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 'license()' or 'licence()' for distribution details.
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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(3.58,98.2,3.52,98.71,3.45,98.54,3.36,98.2,3.27,96.92,3.21,99.06,3.19,99.65,3.16,99.82,3.12,99.99,3.06,100.33,3.01,99.31,2.98,101.1,2.97,101.1,3.02,100.93,3.07,100.85,3.18,100.93,3.29,99.6,3.43,101.88,3.61,101.81,3.74,102.38,3.87,102.74,3.88,102.82,4.09,101.72,4.19,103.47,4.2,102.98,4.29,102.68,4.37,102.9,4.47,103.03,4.61,101.29,4.65,103.69,4.69,103.68,4.82,104.2,4.86,104.08,4.87,104.16,5.01,103.05,5.03,104.66,5.13,104.46,5.18,104.95,5.21,105.85,5.26,106.23,5.25,104.86,5.2,107.44,5.16,108.23,5.19,108.45,5.39,109.39,5.58,110.15,5.76,109.13,5.89,110.28,5.98,110.17,6.02,109.99,5.62,109.26,4.87,109.11,4.24,107.06,4.02,109.53,3.74,108.92,3.45,109.24,3.34,109.12,3.21,109,3.12,107.23,3.04,109.49),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),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 X
1 3.58 98.20
2 3.52 98.71
3 3.45 98.54
4 3.36 98.20
5 3.27 96.92
6 3.21 99.06
7 3.19 99.65
8 3.16 99.82
9 3.12 99.99
10 3.06 100.33
11 3.01 99.31
12 2.98 101.10
13 2.97 101.10
14 3.02 100.93
15 3.07 100.85
16 3.18 100.93
17 3.29 99.60
18 3.43 101.88
19 3.61 101.81
20 3.74 102.38
21 3.87 102.74
22 3.88 102.82
23 4.09 101.72
24 4.19 103.47
25 4.20 102.98
26 4.29 102.68
27 4.37 102.90
28 4.47 103.03
29 4.61 101.29
30 4.65 103.69
31 4.69 103.68
32 4.82 104.20
33 4.86 104.08
34 4.87 104.16
35 5.01 103.05
36 5.03 104.66
37 5.13 104.46
38 5.18 104.95
39 5.21 105.85
40 5.26 106.23
41 5.25 104.86
42 5.20 107.44
43 5.16 108.23
44 5.19 108.45
45 5.39 109.39
46 5.58 110.15
47 5.76 109.13
48 5.89 110.28
49 5.98 110.17
50 6.02 109.99
51 5.62 109.26
52 4.87 109.11
53 4.24 107.06
54 4.02 109.53
55 3.74 108.92
56 3.45 109.24
57 3.34 109.12
58 3.21 109.00
59 3.12 107.23
60 3.04 109.49
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
-11.3900 0.1497
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.9614 -0.4389 0.1414 0.6211 0.9727
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -11.38998 2.62920 -4.332 5.94e-05 ***
X 0.14971 0.02523 5.933 1.75e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7524 on 58 degrees of freedom
Multiple R-squared: 0.3777, Adjusted R-squared: 0.367
F-statistic: 35.21 on 1 and 58 DF, p-value: 1.755e-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,] 2.452648e-03 4.905296e-03 0.99754735
[2,] 3.199230e-03 6.398459e-03 0.99680077
[3,] 1.081452e-03 2.162904e-03 0.99891855
[4,] 2.677921e-04 5.355842e-04 0.99973221
[5,] 6.100051e-05 1.220010e-04 0.99993900
[6,] 1.351710e-05 2.703420e-05 0.99998648
[7,] 7.378202e-06 1.475640e-05 0.99999262
[8,] 1.504740e-06 3.009481e-06 0.99999850
[9,] 3.056559e-07 6.113118e-07 0.99999969
[10,] 5.948841e-08 1.189768e-07 0.99999994
[11,] 1.192441e-08 2.384882e-08 0.99999999
[12,] 4.046123e-09 8.092247e-09 1.00000000
[13,] 9.319110e-10 1.863822e-09 1.00000000
[14,] 1.136625e-08 2.273251e-08 0.99999999
[15,] 9.933168e-08 1.986634e-07 0.99999990
