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(96.92
+ ,148.3
+ ,98.2
+ ,98.54
+ ,99.06
+ ,152.2
+ ,96.92
+ ,98.2
+ ,99.65
+ ,169.4
+ ,99.06
+ ,96.92
+ ,99.82
+ ,168.6
+ ,99.65
+ ,99.06
+ ,99.99
+ ,161.1
+ ,99.82
+ ,99.65
+ ,100.33
+ ,174.1
+ ,99.99
+ ,99.82
+ ,99.31
+ ,179
+ ,100.33
+ ,99.99
+ ,101.1
+ ,190.6
+ ,99.31
+ ,100.33
+ ,101.1
+ ,190
+ ,101.1
+ ,99.31
+ ,100.93
+ ,181.6
+ ,101.1
+ ,101.1
+ ,100.85
+ ,174.8
+ ,100.93
+ ,101.1
+ ,100.93
+ ,180.5
+ ,100.85
+ ,100.93
+ ,99.6
+ ,196.8
+ ,100.93
+ ,100.85
+ ,101.88
+ ,193.8
+ ,99.6
+ ,100.93
+ ,101.81
+ ,197
+ ,101.88
+ ,99.6
+ ,102.38
+ ,216.3
+ ,101.81
+ ,101.88
+ ,102.74
+ ,221.4
+ ,102.38
+ ,101.81
+ ,102.82
+ ,217.9
+ ,102.74
+ ,102.38
+ ,101.72
+ ,229.7
+ ,102.82
+ ,102.74
+ ,103.47
+ ,227.4
+ ,101.72
+ ,102.82
+ ,102.98
+ ,204.2
+ ,103.47
+ ,101.72
+ ,102.68
+ ,196.6
+ ,102.98
+ ,103.47
+ ,102.9
+ ,198.8
+ ,102.68
+ ,102.98
+ ,103.03
+ ,207.5
+ ,102.9
+ ,102.68
+ ,101.29
+ ,190.7
+ ,103.03
+ ,102.9
+ ,103.69
+ ,201.6
+ ,101.29
+ ,103.03
+ ,103.68
+ ,210.5
+ ,103.69
+ ,101.29
+ ,104.2
+ ,223.5
+ ,103.68
+ ,103.69
+ ,104.08
+ ,223.8
+ ,104.2
+ ,103.68
+ ,104.16
+ ,231.2
+ ,104.08
+ ,104.2
+ ,103.05
+ ,244
+ ,104.16
+ ,104.08
+ ,104.66
+ ,234.7
+ ,103.05
+ ,104.16
+ ,104.46
+ ,250.2
+ ,104.66
+ ,103.05
+ ,104.95
+ ,265.7
+ ,104.46
+ ,104.66
+ ,105.85
+ ,287.6
+ ,104.95
+ ,104.46
+ ,106.23
+ ,283.3
+ ,105.85
+ ,104.95
+ ,104.86
+ ,295.4
+ ,106.23
+ ,105.85
+ ,107.44
+ ,312.3
+ ,104.86
+ ,106.23
+ ,108.23
+ ,333.8
+ ,107.44
+ ,104.86
+ ,108.45
+ ,347.7
+ ,108.23
+ ,107.44
+ ,109.39
+ ,383.2
+ ,108.45
+ ,108.23
+ ,110.15
+ ,407.1
+ ,109.39
+ ,108.45
+ ,109.13
+ ,413.6
+ ,110.15
+ ,109.39
+ ,110.28
+ ,362.7
+ ,109.13
+ ,110.15
+ ,110.17
+ ,321.9
+ ,110.28
+ ,109.13
+ ,109.99
+ ,239.4
+ ,110.17
+ ,110.28
+ ,109.26
+ ,191
+ ,109.99
+ ,110.17
+ ,109.11
+ ,159.7
+ ,109.26
+ ,109.99
+ ,107.06
+ ,163.4
+ ,109.11
+ ,109.26
+ ,109.53
+ ,157.6
+ ,107.06
+ ,109.11
+ ,108.92
+ ,166.2
+ ,109.53
+ ,107.06
+ ,109.24
+ ,176.7
+ ,108.92
+ ,109.53
+ ,109.12
+ ,198.3
+ ,109.24
+ ,108.92
+ ,109
+ ,226.2
+ ,109.12
+ ,109.24
+ ,107.23
+ ,216.2
+ ,109
+ ,109.12
+ ,109.49
+ ,235.9
+ ,107.23
+ ,109
+ ,109.04
+ ,226.9
+ ,109.49
+ ,107.23)
+ ,dim=c(4
+ ,57)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2')
+ ,1:57))
> y <- array(NA,dim=c(4,57),dimnames=list(c('Y','X','Y1','Y2'),1:57))
> 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 96.92 148.3 98.20 98.54 1 0 0 0 0 0 0 0 0 0 0 1
2 99.06 152.2 96.92 98.20 0 1 0 0 0 0 0 0 0 0 0 2
3 99.65 169.4 99.06 96.92 0 0 1 0 0 0 0 0 0 0 0 3
4 99.82 168.6 99.65 99.06 0 0 0 1 0 0 0 0 0 0 0 4
5 99.99 161.1 99.82 99.65 0 0 0 0 1 0 0 0 0 0 0 5
6 100.33 174.1 99.99 99.82 0 0 0 0 0 1 0 0 0 0 0 6
7 99.31 179.0 100.33 99.99 0 0 0 0 0 0 1 0 0 0 0 7
8 101.10 190.6 99.31 100.33 0 0 0 0 0 0 0 1 0 0 0 8
9 101.10 190.0 101.10 99.31 0 0 0 0 0 0 0 0 1 0 0 9
10 100.93 181.6 101.10 101.10 0 0 0 0 0 0 0 0 0 1 0 10
11 100.85 174.8 100.93 101.10 0 0 0 0 0 0 0 0 0 0 1 11
12 100.93 180.5 100.85 100.93 0 0 0 0 0 0 0 0 0 0 0 12
13 99.60 196.8 100.93 100.85 1 0 0 0 0 0 0 0 0 0 0 13
