R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(90.7,0,94.3,0,104.6,0,111.1,0,110.8,0,107.2,0,99,0,99,0,91,0,96.2,0,96.9,0,96.2,0,100.1,0,99,0,115.4,0,106.9,0,107.1,0,99.3,0,99.2,0,108.3,0,105.6,0,99.5,0,107.4,0,93.1,0,88.1,0,110.7,0,113.1,0,99.6,0,93.6,0,98.6,0,99.6,0,114.3,0,107.8,0,101.2,0,112.5,0,100.5,0,93.9,0,116.2,0,112,0,106.4,0,95.7,0,96,0,95.8,0,103,0,102.2,0,98.4,0,111.4,1,86.6,1,91.3,1,107.9,1,101.8,1,104.4,1,93.4,1,100.1,1,98.5,1,112.9,1,101.4,1,107.1,1,110.8,1,90.3,1,95.5,1,111.4,1,113,1,107.5,1,95.9,1,106.3,1,105.2,1,117.2,1,106.9,1,108.2,1,113,1,97.2,1,99.9,1,108.1,1,118.1,1,109.1,1,93.3,1,112.1,1,111.8,1,112.5,1,116.3,1,110.3,1,117.1,1,103.4,1,96.2,1),dim=c(2,85),dimnames=list(c('Prodintergoed','invest'),1:85))
> y <- array(NA,dim=c(2,85),dimnames=list(c('Prodintergoed','invest'),1:85))
> 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
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
Prodintergoed invest
1 90.7 0
2 94.3 0
3 104.6 0
4 111.1 0
5 110.8 0
6 107.2 0
7 99.0 0
8 99.0 0
9 91.0 0
10 96.2 0
11 96.9 0
12 96.2 0
13 100.1 0
14 99.0 0
15 115.4 0
16 106.9 0
17 107.1 0
18 99.3 0
19 99.2 0
20 108.3 0
21 105.6 0
22 99.5 0
23 107.4 0
24 93.1 0
25 88.1 0
26 110.7 0
27 113.1 0
28 99.6 0
29 93.6 0
30 98.6 0
31 99.6 0
32 114.3 0
33 107.8 0
34 101.2 0
35 112.5 0
36 100.5 0
37 93.9 0
38 116.2 0
39 112.0 0
40 106.4 0
41 95.7 0
42 96.0 0
43 95.8 0
44 103.0 0
45 102.2 0
46 98.4 0
47 111.4 1
48 86.6 1
49 91.3 1
50 107.9 1
51 101.8 1
52 104.4 1
53 93.4 1
54 100.1 1
55 98.5 1
56 112.9 1
57 101.4 1
58 107.1 1
59 110.8 1
60 90.3 1
61 95.5 1
62 111.4 1
63 113.0 1
64 107.5 1
65 95.9 1
66 106.3 1
67 105.2 1
68 117.2 1
69 106.9 1
70 108.2 1
71 113.0 1
72 97.2 1
73 99.9 1
74 108.1 1
75 118.1 1
76 109.1 1
77 93.3 1
78 112.1 1
79 111.8 1
80 112.5 1
81 116.3 1
82 110.3 1
83 117.1 1
84 103.4 1
85 96.2 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) invest
102.111 3.105
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-18.61538 -5.91087 -0.01538 6.18462 14.08913
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 102.111 1.135 89.950 <2e-16 ***
invest 3.105 1.676 1.852 0.0675 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.699 on 83 degrees of freedom
Multiple R-squared: 0.0397, Adjusted R-squared: 0.02813
F-statistic: 3.432 on 1 and 83 DF, p-value: 0.06752
> 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,] 0.8810324 0.2379353 0.1189676
[2,] 0.8129681 0.3740638 0.1870319
[3,] 0.7279104 0.5441793 0.2720896
[4,] 0.6313386 0.7373229 0.3686614
[5,] 0.6986840 0.6026320 0.3013160
[6,] 0.6306045 0.7387909 0.3693955
[7,] 0.5499598 0.9000804 0.4500402
[8,] 0.4774772 0.9549544 0.5225228
[9,] 0.3833463 0.7666926 0.6166537
[10,] 0.3001103 0.6002205 0.6998897
[11,] 0.5380539 0.9238923 0.4619461
[12,] 0.4971700 0.9943400 0.5028300
[13,] 0.4561827 0.9123654 0.5438173
[14,] 0.3832784 0.7665568 0.6167216
[15,] 0.3158892 0.6317784 0.6841108
[16,] 0.2997152 0.5994303 0.7002848
[17,] 0.2508083 0.5016166 0.7491917
[18,] 0.1992099 0.3984198 0.8007901
[19,] 0.1756118 0.3512236 0.8243882
[20,] 0.1946594 0.3893189 0.8053406
