R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(1
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+ ,2)
+ ,dim=c(10
+ ,146)
+ ,dimnames=list(c('G'
+ ,'Career'
+ ,'PersonalStandards'
+ ,'PeG'
+ ,'ParentalExpectations'
+ ,'PaG'
+ ,'Doubts'
+ ,'DoG'
+ ,'LeadershipPreference'
+ ,'LeaderG')
+ ,1:146))
> y <- array(NA,dim=c(10,146),dimnames=list(c('G','Career','PersonalStandards','PeG','ParentalExpectations','PaG','Doubts','DoG','LeadershipPreference','LeaderG'),1:146))
> 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
> 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 36 0 18 36 10 20 14 28
5 42 1 18 18 10 10 8 8
6 44 0 23 46 9 18 14 28
7 40 1 23 23 18 18 15 15
8 43 1 25 25 14 14 9 9
9 40 1 23 23 11 11 11 11
10 45 0 24 48 11 22 14 28
11 47 0 32 64 9 18 14 28
12 45 1 30 30 17 17 6 6
13 45 1 32 32 21 21 10 10
14 40 0 24 48 16 32 9 18
15 49 0 17 34 14 28 14 28
16 48 0 30 60 24 48 8 16
17 44 1 25 25 7 7 11 11
18 29 0 25 50 9 18 10 20
19 42 1 26 26 18 18 16 16
20 45 0 23 46 11 22 11 22
21 32 1 25 25 13 13 11 11
22 32 1 25 25 13 13 11 11
23 41 1 35 35 18 18 7 7
24 29 0 19 38 14 28 13 26
25 38 1 20 20 12 12 10 10
26 41 0 21 42 12 24 9 18
27 38 1 21 21 9 9 9 9
28 24 1 23 23 11 11 15 15
29 34 0 24 48 8 16 13 26
30 38 0 23 46 5 10 16 32
31 37 0 19 38 10 20 12 24
32 46 1 17 17 11 11 6 6
33 48 0 27 54 15 30 4 8
34 42 1 27 27 16 16 12 12
35 46 1 25 25 12 12 10 10
36 43 1 18 18 14 14 14 14
37 38 1 22 22 13 13 9 9
38 39 0 26 52 10 20 10 20
39 34 0 26 52 18 36 14 28
40 39 1 23 23 17 17 14 14
41 35 0 16 32 12 24 10 20
42 41 0 27 54 13 26 9 18
43 40 1 25 25 13 13 14 14
44 43 0 14 28 11 22 8 16
45 37 1 19 19 13 13 9 9
46 41 1 20 20 12 12 8 8
47 46 1 26 26 12 12 10 10
48 26 1 16 16 12 12 9 9
49 41 0 18 36 12 24 9 18
50 37 1 22 22 9 9 9 9
51 39 1 25 25 17 17 9 9
52 44 1 29 29 18 18 11 11
53 39 0 21 42 7 14 15 30
54 36 0 22 44 17 34 8 16
55 38 1 22 22 12 12 10 10
56 38 1 32 32 12 12 8 8
57 38 1 23 23 9 9 14 14
58 32 0 31 62 9 18 11 22
59 33 1 18 18 13 13 10 10
60 46 0 23 46 10 20 12 24
61 42 0 24 48 12 24 9 18
62 42 0 19 38 10 20 13 26
63 43 1 26 26 11 11 14 14
64 41 1 14 14 13 13 15 15
65 49 1 20 20 6 6 8 8
66 45 0 22 44 7 14 7 14
67 39 0 24 48 13 26 10 20
68 45 1 25 25 11 11 10 10
69 31 1 21 21 18 18 13 13
70 30 1 21 21 18 18 13 13
71 45 0 28 56 9 18 11 22
72 48 0 24 48 9 18 8 16
73 28 0 15 30 12 24 14 28
74 35 0 21 42 11 22 9 18
75 38 1 23 23 15 15 10 10
76 39 1 24 24 11 11 11 11
77 40 1 21 21 14 14 10 10
78 38 0 21 42 14 28 16 32
79 42 0 13 26 8 16 11 22
80 36 1 17 17 12 12 16 16
81 49 1 29 29 8 8 6 6
82 41 1 25 25 11 11 11 11
83 18 1 16 16 10 10 12 12
84 36 0 20 40 11 22 12 24
85 42 1 25 25 17 17 14 14
86 41 1 25 25 16 16 9 9
87 43 1 21 21 13 13 11 11
88 46 1 23 23 15 15 8 8
89 37 0 22 44 11 22 8 16
90 38 0 19 38 12 24 7 14
91 43 0 26 52 20 40 13 26
92 41 1 25 25 16 16 8 8
93 35 0 19 38 8 16 20 40
94 39 1 25 25 7 7 11 11
95 42 1 24 24 16 16 16 16
96 36 0 20 40 11 22 11 22
97 35 1 21 21 13 13 12 12
98 33 0 14 28 15 30 10 20
99 36 1 22 22 15 15 14 14
100 48 1 14 14 12 12 8 8
101 41 1 20 20 12 12 10 10
102 47 1 21 21 24 24 14 14
103 41 1 22 22 15 15 10 10
104 31 1 19 19 8 8 5 5
105 36 1 28 28 18 18 12 12
106 46 1 25 25 17 17 9 9
107 39 0 17 34 12 24 16 32
108 44 1 21 21 15 15 8 8
109 43 1 27 27 11 11 16 16
110 32 0 29 58 12 24 12 24
111 40 1 19 19 12 12 13 13
112 40 1 20 20 14 14 8 8
113 46 1 17 17 11 11 14 14
114 45 0 21 42 12 24 8 16
115 39 1 22 22 10 10 8 8
116 44 1 26 26 11 11 7 7
