R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(41
+ ,38
+ ,39
+ ,32
+ ,30
+ ,35
+ ,31
+ ,33
+ ,34
+ ,37
+ ,35
+ ,29
+ ,39
+ ,31
+ ,34
+ ,36
+ ,36
+ ,35
+ ,37
+ ,38
+ ,38
+ ,31
+ ,36
+ ,34
+ ,38
+ ,35
+ ,39
+ ,38
+ ,33
+ ,37
+ ,32
+ ,33
+ ,36
+ ,32
+ ,38
+ ,38
+ ,39
+ ,38
+ ,32
+ ,32
+ ,32
+ ,33
+ ,31
+ ,31
+ ,39
+ ,38
+ ,37
+ ,39
+ ,39
+ ,32
+ ,41
+ ,32
+ ,36
+ ,35
+ ,33
+ ,37
+ ,33
+ ,33
+ ,34
+ ,33
+ ,31
+ ,28
+ ,27
+ ,32
+ ,37
+ ,31
+ ,34
+ ,37
+ ,34
+ ,30
+ ,32
+ ,33
+ ,29
+ ,31
+ ,36
+ ,33
+ ,29
+ ,31
+ ,35
+ ,33
+ ,37
+ ,32
+ ,34
+ ,33
+ ,38
+ ,32
+ ,35
+ ,33
+ ,38
+ ,28
+ ,37
+ ,35
+ ,38
+ ,39
+ ,33
+ ,34
+ ,36
+ ,38
+ ,38
+ ,32
+ ,32
+ ,38
+ ,32
+ ,30
+ ,32
+ ,33
+ ,34
+ ,38
+ ,32
+ ,32
+ ,37
+ ,32
+ ,39
+ ,34
+ ,29
+ ,34
+ ,37
+ ,36
+ ,35
+ ,34
+ ,30
+ ,28
+ ,38
+ ,34
+ ,34
+ ,35
+ ,31
+ ,35
+ ,34
+ ,31
+ ,35
+ ,37
+ ,36
+ ,35
+ ,30
+ ,27
+ ,39
+ ,40
+ ,35
+ ,37
+ ,38
+ ,36
+ ,31
+ ,38
+ ,34
+ ,39
+ ,38
+ ,41
+ ,34
+ ,27
+ ,39
+ ,30
+ ,37
+ ,37
+ ,34
+ ,31
+ ,28
+ ,31
+ ,37
+ ,27
+ ,33
+ ,36
+ ,37
+ ,38
+ ,35
+ ,37
+ ,37
+ ,33
+ ,32
+ ,34
+ ,33
+ ,31
+ ,38
+ ,39
+ ,33
+ ,34
+ ,29
+ ,32
+ ,33
+ ,33
+ ,31
+ ,36
+ ,36
+ ,32
+ ,35
+ ,41
+ ,32
+ ,28
+ ,29
+ ,30
+ ,39
+ ,36
+ ,37
+ ,35
+ ,35
+ ,31
+ ,37
+ ,34
+ ,32
+ ,36
+ ,38
+ ,36
+ ,37
+ ,35
+ ,36
+ ,37
+ ,32
+ ,28
+ ,33
+ ,39
+ ,40
+ ,32
+ ,38
+ ,35
+ ,41
+ ,39
+ ,36
+ ,35
+ ,43
+ ,42
+ ,30
+ ,34
+ ,31
+ ,33
+ ,32
+ ,41
+ ,32
+ ,33
+ ,37
+ ,34
+ ,37
+ ,32
+ ,33
+ ,40
+ ,34
+ ,40
+ ,33
+ ,35
+ ,38
+ ,36
+ ,33
+ ,37
+ ,31
+ ,27
+ ,38
+ ,39
+ ,37
+ ,38
+ ,33
+ ,31
+ ,31
+ ,33
+ ,39
+ ,32
+ ,44
+ ,39
+ ,33
+ ,36
+ ,35
+ ,33
+ ,32
+ ,33
+ ,28
+ ,32
+ ,40
+ ,37
+ ,27
+ ,30
+ ,37
+ ,38
+ ,32
+ ,29
+ ,28
+ ,22
+ ,34
+ ,35
+ ,30
+ ,35
+ ,35
+ ,34
+ ,31
+ ,35
+ ,32
+ ,34
+ ,30
+ ,34
+ ,30
+ ,35
+ ,31
+ ,23
+ ,40
+ ,31
+ ,32
+ ,27
+ ,36
+ ,36
+ ,32
+ ,31
+ ,35
+ ,32
+ ,38
+ ,39
+ ,42
+ ,37
+ ,34
+ ,38
+ ,35
+ ,39
+ ,35
+ ,34
+ ,33
+ ,31
+ ,36
+ ,32
+ ,32
+ ,37
+ ,33
+ ,36
+ ,34
+ ,32
+ ,32
+ ,35
+ ,34
+ ,36)
+ ,dim=c(2
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Separate')
+ ,1:162))
> y <- array(NA,dim=c(2,162),dimnames=list(c('Connected','Separate'),1:162))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Separate Connected
1 38 41
2 32 39
3 35 30
4 33 31
5 37 34
6 29 35
7 31 39
8 36 34
9 35 36
10 38 37
11 31 38
12 34 36
13 35 38
14 38 39
15 37 33
16 33 32
17 32 36
18 38 38
19 38 39
20 32 32
21 33 32
22 31 31
23 38 39
24 39 37
25 32 39
26 32 41
27 35 36
28 37 33
29 33 33
30 33 34
31 28 31
32 32 27
33 31 37
34 37 34
35 30 34
36 33 32
37 31 29
38 33 36
39 31 29
40 33 35
41 32 37
42 33 34
43 32 38
44 33 35
45 28 38
46 35 37
47 39 38
48 34 33
49 38 36
50 32 38
51 38 32
52 30 32
53 33 32
54 38 34
55 32 32
56 32 37
57 34 39
58 34 29
59 36 37
60 34 35
61 28 30
62 34 38
63 35 34
64 35 31
65 31 34
66 37 35
67 35 36
68 27 30
69 40 39
70 37 35
71 36 38
72 38 31
73 39 34
74 41 38
75 27 34
76 30 39
77 37 37
78 31 34
79 31 28
80 27 37
81 36 33
82 38 37
83 37 35
84 33 37
85 34 32
86 31 33
87 39 38
88 34 33
89 32 29
90 33 33
91 36 31
92 32 36
93 41 35
94 28 32
95 30 29
96 36 39
97 35 37
98 31 35
99 34 37
100 36 32
101 36 38
102 35 37
103 37 36
104 28 32
105 39 33
106 32 40
107 35 38
108 39 41
109 35 36
110 42 43
111 34 30
112 33 31
113 41 32
114 33 32
115 34 37
116 32 37
117 40 33
118 40 34
119 35 33
120 36 38
121 37 33
122 27 31
123 39 38
124 38 37
125 31 33
126 33 31
127 32 39
128 39 44
129 36 33
130 33 35
131 33 32
132 32 28
133 37 40
134 30 27
135 38 37
136 29 32
137 22 28
138 35 34
139 35 30
140 34 35
141 35 31
142 34 32
