R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(1
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+ ,dim=c(10
+ ,145)
+ ,dimnames=list(c('G'
+ ,'Career'
+ ,'PersonalStandards'
+ ,'PeG'
+ ,'ParentalExpectations'
+ ,'PaG'
+ ,'Doubts'
+ ,'DoG'
+ ,'LeadershipPreference'
+ ,'LeaderG')
+ ,1:145))
> y <- array(NA,dim=c(10,145),dimnames=list(c('G','Career','PersonalStandards','PeG','ParentalExpectations','PaG','Doubts','DoG','LeadershipPreference','LeaderG'),1:145))
> 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 = '2'
> #'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
Career G PersonalStandards PeG ParentalExpectations PaG Doubts DoG
1 41 1 25 25 15 15 9 9
2 38 1 25 25 15 15 9 9
3 37 1 19 19 14 14 9 9
4 42 1 18 18 10 10 8 8
5 40 1 23 23 18 18 15 15
6 43 1 25 25 14 14 9 9
7 40 1 23 23 11 11 11 11
8 45 1 30 30 17 17 6 6
9 45 1 32 32 21 21 10 10
10 44 1 25 25 7 7 11 11
11 42 1 26 26 18 18 16 16
12 32 1 25 25 13 13 11 11
13 32 1 25 25 13 13 11 11
14 41 1 35 35 18 18 7 7
15 38 1 20 20 12 12 10 10
16 38 1 21 21 9 9 9 9
17 24 1 23 23 11 11 15 15
18 46 1 17 17 11 11 6 6
19 42 1 27 27 16 16 12 12
20 46 1 25 25 12 12 10 10
21 43 1 18 18 14 14 14 14
22 38 1 22 22 13 13 9 9
23 39 1 23 23 17 17 14 14
24 40 1 25 25 13 13 14 14
25 37 1 19 19 13 13 9 9
26 41 1 20 20 12 12 8 8
27 46 1 26 26 12 12 10 10
28 26 1 16 16 12 12 9 9
29 37 1 22 22 9 9 9 9
30 39 1 25 25 17 17 9 9
31 44 1 29 29 18 18 11 11
32 38 1 22 22 12 12 10 10
33 38 1 32 32 12 12 8 8
34 38 1 23 23 9 9 14 14
35 33 1 18 18 13 13 10 10
36 43 1 26 26 11 11 14 14
37 41 1 14 14 13 13 15 15
38 49 1 20 20 6 6 8 8
39 45 1 25 25 11 11 10 10
40 31 1 21 21 18 18 13 13
41 30 1 21 21 18 18 13 13
42 38 1 23 23 15 15 10 10
43 39 1 24 24 11 11 11 11
44 40 1 21 21 14 14 10 10
45 36 1 17 17 12 12 16 16
46 49 1 29 29 8 8 6 6
47 41 1 25 25 11 11 11 11
48 42 1 25 25 17 17 14 14
49 41 1 25 25 16 16 9 9
50 43 1 21 21 13 13 11 11
51 46 1 23 23 15 15 8 8
52 41 1 25 25 16 16 8 8
53 39 1 25 25 7 7 11 11
54 42 1 24 24 16 16 16 16
55 35 1 21 21 13 13 12 12
56 36 1 22 22 15 15 14 14
57 48 1 14 14 12 12 8 8
58 41 1 20 20 12 12 10 10
59 47 1 21 21 24 24 14 14
60 41 1 22 22 15 15 10 10
61 31 1 19 19 8 8 5 5
62 36 1 28 28 18 18 12 12
63 46 1 25 25 17 17 9 9
64 44 1 21 21 15 15 8 8
65 43 1 27 27 11 11 16 16
66 40 1 19 19 12 12 13 13
67 40 1 20 20 14 14 8 8
68 46 1 17 17 11 11 14 14
69 39 1 22 22 10 10 8 8
70 44 1 26 26 11 11 7 7
71 38 1 17 17 12 12 11 11
72 39 1 15 15 6 6 6 6
73 41 1 27 27 15 15 9 9
74 39 1 25 25 14 14 14 14
75 40 1 19 19 16 16 12 12
76 44 1 18 18 16 16 8 8
77 42 1 15 15 11 11 8 8
78 46 1 29 29 15 15 12 12
79 44 1 24 24 12 12 13 13
80 37 1 24 24 13 13 11 11
81 39 1 22 22 14 14 12 12
82 40 1 22 22 12 12 13 13
83 42 1 25 25 17 17 14 14
84 37 1 21 21 11 11 9 9
85 33 1 21 21 13 13 8 8
86 35 1 18 18 9 9 8 8
87 42 1 10 10 12 12 9 9
88 36 0 18 36 10 20 14 28
89 44 0 23 46 9 18 14 28
90 45 0 24 48 11 22 14 28
91 47 0 32 64 9 18 14 28
92 40 0 24 48 16 32 9 18
93 49 0 17 34 14 28 14 28
94 48 0 30 60 24 48 8 16
95 29 0 25 50 9 18 10 20
96 45 0 23 46 11 22 11 22
97 29 0 19 38 14 28 13 26
98 41 0 21 42 12 24 9 18
99 34 0 24 48 8 16 13 26
100 38 0 23 46 5 10 16 32
101 37 0 19 38 10 20 12 24
102 48 0 27 54 15 30 4 8
103 39 0 26 52 10 20 10 20
104 34 0 26 52 18 36 14 28
105 35 0 16 32 12 24 10 20
106 41 0 27 54 13 26 9 18
107 43 0 14 28 11 22 8 16
108 41 0 18 36 12 24 9 18
109 39 0 21 42 7 14 15 30
110 36 0 22 44 17 34 8 16
111 32 0 31 62 9 18 11 22
