R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(24
+ ,26
+ ,38
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+ ,10
+ ,11)
+ ,dim=c(6
+ ,126)
+ ,dimnames=list(c('PS'
+ ,'O'
+ ,'CMD'
+ ,'PEC'
+ ,'happiness'
+ ,'depression')
+ ,1:126))
> y <- array(NA,dim=c(6,126),dimnames=list(c('PS','O','CMD','PEC','happiness','depression'),1:126))
> 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
PS O CMD PEC happiness depression
1 24 26 38 23 10 11
2 25 23 36 15 10 11
3 30 25 23 25 10 11
4 19 23 30 18 10 11
5 22 19 26 21 10 11
6 22 29 26 19 10 11
7 25 25 30 15 13 12
8 23 21 27 22 10 11
9 17 22 34 19 10 11
10 21 25 28 20 13 9
11 19 24 36 26 10 11
12 19 18 42 26 10 11
13 15 22 31 21 10 11
14 23 22 26 19 10 11
15 27 28 16 19 13 12
16 14 12 23 19 10 11
17 23 20 45 28 10 11
18 19 21 30 27 10 11
19 18 23 45 18 10 11
20 20 28 30 19 10 11
21 23 24 24 24 10 11
22 25 24 29 21 13 12
23 19 24 30 22 13 9
24 24 23 31 25 10 11
25 25 29 34 15 10 11
26 26 24 41 34 10 11
27 29 18 37 23 10 11
28 32 25 33 19 10 11
29 29 26 48 15 10 11
30 28 22 44 15 10 11
31 17 22 29 17 10 11
32 28 22 44 30 13 9
33 26 30 43 28 10 11
34 25 23 31 23 10 11
35 14 17 28 23 10 11
36 25 23 26 21 10 11
37 26 23 30 18 10 11
38 20 25 27 19 15 11
39 18 24 34 24 10 11
40 32 24 47 15 10 11
41 25 21 37 24 13 16
42 21 24 27 20 10 11
43 20 28 30 20 10 11
44 30 20 36 44 10 11
45 24 29 39 20 10 11
46 26 27 32 20 10 11
47 24 22 25 20 10 11
48 22 28 19 11 10 11
49 14 16 29 21 10 11
50 24 25 26 21 13 9
51 24 24 31 19 13 12
52 24 28 31 21 10 11
53 24 24 31 17 10 11
54 22 24 39 19 10 11
55 27 21 28 21 10 11
56 19 25 22 16 10 11
57 25 25 31 19 10 11
58 20 22 36 19 10 11
59 21 23 28 16 10 11
60 27 26 39 24 10 11
61 25 25 35 21 10 11
62 20 21 33 20 10 11
63 21 25 27 19 10 11
64 22 24 33 23 10 11
65 23 29 31 18 10 11
66 25 22 39 19 10 11
67 25 27 37 23 10 11
68 17 26 24 19 10 11
69 25 24 28 26 13 12
70 19 27 37 13 13 12
71 20 24 32 23 10 11
72 26 24 31 16 13 12
73 23 29 29 17 13 12
74 27 22 40 30 10 11
75 17 24 40 22 10 11
76 19 24 15 14 10 11
77 17 23 27 14 13 9
78 22 20 32 21 13 9
79 21 27 28 21 10 11
80 32 26 41 33 10 11
81 21 25 47 23 10 11
82 21 21 42 30 10 11
83 18 19 32 21 11 17
84 23 21 33 25 10 11
85 20 16 29 29 10 11
86 20 29 37 21 10 11
87 17 15 39 16 10 11
88 18 17 29 17 10 11
89 19 15 33 23 10 11
90 15 21 31 18 13 9
91 14 19 21 19 10 11
92 18 24 36 28 10 11
93 35 17 32 29 10 11
94 29 23 15 19 10 11
95 25 14 25 25 13 9
96 20 19 28 15 10 11
97 22 24 39 24 10 11
98 13 13 31 12 13 9
99 26 22 40 11 10 11
100 17 16 25 19 10 11
101 25 19 36 25 10 11
102 20 25 23 12 10 11
103 19 25 39 15 10 11
104 21 23 31 25 10 11
105 22 24 23 14 10 11
106 24 26 31 19 10 11
107 21 26 28 23 13 9
108 26 25 47 19 13 9
109 16 21 25 20 10 11
110 23 26 26 16 13 9
111 18 23 24 13 12 18
112 21 13 30 22 10 11
113 21 24 25 21 13 16
114 23 14 44 18 15 13
