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(3.75,0,3.75,0,3.55,0,3.5,0,3.5,0,3.1,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3,0,3.21,0,3.25,0,3.25,0,3.45,0,3.5,0,3.5,0,3.64,0,3.75,0,3.93,0,4,0,4.17,0,4.25,0,4.39,0,4.5,0,4.5,0,4.65,0,4.75,0,4.75,0,4.9,0,5,0,5,0,5,0,5,0,5,0,5,0,5,1,5,1,5,1,5,1,5,1,5,1,5.18,1,5.25,1,5.25,1,4.49,1,3.92,1,3.25,1),dim=c(2,72),dimnames=list(c('Yt','Xt'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('Yt','Xt'),1:72))
> 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
Yt Xt M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 3.75 0 1 0 0 0 0 0 0 0 0 0 0
2 3.75 0 0 1 0 0 0 0 0 0 0 0 0
3 3.55 0 0 0 1 0 0 0 0 0 0 0 0
4 3.50 0 0 0 0 1 0 0 0 0 0 0 0
5 3.50 0 0 0 0 0 1 0 0 0 0 0 0
6 3.10 0 0 0 0 0 0 1 0 0 0 0 0
7 3.00 0 0 0 0 0 0 0 1 0 0 0 0
8 3.00 0 0 0 0 0 0 0 0 1 0 0 0
9 3.00 0 0 0 0 0 0 0 0 0 1 0 0
10 3.00 0 0 0 0 0 0 0 0 0 0 1 0
11 3.00 0 0 0 0 0 0 0 0 0 0 0 1
12 3.00 0 0 0 0 0 0 0 0 0 0 0 0
13 3.00 0 1 0 0 0 0 0 0 0 0 0 0
14 3.00 0 0 1 0 0 0 0 0 0 0 0 0
15 3.00 0 0 0 1 0 0 0 0 0 0 0 0
16 3.00 0 0 0 0 1 0 0 0 0 0 0 0
17 3.00 0 0 0 0 0 1 0 0 0 0 0 0
18 3.00 0 0 0 0 0 0 1 0 0 0 0 0
19 3.00 0 0 0 0 0 0 0 1 0 0 0 0
20 3.00 0 0 0 0 0 0 0 0 1 0 0 0
21 3.00 0 0 0 0 0 0 0 0 0 1 0 0
22 3.00 0 0 0 0 0 0 0 0 0 0 1 0
23 3.00 0 0 0 0 0 0 0 0 0 0 0 1
24 3.00 0 0 0 0 0 0 0 0 0 0 0 0
25 3.00 0 1 0 0 0 0 0 0 0 0 0 0
26 3.00 0 0 1 0 0 0 0 0 0 0 0 0
27 3.00 0 0 0 1 0 0 0 0 0 0 0 0
28 3.00 0 0 0 0 1 0 0 0 0 0 0 0
29 3.00 0 0 0 0 0 1 0 0 0 0 0 0
30 3.00 0 0 0 0 0 0 1 0 0 0 0 0
31 3.00 0 0 0 0 0 0 0 1 0 0 0 0
32 3.00 0 0 0 0 0 0 0 0 1 0 0 0
33 3.00 0 0 0 0 0 0 0 0 0 1 0 0
34 3.00 0 0 0 0 0 0 0 0 0 0 1 0
35 3.00 0 0 0 0 0 0 0 0 0 0 0 1
36 3.21 0 0 0 0 0 0 0 0 0 0 0 0
37 3.25 0 1 0 0 0 0 0 0 0 0 0 0
38 3.25 0 0 1 0 0 0 0 0 0 0 0 0
39 3.45 0 0 0 1 0 0 0 0 0 0 0 0
40 3.50 0 0 0 0 1 0 0 0 0 0 0 0
41 3.50 0 0 0 0 0 1 0 0 0 0 0 0
42 3.64 0 0 0 0 0 0 1 0 0 0 0 0
43 3.75 0 0 0 0 0 0 0 1 0 0 0 0
44 3.93 0 0 0 0 0 0 0 0 1 0 0 0
45 4.00 0 0 0 0 0 0 0 0 0 1 0 0
46 4.17 0 0 0 0 0 0 0 0 0 0 1 0
47 4.25 0 0 0 0 0 0 0 0 0 0 0 1
48 4.39 0 0 0 0 0 0 0 0 0 0 0 0
49 4.50 0 1 0 0 0 0 0 0 0 0 0 0
50 4.50 0 0 1 0 0 0 0 0 0 0 0 0
51 4.65 0 0 0 1 0 0 0 0 0 0 0 0
52 4.75 0 0 0 0 1 0 0 0 0 0 0 0
53 4.75 0 0 0 0 0 1 0 0 0 0 0 0
54 4.90 0 0 0 0 0 0 1 0 0 0 0 0
55 5.00 0 0 0 0 0 0 0 1 0 0 0 0
56 5.00 0 0 0 0 0 0 0 0 1 0 0 0
57 5.00 0 0 0 0 0 0 0 0 0 1 0 0
58 5.00 0 0 0 0 0 0 0 0 0 0 1 0
59 5.00 0 0 0 0 0 0 0 0 0 0 0 1
60 5.00 0 0 0 0 0 0 0 0 0 0 0 0
61 5.00 1 1 0 0 0 0 0 0 0 0 0 0
62 5.00 1 0 1 0 0 0 0 0 0 0 0 0
63 5.00 1 0 0 1 0 0 0 0 0 0 0 0
64 5.00 1 0 0 0 1 0 0 0 0 0 0 0
65 5.00 1 0 0 0 0 1 0 0 0 0 0 0
66 5.00 1 0 0 0 0 0 1 0 0 0 0 0
67 5.18 1 0 0 0 0 0 0 1 0 0 0 0
68 5.25 1 0 0 0 0 0 0 0 1 0 0 0
69 5.25 1 0 0 0 0 0 0 0 0 1 0 0
70 4.49 1 0 0 0 0 0 0 0 0 0 1 0
71 3.92 1 0 0 0 0 0 0 0 0 0 0 1
72 3.25 1 0 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) Xt M1 M2 M3 M4
