R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(8776
+ ,0
+ ,8823
+ ,9051
+ ,8255
+ ,0
+ ,8776
+ ,8823
+ ,7969
+ ,0
+ ,8255
+ ,8776
+ ,8758
+ ,0
+ ,7969
+ ,8255
+ ,8693
+ ,0
+ ,8758
+ ,7969
+ ,8271
+ ,0
+ ,8693
+ ,8758
+ ,7790
+ ,0
+ ,8271
+ ,8693
+ ,7769
+ ,0
+ ,7790
+ ,8271
+ ,8170
+ ,0
+ ,7769
+ ,7790
+ ,8209
+ ,0
+ ,8170
+ ,7769
+ ,9395
+ ,0
+ ,8209
+ ,8170
+ ,9260
+ ,0
+ ,9395
+ ,8209
+ ,9018
+ ,0
+ ,9260
+ ,9395
+ ,8501
+ ,0
+ ,9018
+ ,9260
+ ,8500
+ ,0
+ ,8501
+ ,9018
+ ,9649
+ ,0
+ ,8500
+ ,8501
+ ,9319
+ ,0
+ ,9649
+ ,8500
+ ,8830
+ ,0
+ ,9319
+ ,9649
+ ,8436
+ ,0
+ ,8830
+ ,9319
+ ,8169
+ ,0
+ ,8436
+ ,8830
+ ,8269
+ ,0
+ ,8169
+ ,8436
+ ,7945
+ ,0
+ ,8269
+ ,8169
+ ,9144
+ ,0
+ ,7945
+ ,8269
+ ,8770
+ ,0
+ ,9144
+ ,7945
+ ,8834
+ ,0
+ ,8770
+ ,9144
+ ,7837
+ ,0
+ ,8834
+ ,8770
+ ,7792
+ ,0
+ ,7837
+ ,8834
+ ,8616
+ ,0
+ ,7792
+ ,7837
+ ,8518
+ ,0
+ ,8616
+ ,7792
+ ,7940
+ ,0
+ ,8518
+ ,8616
+ ,7545
+ ,0
+ ,7940
+ ,8518
+ ,7531
+ ,0
+ ,7545
+ ,7940
+ ,7665
+ ,0
+ ,7531
+ ,7545
+ ,7599
+ ,0
+ ,7665
+ ,7531
+ ,8444
+ ,0
+ ,7599
+ ,7665
+ ,8549
+ ,0
+ ,8444
+ ,7599
+ ,7986
+ ,0
+ ,8549
+ ,8444
+ ,7335
+ ,0
+ ,7986
+ ,8549
+ ,7287
+ ,0
+ ,7335
+ ,7986
+ ,7870
+ ,0
+ ,7287
+ ,7335
+ ,7839
+ ,0
+ ,7870
+ ,7287
+ ,7327
+ ,0
+ ,7839
+ ,7870
+ ,7259
+ ,0
+ ,7327
+ ,7839
+ ,6964
+ ,0
+ ,7259
+ ,7327
+ ,7271
+ ,0
+ ,6964
+ ,7259
+ ,6956
+ ,0
+ ,7271
+ ,6964
+ ,7608
+ ,0
+ ,6956
+ ,7271
+ ,7692
+ ,0
+ ,7608
+ ,6956
+ ,7255
+ ,0
+ ,7692
+ ,7608
+ ,6804
+ ,0
+ ,7255
+ ,7692
+ ,6655
+ ,0
+ ,6804
+ ,7255
+ ,7341
+ ,0
+ ,6655
+ ,6804
+ ,7602
+ ,0
+ ,7341
+ ,6655
+ ,7086
+ ,0
+ ,7602
+ ,7341
+ ,6625
+ ,0
+ ,7086
+ ,7602
+ ,6272
+ ,0
+ ,6625
+ ,7086
+ ,6576
+ ,0
+ ,6272
+ ,6625
+ ,6491
+ ,0
+ ,6576
+ ,6272
+ ,7649
+ ,0
+ ,6491
+ ,6576
+ ,7400
+ ,0
+ ,7649
+ ,6491
+ ,6913
+ ,0
+ ,7400
+ ,7649
+ ,6532
+ ,0
+ ,6913
+ ,7400
+ ,6486
+ ,0
+ ,6532
+ ,6913
+ ,7295
+ ,0
+ ,6486
+ ,6532
+ ,7556
+ ,0
+ ,7295
+ ,6486
+ ,7088
+ ,1
+ ,7556
+ ,7295
+ ,6952
+ ,1
+ ,7088
+ ,7556
+ ,6773
+ ,1
+ ,6952
+ ,7088
+ ,6917
+ ,1
+ ,6773
+ ,6952
+ ,7371
+ ,1
+ ,6917
+ ,6773
+ ,8221
+ ,1
+ ,7371
+ ,6917
+ ,7953
+ ,1
+ ,8221
+ ,7371
+ ,8027
+ ,1
+ ,7953
+ ,8221
+ ,7287
+ ,1
+ ,8027
+ ,7953
+ ,8076
+ ,1
+ ,7287
+ ,8027
+ ,8933
+ ,1
+ ,8076
+ ,7287
+ ,9433
+ ,1
+ ,8933
+ ,8076
+ ,9479
+ ,1
+ ,9433
+ ,8933
+ ,9199
+ ,1
+ ,9479
+ ,9433
+ ,9469
+ ,1
+ ,9199
+ ,9479
+ ,10015
+ ,1
+ ,9469
+ ,9199
+ ,10999
+ ,1
+ ,10015
+ ,9469
+ ,13009
+ ,1
+ ,10999
+ ,10015
+ ,13699
+ ,1
+ ,13009
+ ,10999
+ ,13895
+ ,1
+ ,13699
+ ,13009
+ ,13248
+ ,1
+ ,13895
+ ,13699
+ ,13973
+ ,1
+ ,13248
+ ,13895
+ ,15095
+ ,1
+ ,13973
+ ,13248
+ ,15201
+ ,1
+ ,15095
+ ,13973
+ ,14823
+ ,1
+ ,15201
+ ,15095
