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(90269
+ ,561000
+ ,90398
+ ,90390
+ ,555000
+ ,90269
+ ,88219
+ ,544000
+ ,90390
+ ,87032
+ ,537000
+ ,88219
+ ,87175
+ ,543000
+ ,87032
+ ,92603
+ ,594000
+ ,87175
+ ,93571
+ ,611000
+ ,92603
+ ,94118
+ ,613000
+ ,93571
+ ,92159
+ ,611000
+ ,94118
+ ,89528
+ ,594000
+ ,92159
+ ,89955
+ ,595000
+ ,89528
+ ,89587
+ ,591000
+ ,89955
+ ,89488
+ ,589000
+ ,89587
+ ,88521
+ ,584000
+ ,89488
+ ,86587
+ ,573000
+ ,88521
+ ,85159
+ ,567000
+ ,86587
+ ,84915
+ ,569000
+ ,85159
+ ,91378
+ ,621000
+ ,84915
+ ,92729
+ ,629000
+ ,91378
+ ,92194
+ ,628000
+ ,92729
+ ,89664
+ ,612000
+ ,92194
+ ,86285
+ ,595000
+ ,89664
+ ,86858
+ ,597000
+ ,86285
+ ,87184
+ ,593000
+ ,86858
+ ,86629
+ ,590000
+ ,87184
+ ,85220
+ ,580000
+ ,86629
+ ,84816
+ ,574000
+ ,85220
+ ,84831
+ ,573000
+ ,84816
+ ,84957
+ ,573000
+ ,84831
+ ,90951
+ ,620000
+ ,84957
+ ,92134
+ ,626000
+ ,90951
+ ,91790
+ ,620000
+ ,92134
+ ,86625
+ ,588000
+ ,91790
+ ,83324
+ ,566000
+ ,86625
+ ,82719
+ ,557000
+ ,83324
+ ,83614
+ ,561000
+ ,82719
+ ,81640
+ ,549000
+ ,83614
+ ,78665
+ ,532000
+ ,81640
+ ,77828
+ ,526000
+ ,78665
+ ,75728
+ ,511000
+ ,77828
+ ,72187
+ ,499000
+ ,75728
+ ,79357
+ ,555000
+ ,72187
+ ,81329
+ ,565000
+ ,79357
+ ,77304
+ ,542000
+ ,81329
+ ,75576
+ ,527000
+ ,77304
+ ,72932
+ ,510000
+ ,75576
+ ,74291
+ ,514000
+ ,72932
+ ,74988
+ ,517000
+ ,74291
+ ,73302
+ ,508000
+ ,74988
+ ,70483
+ ,493000
+ ,73302
+ ,69848
+ ,490000
+ ,70483
+ ,66466
+ ,469000
+ ,69848
+ ,67610
+ ,478000
+ ,66466
+ ,75091
+ ,528000
+ ,67610
+ ,76207
+ ,534000
+ ,75091
+ ,73454
+ ,518000
+ ,76207
+ ,72008
+ ,506000
+ ,73454
+ ,71362
+ ,502000
+ ,72008
+ ,74250
+ ,516000
+ ,71362)
+ ,dim=c(3
+ ,59)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1')
+ ,1:59))
> y <- array(NA,dim=c(3,59),dimnames=list(c('Y','X','Y1'),1:59))
> 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 = '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 Y1 t
1 90269 561000 90398 1
2 90390 555000 90269 2
3 88219 544000 90390 3
4 87032 537000 88219 4
5 87175 543000 87032 5
6 92603 594000 87175 6
7 93571 611000 92603 7
8 94118 613000 93571 8
9 92159 611000 94118 9
10 89528 594000 92159 10
11 89955 595000 89528 11
12 89587 591000 89955 12
13 89488 589000 89587 13
14 88521 584000 89488 14
15 86587 573000 88521 15
16 85159 567000 86587 16
17 84915 569000 85159 17
18 91378 621000 84915 18
19 92729 629000 91378 19
20 92194 628000 92729 20
21 89664 612000 92194 21
22 86285 595000 89664 22
23 86858 597000 86285 23
24 87184 593000 86858 24
25 86629 590000 87184 25
26 85220 580000 86629 26
27 84816 574000 85220 27
28 84831 573000 84816 28
29 84957 573000 84831 29
30 90951 620000 84957 30
31 92134 626000 90951 31
32 91790 620000 92134 32
33 86625 588000 91790 33
34 83324 566000 86625 34
35 82719 557000 83324 35
36 83614 561000 82719 36
37 81640 549000 83614 37
38 78665 532000 81640 38
39 77828 526000 78665 39
40 75728 511000 77828 40
41 72187 499000 75728 41
42 79357 555000 72187 42
43 81329 565000 79357 43
44 77304 542000 81329 44
45 75576 527000 77304 45
46 72932 510000 75576 46
47 74291 514000 72932 47
48 74988 517000 74291 48
49 73302 508000 74988 49
50 70483 493000 73302 50
51 69848 490000 70483 51
52 66466 469000 69848 52
53 67610 478000 66466 53
54 75091 528000 67610 54
55 76207 534000 75091 55
56 73454 518000 76207 56
57 72008 506000 73454 57
58 71362 502000 72008 58
59 74250 516000 71362 59
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 t
20602.0407 0.1056 0.1025 -175.1823
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2467.70 -867.71 46.49 758.60 2294.35
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.060e+04 3.776e+03 5.455 1.20e-06 ***
X 1.056e-01 7.142e-03 14.781 < 2e-16 ***
Y1 1.025e-01 5.890e-02 1.740 0.0874 .
