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(95.1,93.8,97,93.8,112.7,107.6,102.9,101,97.4,95.4,111.4,96.5,87.4,89.2,96.8,87.1,114.1,110.5,110.3,110.8,103.9,104.2,101.6,88.9,94.6,89.8,95.9,90,104.7,93.9,102.8,91.3,98.1,87.8,113.9,99.7,80.9,73.5,95.7,79.2,113.2,96.9,105.9,95.2,108.8,95.6,102.3,89.7,99,92.8,100.7,88,115.5,101.1,100.7,92.7,109.9,95.8,114.6,103.8,85.4,81.8,100.5,87.1,114.8,105.9,116.5,108.1,112.9,102.6,102,93.7,106,103.5,105.3,100.6,118.8,113.3,106.1,102.4,109.3,102.1,117.2,106.9,92.5,87.3,104.2,93.1,112.5,109.1,122.4,120.3,113.3,104.9,100,92.6,110.7,109.8,112.8,111.4,109.8,117.9,117.3,121.6,109.1,117.8,115.9,124.2,96,106.8,99.8,102.7,116.8,116.8,115.7,113.6,99.4,96.1,94.3,85),dim=c(2,60),dimnames=list(c('TIA','IAidM'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('TIA','IAidM'),1:60))
> 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
TIA IAidM M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 95.1 93.8 1 0 0 0 0 0 0 0 0 0 0 1
2 97.0 93.8 0 1 0 0 0 0 0 0 0 0 0 2
3 112.7 107.6 0 0 1 0 0 0 0 0 0 0 0 3
4 102.9 101.0 0 0 0 1 0 0 0 0 0 0 0 4
5 97.4 95.4 0 0 0 0 1 0 0 0 0 0 0 5
6 111.4 96.5 0 0 0 0 0 1 0 0 0 0 0 6
7 87.4 89.2 0 0 0 0 0 0 1 0 0 0 0 7
8 96.8 87.1 0 0 0 0 0 0 0 1 0 0 0 8
9 114.1 110.5 0 0 0 0 0 0 0 0 1 0 0 9
10 110.3 110.8 0 0 0 0 0 0 0 0 0 1 0 10
11 103.9 104.2 0 0 0 0 0 0 0 0 0 0 1 11
12 101.6 88.9 0 0 0 0 0 0 0 0 0 0 0 12
13 94.6 89.8 1 0 0 0 0 0 0 0 0 0 0 13
14 95.9 90.0 0 1 0 0 0 0 0 0 0 0 0 14
15 104.7 93.9 0 0 1 0 0 0 0 0 0 0 0 15
16 102.8 91.3 0 0 0 1 0 0 0 0 0 0 0 16
17 98.1 87.8 0 0 0 0 1 0 0 0 0 0 0 17
18 113.9 99.7 0 0 0 0 0 1 0 0 0 0 0 18
19 80.9 73.5 0 0 0 0 0 0 1 0 0 0 0 19
20 95.7 79.2 0 0 0 0 0 0 0 1 0 0 0 20
21 113.2 96.9 0 0 0 0 0 0 0 0 1 0 0 21
22 105.9 95.2 0 0 0 0 0 0 0 0 0 1 0 22
23 108.8 95.6 0 0 0 0 0 0 0 0 0 0 1 23
24 102.3 89.7 0 0 0 0 0 0 0 0 0 0 0 24
25 99.0 92.8 1 0 0 0 0 0 0 0 0 0 0 25
26 100.7 88.0 0 1 0 0 0 0 0 0 0 0 0 26
27 115.5 101.1 0 0 1 0 0 0 0 0 0 0 0 27
28 100.7 92.7 0 0 0 1 0 0 0 0 0 0 0 28
29 109.9 95.8 0 0 0 0 1 0 0 0 0 0 0 29
30 114.6 103.8 0 0 0 0 0 1 0 0 0 0 0 30
31 85.4 81.8 0 0 0 0 0 0 1 0 0 0 0 31
32 100.5 87.1 0 0 0 0 0 0 0 1 0 0 0 32
33 114.8 105.9 0 0 0 0 0 0 0 0 1 0 0 33
34 116.5 108.1 0 0 0 0 0 0 0 0 0 1 0 34
35 112.9 102.6 0 0 0 0 0 0 0 0 0 0 1 35
36 102.0 93.7 0 0 0 0 0 0 0 0 0 0 0 36
37 106.0 103.5 1 0 0 0 0 0 0 0 0 0 0 37
38 105.3 100.6 0 1 0 0 0 0 0 0 0 0 0 38
39 118.8 113.3 0 0 1 0 0 0 0 0 0 0 0 39
40 106.1 102.4 0 0 0 1 0 0 0 0 0 0 0 40
41 109.3 102.1 0 0 0 0 1 0 0 0 0 0 0 41
42 117.2 106.9 0 0 0 0 0 1 0 0 0 0 0 42
43 92.5 87.3 0 0 0 0 0 0 1 0 0 0 0 43
44 104.2 93.1 0 0 0 0 0 0 0 1 0 0 0 44
45 112.5 109.1 0 0 0 0 0 0 0 0 1 0 0 45
46 122.4 120.3 0 0 0 0 0 0 0 0 0 1 0 46
47 113.3 104.9 0 0 0 0 0 0 0 0 0 0 1 47
48 100.0 92.6 0 0 0 0 0 0 0 0 0 0 0 48
49 110.7 109.8 1 0 0 0 0 0 0 0 0 0 0 49
50 112.8 111.4 0 1 0 0 0 0 0 0 0 0 0 50
51 109.8 117.9 0 0 1 0 0 0 0 0 0 0 0 51
52 117.3 121.6 0 0 0 1 0 0 0 0 0 0 0 52
53 109.1 117.8 0 0 0 0 1 0 0 0 0 0 0 53
54 115.9 124.2 0 0 0 0 0 1 0 0 0 0 0 54
55 96.0 106.8 0 0 0 0 0 0 1 0 0 0 0 55
56 99.8 102.7 0 0 0 0 0 0 0 1 0 0 0 56
57 116.8 116.8 0 0 0 0 0 0 0 0 1 0 0 57
