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(8.9,95.05,8.8,96.84,8.3,96.92,7.5,97.44,7.2,97.78,7.4,97.69,8.8,96.67,9.3,98.29,9.3,98.2,8.7,98.71,8.2,98.54,8.3,98.2,8.5,96.92,8.6,99.06,8.5,99.65,8.2,99.82,8.1,99.99,7.9,100.33,8.6,99.31,8.7,101.1,8.7,101.1,8.5,100.93,8.4,100.85,8.5,100.93,8.7,99.6,8.7,101.88,8.6,101.81,8.5,102.38,8.3,102.74,8,102.82,8.2,101.72,8.1,103.47,8.1,102.98,8,102.68,7.9,102.9,7.9,103.03,8,101.29,8,103.69,7.9,103.68,8,104.2,7.7,104.08,7.2,104.16,7.5,103.05,7.3,104.66,7,104.46,7,104.95,7,105.85,7.2,106.23,7.3,104.86,7.1,107.44,6.8,108.23,6.4,108.45,6.1,109.39,6.5,110.15,7.7,109.13,7.9,110.28,7.5,110.17,6.9,109.99,6.6,109.26,6.9,109.11),dim=c(2,60),dimnames=list(c('Werkloosheidsgraad','Consumptieprijs'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Werkloosheidsgraad','Consumptieprijs'),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
Werkloosheidsgraad Consumptieprijs M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.9 95.05 1 0 0 0 0 0 0 0 0 0 0 1
2 8.8 96.84 0 1 0 0 0 0 0 0 0 0 0 2
3 8.3 96.92 0 0 1 0 0 0 0 0 0 0 0 3
4 7.5 97.44 0 0 0 1 0 0 0 0 0 0 0 4
5 7.2 97.78 0 0 0 0 1 0 0 0 0 0 0 5
6 7.4 97.69 0 0 0 0 0 1 0 0 0 0 0 6
7 8.8 96.67 0 0 0 0 0 0 1 0 0 0 0 7
8 9.3 98.29 0 0 0 0 0 0 0 1 0 0 0 8
9 9.3 98.20 0 0 0 0 0 0 0 0 1 0 0 9
10 8.7 98.71 0 0 0 0 0 0 0 0 0 1 0 10
11 8.2 98.54 0 0 0 0 0 0 0 0 0 0 1 11
12 8.3 98.20 0 0 0 0 0 0 0 0 0 0 0 12
13 8.5 96.92 1 0 0 0 0 0 0 0 0 0 0 13
14 8.6 99.06 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 99.65 0 0 1 0 0 0 0 0 0 0 0 15
16 8.2 99.82 0 0 0 1 0 0 0 0 0 0 0 16
17 8.1 99.99 0 0 0 0 1 0 0 0 0 0 0 17
18 7.9 100.33 0 0 0 0 0 1 0 0 0 0 0 18
19 8.6 99.31 0 0 0 0 0 0 1 0 0 0 0 19
20 8.7 101.10 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 101.10 0 0 0 0 0 0 0 0 1 0 0 21
22 8.5 100.93 0 0 0 0 0 0 0 0 0 1 0 22
23 8.4 100.85 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 100.93 0 0 0 0 0 0 0 0 0 0 0 24
25 8.7 99.60 1 0 0 0 0 0 0 0 0 0 0 25
26 8.7 101.88 0 1 0 0 0 0 0 0 0 0 0 26
27 8.6 101.81 0 0 1 0 0 0 0 0 0 0 0 27
28 8.5 102.38 0 0 0 1 0 0 0 0 0 0 0 28
29 8.3 102.74 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 102.82 0 0 0 0 0 1 0 0 0 0 0 30
31 8.2 101.72 0 0 0 0 0 0 1 0 0 0 0 31
32 8.1 103.47 0 0 0 0 0 0 0 1 0 0 0 32
33 8.1 102.98 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 102.68 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 102.90 0 0 0 0 0 0 0 0 0 0 1 35
36 7.9 103.03 0 0 0 0 0 0 0 0 0 0 0 36
37 8.0 101.29 1 0 0 0 0 0 0 0 0 0 0 37
38 8.0 103.69 0 1 0 0 0 0 0 0 0 0 0 38
39 7.9 103.68 0 0 1 0 0 0 0 0 0 0 0 39
40 8.0 104.20 0 0 0 1 0 0 0 0 0 0 0 40
41 7.7 104.08 0 0 0 0 1 0 0 0 0 0 0 41
42 7.2 104.16 0 0 0 0 0 1 0 0 0 0 0 42
43 7.5 103.05 0 0 0 0 0 0 1 0 0 0 0 43
44 7.3 104.66 0 0 0 0 0 0 0 1 0 0 0 44
45 7.0 104.46 0 0 0 0 0 0 0 0 1 0 0 45
46 7.0 104.95 0 0 0 0 0 0 0 0 0 1 0 46
47 7.0 105.85 0 0 0 0 0 0 0 0 0 0 1 47
48 7.2 106.23 0 0 0 0 0 0 0 0 0 0 0 48
49 7.3 104.86 1 0 0 0 0 0 0 0 0 0 0 49
50 7.1 107.44 0 1 0 0 0 0 0 0 0 0 0 50
51 6.8 108.23 0 0 1 0 0 0 0 0 0 0 0 51
52 6.4 108.45 0 0 0 1 0 0 0 0 0 0 0 52
53 6.1 109.39 0 0 0 0 1 0 0 0 0 0 0 53
54 6.5 110.15 0 0 0 0 0 1 0 0 0 0 0 54
55 7.7 109.13 0 0 0 0 0 0 1 0 0 0 0 55
56 7.9 110.28 0 0 0 0 0 0 0 1 0 0 0 56
57 7.5 110.17 0 0 0 0 0 0 0 0 1 0 0 57
