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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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(296.95,17.20,296.84,17.20,287.54,17.20,287.81,17.20,283.99,20.63,275.79,20.63,269.52,20.63,278.35,20.63,283.43,19.32,289.46,19.32,282.30,19.32,293.55,19.32,304.78,12.99,300.99,12.99,315.29,12.99,316.21,12.99,331.79,18.13,329.38,18.13,317.27,18.13,317.98,18.13,340.28,28.37,339.21,28.37,336.71,28.37,340.11,28.37,347.72,24.35,328.68,24.35,303.05,24.35,299.83,24.35,320.04,24.99,317.94,24.99,303.31,24.99,308.85,24.99,319.19,28.84,314.52,28.84,312.39,28.84,315.77,28.84,320.23,37.88,309.45,37.88,296.54,37.88,297.28,37.88,301.39,54.04,306.68,54.04,305.91,54.04,314.76,54.04,323.34,64.93,341.58,64.93,330.12,64.93,318.16,64.93,317.84,71.81,325.39,71.81,327.56,71.81,329.77,71.81,333.29,99.75,346.10,99.75,358.00,99.75,344.82,99.75,313.30,61.25,301.26,61.25,306.38,61.25,319.31,61.25),dim=c(2,60),dimnames=list(c('Gemiddelde_prijs_vliegticket_in$','Gemiddelde_olieprijs_in$'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Gemiddelde_prijs_vliegticket_in$','Gemiddelde_olieprijs_in$'),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 Quarterly 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Gemiddelde_prijs_vliegticket_in$ Gemiddelde_olieprijs_in$ Q1 Q2 Q3 t
1 296.95 17.20 1 0 0 1
2 296.84 17.20 0 1 0 2
3 287.54 17.20 0 0 1 3
4 287.81 17.20 0 0 0 4
5 283.99 20.63 1 0 0 5
6 275.79 20.63 0 1 0 6
7 269.52 20.63 0 0 1 7
8 278.35 20.63 0 0 0 8
9 283.43 19.32 1 0 0 9
10 289.46 19.32 0 1 0 10
11 282.30 19.32 0 0 1 11
12 293.55 19.32 0 0 0 12
13 304.78 12.99 1 0 0 13
14 300.99 12.99 0 1 0 14
15 315.29 12.99 0 0 1 15
16 316.21 12.99 0 0 0 16
17 331.79 18.13 1 0 0 17
18 329.38 18.13 0 1 0 18
19 317.27 18.13 0 0 1 19
20 317.98 18.13 0 0 0 20
21 340.28 28.37 1 0 0 21
22 339.21 28.37 0 1 0 22
23 336.71 28.37 0 0 1 23
24 340.11 28.37 0 0 0 24
25 347.72 24.35 1 0 0 25
26 328.68 24.35 0 1 0 26
27 303.05 24.35 0 0 1 27
28 299.83 24.35 0 0 0 28
29 320.04 24.99 1 0 0 29
30 317.94 24.99 0 1 0 30
31 303.31 24.99 0 0 1 31
32 308.85 24.99 0 0 0 32
33 319.19 28.84 1 0 0 33
34 314.52 28.84 0 1 0 34
35 312.39 28.84 0 0 1 35
36 315.77 28.84 0 0 0 36
37 320.23 37.88 1 0 0 37
38 309.45 37.88 0 1 0 38
39 296.54 37.88 0 0 1 39
40 297.28 37.88 0 0 0 40
41 301.39 54.04 1 0 0 41
42 306.68 54.04 0 1 0 42
43 305.91 54.04 0 0 1 43
44 314.76 54.04 0 0 0 44
45 323.34 64.93 1 0 0 45
46 341.58 64.93 0 1 0 46
47 330.12 64.93 0 0 1 47
48 318.16 64.93 0 0 0 48
49 317.84 71.81 1 0 0 49
50 325.39 71.81 0 1 0 50
51 327.56 71.81 0 0 1 51
52 329.77 71.81 0 0 0 52
53 333.29 99.75 1 0 0 53
54 346.10 99.75 0 1 0 54
55 358.00 99.75 0 0 1 55
56 344.82 99.75 0 0 0 56
57 313.30 61.25 1 0 0 57
58 301.26 61.25 0 1 0 58
59 306.38 61.25 0 0 1 59
60 319.31 61.25 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Gemiddelde_olieprijs_in$`
292.8217 0.1570
Q1 Q2
4.9071 3.5409
Q3 t
-1.6312 0.4135
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-28.701 -11.454 -2.986 12.107 35.831
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 292.8217 6.0181 48.657 <2e-16 ***
`Gemiddelde_olieprijs_in$` 0.1570 0.1801 0.872 0.387
Q1 4.9071 6.2915 0.780 0.439
Q2 3.5409 6.2653 0.565 0.574
Q3 -1.6312 6.2495 -0.261 0.795
t 0.4135 0.2566 1.611 0.113