115 21 10 38 44 10 11
116 21 24 36 12 10 11
117 23 22 34 28 13 12
118 27 24 45 17 13 16
119 21 20 29 18 10 11
120 10 13 25 21 10 11
121 20 20 30 24 10 11
122 26 22 27 20 10 11
123 24 24 44 24 10 11
124 24 20 31 33 10 11
125 22 22 35 25 10 11
126 17 20 47 35 10 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) O CMD PEC happiness depression
2.5618 0.4012 0.1046 0.1864 0.1836 0.1132
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.1064 -2.8330 -0.3734 2.3591 13.7844
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.56179 5.07120 0.505 0.61437
O 0.40122 0.09102 4.408 2.29e-05 ***
CMD 0.10459 0.05173 2.022 0.04541 *
PEC 0.18639 0.06747 2.763 0.00664 **
happiness 0.18360 0.27042 0.679 0.49847
depression 0.11318 0.26489 0.427 0.66996
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.897 on 120 degrees of freedom
Multiple R-squared: 0.2092, Adjusted R-squared: 0.1762
F-statistic: 6.347 on 5 and 120 DF, p-value: 2.903e-05
> 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,] 0.81020034 0.37959931 0.18979966
[2,] 0.68612876 0.62774248 0.31387124
[3,] 0.68557499 0.62885001 0.31442501
[4,] 0.56949411 0.86101177 0.43050589
[5,] 0.72508945 0.54982110 0.27491055
[6,] 0.63598767 0.72802466 0.36401233
[7,] 0.56910534 0.86178933 0.43089466
[8,] 0.53296170 0.93407660 0.46703830
[9,] 0.48944929 0.97889858 0.51055071
[10,] 0.44877005 0.89754011 0.55122995
[11,] 0.39549239 0.79098479 0.60450761
[12,] 0.37558745 0.75117489 0.62441255
[13,] 0.30032054 0.60064108 0.69967946
[14,] 0.23717348 0.47434696 0.76282652
[15,] 0.19170075 0.38340149 0.80829925
[16,] 0.15407729 0.30815457 0.84592271
[17,] 0.12971318 0.25942637 0.87028682
[18,] 0.10027236 0.20054471 0.89972764
[19,] 0.37324116 0.74648232 0.62675884
[20,] 0.69711955 0.60576089 0.30288045
[21,] 0.74428665 0.51142670 0.25571335
[22,] 0.78683492 0.42633017 0.21316508
[23,] 0.79148549 0.41702902 0.20851451
[24,] 0.80319608 0.39360784 0.19680392
[25,] 0.76590727 0.46818546 0.23409273
[26,] 0.73698592 0.52602816 0.26301408
[27,] 0.77247981 0.45504039 0.22752019
[28,] 0.76238126 0.47523748 0.23761874
[29,] 0.76949769 0.46100462 0.23050231
[30,] 0.76912589 0.46174821 0.23087411
[31,] 0.80182003 0.39635994 0.19817997
[32,] 0.89998490 0.20003020 0.10001510
[33,] 0.87760164 0.24479672 0.12239836
[34,] 0.84881842 0.30236316 0.15118158
[35,] 0.85142364 0.29715271 0.14857636
[36,] 0.87958134 0.24083731 0.12041866
[37,] 0.85534241 0.28931519 0.14465759
[38,] 0.83345847 0.33308307 0.16654153
[39,] 0.82012580 0.35974840 0.17987420
[40,] 0.78436981 0.43126038 0.21563019
[41,] 0.80441785 0.39116431 0.19558215
[42,] 0.77274599 0.45450802 0.22725401
[43,] 0.73313079 0.53373842 0.26686921
[44,] 0.68842817 0.62314367 0.31157183
[45,] 0.65560940 0.68878120 0.34439060
[46,] 0.61115707 0.77768585 0.38884293
[47,] 0.68048003 0.63903994 0.31951997