112 46 0 23 46 10 20 12 24
113 42 0 24 48 12 24 9 18
114 42 0 19 38 10 20 13 26
115 45 0 22 44 7 14 7 14
116 39 0 24 48 13 26 10 20
117 45 0 28 56 9 18 11 22
118 48 0 24 48 9 18 8 16
119 28 0 15 30 12 24 14 28
120 35 0 21 42 11 22 9 18
121 38 0 21 42 14 28 16 32
122 42 0 13 26 8 16 11 22
123 36 0 20 40 11 22 12 24
124 37 0 22 44 11 22 8 16
125 38 0 19 38 12 24 7 14
126 43 0 26 52 20 40 13 26
127 35 0 19 38 8 16 20 40
128 36 0 20 40 11 22 11 22
129 33 0 14 28 15 30 10 20
130 39 0 17 34 12 24 16 32
131 32 0 29 58 12 24 12 24
132 45 0 21 42 12 24 8 16
133 35 0 19 38 11 22 10 20
134 38 0 17 34 9 18 11 22
135 36 0 19 38 8 16 14 28
136 42 0 17 34 12 24 10 20
137 41 0 19 38 13 26 12 24
138 47 0 21 42 17 34 11 22
139 35 0 20 40 16 32 11 22
140 43 0 20 40 11 22 14 28
141 40 0 29 58 9 18 16 32
142 46 0 23 46 11 22 9 18
143 44 0 23 46 11 22 11 22
144 35 0 19 38 13 26 9 18
145 29 0 22 44 15 30 14 28
LeadershipPreference LeaderG
1 3 3
2 4 4
3 4 4
4 4 4
5 3 3
6 4 4
7 4 4
8 5 5
9 4 4
10 4 4
11 4 4
12 5 5
13 5 5
14 4 4
15 4 4
16 4 4
17 3 3
18 5 5
19 4 4
20 4 4
21 5 5
22 4 4
23 4 4
24 3 3
25 2 2
26 4 4
27 4 4
28 3 3
29 3 3
30 4 4
31 5 5
32 2 2
33 0 0
34 4 4
35 3 3
36 4 4
37 2 2
38 4 4
39 5 5
40 3 3
41 3 3
42 4 4
43 4 4
44 4 4
45 2 2
46 5 5
47 4 4
48 3 3
49 5 5
50 4 4
51 3 3
52 5 5
53 4 4
54 4 4
55 5 5
56 3 3
57 4 4
58 4 4
59 3 3
60 3 3
61 5 5
62 4 4
63 4 4
64 4 4
65 2 2
66 5 5
67 3 3
68 3 3
69 4 4
70 4 4
71 2 2
72 4 4
73 5 5
74 3 3
75 4 4
76 4 4
77 4 4
78 5 5
79 4 4
80 4 4
81 2 2
82 3 3
83 3 3
84 3 3
85 2 2
86 4 4
87 2 2
88 2 4
89 4 8
90 4 8
91 4 8
92 4 8
93 4 8
94 5 10
95 4 8
96 5 10
97 2 4
98 4 8
99 2 4
100 2 4
101 3 6
102 5 10
103 4 8
104 4 8
105 2 4
106 3 6
107 4 8
108 3 6
109 2 4
110 4 8
111 4 8
112 4 8
113 4 8
114 2 4
115 3 6
116 4 8
117 4 8
118 5 10
119 4 8
120 2 4
121 4 8
122 4 8
123 3 6
124 4 8
125 3 6
126 4 8
127 2 4
128 4 8
129 2 4
130 4 8
131 4 8
132 3 6
133 4 8
134 3 6
135 3 6
136 3 6
137 4 8
138 3 6
139 3 6
140 3 6
141 4 8
142 4 8
143 5 10
144 3 6
145 4 8
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) G PersonalStandards
35.61971 -1.39109 0.20799
PeG ParentalExpectations PaG
-0.06194 0.30477 -0.22289
Doubts DoG LeadershipPreference
-0.02800 -0.14374 -0.28608
LeaderG
1.16964
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.5627 -2.2601 0.4778 3.2208 10.1289
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 35.61971 5.25362 6.780 3.42e-10 ***
G -1.39109 6.62259 -0.210 0.834
PersonalStandards 0.20799 0.30861 0.674 0.501
PeG -0.06194 0.20972 -0.295 0.768
ParentalExpectations 0.30477 0.40508 0.752 0.453
PaG -0.22289 0.26823 -0.831 0.407
Doubts -0.02800 0.47531 -0.059 0.953
DoG -0.14374 0.31198 -0.461 0.646
LeadershipPreference -0.28608 1.41851 -0.202 0.840
LeaderG 1.16964 1.01608 1.151 0.252
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.801 on 135 degrees of freedom
Multiple R-squared: 0.1299, Adjusted R-squared: 0.07193
F-statistic: 2.24 on 9 and 135 DF, p-value: 0.02301
> 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,] 0.839064147 0.32187171 0.16093585
[2,] 0.819316656 0.36136669 0.18068334
[3,] 0.718067015 0.56386597 0.28193299
[4,] 0.613031359 0.77393728 0.38696864
[5,] 0.958907349 0.08218530 0.04109265
[6,] 0.951108018 0.09778396 0.04889198
[7,] 0.933089920 0.13382016 0.06691008
[8,] 0.945430357 0.10913929 0.05456964
[9,] 0.941539424 0.11692115 0.05846058
[10,] 0.920650535 0.15869893 0.07934946
[11,] 0.886943525 0.22611295 0.11305648
[12,] 0.862599100 0.27480180 0.13740090
