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)
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> x <- array(list(2
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+ ,0)
+ ,dim=c(7
+ ,162)
+ ,dimnames=list(c('Gender'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Population')
+ ,1:162))
> y <- array(NA,dim=c(7,162),dimnames=list(c('Gender','Connected','Separate','Learning','Software','Happiness','Population'),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
Connected Gender Separate Learning Software Happiness Population
1 41 2 38 13 12 14 1
2 39 2 32 16 11 18 1
3 30 2 35 19 15 11 0
4 31 1 33 15 6 12 0
5 34 2 37 14 13 16 1
6 35 2 29 13 10 18 1
7 39 2 31 19 12 14 1
8 34 2 36 15 14 14 0
9 36 2 35 14 12 15 0
10 37 2 38 15 6 15 1
11 38 1 31 16 10 17 0
12 36 2 34 16 12 19 0
13 38 1 35 16 12 10 0
14 39 2 38 16 11 16 0
15 33 2 37 17 15 18 0
16 32 1 33 15 12 14 1
17 36 1 32 15 10 14 1
18 38 2 38 20 12 17 1
19 39 1 38 18 11 14 0
20 32 2 32 16 12 16 0
21 32 1 33 16 11 18 1
22 31 2 31 16 12 11 1
23 39 2 38 19 13 14 0
24 37 2 39 16 11 12 1
25 39 1 32 17 9 17 1
26 41 2 32 17 13 9 1
27 36 1 35 16 10 16 1
28 33 2 37 15 14 14 0
29 33 2 33 16 12 15 0
30 34 1 33 14 10 11 1
31 31 2 28 15 12 16 1
32 27 1 32 12 8 13 1
33 37 2 31 14 10 17 0
34 34 2 37 16 12 15 0
35 34 1 30 14 12 14 0
36 32 1 33 7 7 16 1
37 29 1 31 10 6 9 1
38 36 1 33 14 12 15 0
39 29 2 31 16 10 17 0
40 35 1 33 16 10 13 1
41 37 1 32 16 10 15 1
42 34 2 33 14 12 16 1
43 38 1 32 20 15 16 1
44 35 1 33 14 10 12 1
45 38 2 28 14 10 12 0
46 37 2 35 11 12 11 0
47 38 2 39 14 13 15 0
48 33 2 34 15 11 15 1
49 36 2 38 16 11 17 1
50 38 1 32 14 12 13 0
51 32 2 38 16 14 16 0
52 32 1 30 14 10 14 1
53 32 1 33 12 12 11 0
54 34 2 38 16 13 12 0
55 32 1 32 9 5 12 0
56 37 2 32 14 6 15 0
57 39 2 34 16 12 16 0
58 29 2 34 16 12 15 0
59 37 1 36 15 11 12 0
60 35 2 34 16 10 12 0
61 30 1 28 12 7 8 0
62 38 1 34 16 12 13 1
63 34 2 35 16 14 11 1
64 31 2 35 14 11 14 1
65 34 2 31 16 12 15 0
66 35 1 37 17 13 10 0
67 36 2 35 18 14 11 0
68 30 1 27 18 11 12 0
69 39 2 40 12 12 15 1
70 35 1 37 16 12 15 1
71 38 1 36 10 8 14 0
72 31 2 38 14 11 16 0
73 34 2 39 18 14 15 0
74 38 1 41 18 14 15 0
75 34 1 27 16 12 13 1
76 39 2 30 17 9 12 1
77 37 2 37 16 13 17 0
78 34 2 31 16 11 13 1
79 28 1 31 13 12 15 1
80 37 1 27 16 12 13 1
81 33 1 36 16 12 15 0
82 37 1 38 20 12 16 0
83 35 2 37 16 12 15 0
84 37 1 33 15 12 16 1
85 32 2 34 15 11 15 0
86 33 2 31 16 10 14 0
87 38 1 39 14 9 15 1
88 33 2 34 16 12 14 1
89 29 2 32 16 12 13 0
90 33 2 33 15 12 7 0
91 31 2 36 12 9 17 1
92 36 2 32 17 15 13 1
93 35 2 41 16 12 15 1
94 32 2 28 15 12 14 0
95 29 2 30 13 12 13 1
96 39 2 36 16 10 16 1
97 37 2 35 16 13 12 1
98 35 2 31 16 9 14 1
99 37 1 34 16 12 17 0
100 32 1 36 14 10 15 0
101 38 2 36 16 14 17 1
102 37 1 35 16 11 12 0
103 36 2 37 20 15 16 0
104 32 1 28 15 11 11 0
105 33 2 39 16 11 15 0
106 40 1 32 13 12 9 1
107 38 2 35 17 12 16 1
108 41 1 39 16 12 15 0
109 36 1 35 16 11 10 0
110 43 2 42 12 7 10 1
111 30 2 34 16 12 15 1
112 31 2 33 16 14 11 1
113 32 2 41 17 11 13 0
114 32 1 33 13 11 14 0
