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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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(24
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+ ,dim=c(5
+ ,159)
+ ,dimnames=list(c('CM'
+ ,'D'
+ ,'PE'
+ ,'PS'
+ ,'O')
+ ,1:159))
> y <- array(NA,dim=c(5,159),dimnames=list(c('CM','D','PE','PS','O'),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 = '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
PS CM D PE O
1 24 24 14 11 26
2 25 25 11 7 23
3 30 17 6 17 25
4 19 18 12 10 23
5 22 18 8 12 19
6 22 16 10 12 29
7 25 20 10 11 25
8 23 16 11 11 21
9 17 18 16 12 22
10 21 17 11 13 25
11 19 23 13 14 24
12 19 30 12 16 18
13 15 23 8 11 22
14 16 18 12 10 15
15 23 15 11 11 22
16 27 12 4 15 28
17 22 21 9 9 20
18 14 15 8 11 12
19 22 20 8 17 24
20 23 31 14 17 20
21 23 27 15 11 21
22 21 34 16 18 20
23 19 21 9 14 21
24 18 31 14 10 23
25 20 19 11 11 28
26 23 16 8 15 24
27 25 20 9 15 24
28 19 21 9 13 24
29 24 22 9 16 23
30 22 17 9 13 23
31 25 24 10 9 29
32 26 25 16 18 24
33 29 26 11 18 18
34 32 25 8 12 25
35 25 17 9 17 21
36 29 32 16 9 26
37 28 33 11 9 22
38 17 13 16 12 22
39 28 32 12 18 22
40 29 25 12 12 23
41 26 29 14 18 30
42 25 22 9 14 23
43 14 18 10 15 17
44 25 17 9 16 23
45 26 20 10 10 23
46 20 15 12 11 25
47 18 20 14 14 24
48 32 33 14 9 24
49 25 29 10 12 23
50 25 23 14 17 21
51 23 26 16 5 24
52 21 18 9 12 24
53 20 20 10 12 28
54 15 11 6 6 16
55 30 28 8 24 20
56 24 26 13 12 29
57 26 22 10 12 27
58 24 17 8 14 22
59 22 12 7 7 28
60 14 14 15 13 16
61 24 17 9 12 25
62 24 21 10 13 24
63 24 19 12 14 28
64 24 18 13 8 24
65 19 10 10 11 23
66 31 29 11 9 30
67 22 31 8 11 24
68 27 19 9 13 21
69 19 9 13 10 25
70 25 20 11 11 25
71 20 28 8 12 22
72 21 19 9 9 23
73 27 30 9 15 26
74 23 29 15 18 23
75 25 26 9 15 25
76 20 23 10 12 21
77 21 13 14 13 25
78 22 21 12 14 24
79 23 19 12 10 29
80 25 28 11 13 22
81 25 23 14 13 27
82 17 18 6 11 26
83 19 21 12 13 22
84 25 20 8 16 24
85 19 23 14 8 27
86 20 21 11 16 24
87 26 21 10 11 24
88 23 15 14 9 29
89 27 28 12 16 22
90 17 19 10 12 21
91 17 26 14 14 24
92 19 10 5 8 24
93 17 16 11 9 23
94 22 22 10 15 20
95 21 19 9 11 27
96 32 31 10 21 26
97 21 31 16 14 25
98 21 29 13 18 21
99 18 19 9 12 21
100 18 22 10 13 19
101 23 23 10 15 21
102 19 15 7 12 21
103 20 20 9 19 16
104 21 18 8 15 22
105 20 23 14 11 29
106 17 25 14 11 15
107 18 21 8 10 17
108 19 24 9 13 15
109 22 25 14 15 21
110 15 17 14 12 21
111 14 13 8 12 19
112 18 28 8 16 24
113 24 21 8 9 20
114 35 25 7 18 17
115 29 9 6 8 23
116 21 16 8 13 24
117 25 19 6 17 14
118 20 17 11 9 19
119 22 25 14 15 24
120 13 20 11 8 13
121 26 29 11 7 22
122 17 14 11 12 16
123 25 22 14 14 19
124 20 15 8 6 25
125 19 19 20 8 25
126 21 20 11 17 23
127 22 15 8 10 24
128 24 20 11 11 26
129 21 18 10 14 26
130 26 33 14 11 25
131 24 22 11 13 18
132 16 16 9 12 21
133 23 17 9 11 26
134 18 16 8 9 23
135 16 21 10 12 23
136 26 26 13 20 22
137 19 18 13 12 20
138 21 18 12 13 13
139 21 17 8 12 24
140 22 22 13 12 15
141 23 30 14 9 14
142 29 30 12 15 22
143 21 24 14 24 10
144 21 21 15 7 24
145 23 21 13 17 22
146 27 29 16 11 24
147 25 31 9 17 19
148 21 20 9 11 20
149 10 16 9 12 13
150 20 22 8 14 20
151 26 20 7 11 22
152 24 28 16 16 24
153 29 38 11 21 29
154 19 22 9 14 12
155 24 20 11 20 20
156 19 17 9 13 21
157 24 28 14 11 24
158 22 22 13 15 22
159 17 31 16 19 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CM D PE O
7.5234 0.3292 -0.3602 0.1964 0.3991
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.581860 -2.188259 -0.005745 2.175091 11.446565