14 101.88 193.8 99.60 100.93 0 1 0 0 0 0 0 0 0 0 0 14
15 101.81 197.0 101.88 99.60 0 0 1 0 0 0 0 0 0 0 0 15
16 102.38 216.3 101.81 101.88 0 0 0 1 0 0 0 0 0 0 0 16
17 102.74 221.4 102.38 101.81 0 0 0 0 1 0 0 0 0 0 0 17
18 102.82 217.9 102.74 102.38 0 0 0 0 0 1 0 0 0 0 0 18
19 101.72 229.7 102.82 102.74 0 0 0 0 0 0 1 0 0 0 0 19
20 103.47 227.4 101.72 102.82 0 0 0 0 0 0 0 1 0 0 0 20
21 102.98 204.2 103.47 101.72 0 0 0 0 0 0 0 0 1 0 0 21
22 102.68 196.6 102.98 103.47 0 0 0 0 0 0 0 0 0 1 0 22
23 102.90 198.8 102.68 102.98 0 0 0 0 0 0 0 0 0 0 1 23
24 103.03 207.5 102.90 102.68 0 0 0 0 0 0 0 0 0 0 0 24
25 101.29 190.7 103.03 102.90 1 0 0 0 0 0 0 0 0 0 0 25
26 103.69 201.6 101.29 103.03 0 1 0 0 0 0 0 0 0 0 0 26
27 103.68 210.5 103.69 101.29 0 0 1 0 0 0 0 0 0 0 0 27
28 104.20 223.5 103.68 103.69 0 0 0 1 0 0 0 0 0 0 0 28
29 104.08 223.8 104.20 103.68 0 0 0 0 1 0 0 0 0 0 0 29
30 104.16 231.2 104.08 104.20 0 0 0 0 0 1 0 0 0 0 0 30
31 103.05 244.0 104.16 104.08 0 0 0 0 0 0 1 0 0 0 0 31
32 104.66 234.7 103.05 104.16 0 0 0 0 0 0 0 1 0 0 0 32
33 104.46 250.2 104.66 103.05 0 0 0 0 0 0 0 0 1 0 0 33
34 104.95 265.7 104.46 104.66 0 0 0 0 0 0 0 0 0 1 0 34
35 105.85 287.6 104.95 104.46 0 0 0 0 0 0 0 0 0 0 1 35
36 106.23 283.3 105.85 104.95 0 0 0 0 0 0 0 0 0 0 0 36
37 104.86 295.4 106.23 105.85 1 0 0 0 0 0 0 0 0 0 0 37
38 107.44 312.3 104.86 106.23 0 1 0 0 0 0 0 0 0 0 0 38
39 108.23 333.8 107.44 104.86 0 0 1 0 0 0 0 0 0 0 0 39
40 108.45 347.7 108.23 107.44 0 0 0 1 0 0 0 0 0 0 0 40
41 109.39 383.2 108.45 108.23 0 0 0 0 1 0 0 0 0 0 0 41
42 110.15 407.1 109.39 108.45 0 0 0 0 0 1 0 0 0 0 0 42
43 109.13 413.6 110.15 109.39 0 0 0 0 0 0 1 0 0 0 0 43
44 110.28 362.7 109.13 110.15 0 0 0 0 0 0 0 1 0 0 0 44
45 110.17 321.9 110.28 109.13 0 0 0 0 0 0 0 0 1 0 0 45
46 109.99 239.4 110.17 110.28 0 0 0 0 0 0 0 0 0 1 0 46
47 109.26 191.0 109.99 110.17 0 0 0 0 0 0 0 0 0 0 1 47
48 109.11 159.7 109.26 109.99 0 0 0 0 0 0 0 0 0 0 0 48
49 107.06 163.4 109.11 109.26 1 0 0 0 0 0 0 0 0 0 0 49
50 109.53 157.6 107.06 109.11 0 1 0 0 0 0 0 0 0 0 0 50
51 108.92 166.2 109.53 107.06 0 0 1 0 0 0 0 0 0 0 0 51
52 109.24 176.7 108.92 109.53 0 0 0 1 0 0 0 0 0 0 0 52
53 109.12 198.3 109.24 108.92 0 0 0 0 1 0 0 0 0 0 0 53
54 109.00 226.2 109.12 109.24 0 0 0 0 0 1 0 0 0 0 0 54
55 107.23 216.2 109.00 109.12 0 0 0 0 0 0 1 0 0 0 0 55
56 109.49 235.9 107.23 109.00 0 0 0 0 0 0 0 1 0 0 0 56
57 109.04 226.9 109.49 107.23 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
15.862808 0.005488 0.547489 0.284805 -1.720614 1.450338
M3 M4 M5 M6 M7 M8
0.642971 0.166917 0.093132 -0.014504 -1.464946 0.852531
M9 M10 M11 t
0.048899 -0.261059 -0.085038 0.023012
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.557294 -0.163907 0.002809 0.129784 0.512009
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.8628084 4.2896395 3.698 0.000638 ***
X 0.0054885 0.0008545 6.423 1.08e-07 ***
Y1 0.5474891 0.1374757 3.982 0.000273 ***
Y2 0.2848049 0.1289307 2.209 0.032822 *
M1 -1.7206141 0.1807498 -9.519 6.10e-12 ***
M2 1.4503383 0.2932000 4.947 1.33e-05 ***