115 37 2 34 12 10 18 1
116 37 1 32 18 13 16 0
117 33 2 40 14 13 14 0
118 34 2 40 14 8 14 0
119 33 2 35 13 11 14 0
120 38 2 36 16 12 14 0
121 33 2 37 13 11 12 1
122 31 2 27 16 13 14 1
123 38 2 39 13 12 15 1
124 37 2 38 16 14 15 0
125 33 2 31 15 13 15 0
126 31 2 33 16 15 13 1
127 39 1 32 15 10 17 1
128 44 2 39 17 11 17 0
129 33 2 36 15 9 19 1
130 35 2 33 12 11 15 1
131 32 1 33 16 10 13 1
132 28 1 32 10 11 9 1
133 40 2 37 16 8 15 0
134 27 1 30 12 11 15 0
135 37 1 38 14 12 15 1
136 32 2 29 15 12 16 1
137 28 1 22 13 9 11 1
138 34 1 35 15 11 14 0
139 30 2 35 11 10 11 0
140 35 2 34 12 8 15 0
141 31 1 35 8 9 13 1
142 32 2 34 16 8 15 1
143 30 1 34 15 9 16 0
144 30 2 35 17 15 14 0
145 31 1 23 16 11 15 1
146 40 2 31 10 8 16 1
147 32 2 27 18 13 16 1
148 36 1 36 13 12 11 1
149 32 1 31 16 12 12 1
150 35 1 32 13 9 9 0
151 38 2 39 10 7 16 0
152 42 2 37 15 13 13 0
153 34 1 38 16 9 16 1
154 35 2 39 16 6 12 1
155 35 2 34 14 8 9 0
156 33 2 31 10 8 13 0
157 36 2 32 17 15 13 1
158 32 2 37 13 6 14 0
159 33 2 36 15 9 19 0
160 34 2 32 16 11 13 0
161 32 2 35 12 8 12 0
162 34 2 36 13 8 13 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender Separate Learning Software Happiness
17.77763 -0.28835 0.35851 0.34455 -0.12990 0.07486
Population
0.68519
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7152 -2.3509 -0.1722 2.0842 7.3962
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.77763 3.00718 5.912 2.08e-08 ***
Gender -0.28835 0.52905 -0.545 0.5865
Separate 0.35851 0.07259 4.939 2.02e-06 ***
Learning 0.34455 0.13119 2.626 0.0095 **
Software -0.12990 0.13709 -0.948 0.3448
Happiness 0.07486 0.10885 0.688 0.4927
Population 0.68519 0.49946 1.372 0.1721
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.096 on 155 degrees of freedom
Multiple R-squared: 0.19, Adjusted R-squared: 0.1586
F-statistic: 6.058 on 6 and 155 DF, p-value: 1.018e-05
> 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,] 0.85931009 0.28137982 0.14068991
[2,] 0.84682251 0.30635497 0.15317749
[3,] 0.75847389 0.48305222 0.24152611
[4,] 0.75258150 0.49483700 0.24741850
[5,] 0.74881316 0.50237367 0.25118684
[6,] 0.79108005 0.41783989 0.20891995
[7,] 0.86352860 0.27294280 0.13647140
[8,] 0.80795450 0.38409099 0.19204550
[9,] 0.74116707 0.51766585 0.25883293
[10,] 0.71850691 0.56298617 0.28149309
[11,] 0.68163435 0.63673129 0.31836565
[12,] 0.74734652 0.50530696 0.25265348
[13,] 0.73217331 0.53565337 0.26782669
[14,] 0.70464199 0.59071603 0.29535801
[15,] 0.63858329 0.72283341 0.36141671
[16,] 0.61605623 0.76788754 0.38394377
[17,] 0.79589068 0.40821865 0.20410932
[18,] 0.74713766 0.50572467 0.25286234
[19,] 0.70988089 0.58023822 0.29011911
[20,] 0.66465500 0.67069000 0.33534500
[21,] 0.61047485 0.77905031 0.38952515
[22,] 0.59138408 0.81723183 0.40861592
[23,] 0.76478191 0.47043618 0.23521809
[24,] 0.77652056 0.44695888 0.22347944
[25,] 0.74531869 0.50936261 0.25468131
[26,] 0.71702612 0.56594776 0.28297388
[27,] 0.66786156 0.66427688 0.33213844
[28,] 0.65891363 0.68217273 0.34108637
[29,] 0.64291749 0.71416503 0.35708251
[30,] 0.72693185 0.54613630 0.27306815