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.52339 2.21429 3.398 0.000865 ***
CM 0.32923 0.05505 5.980 1.50e-08 ***
D -0.36024 0.10590 -3.402 0.000853 ***
PE 0.19644 0.08505 2.310 0.022230 *
O 0.39912 0.07057 5.656 7.35e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.399 on 154 degrees of freedom
Multiple R-squared: 0.3669, Adjusted R-squared: 0.3505
F-statistic: 22.31 on 4 and 154 DF, p-value: 1.507e-14
> 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,] 0.31390809 0.62781618 0.6860919
[2,] 0.18416143 0.36832286 0.8158386
[3,] 0.11305535 0.22611071 0.8869446
[4,] 0.22835872 0.45671745 0.7716413
[5,] 0.20973554 0.41947108 0.7902645
[6,] 0.81275056 0.37449887 0.1872494
[7,] 0.74190122 0.51619756 0.2580988
[8,] 0.69342929 0.61314141 0.3065707
[9,] 0.62476570 0.75046860 0.3752343
[10,] 0.54487503 0.91024994 0.4551250
[11,] 0.51912071 0.96175859 0.4808793
[12,] 0.45418587 0.90837174 0.5458141
[13,] 0.45189011 0.90378023 0.5481099
[14,] 0.43851159 0.87702319 0.5614884
[15,] 0.37533296 0.75066591 0.6246670
[16,] 0.35214447 0.70428895 0.6478555
[17,] 0.38451720 0.76903439 0.6154828
[18,] 0.40096458 0.80192917 0.5990354
[19,] 0.33630755 0.67261511 0.6636924
[20,] 0.29485340 0.58970680 0.7051466
[21,] 0.31591637 0.63183274 0.6840836
[22,] 0.26485639 0.52971279 0.7351436
[23,] 0.21463439 0.42926879 0.7853656
[24,] 0.17312483 0.34624967 0.8268752
[25,] 0.17922309 0.35844619 0.8207769
[26,] 0.36165142 0.72330283 0.6383486
[27,] 0.59728455 0.80543089 0.4027154
[28,] 0.57147563 0.85704873 0.4285244
[29,] 0.67519602 0.64960796 0.3248040
[30,] 0.67923430 0.64153141 0.3207657
[31,] 0.62761688 0.74476623 0.3723831
[32,] 0.58886387 0.82227227 0.4111361
[33,] 0.68532488 0.62935023 0.3146751
[34,] 0.65591490 0.68817019 0.3440851
[35,] 0.61126750 0.77746500 0.3887325
[36,] 0.68053247 0.63893506 0.3194675
[37,] 0.65832081 0.68335838 0.3416792
[38,] 0.67551281 0.64897438 0.3244872
[39,] 0.62900258 0.74199484 0.3709974
[40,] 0.62413823 0.75172354 0.3758618
[41,] 0.75435311 0.49129378 0.2456469
[42,] 0.71702154 0.56595692 0.2829785
[43,] 0.71990236 0.56019527 0.2800976
[44,] 0.68893348 0.62213305 0.3110665
[45,] 0.65362570 0.69274859 0.3463743
[46,] 0.68946989 0.62106022 0.3105301
[47,] 0.65294711 0.69410578 0.3470529
[48,] 0.64523427 0.70953146 0.3547657
[49,] 0.61302174 0.77395652 0.3869783
[50,] 0.57441639 0.85116722 0.4255836
[51,] 0.54429148 0.91141704 0.4557085
[52,] 0.49721334 0.99442669 0.5027867
[53,] 0.45775857 0.91551715 0.5422414
[54,] 0.42156052 0.84312103 0.5784395
[55,] 0.37875671 0.75751341 0.6212433
[56,] 0.33574040 0.67148081 0.6642596
[57,] 0.35650304 0.71300608 0.6434970
[58,] 0.31420383 0.62840765 0.6857962
[59,] 0.32879854 0.65759708 0.6712015
[60,] 0.38676683 0.77353366 0.6132332
[61,] 0.46037290 0.92074579 0.5396271
[62,] 0.42021932 0.84043865 0.5797807
[63,] 0.40369821 0.80739642 0.5963018
[64,] 0.46152633 0.92305267 0.5384737
[65,] 0.41742211 0.83484422 0.5825779
[66,] 0.37638820 0.75277640 0.6236118
[67,] 0.34067646 0.68135293 0.6593235
[68,] 0.30270857 0.60541715 0.6972914
[69,] 0.27910020 0.55820041 0.7208998
[70,] 0.25168086 0.50336172 0.7483191
[71,] 0.21753979 0.43507958 0.7824602
[72,] 0.18864632 0.37729264 0.8113537
[73,] 0.16110915 0.32221829 0.8388909
[74,] 0.14243235 0.28486470 0.8575676
[75,] 0.23241229 0.46482457 0.7675877
[76,] 0.21448735 0.42897470 0.7855126
[77,] 0.18650212 0.37300424 0.8134979
[78,] 0.18556385 0.37112770 0.8144362
[79,] 0.17981467 0.35962933 0.8201853
[80,] 0.18240979 0.36481959 0.8175902
[81,] 0.17263964 0.34527927 0.8273604
