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 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list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dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> y <- array(NA,dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 595 0 594 611 1 0 0 0 0 0 0 0 0 0 0 1
2 591 0 595 594 0 1 0 0 0 0 0 0 0 0 0 2
3 589 0 591 595 0 0 1 0 0 0 0 0 0 0 0 3
4 584 0 589 591 0 0 0 1 0 0 0 0 0 0 0 4
5 573 0 584 589 0 0 0 0 1 0 0 0 0 0 0 5
6 567 0 573 584 0 0 0 0 0 1 0 0 0 0 0 6
7 569 0 567 573 0 0 0 0 0 0 1 0 0 0 0 7
8 621 0 569 567 0 0 0 0 0 0 0 1 0 0 0 8
9 629 0 621 569 0 0 0 0 0 0 0 0 1 0 0 9
10 628 0 629 621 0 0 0 0 0 0 0 0 0 1 0 10
11 612 0 628 629 0 0 0 0 0 0 0 0 0 0 1 11
12 595 0 612 628 0 0 0 0 0 0 0 0 0 0 0 12
13 597 0 595 612 1 0 0 0 0 0 0 0 0 0 0 13
14 593 0 597 595 0 1 0 0 0 0 0 0 0 0 0 14
15 590 0 593 597 0 0 1 0 0 0 0 0 0 0 0 15
16 580 0 590 593 0 0 0 1 0 0 0 0 0 0 0 16
17 574 0 580 590 0 0 0 0 1 0 0 0 0 0 0 17
18 573 0 574 580 0 0 0 0 0 1 0 0 0 0 0 18
19 573 0 573 574 0 0 0 0 0 0 1 0 0 0 0 19
20 620 0 573 573 0 0 0 0 0 0 0 1 0 0 0 20
21 626 0 620 573 0 0 0 0 0 0 0 0 1 0 0 21
22 620 0 626 620 0 0 0 0 0 0 0 0 0 1 0 22
23 588 0 620 626 0 0 0 0 0 0 0 0 0 0 1 23
24 566 0 588 620 0 0 0 0 0 0 0 0 0 0 0 24
25 557 0 566 588 1 0 0 0 0 0 0 0 0 0 0 25
26 561 0 557 566 0 1 0 0 0 0 0 0 0 0 0 26
27 549 0 561 557 0 0 1 0 0 0 0 0 0 0 0 27
28 532 0 549 561 0 0 0 1 0 0 0 0 0 0 0 28
29 526 0 532 549 0 0 0 0 1 0 0 0 0 0 0 29
30 511 0 526 532 0 0 0 0 0 1 0 0 0 0 0 30
31 499 0 511 526 0 0 0 0 0 0 1 0 0 0 0 31
32 555 0 499 511 0 0 0 0 0 0 0 1 0 0 0 32
33 565 0 555 499 0 0 0 0 0 0 0 0 1 0 0 33
34 542 0 565 555 0 0 0 0 0 0 0 0 0 1 0 34
35 527 0 542 565 0 0 0 0 0 0 0 0 0 0 1 35
36 510 0 527 542 0 0 0 0 0 0 0 0 0 0 0 36
37 514 0 510 527 1 0 0 0 0 0 0 0 0 0 0 37
38 517 0 514 510 0 1 0 0 0 0 0 0 0 0 0 38
39 508 0 517 514 0 0 1 0 0 0 0 0 0 0 0 39
40 493 0 508 517 0 0 0 1 0 0 0 0 0 0 0 40
41 490 0 493 508 0 0 0 0 1 0 0 0 0 0 0 41
42 469 0 490 493 0 0 0 0 0 1 0 0 0 0 0 42
43 478 0 469 490 0 0 0 0 0 0 1 0 0 0 0 43
44 528 0 478 469 0 0 0 0 0 0 0 1 0 0 0 44
45 534 0 528 478 0 0 0 0 0 0 0 0 1 0 0 45
46 518 1 534 528 0 0 0 0 0 0 0 0 0 1 0 46
47 506 1 518 534 0 0 0 0 0 0 0 0 0 0 1 47
48 502 1 506 518 0 0 0 0 0 0 0 0 0 0 0 48
49 516 1 502 506 1 0 0 0 0 0 0 0 0 0 0 49
50 528 1 516 502 0 1 0 0 0 0 0 0 0 0 0 50
51 533 1 528 516 0 0 1 0 0 0 0 0 0 0 0 51
52 536 1 533 528 0 0 0 1 0 0 0 0 0 0 0 52
53 537 1 536 533 0 0 0 0 1 0 0 0 0 0 0 53
54 524 1 537 536 0 0 0 0 0 1 0 0 0 0 0 54
55 536 1 524 537 0 0 0 0 0 0 1 0 0 0 0 55
56 587 1 536 524 0 0 0 0 0 0 0 1 0 0 0 56
57 597 1 587 536 0 0 0 0 0 0 0 0 1 0 0 57
58 581 1 597 587 0 0 0 0 0 0 0 0 0 1 0 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
32.01122 12.85177 0.91262 0.01211 16.31280 16.79000
M3 M4 M5 M6 M7 M8
10.83423 6.12167 9.48466 3.23537 15.99835 65.60722
M9 M10 M11 t
27.13532 4.52518 -2.53197 -0.28106
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.015 -3.356 0.441 3.502 12.716
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 32.01122 26.74681 1.197 0.238087
X 12.85177 3.57684 3.593 0.000851 ***
Y1 0.91262 0.15037 6.069 3.16e-07 ***
