R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(82.7,0,88.9,0,105.9,0,100.8,0,94,0,105,0,58.5,0,87.6,0,113.1,0,112.5,0,89.6,0,74.5,0,82.7,0,90.1,0,109.4,0,96,0,89.2,0,109.1,0,49.1,0,92.9,0,107.7,0,103.5,0,91.1,0,79.8,0,71.9,0,82.9,0,90.1,0,100.7,0,90.7,0,108.8,0,44.1,0,93.6,0,107.4,0,96.5,0,93.6,0,76.5,0,76.7,0,84,0,103.3,0,88.5,1,99,1,105.9,1,44.7,1,94,1,107.1,1,104.8,1,102.5,1,77.7,1,85.2,1,91.3,1,106.5,1,92.4,1,97.5,1,107,1,51.1,1,98.6,1,102.2,1,114.3,1,99.4,1,72.5,1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 82.7 0 1 0 0 0 0 0 0 0 0 0 0 1
2 88.9 0 0 1 0 0 0 0 0 0 0 0 0 2
3 105.9 0 0 0 1 0 0 0 0 0 0 0 0 3
4 100.8 0 0 0 0 1 0 0 0 0 0 0 0 4
5 94.0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 105.0 0 0 0 0 0 0 1 0 0 0 0 0 6
7 58.5 0 0 0 0 0 0 0 1 0 0 0 0 7
8 87.6 0 0 0 0 0 0 0 0 1 0 0 0 8
9 113.1 0 0 0 0 0 0 0 0 0 1 0 0 9
10 112.5 0 0 0 0 0 0 0 0 0 0 1 0 10
11 89.6 0 0 0 0 0 0 0 0 0 0 0 1 11
12 74.5 0 0 0 0 0 0 0 0 0 0 0 0 12
13 82.7 0 1 0 0 0 0 0 0 0 0 0 0 13
14 90.1 0 0 1 0 0 0 0 0 0 0 0 0 14
15 109.4 0 0 0 1 0 0 0 0 0 0 0 0 15
16 96.0 0 0 0 0 1 0 0 0 0 0 0 0 16
17 89.2 0 0 0 0 0 1 0 0 0 0 0 0 17
18 109.1 0 0 0 0 0 0 1 0 0 0 0 0 18
19 49.1 0 0 0 0 0 0 0 1 0 0 0 0 19
20 92.9 0 0 0 0 0 0 0 0 1 0 0 0 20
21 107.7 0 0 0 0 0 0 0 0 0 1 0 0 21
22 103.5 0 0 0 0 0 0 0 0 0 0 1 0 22
23 91.1 0 0 0 0 0 0 0 0 0 0 0 1 23
24 79.8 0 0 0 0 0 0 0 0 0 0 0 0 24
25 71.9 0 1 0 0 0 0 0 0 0 0 0 0 25
26 82.9 0 0 1 0 0 0 0 0 0 0 0 0 26
27 90.1 0 0 0 1 0 0 0 0 0 0 0 0 27
28 100.7 0 0 0 0 1 0 0 0 0 0 0 0 28
29 90.7 0 0 0 0 0 1 0 0 0 0 0 0 29
30 108.8 0 0 0 0 0 0 1 0 0 0 0 0 30
31 44.1 0 0 0 0 0 0 0 1 0 0 0 0 31
32 93.6 0 0 0 0 0 0 0 0 1 0 0 0 32
33 107.4 0 0 0 0 0 0 0 0 0 1 0 0 33
34 96.5 0 0 0 0 0 0 0 0 0 0 1 0 34
35 93.6 0 0 0 0 0 0 0 0 0 0 0 1 35
36 76.5 0 0 0 0 0 0 0 0 0 0 0 0 36
37 76.7 0 1 0 0 0 0 0 0 0 0 0 0 37
38 84.0 0 0 1 0 0 0 0 0 0 0 0 0 38
39 103.3 0 0 0 1 0 0 0 0 0 0 0 0 39
40 88.5 1 0 0 0 1 0 0 0 0 0 0 0 40
41 99.0 1 0 0 0 0 1 0 0 0 0 0 0 41
42 105.9 1 0 0 0 0 0 1 0 0 0 0 0 42
43 44.7 1 0 0 0 0 0 0 1 0 0 0 0 43
44 94.0 1 0 0 0 0 0 0 0 1 0 0 0 44
45 107.1 1 0 0 0 0 0 0 0 0 1 0 0 45
46 104.8 1 0 0 0 0 0 0 0 0 0 1 0 46
47 102.5 1 0 0 0 0 0 0 0 0 0 0 1 47
48 77.7 1 0 0 0 0 0 0 0 0 0 0 0 48
49 85.2 1 1 0 0 0 0 0 0 0 0 0 0 49
50 91.3 1 0 1 0 0 0 0 0 0 0 0 0 50
51 106.5 1 0 0 1 0 0 0 0 0 0 0 0 51
52 92.4 1 0 0 0 1 0 0 0 0 0 0 0 52
53 97.5 1 0 0 0 0 1 0 0 0 0 0 0 53
54 107.0 1 0 0 0 0 0 1 0 0 0 0 0 54
55 51.1 1 0 0 0 0 0 0 1 0 0 0 0 55
56 98.6 1 0 0 0 0 0 0 0 1 0 0 0 56
57 102.2 1 0 0 0 0 0 0 0 0 1 0 0 57
58 114.3 1 0 0 0 0 0 0 0 0 0 1 0 58
59 99.4 1 0 0 0 0 0 0 0 0 0 0 1 59
60 72.5 1 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) X M1 M2 M3 M4
78.5888 5.4679 3.3354 11.0625 26.7896 18.4631
M5 M6 M7 M8 M9 M10
16.9902 30.1973 -27.3356 16.6316 30.9187 29.8658
M11 t
18.9129 -0.1271
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.8464 -3.0708 0.4045 2.9503 8.1365
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 78.58885 2.75595 28.516 < 2e-16 ***
