R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(564260,0,491117,0,621769,0,642302,0,611278,0,846462,0,607912,0,547550,0,715309,0,695634,0,779700,0,1303196,0,540356,0,532917,0,680054,0,663715,0,711397,0,801442,0,589042,0,611648,0,852471,0,703403,0,701913,0,1277262,0,552924,0,624650,0,785161,0,683755,0,637168,0,766338,1,590239,1,724734,1,797947,1,734796,1,741821,1,1352663,1,586784,0,619788,0,817280,0,670827,0,741638,0,791051,0,614362,0,684702,0,815746,0,740751,0,787766,0,1403677,0,704144,0,609141,0,770951,0,664689,0,719533,0,799724,0,683953,0,723532,0,705441,0,711204,0,792322,0,1360777,0),dim=c(2,60),dimnames=list(c('x','y'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('x','y'),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 = 'Include Monthly 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
x y M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 564260 0 1 0 0 0 0 0 0 0 0 0 0 1
2 491117 0 0 1 0 0 0 0 0 0 0 0 0 2
3 621769 0 0 0 1 0 0 0 0 0 0 0 0 3
4 642302 0 0 0 0 1 0 0 0 0 0 0 0 4
5 611278 0 0 0 0 0 1 0 0 0 0 0 0 5
6 846462 0 0 0 0 0 0 1 0 0 0 0 0 6
7 607912 0 0 0 0 0 0 0 1 0 0 0 0 7
8 547550 0 0 0 0 0 0 0 0 1 0 0 0 8
9 715309 0 0 0 0 0 0 0 0 0 1 0 0 9
10 695634 0 0 0 0 0 0 0 0 0 0 1 0 10
11 779700 0 0 0 0 0 0 0 0 0 0 0 1 11
12 1303196 0 0 0 0 0 0 0 0 0 0 0 0 12
13 540356 0 1 0 0 0 0 0 0 0 0 0 0 13
14 532917 0 0 1 0 0 0 0 0 0 0 0 0 14
15 680054 0 0 0 1 0 0 0 0 0 0 0 0 15
16 663715 0 0 0 0 1 0 0 0 0 0 0 0 16
17 711397 0 0 0 0 0 1 0 0 0 0 0 0 17
18 801442 0 0 0 0 0 0 1 0 0 0 0 0 18
19 589042 0 0 0 0 0 0 0 1 0 0 0 0 19
20 611648 0 0 0 0 0 0 0 0 1 0 0 0 20
21 852471 0 0 0 0 0 0 0 0 0 1 0 0 21
22 703403 0 0 0 0 0 0 0 0 0 0 1 0 22
23 701913 0 0 0 0 0 0 0 0 0 0 0 1 23
24 1277262 0 0 0 0 0 0 0 0 0 0 0 0 24
25 552924 0 1 0 0 0 0 0 0 0 0 0 0 25
26 624650 0 0 1 0 0 0 0 0 0 0 0 0 26
27 785161 0 0 0 1 0 0 0 0 0 0 0 0 27
28 683755 0 0 0 0 1 0 0 0 0 0 0 0 28
29 637168 0 0 0 0 0 1 0 0 0 0 0 0 29
30 766338 1 0 0 0 0 0 1 0 0 0 0 0 30
31 590239 1 0 0 0 0 0 0 1 0 0 0 0 31
32 724734 1 0 0 0 0 0 0 0 1 0 0 0 32
33 797947 1 0 0 0 0 0 0 0 0 1 0 0 33
34 734796 1 0 0 0 0 0 0 0 0 0 1 0 34
35 741821 1 0 0 0 0 0 0 0 0 0 0 1 35
36 1352663 1 0 0 0 0 0 0 0 0 0 0 0 36
37 586784 0 1 0 0 0 0 0 0 0 0 0 0 37
38 619788 0 0 1 0 0 0 0 0 0 0 0 0 38
39 817280 0 0 0 1 0 0 0 0 0 0 0 0 39
40 670827 0 0 0 0 1 0 0 0 0 0 0 0 40
41 741638 0 0 0 0 0 1 0 0 0 0 0 0 41
42 791051 0 0 0 0 0 0 1 0 0 0 0 0 42
43 614362 0 0 0 0 0 0 0 1 0 0 0 0 43
44 684702 0 0 0 0 0 0 0 0 1 0 0 0 44
45 815746 0 0 0 0 0 0 0 0 0 1 0 0 45
46 740751 0 0 0 0 0 0 0 0 0 0 1 0 46
47 787766 0 0 0 0 0 0 0 0 0 0 0 1 47
48 1403677 0 0 0 0 0 0 0 0 0 0 0 0 48
49 704144 0 1 0 0 0 0 0 0 0 0 0 0 49
50 609141 0 0 1 0 0 0 0 0 0 0 0 0 50
51 770951 0 0 0 1 0 0 0 0 0 0 0 0 51
52 664689 0 0 0 0 1 0 0 0 0 0 0 0 52
53 719533 0 0 0 0 0 1 0 0 0 0 0 0 53
54 799724 0 0 0 0 0 0 1 0 0 0 0 0 54
55 683953 0 0 0 0 0 0 0 1 0 0 0 0 55
56 723532 0 0 0 0 0 0 0 0 1 0 0 0 56
57 705441 0 0 0 0 0 0 0 0 0 1 0 0 57
58 711204 0 0 0 0 0 0 0 0 0 0 1 0 58
59 792322 0 0 0 0 0 0 0 0 0 0 0 1 59
