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(0
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+ ,20)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Gender'
+ ,'Concernovermistakes'
+ ,'Doubtsaboutactions'
+ ,'Parentalexpectations'
+ ,'Parentalcritism'
+ ,'Personalstandars'
+ ,'organization')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Gender','Concernovermistakes','Doubtsaboutactions','Parentalexpectations','Parentalcritism','Personalstandars','organization'),1:159))
> 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
Concernovermistakes Gender Doubtsaboutactions Parentalexpectations
1 24 0 14 11
2 25 0 11 7
3 17 0 6 17
4 18 1 12 10
5 18 1 8 12
6 16 1 10 12
7 20 1 10 11
8 16 1 11 11
9 18 1 16 12
10 17 1 11 13
11 23 0 13 14
12 30 0 12 16
13 23 1 8 11
14 18 1 12 10
15 15 1 11 11
16 12 1 4 15
17 21 0 9 9
18 15 1 8 11
19 20 1 8 17
20 31 0 14 17
21 27 0 15 11
22 34 1 16 18
23 21 1 9 14
24 31 1 14 10
25 19 1 11 11
26 16 0 8 15
27 20 1 9 15
28 21 1 9 13
29 22 1 9 16
30 17 1 9 13
31 24 1 10 9
32 25 0 16 18
33 26 0 11 18
34 25 1 8 12
35 17 1 9 17
36 32 1 16 9
37 33 1 11 9
38 13 1 16 12
39 32 1 12 18
40 25 1 12 12
41 29 1 14 18
42 22 1 9 14
43 18 1 10 15
44 17 1 9 16
45 20 0 10 10
46 15 1 12 11
47 20 1 14 14
48 33 1 14 9
49 29 0 10 12
50 23 1 14 17
51 26 0 16 5
52 18 1 9 12
53 20 0 10 12
54 11 1 6 6
55 28 1 8 24
56 26 1 13 12
57 22 0 10 12
58 17 1 8 14
59 12 0 7 7
60 14 1 15 13
61 17 1 9 12
62 21 1 10 13
63 19 1 12 14
64 18 1 13 8
65 10 0 10 11
66 29 0 11 9
67 31 1 8 11
68 19 0 9 13
69 9 1 13 10
70 20 1 11 11
71 28 1 8 12
72 19 0 9 9
73 30 0 9 15
74 29 0 15 18
75 26 0 9 15
76 23 0 10 12
77 13 1 14 13
78 21 1 12 14
79 19 1 12 10
80 28 1 11 13
81 23 1 14 13
82 18 1 6 11
83 21 0 12 13
84 20 1 8 16
85 23 1 14 8
86 21 1 11 16
87 21 1 10 11
88 15 1 14 9
89 28 1 12 16
90 19 1 10 12
91 26 1 14 14
92 10 1 5 8
93 16 0 11 9
94 22 1 10 15
95 19 1 9 11
96 31 1 10 21
97 31 0 16 14
98 29 1 13 18
99 19 0 9 12
100 22 1 10 13
101 23 1 10 15
102 15 0 7 12
103 20 0 9 19
104 18 1 8 15
105 23 1 14 11
106 25 1 14 11
107 21 1 8 10
108 24 1 9 13
109 25 1 14 15
110 17 1 14 12
111 13 1 8 12
112 28 1 8 16
113 21 0 8 9
114 25 1 7 18
115 9 0 6 8
116 16 1 8 13
117 19 1 6 17
118 17 1 11 9
119 25 1 14 15
120 20 1 11 8
121 29 1 11 7
122 14 1 11 12
123 22 1 14 14
124 15 1 8 6
125 19 0 20 8
126 20 1 11 17
127 15 0 8 10
128 20 1 11 11
129 18 1 10 14
130 33 1 14 11
131 22 1 11 13
132 16 1 9 12
133 17 1 9 11
134 16 1 8 9
135 21 0 10 12
136 26 0 13 20
137 18 1 13 12
138 18 1 12 13
139 17 1 8 12
140 22 1 13 12
141 30 1 14 9
142 30 0 12 15
143 24 1 14 24
144 21 1 15 7
145 21 1 13 17
146 29 1 16 11
147 31 1 9 17
148 20 1 9 11
149 16 0 9 12
150 22 0 8 14
151 20 1 7 11
152 28 1 16 16
153 38 1 11 21
154 22 0 9 14
155 20 1 11 20
156 17 0 9 13
157 28 1 14 11
158 22 1 13 15
159 31 0 16 19
Parentalcritism Personalstandars organization
1 12 24 26
2 8 25 23
3 8 30 25
4 8 19 23
5 9 22 19
6 7 22 29
7 4 25 25
8 11 23 21
9 7 17 22
10 7 21 25
11 12 19 24
12 10 19 18
13 10 15 22
14 8 16 15
15 8 23 22
16 4 27 28
17 9 22 20
18 8 14 12
19 7 22 24
20 11 23 20
21 9 23 21
22 11 21 20
23 13 19 21
24 8 18 23
25 8 20 28
26 9 23 24
27 6 25 24
28 9 19 24
29 9 24 23
30 6 22 23
31 6 25 29
32 16 26 24
33 5 29 18
34 7 32 25
35 9 25 21
36 6 29 26
37 6 28 22
38 5 17 22
39 12 28 22
40 7 29 23
41 10 26 30
42 9 25 23
43 8 14 17
44 5 25 23
45 8 26 23
46 8 20 25
47 10 18 24
48 6 32 24
49 8 25 23
50 7 25 21
51 4 23 24
52 8 21 24
53 8 20 28
54 4 15 16
55 20 30 20
56 8 24 29
57 8 26 27
58 6 24 22
59 4 22 28
60 8 14 16
61 9 24 25
62 6 24 24
63 7 24 28
64 9 24 24
65 5 19 23
66 5 31 30
67 8 22 24
68 8 27 21
