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.
R is a collaborative project with many contributors.
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(2360
+ ,2
+ ,2267
+ ,1746
+ ,2214
+ ,2
+ ,2360
+ ,2267
+ ,2825
+ ,2
+ ,2214
+ ,2360
+ ,2355
+ ,2
+ ,2825
+ ,2214
+ ,2333
+ ,2
+ ,2355
+ ,2825
+ ,3016
+ ,2
+ ,2333
+ ,2355
+ ,2155
+ ,2
+ ,3016
+ ,2333
+ ,2172
+ ,2
+ ,2155
+ ,3016
+ ,2150
+ ,2
+ ,2172
+ ,2155
+ ,2533
+ ,2
+ ,2150
+ ,2172
+ ,2058
+ ,2
+ ,2533
+ ,2150
+ ,2160
+ ,2
+ ,2058
+ ,2533
+ ,2260
+ ,2
+ ,2160
+ ,2058
+ ,2498
+ ,2
+ ,2260
+ ,2160
+ ,2695
+ ,2
+ ,2498
+ ,2260
+ ,2799
+ ,2
+ ,2695
+ ,2498
+ ,2947
+ ,2
+ ,2799
+ ,2695
+ ,2930
+ ,2
+ ,2947
+ ,2799
+ ,2318
+ ,2
+ ,2930
+ ,2947
+ ,2540
+ ,2
+ ,2318
+ ,2930
+ ,2570
+ ,2
+ ,2540
+ ,2318
+ ,2669
+ ,2
+ ,2570
+ ,2540
+ ,2450
+ ,2
+ ,2669
+ ,2570
+ ,2842
+ ,2
+ ,2450
+ ,2669
+ ,3440
+ ,2
+ ,2842
+ ,2450
+ ,2678
+ ,2
+ ,3440
+ ,2842
+ ,2981
+ ,2
+ ,2678
+ ,3440
+ ,2260
+ ,2.21
+ ,2981
+ ,2678
+ ,2844
+ ,2.25
+ ,2260
+ ,2981
+ ,2546
+ ,2.25
+ ,2844
+ ,2260
+ ,2456
+ ,2.45
+ ,2546
+ ,2844
+ ,2295
+ ,2.5
+ ,2456
+ ,2546
+ ,2379
+ ,2.5
+ ,2295
+ ,2456
+ ,2479
+ ,2.64
+ ,2379
+ ,2295
+ ,2057
+ ,2.75
+ ,2479
+ ,2379
+ ,2280
+ ,2.93
+ ,2057
+ ,2479
+ ,2351
+ ,3
+ ,2280
+ ,2057
+ ,2276
+ ,3.17
+ ,2351
+ ,2280
+ ,2548
+ ,3.25
+ ,2276
+ ,2351
+ ,2311
+ ,3.39
+ ,2548
+ ,2276
+ ,2201
+ ,3.5
+ ,2311
+ ,2548
+ ,2725
+ ,3.5
+ ,2201
+ ,2311
+ ,2408
+ ,3.65
+ ,2725
+ ,2201
+ ,2139
+ ,3.75
+ ,2408
+ ,2725
+ ,1898
+ ,3.75
+ ,2139
+ ,2408
+ ,2537
+ ,3.9
+ ,1898
+ ,2139
+ ,2069
+ ,4
+ ,2537
+ ,1898
+ ,2063
+ ,4
+ ,2069
+ ,2537
+ ,2524
+ ,4
+ ,2063
+ ,2069
+ ,2437
+ ,4
+ ,2524
+ ,2063
+ ,2189
+ ,4
+ ,2437
+ ,2524
+ ,2793
+ ,4
+ ,2189
+ ,2437
+ ,2074
+ ,4
+ ,2793
+ ,2189
+ ,2622
+ ,4
+ ,2074
+ ,2793
+ ,2278
+ ,4
+ ,2622
+ ,2074
+ ,2144
+ ,4
+ ,2278
+ ,2622
+ ,2427
+ ,4
+ ,2144
+ ,2278
+ ,2139
+ ,4
+ ,2427
+ ,2144
+ ,1828
+ ,4.18
+ ,2139
+ ,2427
+ ,2072
+ ,4.25
+ ,1828
+ ,2139
+ ,1800
+ ,4.25
+ ,2072
+ ,1828)
+ ,dim=c(4
+ ,61)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2')
+ ,1:61))
> y <- array(NA,dim=c(4,61),dimnames=list(c('Y','X','Y1','Y2'),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
Y X Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 2360 2.00 2267 1746 1 0 0 0 0 0 0 0 0 0 0 1
2 2214 2.00 2360 2267 0 1 0 0 0 0 0 0 0 0 0 2
3 2825 2.00 2214 2360 0 0 1 0 0 0 0 0 0 0 0 3
4 2355 2.00 2825 2214 0 0 0 1 0 0 0 0 0 0 0 4
5 2333 2.00 2355 2825 0 0 0 0 1 0 0 0 0 0 0 5
6 3016 2.00 2333 2355 0 0 0 0 0 1 0 0 0 0 0 6
7 2155 2.00 3016 2333 0 0 0 0 0 0 1 0 0 0 0 7
8 2172 2.00 2155 3016 0 0 0 0 0 0 0 1 0 0 0 8
9 2150 2.00 2172 2155 0 0 0 0 0 0 0 0 1 0 0 9
10 2533 2.00 2150 2172 0 0 0 0 0 0 0 0 0 1 0 10
11 2058 2.00 2533 2150 0 0 0 0 0 0 0 0 0 0 1 11
12 2160 2.00 2058 2533 0 0 0 0 0 0 0 0 0 0 0 12
13 2260 2.00 2160 2058 1 0 0 0 0 0 0 0 0 0 0 13
14 2498 2.00 2260 2160 0 1 0 0 0 0 0 0 0 0 0 14
15 2695 2.00 2498 2260 0 0 1 0 0 0 0 0 0 0 0 15
16 2799 2.00 2695 2498 0 0 0 1 0 0 0 0 0 0 0 16