M3 0.6429706 0.3416281 1.882 0.066937 .
M4 0.1669169 0.1807103 0.924 0.361062
M5 0.0931323 0.1826589 0.510 0.612876
M6 -0.0145037 0.1822573 -0.080 0.936960
M7 -1.4649456 0.1826195 -8.022 6.19e-10 ***
M8 0.8525315 0.2652558 3.214 0.002552 **
M9 0.0488988 0.2704487 0.181 0.857411
M10 -0.2610593 0.1953144 -1.337 0.188720
M11 -0.0850383 0.1908207 -0.446 0.658198
t 0.0230124 0.0092104 2.499 0.016572 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2685 on 41 degrees of freedom
Multiple R-squared: 0.9962, Adjusted R-squared: 0.9947
F-statistic: 708.2 on 15 and 41 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,] 0.2263898696 0.452779739 0.7736101
[2,] 0.1292813865 0.258562773 0.8707186
[3,] 0.0674370656 0.134874131 0.9325629
[4,] 0.0306494994 0.061298999 0.9693505
[5,] 0.0150687757 0.030137551 0.9849312
[6,] 0.0055383445 0.011076689 0.9944617
[7,] 0.0034633024 0.006926605 0.9965367
[8,] 0.0029424465 0.005884893 0.9970576
[9,] 0.0012986441 0.002597288 0.9987014
[10,] 0.0005041847 0.001008369 0.9994958
[11,] 0.0014293290 0.002858658 0.9985707
[12,] 0.0029110005 0.005822001 0.9970890
[13,] 0.0028563762 0.005712752 0.9971436
[14,] 0.0020815339 0.004163068 0.9979185
[15,] 0.0021887265 0.004377453 0.9978113
[16,] 0.0014489827 0.002897965 0.9985510
[17,] 0.0045896653 0.009179331 0.9954103
[18,] 0.0108611411 0.021722282 0.9891389
[19,] 0.0136346507 0.027269301 0.9863653
[20,] 0.0097681225 0.019536245 0.9902319
> postscript(file="/var/www/html/rcomp/tmp/1ctkk1258723654.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/243ou1258723654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3nny71258723654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4drlr1258723654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5aa1t1258723654.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 = 57
Frequency = 1
1 2 3 4 5 6
0.112751205 -0.164998903 0.307878376 0.002809462 0.003637220 0.215420782
7 8 9 10 11 12
0.361393711 0.208843330 0.303252185 -0.043499698 -0.192138342 -0.159257303
13 14 15 16 17 18
0.097867719 -0.094255560 -0.266947986 0.039135061 0.129783710 -0.045817912
19 20 21 22 23 24
0.070518845 0.072106540 -0.154761313 -0.356242109 -0.043548979 -0.104355441
25 26 27 28 29 30
-0.188378101 -0.126560768 -0.219466150 0.004168304 -0.348552245 -0.306943132
31 32 33 34 35 36
-0.069388505 -0.163906655 -0.233681629 0.109154842 0.478615374 0.141870532
37 38 39 40 41 42
-0.061308392 -0.126194046 0.307820055 -0.262741278 0.187747000 0.323899419
43 44 45 46 47 48
0.011845627 -0.557293655 -0.001855421 0.290586965 -0.242928053 0.121742211
49 50 51 52 53 54
0.039067568 0.512009276 -0.129284295 0.216628452 0.027384314 -0.186559158
55 56 57
-0.374369677 0.440250439 0.087046178
> postscript(file="/var/www/html/rcomp/tmp/6kktg1258723654.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 0.112751205 NA
1 -0.164998903 0.112751205
2 0.307878376 -0.164998903
3 0.002809462 0.307878376
4 0.003637220 0.002809462
5 0.215420782 0.003637220
6 0.361393711 0.215420782
7 0.208843330 0.361393711
8 0.303252185 0.208843330
9 -0.043499698 0.303252185
10 -0.192138342 -0.043499698
