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(124,104.89,118.63,105.15,121.86,105.24,119.97,105.57,125.03,105.62,130.09,106.17,126.65,106.27,121.7,106.41,119.24,106.94,122.63,107.16,116.66,107.32,114.12,107.32,113.11,107.35,112.61,107.55,113.4,107.87,115.18,108.37,121.01,108.38,119.44,107.92,116.68,108.03,117.07,108.14,117.41,108.3,119.58,108.64,120.92,108.66,117.09,109.04,116.77,109.03,119.39,109.03,122.49,109.54,124.08,109.75,118.29,109.83,112.94,109.65,113.79,109.82,114.43,109.95,118.7,110.12,120.36,110.15,118.27,110.21,118.34,109.99,117.82,110.14,117.65,110.14,118.18,110.81,121.02,110.97,124.78,110.99,131.16,109.73,130.14,109.81,131.75,110.02,134.73,110.18,135.35,110.21,140.32,110.25,136.35,110.36,131.6,110.51,128.9,110.6,133.89,110.95,138.25,111.18,146.23,111.19,144.76,111.69,149.3,111.7,156.8,111.83,159.08,111.77,165.12,111.73,163.14,112.01,153.43,111.86,151.01,112.04),dim=c(2,61),dimnames=list(c('AKB','AKW'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('AKB','AKW'),1:61))
> 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
AKB AKW
1 124.00 104.89
2 118.63 105.15
3 121.86 105.24
4 119.97 105.57
5 125.03 105.62
6 130.09 106.17
7 126.65 106.27
8 121.70 106.41
9 119.24 106.94
10 122.63 107.16
11 116.66 107.32
12 114.12 107.32
13 113.11 107.35
14 112.61 107.55
15 113.40 107.87
16 115.18 108.37
17 121.01 108.38
18 119.44 107.92
19 116.68 108.03
20 117.07 108.14
21 117.41 108.30
22 119.58 108.64
23 120.92 108.66
24 117.09 109.04
25 116.77 109.03
26 119.39 109.03
27 122.49 109.54
28 124.08 109.75
29 118.29 109.83
30 112.94 109.65
31 113.79 109.82
32 114.43 109.95
33 118.70 110.12
34 120.36 110.15
35 118.27 110.21
36 118.34 109.99
37 117.82 110.14
38 117.65 110.14
39 118.18 110.81
40 121.02 110.97
41 124.78 110.99
42 131.16 109.73
43 130.14 109.81
44 131.75 110.02
45 134.73 110.18
46 135.35 110.21
47 140.32 110.25
48 136.35 110.36
49 131.60 110.51
50 128.90 110.60
51 133.89 110.95
52 138.25 111.18
53 146.23 111.19
54 144.76 111.69
55 149.30 111.70
56 156.80 111.83
57 159.08 111.77
58 165.12 111.73
59 163.14 112.01
60 153.43 111.86
61 151.01 112.04
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) AKW
-324.264 4.131
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-15.791 -8.263 -2.445 8.095 27.796
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -324.2637 80.0443 -4.051 0.000151 ***
AKW 4.1313 0.7328 5.638 5.12e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11 on 59 degrees of freedom
Multiple R-squared: 0.3501, Adjusted R-squared: 0.3391
F-statistic: 31.78 on 1 and 59 DF, p-value: 5.119e-07
> 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,] 3.233398e-02 6.466796e-02 0.9676660
[2,] 2.369546e-02 4.739092e-02 0.9763045
[3,] 8.895680e-03 1.779136e-02 0.9911043
[4,] 9.810271e-03 1.962054e-02 0.9901897
[5,] 1.202303e-02 2.404605e-02 0.9879770
[6,] 5.788725e-03 1.157745e-02 0.9942113
[7,] 5.931981e-03 1.186396e-02 0.9940680
[8,] 6.752332e-03 1.350466e-02 0.9932477
[9,] 6.382781e-03 1.276556e-02 0.9936172
[10,] 4.622555e-03 9.245110e-03 0.9953774
[11,] 2.334588e-03 4.669175e-03 0.9976654