[21,] 0.3237023 0.6474046 0.6762977
[22,] 0.3478929 0.6957858 0.6521071
[23,] 0.4193391 0.8386783 0.5806609
[24,] 0.3598159 0.7196318 0.6401841
[25,] 0.3714957 0.7429914 0.6285043
[26,] 0.3220456 0.6440912 0.6779544
[27,] 0.2709278 0.5418556 0.7290722
[28,] 0.3590291 0.7180582 0.6409709
[29,] 0.3304725 0.6609449 0.6695275
[30,] 0.2743071 0.5486141 0.7256929
[31,] 0.3183439 0.6366878 0.6816561
[32,] 0.2650386 0.5300772 0.7349614
[33,] 0.2721794 0.5443587 0.7278206
[34,] 0.4043404 0.8086807 0.5956596
[35,] 0.4542643 0.9085285 0.5457357
[36,] 0.4244562 0.8489124 0.5755438
[37,] 0.3931568 0.7863136 0.6068432
[38,] 0.3599993 0.7199985 0.6400007
[39,] 0.3322736 0.6645472 0.6677264
[40,] 0.2790688 0.5581376 0.7209312
[41,] 0.2309793 0.4619587 0.7690207
[42,] 0.1906655 0.3813310 0.8093345
[43,] 0.1620302 0.3240604 0.8379698
[44,] 0.3862119 0.7724239 0.6137881
[45,] 0.4642112 0.9284225 0.5357888
[46,] 0.4474194 0.8948387 0.5525806
[47,] 0.3969694 0.7939388 0.6030306
[48,] 0.3446603 0.6893206 0.6553397
[49,] 0.4037883 0.8075767 0.5962117
[50,] 0.3684953 0.7369906 0.6315047
[51,] 0.3525966 0.7051933 0.6474034
[52,] 0.3777440 0.7554880 0.6222560
[53,] 0.3351930 0.6703860 0.6648070
[54,] 0.2876211 0.5752423 0.7123789
[55,] 0.2662462 0.5324925 0.7337538
[56,] 0.4530452 0.9060903 0.5469548
[57,] 0.5264202 0.9471595 0.4735798
[58,] 0.5005584 0.9988831 0.4994416
[59,] 0.4935612 0.9871225 0.5064388
[60,] 0.4276392 0.8552783 0.5723608
[61,] 0.5053993 0.9892015 0.4946007
[62,] 0.4365016 0.8730032 0.5634984
[63,] 0.3719183 0.7438366 0.6280817
[64,] 0.4334415 0.8668830 0.5665585
[65,] 0.3579561 0.7159121 0.6420439
[66,] 0.2873655 0.5747310 0.7126345
[67,] 0.2579789 0.5159578 0.7420211
[68,] 0.3012478 0.6024956 0.6987522
[69,] 0.3051304 0.6102607 0.6948696
[70,] 0.2296701 0.4593402 0.7703299
[71,] 0.2733110 0.5466220 0.7266890
[72,] 0.1951971 0.3903943 0.8048029
[73,] 0.4512780 0.9025559 0.5487220
[74,] 0.3442522 0.6885044 0.6557478
[75,] 0.2389380 0.4778760 0.7610620
[76,] 0.1529000 0.3057999 0.8471000
> postscript(file="/var/www/html/rcomp/tmp/1bnas1227512086.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2j7ni1227512086.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/349811227512086.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ryh31227512086.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/51lrf1227512086.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 = 85
Frequency = 1
1 2 3 4 5 6
-11.41086957 -7.81086957 2.48913043 8.98913043 8.68913043 5.08913043
7 8 9 10 11 12
-3.11086957 -3.11086957 -11.11086957 -5.91086957 -5.21086957 -5.91086957
13 14 15 16 17 18
-2.01086957 -3.11086957 13.28913043 4.78913043 4.98913043 -2.81086957
19 20 21 22 23 24
-2.91086957 6.18913043 3.48913043 -2.61086957 5.28913043 -9.01086957
25 26 27 28 29 30
-14.01086957 8.58913043 10.98913043 -2.51086957 -8.51086957 -3.51086957
31 32 33 34 35 36
-2.51086957 12.18913043 5.68913043 -0.91086957 10.38913043 -1.61086957
37 38 39 40 41 42
-8.21086957 14.08913043 9.88913043 4.28913043 -6.41086957 -6.11086957
43 44 45 46 47 48
