R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(14.2,-0.8,13.5,-0.2,11.9,0.2,14.6,1,15.6,0,14.1,-0.2,14.9,1,14.2,0.4,14.6,1,17.2,1.7,15.4,3.1,14.3,3.3,17.5,3.1,14.5,3.5,14.4,6,16.6,5.7,16.7,4.7,16.6,4.2,16.9,3.6,15.7,4.4,16.4,2.5,18.4,-0.6,16.9,-1.9,16.5,-1.9,18.3,0.7,15.1,-0.9,15.7,-1.7,18.1,-3.1,16.8,-2.1,18.9,0.2,19,1.2,18.1,3.8,17.8,4,21.5,6.6,17.1,5.3,18.7,7.6,19,4.7,16.4,6.6,16.9,4.4,18.6,4.6,19.3,6,19.4,4.8,17.6,4,18.6,2.7,18.1,3,20.4,4.1,18.1,4,19.6,2.7,19.9,2.6,19.2,3.1,17.8,4.4,19.2,3,22,2,21.1,1.3,19.5,1.5,22.2,1.3,20.9,3.2,22.2,1.8,23.5,3.3,21.5,1,24.3,2.4,22.8,0.4,20.3,-0.1,23.7,1.3,23.3,-1.1,19.6,-4.4,18,-7.5,17.3,-12.2,16.8,-14.5,18.2,-16,16.5,-16.7,16,-16.3,18.4,-16.9),dim=c(2,73),dimnames=list(c('Y','X'),1:73))
> y <- array(NA,dim=c(2,73),dimnames=list(c('Y','X'),1:73))
> 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 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 14.2 -0.8 1 0 0 0 0 0 0 0 0 0 0
2 13.5 -0.2 0 1 0 0 0 0 0 0 0 0 0
3 11.9 0.2 0 0 1 0 0 0 0 0 0 0 0
4 14.6 1.0 0 0 0 1 0 0 0 0 0 0 0
5 15.6 0.0 0 0 0 0 1 0 0 0 0 0 0
6 14.1 -0.2 0 0 0 0 0 1 0 0 0 0 0
7 14.9 1.0 0 0 0 0 0 0 1 0 0 0 0
8 14.2 0.4 0 0 0 0 0 0 0 1 0 0 0
9 14.6 1.0 0 0 0 0 0 0 0 0 1 0 0
10 17.2 1.7 0 0 0 0 0 0 0 0 0 1 0
11 15.4 3.1 0 0 0 0 0 0 0 0 0 0 1
12 14.3 3.3 0 0 0 0 0 0 0 0 0 0 0
13 17.5 3.1 1 0 0 0 0 0 0 0 0 0 0
14 14.5 3.5 0 1 0 0 0 0 0 0 0 0 0
15 14.4 6.0 0 0 1 0 0 0 0 0 0 0 0
16 16.6 5.7 0 0 0 1 0 0 0 0 0 0 0
17 16.7 4.7 0 0 0 0 1 0 0 0 0 0 0
18 16.6 4.2 0 0 0 0 0 1 0 0 0 0 0
19 16.9 3.6 0 0 0 0 0 0 1 0 0 0 0
20 15.7 4.4 0 0 0 0 0 0 0 1 0 0 0
21 16.4 2.5 0 0 0 0 0 0 0 0 1 0 0
22 18.4 -0.6 0 0 0 0 0 0 0 0 0 1 0
23 16.9 -1.9 0 0 0 0 0 0 0 0 0 0 1
24 16.5 -1.9 0 0 0 0 0 0 0 0 0 0 0
25 18.3 0.7 1 0 0 0 0 0 0 0 0 0 0
26 15.1 -0.9 0 1 0 0 0 0 0 0 0 0 0
27 15.7 -1.7 0 0 1 0 0 0 0 0 0 0 0
28 18.1 -3.1 0 0 0 1 0 0 0 0 0 0 0
29 16.8 -2.1 0 0 0 0 1 0 0 0 0 0 0
30 18.9 0.2 0 0 0 0 0 1 0 0 0 0 0
31 19.0 1.2 0 0 0 0 0 0 1 0 0 0 0
32 18.1 3.8 0 0 0 0 0 0 0 1 0 0 0
33 17.8 4.0 0 0 0 0 0 0 0 0 1 0 0
34 21.5 6.6 0 0 0 0 0 0 0 0 0 1 0
35 17.1 5.3 0 0 0 0 0 0 0 0 0 0 1
36 18.7 7.6 0 0 0 0 0 0 0 0 0 0 0
37 19.0 4.7 1 0 0 0 0 0 0 0 0 0 0
38 16.4 6.6 0 1 0 0 0 0 0 0 0 0 0
39 16.9 4.4 0 0 1 0 0 0 0 0 0 0 0
40 18.6 4.6 0 0 0 1 0 0 0 0 0 0 0
41 19.3 6.0 0 0 0 0 1 0 0 0 0 0 0
42 19.4 4.8 0 0 0 0 0 1 0 0 0 0 0
43 17.6 4.0 0 0 0 0 0 0 1 0 0 0 0
44 18.6 2.7 0 0 0 0 0 0 0 1 0 0 0
45 18.1 3.0 0 0 0 0 0 0 0 0 1 0 0
46 20.4 4.1 0 0 0 0 0 0 0 0 0 1 0
47 18.1 4.0 0 0 0 0 0 0 0 0 0 0 1
48 19.6 2.7 0 0 0 0 0 0 0 0 0 0 0
49 19.9 2.6 1 0 0 0 0 0 0 0 0 0 0
50 19.2 3.1 0 1 0 0 0 0 0 0 0 0 0
51 17.8 4.4 0 0 1 0 0 0 0 0 0 0 0
52 19.2 3.0 0 0 0 1 0 0 0 0 0 0 0
53 22.0 2.0 0 0 0 0 1 0 0 0 0 0 0
54 21.1 1.3 0 0 0 0 0 1 0 0 0 0 0
55 19.5 1.5 0 0 0 0 0 0 1 0 0 0 0
56 22.2 1.3 0 0 0 0 0 0 0 1 0 0 0
57 20.9 3.2 0 0 0 0 0 0 0 0 1 0 0
58 22.2 1.8 0 0 0 0 0 0 0 0 0 1 0
59 23.5 3.3 0 0 0 0 0 0 0 0 0 0 1
60 21.5 1.0 0 0 0 0 0 0 0 0 0 0 0
61 24.3 2.4 1 0 0 0 0 0 0 0 0 0 0
62 22.8 0.4 0 1 0 0 0 0 0 0 0 0 0