117 35 0 19 38 11 22 10 20
118 38 0 17 34 9 18 11 22
119 38 1 17 17 12 12 11 11
120 36 0 19 38 8 16 14 28
121 42 0 17 34 12 24 10 20
122 39 1 15 15 6 6 6 6
123 41 1 27 27 15 15 9 9
124 41 0 19 38 13 26 12 24
125 47 0 21 42 17 34 11 22
126 39 1 25 25 14 14 14 14
127 40 1 19 19 16 16 12 12
128 44 1 18 18 16 16 8 8
129 42 1 15 15 11 11 8 8
130 35 0 20 40 16 32 11 22
131 46 1 29 29 15 15 12 12
132 43 0 20 40 11 22 14 28
133 40 0 29 58 9 18 16 32
134 44 1 24 24 12 12 13 13
135 37 1 24 24 13 13 11 11
136 46 0 23 46 11 22 9 18
137 44 0 23 46 11 22 11 22
138 35 0 19 38 13 26 9 18
139 39 1 22 22 14 14 12 12
140 40 1 22 22 12 12 13 13
141 42 1 25 25 17 17 14 14
142 37 1 21 21 11 11 9 9
143 29 0 22 44 15 30 14 28
144 33 1 21 21 13 13 8 8
145 35 1 18 18 9 9 8 8
146 42 1 10 10 12 12 9 9
LeadershipPreference LeaderG
1 3 3
2 4 4
3 4 4
4 2 4
5 4 4
6 4 8
7 3 3
8 4 4
9 4 4
10 4 8
11 4 8
12 5 5
13 4 4
14 4 8
15 4 8
16 5 10
17 4 4
18 4 8
19 4 4
20 5 10
21 5 5
22 5 5
23 4 4
24 2 4
25 4 4
26 4 8
27 4 4
28 3 3
29 2 4
30 2 4
31 3 6
32 5 5
33 5 10
34 4 4
35 4 4
36 5 5
37 4 4
38 4 8
39 4 8
40 4 4
41 2 4
42 3 6
43 3 3
44 4 8
45 2 2
46 4 4
47 4 4
48 3 3
49 3 6
50 3 3
51 4 4
52 5 5
53 2 4
54 4 8
55 2 2
56 0 0
57 4 4
58 4 8
59 3 3
60 4 8
61 4 8
62 2 4
63 4 4
64 2 2
65 4 4
66 3 6
67 4 8
68 5 5
69 3 3
70 3 3
71 4 8
72 5 10
73 4 8
74 2 4
75 4 4
76 4 4
77 4 4
78 4 8
79 4 8
80 2 2
81 5 5
82 4 4
83 2 2
84 3 6
85 3 3
86 5 5
87 4 4
88 3 3
89 4 8
90 3 6
91 4 8
92 5 5
93 2 4
94 4 4
95 4 4
96 4 8
97 5 5
98 2 4
99 3 3
100 4 4
101 4 4
102 3 3
103 3 3
104 5 5
105 4 4
106 4 4
107 4 8
108 4 4
109 2 2
110 4 8
111 5 5
112 3 3
113 3 3
114 3 6
115 4 4
116 4 4
117 4 8
118 3 6
119 2 2
120 3 6
121 3 6
122 4 4
123 5 5
124 4 8
125 3 6
126 3 3
127 4 4
128 4 4
129 4 4
130 3 6
131 5 5
132 3 6
133 4 8
134 4 4
135 4 4
136 4 8
137 5 10
138 3 6
139 2 2
140 3 3
141 3 3
142 3 3
143 4 8
144 2 2
145 4 4
146 2 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) G PersonalStandards
35.61971 -4.20094 0.30303
PeG ParentalExpectations PaG
-0.10947 0.41231 -0.27666
Doubts DoG LeadershipPreference
-0.09705 -0.10922 0.35718
LeaderG
0.84801
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.8074 -2.3348 0.6335 3.2159 10.1289
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 35.61971 5.51278 6.461 1.71e-09 ***
G -4.20094 6.90674 -0.608 0.544
PersonalStandards 0.30303 0.32279 0.939 0.349
PeG -0.10947 0.21968 -0.498 0.619
ParentalExpectations 0.41231 0.42404 0.972 0.333
PaG -0.27666 0.28108 -0.984 0.327
Doubts -0.09705 0.49840 -0.195 0.846
DoG -0.10922 0.32723 -0.334 0.739
LeadershipPreference 0.35718 1.47807 0.242 0.809
LeaderG 0.84801 1.06258 0.798 0.426
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.038 on 136 degrees of freedom
Multiple R-squared: 0.1473, Adjusted R-squared: 0.09084
F-statistic: 2.61 on 9 and 136 DF, p-value: 0.008226
> 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,] 0.222726010 0.44545202 0.77727399
[2,] 0.104911851 0.20982370 0.89508815
[3,] 0.060792196 0.12158439 0.93920780
[4,] 0.032058471 0.06411694 0.96794153
[5,] 0.013898300 0.02779660 0.98610170
[6,] 0.019274163 0.03854833 0.98072584
[7,] 0.008844012 0.01768802 0.99115599
[8,] 0.009961318 0.01992264 0.99003868
[9,] 0.084306268 0.16861254 0.91569373
[10,] 0.114335458 0.22867092 0.88566454
[11,] 0.135418268 0.27083654 0.86458173
[12,] 0.169064059 0.33812812 0.83093594
[13,] 0.121934548 0.24386910 0.87806545
[14,] 0.182101601 0.36420320 0.81789840
[15,] 0.139078074 0.27815615 0.86092193
