R version 2.13.0 (2011-04-13)
Copyright (C) 2011 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(9,676,8,642,9,402,9,610,9,294,9,448,10,319,9,548,9,801,9,596,8,923,9,746,9,829,9,125,9,782,9,441,9,162,9,915,10,444,10,209,9,985,9,842,9,429,10,132,9,849,9,172,10,313,9,819,9,955,10,048,10,082,10,541,10,208,10,233,9,439,9,963,10,158,9,225,10,474,9,757,10,490,10,281,10,444,10,640,10,695,10,786,9,832,9,747,10,411,9,511,10,402,9,701,10,540,10,112,10,915,11,183,10,384,10,834,9,886,10,216,10,943,9,867,10,203,10,837,10,573,10,647,11,502,10,656,10,866,10,835,9,945,10,331),dim=c(2,72),dimnames=list(c('Monthyly','births'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('Monthyly','births'),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 = '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
> 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
Monthyly births M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9 676 1 0 0 0 0 0 0 0 0 0 0 1
2 8 642 0 1 0 0 0 0 0 0 0 0 0 2
3 9 402 0 0 1 0 0 0 0 0 0 0 0 3
4 9 610 0 0 0 1 0 0 0 0 0 0 0 4
5 9 294 0 0 0 0 1 0 0 0 0 0 0 5
6 9 448 0 0 0 0 0 1 0 0 0 0 0 6
7 10 319 0 0 0 0 0 0 1 0 0 0 0 7
8 9 548 0 0 0 0 0 0 0 1 0 0 0 8
9 9 801 0 0 0 0 0 0 0 0 1 0 0 9
10 9 596 0 0 0 0 0 0 0 0 0 1 0 10
11 8 923 0 0 0 0 0 0 0 0 0 0 1 11
12 9 746 0 0 0 0 0 0 0 0 0 0 0 12
13 9 829 1 0 0 0 0 0 0 0 0 0 0 13
14 9 125 0 1 0 0 0 0 0 0 0 0 0 14
15 9 782 0 0 1 0 0 0 0 0 0 0 0 15
16 9 441 0 0 0 1 0 0 0 0 0 0 0 16
17 9 162 0 0 0 0 1 0 0 0 0 0 0 17
18 9 915 0 0 0 0 0 1 0 0 0 0 0 18
19 10 444 0 0 0 0 0 0 1 0 0 0 0 19
20 10 209 0 0 0 0 0 0 0 1 0 0 0 20
21 9 985 0 0 0 0 0 0 0 0 1 0 0 21
22 9 842 0 0 0 0 0 0 0 0 0 1 0 22
23 9 429 0 0 0 0 0 0 0 0 0 0 1 23
24 10 132 0 0 0 0 0 0 0 0 0 0 0 24
25 9 849 1 0 0 0 0 0 0 0 0 0 0 25
26 9 172 0 1 0 0 0 0 0 0 0 0 0 26
27 10 313 0 0 1 0 0 0 0 0 0 0 0 27
28 9 819 0 0 0 1 0 0 0 0 0 0 0 28
29 9 955 0 0 0 0 1 0 0 0 0 0 0 29
30 10 48 0 0 0 0 0 1 0 0 0 0 0 30
31 10 82 0 0 0 0 0 0 1 0 0 0 0 31
32 10 541 0 0 0 0 0 0 0 1 0 0 0 32
33 10 208 0 0 0 0 0 0 0 0 1 0 0 33
34 10 233 0 0 0 0 0 0 0 0 0 1 0 34
35 9 439 0 0 0 0 0 0 0 0 0 0 1 35
36 9 963 0 0 0 0 0 0 0 0 0 0 0 36
37 10 158 1 0 0 0 0 0 0 0 0 0 0 37
38 9 225 0 1 0 0 0 0 0 0 0 0 0 38
39 10 474 0 0 1 0 0 0 0 0 0 0 0 39
40 9 757 0 0 0 1 0 0 0 0 0 0 0 40
41 10 490 0 0 0 0 1 0 0 0 0 0 0 41
42 10 281 0 0 0 0 0 1 0 0 0 0 0 42
43 10 444 0 0 0 0 0 0 1 0 0 0 0 43
44 10 640 0 0 0 0 0 0 0 1 0 0 0 44
45 10 695 0 0 0 0 0 0 0 0 1 0 0 45
46 10 786 0 0 0 0 0 0 0 0 0 1 0 46
47 9 832 0 0 0 0 0 0 0 0 0 0 1 47
48 9 747 0 0 0 0 0 0 0 0 0 0 0 48
49 10 411 1 0 0 0 0 0 0 0 0 0 0 49
50 9 511 0 1 0 0 0 0 0 0 0 0 0 50
51 10 402 0 0 1 0 0 0 0 0 0 0 0 51
52 9 701 0 0 0 1 0 0 0 0 0 0 0 52
53 10 540 0 0 0 0 1 0 0 0 0 0 0 53
54 10 112 0 0 0 0 0 1 0 0 0 0 0 54
55 10 915 0 0 0 0 0 0 1 0 0 0 0 55
56 11 183 0 0 0 0 0 0 0 1 0 0 0 56
57 10 384 0 0 0 0 0 0 0 0 1 0 0 57
58 10 834 0 0 0 0 0 0 0 0 0 1 0 58
59 9 886 0 0 0 0 0 0 0 0 0 0 1 59
60 10 216 0 0 0 0 0 0 0 0 0 0 0 60
61 10 943 1 0 0 0 0 0 0 0 0 0 0 61
62 9 867 0 1 0 0 0 0 0 0 0 0 0 62
63 10 203 0 0 1 0 0 0 0 0 0 0 0 63
64 10 837 0 0 0 1 0 0 0 0 0 0 0 64
65 10 573 0 0 0 0 1 0 0 0 0 0 0 65