[82,] 0.16258548 0.32517096 0.8374145
[83,] 0.16794852 0.33589703 0.8320515
[84,] 0.24133462 0.48266924 0.7586654
[85,] 0.20893944 0.41787887 0.7910606
[86,] 0.19370591 0.38741183 0.8062941
[87,] 0.16298376 0.32596752 0.8370162
[88,] 0.14692995 0.29385990 0.8530700
[89,] 0.15125654 0.30251308 0.8487435
[90,] 0.15208186 0.30416372 0.8479181
[91,] 0.14704716 0.29409432 0.8529528
[92,] 0.14138121 0.28276241 0.8586188
[93,] 0.13741231 0.27482462 0.8625877
[94,] 0.11294939 0.22589877 0.8870506
[95,] 0.09511927 0.19023855 0.9048807
[96,] 0.07751926 0.15503852 0.9224807
[97,] 0.06287594 0.12575189 0.9371241
[98,] 0.06378982 0.12757964 0.9362102
[99,] 0.05512376 0.11024751 0.9448762
[100,] 0.04900256 0.09800512 0.9509974
[101,] 0.04210382 0.08420764 0.9578962
[102,] 0.03216707 0.06433414 0.9678329
[103,] 0.03342375 0.06684750 0.9665763
[104,] 0.04387513 0.08775026 0.9561249
[105,] 0.14445318 0.28890636 0.8555468
[106,] 0.12919215 0.25838431 0.8708078
[107,] 0.53839411 0.92321178 0.4616059
[108,] 0.88813836 0.22372327 0.1118616
[109,] 0.86092683 0.27814634 0.1390732
[110,] 0.90239189 0.19521622 0.0976081
[111,] 0.88139175 0.23721651 0.1186083
[112,] 0.85828489 0.28343022 0.1417151
[113,] 0.89461511 0.21076977 0.1053849
[114,] 0.87655186 0.24689627 0.1234481
[115,] 0.84449058 0.31101885 0.1555094
[116,] 0.87160405 0.25679191 0.1283960
[117,] 0.83792186 0.32415628 0.1620781
[118,] 0.80599532 0.38800936 0.1940047
[119,] 0.76549565 0.46900869 0.2345043
[120,] 0.73410337 0.53179327 0.2658966
[121,] 0.69828730 0.60342539 0.3017127
[122,] 0.64583384 0.70833232 0.3541662
[123,] 0.58688051 0.82623898 0.4131195
[124,] 0.58681734 0.82636531 0.4131827
[125,] 0.58922905 0.82154191 0.4107710
[126,] 0.54187164 0.91625672 0.4581284
[127,] 0.49515839 0.99031678 0.5048416
[128,] 0.64944151 0.70111698 0.3505585
[129,] 0.61290787 0.77418426 0.3870921
[130,] 0.54565508 0.90868985 0.4543449
[131,] 0.54990217 0.90019567 0.4500978
[132,] 0.47554998 0.95109995 0.5244500
[133,] 0.45447522 0.90895044 0.5455248
[134,] 0.42487834 0.84975667 0.5751217
[135,] 0.49106582 0.98213163 0.5089342
[136,] 0.61668357 0.76663285 0.3833164
[137,] 0.54208388 0.91583224 0.4579161
[138,] 0.46693140 0.93386281 0.5330686
[139,] 0.49227394 0.98454788 0.5077261
[140,] 0.43877844 0.87755689 0.5612216
[141,] 0.32747328 0.65494656 0.6725267
[142,] 0.56154023 0.87691955 0.4384598
[143,] 0.53960365 0.92079271 0.4603964
[144,] 0.40081178 0.80162356 0.5991882
> postscript(file="/var/www/html/rcomp/tmp/1mgjw1292685631.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mgjw1292685631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mgjw1292685631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4mgjw1292685631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5mgjw1292685631.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
1.080477379 2.653616944 5.723679458 -1.270806365 1.491827774 -1.120411792
7 8 9 10 11 12
2.355563737 3.629222746 -1.823587670 -0.689361832 -3.741600299 -4.404640808
13 14 15 16 17 18
-8.155267416 -1.077850789 3.559337889 2.844880542 1.054555720 -2.530196227
19 20 21 22 23 24
-2.144419140 -1.008064667 1.448613707 -3.471718812 -3.326744227 -5.830370308
25 26 27 28 29 30
-3.152317152 0.565391419 1.608695922 -4.327666468 0.152910089 0.388391338
31 32 33 34 35 36
-0.164980210 2.894914708 6.159182490 6.792468964 3.400885832 4.559958585
37 38 39 40 41 42
3.025987473 -0.177414721 1.947540025 6.031679306 -1.537226058 1.545782289
43 44 45 46 47 48
-5.578755907 2.799083038 4.350238731 -0.277777590 -3.393653668 7.308477165