[16,] 0.621037875 0.75792425 0.37896213
[17,] 0.603488191 0.79302362 0.39651181
[18,] 0.548447137 0.90310573 0.45155286
[19,] 0.484928520 0.96985704 0.51507148
[20,] 0.480730407 0.96146081 0.51926959
[21,] 0.644187155 0.71162569 0.35581285
[22,] 0.606391146 0.78721771 0.39360885
[23,] 0.649601327 0.70079735 0.35039867
[24,] 0.646978785 0.70604243 0.35302122
[25,] 0.604631319 0.79073736 0.39536868
[26,] 0.551305693 0.89738861 0.44869431
[27,] 0.694334016 0.61133197 0.30566598
[28,] 0.642099869 0.71580026 0.35790013
[29,] 0.603125045 0.79374991 0.39687496
[30,] 0.579236464 0.84152707 0.42076354
[31,] 0.543907724 0.91218455 0.45609228
[32,] 0.494023412 0.98804682 0.50597659
[33,] 0.445665670 0.89133134 0.55433433
[34,] 0.391013165 0.78202633 0.60898684
[35,] 0.413367987 0.82673597 0.58663201
[36,] 0.643390809 0.71321838 0.35660919
[37,] 0.607278557 0.78544289 0.39272144
[38,] 0.556800572 0.88639886 0.44319943
[39,] 0.514210500 0.97157900 0.48578950
[40,] 0.461146384 0.92229277 0.53885362
[41,] 0.430619052 0.86123810 0.56938095
[42,] 0.421187631 0.84237526 0.57881237
[43,] 0.376450183 0.75290037 0.62354982
[44,] 0.328692304 0.65738461 0.67130770
[45,] 0.285399120 0.57079824 0.71460088
[46,] 0.402741648 0.80548330 0.59725835
[47,] 0.391005844 0.78201169 0.60899416
[48,] 0.383581854 0.76716371 0.61641815
[49,] 0.336759645 0.67351929 0.66324036
[50,] 0.357450874 0.71490175 0.64254913
[51,] 0.331390233 0.66278047 0.66860977
[52,] 0.374613183 0.74922637 0.62538682
[53,] 0.503479214 0.99304157 0.49652079
[54,] 0.512700162 0.97459968 0.48729984
[55,] 0.470924802 0.94184960 0.52907520
[56,] 0.438216778 0.87643356 0.56178322
[57,] 0.491932303 0.98386461 0.50806770
[58,] 0.585038803 0.82992239 0.41496120
[59,] 0.558307760 0.88338448 0.44169224
[60,] 0.544494642 0.91101072 0.45550536
[61,] 0.752888334 0.49422333 0.24711167
[62,] 0.717157980 0.56568404 0.28284202
[63,] 0.688001025 0.62399795 0.31199897
[64,] 0.644520357 0.71095929 0.35547964
[65,] 0.598468755 0.80306249 0.40153125
[66,] 0.549955830 0.90008834 0.45004417
[67,] 0.511446903 0.97710619 0.48855310
[68,] 0.467481790 0.93496358 0.53251821
[69,] 0.515445213 0.96910957 0.48455479
[70,] 0.468395888 0.93679178 0.53160411
[71,] 0.949544425 0.10091115 0.05045558
[72,] 0.937521609 0.12495678 0.06247839
[73,] 0.927163919 0.14567216 0.07283608
[74,] 0.907995974 0.18400805 0.09200403
[75,] 0.894257981 0.21148404 0.10574202
[76,] 0.911028922 0.17794216 0.08897108
[77,] 0.903151598 0.19369680 0.09684840
[78,] 0.881884245 0.23623151 0.11811576
[79,] 0.879307926 0.24138415 0.12069207
[80,] 0.851379644 0.29724071 0.14862036
[81,] 0.818352494 0.36329501 0.18164751
[82,] 0.786746659 0.42650668 0.21325334
[83,] 0.753114400 0.49377120 0.24688560
[84,] 0.741430315 0.51713937 0.25856969
[85,] 0.778747365 0.44250527 0.22125263
[86,] 0.776563553 0.44687289 0.22343645
[87,] 0.787374158 0.42525168 0.21262584
[88,] 0.854538973 0.29092205 0.14546103
[89,] 0.820445610 0.35910878 0.17955439
[90,] 0.824249053 0.35150189 0.17575095
[91,] 0.786381663 0.42723667 0.21361834
[92,] 0.861240065 0.27751987 0.13875993
[93,] 0.886066129 0.22786774 0.11393387
[94,] 0.879179725 0.24164055 0.12082028
[95,] 0.857032527 0.28593495 0.14296747
[96,] 0.838743201 0.32251360 0.16125680
[97,] 0.826690756 0.34661849 0.17330924
[98,] 0.926033626 0.14793275 0.07396637
[99,] 0.921524387 0.15695123 0.07847561
[100,] 0.896243905 0.20751219 0.10375609
[101,] 0.908689617 0.18262077 0.09131038