143 34 30
144 35 30
145 23 31
146 31 40
147 27 32
148 36 36
149 31 32
150 32 35
151 39 38
152 37 42
153 38 34
154 39 35
155 34 35
156 31 33
157 32 36
158 37 32
159 36 33
160 32 34
161 35 32
162 36 34
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected
20.6767 0.3875
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.689 -2.198 0.149 2.537 7.924
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.67674 2.69117 7.683 1.47e-12 ***
Connected 0.38748 0.07736 5.009 1.44e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.313 on 160 degrees of freedom
Multiple R-squared: 0.1355, Adjusted R-squared: 0.1301
F-statistic: 25.09 on 1 and 160 DF, p-value: 1.436e-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,] 0.46682134 0.93364268 0.53317866
[2,] 0.70261529 0.59476942 0.29738471
[3,] 0.67175598 0.65648805 0.32824402
[4,] 0.60264198 0.79471604 0.39735802
[5,] 0.49662271 0.99324541 0.50337729
[6,] 0.52453403 0.95093195 0.47546597
[7,] 0.52365030 0.95269940 0.47634970
[8,] 0.42573252 0.85146504 0.57426748
[9,] 0.34183028 0.68366056 0.65816972
[10,] 0.36050685 0.72101370 0.63949315
[11,] 0.33647173 0.67294345 0.66352827
[12,] 0.27579621 0.55159242 0.72420379
[13,] 0.24948131 0.49896262 0.75051869
[14,] 0.25719626 0.51439252 0.74280374
[15,] 0.24820497 0.49640994 0.75179503
[16,] 0.21214589 0.42429177 0.78785411
[17,] 0.16509145 0.33018290 0.83490855
[18,] 0.14581228 0.29162456 0.85418772
[19,] 0.13304109 0.26608218 0.86695891
[20,] 0.15792727 0.31585453 0.84207273
[21,] 0.17299410 0.34598819 0.82700590
[22,] 0.19568251 0.39136502 0.80431749
[23,] 0.15461905 0.30923811 0.84538095
[24,] 0.15273468 0.30546935 0.84726532
[25,] 0.12175509 0.24351018 0.87824491
[26,] 0.09663242 0.19326485 0.90336758
[27,] 0.15312982 0.30625964 0.84687018
[28,] 0.12059272 0.24118544 0.87940728
[29,] 0.13220717 0.26441435 0.86779283
[30,] 0.13232201 0.26464401 0.86767799
[31,] 0.14732819 0.29465637 0.85267181
[32,] 0.11687148 0.23374296 0.88312852
[33,] 0.09530980 0.19061960 0.90469020
[34,] 0.07714925 0.15429850 0.92285075
[35,] 0.06115956 0.12231912 0.93884044
[36,] 0.04744257 0.09488514 0.95255743
[37,] 0.04337919 0.08675838 0.95662081
[38,] 0.03260071 0.06520142 0.96739929
[39,] 0.03104845 0.06209690 0.96895155
[40,] 0.02336876 0.04673752 0.97663124
[41,] 0.06671959 0.13343917 0.93328041
[42,] 0.05245209 0.10490418 0.94754791
[43,] 0.06678203 0.13356406 0.93321797
[44,] 0.05237733 0.10475465 0.94762267
[45,] 0.05905927 0.11811853 0.94094073
[46,] 0.05665562 0.11331123 0.94334438
[47,] 0.07963004 0.15926007 0.92036996
[48,] 0.07911459 0.15822918 0.92088541
[49,] 0.06234702 0.12469404 0.93765298
[50,] 0.07597996 0.15195991 0.92402004
[51,] 0.06182432 0.12364863 0.93817568
[52,] 0.05724996 0.11449993 0.94275004
[53,] 0.04674617 0.09349234 0.95325383
[54,] 0.03908689 0.07817378 0.96091311
[55,] 0.03185529 0.06371058 0.96814471
[56,] 0.02420805 0.04841610 0.97579195
[57,] 0.03238958 0.06477917 0.96761042
[58,] 0.02554314 0.05108628 0.97445686
[59,] 0.02024717 0.04049434 0.97975283
[60,] 0.01745611 0.03491222 0.98254389
[61,] 0.01620365 0.03240731 0.98379635
[62,] 0.01571636 0.03143272 0.98428364
[63,] 0.01184981 0.02369962 0.98815019
[64,] 0.02088808 0.04177615 0.97911192
[65,] 0.02811306 0.05622611 0.97188694
[66,] 0.02684434 0.05368867 0.97315566
[67,] 0.02093955 0.04187911 0.97906045
[68,] 0.03265241 0.06530481 0.96734759
[69,] 0.04784185 0.09568369 0.95215815
[70,] 0.07690567 0.15381134 0.92309433
[71,] 0.14746814 0.29493627 0.85253186
[72,] 0.20489506 0.40979012 0.79510494