[31,] 0.67899716 0.64200568 0.32100284
[32,] 0.64331128 0.71337745 0.35668872
[33,] 0.59207756 0.81584489 0.40792244
[34,] 0.54967609 0.90064783 0.45032391
[35,] 0.49816049 0.99632097 0.50183951
[36,] 0.64990316 0.70019368 0.35009684
[37,] 0.68532545 0.62934909 0.31467455
[38,] 0.66527353 0.66945294 0.33472647
[39,] 0.64157953 0.71684094 0.35842047
[40,] 0.59574165 0.80851670 0.40425835
[41,] 0.63925182 0.72149636 0.36074818
[42,] 0.66716069 0.66567863 0.33283931
[43,] 0.63508156 0.72983688 0.36491844
[44,] 0.59300444 0.81399112 0.40699556
[45,] 0.55941595 0.88116809 0.44058405
[46,] 0.51010786 0.97978427 0.48989214
[47,] 0.50057040 0.99885921 0.49942960
[48,] 0.53556647 0.92886706 0.46443353
[49,] 0.65345442 0.69309117 0.34654558
[50,] 0.62466633 0.75066735 0.37533367
[51,] 0.57949539 0.84100922 0.42050461
[52,] 0.54855169 0.90289663 0.45144831
[53,] 0.53391991 0.93216019 0.46608009
[54,] 0.49204438 0.98408877 0.50795562
[55,] 0.51349994 0.97300013 0.48650006
[56,] 0.46910360 0.93820719 0.53089640
[57,] 0.42518254 0.85036509 0.57481746
[58,] 0.38349913 0.76699826 0.61650087
[59,] 0.39194031 0.78388062 0.60805969
[60,] 0.39122801 0.78245603 0.60877199
[61,] 0.35677759 0.71355518 0.64322241
[62,] 0.38672185 0.77344371 0.61327815
[63,] 0.43501555 0.87003109 0.56498445
[64,] 0.42204759 0.84409519 0.57795241
[65,] 0.37731862 0.75463723 0.62268138
[66,] 0.33902813 0.67805626 0.66097187
[67,] 0.39315158 0.78630316 0.60684842
[68,] 0.35789450 0.71578900 0.64210550
[69,] 0.31637363 0.63274727 0.68362637
[70,] 0.39869945 0.79739889 0.60130055
[71,] 0.43624966 0.87249932 0.56375034
[72,] 0.41702385 0.83404769 0.58297615
[73,] 0.37297094 0.74594189 0.62702906
[74,] 0.33101517 0.66203033 0.66898483
[75,] 0.31006593 0.62013186 0.68993407
[76,] 0.29094359 0.58188717 0.70905641
[77,] 0.25510199 0.51020398 0.74489801
[78,] 0.22420284 0.44840569 0.77579716
[79,] 0.20506127 0.41012254 0.79493873
[80,] 0.24393376 0.48786753 0.75606624
[81,] 0.20880760 0.41761519 0.79119240
[82,] 0.23021026 0.46042053 0.76978974
[83,] 0.20645685 0.41291370 0.79354315
[84,] 0.19707313 0.39414627 0.80292687
[85,] 0.16740748 0.33481497 0.83259252
[86,] 0.17481347 0.34962695 0.82518653
[87,] 0.16902016 0.33804033 0.83097984
[88,] 0.15047159 0.30094318 0.84952841
[89,] 0.12732800 0.25465600 0.87267200
[90,] 0.11426984 0.22853968 0.88573016
[91,] 0.11171087 0.22342174 0.88828913
[92,] 0.10014147 0.20028294 0.89985853
[93,] 0.08940739 0.17881479 0.91059261
[94,] 0.07198534 0.14397068 0.92801466
[95,] 0.05770282 0.11540564 0.94229718
[96,] 0.05862846 0.11725692 0.94137154
[97,] 0.14028048 0.28056096 0.85971952
[98,] 0.12708212 0.25416424 0.87291788
[99,] 0.15994661 0.31989322 0.84005339
[100,] 0.14694360 0.29388720 0.85305640
[101,] 0.27002466 0.54004932 0.72997534
[102,] 0.33247567 0.66495135 0.66752433
[103,] 0.31986421 0.63972842 0.68013579
[104,] 0.38943656 0.77887311 0.61056344
[105,] 0.34687691 0.69375382 0.65312309
[106,] 0.32447040 0.64894080 0.67552960
[107,] 0.31616655 0.63233311 0.68383345
