R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Natural language support but running in an English locale
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Type 'contributors()' for more information and
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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'
+ ,'PE'
+ ,'PC'
+ ,'O'
+ ,'D')
+ ,1:159))
> y <- array(NA,dim=c(5,159),dimnames=list(c('CM','PE','PC','O','D'),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 = '5'
> #'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
D CM PE PC O
1 14 24 11 12 26
2 11 25 7 8 23
3 6 17 17 8 25
4 12 18 10 8 23
5 8 18 12 9 19
6 10 16 12 7 29
7 10 20 11 4 25
8 11 16 11 11 21
9 16 18 12 7 22
10 11 17 13 7 25
11 13 23 14 12 24
12 12 30 16 10 18
13 8 23 11 10 22
14 12 18 10 8 15
15 11 15 11 8 22
16 4 12 15 4 28
17 9 21 9 9 20
18 8 15 11 8 12
19 8 20 17 7 24
20 14 31 17 11 20
21 15 27 11 9 21
22 16 34 18 11 20
23 9 21 14 13 21
24 14 31 10 8 23
25 11 19 11 8 28
26 8 16 15 9 24
27 9 20 15 6 24
28 9 21 13 9 24
29 9 22 16 9 23
30 9 17 13 6 23
31 10 24 9 6 29
32 16 25 18 16 24
33 11 26 18 5 18
34 8 25 12 7 25
35 9 17 17 9 21
36 16 32 9 6 26
37 11 33 9 6 22
38 16 13 12 5 22
39 12 32 18 12 22
40 12 25 12 7 23
41 14 29 18 10 30
42 9 22 14 9 23
43 10 18 15 8 17
44 9 17 16 5 23
45 10 20 10 8 23
46 12 15 11 8 25
47 14 20 14 10 24
48 14 33 9 6 24
49 10 29 12 8 23
50 14 23 17 7 21
51 16 26 5 4 24
52 9 18 12 8 24
53 10 20 12 8 28
54 6 11 6 4 16
55 8 28 24 20 20
56 13 26 12 8 29
57 10 22 12 8 27
58 8 17 14 6 22
59 7 12 7 4 28
60 15 14 13 8 16
61 9 17 12 9 25
62 10 21 13 6 24
63 12 19 14 7 28
64 13 18 8 9 24
65 10 10 11 5 23
66 11 29 9 5 30
67 8 31 11 8 24
68 9 19 13 8 21
69 13 9 10 6 25
70 11 20 11 8 25
71 8 28 12 7 22
72 9 19 9 7 23
73 9 30 15 9 26
74 15 29 18 11 23
75 9 26 15 6 25
76 10 23 12 8 21
77 14 13 13 6 25
78 12 21 14 9 24
79 12 19 10 8 29
80 11 28 13 6 22
81 14 23 13 10 27
82 6 18 11 8 26
83 12 21 13 8 22
84 8 20 16 10 24
85 14 23 8 5 27
86 11 21 16 7 24
87 10 21 11 5 24
88 14 15 9 8 29
89 12 28 16 14 22
90 10 19 12 7 21
91 14 26 14 8 24
92 5 10 8 6 24
93 11 16 9 5 23
94 10 22 15 6 20
95 9 19 11 10 27
96 10 31 21 12 26
97 16 31 14 9 25
98 13 29 18 12 21
99 9 19 12 7 21
100 10 22 13 8 19
101 10 23 15 10 21
102 7 15 12 6 21
103 9 20 19 10 16
104 8 18 15 10 22
105 14 23 11 10 29
106 14 25 11 5 15
107 8 21 10 7 17
108 9 24 13 10 15
109 14 25 15 11 21
110 14 17 12 6 21
111 8 13 12 7 19
112 8 28 16 12 24
113 8 21 9 11 20
114 7 25 18 11 17
115 6 9 8 11 23
116 8 16 13 5 24
117 6 19 17 8 14
118 11 17 9 6 19
119 14 25 15 9 24
120 11 20 8 4 13
121 11 29 7 4 22
122 11 14 12 7 16
123 14 22 14 11 19
124 8 15 6 6 25
125 20 19 8 7 25
126 11 20 17 8 23
127 8 15 10 4 24
128 11 20 11 8 26
129 10 18 14 9 26
130 14 33 11 8 25
131 11 22 13 11 18
132 9 16 12 8 21
133 9 17 11 5 26
134 8 16 9 4 23
135 10 21 12 8 23
136 13 26 20 10 22
137 13 18 12 6 20
138 12 18 13 9 13
139 8 17 12 9 24
140 13 22 12 13 15
141 14 30 9 9 14
142 12 30 15 10 22
143 14 24 24 20 10
144 15 21 7 5 24
145 13 21 17 11 22
146 16 29 11 6 24
147 9 31 17 9 19
148 9 20 11 7 20
149 9 16 12 9 13
150 8 22 14 10 20
151 7 20 11 9 22
152 16 28 16 8 24
153 11 38 21 7 29
154 9 22 14 6 12
155 11 20 20 13 20
156 9 17 13 6 21
157 14 28 11 8 24
158 13 22 15 10 22
159 16 31 19 16 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CM PE PC O
6.49443 0.19971 -0.15244 0.15375 0.03505