[73,] 0.18625310 0.37250620 0.81374690
[74,] 0.17860404 0.35720808 0.82139596
[75,] 0.15173991 0.30347983 0.84826009
[76,] 0.31517182 0.63034363 0.68482818
[77,] 0.29857042 0.59714084 0.70142958
[78,] 0.29255715 0.58511430 0.70744285
[79,] 0.28072667 0.56145335 0.71927333
[80,] 0.25822748 0.51645495 0.74177252
[81,] 0.22541154 0.45082308 0.77458846
[82,] 0.21114535 0.42229070 0.78885465
[83,] 0.21726239 0.43452479 0.78273761
[84,] 0.18615246 0.37230491 0.81384754
[85,] 0.15751749 0.31503498 0.84248251
[86,] 0.13232924 0.26465849 0.86767076
[87,] 0.13157886 0.26315772 0.86842114
[88,] 0.12349723 0.24699445 0.87650277
[89,] 0.20880385 0.41760769 0.79119615
[90,] 0.25538771 0.51077543 0.74461229
[91,] 0.23153266 0.46306531 0.76846734
[92,] 0.19915605 0.39831210 0.80084395
[93,] 0.16923578 0.33847156 0.83076422
[94,] 0.16930304 0.33860609 0.83069696
[95,] 0.14561598 0.29123197 0.85438402
[96,] 0.13860048 0.27720097 0.86139952
[97,] 0.11562161 0.23124322 0.88437839
[98,] 0.09505537 0.19011074 0.90494463
[99,] 0.08461219 0.16922438 0.91538781
[100,] 0.11119939 0.22239878 0.88880061
[101,] 0.15079255 0.30158510 0.84920745
[102,] 0.17605269 0.35210539 0.82394731
[103,] 0.14966949 0.29933899 0.85033051
[104,] 0.13371553 0.26743105 0.86628447
[105,] 0.11008550 0.22017100 0.88991450
[106,] 0.12200647 0.24401294 0.87799353
[107,] 0.10492730 0.20985461 0.89507270
[108,] 0.08480918 0.16961836 0.91519082
[109,] 0.20149430 0.40298860 0.79850570
[110,] 0.16886496 0.33772991 0.83113504
[111,] 0.14378675 0.28757351 0.85621325
[112,] 0.14282992 0.28565984 0.85717008
[113,] 0.23213626 0.46427251 0.76786374
[114,] 0.32971337 0.65942673 0.67028663
[115,] 0.29588394 0.59176789 0.70411606
[116,] 0.25381332 0.50762663 0.74618668
[117,] 0.26274765 0.52549529 0.73725235
[118,] 0.32971527 0.65943054 0.67028473
[119,] 0.32904393 0.65808787 0.67095607
[120,] 0.31660119 0.63320239 0.68339881
[121,] 0.29049350 0.58098699 0.70950650
[122,] 0.24784374 0.49568748 0.75215626
[123,] 0.26747644 0.53495288 0.73252356
[124,] 0.22654185 0.45308369 0.77345815
[125,] 0.21135701 0.42271402 0.78864299
[126,] 0.17776275 0.35552550 0.82223725
[127,] 0.14360289 0.28720578 0.85639711
[128,] 0.11861915 0.23723831 0.88138085
[129,] 0.09281052 0.18562103 0.90718948
[130,] 0.07188275 0.14376550 0.92811725
[131,] 0.06427963 0.12855926 0.93572037
[132,] 0.06557843 0.13115686 0.93442157
[133,] 0.28477937 0.56955874 0.71522063
[134,] 0.23794944 0.47589888 0.76205056
[135,] 0.21335514 0.42671028 0.78664486
[136,] 0.16882568 0.33765136 0.83117432
[137,] 0.14550936 0.29101871 0.85449064
[138,] 0.11286903 0.22573807 0.88713097
[139,] 0.09136688 0.18273376 0.90863312
[140,] 0.08765308 0.17530616 0.91234692
[141,] 0.41684276 0.83368552 0.58315724
[142,] 0.54907339 0.90185321 0.45092661
[143,] 0.78601232 0.42797537 0.21398768
[144,] 0.71853777 0.56292447 0.28146223
[145,] 0.72088392 0.55823217 0.27911608
[146,] 0.73418859 0.53162281 0.26581141
[147,] 0.73008024 0.53983951 0.26991976
[148,] 0.66471304 0.67057392 0.33528696
[149,] 0.67331897 0.65336206 0.32668103
[150,] 0.88055670 0.23888660 0.11944330
[151,] 0.80713049 0.38573902 0.19286951
[152,] 0.91372503 0.17254994 0.08627497
[153,] 0.80797292 0.38405416 0.19202708
> postscript(file="/var/wessaorg/rcomp/tmp/1gpjt1322157935.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/29x8r1322157935.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3tjxf1322157935.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ojj21322157935.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5nvgy1322157935.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 = 162
