R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(130,87.1,136.7,110.5,138.1,110.8,139.5,104.2,140.4,88.9,144.6,89.8,151.4,90,147.9,93.9,141.5,91.3,143.8,87.8,143.6,99.7,150.5,73.5,150.1,79.2,154.9,96.9,162.1,95.2,176.7,95.6,186.6,89.7,194.8,92.8,196.3,88,228.8,101.1,267.2,92.7,237.2,95.8,254.7,103.8,258.2,81.8,257.9,87.1,269.6,105.9,266.9,108.1,269.6,102.6,253.9,93.7,258.6,103.5,274.2,100.6,301.5,113.3,304.5,102.4,285.1,102.1,287.7,106.9,265.5,87.3,264.1,93.1,276.1,109.1,258.9,120.3,239.1,104.9,250.1,92.6,276.8,109.8,297.6,111.4,295.4,117.9,283,121.6,275.8,117.8,279.7,124.2,254.6,106.8,234.6,102.7,176.9,116.8,148.1,113.6,122.7,96.1,124.9,85,121.6,83.2,128.4,84.9,144.5,83,151.8,79.6,167.1,83.2,173.8,83.8,203.7,82.8,199.8,71.4),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),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
Y X
1 130.0 87.1
2 136.7 110.5
3 138.1 110.8
4 139.5 104.2
5 140.4 88.9
6 144.6 89.8
7 151.4 90.0
8 147.9 93.9
9 141.5 91.3
10 143.8 87.8
11 143.6 99.7
12 150.5 73.5
13 150.1 79.2
14 154.9 96.9
15 162.1 95.2
16 176.7 95.6
17 186.6 89.7
18 194.8 92.8
19 196.3 88.0
20 228.8 101.1
21 267.2 92.7
22 237.2 95.8
23 254.7 103.8
24 258.2 81.8
25 257.9 87.1
26 269.6 105.9
27 266.9 108.1
28 269.6 102.6
29 253.9 93.7
30 258.6 103.5
31 274.2 100.6
32 301.5 113.3
33 304.5 102.4
34 285.1 102.1
35 287.7 106.9
36 265.5 87.3
37 264.1 93.1
38 276.1 109.1
39 258.9 120.3
40 239.1 104.9
41 250.1 92.6
42 276.8 109.8
43 297.6 111.4
44 295.4 117.9
45 283.0 121.6
46 275.8 117.8
47 279.7 124.2
48 254.6 106.8
49 234.6 102.7
50 176.9 116.8
51 148.1 113.6
52 122.7 96.1
53 124.9 85.0
54 121.6 83.2
55 128.4 84.9
56 144.5 83.0
57 151.8 79.6
58 167.1 83.2
59 173.8 83.8
60 203.7 82.8
61 199.8 71.4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
-32.126 2.484
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-105.685 -46.361 9.767 38.642 87.113
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -32.1257 53.3655 -0.602 0.55
X 2.4843 0.5428 4.577 2.48e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 52.7 on 59 degrees of freedom
Multiple R-squared: 0.262, Adjusted R-squared: 0.2495
F-statistic: 20.95 on 1 and 59 DF, p-value: 2.482e-05
> 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,] 1.069250e-03 2.138500e-03 0.99893075
[2,] 3.260394e-04 6.520789e-04 0.99967396
[3,] 2.177573e-04 4.355145e-04 0.99978224
[4,] 5.023915e-05 1.004783e-04 0.99994976
[5,] 7.381104e-06 1.476221e-05 0.99999262
[6,] 1.023793e-06 2.047586e-06 0.99999898
[7,] 1.820019e-07 3.640038e-07 0.99999982
[8,] 2.745694e-08 5.491387e-08 0.99999997
[9,] 4.023443e-09 8.046887e-09 1.00000000
[10,] 5.595128e-09 1.119026e-08 0.99999999
[11,] 2.287868e-08 4.575737e-08 0.99999998
[12,] 7.607926e-07 1.521585e-06 0.99999924
[13,] 9.703635e-06 1.940727e-05 0.99999030
[14,] 7.815549e-05 1.563110e-04 0.99992184
[15,] 2.075953e-04 4.151905e-04 0.99979240
[16,] 4.143302e-03 8.286605e-03 0.99585670
[17,] 8.072268e-02 1.614454e-01 0.91927732
[18,] 1.274700e-01 2.549399e-01 0.87253003
[19,] 2.061133e-01 4.122265e-01 0.79388674
[20,] 3.519081e-01 7.038163e-01 0.64809187
[21,] 4.582665e-01 9.165330e-01 0.54173349
