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)
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> x <- array(list(7
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+ ,dim=c(10
+ ,101)
+ ,dimnames=list(c('Q1'
+ ,'Q2'
+ ,'Q3'
+ ,'Q4'
+ ,'Q5'
+ ,'Q1-V'
+ ,'Q2-v'
+ ,'Q3-v'
+ ,'Q4-v'
+ ,'Q5-v')
+ ,1:101))
> y <- array(NA,dim=c(10,101),dimnames=list(c('Q1','Q2','Q3','Q4','Q5','Q1-V','Q2-v','Q3-v','Q4-v','Q5-v'),1:101))
> 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
> 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
Q1 Q2 Q3 Q4 Q5 Q1-V Q2-v Q3-v Q4-v Q5-v
1 7 7 7 7 7 4 5 6 5 6
2 5 5 5 5 5 7 7 7 7 6
3 6 5 6 4 5 5 7 7 4 7
4 5 5 6 5 6 5 6 7 6 7
5 6 7 5 6 7 5 5 6 6 6
6 6 5 6 5 7 7 7 7 6 7
7 6 3 7 7 7 3 7 7 6 7
8 6 6 6 5 6 3 5 5 4 4
9 4 5 6 4 5 6 7 6 6 7
10 6 3 6 6 6 5 7 6 5 6
11 6 7 7 7 7 6 7 6 6 6
12 3 7 7 4 7 4 4 3 4 5
13 5 6 7 6 6 4 5 5 6 7
14 5 7 7 5 7 6 6 6 5 5
15 2 4 5 2 6 5 5 7 5 5
16 3 7 7 5 7 7 7 7 7 7
17 6 7 6 6 5 6 7 6 7 5
18 6 7 6 6 5 7 6 5 6 6
19 5 3 6 5 7 5 4 6 4 5
20 7 5 6 5 6 5 7 7 6 7
21 5 5 5 6 6 2 6 7 4 7
22 5 5 3 5 1 6 6 7 6 6
23 5 7 7 5 7 1 7 7 6 6
24 5 7 6 5 6 5 7 7 6 7
25 5 6 7 5 7 6 7 6 5 4
26 6 6 7 7 6 6 7 6 5 6
27 5 7 6 5 6 6 6 6 6 6
28 5 6 6 3 6 5 5 7 6 7
29 6 5 6 5 6 6 6 7 6 7
30 4 5 6 4 5 5 6 6 6 6
31 4 3 5 6 5 6 7 7 5 6
32 6 7 7 5 7 7 7 7 6 7
33 3 6 4 4 3 4 6 2 3 3
34 6 5 5 5 6 5 7 6 7 4
35 5 5 6 5 5 3 6 5 5 6
36 6 7 7 6 6 7 7 6 6 6
37 7 6 7 5 7 7 5 6 7 5
38 4 6 6 5 6 6 6 6 6 5
39 5 7 6 5 5 6 6 5 4 6
40 4 5 4 4 5 6 7 6 7 6
41 5 6 7 5 6 5 5 6 5 4
42 3 5 7 5 7 5 6 5 5 5
43 5 5 7 5 7 4 5 5 5 5
44 6 6 5 6 5 4 3 7 4 7
45 6 7 7 6 7 6 7 5 5 5
46 4 6 5 4 5 5 6 6 6 7
47 4 5 5 4 5 4 5 5 4 6
48 6 6 6 5 5 6 6 6 6 6
49 6 6 6 6 6 4 6 7 6 6
50 5 7 6 6 6 4 2 6 2 5
51 6 7 7 6 7 4 6 7 5 6
52 4 5 5 4 7 6 7 6 5 7
53 4 3 7 6 7 3 7 7 4 7
54 5 6 6 5 7 6 6 7 6 6
55 3 6 5 4 2 5 5 6 6 6
56 6 6 7 6 6 4 5 7 6 7
57 6 6 7 6 6 7 6 6 7 5
58 4 6 6 4 6 6 6 5 5 6
59 5 7 7 5 7 5 6 4 5 5
60 5 6 5 5 5 6 7 7 7 7
61 4 6 6 6 7 6 6 6 6 6
62 6 5 6 6 6 5 6 5 5 6
63 5 6 6 6 6 5 5 5 4 5
64 4 6 5 5 5 0 0 0 0 0
65 6 6 7 5 6 0 0 0 0 0
66 5 4 7 7 7 0 0 0 0 0
67 6 6 6 6 6 0 0 0 0 0
68 5 7 7 7 7 0 0 0 0 0
69 6 7 7 6 7 0 0 0 0 0
70 5 5 4 5 5 0 0 0 0 0
71 4 5 5 4 6 0 0 0 0 0
72 6 7 7 6 7 0 0 0 0 0
73 5 7 7 3 7 0 0 0 0 0
74 5 5 6 5 7 0 0 0 0 0
75 3 5 7 5 7 0 0 0 0 0
76 5 3 0 5 7 0 0 0 0 0
77 4 6 6 5 6 0 0 0 0 0
78 5 5 6 5 5 0 0 0 0 0
79 5 4 3 3 5 0 0 0 0 0
80 7 7 7 7 7 0 0 0 0 0
81 7 7 7 6 6 0 0 0 0 0
82 5 2 6 4 6 0 0 0 0 0
83 4 6 6 4 6 0 0 0 0 0
84 6 4 6 6 6 0 0 0 0 0
85 5 7 7 5 7 0 0 0 0 0
86 5 6 7 6 6 0 0 0 0 0
87 4 2 6 5 7 0 0 0 0 0
88 5 7 7 5 5 0 0 0 0 0
89 2 7 7 2 5 0 0 0 0 0
90 7 5 7 6 7 0 0 0 0 0
91 4 6 6 5 5 0 0 0 0 0
92 5 5 7 5 7 0 0 0 0 0
93 5 6 7 6 7 0 0 0 0 0
94 7 7 5 7 5 0 0 0 0 0
95 2 6 6 6 6 0 0 0 0 0
96 4 7 7 4 7 0 0 0 0 0