Y2 0.01211 0.15013 0.081 0.936111
M1 16.31280 4.32522 3.772 0.000502 ***
M2 16.79000 5.35798 3.134 0.003144 **
M3 10.83423 5.31669 2.038 0.047903 *
M4 6.12167 4.81022 1.273 0.210146
M5 9.48466 4.56635 2.077 0.043948 *
M6 3.23537 4.82529 0.671 0.506207
M7 15.99835 4.56062 3.508 0.001090 **
M8 65.60722 5.46423 12.007 3.64e-15 ***
M9 27.13532 11.33384 2.394 0.021199 *
M10 4.52518 5.78782 0.782 0.438691
M11 -2.53197 4.61378 -0.549 0.586059
t -0.28106 0.12654 -2.221 0.031792 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.302 on 42 degrees of freedom
Multiple R-squared: 0.9831, Adjusted R-squared: 0.977
F-statistic: 162.7 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,] 0.034473979 0.068947957 0.965526021
[2,] 0.009776078 0.019552155 0.990223922
[3,] 0.002274140 0.004548279 0.997725860
[4,] 0.061495726 0.122991452 0.938504274
[5,] 0.337832471 0.675664943 0.662167529
[6,] 0.277662238 0.555324475 0.722337762
[7,] 0.242578417 0.485156834 0.757421583
[8,] 0.496626159 0.993252318 0.503373841
[9,] 0.467616680 0.935233360 0.532383320
[10,] 0.418347127 0.836694254 0.581652873
[11,] 0.376302401 0.752604803 0.623697599
[12,] 0.360064808 0.720129616 0.639935192
[13,] 0.831674726 0.336650547 0.168325274
[14,] 0.929782113 0.140435774 0.070217887
[15,] 0.922170572 0.155658855 0.077829428
[16,] 0.922823113 0.154353774 0.077176887
[17,] 0.966156467 0.067687065 0.033843533
[18,] 0.937003067 0.125993865 0.062996933
[19,] 0.922381687 0.155236627 0.077618313
[20,] 0.953076964 0.093846072 0.046923036
[21,] 0.994762308 0.010475384 0.005237692
> postscript(file="/var/www/html/rcomp/tmp/1dvw01258658711.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22v391258658711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3gige1258658711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4kx101258658711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hgz21258658711.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
-2.538406997 -7.441363683 0.433851580 2.301139547 -7.193464243
6 7 8 9 10
3.436272866 -1.436731224 -0.517159942 -1.244837529 12.715830915
11 12 13 14 15
4.869814857 0.232982468 1.909538716 -3.906041560 2.957067273
16 17 18 19 20
0.736978830 0.817599423 11.944750729 0.448096536 -1.867617150
21 22 23 24 25
-0.007963925 10.838483849 -8.420201397 -3.394524188 -7.961147108
26 27 28 29 30
4.322664261 -4.982044900 -6.085372469 0.492571127 -2.295532557
31 32 33 34 35
-13.015456483 4.789802935 2.581121007 -7.331883451 5.875606621
36 37 38 39 40
0.592492102 4.256941919 3.616114463 -1.933354698 -3.762446608
41 42 43 44 45
3.953930517 -7.596256798 8.123241529 0.836044127 -0.151131288
46 47 48 49 50
-12.192770740 -2.325220081 2.569049618 4.333073470 3.408626519
51 52 53 54 55
3.524480745 6.809700699 1.929363176 -5.489234241 5.880849642
56 57 58
-3.241069970 -1.177188266 -4.029660574
> postscript(file="/var/www/html/rcomp/tmp/672wh1258658711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.538406997 NA
1 -7.441363683 -2.538406997
2 0.433851580 -7.441363683
3 2.301139547 0.433851580
4 -7.193464243 2.301139547
5 3.436272866 -7.193464243
6 -1.436731224 3.436272866
7 -0.517159942 -1.436731224