17 2947 2.00 2799 2695 0 0 0 0 1 0 0 0 0 0 0 17
18 2930 2.00 2947 2799 0 0 0 0 0 1 0 0 0 0 0 18
19 2318 2.00 2930 2947 0 0 0 0 0 0 1 0 0 0 0 19
20 2540 2.00 2318 2930 0 0 0 0 0 0 0 1 0 0 0 20
21 2570 2.00 2540 2318 0 0 0 0 0 0 0 0 1 0 0 21
22 2669 2.00 2570 2540 0 0 0 0 0 0 0 0 0 1 0 22
23 2450 2.00 2669 2570 0 0 0 0 0 0 0 0 0 0 1 23
24 2842 2.00 2450 2669 0 0 0 0 0 0 0 0 0 0 0 24
25 3440 2.00 2842 2450 1 0 0 0 0 0 0 0 0 0 0 25
26 2678 2.00 3440 2842 0 1 0 0 0 0 0 0 0 0 0 26
27 2981 2.00 2678 3440 0 0 1 0 0 0 0 0 0 0 0 27
28 2260 2.21 2981 2678 0 0 0 1 0 0 0 0 0 0 0 28
29 2844 2.25 2260 2981 0 0 0 0 1 0 0 0 0 0 0 29
30 2546 2.25 2844 2260 0 0 0 0 0 1 0 0 0 0 0 30
31 2456 2.45 2546 2844 0 0 0 0 0 0 1 0 0 0 0 31
32 2295 2.50 2456 2546 0 0 0 0 0 0 0 1 0 0 0 32
33 2379 2.50 2295 2456 0 0 0 0 0 0 0 0 1 0 0 33
34 2479 2.64 2379 2295 0 0 0 0 0 0 0 0 0 1 0 34
35 2057 2.75 2479 2379 0 0 0 0 0 0 0 0 0 0 1 35
36 2280 2.93 2057 2479 0 0 0 0 0 0 0 0 0 0 0 36
37 2351 3.00 2280 2057 1 0 0 0 0 0 0 0 0 0 0 37
38 2276 3.17 2351 2280 0 1 0 0 0 0 0 0 0 0 0 38
39 2548 3.25 2276 2351 0 0 1 0 0 0 0 0 0 0 0 39
40 2311 3.39 2548 2276 0 0 0 1 0 0 0 0 0 0 0 40
41 2201 3.50 2311 2548 0 0 0 0 1 0 0 0 0 0 0 41
42 2725 3.50 2201 2311 0 0 0 0 0 1 0 0 0 0 0 42
43 2408 3.65 2725 2201 0 0 0 0 0 0 1 0 0 0 0 43
44 2139 3.75 2408 2725 0 0 0 0 0 0 0 1 0 0 0 44
45 1898 3.75 2139 2408 0 0 0 0 0 0 0 0 1 0 0 45
46 2537 3.90 1898 2139 0 0 0 0 0 0 0 0 0 1 0 46
47 2069 4.00 2537 1898 0 0 0 0 0 0 0 0 0 0 1 47
48 2063 4.00 2069 2537 0 0 0 0 0 0 0 0 0 0 0 48
49 2524 4.00 2063 2069 1 0 0 0 0 0 0 0 0 0 0 49
50 2437 4.00 2524 2063 0 1 0 0 0 0 0 0 0 0 0 50
51 2189 4.00 2437 2524 0 0 1 0 0 0 0 0 0 0 0 51
52 2793 4.00 2189 2437 0 0 0 1 0 0 0 0 0 0 0 52
53 2074 4.00 2793 2189 0 0 0 0 1 0 0 0 0 0 0 53
54 2622 4.00 2074 2793 0 0 0 0 0 1 0 0 0 0 0 54
55 2278 4.00 2622 2074 0 0 0 0 0 0 1 0 0 0 0 55
56 2144 4.00 2278 2622 0 0 0 0 0 0 0 1 0 0 0 56
57 2427 4.00 2144 2278 0 0 0 0 0 0 0 0 1 0 0 57
58 2139 4.00 2427 2144 0 0 0 0 0 0 0 0 0 1 0 58
59 1828 4.18 2139 2427 0 0 0 0 0 0 0 0 0 0 1 59
60 2072 4.25 1828 2139 0 0 0 0 0 0 0 0 0 0 0 60
61 1800 4.25 2072 1828 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) X Y1 Y2 M1 M2
2.433e+03 -3.151e+02 -1.258e-03 1.771e-01 2.509e+02 1.407e+02
M3 M4 M5 M6 M7 M8
3.154e+02 2.129e+02 1.478e+02 4.510e+02 2.248e+01 -9.496e+01
M9 M10 M11 t
2.392e-01 2.063e+02 -1.629e+02 1.032e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-495.72 -148.88 -33.33 142.41 697.50
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.433e+03 7.516e+02 3.238 0.00227 **
X -3.151e+02 1.487e+02 -2.119 0.03968 *
Y1 -1.258e-03 1.626e-01 -0.008 0.99386
Y2 1.771e-01 1.635e-01 1.083 0.28443
M1 2.509e+02 1.674e+02 1.499 0.14085
M2 1.407e+02 1.776e+02 0.792 0.43225
M3 3.154e+02 1.674e+02 1.884 0.06610 .
M4 2.129e+02 1.814e+02 1.173 0.24685
M5 1.478e+02 1.744e+02 0.847 0.40123
M6 4.510e+02 1.683e+02 2.679 0.01028 *
M7 2.248e+01 1.910e+02 0.118 0.90683