97 6 6 7 6 6 0 0 0 0 0
98 5 5 6 6 5 0 0 0 0 0
99 5 5 6 5 5 0 0 0 0 0
100 4 4 5 5 7 0 0 0 0 0
101 4 4 6 5 7 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Q2 Q3 Q4 Q5 `Q1-V`
1.05598 0.09054 -0.03220 0.61857 0.04979 -0.01120
`Q2-v` `Q3-v` `Q4-v` `Q5-v`
-0.10548 0.16757 0.10915 -0.11978
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.4162 -0.4338 0.0018 0.4803 2.0979
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.05598 0.76723 1.376 0.172
Q2 0.09054 0.08765 1.033 0.304
Q3 -0.03220 0.10917 -0.295 0.769
Q4 0.61857 0.09795 6.315 9.71e-09 ***
Q5 0.04979 0.10441 0.477 0.635
`Q1-V` -0.01120 0.10503 -0.107 0.915
`Q2-v` -0.10548 0.12667 -0.833 0.407
`Q3-v` 0.16757 0.15288 1.096 0.276
`Q4-v` 0.10915 0.15350 0.711 0.479
`Q5-v` -0.11978 0.14742 -0.813 0.419
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9404 on 91 degrees of freedom
Multiple R-squared: 0.3663, Adjusted R-squared: 0.3037
F-statistic: 5.845 on 9 and 91 DF, p-value: 2.122e-06
> 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,] 0.2128979482 0.425795896 0.7871021
[2,] 0.1153008371 0.230601674 0.8846992
[3,] 0.1423839202 0.284767840 0.8576161
[4,] 0.1543249966 0.308649993 0.8456750
[5,] 0.1333583478 0.266716696 0.8666417
[6,] 0.0834959130 0.166991826 0.9165041
[7,] 0.0467115128 0.093423026 0.9532885
[8,] 0.2845040731 0.569008146 0.7154959
[9,] 0.7759346070 0.448130786 0.2240654
[10,] 0.7847753398 0.430449320 0.2152247
[11,] 0.7233462548 0.553307490 0.2766537
[12,] 0.6498025243 0.700394951 0.3501975
[13,] 0.5950471332 0.809905734 0.4049529
[14,] 0.5849158437 0.830168313 0.4150842
[15,] 0.5104888916 0.979022217 0.4895111
[16,] 0.6097516345 0.780496731 0.3902484
[17,] 0.5962471138 0.807505772 0.4037529
[18,] 0.5267970048 0.946405990 0.4732030
[19,] 0.6564948033 0.687010393 0.3435052
[20,] 0.6246432393 0.750713521 0.3753568
[21,] 0.5614689583 0.877062083 0.4385310
[22,] 0.6150412465 0.769917507 0.3849588
[23,] 0.5606098369 0.878780326 0.4393902
[24,] 0.5011894435 0.997621113 0.4988106
[25,] 0.6160939329 0.767812134 0.3839061
[26,] 0.6337413008 0.732517398 0.3662587
[27,] 0.5776807431 0.844638514 0.4223193
[28,] 0.5152379376 0.969524125 0.4847621
[29,] 0.4583748724 0.916749745 0.5416251
[30,] 0.5816327780 0.836734444 0.4183672
[31,] 0.5212658568 0.957468286 0.4787341
[32,] 0.4823920303 0.964784061 0.5176080
[33,] 0.4362883344 0.872576669 0.5637117
[34,] 0.3790672407 0.758134481 0.6209328
[35,] 0.3211354156 0.642270831 0.6788646
[36,] 0.3290666892 0.658133378 0.6709333
[37,] 0.2771331020 0.554266204 0.7228669
[38,] 0.2490893234 0.498178647 0.7509107
[39,] 0.2129051764 0.425810353 0.7870948
[40,] 0.1710591039 0.342118208 0.8289409
[41,] 0.1893970125 0.378794025 0.8106030
[42,] 0.1516414800 0.303282960 0.8483585
[43,] 0.1856285564 0.371257113 0.8143714
[44,] 0.1545390493 0.309078099 0.8454610