63 20.3 -0.1 0 0 1 0 0 0 0 0 0 0 0
64 23.7 1.3 0 0 0 1 0 0 0 0 0 0 0
65 23.3 -1.1 0 0 0 0 1 0 0 0 0 0 0
66 19.6 -4.4 0 0 0 0 0 1 0 0 0 0 0
67 18.0 -7.5 0 0 0 0 0 0 1 0 0 0 0
68 17.3 -12.2 0 0 0 0 0 0 0 1 0 0 0
69 16.8 -14.5 0 0 0 0 0 0 0 0 1 0 0
70 18.2 -16.0 0 0 0 0 0 0 0 0 0 1 0
71 16.5 -16.7 0 0 0 0 0 0 0 0 0 0 1
72 16.0 -16.3 0 0 0 0 0 0 0 0 0 0 0
73 18.4 -16.9 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
17.80625 0.06597 1.03333 -1.02701 -1.78470 0.52299
M5 M6 M7 M8 M9 M10
1.03931 0.41222 -0.19802 -0.12731 -0.36412 1.87014
M11
0.14230
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.58681 -1.89552 -0.03268 1.31262 5.99438
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.80625 1.11264 16.004 <2e-16 ***
X 0.06597 0.05918 1.115 0.269
M1 1.03333 1.51550 0.682 0.498
M2 -1.02701 1.58070 -0.650 0.518
M3 -1.78470 1.58141 -1.129 0.264
M4 0.52299 1.58070 0.331 0.742
M5 1.03931 1.57801 0.659 0.513
M6 0.41222 1.57550 0.262 0.794
M7 -0.19802 1.57440 -0.126 0.900
M8 -0.12731 1.57320 -0.081 0.936
M9 -0.36412 1.57295 -0.231 0.818
M10 1.87014 1.57275 1.189 0.239
M11 0.14230 1.57272 0.090 0.928
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.724 on 60 degrees of freedom
Multiple R-squared: 0.1326, Adjusted R-squared: -0.04088
F-statistic: 0.7644 on 12 and 60 DF, p-value: 0.6836
> 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,] 0.034408847 0.068817695 0.965591153
[2,] 0.016516035 0.033032069 0.983483965
[3,] 0.005687145 0.011374290 0.994312855
[4,] 0.002225177 0.004450354 0.997774823
[5,] 0.000731527 0.001463054 0.999268473
[6,] 0.000440695 0.000881390 0.999559305
[7,] 0.001370594 0.002741189 0.998629406
[8,] 0.009199902 0.018399803 0.990800098
[9,] 0.028877448 0.057754895 0.971122552
[10,] 0.039065524 0.078131049 0.960934476
[11,] 0.039060263 0.078120526 0.960939737
[12,] 0.069326819 0.138653638 0.930673181
[13,] 0.086092181 0.172184362 0.913907819
[14,] 0.082785647 0.165571293 0.917214353
[15,] 0.120085877 0.240171755 0.879914123
[16,] 0.132632838 0.265265677 0.867367162
[17,] 0.154552375 0.309104749 0.845447625
[18,] 0.141687525 0.283375050 0.858312475
[19,] 0.155586350 0.311172701 0.844413650
[20,] 0.138989183 0.277978366 0.861010817
[21,] 0.131445089 0.262890177 0.868554911
[22,] 0.129933025 0.259866050 0.870066975
[23,] 0.183272214 0.366544429 0.816727786
[24,] 0.187517462 0.375034924 0.812482538
[25,] 0.192092941 0.384185882 0.807907059
[26,] 0.251973407 0.503946814 0.748026593
[27,] 0.238366253 0.476732505 0.761633747
[28,] 0.210449123 0.420898245 0.789550877
[29,] 0.232712776 0.465425553 0.767287224
[30,] 0.224573157 0.449146313 0.775426843
[31,] 0.196250344 0.392500687 0.803749656
[32,] 0.293876729 0.587753458 0.706123271
[33,] 0.297357923 0.594715846 0.702642077
[34,] 0.451506582 0.903013164 0.548493418
[35,] 0.677119431 0.645761137 0.322880569
[36,] 0.797704234 0.404591531 0.202295766
[37,] 0.987927838 0.024144325 0.012072162
[38,] 0.996466464 0.007067072 0.003533536
[39,] 0.991508895 0.016982210 0.008491105
[40,] 0.986133396 0.027733208 0.013866604
