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(110.40,0,96.40,0,101.90,0,106.20,0,81.00,0,94.70,0,101.00,1,109.40,1,102.30,1,90.70,1,96.20,1,96.10,1,106.00,1,103.10,1,102.00,1,104.70,1,86.00,1,92.10,1,106.90,1,112.60,1,101.70,1,92.00,1,97.40,1,97.00,1,105.40,1,102.70,1,98.10,1,104.50,1,87.40,1,89.90,1,109.80,1,111.70,1,98.60,1,96.90,1,95.10,1,97.00,1,112.70,1,102.90,1,97.40,1,111.40,1,87.40,1,96.80,1,114.10,1,110.30,1,103.90,1,101.60,1,94.60,1,95.90,1,104.70,1,102.80,1,98.10,1,113.90,1,80.90,1,95.70,1,113.20,1,105.90,1,108.80,1,102.30,1,99.00,1,100.70,1,115.50,1),dim=c(2,61),dimnames=list(c('IP','d'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('IP','d'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
IP d M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 110.4 0 1 0 0 0 0 0 0 0 0 0 0 1
2 96.4 0 0 1 0 0 0 0 0 0 0 0 0 2
3 101.9 0 0 0 1 0 0 0 0 0 0 0 0 3
4 106.2 0 0 0 0 1 0 0 0 0 0 0 0 4
5 81.0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 94.7 0 0 0 0 0 0 1 0 0 0 0 0 6
7 101.0 1 0 0 0 0 0 0 1 0 0 0 0 7
8 109.4 1 0 0 0 0 0 0 0 1 0 0 0 8
9 102.3 1 0 0 0 0 0 0 0 0 1 0 0 9
10 90.7 1 0 0 0 0 0 0 0 0 0 1 0 10
11 96.2 1 0 0 0 0 0 0 0 0 0 0 1 11
12 96.1 1 0 0 0 0 0 0 0 0 0 0 0 12
13 106.0 1 1 0 0 0 0 0 0 0 0 0 0 13
14 103.1 1 0 1 0 0 0 0 0 0 0 0 0 14
15 102.0 1 0 0 1 0 0 0 0 0 0 0 0 15
16 104.7 1 0 0 0 1 0 0 0 0 0 0 0 16
17 86.0 1 0 0 0 0 1 0 0 0 0 0 0 17
18 92.1 1 0 0 0 0 0 1 0 0 0 0 0 18
19 106.9 1 0 0 0 0 0 0 1 0 0 0 0 19
20 112.6 1 0 0 0 0 0 0 0 1 0 0 0 20
21 101.7 1 0 0 0 0 0 0 0 0 1 0 0 21
22 92.0 1 0 0 0 0 0 0 0 0 0 1 0 22
23 97.4 1 0 0 0 0 0 0 0 0 0 0 1 23
24 97.0 1 0 0 0 0 0 0 0 0 0 0 0 24
25 105.4 1 1 0 0 0 0 0 0 0 0 0 0 25
26 102.7 1 0 1 0 0 0 0 0 0 0 0 0 26
27 98.1 1 0 0 1 0 0 0 0 0 0 0 0 27
28 104.5 1 0 0 0 1 0 0 0 0 0 0 0 28
29 87.4 1 0 0 0 0 1 0 0 0 0 0 0 29
30 89.9 1 0 0 0 0 0 1 0 0 0 0 0 30
31 109.8 1 0 0 0 0 0 0 1 0 0 0 0 31
32 111.7 1 0 0 0 0 0 0 0 1 0 0 0 32
33 98.6 1 0 0 0 0 0 0 0 0 1 0 0 33
34 96.9 1 0 0 0 0 0 0 0 0 0 1 0 34
35 95.1 1 0 0 0 0 0 0 0 0 0 0 1 35
36 97.0 1 0 0 0 0 0 0 0 0 0 0 0 36
37 112.7 1 1 0 0 0 0 0 0 0 0 0 0 37
38 102.9 1 0 1 0 0 0 0 0 0 0 0 0 38
39 97.4 1 0 0 1 0 0 0 0 0 0 0 0 39
40 111.4 1 0 0 0 1 0 0 0 0 0 0 0 40
41 87.4 1 0 0 0 0 1 0 0 0 0 0 0 41
42 96.8 1 0 0 0 0 0 1 0 0 0 0 0 42
43 114.1 1 0 0 0 0 0 0 1 0 0 0 0 43
44 110.3 1 0 0 0 0 0 0 0 1 0 0 0 44
45 103.9 1 0 0 0 0 0 0 0 0 1 0 0 45
46 101.6 1 0 0 0 0 0 0 0 0 0 1 0 46
47 94.6 1 0 0 0 0 0 0 0 0 0 0 1 47
48 95.9 1 0 0 0 0 0 0 0 0 0 0 0 48
49 104.7 1 1 0 0 0 0 0 0 0 0 0 0 49
50 102.8 1 0 1 0 0 0 0 0 0 0 0 0 50
51 98.1 1 0 0 1 0 0 0 0 0 0 0 0 51
52 113.9 1 0 0 0 1 0 0 0 0 0 0 0 52
53 80.9 1 0 0 0 0 1 0 0 0 0 0 0 53
54 95.7 1 0 0 0 0 0 1 0 0 0 0 0 54
55 113.2 1 0 0 0 0 0 0 1 0 0 0 0 55
56 105.9 1 0 0 0 0 0 0 0 1 0 0 0 56
57 108.8 1 0 0 0 0 0 0 0 0 1 0 0 57
58 102.3 1 0 0 0 0 0 0 0 0 0 1 0 58
59 99.0 1 0 0 0 0 0 0 0 0 0 0 1 59
60 100.7 1 0 0 0 0 0 0 0 0 0 0 0 60
61 115.5 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) d M1 M2 M3 M4
95.59522 -1.72989 11.97095 4.85921 2.68269 11.22617
M5 M6 M7 M8 M9 M10