60 1360777 0 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) y M1 M2 M3 M4
1282020 6650 -731330 -747061 -589101 -660646
M5 M6 M7 M8 M9 M10
-643061 -529151 -714613 -674841 -557452 -619237
M11 t
-577250 1560
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-108055.1 -31007.6 459.5 22114.6 95139.8
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1282020.1 24394.2 52.554 < 2e-16 ***
y 6650.0 19407.0 0.343 0.733
M1 -731329.9 29554.8 -24.745 < 2e-16 ***
M2 -747061.0 29511.4 -25.314 < 2e-16 ***
M3 -589100.8 29472.1 -19.988 < 2e-16 ***
M4 -660646.3 29436.9 -22.443 < 2e-16 ***
M5 -643061.2 29405.7 -21.869 < 2e-16 ***
M6 -529150.8 29121.2 -18.171 < 2e-16 ***
M7 -714612.7 29098.2 -24.559 < 2e-16 ***
M8 -674841.3 29079.3 -23.207 < 2e-16 ***
M9 -557451.8 29064.6 -19.180 < 2e-16 ***
M10 -619237.1 29054.1 -21.313 < 2e-16 ***
M11 -577250.5 29047.8 -19.872 < 2e-16 ***
t 1560.1 349.4 4.466 5.15e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 45930 on 46 degrees of freedom
Multiple R-squared: 0.959, Adjusted R-squared: 0.9474
F-statistic: 82.78 on 13 and 46 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,] 0.4752661 0.9505321 0.5247339
[2,] 0.5644264 0.8711472 0.4355736
[3,] 0.4686576 0.9373151 0.5313424
[4,] 0.4251410 0.8502820 0.5748590
[5,] 0.7368470 0.5263060 0.2631530
[6,] 0.6533756 0.6932487 0.3466244
[7,] 0.7626198 0.4747604 0.2373802
[8,] 0.7533761 0.4932478 0.2466239
[9,] 0.7604678 0.4790643 0.2395322
[10,] 0.7900876 0.4198248 0.2099124
[11,] 0.8150849 0.3698301 0.1849151
[12,] 0.7557904 0.4884193 0.2442096
[13,] 0.8398926 0.3202147 0.1601074
[14,] 0.7736097 0.4527807 0.2263903
[15,] 0.7539495 0.4921009 0.2460505
[16,] 0.8624585 0.2750831 0.1375415
[17,] 0.8389594 0.3220812 0.1610406
[18,] 0.7933298 0.4133404 0.2066702
[19,] 0.7146530 0.5706939 0.2853470
[20,] 0.6258196 0.7483609 0.3741804
[21,] 0.8261025 0.3477950 0.1738975
[22,] 0.7386737 0.5226525 0.2613263
[23,] 0.6875836 0.6248327 0.3124164
[24,] 0.5827127 0.8345747 0.4172873
[25,] 0.4528704 0.9057407 0.5471296
[26,] 0.3507420 0.7014840 0.6492580
[27,] 0.4218246 0.8436492 0.5781754
> postscript(file="/var/www/html/rcomp/tmp/1l2p11228158146.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zgfn1228158146.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30o571228158146.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4buw51228158146.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5z28j1228158146.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
12009.667 -46962.333 -75830.733 14687.667 -35481.533 84231.867
7 8 9 10 11 12
29583.667 -72109.933 -23300.533 17249.667 57768.867 2454.267
13 14 15 16 17 18
-30615.967 -23883.967 -36267.367 17379.033 45915.833 20490.233
19 20 21 22 23 24
-8007.967 -26733.567 95139.833 6297.033 -38739.767 -42201.367
25 26 27 28 29 30
-36769.600 49127.400 50118.000 18697.400 -47034.800 -39985.400
31 32 33 34 35 36
-32182.600 60980.800 15244.200 12318.400 -24203.400 7828.000
37 38 39 40 41 42
-21631.233 25543.767 63515.367 -12952.233 38713.567 -27344.033
43 44 45 46 47 48
-20131.233 8877.167 20971.567 6201.767 9669.967 46770.367
49 50 51 52 53 54