X 5.46788 2.40330 2.275 0.027603 *
M1 3.33535 3.11652 1.070 0.290105
M2 11.06246 3.11014 3.557 0.000883 ***
M3 26.78958 3.10517 8.627 3.58e-11 ***
M4 18.46311 3.13250 5.894 4.17e-07 ***
M5 16.99022 3.12192 5.442 1.97e-06 ***
M6 30.19733 3.11272 9.701 1.06e-12 ***
M7 -27.33556 3.10491 -8.804 1.99e-11 ***
M8 16.63156 3.09851 5.368 2.54e-06 ***
M9 30.91867 3.09353 9.995 4.14e-13 ***
M10 29.86578 3.08996 9.665 1.19e-12 ***
M11 18.91289 3.08781 6.125 1.88e-07 ***
t -0.12711 0.06642 -1.914 0.061901 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.881 on 46 degrees of freedom
Multiple R-squared: 0.9334, Adjusted R-squared: 0.9145
F-statistic: 49.56 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,] 0.2493767 0.4987533 0.7506233
[2,] 0.2052712 0.4105425 0.7947288
[3,] 0.3393458 0.6786917 0.6606542
[4,] 0.3220256 0.6440512 0.6779744
[5,] 0.2754250 0.5508500 0.7245750
[6,] 0.2925526 0.5851051 0.7074474
[7,] 0.2227498 0.4454995 0.7772502
[8,] 0.2610655 0.5221310 0.7389345
[9,] 0.3413008 0.6826016 0.6586992
[10,] 0.2654689 0.5309379 0.7345311
[11,] 0.6181670 0.7636660 0.3818330
[12,] 0.8434451 0.3131097 0.1565549
[13,] 0.8056031 0.3887938 0.1943969
[14,] 0.8271272 0.3457455 0.1728728
[15,] 0.7853978 0.4292044 0.2146022
[16,] 0.7635646 0.4728708 0.2364354
[17,] 0.8122946 0.3754108 0.1877054
[18,] 0.8718974 0.2562053 0.1281026
[19,] 0.8496822 0.3006356 0.1503178
[20,] 0.8778994 0.2442013 0.1221006
[21,] 0.8231091 0.3537819 0.1768909
[22,] 0.7524639 0.4950722 0.2475361
[23,] 0.6659660 0.6680679 0.3340340
[24,] 0.5602251 0.8795497 0.4397749
[25,] 0.5513077 0.8973845 0.4486923
[26,] 0.3980465 0.7960929 0.6019535
[27,] 0.3251483 0.6502967 0.6748517
> postscript(file="/var/www/html/rcomp/tmp/1npai1227721969.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ik671227721969.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ga0d1227721969.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/430d41227721969.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50zsz1227721969.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
0.9029091 -0.4970909 0.9029091 4.2564848 -0.9435152 -3.0235152
7 8 9 10 11 12
8.1364848 -6.6035152 4.7364848 5.3164848 -6.5035152 -2.5635152
13 14 15 16 17 18
2.4282424 2.2282424 5.9282424 0.9818182 -4.2181818 2.6018182
19 20 21 22 23 24
0.2618182 0.2218182 0.8618182 -2.1581818 -3.4781818 4.2618182
25 26 27 28 29 30
-6.8464242 -3.4464242 -11.8464242 7.2071515 -1.1928485 3.8271515
31 32 33 34 35 36
-3.2128485 2.4471515 2.0871515 -7.6328485 0.5471515 2.4871515
37 38 39 40 41 42
-0.5210909 -0.8210909 2.8789091 -8.9353939 3.1646061 -3.0153939
43 44 45 46 47 48
-6.5553939 -1.0953939 -2.1553939 -3.2753939 5.5046061 -0.2553939
49 50 51 52 53 54
4.0363636 2.5363636 2.1363636 -3.5100606 3.1899394 -0.3900606
55 56 57 58 59 60
1.3699394 5.0299394 -5.5300606 7.7499394 3.9299394 -3.9300606
> postscript(file="/var/www/html/rcomp/tmp/6z0oz1227721969.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 0.9029091 NA