[22,] 5.444768e-01 9.110464e-01 0.45552322
[23,] 5.709155e-01 8.581690e-01 0.42908450
[24,] 5.996510e-01 8.006980e-01 0.40034902
[25,] 6.116118e-01 7.767765e-01 0.38838823
[26,] 5.890656e-01 8.218689e-01 0.41093444
[27,] 6.130586e-01 7.738827e-01 0.38694137
[28,] 6.297782e-01 7.404435e-01 0.37022175
[29,] 7.235828e-01 5.528345e-01 0.27641723
[30,] 7.480136e-01 5.039729e-01 0.25198644
[31,] 7.487804e-01 5.024392e-01 0.25121962
[32,] 8.240512e-01 3.518976e-01 0.17594881
[33,] 8.552323e-01 2.895355e-01 0.14476774
[34,] 8.355173e-01 3.289654e-01 0.16448271
[35,] 7.812894e-01 4.374211e-01 0.21871056
[36,] 7.218896e-01 5.562209e-01 0.27811044
[37,] 7.382546e-01 5.234909e-01 0.26174544
[38,] 7.159797e-01 5.680406e-01 0.28402031
[39,] 7.512598e-01 4.974805e-01 0.24874023
[40,] 7.531123e-01 4.937754e-01 0.24688772
[41,] 7.267320e-01 5.465360e-01 0.27326798
[42,] 7.283022e-01 5.433955e-01 0.27169776
[43,] 7.806703e-01 4.386595e-01 0.21932973
[44,] 8.873205e-01 2.253591e-01 0.11267954
[45,] 9.617453e-01 7.650938e-02 0.03825469
[46,] 9.688997e-01 6.220061e-02 0.03110031
[47,] 9.824801e-01 3.503987e-02 0.01751994
[48,] 9.705777e-01 5.884468e-02 0.02942234
[49,] 9.515498e-01 9.690045e-02 0.04845023
[50,] 9.485548e-01 1.028903e-01 0.05144516
[51,] 9.346761e-01 1.306478e-01 0.06532389
[52,] 8.998465e-01 2.003071e-01 0.10015354
> postscript(file="/var/www/html/rcomp/tmp/1np2l1258731654.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zdlp1258731654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3kemt1258731654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hbyv1258731654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5o7qv1258731654.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
-54.25346947 -105.68517467 -105.03045295 -87.23433096 -48.32513910
6 7 8 9 10
-46.36097391 -40.05782610 -53.24644363 -53.18736527 -42.19245210
11 12 13 14 15
-71.95515689 0.03247886 -14.52780831 -53.69922635 -42.27598281
16 17 18 19 20
-28.66968717 -4.11254782 -3.61375663 9.81069572 9.76687784
21 22 23 24 25
69.03466946 31.33346065 28.95937340 87.11311333 73.64653053
26 27 28 29 30
38.64242549 30.47705150 46.84048649 53.25040855 33.60465167
31 32 33 34 35
56.40900830 52.15889479 82.23733867 63.58261694 54.25816459
36 37 38 39 40
80.74967835 64.94096509 37.19279060 -7.83093155 10.62668640
41 42 43 44 45
52.18309555 36.15380796 52.97899051 34.63129462 13.03952927
46 47 48 49 50
15.27972071 3.28045091 21.40659068 11.59206039 -81.13601838
51 52 53 54 55
-101.98638348 -83.91181762 -54.13652156 -52.96485193 -50.38809547
56 57 58 59 60
-29.56799975 -13.82151267 -7.46485193 -2.25540848 30.12885243
61
54.54942676
> postscript(file="/var/www/html/rcomp/tmp/64uzu1258731655.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 -54.25346947 NA
1 -105.68517467 -54.25346947
2 -105.03045295 -105.68517467
3 -87.23433096 -105.03045295
4 -48.32513910 -87.23433096
5 -46.36097391 -48.32513910
6 -40.05782610 -46.36097391
7 -53.24644363 -40.05782610
8 -53.18736527 -53.24644363
9 -42.19245210 -53.18736527
10 -71.95515689 -42.19245210
11 0.03247886 -71.95515689
12 -14.52780831 0.03247886
13 -53.69922635 -14.52780831
14 -42.27598281 -53.69922635