77007.133 -3824.867 -1535.267 -37811.867 -2113.067 -37392.667
55 56 57 58 59 60
30738.133 28985.533 -108055.067 -42066.867 -4495.667 -14851.267
> postscript(file="/var/www/html/rcomp/tmp/6r6ci1228158146.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 12009.667 NA
1 -46962.333 12009.667
2 -75830.733 -46962.333
3 14687.667 -75830.733
4 -35481.533 14687.667
5 84231.867 -35481.533
6 29583.667 84231.867
7 -72109.933 29583.667
8 -23300.533 -72109.933
9 17249.667 -23300.533
10 57768.867 17249.667
11 2454.267 57768.867
12 -30615.967 2454.267
13 -23883.967 -30615.967
14 -36267.367 -23883.967
15 17379.033 -36267.367
16 45915.833 17379.033
17 20490.233 45915.833
18 -8007.967 20490.233
19 -26733.567 -8007.967
20 95139.833 -26733.567
21 6297.033 95139.833
22 -38739.767 6297.033
23 -42201.367 -38739.767
24 -36769.600 -42201.367
25 49127.400 -36769.600
26 50118.000 49127.400
27 18697.400 50118.000
28 -47034.800 18697.400
29 -39985.400 -47034.800
30 -32182.600 -39985.400
31 60980.800 -32182.600
32 15244.200 60980.800
33 12318.400 15244.200
34 -24203.400 12318.400
35 7828.000 -24203.400
36 -21631.233 7828.000
37 25543.767 -21631.233
38 63515.367 25543.767
39 -12952.233 63515.367
40 38713.567 -12952.233
41 -27344.033 38713.567
42 -20131.233 -27344.033
43 8877.167 -20131.233
44 20971.567 8877.167
45 6201.767 20971.567
46 9669.967 6201.767
47 46770.367 9669.967
48 77007.133 46770.367
49 -3824.867 77007.133
50 -1535.267 -3824.867
51 -37811.867 -1535.267
52 -2113.067 -37811.867
53 -37392.667 -2113.067
54 30738.133 -37392.667
55 28985.533 30738.133
56 -108055.067 28985.533
57 -42066.867 -108055.067
58 -4495.667 -42066.867
59 -14851.267 -4495.667
60 NA -14851.267
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -46962.333 12009.667
[2,] -75830.733 -46962.333
[3,] 14687.667 -75830.733
[4,] -35481.533 14687.667
[5,] 84231.867 -35481.533
[6,] 29583.667 84231.867
[7,] -72109.933 29583.667
[8,] -23300.533 -72109.933
[9,] 17249.667 -23300.533
[10,] 57768.867 17249.667
[11,] 2454.267 57768.867
[12,] -30615.967 2454.267
[13,] -23883.967 -30615.967
[14,] -36267.367 -23883.967
[15,] 17379.033 -36267.367
[16,] 45915.833 17379.033
[17,] 20490.233 45915.833
[18,] -8007.967 20490.233
[19,] -26733.567 -8007.967
[20,] 95139.833 -26733.567
[21,] 6297.033 95139.833
[22,] -38739.767 6297.033
[23,] -42201.367 -38739.767
[24,] -36769.600 -42201.367
[25,] 49127.400 -36769.600
[26,] 50118.000 49127.400
[27,] 18697.400 50118.000
[28,] -47034.800 18697.400
[29,] -39985.400 -47034.800
[30,] -32182.600 -39985.400
[31,] 60980.800 -32182.600
[32,] 15244.200 60980.800
[33,] 12318.400 15244.200
[34,] -24203.400 12318.400
[35,] 7828.000 -24203.400
[36,] -21631.233 7828.000
[37,] 25543.767 -21631.233
[38,] 63515.367 25543.767
[39,] -12952.233 63515.367
[40,] 38713.567 -12952.233
[41,] -27344.033 38713.567
[42,] -20131.233 -27344.033
[43,] 8877.167 -20131.233
[44,] 20971.567 8877.167
[45,] 6201.767 20971.567
[46,] 9669.967 6201.767
[47,] 46770.367 9669.967
[48,] 77007.133 46770.367
[49,] -3824.867 77007.133
[50,] -1535.267 -3824.867
[51,] -37811.867 -1535.267
[52,] -2113.067 -37811.867