49 50 51 52 53 54
-0.005744777 3.226692604 2.119349417 -1.143526599 -4.038230704 -1.548040880
55 56 57 58 59 60
3.443129230 -1.332029103 1.702419563 2.230831823 0.497097928 -1.668611591
61 62 63 64 65 66
1.786588544 1.032576394 0.618617410 4.083189250 0.446148529 4.149970256
67 68 69 70 71 72
-4.587383028 5.528161053 1.254308911 2.715806599 -4.997876464 -0.484333441
73 74 75 76 77 78
-0.481888870 -1.383147067 -0.765831064 -2.232098344 1.708305114 -0.443373982
79 80 81 82 83 84
0.005242363 0.886416022 1.617720322 -6.826057979 -2.448698988 1.052016960
85 86 87 88 89 90
-3.400099178 -3.196489044 3.425448594 2.239102546 2.657350584 -3.915159985
91 92 93 94 95 96
-6.369061206 -1.164876929 -2.776143948 -0.093052608 -2.473683429 3.370502803
97 98 99 100 101 102
-3.693867878 -3.305393897 -3.275402847 -3.301060961 0.178593356 -1.678950212
103 104 105 106 107 108
-0.984092902 -1.294838867 -3.787646372 -1.858443294 -2.304764901 -1.723295214
109 110 111 112 113 114
-0.038904376 -3.815719357 -4.861999277 -8.581859758 2.694312858 11.446565078
115 116 117 118 119 120
9.923719970 -1.041736381 4.455524196 1.491099250 -1.236262717 -3.905451737
121 122 123 124 125 126
2.735798032 0.086853061 4.943474388 -0.736568538 0.876535247 -1.664571107
127 128 129 130 131 132
0.876806509 1.316687152 -1.974394830 0.516485518 3.458301348 -4.287699078
133 134 135 136 137 138
0.583905197 -2.856872534 -6.371868059 1.890318226 -0.106077362 4.131079805
139 140 141 142 143 144
-1.174534871 3.572581514 3.287375404 4.195317504 1.912719231 1.012407304
145 146 147 148 149 150
1.125799475 3.953029048 -0.410159531 -0.009081890 -7.094743502 -2.617102232
151 152 153 154 155 156
3.472193492 0.300083138 -2.771254805 -0.063903794 1.943478934 -1.813369768
157 158 159
0.561777914 0.189437085 -6.680451143
> postscript(file="/var/www/html/rcomp/tmp/6x7ih1292685631.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 1.080477379 NA
1 2.653616944 1.080477379
2 5.723679458 2.653616944
3 -1.270806365 5.723679458
4 1.491827774 -1.270806365
5 -1.120411792 1.491827774
6 2.355563737 -1.120411792
7 3.629222746 2.355563737
8 -1.823587670 3.629222746
9 -0.689361832 -1.823587670
10 -3.741600299 -0.689361832
11 -4.404640808 -3.741600299
12 -8.155267416 -4.404640808
13 -1.077850789 -8.155267416
14 3.559337889 -1.077850789
15 2.844880542 3.559337889
16 1.054555720 2.844880542
17 -2.530196227 1.054555720
18 -2.144419140 -2.530196227
19 -1.008064667 -2.144419140
20 1.448613707 -1.008064667
21 -3.471718812 1.448613707
22 -3.326744227 -3.471718812
23 -5.830370308 -3.326744227
24 -3.152317152 -5.830370308
25 0.565391419 -3.152317152
26 1.608695922 0.565391419
27 -4.327666468 1.608695922
28 0.152910089 -4.327666468
29 0.388391338 0.152910089
30 -0.164980210 0.388391338
31 2.894914708 -0.164980210
32 6.159182490 2.894914708
33 6.792468964 6.159182490
34 3.400885832 6.792468964
35 4.559958585 3.400885832
36 3.025987473 4.559958585
37 -0.177414721 3.025987473
38 1.947540025 -0.177414721
39 6.031679306 1.947540025
40 -1.537226058 6.031679306
41 1.545782289 -1.537226058
42 -5.578755907 1.545782289
43 2.799083038 -5.578755907
44 4.350238731 2.799083038
45 -0.277777590 4.350238731
46 -3.393653668 -0.277777590
47 7.308477165 -3.393653668
48 -0.005744777 7.308477165
49 3.226692604 -0.005744777
50 2.119349417 3.226692604
51 -1.143526599 2.119349417
52 -4.038230704 -1.143526599
53 -1.548040880 -4.038230704
54 3.443129230 -1.548040880
55 -1.332029103 3.443129230
56 1.702419563 -1.332029103
57 2.230831823 1.702419563
58 0.497097928 2.230831823