[41,] 0.977369920 0.045260159 0.022630080
[42,] 0.959617900 0.080764201 0.040382100
> postscript(file="/var/www/html/rcomp/tmp/1o25j1258723296.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2vt6n1258723296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/39trc1258723296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4lnhe1258723296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vfgc1258723296.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 = 73
Frequency = 1
1 2 3 4 5 6
-4.58680690 -3.26604540 -4.13473563 -3.79520402 -3.24555460 -4.10527414
7 8 9 10 11 12
-2.77418736 -3.50532184 -2.90809425 -2.58852758 -2.75304310 -3.72393218
13 14 15 16 17 18
-1.54407241 -2.51011781 -2.01733563 -2.10524195 -2.45559253 -1.89552241
19 20 21 22 23 24
-0.94569770 -2.26918390 -1.20704253 -1.23680690 -0.92321552 -1.18091150
25 26 27 28 29 30
-0.58575517 -1.61986954 -0.20940115 -0.02474541 -1.90702702 0.66833965
31 32 33 34 35 36
1.31261954 0.17039541 0.09400920 1.38824139 -1.19816724 0.39241610
37 38 39 40 41 42
-0.14961724 -0.81461092 0.58820920 -0.03267988 0.05865230 0.86489828
43 44 45 46 47 48
-0.27208390 0.74295747 0.45997472 0.45315518 -0.11241206 1.61564713
49 50 51 52 53 54
0.88891035 2.21626839 1.48820920 0.67286494 3.02251437 2.79577759
55 56 57 58 59 60
1.79282989 4.43530920 3.24678161 2.40487586 5.33376380 3.62778851
61 62 63 64 65 66
5.30210345 5.99437529 4.28505402 5.28500632 4.52700747 1.67178103
67 68 69 70 71 72
0.88651953 0.42584367 0.31437125 -0.42093795 -0.34692588 -0.73100806
73
0.67523791
> postscript(file="/var/www/html/rcomp/tmp/6j3uw1258723296.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 = 73
Frequency = 1
lag(myerror, k = 1) myerror
0 -4.58680690 NA
1 -3.26604540 -4.58680690
2 -4.13473563 -3.26604540
3 -3.79520402 -4.13473563
4 -3.24555460 -3.79520402
5 -4.10527414 -3.24555460
6 -2.77418736 -4.10527414
7 -3.50532184 -2.77418736
8 -2.90809425 -3.50532184
9 -2.58852758 -2.90809425
10 -2.75304310 -2.58852758
11 -3.72393218 -2.75304310
12 -1.54407241 -3.72393218
13 -2.51011781 -1.54407241
14 -2.01733563 -2.51011781
15 -2.10524195 -2.01733563
16 -2.45559253 -2.10524195
17 -1.89552241 -2.45559253
18 -0.94569770 -1.89552241
19 -2.26918390 -0.94569770
20 -1.20704253 -2.26918390
21 -1.23680690 -1.20704253
22 -0.92321552 -1.23680690
23 -1.18091150 -0.92321552
24 -0.58575517 -1.18091150
25 -1.61986954 -0.58575517
26 -0.20940115 -1.61986954
27 -0.02474541 -0.20940115
28 -1.90702702 -0.02474541
29 0.66833965 -1.90702702
30 1.31261954 0.66833965
31 0.17039541 1.31261954
32 0.09400920 0.17039541
33 1.38824139 0.09400920
34 -1.19816724 1.38824139
35 0.39241610 -1.19816724
36 -0.14961724 0.39241610
37 -0.81461092 -0.14961724
38 0.58820920 -0.81461092
39 -0.03267988 0.58820920
40 0.05865230 -0.03267988
41 0.86489828 0.05865230
42 -0.27208390 0.86489828
43 0.74295747 -0.27208390
44 0.45997472 0.74295747
45 0.45315518 0.45997472
46 -0.11241206 0.45315518
47 1.61564713 -0.11241206
48 0.88891035 1.61564713
49 2.21626839 0.88891035
50 1.48820920 2.21626839
