R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(0,0,9,0,1,0,4,0,6,0,21,0,24,0,23,0,22,0,21,0,20,0,16,0,18,0,18,0,24,0,16,0,15,0,24,0,18,0,15,0,4,0,3,0,6,0,5,0,12,0,12,0,12,0,14,0,12,0,17,0,12,0,20,0,21,0,15,0,22,0,19,0,19,0,26,0,25,0,19,0,20,0,30,0,31,0,35,0,33,0,26,0,25,0,17,0,14,0,8,0,12,0,7,0,4,0,10,0,8,0,16,1,14,1,20,1,9,1,10,1),dim=c(2,60),dimnames=list(c('Spa','Val'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Spa','Val'),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 = '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
Val Spa M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 0 9 0 1 0 0 0 0 0 0 0 0 0 2
3 0 1 0 0 1 0 0 0 0 0 0 0 0 3
4 0 4 0 0 0 1 0 0 0 0 0 0 0 4
5 0 6 0 0 0 0 1 0 0 0 0 0 0 5
6 0 21 0 0 0 0 0 1 0 0 0 0 0 6
7 0 24 0 0 0 0 0 0 1 0 0 0 0 7
8 0 23 0 0 0 0 0 0 0 1 0 0 0 8
9 0 22 0 0 0 0 0 0 0 0 1 0 0 9
10 0 21 0 0 0 0 0 0 0 0 0 1 0 10
11 0 20 0 0 0 0 0 0 0 0 0 0 1 11
12 0 16 0 0 0 0 0 0 0 0 0 0 0 12
13 0 18 1 0 0 0 0 0 0 0 0 0 0 13
14 0 18 0 1 0 0 0 0 0 0 0 0 0 14
15 0 24 0 0 1 0 0 0 0 0 0 0 0 15
16 0 16 0 0 0 1 0 0 0 0 0 0 0 16
17 0 15 0 0 0 0 1 0 0 0 0 0 0 17
18 0 24 0 0 0 0 0 1 0 0 0 0 0 18
19 0 18 0 0 0 0 0 0 1 0 0 0 0 19
20 0 15 0 0 0 0 0 0 0 1 0 0 0 20
21 0 4 0 0 0 0 0 0 0 0 1 0 0 21
22 0 3 0 0 0 0 0 0 0 0 0 1 0 22
23 0 6 0 0 0 0 0 0 0 0 0 0 1 23
24 0 5 0 0 0 0 0 0 0 0 0 0 0 24
25 0 12 1 0 0 0 0 0 0 0 0 0 0 25
26 0 12 0 1 0 0 0 0 0 0 0 0 0 26
27 0 12 0 0 1 0 0 0 0 0 0 0 0 27
28 0 14 0 0 0 1 0 0 0 0 0 0 0 28
29 0 12 0 0 0 0 1 0 0 0 0 0 0 29
30 0 17 0 0 0 0 0 1 0 0 0 0 0 30
31 0 12 0 0 0 0 0 0 1 0 0 0 0 31
32 0 20 0 0 0 0 0 0 0 1 0 0 0 32
33 0 21 0 0 0 0 0 0 0 0 1 0 0 33
34 0 15 0 0 0 0 0 0 0 0 0 1 0 34
35 0 22 0 0 0 0 0 0 0 0 0 0 1 35
36 0 19 0 0 0 0 0 0 0 0 0 0 0 36
37 0 19 1 0 0 0 0 0 0 0 0 0 0 37
38 0 26 0 1 0 0 0 0 0 0 0 0 0 38
39 0 25 0 0 1 0 0 0 0 0 0 0 0 39
40 0 19 0 0 0 1 0 0 0 0 0 0 0 40
41 0 20 0 0 0 0 1 0 0 0 0 0 0 41
42 0 30 0 0 0 0 0 1 0 0 0 0 0 42
43 0 31 0 0 0 0 0 0 1 0 0 0 0 43
44 0 35 0 0 0 0 0 0 0 1 0 0 0 44
45 0 33 0 0 0 0 0 0 0 0 1 0 0 45
46 0 26 0 0 0 0 0 0 0 0 0 1 0 46
47 0 25 0 0 0 0 0 0 0 0 0 0 1 47
48 0 17 0 0 0 0 0 0 0 0 0 0 0 48
49 0 14 1 0 0 0 0 0 0 0 0 0 0 49
50 0 8 0 1 0 0 0 0 0 0 0 0 0 50
51 0 12 0 0 1 0 0 0 0 0 0 0 0 51
52 0 7 0 0 0 1 0 0 0 0 0 0 0 52
53 0 4 0 0 0 0 1 0 0 0 0 0 0 53
54 0 10 0 0 0 0 0 1 0 0 0 0 0 54
55 0 8 0 0 0 0 0 0 1 0 0 0 0 55
56 1 16 0 0 0 0 0 0 0 1 0 0 0 56
57 1 14 0 0 0 0 0 0 0 0 1 0 0 57
58 1 20 0 0 0 0 0 0 0 0 0 1 0 58
59 1 9 0 0 0 0 0 0 0 0 0 0 1 59
60 1 10 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) Spa M1 M2 M3 M4
0.039911 -0.007718 -0.125658 -0.117543 -0.123319 -0.152248
M5 M6 M7 M8 M9 M10
-0.164198 -0.102058 -0.123270 0.094107 0.063634 0.042423
M11 t
0.030473 0.007320
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.26005 -0.18376 -0.01441 0.11397 0.64748
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.039911 0.143572 0.278 0.782267
Spa -0.007718 0.004542 -1.699 0.096033 .