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.080 -1.807 -0.303 1.734 8.978
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.49443 1.60818 4.038 8.46e-05 ***
CM 0.19971 0.03856 5.179 6.88e-07 ***
PE -0.15244 0.07509 -2.030 0.0441 *
PC 0.15375 0.09596 1.602 0.1111
O 0.03505 0.05392 0.650 0.5167
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.565 on 154 degrees of freedom
Multiple R-squared: 0.1826, Adjusted R-squared: 0.1613
F-statistic: 8.598 on 4 and 154 DF, p-value: 2.736e-06
> 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,] 0.2669435 0.5338869 0.7330565
[2,] 0.9019544 0.1960912 0.0980456
[3,] 0.8412087 0.3175825 0.1587913
[4,] 0.7954734 0.4090532 0.2045266
[5,] 0.7131607 0.5736785 0.2868393
[6,] 0.7930321 0.4139357 0.2069679
[7,] 0.7302362 0.5395276 0.2697638
[8,] 0.6491306 0.7017388 0.3508694
[9,] 0.7033721 0.5932559 0.2966279
[10,] 0.7218337 0.5563326 0.2781663
[11,] 0.6751081 0.6497838 0.3248919
[12,] 0.6050400 0.7899200 0.3949600
[13,] 0.5685995 0.8628010 0.4314005
[14,] 0.5494091 0.9011819 0.4505909
[15,] 0.5298331 0.9403338 0.4701669
[16,] 0.5161461 0.9677078 0.4838539
[17,] 0.4528916 0.9057832 0.5471084
[18,] 0.3858372 0.7716744 0.6141628
[19,] 0.3285941 0.6571882 0.6714059
[20,] 0.2723304 0.5446607 0.7276696
[21,] 0.2451951 0.4903902 0.7548049
[22,] 0.2080081 0.4160161 0.7919919
[23,] 0.1651980 0.3303960 0.8348020
[24,] 0.1542896 0.3085792 0.8457104
[25,] 0.1906218 0.3812436 0.8093782
[26,] 0.1569680 0.3139359 0.8430320
[27,] 0.1955458 0.3910916 0.8044542
[28,] 0.1570148 0.3140297 0.8429852
[29,] 0.1554460 0.3108920 0.8445540
[30,] 0.1680072 0.3360144 0.8319928
[31,] 0.6172093 0.7655813 0.3827907
[32,] 0.5735402 0.8529196 0.4264598
[33,] 0.5211332 0.9577335 0.4788668
[34,] 0.4966106 0.9932212 0.5033894
[35,] 0.4732746 0.9465493 0.5267254
[36,] 0.4208455 0.8416910 0.5791545
[37,] 0.3706632 0.7413263 0.6293368
[38,] 0.3271925 0.6543850 0.6728075
[39,] 0.3173419 0.6346838 0.6826581
[40,] 0.3417084 0.6834167 0.6582916
[41,] 0.2975835 0.5951671 0.7024165
[42,] 0.2964128 0.5928255 0.7035872
[43,] 0.3415938 0.6831876 0.6584062
[44,] 0.3961996 0.7923992 0.6038004
[45,] 0.3609591 0.7219183 0.6390409
[46,] 0.3191609 0.6383219 0.6808391
[47,] 0.3256008 0.6512015 0.6743992
[48,] 0.4096888 0.8193775 0.5903112
[49,] 0.3668574 0.7337148 0.6331426
[50,] 0.3319738 0.6639476 0.6680262
[51,] 0.3011674 0.6023348 0.6988326
[52,] 0.2924357 0.5848713 0.7075643
[53,] 0.4855912 0.9711824 0.5144088
[54,] 0.4482319 0.8964637 0.5517681
[55,] 0.4027766 0.8055532 0.5972234
[56,] 0.3822802 0.7645603 0.6177198
[57,] 0.3630370 0.7260740 0.6369630
[58,] 0.3357710 0.6715421 0.6642290
[59,] 0.3111284 0.6222567 0.6888716
[60,] 0.4381830 0.8763659 0.5618170
[61,] 0.4040185 0.8080370 0.5959815
[62,] 0.4910176 0.9820352 0.5089824
[63,] 0.4443145 0.8886290 0.5556855
[64,] 0.5147665 0.9704670 0.4852335
[65,] 0.4919462 0.9838923 0.5080538
[66,] 0.5293544 0.9412911 0.4706456
[67,] 0.5459894 0.9080213 0.4540106
[68,] 0.5328910 0.9342180 0.4671090
[69,] 0.4979594 0.9959187 0.5020406
[70,] 0.6367510 0.7264979 0.3632490
[71,] 0.6025792 0.7948415 0.3974208
[72,] 0.5636926 0.8726148 0.4363074
[73,] 0.5247086 0.9505828 0.4752914
[74,] 0.5177227 0.9645545 0.4822773
[75,] 0.6139895 0.7720210 0.3860105
[76,] 0.5798067 0.8403866 0.4201933
[77,] 0.5725900 0.8548199 0.4274100
[78,] 0.5628986 0.8742028 0.4371014
[79,] 0.5224891 0.9550218 0.4775109