+ ,14538
+ ,1
+ ,14823
+ ,15201
+ ,14547
+ ,1
+ ,14538
+ ,14823
+ ,14407
+ ,1
+ ,14547
+ ,14538)
+ ,dim=c(4
+ ,93)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2')
+ ,1:93))
> y <- array(NA,dim=c(4,93),dimnames=list(c('Y','X','Y1','Y2'),1:93))
> 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 Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8776 0 8823 9051 1 0 0 0 0 0 0 0 0 0 0 1
2 8255 0 8776 8823 0 1 0 0 0 0 0 0 0 0 0 2
3 7969 0 8255 8776 0 0 1 0 0 0 0 0 0 0 0 3
4 8758 0 7969 8255 0 0 0 1 0 0 0 0 0 0 0 4
5 8693 0 8758 7969 0 0 0 0 1 0 0 0 0 0 0 5
6 8271 0 8693 8758 0 0 0 0 0 1 0 0 0 0 0 6
7 7790 0 8271 8693 0 0 0 0 0 0 1 0 0 0 0 7
8 7769 0 7790 8271 0 0 0 0 0 0 0 1 0 0 0 8
9 8170 0 7769 7790 0 0 0 0 0 0 0 0 1 0 0 9
10 8209 0 8170 7769 0 0 0 0 0 0 0 0 0 1 0 10
11 9395 0 8209 8170 0 0 0 0 0 0 0 0 0 0 1 11
12 9260 0 9395 8209 0 0 0 0 0 0 0 0 0 0 0 12
13 9018 0 9260 9395 1 0 0 0 0 0 0 0 0 0 0 13
14 8501 0 9018 9260 0 1 0 0 0 0 0 0 0 0 0 14
15 8500 0 8501 9018 0 0 1 0 0 0 0 0 0 0 0 15
16 9649 0 8500 8501 0 0 0 1 0 0 0 0 0 0 0 16
17 9319 0 9649 8500 0 0 0 0 1 0 0 0 0 0 0 17
18 8830 0 9319 9649 0 0 0 0 0 1 0 0 0 0 0 18
19 8436 0 8830 9319 0 0 0 0 0 0 1 0 0 0 0 19
20 8169 0 8436 8830 0 0 0 0 0 0 0 1 0 0 0 20
21 8269 0 8169 8436 0 0 0 0 0 0 0 0 1 0 0 21
22 7945 0 8269 8169 0 0 0 0 0 0 0 0 0 1 0 22
23 9144 0 7945 8269 0 0 0 0 0 0 0 0 0 0 1 23
24 8770 0 9144 7945 0 0 0 0 0 0 0 0 0 0 0 24
25 8834 0 8770 9144 1 0 0 0 0 0 0 0 0 0 0 25
26 7837 0 8834 8770 0 1 0 0 0 0 0 0 0 0 0 26
27 7792 0 7837 8834 0 0 1 0 0 0 0 0 0 0 0 27
28 8616 0 7792 7837 0 0 0 1 0 0 0 0 0 0 0 28
29 8518 0 8616 7792 0 0 0 0 1 0 0 0 0 0 0 29
30 7940 0 8518 8616 0 0 0 0 0 1 0 0 0 0 0 30
31 7545 0 7940 8518 0 0 0 0 0 0 1 0 0 0 0 31
32 7531 0 7545 7940 0 0 0 0 0 0 0 1 0 0 0 32
33 7665 0 7531 7545 0 0 0 0 0 0 0 0 1 0 0 33
34 7599 0 7665 7531 0 0 0 0 0 0 0 0 0 1 0 34
35 8444 0 7599 7665 0 0 0 0 0 0 0 0 0 0 1 35
36 8549 0 8444 7599 0 0 0 0 0 0 0 0 0 0 0 36
37 7986 0 8549 8444 1 0 0 0 0 0 0 0 0 0 0 37
38 7335 0 7986 8549 0 1 0 0 0 0 0 0 0 0 0 38
39 7287 0 7335 7986 0 0 1 0 0 0 0 0 0 0 0 39
40 7870 0 7287 7335 0 0 0 1 0 0 0 0 0 0 0 40
41 7839 0 7870 7287 0 0 0 0 1 0 0 0 0 0 0 41
42 7327 0 7839 7870 0 0 0 0 0 1 0 0 0 0 0 42
43 7259 0 7327 7839 0 0 0 0 0 0 1 0 0 0 0 43
44 6964 0 7259 7327 0 0 0 0 0 0 0 1 0 0 0 44
45 7271 0 6964 7259 0 0 0 0 0 0 0 0 1 0 0 45
46 6956 0 7271 6964 0 0 0 0 0 0 0 0 0 1 0 46
47 7608 0 6956 7271 0 0 0 0 0 0 0 0 0 0 1 47
48 7692 0 7608 6956 0 0 0 0 0 0 0 0 0 0 0 48
49 7255 0 7692 7608 1 0 0 0 0 0 0 0 0 0 0 49
50 6804 0 7255 7692 0 1 0 0 0 0 0 0 0 0 0 50
51 6655 0 6804 7255 0 0 1 0 0 0 0 0 0 0 0 51
52 7341 0 6655 6804 0 0 0 1 0 0 0 0 0 0 0 52
