R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(0
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+ ,20)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Gender'
+ ,'ConcMistakes'
+ ,'DoubtsActions'
+ ,'ParExp'
+ ,'ParCrit'
+ ,'PersonalStandards'
+ ,'Organisation')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Gender','ConcMistakes','DoubtsActions','ParExp','ParCrit','PersonalStandards','Organisation'),1:159))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
ConcMistakes Gender DoubtsActions ParExp ParCrit PersonalStandards
1 24 0 14 11 12 24
2 25 0 11 7 8 25
3 17 0 6 17 8 30
4 18 1 12 10 8 19
5 18 1 8 12 9 22
6 16 1 10 12 7 22
7 20 1 10 11 4 25
8 16 1 11 11 11 23
9 18 1 16 12 7 17
10 17 1 11 13 7 21
11 23 0 13 14 12 19
12 30 0 12 16 10 19
13 23 1 8 11 10 15
14 18 1 12 10 8 16
15 15 1 11 11 8 23
16 12 1 4 15 4 27
17 21 0 9 9 9 22
18 15 1 8 11 8 14
19 20 1 8 17 7 22
20 31 0 14 17 11 23
21 27 0 15 11 9 23
22 34 1 16 18 11 21
23 21 1 9 14 13 19
24 31 1 14 10 8 18
25 19 1 11 11 8 20
26 16 0 8 15 9 23
27 20 1 9 15 6 25
28 21 1 9 13 9 19
29 22 1 9 16 9 24
30 17 1 9 13 6 22
31 24 1 10 9 6 25
32 25 0 16 18 16 26
33 26 0 11 18 5 29
34 25 1 8 12 7 32
35 17 1 9 17 9 25
36 32 1 16 9 6 29
37 33 1 11 9 6 28
38 13 1 16 12 5 17
39 32 1 12 18 12 28
40 25 1 12 12 7 29
41 29 1 14 18 10 26
42 22 1 9 14 9 25
43 18 1 10 15 8 14
44 17 1 9 16 5 25
45 20 0 10 10 8 26
46 15 1 12 11 8 20
47 20 1 14 14 10 18
48 33 1 14 9 6 32
49 29 0 10 12 8 25
50 23 1 14 17 7 25
51 26 0 16 5 4 23
52 18 1 9 12 8 21
53 20 0 10 12 8 20
54 11 1 6 6 4 15
55 28 1 8 24 20 30
56 26 1 13 12 8 24
57 22 0 10 12 8 26
58 17 1 8 14 6 24
59 12 0 7 7 4 22
60 14 1 15 13 8 14
61 17 1 9 12 9 24
62 21 1 10 13 6 24
63 19 1 12 14 7 24
64 18 1 13 8 9 24
65 10 0 10 11 5 19
66 29 0 11 9 5 31
67 31 1 8 11 8 22
68 19 0 9 13 8 27
69 9 1 13 10 6 19
70 20 1 11 11 8 25
71 28 1 8 12 7 20
72 19 0 9 9 7 21
73 30 0 9 15 9 27
74 29 0 15 18 11 23
75 26 0 9 15 6 25
76 23 0 10 12 8 20
77 13 1 14 13 6 21
78 21 1 12 14 9 22
79 19 1 12 10 8 23
80 28 1 11 13 6 25
81 23 1 14 13 10 25
82 18 1 6 11 8 17
83 21 0 12 13 8 19
84 20 1 8 16 10 25
85 23 1 14 8 5 19
86 21 1 11 16 7 20
87 21 1 10 11 5 26
88 15 1 14 9 8 23
89 28 1 12 16 14 27
90 19 1 10 12 7 17
91 26 1 14 14 8 17
92 10 1 5 8 6 19
93 16 0 11 9 5 17
94 22 1 10 15 6 22
95 19 1 9 11 10 21
96 31 1 10 21 12 32
97 31 0 16 14 9 21
98 29 1 13 18 12 21
99 19 0 9 12 7 18
100 22 1 10 13 8 18
101 23 1 10 15 10 23
102 15 0 7 12 6 19
103 20 0 9 19 10 20
104 18 1 8 15 10 21
105 23 1 14 11 10 20
106 25 1 14 11 5 17
107 21 1 8 10 7 18
108 24 1 9 13 10 19
109 25 1 14 15 11 22
110 17 1 14 12 6 15
111 13 1 8 12 7 14
112 28 1 8 16 12 18
113 21 0 8 9 11 24
114 25 1 7 18 11 35
115 9 0 6 8 11 29
116 16 1 8 13 5 21
117 19 1 6 17 8 25
118 17 1 11 9 6 20
119 25 1 14 15 9 22
120 20 1 11 8 4 13
121 29 1 11 7 4 26
122 14 1 11 12 7 17
123 22 1 14 14 11 25
124 15 1 8 6 6 20
125 19 0 20 8 7 19
126 20 1 11 17 8 21
127 15 0 8 10 4 22
128 20 1 11 11 8 24
129 18 1 10 14 9 21
130 33 1 14 11 8 26
131 22 1 11 13 11 24
132 16 1 9 12 8 16
133 17 1 9 11 5 23
134 16 1 8 9 4 18
135 21 0 10 12 8 16
136 26 0 13 20 10 26
137 18 1 13 12 6 19
138 18 1 12 13 9 21
139 17 1 8 12 9 21
140 22 1 13 12 13 22
141 30 1 14 9 9 23
142 30 0 12 15 10 29
143 24 1 14 24 20 21
144 21 1 15 7 5 21
145 21 1 13 17 11 23
146 29 1 16 11 6 27
147 31 1 9 17 9 25
148 20 1 9 11 7 21
149 16 0 9 12 9 10
150 22 0 8 14 10 20
151 20 1 7 11 9 26
152 28 1 16 16 8 24
153 38 1 11 21 7 29
154 22 0 9 14 6 19
155 20 1 11 20 13 24
156 17 0 9 13 6 19
157 28 1 14 11 8 24
158 22 1 13 15 10 22
159 31 0 16 19 16 17
Organisation
1 26
2 23
3 25
4 23
5 19
6 29
7 25
8 21
9 22
10 25
11 24
12 18
13 22
