R version 2.12.0 (2010-10-15)
Copyright (C) 2010 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 '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
+ ,14
+ ,39
+ ,18
+ ,30
+ ,11
+ ,31
+ ,12
+ ,34
+ ,16
+ ,35
+ ,18
+ ,39
+ ,14
+ ,34
+ ,14
+ ,36
+ ,15
+ ,37
+ ,15
+ ,38
+ ,17
+ ,36
+ ,19
+ ,38
+ ,10
+ ,39
+ ,16
+ ,33
+ ,18
+ ,32
+ ,14
+ ,36
+ ,14
+ ,38
+ ,17
+ ,39
+ ,14
+ ,32
+ ,16
+ ,32
+ ,18
+ ,31
+ ,11
+ ,39
+ ,14
+ ,37
+ ,12
+ ,39
+ ,17
+ ,41
+ ,9
+ ,36
+ ,16
+ ,33
+ ,14
+ ,33
+ ,15
+ ,34
+ ,11
+ ,31
+ ,16
+ ,27
+ ,13
+ ,37
+ ,17
+ ,34
+ ,15
+ ,34
+ ,14
+ ,32
+ ,16
+ ,29
+ ,9
+ ,36
+ ,15
+ ,29
+ ,17
+ ,35
+ ,13
+ ,37
+ ,15
+ ,34
+ ,16
+ ,38
+ ,16
+ ,35
+ ,12
+ ,38
+ ,12
+ ,37
+ ,11
+ ,38
+ ,15
+ ,33
+ ,15
+ ,36
+ ,17
+ ,38
+ ,13
+ ,32
+ ,16
+ ,32
+ ,14
+ ,32
+ ,11
+ ,34
+ ,12
+ ,32
+ ,12
+ ,37
+ ,15
+ ,39
+ ,16
+ ,29
+ ,15
+ ,37
+ ,12
+ ,35
+ ,12
+ ,30
+ ,8
+ ,38
+ ,13
+ ,34
+ ,11
+ ,31
+ ,14
+ ,34
+ ,15
+ ,35
+ ,10
+ ,36
+ ,11
+ ,30
+ ,12
+ ,39
+ ,15
+ ,35
+ ,15
+ ,38
+ ,14
+ ,31
+ ,16
+ ,34
+ ,15
+ ,38
+ ,15
+ ,34
+ ,13
+ ,39
+ ,12
+ ,37
+ ,17
+ ,34
+ ,13
+ ,28
+ ,15
+ ,37
+ ,13
+ ,33
+ ,15
+ ,37
+ ,16
+ ,35
+ ,15
+ ,37
+ ,16
+ ,32
+ ,15
+ ,33
+ ,14
+ ,38
+ ,15
+ ,33
+ ,14
+ ,29
+ ,13
+ ,33
+ ,7
+ ,31
+ ,17
+ ,36
+ ,13
+ ,35
+ ,15
+ ,32
+ ,14
+ ,29
+ ,13
+ ,39
+ ,16
+ ,37
+ ,12
+ ,35
+ ,14
+ ,37
+ ,17
+ ,32
+ ,15
+ ,38
+ ,17
+ ,37
+ ,12
+ ,36
+ ,16
+ ,32
+ ,11
+ ,33
+ ,15
+ ,40
+ ,9
+ ,38
+ ,16
+ ,41
+ ,15
+ ,36
+ ,10
+ ,43
+ ,10
+ ,30
+ ,15
+ ,31
+ ,11
+ ,32
+ ,13
+ ,32
+ ,14
+ ,37
+ ,18
+ ,37
+ ,16
+ ,33
+ ,14
+ ,34
+ ,14
+ ,33
+ ,14
+ ,38
+ ,14
+ ,33
+ ,12
+ ,31
+ ,14
+ ,38
+ ,15
+ ,37
+ ,15
+ ,33
+ ,15
+ ,31
+ ,13
+ ,39
+ ,17
+ ,44
+ ,17
+ ,33
+ ,19
+ ,35
+ ,15
+ ,32
+ ,13
+ ,28
+ ,9
+ ,40
+ ,15
+ ,27
+ ,15
+ ,37
+ ,15
+ ,32
+ ,16
+ ,28
+ ,11
+ ,34
+ ,14
+ ,30
+ ,11
+ ,35
+ ,15
+ ,31
+ ,13
+ ,32
+ ,15
+ ,30
+ ,16
+ ,30
+ ,14
+ ,31
+ ,15
+ ,40
+ ,16
+ ,32
+ ,16
+ ,36
+ ,11
+ ,32
+ ,12
+ ,35
+ ,9
+ ,38
+ ,16
+ ,42
+ ,13
+ ,34
+ ,16
+ ,35
+ ,12
+ ,35
+ ,9
+ ,33
+ ,13
+ ,36
+ ,13
+ ,32
+ ,14
+ ,33
+ ,19
+ ,34
+ ,13
+ ,32
+ ,12
+ ,34
+ ,13)
+ ,dim=c(2
+ ,162)
+ ,dimnames=list(c('Connected'
+ ,'Happiness')
+ ,1:162))
> y <- array(NA,dim=c(2,162),dimnames=list(c('Connected','Happiness'),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
Happiness Connected
1 14 41
2 18 39
3 11 30
4 12 31
5 16 34
6 18 35
7 14 39
8 14 34
9 15 36
10 15 37
11 17 38
12 19 36
13 10 38
14 16 39
15 18 33
16 14 32
17 14 36
18 17 38
19 14 39
20 16 32
21 18 32
22 11 31
23 14 39
24 12 37
25 17 39
26 9 41
27 16 36
28 14 33
29 15 33
30 11 34
31 16 31
32 13 27
33 17 37
34 15 34
35 14 34
36 16 32
37 9 29
38 15 36
39 17 29
40 13 35
41 15 37
42 16 34
43 16 38
44 12 35
45 12 38
46 11 37
47 15 38
48 15 33
49 17 36
50 13 38
51 16 32
52 14 32
53 11 32
54 12 34
55 12 32
56 15 37
57 16 39
58 15 29
59 12 37
60 12 35
61 8 30
62 13 38
63 11 34
64 14 31
65 15 34
66 10 35
67 11 36
68 12 30
69 15 39
70 15 35
71 14 38
72 16 31
73 15 34
74 15 38
75 13 34
76 12 39
77 17 37
78 13 34
79 15 28
80 13 37
81 15 33
82 16 37
83 15 35
84 16 37
85 15 32
86 14 33
87 15 38
88 14 33
89 13 29
90 7 33
91 17 31
92 13 36
93 15 35
94 14 32
95 13 29
96 16 39
97 12 37
98 14 35
99 17 37
100 15 32
101 17 38
102 12 37
103 16 36
104 11 32
105 15 33
106 9 40
107 16 38
108 15 41
109 10 36
110 10 43
111 15 30
112 11 31
113 13 32
114 14 32
115 18 37
116 16 37
117 14 33
118 14 34
119 14 33
120 14 38
121 12 33
122 14 31
123 15 38
124 15 37
125 15 33
126 13 31
127 17 39
128 17 44
129 19 33