Frequency = 1
1 2 3 4 5 6
1.43663185 -3.78841047 2.69889907 0.31142023 3.14898372 -5.23849512
7 8 9 10 11 12
-4.78841047 2.14898372 0.37402604 2.98654721 -4.40093163 -0.62597396
13 14 15 16 17 18
-0.40093163 2.21158953 3.53646256 -0.07605860 -2.62597396 2.59906837
19 20 21 22 23 24
2.21158953 -1.07605860 -0.07605860 -1.68857977 2.21158953 3.98654721
25 26 27 28 29 30
-3.78841047 -4.56336815 0.37402604 3.53646256 -0.46353744 -0.85101628
31 32 33 34 35 36
-4.68857977 0.86133559 -4.01345279 3.14898372 -3.85101628 -0.07605860
37 38 39 40 41 42
-0.91362209 -1.62597396 -0.91362209 -1.23849512 -3.01345279 -0.85101628
43 44 45 46 47 48
-3.40093163 -1.23849512 -7.40093163 -0.01345279 3.59906837 0.53646256
49 50 51 52 53 54
3.37402604 -3.40093163 4.92394140 -3.07605860 -0.07605860 4.14898372
55 56 57 58 59 60
-1.07605860 -3.01345279 -1.78841047 2.08637791 0.98654721 -0.23849512
61 62 63 64 65 66
-4.30110093 -1.40093163 1.14898372 2.31142023 -2.85101628 2.76150488
67 68 69 70 71 72
0.37402604 -5.30110093 4.21158953 2.76150488 0.59906837 5.31142023
73 74 75 76 77 78
5.14898372 5.59906837 -6.85101628 -5.78841047 1.98654721 -2.85101628
79 80 81 82 83 84
-0.52614325 -8.01345279 2.53646256 2.98654721 2.76150488 -2.01345279
85 86 87 88 89 90
0.92394140 -2.46353744 3.59906837 0.53646256 0.08637791 -0.46353744
91 92 93 94 95 96
3.31142023 -2.62597396 6.76150488 -5.07605860 -1.91362209 0.21158953
97 98 99 100 101 102
-0.01345279 -3.23849512 -1.01345279 2.92394140 0.59906837 -0.01345279
103 104 105 106 107 108
2.37402604 -5.07605860 5.53646256 -4.17588931 -0.40093163 2.43663185
109 110 111 112 113 114
0.37402604 4.66167418 1.69889907 0.31142023 7.92394140 -0.07605860
115 116 117 118 119 120
-1.01345279 -3.01345279 6.53646256 6.14898372 1.53646256 0.59906837
121 122 123 124 125 126
3.53646256 -5.68857977 3.59906837 2.98654721 -2.46353744 0.31142023
127 128 129 130 131 132
-3.78841047 1.27419534 2.53646256 -1.23849512 -0.07605860 0.47385675
133 134 135 136 137 138
0.82411069 -1.13866441 2.98654721 -4.07605860 -9.52614325 1.14898372
139 140 141 142 143 144
2.69889907 -0.23849512 2.31142023 0.92394140 1.69889907 2.69889907
145 146 147 148 149 150
-9.68857977 -5.17588931 -6.07605860 1.37402604 -2.07605860 -2.23849512
151 152 153 154 155 156
3.59906837 0.04915302 4.14898372 4.76150488 -0.23849512 -2.46353744
157 158 159 160 161 162
-2.62597396 3.92394140 2.53646256 -1.85101628 1.92394140 2.14898372
> postscript(file="/var/wessaorg/rcomp/tmp/6nnar1322157935.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 1.43663185 NA
1 -3.78841047 1.43663185
2 2.69889907 -3.78841047
3 0.31142023 2.69889907
4 3.14898372 0.31142023
5 -5.23849512 3.14898372
6 -4.78841047 -5.23849512
7 2.14898372 -4.78841047
8 0.37402604 2.14898372
9 2.98654721 0.37402604
10 -4.40093163 2.98654721
11 -0.62597396 -4.40093163
12 -0.40093163 -0.62597396
13 2.21158953 -0.40093163
14 3.53646256 2.21158953
15 -0.07605860 3.53646256
16 -2.62597396 -0.07605860
17 2.59906837 -2.62597396
18 2.21158953 2.59906837
19 -1.07605860 2.21158953
20 -0.07605860 -1.07605860
21 -1.68857977 -0.07605860
22 2.21158953 -1.68857977
23 3.98654721 2.21158953
24 -3.78841047 3.98654721
25 -4.56336815 -3.78841047
26 0.37402604 -4.56336815
27 3.53646256 0.37402604
28 -0.46353744 3.53646256
29 -0.85101628 -0.46353744
30 -4.68857977 -0.85101628
31 0.86133559 -4.68857977
32 -4.01345279 0.86133559
33 3.14898372 -4.01345279
34 -3.85101628 3.14898372
35 -0.07605860 -3.85101628
36 -0.91362209 -0.07605860
37 -1.62597396 -0.91362209