[102,] 0.884045430 0.23190914 0.11595457
[103,] 0.846156839 0.30768632 0.15384316
[104,] 0.871771289 0.25645742 0.12822871
[105,] 0.882927780 0.23414444 0.11707222
[106,] 0.848372807 0.30325439 0.15162719
[107,] 0.799266925 0.40146615 0.20073307
[108,] 0.767342058 0.46531588 0.23265794
[109,] 0.706325698 0.58734860 0.29367430
[110,] 0.643908261 0.71218348 0.35609174
[111,] 0.563349119 0.87330176 0.43665088
[112,] 0.511350749 0.97729850 0.48864925
[113,] 0.916317950 0.16736410 0.08368205
[114,] 0.875966169 0.24806766 0.12403383
[115,] 0.928407825 0.14318435 0.07159218
[116,] 0.881209621 0.23758076 0.11879038
[117,] 0.813574938 0.37285012 0.18642506
[118,] 0.890780348 0.21843930 0.10921965
[119,] 0.928123455 0.14375309 0.07187655
[120,] 0.963257277 0.07348545 0.03674272
[121,] 0.896272963 0.20745407 0.10372704
> postscript(file="/var/www/rcomp/tmp/1cpiq1290518064.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2cpiq1290518064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3ng0t1290518064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4ng0t1290518064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5ng0t1290518064.ps",horizontal=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 = 146
Frequency = 1
1 2 3 4 5 6
0.94823308 -3.25695789 -2.95991653 0.58710400 2.56996986 3.91920277
7 8 9 10 11 12
1.16604215 1.87868985 0.08530464 5.11713313 6.16233119 0.67991710
13 14 15 16 17 18
1.78046663 -0.75523509 10.12885183 5.49960373 4.24076440 -12.51093990
19 20 21 22 23 24
1.58642456 2.20156652 -9.77831301 -9.77831301 -3.01209751 -6.24842568
25 26 27 28 29 30
-1.67591647 -0.06699898 -1.66880903 -13.88442367 -2.51499150 2.09252627
31 32 33 34 35 36
-1.18116905 5.01015639 3.22082284 0.83907371 5.35625551 3.05980902
37 38 39 40 41 42
-2.40496560 -2.45402314 -5.06396533 -1.10977125 -1.22462410 1.62263529
43 44 45 46 47 48
1.25087947 2.06517806 -0.41388685 0.91154317 5.16268991 -11.90273327
49 50 51 52 53 54
2.23849326 -1.65718367 -2.52825337 0.76918587 3.22725991 -4.76151505
55 56 57 58 59 60
0.34733426 0.40951979 -1.02458933 -9.70003370 -5.21924203 5.42924187
61 62 63 64 65 66
0.68071050 6.18751991 3.12341838 5.79156225 9.72542961 4.56606339
67 68 69 70 71 72
-1.86278865 3.28671229 -7.85936700 -8.85936700 3.55225682 3.88898073
73 74 75 76 77 78
-10.98498170 -2.10160916 -2.66355650 -1.10826096 -0.14077755 -0.57656104
79 80 81 82 83 84
1.67269583 0.55278336 6.09431237 0.69817344 -17.80743628 -2.12425229
85 86 87 88 89 90
2.70828850 -1.59779660 3.20114037 6.12909410 -4.60759667 -1.47657808
91 92 93 94 95 96
3.90257463 -1.80406678 1.11390343 -0.75923560 2.24485124 -4.49294125
97 98 99 100 101 102
-5.79778042 -2.63338961 -2.43971920 9.07293679 1.32408353 7.53301674
103 104 105 106 107 108
1.73520007 -10.17630178 -5.62578738 4.47174663 0.47779912 3.31103435
109 110 111 112 113 114
5.75277507 -8.79331197 -0.06873129 0.84543866 9.07069978 5.67071549
115 116 117 118 119 120
-1.20429256 2.67952711 -5.72433165 -0.46947621 1.52143246 -1.83222176
121 122 123 124 125 126
3.63807735 0.28071727 -1.84928006 1.18867005 9.32224524 0.11523173
127 128 129 130 131 132
0.38759854 3.75608342 3.01501893 -2.73467152 3.38239927 5.50672221
133 134 135 136 137 138
0.04559621 4.16863166 -3.37955644 4.62379374 1.20156652 -3.70458998
139 140 141 142 143 144
1.48857914 1.76095384 2.70828850 -1.73491355 -10.15061878 -5.00728824
145 146
-4.29438240 6.46385132
> postscript(file="/var/www/rcomp/tmp/6g7zw1290518064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 146