[80,] 0.4787678 0.9575355 0.5212322
[81,] 0.5278789 0.9442422 0.4721211
[82,] 0.4856246 0.9712491 0.5143754
[83,] 0.4395422 0.8790844 0.5604578
[84,] 0.4310678 0.8621355 0.5689322
[85,] 0.4884975 0.9769950 0.5115025
[86,] 0.4518038 0.9036077 0.5481962
[87,] 0.4054944 0.8109887 0.5945056
[88,] 0.3895746 0.7791493 0.6104254
[89,] 0.3845566 0.7691132 0.6154434
[90,] 0.3972333 0.7944667 0.6027667
[91,] 0.3556968 0.7113936 0.6443032
[92,] 0.3215117 0.6430233 0.6784883
[93,] 0.2837171 0.5674341 0.7162829
[94,] 0.2516149 0.5032297 0.7483851
[95,] 0.2398160 0.4796320 0.7601840
[96,] 0.2055099 0.4110197 0.7944901
[97,] 0.1931255 0.3862510 0.8068745
[98,] 0.1763854 0.3527707 0.8236146
[99,] 0.1825324 0.3650648 0.8174676
[100,] 0.1866757 0.3733514 0.8133243
[101,] 0.1823248 0.3646496 0.8176752
[102,] 0.1727281 0.3454562 0.8272719
[103,] 0.2344217 0.4688435 0.7655783
[104,] 0.2005430 0.4010860 0.7994570
[105,] 0.2890126 0.5780252 0.7109874
[106,] 0.3645711 0.7291421 0.6354289
[107,] 0.4520552 0.9041104 0.5479448
[108,] 0.5434120 0.9131761 0.4565880
[109,] 0.4986325 0.9972651 0.5013675
[110,] 0.5200105 0.9599790 0.4799895
[111,] 0.4713005 0.9426011 0.5286995
[112,] 0.4555952 0.9111903 0.5444048
[113,] 0.4096494 0.8192988 0.5903506
[114,] 0.3817009 0.7634018 0.6182991
[115,] 0.3651317 0.7302634 0.6348683
[116,] 0.3550213 0.7100427 0.6449787
[117,] 0.3714592 0.7429183 0.6285408
[118,] 0.8879528 0.2240943 0.1120472
[119,] 0.8642019 0.2715961 0.1357981
[120,] 0.8315738 0.3368523 0.1684262
[121,] 0.7888014 0.4223971 0.2111986
[122,] 0.7403578 0.5192845 0.2596422
[123,] 0.6871873 0.6256253 0.3128127
[124,] 0.6358321 0.7283357 0.3641679
[125,] 0.5780304 0.8439392 0.4219696
[126,] 0.5148788 0.9702425 0.4851212
[127,] 0.4670790 0.9341580 0.5329210
[128,] 0.4163713 0.8327427 0.5836287
[129,] 0.3812882 0.7625764 0.6187118
[130,] 0.4300106 0.8600213 0.5699894
[131,] 0.4166673 0.8333346 0.5833327
[132,] 0.4336840 0.8673679 0.5663160
[133,] 0.3619304 0.7238608 0.6380696
[134,] 0.2913488 0.5826976 0.7086512
[135,] 0.2448634 0.4897268 0.7551366
[136,] 0.2303895 0.4607790 0.7696105
[137,] 0.2435469 0.4870937 0.7564531
[138,] 0.2142421 0.4284843 0.7857579
[139,] 0.2884901 0.5769801 0.7115099
[140,] 0.3205216 0.6410431 0.6794784
[141,] 0.2307917 0.4615835 0.7692083
[142,] 0.1495431 0.2990861 0.8504569
[143,] 0.1649303 0.3298606 0.8350697
[144,] 0.4968480 0.9936960 0.5031520
> postscript(file="/var/www/html/freestat/rcomp/tmp/16ii61290544173.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/26ii61290544173.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3zs0r1290544173.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4zs0r1290544173.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5zs0r1290544173.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 159
Frequency = 1
1 2 3 4 5 6
1.63306064 -1.45623452 -3.40428257 1.39904173 -2.30964604 0.04680171
7 8 9 10 11 12
-0.30301509 0.55972858 5.89271980 1.13972230 1.36017839 -0.21511203
13 14 15 16 17 18
-3.71952815 1.67942604 1.18565458 -4.20073634 -2.40113395 -1.46386503
19 20 21 22 23 24
-1.81460684 1.51376534 2.67043849 3.06707559 -2.28901428 0.80282349
25 26 27 28 29 30
0.17652996 -1.62815547 -0.96572691 -1.93157600 -1.63892448 -0.63642664
31 32 33 34 35 36
-1.85442864 3.95549038 0.65737432 -3.61038798 -0.41784540 2.65304271
37 38 39 40 41 42