59 -1.668611591 0.497097928
60 1.786588544 -1.668611591
61 1.032576394 1.786588544
62 0.618617410 1.032576394
63 4.083189250 0.618617410
64 0.446148529 4.083189250
65 4.149970256 0.446148529
66 -4.587383028 4.149970256
67 5.528161053 -4.587383028
68 1.254308911 5.528161053
69 2.715806599 1.254308911
70 -4.997876464 2.715806599
71 -0.484333441 -4.997876464
72 -0.481888870 -0.484333441
73 -1.383147067 -0.481888870
74 -0.765831064 -1.383147067
75 -2.232098344 -0.765831064
76 1.708305114 -2.232098344
77 -0.443373982 1.708305114
78 0.005242363 -0.443373982
79 0.886416022 0.005242363
80 1.617720322 0.886416022
81 -6.826057979 1.617720322
82 -2.448698988 -6.826057979
83 1.052016960 -2.448698988
84 -3.400099178 1.052016960
85 -3.196489044 -3.400099178
86 3.425448594 -3.196489044
87 2.239102546 3.425448594
88 2.657350584 2.239102546
89 -3.915159985 2.657350584
90 -6.369061206 -3.915159985
91 -1.164876929 -6.369061206
92 -2.776143948 -1.164876929
93 -0.093052608 -2.776143948
94 -2.473683429 -0.093052608
95 3.370502803 -2.473683429
96 -3.693867878 3.370502803
97 -3.305393897 -3.693867878
98 -3.275402847 -3.305393897
99 -3.301060961 -3.275402847
100 0.178593356 -3.301060961
101 -1.678950212 0.178593356
102 -0.984092902 -1.678950212
103 -1.294838867 -0.984092902
104 -3.787646372 -1.294838867
105 -1.858443294 -3.787646372
106 -2.304764901 -1.858443294
107 -1.723295214 -2.304764901
108 -0.038904376 -1.723295214
109 -3.815719357 -0.038904376
110 -4.861999277 -3.815719357
111 -8.581859758 -4.861999277
112 2.694312858 -8.581859758
113 11.446565078 2.694312858
114 9.923719970 11.446565078
115 -1.041736381 9.923719970
116 4.455524196 -1.041736381
117 1.491099250 4.455524196
118 -1.236262717 1.491099250
119 -3.905451737 -1.236262717
120 2.735798032 -3.905451737
121 0.086853061 2.735798032
122 4.943474388 0.086853061
123 -0.736568538 4.943474388
124 0.876535247 -0.736568538
125 -1.664571107 0.876535247
126 0.876806509 -1.664571107
127 1.316687152 0.876806509
128 -1.974394830 1.316687152
129 0.516485518 -1.974394830
130 3.458301348 0.516485518
131 -4.287699078 3.458301348
132 0.583905197 -4.287699078
133 -2.856872534 0.583905197
134 -6.371868059 -2.856872534
135 1.890318226 -6.371868059
136 -0.106077362 1.890318226
137 4.131079805 -0.106077362
138 -1.174534871 4.131079805
139 3.572581514 -1.174534871
140 3.287375404 3.572581514
141 4.195317504 3.287375404
142 1.912719231 4.195317504
143 1.012407304 1.912719231
144 1.125799475 1.012407304
145 3.953029048 1.125799475
146 -0.410159531 3.953029048
147 -0.009081890 -0.410159531
148 -7.094743502 -0.009081890
149 -2.617102232 -7.094743502
150 3.472193492 -2.617102232
151 0.300083138 3.472193492
152 -2.771254805 0.300083138
153 -0.063903794 -2.771254805
154 1.943478934 -0.063903794
155 -1.813369768 1.943478934
156 0.561777914 -1.813369768
157 0.189437085 0.561777914
158 -6.680451143 0.189437085
159 NA -6.680451143
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.653616944 1.080477379
[2,] 5.723679458 2.653616944
[3,] -1.270806365 5.723679458
[4,] 1.491827774 -1.270806365
[5,] -1.120411792 1.491827774
[6,] 2.355563737 -1.120411792
[7,] 3.629222746 2.355563737
[8,] -1.823587670 3.629222746
[9,] -0.689361832 -1.823587670
[10,] -3.741600299 -0.689361832
[11,] -4.404640808 -3.741600299
[12,] -8.155267416 -4.404640808
[13,] -1.077850789 -8.155267416
[14,] 3.559337889 -1.077850789
[15,] 2.844880542 3.559337889
[16,] 1.054555720 2.844880542
[17,] -2.530196227 1.054555720
[18,] -2.144419140 -2.530196227
[19,] -1.008064667 -2.144419140