[45,] 0.1571800783 0.314360157 0.8428199
[46,] 0.1242724983 0.248544997 0.8757275
[47,] 0.0958498897 0.191699779 0.9041501
[48,] 0.0722337248 0.144467450 0.9277663
[49,] 0.0839219952 0.167843990 0.9160780
[50,] 0.0679775121 0.135955024 0.9320225
[51,] 0.0498201222 0.099640244 0.9501799
[52,] 0.0431339498 0.086267900 0.9568661
[53,] 0.0627754110 0.125550822 0.9372246
[54,] 0.0519075902 0.103815180 0.9480924
[55,] 0.0437744445 0.087548889 0.9562256
[56,] 0.0437630232 0.087526046 0.9562370
[57,] 0.0340833766 0.068166753 0.9659166
[58,] 0.0247416627 0.049483325 0.9752583
[59,] 0.0164469046 0.032893809 0.9835531
[60,] 0.0117411412 0.023482282 0.9882589
[61,] 0.0184989952 0.036997990 0.9815010
[62,] 0.0123383887 0.024676777 0.9876616
[63,] 0.0227022818 0.045404564 0.9772977
[64,] 0.0150814532 0.030162906 0.9849185
[65,] 0.0117581313 0.023516263 0.9882419
[66,] 0.0072218261 0.014443652 0.9927782
[67,] 0.0284870282 0.056974056 0.9715130
[68,] 0.0205414149 0.041082830 0.9794586
[69,] 0.0281648092 0.056329618 0.9718352
[70,] 0.0286034978 0.057206996 0.9713965
[71,] 0.0187693899 0.037538780 0.9812306
[72,] 0.0132867686 0.026573537 0.9867132
[73,] 0.0070016652 0.014003330 0.9929983
[74,] 0.0039471954 0.007894391 0.9960528
[75,] 0.0017312659 0.003462532 0.9982687
[76,] 0.0006064181 0.001212836 0.9993936
> postscript(file="/var/wessaorg/rcomp/tmp/19r7w1322067245.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2smr41322067245.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34ud11322067245.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4tjeo1322067245.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5b7pl1322067245.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 = 101
Frequency = 1
1 2 3 4 5 6
0.596834156 -0.091062138 1.984528302 -0.007607870 0.053047354 1.070477256
7 8 9 10 11 12
0.001838128 0.968067595 -0.055000758 0.817330007 -0.278952908 -1.160873552
13 14 15 16 17 18
-0.466065413 -0.157926083 -1.329447499 -2.187554139 0.178076191 0.480281285
19 20 21 22 23 24
0.059032656 2.097873380 -0.473682304 0.036170233 -0.265374223 -0.083214993
25 26 27 28 29 30
-0.081676180 -0.029468917 -0.129707591 1.033503680 1.003590659 -0.291456071
31 32 33 34 35 36
-1.321447541 0.921592483 -0.719643725 0.764762213 0.344290659 0.400607296
37 38 39 40 41 42
1.620042136 -1.158938938 0.305944495 -0.348330116 -0.254044028 -1.820470581
43 44 45 46 47 48
0.062849640 0.191519997 0.496552332 -0.294428324 -0.054480547 1.010629778
49 50 51 52 53 54
0.152305387 -0.875787186 0.153318242 -0.077644099 -1.161300133 -0.256522244
55 56 57 58 59 60
-1.370305563 0.198803271 0.156748075 -0.143882630 0.166006704 -0.073029319
61 62 63 64 65 66
-1.707525079 0.698326041 -0.508328307 -0.780032018 1.234582002 -0.871259794
67 68 69 70 71 72