[13,] 0.824005102 0.35198980 0.17599490
[14,] 0.771329326 0.45734135 0.22867067
[15,] 0.794116381 0.41176724 0.20588362
[16,] 0.937538146 0.12492371 0.06246185
[17,] 0.915264735 0.16947053 0.08473527
[18,] 0.891234188 0.21753162 0.10876581
[19,] 0.858345697 0.28330861 0.14165430
[20,] 0.825006471 0.34998706 0.17499353
[21,] 0.782055732 0.43588854 0.21794427
[22,] 0.736094026 0.52781195 0.26390597
[23,] 0.723467079 0.55306584 0.27653292
[24,] 0.697365467 0.60526907 0.30263453
[25,] 0.749599473 0.50080105 0.25040053
[26,] 0.847403346 0.30519331 0.15259665
[27,] 0.824055794 0.35188841 0.17594421
[28,] 0.853131226 0.29373755 0.14686877
[29,] 0.894992164 0.21001567 0.10500784
[30,] 0.873034148 0.25393170 0.12696585
[31,] 0.843842589 0.31231482 0.15615741
[32,] 0.809525580 0.38094884 0.19047442
[33,] 0.783549968 0.43290006 0.21645003
[34,] 0.808892739 0.38221452 0.19110726
[35,] 0.770559087 0.45888183 0.22944091
[36,] 0.757919634 0.48416073 0.24208037
[37,] 0.714183969 0.57163206 0.28581603
[38,] 0.690413827 0.61917235 0.30958617
[39,] 0.731461972 0.53707606 0.26853803
[40,] 0.687398114 0.62520377 0.31260189
[41,] 0.652277298 0.69544540 0.34772270
[42,] 0.622783869 0.75443226 0.37721613
[43,] 0.649401564 0.70119687 0.35059844
[44,] 0.632986761 0.73402648 0.36701324
[45,] 0.734241182 0.53151764 0.26575882
[46,] 0.691539148 0.61692170 0.30846085
[47,] 0.762134607 0.47573079 0.23786539
[48,] 0.723716261 0.55256748 0.27628374
[49,] 0.830483417 0.33903317 0.16951658
[50,] 0.842735085 0.31452983 0.15726491
[51,] 0.837415450 0.32516910 0.16258455
[52,] 0.819823633 0.36035273 0.18017637
[53,] 0.816022609 0.36795478 0.18397739
[54,] 0.795780563 0.40843887 0.20421944
[55,] 0.759517073 0.48096585 0.24048293
[56,] 0.796827103 0.40634579 0.20317290
[57,] 0.760899179 0.47820164 0.23910082
[58,] 0.760634410 0.47873118 0.23936559
[59,] 0.719620054 0.56075989 0.28037995
[60,] 0.679779470 0.64044106 0.32022053
[61,] 0.634327048 0.73134590 0.36567295
[62,] 0.588148664 0.82370267 0.41185134
[63,] 0.582702754 0.83459449 0.41729725
[64,] 0.546866005 0.90626799 0.45313399
[65,] 0.502569774 0.99486045 0.49743023
[66,] 0.495273605 0.99054721 0.50472640
[67,] 0.471595064 0.94319013 0.52840494
[68,] 0.432770819 0.86554164 0.56722918
[69,] 0.383473995 0.76694799 0.61652600
[70,] 0.341597927 0.68319585 0.65840207
[71,] 0.301004144 0.60200829 0.69899586
[72,] 0.262401218 0.52480244 0.73759878
[73,] 0.244941677 0.48988335 0.75505832
[74,] 0.220163346 0.44032669 0.77983665
[75,] 0.202212130 0.40442426 0.79778787
[76,] 0.167808769 0.33561754 0.83219123
[77,] 0.147698993 0.29539799 0.85230101
[78,] 0.137793745 0.27558749 0.86220625
[79,] 0.144813185 0.28962637 0.85518682
[80,] 0.117114148 0.23422830 0.88288585
[81,] 0.173809770 0.34761954 0.82619023
[82,] 0.185386718 0.37077344 0.81461328
[83,] 0.333513515 0.66702703 0.66648648
[84,] 0.309353062 0.61870612 0.69064694
[85,] 0.336011752 0.67202350 0.66398825
[86,] 0.320742174 0.64148435 0.67925783
[87,] 0.305669105 0.61133821 0.69433090
[88,] 0.262837048 0.52567410 0.73716295
[89,] 0.222678304 0.44535661 0.77732170
[90,] 0.260912127 0.52182425 0.73908787
[91,] 0.222951722 0.44590344 0.77704828
[92,] 0.248934735 0.49786947 0.75106527
[93,] 0.218679282 0.43735856 0.78132072
[94,] 0.187633757 0.37526751 0.81236624
[95,] 0.157381034 0.31476207 0.84261897
[96,] 0.130206167 0.26041233 0.86979383
[97,] 0.106764809 0.21352962 0.89323519
[98,] 0.097058170 0.19411634 0.90294183