38 -0.91362209 -1.62597396
39 -1.23849512 -0.91362209
40 -3.01345279 -1.23849512
41 -0.85101628 -3.01345279
42 -3.40093163 -0.85101628
43 -1.23849512 -3.40093163
44 -7.40093163 -1.23849512
45 -0.01345279 -7.40093163
46 3.59906837 -0.01345279
47 0.53646256 3.59906837
48 3.37402604 0.53646256
49 -3.40093163 3.37402604
50 4.92394140 -3.40093163
51 -3.07605860 4.92394140
52 -0.07605860 -3.07605860
53 4.14898372 -0.07605860
54 -1.07605860 4.14898372
55 -3.01345279 -1.07605860
56 -1.78841047 -3.01345279
57 2.08637791 -1.78841047
58 0.98654721 2.08637791
59 -0.23849512 0.98654721
60 -4.30110093 -0.23849512
61 -1.40093163 -4.30110093
62 1.14898372 -1.40093163
63 2.31142023 1.14898372
64 -2.85101628 2.31142023
65 2.76150488 -2.85101628
66 0.37402604 2.76150488
67 -5.30110093 0.37402604
68 4.21158953 -5.30110093
69 2.76150488 4.21158953
70 0.59906837 2.76150488
71 5.31142023 0.59906837
72 5.14898372 5.31142023
73 5.59906837 5.14898372
74 -6.85101628 5.59906837
75 -5.78841047 -6.85101628
76 1.98654721 -5.78841047
77 -2.85101628 1.98654721
78 -0.52614325 -2.85101628
79 -8.01345279 -0.52614325
80 2.53646256 -8.01345279
81 2.98654721 2.53646256
82 2.76150488 2.98654721
83 -2.01345279 2.76150488
84 0.92394140 -2.01345279
85 -2.46353744 0.92394140
86 3.59906837 -2.46353744
87 0.53646256 3.59906837
88 0.08637791 0.53646256
89 -0.46353744 0.08637791
90 3.31142023 -0.46353744
91 -2.62597396 3.31142023
92 6.76150488 -2.62597396
93 -5.07605860 6.76150488
94 -1.91362209 -5.07605860
95 0.21158953 -1.91362209
96 -0.01345279 0.21158953
97 -3.23849512 -0.01345279
98 -1.01345279 -3.23849512
99 2.92394140 -1.01345279
100 0.59906837 2.92394140
101 -0.01345279 0.59906837
102 2.37402604 -0.01345279
103 -5.07605860 2.37402604
104 5.53646256 -5.07605860
105 -4.17588931 5.53646256
106 -0.40093163 -4.17588931
107 2.43663185 -0.40093163
108 0.37402604 2.43663185
109 4.66167418 0.37402604
110 1.69889907 4.66167418
111 0.31142023 1.69889907
112 7.92394140 0.31142023
113 -0.07605860 7.92394140
114 -1.01345279 -0.07605860
115 -3.01345279 -1.01345279
116 6.53646256 -3.01345279
117 6.14898372 6.53646256
118 1.53646256 6.14898372
119 0.59906837 1.53646256
120 3.53646256 0.59906837
121 -5.68857977 3.53646256
122 3.59906837 -5.68857977
123 2.98654721 3.59906837
124 -2.46353744 2.98654721
125 0.31142023 -2.46353744
126 -3.78841047 0.31142023
127 1.27419534 -3.78841047
128 2.53646256 1.27419534
129 -1.23849512 2.53646256
130 -0.07605860 -1.23849512
131 0.47385675 -0.07605860
132 0.82411069 0.47385675
133 -1.13866441 0.82411069
134 2.98654721 -1.13866441
135 -4.07605860 2.98654721
136 -9.52614325 -4.07605860
137 1.14898372 -9.52614325
138 2.69889907 1.14898372
139 -0.23849512 2.69889907
140 2.31142023 -0.23849512
141 0.92394140 2.31142023
142 1.69889907 0.92394140
143 2.69889907 1.69889907
144 -9.68857977 2.69889907
145 -5.17588931 -9.68857977
146 -6.07605860 -5.17588931
147 1.37402604 -6.07605860
148 -2.07605860 1.37402604
149 -2.23849512 -2.07605860
150 3.59906837 -2.23849512
151 0.04915302 3.59906837
152 4.14898372 0.04915302
153 4.76150488 4.14898372
154 -0.23849512 4.76150488
155 -2.46353744 -0.23849512
156 -2.62597396 -2.46353744
157 3.92394140 -2.62597396
158 2.53646256 3.92394140
159 -1.85101628 2.53646256
160 1.92394140 -1.85101628
161 2.14898372 1.92394140
162 NA 2.14898372
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.78841047 1.43663185
[2,] 2.69889907 -3.78841047
[3,] 0.31142023 2.69889907
[4,] 3.14898372 0.31142023
[5,] -5.23849512 3.14898372
[6,] -4.78841047 -5.23849512
[7,] 2.14898372 -4.78841047