69 6 19 25
70 8 25 25
71 7 20 22
72 7 21 23
73 9 27 26
74 11 23 23
75 6 25 25
76 8 20 21
77 6 21 25
78 9 22 24
79 8 23 29
80 6 25 22
81 10 25 27
82 8 17 26
83 8 19 22
84 10 25 24
85 5 19 27
86 7 20 24
87 5 26 24
88 8 23 29
89 14 27 22
90 7 17 21
91 8 17 24
92 6 19 24
93 5 17 23
94 6 22 20
95 10 21 27
96 12 32 26
97 9 21 25
98 12 21 21
99 7 18 21
100 8 18 19
101 10 23 21
102 6 19 21
103 10 20 16
104 10 21 22
105 10 20 29
106 5 17 15
107 7 18 17
108 10 19 15
109 11 22 21
110 6 15 21
111 7 14 19
112 12 18 24
113 11 24 20
114 11 35 17
115 11 29 23
116 5 21 24
117 8 25 14
118 6 20 19
119 9 22 24
120 4 13 13
121 4 26 22
122 7 17 16
123 11 25 19
124 6 20 25
125 7 19 25
126 8 21 23
127 4 22 24
128 8 24 26
129 9 21 26
130 8 26 25
131 11 24 18
132 8 16 21
133 5 23 26
134 4 18 23
135 8 16 23
136 10 26 22
137 6 19 20
138 9 21 13
139 9 21 24
140 13 22 15
141 9 23 14
142 10 29 22
143 20 21 10
144 5 21 24
145 11 23 22
146 6 27 24
147 9 25 19
148 7 21 20
149 9 10 13
150 10 20 20
151 9 26 22
152 8 24 24
153 7 29 29
154 6 19 12
155 13 24 20
156 6 19 21
157 8 24 24
158 10 22 22
159 16 17 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender Doubtsaboutactions
-1.5249 -0.5998 0.8121
Parentalexpectations Parentalcritism Personalstandars
0.2584 0.1798 0.5631
organization
-0.1151
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.0763 -2.4138 -0.3167 2.7511 12.7204
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.52489 3.11538 -0.489 0.6252
Gender -0.59978 0.80372 -0.746 0.4567
Doubtsaboutactions 0.81215 0.13055 6.221 4.57e-09 ***
Parentalexpectations 0.25842 0.13330 1.939 0.0544 .
Parentalcritism 0.17980 0.16891 1.064 0.2888
Personalstandars 0.56313 0.09603 5.864 2.72e-08 ***
organization -0.11512 0.10318 -1.116 0.2663
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.484 on 152 degrees of freedom
Multiple R-squared: 0.4093, Adjusted R-squared: 0.386
F-statistic: 17.56 on 6 and 152 DF, p-value: 2.186e-15
> 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,] 0.08444205 0.16888410 0.91555795
[2,] 0.04647528 0.09295056 0.95352472
[3,] 0.13027888 0.26055775 0.86972112
[4,] 0.08449614 0.16899229 0.91550386
[5,] 0.12977175 0.25954351 0.87022825
[6,] 0.08379025 0.16758050 0.91620975
[7,] 0.06163554 0.12327108 0.93836446
[8,] 0.08234061 0.16468122 0.91765939
[9,] 0.10343103 0.20686206 0.89656897
[10,] 0.09895159 0.19790318 0.90104841
[11,] 0.13523318 0.27046636 0.86476682
[12,] 0.09646720 0.19293441 0.90353280
[13,] 0.27377446 0.54754891 0.72622554
[14,] 0.21365065 0.42730129 0.78634935
[15,] 0.51765117 0.96469767 0.48234883
[16,] 0.44430888 0.88861777 0.55569112
[17,] 0.47172752 0.94345503 0.52827248
[18,] 0.41557791 0.83115581 0.58442209
[19,] 0.37256664 0.74513328 0.62743336
[20,] 0.32173015 0.64346030 0.67826985
[21,] 0.26875530 0.53751060 0.73124470
[22,] 0.34826040 0.69652079 0.65173960
[23,] 0.39062172 0.78124344 0.60937828
[24,] 0.33986006 0.67972013 0.66013994
[25,] 0.39451522 0.78903044 0.60548478
[26,] 0.39296076 0.78592151 0.60703924
[27,] 0.39933336 0.79866673 0.60066664
[28,] 0.59235098 0.81529803 0.40764902
[29,] 0.78555031 0.42889937 0.21444969
[30,] 0.78087734 0.43824533 0.21912266
[31,] 0.74129424 0.51741153 0.25870576
[32,] 0.71550836 0.56898329 0.28449164
[33,] 0.66716257 0.66567487 0.33283743
[34,] 0.61921703 0.76156593 0.38078297
[35,] 0.60420439 0.79159122 0.39579561
[36,] 0.56708800 0.86582400 0.43291200
[37,] 0.58842406 0.82315187 0.41157594
[38,] 0.54773083 0.90453834 0.45226917
[39,] 0.53540116 0.92919767 0.46459884
[40,] 0.60186151 0.79627697 0.39813849
[41,] 0.58008264 0.83983471 0.41991736
[42,] 0.54271728 0.91456544 0.45728272