-2.40647422 7.19877524 -0.75735728 0.45970809 1.86889565 -1.94379953
43 44 45 46 47 48
0.37151759 -0.02535908 -1.00037646 2.08051046 3.26681564 0.52342970
49 50 51 52 53 54
-2.49288327 3.69140999 3.61915322 -1.33113126 -0.87074161 -2.95238851
55 56 57 58 59 60
-4.20383952 0.89595578 -1.23511176 -1.44894107 -2.42023655 5.90052696
61 62 63 64 65 66
-1.32022519 -0.47031106 1.78759752 1.90536365 1.61041696 -1.73426718
67 68 69 70 71 72
-5.07978702 -1.27325871 4.43383747 0.08196498 -4.10437115 -1.79934991
73 74 75 76 77 78
-3.49417889 2.96047694 -2.19902952 -1.22453262 5.09231367 1.22086152
79 80 81 82 83 84
0.98904440 -0.79817864 2.41010671 -4.55366487 1.29227506 -2.42830931
85 86 87 88 89 90
2.41669399 0.83324654 -0.62143113 3.63544325 -0.57090592 -0.27194126
91 92 93 94 95 96
2.37607103 -4.03569864 1.10728734 -0.22495294 -2.09593196 -2.24052777
97 98 99 100 101 102
3.18872253 0.87681804 -1.27194126 -0.80228992 -1.07473000 -2.31934989
103 104 105 106 107 108
-0.69061242 -2.11123257 2.03513558 2.89516485 -2.83604234 -2.36902591
109 110 111 112 113 114
2.37209682 4.28123192 -1.00359061 -4.33349204 -3.70864391 -4.03039844
115 116 117 118 119 120
-3.56971641 -1.31801060 -3.41817233 0.89401542 2.57446267 0.66024881
121 122 123 124 125 126
-1.60500293 1.90184442 2.88888266 -2.37416721 8.97811648 1.06668622
127 128 129 130 131 132
-1.42185910 0.04691694 -0.25010727 0.48574675 -0.22850682 -0.82656895
133 134 135 136 137 138
-0.89269082 -1.73895768 -0.89521051 2.05328230 3.11657086 2.05307972
139 140 141 142 143 144
-2.28517715 1.41668981 1.01177243 -0.50774171 2.94547725 3.76881877
145 146 147 148 149 150
2.44076022 3.62714113 -3.14367665 -1.58903984 -0.69993962 -2.99241039
151 152 153 154 155 156
-3.96664588 4.28152789 -1.97486065 -1.09700615 0.86036801 -0.56633056
157 158 159
1.51934026 2.08993105 3.04986549
> postscript(file="/var/www/html/freestat/rcomp/tmp/6sjzu1290544173.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 1.63306064 NA
1 -1.45623452 1.63306064
2 -3.40428257 -1.45623452
3 1.39904173 -3.40428257
4 -2.30964604 1.39904173
5 0.04680171 -2.30964604
6 -0.30301509 0.04680171
7 0.55972858 -0.30301509
8 5.89271980 0.55972858
9 1.13972230 5.89271980
10 1.36017839 1.13972230
11 -0.21511203 1.36017839
12 -3.71952815 -0.21511203
13 1.67942604 -3.71952815
14 1.18565458 1.67942604
15 -4.20073634 1.18565458
16 -2.40113395 -4.20073634
17 -1.46386503 -2.40113395
18 -1.81460684 -1.46386503
19 1.51376534 -1.81460684
20 2.67043849 1.51376534
21 3.06707559 2.67043849
22 -2.28901428 3.06707559
23 0.80282349 -2.28901428
24 0.17652996 0.80282349
25 -1.62815547 0.17652996
26 -0.96572691 -1.62815547
27 -1.93157600 -0.96572691
28 -1.63892448 -1.93157600
29 -0.63642664 -1.63892448
30 -1.85442864 -0.63642664
31 3.95549038 -1.85442864
32 0.65737432 3.95549038
33 -3.61038798 0.65737432
34 -0.41784540 -3.61038798
35 2.65304271 -0.41784540
36 -2.40647422 2.65304271
37 7.19877524 -2.40647422
38 -0.75735728 7.19877524
39 0.45970809 -0.75735728
40 1.86889565 0.45970809
41 -1.94379953 1.86889565
42 0.37151759 -1.94379953
43 -0.02535908 0.37151759
44 -1.00037646 -0.02535908
45 2.08051046 -1.00037646
46 3.26681564 2.08051046
47 0.52342970 3.26681564
48 -2.49288327 0.52342970
49 3.69140999 -2.49288327
50 3.61915322 3.69140999
51 -1.33113126 3.61915322
52 -0.87074161 -1.33113126
53 -2.95238851 -0.87074161
54 -4.20383952 -2.95238851
55 0.89595578 -4.20383952