-6.31086957 0.88913043 0.08913043 -3.71086957 6.18461538 -18.61538462
49 50 51 52 53 54
-13.91538462 2.68461538 -3.41538462 -0.81538462 -11.81538462 -5.11538462
55 56 57 58 59 60
-6.71538462 7.68461538 -3.81538462 1.88461538 5.58461538 -14.91538462
61 62 63 64 65 66
-9.71538462 6.18461538 7.78461538 2.28461538 -9.31538462 1.08461538
67 68 69 70 71 72
-0.01538462 11.98461538 1.68461538 2.98461538 7.78461538 -8.01538462
73 74 75 76 77 78
-5.31538462 2.88461538 12.88461538 3.88461538 -11.91538462 6.88461538
79 80 81 82 83 84
6.58461538 7.28461538 11.08461538 5.08461538 11.88461538 -1.81538462
85
-9.01538462
> postscript(file="/var/www/html/rcomp/tmp/6yto11227512086.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 = 85
Frequency = 1
lag(myerror, k = 1) myerror
0 -11.41086957 NA
1 -7.81086957 -11.41086957
2 2.48913043 -7.81086957
3 8.98913043 2.48913043
4 8.68913043 8.98913043
5 5.08913043 8.68913043
6 -3.11086957 5.08913043
7 -3.11086957 -3.11086957
8 -11.11086957 -3.11086957
9 -5.91086957 -11.11086957
10 -5.21086957 -5.91086957
11 -5.91086957 -5.21086957
12 -2.01086957 -5.91086957
13 -3.11086957 -2.01086957
14 13.28913043 -3.11086957
15 4.78913043 13.28913043
16 4.98913043 4.78913043
17 -2.81086957 4.98913043
18 -2.91086957 -2.81086957
19 6.18913043 -2.91086957
20 3.48913043 6.18913043
21 -2.61086957 3.48913043
22 5.28913043 -2.61086957
23 -9.01086957 5.28913043
24 -14.01086957 -9.01086957
25 8.58913043 -14.01086957
26 10.98913043 8.58913043
27 -2.51086957 10.98913043
28 -8.51086957 -2.51086957
29 -3.51086957 -8.51086957
30 -2.51086957 -3.51086957
31 12.18913043 -2.51086957
32 5.68913043 12.18913043
33 -0.91086957 5.68913043
34 10.38913043 -0.91086957
35 -1.61086957 10.38913043
36 -8.21086957 -1.61086957
37 14.08913043 -8.21086957
38 9.88913043 14.08913043
39 4.28913043 9.88913043
40 -6.41086957 4.28913043
41 -6.11086957 -6.41086957
42 -6.31086957 -6.11086957
43 0.88913043 -6.31086957
44 0.08913043 0.88913043
45 -3.71086957 0.08913043
46 6.18461538 -3.71086957
47 -18.61538462 6.18461538
48 -13.91538462 -18.61538462
49 2.68461538 -13.91538462
50 -3.41538462 2.68461538
51 -0.81538462 -3.41538462
52 -11.81538462 -0.81538462
53 -5.11538462 -11.81538462
54 -6.71538462 -5.11538462
55 7.68461538 -6.71538462
56 -3.81538462 7.68461538
57 1.88461538 -3.81538462
58 5.58461538 1.88461538
59 -14.91538462 5.58461538
60 -9.71538462 -14.91538462
61 6.18461538 -9.71538462
62 7.78461538 6.18461538
63 2.28461538 7.78461538
64 -9.31538462 2.28461538
65 1.08461538 -9.31538462
66 -0.01538462 1.08461538
67 11.98461538 -0.01538462
68 1.68461538 11.98461538
69 2.98461538 1.68461538
70 7.78461538 2.98461538
71 -8.01538462 7.78461538
72 -5.31538462 -8.01538462
73 2.88461538 -5.31538462
74 12.88461538 2.88461538
75 3.88461538 12.88461538
76 -11.91538462 3.88461538
77 6.88461538 -11.91538462
78 6.58461538 6.88461538
79 7.28461538 6.58461538
80 11.08461538 7.28461538
81 5.08461538 11.08461538
82 11.88461538 5.08461538
83 -1.81538462 11.88461538
84 -9.01538462 -1.81538462
85 NA -9.01538462
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -7.81086957 -11.41086957