[108,] 0.31953566 0.63907132 0.68046434
[109,] 0.30335164 0.60670328 0.69664836
[110,] 0.26711805 0.53423610 0.73288195
[111,] 0.25378426 0.50756852 0.74621574
[112,] 0.23254357 0.46508713 0.76745643
[113,] 0.19849060 0.39698120 0.80150940
[114,] 0.16955832 0.33911665 0.83044168
[115,] 0.13924237 0.27848475 0.86075763
[116,] 0.11070443 0.22140886 0.88929557
[117,] 0.11828544 0.23657088 0.88171456
[118,] 0.18903038 0.37806076 0.81096962
[119,] 0.39523353 0.79046706 0.60476647
[120,] 0.38746251 0.77492503 0.61253749
[121,] 0.33273744 0.66547489 0.66726256
[122,] 0.29403941 0.58807882 0.70596059
[123,] 0.35012389 0.70024778 0.64987611
[124,] 0.47347573 0.94695146 0.52652427
[125,] 0.53649975 0.92700050 0.46350025
[126,] 0.48574599 0.97149199 0.51425401
[127,] 0.43549613 0.87099226 0.56450387
[128,] 0.39911722 0.79823444 0.60088278
[129,] 0.34977600 0.69955200 0.65022400
[130,] 0.49018114 0.98036229 0.50981886
[131,] 0.42030957 0.84061913 0.57969043
[132,] 0.66462945 0.67074110 0.33537055
[133,] 0.62146380 0.75707240 0.37853620
[134,] 0.55986275 0.88027450 0.44013725
[135,] 0.75425679 0.49148642 0.24574321
[136,] 0.70670399 0.58659203 0.29329601
[137,] 0.95484659 0.09030682 0.04515341
[138,] 0.95149383 0.09701234 0.04850617
[139,] 0.95134442 0.09731115 0.04865558
[140,] 0.91434903 0.17130194 0.08565097
[141,] 0.86079603 0.27840793 0.13920397
[142,] 0.79591514 0.40816971 0.20408486
[143,] 0.95036184 0.09927631 0.04963816
> postscript(file="/var/www/rcomp/tmp/1sfth1323961823.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2xmhl1323961823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xpqm1323961823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4ult31323961823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5n3t81323961823.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
5.52229897 4.21038932 -5.16996668 -3.60711890 -1.48354501 2.18964808
7 8 9 10 11 12
3.96459499 -0.50477067 1.86361754 -0.02107686 3.91069126 1.23361040
13 14 15 16 17 18
3.26045944 2.89423752 -2.72189192 -2.66259487 1.43610491 -0.11408630
19 20 21 22 23 24
2.06650953 -1.82480076 -3.43647129 -2.77719996 2.27012530 0.14996273
25 26 27 28 29 30
3.39254006 6.79936361 -0.13368184 -1.86328014 -1.10845361 -0.35328963
31 32 33 34 35 36
-1.73140960 -6.71521299 3.88813251 -1.54249153 1.44267044 -0.70547192
37 38 39 40 41 42
-3.62799659 2.29228537 -4.80095761 -0.19209300 2.01670322 -0.17941194
43 44 45 46 47 48
2.21319061 0.57185374 6.33794408 4.19667922 2.55948448 -1.93751470
49 50 51 52 53 54
-0.86581093 4.80050811 -3.71604793 -1.50233108 -0.71919801 -1.54652627
55 56 57 58 59 60
-0.31124393 3.15971689 4.45818028 -5.46696309 1.96687691 0.49779709
61 62 63 64 65 66
-1.35160497 2.70920722 -0.95142818 -3.87662249 0.60856535 -0.67119973
67 68 69 70 71 72
1.04467353 -2.84017295 3.07496845 -1.51603447 4.15017439 -4.41667237
73 74 75 76 77 78
-2.68879090 0.30583901 1.21877357 4.77219329 1.43770007 -0.05681806
79 80 81 82 83 84
-5.33134243 4.21877357 -2.47233318 -0.64238899 -0.54249153 2.18769187
85 86 87 88 89 90