53 7602 0 7341 6655 0 0 0 0 1 0 0 0 0 0 0 53
54 7086 0 7602 7341 0 0 0 0 0 1 0 0 0 0 0 54
55 6625 0 7086 7602 0 0 0 0 0 0 1 0 0 0 0 55
56 6272 0 6625 7086 0 0 0 0 0 0 0 1 0 0 0 56
57 6576 0 6272 6625 0 0 0 0 0 0 0 0 1 0 0 57
58 6491 0 6576 6272 0 0 0 0 0 0 0 0 0 1 0 58
59 7649 0 6491 6576 0 0 0 0 0 0 0 0 0 0 1 59
60 7400 0 7649 6491 0 0 0 0 0 0 0 0 0 0 0 60
61 6913 0 7400 7649 1 0 0 0 0 0 0 0 0 0 0 61
62 6532 0 6913 7400 0 1 0 0 0 0 0 0 0 0 0 62
63 6486 0 6532 6913 0 0 1 0 0 0 0 0 0 0 0 63
64 7295 0 6486 6532 0 0 0 1 0 0 0 0 0 0 0 64
65 7556 0 7295 6486 0 0 0 0 1 0 0 0 0 0 0 65
66 7088 1 7556 7295 0 0 0 0 0 1 0 0 0 0 0 66
67 6952 1 7088 7556 0 0 0 0 0 0 1 0 0 0 0 67
68 6773 1 6952 7088 0 0 0 0 0 0 0 1 0 0 0 68
69 6917 1 6773 6952 0 0 0 0 0 0 0 0 1 0 0 69
70 7371 1 6917 6773 0 0 0 0 0 0 0 0 0 1 0 70
71 8221 1 7371 6917 0 0 0 0 0 0 0 0 0 0 1 71
72 7953 1 8221 7371 0 0 0 0 0 0 0 0 0 0 0 72
73 8027 1 7953 8221 1 0 0 0 0 0 0 0 0 0 0 73
74 7287 1 8027 7953 0 1 0 0 0 0 0 0 0 0 0 74
75 8076 1 7287 8027 0 0 1 0 0 0 0 0 0 0 0 75
76 8933 1 8076 7287 0 0 0 1 0 0 0 0 0 0 0 76
77 9433 1 8933 8076 0 0 0 0 1 0 0 0 0 0 0 77
78 9479 1 9433 8933 0 0 0 0 0 1 0 0 0 0 0 78
79 9199 1 9479 9433 0 0 0 0 0 0 1 0 0 0 0 79
80 9469 1 9199 9479 0 0 0 0 0 0 0 1 0 0 0 80
81 10015 1 9469 9199 0 0 0 0 0 0 0 0 1 0 0 81
82 10999 1 10015 9469 0 0 0 0 0 0 0 0 0 1 0 82
83 13009 1 10999 10015 0 0 0 0 0 0 0 0 0 0 1 83
84 13699 1 13009 10999 0 0 0 0 0 0 0 0 0 0 0 84
85 13895 1 13699 13009 1 0 0 0 0 0 0 0 0 0 0 85
86 13248 1 13895 13699 0 1 0 0 0 0 0 0 0 0 0 86
87 13973 1 13248 13895 0 0 1 0 0 0 0 0 0 0 0 87
88 15095 1 13973 13248 0 0 0 1 0 0 0 0 0 0 0 88
89 15201 1 15095 13973 0 0 0 0 1 0 0 0 0 0 0 89
90 14823 1 15201 15095 0 0 0 0 0 1 0 0 0 0 0 90
91 14538 1 14823 15201 0 0 0 0 0 0 1 0 0 0 0 91
92 14547 1 14538 14823 0 0 0 0 0 0 0 1 0 0 0 92
93 14407 1 14547 14538 0 0 0 0 0 0 0 0 1 0 0 93
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
-1.183e+02 2.984e+02 1.002e+00 4.471e-06 -1.490e+02 -5.814e+02
M3 M4 M5 M6 M7 M8
1.503e+02 8.852e+02 1.068e+02 -4.206e+02 -3.177e+02 -1.108e+02
M9 M10 M11 t
2.203e+02 1.212e+02 1.152e+03 -9.217e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-558.229 -165.970 -5.163 167.644 672.063
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.183e+02 2.014e+02 -0.587 0.55883
X 2.984e+02 1.121e+02 2.663 0.00943 **
Y1 1.002e+00 1.194e-01 8.387 1.82e-12 ***
Y2 4.471e-06 1.223e-01 3.66e-05 0.99997
M1 -1.490e+02 1.972e+02 -0.755 0.45226
M2 -5.814e+02 2.087e+02 -2.786 0.00672 **
M3 1.503e+02 2.495e+02 0.602 0.54868
M4 8.852e+02 1.817e+02 4.871 5.80e-06 ***
M5 1.068e+02 1.403e+02 0.761 0.44882
M6 -4.206e+02 1.900e+02 -2.214 0.02979 *
M7 -3.177e+02 2.344e+02 -1.355 0.17930