Frequency = 1
lag(myerror, k = 1) myerror
0 0.94823308 NA
1 -3.25695789 0.94823308
2 -2.95991653 -3.25695789
3 0.58710400 -2.95991653
4 2.56996986 0.58710400
5 3.91920277 2.56996986
6 1.16604215 3.91920277
7 1.87868985 1.16604215
8 0.08530464 1.87868985
9 5.11713313 0.08530464
10 6.16233119 5.11713313
11 0.67991710 6.16233119
12 1.78046663 0.67991710
13 -0.75523509 1.78046663
14 10.12885183 -0.75523509
15 5.49960373 10.12885183
16 4.24076440 5.49960373
17 -12.51093990 4.24076440
18 1.58642456 -12.51093990
19 2.20156652 1.58642456
20 -9.77831301 2.20156652
21 -9.77831301 -9.77831301
22 -3.01209751 -9.77831301
23 -6.24842568 -3.01209751
24 -1.67591647 -6.24842568
25 -0.06699898 -1.67591647
26 -1.66880903 -0.06699898
27 -13.88442367 -1.66880903
28 -2.51499150 -13.88442367
29 2.09252627 -2.51499150
30 -1.18116905 2.09252627
31 5.01015639 -1.18116905
32 3.22082284 5.01015639
33 0.83907371 3.22082284
34 5.35625551 0.83907371
35 3.05980902 5.35625551
36 -2.40496560 3.05980902
37 -2.45402314 -2.40496560
38 -5.06396533 -2.45402314
39 -1.10977125 -5.06396533
40 -1.22462410 -1.10977125
41 1.62263529 -1.22462410
42 1.25087947 1.62263529
43 2.06517806 1.25087947
44 -0.41388685 2.06517806
45 0.91154317 -0.41388685
46 5.16268991 0.91154317
47 -11.90273327 5.16268991
48 2.23849326 -11.90273327
49 -1.65718367 2.23849326
50 -2.52825337 -1.65718367
51 0.76918587 -2.52825337
52 3.22725991 0.76918587
53 -4.76151505 3.22725991
54 0.34733426 -4.76151505
55 0.40951979 0.34733426
56 -1.02458933 0.40951979
57 -9.70003370 -1.02458933
58 -5.21924203 -9.70003370
59 5.42924187 -5.21924203
60 0.68071050 5.42924187
61 6.18751991 0.68071050
62 3.12341838 6.18751991
63 5.79156225 3.12341838
64 9.72542961 5.79156225
65 4.56606339 9.72542961
66 -1.86278865 4.56606339
67 3.28671229 -1.86278865
68 -7.85936700 3.28671229
69 -8.85936700 -7.85936700
70 3.55225682 -8.85936700
71 3.88898073 3.55225682
72 -10.98498170 3.88898073
73 -2.10160916 -10.98498170
74 -2.66355650 -2.10160916
75 -1.10826096 -2.66355650
76 -0.14077755 -1.10826096
77 -0.57656104 -0.14077755
78 1.67269583 -0.57656104
79 0.55278336 1.67269583
80 6.09431237 0.55278336
81 0.69817344 6.09431237
82 -17.80743628 0.69817344
83 -2.12425229 -17.80743628
84 2.70828850 -2.12425229
85 -1.59779660 2.70828850
86 3.20114037 -1.59779660
87 6.12909410 3.20114037
88 -4.60759667 6.12909410
89 -1.47657808 -4.60759667
90 3.90257463 -1.47657808
91 -1.80406678 3.90257463
92 1.11390343 -1.80406678
93 -0.75923560 1.11390343
94 2.24485124 -0.75923560
95 -4.49294125 2.24485124
96 -5.79778042 -4.49294125
97 -2.63338961 -5.79778042
98 -2.43971920 -2.63338961
99 9.07293679 -2.43971920
100 1.32408353 9.07293679
101 7.53301674 1.32408353
102 1.73520007 7.53301674
103 -10.17630178 1.73520007
104 -5.62578738 -10.17630178
105 4.47174663 -5.62578738
106 0.47779912 4.47174663
107 3.31103435 0.47779912
108 5.75277507 3.31103435
109 -8.79331197 5.75277507
110 -0.06873129 -8.79331197
111 0.84543866 -0.06873129
112 9.07069978 0.84543866
113 5.67071549 9.07069978
114 -1.20429256 5.67071549
115 2.67952711 -1.20429256
116 -5.72433165 2.67952711
117 -0.46947621 -5.72433165
118 1.52143246 -0.46947621
119 -1.83222176 1.52143246
120 3.63807735 -1.83222176
121 0.28071727 3.63807735
122 -1.84928006 0.28071727
123 1.18867005 -1.84928006
124 9.32224524 1.18867005
125 0.11523173 9.32224524
126 0.38759854 0.11523173
127 3.75608342 0.38759854
128 3.01501893 3.75608342
