929
55 56 57 58
-1.120688169 1.145931249 -5.229441881 -0.518795936
> postscript(file="/var/www/html/rcomp/tmp/6q3ky1258715376.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 2.212382022 NA
1 2.713169157 2.212382022
2 -2.657054277 2.713169157
3 -3.503064862 -2.657054277
4 2.912042972 -3.503064862
5 -0.606049564 2.912042972
6 2.221448580 -0.606049564
7 -2.116895916 2.221448580
8 -6.640275619 -2.116895916
9 1.442138520 -6.640275619
10 3.090909033 1.442138520
11 4.479029055 3.090909033
12 -1.822402764 4.479029055
13 -6.406251481 -1.822402764
14 -2.458607256 -6.406251481
15 -5.130520550 -2.458607256
16 -2.081691117 -5.130520550
17 -0.353434560 -2.081691117
18 -1.504143489 -0.353434560
19 -5.887157232 -1.504143489
20 7.476590467 -5.887157232
21 1.045236926 7.476590467
22 -5.259750824 1.045236926
23 1.288233498 -5.259750824
24 1.530830682 1.288233498
25 2.180736156 1.530830682
26 3.602618270 2.180736156
27 0.314232122 3.602618270
28 3.315122473 0.314232122
29 -0.837040940 3.315122473
30 0.791262381 -0.837040940
31 3.989739394 0.791262381
32 4.140665745 3.989739394
33 6.976575166 4.140665745
34 2.159751687 6.976575166
35 -3.213088806 2.159751687
36 3.244193350 -3.213088806
37 1.710714473 3.244193350
38 2.798164659 1.710714473
39 3.034430350 2.798164659
40 -4.499973060 3.034430350
41 1.657514134 -4.499973060
42 -0.387879303 1.657514134
43 2.868382505 -0.387879303
44 0.252461288 2.868382505
45 -8.945154676 0.252461288
46 0.009090105 -8.945154676
47 -2.554173747 0.009090105
48 -5.165003290 -2.554173747
49 -0.198368305 -5.165003290
50 -1.285121397 -0.198368305
51 5.284922940 -1.285121397
52 0.354498732 5.284922940
53 0.139010929 0.354498732
54 -1.120688169 0.139010929
55 1.145931249 -1.120688169
56 -5.229441881 1.145931249
57 -0.518795936 -5.229441881
58 NA -0.518795936
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.713169157 2.212382022
[2,] -2.657054277 2.713169157
[3,] -3.503064862 -2.657054277
[4,] 2.912042972 -3.503064862
[5,] -0.606049564 2.912042972
[6,] 2.221448580 -0.606049564
[7,] -2.116895916 2.221448580
[8,] -6.640275619 -2.116895916
[9,] 1.442138520 -6.640275619
[10,] 3.090909033 1.442138520
[11,] 4.479029055 3.090909033
[12,] -1.822402764 4.479029055
[13,] -6.406251481 -1.822402764
[14,] -2.458607256 -6.406251481
[15,] -5.130520550 -2.458607256
[16,] -2.081691117 -5.130520550
[17,] -0.353434560 -2.081691117
[18,] -1.504143489 -0.353434560
[19,] -5.887157232 -1.504143489
[20,] 7.476590467 -5.887157232
[21,] 1.045236926 7.476590467
[22,] -5.259750824 1.045236926
[23,] 1.288233498 -5.259750824
[24,] 1.530830682 1.288233498
[25,] 2.180736156 1.530830682
[26,] 3.602618270 2.180736156
[27,] 0.314232122 3.602618270
[28,] 3.315122473 0.314232122
[29,] -0.837040940 3.315122473
[30,] 0.791262381 -0.837040940
[31,] 3.989739394 0.791262381
[32,] 4.140665745 3.989739394
[33,] 6.976575166 4.140665745
[34,] 2.159751687 6.976575166
[35,] -3.213088806 2.159751687
[36,] 3.244193350 -3.213088806
[37,] 1.710714473 3.244193350
[38,] 2.798164659 1.710714473
[39,] 3.034430350 2.798164659
[40,] -4.499973060 3.034430350
[41,] 1.657514134 -4.499973060
[42,] -0.387879303 1.657514134
[43,] 2.868382505 -0.387879303
[44,] 0.252461288 2.868382505
[45,] -8.945154676 0.252461288
[46,] 0.009090105 -8.945154676
[47,] -2.554173747 0.009090105
[48,] -5.165003290 -2.554173747
[49,] -0.198368305 -5.165003290
