R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1
+ ,1
+ ,4
+ ,0
+ ,2
+ ,1
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+ ,0
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+ ,0
+ ,0
+ ,2
+ ,1
+ ,2
+ ,0
+ ,1
+ ,1
+ ,0
+ ,0)
+ ,dim=c(5
+ ,105)
+ ,dimnames=list(c('pre'
+ ,'post1'
+ ,'post2'
+ ,'post3'
+ ,'post4')
+ ,1:105))
> y <- array(NA,dim=c(5,105),dimnames=list(c('pre','post1','post2','post3','post4'),1:105))
> 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'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
pre post1 post2 post3 post4
1 1 1 4 0 2.0
2 1 1 0 0 2.0
3 0 1 4 1 1.5
4 0 0 0 0 0.0
5 1 1 0 1 1.0
6 1 1 0 1 2.0
7 1 1 0 1 2.0
8 0 1 0 1 1.0
9 0 1 4 1 2.0
10 1 1 1 0 2.0
11 0 0 4 0 2.0
12 0 1 0 1 0.0
13 0 1 2 1 0.0
14 0 1 0 0 2.0
15 0 0 0 0 0.0
16 1 1 0 1 2.0
17 1 1 1 0 2.0
18 1 1 0 1 0.5
19 0 1 0 1 2.0
20 0 0 2 1 0.0
21 1 1 2 1 2.0
22 1 1 1 0 0.0
23 0 0 2 0 0.0
24 1 0 0 0 0.0
25 1 1 3 1 2.0
26 1 0 0 1 0.0
27 1 1 0 0 0.0
28 0 0 0 0 0.0
29 0 0 1 0 2.0
30 1 1 0 1 1.0
31 1 0 0 0 0.5
32 1 1 4 0 2.0
33 0 0 0 1 0.5
34 0 0 1 0 0.0
35 0 0 0 1 0.5
36 1 1 0 0 0.0
37 1 1 4 0 2.0
38 0 1 1 1 0.0
39 0 1 0 1 1.0
40 1 1 4 1 2.0
41 1 1 0 1 1.0
42 1 1 4 1 2.0
43 1 1 0 0 0.0
44 1 1 0 1 0.5
45 0 0 0 1 0.0
46 0 1 4 1 2.0
47 0 1 0 0 0.0
48 1 1 0 0 1.0
49 1 1 4 1 2.0
50 0 0 4 0 0.5
51 0 1 0 1 2.0
52 1 1 1 1 2.0
53 0 1 0 1 2.0
54 0 0 4 0 0.0
55 0 1 0 0 0.0
56 0 1 2 1 0.0
57 0 1 0 1 0.5
58 0 1 4 0 0.0
59 0 0 4 0 2.0
60 0 0 0 0 0.0
61 0 1 0 1 0.0
62 1 1 4 1 2.0
63 1 1 0 1 1.0
64 1 0 0 1 0.0
65 0 0 2 1 2.0
66 0 1 0 0 1.0
67 0 1 0 1 2.0
68 0 0 0 0 0.0
69 1 1 4 1 1.0
70 1 1 4 1 2.0
71 0 1 2 0 0.0
72 0 1 0 0 0.0
73 0 1 0 0 0.0
74 0 1 4 0 0.0
75 1 1 0 1 2.0
76 1 0 0 1 2.0
77 0 0 1 1 2.0
78 1 1 2 1 2.0
79 1 0 0 1 2.0
80 1 1 2 1 2.0
81 0 0 0 1 2.0
82 0 0 4 1 2.0
83 0 0 4 1 2.0
84 1 0 0 1 2.0
85 0 0 0 0 0.0
86 0 0 4 1 2.0
87 1 0 0 0 0.0
88 1 1 4 1 2.0
89 0 0 2 1 2.0
90 0 0 2 0 0.0
91 1 1 0 0 0.0
92 1 1 0 1 2.0
93 1 1 4 0 0.0
94 0 1 0 1 2.0
95 1 1 0 1 2.0
96 1 1 0 1 2.0
97 1 1 4 1 2.0
98 1 1 4 1 2.0
99 0 0 0 0 0.0
100 0 0 0 0 0.0
101 1 1 2 0 0.0
102 0 0 1 1 2.0
103 0 0 0 0 0.0
104 0 0 2 1 2.0
105 0 1 1 0 0.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) post1 post2 post3 post4
0.14340 0.36663 -0.02393 -0.04585 0.13841
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.78685 -0.39628 -0.09554 0.35471 0.90245
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.14340 0.09216 1.556 0.1229
post1 0.36663 0.09493 3.862 0.0002 ***
post2 -0.02393 0.02867 -0.835 0.4059
post3 -0.04585 0.10583 -0.433 0.6658
post4 0.13841 0.05949 2.327 0.0220 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4577 on 100 degrees of freedom
Multiple R-squared: 0.1985, Adjusted R-squared: 0.1664
F-statistic: 6.191 on 4 and 100 DF, p-value: 0.0001714
> 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,] 0.6235076 0.7529849 0.37649244
[2,] 0.5518955 0.8962091 0.44810453
[3,] 0.4165335 0.8330670 0.58346650
[4,] 0.2940744 0.5881488 0.70592560
[5,] 0.2071615 0.4143231 0.79283845
[6,] 0.1476386 0.2952772 0.85236141
[7,] 0.5750424 0.8499152 0.42495758