M8 -1.108e+02 2.236e+02 -0.495 0.62176
M9 2.203e+02 2.041e+02 1.079 0.28377
M10 1.212e+02 1.734e+02 0.699 0.48686
M11 1.152e+03 1.869e+02 6.162 3.05e-08 ***
t -9.217e-02 1.683e+00 -0.055 0.95648
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 263.3 on 77 degrees of freedom
Multiple R-squared: 0.9883, Adjusted R-squared: 0.986
F-statistic: 432.9 on 15 and 77 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,] 0.2996351025 0.5992702049 0.7003649
[2,] 0.1940842073 0.3881684146 0.8059158
[3,] 0.1524739544 0.3049479088 0.8475260
[4,] 0.1804426611 0.3608853223 0.8195573
[5,] 0.1097935686 0.2195871372 0.8902064
[6,] 0.1115455338 0.2230910676 0.8884545
[7,] 0.1001690816 0.2003381632 0.8998309
[8,] 0.1791749789 0.3583499578 0.8208250
[9,] 0.1279598147 0.2559196294 0.8720402
[10,] 0.0858777441 0.1717554882 0.9141223
[11,] 0.0602859512 0.1205719024 0.9397140
[12,] 0.0368083687 0.0736167374 0.9631916
[13,] 0.0223349986 0.0446699973 0.9776650
[14,] 0.0174874889 0.0349749778 0.9825125
[15,] 0.0103109706 0.0206219411 0.9896890
[16,] 0.0058983199 0.0117966397 0.9941017
[17,] 0.0058856964 0.0117713927 0.9941143
[18,] 0.0064400772 0.0128801544 0.9935599
[19,] 0.0061247775 0.0122495549 0.9938752
[20,] 0.0038121825 0.0076243651 0.9961878
[21,] 0.0024625607 0.0049251214 0.9975374
[22,] 0.0019420518 0.0038841035 0.9980579
[23,] 0.0010486854 0.0020973707 0.9989513
[24,] 0.0006003204 0.0012006407 0.9993997
[25,] 0.0016683596 0.0033367193 0.9983316
[26,] 0.0008951255 0.0017902509 0.9991049
[27,] 0.0008521826 0.0017043652 0.9991478
[28,] 0.0006582757 0.0013165514 0.9993417
[29,] 0.0016653625 0.0033307250 0.9983346
[30,] 0.0013673998 0.0027347996 0.9986326
[31,] 0.0007665078 0.0015330156 0.9992335
[32,] 0.0010362384 0.0020724769 0.9989638
[33,] 0.0007461586 0.0014923173 0.9992538
[34,] 0.0004929152 0.0009858304 0.9995071
[35,] 0.0009474891 0.0018949783 0.9990525
[36,] 0.0006477990 0.0012955980 0.9993522
[37,] 0.0004152813 0.0008305626 0.9995847
[38,] 0.0002834352 0.0005668705 0.9997166
[39,] 0.0006690300 0.0013380600 0.9993310
[40,] 0.0005457383 0.0010914765 0.9994543
[41,] 0.0005212206 0.0010424412 0.9994788
[42,] 0.0002705894 0.0005411787 0.9997294
[43,] 0.0002339248 0.0004678495 0.9997661
[44,] 0.0010966208 0.0021932417 0.9989034
[45,] 0.0104353566 0.0208707132 0.9895646
[46,] 0.0070678362 0.0141356724 0.9929322
[47,] 0.0066221938 0.0132443876 0.9933778
[48,] 0.0037031452 0.0074062903 0.9962969
[49,] 0.0098691913 0.0197383825 0.9901308
[50,] 0.0056894093 0.0113788187 0.9943106
[51,] 0.0203485741 0.0406971483 0.9796514
[52,] 0.0379147853 0.0758295706 0.9620852
[53,] 0.0362354258 0.0724708517 0.9637646
[54,] 0.1090865462 0.2181730924 0.8909135
