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(3353,1,3186,1,3902,1,4164,1,3499,1,4145,1,3796,1,3711,1,3949,1,3740,1,3243,1,4407,1,4814,1,3908,1,5250,1,3937,1,4004,1,5560,1,3922,1,3759,1,4138,1,4634,1,3996,1,4308,1,4143,0,4429,0,5219,0,4929,0,5755,0,5592,0,4163,0,4962,0,5208,0,4755,0,4491,0,5732,0,5731,0,5040,0,6102,0,4904,0,5369,0,5578,0,4619,0,4731,0,5011,0,5299,0,4146,0,4625,0,4736,0,4219,0,5116,0,4205,0,4121,0,5103,1,4300,1,4578,1,3809,1,5526,1,4247,1,3830,1,4394,1),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 3353 1 1 0 0 0 0 0 0 0 0 0 0 1
2 3186 1 0 1 0 0 0 0 0 0 0 0 0 2
3 3902 1 0 0 1 0 0 0 0 0 0 0 0 3
4 4164 1 0 0 0 1 0 0 0 0 0 0 0 4
5 3499 1 0 0 0 0 1 0 0 0 0 0 0 5
6 4145 1 0 0 0 0 0 1 0 0 0 0 0 6
7 3796 1 0 0 0 0 0 0 1 0 0 0 0 7
8 3711 1 0 0 0 0 0 0 0 1 0 0 0 8
9 3949 1 0 0 0 0 0 0 0 0 1 0 0 9
10 3740 1 0 0 0 0 0 0 0 0 0 1 0 10
11 3243 1 0 0 0 0 0 0 0 0 0 0 1 11
12 4407 1 0 0 0 0 0 0 0 0 0 0 0 12
13 4814 1 1 0 0 0 0 0 0 0 0 0 0 13
14 3908 1 0 1 0 0 0 0 0 0 0 0 0 14
15 5250 1 0 0 1 0 0 0 0 0 0 0 0 15
16 3937 1 0 0 0 1 0 0 0 0 0 0 0 16
17 4004 1 0 0 0 0 1 0 0 0 0 0 0 17
18 5560 1 0 0 0 0 0 1 0 0 0 0 0 18
19 3922 1 0 0 0 0 0 0 1 0 0 0 0 19
20 3759 1 0 0 0 0 0 0 0 1 0 0 0 20
21 4138 1 0 0 0 0 0 0 0 0 1 0 0 21
22 4634 1 0 0 0 0 0 0 0 0 0 1 0 22
23 3996 1 0 0 0 0 0 0 0 0 0 0 1 23
24 4308 1 0 0 0 0 0 0 0 0 0 0 0 24
25 4143 0 1 0 0 0 0 0 0 0 0 0 0 25
26 4429 0 0 1 0 0 0 0 0 0 0 0 0 26
27 5219 0 0 0 1 0 0 0 0 0 0 0 0 27
28 4929 0 0 0 0 1 0 0 0 0 0 0 0 28
29 5755 0 0 0 0 0 1 0 0 0 0 0 0 29
30 5592 0 0 0 0 0 0 1 0 0 0 0 0 30
31 4163 0 0 0 0 0 0 0 1 0 0 0 0 31
32 4962 0 0 0 0 0 0 0 0 1 0 0 0 32
33 5208 0 0 0 0 0 0 0 0 0 1 0 0 33
34 4755 0 0 0 0 0 0 0 0 0 0 1 0 34
35 4491 0 0 0 0 0 0 0 0 0 0 0 1 35
36 5732 0 0 0 0 0 0 0 0 0 0 0 0 36
37 5731 0 1 0 0 0 0 0 0 0 0 0 0 37
38 5040 0 0 1 0 0 0 0 0 0 0 0 0 38
39 6102 0 0 0 1 0 0 0 0 0 0 0 0 39
40 4904 0 0 0 0 1 0 0 0 0 0 0 0 40
41 5369 0 0 0 0 0 1 0 0 0 0 0 0 41
42 5578 0 0 0 0 0 0 1 0 0 0 0 0 42
43 4619 0 0 0 0 0 0 0 1 0 0 0 0 43
44 4731 0 0 0 0 0 0 0 0 1 0 0 0 44
45 5011 0 0 0 0 0 0 0 0 0 1 0 0 45
46 5299 0 0 0 0 0 0 0 0 0 0 1 0 46
47 4146 0 0 0 0 0 0 0 0 0 0 0 1 47
48 4625 0 0 0 0 0 0 0 0 0 0 0 0 48
49 4736 0 1 0 0 0 0 0 0 0 0 0 0 49
50 4219 0 0 1 0 0 0 0 0 0 0 0 0 50
51 5116 0 0 0 1 0 0 0 0 0 0 0 0 51
52 4205 0 0 0 0 1 0 0 0 0 0 0 0 52
53 4121 0 0 0 0 0 1 0 0 0 0 0 0 53
54 5103 1 0 0 0 0 0 1 0 0 0 0 0 54
55 4300 1 0 0 0 0 0 0 1 0 0 0 0 55
56 4578 1 0 0 0 0 0 0 0 1 0 0 0 56
57 3809 1 0 0 0 0 0 0 0 0 1 0 0 57
58 5526 1 0 0 0 0 0 0 0 0 0 1 0 58