56 -1.23511176 0.89595578
57 -1.44894107 -1.23511176
58 -2.42023655 -1.44894107
59 5.90052696 -2.42023655
60 -1.32022519 5.90052696
61 -0.47031106 -1.32022519
62 1.78759752 -0.47031106
63 1.90536365 1.78759752
64 1.61041696 1.90536365
65 -1.73426718 1.61041696
66 -5.07978702 -1.73426718
67 -1.27325871 -5.07978702
68 4.43383747 -1.27325871
69 0.08196498 4.43383747
70 -4.10437115 0.08196498
71 -1.79934991 -4.10437115
72 -3.49417889 -1.79934991
73 2.96047694 -3.49417889
74 -2.19902952 2.96047694
75 -1.22453262 -2.19902952
76 5.09231367 -1.22453262
77 1.22086152 5.09231367
78 0.98904440 1.22086152
79 -0.79817864 0.98904440
80 2.41010671 -0.79817864
81 -4.55366487 2.41010671
82 1.29227506 -4.55366487
83 -2.42830931 1.29227506
84 2.41669399 -2.42830931
85 0.83324654 2.41669399
86 -0.62143113 0.83324654
87 3.63544325 -0.62143113
88 -0.57090592 3.63544325
89 -0.27194126 -0.57090592
90 2.37607103 -0.27194126
91 -4.03569864 2.37607103
92 1.10728734 -4.03569864
93 -0.22495294 1.10728734
94 -2.09593196 -0.22495294
95 -2.24052777 -2.09593196
96 3.18872253 -2.24052777
97 0.87681804 3.18872253
98 -1.27194126 0.87681804
99 -0.80228992 -1.27194126
100 -1.07473000 -0.80228992
101 -2.31934989 -1.07473000
102 -0.69061242 -2.31934989
103 -2.11123257 -0.69061242
104 2.03513558 -2.11123257
105 2.89516485 2.03513558
106 -2.83604234 2.89516485
107 -2.36902591 -2.83604234
108 2.37209682 -2.36902591
109 4.28123192 2.37209682
110 -1.00359061 4.28123192
111 -4.33349204 -1.00359061
112 -3.70864391 -4.33349204
113 -4.03039844 -3.70864391
114 -3.56971641 -4.03039844
115 -1.31801060 -3.56971641
116 -3.41817233 -1.31801060
117 0.89401542 -3.41817233
118 2.57446267 0.89401542
119 0.66024881 2.57446267
120 -1.60500293 0.66024881
121 1.90184442 -1.60500293
122 2.88888266 1.90184442
123 -2.37416721 2.88888266
124 8.97811648 -2.37416721
125 1.06668622 8.97811648
126 -1.42185910 1.06668622
127 0.04691694 -1.42185910
128 -0.25010727 0.04691694
129 0.48574675 -0.25010727
130 -0.22850682 0.48574675
131 -0.82656895 -0.22850682
132 -0.89269082 -0.82656895
133 -1.73895768 -0.89269082
134 -0.89521051 -1.73895768
135 2.05328230 -0.89521051
136 3.11657086 2.05328230
137 2.05307972 3.11657086
138 -2.28517715 2.05307972
139 1.41668981 -2.28517715
140 1.01177243 1.41668981
141 -0.50774171 1.01177243
142 2.94547725 -0.50774171
143 3.76881877 2.94547725
144 2.44076022 3.76881877
145 3.62714113 2.44076022
146 -3.14367665 3.62714113
147 -1.58903984 -3.14367665
148 -0.69993962 -1.58903984
149 -2.99241039 -0.69993962
150 -3.96664588 -2.99241039
151 4.28152789 -3.96664588
152 -1.97486065 4.28152789
153 -1.09700615 -1.97486065
154 0.86036801 -1.09700615
155 -0.56633056 0.86036801
156 1.51934026 -0.56633056
157 2.08993105 1.51934026
158 3.04986549 2.08993105
159 NA 3.04986549
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.45623452 1.63306064
[2,] -3.40428257 -1.45623452
[3,] 1.39904173 -3.40428257
[4,] -2.30964604 1.39904173
[5,] 0.04680171 -2.30964604
[6,] -0.30301509 0.04680171
[7,] 0.55972858 -0.30301509
[8,] 5.89271980 0.55972858
[9,] 1.13972230 5.89271980
[10,] 1.36017839 1.13972230
[11,] -0.21511203 1.36017839
[12,] -3.71952815 -0.21511203
[13,] 1.67942604 -3.71952815
[14,] 1.18565458 1.67942604
[15,] -4.20073634 1.18565458
[16,] -2.40113395 -4.20073634
[17,] -1.46386503 -2.40113395
[18,] -1.81460684 -1.46386503
[19,] 1.51376534 -1.81460684
[20,] 2.67043849 1.51376534