0.583809908 -1.142892354 0.475676140 0.278308569 -0.120712519 0.475676140
73 74 75 76 77 78
1.331381620 0.243129406 -1.724666994 0.230996176 -0.797621599 0.342715770
79 80 81 82 83 84
1.573786141 0.857107646 1.525469322 1.183123641 -0.179053106 0.764898281
85 86 87 88 89 90
0.094244633 -0.383986492 -0.485238035 0.193830997 -0.950463523 1.656764513
91 92 93 94 95 96
-0.747828417 0.275333006 -0.433779674 0.892286809 -3.416190092 -0.287186873
97 98 99 100 101
0.616013508 -0.275852724 0.342715770 -0.698530008 -0.666326408
> postscript(file="/var/wessaorg/rcomp/tmp/6qcya1322067245.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 = 101
Frequency = 1
lag(myerror, k = 1) myerror
0 0.596834156 NA
1 -0.091062138 0.596834156
2 1.984528302 -0.091062138
3 -0.007607870 1.984528302
4 0.053047354 -0.007607870
5 1.070477256 0.053047354
6 0.001838128 1.070477256
7 0.968067595 0.001838128
8 -0.055000758 0.968067595
9 0.817330007 -0.055000758
10 -0.278952908 0.817330007
11 -1.160873552 -0.278952908
12 -0.466065413 -1.160873552
13 -0.157926083 -0.466065413
14 -1.329447499 -0.157926083
15 -2.187554139 -1.329447499
16 0.178076191 -2.187554139
17 0.480281285 0.178076191
18 0.059032656 0.480281285
19 2.097873380 0.059032656
20 -0.473682304 2.097873380
21 0.036170233 -0.473682304
22 -0.265374223 0.036170233
23 -0.083214993 -0.265374223
24 -0.081676180 -0.083214993
25 -0.029468917 -0.081676180
26 -0.129707591 -0.029468917
27 1.033503680 -0.129707591
28 1.003590659 1.033503680
29 -0.291456071 1.003590659
30 -1.321447541 -0.291456071
31 0.921592483 -1.321447541
32 -0.719643725 0.921592483
33 0.764762213 -0.719643725
34 0.344290659 0.764762213
35 0.400607296 0.344290659
36 1.620042136 0.400607296
37 -1.158938938 1.620042136
38 0.305944495 -1.158938938
39 -0.348330116 0.305944495
40 -0.254044028 -0.348330116
41 -1.820470581 -0.254044028
42 0.062849640 -1.820470581
43 0.191519997 0.062849640
44 0.496552332 0.191519997
45 -0.294428324 0.496552332
46 -0.054480547 -0.294428324
47 1.010629778 -0.054480547
48 0.152305387 1.010629778
49 -0.875787186 0.152305387
50 0.153318242 -0.875787186
51 -0.077644099 0.153318242
52 -1.161300133 -0.077644099
53 -0.256522244 -1.161300133
54 -1.370305563 -0.256522244
55 0.198803271 -1.370305563
56 0.156748075 0.198803271
57 -0.143882630 0.156748075
58 0.166006704 -0.143882630
59 -0.073029319 0.166006704
60 -1.707525079 -0.073029319
61 0.698326041 -1.707525079
62 -0.508328307 0.698326041
63 -0.780032018 -0.508328307
64 1.234582002 -0.780032018
65 -0.871259794 1.234582002
66 0.583809908 -0.871259794
67 -1.142892354 0.583809908
68 0.475676140 -1.142892354
69 0.278308569 0.475676140
70 -0.120712519 0.278308569
71 0.475676140 -0.120712519
72 1.331381620 0.475676140
73 0.243129406 1.331381620
74 -1.724666994 0.243129406
75 0.230996176 -1.724666994
76 -0.797621599 0.230996176
