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(3030.29,101.2,2803.47,101.1,2767.63,100.7,2882.6,100.1,2863.36,99.9,2897.06,99.7,3012.61,99.5,3142.95,99.2,3032.93,99,3045.78,99,3110.52,99.3,3013.24,99.5,2987.1,99.7,2995.55,100,2833.18,100.4,2848.96,100.6,2794.83,100.7,2845.26,100.7,2915.02,100.6,2892.63,100.5,2604.42,100.6,2641.65,100.5,2659.81,100.4,2638.53,100.3,2720.25,100.4,2745.88,100.4,2735.7,100.4,2811.7,100.4,2799.43,100.4,2555.28,100.5,2304.98,100.6,2214.95,100.6,2065.81,100.5,1940.49,100.5,2042.00,100.7,1995.37,101.1,1946.81,101.5,1765.9,101.9,1635.25,102.1,1833.42,102.1,1910.43,102.1,1959.67,102.4,1969.6,102.8,2061.41,103.1,2093.48,103.1,2120.88,102.9,2174.56,102.4,2196.72,101.9,2350.44,101.3,2440.25,100.7,2408.64,100.6,2472.81,101,2407.6,101.5,2454.62,101.9,2448.05,102.1,2497.84,102.3,2645.64,102.5,2756.76,102.9,2849.27,103.6,2921.44,104.3),dim=c(2,60),dimnames=list(c('Bel20','Gzhidx'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Bel20','Gzhidx'),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 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'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
Gzhidx Bel20 t
1 101.2 3030.29 1
2 101.1 2803.47 2
3 100.7 2767.63 3
4 100.1 2882.60 4
5 99.9 2863.36 5
6 99.7 2897.06 6
7 99.5 3012.61 7
8 99.2 3142.95 8
9 99.0 3032.93 9
10 99.0 3045.78 10
11 99.3 3110.52 11
12 99.5 3013.24 12
13 99.7 2987.10 13
14 100.0 2995.55 14
15 100.4 2833.18 15
16 100.6 2848.96 16
17 100.7 2794.83 17
18 100.7 2845.26 18
19 100.6 2915.02 19
20 100.5 2892.63 20
21 100.6 2604.42 21
22 100.5 2641.65 22
23 100.4 2659.81 23
24 100.3 2638.53 24
25 100.4 2720.25 25
26 100.4 2745.88 26
27 100.4 2735.70 27
28 100.4 2811.70 28
29 100.4 2799.43 29
30 100.5 2555.28 30
31 100.6 2304.98 31
32 100.6 2214.95 32
33 100.5 2065.81 33
34 100.5 1940.49 34
35 100.7 2042.00 35
36 101.1 1995.37 36
37 101.5 1946.81 37
38 101.9 1765.90 38
39 102.1 1635.25 39
40 102.1 1833.42 40
41 102.1 1910.43 41
42 102.4 1959.67 42
43 102.8 1969.60 43
44 103.1 2061.41 44
45 103.1 2093.48 45
46 102.9 2120.88 46
47 102.4 2174.56 47
48 101.9 2196.72 48
49 101.3 2350.44 49
50 100.7 2440.25 50
51 100.6 2408.64 51
52 101.0 2472.81 52
53 101.5 2407.60 53
54 101.9 2454.62 54
55 102.1 2448.05 55
56 102.3 2497.84 56
57 102.5 2645.64 57
58 102.9 2756.76 58
59 103.6 2849.27 59
60 104.3 2921.44 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Bel20 t
1.004e+02 -3.449e-04 4.839e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.48294 -0.49446 -0.09469 0.36305 1.95842
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.004e+02 9.007e-01 111.514 < 2e-16 ***
Bel20 -3.449e-04 2.981e-04 -1.157 0.252
t 4.839e-02 6.859e-03 7.055 2.58e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7492 on 57 degrees of freedom
Multiple R-squared: 0.6158, Adjusted R-squared: 0.6023
F-statistic: 45.68 on 2 and 57 DF, p-value: 1.443e-12
> 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,] 0.0107768402 0.0215536805 0.989223160
[2,] 0.0060883855 0.0121767711 0.993911614
[3,] 0.0015578370 0.0031156740 0.998442163
[4,] 0.0004772870 0.0009545741 0.999522713
[5,] 0.0006452738 0.0012905476 0.999354726
[6,] 0.0077001872 0.0154003744 0.992299813
[7,] 0.0318209203 0.0636418406 0.968179080
[8,] 0.0630880485 0.1261760970 0.936911951
[9,] 0.1135605888 0.2271211777 0.886439411