58 115.7 113.6 0 0 0 0 0 0 0 0 0 1 0 58
59 99.4 96.1 0 0 0 0 0 0 0 0 0 0 1 59
60 94.3 85.0 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) IAidM M1 M2 M3 M4
65.18435 0.37055 -1.44718 0.20803 6.42047 1.87637
M5 M6 M7 M8 M9 M10
1.38285 8.79446 -10.55236 -0.41997 7.74805 6.93384
M11 t
3.69713 0.04204
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.6369 -2.3526 0.2124 2.9141 6.6148
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 65.18435 6.18655 10.536 7.52e-14 ***
IAidM 0.37055 0.07438 4.982 9.35e-06 ***
M1 -1.44718 2.52705 -0.573 0.56965
M2 0.20803 2.48887 0.084 0.93375
M3 6.42047 2.78499 2.305 0.02571 *
M4 1.87637 2.59930 0.722 0.47402
M5 1.38285 2.53107 0.546 0.58747
M6 8.79446 2.72307 3.230 0.00229 **
M7 -10.55236 2.36625 -4.460 5.25e-05 ***
M8 -0.41997 2.36550 -0.178 0.85986
M9 7.74805 2.74506 2.823 0.00702 **
M10 6.93384 2.80086 2.476 0.01704 *
M11 3.69713 2.49946 1.479 0.14591
t 0.04204 0.03648 1.152 0.25518
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.734 on 46 degrees of freedom
Multiple R-squared: 0.8658, Adjusted R-squared: 0.8279
F-statistic: 22.83 on 13 and 46 DF, p-value: 8.927e-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,] 0.22421124 0.44842249 0.7757888
[2,] 0.11618349 0.23236698 0.8838165
[3,] 0.05652834 0.11305667 0.9434717
[4,] 0.02552554 0.05105108 0.9744745
[5,] 0.02801078 0.05602156 0.9719892
[6,] 0.01769785 0.03539570 0.9823021
[7,] 0.07517465 0.15034931 0.9248253
[8,] 0.04675064 0.09350129 0.9532494
[9,] 0.03609707 0.07219414 0.9639029
[10,] 0.02856037 0.05712073 0.9714396
[11,] 0.02557920 0.05115840 0.9744208
[12,] 0.05542064 0.11084127 0.9445794
[13,] 0.10653003 0.21306007 0.8934700
[14,] 0.12928271 0.25856542 0.8707173
[15,] 0.14914656 0.29829312 0.8508534
[16,] 0.10153883 0.20307765 0.8984612
[17,] 0.08083359 0.16166719 0.9191664
[18,] 0.07287655 0.14575310 0.9271235
[19,] 0.04618846 0.09237692 0.9538115
[20,] 0.05930886 0.11861773 0.9406911
[21,] 0.06195698 0.12391396 0.9380430
[22,] 0.09190861 0.18381722 0.9080914
[23,] 0.10744199 0.21488397 0.8925580
[24,] 0.26754381 0.53508762 0.7324562
[25,] 0.17560331 0.35120662 0.8243967
[26,] 0.15941622 0.31883245 0.8405838
[27,] 0.09051138 0.18102277 0.9094886
> postscript(file="/var/www/html/rcomp/tmp/1ndu51258744597.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lhjm1258744597.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3rhq81258744597.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4snjx1258744597.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5k0em1258744597.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 = 60
Frequency = 1
1 2 3 4 5 6 7
-3.4370105 -3.2342622 1.0976393 -1.7546550 -4.7280780 1.4106710 -0.5795142
8 9 10 11 12 13 14
-0.5757837 -0.1567658 -3.2957595 -4.0554407 2.9690996 -2.9592531 -3.4306152
15 16 17 18 19 20 21
-2.3302466 1.2352502 -1.7163325 2.2204522 -1.7662955 0.7471275 3.4782931
22 23 24 25 26 27 28
-2.4195961 3.5268570 2.8682062 -0.1753615 1.6060377 5.2973257 -1.8879746
29 30 31 32 33 34 35
6.6147979 0.8967364 -0.8463307 2.1153132 1.2388714 2.8958284 4.5285397
36 37 38 39 40 41 42
0.5815457 2.3552779 1.0326278 3.5721367 -0.5867829 3.1758672 1.8435729