[55,] 0.0781665887 0.1563331773 0.9218334
[56,] 0.0570658190 0.1141316380 0.9429342
> postscript(file="/var/www/html/rcomp/tmp/1h6sp1260039641.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2kls91260039641.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/381x01260039641.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lup91260039641.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5s1en1260039641.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 = 93
Frequency = 1
1 2 3 4 5 6
204.342370 162.977267 -332.689889 8.038991 -68.969625 101.714824
7 8 9 10 11 12
-59.396214 194.658462 285.755086 22.211279 138.889080 -32.643056
13 14 15 16 17 18
9.656743 167.643780 -47.029736 368.183854 -334.476908 34.685044
19 20 21 22 23 24
27.696325 -51.405992 -14.864996 -339.863502 153.471940 -270.082077
25 26 27 28 29 30
317.649770 -310.915365 -88.722420 45.572866 -99.500107 -51.756152
31 32 33 34 35 36
29.415002 204.314890 21.398473 -79.662635 -198.793803 211.291050
37 38 39 40 41 42
-307.841540 37.724783 -89.705028 -193.405865 -30.043102 16.580796
43 44 45 46 47 48
358.632323 -75.059291 196.530871 -326.841761 -389.523516 192.911389
49 50 51 52 53 54
-179.182371 240.156242 -188.635728 -88.154854 264.022139 14.117648
55 56 57 58 59 60
-32.824898 -130.805960 195.890822 -94.476127 118.426296 -139.054662
61 62 63 64 65 66
-227.548628 311.881872 -84.036433 36.257907 265.212059 -235.134137
67 68 69 70 71 72
-5.163466 -254.732335 -262.351594 146.569956 -488.501404 -456.427916
73 74 75 76 77 78
35.113846 -346.469843 452.258407 -215.955307 203.908328 276.570967
79 80 81 82 83 84
-152.388730 191.300451 135.870055 672.062791 666.031407 494.005274
85 86 87 88 89 90
147.809809 -262.998736 378.560827 39.462407 -200.152785 -156.778989
91 92 93
-165.970342 -78.270226 -558.228718
> postscript(file="/var/www/html/rcomp/tmp/69zih1260039641.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 = 93
Frequency = 1
lag(myerror, k = 1) myerror
0 204.342370 NA
1 162.977267 204.342370
2 -332.689889 162.977267
3 8.038991 -332.689889
4 -68.969625 8.038991
5 101.714824 -68.969625
6 -59.396214 101.714824
7 194.658462 -59.396214
8 285.755086 194.658462
9 22.211279 285.755086
10 138.889080 22.211279
11 -32.643056 138.889080
12 9.656743 -32.643056
13 167.643780 9.656743
14 -47.029736 167.643780
15 368.183854 -47.029736
16 -334.476908 368.183854
17 34.685044 -334.476908
18 27.696325 34.685044
19 -51.405992 27.696325
20 -14.864996 -51.405992
21 -339.863502 -14.864996
22 153.471940 -339.863502
23 -270.082077 153.471940
24 317.649770 -270.082077
25 -310.915365 317.649770
26 -88.722420 -310.915365
27 45.572866 -88.722420
28 -99.500107 45.572866
29 -51.756152 -99.500107