129 -2.73467152 3.01501893
130 3.38239927 -2.73467152
131 5.50672221 3.38239927
132 0.04559621 5.50672221
133 4.16863166 0.04559621
134 -3.37955644 4.16863166
135 4.62379374 -3.37955644
136 1.20156652 4.62379374
137 -3.70458998 1.20156652
138 1.48857914 -3.70458998
139 1.76095384 1.48857914
140 2.70828850 1.76095384
141 -1.73491355 2.70828850
142 -10.15061878 -1.73491355
143 -5.00728824 -10.15061878
144 -4.29438240 -5.00728824
145 6.46385132 -4.29438240
146 NA 6.46385132
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.25695789 0.94823308
[2,] -2.95991653 -3.25695789
[3,] 0.58710400 -2.95991653
[4,] 2.56996986 0.58710400
[5,] 3.91920277 2.56996986
[6,] 1.16604215 3.91920277
[7,] 1.87868985 1.16604215
[8,] 0.08530464 1.87868985
[9,] 5.11713313 0.08530464
[10,] 6.16233119 5.11713313
[11,] 0.67991710 6.16233119
[12,] 1.78046663 0.67991710
[13,] -0.75523509 1.78046663
[14,] 10.12885183 -0.75523509
[15,] 5.49960373 10.12885183
[16,] 4.24076440 5.49960373
[17,] -12.51093990 4.24076440
[18,] 1.58642456 -12.51093990
[19,] 2.20156652 1.58642456
[20,] -9.77831301 2.20156652
[21,] -9.77831301 -9.77831301
[22,] -3.01209751 -9.77831301
[23,] -6.24842568 -3.01209751
[24,] -1.67591647 -6.24842568
[25,] -0.06699898 -1.67591647
[26,] -1.66880903 -0.06699898
[27,] -13.88442367 -1.66880903
[28,] -2.51499150 -13.88442367
[29,] 2.09252627 -2.51499150
[30,] -1.18116905 2.09252627
[31,] 5.01015639 -1.18116905
[32,] 3.22082284 5.01015639
[33,] 0.83907371 3.22082284
[34,] 5.35625551 0.83907371
[35,] 3.05980902 5.35625551
[36,] -2.40496560 3.05980902
[37,] -2.45402314 -2.40496560
[38,] -5.06396533 -2.45402314
[39,] -1.10977125 -5.06396533
[40,] -1.22462410 -1.10977125
[41,] 1.62263529 -1.22462410
[42,] 1.25087947 1.62263529
[43,] 2.06517806 1.25087947
[44,] -0.41388685 2.06517806
[45,] 0.91154317 -0.41388685
[46,] 5.16268991 0.91154317
[47,] -11.90273327 5.16268991
[48,] 2.23849326 -11.90273327
[49,] -1.65718367 2.23849326
[50,] -2.52825337 -1.65718367
[51,] 0.76918587 -2.52825337
[52,] 3.22725991 0.76918587
[53,] -4.76151505 3.22725991
[54,] 0.34733426 -4.76151505
[55,] 0.40951979 0.34733426
[56,] -1.02458933 0.40951979
[57,] -9.70003370 -1.02458933
[58,] -5.21924203 -9.70003370
[59,] 5.42924187 -5.21924203
[60,] 0.68071050 5.42924187
[61,] 6.18751991 0.68071050
[62,] 3.12341838 6.18751991
[63,] 5.79156225 3.12341838
[64,] 9.72542961 5.79156225
[65,] 4.56606339 9.72542961
[66,] -1.86278865 4.56606339
[67,] 3.28671229 -1.86278865
[68,] -7.85936700 3.28671229
[69,] -8.85936700 -7.85936700
[70,] 3.55225682 -8.85936700
[71,] 3.88898073 3.55225682
[72,] -10.98498170 3.88898073
[73,] -2.10160916 -10.98498170
[74,] -2.66355650 -2.10160916
[75,] -1.10826096 -2.66355650
[76,] -0.14077755 -1.10826096
[77,] -0.57656104 -0.14077755
[78,] 1.67269583 -0.57656104
[79,] 0.55278336 1.67269583
[80,] 6.09431237 0.55278336
[81,] 0.69817344 6.09431237
[82,] -17.80743628 0.69817344
[83,] -2.12425229 -17.80743628
[84,] 2.70828850 -2.12425229
[85,] -1.59779660 2.70828850
[86,] 3.20114037 -1.59779660
[87,] 6.12909410 3.20114037
[88,] -4.60759667 6.12909410
[89,] -1.47657808 -4.60759667
[90,] 3.90257463 -1.47657808
[91,] -1.80406678 3.90257463
[92,] 1.11390343 -1.80406678
[93,] -0.75923560 1.11390343
[94,] 2.24485124 -0.75923560
[95,] -4.49294125 2.24485124
[96,] -5.79778042 -4.49294125
[97,] -2.63338961 -5.79778042
[98,] -2.43971920 -2.63338961
[99,] 9.07293679 -2.43971920
[100,] 1.32408353 9.07293679
[101,] 7.53301674 1.32408353