3.44108 1.20350 0.10833 0.10833 0.13333 0.15000
M5 M6 M7 M8 M9 M10
0.15000 0.13167 0.18000 0.22167 0.23333 0.13500
M11
0.05333
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.3946 -0.5748 -0.1777 0.3330 1.5589
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.44108 0.31895 10.789 1.39e-15 ***
Xt 1.20350 0.24503 4.912 7.50e-06 ***
M1 0.10833 0.44735 0.242 0.809
M2 0.10833 0.44735 0.242 0.809
M3 0.13333 0.44735 0.298 0.767
M4 0.15000 0.44735 0.335 0.739
M5 0.15000 0.44735 0.335 0.739
M6 0.13167 0.44735 0.294 0.770
M7 0.18000 0.44735 0.402 0.689
M8 0.22167 0.44735 0.496 0.622
M9 0.23333 0.44735 0.522 0.604
M10 0.13500 0.44735 0.302 0.764
M11 0.05333 0.44735 0.119 0.906
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7748 on 59 degrees of freedom
Multiple R-squared: 0.2941, Adjusted R-squared: 0.1506
F-statistic: 2.049 on 12 and 59 DF, p-value: 0.03523
> 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,] 2.388826e-01 4.777652e-01 0.7611174
[2,] 1.460649e-01 2.921298e-01 0.8539351
[3,] 7.152206e-02 1.430441e-01 0.9284779
[4,] 3.293611e-02 6.587222e-02 0.9670639
[5,] 1.457584e-02 2.915168e-02 0.9854242
[6,] 6.236036e-03 1.247207e-02 0.9937640
[7,] 2.505364e-03 5.010729e-03 0.9974946
[8,] 9.469596e-04 1.893919e-03 0.9990530
[9,] 3.391189e-04 6.782378e-04 0.9996609
[10,] 2.000821e-04 4.001642e-04 0.9997999
[11,] 1.201077e-04 2.402155e-04 0.9998799
[12,] 6.224029e-05 1.244806e-04 0.9999378
[13,] 3.247031e-05 6.494062e-05 0.9999675
[14,] 1.754359e-05 3.508719e-05 0.9999825
[15,] 8.436398e-06 1.687280e-05 0.9999916
[16,] 4.690987e-06 9.381973e-06 0.9999953
[17,] 3.115942e-06 6.231885e-06 0.9999969
[18,] 2.461835e-06 4.923670e-06 0.9999975
[19,] 1.788839e-06 3.577678e-06 0.9999982
[20,] 1.200728e-06 2.401456e-06 0.9999988
[21,] 6.932683e-07 1.386537e-06 0.9999993
[22,] 5.357305e-07 1.071461e-06 0.9999995
[23,] 4.937570e-07 9.875140e-07 0.9999995
[24,] 6.695297e-07 1.339059e-06 0.9999993
[25,] 1.382969e-06 2.765938e-06 0.9999986
[26,] 3.644340e-06 7.288681e-06 0.9999964
[27,] 3.226867e-05 6.453733e-05 0.9999677
[28,] 5.531305e-04 1.106261e-03 0.9994469
[29,] 8.503876e-03 1.700775e-02 0.9914961
[30,] 6.872543e-02 1.374509e-01 0.9312746
[31,] 1.695015e-01 3.390029e-01 0.8304985
[32,] 2.532289e-01 5.064577e-01 0.7467711
[33,] 3.265012e-01 6.530024e-01 0.6734988
[34,] 4.046157e-01 8.092314e-01 0.5953843
[35,] 4.699462e-01 9.398923e-01 0.5300538
[36,] 5.162858e-01 9.674285e-01 0.4837142
[37,] 5.403610e-01 9.192780e-01 0.4596390
[38,] 5.485998e-01 9.028004e-01 0.4514002
[39,] 5.370771e-01 9.258459e-01 0.4629229
[40,] 5.345320e-01 9.309360e-01 0.4654680
[41,] 5.596540e-01 8.806920e-01 0.4403460
> postscript(file="/var/www/html/rcomp/tmp/1q8i01258656577.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/24omg1258656577.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3vnyf1258656577.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4u54a1258656577.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55dmo1258656577.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 = 72