M1 -0.125658 0.162373 -0.774 0.442956
M2 -0.117543 0.162307 -0.724 0.472609
M3 -0.123319 0.162121 -0.761 0.450743
M4 -0.152248 0.161774 -0.941 0.351563
M5 -0.164198 0.161711 -1.015 0.315236
M6 -0.102058 0.164747 -0.619 0.538654
M7 -0.123270 0.163140 -0.756 0.453738
M8 0.094107 0.165812 0.568 0.573098
M9 0.063634 0.163028 0.390 0.698096
M10 0.042423 0.161885 0.262 0.794449
M11 0.030473 0.161572 0.189 0.851234
t 0.007320 0.001949 3.756 0.000484 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2545 on 46 degrees of freedom
Multiple R-squared: 0.3499, Adjusted R-squared: 0.1662
F-statistic: 1.904 on 13 and 46 DF, p-value: 0.05483
> 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 0 1
[2,] 0 0 1
[3,] 0 0 1
[4,] 0 0 1
[5,] 0 0 1
[6,] 0 0 1
[7,] 0 0 1
[8,] 0 0 1
[9,] 0 0 1
[10,] 0 0 1
[11,] 0 0 1
[12,] 0 0 1
[13,] 0 0 1
[14,] 0 0 1
[15,] 0 0 1
[16,] 0 0 1
[17,] 0 0 1
[18,] 0 0 1
[19,] 0 0 1
[20,] 0 0 1
[21,] 0 0 1
[22,] 0 0 1
[23,] 0 0 1
[24,] 0 0 1
[25,] 0 0 1
[26,] 0 0 1
[27,] 0 0 1
> postscript(file="/var/www/html/rcomp/tmp/1qgwz1228687207.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2thy31228687207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3a33q1228687207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/40aw71228687207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50xhx1228687207.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
0.0784275842 0.1324515122 0.0691663393 0.1139290226 0.1339950530
6 7 8 9 10
0.1803012771 0.2173462563 -0.0150681005 0.0003673075 0.0065414707
11 12 13 14 15
0.0034543891 -0.0042633149 0.1295109290 0.1140755210 0.1588382042
16 17 18 19 20
0.1187061434 0.1156190618 0.1156190618 0.0832047049 -0.1646450599
21 22 23 24 25
-0.2263866920 -0.2202125288 -0.1924287944 -0.1769933863 -0.0046306224
26 27 28 29 30
-0.0200660304 -0.0216095712 0.0154354080 0.0046306224 -0.0262401936
31 32 33 34 35
-0.0509368465 -0.2138918672 -0.1830210512 -0.2154354080 -0.1567808575
36 37 38 39 40
-0.1567808575 -0.0384420217 0.0001464984 -0.0091147464 -0.0338113993
41 42 43 44 45
-0.0214630729 -0.0137453688 0.0078642024 -0.1859616344 -0.1782439304
46 47 48 49 50
-0.2183759913 -0.2214630729 -0.2600515929 -0.1648658691 -0.2266075011
51 52 53 54 55
-0.1972802259 -0.2142591747 -0.2327816643 -0.2559347764 -0.2574783172
56 57 58 59 60
0.5795666621 0.5872843661 0.6474824574 0.5672183357 0.5980891517
> postscript(file="/var/www/html/rcomp/tmp/6rnk21228687207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.0784275842 NA
1 0.1324515122 0.0784275842
2 0.0691663393 0.1324515122
3 0.1139290226 0.0691663393
4 0.1339950530 0.1139290226
5 0.1803012771 0.1339950530
6 0.2173462563 0.1803012771
7 -0.0150681005 0.2173462563
8 0.0003673075 -0.0150681005
9 0.0065414707 0.0003673075
10 0.0034543891 0.0065414707
11 -0.0042633149 0.0034543891
12 0.1295109290 -0.0042633149
13 0.1140755210 0.1295109290
14 0.1588382042 0.1140755210
15 0.1187061434 0.1588382042
16 0.1156190618 0.1187061434
17 0.1156190618 0.1156190618
18 0.0832047049 0.1156190618
19 -0.1646450599 0.0832047049
20 -0.2263866920 -0.1646450599
21 -0.2202125288 -0.2263866920
22 -0.1924287944 -0.2202125288
23 -0.1769933863 -0.1924287944
24 -0.0046306224 -0.1769933863
