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(108.01,102.9,101.21,97.4,119.93,111.4,94.76,87.4,95.26,96.8,117.96,114.1,115.86,110.3,111.44,103.9,108.16,101.6,108.77,94.6,109.45,95.9,124.83,104.7,115.31,102.8,109.49,98.1,124.24,113.9,92.85,80.9,98.42,95.7,120.88,113.2,111.72,105.9,116.1,108.8,109.37,102.3,111.65,99,114.29,100.7,133.68,115.5,114.27,100.7,126.49,109.9,131,114.6,104,85.4,108.88,100.5,128.48,114.8,132.44,116.5,128.04,112.9,116.35,102,120.93,106,118.59,105.3,133.1,118.8,121.05,106.1,127.62,109.3,135.44,117.2,114.88,92.5,114.34,104.2,128.85,112.5,138.9,122.4,129.44,113.3,114.96,100,127.98,110.7,127.03,112.8,128.75,109.8,137.91,117.3,128.37,109.1,135.9,115.9,122.19,96,113.08,99.8,136.2,116.8,138,115.7,115.24,99.4,110.95,94.3,99.23,91,102.39,93.2,112.67,103.1),dim=c(2,60),dimnames=list(c('Y(omzet)','X(prod)'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y(omzet)','X(prod)'),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
Y(omzet) X(prod)
1 108.01 102.9
2 101.21 97.4
3 119.93 111.4
4 94.76 87.4
5 95.26 96.8
6 117.96 114.1
7 115.86 110.3
8 111.44 103.9
9 108.16 101.6
10 108.77 94.6
11 109.45 95.9
12 124.83 104.7
13 115.31 102.8
14 109.49 98.1
15 124.24 113.9
16 92.85 80.9
17 98.42 95.7
18 120.88 113.2
19 111.72 105.9
20 116.10 108.8
21 109.37 102.3
22 111.65 99.0
23 114.29 100.7
24 133.68 115.5
25 114.27 100.7
26 126.49 109.9
27 131.00 114.6
28 104.00 85.4
29 108.88 100.5
30 128.48 114.8
31 132.44 116.5
32 128.04 112.9
33 116.35 102.0
34 120.93 106.0
35 118.59 105.3
36 133.10 118.8
37 121.05 106.1
38 127.62 109.3
39 135.44 117.2
40 114.88 92.5
41 114.34 104.2
42 128.85 112.5
43 138.90 122.4
44 129.44 113.3
45 114.96 100.0
46 127.98 110.7
47 127.03 112.8
48 128.75 109.8
49 137.91 117.3
50 128.37 109.1
51 135.90 115.9
52 122.19 96.0
53 113.08 99.8
54 136.20 116.8
55 138.00 115.7
56 115.24 99.4
57 110.95 94.3
58 99.23 91.0
59 102.39 93.2
60 112.67 103.1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `X(prod)`
-2.977 1.154
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.4277 -3.4828 0.9794 3.2292 14.4252
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.97734 8.18347 -0.364 0.717
`X(prod)` 1.15356 0.07756 14.874 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.432 on 58 degrees of freedom
Multiple R-squared: 0.7923, Adjusted R-squared: 0.7887
F-statistic: 221.2 on 1 and 58 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,] 0.4859858 0.9719715 0.51401423
[2,] 0.3775787 0.7551574 0.62242130
[3,] 0.2703963 0.5407925 0.72960373
[4,] 0.2175040 0.4350080 0.78249600
[5,] 0.1597145 0.3194290 0.84028551
[6,] 0.4305000 0.8610000 0.56950002
[7,] 0.5142435 0.9715131 0.48575654
[8,] 0.8827244 0.2345512 0.11727560
[9,] 0.8600717 0.2798566 0.13992832
[10,] 0.8182245 0.3635510 0.18177549
[11,] 0.7832398 0.4335203 0.21676016
[12,] 0.7229937 0.5540126 0.27700631
[13,] 0.8203017 0.3593967 0.17969834