Frequency = 1
1 2 3 4 5 6
0.20058333 0.20058333 -0.02441667 -0.09108333 -0.09108333 -0.47275000
7 8 9 10 11 12
-0.62108333 -0.66275000 -0.67441667 -0.57608333 -0.49441667 -0.44108333
13 14 15 16 17 18
-0.54941667 -0.54941667 -0.57441667 -0.59108333 -0.59108333 -0.57275000
19 20 21 22 23 24
-0.62108333 -0.66275000 -0.67441667 -0.57608333 -0.49441667 -0.44108333
25 26 27 28 29 30
-0.54941667 -0.54941667 -0.57441667 -0.59108333 -0.59108333 -0.57275000
31 32 33 34 35 36
-0.62108333 -0.66275000 -0.67441667 -0.57608333 -0.49441667 -0.23108333
37 38 39 40 41 42
-0.29941667 -0.29941667 -0.12441667 -0.09108333 -0.09108333 0.06725000
43 44 45 46 47 48
0.12891667 0.26725000 0.32558333 0.59391667 0.75558333 0.94891667
49 50 51 52 53 54
0.95058333 0.95058333 1.07558333 1.15891667 1.15891667 1.32725000
55 56 57 58 59 60
1.37891667 1.33725000 1.32558333 1.42391667 1.50558333 1.55891667
61 62 63 64 65 66
0.24708333 0.24708333 0.22208333 0.20541667 0.20541667 0.22375000
67 68 69 70 71 72
0.35541667 0.38375000 0.37208333 -0.28958333 -0.77791667 -1.39458333
> postscript(file="/var/www/html/rcomp/tmp/6v1rp1258656577.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 = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 0.20058333 NA
1 0.20058333 0.20058333
2 -0.02441667 0.20058333
3 -0.09108333 -0.02441667
4 -0.09108333 -0.09108333
5 -0.47275000 -0.09108333
6 -0.62108333 -0.47275000
7 -0.66275000 -0.62108333
8 -0.67441667 -0.66275000
9 -0.57608333 -0.67441667
10 -0.49441667 -0.57608333
11 -0.44108333 -0.49441667
12 -0.54941667 -0.44108333
13 -0.54941667 -0.54941667
14 -0.57441667 -0.54941667
15 -0.59108333 -0.57441667
16 -0.59108333 -0.59108333
17 -0.57275000 -0.59108333
18 -0.62108333 -0.57275000
19 -0.66275000 -0.62108333
20 -0.67441667 -0.66275000
21 -0.57608333 -0.67441667
22 -0.49441667 -0.57608333
23 -0.44108333 -0.49441667
24 -0.54941667 -0.44108333
25 -0.54941667 -0.54941667
26 -0.57441667 -0.54941667
27 -0.59108333 -0.57441667
28 -0.59108333 -0.59108333
29 -0.57275000 -0.59108333
30 -0.62108333 -0.57275000
31 -0.66275000 -0.62108333
32 -0.67441667 -0.66275000
33 -0.57608333 -0.67441667
34 -0.49441667 -0.57608333
35 -0.23108333 -0.49441667
36 -0.29941667 -0.23108333
37 -0.29941667 -0.29941667
38 -0.12441667 -0.29941667
39 -0.09108333 -0.12441667
40 -0.09108333 -0.09108333
41 0.06725000 -0.09108333
42 0.12891667 0.06725000
43 0.26725000 0.12891667
44 0.32558333 0.26725000
45 0.59391667 0.32558333
46 0.75558333 0.59391667
47 0.94891667 0.75558333
48 0.95058333 0.94891667
49 0.95058333 0.95058333
50 1.07558333 0.95058333
51 1.15891667 1.07558333
52 1.15891667 1.15891667
53 1.32725000 1.15891667
54 1.37891667 1.32725000
55 1.33725000 1.37891667
56 1.32558333 1.33725000
57 1.42391667 1.32558333
58 1.50558333 1.42391667