[48,] 0.64427674 0.71144652 0.35572326
[49,] 0.61552521 0.76894958 0.38447479
[50,] 0.57613804 0.84772393 0.42386196
[51,] 0.52444136 0.95111728 0.47555864
[52,] 0.49336693 0.98673386 0.50663307
[53,] 0.45400398 0.90800795 0.54599602
[54,] 0.40694345 0.81388690 0.59305655
[55,] 0.36001737 0.72003474 0.63998263
[56,] 0.31501967 0.63003933 0.68498033
[57,] 0.27438761 0.54877523 0.72561239
[58,] 0.25890517 0.51781034 0.74109483
[59,] 0.22177523 0.44355045 0.77822477
[60,] 0.24359893 0.48719787 0.75640107
[61,] 0.20620683 0.41241365 0.79379317
[62,] 0.22351010 0.44702020 0.77648990
[63,] 0.20326911 0.40653822 0.79673089
[64,] 0.20008141 0.40016282 0.79991859
[65,] 0.16834943 0.33669885 0.83165057
[66,] 0.15406839 0.30813678 0.84593161
[67,] 0.20713021 0.41426041 0.79286979
[68,] 0.17086844 0.34173688 0.82913156
[69,] 0.16057979 0.32115959 0.83942021
[70,] 0.13168508 0.26337016 0.86831492
[71,] 0.11180307 0.22360614 0.88819693
[72,] 0.14826140 0.29652280 0.85173860
[73,] 0.14017525 0.28035050 0.85982475
[74,] 0.12393177 0.24786353 0.87606823
[75,] 0.11865019 0.23730038 0.88134981
[76,] 0.09483323 0.18966645 0.90516677
[77,] 0.07338434 0.14676868 0.92661566
[78,] 0.07856709 0.15713419 0.92143291
[79,] 0.06249128 0.12498255 0.93750872
[80,] 0.04728758 0.09457516 0.95271242
[81,] 0.03483564 0.06967128 0.96516436
[82,] 0.05026702 0.10053404 0.94973298
[83,] 0.06198370 0.12396740 0.93801630
[84,] 0.08548351 0.17096702 0.91451649
[85,] 0.61621712 0.76756576 0.38378288
[86,] 0.84256902 0.31486195 0.15743098
[87,] 0.91356495 0.17287009 0.08643505
[88,] 0.88667795 0.22664409 0.11332205
[89,] 0.85799209 0.28401581 0.14200791
[90,] 0.87956477 0.24087045 0.12043523
[91,] 0.89227869 0.21544263 0.10772131
[92,] 0.85715062 0.28569876 0.14284938
[93,] 0.86809390 0.26381221 0.13190610
[94,] 0.82280063 0.35439873 0.17719937
[95,] 0.82267255 0.35465491 0.17732745
[96,] 0.76914140 0.46171719 0.23085860
[97,] 0.72199307 0.55601386 0.27800693
[98,] 0.66810362 0.66379276 0.33189638
[99,] 0.62655025 0.74689950 0.37344975
[100,] 0.54521060 0.90957879 0.45478940
[101,] 0.53565368 0.92869264 0.46434632
[102,] 0.46410910 0.92821821 0.53589090
[103,] 0.39382889 0.78765779 0.60617111
[104,] 0.39039063 0.78078126 0.60960937
[105,] 0.40890992 0.81781984 0.59109008
[106,] 0.54178234 0.91643531 0.45821766
[107,] 0.98762972 0.02474056 0.01237028
[108,] 0.98229828 0.03540344 0.01770172
[109,] 0.93947148 0.12105703 0.06052852
> postscript(file="/var/www/html/rcomp/tmp/1nm3n1292778568.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2nm3n1292778568.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3nm3n1292778568.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gdkp1292778568.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5gdkp1292778568.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 = 126