[12,] 1.057447e-03 2.114895e-03 0.9989426
[13,] 1.213122e-03 2.426244e-03 0.9987869
[14,] 8.447060e-04 1.689412e-03 0.9991553
[15,] 4.658827e-04 9.317653e-04 0.9995341
[16,] 2.715095e-04 5.430191e-04 0.9997285
[17,] 1.675188e-04 3.350377e-04 0.9998325
[18,] 1.433485e-04 2.866971e-04 0.9998567
[19,] 1.846622e-04 3.693243e-04 0.9998153
[20,] 9.377798e-05 1.875560e-04 0.9999062
[21,] 4.733261e-05 9.466523e-05 0.9999527
[22,] 3.864916e-05 7.729831e-05 0.9999614
[23,] 5.347639e-05 1.069528e-04 0.9999465
[24,] 8.420977e-05 1.684195e-04 0.9999158
[25,] 3.757475e-05 7.514950e-05 0.9999624
[26,] 2.555948e-05 5.111896e-05 0.9999744
[27,] 1.503789e-05 3.007578e-05 0.9999850
[28,] 8.862790e-06 1.772558e-05 0.9999911
[29,] 5.139180e-06 1.027836e-05 0.9999949
[30,] 3.460758e-06 6.921516e-06 0.9999965
[31,] 2.314191e-06 4.628382e-06 0.9999977
[32,] 1.210555e-06 2.421110e-06 0.9999988
[33,] 8.732106e-07 1.746421e-06 0.9999991
[34,] 7.645527e-07 1.529105e-06 0.9999992
[35,] 7.127263e-06 1.425453e-05 0.9999929
[36,] 2.156043e-04 4.312085e-04 0.9997844
[37,] 8.233963e-03 1.646793e-02 0.9917660
[38,] 2.989470e-02 5.978940e-02 0.9701053
[39,] 5.006324e-02 1.001265e-01 0.9499368
[40,] 7.860390e-02 1.572078e-01 0.9213961
[41,] 1.373692e-01 2.747383e-01 0.8626308
[42,] 2.021628e-01 4.043255e-01 0.7978372
[43,] 4.930146e-01 9.860293e-01 0.5069854
[44,] 6.339958e-01 7.320084e-01 0.3660042
[45,] 5.927212e-01 8.145576e-01 0.4072788
[46,] 5.009180e-01 9.981640e-01 0.4990820
[47,] 4.498769e-01 8.997539e-01 0.5501231
[48,] 4.674683e-01 9.349365e-01 0.5325317
[49,] 4.518675e-01 9.037350e-01 0.5481325
[50,] 6.153506e-01 7.692989e-01 0.3846494
[51,] 7.688817e-01 4.622366e-01 0.2311183
[52,] 6.880002e-01 6.239996e-01 0.3119998
> postscript(file="/var/www/html/rcomp/tmp/1s8751258912357.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2enj21258912357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lmyk1258912357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4t83l1258912357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/532eg1258912357.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 = 61
Frequency = 1
1 2 3 4 5 6
14.9340285 8.4898964 11.3480814 8.0947598 12.9481960 15.7359934
7 8 9 10 11 12
11.8828656 6.3544868 1.7049097 4.1860287 -2.4449757 -4.9849757
13 14 15 16 17 18
-6.1189140 -7.4451695 -7.9771783 -8.2628170 -2.4741298 -2.1437422
19 20 21 22 23 24
-5.3581827 -5.4226232 -5.7436276 -4.9782619 -3.7208875 -9.1207729
25 26 27 28 29 30
-9.3994601 -6.7794601 -5.7864116 -5.0639799 -11.1844821 -15.7908521
31 32 33 34 35 36
-15.6431693 -15.5402354 -11.9725525 -10.4364909 -12.7743675 -11.7954865
37 38 39 40 41 42
-12.9351781 -13.1051781 -15.3431340 -13.1641384 -9.4867639 2.0986457
43 44 45 46 47 48
0.7481435 1.4905752 3.8095708 4.3056325 9.1103814 4.6859409
49 50 51 52 53 54
-0.6837507 -3.7555657 -0.2115128 3.1982934 11.1369806 7.6013419
55 56 57 58 59 60
12.1000291 19.0629630 21.5908397 27.7960908 24.6593331 15.5690247
61
12.4053948
> postscript(file="/var/www/html/rcomp/tmp/62q0u1258912357.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 14.9340285 NA