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 17.1 on 54 degrees of freedom
Multiple R-squared: 0.3114, Adjusted R-squared: 0.2477
F-statistic: 4.884 on 5 and 54 DF, p-value: 0.000929
> 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,] 0.01998978 0.039979562 0.980010219
[2,] 0.01623481 0.032469624 0.983765188
[3,] 0.01004845 0.020096906 0.989951547
[4,] 0.01663012 0.033260234 0.983369883
[5,] 0.02753437 0.055068742 0.972465629
[6,] 0.02503592 0.050071845 0.974964077
[7,] 0.05986749 0.119734986 0.940132507
[8,] 0.04071018 0.081420362 0.959289819
[9,] 0.51135430 0.977291392 0.488645696
[10,] 0.66394338 0.672113243 0.336056621
[11,] 0.62113162 0.757736757 0.378868379
[12,] 0.54716480 0.905670391 0.452835196
[13,] 0.67331743 0.653365134 0.326682567
[14,] 0.65755011 0.684899785 0.342449892
[15,] 0.63709943 0.725801132 0.362900566
[16,] 0.62866408 0.742671846 0.371335923
[17,] 0.79272256 0.414554880 0.207277440
[18,] 0.82979952 0.340400955 0.170200478
[19,] 0.93361326 0.132773485 0.066386742
[20,] 0.98094231 0.038115382 0.019057691
[21,] 0.98759295 0.024814099 0.012407050
[22,] 0.98800162 0.023996766 0.011998383
[23,] 0.98856092 0.022878169 0.011439085
[24,] 0.98602359 0.027952817 0.013976409
[25,] 0.99040240 0.019195204 0.009597602
[26,] 0.98964110 0.020717798 0.010358899
[27,] 0.98698029 0.026039417 0.013019708
[28,] 0.98829967 0.023400654 0.011700327
[29,] 0.99667777 0.006644453 0.003322226
[30,] 0.99592990 0.008140207 0.004070103
[31,] 0.99389125 0.012217501 0.006108750
[32,] 0.99068689 0.018626215 0.009313107
[33,] 0.98557375 0.028852499 0.014426250
[34,] 0.98122093 0.037558147 0.018779074
[35,] 0.98616609 0.027667815 0.013833908
[36,] 0.97899014 0.042019718 0.021009859
[37,] 0.96215715 0.075685699 0.037842850
[38,] 0.98980764 0.020384724 0.010192362
[39,] 0.98184119 0.036317628 0.018158814
[40,] 0.96648655 0.067026898 0.033513449
[41,] 0.92678889 0.146422215 0.073211107
[42,] 0.85372241 0.292555177 0.146277588
[43,] 0.72111307 0.557773855 0.278886928
> postscript(file="/var/www/html/rcomp/tmp/1fawb1292003171.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/2fawb1292003171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3fawb1292003171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pjee1292003171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5pjee1292003171.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-3.8928302 -3.0501636 -7.5914969 -9.3661636 -19.0452509 -26.2925842
7 8 9 10 11 12
-27.8039175 -21.0185842 -21.0534312 -14.0707646 -16.4720979 -7.2667646
13 14 15 16 17 18
-0.3634078 -3.2007412 15.8579255 14.7332588 24.1856798 22.7283465
19 20 21 22 23 24
15.3770132 14.0423465 29.4140027 29.2966693 31.5553360 32.9106693
25 26 27 28 29 30
35.8313267 17.7439934 -3.1273399 -8.3920066 6.3969716 5.2496382
31 32 33 34 35 36
-4.6216951 -1.1263618 3.2886056 -0.4287277 2.1999390 3.5352723
37 38 39 40 41 42
1.2553437 -8.5719896 -16.7233229 -18.0279896 -21.7758486 -15.5331820
43 44 45 46 47 48
-11.5445153 -4.7391820 -3.1895840 16.0030826 9.3017493 -4.7029174
49 50 51 52 53 54
-11.4236985 -2.9210318 4.0076349 4.1729682 -2.0145005 11.7481662
55 56 57 58 59 60
28.4068328 13.1821662 -17.6133784 -28.7007117 -18.8220450 -7.9367117
> postscript(file="/var/www/html/rcomp/tmp/6pjee1292003171.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.8928302 NA