[102,] 1.73520007 7.53301674
[103,] -10.17630178 1.73520007
[104,] -5.62578738 -10.17630178
[105,] 4.47174663 -5.62578738
[106,] 0.47779912 4.47174663
[107,] 3.31103435 0.47779912
[108,] 5.75277507 3.31103435
[109,] -8.79331197 5.75277507
[110,] -0.06873129 -8.79331197
[111,] 0.84543866 -0.06873129
[112,] 9.07069978 0.84543866
[113,] 5.67071549 9.07069978
[114,] -1.20429256 5.67071549
[115,] 2.67952711 -1.20429256
[116,] -5.72433165 2.67952711
[117,] -0.46947621 -5.72433165
[118,] 1.52143246 -0.46947621
[119,] -1.83222176 1.52143246
[120,] 3.63807735 -1.83222176
[121,] 0.28071727 3.63807735
[122,] -1.84928006 0.28071727
[123,] 1.18867005 -1.84928006
[124,] 9.32224524 1.18867005
[125,] 0.11523173 9.32224524
[126,] 0.38759854 0.11523173
[127,] 3.75608342 0.38759854
[128,] 3.01501893 3.75608342
[129,] -2.73467152 3.01501893
[130,] 3.38239927 -2.73467152
[131,] 5.50672221 3.38239927
[132,] 0.04559621 5.50672221
[133,] 4.16863166 0.04559621
[134,] -3.37955644 4.16863166
[135,] 4.62379374 -3.37955644
[136,] 1.20156652 4.62379374
[137,] -3.70458998 1.20156652
[138,] 1.48857914 -3.70458998
[139,] 1.76095384 1.48857914
[140,] 2.70828850 1.76095384
[141,] -1.73491355 2.70828850
[142,] -10.15061878 -1.73491355
[143,] -5.00728824 -10.15061878
[144,] -4.29438240 -5.00728824
[145,] 6.46385132 -4.29438240
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.25695789 0.94823308
2 -2.95991653 -3.25695789
3 0.58710400 -2.95991653
4 2.56996986 0.58710400
5 3.91920277 2.56996986
6 1.16604215 3.91920277
7 1.87868985 1.16604215
8 0.08530464 1.87868985
9 5.11713313 0.08530464
10 6.16233119 5.11713313
11 0.67991710 6.16233119
12 1.78046663 0.67991710
13 -0.75523509 1.78046663
14 10.12885183 -0.75523509
15 5.49960373 10.12885183
16 4.24076440 5.49960373
17 -12.51093990 4.24076440
18 1.58642456 -12.51093990
19 2.20156652 1.58642456
20 -9.77831301 2.20156652
21 -9.77831301 -9.77831301
22 -3.01209751 -9.77831301
23 -6.24842568 -3.01209751
24 -1.67591647 -6.24842568
25 -0.06699898 -1.67591647
26 -1.66880903 -0.06699898
27 -13.88442367 -1.66880903
28 -2.51499150 -13.88442367
29 2.09252627 -2.51499150
30 -1.18116905 2.09252627
31 5.01015639 -1.18116905
32 3.22082284 5.01015639
33 0.83907371 3.22082284
34 5.35625551 0.83907371
35 3.05980902 5.35625551
36 -2.40496560 3.05980902
37 -2.45402314 -2.40496560
38 -5.06396533 -2.45402314
39 -1.10977125 -5.06396533
40 -1.22462410 -1.10977125
41 1.62263529 -1.22462410
42 1.25087947 1.62263529
43 2.06517806 1.25087947
44 -0.41388685 2.06517806
45 0.91154317 -0.41388685
46 5.16268991 0.91154317
47 -11.90273327 5.16268991
48 2.23849326 -11.90273327
49 -1.65718367 2.23849326
50 -2.52825337 -1.65718367
51 0.76918587 -2.52825337
52 3.22725991 0.76918587
53 -4.76151505 3.22725991
54 0.34733426 -4.76151505
55 0.40951979 0.34733426
56 -1.02458933 0.40951979
57 -9.70003370 -1.02458933
58 -5.21924203 -9.70003370
59 5.42924187 -5.21924203
60 0.68071050 5.42924187
61 6.18751991 0.68071050
62 3.12341838 6.18751991
63 5.79156225 3.12341838
64 9.72542961 5.79156225
65 4.56606339 9.72542961
66 -1.86278865 4.56606339
67 3.28671229 -1.86278865
68 -7.85936700 3.28671229
69 -8.85936700 -7.85936700
70 3.55225682 -8.85936700
71 3.88898073 3.55225682
72 -10.98498170 3.88898073
73 -2.10160916 -10.98498170
74 -2.66355650 -2.10160916
75 -1.10826096 -2.66355650
76 -0.14077755 -1.10826096
77 -0.57656104 -0.14077755
78 1.67269583 -0.57656104
79 0.55278336 1.67269583
80 6.09431237 0.55278336
81 0.69817344 6.09431237