66 10 647 0 0 0 0 0 1 0 0 0 0 0 66
67 11 502 0 0 0 0 0 0 1 0 0 0 0 67
68 10 656 0 0 0 0 0 0 0 1 0 0 0 68
69 10 866 0 0 0 0 0 0 0 0 1 0 0 69
70 10 835 0 0 0 0 0 0 0 0 0 1 0 70
71 9 945 0 0 0 0 0 0 0 0 0 0 1 71
72 10 331 0 0 0 0 0 0 0 0 0 0 0 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) births M1 M2 M3 M4
9.22660 -0.00089 0.30184 -0.57881 0.24198 -0.03989
M5 M6 M7 M8 M9 M10
0.10513 0.17069 0.69094 0.51722 0.33868 0.34884
M11 t
-0.45342 0.01758
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.48644 -0.14829 -0.00496 0.14446 0.46891
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.2266026 0.1304840 70.711 < 2e-16 ***
births -0.0008900 0.0001199 -7.425 5.65e-10 ***
M1 0.3018397 0.1450288 2.081 0.041837 *
M2 -0.5788123 0.1443381 -4.010 0.000176 ***
M3 0.2419826 0.1441670 1.678 0.098633 .
M4 -0.0398859 0.1453526 -0.274 0.784746
M5 0.1051251 0.1436122 0.732 0.467112
M6 0.1706938 0.1440800 1.185 0.240962
M7 0.6909387 0.1436485 4.810 1.11e-05 ***
M8 0.5172222 0.1435212 3.604 0.000652 ***
M9 0.3386788 0.1442699 2.348 0.022332 *
M10 0.3488365 0.1446890 2.411 0.019100 *
M11 -0.4534230 0.1456954 -3.112 0.002882 **
t 0.0175820 0.0014347 12.254 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2481 on 58 degrees of freedom
Multiple R-squared: 0.8626, Adjusted R-squared: 0.8318
F-statistic: 28.01 on 13 and 58 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.56813974 0.86372051 0.4318603
[2,] 0.46648563 0.93297126 0.5335144
[3,] 0.32981246 0.65962492 0.6701875
[4,] 0.42862292 0.85724584 0.5713771
[5,] 0.31192986 0.62385973 0.6880701
[6,] 0.23810316 0.47620633 0.7618968
[7,] 0.18730271 0.37460541 0.8126973
[8,] 0.17322041 0.34644081 0.8267796
[9,] 0.18197358 0.36394715 0.8180264
[10,] 0.12656370 0.25312739 0.8734363
[11,] 0.15616135 0.31232269 0.8438387
[12,] 0.10545012 0.21090023 0.8945499
[13,] 0.17456669 0.34913337 0.8254333
[14,] 0.12734312 0.25468624 0.8726569
[15,] 0.37041906 0.74083812 0.6295809
[16,] 0.33781219 0.67562438 0.6621878
[17,] 0.26131697 0.52263394 0.7386830
[18,] 0.20909955 0.41819911 0.7909004
[19,] 0.15604387 0.31208774 0.8439561
[20,] 0.15144112 0.30288225 0.8485589
[21,] 0.11157548 0.22315096 0.8884245
[22,] 0.08728706 0.17457413 0.9127129
[23,] 0.08972965 0.17945930 0.9102703
[24,] 0.08659696 0.17319391 0.9134030
[25,] 0.13645179 0.27290358 0.8635482
[26,] 0.10243116 0.20486232 0.8975688
[27,] 0.12509234 0.25018467 0.8749077
[28,] 0.08594537 0.17189075 0.9140546
[29,] 0.08395682 0.16791364 0.9160432
[30,] 0.09155460 0.18310921 0.9084454
[31,] 0.07472270 0.14944540 0.9252773
[32,] 0.10748299 0.21496597 0.8925170
[33,] 0.07803802 0.15607604 0.9219620
[34,] 0.04928441 0.09856881 0.9507156
[35,] 0.05037952 0.10075905 0.9496205
[36,] 0.28344917 0.56689833 0.7165508
[37,] 0.21159809 0.42319617 0.7884019
[38,] 0.31152124 0.62304248 0.6884788
[39,] 0.23446133 0.46892267 0.7655387
> postscript(file="/var/wessaorg/rcomp/tmp/1moww1322500135.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2237l1322500135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3260i1322500135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4gvo51322500135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5xf7a1322500135.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 = 72