[21,] 3.06707559 2.67043849
[22,] -2.28901428 3.06707559
[23,] 0.80282349 -2.28901428
[24,] 0.17652996 0.80282349
[25,] -1.62815547 0.17652996
[26,] -0.96572691 -1.62815547
[27,] -1.93157600 -0.96572691
[28,] -1.63892448 -1.93157600
[29,] -0.63642664 -1.63892448
[30,] -1.85442864 -0.63642664
[31,] 3.95549038 -1.85442864
[32,] 0.65737432 3.95549038
[33,] -3.61038798 0.65737432
[34,] -0.41784540 -3.61038798
[35,] 2.65304271 -0.41784540
[36,] -2.40647422 2.65304271
[37,] 7.19877524 -2.40647422
[38,] -0.75735728 7.19877524
[39,] 0.45970809 -0.75735728
[40,] 1.86889565 0.45970809
[41,] -1.94379953 1.86889565
[42,] 0.37151759 -1.94379953
[43,] -0.02535908 0.37151759
[44,] -1.00037646 -0.02535908
[45,] 2.08051046 -1.00037646
[46,] 3.26681564 2.08051046
[47,] 0.52342970 3.26681564
[48,] -2.49288327 0.52342970
[49,] 3.69140999 -2.49288327
[50,] 3.61915322 3.69140999
[51,] -1.33113126 3.61915322
[52,] -0.87074161 -1.33113126
[53,] -2.95238851 -0.87074161
[54,] -4.20383952 -2.95238851
[55,] 0.89595578 -4.20383952
[56,] -1.23511176 0.89595578
[57,] -1.44894107 -1.23511176
[58,] -2.42023655 -1.44894107
[59,] 5.90052696 -2.42023655
[60,] -1.32022519 5.90052696
[61,] -0.47031106 -1.32022519
[62,] 1.78759752 -0.47031106
[63,] 1.90536365 1.78759752
[64,] 1.61041696 1.90536365
[65,] -1.73426718 1.61041696
[66,] -5.07978702 -1.73426718
[67,] -1.27325871 -5.07978702
[68,] 4.43383747 -1.27325871
[69,] 0.08196498 4.43383747
[70,] -4.10437115 0.08196498
[71,] -1.79934991 -4.10437115
[72,] -3.49417889 -1.79934991
[73,] 2.96047694 -3.49417889
[74,] -2.19902952 2.96047694
[75,] -1.22453262 -2.19902952
[76,] 5.09231367 -1.22453262
[77,] 1.22086152 5.09231367
[78,] 0.98904440 1.22086152
[79,] -0.79817864 0.98904440
[80,] 2.41010671 -0.79817864
[81,] -4.55366487 2.41010671
[82,] 1.29227506 -4.55366487
[83,] -2.42830931 1.29227506
[84,] 2.41669399 -2.42830931
[85,] 0.83324654 2.41669399
[86,] -0.62143113 0.83324654
[87,] 3.63544325 -0.62143113
[88,] -0.57090592 3.63544325
[89,] -0.27194126 -0.57090592
[90,] 2.37607103 -0.27194126
[91,] -4.03569864 2.37607103
[92,] 1.10728734 -4.03569864
[93,] -0.22495294 1.10728734
[94,] -2.09593196 -0.22495294
[95,] -2.24052777 -2.09593196
[96,] 3.18872253 -2.24052777
[97,] 0.87681804 3.18872253
[98,] -1.27194126 0.87681804
[99,] -0.80228992 -1.27194126
[100,] -1.07473000 -0.80228992
[101,] -2.31934989 -1.07473000
[102,] -0.69061242 -2.31934989
[103,] -2.11123257 -0.69061242
[104,] 2.03513558 -2.11123257
[105,] 2.89516485 2.03513558
[106,] -2.83604234 2.89516485
[107,] -2.36902591 -2.83604234
[108,] 2.37209682 -2.36902591
[109,] 4.28123192 2.37209682
[110,] -1.00359061 4.28123192
[111,] -4.33349204 -1.00359061
[112,] -3.70864391 -4.33349204
[113,] -4.03039844 -3.70864391
[114,] -3.56971641 -4.03039844
[115,] -1.31801060 -3.56971641
[116,] -3.41817233 -1.31801060
[117,] 0.89401542 -3.41817233
[118,] 2.57446267 0.89401542
[119,] 0.66024881 2.57446267
[120,] -1.60500293 0.66024881
[121,] 1.90184442 -1.60500293
[122,] 2.88888266 1.90184442
[123,] -2.37416721 2.88888266
[124,] 8.97811648 -2.37416721
[125,] 1.06668622 8.97811648
[126,] -1.42185910 1.06668622
[127,] 0.04691694 -1.42185910
[128,] -0.25010727 0.04691694
[129,] 0.48574675 -0.25010727
[130,] -0.22850682 0.48574675
[131,] -0.82656895 -0.22850682
[132,] -0.89269082 -0.82656895
[133,] -1.73895768 -0.89269082
[134,] -0.89521051 -1.73895768
