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(555,0,562,0,561,0,555,0,544,0,537,0,543,0,594,0,611,0,613,0,611,0,594,0,595,0,591,0,589,0,584,0,573,0,567,0,569,0,621,0,629,0,628,0,612,0,595,0,597,0,593,0,590,0,580,0,574,0,573,0,573,0,620,0,626,0,620,0,588,0,566,0,557,0,561,0,549,0,532,0,526,0,511,0,499,0,555,0,565,0,542,0,527,0,510,0,514,0,517,0,508,0,493,0,490,1,469,1,478,1,528,1,534,1,518,1,506,1,502,1,516,1,528,1,533,1,536,1,537,1,524,1,536,1,587,1,597,1,581,1),dim=c(2,70),dimnames=list(c('Y','X'),1:70))
> y <- array(NA,dim=c(2,70),dimnames=list(c('Y','X'),1:70))
> 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
1 555 0 1 0 0 0 0 0 0 0 0 0 0 1
2 562 0 0 1 0 0 0 0 0 0 0 0 0 2
3 561 0 0 0 1 0 0 0 0 0 0 0 0 3
4 555 0 0 0 0 1 0 0 0 0 0 0 0 4
5 544 0 0 0 0 0 1 0 0 0 0 0 0 5
6 537 0 0 0 0 0 0 1 0 0 0 0 0 6
7 543 0 0 0 0 0 0 0 1 0 0 0 0 7
8 594 0 0 0 0 0 0 0 0 1 0 0 0 8
9 611 0 0 0 0 0 0 0 0 0 1 0 0 9
10 613 0 0 0 0 0 0 0 0 0 0 1 0 10
11 611 0 0 0 0 0 0 0 0 0 0 0 1 11
12 594 0 0 0 0 0 0 0 0 0 0 0 0 12
13 595 0 1 0 0 0 0 0 0 0 0 0 0 13
14 591 0 0 1 0 0 0 0 0 0 0 0 0 14
15 589 0 0 0 1 0 0 0 0 0 0 0 0 15
16 584 0 0 0 0 1 0 0 0 0 0 0 0 16
17 573 0 0 0 0 0 1 0 0 0 0 0 0 17
18 567 0 0 0 0 0 0 1 0 0 0 0 0 18
19 569 0 0 0 0 0 0 0 1 0 0 0 0 19
20 621 0 0 0 0 0 0 0 0 1 0 0 0 20
21 629 0 0 0 0 0 0 0 0 0 1 0 0 21
22 628 0 0 0 0 0 0 0 0 0 0 1 0 22
23 612 0 0 0 0 0 0 0 0 0 0 0 1 23
24 595 0 0 0 0 0 0 0 0 0 0 0 0 24
25 597 0 1 0 0 0 0 0 0 0 0 0 0 25
26 593 0 0 1 0 0 0 0 0 0 0 0 0 26
27 590 0 0 0 1 0 0 0 0 0 0 0 0 27
28 580 0 0 0 0 1 0 0 0 0 0 0 0 28
29 574 0 0 0 0 0 1 0 0 0 0 0 0 29
30 573 0 0 0 0 0 0 1 0 0 0 0 0 30
31 573 0 0 0 0 0 0 0 1 0 0 0 0 31
32 620 0 0 0 0 0 0 0 0 1 0 0 0 32
33 626 0 0 0 0 0 0 0 0 0 1 0 0 33
34 620 0 0 0 0 0 0 0 0 0 0 1 0 34
35 588 0 0 0 0 0 0 0 0 0 0 0 1 35
36 566 0 0 0 0 0 0 0 0 0 0 0 0 36