[14,] 0.8188437 0.3623125 0.18115625
[15,] 0.8513473 0.2973054 0.14865269
[16,] 0.8701643 0.2596714 0.12983572
[17,] 0.8916205 0.2167590 0.10837952
[18,] 0.8784208 0.2431584 0.12157918
[19,] 0.8697606 0.2604788 0.13023939
[20,] 0.9205103 0.1589793 0.07948966
[21,] 0.9068475 0.1863050 0.09315249
[22,] 0.9099867 0.1800266 0.09001329
[23,] 0.9024152 0.1951696 0.09758480
[24,] 0.9393263 0.1213475 0.06067373
[25,] 0.9469450 0.1061100 0.05305502
[26,] 0.9369679 0.1260642 0.06303209
[27,] 0.9248346 0.1503309 0.07516543
[28,] 0.9067469 0.1865062 0.09325311
[29,] 0.8815021 0.2369958 0.11849791
[30,] 0.8524526 0.2950948 0.14754741
[31,] 0.8206451 0.3587098 0.17935491
[32,] 0.7965437 0.4069126 0.20345630
[33,] 0.7538462 0.4923077 0.24615383
[34,] 0.7316457 0.5367086 0.26835430
[35,] 0.6890993 0.6218013 0.31090067
[36,] 0.8614783 0.2770434 0.13852170
[37,] 0.8667290 0.2665420 0.13327101
[38,] 0.8264770 0.3470460 0.17352300
[39,] 0.8031212 0.3937576 0.19687878
[40,] 0.7571343 0.4857315 0.24286574
[41,] 0.6872576 0.6254847 0.31274236
[42,] 0.6158448 0.7683103 0.38415516
[43,] 0.5936894 0.8126212 0.40631059
[44,] 0.5201468 0.9597065 0.47985323
[45,] 0.4442009 0.8884017 0.55579914
[46,] 0.3657093 0.7314187 0.63429066
[47,] 0.2813728 0.5627455 0.71862723
[48,] 0.8758354 0.2483292 0.12416460
[49,] 0.7835582 0.4328836 0.21644180
[50,] 0.6665780 0.6668440 0.33342198
[51,] 0.5771289 0.8457422 0.42287109
> postscript(file="/var/www/html/rcomp/tmp/1xrjc1258656894.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21ql51258656894.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/363b71258656894.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4czka1258656894.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5aclr1258656894.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6
-7.7143974 -8.1697953 -5.5996917 -3.0841550 -13.4276568 -10.6843146
7 8 9 10 11 12
-8.4007712 -5.4379615 -6.0647642 2.6201840 1.8005508 7.0291873
13 14 15 16 17 18
-0.2990410 -0.6972901 -4.1736018 2.5040112 -8.9987364 -6.7261069
19 20 21 22 23 24
-7.4650895 -6.4304252 -5.6622590 0.4245023 1.1034434 3.4206958
25 26 27 28 29 30
1.0834434 2.6906544 1.7789034 8.4629731 -4.0758438 -0.9718094
31 32 33 34 35 36
1.0271318 0.7799623 1.6638102 1.6295541 0.0970489 -0.9660655
37 38 39 40 41 42
1.6341977 4.5127928 3.2196369 11.1526685 -2.8840307 2.0513879
43 44 45 46 47 48
0.6811040 1.7185367 2.5809383 3.2578031 -0.1146813 5.0660108
49 50 51 52 53 54
5.5742805 5.4935056 5.1792702 14.4251944 0.9316511 4.4410626
55 56 57 58 59 60
7.5099830 3.5530767 5.1462532 -2.7669855 -2.1448263 -3.2851102
> postscript(file="/var/www/html/rcomp/tmp/6r6v31258656894.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 -7.7143974 NA
1 -8.1697953 -7.7143974
2 -5.5996917 -8.1697953
3 -3.0841550 -5.5996917
4 -13.4276568 -3.0841550
5 -10.6843146 -13.4276568
6 -8.4007712 -10.6843146
7 -5.4379615 -8.4007712