[135,] 2.05328230 -0.89521051
[136,] 3.11657086 2.05328230
[137,] 2.05307972 3.11657086
[138,] -2.28517715 2.05307972
[139,] 1.41668981 -2.28517715
[140,] 1.01177243 1.41668981
[141,] -0.50774171 1.01177243
[142,] 2.94547725 -0.50774171
[143,] 3.76881877 2.94547725
[144,] 2.44076022 3.76881877
[145,] 3.62714113 2.44076022
[146,] -3.14367665 3.62714113
[147,] -1.58903984 -3.14367665
[148,] -0.69993962 -1.58903984
[149,] -2.99241039 -0.69993962
[150,] -3.96664588 -2.99241039
[151,] 4.28152789 -3.96664588
[152,] -1.97486065 4.28152789
[153,] -1.09700615 -1.97486065
[154,] 0.86036801 -1.09700615
[155,] -0.56633056 0.86036801
[156,] 1.51934026 -0.56633056
[157,] 2.08993105 1.51934026
[158,] 3.04986549 2.08993105
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.45623452 1.63306064
2 -3.40428257 -1.45623452
3 1.39904173 -3.40428257
4 -2.30964604 1.39904173
5 0.04680171 -2.30964604
6 -0.30301509 0.04680171
7 0.55972858 -0.30301509
8 5.89271980 0.55972858
9 1.13972230 5.89271980
10 1.36017839 1.13972230
11 -0.21511203 1.36017839
12 -3.71952815 -0.21511203
13 1.67942604 -3.71952815
14 1.18565458 1.67942604
15 -4.20073634 1.18565458
16 -2.40113395 -4.20073634
17 -1.46386503 -2.40113395
18 -1.81460684 -1.46386503
19 1.51376534 -1.81460684
20 2.67043849 1.51376534
21 3.06707559 2.67043849
22 -2.28901428 3.06707559
23 0.80282349 -2.28901428
24 0.17652996 0.80282349
25 -1.62815547 0.17652996
26 -0.96572691 -1.62815547
27 -1.93157600 -0.96572691
28 -1.63892448 -1.93157600
29 -0.63642664 -1.63892448
30 -1.85442864 -0.63642664
31 3.95549038 -1.85442864
32 0.65737432 3.95549038
33 -3.61038798 0.65737432
34 -0.41784540 -3.61038798
35 2.65304271 -0.41784540
36 -2.40647422 2.65304271
37 7.19877524 -2.40647422
38 -0.75735728 7.19877524
39 0.45970809 -0.75735728
40 1.86889565 0.45970809
41 -1.94379953 1.86889565
42 0.37151759 -1.94379953
43 -0.02535908 0.37151759
44 -1.00037646 -0.02535908
45 2.08051046 -1.00037646
46 3.26681564 2.08051046
47 0.52342970 3.26681564
48 -2.49288327 0.52342970
49 3.69140999 -2.49288327
50 3.61915322 3.69140999
51 -1.33113126 3.61915322
52 -0.87074161 -1.33113126
53 -2.95238851 -0.87074161
54 -4.20383952 -2.95238851
55 0.89595578 -4.20383952
56 -1.23511176 0.89595578
57 -1.44894107 -1.23511176
58 -2.42023655 -1.44894107
59 5.90052696 -2.42023655
60 -1.32022519 5.90052696
61 -0.47031106 -1.32022519
62 1.78759752 -0.47031106
63 1.90536365 1.78759752
64 1.61041696 1.90536365
65 -1.73426718 1.61041696
66 -5.07978702 -1.73426718
67 -1.27325871 -5.07978702
68 4.43383747 -1.27325871
69 0.08196498 4.43383747
70 -4.10437115 0.08196498
71 -1.79934991 -4.10437115
72 -3.49417889 -1.79934991
73 2.96047694 -3.49417889
74 -2.19902952 2.96047694
75 -1.22453262 -2.19902952
76 5.09231367 -1.22453262
77 1.22086152 5.09231367
78 0.98904440 1.22086152
79 -0.79817864 0.98904440
80 2.41010671 -0.79817864
81 -4.55366487 2.41010671
82 1.29227506 -4.55366487
83 -2.42830931 1.29227506
84 2.41669399 -2.42830931
85 0.83324654 2.41669399
86 -0.62143113 0.83324654
87 3.63544325 -0.62143113
88 -0.57090592 3.63544325
89 -0.27194126 -0.57090592
90 2.37607103 -0.27194126
91 -4.03569864 2.37607103
92 1.10728734 -4.03569864
93 -0.22495294 1.10728734
94 -2.09593196 -0.22495294
95 -2.24052777 -2.09593196
96 3.18872253 -2.24052777
97 0.87681804 3.18872253
98 -1.27194126 0.87681804
99 -0.80228992 -1.27194126
100 -1.07473000 -0.80228992
101 -2.31934989 -1.07473000