59 4247 1 0 0 0 0 0 0 0 0 0 0 1 59
60 3830 1 0 0 0 0 0 0 0 0 0 0 0 60
61 4394 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
4671.000 -647.000 -75.267 -470.733 482.400 -215.867
M5 M6 M7 M8 M9 M10
-102.333 664.800 -379.067 -199.133 -132.600 226.933
M11 t
-547.533 8.267
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-885.8 -383.8 8.2 282.8 946.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4671.000 305.156 15.307 < 2e-16 ***
X -647.000 147.896 -4.375 6.71e-05 ***
M1 -75.267 303.993 -0.248 0.8055
M2 -470.733 321.831 -1.463 0.1502
M3 482.400 321.131 1.502 0.1397
M4 -215.867 320.484 -0.674 0.5039
M5 -102.333 319.891 -0.320 0.7505
M6 664.800 316.871 2.098 0.0413 *
M7 -379.067 316.567 -1.197 0.2371
M8 -199.133 316.318 -0.630 0.5320
M9 -132.600 316.124 -0.419 0.6768
M10 226.933 315.985 0.718 0.4762
M11 -547.533 315.902 -1.733 0.0896 .
t 8.267 4.185 1.975 0.0541 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 499.4 on 47 degrees of freedom
Multiple R-squared: 0.5956, Adjusted R-squared: 0.4838
F-statistic: 5.326 on 13 and 47 DF, p-value: 9.493e-06
> 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,] 0.6963926 0.6072149 0.30360743
[2,] 0.6321740 0.7356520 0.36782601
[3,] 0.5897342 0.8205316 0.41026581
[4,] 0.5746086 0.8507827 0.42539137
[5,] 0.4936421 0.9872843 0.50635785
[6,] 0.3978532 0.7957064 0.60214680
[7,] 0.2948837 0.5897674 0.70511629
[8,] 0.3000688 0.6001376 0.69993119
[9,] 0.3497079 0.6994159 0.65029207
[10,] 0.3700643 0.7401287 0.62993565
[11,] 0.3873119 0.7746237 0.61268814
[12,] 0.3259406 0.6518811 0.67405943
[13,] 0.5203545 0.9592909 0.47964546
[14,] 0.4408264 0.8816527 0.55917364
[15,] 0.5290589 0.9418821 0.47094107
[16,] 0.4894227 0.9788453 0.51057734
[17,] 0.4023769 0.8047539 0.59762307
[18,] 0.8520356 0.2959288 0.14796439
[19,] 0.9343336 0.1313328 0.06566639
[20,] 0.9252434 0.1495132 0.07475662
[21,] 0.8860638 0.2278724 0.11393619
[22,] 0.8233461 0.3533077 0.17665387
[23,] 0.7337227 0.5325547 0.26627735
[24,] 0.7154443 0.5691114 0.28455570
[25,] 0.5971496 0.8057007 0.40285036
[26,] 0.5008889 0.9982223 0.49911114
[27,] 0.3668812 0.7337624 0.63311878
[28,] 0.2503988 0.5007977 0.74960115
> postscript(file="/var/www/html/rcomp/tmp/1h6ob1258621492.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23e5b1258621492.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ykfo1258621492.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ti4x1258621492.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59dnw1258621492.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6 7 8 9 10 11