[8,] 0.4787009 0.9574017 0.52129914
[9,] 0.4076275 0.8152550 0.59237252
[10,] 0.3384677 0.6769355 0.66153226
[11,] 0.4061636 0.8123272 0.59383640
[12,] 0.5463131 0.9073737 0.45368687
[13,] 0.5003447 0.9993105 0.49965527
[14,] 0.4882789 0.9765577 0.51172113
[15,] 0.5057323 0.9885354 0.49426772
[16,] 0.4309332 0.8618664 0.56906680
[17,] 0.5787194 0.8425613 0.42128063
[18,] 0.5905768 0.8188465 0.40942324
[19,] 0.7315197 0.5369607 0.26848034
[20,] 0.7131017 0.5737966 0.28689830
[21,] 0.6814652 0.6370695 0.31853477
[22,] 0.6626539 0.6746921 0.33734606
[23,] 0.6353674 0.7292653 0.36463263
[24,] 0.7072398 0.5855203 0.29276016
[25,] 0.6912110 0.6175780 0.30878899
[26,] 0.6424508 0.7150984 0.35754922
[27,] 0.5957423 0.8085155 0.40425774
[28,] 0.5406174 0.9187651 0.45938256
[29,] 0.5182261 0.9635478 0.48177391
[30,] 0.4898943 0.9797885 0.51010573
[31,] 0.5029959 0.9940082 0.49700408
[32,] 0.5519854 0.8960292 0.44801461
[33,] 0.5513104 0.8973791 0.44868957
[34,] 0.5317890 0.9364220 0.46821100
[35,] 0.5187458 0.9625083 0.48125415
[36,] 0.5125004 0.9749992 0.48749958
[37,] 0.5028156 0.9943689 0.49718443
[38,] 0.4478634 0.8957268 0.55213658
[39,] 0.4918414 0.9836828 0.50815858
[40,] 0.5415953 0.9168093 0.45840467
[41,] 0.5315201 0.9369598 0.46847989
[42,] 0.5154692 0.9690615 0.48453077
[43,] 0.4605029 0.9210059 0.53949706
[44,] 0.5367053 0.9265893 0.46329466
[45,] 0.5074357 0.9851287 0.49256435
[46,] 0.5806067 0.8387866 0.41939328
[47,] 0.5258185 0.9483630 0.47418149
[48,] 0.5498547 0.9002906 0.45014529
[49,] 0.5666997 0.8666006 0.43330029
[50,] 0.6261299 0.7477402 0.37387009
[51,] 0.6187471 0.7625058 0.38125291
[52,] 0.5751587 0.8496825 0.42484126
[53,] 0.5223056 0.9553889 0.47769443
[54,] 0.6955605 0.6088790 0.30443950
[55,] 0.6767721 0.6464558 0.32322788
[56,] 0.6438492 0.7123015 0.35615076
[57,] 0.6708987 0.6582026 0.32910132
[58,] 0.6407419 0.7185163 0.35925813
[59,] 0.6317445 0.7365111 0.36825553
[60,] 0.7438023 0.5123955 0.25619775
[61,] 0.6949489 0.6101021 0.30505106
[62,] 0.6620078 0.6759844 0.33799222
[63,] 0.6294588 0.7410824 0.37054122
[64,] 0.6337663 0.7324674 0.36623368
[65,] 0.6698102 0.6603796 0.33018981
[66,] 0.7294578 0.5410844 0.27054222
[67,] 0.7527264 0.4945471 0.24727356
[68,] 0.7025651 0.5948697 0.29743486
[69,] 0.7716343 0.4567315 0.22836575
[70,] 0.7412796 0.5174408 0.25872041
[71,] 0.6920180 0.6159640 0.30798199
[72,] 0.7919439 0.4161123 0.20805614
[73,] 0.7471613 0.5056773 0.25283866
[74,] 0.7064399 0.5871203 0.29356014
[75,] 0.6526999 0.6946002 0.34730010
[76,] 0.5974651 0.8050699 0.40253493
[77,] 0.7711505 0.4576990 0.22884948
[78,] 0.7079735 0.5840530 0.29202648
[79,] 0.6486861 0.7026279 0.35131395
[80,] 0.9054865 0.1890270 0.09451349
[81,] 0.8639621 0.2720758 0.13603789
[82,] 0.8107283 0.3785433 0.18927167
[83,] 0.7369795 0.5260410 0.26302049
[84,] 0.7165590 0.5668821 0.28344103
[85,] 0.6831917 0.6336165 0.31680827
[86,] 0.5962250 0.8075500 0.40377498
[87,] 0.8106084 0.3787832 0.18939162
[88,] 0.7148171 0.5703659 0.28518293
[89,] 0.8855647 0.2288705 0.11443526
[90,] 0.7645377 0.4709247 0.23546234