11 -0.159257303 -0.192138342
12 0.097867719 -0.159257303
13 -0.094255560 0.097867719
14 -0.266947986 -0.094255560
15 0.039135061 -0.266947986
16 0.129783710 0.039135061
17 -0.045817912 0.129783710
18 0.070518845 -0.045817912
19 0.072106540 0.070518845
20 -0.154761313 0.072106540
21 -0.356242109 -0.154761313
22 -0.043548979 -0.356242109
23 -0.104355441 -0.043548979
24 -0.188378101 -0.104355441
25 -0.126560768 -0.188378101
26 -0.219466150 -0.126560768
27 0.004168304 -0.219466150
28 -0.348552245 0.004168304
29 -0.306943132 -0.348552245
30 -0.069388505 -0.306943132
31 -0.163906655 -0.069388505
32 -0.233681629 -0.163906655
33 0.109154842 -0.233681629
34 0.478615374 0.109154842
35 0.141870532 0.478615374
36 -0.061308392 0.141870532
37 -0.126194046 -0.061308392
38 0.307820055 -0.126194046
39 -0.262741278 0.307820055
40 0.187747000 -0.262741278
41 0.323899419 0.187747000
42 0.011845627 0.323899419
43 -0.557293655 0.011845627
44 -0.001855421 -0.557293655
45 0.290586965 -0.001855421
46 -0.242928053 0.290586965
47 0.121742211 -0.242928053
48 0.039067568 0.121742211
49 0.512009276 0.039067568
50 -0.129284295 0.512009276
51 0.216628452 -0.129284295
52 0.027384314 0.216628452
53 -0.186559158 0.027384314
54 -0.374369677 -0.186559158
55 0.440250439 -0.374369677
56 0.087046178 0.440250439
57 NA 0.087046178
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.164998903 0.112751205
[2,] 0.307878376 -0.164998903
[3,] 0.002809462 0.307878376
[4,] 0.003637220 0.002809462
[5,] 0.215420782 0.003637220
[6,] 0.361393711 0.215420782
[7,] 0.208843330 0.361393711
[8,] 0.303252185 0.208843330
[9,] -0.043499698 0.303252185
[10,] -0.192138342 -0.043499698
[11,] -0.159257303 -0.192138342
[12,] 0.097867719 -0.159257303
[13,] -0.094255560 0.097867719
[14,] -0.266947986 -0.094255560
[15,] 0.039135061 -0.266947986
[16,] 0.129783710 0.039135061
[17,] -0.045817912 0.129783710
[18,] 0.070518845 -0.045817912
[19,] 0.072106540 0.070518845
[20,] -0.154761313 0.072106540
[21,] -0.356242109 -0.154761313
[22,] -0.043548979 -0.356242109
[23,] -0.104355441 -0.043548979
[24,] -0.188378101 -0.104355441
[25,] -0.126560768 -0.188378101
[26,] -0.219466150 -0.126560768
[27,] 0.004168304 -0.219466150
[28,] -0.348552245 0.004168304
[29,] -0.306943132 -0.348552245
[30,] -0.069388505 -0.306943132
[31,] -0.163906655 -0.069388505
[32,] -0.233681629 -0.163906655
[33,] 0.109154842 -0.233681629
[34,] 0.478615374 0.109154842
[35,] 0.141870532 0.478615374
[36,] -0.061308392 0.141870532
[37,] -0.126194046 -0.061308392
[38,] 0.307820055 -0.126194046
[39,] -0.262741278 0.307820055
[40,] 0.187747000 -0.262741278
[41,] 0.323899419 0.187747000
[42,] 0.011845627 0.323899419
[43,] -0.557293655 0.011845627
[44,] -0.001855421 -0.557293655
[45,] 0.290586965 -0.001855421
[46,] -0.242928053 0.290586965
[47,] 0.121742211 -0.242928053
[48,] 0.039067568 0.121742211
[49,] 0.512009276 0.039067568
[50,] -0.129284295 0.512009276
[51,] 0.216628452 -0.129284295
[52,] 0.027384314 0.216628452
[53,] -0.186559158 0.027384314
[54,] -0.374369677 -0.186559158
[55,] 0.440250439 -0.374369677