t -1.752e+02 1.838e+01 -9.529 3.05e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1135 on 55 degrees of freedom
Multiple R-squared: 0.9794, Adjusted R-squared: 0.9783
F-statistic: 872 on 3 and 55 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.22725299 0.454505977 0.7727470116
[2,] 0.16801071 0.336021411 0.8319892946
[3,] 0.21821521 0.436430423 0.7817847887
[4,] 0.22135744 0.442714880 0.7786425601
[5,] 0.13788787 0.275775732 0.8621121338
[6,] 0.11919590 0.238391793 0.8808041036
[7,] 0.15454364 0.309087284 0.8454563581
[8,] 0.14200191 0.284003823 0.8579980885
[9,] 0.09963156 0.199263127 0.9003684367
[10,] 0.07907636 0.158152720 0.9209236402
[11,] 0.05965025 0.119300499 0.9403497507
[12,] 0.05137919 0.102758389 0.9486208053
[13,] 0.21134531 0.422690628 0.7886546862
[14,] 0.29378891 0.587577826 0.7062110872
[15,] 0.29098069 0.581961381 0.7090193096
[16,] 0.41174087 0.823481731 0.5882591343
[17,] 0.45106552 0.902131039 0.5489344806
[18,] 0.54508823 0.909823535 0.4549117675
[19,] 0.62354588 0.752908234 0.3764541168
[20,] 0.66850188 0.662996236 0.3314981180
[21,] 0.70437587 0.591248262 0.2956241308
[22,] 0.73920245 0.521595102 0.2607975511
[23,] 0.77491381 0.450172380 0.2250861899
[24,] 0.91696000 0.166079997 0.0830399985
[25,] 0.97821967 0.043560668 0.0217803342
[26,] 0.98872240 0.022555203 0.0112776017
[27,] 0.98538759 0.029224816 0.0146124082
[28,] 0.97927900 0.041441993 0.0207209967
[29,] 0.97222482 0.055550361 0.0277751804
[30,] 0.97946023 0.041079548 0.0205397739
[31,] 0.98002970 0.039940600 0.0199703002
[32,] 0.97764922 0.044701568 0.0223507841
[33,] 0.98768754 0.024624911 0.0123124553
[34,] 0.99928171 0.001436585 0.0007182924
[35,] 0.99925004 0.001499927 0.0007499633
[36,] 0.99879250 0.002415006 0.0012075029
[37,] 0.99727668 0.005446633 0.0027233166
[38,] 0.99812965 0.003740694 0.0018703469
[39,] 0.99713065 0.005738705 0.0028693523
[40,] 0.99681910 0.006361807 0.0031809035
[41,] 0.99231545 0.015369102 0.0076845509
[42,] 0.99120550 0.017589000 0.0087944998
[43,] 0.99226668 0.015466641 0.0077333205
[44,] 0.98848824 0.023023511 0.0115117556
[45,] 0.98367857 0.032642860 0.0163214300
[46,] 0.97279797 0.054404062 0.0272020310
> postscript(file="/var/www/html/rcomp/tmp/1xim81258573753.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2hoqy1258573753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31efw1258573753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/48kzt1258573753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5iez41258573753.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 = 59
Frequency = 1
1 2 3 4 5 6
1351.53297 2294.34673 1447.37134 1397.08607 1203.54508 1408.10711
7 8 9 10 11 12
200.19109 612.00437 -1016.75261 -1477.09220 -710.76402 -525.08363
13 14 15 16 17 18
-200.04052 -453.87004 -952.31044 -1373.45933 -1507.02307 -333.35608
19 20 21 22 23 24
-314.26310 -707.00902 -1317.89607 -2467.70031 -1584.25968 -719.54631
25 26 27 28 29 30
-816.07989 -937.32383 -388.29245 -51.12670 248.51792 1443.09411
31 32 33 34 35 36
1553.40176 1896.71746 320.33603 46.49487 905.18587 1615.11759
37 38 39 40 41 42
991.36441 188.56252 465.13046 209.63470 -1674.08975 122.29369
43 44 45 46 47 48