51 0.67286494 1.48820920
52 3.02251437 0.67286494
53 2.79577759 3.02251437
54 1.79282989 2.79577759
55 4.43530920 1.79282989
56 3.24678161 4.43530920
57 2.40487586 3.24678161
58 5.33376380 2.40487586
59 3.62778851 5.33376380
60 5.30210345 3.62778851
61 5.99437529 5.30210345
62 4.28505402 5.99437529
63 5.28500632 4.28505402
64 4.52700747 5.28500632
65 1.67178103 4.52700747
66 0.88651953 1.67178103
67 0.42584367 0.88651953
68 0.31437125 0.42584367
69 -0.42093795 0.31437125
70 -0.34692588 -0.42093795
71 -0.73100806 -0.34692588
72 0.67523791 -0.73100806
73 NA 0.67523791
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.26604540 -4.58680690
[2,] -4.13473563 -3.26604540
[3,] -3.79520402 -4.13473563
[4,] -3.24555460 -3.79520402
[5,] -4.10527414 -3.24555460
[6,] -2.77418736 -4.10527414
[7,] -3.50532184 -2.77418736
[8,] -2.90809425 -3.50532184
[9,] -2.58852758 -2.90809425
[10,] -2.75304310 -2.58852758
[11,] -3.72393218 -2.75304310
[12,] -1.54407241 -3.72393218
[13,] -2.51011781 -1.54407241
[14,] -2.01733563 -2.51011781
[15,] -2.10524195 -2.01733563
[16,] -2.45559253 -2.10524195
[17,] -1.89552241 -2.45559253
[18,] -0.94569770 -1.89552241
[19,] -2.26918390 -0.94569770
[20,] -1.20704253 -2.26918390
[21,] -1.23680690 -1.20704253
[22,] -0.92321552 -1.23680690
[23,] -1.18091150 -0.92321552
[24,] -0.58575517 -1.18091150
[25,] -1.61986954 -0.58575517
[26,] -0.20940115 -1.61986954
[27,] -0.02474541 -0.20940115
[28,] -1.90702702 -0.02474541
[29,] 0.66833965 -1.90702702
[30,] 1.31261954 0.66833965
[31,] 0.17039541 1.31261954
[32,] 0.09400920 0.17039541
[33,] 1.38824139 0.09400920
[34,] -1.19816724 1.38824139
[35,] 0.39241610 -1.19816724
[36,] -0.14961724 0.39241610
[37,] -0.81461092 -0.14961724
[38,] 0.58820920 -0.81461092
[39,] -0.03267988 0.58820920
[40,] 0.05865230 -0.03267988
[41,] 0.86489828 0.05865230
[42,] -0.27208390 0.86489828
[43,] 0.74295747 -0.27208390
[44,] 0.45997472 0.74295747
[45,] 0.45315518 0.45997472
[46,] -0.11241206 0.45315518
[47,] 1.61564713 -0.11241206
[48,] 0.88891035 1.61564713
[49,] 2.21626839 0.88891035
[50,] 1.48820920 2.21626839
[51,] 0.67286494 1.48820920
[52,] 3.02251437 0.67286494
[53,] 2.79577759 3.02251437
[54,] 1.79282989 2.79577759
[55,] 4.43530920 1.79282989
[56,] 3.24678161 4.43530920
[57,] 2.40487586 3.24678161
[58,] 5.33376380 2.40487586
[59,] 3.62778851 5.33376380
[60,] 5.30210345 3.62778851
[61,] 5.99437529 5.30210345
[62,] 4.28505402 5.99437529
[63,] 5.28500632 4.28505402
[64,] 4.52700747 5.28500632
[65,] 1.67178103 4.52700747
[66,] 0.88651953 1.67178103
[67,] 0.42584367 0.88651953
[68,] 0.31437125 0.42584367
[69,] -0.42093795 0.31437125
[70,] -0.34692588 -0.42093795
[71,] -0.73100806 -0.34692588
[72,] 0.67523791 -0.73100806
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.26604540 -4.58680690
2 -4.13473563 -3.26604540
3 -3.79520402 -4.13473563
4 -3.24555460 -3.79520402
5 -4.10527414 -3.24555460
6 -2.77418736 -4.10527414
7 -3.50532184 -2.77418736
8 -2.90809425 -3.50532184
9 -2.58852758 -2.90809425
10 -2.75304310 -2.58852758
11 -3.72393218 -2.75304310