30 29.415002 -51.756152
31 204.314890 29.415002
32 21.398473 204.314890
33 -79.662635 21.398473
34 -198.793803 -79.662635
35 211.291050 -198.793803
36 -307.841540 211.291050
37 37.724783 -307.841540
38 -89.705028 37.724783
39 -193.405865 -89.705028
40 -30.043102 -193.405865
41 16.580796 -30.043102
42 358.632323 16.580796
43 -75.059291 358.632323
44 196.530871 -75.059291
45 -326.841761 196.530871
46 -389.523516 -326.841761
47 192.911389 -389.523516
48 -179.182371 192.911389
49 240.156242 -179.182371
50 -188.635728 240.156242
51 -88.154854 -188.635728
52 264.022139 -88.154854
53 14.117648 264.022139
54 -32.824898 14.117648
55 -130.805960 -32.824898
56 195.890822 -130.805960
57 -94.476127 195.890822
58 118.426296 -94.476127
59 -139.054662 118.426296
60 -227.548628 -139.054662
61 311.881872 -227.548628
62 -84.036433 311.881872
63 36.257907 -84.036433
64 265.212059 36.257907
65 -235.134137 265.212059
66 -5.163466 -235.134137
67 -254.732335 -5.163466
68 -262.351594 -254.732335
69 146.569956 -262.351594
70 -488.501404 146.569956
71 -456.427916 -488.501404
72 35.113846 -456.427916
73 -346.469843 35.113846
74 452.258407 -346.469843
75 -215.955307 452.258407
76 203.908328 -215.955307
77 276.570967 203.908328
78 -152.388730 276.570967
79 191.300451 -152.388730
80 135.870055 191.300451
81 672.062791 135.870055
82 666.031407 672.062791
83 494.005274 666.031407
84 147.809809 494.005274
85 -262.998736 147.809809
86 378.560827 -262.998736
87 39.462407 378.560827
88 -200.152785 39.462407
89 -156.778989 -200.152785
90 -165.970342 -156.778989
91 -78.270226 -165.970342
92 -558.228718 -78.270226
93 NA -558.228718
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 162.977267 204.342370
[2,] -332.689889 162.977267
[3,] 8.038991 -332.689889
[4,] -68.969625 8.038991
[5,] 101.714824 -68.969625
[6,] -59.396214 101.714824
[7,] 194.658462 -59.396214
[8,] 285.755086 194.658462
[9,] 22.211279 285.755086
[10,] 138.889080 22.211279
[11,] -32.643056 138.889080
[12,] 9.656743 -32.643056
[13,] 167.643780 9.656743
[14,] -47.029736 167.643780
[15,] 368.183854 -47.029736
[16,] -334.476908 368.183854
[17,] 34.685044 -334.476908
[18,] 27.696325 34.685044
[19,] -51.405992 27.696325
[20,] -14.864996 -51.405992
[21,] -339.863502 -14.864996
[22,] 153.471940 -339.863502
[23,] -270.082077 153.471940
[24,] 317.649770 -270.082077
[25,] -310.915365 317.649770
[26,] -88.722420 -310.915365
[27,] 45.572866 -88.722420
[28,] -99.500107 45.572866
[29,] -51.756152 -99.500107
[30,] 29.415002 -51.756152
[31,] 204.314890 29.415002
[32,] 21.398473 204.314890
[33,] -79.662635 21.398473
[34,] -198.793803 -79.662635
[35,] 211.291050 -198.793803
[36,] -307.841540 211.291050
[37,] 37.724783 -307.841540
