R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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(1.0622
+ ,2.1
+ ,8.93
+ ,2.5974
+ ,5.8
+ ,1.0773
+ ,2.4
+ ,8.96
+ ,2.9809
+ ,5.9
+ ,1.0807
+ ,2.5
+ ,8.99
+ ,3.0201
+ ,5.9
+ ,1.0848
+ ,2.1
+ ,8.98
+ ,2.2247
+ ,6
+ ,1.1582
+ ,1.8
+ ,9
+ ,2.0578
+ ,6.1
+ ,1.1663
+ ,1.9
+ ,9.03
+ ,2.1123
+ ,6.3
+ ,1.1372
+ ,1.9
+ ,9.02
+ ,2.1099
+ ,6.2
+ ,1.1139
+ ,2.1
+ ,9
+ ,2.1583
+ ,6.1
+ ,1.1222
+ ,2.2
+ ,9.03
+ ,2.3204
+ ,6.1
+ ,1.1692
+ ,2
+ ,9.03
+ ,2.0408
+ ,6
+ ,1.1702
+ ,2.2
+ ,9.03
+ ,1.765
+ ,5.8
+ ,1.2286
+ ,2
+ ,9.07
+ ,1.8795
+ ,5.7
+ ,1.2613
+ ,1.9
+ ,9.15
+ ,1.9263
+ ,5.7
+ ,1.2646
+ ,1.6
+ ,9.1
+ ,1.6931
+ ,5.6
+ ,1.2262
+ ,1.7
+ ,9.15
+ ,1.7372
+ ,5.8
+ ,1.1985
+ ,2
+ ,9.15
+ ,2.2851
+ ,5.6
+ ,1.2007
+ ,2.5
+ ,9.22
+ ,3.0518
+ ,5.6
+ ,1.2138
+ ,2.4
+ ,9.22
+ ,3.2662
+ ,5.6
+ ,1.2266
+ ,2.3
+ ,9.24
+ ,2.9908
+ ,5.5
+ ,1.2176
+ ,2.3
+ ,9.26
+ ,2.6544
+ ,5.4
+ ,1.2218
+ ,2.1
+ ,9.3
+ ,2.5378
+ ,5.4
+ ,1.249
+ ,2.4
+ ,9.27
+ ,3.1892
+ ,5.5
+ ,1.2991
+ ,2.2
+ ,9.32
+ ,3.523
+ ,5.4
+ ,1.3408
+ ,2.4
+ ,9.33
+ ,3.2556
+ ,5.4
+ ,1.3119
+ ,1.9
+ ,9.32
+ ,2.9698
+ ,5.3
+ ,1.3014
+ ,2.1
+ ,9.34
+ ,3.0075
+ ,5.4
+ ,1.3201
+ ,2.1
+ ,9.32
+ ,3.1483
+ ,5.2
+ ,1.2938
+ ,2.1
+ ,9.32
+ ,3.5106
+ ,5.2
+ ,1.2694
+ ,2
+ ,9.24
+ ,2.8027
+ ,5.1
+ ,1.2165
+ ,2.1
+ ,9.24
+ ,2.5303
+ ,5
+ ,1.2037
+ ,2.2
+ ,9.15
+ ,3.1679
+ ,5
+ ,1.2292
+ ,2.2
+ ,9.17
+ ,3.6412
+ ,4.9
+ ,1.2256
+ ,2.6
+ ,9.14
+ ,4.6867
+ ,5
+ ,1.2015
+ ,2.5
+ ,9.11
+ ,4.3478
+ ,5
+ ,1.1786
+ ,2.3
+ ,9.04
+ ,3.4555
+ ,5
+ ,1.1856
+ ,2.2
+ ,8.96
+ ,3.4157
+ ,4.9
+ ,1.2103
+ ,2.4
+ ,8.86
+ ,3.9853
+ ,4.7
+ ,1.1938
+ ,2.3
+ ,8.85
+ ,3.5975
+ ,4.8
+ ,1.202
+ ,2.2
+ ,8.75
+ ,3.3626
+ ,4.7
+ ,1.2271
+ ,2.5
+ ,8.65
+ ,3.5457
+ ,4.7
+ ,1.277
+ ,2.5
+ ,8.61
+ ,4.1667
+ ,4.6
+ ,1.265
+ ,2.5
+ ,8.46
+ ,4.3188
+ ,4.6
+ ,1.2684
+ ,2.4
+ ,8.38
+ ,4.1453
+ ,4.7
+ ,1.2811
+ ,2.3
+ ,8.33
+ ,3.8187
+ ,4.7
+ ,1.2727
+ ,1.7
+ ,8.27
+ ,2.0624
+ ,4.5
+ ,1.2611
+ ,1.6
+ ,8.21
+ ,1.3052
+ ,4.4
+ ,1.2881
+ ,1.9
+ ,8.18
+ ,1.9737
+ ,4.5
+ ,1.3213
+ ,1.9
+ ,8.04
+ ,2.5407
+ ,4.4
+ ,1.2999
+ ,1.8
+ ,7.97
+ ,2.0756
+ ,4.6
+ ,1.3074
+ ,1.8
+ ,7.86
+ ,2.4152
+ ,4.5
+ ,1.3242
+ ,1.9
+ ,7.75
+ ,2.7788
+ ,4.4
+ ,1.3516
+ ,1.9
+ ,7.65
+ ,2.5737
+ ,4.5
+ ,1.3511
+ ,1.9
+ ,7.62
+ ,2.6909
+ ,4.4
+ ,1.3419
+ ,1.9
+ ,7.55
+ ,2.687
+ ,4.6
+ ,1.3716
+ ,1.8
+ ,7.6
+ ,2.3582
+ ,4.7
+ ,1.3622
+ ,1.7
+ ,7.54
+ ,1.9701
+ ,4.6
+ ,1.3896
+ ,2.1
+ ,7.48
+ ,2.7551
+ ,4.7
+ ,1.4227
+ ,2.6
+ ,7.44
+ ,3.5362
+ ,4.7
+ ,1.4684
+ ,3.1
+ ,7.41
+ ,4.3062
+ ,4.7
+ ,1.457
+ ,3.1
+ ,7.45
+ ,4.0813
+ ,5)
+ ,dim=c(5
+ ,60)
+ ,dimnames=list(c('Dollar/Euro'
+ ,'Inf-Eu'
+ ,'Werkl-Eu'