-12.47035 -3.26687 12.14259 13.02607 6.00956 -0.44696
M11 t
-0.78348 0.09652
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.3964 -2.5982 0.2236 2.1478 4.0042
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 95.59522 2.08969 45.746 < 2e-16 ***
d -1.72989 1.72091 -1.005 0.31994
M1 11.97095 1.99479 6.001 2.68e-07 ***
M2 4.85921 2.09130 2.324 0.02453 *
M3 2.68269 2.09007 1.284 0.20560
M4 11.22617 2.08922 5.373 2.36e-06 ***
M5 -12.47035 2.08874 -5.970 2.99e-07 ***
M6 -3.26687 2.08863 -1.564 0.12450
M7 12.14259 2.07205 5.860 4.38e-07 ***
M8 13.02607 2.07036 6.292 9.73e-08 ***
M9 6.00956 2.06904 2.905 0.00559 **
M10 -0.44696 2.06810 -0.216 0.82983
M11 -0.78348 2.06753 -0.379 0.70643
t 0.09652 0.02792 3.457 0.00117 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.269 on 47 degrees of freedom
Multiple R-squared: 0.8718, Adjusted R-squared: 0.8364
F-statistic: 24.59 on 13 and 47 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,] 0.71281355 0.5743729 0.2871865
[2,] 0.62013930 0.7597214 0.3798607
[3,] 0.47967392 0.9593478 0.5203261
[4,] 0.40894284 0.8178857 0.5910572
[5,] 0.37591083 0.7518217 0.6240892
[6,] 0.31763663 0.6352733 0.6823634
[7,] 0.24453782 0.4890756 0.7554622
[8,] 0.17251051 0.3450210 0.8274895
[9,] 0.19807701 0.3961540 0.8019230
[10,] 0.14377564 0.2875513 0.8562244
[11,] 0.17138620 0.3427724 0.8286138
[12,] 0.16413606 0.3282721 0.8358639
[13,] 0.17901024 0.3580205 0.8209898
[14,] 0.19972152 0.3994430 0.8002785
[15,] 0.20155969 0.4031194 0.7984403
[16,] 0.20357659 0.4071532 0.7964234
[17,] 0.28353994 0.5670799 0.7164601
[18,] 0.29990534 0.5998107 0.7000947
[19,] 0.24044895 0.4808979 0.7595510
[20,] 0.16792077 0.3358415 0.8320792
[21,] 0.18334725 0.3666945 0.8166527
[22,] 0.12423624 0.2484725 0.8757638
[23,] 0.10435986 0.2087197 0.8956401
[24,] 0.08521831 0.1704366 0.9147817
[25,] 0.15387032 0.3077406 0.8461297
[26,] 0.12570019 0.2514004 0.8742998
[27,] 0.12624550 0.2524910 0.8737545
[28,] 0.37880286 0.7576057 0.6211971
> postscript(file="/var/www/html/rcomp/tmp/17bwc1227799916.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22qqc1227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3axi41227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41l821227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53wt01227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6 7
2.7373188 -4.2474638 3.3325362 -1.0074638 -2.6074638 1.7925362 -5.6835507
8 9 10 11 12 13 14
1.7364493 1.5564493 -3.6835507 2.0564493 1.0764493 -1.0910145 3.0242029
15 16 17 18 19 20 21
4.0042029 -1.9357971 2.9642029 -0.2357971 -0.9417754 3.7782246 -0.2017754
22 23 24 25 26 27 28
-3.5417754 2.0982246 0.8182246 -2.8492391 1.4659783 -1.0540217 -3.2940217
29 30 31 32 33 34 35
3.2059783 -3.5940217 0.8000000 1.7200000 -4.4600000 0.2000000 -1.3600000
36 37 38 39 40 41 42
-0.3400000 3.2925362 0.5077536 -2.9122464 2.4477536 2.0477536 2.1477536
43 44 45 46 47 48 49
3.9417754 -0.8382246 -0.3182246 3.7417754 -3.0182246 -2.5982246 -5.8656884
50 51 52 53 54 55 56
-0.7504710 -3.3704710 3.7895290 -5.6104710 -0.1104710 1.8835507 -6.3964493
57 58 59 60 61