[43,] 0.49284617 0.98569234 0.50715383
[44,] 0.45222266 0.90444533 0.54777734
[45,] 0.40451683 0.80903366 0.59548317
[46,] 0.35941119 0.71882238 0.64058881
[47,] 0.33253380 0.66506761 0.66746620
[48,] 0.28860123 0.57720246 0.71139877
[49,] 0.26330461 0.52660922 0.73669539
[50,] 0.24494318 0.48988636 0.75505682
[51,] 0.30306524 0.60613049 0.69693476
[52,] 0.28936945 0.57873889 0.71063055
[53,] 0.25111593 0.50223186 0.74888407
[54,] 0.23712794 0.47425589 0.76287206
[55,] 0.26632686 0.53265373 0.73367314
[56,] 0.35117424 0.70234849 0.64882576
[57,] 0.34938391 0.69876782 0.65061609
[58,] 0.68358034 0.63283932 0.31641966
[59,] 0.67877963 0.64244074 0.32122037
[60,] 0.84901838 0.30196323 0.15098162
[61,] 0.82902960 0.34194081 0.17097040
[62,] 0.93348828 0.13302344 0.06651172
[63,] 0.91720862 0.16558277 0.08279138
[64,] 0.93738793 0.12522413 0.06261207
[65,] 0.92463815 0.15072369 0.07536185
[66,] 0.92401950 0.15196100 0.07598050
[67,] 0.91589200 0.16821601 0.08410800
[68,] 0.96709838 0.06580324 0.03290162
[69,] 0.95904837 0.08190325 0.04095163
[70,] 0.95065727 0.09868546 0.04934273
[71,] 0.95416057 0.09167887 0.04583943
[72,] 0.94546104 0.10907793 0.05453896
[73,] 0.94513104 0.10973792 0.05486896
[74,] 0.93077367 0.13845266 0.06922633
[75,] 0.91662897 0.16674205 0.08337103
[76,] 0.90795664 0.18408672 0.09204336
[77,] 0.89185919 0.21628163 0.10814081
[78,] 0.86941097 0.26117805 0.13058903
[79,] 0.91488472 0.17023055 0.08511528
[80,] 0.89716091 0.20567819 0.10283909
[81,] 0.87648561 0.24702879 0.12351439
[82,] 0.87860526 0.24278949 0.12139474
[83,] 0.86687577 0.26624846 0.13312423
[84,] 0.84359611 0.31280777 0.15640389
[85,] 0.81547333 0.36905333 0.18452667
[86,] 0.78149149 0.43701701 0.21850851
[87,] 0.75207932 0.49584135 0.24792068
[88,] 0.77006879 0.45986242 0.22993121
[89,] 0.76436930 0.47126140 0.23563070
[90,] 0.72774069 0.54451862 0.27225931
[91,] 0.70283168 0.59433664 0.29716832
[92,] 0.65937763 0.68124475 0.34062237
[93,] 0.61957469 0.76085062 0.38042531
[94,] 0.58096015 0.83807970 0.41903985
[95,] 0.53798948 0.92402103 0.46201052
[96,] 0.49179655 0.98359311 0.50820345
[97,] 0.47345429 0.94690859 0.52654571
[98,] 0.47071276 0.94142552 0.52928724
[99,] 0.48282195 0.96564390 0.51717805
[100,] 0.43230739 0.86461478 0.56769261
[101,] 0.40822862 0.81645724 0.59177138
[102,] 0.36864198 0.73728396 0.63135802
[103,] 0.59692555 0.80614889 0.40307445
[104,] 0.58411803 0.83176394 0.41588197
[105,] 0.55262264 0.89475471 0.44737736
[106,] 0.79288271 0.41423457 0.20711729
[107,] 0.76597364 0.46805272 0.23402636
[108,] 0.75162512 0.49674976 0.24837488
[109,] 0.72077526 0.55844947 0.27922474
[110,] 0.67330355 0.65339290 0.32669645
[111,] 0.70240711 0.59518577 0.29759289
[112,] 0.75418327 0.49163345 0.24581673
[113,] 0.74479461 0.51041078 0.25520539
[114,] 0.74670470 0.50659060 0.25329530
[115,] 0.69573642 0.60852716 0.30426358
[116,] 0.78535276 0.42929448 0.21464724
[117,] 0.74739683 0.50520634 0.25260317
[118,] 0.78617093 0.42765815 0.21382907
[119,] 0.75642040 0.48715920 0.24357960
[120,] 0.72105719 0.55788562 0.27894281
[121,] 0.78127305 0.43745391 0.21872695
[122,] 0.73060128 0.53879744 0.26939872
[123,] 0.67085551 0.65828897 0.32914449
[124,] 0.65357762 0.69284475 0.34642238
[125,] 0.58545894 0.82908213 0.41454106
[126,] 0.52829039 0.94341922 0.47170961
[127,] 0.58570912 0.82858177 0.41429088
[128,] 0.55016840 0.89966320 0.44983160
[129,] 0.55496108 0.89007783 0.44503892
[130,] 0.47440298 0.94880595 0.52559702
[131,] 0.39456077 0.78912154 0.60543923
[132,] 0.54014405 0.91971190 0.45985595
[133,] 0.45901632 0.91803265 0.54098368