Frequency = 1
1 2 3 4 5 6
-0.33584762 3.56809007 7.26145273 -2.36352790 2.10057200 -1.53890051
7 8 9 10 11 12
2.72920345 2.00714782 -4.56704840 -1.65401413 -4.88337609 -3.10356873
13 14 15 16 17 18
-6.62605019 2.26967011 4.24424525 -2.40431670 -0.59255816 -3.23854971
19 20 21 22 23 24
-4.93237510 -3.55603539 0.74447290 2.11670393 -3.83474062 1.22718297
25 26 27 28 29 30
1.36992345 0.10258990 7.97853719 9.33386829 5.10933918 6.13259594
31 32 33 34 35 36
-3.67132809 3.01236580 -1.39562227 2.59995420 -5.67893011 3.49567450
37 38 39 40 41 42
4.63647210 -2.95659249 -5.30142523 9.01637775 1.47179385 -0.82375407
43 44 45 46 47 48
-3.74242101 4.36658029 -1.08495370 2.44962374 3.18787431 1.08553750
49 50 51 52 53 54
-5.00952432 1.36877988 1.28029554 -0.03339644 2.31704353 -0.89244621
55 56 57 58 59 60
6.08894362 -1.95648690 2.54304792 -1.77622802 0.21842296 2.37317695
61 62 63 64 65 66
1.75191743 -1.24761983 -1.03859282 -1.01044980 -0.87546396 2.91000253
67 68 69 70 71 72
0.36751782 -5.12604776 1.28936566 -4.43260276 -2.90585999 3.83945239
73 74 75 76 77 78
-1.14387547 2.75517093 -6.55619288 -0.45036260 -3.62866187 0.74736286
79 80 81 82 83 84
-2.31840262 5.48652677 -3.87593157 -3.05278432 -3.38962857 0.82045209
85 86 87 88 89 90
-0.50060926 -5.06215969 -1.72226999 -0.66520622 -0.39943043 -5.99011484
91 92 93 94 95 96
-5.00370770 -6.25614733 13.78439693 9.01893369 6.14129534 1.00970608
97 98 99 100 101 102
-1.82437430 -3.66200615 5.29649766 -1.21839383 3.30913139 -0.31553425
103 104 105 106 107 108
-3.54812812 -1.77281703 1.71291889 1.14182355 -2.61439536 1.54516502
109 110 111 112 113 114
-4.41090132 0.89948359 -2.96349951 2.90317337 -1.91764465 2.63888050
115 116 117 118 119 120
-0.83035560 -0.27397745 -0.90849571 2.73610154 0.94473504 -7.38749194
121 122 123 124 125 126
-1.27816848 4.97869468 -0.34732337 0.93977115 -0.78995192 -8.10643711
> postscript(file="/var/www/html/rcomp/tmp/6gdkp1292778568.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 = 126
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.33584762 NA
1 3.56809007 -0.33584762
2 7.26145273 3.56809007
3 -2.36352790 7.26145273
4 2.10057200 -2.36352790
5 -1.53890051 2.10057200
6 2.72920345 -1.53890051
7 2.00714782 2.72920345
8 -4.56704840 2.00714782
9 -1.65401413 -4.56704840
10 -4.88337609 -1.65401413
11 -3.10356873 -4.88337609
12 -6.62605019 -3.10356873
13 2.26967011 -6.62605019
14 4.24424525 2.26967011
15 -2.40431670 4.24424525
16 -0.59255816 -2.40431670
17 -3.23854971 -0.59255816
18 -4.93237510 -3.23854971
19 -3.55603539 -4.93237510
20 0.74447290 -3.55603539
21 2.11670393 0.74447290
22 -3.83474062 2.11670393
23 1.22718297 -3.83474062
24 1.36992345 1.22718297
25 0.10258990 1.36992345
26 7.97853719 0.10258990
27 9.33386829 7.97853719
28 5.10933918 9.33386829
29 6.13259594 5.10933918