478.77371 -1145.14034 -701.82221 -1198.84248 184.11480 600.27734
49 50 51 52 53 54
-31.88169 -919.34326 -773.47105 -1698.26743 -982.49428 1278.01932
55 56 57 58 59
1168.88900 165.75171 443.96875 543.65742 2195.11361
> postscript(file="/var/www/html/rcomp/tmp/68vgs1258573753.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 1351.53297 NA
1 2294.34673 1351.53297
2 1447.37134 2294.34673
3 1397.08607 1447.37134
4 1203.54508 1397.08607
5 1408.10711 1203.54508
6 200.19109 1408.10711
7 612.00437 200.19109
8 -1016.75261 612.00437
9 -1477.09220 -1016.75261
10 -710.76402 -1477.09220
11 -525.08363 -710.76402
12 -200.04052 -525.08363
13 -453.87004 -200.04052
14 -952.31044 -453.87004
15 -1373.45933 -952.31044
16 -1507.02307 -1373.45933
17 -333.35608 -1507.02307
18 -314.26310 -333.35608
19 -707.00902 -314.26310
20 -1317.89607 -707.00902
21 -2467.70031 -1317.89607
22 -1584.25968 -2467.70031
23 -719.54631 -1584.25968
24 -816.07989 -719.54631
25 -937.32383 -816.07989
26 -388.29245 -937.32383
27 -51.12670 -388.29245
28 248.51792 -51.12670
29 1443.09411 248.51792
30 1553.40176 1443.09411
31 1896.71746 1553.40176
32 320.33603 1896.71746
33 46.49487 320.33603
34 905.18587 46.49487
35 1615.11759 905.18587
36 991.36441 1615.11759
37 188.56252 991.36441
38 465.13046 188.56252
39 209.63470 465.13046
40 -1674.08975 209.63470
41 122.29369 -1674.08975
42 478.77371 122.29369
43 -1145.14034 478.77371
44 -701.82221 -1145.14034
45 -1198.84248 -701.82221
46 184.11480 -1198.84248
47 600.27734 184.11480
48 -31.88169 600.27734
49 -919.34326 -31.88169
50 -773.47105 -919.34326
51 -1698.26743 -773.47105
52 -982.49428 -1698.26743
53 1278.01932 -982.49428
54 1168.88900 1278.01932
55 165.75171 1168.88900
56 443.96875 165.75171
57 543.65742 443.96875
58 2195.11361 543.65742
59 NA 2195.11361
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2294.34673 1351.53297
[2,] 1447.37134 2294.34673
[3,] 1397.08607 1447.37134
[4,] 1203.54508 1397.08607
[5,] 1408.10711 1203.54508
[6,] 200.19109 1408.10711
[7,] 612.00437 200.19109
[8,] -1016.75261 612.00437
[9,] -1477.09220 -1016.75261
[10,] -710.76402 -1477.09220
[11,] -525.08363 -710.76402
[12,] -200.04052 -525.08363
[13,] -453.87004 -200.04052
[14,] -952.31044 -453.87004
[15,] -1373.45933 -952.31044
[16,] -1507.02307 -1373.45933
[17,] -333.35608 -1507.02307
[18,] -314.26310 -333.35608
[19,] -707.00902 -314.26310
[20,] -1317.89607 -707.00902
[21,] -2467.70031 -1317.89607
[22,] -1584.25968 -2467.70031
[23,] -719.54631 -1584.25968
[24,] -816.07989 -719.54631
[25,] -937.32383 -816.07989
[26,] -388.29245 -937.32383
[27,] -51.12670 -388.29245
[28,] 248.51792 -51.12670
[29,] 1443.09411 248.51792
[30,] 1553.40176 1443.09411
[31,] 1896.71746 1553.40176
[32,] 320.33603 1896.71746
[33,] 46.49487 320.33603
[34,] 905.18587 46.49487
[35,] 1615.11759 905.18587
[36,] 991.36441 1615.11759
[37,] 188.56252 991.36441
[38,] 465.13046 188.56252
[39,] 209.63470 465.13046
[40,] -1674.08975 209.63470
[41,] 122.29369 -1674.08975
[42,] 478.77371 122.29369
[43,] -1145.14034 478.77371
[44,] -701.82221 -1145.14034
[45,] -1198.84248 -701.82221
[46,] 184.11480 -1198.84248
[47,] 600.27734 184.11480
[48,] -31.88169 600.27734
[49,] -919.34326 -31.88169