8 -1.244837529 -0.517159942
9 12.715830915 -1.244837529
10 4.869814857 12.715830915
11 0.232982468 4.869814857
12 1.909538716 0.232982468
13 -3.906041560 1.909538716
14 2.957067273 -3.906041560
15 0.736978830 2.957067273
16 0.817599423 0.736978830
17 11.944750729 0.817599423
18 0.448096536 11.944750729
19 -1.867617150 0.448096536
20 -0.007963925 -1.867617150
21 10.838483849 -0.007963925
22 -8.420201397 10.838483849
23 -3.394524188 -8.420201397
24 -7.961147108 -3.394524188
25 4.322664261 -7.961147108
26 -4.982044900 4.322664261
27 -6.085372469 -4.982044900
28 0.492571127 -6.085372469
29 -2.295532557 0.492571127
30 -13.015456483 -2.295532557
31 4.789802935 -13.015456483
32 2.581121007 4.789802935
33 -7.331883451 2.581121007
34 5.875606621 -7.331883451
35 0.592492102 5.875606621
36 4.256941919 0.592492102
37 3.616114463 4.256941919
38 -1.933354698 3.616114463
39 -3.762446608 -1.933354698
40 3.953930517 -3.762446608
41 -7.596256798 3.953930517
42 8.123241529 -7.596256798
43 0.836044127 8.123241529
44 -0.151131288 0.836044127
45 -12.192770740 -0.151131288
46 -2.325220081 -12.192770740
47 2.569049618 -2.325220081
48 4.333073470 2.569049618
49 3.408626519 4.333073470
50 3.524480745 3.408626519
51 6.809700699 3.524480745
52 1.929363176 6.809700699
53 -5.489234241 1.929363176
54 5.880849642 -5.489234241
55 -3.241069970 5.880849642
56 -1.177188266 -3.241069970
57 -4.029660574 -1.177188266
58 NA -4.029660574
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -7.441363683 -2.538406997
[2,] 0.433851580 -7.441363683
[3,] 2.301139547 0.433851580
[4,] -7.193464243 2.301139547
[5,] 3.436272866 -7.193464243
[6,] -1.436731224 3.436272866
[7,] -0.517159942 -1.436731224
[8,] -1.244837529 -0.517159942
[9,] 12.715830915 -1.244837529
[10,] 4.869814857 12.715830915
[11,] 0.232982468 4.869814857
[12,] 1.909538716 0.232982468
[13,] -3.906041560 1.909538716
[14,] 2.957067273 -3.906041560
[15,] 0.736978830 2.957067273
[16,] 0.817599423 0.736978830
[17,] 11.944750729 0.817599423
[18,] 0.448096536 11.944750729
[19,] -1.867617150 0.448096536
[20,] -0.007963925 -1.867617150
[21,] 10.838483849 -0.007963925
[22,] -8.420201397 10.838483849
[23,] -3.394524188 -8.420201397
[24,] -7.961147108 -3.394524188
[25,] 4.322664261 -7.961147108
[26,] -4.982044900 4.322664261
[27,] -6.085372469 -4.982044900
[28,] 0.492571127 -6.085372469
[29,] -2.295532557 0.492571127
[30,] -13.015456483 -2.295532557
[31,] 4.789802935 -13.015456483
[32,] 2.581121007 4.789802935
[33,] -7.331883451 2.581121007
[34,] 5.875606621 -7.331883451
[35,] 0.592492102 5.875606621
[36,] 4.256941919 0.592492102
[37,] 3.616114463 4.256941919
[38,] -1.933354698 3.616114463
[39,] -3.762446608 -1.933354698
[40,] 3.953930517 -3.762446608
[41,] -7.596256798 3.953930517
[42,] 8.123241529 -7.596256798
[43,] 0.836044127 8.123241529
[44,] -0.151131288 0.836044127
[45,] -12.192770740 -0.151131288
[46,] -2.325220081 -12.192770740
[47,] 2.569049618 -2.325220081
[48,] 4.333073470 2.569049618
[49,] 3.408626519 4.333073470
[50,] 3.524480745 3.408626519
[51,] 6.809700699 3.524480745
[52,] 1.929363176 6.809700699
[53,] -5.489234241 1.929363176
[54,] 5.880849642 -5.489234241
[55,] -3.241069970 5.880849642