130 15 35
131 13 32
132 9 28
133 15 40
134 15 27
135 15 37
136 16 32
137 11 28
138 14 34
139 11 30
140 15 35
141 13 31
142 15 32
143 16 30
144 14 30
145 15 31
146 16 40
147 16 32
148 11 36
149 12 32
150 9 35
151 16 38
152 13 42
153 16 34
154 12 35
155 9 35
156 13 33
157 13 36
158 14 32
159 19 33
160 13 34
161 12 32
162 13 34
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected
10.61516 0.09883
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.8766 -1.5278 0.2717 1.5958 5.1234
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.61516 1.88524 5.631 7.88e-08 ***
Connected 0.09883 0.05419 1.824 0.0701 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.321 on 160 degrees of freedom
Multiple R-squared: 0.02036, Adjusted R-squared: 0.01424
F-statistic: 3.326 on 1 and 160 DF, p-value: 0.07007
> 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,] 0.6605888 0.6788225 0.33941124
[2,] 0.7708597 0.4582806 0.22914030
[3,] 0.7300897 0.5398206 0.26991030
[4,] 0.6178470 0.7643060 0.38215300
[5,] 0.5025425 0.9949151 0.49745755
[6,] 0.3926467 0.7852934 0.60735331
[7,] 0.3381636 0.6763273 0.66183636
[8,] 0.5297019 0.9405961 0.47029807
[9,] 0.8466930 0.3066139 0.15330697
[10,] 0.7950866 0.4098269 0.20491344
[11,] 0.8567692 0.2864616 0.14323081
[12,] 0.8089703 0.3820595 0.19102975
[13,] 0.7626124 0.4747751 0.23738756
[14,] 0.7327190 0.5345620 0.26728101
[15,] 0.6966682 0.6066637 0.30333185
[16,] 0.6635966 0.6728067 0.33640336
[17,] 0.7267643 0.5464714 0.27323569
[18,] 0.7836660 0.4326680 0.21633398
[19,] 0.7502711 0.4994578 0.24972892
[20,] 0.7757287 0.4485426 0.22427128
[21,] 0.7571207 0.4857587 0.24287933
[22,] 0.9324541 0.1350918 0.06754588
[23,] 0.9182876 0.1634247 0.08171236
[24,] 0.8952815 0.2094370 0.10471851
[25,] 0.8682884 0.2634232 0.13171162
[26,] 0.8972867 0.2054267 0.10271334
[27,] 0.8843496 0.2313008 0.11565042
[28,] 0.8618770 0.2762461 0.13812305
[29,] 0.8614754 0.2770492 0.13852459
[30,] 0.8312692 0.3374617 0.16873084
[31,] 0.7964037 0.4071926 0.20359631
[32,] 0.7780577 0.4438845 0.22194227
[33,] 0.8838986 0.2322028 0.11610138
[34,] 0.8577051 0.2845899 0.14229493
[35,] 0.8794192 0.2411616 0.12058078
[36,] 0.8620578 0.2758845 0.13794223
[37,] 0.8331124 0.3337753 0.16688763
[38,] 0.8171296 0.3657409 0.18287043
[39,] 0.7934561 0.4130879 0.20654393
[40,] 0.7954209 0.4091581 0.20457906
[41,] 0.8037645 0.3924710 0.19623550
[42,] 0.8406405 0.3187190 0.15935949
[43,] 0.8103307 0.3793386 0.18966932
[44,] 0.7804572 0.4390856 0.21954278
[45,] 0.7889767 0.4220467 0.21102333
[46,] 0.7681959 0.4636082 0.23180410
[47,] 0.7560518 0.4878965 0.24394823
[48,] 0.7178955 0.5642089 0.28210445
[49,] 0.7464197 0.5071606 0.25358031
[50,] 0.7410505 0.5178991 0.25894953
[51,] 0.7298109 0.5403781 0.27018905
[52,] 0.6922996 0.6154009 0.30770043
[53,] 0.6658678 0.6682643 0.33413216
[54,] 0.6365780 0.7268440 0.36342201
[55,] 0.6386130 0.7227739 0.36138697
[56,] 0.6334998 0.7330003 0.36650016
[57,] 0.8167873 0.3664254 0.18321271
[58,] 0.7968603 0.4062794 0.20313971
[59,] 0.8166382 0.3667235 0.18336176
[60,] 0.7850315 0.4299371 0.21496854
[61,] 0.7564483 0.4871034 0.24355171
[62,] 0.8228814 0.3542371 0.17711856
[63,] 0.8461547 0.3076907 0.15384535
[64,] 0.8305208 0.3389584 0.16947919
[65,] 0.8016995 0.3966010 0.19830052
[66,] 0.7738132 0.4523736 0.22618680
[67,] 0.7391525 0.5216950 0.26084750
[68,] 0.7384320 0.5231360 0.26156801
[69,] 0.7076015 0.5847971 0.29239854
[70,] 0.6703998 0.6592005 0.32960023
[71,] 0.6358535 0.7282931 0.36414654
