R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list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dim=c(4,58),dimnames=list(c('WLH','X','Y(t-1)','Y(t-2)'),1:58))
> y <- array(NA,dim=c(4,58),dimnames=list(c('WLH','X','Y(t-1)','Y(t-2)'),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
WLH X Y(t-1) Y(t-2) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 135 0 139 149 1 0 0 0 0 0 0 0 0 0 0 1
2 130 0 135 139 0 1 0 0 0 0 0 0 0 0 0 2
3 127 0 130 135 0 0 1 0 0 0 0 0 0 0 0 3
4 122 0 127 130 0 0 0 1 0 0 0 0 0 0 0 4
5 117 0 122 127 0 0 0 0 1 0 0 0 0 0 0 5
6 112 0 117 122 0 0 0 0 0 1 0 0 0 0 0 6
7 113 0 112 117 0 0 0 0 0 0 1 0 0 0 0 7
8 149 0 113 112 0 0 0 0 0 0 0 1 0 0 0 8
9 157 0 149 113 0 0 0 0 0 0 0 0 1 0 0 9
10 157 0 157 149 0 0 0 0 0 0 0 0 0 1 0 10
11 147 0 157 157 0 0 0 0 0 0 0 0 0 0 1 11
12 137 0 147 157 0 0 0 0 0 0 0 0 0 0 0 12
13 132 0 137 147 1 0 0 0 0 0 0 0 0 0 0 13
14 125 0 132 137 0 1 0 0 0 0 0 0 0 0 0 14
15 123 0 125 132 0 0 1 0 0 0 0 0 0 0 0 15
16 117 0 123 125 0 0 0 1 0 0 0 0 0 0 0 16
17 114 0 117 123 0 0 0 0 1 0 0 0 0 0 0 17
18 111 0 114 117 0 0 0 0 0 1 0 0 0 0 0 18
19 112 0 111 114 0 0 0 0 0 0 1 0 0 0 0 19
20 144 0 112 111 0 0 0 0 0 0 0 1 0 0 0 20
21 150 0 144 112 0 0 0 0 0 0 0 0 1 0 0 21
22 149 0 150 144 0 0 0 0 0 0 0 0 0 1 0 22
23 134 0 149 150 0 0 0 0 0 0 0 0 0 0 1 23
24 123 0 134 149 0 0 0 0 0 0 0 0 0 0 0 24
25 116 0 123 134 1 0 0 0 0 0 0 0 0 0 0 25
26 117 0 116 123 0 1 0 0 0 0 0 0 0 0 0 26
27 111 0 117 116 0 0 1 0 0 0 0 0 0 0 0 27
28 105 0 111 117 0 0 0 1 0 0 0 0 0 0 0 28
29 102 0 105 111 0 0 0 0 1 0 0 0 0 0 0 29
30 95 0 102 105 0 0 0 0 0 1 0 0 0 0 0 30
31 93 0 95 102 0 0 0 0 0 0 1 0 0 0 0 31
32 124 0 93 95 0 0 0 0 0 0 0 1 0 0 0 32
33 130 0 124 93 0 0 0 0 0 0 0 0 1 0 0 33
34 124 0 130 124 0 0 0 0 0 0 0 0 0 1 0 34
35 115 0 124 130 0 0 0 0 0 0 0 0 0 0 1 35
36 106 0 115 124 0 0 0 0 0 0 0 0 0 0 0 36
37 105 0 106 115 1 0 0 0 0 0 0 0 0 0 0 37
38 105 0 105 106 0 1 0 0 0 0 0 0 0 0 0 38
39 101 0 105 105 0 0 1 0 0 0 0 0 0 0 0 39
40 95 0 101 105 0 0 0 1 0 0 0 0 0 0 0 40
41 93 0 95 101 0 0 0 0 1 0 0 0 0 0 0 41
42 84 0 93 95 0 0 0 0 0 1 0 0 0 0 0 42
43 87 0 84 93 0 0 0 0 0 0 1 0 0 0 0 43
44 116 0 87 84 0 0 0 0 0 0 0 1 0 0 0 44
45 120 0 116 87 0 0 0 0 0 0 0 0 1 0 0 45
46 117 1 120 116 0 0 0 0 0 0 0 0 0 1 0 46
47 109 1 117 120 0 0 0 0 0 0 0 0 0 0 1 47
48 105 1 109 117 0 0 0 0 0 0 0 0 0 0 0 48
49 107 1 105 109 1 0 0 0 0 0 0 0 0 0 0 49
50 109 1 107 105 0 1 0 0 0 0 0 0 0 0 0 50
51 109 1 109 107 0 0 1 0 0 0 0 0 0 0 0 51
52 108 1 109 109 0 0 0 1 0 0 0 0 0 0 0 52
53 107 1 108 109 0 0 0 0 1 0 0 0 0 0 0 53
54 99 1 107 108 0 0 0 0 0 1 0 0 0 0 0 54
55 103 1 99 107 0 0 0 0 0 0 1 0 0 0 0 55
56 131 1 103 99 0 0 0 0 0 0 0 1 0 0 0 56
57 137 1 131 103 0 0 0 0 0 0 0 0 1 0 0 57
58 135 1 137 131 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 `Y(t-1)` `Y(t-2)` M1 M2
12.3068 4.3301 1.0187 -0.1490 4.2634 4.3378
M3 M4 M5 M6 M7 M8
2.8537 0.9708 2.7428 -1.3907 6.2410 35.1906
M9 M10 M11 t
9.7455 5.1444 -1.9530 -0.1292
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.8929 -1.2099 -0.1176 1.5217 4.1096