14 15
15 22
16 28
17 20
18 12
19 24
20 20
21 21
22 20
23 21
24 23
25 28
26 24
27 24
28 24
29 23
30 23
31 29
32 24
33 18
34 25
35 21
36 26
37 22
38 22
39 22
40 23
41 30
42 23
43 17
44 23
45 23
46 25
47 24
48 24
49 23
50 21
51 24
52 24
53 28
54 16
55 20
56 29
57 27
58 22
59 28
60 16
61 25
62 24
63 28
64 24
65 23
66 30
67 24
68 21
69 25
70 25
71 22
72 23
73 26
74 23
75 25
76 21
77 25
78 24
79 29
80 22
81 27
82 26
83 22
84 24
85 27
86 24
87 24
88 29
89 22
90 21
91 24
92 24
93 23
94 20
95 27
96 26
97 25
98 21
99 21
100 19
101 21
102 21
103 16
104 22
105 29
106 15
107 17
108 15
109 21
110 21
111 19
112 24
113 20
114 17
115 23
116 24
117 14
118 19
119 24
120 13
121 22
122 16
123 19
124 25
125 25
126 23
127 24
128 26
129 26
130 25
131 18
132 21
133 26
134 23
135 23
136 22
137 20
138 13
139 24
140 15
141 14
142 22
143 10
144 24
145 22
146 24
147 19
148 20
149 13
150 20
151 22
152 24
153 29
154 12
155 20
156 21
157 24
158 22
159 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender DoubtsActions ParExp
-1.5249 -0.5998 0.8121 0.2584
ParCrit PersonalStandards Organisation
0.1798 0.5631 -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
DoubtsActions 0.81215 0.13055 6.221 4.57e-09 ***
ParExp 0.25842 0.13330 1.939 0.0544 .
ParCrit 0.17980 0.16891 1.064 0.2888
PersonalStandards 0.56313 0.09603 5.864 2.72e-08 ***
Organisation -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/fisher/rcomp/tmp/1nwmh1355148973.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/fisher/rcomp/tmp/25xw11355148973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3vh6k1355148973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4rv321355148973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/57ddg1355148973.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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/fisher/rcomp/tmp/6k5dj1355148973.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/fisher/rcomp/tmp/7h2ph1355148973.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/fisher/rcomp/tmp/876qv1355148973.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/fisher/rcomp/tmp/9ml5z1355148973.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/fisher/rcomp/tmp/10cpzr1355148973.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11zhf31355148973.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12xlhf1355148973.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13ywck1355148973.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14hvws1355148973.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/fisher/rcomp/tmp/15ag1p1355148973.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/fisher/rcomp/tmp/16bdiz1355148973.tab")
+ }
>
> try(system("convert tmp/1nwmh1355148973.ps tmp/1nwmh1355148973.png",intern=TRUE))
character(0)
> try(system("convert tmp/25xw11355148973.ps tmp/25xw11355148973.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vh6k1355148973.ps tmp/3vh6k1355148973.png",intern=TRUE))
character(0)
> try(system("convert tmp/4rv321355148973.ps tmp/4rv321355148973.png",intern=TRUE))
character(0)
> try(system("convert tmp/57ddg1355148973.ps tmp/57ddg1355148973.png",intern=TRUE))
character(0)
> try(system("convert tmp/6k5dj1355148973.ps tmp/6k5dj1355148973.png",intern=TRUE))
character(0)
> try(system("convert tmp/7h2ph1355148973.ps tmp/7h2ph1355148973.png",intern=TRUE))
character(0)
> try(system("convert tmp/876qv1355148973.ps tmp/876qv1355148973.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ml5z1355148973.ps tmp/9ml5z1355148973.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cpzr1355148973.ps tmp/10cpzr1355148973.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.128 1.674 9.831