30 -3.67132809 6.13259594
31 3.01236580 -3.67132809
32 -1.39562227 3.01236580
33 2.59995420 -1.39562227
34 -5.67893011 2.59995420
35 3.49567450 -5.67893011
36 4.63647210 3.49567450
37 -2.95659249 4.63647210
38 -5.30142523 -2.95659249
39 9.01637775 -5.30142523
40 1.47179385 9.01637775
41 -0.82375407 1.47179385
42 -3.74242101 -0.82375407
43 4.36658029 -3.74242101
44 -1.08495370 4.36658029
45 2.44962374 -1.08495370
46 3.18787431 2.44962374
47 1.08553750 3.18787431
48 -5.00952432 1.08553750
49 1.36877988 -5.00952432
50 1.28029554 1.36877988
51 -0.03339644 1.28029554
52 2.31704353 -0.03339644
53 -0.89244621 2.31704353
54 6.08894362 -0.89244621
55 -1.95648690 6.08894362
56 2.54304792 -1.95648690
57 -1.77622802 2.54304792
58 0.21842296 -1.77622802
59 2.37317695 0.21842296
60 1.75191743 2.37317695
61 -1.24761983 1.75191743
62 -1.03859282 -1.24761983
63 -1.01044980 -1.03859282
64 -0.87546396 -1.01044980
65 2.91000253 -0.87546396
66 0.36751782 2.91000253
67 -5.12604776 0.36751782
68 1.28936566 -5.12604776
69 -4.43260276 1.28936566
70 -2.90585999 -4.43260276
71 3.83945239 -2.90585999
72 -1.14387547 3.83945239
73 2.75517093 -1.14387547
74 -6.55619288 2.75517093
75 -0.45036260 -6.55619288
76 -3.62866187 -0.45036260
77 0.74736286 -3.62866187
78 -2.31840262 0.74736286
79 5.48652677 -2.31840262
80 -3.87593157 5.48652677
81 -3.05278432 -3.87593157
82 -3.38962857 -3.05278432
83 0.82045209 -3.38962857
84 -0.50060926 0.82045209
85 -5.06215969 -0.50060926
86 -1.72226999 -5.06215969
87 -0.66520622 -1.72226999
88 -0.39943043 -0.66520622
89 -5.99011484 -0.39943043
90 -5.00370770 -5.99011484
91 -6.25614733 -5.00370770
92 13.78439693 -6.25614733
93 9.01893369 13.78439693
94 6.14129534 9.01893369
95 1.00970608 6.14129534
96 -1.82437430 1.00970608
97 -3.66200615 -1.82437430
98 5.29649766 -3.66200615
99 -1.21839383 5.29649766
100 3.30913139 -1.21839383
101 -0.31553425 3.30913139
102 -3.54812812 -0.31553425
103 -1.77281703 -3.54812812
104 1.71291889 -1.77281703
105 1.14182355 1.71291889
106 -2.61439536 1.14182355
107 1.54516502 -2.61439536
108 -4.41090132 1.54516502
109 0.89948359 -4.41090132
110 -2.96349951 0.89948359
111 2.90317337 -2.96349951
112 -1.91764465 2.90317337
113 2.63888050 -1.91764465
114 -0.83035560 2.63888050
115 -0.27397745 -0.83035560
116 -0.90849571 -0.27397745
117 2.73610154 -0.90849571
118 0.94473504 2.73610154
119 -7.38749194 0.94473504
120 -1.27816848 -7.38749194
121 4.97869468 -1.27816848
122 -0.34732337 4.97869468
123 0.93977115 -0.34732337
124 -0.78995192 0.93977115
125 -8.10643711 -0.78995192
126 NA -8.10643711
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.56809007 -0.33584762
[2,] 7.26145273 3.56809007
[3,] -2.36352790 7.26145273
[4,] 2.10057200 -2.36352790
[5,] -1.53890051 2.10057200
[6,] 2.72920345 -1.53890051
[7,] 2.00714782 2.72920345
[8,] -4.56704840 2.00714782