102 -0.69061242 -2.31934989
103 -2.11123257 -0.69061242
104 2.03513558 -2.11123257
105 2.89516485 2.03513558
106 -2.83604234 2.89516485
107 -2.36902591 -2.83604234
108 2.37209682 -2.36902591
109 4.28123192 2.37209682
110 -1.00359061 4.28123192
111 -4.33349204 -1.00359061
112 -3.70864391 -4.33349204
113 -4.03039844 -3.70864391
114 -3.56971641 -4.03039844
115 -1.31801060 -3.56971641
116 -3.41817233 -1.31801060
117 0.89401542 -3.41817233
118 2.57446267 0.89401542
119 0.66024881 2.57446267
120 -1.60500293 0.66024881
121 1.90184442 -1.60500293
122 2.88888266 1.90184442
123 -2.37416721 2.88888266
124 8.97811648 -2.37416721
125 1.06668622 8.97811648
126 -1.42185910 1.06668622
127 0.04691694 -1.42185910
128 -0.25010727 0.04691694
129 0.48574675 -0.25010727
130 -0.22850682 0.48574675
131 -0.82656895 -0.22850682
132 -0.89269082 -0.82656895
133 -1.73895768 -0.89269082
134 -0.89521051 -1.73895768
135 2.05328230 -0.89521051
136 3.11657086 2.05328230
137 2.05307972 3.11657086
138 -2.28517715 2.05307972
139 1.41668981 -2.28517715
140 1.01177243 1.41668981
141 -0.50774171 1.01177243
142 2.94547725 -0.50774171
143 3.76881877 2.94547725
144 2.44076022 3.76881877
145 3.62714113 2.44076022
146 -3.14367665 3.62714113
147 -1.58903984 -3.14367665
148 -0.69993962 -1.58903984
149 -2.99241039 -0.69993962
150 -3.96664588 -2.99241039
151 4.28152789 -3.96664588
152 -1.97486065 4.28152789
153 -1.09700615 -1.97486065
154 0.86036801 -1.09700615
155 -0.56633056 0.86036801
156 1.51934026 -0.56633056
157 2.08993105 1.51934026
158 3.04986549 2.08993105
> 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/freestat/rcomp/tmp/72sgx1290544173.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/82sgx1290544173.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/92sgx1290544173.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10d2yi1290544173.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11g2w51290544173.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/freestat/rcomp/tmp/1222cb1290544173.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/freestat/rcomp/tmp/1383951290544173.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/freestat/rcomp/tmp/146gge1290544173.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/freestat/rcomp/tmp/15mvpe1290544173.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/freestat/rcomp/tmp/1615nn1290544173.tab")
+ }
>
> try(system("convert tmp/16ii61290544173.ps tmp/16ii61290544173.png",intern=TRUE))
character(0)
> try(system("convert tmp/26ii61290544173.ps tmp/26ii61290544173.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zs0r1290544173.ps tmp/3zs0r1290544173.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zs0r1290544173.ps tmp/4zs0r1290544173.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zs0r1290544173.ps tmp/5zs0r1290544173.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sjzu1290544173.ps tmp/6sjzu1290544173.png",intern=TRUE))
character(0)
> try(system("convert tmp/72sgx1290544173.ps tmp/72sgx1290544173.png",intern=TRUE))
character(0)
> try(system("convert tmp/82sgx1290544173.ps tmp/82sgx1290544173.png",intern=TRUE))
character(0)
> try(system("convert tmp/92sgx1290544173.ps tmp/92sgx1290544173.png",intern=TRUE))
character(0)
> try(system("convert tmp/10d2yi1290544173.ps tmp/10d2yi1290544173.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.704 2.766 7.586