> postscript(file="/var/wessaorg/rcomp/tmp/19h061354876959.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/2fcg81354876959.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/32ce01354876959.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4vggc1354876959.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/529261354876959.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 105
Frequency = 1
1 2 3 4 5 6
0.30886259 0.21315323 -0.57608183 -0.14339717 0.39741260 0.25900502
7 8 9 10 11 12
0.25900502 -0.60258740 -0.64528562 0.23708057 -0.32450297 -0.46417982
13 14 15 16 17 18
-0.41632514 -0.78684677 -0.14339717 0.25900502 0.23708057 0.46661639
19 20 21 22 23 24
-0.74099498 -0.04969071 0.30685970 0.51389573 -0.09554249 0.85660283
25 26 27 28 29 30
0.33078704 0.90245461 0.48996839 -0.14339717 -0.39628499 0.39741260
31 32 33 34 35 36
0.78739904 0.30886259 -0.16674918 -0.11946983 -0.16674918 0.48996839
37 38 39 40 41 42
0.30886259 -0.44025248 -0.60258740 0.35471438 0.39741260 0.35471438
43 44 45 46 47 48
0.48996839 0.46661639 -0.09754539 -0.64528562 -0.51003161 0.35156081
49 50 51 52 53 54
0.35471438 -0.11689160 -0.74099498 0.28293236 -0.74099498 -0.04768781
55 56 57 58 59 60
-0.51003161 -0.41632514 -0.53338361 -0.41432225 -0.32450297 -0.14339717
61 62 63 64 65 66
-0.46417982 0.35471438 0.39741260 0.90245461 -0.32650587 -0.64843919
67 68 69 70 71 72
-0.74099498 -0.14339717 0.49312196 0.35471438 -0.46217693 -0.51003161
73 74 75 76 77 78
-0.51003161 -0.41432225 0.25900502 0.62563945 -0.35043321 0.30685970
79 80 81 82 83 84
0.62563945 0.30685970 -0.37436055 -0.27865118 -0.27865118 0.62563945
85 86 87 88 89 90
-0.14339717 -0.27865118 0.85660283 0.35471438 -0.32650587 -0.09554249
91 92 93 94 95 96
0.48996839 0.25900502 0.58567775 -0.74099498 0.25900502 0.25900502
97 98 99 100 101 102
0.35471438 0.35471438 -0.14339717 -0.14339717 0.53782307 -0.35043321
103 104 105
-0.14339717 -0.32650587 -0.48610427
> postscript(file="/var/wessaorg/rcomp/tmp/68hvm1354876959.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 = 105
Frequency = 1
lag(myerror, k = 1) myerror
0 0.30886259 NA
1 0.21315323 0.30886259
2 -0.57608183 0.21315323
3 -0.14339717 -0.57608183
4 0.39741260 -0.14339717
5 0.25900502 0.39741260
6 0.25900502 0.25900502
7 -0.60258740 0.25900502
8 -0.64528562 -0.60258740
9 0.23708057 -0.64528562
10 -0.32450297 0.23708057
11 -0.46417982 -0.32450297
12 -0.41632514 -0.46417982
13 -0.78684677 -0.41632514
14 -0.14339717 -0.78684677
15 0.25900502 -0.14339717
16 0.23708057 0.25900502
17 0.46661639 0.23708057
18 -0.74099498 0.46661639
19 -0.04969071 -0.74099498
20 0.30685970 -0.04969071
21 0.51389573 0.30685970
22 -0.09554249 0.51389573
23 0.85660283 -0.09554249
24 0.33078704 0.85660283
25 0.90245461 0.33078704
26 0.48996839 0.90245461
27 -0.14339717 0.48996839
28 -0.39628499 -0.14339717
29 0.39741260 -0.39628499
30 0.78739904 0.39741260
31 0.30886259 0.78739904
32 -0.16674918 0.30886259
33 -0.11946983 -0.16674918
34 -0.16674918 -0.11946983
35 0.48996839 -0.16674918
36 0.30886259 0.48996839
37 -0.44025248 0.30886259
38 -0.60258740 -0.44025248
39 0.35471438 -0.60258740