[9,] -1.65401413 -4.56704840
[10,] -4.88337609 -1.65401413
[11,] -3.10356873 -4.88337609
[12,] -6.62605019 -3.10356873
[13,] 2.26967011 -6.62605019
[14,] 4.24424525 2.26967011
[15,] -2.40431670 4.24424525
[16,] -0.59255816 -2.40431670
[17,] -3.23854971 -0.59255816
[18,] -4.93237510 -3.23854971
[19,] -3.55603539 -4.93237510
[20,] 0.74447290 -3.55603539
[21,] 2.11670393 0.74447290
[22,] -3.83474062 2.11670393
[23,] 1.22718297 -3.83474062
[24,] 1.36992345 1.22718297
[25,] 0.10258990 1.36992345
[26,] 7.97853719 0.10258990
[27,] 9.33386829 7.97853719
[28,] 5.10933918 9.33386829
[29,] 6.13259594 5.10933918
[30,] -3.67132809 6.13259594
[31,] 3.01236580 -3.67132809
[32,] -1.39562227 3.01236580
[33,] 2.59995420 -1.39562227
[34,] -5.67893011 2.59995420
[35,] 3.49567450 -5.67893011
[36,] 4.63647210 3.49567450
[37,] -2.95659249 4.63647210
[38,] -5.30142523 -2.95659249
[39,] 9.01637775 -5.30142523
[40,] 1.47179385 9.01637775
[41,] -0.82375407 1.47179385
[42,] -3.74242101 -0.82375407
[43,] 4.36658029 -3.74242101
[44,] -1.08495370 4.36658029
[45,] 2.44962374 -1.08495370
[46,] 3.18787431 2.44962374
[47,] 1.08553750 3.18787431
[48,] -5.00952432 1.08553750
[49,] 1.36877988 -5.00952432
[50,] 1.28029554 1.36877988
[51,] -0.03339644 1.28029554
[52,] 2.31704353 -0.03339644
[53,] -0.89244621 2.31704353
[54,] 6.08894362 -0.89244621
[55,] -1.95648690 6.08894362
[56,] 2.54304792 -1.95648690
[57,] -1.77622802 2.54304792
[58,] 0.21842296 -1.77622802
[59,] 2.37317695 0.21842296
[60,] 1.75191743 2.37317695
[61,] -1.24761983 1.75191743
[62,] -1.03859282 -1.24761983
[63,] -1.01044980 -1.03859282
[64,] -0.87546396 -1.01044980
[65,] 2.91000253 -0.87546396
[66,] 0.36751782 2.91000253
[67,] -5.12604776 0.36751782
[68,] 1.28936566 -5.12604776
[69,] -4.43260276 1.28936566
[70,] -2.90585999 -4.43260276
[71,] 3.83945239 -2.90585999
[72,] -1.14387547 3.83945239
[73,] 2.75517093 -1.14387547
[74,] -6.55619288 2.75517093
[75,] -0.45036260 -6.55619288
[76,] -3.62866187 -0.45036260
[77,] 0.74736286 -3.62866187
[78,] -2.31840262 0.74736286
[79,] 5.48652677 -2.31840262
[80,] -3.87593157 5.48652677
[81,] -3.05278432 -3.87593157
[82,] -3.38962857 -3.05278432
[83,] 0.82045209 -3.38962857
[84,] -0.50060926 0.82045209
[85,] -5.06215969 -0.50060926
[86,] -1.72226999 -5.06215969
[87,] -0.66520622 -1.72226999
[88,] -0.39943043 -0.66520622
[89,] -5.99011484 -0.39943043
[90,] -5.00370770 -5.99011484
[91,] -6.25614733 -5.00370770
[92,] 13.78439693 -6.25614733
[93,] 9.01893369 13.78439693
[94,] 6.14129534 9.01893369
[95,] 1.00970608 6.14129534
[96,] -1.82437430 1.00970608
[97,] -3.66200615 -1.82437430
[98,] 5.29649766 -3.66200615
[99,] -1.21839383 5.29649766
[100,] 3.30913139 -1.21839383
[101,] -0.31553425 3.30913139
[102,] -3.54812812 -0.31553425
[103,] -1.77281703 -3.54812812
[104,] 1.71291889 -1.77281703
[105,] 1.14182355 1.71291889