58 6.9 109.99 0 0 0 0 0 0 0 0 0 1 0 58
59 6.6 109.26 0 0 0 0 0 0 0 0 0 0 1 59
60 6.9 109.11 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) Consumptieprijs M1 M2
12.623217 -0.039065 0.114912 0.185117
M3 M4 M5 M6
-0.001324 -0.262921 -0.466940 -0.515022
M7 M8 M9 M10
0.226580 0.411236 0.287060 0.012571
M11 t
-0.163558 -0.022777
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.02260 -0.21669 0.06203 0.26488 0.81781
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.623217 7.494561 1.684 0.0989 .
Consumptieprijs -0.039065 0.078488 -0.498 0.6211
M1 0.114912 0.319037 0.360 0.7204
M2 0.185117 0.299146 0.619 0.5391
M3 -0.001324 0.299335 -0.004 0.9965
M4 -0.262921 0.301371 -0.872 0.3875
M5 -0.466940 0.302935 -1.541 0.1301
M6 -0.515022 0.302843 -1.701 0.0958 .
M7 0.226580 0.296438 0.764 0.4486
M8 0.411236 0.303954 1.353 0.1827
M9 0.287060 0.297591 0.965 0.3398
M10 0.012571 0.295961 0.042 0.9663
M11 -0.163558 0.294662 -0.555 0.5815
t -0.022777 0.017935 -1.270 0.2105
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4652 on 46 degrees of freedom
Multiple R-squared: 0.6946, Adjusted R-squared: 0.6083
F-statistic: 8.049 on 13 and 46 DF, p-value: 4.648e-08
> 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,] 0.71431290 0.57137419 0.28568710
[2,] 0.58482769 0.83034462 0.41517231
[3,] 0.61445199 0.77109602 0.38554801
[4,] 0.72099247 0.55801507 0.27900753
[5,] 0.69724681 0.60550637 0.30275319
[6,] 0.61706931 0.76586138 0.38293069
[7,] 0.51131880 0.97736240 0.48868120
[8,] 0.42629357 0.85258714 0.57370643
[9,] 0.32462238 0.64924476 0.67537762
[10,] 0.23341791 0.46683583 0.76658209
[11,] 0.16521218 0.33042437 0.83478782
[12,] 0.17519789 0.35039579 0.82480211
[13,] 0.17924837 0.35849674 0.82075163
[14,] 0.12831155 0.25662310 0.87168845
[15,] 0.15255959 0.30511919 0.84744041
[16,] 0.27854013 0.55708025 0.72145987
[17,] 0.33278525 0.66557050 0.66721475
[18,] 0.25906160 0.51812320 0.74093840
[19,] 0.18431966 0.36863933 0.81568034
[20,] 0.14174395 0.28348790 0.85825605
[21,] 0.09263630 0.18527260 0.90736370
[22,] 0.06093603 0.12187207 0.93906397
[23,] 0.04523796 0.09047591 0.95476204
[24,] 0.09368758 0.18737515 0.90631242
[25,] 0.53384356 0.93231287 0.46615644
[26,] 0.91205812 0.17588377 0.08794188
[27,] 0.87651007 0.24697987 0.12348993
> postscript(file="/var/www/html/rcomp/tmp/1zor21258555889.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mz461258555889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/395n51258555889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zv6g1258555889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/596xg1258555889.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
-0.10220465 -0.17970586 -0.46736264 -0.96267481 -1.02259668 -0.75525381
7 8 9 10 11 12
-0.11392559 0.28748076 0.43091849 0.14810718 -0.15962773 -0.21369120
13 14 15 16 17 18
-0.15582991 -0.01965829 0.11260818 0.10362318 0.23706023 0.12120114
19 20 21 22 23 24
0.06252936 0.07057679 0.21753039 0.30815475 0.40393570 0.36627962
25 26 27 28 29 30
0.42218765 0.46382839 0.57031183 0.77695291 0.81781235 0.59179631
31 32 33 34 35 36
0.02999931 -0.16351587 -0.03570421 0.14984167 0.25734218 0.12163936
37 38 39 40 41 42
0.06153066 0.10785922 0.21668658 0.62137440 0.54348254 0.11746650
43 44 45 46 47 48
-0.34472115 -0.64370546 -0.80456489 -0.48815751 -0.25409266 -0.18002918