[20,] 1.448613707 -1.008064667
[21,] -3.471718812 1.448613707
[22,] -3.326744227 -3.471718812
[23,] -5.830370308 -3.326744227
[24,] -3.152317152 -5.830370308
[25,] 0.565391419 -3.152317152
[26,] 1.608695922 0.565391419
[27,] -4.327666468 1.608695922
[28,] 0.152910089 -4.327666468
[29,] 0.388391338 0.152910089
[30,] -0.164980210 0.388391338
[31,] 2.894914708 -0.164980210
[32,] 6.159182490 2.894914708
[33,] 6.792468964 6.159182490
[34,] 3.400885832 6.792468964
[35,] 4.559958585 3.400885832
[36,] 3.025987473 4.559958585
[37,] -0.177414721 3.025987473
[38,] 1.947540025 -0.177414721
[39,] 6.031679306 1.947540025
[40,] -1.537226058 6.031679306
[41,] 1.545782289 -1.537226058
[42,] -5.578755907 1.545782289
[43,] 2.799083038 -5.578755907
[44,] 4.350238731 2.799083038
[45,] -0.277777590 4.350238731
[46,] -3.393653668 -0.277777590
[47,] 7.308477165 -3.393653668
[48,] -0.005744777 7.308477165
[49,] 3.226692604 -0.005744777
[50,] 2.119349417 3.226692604
[51,] -1.143526599 2.119349417
[52,] -4.038230704 -1.143526599
[53,] -1.548040880 -4.038230704
[54,] 3.443129230 -1.548040880
[55,] -1.332029103 3.443129230
[56,] 1.702419563 -1.332029103
[57,] 2.230831823 1.702419563
[58,] 0.497097928 2.230831823
[59,] -1.668611591 0.497097928
[60,] 1.786588544 -1.668611591
[61,] 1.032576394 1.786588544
[62,] 0.618617410 1.032576394
[63,] 4.083189250 0.618617410
[64,] 0.446148529 4.083189250
[65,] 4.149970256 0.446148529
[66,] -4.587383028 4.149970256
[67,] 5.528161053 -4.587383028
[68,] 1.254308911 5.528161053
[69,] 2.715806599 1.254308911
[70,] -4.997876464 2.715806599
[71,] -0.484333441 -4.997876464
[72,] -0.481888870 -0.484333441
[73,] -1.383147067 -0.481888870
[74,] -0.765831064 -1.383147067
[75,] -2.232098344 -0.765831064
[76,] 1.708305114 -2.232098344
[77,] -0.443373982 1.708305114
[78,] 0.005242363 -0.443373982
[79,] 0.886416022 0.005242363
[80,] 1.617720322 0.886416022
[81,] -6.826057979 1.617720322
[82,] -2.448698988 -6.826057979
[83,] 1.052016960 -2.448698988
[84,] -3.400099178 1.052016960
[85,] -3.196489044 -3.400099178
[86,] 3.425448594 -3.196489044
[87,] 2.239102546 3.425448594
[88,] 2.657350584 2.239102546
[89,] -3.915159985 2.657350584
[90,] -6.369061206 -3.915159985
[91,] -1.164876929 -6.369061206
[92,] -2.776143948 -1.164876929
[93,] -0.093052608 -2.776143948
[94,] -2.473683429 -0.093052608
[95,] 3.370502803 -2.473683429
[96,] -3.693867878 3.370502803
[97,] -3.305393897 -3.693867878
[98,] -3.275402847 -3.305393897
[99,] -3.301060961 -3.275402847
[100,] 0.178593356 -3.301060961
[101,] -1.678950212 0.178593356
[102,] -0.984092902 -1.678950212
[103,] -1.294838867 -0.984092902
[104,] -3.787646372 -1.294838867
[105,] -1.858443294 -3.787646372
[106,] -2.304764901 -1.858443294
[107,] -1.723295214 -2.304764901
[108,] -0.038904376 -1.723295214
[109,] -3.815719357 -0.038904376
[110,] -4.861999277 -3.815719357
[111,] -8.581859758 -4.861999277
[112,] 2.694312858 -8.581859758
[113,] 11.446565078 2.694312858
[114,] 9.923719970 11.446565078
[115,] -1.041736381 9.923719970
[116,] 4.455524196 -1.041736381
[117,] 1.491099250 4.455524196
[118,] -1.236262717 1.491099250
[119,] -3.905451737 -1.236262717
[120,] 2.735798032 -3.905451737
[121,] 0.086853061 2.735798032
[122,] 4.943474388 0.086853061
[123,] -0.736568538 4.943474388
[124,] 0.876535247 -0.736568538
[125,] -1.664571107 0.876535247
[126,] 0.876806509 -1.664571107
[127,] 1.316687152 0.876806509
[128,] -1.974394830 1.316687152
[129,] 0.516485518 -1.974394830
[130,] 3.458301348 0.516485518
[131,] -4.287699078 3.458301348
[132,] 0.583905197 -4.287699078