[16,] 5.282834e-07 1.056567e-06 0.99999947
[17,] 1.621916e-06 3.243833e-06 0.99999838
[18,] 2.131018e-06 4.262036e-06 0.99999787
[19,] 6.804224e-06 1.360845e-05 0.99999320
[20,] 9.204999e-06 1.841000e-05 0.99999080
[21,] 1.023084e-05 2.046168e-05 0.99998977
[22,] 1.321917e-05 2.643835e-05 0.99998678
[23,] 1.512416e-05 3.024831e-05 0.99998488
[24,] 1.708190e-05 3.416381e-05 0.99998292
[25,] 7.028906e-05 1.405781e-04 0.99992971
[26,] 5.737977e-05 1.147595e-04 0.99994262
[27,] 4.372284e-05 8.744569e-05 0.99995628
[28,] 2.991740e-05 5.983481e-05 0.99997008
[29,] 2.040618e-05 4.081236e-05 0.99997959
[30,] 1.235608e-05 2.471215e-05 0.99998764
[31,] 1.650232e-05 3.300464e-05 0.99998350
[32,] 8.910169e-06 1.782034e-05 0.99999109
[33,] 5.761339e-06 1.152268e-05 0.99999424
[34,] 3.280111e-06 6.560223e-06 0.99999672
[35,] 1.630100e-06 3.260199e-06 0.99999837
[36,] 9.100913e-07 1.820183e-06 0.99999909
[37,] 2.182131e-06 4.364261e-06 0.99999782
[38,] 3.520446e-06 7.040891e-06 0.99999648
[39,] 5.227242e-06 1.045448e-05 0.99999477
[40,] 7.405755e-06 1.481151e-05 0.99999259
[41,] 6.063299e-06 1.212660e-05 0.99999394
[42,] 3.449188e-06 6.898375e-06 0.99999655
[43,] 6.923259e-06 1.384652e-05 0.99999308
[44,] 5.849762e-06 1.169952e-05 0.99999415
[45,] 1.382335e-05 2.764670e-05 0.99998618
[46,] 3.334916e-04 6.669832e-04 0.99966651
[47,] 2.802272e-02 5.604545e-02 0.97197728
[48,] 3.301243e-01 6.602486e-01 0.66987570
[49,] 6.469071e-01 7.061859e-01 0.35309293
[50,] 8.586483e-01 2.827033e-01 0.14135165
[51,] 9.495762e-01 1.008476e-01 0.05042378
> postscript(file="/var/www/html/rcomp/tmp/168vx1258559376.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ktlx1258559376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3w5sz1258559376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4u7xf1258559376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5x7s71258559376.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
0.26876129 0.13241074 0.08786092 0.04876129 0.15038619 -0.22998670
7 8 9 10 11 12
-0.33831380 -0.39376399 -0.45921417 -0.57011454 -0.46741344 -0.76538889
13 14 15 16 17 18
-0.77538889 -0.69993871 -0.63796215 -0.53993871 -0.23082846 -0.43216032
19 20 21 22 23 24
-0.24168083 -0.19701380 -0.12090830 -0.12288486 0.25179279 0.08980562
25 26 27 28 29 30
0.17316203 0.30807411 0.35513858 0.43567668 0.83616678 0.51687008
31 32 33 34 35 36
0.55836715 0.61051954 0.66848437 0.66650781 0.97268254 0.75165434
37 38 39 40 41 42
0.88159573 0.85823932 0.75350306 0.74661441 0.94171294 0.50546899
43 44 45 46 47 48
0.34720049 0.34426496 0.40354042 0.47976314 0.81246423 0.77030123
49 50 51 52 53 54
0.87676900 0.94371625 0.65300233 -0.07454163 -0.39764236 -0.98741855
55 56 57 58 59 60
-1.17609731 -1.51400353 -1.60603870 -1.71807386 -1.54309255 -1.96143027
> postscript(file="/var/www/html/rcomp/tmp/66rd61258559376.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 0.26876129 NA
1 0.13241074 0.26876129
2 0.08786092 0.13241074
3 0.04876129 0.08786092
4 0.15038619 0.04876129
5 -0.22998670 0.15038619
6 -0.33831380 -0.22998670
7 -0.39376399 -0.33831380