[8,] 0.37402604 2.14898372
[9,] 2.98654721 0.37402604
[10,] -4.40093163 2.98654721
[11,] -0.62597396 -4.40093163
[12,] -0.40093163 -0.62597396
[13,] 2.21158953 -0.40093163
[14,] 3.53646256 2.21158953
[15,] -0.07605860 3.53646256
[16,] -2.62597396 -0.07605860
[17,] 2.59906837 -2.62597396
[18,] 2.21158953 2.59906837
[19,] -1.07605860 2.21158953
[20,] -0.07605860 -1.07605860
[21,] -1.68857977 -0.07605860
[22,] 2.21158953 -1.68857977
[23,] 3.98654721 2.21158953
[24,] -3.78841047 3.98654721
[25,] -4.56336815 -3.78841047
[26,] 0.37402604 -4.56336815
[27,] 3.53646256 0.37402604
[28,] -0.46353744 3.53646256
[29,] -0.85101628 -0.46353744
[30,] -4.68857977 -0.85101628
[31,] 0.86133559 -4.68857977
[32,] -4.01345279 0.86133559
[33,] 3.14898372 -4.01345279
[34,] -3.85101628 3.14898372
[35,] -0.07605860 -3.85101628
[36,] -0.91362209 -0.07605860
[37,] -1.62597396 -0.91362209
[38,] -0.91362209 -1.62597396
[39,] -1.23849512 -0.91362209
[40,] -3.01345279 -1.23849512
[41,] -0.85101628 -3.01345279
[42,] -3.40093163 -0.85101628
[43,] -1.23849512 -3.40093163
[44,] -7.40093163 -1.23849512
[45,] -0.01345279 -7.40093163
[46,] 3.59906837 -0.01345279
[47,] 0.53646256 3.59906837
[48,] 3.37402604 0.53646256
[49,] -3.40093163 3.37402604
[50,] 4.92394140 -3.40093163
[51,] -3.07605860 4.92394140
[52,] -0.07605860 -3.07605860
[53,] 4.14898372 -0.07605860
[54,] -1.07605860 4.14898372
[55,] -3.01345279 -1.07605860
[56,] -1.78841047 -3.01345279
[57,] 2.08637791 -1.78841047
[58,] 0.98654721 2.08637791
[59,] -0.23849512 0.98654721
[60,] -4.30110093 -0.23849512
[61,] -1.40093163 -4.30110093
[62,] 1.14898372 -1.40093163
[63,] 2.31142023 1.14898372
[64,] -2.85101628 2.31142023
[65,] 2.76150488 -2.85101628
[66,] 0.37402604 2.76150488
[67,] -5.30110093 0.37402604
[68,] 4.21158953 -5.30110093
[69,] 2.76150488 4.21158953
[70,] 0.59906837 2.76150488
[71,] 5.31142023 0.59906837
[72,] 5.14898372 5.31142023
[73,] 5.59906837 5.14898372
[74,] -6.85101628 5.59906837
[75,] -5.78841047 -6.85101628
[76,] 1.98654721 -5.78841047
[77,] -2.85101628 1.98654721
[78,] -0.52614325 -2.85101628
[79,] -8.01345279 -0.52614325
[80,] 2.53646256 -8.01345279
[81,] 2.98654721 2.53646256
[82,] 2.76150488 2.98654721
[83,] -2.01345279 2.76150488
[84,] 0.92394140 -2.01345279
[85,] -2.46353744 0.92394140
[86,] 3.59906837 -2.46353744
[87,] 0.53646256 3.59906837
[88,] 0.08637791 0.53646256
[89,] -0.46353744 0.08637791
[90,] 3.31142023 -0.46353744
[91,] -2.62597396 3.31142023
[92,] 6.76150488 -2.62597396
[93,] -5.07605860 6.76150488
[94,] -1.91362209 -5.07605860
[95,] 0.21158953 -1.91362209
[96,] -0.01345279 0.21158953
[97,] -3.23849512 -0.01345279
[98,] -1.01345279 -3.23849512
[99,] 2.92394140 -1.01345279
[100,] 0.59906837 2.92394140
[101,] -0.01345279 0.59906837
[102,] 2.37402604 -0.01345279
[103,] -5.07605860 2.37402604
[104,] 5.53646256 -5.07605860
[105,] -4.17588931 5.53646256
[106,] -0.40093163 -4.17588931
[107,] 2.43663185 -0.40093163
[108,] 0.37402604 2.43663185
[109,] 4.66167418 0.37402604
[110,] 1.69889907 4.66167418
[111,] 0.31142023 1.69889907
[112,] 7.92394140 0.31142023
[113,] -0.07605860 7.92394140
[114,] -1.01345279 -0.07605860
[115,] -3.01345279 -1.01345279
[116,] 6.53646256 -3.01345279
[117,] 6.14898372 6.53646256
[118,] 1.53646256 6.14898372
[119,] 0.59906837 1.53646256
[120,] 3.53646256 0.59906837
[121,] -5.68857977 3.53646256
[122,] 3.59906837 -5.68857977
[123,] 2.98654721 3.59906837
[124,] -2.46353744 2.98654721
[125,] 0.31142023 -2.46353744
[126,] -3.78841047 0.31142023
[127,] 1.27419534 -3.78841047
[128,] 2.53646256 1.27419534