[99,] 0.200014629 0.40002926 0.79998537
[100,] 0.203653112 0.40730622 0.79634689
[101,] 0.161646527 0.32329305 0.83835347
[102,] 0.167228840 0.33445768 0.83277116
[103,] 0.153984387 0.30796877 0.84601561
[104,] 0.121653139 0.24330628 0.87834686
[105,] 0.101913036 0.20382607 0.89808696
[106,] 0.101169061 0.20233812 0.89883094
[107,] 0.238924149 0.47784830 0.76107585
[108,] 0.192392194 0.38478439 0.80760781
[109,] 0.144473351 0.28894670 0.85552665
[110,] 0.116240321 0.23248064 0.88375968
[111,] 0.085487489 0.17097498 0.91451251
[112,] 0.067999082 0.13599816 0.93200092
[113,] 0.046431818 0.09286364 0.95356818
[114,] 0.040001594 0.08000319 0.95999841
[115,] 0.023898213 0.04779643 0.97610179
[116,] 0.017392065 0.03478413 0.98260794
[117,] 0.014272220 0.02854444 0.98572778
[118,] 0.008086737 0.01617347 0.99191326
[119,] 0.022237339 0.04447468 0.97776266
[120,] 0.010189982 0.02037996 0.98981002
> postscript(file="/var/www/html/rcomp/tmp/185un1292686850.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jfc81292686850.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jfc81292686850.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4jfc81292686850.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5t6bt1292686850.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 = 145
Frequency = 1
1 2 3 4 5 6
0.78719820 -3.09636453 -3.13823858 2.16357032 0.86411840 1.98551373
7 8 9 10 11 12
-0.13327815 1.61087363 2.56182238 3.90215230 1.71417723 -9.47267994
13 14 15 16 17 18
-9.47267994 -2.14590292 -1.94877795 -2.02092988 -14.56273382 5.00067983
19 20 21 22 23 24
1.04491086 5.32101563 2.98296699 -2.49448417 -1.10931147 0.80968170
25 26 27 28 29 30
-1.28923488 0.70773125 5.17497435 -12.65279550 -2.28340844 -2.26012103
31 32 33 34 35 36
1.53376366 -0.47373507 -0.51051326 -1.45428544 -5.85501092 2.94383421
37 38 39 40 41 42
4.47144394 9.19900077 3.51933116 -8.18728983 -9.18728983 -2.63253656
43 44 45 46 47 48
-1.27931943 -0.25857574 -0.71305626 6.49381919 0.57463929 2.48216869
49 50 51 52 53 54
-1.06180550 2.99504791 5.90753536 -1.23355090 -1.09784770 2.17001631
55 56 57 58 59 60
-5.71676941 -2.91595095 8.58397894 1.05122205 7.49318605 1.39706744
61 62 63 64 65 66
-10.21751338 -5.26488693 4.73987897 3.31605520 4.90840917 -0.17106319
67 68 69 70 71 72
0.42753746 8.14176847 -1.42059481 2.74161640 0.42821674 -0.41428362
73 74 75 76 77 78
-1.27200981 -0.27219655 0.21324112 3.67230080 2.51981591 3.95114382
79 80 81 82 83 84
3.98229312 -3.44307594 0.70599922 1.15793840 2.48216869 -2.30112366
85 86 87 88 89 90
-5.75306285 -4.75455143 5.10701492 0.58710400 3.91920277 5.11713313
91 92 93 94 95 96
6.16233119 -0.75523509 10.12885183 5.49960373 -12.51093990 2.20156652
97 98 99 100 101 102
-6.24842568 -0.06699898 -2.51499150 2.09252627 -1.18116905 3.22082284
103 104 105 106 107 108
-2.45402314 -5.06396533 -1.22462410 1.62263529 2.06517806 2.23849326
109 110 111 112 113 114
3.22725991 -4.76151505 -9.70003370 5.42924187 0.68071050 6.18751991
115 116 117 118 119 120
4.56606339 -1.86278865 3.55225682 3.88898073 -10.98498170 -2.10160916
121 122 123 124 125 126
-0.57656104 1.67269583 -2.12425229 -4.60759667 -1.47657808 3.90257463
127 128 129 130 131 132
1.11390343 -4.49294125 -2.63338961 0.47779912 -8.79331197 5.67071549
133 134 135 136 137 138
-5.72433165 -0.46947621 -1.83222176 3.63807735 1.18867005 9.32224524
139 140 141 142 143 144
-2.73467152 5.50672221 0.04559621 4.62379374 1.20156652 -3.70458998
145
