R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(104.08,99.2,103.86,93.6,107.47,104.2,111.1,95.3,117.33,102.7,119.04,103.1,123.68,100,125.9,107.2,124.54,107,119.39,119,118.8,110.4,114.81,101.7,117.9,102.4,120.53,98.8,125.15,105.6,126.49,104.4,131.85,106.3,127.4,107.2,131.08,108.5,122.37,106.9,124.34,114.2,119.61,125.9,119.97,110.6,116.46,110.5,117.03,106.7,120.96,104.7,124.71,107.4,127.08,109.8,131.91,103.4,137.69,114.8,142.46,114.3,144.32,109.6,138.06,118.3,124.45,127.3,126.71,112.3,121.83,114.9,122.51,108.2,125.48,105.4,127.77,122.1,128.03,113.5,132.84,110,133.41,125.3,139.99,114.3,138.53,115.6,136.12,127.1,124.75,123,122.88,122.2,121.46,126.4,118.4,112.7,122.45,105.8,128.94,120.9,133.25,116.3,137.94,115.7,140.04,127.9,130.74,108.3,131.55,121.1,129.47,128.6,125.45,123.1,127.87,127.7,124.68,126.6),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
1 104.08 99.2 1 0 0 0 0 0 0 0 0 0 0 1
2 103.86 93.6 0 1 0 0 0 0 0 0 0 0 0 2
3 107.47 104.2 0 0 1 0 0 0 0 0 0 0 0 3
4 111.10 95.3 0 0 0 1 0 0 0 0 0 0 0 4
5 117.33 102.7 0 0 0 0 1 0 0 0 0 0 0 5
6 119.04 103.1 0 0 0 0 0 1 0 0 0 0 0 6
7 123.68 100.0 0 0 0 0 0 0 1 0 0 0 0 7
8 125.90 107.2 0 0 0 0 0 0 0 1 0 0 0 8
9 124.54 107.0 0 0 0 0 0 0 0 0 1 0 0 9
10 119.39 119.0 0 0 0 0 0 0 0 0 0 1 0 10
11 118.80 110.4 0 0 0 0 0 0 0 0 0 0 1 11
12 114.81 101.7 0 0 0 0 0 0 0 0 0 0 0 12
13 117.90 102.4 1 0 0 0 0 0 0 0 0 0 0 13
14 120.53 98.8 0 1 0 0 0 0 0 0 0 0 0 14
15 125.15 105.6 0 0 1 0 0 0 0 0 0 0 0 15
16 126.49 104.4 0 0 0 1 0 0 0 0 0 0 0 16
17 131.85 106.3 0 0 0 0 1 0 0 0 0 0 0 17
18 127.40 107.2 0 0 0 0 0 1 0 0 0 0 0 18
19 131.08 108.5 0 0 0 0 0 0 1 0 0 0 0 19
20 122.37 106.9 0 0 0 0 0 0 0 1 0 0 0 20
21 124.34 114.2 0 0 0 0 0 0 0 0 1 0 0 21
22 119.61 125.9 0 0 0 0 0 0 0 0 0 1 0 22
23 119.97 110.6 0 0 0 0 0 0 0 0 0 0 1 23
24 116.46 110.5 0 0 0 0 0 0 0 0 0 0 0 24
25 117.03 106.7 1 0 0 0 0 0 0 0 0 0 0 25
26 120.96 104.7 0 1 0 0 0 0 0 0 0 0 0 26
27 124.71 107.4 0 0 1 0 0 0 0 0 0 0 0 27
28 127.08 109.8 0 0 0 1 0 0 0 0 0 0 0 28
29 131.91 103.4 0 0 0 0 1 0 0 0 0 0 0 29
30 137.69 114.8 0 0 0 0 0 1 0 0 0 0 0 30
31 142.46 114.3 0 0 0 0 0 0 1 0 0 0 0 31
32 144.32 109.6 0 0 0 0 0 0 0 1 0 0 0 32
33 138.06 118.3 0 0 0 0 0 0 0 0 1 0 0 33
34 124.45 127.3 0 0 0 0 0 0 0 0 0 1 0 34
35 126.71 112.3 0 0 0 0 0 0 0 0 0 0 1 35
36 121.83 114.9 0 0 0 0 0 0 0 0 0 0 0 36
37 122.51 108.2 1 0 0 0 0 0 0 0 0 0 0 37
38 125.48 105.4 0 1 0 0 0 0 0 0 0 0 0 38
39 127.77 122.1 0 0 1 0 0 0 0 0 0 0 0 39
40 128.03 113.5 0 0 0 1 0 0 0 0 0 0 0 40
41 132.84 110.0 0 0 0 0 1 0 0 0 0 0 0 41
42 133.41 125.3 0 0 0 0 0 1 0 0 0 0 0 42
43 139.99 114.3 0 0 0 0 0 0 1 0 0 0 0 43
44 138.53 115.6 0 0 0 0 0 0 0 1 0 0 0 44
45 136.12 127.1 0 0 0 0 0 0 0 0 1 0 0 45
46 124.75 123.0 0 0 0 0 0 0 0 0 0 1 0 46
47 122.88 122.2 0 0 0 0 0 0 0 0 0 0 1 47
48 121.46 126.4 0 0 0 0 0 0 0 0 0 0 0 48
49 118.40 112.7 1 0 0 0 0 0 0 0 0 0 0 49
50 122.45 105.8 0 1 0 0 0 0 0 0 0 0 0 50
51 128.94 120.9 0 0 1 0 0 0 0 0 0 0 0 51
52 133.25 116.3 0 0 0 1 0 0 0 0 0 0 0 52
53 137.94 115.7 0 0 0 0 1 0 0 0 0 0 0 53
54 140.04 127.9 0 0 0 0 0 1 0 0 0 0 0 54
55 130.74 108.3 0 0 0 0 0 0 1 0 0 0 0 55
56 131.55 121.1 0 0 0 0 0 0 0 1 0 0 0 56