37 557 0 1 0 0 0 0 0 0 0 0 0 0 37
38 561 0 0 1 0 0 0 0 0 0 0 0 0 38
39 549 0 0 0 1 0 0 0 0 0 0 0 0 39
40 532 0 0 0 0 1 0 0 0 0 0 0 0 40
41 526 0 0 0 0 0 1 0 0 0 0 0 0 41
42 511 0 0 0 0 0 0 1 0 0 0 0 0 42
43 499 0 0 0 0 0 0 0 1 0 0 0 0 43
44 555 0 0 0 0 0 0 0 0 1 0 0 0 44
45 565 0 0 0 0 0 0 0 0 0 1 0 0 45
46 542 0 0 0 0 0 0 0 0 0 0 1 0 46
47 527 0 0 0 0 0 0 0 0 0 0 0 1 47
48 510 0 0 0 0 0 0 0 0 0 0 0 0 48
49 514 0 1 0 0 0 0 0 0 0 0 0 0 49
50 517 0 0 1 0 0 0 0 0 0 0 0 0 50
51 508 0 0 0 1 0 0 0 0 0 0 0 0 51
52 493 0 0 0 0 1 0 0 0 0 0 0 0 52
53 490 1 0 0 0 0 1 0 0 0 0 0 0 53
54 469 1 0 0 0 0 0 1 0 0 0 0 0 54
55 478 1 0 0 0 0 0 0 1 0 0 0 0 55
56 528 1 0 0 0 0 0 0 0 1 0 0 0 56
57 534 1 0 0 0 0 0 0 0 0 1 0 0 57
58 518 1 0 0 0 0 0 0 0 0 0 1 0 58
59 506 1 0 0 0 0 0 0 0 0 0 0 1 59
60 502 1 0 0 0 0 0 0 0 0 0 0 0 60
61 516 1 1 0 0 0 0 0 0 0 0 0 0 61
62 528 1 0 1 0 0 0 0 0 0 0 0 0 62
63 533 1 0 0 1 0 0 0 0 0 0 0 0 63
64 536 1 0 0 0 1 0 0 0 0 0 0 0 64
65 537 1 0 0 0 0 1 0 0 0 0 0 0 65
66 524 1 0 0 0 0 0 1 0 0 0 0 0 66
67 536 1 0 0 0 0 0 0 1 0 0 0 0 67
68 587 1 0 0 0 0 0 0 0 1 0 0 0 68
69 597 1 0 0 0 0 0 0 0 0 1 0 0 69
70 581 1 0 0 0 0 0 0 0 0 0 1 0 70
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
591.5037 -8.2904 -3.0716 0.9408 -1.7135 -9.0344
M5 M6 M7 M8 M9 M10
-12.6403 -22.1280 -18.2822 33.8968 44.4092 35.4216
M11 t
14.3876 -1.0124
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-41.92 -25.83 6.82 24.25 39.23
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 591.5037 15.0270 39.363 <2e-16 ***
X -8.2904 12.2340 -0.678 0.5008
M1 -3.0716 17.4489 -0.176 0.8609
M2 0.9408 17.4357 0.054 0.9572
M3 -1.7135 17.4265 -0.098 0.9220
M4 -9.0344 17.4213 -0.519 0.6061
M5 -12.6403 17.5146 -0.722 0.4735
M6 -22.1280 17.4941 -1.265 0.2112
M7 -18.2822 17.4775 -1.046 0.3000
M8 33.8968 17.4649 1.941 0.0573 .