[134,] 0.37658010 0.75316020 0.62341990
[135,] 0.28675268 0.57350536 0.71324732
[136,] 0.30398420 0.60796839 0.69601580
[137,] 0.21400784 0.42801569 0.78599216
[138,] 0.32942944 0.65885889 0.67057056
[139,] 0.23470055 0.46940111 0.76529945
[140,] 0.60935676 0.78128648 0.39064324
> postscript(file="/var/www/html/rcomp/tmp/1vth41292144489.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2vth41292144489.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3n2zp1292144489.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4n2zp1292144489.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5n2zp1292144489.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 = 159
Frequency = 1
1 2 3 4 5 6
-1.36747139 2.91335235 -6.19556198 -1.69548527 -1.29341173 -3.40692848
7 8 9 10 11 12
-0.75899047 -6.16391810 -4.26997117 -3.37483737 0.25483781 7.21902277
13 14 15 16 17 18
8.07250009 -0.92703330 -6.50941103 -5.70070973 1.18504930 -0.15595825
19 20 21 22 23 24
0.34966333 4.13421639 1.34731189 6.97754395 1.57804890 10.24335472
25 26 27 28 29 30
-0.12930172 -4.65599484 -1.45524225 2.90101083 0.19496183 -2.36411789
31 32 33 34 35 36
3.85873431 -5.87640513 -1.21802337 1.12556094 -5.85682981 4.38796808
37 38 39 40 41 42
9.55136011 -8.91037871 4.33463947 -0.66386284 2.11715198 0.14867254
43 44 45 46 47 48
0.76165319 -4.64898626 -2.61289973 -5.28680338 -2.03480681 5.09262532
49 50 51 52 53 54
6.43338956 -3.55796990 2.33003284 -0.78703710 0.82464650 -0.62303049
55 56 57 58 59 60
-0.76217827 2.85056975 -0.66926992 -3.05177764 -3.85388576 -6.89735402
61 62 63 64 65 66
-3.54111397 -0.18741203 -3.78944961 -4.87113094 -8.39000185 4.36292795
67 68 69 70 71 72
12.72039827 -4.36938857 -10.91780256 -2.29032190 10.53780189 0.45312991
73 74 75 76 77 78
6.50956338 1.40900317 4.06009971 3.01881783 -9.63148067 -1.48324959
79 80 81 82 83 84
-2.25730701 5.20707169 -2.37296085 4.39059309 -0.18564566 -1.62070252
85 86 87 88 89 90
3.19692765 0.29791168 -0.61703809 -7.62317817 1.05502375 1.48778951
91 92 93 94 95 96
4.88791866 -3.01890514 -1.55903862 0.96153674 0.45714745 2.39160841
97 98 99 100 101 102
5.34663934 4.34930563 1.13702658 3.25620165 0.79433719 -1.62201717
103 104 105 106 107 108
-1.91317306 -1.33998539 1.18978467 4.16650750 4.60531972 4.68514916
109 110 111 112 113 114
-0.07091207 -2.45473429 -1.42875522 9.96163607 0.51133733 -2.94235471
115 116 117 118 119 120
-12.07625768 -1.69392376 -2.04642272 -2.28893097 0.63403553 4.58030411
121 122 123 124 125 126
7.55406231 -4.89994891 -4.73212599 -0.38651557 -6.86555706 -1.81855780
127 128 129 130 131 132
-2.90177138 -1.61207051 -2.06558680 7.71010556 -1.58924998 -0.31672721
133 134 135 136 137 138
-1.88525580 1.09384055 3.50158662 -2.10826871 -3.01023819 -4.92799691
139 140 141 142 143 144
-1.15468682 -2.53380273 5.47024988 2.30658808 -5.71804408 -0.82841816
145 146 147 148 149 150
-4.22362391 1.76715361 7.91293343 1.19070747 1.36155113 2.65155629
151 152 153 154 155 156
-0.13002025 0.80485087 11.51319721 2.20078065 -5.52755870 -1.50473206
157 158 159
3.72125319 -1.96385095 4.47289655
> postscript(file="/var/www/html/rcomp/tmp/6gcga1292144489.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.36747139 NA
1 2.91335235 -1.36747139
2 -6.19556198 2.91335235
3 -1.69548527 -6.19556198
4 -1.29341173 -1.69548527
5 -3.40692848 -1.29341173
6 -0.75899047 -3.40692848
7 -6.16391810 -0.75899047
8 -4.26997117 -6.16391810
9 -3.37483737 -4.26997117
10 0.25483781 -3.37483737
11 7.21902277 0.25483781
12 8.07250009 7.21902277
13 -0.92703330 8.07250009
14 -6.50941103 -0.92703330
15 -5.70070973 -6.50941103
16 1.18504930 -5.70070973
17 -0.15595825 1.18504930