8 -0.45921417 -0.39376399
9 -0.57011454 -0.45921417
10 -0.46741344 -0.57011454
11 -0.76538889 -0.46741344
12 -0.77538889 -0.76538889
13 -0.69993871 -0.77538889
14 -0.63796215 -0.69993871
15 -0.53993871 -0.63796215
16 -0.23082846 -0.53993871
17 -0.43216032 -0.23082846
18 -0.24168083 -0.43216032
19 -0.19701380 -0.24168083
20 -0.12090830 -0.19701380
21 -0.12288486 -0.12090830
22 0.25179279 -0.12288486
23 0.08980562 0.25179279
24 0.17316203 0.08980562
25 0.30807411 0.17316203
26 0.35513858 0.30807411
27 0.43567668 0.35513858
28 0.83616678 0.43567668
29 0.51687008 0.83616678
30 0.55836715 0.51687008
31 0.61051954 0.55836715
32 0.66848437 0.61051954
33 0.66650781 0.66848437
34 0.97268254 0.66650781
35 0.75165434 0.97268254
36 0.88159573 0.75165434
37 0.85823932 0.88159573
38 0.75350306 0.85823932
39 0.74661441 0.75350306
40 0.94171294 0.74661441
41 0.50546899 0.94171294
42 0.34720049 0.50546899
43 0.34426496 0.34720049
44 0.40354042 0.34426496
45 0.47976314 0.40354042
46 0.81246423 0.47976314
47 0.77030123 0.81246423
48 0.87676900 0.77030123
49 0.94371625 0.87676900
50 0.65300233 0.94371625
51 -0.07454163 0.65300233
52 -0.39764236 -0.07454163
53 -0.98741855 -0.39764236
54 -1.17609731 -0.98741855
55 -1.51400353 -1.17609731
56 -1.60603870 -1.51400353
57 -1.71807386 -1.60603870
58 -1.54309255 -1.71807386
59 -1.96143027 -1.54309255
60 NA -1.96143027
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.13241074 0.26876129
[2,] 0.08786092 0.13241074
[3,] 0.04876129 0.08786092
[4,] 0.15038619 0.04876129
[5,] -0.22998670 0.15038619
[6,] -0.33831380 -0.22998670
[7,] -0.39376399 -0.33831380
[8,] -0.45921417 -0.39376399
[9,] -0.57011454 -0.45921417
[10,] -0.46741344 -0.57011454
[11,] -0.76538889 -0.46741344
[12,] -0.77538889 -0.76538889
[13,] -0.69993871 -0.77538889
[14,] -0.63796215 -0.69993871
[15,] -0.53993871 -0.63796215
[16,] -0.23082846 -0.53993871
[17,] -0.43216032 -0.23082846
[18,] -0.24168083 -0.43216032
[19,] -0.19701380 -0.24168083
[20,] -0.12090830 -0.19701380
[21,] -0.12288486 -0.12090830
[22,] 0.25179279 -0.12288486
[23,] 0.08980562 0.25179279
[24,] 0.17316203 0.08980562
[25,] 0.30807411 0.17316203
[26,] 0.35513858 0.30807411
[27,] 0.43567668 0.35513858
[28,] 0.83616678 0.43567668
[29,] 0.51687008 0.83616678
[30,] 0.55836715 0.51687008
[31,] 0.61051954 0.55836715
[32,] 0.66848437 0.61051954
[33,] 0.66650781 0.66848437
[34,] 0.97268254 0.66650781
[35,] 0.75165434 0.97268254
[36,] 0.88159573 0.75165434
[37,] 0.85823932 0.88159573
[38,] 0.75350306 0.85823932
[39,] 0.74661441 0.75350306
[40,] 0.94171294 0.74661441
[41,] 0.50546899 0.94171294
[42,] 0.34720049 0.50546899
[43,] 0.34426496 0.34720049
[44,] 0.40354042 0.34426496
[45,] 0.47976314 0.40354042
[46,] 0.81246423 0.47976314
[47,] 0.77030123 0.81246423
[48,] 0.87676900 0.77030123
[49,] 0.94371625 0.87676900
[50,] 0.65300233 0.94371625
[51,] -0.07454163 0.65300233
[52,] -0.39764236 -0.07454163
[53,] -0.98741855 -0.39764236
[54,] -1.17609731 -0.98741855
[55,] -1.51400353 -1.17609731
[56,] -1.60603870 -1.51400353
[57,] -1.71807386 -1.60603870