1 -0.4970909 0.9029091
2 0.9029091 -0.4970909
3 4.2564848 0.9029091
4 -0.9435152 4.2564848
5 -3.0235152 -0.9435152
6 8.1364848 -3.0235152
7 -6.6035152 8.1364848
8 4.7364848 -6.6035152
9 5.3164848 4.7364848
10 -6.5035152 5.3164848
11 -2.5635152 -6.5035152
12 2.4282424 -2.5635152
13 2.2282424 2.4282424
14 5.9282424 2.2282424
15 0.9818182 5.9282424
16 -4.2181818 0.9818182
17 2.6018182 -4.2181818
18 0.2618182 2.6018182
19 0.2218182 0.2618182
20 0.8618182 0.2218182
21 -2.1581818 0.8618182
22 -3.4781818 -2.1581818
23 4.2618182 -3.4781818
24 -6.8464242 4.2618182
25 -3.4464242 -6.8464242
26 -11.8464242 -3.4464242
27 7.2071515 -11.8464242
28 -1.1928485 7.2071515
29 3.8271515 -1.1928485
30 -3.2128485 3.8271515
31 2.4471515 -3.2128485
32 2.0871515 2.4471515
33 -7.6328485 2.0871515
34 0.5471515 -7.6328485
35 2.4871515 0.5471515
36 -0.5210909 2.4871515
37 -0.8210909 -0.5210909
38 2.8789091 -0.8210909
39 -8.9353939 2.8789091
40 3.1646061 -8.9353939
41 -3.0153939 3.1646061
42 -6.5553939 -3.0153939
43 -1.0953939 -6.5553939
44 -2.1553939 -1.0953939
45 -3.2753939 -2.1553939
46 5.5046061 -3.2753939
47 -0.2553939 5.5046061
48 4.0363636 -0.2553939
49 2.5363636 4.0363636
50 2.1363636 2.5363636
51 -3.5100606 2.1363636
52 3.1899394 -3.5100606
53 -0.3900606 3.1899394
54 1.3699394 -0.3900606
55 5.0299394 1.3699394
56 -5.5300606 5.0299394
57 7.7499394 -5.5300606
58 3.9299394 7.7499394
59 -3.9300606 3.9299394
60 NA -3.9300606
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.4970909 0.9029091
[2,] 0.9029091 -0.4970909
[3,] 4.2564848 0.9029091
[4,] -0.9435152 4.2564848
[5,] -3.0235152 -0.9435152
[6,] 8.1364848 -3.0235152
[7,] -6.6035152 8.1364848
[8,] 4.7364848 -6.6035152
[9,] 5.3164848 4.7364848
[10,] -6.5035152 5.3164848
[11,] -2.5635152 -6.5035152
[12,] 2.4282424 -2.5635152
[13,] 2.2282424 2.4282424
[14,] 5.9282424 2.2282424
[15,] 0.9818182 5.9282424
[16,] -4.2181818 0.9818182
[17,] 2.6018182 -4.2181818
[18,] 0.2618182 2.6018182
[19,] 0.2218182 0.2618182
[20,] 0.8618182 0.2218182
[21,] -2.1581818 0.8618182
[22,] -3.4781818 -2.1581818
[23,] 4.2618182 -3.4781818
[24,] -6.8464242 4.2618182
[25,] -3.4464242 -6.8464242
[26,] -11.8464242 -3.4464242
[27,] 7.2071515 -11.8464242
[28,] -1.1928485 7.2071515
[29,] 3.8271515 -1.1928485
[30,] -3.2128485 3.8271515
[31,] 2.4471515 -3.2128485
[32,] 2.0871515 2.4471515
[33,] -7.6328485 2.0871515
[34,] 0.5471515 -7.6328485
[35,] 2.4871515 0.5471515
[36,] -0.5210909 2.4871515
[37,] -0.8210909 -0.5210909
[38,] 2.8789091 -0.8210909
[39,] -8.9353939 2.8789091
[40,] 3.1646061 -8.9353939
[41,] -3.0153939 3.1646061
[42,] -6.5553939 -3.0153939
[43,] -1.0953939 -6.5553939
[44,] -2.1553939 -1.0953939
[45,] -3.2753939 -2.1553939
[46,] 5.5046061 -3.2753939
[47,] -0.2553939 5.5046061
[48,] 4.0363636 -0.2553939
[49,] 2.5363636 4.0363636
[50,] 2.1363636 2.5363636
[51,] -3.5100606 2.1363636
[52,] 3.1899394 -3.5100606
[53,] -0.3900606 3.1899394
[54,] 1.3699394 -0.3900606
[55,] 5.0299394 1.3699394
[56,] -5.5300606 5.0299394