25 -0.0200660304 -0.0046306224
26 -0.0216095712 -0.0200660304
27 0.0154354080 -0.0216095712
28 0.0046306224 0.0154354080
29 -0.0262401936 0.0046306224
30 -0.0509368465 -0.0262401936
31 -0.2138918672 -0.0509368465
32 -0.1830210512 -0.2138918672
33 -0.2154354080 -0.1830210512
34 -0.1567808575 -0.2154354080
35 -0.1567808575 -0.1567808575
36 -0.0384420217 -0.1567808575
37 0.0001464984 -0.0384420217
38 -0.0091147464 0.0001464984
39 -0.0338113993 -0.0091147464
40 -0.0214630729 -0.0338113993
41 -0.0137453688 -0.0214630729
42 0.0078642024 -0.0137453688
43 -0.1859616344 0.0078642024
44 -0.1782439304 -0.1859616344
45 -0.2183759913 -0.1782439304
46 -0.2214630729 -0.2183759913
47 -0.2600515929 -0.2214630729
48 -0.1648658691 -0.2600515929
49 -0.2266075011 -0.1648658691
50 -0.1972802259 -0.2266075011
51 -0.2142591747 -0.1972802259
52 -0.2327816643 -0.2142591747
53 -0.2559347764 -0.2327816643
54 -0.2574783172 -0.2559347764
55 0.5795666621 -0.2574783172
56 0.5872843661 0.5795666621
57 0.6474824574 0.5872843661
58 0.5672183357 0.6474824574
59 0.5980891517 0.5672183357
60 NA 0.5980891517
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.1324515122 0.0784275842
[2,] 0.0691663393 0.1324515122
[3,] 0.1139290226 0.0691663393
[4,] 0.1339950530 0.1139290226
[5,] 0.1803012771 0.1339950530
[6,] 0.2173462563 0.1803012771
[7,] -0.0150681005 0.2173462563
[8,] 0.0003673075 -0.0150681005
[9,] 0.0065414707 0.0003673075
[10,] 0.0034543891 0.0065414707
[11,] -0.0042633149 0.0034543891
[12,] 0.1295109290 -0.0042633149
[13,] 0.1140755210 0.1295109290
[14,] 0.1588382042 0.1140755210
[15,] 0.1187061434 0.1588382042
[16,] 0.1156190618 0.1187061434
[17,] 0.1156190618 0.1156190618
[18,] 0.0832047049 0.1156190618
[19,] -0.1646450599 0.0832047049
[20,] -0.2263866920 -0.1646450599
[21,] -0.2202125288 -0.2263866920
[22,] -0.1924287944 -0.2202125288
[23,] -0.1769933863 -0.1924287944
[24,] -0.0046306224 -0.1769933863
[25,] -0.0200660304 -0.0046306224
[26,] -0.0216095712 -0.0200660304
[27,] 0.0154354080 -0.0216095712
[28,] 0.0046306224 0.0154354080
[29,] -0.0262401936 0.0046306224
[30,] -0.0509368465 -0.0262401936
[31,] -0.2138918672 -0.0509368465
[32,] -0.1830210512 -0.2138918672
[33,] -0.2154354080 -0.1830210512
[34,] -0.1567808575 -0.2154354080
[35,] -0.1567808575 -0.1567808575
[36,] -0.0384420217 -0.1567808575
[37,] 0.0001464984 -0.0384420217
[38,] -0.0091147464 0.0001464984
[39,] -0.0338113993 -0.0091147464
[40,] -0.0214630729 -0.0338113993
[41,] -0.0137453688 -0.0214630729
[42,] 0.0078642024 -0.0137453688
[43,] -0.1859616344 0.0078642024
[44,] -0.1782439304 -0.1859616344
[45,] -0.2183759913 -0.1782439304
[46,] -0.2214630729 -0.2183759913
[47,] -0.2600515929 -0.2214630729
[48,] -0.1648658691 -0.2600515929
[49,] -0.2266075011 -0.1648658691
[50,] -0.1972802259 -0.2266075011
[51,] -0.2142591747 -0.1972802259
[52,] -0.2327816643 -0.2142591747
[53,] -0.2559347764 -0.2327816643
[54,] -0.2574783172 -0.2559347764
[55,] 0.5795666621 -0.2574783172
[56,] 0.5872843661 0.5795666621
[57,] 0.6474824574 0.5872843661
[58,] 0.5672183357 0.6474824574
[59,] 0.5980891517 0.5672183357
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.1324515122 0.0784275842