15 -28.66968717 -42.27598281
16 -4.11254782 -28.66968717
17 -3.61375663 -4.11254782
18 9.81069572 -3.61375663
19 9.76687784 9.81069572
20 69.03466946 9.76687784
21 31.33346065 69.03466946
22 28.95937340 31.33346065
23 87.11311333 28.95937340
24 73.64653053 87.11311333
25 38.64242549 73.64653053
26 30.47705150 38.64242549
27 46.84048649 30.47705150
28 53.25040855 46.84048649
29 33.60465167 53.25040855
30 56.40900830 33.60465167
31 52.15889479 56.40900830
32 82.23733867 52.15889479
33 63.58261694 82.23733867
34 54.25816459 63.58261694
35 80.74967835 54.25816459
36 64.94096509 80.74967835
37 37.19279060 64.94096509
38 -7.83093155 37.19279060
39 10.62668640 -7.83093155
40 52.18309555 10.62668640
41 36.15380796 52.18309555
42 52.97899051 36.15380796
43 34.63129462 52.97899051
44 13.03952927 34.63129462
45 15.27972071 13.03952927
46 3.28045091 15.27972071
47 21.40659068 3.28045091
48 11.59206039 21.40659068
49 -81.13601838 11.59206039
50 -101.98638348 -81.13601838
51 -83.91181762 -101.98638348
52 -54.13652156 -83.91181762
53 -52.96485193 -54.13652156
54 -50.38809547 -52.96485193
55 -29.56799975 -50.38809547
56 -13.82151267 -29.56799975
57 -7.46485193 -13.82151267
58 -2.25540848 -7.46485193
59 30.12885243 -2.25540848
60 54.54942676 30.12885243
61 NA 54.54942676
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -105.68517467 -54.25346947
[2,] -105.03045295 -105.68517467
[3,] -87.23433096 -105.03045295
[4,] -48.32513910 -87.23433096
[5,] -46.36097391 -48.32513910
[6,] -40.05782610 -46.36097391
[7,] -53.24644363 -40.05782610
[8,] -53.18736527 -53.24644363
[9,] -42.19245210 -53.18736527
[10,] -71.95515689 -42.19245210
[11,] 0.03247886 -71.95515689
[12,] -14.52780831 0.03247886
[13,] -53.69922635 -14.52780831
[14,] -42.27598281 -53.69922635
[15,] -28.66968717 -42.27598281
[16,] -4.11254782 -28.66968717
[17,] -3.61375663 -4.11254782
[18,] 9.81069572 -3.61375663
[19,] 9.76687784 9.81069572
[20,] 69.03466946 9.76687784
[21,] 31.33346065 69.03466946
[22,] 28.95937340 31.33346065
[23,] 87.11311333 28.95937340
[24,] 73.64653053 87.11311333
[25,] 38.64242549 73.64653053
[26,] 30.47705150 38.64242549
[27,] 46.84048649 30.47705150
[28,] 53.25040855 46.84048649
[29,] 33.60465167 53.25040855
[30,] 56.40900830 33.60465167
[31,] 52.15889479 56.40900830
[32,] 82.23733867 52.15889479
[33,] 63.58261694 82.23733867
[34,] 54.25816459 63.58261694
[35,] 80.74967835 54.25816459
[36,] 64.94096509 80.74967835
[37,] 37.19279060 64.94096509
[38,] -7.83093155 37.19279060
[39,] 10.62668640 -7.83093155
[40,] 52.18309555 10.62668640
[41,] 36.15380796 52.18309555
[42,] 52.97899051 36.15380796
[43,] 34.63129462 52.97899051
[44,] 13.03952927 34.63129462
[45,] 15.27972071 13.03952927
[46,] 3.28045091 15.27972071
[47,] 21.40659068 3.28045091
[48,] 11.59206039 21.40659068
[49,] -81.13601838 11.59206039
[50,] -101.98638348 -81.13601838
[51,] -83.91181762 -101.98638348
[52,] -54.13652156 -83.91181762
[53,] -52.96485193 -54.13652156
[54,] -50.38809547 -52.96485193
[55,] -29.56799975 -50.38809547
[56,] -13.82151267 -29.56799975
[57,] -7.46485193 -13.82151267
[58,] -2.25540848 -7.46485193
[59,] 30.12885243 -2.25540848
[60,] 54.54942676 30.12885243