M8 -9.496e+01 1.689e+02 -0.562 0.57666
M9 2.392e-01 1.610e+02 0.001 0.99882
M10 2.063e+02 1.635e+02 1.262 0.21345
M11 -1.629e+02 1.702e+02 -0.957 0.34367
t 1.032e+01 6.705e+00 1.538 0.13096
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 248.4 on 45 degrees of freedom
Multiple R-squared: 0.5442, Adjusted R-squared: 0.3922
F-statistic: 3.581 on 15 and 45 DF, p-value: 0.0004559
> 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,] 0.62305651 0.7538870 0.3769435
[2,] 0.45893028 0.9178606 0.5410697
[3,] 0.36310612 0.7262122 0.6368939
[4,] 0.23681690 0.4736338 0.7631831
[5,] 0.17917826 0.3583565 0.8208217
[6,] 0.22076587 0.4415317 0.7792341
[7,] 0.74515871 0.5096826 0.2548413
[8,] 0.75657768 0.4868446 0.2434223
[9,] 0.79001478 0.4199704 0.2099852
[10,] 0.71825591 0.5634882 0.2817441
[11,] 0.82261967 0.3547607 0.1773803
[12,] 0.79866808 0.4026638 0.2013319
[13,] 0.84851955 0.3029609 0.1514805
[14,] 0.78557489 0.4288502 0.2144251
[15,] 0.71149691 0.5770062 0.2885031
[16,] 0.61835889 0.7632822 0.3816411
[17,] 0.51172139 0.9765572 0.4882786
[18,] 0.42872607 0.8574521 0.5712739
[19,] 0.39219977 0.7843995 0.6078002
[20,] 0.29941023 0.5988205 0.7005898
[21,] 0.27879021 0.5575804 0.7212098
[22,] 0.23025768 0.4605154 0.7697423
[23,] 0.13660367 0.2732073 0.8633963
[24,] 0.07747479 0.1549496 0.9225252
> postscript(file="/var/www/html/rcomp/tmp/15vqg1258655144.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2y6tl1258655144.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3je4f1258655144.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43sbk1258655144.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5q78d1258655144.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
-10.944083 -149.260739 260.122092 -91.063637 -167.150390 285.601644
7 8 9 10 11 12
-152.475316 -150.424195 -125.392614 38.145656 -73.538214 -213.204314
13 14 15 16 17 18
-290.132472 29.784077 24.410134 178.679132 346.653403 -2.062732
19 20 21 22 23 24
-222.135530 109.231522 142.411602 -14.299726 120.447378 321.413062
25 26 27 28 29 30
697.500347 -33.328881 -22.180264 -449.465764 147.295705 -335.725850
31 32 33 34 35 36
-48.373333 -33.817478 -39.589881 -83.275748 -126.435140 -38.188724
37 38 39 40 41 42
-131.298183 -92.290253 7.285086 -79.794285 -148.876984 103.489164
43 44 45 46 47 48
272.062126 48.471568 -242.224486 274.963211 240.904014 -52.102788
49 50 51 52 53 54
230.593113 245.095795 -269.637048 441.644554 -177.921734 -51.302226
55 56 57 58 59 60
150.922053 26.538584 264.795379 -215.533392 -161.378038 -17.917236
61
-495.718723
> postscript(file="/var/www/html/rcomp/tmp/6t6861258655144.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 -10.944083 NA
1 -149.260739 -10.944083
2 260.122092 -149.260739
3 -91.063637 260.122092
4 -167.150390 -91.063637
5 285.601644 -167.150390
6 -152.475316 285.601644
7 -150.424195 -152.475316
8 -125.392614 -150.424195
9 38.145656 -125.392614
10 -73.538214 38.145656
11 -213.204314 -73.538214
12 -290.132472 -213.204314
13 29.784077 -290.132472