8 -6.0647642 -5.4379615
9 2.6201840 -6.0647642
10 1.8005508 2.6201840
11 7.0291873 1.8005508
12 -0.2990410 7.0291873
13 -0.6972901 -0.2990410
14 -4.1736018 -0.6972901
15 2.5040112 -4.1736018
16 -8.9987364 2.5040112
17 -6.7261069 -8.9987364
18 -7.4650895 -6.7261069
19 -6.4304252 -7.4650895
20 -5.6622590 -6.4304252
21 0.4245023 -5.6622590
22 1.1034434 0.4245023
23 3.4206958 1.1034434
24 1.0834434 3.4206958
25 2.6906544 1.0834434
26 1.7789034 2.6906544
27 8.4629731 1.7789034
28 -4.0758438 8.4629731
29 -0.9718094 -4.0758438
30 1.0271318 -0.9718094
31 0.7799623 1.0271318
32 1.6638102 0.7799623
33 1.6295541 1.6638102
34 0.0970489 1.6295541
35 -0.9660655 0.0970489
36 1.6341977 -0.9660655
37 4.5127928 1.6341977
38 3.2196369 4.5127928
39 11.1526685 3.2196369
40 -2.8840307 11.1526685
41 2.0513879 -2.8840307
42 0.6811040 2.0513879
43 1.7185367 0.6811040
44 2.5809383 1.7185367
45 3.2578031 2.5809383
46 -0.1146813 3.2578031
47 5.0660108 -0.1146813
48 5.5742805 5.0660108
49 5.4935056 5.5742805
50 5.1792702 5.4935056
51 14.4251944 5.1792702
52 0.9316511 14.4251944
53 4.4410626 0.9316511
54 7.5099830 4.4410626
55 3.5530767 7.5099830
56 5.1462532 3.5530767
57 -2.7669855 5.1462532
58 -2.1448263 -2.7669855
59 -3.2851102 -2.1448263
60 NA -3.2851102
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -8.1697953 -7.7143974
[2,] -5.5996917 -8.1697953
[3,] -3.0841550 -5.5996917
[4,] -13.4276568 -3.0841550
[5,] -10.6843146 -13.4276568
[6,] -8.4007712 -10.6843146
[7,] -5.4379615 -8.4007712
[8,] -6.0647642 -5.4379615
[9,] 2.6201840 -6.0647642
[10,] 1.8005508 2.6201840
[11,] 7.0291873 1.8005508
[12,] -0.2990410 7.0291873
[13,] -0.6972901 -0.2990410
[14,] -4.1736018 -0.6972901
[15,] 2.5040112 -4.1736018
[16,] -8.9987364 2.5040112
[17,] -6.7261069 -8.9987364
[18,] -7.4650895 -6.7261069
[19,] -6.4304252 -7.4650895
[20,] -5.6622590 -6.4304252
[21,] 0.4245023 -5.6622590
[22,] 1.1034434 0.4245023
[23,] 3.4206958 1.1034434
[24,] 1.0834434 3.4206958
[25,] 2.6906544 1.0834434
[26,] 1.7789034 2.6906544
[27,] 8.4629731 1.7789034
[28,] -4.0758438 8.4629731
[29,] -0.9718094 -4.0758438
[30,] 1.0271318 -0.9718094
[31,] 0.7799623 1.0271318
[32,] 1.6638102 0.7799623
[33,] 1.6295541 1.6638102
[34,] 0.0970489 1.6295541
[35,] -0.9660655 0.0970489
[36,] 1.6341977 -0.9660655
[37,] 4.5127928 1.6341977
[38,] 3.2196369 4.5127928
[39,] 11.1526685 3.2196369
[40,] -2.8840307 11.1526685
[41,] 2.0513879 -2.8840307
[42,] 0.6811040 2.0513879
[43,] 1.7185367 0.6811040
[44,] 2.5809383 1.7185367
[45,] 3.2578031 2.5809383
[46,] -0.1146813 3.2578031
[47,] 5.0660108 -0.1146813
[48,] 5.5742805 5.0660108
[49,] 5.4935056 5.5742805
[50,] 5.1792702 5.4935056
[51,] 14.4251944 5.1792702
[52,] 0.9316511 14.4251944
[53,] 4.4410626 0.9316511
[54,] 7.5099830 4.4410626
[55,] 3.5530767 7.5099830
[56,] 5.1462532 3.5530767
[57,] -2.7669855 5.1462532
[58,] -2.1448263 -2.7669855
[59,] -3.2851102 -2.1448263