57 129.47 128.6 0 0 0 0 0 0 0 0 1 0 0 57
58 125.45 123.1 0 0 0 0 0 0 0 0 0 1 0 58
59 127.87 127.7 0 0 0 0 0 0 0 0 0 0 1 59
60 124.68 126.6 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
85.7644 0.2342 0.6316 4.0907 5.6196 8.7887
M5 M6 M7 M8 M9 M10
13.8370 12.9039 16.3271 14.3765 10.5265 1.4764
M11 t
3.4447 0.1919
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.8954 -2.6826 0.3722 2.8698 12.3669
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 85.76443 19.64365 4.366 7.11e-05 ***
X 0.23422 0.18780 1.247 0.218639
M1 0.63160 3.41665 0.185 0.854153
M2 4.09070 3.79573 1.078 0.286783
M3 5.61958 3.19331 1.760 0.085091 .
M4 8.78868 3.34336 2.629 0.011613 *
M5 13.83696 3.37505 4.100 0.000167 ***
M6 12.90391 3.19465 4.039 0.000202 ***
M7 16.32714 3.32416 4.912 1.18e-05 ***
M8 14.37655 3.21047 4.478 4.95e-05 ***
M9 10.52646 3.26237 3.227 0.002310 **
M10 1.47644 3.53589 0.418 0.678214
M11 3.44472 3.17580 1.085 0.283717
t 0.19193 0.07491 2.562 0.013742 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.013 on 46 degrees of freedom
Multiple R-squared: 0.7387, Adjusted R-squared: 0.6648
F-statistic: 10 on 13 and 46 DF, p-value: 1.777e-09
> 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,] 0.02861769 0.05723538 0.97138231
[2,] 0.14365228 0.28730456 0.85634772
[3,] 0.15414700 0.30829400 0.84585300
[4,] 0.79898903 0.40202193 0.20101097
[5,] 0.92549329 0.14901341 0.07450671
[6,] 0.94845883 0.10308234 0.05154117
[7,] 0.94480697 0.11038606 0.05519303
[8,] 0.94893511 0.10212978 0.05106489
[9,] 0.93820166 0.12359669 0.06179834
[10,] 0.91794237 0.16411526 0.08205763
[11,] 0.88191671 0.23616658 0.11808329
[12,] 0.85763841 0.28472317 0.14236159
[13,] 0.82590503 0.34818994 0.17409497
[14,] 0.77078778 0.45842443 0.22921222
[15,] 0.74297834 0.51404333 0.25702166
[16,] 0.87209043 0.25581915 0.12790957
[17,] 0.86293302 0.27413396 0.13706698
[18,] 0.83827727 0.32344547 0.16172273
[19,] 0.80097267 0.39805467 0.19902733
[20,] 0.75197588 0.49604825 0.24802412
[21,] 0.74097498 0.51805003 0.25902502
[22,] 0.65438052 0.69123897 0.34561948
[23,] 0.58611718 0.82776565 0.41388282
[24,] 0.54012209 0.91975582 0.45987791
[25,] 0.44793133 0.89586266 0.55206867
[26,] 0.48458865 0.96917729 0.51541135
[27,] 0.42447662 0.84895324 0.57552338
> postscript(file="/var/www/html/rcomp/tmp/1kqid1258763158.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mzab1258763158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34hrs1258763158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xtv11258763158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5q9871258763158.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 7
-5.7424159 -8.3018260 -8.8953540 -6.5418438 -7.2852713 -4.9278438 -3.1769233
8 9 10 11 12 13 14
-0.8846400 1.4603628 2.3578319 1.6218968 2.9223804 5.0248982 4.8470516
15 16 17 18 19 20 21
6.1535530 4.4135826 4.0883555 0.1686739 -0.0709660 -6.6475620 -2.7291962
22 23 24 25 26 27 28
-1.3414615 0.4418657 0.2080722 0.8445723 1.5919765 2.9887728 1.4356166
29 30 31 32 33 34 35
2.5244011 6.3754277 7.6473806 12.3668613 7.7273215 0.8674455 4.4805073
36 37 38 39 40 41 42
2.2443245 3.6700575 3.6448363 0.3025768 -0.7841784 -0.3946269 -2.6670515