49 50 51 52 53 54
-0.22568376 -0.37232346 -0.43224395 -0.53927568 -0.57575843 -0.07521014
55 56 57 58 59 60
0.36611808 0.44916378 0.19182022 -0.11794608 -0.24755750 -0.09419859
> postscript(file="/var/www/html/rcomp/tmp/6hspz1258555889.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 -0.10220465 NA
1 -0.17970586 -0.10220465
2 -0.46736264 -0.17970586
3 -0.96267481 -0.46736264
4 -1.02259668 -0.96267481
5 -0.75525381 -1.02259668
6 -0.11392559 -0.75525381
7 0.28748076 -0.11392559
8 0.43091849 0.28748076
9 0.14810718 0.43091849
10 -0.15962773 0.14810718
11 -0.21369120 -0.15962773
12 -0.15582991 -0.21369120
13 -0.01965829 -0.15582991
14 0.11260818 -0.01965829
15 0.10362318 0.11260818
16 0.23706023 0.10362318
17 0.12120114 0.23706023
18 0.06252936 0.12120114
19 0.07057679 0.06252936
20 0.21753039 0.07057679
21 0.30815475 0.21753039
22 0.40393570 0.30815475
23 0.36627962 0.40393570
24 0.42218765 0.36627962
25 0.46382839 0.42218765
26 0.57031183 0.46382839
27 0.77695291 0.57031183
28 0.81781235 0.77695291
29 0.59179631 0.81781235
30 0.02999931 0.59179631
31 -0.16351587 0.02999931
32 -0.03570421 -0.16351587
33 0.14984167 -0.03570421
34 0.25734218 0.14984167
35 0.12163936 0.25734218
36 0.06153066 0.12163936
37 0.10785922 0.06153066
38 0.21668658 0.10785922
39 0.62137440 0.21668658
40 0.54348254 0.62137440
41 0.11746650 0.54348254
42 -0.34472115 0.11746650
43 -0.64370546 -0.34472115
44 -0.80456489 -0.64370546
45 -0.48815751 -0.80456489
46 -0.25409266 -0.48815751
47 -0.18002918 -0.25409266
48 -0.22568376 -0.18002918
49 -0.37232346 -0.22568376
50 -0.43224395 -0.37232346
51 -0.53927568 -0.43224395
52 -0.57575843 -0.53927568
53 -0.07521014 -0.57575843
54 0.36611808 -0.07521014
55 0.44916378 0.36611808
56 0.19182022 0.44916378
57 -0.11794608 0.19182022
58 -0.24755750 -0.11794608
59 -0.09419859 -0.24755750
60 NA -0.09419859
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.17970586 -0.10220465
[2,] -0.46736264 -0.17970586
[3,] -0.96267481 -0.46736264
[4,] -1.02259668 -0.96267481
[5,] -0.75525381 -1.02259668
[6,] -0.11392559 -0.75525381
[7,] 0.28748076 -0.11392559
[8,] 0.43091849 0.28748076
[9,] 0.14810718 0.43091849
[10,] -0.15962773 0.14810718
[11,] -0.21369120 -0.15962773
[12,] -0.15582991 -0.21369120
[13,] -0.01965829 -0.15582991
[14,] 0.11260818 -0.01965829
[15,] 0.10362318 0.11260818
[16,] 0.23706023 0.10362318
[17,] 0.12120114 0.23706023
[18,] 0.06252936 0.12120114
[19,] 0.07057679 0.06252936
[20,] 0.21753039 0.07057679
[21,] 0.30815475 0.21753039
[22,] 0.40393570 0.30815475
[23,] 0.36627962 0.40393570
[24,] 0.42218765 0.36627962
[25,] 0.46382839 0.42218765
[26,] 0.57031183 0.46382839
[27,] 0.77695291 0.57031183
[28,] 0.81781235 0.77695291
[29,] 0.59179631 0.81781235
[30,] 0.02999931 0.59179631
[31,] -0.16351587 0.02999931
[32,] -0.03570421 -0.16351587
[33,] 0.14984167 -0.03570421
[34,] 0.25734218 0.14984167
[35,] 0.12163936 0.25734218
[36,] 0.06153066 0.12163936
[37,] 0.10785922 0.06153066
[38,] 0.21668658 0.10785922
[39,] 0.62137440 0.21668658
[40,] 0.54348254 0.62137440
[41,] 0.11746650 0.54348254
[42,] -0.34472115 0.11746650
[43,] -0.64370546 -0.34472115
[44,] -0.80456489 -0.64370546
[45,] -0.48815751 -0.80456489
[46,] -0.25409266 -0.48815751
[47,] -0.18002918 -0.25409266
[48,] -0.22568376 -0.18002918
[49,] -0.37232346 -0.22568376