[58,] -1.54309255 -1.71807386
[59,] -1.96143027 -1.54309255
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.13241074 0.26876129
2 0.08786092 0.13241074
3 0.04876129 0.08786092
4 0.15038619 0.04876129
5 -0.22998670 0.15038619
6 -0.33831380 -0.22998670
7 -0.39376399 -0.33831380
8 -0.45921417 -0.39376399
9 -0.57011454 -0.45921417
10 -0.46741344 -0.57011454
11 -0.76538889 -0.46741344
12 -0.77538889 -0.76538889
13 -0.69993871 -0.77538889
14 -0.63796215 -0.69993871
15 -0.53993871 -0.63796215
16 -0.23082846 -0.53993871
17 -0.43216032 -0.23082846
18 -0.24168083 -0.43216032
19 -0.19701380 -0.24168083
20 -0.12090830 -0.19701380
21 -0.12288486 -0.12090830
22 0.25179279 -0.12288486
23 0.08980562 0.25179279
24 0.17316203 0.08980562
25 0.30807411 0.17316203
26 0.35513858 0.30807411
27 0.43567668 0.35513858
28 0.83616678 0.43567668
29 0.51687008 0.83616678
30 0.55836715 0.51687008
31 0.61051954 0.55836715
32 0.66848437 0.61051954
33 0.66650781 0.66848437
34 0.97268254 0.66650781
35 0.75165434 0.97268254
36 0.88159573 0.75165434
37 0.85823932 0.88159573
38 0.75350306 0.85823932
39 0.74661441 0.75350306
40 0.94171294 0.74661441
41 0.50546899 0.94171294
42 0.34720049 0.50546899
43 0.34426496 0.34720049
44 0.40354042 0.34426496
45 0.47976314 0.40354042
46 0.81246423 0.47976314
47 0.77030123 0.81246423
48 0.87676900 0.77030123
49 0.94371625 0.87676900
50 0.65300233 0.94371625
51 -0.07454163 0.65300233
52 -0.39764236 -0.07454163
53 -0.98741855 -0.39764236
54 -1.17609731 -0.98741855
55 -1.51400353 -1.17609731
56 -1.60603870 -1.51400353
57 -1.71807386 -1.60603870
58 -1.54309255 -1.71807386
59 -1.96143027 -1.54309255
> 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/7xrlz1258559376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kotm1258559376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9aid81258559376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10b7i41258559376.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11giuw1258559376.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/12qzp71258559376.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/134apj1258559376.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/14u8k71258559376.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/15ym2u1258559376.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/16a7u21258559376.tab")
+ }
>
> system("convert tmp/168vx1258559376.ps tmp/168vx1258559376.png")
> system("convert tmp/2ktlx1258559376.ps tmp/2ktlx1258559376.png")
> system("convert tmp/3w5sz1258559376.ps tmp/3w5sz1258559376.png")
> system("convert tmp/4u7xf1258559376.ps tmp/4u7xf1258559376.png")
> system("convert tmp/5x7s71258559376.ps tmp/5x7s71258559376.png")
> system("convert tmp/66rd61258559376.ps tmp/66rd61258559376.png")
> system("convert tmp/7xrlz1258559376.ps tmp/7xrlz1258559376.png")
> system("convert tmp/8kotm1258559376.ps tmp/8kotm1258559376.png")
> system("convert tmp/9aid81258559376.ps tmp/9aid81258559376.png")
> system("convert tmp/10b7i41258559376.ps tmp/10b7i41258559376.png")
>
>
> proc.time()
user system elapsed
2.511 1.580 4.061