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(8.9,1.6,8.8,1.8,8.3,1.6,7.5,1.5,7.2,1.5,7.4,1.3,8.8,1.4,9.3,1.4,9.3,1.3,8.7,1.3,8.2,1.2,8.3,1.1,8.5,1.4,8.6,1.2,8.5,1.5,8.2,1.1,8.1,1.3,7.9,1.5,8.6,1.1,8.7,1.4,8.7,1.3,8.5,1.5,8.4,1.6,8.5,1.7,8.7,1.1,8.7,1.6,8.6,1.3,8.5,1.7,8.3,1.6,8,1.7,8.2,1.9,8.1,1.8,8.1,1.9,8,1.6,7.9,1.5,7.9,1.6,8,1.6,8,1.7,7.9,2,8,2,7.7,1.9,7.2,1.7,7.5,1.8,7.3,1.9,7,1.7,7,2,7,2.1,7.2,2.4,7.3,2.5,7.1,2.5,6.8,2.6,6.4,2.2,6.1,2.5,6.5,2.8,7.7,2.8,7.9,2.9,7.5,3,6.9,3.1,6.6,2.9,6.9,2.7),dim=c(2,60),dimnames=list(c('graad','inflatie'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('graad','inflatie'),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 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'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
inflatie graad t
1 1.6 8.9 1
2 1.8 8.8 2
3 1.6 8.3 3
4 1.5 7.5 4
5 1.5 7.2 5
6 1.3 7.4 6
7 1.4 8.8 7
8 1.4 9.3 8
9 1.3 9.3 9
10 1.3 8.7 10
11 1.2 8.2 11
12 1.1 8.3 12
13 1.4 8.5 13
14 1.2 8.6 14
15 1.5 8.5 15
16 1.1 8.2 16
17 1.3 8.1 17
18 1.5 7.9 18
19 1.1 8.6 19
20 1.4 8.7 20
21 1.3 8.7 21
22 1.5 8.5 22
23 1.6 8.4 23
24 1.7 8.5 24
25 1.1 8.7 25
26 1.6 8.7 26
27 1.3 8.6 27
28 1.7 8.5 28
29 1.6 8.3 29
30 1.7 8.0 30
31 1.9 8.2 31
32 1.8 8.1 32
33 1.9 8.1 33
34 1.6 8.0 34
35 1.5 7.9 35
36 1.6 7.9 36
37 1.6 8.0 37
38 1.7 8.0 38
39 2.0 7.9 39
40 2.0 8.0 40
41 1.9 7.7 41
42 1.7 7.2 42
43 1.8 7.5 43
44 1.9 7.3 44
45 1.7 7.0 45
46 2.0 7.0 46
47 2.1 7.0 47
48 2.4 7.2 48
49 2.5 7.3 49
50 2.5 7.1 50
51 2.6 6.8 51
52 2.2 6.4 52
53 2.5 6.1 53
54 2.8 6.5 54
55 2.8 7.7 55
56 2.9 7.9 56
57 3.0 7.5 57
58 3.1 6.9 58
59 2.9 6.6 59
60 2.7 6.9 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) graad t
2.14074 -0.12609 0.02162
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.531190 -0.253037 -0.008696 0.162547 0.725570
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.140743 0.681777 3.140 0.00268 **
graad -0.126086 0.076435 -1.650 0.10453
t 0.021623 0.003253 6.647 1.23e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2954 on 57 degrees of freedom
Multiple R-squared: 0.7071, Adjusted R-squared: 0.6968
F-statistic: 68.81 on 2 and 57 DF, p-value: 6.318e-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,] 0.1330242631 0.2660485262 0.86697574
[2,] 0.0572763654 0.1145527307 0.94272363
[3,] 0.0236361426 0.0472722852 0.97636386
[4,] 0.0090771161 0.0181542323 0.99092288
[5,] 0.0038342841 0.0076685682 0.99616572
[6,] 0.0012893546 0.0025787093 0.99871065
[7,] 0.0004355596 0.0008711193 0.99956444
[8,] 0.0062951399 0.0125902798 0.99370486
[9,] 0.0030130654 0.0060261308 0.99698693
[10,] 0.0327721925 0.0655443849 0.96722781
[11,] 0.0190349218 0.0380698436 0.98096508
[12,] 0.0186052556 0.0372105112 0.98139474
[13,] 0.0792552315 0.1585104631 0.92074477
[14,] 0.0549898166 0.1099796332 0.94501018
[15,] 0.0636140994 0.1272281987 0.93638590
[16,] 0.0458683039 0.0917366079 0.95413170
[17,] 0.0765943920 0.1531887840 0.92340561
[18,] 0.1674232837 0.3348465674 0.83257672
[19,] 0.3690271580 0.7380543160 0.63097284
[20,] 0.3899682225 0.7799364450 0.61003178
[21,] 0.4274749749 0.8549499499 0.57252503