[10,] 0.1144095043 0.2288190085 0.885590496
[11,] 0.1148537746 0.2297075493 0.885146225
[12,] 0.0915666895 0.1831333791 0.908433310
[13,] 0.0799874164 0.1599748328 0.920012584
[14,] 0.0763138750 0.1526277500 0.923686125
[15,] 0.0570226934 0.1140453868 0.942977307
[16,] 0.0643204448 0.1286408897 0.935679555
[17,] 0.0522827634 0.1045655267 0.947717237
[18,] 0.0388294861 0.0776589721 0.961170514
[19,] 0.0292549182 0.0585098364 0.970745082
[20,] 0.0184310272 0.0368620545 0.981568973
[21,] 0.0113781618 0.0227563236 0.988621838
[22,] 0.0067858754 0.0135717507 0.993214125
[23,] 0.0044425849 0.0088851697 0.995557415
[24,] 0.0028482672 0.0056965344 0.997151733
[25,] 0.0020491753 0.0040983506 0.997950825
[26,] 0.0025066582 0.0050133163 0.997493342
[27,] 0.0024557477 0.0049114955 0.997544252
[28,] 0.0025462873 0.0050925747 0.997453713
[29,] 0.0024857919 0.0049715838 0.997514208
[30,] 0.0015859687 0.0031719375 0.998414031
[31,] 0.0009733769 0.0019467538 0.999026623
[32,] 0.0006926264 0.0013852528 0.999307374
[33,] 0.0004742260 0.0009484520 0.999525774
[34,] 0.0002994263 0.0005988525 0.999700574
[35,] 0.0002256613 0.0004513225 0.999774339
[36,] 0.0001744509 0.0003489019 0.999825549
[37,] 0.0002260310 0.0004520620 0.999773969
[38,] 0.0006938449 0.0013876898 0.999306155
[39,] 0.0047616831 0.0095233661 0.995238317
[40,] 0.0312403786 0.0624807571 0.968759621
[41,] 0.1746178381 0.3492356762 0.825382162
[42,] 0.5362260595 0.9275478810 0.463773941
[43,] 0.9494177331 0.1011645338 0.050582267
[44,] 0.9972655105 0.0054689791 0.002734490
[45,] 0.9952904538 0.0094190924 0.004709546
[46,] 0.9884420298 0.0231159405 0.011557970
[47,] 0.9765265867 0.0469468267 0.023473413
[48,] 0.9364122645 0.1271754709 0.063587735
[49,] 0.9087016459 0.1825967082 0.091298354
> postscript(file="/var/www/html/rcomp/tmp/17b3e1258570010.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2d1i11258570010.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/322ps1258570010.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hz301258570010.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5k1cg1258570010.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
1.750944006 1.524324513 1.063573798 0.454837276 0.199811881 -0.036954569
7 8 9 10 11 12
-0.245491050 -0.548926478 -0.835261788 -0.879219378 -0.605280169 -0.487221468
13 14 15 16 17 18
-0.344626664 -0.090101810 0.205507427 0.362560390 0.395501476 0.364505183
19 20 21 22 23 24
0.240175784 0.084063959 0.036271127 -0.099277830 -0.241404008 -0.397132996
25 26 27 28 29 30
-0.317337405 -0.356887189 -0.408787799 -0.430965029 -0.483586479 -0.516183068
31 32 33 34 35 36
-0.550900785 -0.630341567 -0.830169319 -0.921781581 -0.735160441 -0.399632614
37 38 39 40 41 42
-0.064770442 0.224444370 0.330993798 0.350952858 0.329123975 0.597717253
43 44 45 46 47 48
0.952752557 1.236028178 1.198699543 0.959760230 0.429884858 -0.110861725
49 50 51 52 53 54
-0.706233418 -1.323647595 -1.482939388 -1.109196771 -0.680077164 -0.312249562
55 56 57 58 59 60
-0.162905088 0.005877884 0.208464390 0.598400006 1.281917056 1.958418864
> postscript(file="/var/www/html/rcomp/tmp/6wirj1258570010.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 1.750944006 NA
1 1.524324513 1.750944006
2 1.063573798 1.524324513
3 0.454837276 1.063573798
4 0.199811881 0.454837276