[50,] -0.43224395 -0.37232346
[51,] -0.53927568 -0.43224395
[52,] -0.57575843 -0.53927568
[53,] -0.07521014 -0.57575843
[54,] 0.36611808 -0.07521014
[55,] 0.44916378 0.36611808
[56,] 0.19182022 0.44916378
[57,] -0.11794608 0.19182022
[58,] -0.24755750 -0.11794608
[59,] -0.09419859 -0.24755750
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.17970586 -0.10220465
2 -0.46736264 -0.17970586
3 -0.96267481 -0.46736264
4 -1.02259668 -0.96267481
5 -0.75525381 -1.02259668
6 -0.11392559 -0.75525381
7 0.28748076 -0.11392559
8 0.43091849 0.28748076
9 0.14810718 0.43091849
10 -0.15962773 0.14810718
11 -0.21369120 -0.15962773
12 -0.15582991 -0.21369120
13 -0.01965829 -0.15582991
14 0.11260818 -0.01965829
15 0.10362318 0.11260818
16 0.23706023 0.10362318
17 0.12120114 0.23706023
18 0.06252936 0.12120114
19 0.07057679 0.06252936
20 0.21753039 0.07057679
21 0.30815475 0.21753039
22 0.40393570 0.30815475
23 0.36627962 0.40393570
24 0.42218765 0.36627962
25 0.46382839 0.42218765
26 0.57031183 0.46382839
27 0.77695291 0.57031183
28 0.81781235 0.77695291
29 0.59179631 0.81781235
30 0.02999931 0.59179631
31 -0.16351587 0.02999931
32 -0.03570421 -0.16351587
33 0.14984167 -0.03570421
34 0.25734218 0.14984167
35 0.12163936 0.25734218
36 0.06153066 0.12163936
37 0.10785922 0.06153066
38 0.21668658 0.10785922
39 0.62137440 0.21668658
40 0.54348254 0.62137440
41 0.11746650 0.54348254
42 -0.34472115 0.11746650
43 -0.64370546 -0.34472115
44 -0.80456489 -0.64370546
45 -0.48815751 -0.80456489
46 -0.25409266 -0.48815751
47 -0.18002918 -0.25409266
48 -0.22568376 -0.18002918
49 -0.37232346 -0.22568376
50 -0.43224395 -0.37232346
51 -0.53927568 -0.43224395
52 -0.57575843 -0.53927568
53 -0.07521014 -0.57575843
54 0.36611808 -0.07521014
55 0.44916378 0.36611808
56 0.19182022 0.44916378
57 -0.11794608 0.19182022
58 -0.24755750 -0.11794608
59 -0.09419859 -0.24755750
> 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/7sucn1258555889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8azsd1258555889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qlw91258555889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10acpg1258555889.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hi3j1258555889.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/12drhh1258555889.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/13ng0j1258555889.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/149c2m1258555889.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/15p3zc1258555889.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/16234v1258555889.tab")
+ }
>
> system("convert tmp/1zor21258555889.ps tmp/1zor21258555889.png")
> system("convert tmp/2mz461258555889.ps tmp/2mz461258555889.png")
> system("convert tmp/395n51258555889.ps tmp/395n51258555889.png")
> system("convert tmp/4zv6g1258555889.ps tmp/4zv6g1258555889.png")
> system("convert tmp/596xg1258555889.ps tmp/596xg1258555889.png")
> system("convert tmp/6hspz1258555889.ps tmp/6hspz1258555889.png")
> system("convert tmp/7sucn1258555889.ps tmp/7sucn1258555889.png")
> system("convert tmp/8azsd1258555889.ps tmp/8azsd1258555889.png")
> system("convert tmp/9qlw91258555889.ps tmp/9qlw91258555889.png")
> system("convert tmp/10acpg1258555889.ps tmp/10acpg1258555889.png")
>
>
> proc.time()
user system elapsed
2.399 1.571 3.306