[50,] -1.285121397 -0.198368305
[51,] 5.284922940 -1.285121397
[52,] 0.354498732 5.284922940
[53,] 0.139010929 0.354498732
[54,] -1.120688169 0.139010929
[55,] 1.145931249 -1.120688169
[56,] -5.229441881 1.145931249
[57,] -0.518795936 -5.229441881
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.713169157 2.212382022
2 -2.657054277 2.713169157
3 -3.503064862 -2.657054277
4 2.912042972 -3.503064862
5 -0.606049564 2.912042972
6 2.221448580 -0.606049564
7 -2.116895916 2.221448580
8 -6.640275619 -2.116895916
9 1.442138520 -6.640275619
10 3.090909033 1.442138520
11 4.479029055 3.090909033
12 -1.822402764 4.479029055
13 -6.406251481 -1.822402764
14 -2.458607256 -6.406251481
15 -5.130520550 -2.458607256
16 -2.081691117 -5.130520550
17 -0.353434560 -2.081691117
18 -1.504143489 -0.353434560
19 -5.887157232 -1.504143489
20 7.476590467 -5.887157232
21 1.045236926 7.476590467
22 -5.259750824 1.045236926
23 1.288233498 -5.259750824
24 1.530830682 1.288233498
25 2.180736156 1.530830682
26 3.602618270 2.180736156
27 0.314232122 3.602618270
28 3.315122473 0.314232122
29 -0.837040940 3.315122473
30 0.791262381 -0.837040940
31 3.989739394 0.791262381
32 4.140665745 3.989739394
33 6.976575166 4.140665745
34 2.159751687 6.976575166
35 -3.213088806 2.159751687
36 3.244193350 -3.213088806
37 1.710714473 3.244193350
38 2.798164659 1.710714473
39 3.034430350 2.798164659
40 -4.499973060 3.034430350
41 1.657514134 -4.499973060
42 -0.387879303 1.657514134
43 2.868382505 -0.387879303
44 0.252461288 2.868382505
45 -8.945154676 0.252461288
46 0.009090105 -8.945154676
47 -2.554173747 0.009090105
48 -5.165003290 -2.554173747
49 -0.198368305 -5.165003290
50 -1.285121397 -0.198368305
51 5.284922940 -1.285121397
52 0.354498732 5.284922940
53 0.139010929 0.354498732
54 -1.120688169 0.139010929
55 1.145931249 -1.120688169
56 -5.229441881 1.145931249
57 -0.518795936 -5.229441881
> 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/7er971258715376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8otn01258715376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jupa1258715376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1050i51258715376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11b00t1258715376.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/122m551258715376.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/13pqf41258715376.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/14aszb1258715376.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/151j9k1258715376.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16hwai1258715376.tab")
+ }
>
> system("convert tmp/1jl9e1258715376.ps tmp/1jl9e1258715376.png")
> system("convert tmp/2gj351258715376.ps tmp/2gj351258715376.png")
> system("convert tmp/3x9qu1258715376.ps tmp/3x9qu1258715376.png")
> system("convert tmp/4u04b1258715376.ps tmp/4u04b1258715376.png")
> system("convert tmp/5ec131258715376.ps tmp/5ec131258715376.png")
> system("convert tmp/6q3ky1258715376.ps tmp/6q3ky1258715376.png")
> system("convert tmp/7er971258715376.ps tmp/7er971258715376.png")
> system("convert tmp/8otn01258715376.ps tmp/8otn01258715376.png")
> system("convert tmp/9jupa1258715376.ps tmp/9jupa1258715376.png")
> system("convert tmp/1050i51258715376.ps tmp/1050i51258715376.png")
>
>
> proc.time()
user system elapsed
2.326 1.528 2.731