3.4235507 3.2835507 0.2235507 1.0435507 3.7760870
> postscript(file="/var/www/html/rcomp/tmp/6nd271227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 2.7373188 NA
1 -4.2474638 2.7373188
2 3.3325362 -4.2474638
3 -1.0074638 3.3325362
4 -2.6074638 -1.0074638
5 1.7925362 -2.6074638
6 -5.6835507 1.7925362
7 1.7364493 -5.6835507
8 1.5564493 1.7364493
9 -3.6835507 1.5564493
10 2.0564493 -3.6835507
11 1.0764493 2.0564493
12 -1.0910145 1.0764493
13 3.0242029 -1.0910145
14 4.0042029 3.0242029
15 -1.9357971 4.0042029
16 2.9642029 -1.9357971
17 -0.2357971 2.9642029
18 -0.9417754 -0.2357971
19 3.7782246 -0.9417754
20 -0.2017754 3.7782246
21 -3.5417754 -0.2017754
22 2.0982246 -3.5417754
23 0.8182246 2.0982246
24 -2.8492391 0.8182246
25 1.4659783 -2.8492391
26 -1.0540217 1.4659783
27 -3.2940217 -1.0540217
28 3.2059783 -3.2940217
29 -3.5940217 3.2059783
30 0.8000000 -3.5940217
31 1.7200000 0.8000000
32 -4.4600000 1.7200000
33 0.2000000 -4.4600000
34 -1.3600000 0.2000000
35 -0.3400000 -1.3600000
36 3.2925362 -0.3400000
37 0.5077536 3.2925362
38 -2.9122464 0.5077536
39 2.4477536 -2.9122464
40 2.0477536 2.4477536
41 2.1477536 2.0477536
42 3.9417754 2.1477536
43 -0.8382246 3.9417754
44 -0.3182246 -0.8382246
45 3.7417754 -0.3182246
46 -3.0182246 3.7417754
47 -2.5982246 -3.0182246
48 -5.8656884 -2.5982246
49 -0.7504710 -5.8656884
50 -3.3704710 -0.7504710
51 3.7895290 -3.3704710
52 -5.6104710 3.7895290
53 -0.1104710 -5.6104710
54 1.8835507 -0.1104710
55 -6.3964493 1.8835507
56 3.4235507 -6.3964493
57 3.2835507 3.4235507
58 0.2235507 3.2835507
59 1.0435507 0.2235507
60 3.7760870 1.0435507
61 NA 3.7760870
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.2474638 2.7373188
[2,] 3.3325362 -4.2474638
[3,] -1.0074638 3.3325362
[4,] -2.6074638 -1.0074638
[5,] 1.7925362 -2.6074638
[6,] -5.6835507 1.7925362
[7,] 1.7364493 -5.6835507
[8,] 1.5564493 1.7364493
[9,] -3.6835507 1.5564493
[10,] 2.0564493 -3.6835507
[11,] 1.0764493 2.0564493
[12,] -1.0910145 1.0764493
[13,] 3.0242029 -1.0910145
[14,] 4.0042029 3.0242029
[15,] -1.9357971 4.0042029
[16,] 2.9642029 -1.9357971
[17,] -0.2357971 2.9642029
[18,] -0.9417754 -0.2357971
[19,] 3.7782246 -0.9417754
[20,] -0.2017754 3.7782246
[21,] -3.5417754 -0.2017754
[22,] 2.0982246 -3.5417754
[23,] 0.8182246 2.0982246
[24,] -2.8492391 0.8182246
[25,] 1.4659783 -2.8492391
[26,] -1.0540217 1.4659783
[27,] -3.2940217 -1.0540217
[28,] 3.2059783 -3.2940217
[29,] -3.5940217 3.2059783
[30,] 0.8000000 -3.5940217
[31,] 1.7200000 0.8000000
[32,] -4.4600000 1.7200000
[33,] 0.2000000 -4.4600000
[34,] -1.3600000 0.2000000
[35,] -0.3400000 -1.3600000
[36,] 3.2925362 -0.3400000
[37,] 0.5077536 3.2925362
[38,] -2.9122464 0.5077536
[39,] 2.4477536 -2.9122464
[40,] 2.0477536 2.4477536
[41,] 2.1477536 2.0477536
[42,] 3.9417754 2.1477536
[43,] -0.8382246 3.9417754
[44,] -0.3182246 -0.8382246
[45,] 3.7417754 -0.3182246
[46,] -3.0182246 3.7417754
[47,] -2.5982246 -3.0182246
[48,] -5.8656884 -2.5982246
[49,] -0.7504710 -5.8656884
[50,] -3.3704710 -0.7504710
[51,] 3.7895290 -3.3704710
[52,] -5.6104710 3.7895290
[53,] -0.1104710 -5.6104710