-10.15061878
> postscript(file="/var/www/html/rcomp/tmp/6t6bt1292686850.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 = 145
Frequency = 1
lag(myerror, k = 1) myerror
0 0.78719820 NA
1 -3.09636453 0.78719820
2 -3.13823858 -3.09636453
3 2.16357032 -3.13823858
4 0.86411840 2.16357032
5 1.98551373 0.86411840
6 -0.13327815 1.98551373
7 1.61087363 -0.13327815
8 2.56182238 1.61087363
9 3.90215230 2.56182238
10 1.71417723 3.90215230
11 -9.47267994 1.71417723
12 -9.47267994 -9.47267994
13 -2.14590292 -9.47267994
14 -1.94877795 -2.14590292
15 -2.02092988 -1.94877795
16 -14.56273382 -2.02092988
17 5.00067983 -14.56273382
18 1.04491086 5.00067983
19 5.32101563 1.04491086
20 2.98296699 5.32101563
21 -2.49448417 2.98296699
22 -1.10931147 -2.49448417
23 0.80968170 -1.10931147
24 -1.28923488 0.80968170
25 0.70773125 -1.28923488
26 5.17497435 0.70773125
27 -12.65279550 5.17497435
28 -2.28340844 -12.65279550
29 -2.26012103 -2.28340844
30 1.53376366 -2.26012103
31 -0.47373507 1.53376366
32 -0.51051326 -0.47373507
33 -1.45428544 -0.51051326
34 -5.85501092 -1.45428544
35 2.94383421 -5.85501092
36 4.47144394 2.94383421
37 9.19900077 4.47144394
38 3.51933116 9.19900077
39 -8.18728983 3.51933116
40 -9.18728983 -8.18728983
41 -2.63253656 -9.18728983
42 -1.27931943 -2.63253656
43 -0.25857574 -1.27931943
44 -0.71305626 -0.25857574
45 6.49381919 -0.71305626
46 0.57463929 6.49381919
47 2.48216869 0.57463929
48 -1.06180550 2.48216869
49 2.99504791 -1.06180550
50 5.90753536 2.99504791
51 -1.23355090 5.90753536
52 -1.09784770 -1.23355090
53 2.17001631 -1.09784770
54 -5.71676941 2.17001631
55 -2.91595095 -5.71676941
56 8.58397894 -2.91595095
57 1.05122205 8.58397894
58 7.49318605 1.05122205
59 1.39706744 7.49318605
60 -10.21751338 1.39706744
61 -5.26488693 -10.21751338
62 4.73987897 -5.26488693
63 3.31605520 4.73987897
64 4.90840917 3.31605520
65 -0.17106319 4.90840917
66 0.42753746 -0.17106319
67 8.14176847 0.42753746
68 -1.42059481 8.14176847
69 2.74161640 -1.42059481
70 0.42821674 2.74161640
71 -0.41428362 0.42821674
72 -1.27200981 -0.41428362
73 -0.27219655 -1.27200981
74 0.21324112 -0.27219655
75 3.67230080 0.21324112
76 2.51981591 3.67230080
77 3.95114382 2.51981591
78 3.98229312 3.95114382
79 -3.44307594 3.98229312
80 0.70599922 -3.44307594
81 1.15793840 0.70599922
82 2.48216869 1.15793840
83 -2.30112366 2.48216869
84 -5.75306285 -2.30112366
85 -4.75455143 -5.75306285
86 5.10701492 -4.75455143
87 0.58710400 5.10701492
88 3.91920277 0.58710400
89 5.11713313 3.91920277
90 6.16233119 5.11713313
91 -0.75523509 6.16233119
92 10.12885183 -0.75523509
93 5.49960373 10.12885183
94 -12.51093990 5.49960373
95 2.20156652 -12.51093990
96 -6.24842568 2.20156652
97 -0.06699898 -6.24842568
98 -2.51499150 -0.06699898
99 2.09252627 -2.51499150
100 -1.18116905 2.09252627
101 3.22082284 -1.18116905
102 -2.45402314 3.22082284
103 -5.06396533 -2.45402314
104 -1.22462410 -5.06396533
105 1.62263529 -1.22462410
106 2.06517806 1.62263529
107 2.23849326 2.06517806
108 3.22725991 2.23849326
109 -4.76151505 3.22725991
110 -9.70003370 -4.76151505
111 5.42924187 -9.70003370
112 0.68071050 5.42924187
113 6.18751991 0.68071050
114 4.56606339 6.18751991
115 -1.86278865 4.56606339
116 3.55225682 -1.86278865
117 3.88898073 3.55225682
118 -10.98498170 3.88898073
119 -2.10160916 -10.98498170
120 -0.57656104 -2.10160916
121 1.67269583 -0.57656104
122 -2.12425229 1.67269583
123 -4.60759667 -2.12425229
124 -1.47657808 -4.60759667
125 3.90257463 -1.47657808