[53,] -37392.667 -2113.067
[54,] 30738.133 -37392.667
[55,] 28985.533 30738.133
[56,] -108055.067 28985.533
[57,] -42066.867 -108055.067
[58,] -4495.667 -42066.867
[59,] -14851.267 -4495.667
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -46962.333 12009.667
2 -75830.733 -46962.333
3 14687.667 -75830.733
4 -35481.533 14687.667
5 84231.867 -35481.533
6 29583.667 84231.867
7 -72109.933 29583.667
8 -23300.533 -72109.933
9 17249.667 -23300.533
10 57768.867 17249.667
11 2454.267 57768.867
12 -30615.967 2454.267
13 -23883.967 -30615.967
14 -36267.367 -23883.967
15 17379.033 -36267.367
16 45915.833 17379.033
17 20490.233 45915.833
18 -8007.967 20490.233
19 -26733.567 -8007.967
20 95139.833 -26733.567
21 6297.033 95139.833
22 -38739.767 6297.033
23 -42201.367 -38739.767
24 -36769.600 -42201.367
25 49127.400 -36769.600
26 50118.000 49127.400
27 18697.400 50118.000
28 -47034.800 18697.400
29 -39985.400 -47034.800
30 -32182.600 -39985.400
31 60980.800 -32182.600
32 15244.200 60980.800
33 12318.400 15244.200
34 -24203.400 12318.400
35 7828.000 -24203.400
36 -21631.233 7828.000
37 25543.767 -21631.233
38 63515.367 25543.767
39 -12952.233 63515.367
40 38713.567 -12952.233
41 -27344.033 38713.567
42 -20131.233 -27344.033
43 8877.167 -20131.233
44 20971.567 8877.167
45 6201.767 20971.567
46 9669.967 6201.767
47 46770.367 9669.967
48 77007.133 46770.367
49 -3824.867 77007.133
50 -1535.267 -3824.867
51 -37811.867 -1535.267
52 -2113.067 -37811.867
53 -37392.667 -2113.067
54 30738.133 -37392.667
55 28985.533 30738.133
56 -108055.067 28985.533
57 -42066.867 -108055.067
58 -4495.667 -42066.867
59 -14851.267 -4495.667
> 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/79nsf1228158146.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/845lp1228158146.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9gd3p1228158146.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rhgp1228158146.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119up71228158146.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/12ydil1228158146.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/13m4ac1228158146.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/14efzr1228158146.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/158gcz1228158146.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/16po2i1228158146.tab")
+ }
>
> system("convert tmp/1l2p11228158146.ps tmp/1l2p11228158146.png")
> system("convert tmp/2zgfn1228158146.ps tmp/2zgfn1228158146.png")
> system("convert tmp/30o571228158146.ps tmp/30o571228158146.png")
> system("convert tmp/4buw51228158146.ps tmp/4buw51228158146.png")
> system("convert tmp/5z28j1228158146.ps tmp/5z28j1228158146.png")
> system("convert tmp/6r6ci1228158146.ps tmp/6r6ci1228158146.png")
> system("convert tmp/79nsf1228158146.ps tmp/79nsf1228158146.png")
> system("convert tmp/845lp1228158146.ps tmp/845lp1228158146.png")
> system("convert tmp/9gd3p1228158146.ps tmp/9gd3p1228158146.png")
> system("convert tmp/10rhgp1228158146.ps tmp/10rhgp1228158146.png")
>
>
> proc.time()
user system elapsed
4.955 2.748 5.333