18 0.34966333 -0.15595825
19 4.13421639 0.34966333
20 1.34731189 4.13421639
21 6.97754395 1.34731189
22 1.57804890 6.97754395
23 10.24335472 1.57804890
24 -0.12930172 10.24335472
25 -4.65599484 -0.12930172
26 -1.45524225 -4.65599484
27 2.90101083 -1.45524225
28 0.19496183 2.90101083
29 -2.36411789 0.19496183
30 3.85873431 -2.36411789
31 -5.87640513 3.85873431
32 -1.21802337 -5.87640513
33 1.12556094 -1.21802337
34 -5.85682981 1.12556094
35 4.38796808 -5.85682981
36 9.55136011 4.38796808
37 -8.91037871 9.55136011
38 4.33463947 -8.91037871
39 -0.66386284 4.33463947
40 2.11715198 -0.66386284
41 0.14867254 2.11715198
42 0.76165319 0.14867254
43 -4.64898626 0.76165319
44 -2.61289973 -4.64898626
45 -5.28680338 -2.61289973
46 -2.03480681 -5.28680338
47 5.09262532 -2.03480681
48 6.43338956 5.09262532
49 -3.55796990 6.43338956
50 2.33003284 -3.55796990
51 -0.78703710 2.33003284
52 0.82464650 -0.78703710
53 -0.62303049 0.82464650
54 -0.76217827 -0.62303049
55 2.85056975 -0.76217827
56 -0.66926992 2.85056975
57 -3.05177764 -0.66926992
58 -3.85388576 -3.05177764
59 -6.89735402 -3.85388576
60 -3.54111397 -6.89735402
61 -0.18741203 -3.54111397
62 -3.78944961 -0.18741203
63 -4.87113094 -3.78944961
64 -8.39000185 -4.87113094
65 4.36292795 -8.39000185
66 12.72039827 4.36292795
67 -4.36938857 12.72039827
68 -10.91780256 -4.36938857
69 -2.29032190 -10.91780256
70 10.53780189 -2.29032190
71 0.45312991 10.53780189
72 6.50956338 0.45312991
73 1.40900317 6.50956338
74 4.06009971 1.40900317
75 3.01881783 4.06009971
76 -9.63148067 3.01881783
77 -1.48324959 -9.63148067
78 -2.25730701 -1.48324959
79 5.20707169 -2.25730701
80 -2.37296085 5.20707169
81 4.39059309 -2.37296085
82 -0.18564566 4.39059309
83 -1.62070252 -0.18564566
84 3.19692765 -1.62070252
85 0.29791168 3.19692765
86 -0.61703809 0.29791168
87 -7.62317817 -0.61703809
88 1.05502375 -7.62317817
89 1.48778951 1.05502375
90 4.88791866 1.48778951
91 -3.01890514 4.88791866
92 -1.55903862 -3.01890514
93 0.96153674 -1.55903862
94 0.45714745 0.96153674
95 2.39160841 0.45714745
96 5.34663934 2.39160841
97 4.34930563 5.34663934
98 1.13702658 4.34930563
99 3.25620165 1.13702658
100 0.79433719 3.25620165
101 -1.62201717 0.79433719
102 -1.91317306 -1.62201717
103 -1.33998539 -1.91317306
104 1.18978467 -1.33998539
105 4.16650750 1.18978467
106 4.60531972 4.16650750
107 4.68514916 4.60531972
108 -0.07091207 4.68514916
109 -2.45473429 -0.07091207
110 -1.42875522 -2.45473429
111 9.96163607 -1.42875522
112 0.51133733 9.96163607
113 -2.94235471 0.51133733
114 -12.07625768 -2.94235471
115 -1.69392376 -12.07625768
116 -2.04642272 -1.69392376
117 -2.28893097 -2.04642272
118 0.63403553 -2.28893097
119 4.58030411 0.63403553
120 7.55406231 4.58030411
121 -4.89994891 7.55406231
122 -4.73212599 -4.89994891
123 -0.38651557 -4.73212599
124 -6.86555706 -0.38651557
125 -1.81855780 -6.86555706
126 -2.90177138 -1.81855780
127 -1.61207051 -2.90177138
128 -2.06558680 -1.61207051
129 7.71010556 -2.06558680
130 -1.58924998 7.71010556
131 -0.31672721 -1.58924998
132 -1.88525580 -0.31672721
133 1.09384055 -1.88525580
134 3.50158662 1.09384055
135 -2.10826871 3.50158662
136 -3.01023819 -2.10826871
137 -4.92799691 -3.01023819
138 -1.15468682 -4.92799691
139 -2.53380273 -1.15468682
140 5.47024988 -2.53380273
141 2.30658808 5.47024988
142 -5.71804408 2.30658808
143 -0.82841816 -5.71804408
144 -4.22362391 -0.82841816
145 1.76715361 -4.22362391
146 7.91293343 1.76715361
147 1.19070747 7.91293343
148 1.36155113 1.19070747
149 2.65155629 1.36155113
150 -0.13002025 2.65155629
151 0.80485087 -0.13002025
152 11.51319721 0.80485087
153 2.20078065 11.51319721