2 0.0691663393 0.1324515122
3 0.1139290226 0.0691663393
4 0.1339950530 0.1139290226
5 0.1803012771 0.1339950530
6 0.2173462563 0.1803012771
7 -0.0150681005 0.2173462563
8 0.0003673075 -0.0150681005
9 0.0065414707 0.0003673075
10 0.0034543891 0.0065414707
11 -0.0042633149 0.0034543891
12 0.1295109290 -0.0042633149
13 0.1140755210 0.1295109290
14 0.1588382042 0.1140755210
15 0.1187061434 0.1588382042
16 0.1156190618 0.1187061434
17 0.1156190618 0.1156190618
18 0.0832047049 0.1156190618
19 -0.1646450599 0.0832047049
20 -0.2263866920 -0.1646450599
21 -0.2202125288 -0.2263866920
22 -0.1924287944 -0.2202125288
23 -0.1769933863 -0.1924287944
24 -0.0046306224 -0.1769933863
25 -0.0200660304 -0.0046306224
26 -0.0216095712 -0.0200660304
27 0.0154354080 -0.0216095712
28 0.0046306224 0.0154354080
29 -0.0262401936 0.0046306224
30 -0.0509368465 -0.0262401936
31 -0.2138918672 -0.0509368465
32 -0.1830210512 -0.2138918672
33 -0.2154354080 -0.1830210512
34 -0.1567808575 -0.2154354080
35 -0.1567808575 -0.1567808575
36 -0.0384420217 -0.1567808575
37 0.0001464984 -0.0384420217
38 -0.0091147464 0.0001464984
39 -0.0338113993 -0.0091147464
40 -0.0214630729 -0.0338113993
41 -0.0137453688 -0.0214630729
42 0.0078642024 -0.0137453688
43 -0.1859616344 0.0078642024
44 -0.1782439304 -0.1859616344
45 -0.2183759913 -0.1782439304
46 -0.2214630729 -0.2183759913
47 -0.2600515929 -0.2214630729
48 -0.1648658691 -0.2600515929
49 -0.2266075011 -0.1648658691
50 -0.1972802259 -0.2266075011
51 -0.2142591747 -0.1972802259
52 -0.2327816643 -0.2142591747
53 -0.2559347764 -0.2327816643
54 -0.2574783172 -0.2559347764
55 0.5795666621 -0.2574783172
56 0.5872843661 0.5795666621
57 0.6474824574 0.5872843661
58 0.5672183357 0.6474824574
59 0.5980891517 0.5672183357
> 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/70yxj1228687207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8g1nl1228687207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/96kv51228687207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10yr3d1228687207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xqda1228687207.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/12uy751228687207.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/13a37q1228687207.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/14qirg1228687207.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/15oqc41228687207.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/16f4r91228687207.tab")
+ }
>
> system("convert tmp/1qgwz1228687207.ps tmp/1qgwz1228687207.png")
> system("convert tmp/2thy31228687207.ps tmp/2thy31228687207.png")
> system("convert tmp/3a33q1228687207.ps tmp/3a33q1228687207.png")
> system("convert tmp/40aw71228687207.ps tmp/40aw71228687207.png")
> system("convert tmp/50xhx1228687207.ps tmp/50xhx1228687207.png")
> system("convert tmp/6rnk21228687207.ps tmp/6rnk21228687207.png")
> system("convert tmp/70yxj1228687207.ps tmp/70yxj1228687207.png")
> system("convert tmp/8g1nl1228687207.ps tmp/8g1nl1228687207.png")
> system("convert tmp/96kv51228687207.ps tmp/96kv51228687207.png")
> system("convert tmp/10yr3d1228687207.ps tmp/10yr3d1228687207.png")
>
>
> proc.time()
user system elapsed
2.362 1.573 2.978