M9 44.4092 17.4562 2.544 0.0137 *
M10 35.4216 17.4515 2.030 0.0471 *
M11 14.3876 18.1943 0.791 0.4324
t -1.0124 0.2626 -3.856 0.0003 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 28.76 on 56 degrees of freedom
Multiple R-squared: 0.5829, Adjusted R-squared: 0.486
F-statistic: 6.019 on 13 and 56 DF, p-value: 7.926e-07
> 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,] 4.119040e-03 8.238080e-03 0.9958809602
[2,] 4.821346e-04 9.642692e-04 0.9995178654
[3,] 7.766971e-05 1.553394e-04 0.9999223303
[4,] 9.809967e-06 1.961993e-05 0.9999901900
[5,] 9.410108e-06 1.882022e-05 0.9999905899
[6,] 8.187331e-06 1.637466e-05 0.9999918127
[7,] 6.582610e-05 1.316522e-04 0.9999341739
[8,] 1.009787e-04 2.019575e-04 0.9998990213
[9,] 4.496792e-05 8.993585e-05 0.9999550321
[10,] 2.549175e-05 5.098349e-05 0.9999745083
[11,] 1.343070e-05 2.686140e-05 0.9999865693
[12,] 9.025185e-06 1.805037e-05 0.9999909748
[13,] 3.550652e-06 7.101303e-06 0.9999964493
[14,] 1.353380e-06 2.706760e-06 0.9999986466
[15,] 6.106353e-07 1.221271e-06 0.9999993894
[16,] 3.609351e-07 7.218703e-07 0.9999996391
[17,] 4.023880e-07 8.047761e-07 0.9999995976
[18,] 1.718331e-06 3.436662e-06 0.9999982817
[19,] 1.457917e-04 2.915834e-04 0.9998542083
[20,] 3.607436e-03 7.214872e-03 0.9963925642
[21,] 2.614948e-02 5.229896e-02 0.9738505184
[22,] 1.004174e-01 2.008348e-01 0.8995826042
[23,] 3.407568e-01 6.815136e-01 0.6592431754
[24,] 7.511962e-01 4.976077e-01 0.2488038457
[25,] 8.308152e-01 3.383696e-01 0.1691847777
[26,] 9.257291e-01 1.485417e-01 0.0742708686
[27,] 9.472264e-01 1.055473e-01 0.0527736317
[28,] 9.610235e-01 7.795299e-02 0.0389764966
[29,] 9.788759e-01 4.224829e-02 0.0211241432
[30,] 9.918736e-01 1.625286e-02 0.0081264279
[31,] 9.978455e-01 4.309003e-03 0.0021545016
[32,] 9.990850e-01 1.830059e-03 0.0009150293
[33,] 9.994337e-01 1.132617e-03 0.0005663085
[34,] 9.997161e-01 5.677367e-04 0.0002838684
[35,] 9.997022e-01 5.955246e-04 0.0002977623
[36,] 9.981088e-01 3.782453e-03 0.0018912265
[37,] 9.983858e-01 3.228378e-03 0.0016141889
> postscript(file="/var/www/html/rcomp/tmp/1pn411258729741.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2yz751258729741.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jsap1258729741.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wra41258729741.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55c7g1258729741.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 = 70
Frequency = 1
1 2 3 4 5 6 7
-32.419759 -28.419759 -25.753092 -23.419759 -29.801484 -26.301484 -23.134818
8 9 10 11 12 13 14
-23.301484 -15.801484 -3.801484 16.244836 14.644836 19.728788 12.728788
15 16 17 18 19 20 21
14.395455 17.728788 11.347062 15.847062 15.013729 15.847062 14.347062
22 23 24 25 26 27 28
23.347062 29.393383 27.793383 33.877335 26.877335 27.544001 25.877335
29 30 31 32 33 34 35
24.495609 33.995609 31.162276 26.995609 23.495609 27.495609 17.541929
36 37 38 39 40 41 42
10.941929 6.025881 7.025881 -1.307452 -9.974119 -11.355844 -15.855844
43 44 45 46 47 48 49
-30.689177 -25.855844 -25.355844 -38.355844 -31.309524 -32.909524 -24.825572
50 51 52 53 54 55 56
-24.825572 -30.158905 -36.825572 -26.916945 -37.416945 -31.250278 -32.416945
57 58 59 60 61 62 63
-35.916945 -41.916945 -31.870625 -20.470625 -2.386673 6.613327 15.279994
64 65 66 67 68 69 70
26.613327 32.231602 29.731602 38.898268 38.731602 39.231602 33.231602