154 -5.52755870 2.20078065
155 -1.50473206 -5.52755870
156 3.72125319 -1.50473206
157 -1.96385095 3.72125319
158 4.47289655 -1.96385095
159 NA 4.47289655
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.91335235 -1.36747139
[2,] -6.19556198 2.91335235
[3,] -1.69548527 -6.19556198
[4,] -1.29341173 -1.69548527
[5,] -3.40692848 -1.29341173
[6,] -0.75899047 -3.40692848
[7,] -6.16391810 -0.75899047
[8,] -4.26997117 -6.16391810
[9,] -3.37483737 -4.26997117
[10,] 0.25483781 -3.37483737
[11,] 7.21902277 0.25483781
[12,] 8.07250009 7.21902277
[13,] -0.92703330 8.07250009
[14,] -6.50941103 -0.92703330
[15,] -5.70070973 -6.50941103
[16,] 1.18504930 -5.70070973
[17,] -0.15595825 1.18504930
[18,] 0.34966333 -0.15595825
[19,] 4.13421639 0.34966333
[20,] 1.34731189 4.13421639
[21,] 6.97754395 1.34731189
[22,] 1.57804890 6.97754395
[23,] 10.24335472 1.57804890
[24,] -0.12930172 10.24335472
[25,] -4.65599484 -0.12930172
[26,] -1.45524225 -4.65599484
[27,] 2.90101083 -1.45524225
[28,] 0.19496183 2.90101083
[29,] -2.36411789 0.19496183
[30,] 3.85873431 -2.36411789
[31,] -5.87640513 3.85873431
[32,] -1.21802337 -5.87640513
[33,] 1.12556094 -1.21802337
[34,] -5.85682981 1.12556094
[35,] 4.38796808 -5.85682981
[36,] 9.55136011 4.38796808
[37,] -8.91037871 9.55136011
[38,] 4.33463947 -8.91037871
[39,] -0.66386284 4.33463947
[40,] 2.11715198 -0.66386284
[41,] 0.14867254 2.11715198
[42,] 0.76165319 0.14867254
[43,] -4.64898626 0.76165319
[44,] -2.61289973 -4.64898626
[45,] -5.28680338 -2.61289973
[46,] -2.03480681 -5.28680338
[47,] 5.09262532 -2.03480681
[48,] 6.43338956 5.09262532
[49,] -3.55796990 6.43338956
[50,] 2.33003284 -3.55796990
[51,] -0.78703710 2.33003284
[52,] 0.82464650 -0.78703710
[53,] -0.62303049 0.82464650
[54,] -0.76217827 -0.62303049
[55,] 2.85056975 -0.76217827
[56,] -0.66926992 2.85056975
[57,] -3.05177764 -0.66926992
[58,] -3.85388576 -3.05177764
[59,] -6.89735402 -3.85388576
[60,] -3.54111397 -6.89735402
[61,] -0.18741203 -3.54111397
[62,] -3.78944961 -0.18741203
[63,] -4.87113094 -3.78944961
[64,] -8.39000185 -4.87113094
[65,] 4.36292795 -8.39000185
[66,] 12.72039827 4.36292795
[67,] -4.36938857 12.72039827
[68,] -10.91780256 -4.36938857
[69,] -2.29032190 -10.91780256
[70,] 10.53780189 -2.29032190
[71,] 0.45312991 10.53780189
[72,] 6.50956338 0.45312991
[73,] 1.40900317 6.50956338
[74,] 4.06009971 1.40900317
[75,] 3.01881783 4.06009971
[76,] -9.63148067 3.01881783
[77,] -1.48324959 -9.63148067
[78,] -2.25730701 -1.48324959
[79,] 5.20707169 -2.25730701
[80,] -2.37296085 5.20707169
[81,] 4.39059309 -2.37296085
[82,] -0.18564566 4.39059309
[83,] -1.62070252 -0.18564566
[84,] 3.19692765 -1.62070252
[85,] 0.29791168 3.19692765
[86,] -0.61703809 0.29791168
[87,] -7.62317817 -0.61703809
[88,] 1.05502375 -7.62317817
[89,] 1.48778951 1.05502375
[90,] 4.88791866 1.48778951
[91,] -3.01890514 4.88791866
[92,] -1.55903862 -3.01890514
[93,] 0.96153674 -1.55903862
[94,] 0.45714745 0.96153674
[95,] 2.39160841 0.45714745
[96,] 5.34663934 2.39160841
[97,] 4.34930563 5.34663934
[98,] 1.13702658 4.34930563
[99,] 3.25620165 1.13702658
[100,] 0.79433719 3.25620165
[101,] -1.62201717 0.79433719
[102,] -1.91317306 -1.62201717
[103,] -1.33998539 -1.91317306
[104,] 1.18978467 -1.33998539
[105,] 4.16650750 1.18978467
[106,] 4.60531972 4.16650750
[107,] 4.68514916 4.60531972
[108,] -0.07091207 4.68514916
[109,] -2.45473429 -0.07091207
[110,] -1.42875522 -2.45473429
[111,] 9.96163607 -1.42875522
[112,] 0.51133733 9.96163607
[113,] -2.94235471 0.51133733
[114,] -12.07625768 -2.94235471
[115,] -1.69392376 -12.07625768
[116,] -2.04642272 -1.69392376