40 0.39741260 0.35471438
41 0.35471438 0.39741260
42 0.48996839 0.35471438
43 0.46661639 0.48996839
44 -0.09754539 0.46661639
45 -0.64528562 -0.09754539
46 -0.51003161 -0.64528562
47 0.35156081 -0.51003161
48 0.35471438 0.35156081
49 -0.11689160 0.35471438
50 -0.74099498 -0.11689160
51 0.28293236 -0.74099498
52 -0.74099498 0.28293236
53 -0.04768781 -0.74099498
54 -0.51003161 -0.04768781
55 -0.41632514 -0.51003161
56 -0.53338361 -0.41632514
57 -0.41432225 -0.53338361
58 -0.32450297 -0.41432225
59 -0.14339717 -0.32450297
60 -0.46417982 -0.14339717
61 0.35471438 -0.46417982
62 0.39741260 0.35471438
63 0.90245461 0.39741260
64 -0.32650587 0.90245461
65 -0.64843919 -0.32650587
66 -0.74099498 -0.64843919
67 -0.14339717 -0.74099498
68 0.49312196 -0.14339717
69 0.35471438 0.49312196
70 -0.46217693 0.35471438
71 -0.51003161 -0.46217693
72 -0.51003161 -0.51003161
73 -0.41432225 -0.51003161
74 0.25900502 -0.41432225
75 0.62563945 0.25900502
76 -0.35043321 0.62563945
77 0.30685970 -0.35043321
78 0.62563945 0.30685970
79 0.30685970 0.62563945
80 -0.37436055 0.30685970
81 -0.27865118 -0.37436055
82 -0.27865118 -0.27865118
83 0.62563945 -0.27865118
84 -0.14339717 0.62563945
85 -0.27865118 -0.14339717
86 0.85660283 -0.27865118
87 0.35471438 0.85660283
88 -0.32650587 0.35471438
89 -0.09554249 -0.32650587
90 0.48996839 -0.09554249
91 0.25900502 0.48996839
92 0.58567775 0.25900502
93 -0.74099498 0.58567775
94 0.25900502 -0.74099498
95 0.25900502 0.25900502
96 0.35471438 0.25900502
97 0.35471438 0.35471438
98 -0.14339717 0.35471438
99 -0.14339717 -0.14339717
100 0.53782307 -0.14339717
101 -0.35043321 0.53782307
102 -0.14339717 -0.35043321
103 -0.32650587 -0.14339717
104 -0.48610427 -0.32650587
105 NA -0.48610427
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.21315323 0.30886259
[2,] -0.57608183 0.21315323
[3,] -0.14339717 -0.57608183
[4,] 0.39741260 -0.14339717
[5,] 0.25900502 0.39741260
[6,] 0.25900502 0.25900502
[7,] -0.60258740 0.25900502
[8,] -0.64528562 -0.60258740
[9,] 0.23708057 -0.64528562
[10,] -0.32450297 0.23708057
[11,] -0.46417982 -0.32450297
[12,] -0.41632514 -0.46417982
[13,] -0.78684677 -0.41632514
[14,] -0.14339717 -0.78684677
[15,] 0.25900502 -0.14339717
[16,] 0.23708057 0.25900502
[17,] 0.46661639 0.23708057
[18,] -0.74099498 0.46661639
[19,] -0.04969071 -0.74099498
[20,] 0.30685970 -0.04969071
[21,] 0.51389573 0.30685970
[22,] -0.09554249 0.51389573
[23,] 0.85660283 -0.09554249
[24,] 0.33078704 0.85660283
[25,] 0.90245461 0.33078704
[26,] 0.48996839 0.90245461
[27,] -0.14339717 0.48996839
[28,] -0.39628499 -0.14339717
[29,] 0.39741260 -0.39628499
[30,] 0.78739904 0.39741260
[31,] 0.30886259 0.78739904
[32,] -0.16674918 0.30886259
[33,] -0.11946983 -0.16674918
[34,] -0.16674918 -0.11946983
[35,] 0.48996839 -0.16674918
[36,] 0.30886259 0.48996839
[37,] -0.44025248 0.30886259
[38,] -0.60258740 -0.44025248
[39,] 0.35471438 -0.60258740
[40,] 0.39741260 0.35471438
[41,] 0.35471438 0.39741260
[42,] 0.48996839 0.35471438
[43,] 0.46661639 0.48996839
[44,] -0.09754539 0.46661639
[45,] -0.64528562 -0.09754539