12 -1.54407241 -3.72393218
13 -2.51011781 -1.54407241
14 -2.01733563 -2.51011781
15 -2.10524195 -2.01733563
16 -2.45559253 -2.10524195
17 -1.89552241 -2.45559253
18 -0.94569770 -1.89552241
19 -2.26918390 -0.94569770
20 -1.20704253 -2.26918390
21 -1.23680690 -1.20704253
22 -0.92321552 -1.23680690
23 -1.18091150 -0.92321552
24 -0.58575517 -1.18091150
25 -1.61986954 -0.58575517
26 -0.20940115 -1.61986954
27 -0.02474541 -0.20940115
28 -1.90702702 -0.02474541
29 0.66833965 -1.90702702
30 1.31261954 0.66833965
31 0.17039541 1.31261954
32 0.09400920 0.17039541
33 1.38824139 0.09400920
34 -1.19816724 1.38824139
35 0.39241610 -1.19816724
36 -0.14961724 0.39241610
37 -0.81461092 -0.14961724
38 0.58820920 -0.81461092
39 -0.03267988 0.58820920
40 0.05865230 -0.03267988
41 0.86489828 0.05865230
42 -0.27208390 0.86489828
43 0.74295747 -0.27208390
44 0.45997472 0.74295747
45 0.45315518 0.45997472
46 -0.11241206 0.45315518
47 1.61564713 -0.11241206
48 0.88891035 1.61564713
49 2.21626839 0.88891035
50 1.48820920 2.21626839
51 0.67286494 1.48820920
52 3.02251437 0.67286494
53 2.79577759 3.02251437
54 1.79282989 2.79577759
55 4.43530920 1.79282989
56 3.24678161 4.43530920
57 2.40487586 3.24678161
58 5.33376380 2.40487586
59 3.62778851 5.33376380
60 5.30210345 3.62778851
61 5.99437529 5.30210345
62 4.28505402 5.99437529
63 5.28500632 4.28505402
64 4.52700747 5.28500632
65 1.67178103 4.52700747
66 0.88651953 1.67178103
67 0.42584367 0.88651953
68 0.31437125 0.42584367
69 -0.42093795 0.31437125
70 -0.34692588 -0.42093795
71 -0.73100806 -0.34692588
72 0.67523791 -0.73100806
> 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/7til51258723296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/85w291258723296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9fv471258723296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10v3ru1258723296.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113xf11258723296.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/126d0t1258723296.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/13pogc1258723296.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/1411nm1258723296.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/15ycan1258723296.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/16ai401258723296.tab")
+ }
>
> system("convert tmp/1o25j1258723296.ps tmp/1o25j1258723296.png")
> system("convert tmp/2vt6n1258723296.ps tmp/2vt6n1258723296.png")
> system("convert tmp/39trc1258723296.ps tmp/39trc1258723296.png")
> system("convert tmp/4lnhe1258723296.ps tmp/4lnhe1258723296.png")
> system("convert tmp/5vfgc1258723296.ps tmp/5vfgc1258723296.png")
> system("convert tmp/6j3uw1258723296.ps tmp/6j3uw1258723296.png")
> system("convert tmp/7til51258723296.ps tmp/7til51258723296.png")
> system("convert tmp/85w291258723296.ps tmp/85w291258723296.png")
> system("convert tmp/9fv471258723296.ps tmp/9fv471258723296.png")
> system("convert tmp/10v3ru1258723296.ps tmp/10v3ru1258723296.png")
>
>
> proc.time()
user system elapsed
2.577 1.579 2.951