77 0.342715770 -0.797621599
78 1.573786141 0.342715770
79 0.857107646 1.573786141
80 1.525469322 0.857107646
81 1.183123641 1.525469322
82 -0.179053106 1.183123641
83 0.764898281 -0.179053106
84 0.094244633 0.764898281
85 -0.383986492 0.094244633
86 -0.485238035 -0.383986492
87 0.193830997 -0.485238035
88 -0.950463523 0.193830997
89 1.656764513 -0.950463523
90 -0.747828417 1.656764513
91 0.275333006 -0.747828417
92 -0.433779674 0.275333006
93 0.892286809 -0.433779674
94 -3.416190092 0.892286809
95 -0.287186873 -3.416190092
96 0.616013508 -0.287186873
97 -0.275852724 0.616013508
98 0.342715770 -0.275852724
99 -0.698530008 0.342715770
100 -0.666326408 -0.698530008
101 NA -0.666326408
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.091062138 0.596834156
[2,] 1.984528302 -0.091062138
[3,] -0.007607870 1.984528302
[4,] 0.053047354 -0.007607870
[5,] 1.070477256 0.053047354
[6,] 0.001838128 1.070477256
[7,] 0.968067595 0.001838128
[8,] -0.055000758 0.968067595
[9,] 0.817330007 -0.055000758
[10,] -0.278952908 0.817330007
[11,] -1.160873552 -0.278952908
[12,] -0.466065413 -1.160873552
[13,] -0.157926083 -0.466065413
[14,] -1.329447499 -0.157926083
[15,] -2.187554139 -1.329447499
[16,] 0.178076191 -2.187554139
[17,] 0.480281285 0.178076191
[18,] 0.059032656 0.480281285
[19,] 2.097873380 0.059032656
[20,] -0.473682304 2.097873380
[21,] 0.036170233 -0.473682304
[22,] -0.265374223 0.036170233
[23,] -0.083214993 -0.265374223
[24,] -0.081676180 -0.083214993
[25,] -0.029468917 -0.081676180
[26,] -0.129707591 -0.029468917
[27,] 1.033503680 -0.129707591
[28,] 1.003590659 1.033503680
[29,] -0.291456071 1.003590659
[30,] -1.321447541 -0.291456071
[31,] 0.921592483 -1.321447541
[32,] -0.719643725 0.921592483
[33,] 0.764762213 -0.719643725
[34,] 0.344290659 0.764762213
[35,] 0.400607296 0.344290659
[36,] 1.620042136 0.400607296
[37,] -1.158938938 1.620042136
[38,] 0.305944495 -1.158938938
[39,] -0.348330116 0.305944495
[40,] -0.254044028 -0.348330116
[41,] -1.820470581 -0.254044028
[42,] 0.062849640 -1.820470581
[43,] 0.191519997 0.062849640
[44,] 0.496552332 0.191519997
[45,] -0.294428324 0.496552332
[46,] -0.054480547 -0.294428324
[47,] 1.010629778 -0.054480547
[48,] 0.152305387 1.010629778
[49,] -0.875787186 0.152305387
[50,] 0.153318242 -0.875787186
[51,] -0.077644099 0.153318242
[52,] -1.161300133 -0.077644099
[53,] -0.256522244 -1.161300133
[54,] -1.370305563 -0.256522244
[55,] 0.198803271 -1.370305563
[56,] 0.156748075 0.198803271
[57,] -0.143882630 0.156748075
[58,] 0.166006704 -0.143882630
[59,] -0.073029319 0.166006704
[60,] -1.707525079 -0.073029319
[61,] 0.698326041 -1.707525079
[62,] -0.508328307 0.698326041
[63,] -0.780032018 -0.508328307
[64,] 1.234582002 -0.780032018
[65,] -0.871259794 1.234582002
[66,] 0.583809908 -0.871259794