> postscript(file="/var/www/html/rcomp/tmp/6w4hl1258729741.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 = 70
Frequency = 1
lag(myerror, k = 1) myerror
0 -32.419759 NA
1 -28.419759 -32.419759
2 -25.753092 -28.419759
3 -23.419759 -25.753092
4 -29.801484 -23.419759
5 -26.301484 -29.801484
6 -23.134818 -26.301484
7 -23.301484 -23.134818
8 -15.801484 -23.301484
9 -3.801484 -15.801484
10 16.244836 -3.801484
11 14.644836 16.244836
12 19.728788 14.644836
13 12.728788 19.728788
14 14.395455 12.728788
15 17.728788 14.395455
16 11.347062 17.728788
17 15.847062 11.347062
18 15.013729 15.847062
19 15.847062 15.013729
20 14.347062 15.847062
21 23.347062 14.347062
22 29.393383 23.347062
23 27.793383 29.393383
24 33.877335 27.793383
25 26.877335 33.877335
26 27.544001 26.877335
27 25.877335 27.544001
28 24.495609 25.877335
29 33.995609 24.495609
30 31.162276 33.995609
31 26.995609 31.162276
32 23.495609 26.995609
33 27.495609 23.495609
34 17.541929 27.495609
35 10.941929 17.541929
36 6.025881 10.941929
37 7.025881 6.025881
38 -1.307452 7.025881
39 -9.974119 -1.307452
40 -11.355844 -9.974119
41 -15.855844 -11.355844
42 -30.689177 -15.855844
43 -25.855844 -30.689177
44 -25.355844 -25.855844
45 -38.355844 -25.355844
46 -31.309524 -38.355844
47 -32.909524 -31.309524
48 -24.825572 -32.909524
49 -24.825572 -24.825572
50 -30.158905 -24.825572
51 -36.825572 -30.158905
52 -26.916945 -36.825572
53 -37.416945 -26.916945
54 -31.250278 -37.416945
55 -32.416945 -31.250278
56 -35.916945 -32.416945
57 -41.916945 -35.916945
58 -31.870625 -41.916945
59 -20.470625 -31.870625
60 -2.386673 -20.470625
61 6.613327 -2.386673
62 15.279994 6.613327
63 26.613327 15.279994
64 32.231602 26.613327
65 29.731602 32.231602
66 38.898268 29.731602
67 38.731602 38.898268
68 39.231602 38.731602
69 33.231602 39.231602
70 NA 33.231602
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -28.419759 -32.419759
[2,] -25.753092 -28.419759
[3,] -23.419759 -25.753092
[4,] -29.801484 -23.419759
[5,] -26.301484 -29.801484
[6,] -23.134818 -26.301484
[7,] -23.301484 -23.134818
[8,] -15.801484 -23.301484
[9,] -3.801484 -15.801484
[10,] 16.244836 -3.801484
[11,] 14.644836 16.244836
[12,] 19.728788 14.644836
[13,] 12.728788 19.728788
[14,] 14.395455 12.728788
[15,] 17.728788 14.395455
[16,] 11.347062 17.728788
[17,] 15.847062 11.347062
[18,] 15.013729 15.847062
[19,] 15.847062 15.013729
[20,] 14.347062 15.847062
[21,] 23.347062 14.347062
[22,] 29.393383 23.347062
[23,] 27.793383 29.393383
[24,] 33.877335 27.793383
[25,] 26.877335 33.877335
[26,] 27.544001 26.877335
[27,] 25.877335 27.544001
[28,] 24.495609 25.877335
[29,] 33.995609 24.495609
[30,] 31.162276 33.995609
[31,] 26.995609 31.162276
[32,] 23.495609 26.995609
[33,] 27.495609 23.495609
[34,] 17.541929 27.495609
[35,] 10.941929 17.541929
[36,] 6.025881 10.941929
[37,] 7.025881 6.025881
[38,] -1.307452 7.025881
[39,] -9.974119 -1.307452
[40,] -11.355844 -9.974119
[41,] -15.855844 -11.355844
[42,] -30.689177 -15.855844
[43,] -25.855844 -30.689177
[44,] -25.355844 -25.855844
[45,] -38.355844 -25.355844
[46,] -31.309524 -38.355844
[47,] -32.909524 -31.309524
[48,] -24.825572 -32.909524
[49,] -24.825572 -24.825572
[50,] -30.158905 -24.825572
[51,] -36.825572 -30.158905
[52,] -26.916945 -36.825572
[53,] -37.416945 -26.916945
[54,] -31.250278 -37.416945
[55,] -32.416945 -31.250278
[56,] -35.916945 -32.416945
[57,] -41.916945 -35.916945
[58,] -31.870625 -41.916945