-2.25232288 -0.57638772 1.06632213 -2.07729828 -4.60023088 -0.16505554
91 92 93 94 95 96
-4.03042144 1.75974680 -2.66172126 0.10349547 -3.53476857 2.79615981
97 98 99 100 101 102
1.84381035 0.60851561 2.09497253 -3.04305276 2.24092258 1.98084133
103 104 105 106 107 108
-0.60581383 -0.09019063 -3.38941533 6.75928785 2.06993393 4.45213839
109 110 111 112 113 114
1.13055459 6.08270838 -5.15215491 -3.23440922 -5.30126609 -1.41821779
115 116 117 118 119 120
2.74164574 2.32766285 -2.72416837 -2.37369262 -0.84688562 2.89087458
121 122 123 124 125 126
-2.09938314 -1.43782708 2.08893287 1.35880869 0.08301525 -3.25421763
127 128 129 130 131 132
4.21153502 7.11632636 -3.21376986 1.45462995 -3.19209300 -4.33698183
133 134 135 136 137 138
3.93788908 -5.07300093 0.81454616 -1.08991908 -2.19504516 -0.82432686
139 140 141 142 143 144
-3.06313047 1.39159774 -2.28265636 -3.67177430 -4.87534034 -4.70544645
145 146 147 148 149 150
-0.62680662 7.39616785 -1.27663045 1.17553668 -2.14040772 2.05476512
151 152 153 154 155 156
3.08337898 7.08167163 -3.33911513 -2.49956151 1.15164739 1.30592954
157 158 159 160 161 162
1.75974680 -3.21342882 -2.52857805 0.26986427 -1.74234186 -0.52025302
> postscript(file="/var/www/rcomp/tmp/67qow1323961823.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 5.52229897 NA
1 4.21038932 5.52229897
2 -5.16996668 4.21038932
3 -3.60711890 -5.16996668
4 -1.48354501 -3.60711890
5 2.18964808 -1.48354501
6 3.96459499 2.18964808
7 -0.50477067 3.96459499
8 1.86361754 -0.50477067
9 -0.02107686 1.86361754
10 3.91069126 -0.02107686
11 1.23361040 3.91069126
12 3.26045944 1.23361040
13 2.89423752 3.26045944
14 -2.72189192 2.89423752
15 -2.66259487 -2.72189192
16 1.43610491 -2.66259487
17 -0.11408630 1.43610491
18 2.06650953 -0.11408630
19 -1.82480076 2.06650953
20 -3.43647129 -1.82480076
21 -2.77719996 -3.43647129
22 2.27012530 -2.77719996
23 0.14996273 2.27012530
24 3.39254006 0.14996273
25 6.79936361 3.39254006
26 -0.13368184 6.79936361
27 -1.86328014 -0.13368184
28 -1.10845361 -1.86328014
29 -0.35328963 -1.10845361
30 -1.73140960 -0.35328963
31 -6.71521299 -1.73140960
32 3.88813251 -6.71521299
33 -1.54249153 3.88813251
34 1.44267044 -1.54249153
35 -0.70547192 1.44267044
36 -3.62799659 -0.70547192
37 2.29228537 -3.62799659
38 -4.80095761 2.29228537
39 -0.19209300 -4.80095761
40 2.01670322 -0.19209300
41 -0.17941194 2.01670322
42 2.21319061 -0.17941194
43 0.57185374 2.21319061
44 6.33794408 0.57185374
45 4.19667922 6.33794408
46 2.55948448 4.19667922
47 -1.93751470 2.55948448
48 -0.86581093 -1.93751470
49 4.80050811 -0.86581093
50 -3.71604793 4.80050811
51 -1.50233108 -3.71604793
52 -0.71919801 -1.50233108
53 -1.54652627 -0.71919801
54 -0.31124393 -1.54652627
55 3.15971689 -0.31124393
56 4.45818028 3.15971689
57 -5.46696309 4.45818028
58 1.96687691 -5.46696309
59 0.49779709 1.96687691
60 -1.35160497 0.49779709
61 2.70920722 -1.35160497
62 -0.95142818 2.70920722
63 -3.87662249 -0.95142818
64 0.60856535 -3.87662249
65 -0.67119973 0.60856535
66 1.04467353 -0.67119973
67 -2.84017295 1.04467353
68 3.07496845 -2.84017295
69 -1.51603447 3.07496845
