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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> x <- array(list(96.96,89.1,93.11,83.3,95.62,97.7,98.30,100.9,96.38,108.3,100.82,113.2,99.06,105,94.03,104,102.07,109.8,99.31,98.6,98.64,93.5,101.82,98.2,99.14,88,97.63,85.3,100.06,96.8,101.32,98.8,101.49,110.3,105.43,111.6,105.09,111.2,99.48,106.9,108.53,117.6,104.34,97,106.10,97.3,107.35,98.4,103.00,87.6,104.50,87.4,105.17,94.7,104.84,101.5,106.18,110.4,108.86,108.4,107.77,109.7,102.74,105.2,112.63,111.1,106.26,96.2,108.86,97.3,111.38,98.9,106.85,91.7,107.86,90.9,107.94,98.8,111.38,111.5,111.29,119,113.72,115.3,111.88,116.3,109.87,113.6,113.72,115.1,111.71,109.7,114.81,97.6,112.05,100.8,111.54,94,110.87,87.2,110.87,102.9,115.48,111.3,111.63,106.6,116.24,108.9,113.56,108.3,106.01,100.5,110.45,104,107.77,89.9,108.61,86.8,108.19,91.2),dim=c(2,60),dimnames=list(c('Bestc','Industr'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Bestc','Industr'),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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal 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
Bestc Industr
1 96.96 89.1
2 93.11 83.3
3 95.62 97.7
4 98.30 100.9
5 96.38 108.3
6 100.82 113.2
7 99.06 105.0
8 94.03 104.0
9 102.07 109.8
10 99.31 98.6
11 98.64 93.5
12 101.82 98.2
13 99.14 88.0
14 97.63 85.3
15 100.06 96.8
16 101.32 98.8
17 101.49 110.3
18 105.43 111.6
19 105.09 111.2
20 99.48 106.9
21 108.53 117.6
22 104.34 97.0
23 106.10 97.3
24 107.35 98.4
25 103.00 87.6
26 104.50 87.4
27 105.17 94.7
28 104.84 101.5
29 106.18 110.4
30 108.86 108.4
31 107.77 109.7
32 102.74 105.2
33 112.63 111.1
34 106.26 96.2
35 108.86 97.3
36 111.38 98.9
37 106.85 91.7
38 107.86 90.9
39 107.94 98.8
40 111.38 111.5
41 111.29 119.0
42 113.72 115.3
43 111.88 116.3
44 109.87 113.6
45 113.72 115.1
46 111.71 109.7
47 114.81 97.6
48 112.05 100.8
49 111.54 94.0
50 110.87 87.2
51 110.87 102.9
52 115.48 111.3
53 111.63 106.6
54 116.24 108.9
55 113.56 108.3
56 106.01 100.5
57 110.45 104.0
58 107.77 89.9
59 108.61 86.8
60 108.19 91.2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Industr
82.895 0.227
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.472 -4.182 1.154 4.481 9.761
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 82.89471 7.80596 10.619 3.19e-15 ***
Industr 0.22699 0.07645 2.969 0.00434 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.471 on 58 degrees of freedom
Multiple R-squared: 0.1319, Adjusted R-squared: 0.117
F-statistic: 8.815 on 1 and 58 DF, p-value: 0.004337
> 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,] 0.05700432 0.114008650 0.9429956752
[2,] 0.03266338 0.065326759 0.9673366206
[3,] 0.01352109 0.027042180 0.9864789098
[4,] 0.03665135 0.073302695 0.9633486524
[5,] 0.04475523 0.089510453 0.9552447736
[6,] 0.03822084 0.076441682 0.9617791592
[7,] 0.03408130 0.068162591 0.9659187047
[8,] 0.05861370 0.117227409 0.9413862956
[9,] 0.05789127 0.115782550 0.9421087251
[10,] 0.04932736 0.098654728 0.9506726360
[11,] 0.04998290 0.099965801 0.9500170994
[12,] 0.06066227 0.121324542 0.9393377291