[46,] -0.51003161 -0.64528562
[47,] 0.35156081 -0.51003161
[48,] 0.35471438 0.35156081
[49,] -0.11689160 0.35471438
[50,] -0.74099498 -0.11689160
[51,] 0.28293236 -0.74099498
[52,] -0.74099498 0.28293236
[53,] -0.04768781 -0.74099498
[54,] -0.51003161 -0.04768781
[55,] -0.41632514 -0.51003161
[56,] -0.53338361 -0.41632514
[57,] -0.41432225 -0.53338361
[58,] -0.32450297 -0.41432225
[59,] -0.14339717 -0.32450297
[60,] -0.46417982 -0.14339717
[61,] 0.35471438 -0.46417982
[62,] 0.39741260 0.35471438
[63,] 0.90245461 0.39741260
[64,] -0.32650587 0.90245461
[65,] -0.64843919 -0.32650587
[66,] -0.74099498 -0.64843919
[67,] -0.14339717 -0.74099498
[68,] 0.49312196 -0.14339717
[69,] 0.35471438 0.49312196
[70,] -0.46217693 0.35471438
[71,] -0.51003161 -0.46217693
[72,] -0.51003161 -0.51003161
[73,] -0.41432225 -0.51003161
[74,] 0.25900502 -0.41432225
[75,] 0.62563945 0.25900502
[76,] -0.35043321 0.62563945
[77,] 0.30685970 -0.35043321
[78,] 0.62563945 0.30685970
[79,] 0.30685970 0.62563945
[80,] -0.37436055 0.30685970
[81,] -0.27865118 -0.37436055
[82,] -0.27865118 -0.27865118
[83,] 0.62563945 -0.27865118
[84,] -0.14339717 0.62563945
[85,] -0.27865118 -0.14339717
[86,] 0.85660283 -0.27865118
[87,] 0.35471438 0.85660283
[88,] -0.32650587 0.35471438
[89,] -0.09554249 -0.32650587
[90,] 0.48996839 -0.09554249
[91,] 0.25900502 0.48996839
[92,] 0.58567775 0.25900502
[93,] -0.74099498 0.58567775
[94,] 0.25900502 -0.74099498
[95,] 0.25900502 0.25900502
[96,] 0.35471438 0.25900502
[97,] 0.35471438 0.35471438
[98,] -0.14339717 0.35471438
[99,] -0.14339717 -0.14339717
[100,] 0.53782307 -0.14339717
[101,] -0.35043321 0.53782307
[102,] -0.14339717 -0.35043321
[103,] -0.32650587 -0.14339717
[104,] -0.48610427 -0.32650587
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.21315323 0.30886259
2 -0.57608183 0.21315323
3 -0.14339717 -0.57608183
4 0.39741260 -0.14339717
5 0.25900502 0.39741260
6 0.25900502 0.25900502
7 -0.60258740 0.25900502
8 -0.64528562 -0.60258740
9 0.23708057 -0.64528562
10 -0.32450297 0.23708057
11 -0.46417982 -0.32450297
12 -0.41632514 -0.46417982
13 -0.78684677 -0.41632514
14 -0.14339717 -0.78684677
15 0.25900502 -0.14339717
16 0.23708057 0.25900502
17 0.46661639 0.23708057
18 -0.74099498 0.46661639
19 -0.04969071 -0.74099498
20 0.30685970 -0.04969071
21 0.51389573 0.30685970
22 -0.09554249 0.51389573
23 0.85660283 -0.09554249
24 0.33078704 0.85660283
25 0.90245461 0.33078704
26 0.48996839 0.90245461
27 -0.14339717 0.48996839
28 -0.39628499 -0.14339717
29 0.39741260 -0.39628499
30 0.78739904 0.39741260
31 0.30886259 0.78739904
32 -0.16674918 0.30886259
33 -0.11946983 -0.16674918
34 -0.16674918 -0.11946983
35 0.48996839 -0.16674918
36 0.30886259 0.48996839
37 -0.44025248 0.30886259
38 -0.60258740 -0.44025248
39 0.35471438 -0.60258740
40 0.39741260 0.35471438
41 0.35471438 0.39741260
42 0.48996839 0.35471438
43 0.46661639 0.48996839
44 -0.09754539 0.46661639
45 -0.64528562 -0.09754539
46 -0.51003161 -0.64528562
47 0.35156081 -0.51003161
48 0.35471438 0.35156081
49 -0.11689160 0.35471438
50 -0.74099498 -0.11689160
51 0.28293236 -0.74099498