126 1.11390343 3.90257463
127 -4.49294125 1.11390343
128 -2.63338961 -4.49294125
129 0.47779912 -2.63338961
130 -8.79331197 0.47779912
131 5.67071549 -8.79331197
132 -5.72433165 5.67071549
133 -0.46947621 -5.72433165
134 -1.83222176 -0.46947621
135 3.63807735 -1.83222176
136 1.18867005 3.63807735
137 9.32224524 1.18867005
138 -2.73467152 9.32224524
139 5.50672221 -2.73467152
140 0.04559621 5.50672221
141 4.62379374 0.04559621
142 1.20156652 4.62379374
143 -3.70458998 1.20156652
144 -10.15061878 -3.70458998
145 NA -10.15061878
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.09636453 0.78719820
[2,] -3.13823858 -3.09636453
[3,] 2.16357032 -3.13823858
[4,] 0.86411840 2.16357032
[5,] 1.98551373 0.86411840
[6,] -0.13327815 1.98551373
[7,] 1.61087363 -0.13327815
[8,] 2.56182238 1.61087363
[9,] 3.90215230 2.56182238
[10,] 1.71417723 3.90215230
[11,] -9.47267994 1.71417723
[12,] -9.47267994 -9.47267994
[13,] -2.14590292 -9.47267994
[14,] -1.94877795 -2.14590292
[15,] -2.02092988 -1.94877795
[16,] -14.56273382 -2.02092988
[17,] 5.00067983 -14.56273382
[18,] 1.04491086 5.00067983
[19,] 5.32101563 1.04491086
[20,] 2.98296699 5.32101563
[21,] -2.49448417 2.98296699
[22,] -1.10931147 -2.49448417
[23,] 0.80968170 -1.10931147
[24,] -1.28923488 0.80968170
[25,] 0.70773125 -1.28923488
[26,] 5.17497435 0.70773125
[27,] -12.65279550 5.17497435
[28,] -2.28340844 -12.65279550
[29,] -2.26012103 -2.28340844
[30,] 1.53376366 -2.26012103
[31,] -0.47373507 1.53376366
[32,] -0.51051326 -0.47373507
[33,] -1.45428544 -0.51051326
[34,] -5.85501092 -1.45428544
[35,] 2.94383421 -5.85501092
[36,] 4.47144394 2.94383421
[37,] 9.19900077 4.47144394
[38,] 3.51933116 9.19900077
[39,] -8.18728983 3.51933116
[40,] -9.18728983 -8.18728983
[41,] -2.63253656 -9.18728983
[42,] -1.27931943 -2.63253656
[43,] -0.25857574 -1.27931943
[44,] -0.71305626 -0.25857574
[45,] 6.49381919 -0.71305626
[46,] 0.57463929 6.49381919
[47,] 2.48216869 0.57463929
[48,] -1.06180550 2.48216869
[49,] 2.99504791 -1.06180550
[50,] 5.90753536 2.99504791
[51,] -1.23355090 5.90753536
[52,] -1.09784770 -1.23355090
[53,] 2.17001631 -1.09784770
[54,] -5.71676941 2.17001631
[55,] -2.91595095 -5.71676941
[56,] 8.58397894 -2.91595095
[57,] 1.05122205 8.58397894
[58,] 7.49318605 1.05122205
[59,] 1.39706744 7.49318605
[60,] -10.21751338 1.39706744
[61,] -5.26488693 -10.21751338
[62,] 4.73987897 -5.26488693
[63,] 3.31605520 4.73987897
[64,] 4.90840917 3.31605520
[65,] -0.17106319 4.90840917
[66,] 0.42753746 -0.17106319
[67,] 8.14176847 0.42753746
[68,] -1.42059481 8.14176847
[69,] 2.74161640 -1.42059481
[70,] 0.42821674 2.74161640
[71,] -0.41428362 0.42821674
[72,] -1.27200981 -0.41428362
[73,] -0.27219655 -1.27200981
[74,] 0.21324112 -0.27219655
[75,] 3.67230080 0.21324112
[76,] 2.51981591 3.67230080
[77,] 3.95114382 2.51981591
[78,] 3.98229312 3.95114382
[79,] -3.44307594 3.98229312
[80,] 0.70599922 -3.44307594
[81,] 1.15793840 0.70599922
[82,] 2.48216869 1.15793840
[83,] -2.30112366 2.48216869
[84,] -5.75306285 -2.30112366
[85,] -4.75455143 -5.75306285
[86,] 5.10701492 -4.75455143
[87,] 0.58710400 5.10701492
[88,] 3.91920277 0.58710400
[89,] 5.11713313 3.91920277
[90,] 6.16233119 5.11713313
[91,] -0.75523509 6.16233119
[92,] 10.12885183 -0.75523509
[93,] 5.49960373 10.12885183
[94,] -12.51093990 5.49960373
[95,] 2.20156652 -12.51093990
[96,] -6.24842568 2.20156652
[97,] -0.06699898 -6.24842568
[98,] -2.51499150 -0.06699898
[99,] 2.09252627 -2.51499150