[13,] 0.06929593 0.138591853 0.9307040737
[14,] 0.12525783 0.250515662 0.8747421688
[15,] 0.16531413 0.330628251 0.8346858744
[16,] 0.29504650 0.590093000 0.7049535001
[17,] 0.42553041 0.851060826 0.5744695872
[18,] 0.56897333 0.862053330 0.4310266650
[19,] 0.72374976 0.552500472 0.2762502361
[20,] 0.83502459 0.329950814 0.1649754070
[21,] 0.87301303 0.253973935 0.1269869674
[22,] 0.90294448 0.194111034 0.0970555170
[23,] 0.92121662 0.157566760 0.0787833799
[24,] 0.94153338 0.116933238 0.0584666190
[25,] 0.95931619 0.081367614 0.0406838068
[26,] 0.96730308 0.065393837 0.0326969185
[27,] 0.97338926 0.053221475 0.0266107373
[28,] 0.99716093 0.005678141 0.0028390704
[29,] 0.99824634 0.003507316 0.0017536580
[30,] 0.99889567 0.002208666 0.0011043332
[31,] 0.99899449 0.002011012 0.0010055059
[32,] 0.99926092 0.001478158 0.0007390789
[33,] 0.99927689 0.001446214 0.0007231068
[34,] 0.99915169 0.001696611 0.0008483054
[35,] 0.99914298 0.001714038 0.0008570191
[36,] 0.99876975 0.002460492 0.0012302460
[37,] 0.99841050 0.003178996 0.0015894979
[38,] 0.99761097 0.004778061 0.0023890304
[39,] 0.99625769 0.007484615 0.0037423074
[40,] 0.99685295 0.006294097 0.0031470483
[41,] 0.99457073 0.010858536 0.0054292680
[42,] 0.99166722 0.016665565 0.0083327825
[43,] 0.99647590 0.007048201 0.0035241007
[44,] 0.99357026 0.012859472 0.0064297361
[45,] 0.99085941 0.018281174 0.0091405868
[46,] 0.99306776 0.013864471 0.0069322354
[47,] 0.98406880 0.031862408 0.0159312038
[48,] 0.97140025 0.057199490 0.0285997450
[49,] 0.93717612 0.125647759 0.0628238797
[50,] 0.94959729 0.100805428 0.0504027140
[51,] 0.95488927 0.090221455 0.0451107273
> postscript(file="/var/www/html/rcomp/tmp/1459p1258648324.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2c77l1258648324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jxqd1258648324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wuwe1258648324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5u4yt1258648324.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
-6.15950072 -8.69296020 -9.45161253 -7.49797972 -11.09770383 -7.76995359
7 8 9 10 11 12
-7.66863767 -12.47164793 -5.74818845 -5.96590330 -5.47825560 -3.36510740
13 14 15 16 17 18
-3.72981200 -4.62693969 -4.80732176 -4.00130125 -6.44168332 -2.79676999
19 20 21 22 23 24
-3.04597409 -7.67991819 -1.05870847 -0.57271971 1.11918337 2.11949465
25 26 27 28 29 30
0.22098390 1.76638185 0.77935671 -1.09417356 -1.77438230 1.35959719
31 32 33 34 35 36
-0.02548948 -4.03403562 4.51672488 1.52887209 3.87918337 6.03599977
37 38 39 40 41 42
3.14032594 4.33191774 2.61869875 3.17592898 1.38350589 4.65336795
43 44 45 46 47 48
2.58637820 1.18925052 4.69876590 3.91451052 9.76108644 6.27471926
49 50 51 52 53 54
7.30824953 8.18177980 4.61804079 7.32132693 4.53817873 8.62610232
55 56 57 58 59 60
6.08229617 0.30281618 3.94835207 4.46890748 6.01257569 4.59382081
> postscript(file="/var/www/html/rcomp/tmp/6ib7b1258648324.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 -6.15950072 NA
1 -8.69296020 -6.15950072
2 -9.45161253 -8.69296020
3 -7.49797972 -9.45161253
4 -11.09770383 -7.49797972