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.30683 8.19304 1.502 0.14055
X 4.33015 1.34071 3.230 0.00241 **
`Y(t-1)` 1.01869 0.14445 7.052 1.23e-08 ***
`Y(t-2)` -0.14897 0.14104 -1.056 0.29690
M1 4.26344 1.68134 2.536 0.01503 *
M2 4.33778 2.02861 2.138 0.03835 *
M3 2.85372 2.14441 1.331 0.19045
M4 0.97084 2.08789 0.465 0.64434
M5 2.74284 2.05206 1.337 0.18854
M6 -1.39069 2.24537 -0.619 0.53903
M7 6.24100 2.18492 2.856 0.00663 **
M8 35.19065 2.70429 13.013 2.50e-16 ***
M9 9.74545 6.07991 1.603 0.11645
M10 5.14442 2.81462 1.828 0.07470 .
M11 -1.95299 2.05956 -0.948 0.34843
t -0.12922 0.05141 -2.513 0.01588 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.388 on 42 degrees of freedom
Multiple R-squared: 0.9861, Adjusted R-squared: 0.9811
F-statistic: 198 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,] 0.19283014 0.38566029 0.80716986
[2,] 0.14057581 0.28115161 0.85942419
[3,] 0.07171627 0.14343254 0.92828373
[4,] 0.20885123 0.41770247 0.79114877
[5,] 0.56289032 0.87421936 0.43710968
[6,] 0.44876986 0.89753973 0.55123014
[7,] 0.52226363 0.95547274 0.47773637
[8,] 0.88698099 0.22603801 0.11301901
[9,] 0.89323302 0.21353396 0.10676698
[10,] 0.85145444 0.29709113 0.14854556
[11,] 0.78467828 0.43064344 0.21532172
[12,] 0.78627483 0.42745034 0.21372517
[13,] 0.90647891 0.18704219 0.09352109
[14,] 0.94554095 0.10891811 0.05445905
[15,] 0.96153490 0.07693020 0.03846510
[16,] 0.94773855 0.10452290 0.05226145
[17,] 0.98067172 0.03865656 0.01932828
[18,] 0.95888520 0.08222960 0.04111480
[19,] 0.94206818 0.11586364 0.05793182
[20,] 0.96386400 0.07227201 0.03613600
[21,] 0.98872178 0.02255645 0.01127822
> postscript(file="/var/www/html/rcomp/tmp/1z6b21258620691.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2a3p81258620691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3euwh1258620691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4njiz1258620691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58bg61258620691.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
-0.84161433 -3.20169435 -0.09087303 -0.76757994 -2.76384292 0.84747006
7 8 9 10 11 12
-1.30642504 4.10960826 1.16033155 3.10405159 1.52244685 -0.11446842
13 14 15 16 17 18
-0.55155177 -3.89294672 2.10627384 -0.88705995 0.28433304 3.70930497
19 20 21 22 23 24
-0.18401846 1.52995666 0.65542023 3.04062679 -2.82023470 -0.51269551
25 26 27 28 29 30
-2.67594833 3.87105596 -2.57714588 -0.30397212 0.27153724 -0.30349083
31 32 33 34 35 36
-3.12207397 0.05207273 -0.25069134 -3.01445569 2.21810818 -0.33131761
37 38 39 40 41 42
2.36188487 2.09472055 -0.44097077 -0.35413806 1.51931312 -2.07440003
43 44 45 46 47 48
2.29335788 -1.92386259 -1.44440198 -3.79888716 -0.92032033 0.95848154
49 50 51 52 53 54
1.70722957 1.12886457 1.00271583 2.31275007 0.68865952 -2.17888416
55 56 57 58
2.31915959 -3.76777505 -0.12065846 0.66866447
> postscript(file="/var/www/html/rcomp/tmp/68xwq1258620691.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 -0.84161433 NA
1 -3.20169435 -0.84161433
2 -0.09087303 -3.20169435