[100,] -1.18116905 2.09252627
[101,] 3.22082284 -1.18116905
[102,] -2.45402314 3.22082284
[103,] -5.06396533 -2.45402314
[104,] -1.22462410 -5.06396533
[105,] 1.62263529 -1.22462410
[106,] 2.06517806 1.62263529
[107,] 2.23849326 2.06517806
[108,] 3.22725991 2.23849326
[109,] -4.76151505 3.22725991
[110,] -9.70003370 -4.76151505
[111,] 5.42924187 -9.70003370
[112,] 0.68071050 5.42924187
[113,] 6.18751991 0.68071050
[114,] 4.56606339 6.18751991
[115,] -1.86278865 4.56606339
[116,] 3.55225682 -1.86278865
[117,] 3.88898073 3.55225682
[118,] -10.98498170 3.88898073
[119,] -2.10160916 -10.98498170
[120,] -0.57656104 -2.10160916
[121,] 1.67269583 -0.57656104
[122,] -2.12425229 1.67269583
[123,] -4.60759667 -2.12425229
[124,] -1.47657808 -4.60759667
[125,] 3.90257463 -1.47657808
[126,] 1.11390343 3.90257463
[127,] -4.49294125 1.11390343
[128,] -2.63338961 -4.49294125
[129,] 0.47779912 -2.63338961
[130,] -8.79331197 0.47779912
[131,] 5.67071549 -8.79331197
[132,] -5.72433165 5.67071549
[133,] -0.46947621 -5.72433165
[134,] -1.83222176 -0.46947621
[135,] 3.63807735 -1.83222176
[136,] 1.18867005 3.63807735
[137,] 9.32224524 1.18867005
[138,] -2.73467152 9.32224524
[139,] 5.50672221 -2.73467152
[140,] 0.04559621 5.50672221
[141,] 4.62379374 0.04559621
[142,] 1.20156652 4.62379374
[143,] -3.70458998 1.20156652
[144,] -10.15061878 -3.70458998
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.09636453 0.78719820
2 -3.13823858 -3.09636453
3 2.16357032 -3.13823858
4 0.86411840 2.16357032
5 1.98551373 0.86411840
6 -0.13327815 1.98551373
7 1.61087363 -0.13327815
8 2.56182238 1.61087363
9 3.90215230 2.56182238
10 1.71417723 3.90215230
11 -9.47267994 1.71417723
12 -9.47267994 -9.47267994
13 -2.14590292 -9.47267994
14 -1.94877795 -2.14590292
15 -2.02092988 -1.94877795
16 -14.56273382 -2.02092988
17 5.00067983 -14.56273382
18 1.04491086 5.00067983
19 5.32101563 1.04491086
20 2.98296699 5.32101563
21 -2.49448417 2.98296699
22 -1.10931147 -2.49448417
23 0.80968170 -1.10931147
24 -1.28923488 0.80968170
25 0.70773125 -1.28923488
26 5.17497435 0.70773125
27 -12.65279550 5.17497435
28 -2.28340844 -12.65279550
29 -2.26012103 -2.28340844
30 1.53376366 -2.26012103
31 -0.47373507 1.53376366
32 -0.51051326 -0.47373507
33 -1.45428544 -0.51051326
34 -5.85501092 -1.45428544
35 2.94383421 -5.85501092
36 4.47144394 2.94383421
37 9.19900077 4.47144394
38 3.51933116 9.19900077
39 -8.18728983 3.51933116
40 -9.18728983 -8.18728983
41 -2.63253656 -9.18728983
42 -1.27931943 -2.63253656
43 -0.25857574 -1.27931943
44 -0.71305626 -0.25857574
45 6.49381919 -0.71305626
46 0.57463929 6.49381919
47 2.48216869 0.57463929
48 -1.06180550 2.48216869
49 2.99504791 -1.06180550
50 5.90753536 2.99504791
51 -1.23355090 5.90753536
52 -1.09784770 -1.23355090
53 2.17001631 -1.09784770
54 -5.71676941 2.17001631
55 -2.91595095 -5.71676941
56 8.58397894 -2.91595095
57 1.05122205 8.58397894
58 7.49318605 1.05122205
59 1.39706744 7.49318605
60 -10.21751338 1.39706744
61 -5.26488693 -10.21751338
62 4.73987897 -5.26488693
63 3.31605520 4.73987897
64 4.90840917 3.31605520
65 -0.17106319 4.90840917
66 0.42753746 -0.17106319
67 8.14176847 0.42753746
68 -1.42059481 8.14176847
69 2.74161640 -1.42059481
70 0.42821674 2.74161640
71 -0.41428362 0.42821674
72 -1.27200981 -0.41428362
73 -0.27219655 -1.27200981
74 0.21324112 -0.27219655
75 3.67230080 0.21324112
76 2.51981591 3.67230080
77 3.95114382 2.51981591
78 3.98229312 3.95114382
79 -3.44307594 3.98229312