5 -7.76995359 -11.09770383
6 -7.66863767 -7.76995359
7 -12.47164793 -7.66863767
8 -5.74818845 -12.47164793
9 -5.96590330 -5.74818845
10 -5.47825560 -5.96590330
11 -3.36510740 -5.47825560
12 -3.72981200 -3.36510740
13 -4.62693969 -3.72981200
14 -4.80732176 -4.62693969
15 -4.00130125 -4.80732176
16 -6.44168332 -4.00130125
17 -2.79676999 -6.44168332
18 -3.04597409 -2.79676999
19 -7.67991819 -3.04597409
20 -1.05870847 -7.67991819
21 -0.57271971 -1.05870847
22 1.11918337 -0.57271971
23 2.11949465 1.11918337
24 0.22098390 2.11949465
25 1.76638185 0.22098390
26 0.77935671 1.76638185
27 -1.09417356 0.77935671
28 -1.77438230 -1.09417356
29 1.35959719 -1.77438230
30 -0.02548948 1.35959719
31 -4.03403562 -0.02548948
32 4.51672488 -4.03403562
33 1.52887209 4.51672488
34 3.87918337 1.52887209
35 6.03599977 3.87918337
36 3.14032594 6.03599977
37 4.33191774 3.14032594
38 2.61869875 4.33191774
39 3.17592898 2.61869875
40 1.38350589 3.17592898
41 4.65336795 1.38350589
42 2.58637820 4.65336795
43 1.18925052 2.58637820
44 4.69876590 1.18925052
45 3.91451052 4.69876590
46 9.76108644 3.91451052
47 6.27471926 9.76108644
48 7.30824953 6.27471926
49 8.18177980 7.30824953
50 4.61804079 8.18177980
51 7.32132693 4.61804079
52 4.53817873 7.32132693
53 8.62610232 4.53817873
54 6.08229617 8.62610232
55 0.30281618 6.08229617
56 3.94835207 0.30281618
57 4.46890748 3.94835207
58 6.01257569 4.46890748
59 4.59382081 6.01257569
60 NA 4.59382081
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -8.69296020 -6.15950072
[2,] -9.45161253 -8.69296020
[3,] -7.49797972 -9.45161253
[4,] -11.09770383 -7.49797972
[5,] -7.76995359 -11.09770383
[6,] -7.66863767 -7.76995359
[7,] -12.47164793 -7.66863767
[8,] -5.74818845 -12.47164793
[9,] -5.96590330 -5.74818845
[10,] -5.47825560 -5.96590330
[11,] -3.36510740 -5.47825560
[12,] -3.72981200 -3.36510740
[13,] -4.62693969 -3.72981200
[14,] -4.80732176 -4.62693969
[15,] -4.00130125 -4.80732176
[16,] -6.44168332 -4.00130125
[17,] -2.79676999 -6.44168332
[18,] -3.04597409 -2.79676999
[19,] -7.67991819 -3.04597409
[20,] -1.05870847 -7.67991819
[21,] -0.57271971 -1.05870847
[22,] 1.11918337 -0.57271971
[23,] 2.11949465 1.11918337
[24,] 0.22098390 2.11949465
[25,] 1.76638185 0.22098390
[26,] 0.77935671 1.76638185
[27,] -1.09417356 0.77935671
[28,] -1.77438230 -1.09417356
[29,] 1.35959719 -1.77438230
[30,] -0.02548948 1.35959719
[31,] -4.03403562 -0.02548948
[32,] 4.51672488 -4.03403562
[33,] 1.52887209 4.51672488
[34,] 3.87918337 1.52887209
[35,] 6.03599977 3.87918337
[36,] 3.14032594 6.03599977
[37,] 4.33191774 3.14032594
[38,] 2.61869875 4.33191774
[39,] 3.17592898 2.61869875
[40,] 1.38350589 3.17592898
[41,] 4.65336795 1.38350589
[42,] 2.58637820 4.65336795
[43,] 1.18925052 2.58637820
[44,] 4.69876590 1.18925052
[45,] 3.91451052 4.69876590
[46,] 9.76108644 3.91451052
[47,] 6.27471926 9.76108644
[48,] 7.30824953 6.27471926
[49,] 8.18177980 7.30824953
[50,] 4.61804079 8.18177980
[51,] 7.32132693 4.61804079
[52,] 4.53817873 7.32132693
[53,] 8.62610232 4.53817873
[54,] 6.08229617 8.62610232
[55,] 0.30281618 6.08229617
[56,] 3.94835207 0.30281618