[56,] -1.177188266 -3.241069970
[57,] -4.029660574 -1.177188266
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -7.441363683 -2.538406997
2 0.433851580 -7.441363683
3 2.301139547 0.433851580
4 -7.193464243 2.301139547
5 3.436272866 -7.193464243
6 -1.436731224 3.436272866
7 -0.517159942 -1.436731224
8 -1.244837529 -0.517159942
9 12.715830915 -1.244837529
10 4.869814857 12.715830915
11 0.232982468 4.869814857
12 1.909538716 0.232982468
13 -3.906041560 1.909538716
14 2.957067273 -3.906041560
15 0.736978830 2.957067273
16 0.817599423 0.736978830
17 11.944750729 0.817599423
18 0.448096536 11.944750729
19 -1.867617150 0.448096536
20 -0.007963925 -1.867617150
21 10.838483849 -0.007963925
22 -8.420201397 10.838483849
23 -3.394524188 -8.420201397
24 -7.961147108 -3.394524188
25 4.322664261 -7.961147108
26 -4.982044900 4.322664261
27 -6.085372469 -4.982044900
28 0.492571127 -6.085372469
29 -2.295532557 0.492571127
30 -13.015456483 -2.295532557
31 4.789802935 -13.015456483
32 2.581121007 4.789802935
33 -7.331883451 2.581121007
34 5.875606621 -7.331883451
35 0.592492102 5.875606621
36 4.256941919 0.592492102
37 3.616114463 4.256941919
38 -1.933354698 3.616114463
39 -3.762446608 -1.933354698
40 3.953930517 -3.762446608
41 -7.596256798 3.953930517
42 8.123241529 -7.596256798
43 0.836044127 8.123241529
44 -0.151131288 0.836044127
45 -12.192770740 -0.151131288
46 -2.325220081 -12.192770740
47 2.569049618 -2.325220081
48 4.333073470 2.569049618
49 3.408626519 4.333073470
50 3.524480745 3.408626519
51 6.809700699 3.524480745
52 1.929363176 6.809700699
53 -5.489234241 1.929363176
54 5.880849642 -5.489234241
55 -3.241069970 5.880849642
56 -1.177188266 -3.241069970
57 -4.029660574 -1.177188266
> 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/73aoc1258658711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8thmt1258658711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9da0j1258658711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10kc1x1258658711.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11smm21258658711.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/125s4t1258658711.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/13gire1258658711.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/14ho021258658711.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/1567ij1258658711.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/16ghf51258658711.tab")
+ }
> system("convert tmp/1dvw01258658711.ps tmp/1dvw01258658711.png")
> system("convert tmp/22v391258658711.ps tmp/22v391258658711.png")
> system("convert tmp/3gige1258658711.ps tmp/3gige1258658711.png")
> system("convert tmp/4kx101258658711.ps tmp/4kx101258658711.png")
> system("convert tmp/5hgz21258658711.ps tmp/5hgz21258658711.png")
> system("convert tmp/672wh1258658711.ps tmp/672wh1258658711.png")
> system("convert tmp/73aoc1258658711.ps tmp/73aoc1258658711.png")
> system("convert tmp/8thmt1258658711.ps tmp/8thmt1258658711.png")
> system("convert tmp/9da0j1258658711.ps tmp/9da0j1258658711.png")
> system("convert tmp/10kc1x1258658711.ps tmp/10kc1x1258658711.png")
>
>
> proc.time()
user system elapsed
2.365 1.588 2.740