-604.0 -383.8 -629.2 322.8 -464.0 -593.4 93.2 -180.0 -16.8 -593.6 -324.4
12 13 14 15 16 17 18 19 20 21 22
283.8 757.8 239.0 619.6 -3.4 -58.2 722.4 120.0 -231.2 73.0 201.2
23 24 25 26 27 28 29 30 31 32 33
329.4 85.6 -659.4 13.8 -157.6 242.4 946.6 8.2 -385.2 225.6 396.8
34 35 36 37 38 39 40 41 42 43 44
-424.0 78.2 763.4 829.4 525.6 626.2 118.2 461.4 -105.0 -28.4 -104.6
45 46 47 48 49 50 51 52 53 54 55
100.6 20.8 -366.0 -442.8 -264.8 -394.6 -459.0 -680.0 -885.8 -32.2 200.4
56 57 58 59 60 61
290.2 -553.6 795.6 282.8 -690.0 -59.0
> postscript(file="/var/www/html/rcomp/tmp/65ap81258621492.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -604.0 NA
1 -383.8 -604.0
2 -629.2 -383.8
3 322.8 -629.2
4 -464.0 322.8
5 -593.4 -464.0
6 93.2 -593.4
7 -180.0 93.2
8 -16.8 -180.0
9 -593.6 -16.8
10 -324.4 -593.6
11 283.8 -324.4
12 757.8 283.8
13 239.0 757.8
14 619.6 239.0
15 -3.4 619.6
16 -58.2 -3.4
17 722.4 -58.2
18 120.0 722.4
19 -231.2 120.0
20 73.0 -231.2
21 201.2 73.0
22 329.4 201.2
23 85.6 329.4
24 -659.4 85.6
25 13.8 -659.4
26 -157.6 13.8
27 242.4 -157.6
28 946.6 242.4
29 8.2 946.6
30 -385.2 8.2
31 225.6 -385.2
32 396.8 225.6
33 -424.0 396.8
34 78.2 -424.0
35 763.4 78.2
36 829.4 763.4
37 525.6 829.4
38 626.2 525.6
39 118.2 626.2
40 461.4 118.2
41 -105.0 461.4
42 -28.4 -105.0
43 -104.6 -28.4
44 100.6 -104.6
45 20.8 100.6
46 -366.0 20.8
47 -442.8 -366.0
48 -264.8 -442.8
49 -394.6 -264.8
50 -459.0 -394.6
51 -680.0 -459.0
52 -885.8 -680.0
53 -32.2 -885.8
54 200.4 -32.2
55 290.2 200.4
56 -553.6 290.2
57 795.6 -553.6
58 282.8 795.6
59 -690.0 282.8
60 -59.0 -690.0
61 NA -59.0
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -383.8 -604.0
[2,] -629.2 -383.8
[3,] 322.8 -629.2
[4,] -464.0 322.8
[5,] -593.4 -464.0
[6,] 93.2 -593.4
[7,] -180.0 93.2
[8,] -16.8 -180.0
[9,] -593.6 -16.8
[10,] -324.4 -593.6
[11,] 283.8 -324.4
[12,] 757.8 283.8
[13,] 239.0 757.8
[14,] 619.6 239.0
[15,] -3.4 619.6
[16,] -58.2 -3.4
[17,] 722.4 -58.2
[18,] 120.0 722.4
[19,] -231.2 120.0
[20,] 73.0 -231.2
[21,] 201.2 73.0
[22,] 329.4 201.2
[23,] 85.6 329.4
[24,] -659.4 85.6
[25,] 13.8 -659.4
[26,] -157.6 13.8
[27,] 242.4 -157.6
[28,] 946.6 242.4
[29,] 8.2 946.6
[30,] -385.2 8.2
[31,] 225.6 -385.2
[32,] 396.8 225.6
[33,] -424.0 396.8
[34,] 78.2 -424.0
[35,] 763.4 78.2
[36,] 829.4 763.4
[37,] 525.6 829.4
[38,] 626.2 525.6
[39,] 118.2 626.2
[40,] 461.4 118.2
[41,] -105.0 461.4
[42,] -28.4 -105.0
[43,] -104.6 -28.4
[44,] 100.6 -104.6
[45,] 20.8 100.6
[46,] -366.0 20.8
[47,] -442.8 -366.0
[48,] -264.8 -442.8
[49,] -394.6 -264.8
[50,] -459.0 -394.6
[51,] -680.0 -459.0
[52,] -885.8 -680.0