70 4.15017439 -1.51603447
71 -4.41667237 4.15017439
72 -2.68879090 -4.41667237
73 0.30583901 -2.68879090
74 1.21877357 0.30583901
75 4.77219329 1.21877357
76 1.43770007 4.77219329
77 -0.05681806 1.43770007
78 -5.33134243 -0.05681806
79 4.21877357 -5.33134243
80 -2.47233318 4.21877357
81 -0.64238899 -2.47233318
82 -0.54249153 -0.64238899
83 2.18769187 -0.54249153
84 -2.25232288 2.18769187
85 -0.57638772 -2.25232288
86 1.06632213 -0.57638772
87 -2.07729828 1.06632213
88 -4.60023088 -2.07729828
89 -0.16505554 -4.60023088
90 -4.03042144 -0.16505554
91 1.75974680 -4.03042144
92 -2.66172126 1.75974680
93 0.10349547 -2.66172126
94 -3.53476857 0.10349547
95 2.79615981 -3.53476857
96 1.84381035 2.79615981
97 0.60851561 1.84381035
98 2.09497253 0.60851561
99 -3.04305276 2.09497253
100 2.24092258 -3.04305276
101 1.98084133 2.24092258
102 -0.60581383 1.98084133
103 -0.09019063 -0.60581383
104 -3.38941533 -0.09019063
105 6.75928785 -3.38941533
106 2.06993393 6.75928785
107 4.45213839 2.06993393
108 1.13055459 4.45213839
109 6.08270838 1.13055459
110 -5.15215491 6.08270838
111 -3.23440922 -5.15215491
112 -5.30126609 -3.23440922
113 -1.41821779 -5.30126609
114 2.74164574 -1.41821779
115 2.32766285 2.74164574
116 -2.72416837 2.32766285
117 -2.37369262 -2.72416837
118 -0.84688562 -2.37369262
119 2.89087458 -0.84688562
120 -2.09938314 2.89087458
121 -1.43782708 -2.09938314
122 2.08893287 -1.43782708
123 1.35880869 2.08893287
124 0.08301525 1.35880869
125 -3.25421763 0.08301525
126 4.21153502 -3.25421763
127 7.11632636 4.21153502
128 -3.21376986 7.11632636
129 1.45462995 -3.21376986
130 -3.19209300 1.45462995
131 -4.33698183 -3.19209300
132 3.93788908 -4.33698183
133 -5.07300093 3.93788908
134 0.81454616 -5.07300093
135 -1.08991908 0.81454616
136 -2.19504516 -1.08991908
137 -0.82432686 -2.19504516
138 -3.06313047 -0.82432686
139 1.39159774 -3.06313047
140 -2.28265636 1.39159774
141 -3.67177430 -2.28265636
142 -4.87534034 -3.67177430
143 -4.70544645 -4.87534034
144 -0.62680662 -4.70544645
145 7.39616785 -0.62680662
146 -1.27663045 7.39616785
147 1.17553668 -1.27663045
148 -2.14040772 1.17553668
149 2.05476512 -2.14040772
150 3.08337898 2.05476512
151 7.08167163 3.08337898
152 -3.33911513 7.08167163
153 -2.49956151 -3.33911513
154 1.15164739 -2.49956151
155 1.30592954 1.15164739
156 1.75974680 1.30592954
157 -3.21342882 1.75974680
158 -2.52857805 -3.21342882
159 0.26986427 -2.52857805
160 -1.74234186 0.26986427
161 -0.52025302 -1.74234186
162 NA -0.52025302
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.21038932 5.52229897
[2,] -5.16996668 4.21038932
[3,] -3.60711890 -5.16996668
[4,] -1.48354501 -3.60711890
[5,] 2.18964808 -1.48354501
[6,] 3.96459499 2.18964808
[7,] -0.50477067 3.96459499
[8,] 1.86361754 -0.50477067
[9,] -0.02107686 1.86361754
[10,] 3.91069126 -0.02107686
[11,] 1.23361040 3.91069126
[12,] 3.26045944 1.23361040
[13,] 2.89423752 3.26045944
[14,] -2.72189192 2.89423752
[15,] -2.66259487 -2.72189192
[16,] 1.43610491 -2.66259487
[17,] -0.11408630 1.43610491
[18,] 2.06650953 -0.11408630