14 24.410134 29.784077
15 178.679132 24.410134
16 346.653403 178.679132
17 -2.062732 346.653403
18 -222.135530 -2.062732
19 109.231522 -222.135530
20 142.411602 109.231522
21 -14.299726 142.411602
22 120.447378 -14.299726
23 321.413062 120.447378
24 697.500347 321.413062
25 -33.328881 697.500347
26 -22.180264 -33.328881
27 -449.465764 -22.180264
28 147.295705 -449.465764
29 -335.725850 147.295705
30 -48.373333 -335.725850
31 -33.817478 -48.373333
32 -39.589881 -33.817478
33 -83.275748 -39.589881
34 -126.435140 -83.275748
35 -38.188724 -126.435140
36 -131.298183 -38.188724
37 -92.290253 -131.298183
38 7.285086 -92.290253
39 -79.794285 7.285086
40 -148.876984 -79.794285
41 103.489164 -148.876984
42 272.062126 103.489164
43 48.471568 272.062126
44 -242.224486 48.471568
45 274.963211 -242.224486
46 240.904014 274.963211
47 -52.102788 240.904014
48 230.593113 -52.102788
49 245.095795 230.593113
50 -269.637048 245.095795
51 441.644554 -269.637048
52 -177.921734 441.644554
53 -51.302226 -177.921734
54 150.922053 -51.302226
55 26.538584 150.922053
56 264.795379 26.538584
57 -215.533392 264.795379
58 -161.378038 -215.533392
59 -17.917236 -161.378038
60 -495.718723 -17.917236
61 NA -495.718723
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -149.260739 -10.944083
[2,] 260.122092 -149.260739
[3,] -91.063637 260.122092
[4,] -167.150390 -91.063637
[5,] 285.601644 -167.150390
[6,] -152.475316 285.601644
[7,] -150.424195 -152.475316
[8,] -125.392614 -150.424195
[9,] 38.145656 -125.392614
[10,] -73.538214 38.145656
[11,] -213.204314 -73.538214
[12,] -290.132472 -213.204314
[13,] 29.784077 -290.132472
[14,] 24.410134 29.784077
[15,] 178.679132 24.410134
[16,] 346.653403 178.679132
[17,] -2.062732 346.653403
[18,] -222.135530 -2.062732
[19,] 109.231522 -222.135530
[20,] 142.411602 109.231522
[21,] -14.299726 142.411602
[22,] 120.447378 -14.299726
[23,] 321.413062 120.447378
[24,] 697.500347 321.413062
[25,] -33.328881 697.500347
[26,] -22.180264 -33.328881
[27,] -449.465764 -22.180264
[28,] 147.295705 -449.465764
[29,] -335.725850 147.295705
[30,] -48.373333 -335.725850
[31,] -33.817478 -48.373333
[32,] -39.589881 -33.817478
[33,] -83.275748 -39.589881
[34,] -126.435140 -83.275748
[35,] -38.188724 -126.435140
[36,] -131.298183 -38.188724
[37,] -92.290253 -131.298183
[38,] 7.285086 -92.290253
[39,] -79.794285 7.285086
[40,] -148.876984 -79.794285
[41,] 103.489164 -148.876984
[42,] 272.062126 103.489164
[43,] 48.471568 272.062126
[44,] -242.224486 48.471568
[45,] 274.963211 -242.224486
[46,] 240.904014 274.963211
[47,] -52.102788 240.904014
[48,] 230.593113 -52.102788
[49,] 245.095795 230.593113
[50,] -269.637048 245.095795
[51,] 441.644554 -269.637048
[52,] -177.921734 441.644554
[53,] -51.302226 -177.921734
[54,] 150.922053 -51.302226
[55,] 26.538584 150.922053
[56,] 264.795379 26.538584
[57,] -215.533392 264.795379
[58,] -161.378038 -215.533392
[59,] -17.917236 -161.378038
[60,] -495.718723 -17.917236