5 -0.036954569 0.199811881
6 -0.245491050 -0.036954569
7 -0.548926478 -0.245491050
8 -0.835261788 -0.548926478
9 -0.879219378 -0.835261788
10 -0.605280169 -0.879219378
11 -0.487221468 -0.605280169
12 -0.344626664 -0.487221468
13 -0.090101810 -0.344626664
14 0.205507427 -0.090101810
15 0.362560390 0.205507427
16 0.395501476 0.362560390
17 0.364505183 0.395501476
18 0.240175784 0.364505183
19 0.084063959 0.240175784
20 0.036271127 0.084063959
21 -0.099277830 0.036271127
22 -0.241404008 -0.099277830
23 -0.397132996 -0.241404008
24 -0.317337405 -0.397132996
25 -0.356887189 -0.317337405
26 -0.408787799 -0.356887189
27 -0.430965029 -0.408787799
28 -0.483586479 -0.430965029
29 -0.516183068 -0.483586479
30 -0.550900785 -0.516183068
31 -0.630341567 -0.550900785
32 -0.830169319 -0.630341567
33 -0.921781581 -0.830169319
34 -0.735160441 -0.921781581
35 -0.399632614 -0.735160441
36 -0.064770442 -0.399632614
37 0.224444370 -0.064770442
38 0.330993798 0.224444370
39 0.350952858 0.330993798
40 0.329123975 0.350952858
41 0.597717253 0.329123975
42 0.952752557 0.597717253
43 1.236028178 0.952752557
44 1.198699543 1.236028178
45 0.959760230 1.198699543
46 0.429884858 0.959760230
47 -0.110861725 0.429884858
48 -0.706233418 -0.110861725
49 -1.323647595 -0.706233418
50 -1.482939388 -1.323647595
51 -1.109196771 -1.482939388
52 -0.680077164 -1.109196771
53 -0.312249562 -0.680077164
54 -0.162905088 -0.312249562
55 0.005877884 -0.162905088
56 0.208464390 0.005877884
57 0.598400006 0.208464390
58 1.281917056 0.598400006
59 1.958418864 1.281917056
60 NA 1.958418864
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.524324513 1.750944006
[2,] 1.063573798 1.524324513
[3,] 0.454837276 1.063573798
[4,] 0.199811881 0.454837276
[5,] -0.036954569 0.199811881
[6,] -0.245491050 -0.036954569
[7,] -0.548926478 -0.245491050
[8,] -0.835261788 -0.548926478
[9,] -0.879219378 -0.835261788
[10,] -0.605280169 -0.879219378
[11,] -0.487221468 -0.605280169
[12,] -0.344626664 -0.487221468
[13,] -0.090101810 -0.344626664
[14,] 0.205507427 -0.090101810
[15,] 0.362560390 0.205507427
[16,] 0.395501476 0.362560390
[17,] 0.364505183 0.395501476
[18,] 0.240175784 0.364505183
[19,] 0.084063959 0.240175784
[20,] 0.036271127 0.084063959
[21,] -0.099277830 0.036271127
[22,] -0.241404008 -0.099277830
[23,] -0.397132996 -0.241404008
[24,] -0.317337405 -0.397132996
[25,] -0.356887189 -0.317337405
[26,] -0.408787799 -0.356887189
[27,] -0.430965029 -0.408787799
[28,] -0.483586479 -0.430965029
[29,] -0.516183068 -0.483586479
[30,] -0.550900785 -0.516183068
[31,] -0.630341567 -0.550900785
[32,] -0.830169319 -0.630341567
[33,] -0.921781581 -0.830169319
[34,] -0.735160441 -0.921781581
[35,] -0.399632614 -0.735160441
[36,] -0.064770442 -0.399632614
[37,] 0.224444370 -0.064770442
[38,] 0.330993798 0.224444370
[39,] 0.350952858 0.330993798
[40,] 0.329123975 0.350952858
[41,] 0.597717253 0.329123975
[42,] 0.952752557 0.597717253
[43,] 1.236028178 0.952752557
[44,] 1.198699543 1.236028178
[45,] 0.959760230 1.198699543
[46,] 0.429884858 0.959760230
[47,] -0.110861725 0.429884858
[48,] -0.706233418 -0.110861725
[49,] -1.323647595 -0.706233418
[50,] -1.482939388 -1.323647595
[51,] -1.109196771 -1.482939388
[52,] -0.680077164 -1.109196771
[53,] -0.312249562 -0.680077164
[54,] -0.162905088 -0.312249562