[67,] -1.142892354 0.583809908
[68,] 0.475676140 -1.142892354
[69,] 0.278308569 0.475676140
[70,] -0.120712519 0.278308569
[71,] 0.475676140 -0.120712519
[72,] 1.331381620 0.475676140
[73,] 0.243129406 1.331381620
[74,] -1.724666994 0.243129406
[75,] 0.230996176 -1.724666994
[76,] -0.797621599 0.230996176
[77,] 0.342715770 -0.797621599
[78,] 1.573786141 0.342715770
[79,] 0.857107646 1.573786141
[80,] 1.525469322 0.857107646
[81,] 1.183123641 1.525469322
[82,] -0.179053106 1.183123641
[83,] 0.764898281 -0.179053106
[84,] 0.094244633 0.764898281
[85,] -0.383986492 0.094244633
[86,] -0.485238035 -0.383986492
[87,] 0.193830997 -0.485238035
[88,] -0.950463523 0.193830997
[89,] 1.656764513 -0.950463523
[90,] -0.747828417 1.656764513
[91,] 0.275333006 -0.747828417
[92,] -0.433779674 0.275333006
[93,] 0.892286809 -0.433779674
[94,] -3.416190092 0.892286809
[95,] -0.287186873 -3.416190092
[96,] 0.616013508 -0.287186873
[97,] -0.275852724 0.616013508
[98,] 0.342715770 -0.275852724
[99,] -0.698530008 0.342715770
[100,] -0.666326408 -0.698530008
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.091062138 0.596834156
2 1.984528302 -0.091062138
3 -0.007607870 1.984528302
4 0.053047354 -0.007607870
5 1.070477256 0.053047354
6 0.001838128 1.070477256
7 0.968067595 0.001838128
8 -0.055000758 0.968067595
9 0.817330007 -0.055000758
10 -0.278952908 0.817330007
11 -1.160873552 -0.278952908
12 -0.466065413 -1.160873552
13 -0.157926083 -0.466065413
14 -1.329447499 -0.157926083
15 -2.187554139 -1.329447499
16 0.178076191 -2.187554139
17 0.480281285 0.178076191
18 0.059032656 0.480281285
19 2.097873380 0.059032656
20 -0.473682304 2.097873380
21 0.036170233 -0.473682304
22 -0.265374223 0.036170233
23 -0.083214993 -0.265374223
24 -0.081676180 -0.083214993
25 -0.029468917 -0.081676180
26 -0.129707591 -0.029468917
27 1.033503680 -0.129707591
28 1.003590659 1.033503680
29 -0.291456071 1.003590659
30 -1.321447541 -0.291456071
31 0.921592483 -1.321447541
32 -0.719643725 0.921592483
33 0.764762213 -0.719643725
34 0.344290659 0.764762213
35 0.400607296 0.344290659
36 1.620042136 0.400607296
37 -1.158938938 1.620042136
38 0.305944495 -1.158938938
39 -0.348330116 0.305944495
40 -0.254044028 -0.348330116
41 -1.820470581 -0.254044028
42 0.062849640 -1.820470581
43 0.191519997 0.062849640
44 0.496552332 0.191519997
45 -0.294428324 0.496552332
46 -0.054480547 -0.294428324
47 1.010629778 -0.054480547
48 0.152305387 1.010629778
49 -0.875787186 0.152305387
50 0.153318242 -0.875787186
51 -0.077644099 0.153318242
52 -1.161300133 -0.077644099
53 -0.256522244 -1.161300133
54 -1.370305563 -0.256522244
55 0.198803271 -1.370305563
56 0.156748075 0.198803271
57 -0.143882630 0.156748075
58 0.166006704 -0.143882630
59 -0.073029319 0.166006704
60 -1.707525079 -0.073029319
61 0.698326041 -1.707525079