[133,] -2.856872534 0.583905197
[134,] -6.371868059 -2.856872534
[135,] 1.890318226 -6.371868059
[136,] -0.106077362 1.890318226
[137,] 4.131079805 -0.106077362
[138,] -1.174534871 4.131079805
[139,] 3.572581514 -1.174534871
[140,] 3.287375404 3.572581514
[141,] 4.195317504 3.287375404
[142,] 1.912719231 4.195317504
[143,] 1.012407304 1.912719231
[144,] 1.125799475 1.012407304
[145,] 3.953029048 1.125799475
[146,] -0.410159531 3.953029048
[147,] -0.009081890 -0.410159531
[148,] -7.094743502 -0.009081890
[149,] -2.617102232 -7.094743502
[150,] 3.472193492 -2.617102232
[151,] 0.300083138 3.472193492
[152,] -2.771254805 0.300083138
[153,] -0.063903794 -2.771254805
[154,] 1.943478934 -0.063903794
[155,] -1.813369768 1.943478934
[156,] 0.561777914 -1.813369768
[157,] 0.189437085 0.561777914
[158,] -6.680451143 0.189437085
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.653616944 1.080477379
2 5.723679458 2.653616944
3 -1.270806365 5.723679458
4 1.491827774 -1.270806365
5 -1.120411792 1.491827774
6 2.355563737 -1.120411792
7 3.629222746 2.355563737
8 -1.823587670 3.629222746
9 -0.689361832 -1.823587670
10 -3.741600299 -0.689361832
11 -4.404640808 -3.741600299
12 -8.155267416 -4.404640808
13 -1.077850789 -8.155267416
14 3.559337889 -1.077850789
15 2.844880542 3.559337889
16 1.054555720 2.844880542
17 -2.530196227 1.054555720
18 -2.144419140 -2.530196227
19 -1.008064667 -2.144419140
20 1.448613707 -1.008064667
21 -3.471718812 1.448613707
22 -3.326744227 -3.471718812
23 -5.830370308 -3.326744227
24 -3.152317152 -5.830370308
25 0.565391419 -3.152317152
26 1.608695922 0.565391419
27 -4.327666468 1.608695922
28 0.152910089 -4.327666468
29 0.388391338 0.152910089
30 -0.164980210 0.388391338
31 2.894914708 -0.164980210
32 6.159182490 2.894914708
33 6.792468964 6.159182490
34 3.400885832 6.792468964
35 4.559958585 3.400885832
36 3.025987473 4.559958585
37 -0.177414721 3.025987473
38 1.947540025 -0.177414721
39 6.031679306 1.947540025
40 -1.537226058 6.031679306
41 1.545782289 -1.537226058
42 -5.578755907 1.545782289
43 2.799083038 -5.578755907
44 4.350238731 2.799083038
45 -0.277777590 4.350238731
46 -3.393653668 -0.277777590
47 7.308477165 -3.393653668
48 -0.005744777 7.308477165
49 3.226692604 -0.005744777
50 2.119349417 3.226692604
51 -1.143526599 2.119349417
52 -4.038230704 -1.143526599
53 -1.548040880 -4.038230704
54 3.443129230 -1.548040880
55 -1.332029103 3.443129230
56 1.702419563 -1.332029103
57 2.230831823 1.702419563
58 0.497097928 2.230831823
59 -1.668611591 0.497097928
60 1.786588544 -1.668611591
61 1.032576394 1.786588544
62 0.618617410 1.032576394
63 4.083189250 0.618617410
64 0.446148529 4.083189250
65 4.149970256 0.446148529
66 -4.587383028 4.149970256
67 5.528161053 -4.587383028
68 1.254308911 5.528161053
69 2.715806599 1.254308911
70 -4.997876464 2.715806599
71 -0.484333441 -4.997876464
72 -0.481888870 -0.484333441
73 -1.383147067 -0.481888870
74 -0.765831064 -1.383147067
75 -2.232098344 -0.765831064
76 1.708305114 -2.232098344
77 -0.443373982 1.708305114
78 0.005242363 -0.443373982
79 0.886416022 0.005242363
80 1.617720322 0.886416022
81 -6.826057979 1.617720322
82 -2.448698988 -6.826057979
83 1.052016960 -2.448698988
84 -3.400099178 1.052016960
85 -3.196489044 -3.400099178
86 3.425448594 -3.196489044
87 2.239102546 3.425448594
88 2.657350584 2.239102546
89 -3.915159985 2.657350584
90 -6.369061206 -3.915159985
91 -1.164876929 -6.369061206
92 -2.776143948 -1.164876929
93 -0.093052608 -2.776143948
94 -2.473683429 -0.093052608
95 3.370502803 -2.473683429