[53,] -32.2 -885.8
[54,] 200.4 -32.2
[55,] 290.2 200.4
[56,] -553.6 290.2
[57,] 795.6 -553.6
[58,] 282.8 795.6
[59,] -690.0 282.8
[60,] -59.0 -690.0
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -383.8 -604.0
2 -629.2 -383.8
3 322.8 -629.2
4 -464.0 322.8
5 -593.4 -464.0
6 93.2 -593.4
7 -180.0 93.2
8 -16.8 -180.0
9 -593.6 -16.8
10 -324.4 -593.6
11 283.8 -324.4
12 757.8 283.8
13 239.0 757.8
14 619.6 239.0
15 -3.4 619.6
16 -58.2 -3.4
17 722.4 -58.2
18 120.0 722.4
19 -231.2 120.0
20 73.0 -231.2
21 201.2 73.0
22 329.4 201.2
23 85.6 329.4
24 -659.4 85.6
25 13.8 -659.4
26 -157.6 13.8
27 242.4 -157.6
28 946.6 242.4
29 8.2 946.6
30 -385.2 8.2
31 225.6 -385.2
32 396.8 225.6
33 -424.0 396.8
34 78.2 -424.0
35 763.4 78.2
36 829.4 763.4
37 525.6 829.4
38 626.2 525.6
39 118.2 626.2
40 461.4 118.2
41 -105.0 461.4
42 -28.4 -105.0
43 -104.6 -28.4
44 100.6 -104.6
45 20.8 100.6
46 -366.0 20.8
47 -442.8 -366.0
48 -264.8 -442.8
49 -394.6 -264.8
50 -459.0 -394.6
51 -680.0 -459.0
52 -885.8 -680.0
53 -32.2 -885.8
54 200.4 -32.2
55 290.2 200.4
56 -553.6 290.2
57 795.6 -553.6
58 282.8 795.6
59 -690.0 282.8
60 -59.0 -690.0
> 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/7uyix1258621492.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88skq1258621492.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90pe11258621492.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10x6dq1258621492.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11rzkp1258621492.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/12rx7y1258621492.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/13vajj1258621492.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/14d0e11258621492.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/153x9v1258621492.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/16n40z1258621492.tab")
+ }
>
> system("convert tmp/1h6ob1258621492.ps tmp/1h6ob1258621492.png")
> system("convert tmp/23e5b1258621492.ps tmp/23e5b1258621492.png")
> system("convert tmp/3ykfo1258621492.ps tmp/3ykfo1258621492.png")
> system("convert tmp/4ti4x1258621492.ps tmp/4ti4x1258621492.png")
> system("convert tmp/59dnw1258621492.ps tmp/59dnw1258621492.png")
> system("convert tmp/65ap81258621492.ps tmp/65ap81258621492.png")
> system("convert tmp/7uyix1258621492.ps tmp/7uyix1258621492.png")
> system("convert tmp/88skq1258621492.ps tmp/88skq1258621492.png")
> system("convert tmp/90pe11258621492.ps tmp/90pe11258621492.png")
> system("convert tmp/10x6dq1258621492.ps tmp/10x6dq1258621492.png")
>
>
> proc.time()
user system elapsed
2.377 1.565 3.185