62 -0.508328307 0.698326041
63 -0.780032018 -0.508328307
64 1.234582002 -0.780032018
65 -0.871259794 1.234582002
66 0.583809908 -0.871259794
67 -1.142892354 0.583809908
68 0.475676140 -1.142892354
69 0.278308569 0.475676140
70 -0.120712519 0.278308569
71 0.475676140 -0.120712519
72 1.331381620 0.475676140
73 0.243129406 1.331381620
74 -1.724666994 0.243129406
75 0.230996176 -1.724666994
76 -0.797621599 0.230996176
77 0.342715770 -0.797621599
78 1.573786141 0.342715770
79 0.857107646 1.573786141
80 1.525469322 0.857107646
81 1.183123641 1.525469322
82 -0.179053106 1.183123641
83 0.764898281 -0.179053106
84 0.094244633 0.764898281
85 -0.383986492 0.094244633
86 -0.485238035 -0.383986492
87 0.193830997 -0.485238035
88 -0.950463523 0.193830997
89 1.656764513 -0.950463523
90 -0.747828417 1.656764513
91 0.275333006 -0.747828417
92 -0.433779674 0.275333006
93 0.892286809 -0.433779674
94 -3.416190092 0.892286809
95 -0.287186873 -3.416190092
96 0.616013508 -0.287186873
97 -0.275852724 0.616013508
98 0.342715770 -0.275852724
99 -0.698530008 0.342715770
100 -0.666326408 -0.698530008
> 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/7xyp61322067245.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8zqs91322067245.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9hz5a1322067245.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10mweq1322067245.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11rsrl1322067245.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/12b59n1322067245.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/13cy821322067245.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/14b7d91322067245.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/1552f51322067245.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/16t8ea1322067245.tab")
+ }
>
> try(system("convert tmp/19r7w1322067245.ps tmp/19r7w1322067245.png",intern=TRUE))
character(0)
> try(system("convert tmp/2smr41322067245.ps tmp/2smr41322067245.png",intern=TRUE))
character(0)
> try(system("convert tmp/34ud11322067245.ps tmp/34ud11322067245.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tjeo1322067245.ps tmp/4tjeo1322067245.png",intern=TRUE))
character(0)
> try(system("convert tmp/5b7pl1322067245.ps tmp/5b7pl1322067245.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qcya1322067245.ps tmp/6qcya1322067245.png",intern=TRUE))
character(0)
> try(system("convert tmp/7xyp61322067245.ps tmp/7xyp61322067245.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zqs91322067245.ps tmp/8zqs91322067245.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hz5a1322067245.ps tmp/9hz5a1322067245.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mweq1322067245.ps tmp/10mweq1322067245.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.928 0.511 4.476