+ ,'Inf-USA'
+ ,'Werkl-USA')
+ ,1:60))
> y <- array(NA,dim=c(5,60),dimnames=list(c('Dollar/Euro','Inf-Eu','Werkl-Eu','Inf-USA','Werkl-USA'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal 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
> 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
Dollar/Euro Inf-Eu Werkl-Eu Inf-USA Werkl-USA
1 1.0622 2.1 8.93 2.5974 5.8
2 1.0773 2.4 8.96 2.9809 5.9
3 1.0807 2.5 8.99 3.0201 5.9
4 1.0848 2.1 8.98 2.2247 6.0
5 1.1582 1.8 9.00 2.0578 6.1
6 1.1663 1.9 9.03 2.1123 6.3
7 1.1372 1.9 9.02 2.1099 6.2
8 1.1139 2.1 9.00 2.1583 6.1
9 1.1222 2.2 9.03 2.3204 6.1
10 1.1692 2.0 9.03 2.0408 6.0
11 1.1702 2.2 9.03 1.7650 5.8
12 1.2286 2.0 9.07 1.8795 5.7
13 1.2613 1.9 9.15 1.9263 5.7
14 1.2646 1.6 9.10 1.6931 5.6
15 1.2262 1.7 9.15 1.7372 5.8
16 1.1985 2.0 9.15 2.2851 5.6
17 1.2007 2.5 9.22 3.0518 5.6
18 1.2138 2.4 9.22 3.2662 5.6
19 1.2266 2.3 9.24 2.9908 5.5
20 1.2176 2.3 9.26 2.6544 5.4
21 1.2218 2.1 9.30 2.5378 5.4
22 1.2490 2.4 9.27 3.1892 5.5
23 1.2991 2.2 9.32 3.5230 5.4
24 1.3408 2.4 9.33 3.2556 5.4
25 1.3119 1.9 9.32 2.9698 5.3
26 1.3014 2.1 9.34 3.0075 5.4
27 1.3201 2.1 9.32 3.1483 5.2
28 1.2938 2.1 9.32 3.5106 5.2
29 1.2694 2.0 9.24 2.8027 5.1
30 1.2165 2.1 9.24 2.5303 5.0
31 1.2037 2.2 9.15 3.1679 5.0
32 1.2292 2.2 9.17 3.6412 4.9
33 1.2256 2.6 9.14 4.6867 5.0
34 1.2015 2.5 9.11 4.3478 5.0
35 1.1786 2.3 9.04 3.4555 5.0
36 1.1856 2.2 8.96 3.4157 4.9
37 1.2103 2.4 8.86 3.9853 4.7
38 1.1938 2.3 8.85 3.5975 4.8
39 1.2020 2.2 8.75 3.3626 4.7
40 1.2271 2.5 8.65 3.5457 4.7
41 1.2770 2.5 8.61 4.1667 4.6
42 1.2650 2.5 8.46 4.3188 4.6
43 1.2684 2.4 8.38 4.1453 4.7
44 1.2811 2.3 8.33 3.8187 4.7
45 1.2727 1.7 8.27 2.0624 4.5
46 1.2611 1.6 8.21 1.3052 4.4
47 1.2881 1.9 8.18 1.9737 4.5
48 1.3213 1.9 8.04 2.5407 4.4
49 1.2999 1.8 7.97 2.0756 4.6
50 1.3074 1.8 7.86 2.4152 4.5
51 1.3242 1.9 7.75 2.7788 4.4
52 1.3516 1.9 7.65 2.5737 4.5
53 1.3511 1.9 7.62 2.6909 4.4
54 1.3419 1.9 7.55 2.6870 4.6
55 1.3716 1.8 7.60 2.3582 4.7
56 1.3622 1.7 7.54 1.9701 4.6
57 1.3896 2.1 7.48 2.7551 4.7
58 1.4227 2.6 7.44 3.5362 4.7
59 1.4684 3.1 7.41 4.3062 4.7
60 1.4570 3.1 7.45 4.0813 5.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Inf-Eu` `Werkl-Eu` `Inf-USA` `Werkl-USA`
2.018611 0.014703 -0.058657 0.004367 -0.058710
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.134306 -0.037821 -0.007457 0.039973 0.136987
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.018611 0.129038 15.644 <2e-16 ***
`Inf-Eu` 0.014703 0.054838 0.268 0.7896
`Werkl-Eu` -0.058657 0.020246 -2.897 0.0054 **
`Inf-USA` 0.004367 0.024290 0.180 0.8580
`Werkl-USA` -0.058710 0.027100 -2.166 0.0346 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.06354 on 55 degrees of freedom