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -149.260739 -10.944083
2 260.122092 -149.260739
3 -91.063637 260.122092
4 -167.150390 -91.063637
5 285.601644 -167.150390
6 -152.475316 285.601644
7 -150.424195 -152.475316
8 -125.392614 -150.424195
9 38.145656 -125.392614
10 -73.538214 38.145656
11 -213.204314 -73.538214
12 -290.132472 -213.204314
13 29.784077 -290.132472
14 24.410134 29.784077
15 178.679132 24.410134
16 346.653403 178.679132
17 -2.062732 346.653403
18 -222.135530 -2.062732
19 109.231522 -222.135530
20 142.411602 109.231522
21 -14.299726 142.411602
22 120.447378 -14.299726
23 321.413062 120.447378
24 697.500347 321.413062
25 -33.328881 697.500347
26 -22.180264 -33.328881
27 -449.465764 -22.180264
28 147.295705 -449.465764
29 -335.725850 147.295705
30 -48.373333 -335.725850
31 -33.817478 -48.373333
32 -39.589881 -33.817478
33 -83.275748 -39.589881
34 -126.435140 -83.275748
35 -38.188724 -126.435140
36 -131.298183 -38.188724
37 -92.290253 -131.298183
38 7.285086 -92.290253
39 -79.794285 7.285086
40 -148.876984 -79.794285
41 103.489164 -148.876984
42 272.062126 103.489164
43 48.471568 272.062126
44 -242.224486 48.471568
45 274.963211 -242.224486
46 240.904014 274.963211
47 -52.102788 240.904014
48 230.593113 -52.102788
49 245.095795 230.593113
50 -269.637048 245.095795
51 441.644554 -269.637048
52 -177.921734 441.644554
53 -51.302226 -177.921734
54 150.922053 -51.302226
55 26.538584 150.922053
56 264.795379 26.538584
57 -215.533392 264.795379
58 -161.378038 -215.533392
59 -17.917236 -161.378038
60 -495.718723 -17.917236
> 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/7uebi1258655144.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8e3v81258655144.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9q0dl1258655144.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10r93i1258655144.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11az4s1258655144.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/12vrww1258655144.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/13r9p21258655145.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/14lfpk1258655145.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/15labk1258655145.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/16gucc1258655145.tab")
+ }
> system("convert tmp/15vqg1258655144.ps tmp/15vqg1258655144.png")
> system("convert tmp/2y6tl1258655144.ps tmp/2y6tl1258655144.png")
> system("convert tmp/3je4f1258655144.ps tmp/3je4f1258655144.png")
> system("convert tmp/43sbk1258655144.ps tmp/43sbk1258655144.png")
> system("convert tmp/5q78d1258655144.ps tmp/5q78d1258655144.png")
> system("convert tmp/6t6861258655144.ps tmp/6t6861258655144.png")
> system("convert tmp/7uebi1258655144.ps tmp/7uebi1258655144.png")
> system("convert tmp/8e3v81258655144.ps tmp/8e3v81258655144.png")
> system("convert tmp/9q0dl1258655144.ps tmp/9q0dl1258655144.png")
> system("convert tmp/10r93i1258655144.ps tmp/10r93i1258655144.png")
>
>
> proc.time()
user system elapsed
2.463 1.615 7.848