[2,] 2.48913043 -7.81086957
[3,] 8.98913043 2.48913043
[4,] 8.68913043 8.98913043
[5,] 5.08913043 8.68913043
[6,] -3.11086957 5.08913043
[7,] -3.11086957 -3.11086957
[8,] -11.11086957 -3.11086957
[9,] -5.91086957 -11.11086957
[10,] -5.21086957 -5.91086957
[11,] -5.91086957 -5.21086957
[12,] -2.01086957 -5.91086957
[13,] -3.11086957 -2.01086957
[14,] 13.28913043 -3.11086957
[15,] 4.78913043 13.28913043
[16,] 4.98913043 4.78913043
[17,] -2.81086957 4.98913043
[18,] -2.91086957 -2.81086957
[19,] 6.18913043 -2.91086957
[20,] 3.48913043 6.18913043
[21,] -2.61086957 3.48913043
[22,] 5.28913043 -2.61086957
[23,] -9.01086957 5.28913043
[24,] -14.01086957 -9.01086957
[25,] 8.58913043 -14.01086957
[26,] 10.98913043 8.58913043
[27,] -2.51086957 10.98913043
[28,] -8.51086957 -2.51086957
[29,] -3.51086957 -8.51086957
[30,] -2.51086957 -3.51086957
[31,] 12.18913043 -2.51086957
[32,] 5.68913043 12.18913043
[33,] -0.91086957 5.68913043
[34,] 10.38913043 -0.91086957
[35,] -1.61086957 10.38913043
[36,] -8.21086957 -1.61086957
[37,] 14.08913043 -8.21086957
[38,] 9.88913043 14.08913043
[39,] 4.28913043 9.88913043
[40,] -6.41086957 4.28913043
[41,] -6.11086957 -6.41086957
[42,] -6.31086957 -6.11086957
[43,] 0.88913043 -6.31086957
[44,] 0.08913043 0.88913043
[45,] -3.71086957 0.08913043
[46,] 6.18461538 -3.71086957
[47,] -18.61538462 6.18461538
[48,] -13.91538462 -18.61538462
[49,] 2.68461538 -13.91538462
[50,] -3.41538462 2.68461538
[51,] -0.81538462 -3.41538462
[52,] -11.81538462 -0.81538462
[53,] -5.11538462 -11.81538462
[54,] -6.71538462 -5.11538462
[55,] 7.68461538 -6.71538462
[56,] -3.81538462 7.68461538
[57,] 1.88461538 -3.81538462
[58,] 5.58461538 1.88461538
[59,] -14.91538462 5.58461538
[60,] -9.71538462 -14.91538462
[61,] 6.18461538 -9.71538462
[62,] 7.78461538 6.18461538
[63,] 2.28461538 7.78461538
[64,] -9.31538462 2.28461538
[65,] 1.08461538 -9.31538462
[66,] -0.01538462 1.08461538
[67,] 11.98461538 -0.01538462
[68,] 1.68461538 11.98461538
[69,] 2.98461538 1.68461538
[70,] 7.78461538 2.98461538
[71,] -8.01538462 7.78461538
[72,] -5.31538462 -8.01538462
[73,] 2.88461538 -5.31538462
[74,] 12.88461538 2.88461538
[75,] 3.88461538 12.88461538
[76,] -11.91538462 3.88461538
[77,] 6.88461538 -11.91538462
[78,] 6.58461538 6.88461538
[79,] 7.28461538 6.58461538
[80,] 11.08461538 7.28461538
[81,] 5.08461538 11.08461538
[82,] 11.88461538 5.08461538
[83,] -1.81538462 11.88461538
[84,] -9.01538462 -1.81538462
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -7.81086957 -11.41086957
2 2.48913043 -7.81086957
3 8.98913043 2.48913043
4 8.68913043 8.98913043
5 5.08913043 8.68913043
6 -3.11086957 5.08913043
7 -3.11086957 -3.11086957
8 -11.11086957 -3.11086957
9 -5.91086957 -11.11086957
10 -5.21086957 -5.91086957
11 -5.91086957 -5.21086957
12 -2.01086957 -5.91086957
13 -3.11086957 -2.01086957
14 13.28913043 -3.11086957
15 4.78913043 13.28913043
16 4.98913043 4.78913043
17 -2.81086957 4.98913043
18 -2.91086957 -2.81086957
19 6.18913043 -2.91086957
20 3.48913043 6.18913043
21 -2.61086957 3.48913043
22 5.28913043 -2.61086957