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -8.1697953 -7.7143974
2 -5.5996917 -8.1697953
3 -3.0841550 -5.5996917
4 -13.4276568 -3.0841550
5 -10.6843146 -13.4276568
6 -8.4007712 -10.6843146
7 -5.4379615 -8.4007712
8 -6.0647642 -5.4379615
9 2.6201840 -6.0647642
10 1.8005508 2.6201840
11 7.0291873 1.8005508
12 -0.2990410 7.0291873
13 -0.6972901 -0.2990410
14 -4.1736018 -0.6972901
15 2.5040112 -4.1736018
16 -8.9987364 2.5040112
17 -6.7261069 -8.9987364
18 -7.4650895 -6.7261069
19 -6.4304252 -7.4650895
20 -5.6622590 -6.4304252
21 0.4245023 -5.6622590
22 1.1034434 0.4245023
23 3.4206958 1.1034434
24 1.0834434 3.4206958
25 2.6906544 1.0834434
26 1.7789034 2.6906544
27 8.4629731 1.7789034
28 -4.0758438 8.4629731
29 -0.9718094 -4.0758438
30 1.0271318 -0.9718094
31 0.7799623 1.0271318
32 1.6638102 0.7799623
33 1.6295541 1.6638102
34 0.0970489 1.6295541
35 -0.9660655 0.0970489
36 1.6341977 -0.9660655
37 4.5127928 1.6341977
38 3.2196369 4.5127928
39 11.1526685 3.2196369
40 -2.8840307 11.1526685
41 2.0513879 -2.8840307
42 0.6811040 2.0513879
43 1.7185367 0.6811040
44 2.5809383 1.7185367
45 3.2578031 2.5809383
46 -0.1146813 3.2578031
47 5.0660108 -0.1146813
48 5.5742805 5.0660108
49 5.4935056 5.5742805
50 5.1792702 5.4935056
51 14.4251944 5.1792702
52 0.9316511 14.4251944
53 4.4410626 0.9316511
54 7.5099830 4.4410626
55 3.5530767 7.5099830
56 5.1462532 3.5530767
57 -2.7669855 5.1462532
58 -2.1448263 -2.7669855
59 -3.2851102 -2.1448263
> 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/7gv8n1258656894.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tgad1258656894.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/957g41258656894.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10a0451258656894.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11i8ee1258656894.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/12jns91258656894.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/13xu1w1258656894.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/14ewvt1258656894.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/15tfd81258656894.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/16nuok1258656894.tab")
+ }
>
> system("convert tmp/1xrjc1258656894.ps tmp/1xrjc1258656894.png")
> system("convert tmp/21ql51258656894.ps tmp/21ql51258656894.png")
> system("convert tmp/363b71258656894.ps tmp/363b71258656894.png")
> system("convert tmp/4czka1258656894.ps tmp/4czka1258656894.png")
> system("convert tmp/5aclr1258656894.ps tmp/5aclr1258656894.png")
> system("convert tmp/6r6v31258656894.ps tmp/6r6v31258656894.png")
> system("convert tmp/7gv8n1258656894.ps tmp/7gv8n1258656894.png")
> system("convert tmp/8tgad1258656894.ps tmp/8tgad1258656894.png")
> system("convert tmp/957g41258656894.ps tmp/957g41258656894.png")
> system("convert tmp/10a0451258656894.ps tmp/10a0451258656894.png")
>
>
> proc.time()
user system elapsed
2.494 1.605 3.015