1 -3.0501636 -3.8928302
2 -7.5914969 -3.0501636
3 -9.3661636 -7.5914969
4 -19.0452509 -9.3661636
5 -26.2925842 -19.0452509
6 -27.8039175 -26.2925842
7 -21.0185842 -27.8039175
8 -21.0534312 -21.0185842
9 -14.0707646 -21.0534312
10 -16.4720979 -14.0707646
11 -7.2667646 -16.4720979
12 -0.3634078 -7.2667646
13 -3.2007412 -0.3634078
14 15.8579255 -3.2007412
15 14.7332588 15.8579255
16 24.1856798 14.7332588
17 22.7283465 24.1856798
18 15.3770132 22.7283465
19 14.0423465 15.3770132
20 29.4140027 14.0423465
21 29.2966693 29.4140027
22 31.5553360 29.2966693
23 32.9106693 31.5553360
24 35.8313267 32.9106693
25 17.7439934 35.8313267
26 -3.1273399 17.7439934
27 -8.3920066 -3.1273399
28 6.3969716 -8.3920066
29 5.2496382 6.3969716
30 -4.6216951 5.2496382
31 -1.1263618 -4.6216951
32 3.2886056 -1.1263618
33 -0.4287277 3.2886056
34 2.1999390 -0.4287277
35 3.5352723 2.1999390
36 1.2553437 3.5352723
37 -8.5719896 1.2553437
38 -16.7233229 -8.5719896
39 -18.0279896 -16.7233229
40 -21.7758486 -18.0279896
41 -15.5331820 -21.7758486
42 -11.5445153 -15.5331820
43 -4.7391820 -11.5445153
44 -3.1895840 -4.7391820
45 16.0030826 -3.1895840
46 9.3017493 16.0030826
47 -4.7029174 9.3017493
48 -11.4236985 -4.7029174
49 -2.9210318 -11.4236985
50 4.0076349 -2.9210318
51 4.1729682 4.0076349
52 -2.0145005 4.1729682
53 11.7481662 -2.0145005
54 28.4068328 11.7481662
55 13.1821662 28.4068328
56 -17.6133784 13.1821662
57 -28.7007117 -17.6133784
58 -18.8220450 -28.7007117
59 -7.9367117 -18.8220450
60 NA -7.9367117
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.0501636 -3.8928302
[2,] -7.5914969 -3.0501636
[3,] -9.3661636 -7.5914969
[4,] -19.0452509 -9.3661636
[5,] -26.2925842 -19.0452509
[6,] -27.8039175 -26.2925842
[7,] -21.0185842 -27.8039175
[8,] -21.0534312 -21.0185842
[9,] -14.0707646 -21.0534312
[10,] -16.4720979 -14.0707646
[11,] -7.2667646 -16.4720979
[12,] -0.3634078 -7.2667646
[13,] -3.2007412 -0.3634078
[14,] 15.8579255 -3.2007412
[15,] 14.7332588 15.8579255
[16,] 24.1856798 14.7332588
[17,] 22.7283465 24.1856798
[18,] 15.3770132 22.7283465
[19,] 14.0423465 15.3770132
[20,] 29.4140027 14.0423465
[21,] 29.2966693 29.4140027
[22,] 31.5553360 29.2966693
[23,] 32.9106693 31.5553360
[24,] 35.8313267 32.9106693
[25,] 17.7439934 35.8313267
[26,] -3.1273399 17.7439934
[27,] -8.3920066 -3.1273399
[28,] 6.3969716 -8.3920066
[29,] 5.2496382 6.3969716
[30,] -4.6216951 5.2496382
[31,] -1.1263618 -4.6216951
[32,] 3.2886056 -1.1263618
[33,] -0.4287277 3.2886056
[34,] 2.1999390 -0.4287277
[35,] 3.5352723 2.1999390
[36,] 1.2553437 3.5352723
[37,] -8.5719896 1.2553437
[38,] -16.7233229 -8.5719896
[39,] -18.0279896 -16.7233229
[40,] -21.7758486 -18.0279896
[41,] -15.5331820 -21.7758486
[42,] -11.5445153 -15.5331820
[43,] -4.7391820 -11.5445153
[44,] -3.1895840 -4.7391820
[45,] 16.0030826 -3.1895840
[46,] 9.3017493 16.0030826
[47,] -4.7029174 9.3017493
[48,] -11.4236985 -4.7029174
[49,] -2.9210318 -11.4236985
[50,] 4.0076349 -2.9210318
[51,] 4.1729682 4.0076349
[52,] -2.0145005 4.1729682
[53,] 11.7481662 -2.0145005
[54,] 28.4068328 11.7481662
[55,] 13.1821662 28.4068328
[56,] -17.6133784 13.1821662
[57,] -28.7007117 -17.6133784
[58,] -18.8220450 -28.7007117
[59,] -7.9367117 -18.8220450
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.0501636 -3.8928302