Frequency = 1
1 2 3 4 5 6
0.055646155 -0.111545433 -0.163533171 0.285882601 -0.157964661 -0.104048492
7 8 9 10 11 12
0.243308779 -0.396736434 -0.010593760 -0.220792737 -0.145070464 0.226386466
13 14 15 16 17 18
-0.019161508 0.217316710 -0.036300604 -0.075519573 -0.486435167 0.100617996
19 20 21 22 23 24
0.143579854 0.090553731 -0.057810026 -0.212826208 0.204262716 0.468914238
25 26 27 28 29 30
-0.212345164 0.048164271 0.335283703 0.049932905 0.008386012 0.117964366
31 32 33 34 35 36
-0.389601017 0.175064135 0.039640400 0.034151789 0.002178609 -0.002442870
37 38 39 40 41 42
-0.038350862 -0.115647899 0.267596401 -0.216234447 0.383530499 0.114360309
43 44 45 46 47 48
-0.278389261 0.052194039 0.262107790 0.315362153 0.140981762 -0.405677162
49 50 51 52 53 54
-0.024154019 -0.072079567 -0.007471402 -0.477061529 0.217048194 -0.247041865
55 56 57 58 59 60
-0.070162593 0.434458886 -0.225680784 0.147099758 -0.021940362 -0.089275649
61 62 63 64 65 66
0.238365399 0.033791918 -0.395574927 0.433000043 0.035435124 0.018147687
67 68 69 70 71 72
0.351264237 -0.355534356 -0.007663620 -0.062994755 -0.180412261 -0.197905024
> postscript(file="/var/wessaorg/rcomp/tmp/6wsay1322500135.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 = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 0.055646155 NA
1 -0.111545433 0.055646155
2 -0.163533171 -0.111545433
3 0.285882601 -0.163533171
4 -0.157964661 0.285882601
5 -0.104048492 -0.157964661
6 0.243308779 -0.104048492
7 -0.396736434 0.243308779
8 -0.010593760 -0.396736434
9 -0.220792737 -0.010593760
10 -0.145070464 -0.220792737
11 0.226386466 -0.145070464
12 -0.019161508 0.226386466
13 0.217316710 -0.019161508
14 -0.036300604 0.217316710
15 -0.075519573 -0.036300604
16 -0.486435167 -0.075519573
17 0.100617996 -0.486435167
18 0.143579854 0.100617996
19 0.090553731 0.143579854
20 -0.057810026 0.090553731
21 -0.212826208 -0.057810026
22 0.204262716 -0.212826208
23 0.468914238 0.204262716
24 -0.212345164 0.468914238
25 0.048164271 -0.212345164
26 0.335283703 0.048164271
27 0.049932905 0.335283703
28 0.008386012 0.049932905
29 0.117964366 0.008386012
30 -0.389601017 0.117964366
31 0.175064135 -0.389601017
32 0.039640400 0.175064135
33 0.034151789 0.039640400
34 0.002178609 0.034151789
35 -0.002442870 0.002178609
36 -0.038350862 -0.002442870
37 -0.115647899 -0.038350862
38 0.267596401 -0.115647899
39 -0.216234447 0.267596401
40 0.383530499 -0.216234447
41 0.114360309 0.383530499
42 -0.278389261 0.114360309
43 0.052194039 -0.278389261
44 0.262107790 0.052194039
45 0.315362153 0.262107790
46 0.140981762 0.315362153
47 -0.405677162 0.140981762
48 -0.024154019 -0.405677162
49 -0.072079567 -0.024154019
50 -0.007471402 -0.072079567
51 -0.477061529 -0.007471402
52 0.217048194 -0.477061529
53 -0.247041865 0.217048194
54 -0.070162593 -0.247041865
55 0.434458886 -0.070162593
56 -0.225680784 0.434458886
57 0.147099758 -0.225680784
58 -0.021940362 0.147099758
59 -0.089275649 -0.021940362
60 0.238365399 -0.089275649
61 0.033791918 0.238365399
62 -0.395574927 0.033791918
63 0.433000043 -0.395574927
64 0.035435124 0.433000043