59 1.55891667 1.50558333
60 0.24708333 1.55891667
61 0.24708333 0.24708333
62 0.22208333 0.24708333
63 0.20541667 0.22208333
64 0.20541667 0.20541667
65 0.22375000 0.20541667
66 0.35541667 0.22375000
67 0.38375000 0.35541667
68 0.37208333 0.38375000
69 -0.28958333 0.37208333
70 -0.77791667 -0.28958333
71 -1.39458333 -0.77791667
72 NA -1.39458333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.20058333 0.20058333
[2,] -0.02441667 0.20058333
[3,] -0.09108333 -0.02441667
[4,] -0.09108333 -0.09108333
[5,] -0.47275000 -0.09108333
[6,] -0.62108333 -0.47275000
[7,] -0.66275000 -0.62108333
[8,] -0.67441667 -0.66275000
[9,] -0.57608333 -0.67441667
[10,] -0.49441667 -0.57608333
[11,] -0.44108333 -0.49441667
[12,] -0.54941667 -0.44108333
[13,] -0.54941667 -0.54941667
[14,] -0.57441667 -0.54941667
[15,] -0.59108333 -0.57441667
[16,] -0.59108333 -0.59108333
[17,] -0.57275000 -0.59108333
[18,] -0.62108333 -0.57275000
[19,] -0.66275000 -0.62108333
[20,] -0.67441667 -0.66275000
[21,] -0.57608333 -0.67441667
[22,] -0.49441667 -0.57608333
[23,] -0.44108333 -0.49441667
[24,] -0.54941667 -0.44108333
[25,] -0.54941667 -0.54941667
[26,] -0.57441667 -0.54941667
[27,] -0.59108333 -0.57441667
[28,] -0.59108333 -0.59108333
[29,] -0.57275000 -0.59108333
[30,] -0.62108333 -0.57275000
[31,] -0.66275000 -0.62108333
[32,] -0.67441667 -0.66275000
[33,] -0.57608333 -0.67441667
[34,] -0.49441667 -0.57608333
[35,] -0.23108333 -0.49441667
[36,] -0.29941667 -0.23108333
[37,] -0.29941667 -0.29941667
[38,] -0.12441667 -0.29941667
[39,] -0.09108333 -0.12441667
[40,] -0.09108333 -0.09108333
[41,] 0.06725000 -0.09108333
[42,] 0.12891667 0.06725000
[43,] 0.26725000 0.12891667
[44,] 0.32558333 0.26725000
[45,] 0.59391667 0.32558333
[46,] 0.75558333 0.59391667
[47,] 0.94891667 0.75558333
[48,] 0.95058333 0.94891667
[49,] 0.95058333 0.95058333
[50,] 1.07558333 0.95058333
[51,] 1.15891667 1.07558333
[52,] 1.15891667 1.15891667
[53,] 1.32725000 1.15891667
[54,] 1.37891667 1.32725000
[55,] 1.33725000 1.37891667
[56,] 1.32558333 1.33725000
[57,] 1.42391667 1.32558333
[58,] 1.50558333 1.42391667
[59,] 1.55891667 1.50558333
[60,] 0.24708333 1.55891667
[61,] 0.24708333 0.24708333
[62,] 0.22208333 0.24708333
[63,] 0.20541667 0.22208333
[64,] 0.20541667 0.20541667
[65,] 0.22375000 0.20541667
[66,] 0.35541667 0.22375000
[67,] 0.38375000 0.35541667
[68,] 0.37208333 0.38375000
[69,] -0.28958333 0.37208333
[70,] -0.77791667 -0.28958333
[71,] -1.39458333 -0.77791667
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.20058333 0.20058333
2 -0.02441667 0.20058333
3 -0.09108333 -0.02441667
4 -0.09108333 -0.09108333
5 -0.47275000 -0.09108333
6 -0.62108333 -0.47275000
7 -0.66275000 -0.62108333
8 -0.67441667 -0.66275000
9 -0.57608333 -0.67441667
10 -0.49441667 -0.57608333
11 -0.44108333 -0.49441667
12 -0.54941667 -0.44108333
13 -0.54941667 -0.54941667
14 -0.57441667 -0.54941667
15 -0.59108333 -0.57441667