[57,] 4.46890748 3.94835207
[58,] 6.01257569 4.46890748
[59,] 4.59382081 6.01257569
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -8.69296020 -6.15950072
2 -9.45161253 -8.69296020
3 -7.49797972 -9.45161253
4 -11.09770383 -7.49797972
5 -7.76995359 -11.09770383
6 -7.66863767 -7.76995359
7 -12.47164793 -7.66863767
8 -5.74818845 -12.47164793
9 -5.96590330 -5.74818845
10 -5.47825560 -5.96590330
11 -3.36510740 -5.47825560
12 -3.72981200 -3.36510740
13 -4.62693969 -3.72981200
14 -4.80732176 -4.62693969
15 -4.00130125 -4.80732176
16 -6.44168332 -4.00130125
17 -2.79676999 -6.44168332
18 -3.04597409 -2.79676999
19 -7.67991819 -3.04597409
20 -1.05870847 -7.67991819
21 -0.57271971 -1.05870847
22 1.11918337 -0.57271971
23 2.11949465 1.11918337
24 0.22098390 2.11949465
25 1.76638185 0.22098390
26 0.77935671 1.76638185
27 -1.09417356 0.77935671
28 -1.77438230 -1.09417356
29 1.35959719 -1.77438230
30 -0.02548948 1.35959719
31 -4.03403562 -0.02548948
32 4.51672488 -4.03403562
33 1.52887209 4.51672488
34 3.87918337 1.52887209
35 6.03599977 3.87918337
36 3.14032594 6.03599977
37 4.33191774 3.14032594
38 2.61869875 4.33191774
39 3.17592898 2.61869875
40 1.38350589 3.17592898
41 4.65336795 1.38350589
42 2.58637820 4.65336795
43 1.18925052 2.58637820
44 4.69876590 1.18925052
45 3.91451052 4.69876590
46 9.76108644 3.91451052
47 6.27471926 9.76108644
48 7.30824953 6.27471926
49 8.18177980 7.30824953
50 4.61804079 8.18177980
51 7.32132693 4.61804079
52 4.53817873 7.32132693
53 8.62610232 4.53817873
54 6.08229617 8.62610232
55 0.30281618 6.08229617
56 3.94835207 0.30281618
57 4.46890748 3.94835207
58 6.01257569 4.46890748
59 4.59382081 6.01257569
> 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/7s2xp1258648324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xg5a1258648324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9dl9r1258648324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10quhy1258648324.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11tu0g1258648324.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/120skr1258648324.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/13h5fl1258648324.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/14xqq21258648324.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/153st41258648324.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/16vxkb1258648324.tab")
+ }
> system("convert tmp/1459p1258648324.ps tmp/1459p1258648324.png")
> system("convert tmp/2c77l1258648324.ps tmp/2c77l1258648324.png")
> system("convert tmp/3jxqd1258648324.ps tmp/3jxqd1258648324.png")
> system("convert tmp/4wuwe1258648324.ps tmp/4wuwe1258648324.png")
> system("convert tmp/5u4yt1258648324.ps tmp/5u4yt1258648324.png")
> system("convert tmp/6ib7b1258648324.ps tmp/6ib7b1258648324.png")
> system("convert tmp/7s2xp1258648324.ps tmp/7s2xp1258648324.png")
> system("convert tmp/8xg5a1258648324.ps tmp/8xg5a1258648324.png")
> system("convert tmp/9dl9r1258648324.ps tmp/9dl9r1258648324.png")
> system("convert tmp/10quhy1258648324.ps tmp/10quhy1258648324.png")
>
>
> proc.time()
user system elapsed
2.470 1.562 2.841