3 -0.76757994 -0.09087303
4 -2.76384292 -0.76757994
5 0.84747006 -2.76384292
6 -1.30642504 0.84747006
7 4.10960826 -1.30642504
8 1.16033155 4.10960826
9 3.10405159 1.16033155
10 1.52244685 3.10405159
11 -0.11446842 1.52244685
12 -0.55155177 -0.11446842
13 -3.89294672 -0.55155177
14 2.10627384 -3.89294672
15 -0.88705995 2.10627384
16 0.28433304 -0.88705995
17 3.70930497 0.28433304
18 -0.18401846 3.70930497
19 1.52995666 -0.18401846
20 0.65542023 1.52995666
21 3.04062679 0.65542023
22 -2.82023470 3.04062679
23 -0.51269551 -2.82023470
24 -2.67594833 -0.51269551
25 3.87105596 -2.67594833
26 -2.57714588 3.87105596
27 -0.30397212 -2.57714588
28 0.27153724 -0.30397212
29 -0.30349083 0.27153724
30 -3.12207397 -0.30349083
31 0.05207273 -3.12207397
32 -0.25069134 0.05207273
33 -3.01445569 -0.25069134
34 2.21810818 -3.01445569
35 -0.33131761 2.21810818
36 2.36188487 -0.33131761
37 2.09472055 2.36188487
38 -0.44097077 2.09472055
39 -0.35413806 -0.44097077
40 1.51931312 -0.35413806
41 -2.07440003 1.51931312
42 2.29335788 -2.07440003
43 -1.92386259 2.29335788
44 -1.44440198 -1.92386259
45 -3.79888716 -1.44440198
46 -0.92032033 -3.79888716
47 0.95848154 -0.92032033
48 1.70722957 0.95848154
49 1.12886457 1.70722957
50 1.00271583 1.12886457
51 2.31275007 1.00271583
52 0.68865952 2.31275007
53 -2.17888416 0.68865952
54 2.31915959 -2.17888416
55 -3.76777505 2.31915959
56 -0.12065846 -3.76777505
57 0.66866447 -0.12065846
58 NA 0.66866447
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.20169435 -0.84161433
[2,] -0.09087303 -3.20169435
[3,] -0.76757994 -0.09087303
[4,] -2.76384292 -0.76757994
[5,] 0.84747006 -2.76384292
[6,] -1.30642504 0.84747006
[7,] 4.10960826 -1.30642504
[8,] 1.16033155 4.10960826
[9,] 3.10405159 1.16033155
[10,] 1.52244685 3.10405159
[11,] -0.11446842 1.52244685
[12,] -0.55155177 -0.11446842
[13,] -3.89294672 -0.55155177
[14,] 2.10627384 -3.89294672
[15,] -0.88705995 2.10627384
[16,] 0.28433304 -0.88705995
[17,] 3.70930497 0.28433304
[18,] -0.18401846 3.70930497
[19,] 1.52995666 -0.18401846
[20,] 0.65542023 1.52995666
[21,] 3.04062679 0.65542023
[22,] -2.82023470 3.04062679
[23,] -0.51269551 -2.82023470
[24,] -2.67594833 -0.51269551
[25,] 3.87105596 -2.67594833
[26,] -2.57714588 3.87105596
[27,] -0.30397212 -2.57714588
[28,] 0.27153724 -0.30397212
[29,] -0.30349083 0.27153724
[30,] -3.12207397 -0.30349083
[31,] 0.05207273 -3.12207397
[32,] -0.25069134 0.05207273
[33,] -3.01445569 -0.25069134
[34,] 2.21810818 -3.01445569
[35,] -0.33131761 2.21810818
[36,] 2.36188487 -0.33131761
[37,] 2.09472055 2.36188487
[38,] -0.44097077 2.09472055
[39,] -0.35413806 -0.44097077
[40,] 1.51931312 -0.35413806
[41,] -2.07440003 1.51931312
[42,] 2.29335788 -2.07440003
[43,] -1.92386259 2.29335788
[44,] -1.44440198 -1.92386259
[45,] -3.79888716 -1.44440198
[46,] -0.92032033 -3.79888716
[47,] 0.95848154 -0.92032033
[48,] 1.70722957 0.95848154
[49,] 1.12886457 1.70722957
[50,] 1.00271583 1.12886457
[51,] 2.31275007 1.00271583
[52,] 0.68865952 2.31275007
[53,] -2.17888416 0.68865952
[54,] 2.31915959 -2.17888416