96 -3.693867878 3.370502803
97 -3.305393897 -3.693867878
98 -3.275402847 -3.305393897
99 -3.301060961 -3.275402847
100 0.178593356 -3.301060961
101 -1.678950212 0.178593356
102 -0.984092902 -1.678950212
103 -1.294838867 -0.984092902
104 -3.787646372 -1.294838867
105 -1.858443294 -3.787646372
106 -2.304764901 -1.858443294
107 -1.723295214 -2.304764901
108 -0.038904376 -1.723295214
109 -3.815719357 -0.038904376
110 -4.861999277 -3.815719357
111 -8.581859758 -4.861999277
112 2.694312858 -8.581859758
113 11.446565078 2.694312858
114 9.923719970 11.446565078
115 -1.041736381 9.923719970
116 4.455524196 -1.041736381
117 1.491099250 4.455524196
118 -1.236262717 1.491099250
119 -3.905451737 -1.236262717
120 2.735798032 -3.905451737
121 0.086853061 2.735798032
122 4.943474388 0.086853061
123 -0.736568538 4.943474388
124 0.876535247 -0.736568538
125 -1.664571107 0.876535247
126 0.876806509 -1.664571107
127 1.316687152 0.876806509
128 -1.974394830 1.316687152
129 0.516485518 -1.974394830
130 3.458301348 0.516485518
131 -4.287699078 3.458301348
132 0.583905197 -4.287699078
133 -2.856872534 0.583905197
134 -6.371868059 -2.856872534
135 1.890318226 -6.371868059
136 -0.106077362 1.890318226
137 4.131079805 -0.106077362
138 -1.174534871 4.131079805
139 3.572581514 -1.174534871
140 3.287375404 3.572581514
141 4.195317504 3.287375404
142 1.912719231 4.195317504
143 1.012407304 1.912719231
144 1.125799475 1.012407304
145 3.953029048 1.125799475
146 -0.410159531 3.953029048
147 -0.009081890 -0.410159531
148 -7.094743502 -0.009081890
149 -2.617102232 -7.094743502
150 3.472193492 -2.617102232
151 0.300083138 3.472193492
152 -2.771254805 0.300083138
153 -0.063903794 -2.771254805
154 1.943478934 -0.063903794
155 -1.813369768 1.943478934
156 0.561777914 -1.813369768
157 0.189437085 0.561777914
158 -6.680451143 0.189437085
> 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/7pyhk1292685631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8pyhk1292685631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9pyhk1292685631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10iqg51292685631.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11lqfb1292685631.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/1279eh1292685631.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/13lib81292685631.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/14o1se1292685631.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/15ak8j1292685631.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/16v2p71292685631.tab")
+ }
>
> try(system("convert tmp/1mgjw1292685631.ps tmp/1mgjw1292685631.png",intern=TRUE))
character(0)
> try(system("convert tmp/2mgjw1292685631.ps tmp/2mgjw1292685631.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mgjw1292685631.ps tmp/3mgjw1292685631.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mgjw1292685631.ps tmp/4mgjw1292685631.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mgjw1292685631.ps tmp/5mgjw1292685631.png",intern=TRUE))
character(0)
> try(system("convert tmp/6x7ih1292685631.ps tmp/6x7ih1292685631.png",intern=TRUE))
character(0)
> try(system("convert tmp/7pyhk1292685631.ps tmp/7pyhk1292685631.png",intern=TRUE))
character(0)
> try(system("convert tmp/8pyhk1292685631.ps tmp/8pyhk1292685631.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pyhk1292685631.ps tmp/9pyhk1292685631.png",intern=TRUE))
character(0)
> try(system("convert tmp/10iqg51292685631.ps tmp/10iqg51292685631.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.088 1.801 10.024