43 44 45 46 47 48 49
2.8741932 2.8683643 1.4230134 -0.1286033 -3.9714410 -3.1223730 -3.7971121
50 51 52 53 54 55 56
-1.7820384 -0.5495487 1.4768230 1.0671416 1.0507936 -7.2736846 -7.7030236
57 58 59 60
-7.8815014 -1.7552126 -2.5728288 -2.2524041
> postscript(file="/var/www/html/rcomp/tmp/69zsu1258763158.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 -5.7424159 NA
1 -8.3018260 -5.7424159
2 -8.8953540 -8.3018260
3 -6.5418438 -8.8953540
4 -7.2852713 -6.5418438
5 -4.9278438 -7.2852713
6 -3.1769233 -4.9278438
7 -0.8846400 -3.1769233
8 1.4603628 -0.8846400
9 2.3578319 1.4603628
10 1.6218968 2.3578319
11 2.9223804 1.6218968
12 5.0248982 2.9223804
13 4.8470516 5.0248982
14 6.1535530 4.8470516
15 4.4135826 6.1535530
16 4.0883555 4.4135826
17 0.1686739 4.0883555
18 -0.0709660 0.1686739
19 -6.6475620 -0.0709660
20 -2.7291962 -6.6475620
21 -1.3414615 -2.7291962
22 0.4418657 -1.3414615
23 0.2080722 0.4418657
24 0.8445723 0.2080722
25 1.5919765 0.8445723
26 2.9887728 1.5919765
27 1.4356166 2.9887728
28 2.5244011 1.4356166
29 6.3754277 2.5244011
30 7.6473806 6.3754277
31 12.3668613 7.6473806
32 7.7273215 12.3668613
33 0.8674455 7.7273215
34 4.4805073 0.8674455
35 2.2443245 4.4805073
36 3.6700575 2.2443245
37 3.6448363 3.6700575
38 0.3025768 3.6448363
39 -0.7841784 0.3025768
40 -0.3946269 -0.7841784
41 -2.6670515 -0.3946269
42 2.8741932 -2.6670515
43 2.8683643 2.8741932
44 1.4230134 2.8683643
45 -0.1286033 1.4230134
46 -3.9714410 -0.1286033
47 -3.1223730 -3.9714410
48 -3.7971121 -3.1223730
49 -1.7820384 -3.7971121
50 -0.5495487 -1.7820384
51 1.4768230 -0.5495487
52 1.0671416 1.4768230
53 1.0507936 1.0671416
54 -7.2736846 1.0507936
55 -7.7030236 -7.2736846
56 -7.8815014 -7.7030236
57 -1.7552126 -7.8815014
58 -2.5728288 -1.7552126
59 -2.2524041 -2.5728288
60 NA -2.2524041
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -8.3018260 -5.7424159
[2,] -8.8953540 -8.3018260
[3,] -6.5418438 -8.8953540
[4,] -7.2852713 -6.5418438
[5,] -4.9278438 -7.2852713
[6,] -3.1769233 -4.9278438
[7,] -0.8846400 -3.1769233
[8,] 1.4603628 -0.8846400
[9,] 2.3578319 1.4603628
[10,] 1.6218968 2.3578319
[11,] 2.9223804 1.6218968
[12,] 5.0248982 2.9223804
[13,] 4.8470516 5.0248982
[14,] 6.1535530 4.8470516
[15,] 4.4135826 6.1535530
[16,] 4.0883555 4.4135826
[17,] 0.1686739 4.0883555
[18,] -0.0709660 0.1686739
[19,] -6.6475620 -0.0709660
[20,] -2.7291962 -6.6475620
[21,] -1.3414615 -2.7291962
[22,] 0.4418657 -1.3414615
[23,] 0.2080722 0.4418657
[24,] 0.8445723 0.2080722
[25,] 1.5919765 0.8445723
[26,] 2.9887728 1.5919765
[27,] 1.4356166 2.9887728
[28,] 2.5244011 1.4356166
[29,] 6.3754277 2.5244011
[30,] 7.6473806 6.3754277
[31,] 12.3668613 7.6473806
[32,] 7.7273215 12.3668613
[33,] 0.8674455 7.7273215
[34,] 4.4805073 0.8674455
[35,] 2.2443245 4.4805073
[36,] 3.6700575 2.2443245
[37,] 3.6448363 3.6700575
[38,] 0.3025768 3.6448363
[39,] -0.7841784 0.3025768
[40,] -0.3946269 -0.7841784
[41,] -2.6670515 -0.3946269
[42,] 2.8741932 -2.6670515
[43,] 2.8683643 2.8741932
[44,] 1.4230134 2.8683643
[45,] -0.1286033 1.4230134
[46,] -3.9714410 -0.1286033
[47,] -3.1223730 -3.9714410
[48,] -3.7971121 -3.1223730
[49,] -1.7820384 -3.7971121