1 8.4898964 14.9340285
2 11.3480814 8.4898964
3 8.0947598 11.3480814
4 12.9481960 8.0947598
5 15.7359934 12.9481960
6 11.8828656 15.7359934
7 6.3544868 11.8828656
8 1.7049097 6.3544868
9 4.1860287 1.7049097
10 -2.4449757 4.1860287
11 -4.9849757 -2.4449757
12 -6.1189140 -4.9849757
13 -7.4451695 -6.1189140
14 -7.9771783 -7.4451695
15 -8.2628170 -7.9771783
16 -2.4741298 -8.2628170
17 -2.1437422 -2.4741298
18 -5.3581827 -2.1437422
19 -5.4226232 -5.3581827
20 -5.7436276 -5.4226232
21 -4.9782619 -5.7436276
22 -3.7208875 -4.9782619
23 -9.1207729 -3.7208875
24 -9.3994601 -9.1207729
25 -6.7794601 -9.3994601
26 -5.7864116 -6.7794601
27 -5.0639799 -5.7864116
28 -11.1844821 -5.0639799
29 -15.7908521 -11.1844821
30 -15.6431693 -15.7908521
31 -15.5402354 -15.6431693
32 -11.9725525 -15.5402354
33 -10.4364909 -11.9725525
34 -12.7743675 -10.4364909
35 -11.7954865 -12.7743675
36 -12.9351781 -11.7954865
37 -13.1051781 -12.9351781
38 -15.3431340 -13.1051781
39 -13.1641384 -15.3431340
40 -9.4867639 -13.1641384
41 2.0986457 -9.4867639
42 0.7481435 2.0986457
43 1.4905752 0.7481435
44 3.8095708 1.4905752
45 4.3056325 3.8095708
46 9.1103814 4.3056325
47 4.6859409 9.1103814
48 -0.6837507 4.6859409
49 -3.7555657 -0.6837507
50 -0.2115128 -3.7555657
51 3.1982934 -0.2115128
52 11.1369806 3.1982934
53 7.6013419 11.1369806
54 12.1000291 7.6013419
55 19.0629630 12.1000291
56 21.5908397 19.0629630
57 27.7960908 21.5908397
58 24.6593331 27.7960908
59 15.5690247 24.6593331
60 12.4053948 15.5690247
61 NA 12.4053948
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 8.4898964 14.9340285
[2,] 11.3480814 8.4898964
[3,] 8.0947598 11.3480814
[4,] 12.9481960 8.0947598
[5,] 15.7359934 12.9481960
[6,] 11.8828656 15.7359934
[7,] 6.3544868 11.8828656
[8,] 1.7049097 6.3544868
[9,] 4.1860287 1.7049097
[10,] -2.4449757 4.1860287
[11,] -4.9849757 -2.4449757
[12,] -6.1189140 -4.9849757
[13,] -7.4451695 -6.1189140
[14,] -7.9771783 -7.4451695
[15,] -8.2628170 -7.9771783
[16,] -2.4741298 -8.2628170
[17,] -2.1437422 -2.4741298
[18,] -5.3581827 -2.1437422
[19,] -5.4226232 -5.3581827
[20,] -5.7436276 -5.4226232
[21,] -4.9782619 -5.7436276
[22,] -3.7208875 -4.9782619
[23,] -9.1207729 -3.7208875
[24,] -9.3994601 -9.1207729
[25,] -6.7794601 -9.3994601
[26,] -5.7864116 -6.7794601
[27,] -5.0639799 -5.7864116
[28,] -11.1844821 -5.0639799
[29,] -15.7908521 -11.1844821
[30,] -15.6431693 -15.7908521
[31,] -15.5402354 -15.6431693
[32,] -11.9725525 -15.5402354
[33,] -10.4364909 -11.9725525
[34,] -12.7743675 -10.4364909
[35,] -11.7954865 -12.7743675
[36,] -12.9351781 -11.7954865
[37,] -13.1051781 -12.9351781
[38,] -15.3431340 -13.1051781
[39,] -13.1641384 -15.3431340
[40,] -9.4867639 -13.1641384
[41,] 2.0986457 -9.4867639
[42,] 0.7481435 2.0986457
[43,] 1.4905752 0.7481435
[44,] 3.8095708 1.4905752
[45,] 4.3056325 3.8095708
[46,] 9.1103814 4.3056325
[47,] 4.6859409 9.1103814
[48,] -0.6837507 4.6859409
[49,] -3.7555657 -0.6837507
[50,] -0.2115128 -3.7555657
[51,] 3.1982934 -0.2115128
[52,] 11.1369806 3.1982934
[53,] 7.6013419 11.1369806
[54,] 12.1000291 7.6013419
[55,] 19.0629630 12.1000291
[56,] 21.5908397 19.0629630