65 0.018147687 0.035435124
66 0.351264237 0.018147687
67 -0.355534356 0.351264237
68 -0.007663620 -0.355534356
69 -0.062994755 -0.007663620
70 -0.180412261 -0.062994755
71 -0.197905024 -0.180412261
72 NA -0.197905024
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.111545433 0.055646155
[2,] -0.163533171 -0.111545433
[3,] 0.285882601 -0.163533171
[4,] -0.157964661 0.285882601
[5,] -0.104048492 -0.157964661
[6,] 0.243308779 -0.104048492
[7,] -0.396736434 0.243308779
[8,] -0.010593760 -0.396736434
[9,] -0.220792737 -0.010593760
[10,] -0.145070464 -0.220792737
[11,] 0.226386466 -0.145070464
[12,] -0.019161508 0.226386466
[13,] 0.217316710 -0.019161508
[14,] -0.036300604 0.217316710
[15,] -0.075519573 -0.036300604
[16,] -0.486435167 -0.075519573
[17,] 0.100617996 -0.486435167
[18,] 0.143579854 0.100617996
[19,] 0.090553731 0.143579854
[20,] -0.057810026 0.090553731
[21,] -0.212826208 -0.057810026
[22,] 0.204262716 -0.212826208
[23,] 0.468914238 0.204262716
[24,] -0.212345164 0.468914238
[25,] 0.048164271 -0.212345164
[26,] 0.335283703 0.048164271
[27,] 0.049932905 0.335283703
[28,] 0.008386012 0.049932905
[29,] 0.117964366 0.008386012
[30,] -0.389601017 0.117964366
[31,] 0.175064135 -0.389601017
[32,] 0.039640400 0.175064135
[33,] 0.034151789 0.039640400
[34,] 0.002178609 0.034151789
[35,] -0.002442870 0.002178609
[36,] -0.038350862 -0.002442870
[37,] -0.115647899 -0.038350862
[38,] 0.267596401 -0.115647899
[39,] -0.216234447 0.267596401
[40,] 0.383530499 -0.216234447
[41,] 0.114360309 0.383530499
[42,] -0.278389261 0.114360309
[43,] 0.052194039 -0.278389261
[44,] 0.262107790 0.052194039
[45,] 0.315362153 0.262107790
[46,] 0.140981762 0.315362153
[47,] -0.405677162 0.140981762
[48,] -0.024154019 -0.405677162
[49,] -0.072079567 -0.024154019
[50,] -0.007471402 -0.072079567
[51,] -0.477061529 -0.007471402
[52,] 0.217048194 -0.477061529
[53,] -0.247041865 0.217048194
[54,] -0.070162593 -0.247041865
[55,] 0.434458886 -0.070162593
[56,] -0.225680784 0.434458886
[57,] 0.147099758 -0.225680784
[58,] -0.021940362 0.147099758
[59,] -0.089275649 -0.021940362
[60,] 0.238365399 -0.089275649
[61,] 0.033791918 0.238365399
[62,] -0.395574927 0.033791918
[63,] 0.433000043 -0.395574927
[64,] 0.035435124 0.433000043
[65,] 0.018147687 0.035435124
[66,] 0.351264237 0.018147687
[67,] -0.355534356 0.351264237
[68,] -0.007663620 -0.355534356
[69,] -0.062994755 -0.007663620
[70,] -0.180412261 -0.062994755
[71,] -0.197905024 -0.180412261
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.111545433 0.055646155
2 -0.163533171 -0.111545433
3 0.285882601 -0.163533171
4 -0.157964661 0.285882601
5 -0.104048492 -0.157964661
6 0.243308779 -0.104048492
7 -0.396736434 0.243308779
8 -0.010593760 -0.396736434
9 -0.220792737 -0.010593760
10 -0.145070464 -0.220792737
11 0.226386466 -0.145070464
12 -0.019161508 0.226386466
13 0.217316710 -0.019161508
14 -0.036300604 0.217316710
15 -0.075519573 -0.036300604
16 -0.486435167 -0.075519573
17 0.100617996 -0.486435167
18 0.143579854 0.100617996
19 0.090553731 0.143579854
20 -0.057810026 0.090553731
21 -0.212826208 -0.057810026
22 0.204262716 -0.212826208
23 0.468914238 0.204262716