[38,] -89.705028 37.724783
[39,] -193.405865 -89.705028
[40,] -30.043102 -193.405865
[41,] 16.580796 -30.043102
[42,] 358.632323 16.580796
[43,] -75.059291 358.632323
[44,] 196.530871 -75.059291
[45,] -326.841761 196.530871
[46,] -389.523516 -326.841761
[47,] 192.911389 -389.523516
[48,] -179.182371 192.911389
[49,] 240.156242 -179.182371
[50,] -188.635728 240.156242
[51,] -88.154854 -188.635728
[52,] 264.022139 -88.154854
[53,] 14.117648 264.022139
[54,] -32.824898 14.117648
[55,] -130.805960 -32.824898
[56,] 195.890822 -130.805960
[57,] -94.476127 195.890822
[58,] 118.426296 -94.476127
[59,] -139.054662 118.426296
[60,] -227.548628 -139.054662
[61,] 311.881872 -227.548628
[62,] -84.036433 311.881872
[63,] 36.257907 -84.036433
[64,] 265.212059 36.257907
[65,] -235.134137 265.212059
[66,] -5.163466 -235.134137
[67,] -254.732335 -5.163466
[68,] -262.351594 -254.732335
[69,] 146.569956 -262.351594
[70,] -488.501404 146.569956
[71,] -456.427916 -488.501404
[72,] 35.113846 -456.427916
[73,] -346.469843 35.113846
[74,] 452.258407 -346.469843
[75,] -215.955307 452.258407
[76,] 203.908328 -215.955307
[77,] 276.570967 203.908328
[78,] -152.388730 276.570967
[79,] 191.300451 -152.388730
[80,] 135.870055 191.300451
[81,] 672.062791 135.870055
[82,] 666.031407 672.062791
[83,] 494.005274 666.031407
[84,] 147.809809 494.005274
[85,] -262.998736 147.809809
[86,] 378.560827 -262.998736
[87,] 39.462407 378.560827
[88,] -200.152785 39.462407
[89,] -156.778989 -200.152785
[90,] -165.970342 -156.778989
[91,] -78.270226 -165.970342
[92,] -558.228718 -78.270226
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 162.977267 204.342370
2 -332.689889 162.977267
3 8.038991 -332.689889
4 -68.969625 8.038991
5 101.714824 -68.969625
6 -59.396214 101.714824
7 194.658462 -59.396214
8 285.755086 194.658462
9 22.211279 285.755086
10 138.889080 22.211279
11 -32.643056 138.889080
12 9.656743 -32.643056
13 167.643780 9.656743
14 -47.029736 167.643780
15 368.183854 -47.029736
16 -334.476908 368.183854
17 34.685044 -334.476908
18 27.696325 34.685044
19 -51.405992 27.696325
20 -14.864996 -51.405992
21 -339.863502 -14.864996
22 153.471940 -339.863502
23 -270.082077 153.471940
24 317.649770 -270.082077
25 -310.915365 317.649770
26 -88.722420 -310.915365
27 45.572866 -88.722420
28 -99.500107 45.572866
29 -51.756152 -99.500107
30 29.415002 -51.756152
31 204.314890 29.415002
32 21.398473 204.314890
33 -79.662635 21.398473
34 -198.793803 -79.662635
35 211.291050 -198.793803
36 -307.841540 211.291050
37 37.724783 -307.841540
38 -89.705028 37.724783
39 -193.405865 -89.705028
40 -30.043102 -193.405865
41 16.580796 -30.043102
42 358.632323 16.580796
43 -75.059291 358.632323