[57,] 27.7960908 21.5908397
[58,] 24.6593331 27.7960908
[59,] 15.5690247 24.6593331
[60,] 12.4053948 15.5690247
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 8.4898964 14.9340285
2 11.3480814 8.4898964
3 8.0947598 11.3480814
4 12.9481960 8.0947598
5 15.7359934 12.9481960
6 11.8828656 15.7359934
7 6.3544868 11.8828656
8 1.7049097 6.3544868
9 4.1860287 1.7049097
10 -2.4449757 4.1860287
11 -4.9849757 -2.4449757
12 -6.1189140 -4.9849757
13 -7.4451695 -6.1189140
14 -7.9771783 -7.4451695
15 -8.2628170 -7.9771783
16 -2.4741298 -8.2628170
17 -2.1437422 -2.4741298
18 -5.3581827 -2.1437422
19 -5.4226232 -5.3581827
20 -5.7436276 -5.4226232
21 -4.9782619 -5.7436276
22 -3.7208875 -4.9782619
23 -9.1207729 -3.7208875
24 -9.3994601 -9.1207729
25 -6.7794601 -9.3994601
26 -5.7864116 -6.7794601
27 -5.0639799 -5.7864116
28 -11.1844821 -5.0639799
29 -15.7908521 -11.1844821
30 -15.6431693 -15.7908521
31 -15.5402354 -15.6431693
32 -11.9725525 -15.5402354
33 -10.4364909 -11.9725525
34 -12.7743675 -10.4364909
35 -11.7954865 -12.7743675
36 -12.9351781 -11.7954865
37 -13.1051781 -12.9351781
38 -15.3431340 -13.1051781
39 -13.1641384 -15.3431340
40 -9.4867639 -13.1641384
41 2.0986457 -9.4867639
42 0.7481435 2.0986457
43 1.4905752 0.7481435
44 3.8095708 1.4905752
45 4.3056325 3.8095708
46 9.1103814 4.3056325
47 4.6859409 9.1103814
48 -0.6837507 4.6859409
49 -3.7555657 -0.6837507
50 -0.2115128 -3.7555657
51 3.1982934 -0.2115128
52 11.1369806 3.1982934
53 7.6013419 11.1369806
54 12.1000291 7.6013419
55 19.0629630 12.1000291
56 21.5908397 19.0629630
57 27.7960908 21.5908397
58 24.6593331 27.7960908
59 15.5690247 24.6593331
60 12.4053948 15.5690247
> 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/7k0de1258912357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ausy1258912357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9e8yq1258912357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10095y1258912357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112dnb1258912358.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/12p5zl1258912358.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/13djhm1258912358.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/1479221258912358.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/154s081258912358.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/16yy651258912358.tab")
+ }
>
> system("convert tmp/1s8751258912357.ps tmp/1s8751258912357.png")
> system("convert tmp/2enj21258912357.ps tmp/2enj21258912357.png")
> system("convert tmp/3lmyk1258912357.ps tmp/3lmyk1258912357.png")
> system("convert tmp/4t83l1258912357.ps tmp/4t83l1258912357.png")
> system("convert tmp/532eg1258912357.ps tmp/532eg1258912357.png")
> system("convert tmp/62q0u1258912357.ps tmp/62q0u1258912357.png")
> system("convert tmp/7k0de1258912357.ps tmp/7k0de1258912357.png")
> system("convert tmp/8ausy1258912357.ps tmp/8ausy1258912357.png")
> system("convert tmp/9e8yq1258912357.ps tmp/9e8yq1258912357.png")
> system("convert tmp/10095y1258912357.ps tmp/10095y1258912357.png")
>
>
> proc.time()
user system elapsed
2.430 1.509 3.333