[72,] 0.6404431 0.7191137 0.35955687
[73,] 0.6552238 0.6895523 0.34477617
[74,] 0.6201763 0.7596474 0.37982370
[75,] 0.5971618 0.8056764 0.40283818
[76,] 0.5659653 0.8680694 0.43403470
[77,] 0.5313872 0.9372256 0.46861282
[78,] 0.5109299 0.9781402 0.48907009
[79,] 0.4729256 0.9458512 0.52707438
[80,] 0.4527466 0.9054932 0.54725339
[81,] 0.4203939 0.8407878 0.57960611
[82,] 0.3769804 0.7539608 0.62301961
[83,] 0.3380597 0.6761194 0.66194030
[84,] 0.2979309 0.5958619 0.70206907
[85,] 0.2615702 0.5231404 0.73842979
[86,] 0.5876734 0.8246531 0.41232656
[87,] 0.6328074 0.7343853 0.36719264
[88,] 0.5997889 0.8004221 0.40021107
[89,] 0.5624583 0.8750833 0.43754167
[90,] 0.5175564 0.9648871 0.48244356
[91,] 0.4735798 0.9471595 0.52642024
[92,] 0.4482992 0.8965985 0.55170077
[93,] 0.4442081 0.8884162 0.55579188
[94,] 0.3990920 0.7981840 0.60090801
[95,] 0.4165645 0.8331290 0.58343552
[96,] 0.3847556 0.7695111 0.61524444
[97,] 0.3996776 0.7993551 0.60032243
[98,] 0.3938861 0.7877721 0.60611394
[99,] 0.3787194 0.7574388 0.62128062
[100,] 0.3941207 0.7882413 0.60587934
[101,] 0.3606415 0.7212830 0.63935848
[102,] 0.5713838 0.8572323 0.42861616
[103,] 0.5476471 0.9047058 0.45235289
[104,] 0.5009252 0.9981496 0.49907481
[105,] 0.6035444 0.7929113 0.39645563
[106,] 0.7734958 0.4530083 0.22650415
[107,] 0.7545310 0.4909381 0.24546904
[108,] 0.7642183 0.4715634 0.23578171
[109,] 0.7283743 0.5432514 0.27162571
[110,] 0.6858469 0.6283063 0.31415315
[111,] 0.7444949 0.5110102 0.25550509
[112,] 0.7227702 0.5544596 0.27722981
[113,] 0.6785342 0.6429316 0.32146578
[114,] 0.6310819 0.7378362 0.36891809
[115,] 0.5815744 0.8368512 0.41842562
[116,] 0.5331389 0.9337221 0.46686107
[117,] 0.5136659 0.9726682 0.48633408
[118,] 0.4629107 0.9258214 0.53708929
[119,] 0.4125736 0.8251473 0.58742636
[120,] 0.3645933 0.7291865 0.63540673
[121,] 0.3269968 0.6539936 0.67300319
[122,] 0.2821304 0.5642608 0.71786960
[123,] 0.2821212 0.5642424 0.71787878
[124,] 0.2683959 0.5367918 0.73160410
[125,] 0.4771369 0.9542738 0.52286312
[126,] 0.4349846 0.8699692 0.56501538
[127,] 0.3818089 0.7636178 0.61819111
[128,] 0.5223884 0.9552232 0.47761160
[129,] 0.4759047 0.9518093 0.52409535
[130,] 0.4399345 0.8798689 0.56006553
[131,] 0.3957018 0.7914035 0.60429823
[132,] 0.3993802 0.7987604 0.60061980
[133,] 0.4075389 0.8150778 0.59246110
[134,] 0.3477864 0.6955729 0.65221357
[135,] 0.3725112 0.7450225 0.62748877
[136,] 0.3305369 0.6610737 0.66946314
[137,] 0.2795687 0.5591374 0.72043132
[138,] 0.2364297 0.4728594 0.76357032
[139,] 0.2263998 0.4527997 0.77360017
[140,] 0.1769393 0.3538787 0.82306065
[141,] 0.1487194 0.2974387 0.85128064
[142,] 0.1617633 0.3235265 0.83823673
[143,] 0.1665938 0.3331875 0.83340623
[144,] 0.1548006 0.3096012 0.84519940
[145,] 0.1220033 0.2440067 0.87799666
[146,] 0.2414746 0.4829492 0.75852538
[147,] 0.2619914 0.5239829 0.73800857
[148,] 0.2395601 0.4791201 0.76043995
[149,] 0.2628225 0.5256450 0.73717749
[150,] 0.1836308 0.3672615 0.81636925
[151,] 0.3152501 0.6305002 0.68474992
[152,] 0.2203560 0.4407120 0.77964401
[153,] 0.1292329 0.2584657 0.87076713
> postscript(file="/var/www/rcomp/tmp/1mxhb1324681072.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2j1dr1324681072.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3fn381324681072.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4gcx51324681072.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/51ee81324681072.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