[22,] 0.3654612287 0.7309224575 0.63453877
[23,] 0.4566674047 0.9133348094 0.54333260
[24,] 0.4417369457 0.8834738915 0.55826305
[25,] 0.5066701198 0.9866597603 0.49332988
[26,] 0.7429346086 0.5141307828 0.25706539
[27,] 0.8255918810 0.3488162380 0.17440812
[28,] 0.9430083740 0.1139832519 0.05699163
[29,] 0.9275277310 0.1449445381 0.07247227
[30,] 0.8995014871 0.2009970258 0.10049851
[31,] 0.8600422499 0.2799155003 0.13995775
[32,] 0.8126599680 0.3746800641 0.18734003
[33,] 0.7559298536 0.4881402928 0.24407015
[34,] 0.8090364912 0.3819270176 0.19096351
[35,] 0.8299381633 0.3401236734 0.17006184
[36,] 0.7959030034 0.4081939933 0.20409700
[37,] 0.7393657584 0.5212684832 0.26063424
[38,] 0.6812137561 0.6375724878 0.31878624
[39,] 0.6091408419 0.7817183162 0.39085916
[40,] 0.7591287927 0.4817424146 0.24087121
[41,] 0.7568765213 0.4862469575 0.24312348
[42,] 0.7749009292 0.4501981415 0.22509907
[43,] 0.7567872783 0.4864254433 0.24321272
[44,] 0.7342754892 0.5314490216 0.26572451
[45,] 0.6765449783 0.6469100433 0.32345502
[46,] 0.6260841049 0.7478317901 0.37391590
[47,] 0.7647514042 0.4704971915 0.23524860
[48,] 0.7565000968 0.4869998065 0.24349990
[49,] 0.7117708142 0.5764583716 0.28822919
> postscript(file="/var/www/html/rcomp/tmp/1kzt31258564709.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2nw0f1258564709.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jcia1258564709.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kufn1258564709.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5j30i1258564709.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
0.559801809 0.725569808 0.440903283 0.218410865 0.158961603 -0.037444505
7 8 9 10 11 12
0.217452959 0.258872744 0.137249375 0.039974219 -0.144692306 -0.253707045
13 14 15 16 17 18
0.049886848 -0.159127891 0.106640108 -0.352809155 -0.187041155 -0.033881787
19 20 21 22 23 24
-0.367244740 -0.076259479 -0.197882848 -0.044723480 0.021044519 0.112029781
25 26 27 28 29 30
-0.484376327 -0.005999697 -0.340231698 0.025536302 -0.121304330 -0.080753593
31 32 33 34 35 36
0.122840299 -0.011391701 0.066984929 -0.267247072 -0.401479073 -0.323102442
37 38 39 40 41 42
-0.332117181 -0.253740551 0.012027448 0.003012710 -0.156436553 -0.441103078
43 44 45 46 47 48
-0.324900554 -0.271741186 -0.531190449 -0.252813819 -0.174437188 0.129156704
49 50 51 52 53 54
0.220141965 0.173301333 0.213852071 -0.258205823 -0.017655086 0.311156068
55 56 57 58 59 60
0.440836271 0.544430163 0.572372269 0.575097113 0.315647851 0.131850374
> postscript(file="/var/www/html/rcomp/tmp/6ltf51258564709.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 0.559801809 NA
1 0.725569808 0.559801809
2 0.440903283 0.725569808
3 0.218410865 0.440903283
4 0.158961603 0.218410865
5 -0.037444505 0.158961603
6 0.217452959 -0.037444505
7 0.258872744 0.217452959
8 0.137249375 0.258872744
9 0.039974219 0.137249375
10 -0.144692306 0.039974219
11 -0.253707045 -0.144692306
12 0.049886848 -0.253707045
13 -0.159127891 0.049886848
14 0.106640108 -0.159127891
15 -0.352809155 0.106640108
16 -0.187041155 -0.352809155
17 -0.033881787 -0.187041155