[129,] -1.23849512 2.53646256
[130,] -0.07605860 -1.23849512
[131,] 0.47385675 -0.07605860
[132,] 0.82411069 0.47385675
[133,] -1.13866441 0.82411069
[134,] 2.98654721 -1.13866441
[135,] -4.07605860 2.98654721
[136,] -9.52614325 -4.07605860
[137,] 1.14898372 -9.52614325
[138,] 2.69889907 1.14898372
[139,] -0.23849512 2.69889907
[140,] 2.31142023 -0.23849512
[141,] 0.92394140 2.31142023
[142,] 1.69889907 0.92394140
[143,] 2.69889907 1.69889907
[144,] -9.68857977 2.69889907
[145,] -5.17588931 -9.68857977
[146,] -6.07605860 -5.17588931
[147,] 1.37402604 -6.07605860
[148,] -2.07605860 1.37402604
[149,] -2.23849512 -2.07605860
[150,] 3.59906837 -2.23849512
[151,] 0.04915302 3.59906837
[152,] 4.14898372 0.04915302
[153,] 4.76150488 4.14898372
[154,] -0.23849512 4.76150488
[155,] -2.46353744 -0.23849512
[156,] -2.62597396 -2.46353744
[157,] 3.92394140 -2.62597396
[158,] 2.53646256 3.92394140
[159,] -1.85101628 2.53646256
[160,] 1.92394140 -1.85101628
[161,] 2.14898372 1.92394140
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.78841047 1.43663185
2 2.69889907 -3.78841047
3 0.31142023 2.69889907
4 3.14898372 0.31142023
5 -5.23849512 3.14898372
6 -4.78841047 -5.23849512
7 2.14898372 -4.78841047
8 0.37402604 2.14898372
9 2.98654721 0.37402604
10 -4.40093163 2.98654721
11 -0.62597396 -4.40093163
12 -0.40093163 -0.62597396
13 2.21158953 -0.40093163
14 3.53646256 2.21158953
15 -0.07605860 3.53646256
16 -2.62597396 -0.07605860
17 2.59906837 -2.62597396
18 2.21158953 2.59906837
19 -1.07605860 2.21158953
20 -0.07605860 -1.07605860
21 -1.68857977 -0.07605860
22 2.21158953 -1.68857977
23 3.98654721 2.21158953
24 -3.78841047 3.98654721
25 -4.56336815 -3.78841047
26 0.37402604 -4.56336815
27 3.53646256 0.37402604
28 -0.46353744 3.53646256
29 -0.85101628 -0.46353744
30 -4.68857977 -0.85101628
31 0.86133559 -4.68857977
32 -4.01345279 0.86133559
33 3.14898372 -4.01345279
34 -3.85101628 3.14898372
35 -0.07605860 -3.85101628
36 -0.91362209 -0.07605860
37 -1.62597396 -0.91362209
38 -0.91362209 -1.62597396
39 -1.23849512 -0.91362209
40 -3.01345279 -1.23849512
41 -0.85101628 -3.01345279
42 -3.40093163 -0.85101628
43 -1.23849512 -3.40093163
44 -7.40093163 -1.23849512
45 -0.01345279 -7.40093163
46 3.59906837 -0.01345279
47 0.53646256 3.59906837
48 3.37402604 0.53646256
49 -3.40093163 3.37402604
50 4.92394140 -3.40093163
51 -3.07605860 4.92394140
52 -0.07605860 -3.07605860
53 4.14898372 -0.07605860
54 -1.07605860 4.14898372
55 -3.01345279 -1.07605860
56 -1.78841047 -3.01345279
57 2.08637791 -1.78841047
58 0.98654721 2.08637791
59 -0.23849512 0.98654721
60 -4.30110093 -0.23849512
61 -1.40093163 -4.30110093
62 1.14898372 -1.40093163
63 2.31142023 1.14898372
64 -2.85101628 2.31142023
65 2.76150488 -2.85101628
66 0.37402604 2.76150488
67 -5.30110093 0.37402604
68 4.21158953 -5.30110093
69 2.76150488 4.21158953
70 0.59906837 2.76150488
71 5.31142023 0.59906837
72 5.14898372 5.31142023
73 5.59906837 5.14898372
74 -6.85101628 5.59906837
75 -5.78841047 -6.85101628
76 1.98654721 -5.78841047
77 -2.85101628 1.98654721
78 -0.52614325 -2.85101628
79 -8.01345279 -0.52614325
80 2.53646256 -8.01345279
81 2.98654721 2.53646256
82 2.76150488 2.98654721
83 -2.01345279 2.76150488
84 0.92394140 -2.01345279
85 -2.46353744 0.92394140
86 3.59906837 -2.46353744
87 0.53646256 3.59906837
88 0.08637791 0.53646256
89 -0.46353744 0.08637791
90 3.31142023 -0.46353744
91 -2.62597396 3.31142023
92 6.76150488 -2.62597396
93 -5.07605860 6.76150488
94 -1.91362209 -5.07605860
95 0.21158953 -1.91362209
96 -0.01345279 0.21158953