-0.66723772 3.53042445 -2.58009579 -1.67892687 2.02457987 3.92574879
7 8 9 10 11 12
-0.46957555 0.02457987 0.82691770 0.72808662 2.62925554 4.82691770
13 14 15 16 17 18
-4.37074446 1.53042445 4.12341096 0.22224204 -0.17308230 2.62925554
19 20 21 22 23 24
-0.46957555 2.22224204 4.22224204 -2.67892687 -0.46957555 -2.27191338
25 26 27 28 29 30
2.53042445 -5.66723772 1.82691770 0.12341096 1.12341096 -2.97542013
31 32 33 34 35 36
2.32107313 -0.28360254 2.72808662 1.02457987 0.02457987 2.22224204
37 38 39 40 41 42
-4.48126470 0.82691770 3.51873530 -1.07425121 0.72808662 2.02457987
43 44 45 46 47 48
1.62925554 -2.07425121 -2.37074446 -3.27191338 0.62925554 1.12341096
49 50 51 52 53 54
2.82691770 -1.37074446 2.22224204 0.22224204 -2.77775796 -1.97542013
55 56 57 58 59 60
-1.77775796 0.72808662 1.53042445 1.51873530 -2.27191338 -2.07425121
61 62 63 64 65 66
-5.58009579 -1.37074446 -2.97542013 0.32107313 1.02457987 -4.07425121
67 68 69 70 71 72
-3.17308230 -1.58009579 0.53042445 0.92574879 -0.37074446 2.32107313
73 74 75 76 77 78
1.02457987 0.62925554 -0.97542013 -2.46957555 2.72808662 -0.97542013
79 80 81 82 83 84
1.61756638 -1.27191338 1.12341096 1.72808662 0.92574879 1.72808662
85 86 87 88 89 90
1.22224204 0.12341096 0.62925554 0.12341096 -0.48126470 -6.87658904
91 92 93 94 95 96
3.32107313 -1.17308230 0.92574879 0.22224204 -0.48126470 1.53042445
97 98 99 100 101 102
-2.27191338 -0.07425121 2.72808662 1.22224204 2.62925554 -2.27191338
103 104 105 106 107 108
1.82691770 -2.77775796 1.12341096 -5.56840663 1.62925554 0.33276228
109 110 111 112 113 114
-4.17308230 -4.86489989 1.41990421 -2.67892687 -0.77775796 0.22224204
115 116 117 118 119 120
3.72808662 1.72808662 0.12341096 0.02457987 0.12341096 -0.37074446
121 122 123 124 125 126
-1.87658904 0.32107313 0.62925554 0.72808662 1.12341096 -0.67892687
127 128 129 130 131 132
2.53042445 2.03626903 5.12341096 0.92574879 -0.77775796 -4.38243362
133 134 135 136 137 138
0.43159337 1.71639746 0.72808662 2.22224204 -2.38243362 0.02457987
139 140 141 142 143 144
-2.58009579 0.92574879 -0.67892687 1.22224204 2.41990421 0.41990421
145 146 147 148 149 150
1.32107313 1.43159337 2.22224204 -3.17308230 -1.77775796 -5.07425121
151 152 153 154 155 156
1.62925554 -1.76606880 2.02457987 -2.07425121 -5.07425121 -0.87658904
157 158 159 160 161 162
-1.17308230 0.22224204 5.12341096 -0.97542013 -1.77775796 -0.97542013
> postscript(file="/var/www/rcomp/tmp/6gcy61324681072.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 -0.66723772 NA
1 3.53042445 -0.66723772
2 -2.58009579 3.53042445
3 -1.67892687 -2.58009579
4 2.02457987 -1.67892687
5 3.92574879 2.02457987
6 -0.46957555 3.92574879
7 0.02457987 -0.46957555
8 0.82691770 0.02457987
9 0.72808662 0.82691770
10 2.62925554 0.72808662
11 4.82691770 2.62925554
12 -4.37074446 4.82691770
13 1.53042445 -4.37074446
14 4.12341096 1.53042445
15 0.22224204 4.12341096
16 -0.17308230 0.22224204
17 2.62925554 -0.17308230
18 -0.46957555 2.62925554
19 2.22224204 -0.46957555
20 4.22224204 2.22224204
21 -2.67892687 4.22224204
22 -0.46957555 -2.67892687
23 -2.27191338 -0.46957555
24 2.53042445 -2.27191338
25 -5.66723772 2.53042445
26 1.82691770 -5.66723772
27 0.12341096 1.82691770
28 1.12341096 0.12341096
29 -2.97542013 1.12341096
30 2.32107313 -2.97542013
31 -0.28360254 2.32107313
32 2.72808662 -0.28360254
33 1.02457987 2.72808662
34 0.02457987 1.02457987
35 2.22224204 0.02457987
36 -4.48126470 2.22224204
37 0.82691770 -4.48126470
38 3.51873530 0.82691770
39 -1.07425121 3.51873530
40 0.72808662 -1.07425121