[50,] -773.47105 -919.34326
[51,] -1698.26743 -773.47105
[52,] -982.49428 -1698.26743
[53,] 1278.01932 -982.49428
[54,] 1168.88900 1278.01932
[55,] 165.75171 1168.88900
[56,] 443.96875 165.75171
[57,] 543.65742 443.96875
[58,] 2195.11361 543.65742
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2294.34673 1351.53297
2 1447.37134 2294.34673
3 1397.08607 1447.37134
4 1203.54508 1397.08607
5 1408.10711 1203.54508
6 200.19109 1408.10711
7 612.00437 200.19109
8 -1016.75261 612.00437
9 -1477.09220 -1016.75261
10 -710.76402 -1477.09220
11 -525.08363 -710.76402
12 -200.04052 -525.08363
13 -453.87004 -200.04052
14 -952.31044 -453.87004
15 -1373.45933 -952.31044
16 -1507.02307 -1373.45933
17 -333.35608 -1507.02307
18 -314.26310 -333.35608
19 -707.00902 -314.26310
20 -1317.89607 -707.00902
21 -2467.70031 -1317.89607
22 -1584.25968 -2467.70031
23 -719.54631 -1584.25968
24 -816.07989 -719.54631
25 -937.32383 -816.07989
26 -388.29245 -937.32383
27 -51.12670 -388.29245
28 248.51792 -51.12670
29 1443.09411 248.51792
30 1553.40176 1443.09411
31 1896.71746 1553.40176
32 320.33603 1896.71746
33 46.49487 320.33603
34 905.18587 46.49487
35 1615.11759 905.18587
36 991.36441 1615.11759
37 188.56252 991.36441
38 465.13046 188.56252
39 209.63470 465.13046
40 -1674.08975 209.63470
41 122.29369 -1674.08975
42 478.77371 122.29369
43 -1145.14034 478.77371
44 -701.82221 -1145.14034
45 -1198.84248 -701.82221
46 184.11480 -1198.84248
47 600.27734 184.11480
48 -31.88169 600.27734
49 -919.34326 -31.88169
50 -773.47105 -919.34326
51 -1698.26743 -773.47105
52 -982.49428 -1698.26743
53 1278.01932 -982.49428
54 1168.88900 1278.01932
55 165.75171 1168.88900
56 443.96875 165.75171
57 543.65742 443.96875
58 2195.11361 543.65742
> 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/7m6zh1258573753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8w1mm1258573753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9wyrq1258573753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1053w71258573753.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hyyi1258573753.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/1289r01258573753.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/13a4ey1258573753.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/14aonh1258573753.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/15q67t1258573753.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/1626zj1258573753.tab")
+ }
>
> system("convert tmp/1xim81258573753.ps tmp/1xim81258573753.png")
> system("convert tmp/2hoqy1258573753.ps tmp/2hoqy1258573753.png")
> system("convert tmp/31efw1258573753.ps tmp/31efw1258573753.png")
> system("convert tmp/48kzt1258573753.ps tmp/48kzt1258573753.png")
> system("convert tmp/5iez41258573753.ps tmp/5iez41258573753.png")
> system("convert tmp/68vgs1258573753.ps tmp/68vgs1258573753.png")
> system("convert tmp/7m6zh1258573753.ps tmp/7m6zh1258573753.png")
> system("convert tmp/8w1mm1258573753.ps tmp/8w1mm1258573753.png")
> system("convert tmp/9wyrq1258573753.ps tmp/9wyrq1258573753.png")
> system("convert tmp/1053w71258573753.ps tmp/1053w71258573753.png")
>
>
> proc.time()
user system elapsed
2.401 1.550 2.798