52 -0.74099498 0.28293236
53 -0.04768781 -0.74099498
54 -0.51003161 -0.04768781
55 -0.41632514 -0.51003161
56 -0.53338361 -0.41632514
57 -0.41432225 -0.53338361
58 -0.32450297 -0.41432225
59 -0.14339717 -0.32450297
60 -0.46417982 -0.14339717
61 0.35471438 -0.46417982
62 0.39741260 0.35471438
63 0.90245461 0.39741260
64 -0.32650587 0.90245461
65 -0.64843919 -0.32650587
66 -0.74099498 -0.64843919
67 -0.14339717 -0.74099498
68 0.49312196 -0.14339717
69 0.35471438 0.49312196
70 -0.46217693 0.35471438
71 -0.51003161 -0.46217693
72 -0.51003161 -0.51003161
73 -0.41432225 -0.51003161
74 0.25900502 -0.41432225
75 0.62563945 0.25900502
76 -0.35043321 0.62563945
77 0.30685970 -0.35043321
78 0.62563945 0.30685970
79 0.30685970 0.62563945
80 -0.37436055 0.30685970
81 -0.27865118 -0.37436055
82 -0.27865118 -0.27865118
83 0.62563945 -0.27865118
84 -0.14339717 0.62563945
85 -0.27865118 -0.14339717
86 0.85660283 -0.27865118
87 0.35471438 0.85660283
88 -0.32650587 0.35471438
89 -0.09554249 -0.32650587
90 0.48996839 -0.09554249
91 0.25900502 0.48996839
92 0.58567775 0.25900502
93 -0.74099498 0.58567775
94 0.25900502 -0.74099498
95 0.25900502 0.25900502
96 0.35471438 0.25900502
97 0.35471438 0.35471438
98 -0.14339717 0.35471438
99 -0.14339717 -0.14339717
100 0.53782307 -0.14339717
101 -0.35043321 0.53782307
102 -0.14339717 -0.35043321
103 -0.32650587 -0.14339717
104 -0.48610427 -0.32650587
> 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/7tbrx1354876959.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/82po51354876959.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/9j7391354876959.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/10rjsj1354876959.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11lmy31354876959.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12xw6s1354876959.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/138a4q1354876959.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14qsrv1354876959.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/15fmj41354876959.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/16l1sq1354876959.tab")
+ }
>
> try(system("convert tmp/19h061354876959.ps tmp/19h061354876959.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fcg81354876959.ps tmp/2fcg81354876959.png",intern=TRUE))
character(0)
> try(system("convert tmp/32ce01354876959.ps tmp/32ce01354876959.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vggc1354876959.ps tmp/4vggc1354876959.png",intern=TRUE))
character(0)
> try(system("convert tmp/529261354876959.ps tmp/529261354876959.png",intern=TRUE))
character(0)
> try(system("convert tmp/68hvm1354876959.ps tmp/68hvm1354876959.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tbrx1354876959.ps tmp/7tbrx1354876959.png",intern=TRUE))
character(0)
> try(system("convert tmp/82po51354876959.ps tmp/82po51354876959.png",intern=TRUE))
character(0)
> try(system("convert tmp/9j7391354876959.ps tmp/9j7391354876959.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rjsj1354876959.ps tmp/10rjsj1354876959.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.434 0.977 7.400