41 2.02457987 0.72808662
42 1.62925554 2.02457987
43 -2.07425121 1.62925554
44 -2.37074446 -2.07425121
45 -3.27191338 -2.37074446
46 0.62925554 -3.27191338
47 1.12341096 0.62925554
48 2.82691770 1.12341096
49 -1.37074446 2.82691770
50 2.22224204 -1.37074446
51 0.22224204 2.22224204
52 -2.77775796 0.22224204
53 -1.97542013 -2.77775796
54 -1.77775796 -1.97542013
55 0.72808662 -1.77775796
56 1.53042445 0.72808662
57 1.51873530 1.53042445
58 -2.27191338 1.51873530
59 -2.07425121 -2.27191338
60 -5.58009579 -2.07425121
61 -1.37074446 -5.58009579
62 -2.97542013 -1.37074446
63 0.32107313 -2.97542013
64 1.02457987 0.32107313
65 -4.07425121 1.02457987
66 -3.17308230 -4.07425121
67 -1.58009579 -3.17308230
68 0.53042445 -1.58009579
69 0.92574879 0.53042445
70 -0.37074446 0.92574879
71 2.32107313 -0.37074446
72 1.02457987 2.32107313
73 0.62925554 1.02457987
74 -0.97542013 0.62925554
75 -2.46957555 -0.97542013
76 2.72808662 -2.46957555
77 -0.97542013 2.72808662
78 1.61756638 -0.97542013
79 -1.27191338 1.61756638
80 1.12341096 -1.27191338
81 1.72808662 1.12341096
82 0.92574879 1.72808662
83 1.72808662 0.92574879
84 1.22224204 1.72808662
85 0.12341096 1.22224204
86 0.62925554 0.12341096
87 0.12341096 0.62925554
88 -0.48126470 0.12341096
89 -6.87658904 -0.48126470
90 3.32107313 -6.87658904
91 -1.17308230 3.32107313
92 0.92574879 -1.17308230
93 0.22224204 0.92574879
94 -0.48126470 0.22224204
95 1.53042445 -0.48126470
96 -2.27191338 1.53042445
97 -0.07425121 -2.27191338
98 2.72808662 -0.07425121
99 1.22224204 2.72808662
100 2.62925554 1.22224204
101 -2.27191338 2.62925554
102 1.82691770 -2.27191338
103 -2.77775796 1.82691770
104 1.12341096 -2.77775796
105 -5.56840663 1.12341096
106 1.62925554 -5.56840663
107 0.33276228 1.62925554
108 -4.17308230 0.33276228
109 -4.86489989 -4.17308230
110 1.41990421 -4.86489989
111 -2.67892687 1.41990421
112 -0.77775796 -2.67892687
113 0.22224204 -0.77775796
114 3.72808662 0.22224204
115 1.72808662 3.72808662
116 0.12341096 1.72808662
117 0.02457987 0.12341096
118 0.12341096 0.02457987
119 -0.37074446 0.12341096
120 -1.87658904 -0.37074446
121 0.32107313 -1.87658904
122 0.62925554 0.32107313
123 0.72808662 0.62925554
124 1.12341096 0.72808662
125 -0.67892687 1.12341096
126 2.53042445 -0.67892687
127 2.03626903 2.53042445
128 5.12341096 2.03626903
129 0.92574879 5.12341096
130 -0.77775796 0.92574879
131 -4.38243362 -0.77775796
132 0.43159337 -4.38243362
133 1.71639746 0.43159337
134 0.72808662 1.71639746
135 2.22224204 0.72808662
136 -2.38243362 2.22224204
137 0.02457987 -2.38243362
138 -2.58009579 0.02457987
139 0.92574879 -2.58009579
140 -0.67892687 0.92574879
141 1.22224204 -0.67892687
142 2.41990421 1.22224204
143 0.41990421 2.41990421
144 1.32107313 0.41990421
145 1.43159337 1.32107313
146 2.22224204 1.43159337
147 -3.17308230 2.22224204
148 -1.77775796 -3.17308230
149 -5.07425121 -1.77775796
150 1.62925554 -5.07425121
151 -1.76606880 1.62925554
152 2.02457987 -1.76606880
153 -2.07425121 2.02457987
154 -5.07425121 -2.07425121
155 -0.87658904 -5.07425121
156 -1.17308230 -0.87658904
157 0.22224204 -1.17308230
158 5.12341096 0.22224204
159 -0.97542013 5.12341096
160 -1.77775796 -0.97542013
161 -0.97542013 -1.77775796
162 NA -0.97542013
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.53042445 -0.66723772
[2,] -2.58009579 3.53042445
[3,] -1.67892687 -2.58009579
[4,] 2.02457987 -1.67892687
[5,] 3.92574879 2.02457987
[6,] -0.46957555 3.92574879
[7,] 0.02457987 -0.46957555
[8,] 0.82691770 0.02457987
[9,] 0.72808662 0.82691770