80 0.70599922 -3.44307594
81 1.15793840 0.70599922
82 2.48216869 1.15793840
83 -2.30112366 2.48216869
84 -5.75306285 -2.30112366
85 -4.75455143 -5.75306285
86 5.10701492 -4.75455143
87 0.58710400 5.10701492
88 3.91920277 0.58710400
89 5.11713313 3.91920277
90 6.16233119 5.11713313
91 -0.75523509 6.16233119
92 10.12885183 -0.75523509
93 5.49960373 10.12885183
94 -12.51093990 5.49960373
95 2.20156652 -12.51093990
96 -6.24842568 2.20156652
97 -0.06699898 -6.24842568
98 -2.51499150 -0.06699898
99 2.09252627 -2.51499150
100 -1.18116905 2.09252627
101 3.22082284 -1.18116905
102 -2.45402314 3.22082284
103 -5.06396533 -2.45402314
104 -1.22462410 -5.06396533
105 1.62263529 -1.22462410
106 2.06517806 1.62263529
107 2.23849326 2.06517806
108 3.22725991 2.23849326
109 -4.76151505 3.22725991
110 -9.70003370 -4.76151505
111 5.42924187 -9.70003370
112 0.68071050 5.42924187
113 6.18751991 0.68071050
114 4.56606339 6.18751991
115 -1.86278865 4.56606339
116 3.55225682 -1.86278865
117 3.88898073 3.55225682
118 -10.98498170 3.88898073
119 -2.10160916 -10.98498170
120 -0.57656104 -2.10160916
121 1.67269583 -0.57656104
122 -2.12425229 1.67269583
123 -4.60759667 -2.12425229
124 -1.47657808 -4.60759667
125 3.90257463 -1.47657808
126 1.11390343 3.90257463
127 -4.49294125 1.11390343
128 -2.63338961 -4.49294125
129 0.47779912 -2.63338961
130 -8.79331197 0.47779912
131 5.67071549 -8.79331197
132 -5.72433165 5.67071549
133 -0.46947621 -5.72433165
134 -1.83222176 -0.46947621
135 3.63807735 -1.83222176
136 1.18867005 3.63807735
137 9.32224524 1.18867005
138 -2.73467152 9.32224524
139 5.50672221 -2.73467152
140 0.04559621 5.50672221
141 4.62379374 0.04559621
142 1.20156652 4.62379374
143 -3.70458998 1.20156652
144 -10.15061878 -3.70458998
> 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/74xse1292686850.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/84xse1292686850.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9forz1292686850.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10forz1292686850.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1107qn1292686850.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/12l87b1292686850.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/13sr441292686850.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/14li3p1292686850.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/15611d1292686850.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/16a1i11292686850.tab")
+ }
>
> try(system("convert tmp/185un1292686850.ps tmp/185un1292686850.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jfc81292686850.ps tmp/2jfc81292686850.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jfc81292686850.ps tmp/3jfc81292686850.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jfc81292686850.ps tmp/4jfc81292686850.png",intern=TRUE))
character(0)
> try(system("convert tmp/5t6bt1292686850.ps tmp/5t6bt1292686850.png",intern=TRUE))
character(0)
> try(system("convert tmp/6t6bt1292686850.ps tmp/6t6bt1292686850.png",intern=TRUE))
character(0)
> try(system("convert tmp/74xse1292686850.ps tmp/74xse1292686850.png",intern=TRUE))
character(0)
> try(system("convert tmp/84xse1292686850.ps tmp/84xse1292686850.png",intern=TRUE))
character(0)
> try(system("convert tmp/9forz1292686850.ps tmp/9forz1292686850.png",intern=TRUE))
character(0)
> try(system("convert tmp/10forz1292686850.ps tmp/10forz1292686850.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.214 1.808 10.782