[117,] -2.28893097 -2.04642272
[118,] 0.63403553 -2.28893097
[119,] 4.58030411 0.63403553
[120,] 7.55406231 4.58030411
[121,] -4.89994891 7.55406231
[122,] -4.73212599 -4.89994891
[123,] -0.38651557 -4.73212599
[124,] -6.86555706 -0.38651557
[125,] -1.81855780 -6.86555706
[126,] -2.90177138 -1.81855780
[127,] -1.61207051 -2.90177138
[128,] -2.06558680 -1.61207051
[129,] 7.71010556 -2.06558680
[130,] -1.58924998 7.71010556
[131,] -0.31672721 -1.58924998
[132,] -1.88525580 -0.31672721
[133,] 1.09384055 -1.88525580
[134,] 3.50158662 1.09384055
[135,] -2.10826871 3.50158662
[136,] -3.01023819 -2.10826871
[137,] -4.92799691 -3.01023819
[138,] -1.15468682 -4.92799691
[139,] -2.53380273 -1.15468682
[140,] 5.47024988 -2.53380273
[141,] 2.30658808 5.47024988
[142,] -5.71804408 2.30658808
[143,] -0.82841816 -5.71804408
[144,] -4.22362391 -0.82841816
[145,] 1.76715361 -4.22362391
[146,] 7.91293343 1.76715361
[147,] 1.19070747 7.91293343
[148,] 1.36155113 1.19070747
[149,] 2.65155629 1.36155113
[150,] -0.13002025 2.65155629
[151,] 0.80485087 -0.13002025
[152,] 11.51319721 0.80485087
[153,] 2.20078065 11.51319721
[154,] -5.52755870 2.20078065
[155,] -1.50473206 -5.52755870
[156,] 3.72125319 -1.50473206
[157,] -1.96385095 3.72125319
[158,] 4.47289655 -1.96385095
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.91335235 -1.36747139
2 -6.19556198 2.91335235
3 -1.69548527 -6.19556198
4 -1.29341173 -1.69548527
5 -3.40692848 -1.29341173
6 -0.75899047 -3.40692848
7 -6.16391810 -0.75899047
8 -4.26997117 -6.16391810
9 -3.37483737 -4.26997117
10 0.25483781 -3.37483737
11 7.21902277 0.25483781
12 8.07250009 7.21902277
13 -0.92703330 8.07250009
14 -6.50941103 -0.92703330
15 -5.70070973 -6.50941103
16 1.18504930 -5.70070973
17 -0.15595825 1.18504930
18 0.34966333 -0.15595825
19 4.13421639 0.34966333
20 1.34731189 4.13421639
21 6.97754395 1.34731189
22 1.57804890 6.97754395
23 10.24335472 1.57804890
24 -0.12930172 10.24335472
25 -4.65599484 -0.12930172
26 -1.45524225 -4.65599484
27 2.90101083 -1.45524225
28 0.19496183 2.90101083
29 -2.36411789 0.19496183
30 3.85873431 -2.36411789
31 -5.87640513 3.85873431
32 -1.21802337 -5.87640513
33 1.12556094 -1.21802337
34 -5.85682981 1.12556094
35 4.38796808 -5.85682981
36 9.55136011 4.38796808
37 -8.91037871 9.55136011
38 4.33463947 -8.91037871
39 -0.66386284 4.33463947
40 2.11715198 -0.66386284
41 0.14867254 2.11715198
42 0.76165319 0.14867254
43 -4.64898626 0.76165319
44 -2.61289973 -4.64898626
45 -5.28680338 -2.61289973
46 -2.03480681 -5.28680338
47 5.09262532 -2.03480681
48 6.43338956 5.09262532
49 -3.55796990 6.43338956
50 2.33003284 -3.55796990
51 -0.78703710 2.33003284
52 0.82464650 -0.78703710
53 -0.62303049 0.82464650
54 -0.76217827 -0.62303049
55 2.85056975 -0.76217827
56 -0.66926992 2.85056975
57 -3.05177764 -0.66926992
58 -3.85388576 -3.05177764
59 -6.89735402 -3.85388576
60 -3.54111397 -6.89735402
61 -0.18741203 -3.54111397
62 -3.78944961 -0.18741203
63 -4.87113094 -3.78944961
64 -8.39000185 -4.87113094
65 4.36292795 -8.39000185
66 12.72039827 4.36292795
67 -4.36938857 12.72039827
68 -10.91780256 -4.36938857
69 -2.29032190 -10.91780256
70 10.53780189 -2.29032190
71 0.45312991 10.53780189
72 6.50956338 0.45312991
73 1.40900317 6.50956338
74 4.06009971 1.40900317
75 3.01881783 4.06009971
76 -9.63148067 3.01881783
77 -1.48324959 -9.63148067
78 -2.25730701 -1.48324959
79 5.20707169 -2.25730701
80 -2.37296085 5.20707169
81 4.39059309 -2.37296085
82 -0.18564566 4.39059309
83 -1.62070252 -0.18564566
84 3.19692765 -1.62070252
85 0.29791168 3.19692765
86 -0.61703809 0.29791168
87 -7.62317817 -0.61703809
88 1.05502375 -7.62317817