[10,] 2.62925554 0.72808662
[11,] 4.82691770 2.62925554
[12,] -4.37074446 4.82691770
[13,] 1.53042445 -4.37074446
[14,] 4.12341096 1.53042445
[15,] 0.22224204 4.12341096
[16,] -0.17308230 0.22224204
[17,] 2.62925554 -0.17308230
[18,] -0.46957555 2.62925554
[19,] 2.22224204 -0.46957555
[20,] 4.22224204 2.22224204
[21,] -2.67892687 4.22224204
[22,] -0.46957555 -2.67892687
[23,] -2.27191338 -0.46957555
[24,] 2.53042445 -2.27191338
[25,] -5.66723772 2.53042445
[26,] 1.82691770 -5.66723772
[27,] 0.12341096 1.82691770
[28,] 1.12341096 0.12341096
[29,] -2.97542013 1.12341096
[30,] 2.32107313 -2.97542013
[31,] -0.28360254 2.32107313
[32,] 2.72808662 -0.28360254
[33,] 1.02457987 2.72808662
[34,] 0.02457987 1.02457987
[35,] 2.22224204 0.02457987
[36,] -4.48126470 2.22224204
[37,] 0.82691770 -4.48126470
[38,] 3.51873530 0.82691770
[39,] -1.07425121 3.51873530
[40,] 0.72808662 -1.07425121
[41,] 2.02457987 0.72808662
[42,] 1.62925554 2.02457987
[43,] -2.07425121 1.62925554
[44,] -2.37074446 -2.07425121
[45,] -3.27191338 -2.37074446
[46,] 0.62925554 -3.27191338
[47,] 1.12341096 0.62925554
[48,] 2.82691770 1.12341096
[49,] -1.37074446 2.82691770
[50,] 2.22224204 -1.37074446
[51,] 0.22224204 2.22224204
[52,] -2.77775796 0.22224204
[53,] -1.97542013 -2.77775796
[54,] -1.77775796 -1.97542013
[55,] 0.72808662 -1.77775796
[56,] 1.53042445 0.72808662
[57,] 1.51873530 1.53042445
[58,] -2.27191338 1.51873530
[59,] -2.07425121 -2.27191338
[60,] -5.58009579 -2.07425121
[61,] -1.37074446 -5.58009579
[62,] -2.97542013 -1.37074446
[63,] 0.32107313 -2.97542013
[64,] 1.02457987 0.32107313
[65,] -4.07425121 1.02457987
[66,] -3.17308230 -4.07425121
[67,] -1.58009579 -3.17308230
[68,] 0.53042445 -1.58009579
[69,] 0.92574879 0.53042445
[70,] -0.37074446 0.92574879
[71,] 2.32107313 -0.37074446
[72,] 1.02457987 2.32107313
[73,] 0.62925554 1.02457987
[74,] -0.97542013 0.62925554
[75,] -2.46957555 -0.97542013
[76,] 2.72808662 -2.46957555
[77,] -0.97542013 2.72808662
[78,] 1.61756638 -0.97542013
[79,] -1.27191338 1.61756638
[80,] 1.12341096 -1.27191338
[81,] 1.72808662 1.12341096
[82,] 0.92574879 1.72808662
[83,] 1.72808662 0.92574879
[84,] 1.22224204 1.72808662
[85,] 0.12341096 1.22224204
[86,] 0.62925554 0.12341096
[87,] 0.12341096 0.62925554
[88,] -0.48126470 0.12341096
[89,] -6.87658904 -0.48126470
[90,] 3.32107313 -6.87658904
[91,] -1.17308230 3.32107313
[92,] 0.92574879 -1.17308230
[93,] 0.22224204 0.92574879
[94,] -0.48126470 0.22224204
[95,] 1.53042445 -0.48126470
[96,] -2.27191338 1.53042445
[97,] -0.07425121 -2.27191338
[98,] 2.72808662 -0.07425121
[99,] 1.22224204 2.72808662
[100,] 2.62925554 1.22224204
[101,] -2.27191338 2.62925554
[102,] 1.82691770 -2.27191338
[103,] -2.77775796 1.82691770
[104,] 1.12341096 -2.77775796
[105,] -5.56840663 1.12341096
[106,] 1.62925554 -5.56840663
[107,] 0.33276228 1.62925554
[108,] -4.17308230 0.33276228
[109,] -4.86489989 -4.17308230
[110,] 1.41990421 -4.86489989
[111,] -2.67892687 1.41990421
[112,] -0.77775796 -2.67892687
[113,] 0.22224204 -0.77775796
[114,] 3.72808662 0.22224204
[115,] 1.72808662 3.72808662
[116,] 0.12341096 1.72808662
[117,] 0.02457987 0.12341096
[118,] 0.12341096 0.02457987
[119,] -0.37074446 0.12341096
[120,] -1.87658904 -0.37074446
[121,] 0.32107313 -1.87658904
[122,] 0.62925554 0.32107313
[123,] 0.72808662 0.62925554
[124,] 1.12341096 0.72808662
[125,] -0.67892687 1.12341096
[126,] 2.53042445 -0.67892687
[127,] 2.03626903 2.53042445
[128,] 5.12341096 2.03626903
[129,] 0.92574879 5.12341096