97 -3.23849512 -0.01345279
98 -1.01345279 -3.23849512
99 2.92394140 -1.01345279
100 0.59906837 2.92394140
101 -0.01345279 0.59906837
102 2.37402604 -0.01345279
103 -5.07605860 2.37402604
104 5.53646256 -5.07605860
105 -4.17588931 5.53646256
106 -0.40093163 -4.17588931
107 2.43663185 -0.40093163
108 0.37402604 2.43663185
109 4.66167418 0.37402604
110 1.69889907 4.66167418
111 0.31142023 1.69889907
112 7.92394140 0.31142023
113 -0.07605860 7.92394140
114 -1.01345279 -0.07605860
115 -3.01345279 -1.01345279
116 6.53646256 -3.01345279
117 6.14898372 6.53646256
118 1.53646256 6.14898372
119 0.59906837 1.53646256
120 3.53646256 0.59906837
121 -5.68857977 3.53646256
122 3.59906837 -5.68857977
123 2.98654721 3.59906837
124 -2.46353744 2.98654721
125 0.31142023 -2.46353744
126 -3.78841047 0.31142023
127 1.27419534 -3.78841047
128 2.53646256 1.27419534
129 -1.23849512 2.53646256
130 -0.07605860 -1.23849512
131 0.47385675 -0.07605860
132 0.82411069 0.47385675
133 -1.13866441 0.82411069
134 2.98654721 -1.13866441
135 -4.07605860 2.98654721
136 -9.52614325 -4.07605860
137 1.14898372 -9.52614325
138 2.69889907 1.14898372
139 -0.23849512 2.69889907
140 2.31142023 -0.23849512
141 0.92394140 2.31142023
142 1.69889907 0.92394140
143 2.69889907 1.69889907
144 -9.68857977 2.69889907
145 -5.17588931 -9.68857977
146 -6.07605860 -5.17588931
147 1.37402604 -6.07605860
148 -2.07605860 1.37402604
149 -2.23849512 -2.07605860
150 3.59906837 -2.23849512
151 0.04915302 3.59906837
152 4.14898372 0.04915302
153 4.76150488 4.14898372
154 -0.23849512 4.76150488
155 -2.46353744 -0.23849512
156 -2.62597396 -2.46353744
157 3.92394140 -2.62597396
158 2.53646256 3.92394140
159 -1.85101628 2.53646256
160 1.92394140 -1.85101628
161 2.14898372 1.92394140
> 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/wessaorg/rcomp/tmp/7qudb1322157935.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8b9zr1322157935.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9zik81322157935.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10gs091322157935.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11bxp01322157935.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/wessaorg/rcomp/tmp/12g8331322157935.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/wessaorg/rcomp/tmp/134j2s1322157936.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/wessaorg/rcomp/tmp/147cjx1322157936.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/wessaorg/rcomp/tmp/156w9i1322157936.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/wessaorg/rcomp/tmp/1669951322157936.tab")
+ }
>
> try(system("convert tmp/1gpjt1322157935.ps tmp/1gpjt1322157935.png",intern=TRUE))
character(0)
> try(system("convert tmp/29x8r1322157935.ps tmp/29x8r1322157935.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tjxf1322157935.ps tmp/3tjxf1322157935.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ojj21322157935.ps tmp/4ojj21322157935.png",intern=TRUE))
character(0)
> try(system("convert tmp/5nvgy1322157935.ps tmp/5nvgy1322157935.png",intern=TRUE))
character(0)
> try(system("convert tmp/6nnar1322157935.ps tmp/6nnar1322157935.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qudb1322157935.ps tmp/7qudb1322157935.png",intern=TRUE))
character(0)
> try(system("convert tmp/8b9zr1322157935.ps tmp/8b9zr1322157935.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zik81322157935.ps tmp/9zik81322157935.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gs091322157935.ps tmp/10gs091322157935.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.647 0.498 5.168