23 -9.01086957 5.28913043
24 -14.01086957 -9.01086957
25 8.58913043 -14.01086957
26 10.98913043 8.58913043
27 -2.51086957 10.98913043
28 -8.51086957 -2.51086957
29 -3.51086957 -8.51086957
30 -2.51086957 -3.51086957
31 12.18913043 -2.51086957
32 5.68913043 12.18913043
33 -0.91086957 5.68913043
34 10.38913043 -0.91086957
35 -1.61086957 10.38913043
36 -8.21086957 -1.61086957
37 14.08913043 -8.21086957
38 9.88913043 14.08913043
39 4.28913043 9.88913043
40 -6.41086957 4.28913043
41 -6.11086957 -6.41086957
42 -6.31086957 -6.11086957
43 0.88913043 -6.31086957
44 0.08913043 0.88913043
45 -3.71086957 0.08913043
46 6.18461538 -3.71086957
47 -18.61538462 6.18461538
48 -13.91538462 -18.61538462
49 2.68461538 -13.91538462
50 -3.41538462 2.68461538
51 -0.81538462 -3.41538462
52 -11.81538462 -0.81538462
53 -5.11538462 -11.81538462
54 -6.71538462 -5.11538462
55 7.68461538 -6.71538462
56 -3.81538462 7.68461538
57 1.88461538 -3.81538462
58 5.58461538 1.88461538
59 -14.91538462 5.58461538
60 -9.71538462 -14.91538462
61 6.18461538 -9.71538462
62 7.78461538 6.18461538
63 2.28461538 7.78461538
64 -9.31538462 2.28461538
65 1.08461538 -9.31538462
66 -0.01538462 1.08461538
67 11.98461538 -0.01538462
68 1.68461538 11.98461538
69 2.98461538 1.68461538
70 7.78461538 2.98461538
71 -8.01538462 7.78461538
72 -5.31538462 -8.01538462
73 2.88461538 -5.31538462
74 12.88461538 2.88461538
75 3.88461538 12.88461538
76 -11.91538462 3.88461538
77 6.88461538 -11.91538462
78 6.58461538 6.88461538
79 7.28461538 6.58461538
80 11.08461538 7.28461538
81 5.08461538 11.08461538
82 11.88461538 5.08461538
83 -1.81538462 11.88461538
84 -9.01538462 -1.81538462
> 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/7t18c1227512086.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8mxdr1227512086.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90pw31227512086.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/103s9d1227512086.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11c00p1227512086.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/12vbnl1227512087.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/134ktw1227512087.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/14esrg1227512087.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/151kzc1227512087.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/16misr1227512087.tab")
+ }
>
> system("convert tmp/1bnas1227512086.ps tmp/1bnas1227512086.png")
> system("convert tmp/2j7ni1227512086.ps tmp/2j7ni1227512086.png")
> system("convert tmp/349811227512086.ps tmp/349811227512086.png")
> system("convert tmp/4ryh31227512086.ps tmp/4ryh31227512086.png")
> system("convert tmp/51lrf1227512086.ps tmp/51lrf1227512086.png")
> system("convert tmp/6yto11227512086.ps tmp/6yto11227512086.png")
> system("convert tmp/7t18c1227512086.ps tmp/7t18c1227512086.png")
> system("convert tmp/8mxdr1227512086.ps tmp/8mxdr1227512086.png")
> system("convert tmp/90pw31227512086.ps tmp/90pw31227512086.png")
> system("convert tmp/103s9d1227512086.ps tmp/103s9d1227512086.png")
>
>
> proc.time()
user system elapsed
5.469 2.781 5.876