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -105.68517467 -54.25346947
2 -105.03045295 -105.68517467
3 -87.23433096 -105.03045295
4 -48.32513910 -87.23433096
5 -46.36097391 -48.32513910
6 -40.05782610 -46.36097391
7 -53.24644363 -40.05782610
8 -53.18736527 -53.24644363
9 -42.19245210 -53.18736527
10 -71.95515689 -42.19245210
11 0.03247886 -71.95515689
12 -14.52780831 0.03247886
13 -53.69922635 -14.52780831
14 -42.27598281 -53.69922635
15 -28.66968717 -42.27598281
16 -4.11254782 -28.66968717
17 -3.61375663 -4.11254782
18 9.81069572 -3.61375663
19 9.76687784 9.81069572
20 69.03466946 9.76687784
21 31.33346065 69.03466946
22 28.95937340 31.33346065
23 87.11311333 28.95937340
24 73.64653053 87.11311333
25 38.64242549 73.64653053
26 30.47705150 38.64242549
27 46.84048649 30.47705150
28 53.25040855 46.84048649
29 33.60465167 53.25040855
30 56.40900830 33.60465167
31 52.15889479 56.40900830
32 82.23733867 52.15889479
33 63.58261694 82.23733867
34 54.25816459 63.58261694
35 80.74967835 54.25816459
36 64.94096509 80.74967835
37 37.19279060 64.94096509
38 -7.83093155 37.19279060
39 10.62668640 -7.83093155
40 52.18309555 10.62668640
41 36.15380796 52.18309555
42 52.97899051 36.15380796
43 34.63129462 52.97899051
44 13.03952927 34.63129462
45 15.27972071 13.03952927
46 3.28045091 15.27972071
47 21.40659068 3.28045091
48 11.59206039 21.40659068
49 -81.13601838 11.59206039
50 -101.98638348 -81.13601838
51 -83.91181762 -101.98638348
52 -54.13652156 -83.91181762
53 -52.96485193 -54.13652156
54 -50.38809547 -52.96485193
55 -29.56799975 -50.38809547
56 -13.82151267 -29.56799975
57 -7.46485193 -13.82151267
58 -2.25540848 -7.46485193
59 30.12885243 -2.25540848
60 54.54942676 30.12885243
> 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/7x8hg1258731655.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ufjh1258731655.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zi6m1258731655.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/103ahj1258731655.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11n3z51258731655.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/12o0uc1258731655.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/1303nv1258731655.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/14tpq51258731655.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/15izaq1258731655.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/16lkhe1258731655.tab")
+ }
>
> system("convert tmp/1np2l1258731654.ps tmp/1np2l1258731654.png")
> system("convert tmp/2zdlp1258731654.ps tmp/2zdlp1258731654.png")
> system("convert tmp/3kemt1258731654.ps tmp/3kemt1258731654.png")
> system("convert tmp/4hbyv1258731654.ps tmp/4hbyv1258731654.png")
> system("convert tmp/5o7qv1258731654.ps tmp/5o7qv1258731654.png")
> system("convert tmp/64uzu1258731655.ps tmp/64uzu1258731655.png")
> system("convert tmp/7x8hg1258731655.ps tmp/7x8hg1258731655.png")
> system("convert tmp/8ufjh1258731655.ps tmp/8ufjh1258731655.png")
> system("convert tmp/9zi6m1258731655.ps tmp/9zi6m1258731655.png")
> system("convert tmp/103ahj1258731655.ps tmp/103ahj1258731655.png")
>
>
> proc.time()
user system elapsed
2.437 1.508 2.858