[57,] 7.7499394 -5.5300606
[58,] 3.9299394 7.7499394
[59,] -3.9300606 3.9299394
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.4970909 0.9029091
2 0.9029091 -0.4970909
3 4.2564848 0.9029091
4 -0.9435152 4.2564848
5 -3.0235152 -0.9435152
6 8.1364848 -3.0235152
7 -6.6035152 8.1364848
8 4.7364848 -6.6035152
9 5.3164848 4.7364848
10 -6.5035152 5.3164848
11 -2.5635152 -6.5035152
12 2.4282424 -2.5635152
13 2.2282424 2.4282424
14 5.9282424 2.2282424
15 0.9818182 5.9282424
16 -4.2181818 0.9818182
17 2.6018182 -4.2181818
18 0.2618182 2.6018182
19 0.2218182 0.2618182
20 0.8618182 0.2218182
21 -2.1581818 0.8618182
22 -3.4781818 -2.1581818
23 4.2618182 -3.4781818
24 -6.8464242 4.2618182
25 -3.4464242 -6.8464242
26 -11.8464242 -3.4464242
27 7.2071515 -11.8464242
28 -1.1928485 7.2071515
29 3.8271515 -1.1928485
30 -3.2128485 3.8271515
31 2.4471515 -3.2128485
32 2.0871515 2.4471515
33 -7.6328485 2.0871515
34 0.5471515 -7.6328485
35 2.4871515 0.5471515
36 -0.5210909 2.4871515
37 -0.8210909 -0.5210909
38 2.8789091 -0.8210909
39 -8.9353939 2.8789091
40 3.1646061 -8.9353939
41 -3.0153939 3.1646061
42 -6.5553939 -3.0153939
43 -1.0953939 -6.5553939
44 -2.1553939 -1.0953939
45 -3.2753939 -2.1553939
46 5.5046061 -3.2753939
47 -0.2553939 5.5046061
48 4.0363636 -0.2553939
49 2.5363636 4.0363636
50 2.1363636 2.5363636
51 -3.5100606 2.1363636
52 3.1899394 -3.5100606
53 -0.3900606 3.1899394
54 1.3699394 -0.3900606
55 5.0299394 1.3699394
56 -5.5300606 5.0299394
57 7.7499394 -5.5300606
58 3.9299394 7.7499394
59 -3.9300606 3.9299394
> 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/72ykl1227721969.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/82nhh1227721969.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jlth1227721969.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10zr4l1227721969.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11g6tf1227721969.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/12obl21227721970.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/13hvm81227721970.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/14xr521227721970.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/15q7w01227721970.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/16m80o1227721970.tab")
+ }
>
> system("convert tmp/1npai1227721969.ps tmp/1npai1227721969.png")
> system("convert tmp/2ik671227721969.ps tmp/2ik671227721969.png")
> system("convert tmp/3ga0d1227721969.ps tmp/3ga0d1227721969.png")
> system("convert tmp/430d41227721969.ps tmp/430d41227721969.png")
> system("convert tmp/50zsz1227721969.ps tmp/50zsz1227721969.png")
> system("convert tmp/6z0oz1227721969.ps tmp/6z0oz1227721969.png")
> system("convert tmp/72ykl1227721969.ps tmp/72ykl1227721969.png")
> system("convert tmp/82nhh1227721969.ps tmp/82nhh1227721969.png")
> system("convert tmp/9jlth1227721969.ps tmp/9jlth1227721969.png")
> system("convert tmp/10zr4l1227721969.ps tmp/10zr4l1227721969.png")
>
>
> proc.time()
user system elapsed
4.940 2.732 5.321