[19,] -1.82480076 2.06650953
[20,] -3.43647129 -1.82480076
[21,] -2.77719996 -3.43647129
[22,] 2.27012530 -2.77719996
[23,] 0.14996273 2.27012530
[24,] 3.39254006 0.14996273
[25,] 6.79936361 3.39254006
[26,] -0.13368184 6.79936361
[27,] -1.86328014 -0.13368184
[28,] -1.10845361 -1.86328014
[29,] -0.35328963 -1.10845361
[30,] -1.73140960 -0.35328963
[31,] -6.71521299 -1.73140960
[32,] 3.88813251 -6.71521299
[33,] -1.54249153 3.88813251
[34,] 1.44267044 -1.54249153
[35,] -0.70547192 1.44267044
[36,] -3.62799659 -0.70547192
[37,] 2.29228537 -3.62799659
[38,] -4.80095761 2.29228537
[39,] -0.19209300 -4.80095761
[40,] 2.01670322 -0.19209300
[41,] -0.17941194 2.01670322
[42,] 2.21319061 -0.17941194
[43,] 0.57185374 2.21319061
[44,] 6.33794408 0.57185374
[45,] 4.19667922 6.33794408
[46,] 2.55948448 4.19667922
[47,] -1.93751470 2.55948448
[48,] -0.86581093 -1.93751470
[49,] 4.80050811 -0.86581093
[50,] -3.71604793 4.80050811
[51,] -1.50233108 -3.71604793
[52,] -0.71919801 -1.50233108
[53,] -1.54652627 -0.71919801
[54,] -0.31124393 -1.54652627
[55,] 3.15971689 -0.31124393
[56,] 4.45818028 3.15971689
[57,] -5.46696309 4.45818028
[58,] 1.96687691 -5.46696309
[59,] 0.49779709 1.96687691
[60,] -1.35160497 0.49779709
[61,] 2.70920722 -1.35160497
[62,] -0.95142818 2.70920722
[63,] -3.87662249 -0.95142818
[64,] 0.60856535 -3.87662249
[65,] -0.67119973 0.60856535
[66,] 1.04467353 -0.67119973
[67,] -2.84017295 1.04467353
[68,] 3.07496845 -2.84017295
[69,] -1.51603447 3.07496845
[70,] 4.15017439 -1.51603447
[71,] -4.41667237 4.15017439
[72,] -2.68879090 -4.41667237
[73,] 0.30583901 -2.68879090
[74,] 1.21877357 0.30583901
[75,] 4.77219329 1.21877357
[76,] 1.43770007 4.77219329
[77,] -0.05681806 1.43770007
[78,] -5.33134243 -0.05681806
[79,] 4.21877357 -5.33134243
[80,] -2.47233318 4.21877357
[81,] -0.64238899 -2.47233318
[82,] -0.54249153 -0.64238899
[83,] 2.18769187 -0.54249153
[84,] -2.25232288 2.18769187
[85,] -0.57638772 -2.25232288
[86,] 1.06632213 -0.57638772
[87,] -2.07729828 1.06632213
[88,] -4.60023088 -2.07729828
[89,] -0.16505554 -4.60023088
[90,] -4.03042144 -0.16505554
[91,] 1.75974680 -4.03042144
[92,] -2.66172126 1.75974680
[93,] 0.10349547 -2.66172126
[94,] -3.53476857 0.10349547
[95,] 2.79615981 -3.53476857
[96,] 1.84381035 2.79615981
[97,] 0.60851561 1.84381035
[98,] 2.09497253 0.60851561
[99,] -3.04305276 2.09497253
[100,] 2.24092258 -3.04305276
[101,] 1.98084133 2.24092258
[102,] -0.60581383 1.98084133
[103,] -0.09019063 -0.60581383
[104,] -3.38941533 -0.09019063
[105,] 6.75928785 -3.38941533
[106,] 2.06993393 6.75928785
[107,] 4.45213839 2.06993393
[108,] 1.13055459 4.45213839
[109,] 6.08270838 1.13055459
[110,] -5.15215491 6.08270838
[111,] -3.23440922 -5.15215491
[112,] -5.30126609 -3.23440922
[113,] -1.41821779 -5.30126609
[114,] 2.74164574 -1.41821779
[115,] 2.32766285 2.74164574
[116,] -2.72416837 2.32766285
[117,] -2.37369262 -2.72416837
[118,] -0.84688562 -2.37369262
[119,] 2.89087458 -0.84688562
[120,] -2.09938314 2.89087458
[121,] -1.43782708 -2.09938314
[122,] 2.08893287 -1.43782708