[55,] -3.76777505 2.31915959
[56,] -0.12065846 -3.76777505
[57,] 0.66866447 -0.12065846
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.20169435 -0.84161433
2 -0.09087303 -3.20169435
3 -0.76757994 -0.09087303
4 -2.76384292 -0.76757994
5 0.84747006 -2.76384292
6 -1.30642504 0.84747006
7 4.10960826 -1.30642504
8 1.16033155 4.10960826
9 3.10405159 1.16033155
10 1.52244685 3.10405159
11 -0.11446842 1.52244685
12 -0.55155177 -0.11446842
13 -3.89294672 -0.55155177
14 2.10627384 -3.89294672
15 -0.88705995 2.10627384
16 0.28433304 -0.88705995
17 3.70930497 0.28433304
18 -0.18401846 3.70930497
19 1.52995666 -0.18401846
20 0.65542023 1.52995666
21 3.04062679 0.65542023
22 -2.82023470 3.04062679
23 -0.51269551 -2.82023470
24 -2.67594833 -0.51269551
25 3.87105596 -2.67594833
26 -2.57714588 3.87105596
27 -0.30397212 -2.57714588
28 0.27153724 -0.30397212
29 -0.30349083 0.27153724
30 -3.12207397 -0.30349083
31 0.05207273 -3.12207397
32 -0.25069134 0.05207273
33 -3.01445569 -0.25069134
34 2.21810818 -3.01445569
35 -0.33131761 2.21810818
36 2.36188487 -0.33131761
37 2.09472055 2.36188487
38 -0.44097077 2.09472055
39 -0.35413806 -0.44097077
40 1.51931312 -0.35413806
41 -2.07440003 1.51931312
42 2.29335788 -2.07440003
43 -1.92386259 2.29335788
44 -1.44440198 -1.92386259
45 -3.79888716 -1.44440198
46 -0.92032033 -3.79888716
47 0.95848154 -0.92032033
48 1.70722957 0.95848154
49 1.12886457 1.70722957
50 1.00271583 1.12886457
51 2.31275007 1.00271583
52 0.68865952 2.31275007
53 -2.17888416 0.68865952
54 2.31915959 -2.17888416
55 -3.76777505 2.31915959
56 -0.12065846 -3.76777505
57 0.66866447 -0.12065846
> 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/7wjgd1258620691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qdmk1258620691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ixx21258620691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109hsb1258620691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ti3n1258620691.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/127gek1258620691.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/13hpad1258620691.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/14pn6p1258620691.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/158r6q1258620691.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/164vzj1258620691.tab")
+ }
>
> system("convert tmp/1z6b21258620691.ps tmp/1z6b21258620691.png")
> system("convert tmp/2a3p81258620691.ps tmp/2a3p81258620691.png")
> system("convert tmp/3euwh1258620691.ps tmp/3euwh1258620691.png")
> system("convert tmp/4njiz1258620691.ps tmp/4njiz1258620691.png")
> system("convert tmp/58bg61258620691.ps tmp/58bg61258620691.png")
> system("convert tmp/68xwq1258620691.ps tmp/68xwq1258620691.png")
> system("convert tmp/7wjgd1258620691.ps tmp/7wjgd1258620691.png")
> system("convert tmp/8qdmk1258620691.ps tmp/8qdmk1258620691.png")
> system("convert tmp/9ixx21258620691.ps tmp/9ixx21258620691.png")
> system("convert tmp/109hsb1258620691.ps tmp/109hsb1258620691.png")
>
>
> proc.time()
user system elapsed
2.319 1.547 2.774