18 -0.367244740 -0.033881787
19 -0.076259479 -0.367244740
20 -0.197882848 -0.076259479
21 -0.044723480 -0.197882848
22 0.021044519 -0.044723480
23 0.112029781 0.021044519
24 -0.484376327 0.112029781
25 -0.005999697 -0.484376327
26 -0.340231698 -0.005999697
27 0.025536302 -0.340231698
28 -0.121304330 0.025536302
29 -0.080753593 -0.121304330
30 0.122840299 -0.080753593
31 -0.011391701 0.122840299
32 0.066984929 -0.011391701
33 -0.267247072 0.066984929
34 -0.401479073 -0.267247072
35 -0.323102442 -0.401479073
36 -0.332117181 -0.323102442
37 -0.253740551 -0.332117181
38 0.012027448 -0.253740551
39 0.003012710 0.012027448
40 -0.156436553 0.003012710
41 -0.441103078 -0.156436553
42 -0.324900554 -0.441103078
43 -0.271741186 -0.324900554
44 -0.531190449 -0.271741186
45 -0.252813819 -0.531190449
46 -0.174437188 -0.252813819
47 0.129156704 -0.174437188
48 0.220141965 0.129156704
49 0.173301333 0.220141965
50 0.213852071 0.173301333
51 -0.258205823 0.213852071
52 -0.017655086 -0.258205823
53 0.311156068 -0.017655086
54 0.440836271 0.311156068
55 0.544430163 0.440836271
56 0.572372269 0.544430163
57 0.575097113 0.572372269
58 0.315647851 0.575097113
59 0.131850374 0.315647851
60 NA 0.131850374
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.725569808 0.559801809
[2,] 0.440903283 0.725569808
[3,] 0.218410865 0.440903283
[4,] 0.158961603 0.218410865
[5,] -0.037444505 0.158961603
[6,] 0.217452959 -0.037444505
[7,] 0.258872744 0.217452959
[8,] 0.137249375 0.258872744
[9,] 0.039974219 0.137249375
[10,] -0.144692306 0.039974219
[11,] -0.253707045 -0.144692306
[12,] 0.049886848 -0.253707045
[13,] -0.159127891 0.049886848
[14,] 0.106640108 -0.159127891
[15,] -0.352809155 0.106640108
[16,] -0.187041155 -0.352809155
[17,] -0.033881787 -0.187041155
[18,] -0.367244740 -0.033881787
[19,] -0.076259479 -0.367244740
[20,] -0.197882848 -0.076259479
[21,] -0.044723480 -0.197882848
[22,] 0.021044519 -0.044723480
[23,] 0.112029781 0.021044519
[24,] -0.484376327 0.112029781
[25,] -0.005999697 -0.484376327
[26,] -0.340231698 -0.005999697
[27,] 0.025536302 -0.340231698
[28,] -0.121304330 0.025536302
[29,] -0.080753593 -0.121304330
[30,] 0.122840299 -0.080753593
[31,] -0.011391701 0.122840299
[32,] 0.066984929 -0.011391701
[33,] -0.267247072 0.066984929
[34,] -0.401479073 -0.267247072
[35,] -0.323102442 -0.401479073
[36,] -0.332117181 -0.323102442
[37,] -0.253740551 -0.332117181
[38,] 0.012027448 -0.253740551
[39,] 0.003012710 0.012027448
[40,] -0.156436553 0.003012710
[41,] -0.441103078 -0.156436553
[42,] -0.324900554 -0.441103078
[43,] -0.271741186 -0.324900554
[44,] -0.531190449 -0.271741186
[45,] -0.252813819 -0.531190449
[46,] -0.174437188 -0.252813819
[47,] 0.129156704 -0.174437188
[48,] 0.220141965 0.129156704
[49,] 0.173301333 0.220141965
[50,] 0.213852071 0.173301333
[51,] -0.258205823 0.213852071
[52,] -0.017655086 -0.258205823
[53,] 0.311156068 -0.017655086
[54,] 0.440836271 0.311156068
[55,] 0.544430163 0.440836271
[56,] 0.572372269 0.544430163
[57,] 0.575097113 0.572372269
[58,] 0.315647851 0.575097113
[59,] 0.131850374 0.315647851