16 -0.59108333 -0.59108333
17 -0.57275000 -0.59108333
18 -0.62108333 -0.57275000
19 -0.66275000 -0.62108333
20 -0.67441667 -0.66275000
21 -0.57608333 -0.67441667
22 -0.49441667 -0.57608333
23 -0.44108333 -0.49441667
24 -0.54941667 -0.44108333
25 -0.54941667 -0.54941667
26 -0.57441667 -0.54941667
27 -0.59108333 -0.57441667
28 -0.59108333 -0.59108333
29 -0.57275000 -0.59108333
30 -0.62108333 -0.57275000
31 -0.66275000 -0.62108333
32 -0.67441667 -0.66275000
33 -0.57608333 -0.67441667
34 -0.49441667 -0.57608333
35 -0.23108333 -0.49441667
36 -0.29941667 -0.23108333
37 -0.29941667 -0.29941667
38 -0.12441667 -0.29941667
39 -0.09108333 -0.12441667
40 -0.09108333 -0.09108333
41 0.06725000 -0.09108333
42 0.12891667 0.06725000
43 0.26725000 0.12891667
44 0.32558333 0.26725000
45 0.59391667 0.32558333
46 0.75558333 0.59391667
47 0.94891667 0.75558333
48 0.95058333 0.94891667
49 0.95058333 0.95058333
50 1.07558333 0.95058333
51 1.15891667 1.07558333
52 1.15891667 1.15891667
53 1.32725000 1.15891667
54 1.37891667 1.32725000
55 1.33725000 1.37891667
56 1.32558333 1.33725000
57 1.42391667 1.32558333
58 1.50558333 1.42391667
59 1.55891667 1.50558333
60 0.24708333 1.55891667
61 0.24708333 0.24708333
62 0.22208333 0.24708333
63 0.20541667 0.22208333
64 0.20541667 0.20541667
65 0.22375000 0.20541667
66 0.35541667 0.22375000
67 0.38375000 0.35541667
68 0.37208333 0.38375000
69 -0.28958333 0.37208333
70 -0.77791667 -0.28958333
71 -1.39458333 -0.77791667
> 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/7ihb01258656577.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8lawg1258656577.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/967yk1258656577.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10emk01258656577.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11dd0n1258656577.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/12re5k1258656577.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/13yxlk1258656577.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/14pi8c1258656577.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/150sli1258656577.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/1604zb1258656578.tab")
+ }
>
> system("convert tmp/1q8i01258656577.ps tmp/1q8i01258656577.png")
> system("convert tmp/24omg1258656577.ps tmp/24omg1258656577.png")
> system("convert tmp/3vnyf1258656577.ps tmp/3vnyf1258656577.png")
> system("convert tmp/4u54a1258656577.ps tmp/4u54a1258656577.png")
> system("convert tmp/55dmo1258656577.ps tmp/55dmo1258656577.png")
> system("convert tmp/6v1rp1258656577.ps tmp/6v1rp1258656577.png")
> system("convert tmp/7ihb01258656577.ps tmp/7ihb01258656577.png")
> system("convert tmp/8lawg1258656577.ps tmp/8lawg1258656577.png")
> system("convert tmp/967yk1258656577.ps tmp/967yk1258656577.png")
> system("convert tmp/10emk01258656577.ps tmp/10emk01258656577.png")
>
>
> proc.time()
user system elapsed
2.646 1.628 5.987