Multiple R-squared: 0.5293, Adjusted R-squared: 0.4951
F-statistic: 15.46 on 4 and 55 DF, p-value: 1.555e-08
> 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,] 0.024375414 4.875083e-02 9.756246e-01
[2,] 0.007220532 1.444106e-02 9.927795e-01
[3,] 0.006411461 1.282292e-02 9.935885e-01
[4,] 0.006191741 1.238348e-02 9.938083e-01
[5,] 0.002630383 5.260766e-03 9.973696e-01
[6,] 0.005372293 1.074459e-02 9.946277e-01
[7,] 0.001938948 3.877895e-03 9.980611e-01
[8,] 0.012814202 2.562840e-02 9.871858e-01
[9,] 0.019002903 3.800581e-02 9.809971e-01
[10,] 0.014557282 2.911456e-02 9.854427e-01
[11,] 0.015772574 3.154515e-02 9.842274e-01
[12,] 0.012576222 2.515244e-02 9.874238e-01
[13,] 0.026839470 5.367894e-02 9.731605e-01
[14,] 0.104253292 2.085066e-01 8.957467e-01
[15,] 0.163371307 3.267426e-01 8.366287e-01
[16,] 0.182749555 3.654991e-01 8.172504e-01
[17,] 0.398719907 7.974398e-01 6.012801e-01
[18,] 0.371179072 7.423581e-01 6.288209e-01
[19,] 0.311954528 6.239091e-01 6.880455e-01
[20,] 0.504518429 9.909631e-01 4.954816e-01
[21,] 0.750947518 4.981050e-01 2.490525e-01
[22,] 0.940773755 1.184525e-01 5.922625e-02
[23,] 0.973605234 5.278953e-02 2.639477e-02
[24,] 0.968117183 6.376563e-02 3.188282e-02
[25,] 0.997679477 4.641046e-03 2.320523e-03
[26,] 0.999800570 3.988598e-04 1.994299e-04
[27,] 0.999871672 2.566553e-04 1.283277e-04
[28,] 0.999725432 5.491352e-04 2.745676e-04
[29,] 0.999606202 7.875953e-04 3.937977e-04
[30,] 0.999821337 3.573260e-04 1.786630e-04
[31,] 0.999746836 5.063272e-04 2.531636e-04
[32,] 0.999735674 5.286511e-04 2.643256e-04
[33,] 0.999985064 2.987276e-05 1.493638e-05
[34,] 0.999997420 5.160940e-06 2.580470e-06
[35,] 0.999996146 7.707318e-06 3.853659e-06
[36,] 0.999993432 1.313676e-05 6.568380e-06
[37,] 0.999984477 3.104610e-05 1.552305e-05
[38,] 0.999951858 9.628438e-05 4.814219e-05
[39,] 0.999847769 3.044627e-04 1.522314e-04
[40,] 0.999598846 8.023074e-04 4.011537e-04
[41,] 0.999800182 3.996364e-04 1.998182e-04
[42,] 0.999224530 1.550940e-03 7.754700e-04
[43,] 0.997189946 5.620107e-03 2.810054e-03
[44,] 0.990074860 1.985028e-02 9.925140e-03
[45,] 0.965528115 6.894377e-02 3.447189e-02
> postscript(file="/var/www/rcomp/tmp/1s4qf1324494537.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2ppur1324494537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/38t9x1324494537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4vqwz1324494537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5ujy51324494537.ps",horizontal=F,onefile=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 = 60
Frequency = 1
1 2 3 4 5 6
-0.134306249 -0.117661178 -0.114142941 -0.095403714 -0.009819864 0.010073582