43 44 45 46 47 48 49
3.7111804 3.0875481 -2.7513474 3.7706394 3.5718179 -1.5152985 4.2163472
50 51 52 53 54 55 56
4.0262120 -7.6368552 2.9941624 -3.3462546 -6.3714325 -0.5190400 -5.3742050
57 58 59 60
-1.8090513 -0.9511121 -7.5717739 -4.9035530
> postscript(file="/var/www/html/rcomp/tmp/6pnez1258744597.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.4370105 NA
1 -3.2342622 -3.4370105
2 1.0976393 -3.2342622
3 -1.7546550 1.0976393
4 -4.7280780 -1.7546550
5 1.4106710 -4.7280780
6 -0.5795142 1.4106710
7 -0.5757837 -0.5795142
8 -0.1567658 -0.5757837
9 -3.2957595 -0.1567658
10 -4.0554407 -3.2957595
11 2.9690996 -4.0554407
12 -2.9592531 2.9690996
13 -3.4306152 -2.9592531
14 -2.3302466 -3.4306152
15 1.2352502 -2.3302466
16 -1.7163325 1.2352502
17 2.2204522 -1.7163325
18 -1.7662955 2.2204522
19 0.7471275 -1.7662955
20 3.4782931 0.7471275
21 -2.4195961 3.4782931
22 3.5268570 -2.4195961
23 2.8682062 3.5268570
24 -0.1753615 2.8682062
25 1.6060377 -0.1753615
26 5.2973257 1.6060377
27 -1.8879746 5.2973257
28 6.6147979 -1.8879746
29 0.8967364 6.6147979
30 -0.8463307 0.8967364
31 2.1153132 -0.8463307
32 1.2388714 2.1153132
33 2.8958284 1.2388714
34 4.5285397 2.8958284
35 0.5815457 4.5285397
36 2.3552779 0.5815457
37 1.0326278 2.3552779
38 3.5721367 1.0326278
39 -0.5867829 3.5721367
40 3.1758672 -0.5867829
41 1.8435729 3.1758672
42 3.7111804 1.8435729
43 3.0875481 3.7111804
44 -2.7513474 3.0875481
45 3.7706394 -2.7513474
46 3.5718179 3.7706394
47 -1.5152985 3.5718179
48 4.2163472 -1.5152985
49 4.0262120 4.2163472
50 -7.6368552 4.0262120
51 2.9941624 -7.6368552
52 -3.3462546 2.9941624
53 -6.3714325 -3.3462546
54 -0.5190400 -6.3714325
55 -5.3742050 -0.5190400
56 -1.8090513 -5.3742050
57 -0.9511121 -1.8090513
58 -7.5717739 -0.9511121
59 -4.9035530 -7.5717739
60 NA -4.9035530
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.2342622 -3.4370105
[2,] 1.0976393 -3.2342622
[3,] -1.7546550 1.0976393
[4,] -4.7280780 -1.7546550
[5,] 1.4106710 -4.7280780
[6,] -0.5795142 1.4106710
[7,] -0.5757837 -0.5795142
[8,] -0.1567658 -0.5757837
[9,] -3.2957595 -0.1567658
[10,] -4.0554407 -3.2957595
[11,] 2.9690996 -4.0554407
[12,] -2.9592531 2.9690996
[13,] -3.4306152 -2.9592531
[14,] -2.3302466 -3.4306152
[15,] 1.2352502 -2.3302466
[16,] -1.7163325 1.2352502
[17,] 2.2204522 -1.7163325
[18,] -1.7662955 2.2204522
[19,] 0.7471275 -1.7662955
[20,] 3.4782931 0.7471275
[21,] -2.4195961 3.4782931
[22,] 3.5268570 -2.4195961
[23,] 2.8682062 3.5268570
[24,] -0.1753615 2.8682062
[25,] 1.6060377 -0.1753615
[26,] 5.2973257 1.6060377
[27,] -1.8879746 5.2973257
[28,] 6.6147979 -1.8879746
[29,] 0.8967364 6.6147979
[30,] -0.8463307 0.8967364
[31,] 2.1153132 -0.8463307
[32,] 1.2388714 2.1153132
[33,] 2.8958284 1.2388714
[34,] 4.5285397 2.8958284
[35,] 0.5815457 4.5285397
[36,] 2.3552779 0.5815457
[37,] 1.0326278 2.3552779
[38,] 3.5721367 1.0326278
[39,] -0.5867829 3.5721367
[40,] 3.1758672 -0.5867829
[41,] 1.8435729 3.1758672
[42,] 3.7111804 1.8435729
[43,] 3.0875481 3.7111804
[44,] -2.7513474 3.0875481
[45,] 3.7706394 -2.7513474
[46,] 3.5718179 3.7706394
[47,] -1.5152985 3.5718179
[48,] 4.2163472 -1.5152985
[49,] 4.0262120 4.2163472