24 -0.212345164 0.468914238
25 0.048164271 -0.212345164
26 0.335283703 0.048164271
27 0.049932905 0.335283703
28 0.008386012 0.049932905
29 0.117964366 0.008386012
30 -0.389601017 0.117964366
31 0.175064135 -0.389601017
32 0.039640400 0.175064135
33 0.034151789 0.039640400
34 0.002178609 0.034151789
35 -0.002442870 0.002178609
36 -0.038350862 -0.002442870
37 -0.115647899 -0.038350862
38 0.267596401 -0.115647899
39 -0.216234447 0.267596401
40 0.383530499 -0.216234447
41 0.114360309 0.383530499
42 -0.278389261 0.114360309
43 0.052194039 -0.278389261
44 0.262107790 0.052194039
45 0.315362153 0.262107790
46 0.140981762 0.315362153
47 -0.405677162 0.140981762
48 -0.024154019 -0.405677162
49 -0.072079567 -0.024154019
50 -0.007471402 -0.072079567
51 -0.477061529 -0.007471402
52 0.217048194 -0.477061529
53 -0.247041865 0.217048194
54 -0.070162593 -0.247041865
55 0.434458886 -0.070162593
56 -0.225680784 0.434458886
57 0.147099758 -0.225680784
58 -0.021940362 0.147099758
59 -0.089275649 -0.021940362
60 0.238365399 -0.089275649
61 0.033791918 0.238365399
62 -0.395574927 0.033791918
63 0.433000043 -0.395574927
64 0.035435124 0.433000043
65 0.018147687 0.035435124
66 0.351264237 0.018147687
67 -0.355534356 0.351264237
68 -0.007663620 -0.355534356
69 -0.062994755 -0.007663620
70 -0.180412261 -0.062994755
71 -0.197905024 -0.180412261
> 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/wessaorg/rcomp/tmp/75jkm1322500135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8am9x1322500135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9a5uc1322500135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1091yb1322500135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/117zuo1322500136.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/wessaorg/rcomp/tmp/12uitr1322500136.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/wessaorg/rcomp/tmp/13x02u1322500136.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/wessaorg/rcomp/tmp/14tdsy1322500136.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/wessaorg/rcomp/tmp/15wdk71322500136.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/wessaorg/rcomp/tmp/16zu591322500136.tab")
+ }
>
> try(system("convert tmp/1moww1322500135.ps tmp/1moww1322500135.png",intern=TRUE))
character(0)
> try(system("convert tmp/2237l1322500135.ps tmp/2237l1322500135.png",intern=TRUE))
character(0)
> try(system("convert tmp/3260i1322500135.ps tmp/3260i1322500135.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gvo51322500135.ps tmp/4gvo51322500135.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xf7a1322500135.ps tmp/5xf7a1322500135.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wsay1322500135.ps tmp/6wsay1322500135.png",intern=TRUE))
character(0)
> try(system("convert tmp/75jkm1322500135.ps tmp/75jkm1322500135.png",intern=TRUE))
character(0)
> try(system("convert tmp/8am9x1322500135.ps tmp/8am9x1322500135.png",intern=TRUE))
character(0)
> try(system("convert tmp/9a5uc1322500135.ps tmp/9a5uc1322500135.png",intern=TRUE))
character(0)
> try(system("convert tmp/1091yb1322500135.ps tmp/1091yb1322500135.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.285 0.491 3.800