[50,] -0.5495487 -1.7820384
[51,] 1.4768230 -0.5495487
[52,] 1.0671416 1.4768230
[53,] 1.0507936 1.0671416
[54,] -7.2736846 1.0507936
[55,] -7.7030236 -7.2736846
[56,] -7.8815014 -7.7030236
[57,] -1.7552126 -7.8815014
[58,] -2.5728288 -1.7552126
[59,] -2.2524041 -2.5728288
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -8.3018260 -5.7424159
2 -8.8953540 -8.3018260
3 -6.5418438 -8.8953540
4 -7.2852713 -6.5418438
5 -4.9278438 -7.2852713
6 -3.1769233 -4.9278438
7 -0.8846400 -3.1769233
8 1.4603628 -0.8846400
9 2.3578319 1.4603628
10 1.6218968 2.3578319
11 2.9223804 1.6218968
12 5.0248982 2.9223804
13 4.8470516 5.0248982
14 6.1535530 4.8470516
15 4.4135826 6.1535530
16 4.0883555 4.4135826
17 0.1686739 4.0883555
18 -0.0709660 0.1686739
19 -6.6475620 -0.0709660
20 -2.7291962 -6.6475620
21 -1.3414615 -2.7291962
22 0.4418657 -1.3414615
23 0.2080722 0.4418657
24 0.8445723 0.2080722
25 1.5919765 0.8445723
26 2.9887728 1.5919765
27 1.4356166 2.9887728
28 2.5244011 1.4356166
29 6.3754277 2.5244011
30 7.6473806 6.3754277
31 12.3668613 7.6473806
32 7.7273215 12.3668613
33 0.8674455 7.7273215
34 4.4805073 0.8674455
35 2.2443245 4.4805073
36 3.6700575 2.2443245
37 3.6448363 3.6700575
38 0.3025768 3.6448363
39 -0.7841784 0.3025768
40 -0.3946269 -0.7841784
41 -2.6670515 -0.3946269
42 2.8741932 -2.6670515
43 2.8683643 2.8741932
44 1.4230134 2.8683643
45 -0.1286033 1.4230134
46 -3.9714410 -0.1286033
47 -3.1223730 -3.9714410
48 -3.7971121 -3.1223730
49 -1.7820384 -3.7971121
50 -0.5495487 -1.7820384
51 1.4768230 -0.5495487
52 1.0671416 1.4768230
53 1.0507936 1.0671416
54 -7.2736846 1.0507936
55 -7.7030236 -7.2736846
56 -7.8815014 -7.7030236
57 -1.7552126 -7.8815014
58 -2.5728288 -1.7552126
59 -2.2524041 -2.5728288
> 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/7azhi1258763158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80tjm1258763158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/917mb1258763158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hsn81258763158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1172if1258763158.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/1232ge1258763158.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/1364r71258763158.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/14xxeg1258763158.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/15ahov1258763158.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/16z3e91258763158.tab")
+ }
>
> system("convert tmp/1kqid1258763158.ps tmp/1kqid1258763158.png")
> system("convert tmp/2mzab1258763158.ps tmp/2mzab1258763158.png")
> system("convert tmp/34hrs1258763158.ps tmp/34hrs1258763158.png")
> system("convert tmp/4xtv11258763158.ps tmp/4xtv11258763158.png")
> system("convert tmp/5q9871258763158.ps tmp/5q9871258763158.png")
> system("convert tmp/69zsu1258763158.ps tmp/69zsu1258763158.png")
> system("convert tmp/7azhi1258763158.ps tmp/7azhi1258763158.png")
> system("convert tmp/80tjm1258763158.ps tmp/80tjm1258763158.png")
> system("convert tmp/917mb1258763158.ps tmp/917mb1258763158.png")
> system("convert tmp/10hsn81258763158.ps tmp/10hsn81258763158.png")
>
>
> proc.time()
user system elapsed
2.388 1.530 3.691