[130,] -0.77775796 0.92574879
[131,] -4.38243362 -0.77775796
[132,] 0.43159337 -4.38243362
[133,] 1.71639746 0.43159337
[134,] 0.72808662 1.71639746
[135,] 2.22224204 0.72808662
[136,] -2.38243362 2.22224204
[137,] 0.02457987 -2.38243362
[138,] -2.58009579 0.02457987
[139,] 0.92574879 -2.58009579
[140,] -0.67892687 0.92574879
[141,] 1.22224204 -0.67892687
[142,] 2.41990421 1.22224204
[143,] 0.41990421 2.41990421
[144,] 1.32107313 0.41990421
[145,] 1.43159337 1.32107313
[146,] 2.22224204 1.43159337
[147,] -3.17308230 2.22224204
[148,] -1.77775796 -3.17308230
[149,] -5.07425121 -1.77775796
[150,] 1.62925554 -5.07425121
[151,] -1.76606880 1.62925554
[152,] 2.02457987 -1.76606880
[153,] -2.07425121 2.02457987
[154,] -5.07425121 -2.07425121
[155,] -0.87658904 -5.07425121
[156,] -1.17308230 -0.87658904
[157,] 0.22224204 -1.17308230
[158,] 5.12341096 0.22224204
[159,] -0.97542013 5.12341096
[160,] -1.77775796 -0.97542013
[161,] -0.97542013 -1.77775796
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.53042445 -0.66723772
2 -2.58009579 3.53042445
3 -1.67892687 -2.58009579
4 2.02457987 -1.67892687
5 3.92574879 2.02457987
6 -0.46957555 3.92574879
7 0.02457987 -0.46957555
8 0.82691770 0.02457987
9 0.72808662 0.82691770
10 2.62925554 0.72808662
11 4.82691770 2.62925554
12 -4.37074446 4.82691770
13 1.53042445 -4.37074446
14 4.12341096 1.53042445
15 0.22224204 4.12341096
16 -0.17308230 0.22224204
17 2.62925554 -0.17308230
18 -0.46957555 2.62925554
19 2.22224204 -0.46957555
20 4.22224204 2.22224204
21 -2.67892687 4.22224204
22 -0.46957555 -2.67892687
23 -2.27191338 -0.46957555
24 2.53042445 -2.27191338
25 -5.66723772 2.53042445
26 1.82691770 -5.66723772
27 0.12341096 1.82691770
28 1.12341096 0.12341096
29 -2.97542013 1.12341096
30 2.32107313 -2.97542013
31 -0.28360254 2.32107313
32 2.72808662 -0.28360254
33 1.02457987 2.72808662
34 0.02457987 1.02457987
35 2.22224204 0.02457987
36 -4.48126470 2.22224204
37 0.82691770 -4.48126470
38 3.51873530 0.82691770
39 -1.07425121 3.51873530
40 0.72808662 -1.07425121
41 2.02457987 0.72808662
42 1.62925554 2.02457987
43 -2.07425121 1.62925554
44 -2.37074446 -2.07425121
45 -3.27191338 -2.37074446
46 0.62925554 -3.27191338
47 1.12341096 0.62925554
48 2.82691770 1.12341096
49 -1.37074446 2.82691770
50 2.22224204 -1.37074446
51 0.22224204 2.22224204
52 -2.77775796 0.22224204
53 -1.97542013 -2.77775796
54 -1.77775796 -1.97542013
55 0.72808662 -1.77775796
56 1.53042445 0.72808662
57 1.51873530 1.53042445
58 -2.27191338 1.51873530
59 -2.07425121 -2.27191338
60 -5.58009579 -2.07425121
61 -1.37074446 -5.58009579
62 -2.97542013 -1.37074446
63 0.32107313 -2.97542013
64 1.02457987 0.32107313
65 -4.07425121 1.02457987
66 -3.17308230 -4.07425121
67 -1.58009579 -3.17308230
68 0.53042445 -1.58009579
69 0.92574879 0.53042445
70 -0.37074446 0.92574879
71 2.32107313 -0.37074446
72 1.02457987 2.32107313
73 0.62925554 1.02457987
74 -0.97542013 0.62925554
75 -2.46957555 -0.97542013
76 2.72808662 -2.46957555
77 -0.97542013 2.72808662
78 1.61756638 -0.97542013
79 -1.27191338 1.61756638
80 1.12341096 -1.27191338
81 1.72808662 1.12341096
82 0.92574879 1.72808662
83 1.72808662 0.92574879
84 1.22224204 1.72808662
85 0.12341096 1.22224204
86 0.62925554 0.12341096
87 0.12341096 0.62925554
88 -0.48126470 0.12341096
89 -6.87658904 -0.48126470
90 3.32107313 -6.87658904
91 -1.17308230 3.32107313
92 0.92574879 -1.17308230
93 0.22224204 0.92574879
94 -0.48126470 0.22224204
95 1.53042445 -0.48126470
96 -2.27191338 1.53042445