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.725569808 0.559801809
2 0.440903283 0.725569808
3 0.218410865 0.440903283
4 0.158961603 0.218410865
5 -0.037444505 0.158961603
6 0.217452959 -0.037444505
7 0.258872744 0.217452959
8 0.137249375 0.258872744
9 0.039974219 0.137249375
10 -0.144692306 0.039974219
11 -0.253707045 -0.144692306
12 0.049886848 -0.253707045
13 -0.159127891 0.049886848
14 0.106640108 -0.159127891
15 -0.352809155 0.106640108
16 -0.187041155 -0.352809155
17 -0.033881787 -0.187041155
18 -0.367244740 -0.033881787
19 -0.076259479 -0.367244740
20 -0.197882848 -0.076259479
21 -0.044723480 -0.197882848
22 0.021044519 -0.044723480
23 0.112029781 0.021044519
24 -0.484376327 0.112029781
25 -0.005999697 -0.484376327
26 -0.340231698 -0.005999697
27 0.025536302 -0.340231698
28 -0.121304330 0.025536302
29 -0.080753593 -0.121304330
30 0.122840299 -0.080753593
31 -0.011391701 0.122840299
32 0.066984929 -0.011391701
33 -0.267247072 0.066984929
34 -0.401479073 -0.267247072
35 -0.323102442 -0.401479073
36 -0.332117181 -0.323102442
37 -0.253740551 -0.332117181
38 0.012027448 -0.253740551
39 0.003012710 0.012027448
40 -0.156436553 0.003012710
41 -0.441103078 -0.156436553
42 -0.324900554 -0.441103078
43 -0.271741186 -0.324900554
44 -0.531190449 -0.271741186
45 -0.252813819 -0.531190449
46 -0.174437188 -0.252813819
47 0.129156704 -0.174437188
48 0.220141965 0.129156704
49 0.173301333 0.220141965
50 0.213852071 0.173301333
51 -0.258205823 0.213852071
52 -0.017655086 -0.258205823
53 0.311156068 -0.017655086
54 0.440836271 0.311156068
55 0.544430163 0.440836271
56 0.572372269 0.544430163
57 0.575097113 0.572372269
58 0.315647851 0.575097113
59 0.131850374 0.315647851
> 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/7a0gr1258564709.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89i4m1258564709.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9i14y1258564709.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/102k3u1258564709.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hmgs1258564709.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/12qehr1258564710.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/139fhy1258564710.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/148zvp1258564710.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/159x4d1258564710.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/16ed6r1258564710.tab")
+ }
>
> system("convert tmp/1kzt31258564709.ps tmp/1kzt31258564709.png")
> system("convert tmp/2nw0f1258564709.ps tmp/2nw0f1258564709.png")
> system("convert tmp/3jcia1258564709.ps tmp/3jcia1258564709.png")
> system("convert tmp/4kufn1258564709.ps tmp/4kufn1258564709.png")
> system("convert tmp/5j30i1258564709.ps tmp/5j30i1258564709.png")
> system("convert tmp/6ltf51258564709.ps tmp/6ltf51258564709.png")
> system("convert tmp/7a0gr1258564709.ps tmp/7a0gr1258564709.png")
> system("convert tmp/89i4m1258564709.ps tmp/89i4m1258564709.png")
> system("convert tmp/9i14y1258564709.ps tmp/9i14y1258564709.png")
> system("convert tmp/102k3u1258564709.ps tmp/102k3u1258564709.png")
>
>
> proc.time()
user system elapsed
2.453 1.555 2.864