7 8 9 10 11 12
-0.025473518 -0.058969580 -0.051088077 -0.005797472 -0.018275553 0.039040192
13 14 15 16 17 18
0.077698614 0.077624012 0.052236012 0.005990365 0.001596626 0.015230559
19 20 21 22 23 24
0.026005688 0.013776948 0.023772977 0.047828660 0.096473237 0.136987066
25 26 27 28 29 30
0.110228989 0.103667954 0.108837884 0.080955632 0.050553918 -0.008497729
31 32 33 34 35 36
-0.030831657 -0.012096551 -0.022032267 -0.044941642 -0.065110187 -0.067029654
37 38 39 40 41 42
-0.065365475 -0.073417143 -0.074457697 -0.060433821 -0.021463161 -0.042925929
43 44 45 46 47 48
-0.036119469 -0.023455696 -0.030625327 -0.046838602 -0.023057598 -0.006416781
49 50 51 52 53 54
-0.016679252 -0.022985623 -0.021567076 0.006733985 -0.001908572 -0.003455484
55 56 57 58 59 60
0.037954585 0.022329362 0.042771610 0.062762742 0.095988918 0.105530422
> postscript(file="/var/www/rcomp/tmp/6m3i71324494537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.134306249 NA
1 -0.117661178 -0.134306249
2 -0.114142941 -0.117661178
3 -0.095403714 -0.114142941
4 -0.009819864 -0.095403714
5 0.010073582 -0.009819864
6 -0.025473518 0.010073582
7 -0.058969580 -0.025473518
8 -0.051088077 -0.058969580
9 -0.005797472 -0.051088077
10 -0.018275553 -0.005797472
11 0.039040192 -0.018275553
12 0.077698614 0.039040192
13 0.077624012 0.077698614
14 0.052236012 0.077624012
15 0.005990365 0.052236012
16 0.001596626 0.005990365
17 0.015230559 0.001596626
18 0.026005688 0.015230559
19 0.013776948 0.026005688
20 0.023772977 0.013776948
21 0.047828660 0.023772977
22 0.096473237 0.047828660
23 0.136987066 0.096473237
24 0.110228989 0.136987066
25 0.103667954 0.110228989
26 0.108837884 0.103667954
27 0.080955632 0.108837884
28 0.050553918 0.080955632
29 -0.008497729 0.050553918
30 -0.030831657 -0.008497729
31 -0.012096551 -0.030831657
32 -0.022032267 -0.012096551
33 -0.044941642 -0.022032267
34 -0.065110187 -0.044941642
35 -0.067029654 -0.065110187
36 -0.065365475 -0.067029654
37 -0.073417143 -0.065365475
38 -0.074457697 -0.073417143
39 -0.060433821 -0.074457697
40 -0.021463161 -0.060433821
41 -0.042925929 -0.021463161
42 -0.036119469 -0.042925929
43 -0.023455696 -0.036119469
44 -0.030625327 -0.023455696
45 -0.046838602 -0.030625327
46 -0.023057598 -0.046838602
47 -0.006416781 -0.023057598
48 -0.016679252 -0.006416781
49 -0.022985623 -0.016679252
50 -0.021567076 -0.022985623
51 0.006733985 -0.021567076
52 -0.001908572 0.006733985
53 -0.003455484 -0.001908572
54 0.037954585 -0.003455484
55 0.022329362 0.037954585
56 0.042771610 0.022329362
57 0.062762742 0.042771610
58 0.095988918 0.062762742
59 0.105530422 0.095988918
60 NA 0.105530422