97 -0.07425121 -2.27191338
98 2.72808662 -0.07425121
99 1.22224204 2.72808662
100 2.62925554 1.22224204
101 -2.27191338 2.62925554
102 1.82691770 -2.27191338
103 -2.77775796 1.82691770
104 1.12341096 -2.77775796
105 -5.56840663 1.12341096
106 1.62925554 -5.56840663
107 0.33276228 1.62925554
108 -4.17308230 0.33276228
109 -4.86489989 -4.17308230
110 1.41990421 -4.86489989
111 -2.67892687 1.41990421
112 -0.77775796 -2.67892687
113 0.22224204 -0.77775796
114 3.72808662 0.22224204
115 1.72808662 3.72808662
116 0.12341096 1.72808662
117 0.02457987 0.12341096
118 0.12341096 0.02457987
119 -0.37074446 0.12341096
120 -1.87658904 -0.37074446
121 0.32107313 -1.87658904
122 0.62925554 0.32107313
123 0.72808662 0.62925554
124 1.12341096 0.72808662
125 -0.67892687 1.12341096
126 2.53042445 -0.67892687
127 2.03626903 2.53042445
128 5.12341096 2.03626903
129 0.92574879 5.12341096
130 -0.77775796 0.92574879
131 -4.38243362 -0.77775796
132 0.43159337 -4.38243362
133 1.71639746 0.43159337
134 0.72808662 1.71639746
135 2.22224204 0.72808662
136 -2.38243362 2.22224204
137 0.02457987 -2.38243362
138 -2.58009579 0.02457987
139 0.92574879 -2.58009579
140 -0.67892687 0.92574879
141 1.22224204 -0.67892687
142 2.41990421 1.22224204
143 0.41990421 2.41990421
144 1.32107313 0.41990421
145 1.43159337 1.32107313
146 2.22224204 1.43159337
147 -3.17308230 2.22224204
148 -1.77775796 -3.17308230
149 -5.07425121 -1.77775796
150 1.62925554 -5.07425121
151 -1.76606880 1.62925554
152 2.02457987 -1.76606880
153 -2.07425121 2.02457987
154 -5.07425121 -2.07425121
155 -0.87658904 -5.07425121
156 -1.17308230 -0.87658904
157 0.22224204 -1.17308230
158 5.12341096 0.22224204
159 -0.97542013 5.12341096
160 -1.77775796 -0.97542013
161 -0.97542013 -1.77775796
> 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/rcomp/tmp/7zxa61324681072.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8rppe1324681072.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9m7p61324681072.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/1093yp1324681072.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11r1y71324681072.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/rcomp/tmp/12ewdg1324681072.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/rcomp/tmp/13arpr1324681072.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/rcomp/tmp/143ny31324681072.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/rcomp/tmp/15k89c1324681072.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/rcomp/tmp/16xzqk1324681072.tab")
+ }
>
> try(system("convert tmp/1mxhb1324681072.ps tmp/1mxhb1324681072.png",intern=TRUE))
character(0)
> try(system("convert tmp/2j1dr1324681072.ps tmp/2j1dr1324681072.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fn381324681072.ps tmp/3fn381324681072.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gcx51324681072.ps tmp/4gcx51324681072.png",intern=TRUE))
character(0)
> try(system("convert tmp/51ee81324681072.ps tmp/51ee81324681072.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gcy61324681072.ps tmp/6gcy61324681072.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zxa61324681072.ps tmp/7zxa61324681072.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rppe1324681072.ps tmp/8rppe1324681072.png",intern=TRUE))
character(0)
> try(system("convert tmp/9m7p61324681072.ps tmp/9m7p61324681072.png",intern=TRUE))
character(0)
> try(system("convert tmp/1093yp1324681072.ps tmp/1093yp1324681072.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.390 0.320 4.692