[106,] -2.61439536 1.14182355
[107,] 1.54516502 -2.61439536
[108,] -4.41090132 1.54516502
[109,] 0.89948359 -4.41090132
[110,] -2.96349951 0.89948359
[111,] 2.90317337 -2.96349951
[112,] -1.91764465 2.90317337
[113,] 2.63888050 -1.91764465
[114,] -0.83035560 2.63888050
[115,] -0.27397745 -0.83035560
[116,] -0.90849571 -0.27397745
[117,] 2.73610154 -0.90849571
[118,] 0.94473504 2.73610154
[119,] -7.38749194 0.94473504
[120,] -1.27816848 -7.38749194
[121,] 4.97869468 -1.27816848
[122,] -0.34732337 4.97869468
[123,] 0.93977115 -0.34732337
[124,] -0.78995192 0.93977115
[125,] -8.10643711 -0.78995192
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.56809007 -0.33584762
2 7.26145273 3.56809007
3 -2.36352790 7.26145273
4 2.10057200 -2.36352790
5 -1.53890051 2.10057200
6 2.72920345 -1.53890051
7 2.00714782 2.72920345
8 -4.56704840 2.00714782
9 -1.65401413 -4.56704840
10 -4.88337609 -1.65401413
11 -3.10356873 -4.88337609
12 -6.62605019 -3.10356873
13 2.26967011 -6.62605019
14 4.24424525 2.26967011
15 -2.40431670 4.24424525
16 -0.59255816 -2.40431670
17 -3.23854971 -0.59255816
18 -4.93237510 -3.23854971
19 -3.55603539 -4.93237510
20 0.74447290 -3.55603539
21 2.11670393 0.74447290
22 -3.83474062 2.11670393
23 1.22718297 -3.83474062
24 1.36992345 1.22718297
25 0.10258990 1.36992345
26 7.97853719 0.10258990
27 9.33386829 7.97853719
28 5.10933918 9.33386829
29 6.13259594 5.10933918
30 -3.67132809 6.13259594
31 3.01236580 -3.67132809
32 -1.39562227 3.01236580
33 2.59995420 -1.39562227
34 -5.67893011 2.59995420
35 3.49567450 -5.67893011
36 4.63647210 3.49567450
37 -2.95659249 4.63647210
38 -5.30142523 -2.95659249
39 9.01637775 -5.30142523
40 1.47179385 9.01637775
41 -0.82375407 1.47179385
42 -3.74242101 -0.82375407
43 4.36658029 -3.74242101
44 -1.08495370 4.36658029
45 2.44962374 -1.08495370
46 3.18787431 2.44962374
47 1.08553750 3.18787431
48 -5.00952432 1.08553750
49 1.36877988 -5.00952432
50 1.28029554 1.36877988
51 -0.03339644 1.28029554
52 2.31704353 -0.03339644
53 -0.89244621 2.31704353
54 6.08894362 -0.89244621
55 -1.95648690 6.08894362
56 2.54304792 -1.95648690
57 -1.77622802 2.54304792
58 0.21842296 -1.77622802
59 2.37317695 0.21842296
60 1.75191743 2.37317695
61 -1.24761983 1.75191743
62 -1.03859282 -1.24761983
63 -1.01044980 -1.03859282
64 -0.87546396 -1.01044980
65 2.91000253 -0.87546396
66 0.36751782 2.91000253
67 -5.12604776 0.36751782
68 1.28936566 -5.12604776
69 -4.43260276 1.28936566
70 -2.90585999 -4.43260276
71 3.83945239 -2.90585999
72 -1.14387547 3.83945239
73 2.75517093 -1.14387547
74 -6.55619288 2.75517093
75 -0.45036260 -6.55619288
76 -3.62866187 -0.45036260
77 0.74736286 -3.62866187
78 -2.31840262 0.74736286
79 5.48652677 -2.31840262
80 -3.87593157 5.48652677
81 -3.05278432 -3.87593157
82 -3.38962857 -3.05278432
83 0.82045209 -3.38962857