44 196.530871 -75.059291
45 -326.841761 196.530871
46 -389.523516 -326.841761
47 192.911389 -389.523516
48 -179.182371 192.911389
49 240.156242 -179.182371
50 -188.635728 240.156242
51 -88.154854 -188.635728
52 264.022139 -88.154854
53 14.117648 264.022139
54 -32.824898 14.117648
55 -130.805960 -32.824898
56 195.890822 -130.805960
57 -94.476127 195.890822
58 118.426296 -94.476127
59 -139.054662 118.426296
60 -227.548628 -139.054662
61 311.881872 -227.548628
62 -84.036433 311.881872
63 36.257907 -84.036433
64 265.212059 36.257907
65 -235.134137 265.212059
66 -5.163466 -235.134137
67 -254.732335 -5.163466
68 -262.351594 -254.732335
69 146.569956 -262.351594
70 -488.501404 146.569956
71 -456.427916 -488.501404
72 35.113846 -456.427916
73 -346.469843 35.113846
74 452.258407 -346.469843
75 -215.955307 452.258407
76 203.908328 -215.955307
77 276.570967 203.908328
78 -152.388730 276.570967
79 191.300451 -152.388730
80 135.870055 191.300451
81 672.062791 135.870055
82 666.031407 672.062791
83 494.005274 666.031407
84 147.809809 494.005274
85 -262.998736 147.809809
86 378.560827 -262.998736
87 39.462407 378.560827
88 -200.152785 39.462407
89 -156.778989 -200.152785
90 -165.970342 -156.778989
91 -78.270226 -165.970342
92 -558.228718 -78.270226
> 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/71ey11260039641.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8v3py1260039641.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9wsfv1260039641.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10lotb1260039641.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119zm21260039641.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/12a56v1260039641.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/132ymu1260039641.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/14ub1j1260039641.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/15zshs1260039641.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/16m1w01260039641.tab")
+ }
>
> system("convert tmp/1h6sp1260039641.ps tmp/1h6sp1260039641.png")
> system("convert tmp/2kls91260039641.ps tmp/2kls91260039641.png")
> system("convert tmp/381x01260039641.ps tmp/381x01260039641.png")
> system("convert tmp/4lup91260039641.ps tmp/4lup91260039641.png")
> system("convert tmp/5s1en1260039641.ps tmp/5s1en1260039641.png")
> system("convert tmp/69zih1260039641.ps tmp/69zih1260039641.png")
> system("convert tmp/71ey11260039641.ps tmp/71ey11260039641.png")
> system("convert tmp/8v3py1260039641.ps tmp/8v3py1260039641.png")
> system("convert tmp/9wsfv1260039641.ps tmp/9wsfv1260039641.png")
> system("convert tmp/10lotb1260039641.ps tmp/10lotb1260039641.png")
>
>
> proc.time()
user system elapsed
2.925 1.640 4.738