[50,] -7.6368552 4.0262120
[51,] 2.9941624 -7.6368552
[52,] -3.3462546 2.9941624
[53,] -6.3714325 -3.3462546
[54,] -0.5190400 -6.3714325
[55,] -5.3742050 -0.5190400
[56,] -1.8090513 -5.3742050
[57,] -0.9511121 -1.8090513
[58,] -7.5717739 -0.9511121
[59,] -4.9035530 -7.5717739
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.2342622 -3.4370105
2 1.0976393 -3.2342622
3 -1.7546550 1.0976393
4 -4.7280780 -1.7546550
5 1.4106710 -4.7280780
6 -0.5795142 1.4106710
7 -0.5757837 -0.5795142
8 -0.1567658 -0.5757837
9 -3.2957595 -0.1567658
10 -4.0554407 -3.2957595
11 2.9690996 -4.0554407
12 -2.9592531 2.9690996
13 -3.4306152 -2.9592531
14 -2.3302466 -3.4306152
15 1.2352502 -2.3302466
16 -1.7163325 1.2352502
17 2.2204522 -1.7163325
18 -1.7662955 2.2204522
19 0.7471275 -1.7662955
20 3.4782931 0.7471275
21 -2.4195961 3.4782931
22 3.5268570 -2.4195961
23 2.8682062 3.5268570
24 -0.1753615 2.8682062
25 1.6060377 -0.1753615
26 5.2973257 1.6060377
27 -1.8879746 5.2973257
28 6.6147979 -1.8879746
29 0.8967364 6.6147979
30 -0.8463307 0.8967364
31 2.1153132 -0.8463307
32 1.2388714 2.1153132
33 2.8958284 1.2388714
34 4.5285397 2.8958284
35 0.5815457 4.5285397
36 2.3552779 0.5815457
37 1.0326278 2.3552779
38 3.5721367 1.0326278
39 -0.5867829 3.5721367
40 3.1758672 -0.5867829
41 1.8435729 3.1758672
42 3.7111804 1.8435729
43 3.0875481 3.7111804
44 -2.7513474 3.0875481
45 3.7706394 -2.7513474
46 3.5718179 3.7706394
47 -1.5152985 3.5718179
48 4.2163472 -1.5152985
49 4.0262120 4.2163472
50 -7.6368552 4.0262120
51 2.9941624 -7.6368552
52 -3.3462546 2.9941624
53 -6.3714325 -3.3462546
54 -0.5190400 -6.3714325
55 -5.3742050 -0.5190400
56 -1.8090513 -5.3742050
57 -0.9511121 -1.8090513
58 -7.5717739 -0.9511121
59 -4.9035530 -7.5717739
> 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/7rbi51258744597.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ff5s1258744597.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9bh601258744597.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10iajl1258744597.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11q4py1258744597.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/12pwo01258744597.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/139pty1258744597.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/14fula1258744597.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/15d6f51258744597.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/16jtyt1258744597.tab")
+ }
>
> system("convert tmp/1ndu51258744597.ps tmp/1ndu51258744597.png")
> system("convert tmp/2lhjm1258744597.ps tmp/2lhjm1258744597.png")
> system("convert tmp/3rhq81258744597.ps tmp/3rhq81258744597.png")
> system("convert tmp/4snjx1258744597.ps tmp/4snjx1258744597.png")
> system("convert tmp/5k0em1258744597.ps tmp/5k0em1258744597.png")
> system("convert tmp/6pnez1258744597.ps tmp/6pnez1258744597.png")
> system("convert tmp/7rbi51258744597.ps tmp/7rbi51258744597.png")
> system("convert tmp/8ff5s1258744597.ps tmp/8ff5s1258744597.png")
> system("convert tmp/9bh601258744597.ps tmp/9bh601258744597.png")
> system("convert tmp/10iajl1258744597.ps tmp/10iajl1258744597.png")
>
>
> proc.time()
user system elapsed
2.446 1.569 2.795