89 1.48778951 1.05502375
90 4.88791866 1.48778951
91 -3.01890514 4.88791866
92 -1.55903862 -3.01890514
93 0.96153674 -1.55903862
94 0.45714745 0.96153674
95 2.39160841 0.45714745
96 5.34663934 2.39160841
97 4.34930563 5.34663934
98 1.13702658 4.34930563
99 3.25620165 1.13702658
100 0.79433719 3.25620165
101 -1.62201717 0.79433719
102 -1.91317306 -1.62201717
103 -1.33998539 -1.91317306
104 1.18978467 -1.33998539
105 4.16650750 1.18978467
106 4.60531972 4.16650750
107 4.68514916 4.60531972
108 -0.07091207 4.68514916
109 -2.45473429 -0.07091207
110 -1.42875522 -2.45473429
111 9.96163607 -1.42875522
112 0.51133733 9.96163607
113 -2.94235471 0.51133733
114 -12.07625768 -2.94235471
115 -1.69392376 -12.07625768
116 -2.04642272 -1.69392376
117 -2.28893097 -2.04642272
118 0.63403553 -2.28893097
119 4.58030411 0.63403553
120 7.55406231 4.58030411
121 -4.89994891 7.55406231
122 -4.73212599 -4.89994891
123 -0.38651557 -4.73212599
124 -6.86555706 -0.38651557
125 -1.81855780 -6.86555706
126 -2.90177138 -1.81855780
127 -1.61207051 -2.90177138
128 -2.06558680 -1.61207051
129 7.71010556 -2.06558680
130 -1.58924998 7.71010556
131 -0.31672721 -1.58924998
132 -1.88525580 -0.31672721
133 1.09384055 -1.88525580
134 3.50158662 1.09384055
135 -2.10826871 3.50158662
136 -3.01023819 -2.10826871
137 -4.92799691 -3.01023819
138 -1.15468682 -4.92799691
139 -2.53380273 -1.15468682
140 5.47024988 -2.53380273
141 2.30658808 5.47024988
142 -5.71804408 2.30658808
143 -0.82841816 -5.71804408
144 -4.22362391 -0.82841816
145 1.76715361 -4.22362391
146 7.91293343 1.76715361
147 1.19070747 7.91293343
148 1.36155113 1.19070747
149 2.65155629 1.36155113
150 -0.13002025 2.65155629
151 0.80485087 -0.13002025
152 11.51319721 0.80485087
153 2.20078065 11.51319721
154 -5.52755870 2.20078065
155 -1.50473206 -5.52755870
156 3.72125319 -1.50473206
157 -1.96385095 3.72125319
158 4.47289655 -1.96385095
> 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/793fd1292144489.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/893fd1292144489.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/993fd1292144489.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101cfg1292144489.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/115cv31292144489.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/12qvbr1292144489.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/134n901292144489.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/14q6qo1292144489.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/15bo6c1292144489.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/16wo4z1292144489.tab")
+ }
>
> try(system("convert tmp/1vth41292144489.ps tmp/1vth41292144489.png",intern=TRUE))
character(0)
> try(system("convert tmp/2vth41292144489.ps tmp/2vth41292144489.png",intern=TRUE))
character(0)
> try(system("convert tmp/3n2zp1292144489.ps tmp/3n2zp1292144489.png",intern=TRUE))
character(0)
> try(system("convert tmp/4n2zp1292144489.ps tmp/4n2zp1292144489.png",intern=TRUE))
character(0)
> try(system("convert tmp/5n2zp1292144489.ps tmp/5n2zp1292144489.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gcga1292144489.ps tmp/6gcga1292144489.png",intern=TRUE))
character(0)
> try(system("convert tmp/793fd1292144489.ps tmp/793fd1292144489.png",intern=TRUE))
character(0)
> try(system("convert tmp/893fd1292144489.ps tmp/893fd1292144489.png",intern=TRUE))
character(0)
> try(system("convert tmp/993fd1292144489.ps tmp/993fd1292144489.png",intern=TRUE))
character(0)
> try(system("convert tmp/101cfg1292144489.ps tmp/101cfg1292144489.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.162 1.840 13.541