[59,] -20.470625 -31.870625
[60,] -2.386673 -20.470625
[61,] 6.613327 -2.386673
[62,] 15.279994 6.613327
[63,] 26.613327 15.279994
[64,] 32.231602 26.613327
[65,] 29.731602 32.231602
[66,] 38.898268 29.731602
[67,] 38.731602 38.898268
[68,] 39.231602 38.731602
[69,] 33.231602 39.231602
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -28.419759 -32.419759
2 -25.753092 -28.419759
3 -23.419759 -25.753092
4 -29.801484 -23.419759
5 -26.301484 -29.801484
6 -23.134818 -26.301484
7 -23.301484 -23.134818
8 -15.801484 -23.301484
9 -3.801484 -15.801484
10 16.244836 -3.801484
11 14.644836 16.244836
12 19.728788 14.644836
13 12.728788 19.728788
14 14.395455 12.728788
15 17.728788 14.395455
16 11.347062 17.728788
17 15.847062 11.347062
18 15.013729 15.847062
19 15.847062 15.013729
20 14.347062 15.847062
21 23.347062 14.347062
22 29.393383 23.347062
23 27.793383 29.393383
24 33.877335 27.793383
25 26.877335 33.877335
26 27.544001 26.877335
27 25.877335 27.544001
28 24.495609 25.877335
29 33.995609 24.495609
30 31.162276 33.995609
31 26.995609 31.162276
32 23.495609 26.995609
33 27.495609 23.495609
34 17.541929 27.495609
35 10.941929 17.541929
36 6.025881 10.941929
37 7.025881 6.025881
38 -1.307452 7.025881
39 -9.974119 -1.307452
40 -11.355844 -9.974119
41 -15.855844 -11.355844
42 -30.689177 -15.855844
43 -25.855844 -30.689177
44 -25.355844 -25.855844
45 -38.355844 -25.355844
46 -31.309524 -38.355844
47 -32.909524 -31.309524
48 -24.825572 -32.909524
49 -24.825572 -24.825572
50 -30.158905 -24.825572
51 -36.825572 -30.158905
52 -26.916945 -36.825572
53 -37.416945 -26.916945
54 -31.250278 -37.416945
55 -32.416945 -31.250278
56 -35.916945 -32.416945
57 -41.916945 -35.916945
58 -31.870625 -41.916945
59 -20.470625 -31.870625
60 -2.386673 -20.470625
61 6.613327 -2.386673
62 15.279994 6.613327
63 26.613327 15.279994
64 32.231602 26.613327
65 29.731602 32.231602
66 38.898268 29.731602
67 38.731602 38.898268
68 39.231602 38.731602
69 33.231602 39.231602
> 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/7jxn41258729741.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81wtx1258729741.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hm4c1258729741.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10tez91258729741.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11y7nh1258729741.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/12ndvj1258729741.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/13nsp01258729741.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/143z7m1258729741.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/15kqbx1258729741.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/16cgum1258729741.tab")
+ }
>
> system("convert tmp/1pn411258729741.ps tmp/1pn411258729741.png")
> system("convert tmp/2yz751258729741.ps tmp/2yz751258729741.png")
> system("convert tmp/3jsap1258729741.ps tmp/3jsap1258729741.png")
> system("convert tmp/4wra41258729741.ps tmp/4wra41258729741.png")
> system("convert tmp/55c7g1258729741.ps tmp/55c7g1258729741.png")
> system("convert tmp/6w4hl1258729741.ps tmp/6w4hl1258729741.png")
> system("convert tmp/7jxn41258729741.ps tmp/7jxn41258729741.png")
> system("convert tmp/81wtx1258729741.ps tmp/81wtx1258729741.png")
> system("convert tmp/9hm4c1258729741.ps tmp/9hm4c1258729741.png")
> system("convert tmp/10tez91258729741.ps tmp/10tez91258729741.png")
>
>
> proc.time()
user system elapsed
2.552 1.583 3.048