84 -0.50060926 0.82045209
85 -5.06215969 -0.50060926
86 -1.72226999 -5.06215969
87 -0.66520622 -1.72226999
88 -0.39943043 -0.66520622
89 -5.99011484 -0.39943043
90 -5.00370770 -5.99011484
91 -6.25614733 -5.00370770
92 13.78439693 -6.25614733
93 9.01893369 13.78439693
94 6.14129534 9.01893369
95 1.00970608 6.14129534
96 -1.82437430 1.00970608
97 -3.66200615 -1.82437430
98 5.29649766 -3.66200615
99 -1.21839383 5.29649766
100 3.30913139 -1.21839383
101 -0.31553425 3.30913139
102 -3.54812812 -0.31553425
103 -1.77281703 -3.54812812
104 1.71291889 -1.77281703
105 1.14182355 1.71291889
106 -2.61439536 1.14182355
107 1.54516502 -2.61439536
108 -4.41090132 1.54516502
109 0.89948359 -4.41090132
110 -2.96349951 0.89948359
111 2.90317337 -2.96349951
112 -1.91764465 2.90317337
113 2.63888050 -1.91764465
114 -0.83035560 2.63888050
115 -0.27397745 -0.83035560
116 -0.90849571 -0.27397745
117 2.73610154 -0.90849571
118 0.94473504 2.73610154
119 -7.38749194 0.94473504
120 -1.27816848 -7.38749194
121 4.97869468 -1.27816848
122 -0.34732337 4.97869468
123 0.93977115 -0.34732337
124 -0.78995192 0.93977115
125 -8.10643711 -0.78995192
> 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/7q4kb1292778568.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81wje1292778568.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91wje1292778568.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101wje1292778568.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xnz41292778568.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/1216fa1292778568.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/13fyv11292778568.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/14iycp1292778568.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/153zad1292778568.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/16pzr11292778568.tab")
+ }
>
> try(system("convert tmp/1nm3n1292778568.ps tmp/1nm3n1292778568.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nm3n1292778568.ps tmp/2nm3n1292778568.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nm3n1292778568.ps tmp/3nm3n1292778568.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gdkp1292778568.ps tmp/4gdkp1292778568.png",intern=TRUE))
character(0)
> try(system("convert tmp/5gdkp1292778568.ps tmp/5gdkp1292778568.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gdkp1292778568.ps tmp/6gdkp1292778568.png",intern=TRUE))
character(0)
> try(system("convert tmp/7q4kb1292778568.ps tmp/7q4kb1292778568.png",intern=TRUE))
character(0)
> try(system("convert tmp/81wje1292778568.ps tmp/81wje1292778568.png",intern=TRUE))
character(0)
> try(system("convert tmp/91wje1292778568.ps tmp/91wje1292778568.png",intern=TRUE))
character(0)
> try(system("convert tmp/101wje1292778568.ps tmp/101wje1292778568.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.507 1.835 8.114