82 -17.80743628 0.69817344
83 -2.12425229 -17.80743628
84 2.70828850 -2.12425229
85 -1.59779660 2.70828850
86 3.20114037 -1.59779660
87 6.12909410 3.20114037
88 -4.60759667 6.12909410
89 -1.47657808 -4.60759667
90 3.90257463 -1.47657808
91 -1.80406678 3.90257463
92 1.11390343 -1.80406678
93 -0.75923560 1.11390343
94 2.24485124 -0.75923560
95 -4.49294125 2.24485124
96 -5.79778042 -4.49294125
97 -2.63338961 -5.79778042
98 -2.43971920 -2.63338961
99 9.07293679 -2.43971920
100 1.32408353 9.07293679
101 7.53301674 1.32408353
102 1.73520007 7.53301674
103 -10.17630178 1.73520007
104 -5.62578738 -10.17630178
105 4.47174663 -5.62578738
106 0.47779912 4.47174663
107 3.31103435 0.47779912
108 5.75277507 3.31103435
109 -8.79331197 5.75277507
110 -0.06873129 -8.79331197
111 0.84543866 -0.06873129
112 9.07069978 0.84543866
113 5.67071549 9.07069978
114 -1.20429256 5.67071549
115 2.67952711 -1.20429256
116 -5.72433165 2.67952711
117 -0.46947621 -5.72433165
118 1.52143246 -0.46947621
119 -1.83222176 1.52143246
120 3.63807735 -1.83222176
121 0.28071727 3.63807735
122 -1.84928006 0.28071727
123 1.18867005 -1.84928006
124 9.32224524 1.18867005
125 0.11523173 9.32224524
126 0.38759854 0.11523173
127 3.75608342 0.38759854
128 3.01501893 3.75608342
129 -2.73467152 3.01501893
130 3.38239927 -2.73467152
131 5.50672221 3.38239927
132 0.04559621 5.50672221
133 4.16863166 0.04559621
134 -3.37955644 4.16863166
135 4.62379374 -3.37955644
136 1.20156652 4.62379374
137 -3.70458998 1.20156652
138 1.48857914 -3.70458998
139 1.76095384 1.48857914
140 2.70828850 1.76095384
141 -1.73491355 2.70828850
142 -10.15061878 -1.73491355
143 -5.00728824 -10.15061878
144 -4.29438240 -5.00728824
145 6.46385132 -4.29438240
> 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/rcomp/tmp/7qhyz1290518064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8qhyz1290518064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9j8gk1290518064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10j8gk1290518064.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/1149eq1290518064.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/rcomp/tmp/12qruv1290518064.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/rcomp/tmp/13fasp1290518064.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/rcomp/tmp/14pj9s1290518064.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/rcomp/tmp/15b2pg1290518064.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/rcomp/tmp/167c571290518064.tab")
+ }
>
> try(system("convert tmp/1cpiq1290518064.ps tmp/1cpiq1290518064.png",intern=TRUE))
character(0)
> try(system("convert tmp/2cpiq1290518064.ps tmp/2cpiq1290518064.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ng0t1290518064.ps tmp/3ng0t1290518064.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ng0t1290518064.ps tmp/4ng0t1290518064.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ng0t1290518064.ps tmp/5ng0t1290518064.png",intern=TRUE))
character(0)
> try(system("convert tmp/6g7zw1290518064.ps tmp/6g7zw1290518064.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qhyz1290518064.ps tmp/7qhyz1290518064.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qhyz1290518064.ps tmp/8qhyz1290518064.png",intern=TRUE))
character(0)
> try(system("convert tmp/9j8gk1290518064.ps tmp/9j8gk1290518064.png",intern=TRUE))
character(0)
> try(system("convert tmp/10j8gk1290518064.ps tmp/10j8gk1290518064.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.800 1.990 7.785