[54,] 1.8835507 -0.1104710
[55,] -6.3964493 1.8835507
[56,] 3.4235507 -6.3964493
[57,] 3.2835507 3.4235507
[58,] 0.2235507 3.2835507
[59,] 1.0435507 0.2235507
[60,] 3.7760870 1.0435507
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.2474638 2.7373188
2 3.3325362 -4.2474638
3 -1.0074638 3.3325362
4 -2.6074638 -1.0074638
5 1.7925362 -2.6074638
6 -5.6835507 1.7925362
7 1.7364493 -5.6835507
8 1.5564493 1.7364493
9 -3.6835507 1.5564493
10 2.0564493 -3.6835507
11 1.0764493 2.0564493
12 -1.0910145 1.0764493
13 3.0242029 -1.0910145
14 4.0042029 3.0242029
15 -1.9357971 4.0042029
16 2.9642029 -1.9357971
17 -0.2357971 2.9642029
18 -0.9417754 -0.2357971
19 3.7782246 -0.9417754
20 -0.2017754 3.7782246
21 -3.5417754 -0.2017754
22 2.0982246 -3.5417754
23 0.8182246 2.0982246
24 -2.8492391 0.8182246
25 1.4659783 -2.8492391
26 -1.0540217 1.4659783
27 -3.2940217 -1.0540217
28 3.2059783 -3.2940217
29 -3.5940217 3.2059783
30 0.8000000 -3.5940217
31 1.7200000 0.8000000
32 -4.4600000 1.7200000
33 0.2000000 -4.4600000
34 -1.3600000 0.2000000
35 -0.3400000 -1.3600000
36 3.2925362 -0.3400000
37 0.5077536 3.2925362
38 -2.9122464 0.5077536
39 2.4477536 -2.9122464
40 2.0477536 2.4477536
41 2.1477536 2.0477536
42 3.9417754 2.1477536
43 -0.8382246 3.9417754
44 -0.3182246 -0.8382246
45 3.7417754 -0.3182246
46 -3.0182246 3.7417754
47 -2.5982246 -3.0182246
48 -5.8656884 -2.5982246
49 -0.7504710 -5.8656884
50 -3.3704710 -0.7504710
51 3.7895290 -3.3704710
52 -5.6104710 3.7895290
53 -0.1104710 -5.6104710
54 1.8835507 -0.1104710
55 -6.3964493 1.8835507
56 3.4235507 -6.3964493
57 3.2835507 3.4235507
58 0.2235507 3.2835507
59 1.0435507 0.2235507
60 3.7760870 1.0435507
> 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/7prkn1227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hvs01227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9w7fo1227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10izl01227799916.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11j4361227799916.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/1282s31227799916.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/13tm4a1227799916.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/14v7uv1227799916.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/15qpjw1227799916.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/16zahz1227799916.tab")
+ }
>
> system("convert tmp/17bwc1227799916.ps tmp/17bwc1227799916.png")
> system("convert tmp/22qqc1227799916.ps tmp/22qqc1227799916.png")
> system("convert tmp/3axi41227799916.ps tmp/3axi41227799916.png")
> system("convert tmp/41l821227799916.ps tmp/41l821227799916.png")
> system("convert tmp/53wt01227799916.ps tmp/53wt01227799916.png")
> system("convert tmp/6nd271227799916.ps tmp/6nd271227799916.png")
> system("convert tmp/7prkn1227799916.ps tmp/7prkn1227799916.png")
> system("convert tmp/8hvs01227799916.ps tmp/8hvs01227799916.png")
> system("convert tmp/9w7fo1227799916.ps tmp/9w7fo1227799916.png")
> system("convert tmp/10izl01227799916.ps tmp/10izl01227799916.png")
>
>
> proc.time()
user system elapsed
4.987 2.766 5.343