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.117661178 -0.134306249
[2,] -0.114142941 -0.117661178
[3,] -0.095403714 -0.114142941
[4,] -0.009819864 -0.095403714
[5,] 0.010073582 -0.009819864
[6,] -0.025473518 0.010073582
[7,] -0.058969580 -0.025473518
[8,] -0.051088077 -0.058969580
[9,] -0.005797472 -0.051088077
[10,] -0.018275553 -0.005797472
[11,] 0.039040192 -0.018275553
[12,] 0.077698614 0.039040192
[13,] 0.077624012 0.077698614
[14,] 0.052236012 0.077624012
[15,] 0.005990365 0.052236012
[16,] 0.001596626 0.005990365
[17,] 0.015230559 0.001596626
[18,] 0.026005688 0.015230559
[19,] 0.013776948 0.026005688
[20,] 0.023772977 0.013776948
[21,] 0.047828660 0.023772977
[22,] 0.096473237 0.047828660
[23,] 0.136987066 0.096473237
[24,] 0.110228989 0.136987066
[25,] 0.103667954 0.110228989
[26,] 0.108837884 0.103667954
[27,] 0.080955632 0.108837884
[28,] 0.050553918 0.080955632
[29,] -0.008497729 0.050553918
[30,] -0.030831657 -0.008497729
[31,] -0.012096551 -0.030831657
[32,] -0.022032267 -0.012096551
[33,] -0.044941642 -0.022032267
[34,] -0.065110187 -0.044941642
[35,] -0.067029654 -0.065110187
[36,] -0.065365475 -0.067029654
[37,] -0.073417143 -0.065365475
[38,] -0.074457697 -0.073417143
[39,] -0.060433821 -0.074457697
[40,] -0.021463161 -0.060433821
[41,] -0.042925929 -0.021463161
[42,] -0.036119469 -0.042925929
[43,] -0.023455696 -0.036119469
[44,] -0.030625327 -0.023455696
[45,] -0.046838602 -0.030625327
[46,] -0.023057598 -0.046838602
[47,] -0.006416781 -0.023057598
[48,] -0.016679252 -0.006416781
[49,] -0.022985623 -0.016679252
[50,] -0.021567076 -0.022985623
[51,] 0.006733985 -0.021567076
[52,] -0.001908572 0.006733985
[53,] -0.003455484 -0.001908572
[54,] 0.037954585 -0.003455484
[55,] 0.022329362 0.037954585
[56,] 0.042771610 0.022329362
[57,] 0.062762742 0.042771610
[58,] 0.095988918 0.062762742
[59,] 0.105530422 0.095988918
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.117661178 -0.134306249
2 -0.114142941 -0.117661178
3 -0.095403714 -0.114142941
4 -0.009819864 -0.095403714
5 0.010073582 -0.009819864
6 -0.025473518 0.010073582
7 -0.058969580 -0.025473518
8 -0.051088077 -0.058969580
9 -0.005797472 -0.051088077
10 -0.018275553 -0.005797472
11 0.039040192 -0.018275553
12 0.077698614 0.039040192
13 0.077624012 0.077698614
14 0.052236012 0.077624012
15 0.005990365 0.052236012
16 0.001596626 0.005990365
17 0.015230559 0.001596626
18 0.026005688 0.015230559
19 0.013776948 0.026005688
20 0.023772977 0.013776948
21 0.047828660 0.023772977
22 0.096473237 0.047828660
23 0.136987066 0.096473237
24 0.110228989 0.136987066
25 0.103667954 0.110228989
26 0.108837884 0.103667954
27 0.080955632 0.108837884