[56,] 0.087046178 0.440250439
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.164998903 0.112751205
2 0.307878376 -0.164998903
3 0.002809462 0.307878376
4 0.003637220 0.002809462
5 0.215420782 0.003637220
6 0.361393711 0.215420782
7 0.208843330 0.361393711
8 0.303252185 0.208843330
9 -0.043499698 0.303252185
10 -0.192138342 -0.043499698
11 -0.159257303 -0.192138342
12 0.097867719 -0.159257303
13 -0.094255560 0.097867719
14 -0.266947986 -0.094255560
15 0.039135061 -0.266947986
16 0.129783710 0.039135061
17 -0.045817912 0.129783710
18 0.070518845 -0.045817912
19 0.072106540 0.070518845
20 -0.154761313 0.072106540
21 -0.356242109 -0.154761313
22 -0.043548979 -0.356242109
23 -0.104355441 -0.043548979
24 -0.188378101 -0.104355441
25 -0.126560768 -0.188378101
26 -0.219466150 -0.126560768
27 0.004168304 -0.219466150
28 -0.348552245 0.004168304
29 -0.306943132 -0.348552245
30 -0.069388505 -0.306943132
31 -0.163906655 -0.069388505
32 -0.233681629 -0.163906655
33 0.109154842 -0.233681629
34 0.478615374 0.109154842
35 0.141870532 0.478615374
36 -0.061308392 0.141870532
37 -0.126194046 -0.061308392
38 0.307820055 -0.126194046
39 -0.262741278 0.307820055
40 0.187747000 -0.262741278
41 0.323899419 0.187747000
42 0.011845627 0.323899419
43 -0.557293655 0.011845627
44 -0.001855421 -0.557293655
45 0.290586965 -0.001855421
46 -0.242928053 0.290586965
47 0.121742211 -0.242928053
48 0.039067568 0.121742211
49 0.512009276 0.039067568
50 -0.129284295 0.512009276
51 0.216628452 -0.129284295
52 0.027384314 0.216628452
53 -0.186559158 0.027384314
54 -0.374369677 -0.186559158
55 0.440250439 -0.374369677
56 0.087046178 0.440250439
> 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/7v8lx1258723654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tro91258723654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/96ckx1258723654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10r4v01258723654.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wuui1258723654.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/12dsii1258723654.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/13tckq1258723654.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/14tj7y1258723654.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/15gwgi1258723654.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/16fyrr1258723655.tab")
+ }
>
> system("convert tmp/1ctkk1258723654.ps tmp/1ctkk1258723654.png")
> system("convert tmp/243ou1258723654.ps tmp/243ou1258723654.png")
> system("convert tmp/3nny71258723654.ps tmp/3nny71258723654.png")
> system("convert tmp/4drlr1258723654.ps tmp/4drlr1258723654.png")
> system("convert tmp/5aa1t1258723654.ps tmp/5aa1t1258723654.png")
> system("convert tmp/6kktg1258723654.ps tmp/6kktg1258723654.png")
> system("convert tmp/7v8lx1258723654.ps tmp/7v8lx1258723654.png")
> system("convert tmp/8tro91258723654.ps tmp/8tro91258723654.png")
> system("convert tmp/96ckx1258723654.ps tmp/96ckx1258723654.png")
> system("convert tmp/10r4v01258723654.ps tmp/10r4v01258723654.png")
>
>
> proc.time()
user system elapsed
2.394 1.570 3.489