2 -7.5914969 -3.0501636
3 -9.3661636 -7.5914969
4 -19.0452509 -9.3661636
5 -26.2925842 -19.0452509
6 -27.8039175 -26.2925842
7 -21.0185842 -27.8039175
8 -21.0534312 -21.0185842
9 -14.0707646 -21.0534312
10 -16.4720979 -14.0707646
11 -7.2667646 -16.4720979
12 -0.3634078 -7.2667646
13 -3.2007412 -0.3634078
14 15.8579255 -3.2007412
15 14.7332588 15.8579255
16 24.1856798 14.7332588
17 22.7283465 24.1856798
18 15.3770132 22.7283465
19 14.0423465 15.3770132
20 29.4140027 14.0423465
21 29.2966693 29.4140027
22 31.5553360 29.2966693
23 32.9106693 31.5553360
24 35.8313267 32.9106693
25 17.7439934 35.8313267
26 -3.1273399 17.7439934
27 -8.3920066 -3.1273399
28 6.3969716 -8.3920066
29 5.2496382 6.3969716
30 -4.6216951 5.2496382
31 -1.1263618 -4.6216951
32 3.2886056 -1.1263618
33 -0.4287277 3.2886056
34 2.1999390 -0.4287277
35 3.5352723 2.1999390
36 1.2553437 3.5352723
37 -8.5719896 1.2553437
38 -16.7233229 -8.5719896
39 -18.0279896 -16.7233229
40 -21.7758486 -18.0279896
41 -15.5331820 -21.7758486
42 -11.5445153 -15.5331820
43 -4.7391820 -11.5445153
44 -3.1895840 -4.7391820
45 16.0030826 -3.1895840
46 9.3017493 16.0030826
47 -4.7029174 9.3017493
48 -11.4236985 -4.7029174
49 -2.9210318 -11.4236985
50 4.0076349 -2.9210318
51 4.1729682 4.0076349
52 -2.0145005 4.1729682
53 11.7481662 -2.0145005
54 28.4068328 11.7481662
55 13.1821662 28.4068328
56 -17.6133784 13.1821662
57 -28.7007117 -17.6133784
58 -18.8220450 -28.7007117
59 -7.9367117 -18.8220450
> 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/70adh1292003171.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/8t1u21292003171.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/9t1u21292003171.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')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10t1u21292003171.ps",horizontal=F,onefile=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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11pt9t1292003171.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12i39e1292003171.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13herk1292003172.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14r5qn1292003172.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/15vn6t1292003172.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/169f421292003172.tab")
+ }
>
> try(system("convert tmp/1fawb1292003171.ps tmp/1fawb1292003171.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fawb1292003171.ps tmp/2fawb1292003171.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fawb1292003171.ps tmp/3fawb1292003171.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pjee1292003171.ps tmp/4pjee1292003171.png",intern=TRUE))
character(0)
> try(system("convert tmp/5pjee1292003171.ps tmp/5pjee1292003171.png",intern=TRUE))
character(0)
> try(system("convert tmp/6pjee1292003171.ps tmp/6pjee1292003171.png",intern=TRUE))
character(0)
> try(system("convert tmp/70adh1292003171.ps tmp/70adh1292003171.png",intern=TRUE))
character(0)
> try(system("convert tmp/8t1u21292003171.ps tmp/8t1u21292003171.png",intern=TRUE))
character(0)
> try(system("convert tmp/9t1u21292003171.ps tmp/9t1u21292003171.png",intern=TRUE))
character(0)
> try(system("convert tmp/10t1u21292003171.ps tmp/10t1u21292003171.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.502 1.715 6.064