28 0.050553918 0.080955632
29 -0.008497729 0.050553918
30 -0.030831657 -0.008497729
31 -0.012096551 -0.030831657
32 -0.022032267 -0.012096551
33 -0.044941642 -0.022032267
34 -0.065110187 -0.044941642
35 -0.067029654 -0.065110187
36 -0.065365475 -0.067029654
37 -0.073417143 -0.065365475
38 -0.074457697 -0.073417143
39 -0.060433821 -0.074457697
40 -0.021463161 -0.060433821
41 -0.042925929 -0.021463161
42 -0.036119469 -0.042925929
43 -0.023455696 -0.036119469
44 -0.030625327 -0.023455696
45 -0.046838602 -0.030625327
46 -0.023057598 -0.046838602
47 -0.006416781 -0.023057598
48 -0.016679252 -0.006416781
49 -0.022985623 -0.016679252
50 -0.021567076 -0.022985623
51 0.006733985 -0.021567076
52 -0.001908572 0.006733985
53 -0.003455484 -0.001908572
54 0.037954585 -0.003455484
55 0.022329362 0.037954585
56 0.042771610 0.022329362
57 0.062762742 0.042771610
58 0.095988918 0.062762742
59 0.105530422 0.095988918
> 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/rcomp/tmp/7z5xf1324494537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8fco81324494537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9geo51324494537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/103jwc1324494537.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11c6971324494537.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/rcomp/tmp/12w67q1324494537.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/rcomp/tmp/13p6wc1324494537.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/rcomp/tmp/14afj91324494537.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/rcomp/tmp/15kho01324494537.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/rcomp/tmp/16tc991324494537.tab")
+ }
>
> try(system("convert tmp/1s4qf1324494537.ps tmp/1s4qf1324494537.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ppur1324494537.ps tmp/2ppur1324494537.png",intern=TRUE))
character(0)
> try(system("convert tmp/38t9x1324494537.ps tmp/38t9x1324494537.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vqwz1324494537.ps tmp/4vqwz1324494537.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ujy51324494537.ps tmp/5ujy51324494537.png",intern=TRUE))
character(0)
> try(system("convert tmp/6m3i71324494537.ps tmp/6m3i71324494537.png",intern=TRUE))
character(0)
> try(system("convert tmp/7z5xf1324494537.ps tmp/7z5xf1324494537.png",intern=TRUE))
character(0)
> try(system("convert tmp/8fco81324494537.ps tmp/8fco81324494537.png",intern=TRUE))
character(0)
> try(system("convert tmp/9geo51324494537.ps tmp/9geo51324494537.png",intern=TRUE))
character(0)
> try(system("convert tmp/103jwc1324494537.ps tmp/103jwc1324494537.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.750 0.360 4.094