[55,] 0.005877884 -0.162905088
[56,] 0.208464390 0.005877884
[57,] 0.598400006 0.208464390
[58,] 1.281917056 0.598400006
[59,] 1.958418864 1.281917056
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.524324513 1.750944006
2 1.063573798 1.524324513
3 0.454837276 1.063573798
4 0.199811881 0.454837276
5 -0.036954569 0.199811881
6 -0.245491050 -0.036954569
7 -0.548926478 -0.245491050
8 -0.835261788 -0.548926478
9 -0.879219378 -0.835261788
10 -0.605280169 -0.879219378
11 -0.487221468 -0.605280169
12 -0.344626664 -0.487221468
13 -0.090101810 -0.344626664
14 0.205507427 -0.090101810
15 0.362560390 0.205507427
16 0.395501476 0.362560390
17 0.364505183 0.395501476
18 0.240175784 0.364505183
19 0.084063959 0.240175784
20 0.036271127 0.084063959
21 -0.099277830 0.036271127
22 -0.241404008 -0.099277830
23 -0.397132996 -0.241404008
24 -0.317337405 -0.397132996
25 -0.356887189 -0.317337405
26 -0.408787799 -0.356887189
27 -0.430965029 -0.408787799
28 -0.483586479 -0.430965029
29 -0.516183068 -0.483586479
30 -0.550900785 -0.516183068
31 -0.630341567 -0.550900785
32 -0.830169319 -0.630341567
33 -0.921781581 -0.830169319
34 -0.735160441 -0.921781581
35 -0.399632614 -0.735160441
36 -0.064770442 -0.399632614
37 0.224444370 -0.064770442
38 0.330993798 0.224444370
39 0.350952858 0.330993798
40 0.329123975 0.350952858
41 0.597717253 0.329123975
42 0.952752557 0.597717253
43 1.236028178 0.952752557
44 1.198699543 1.236028178
45 0.959760230 1.198699543
46 0.429884858 0.959760230
47 -0.110861725 0.429884858
48 -0.706233418 -0.110861725
49 -1.323647595 -0.706233418
50 -1.482939388 -1.323647595
51 -1.109196771 -1.482939388
52 -0.680077164 -1.109196771
53 -0.312249562 -0.680077164
54 -0.162905088 -0.312249562
55 0.005877884 -0.162905088
56 0.208464390 0.005877884
57 0.598400006 0.208464390
58 1.281917056 0.598400006
59 1.958418864 1.281917056
> 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/7e3q71258570010.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/828p81258570010.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9cyj71258570010.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xef01258570010.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1166y91258570010.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/12vq2y1258570010.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/13q8dn1258570010.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/14y2ag1258570010.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/15k9yx1258570010.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/16e23o1258570010.tab")
+ }
> system("convert tmp/17b3e1258570010.ps tmp/17b3e1258570010.png")
> system("convert tmp/2d1i11258570010.ps tmp/2d1i11258570010.png")
> system("convert tmp/322ps1258570010.ps tmp/322ps1258570010.png")
> system("convert tmp/4hz301258570010.ps tmp/4hz301258570010.png")
> system("convert tmp/5k1cg1258570010.ps tmp/5k1cg1258570010.png")
> system("convert tmp/6wirj1258570010.ps tmp/6wirj1258570010.png")
> system("convert tmp/7e3q71258570010.ps tmp/7e3q71258570010.png")
> system("convert tmp/828p81258570010.ps tmp/828p81258570010.png")
> system("convert tmp/9cyj71258570010.ps tmp/9cyj71258570010.png")
> system("convert tmp/10xef01258570010.ps tmp/10xef01258570010.png")
>
>
> proc.time()
user system elapsed
2.509 1.584 2.882