[123,] 1.35880869 2.08893287
[124,] 0.08301525 1.35880869
[125,] -3.25421763 0.08301525
[126,] 4.21153502 -3.25421763
[127,] 7.11632636 4.21153502
[128,] -3.21376986 7.11632636
[129,] 1.45462995 -3.21376986
[130,] -3.19209300 1.45462995
[131,] -4.33698183 -3.19209300
[132,] 3.93788908 -4.33698183
[133,] -5.07300093 3.93788908
[134,] 0.81454616 -5.07300093
[135,] -1.08991908 0.81454616
[136,] -2.19504516 -1.08991908
[137,] -0.82432686 -2.19504516
[138,] -3.06313047 -0.82432686
[139,] 1.39159774 -3.06313047
[140,] -2.28265636 1.39159774
[141,] -3.67177430 -2.28265636
[142,] -4.87534034 -3.67177430
[143,] -4.70544645 -4.87534034
[144,] -0.62680662 -4.70544645
[145,] 7.39616785 -0.62680662
[146,] -1.27663045 7.39616785
[147,] 1.17553668 -1.27663045
[148,] -2.14040772 1.17553668
[149,] 2.05476512 -2.14040772
[150,] 3.08337898 2.05476512
[151,] 7.08167163 3.08337898
[152,] -3.33911513 7.08167163
[153,] -2.49956151 -3.33911513
[154,] 1.15164739 -2.49956151
[155,] 1.30592954 1.15164739
[156,] 1.75974680 1.30592954
[157,] -3.21342882 1.75974680
[158,] -2.52857805 -3.21342882
[159,] 0.26986427 -2.52857805
[160,] -1.74234186 0.26986427
[161,] -0.52025302 -1.74234186
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.21038932 5.52229897
2 -5.16996668 4.21038932
3 -3.60711890 -5.16996668
4 -1.48354501 -3.60711890
5 2.18964808 -1.48354501
6 3.96459499 2.18964808
7 -0.50477067 3.96459499
8 1.86361754 -0.50477067
9 -0.02107686 1.86361754
10 3.91069126 -0.02107686
11 1.23361040 3.91069126
12 3.26045944 1.23361040
13 2.89423752 3.26045944
14 -2.72189192 2.89423752
15 -2.66259487 -2.72189192
16 1.43610491 -2.66259487
17 -0.11408630 1.43610491
18 2.06650953 -0.11408630
19 -1.82480076 2.06650953
20 -3.43647129 -1.82480076
21 -2.77719996 -3.43647129
22 2.27012530 -2.77719996
23 0.14996273 2.27012530
24 3.39254006 0.14996273
25 6.79936361 3.39254006
26 -0.13368184 6.79936361
27 -1.86328014 -0.13368184
28 -1.10845361 -1.86328014
29 -0.35328963 -1.10845361
30 -1.73140960 -0.35328963
31 -6.71521299 -1.73140960
32 3.88813251 -6.71521299
33 -1.54249153 3.88813251
34 1.44267044 -1.54249153
35 -0.70547192 1.44267044
36 -3.62799659 -0.70547192
37 2.29228537 -3.62799659
38 -4.80095761 2.29228537
39 -0.19209300 -4.80095761
40 2.01670322 -0.19209300
41 -0.17941194 2.01670322
42 2.21319061 -0.17941194
43 0.57185374 2.21319061
44 6.33794408 0.57185374
45 4.19667922 6.33794408
46 2.55948448 4.19667922
47 -1.93751470 2.55948448
48 -0.86581093 -1.93751470
49 4.80050811 -0.86581093
50 -3.71604793 4.80050811
51 -1.50233108 -3.71604793
52 -0.71919801 -1.50233108
53 -1.54652627 -0.71919801
54 -0.31124393 -1.54652627
55 3.15971689 -0.31124393
56 4.45818028 3.15971689
57 -5.46696309 4.45818028
58 1.96687691 -5.46696309
59 0.49779709 1.96687691
60 -1.35160497 0.49779709
61 2.70920722 -1.35160497
62 -0.95142818 2.70920722
63 -3.87662249 -0.95142818
64 0.60856535 -3.87662249
65 -0.67119973 0.60856535
66 1.04467353 -0.67119973
67 -2.84017295 1.04467353
68 3.07496845 -2.84017295
69 -1.51603447 3.07496845
70 4.15017439 -1.51603447
71 -4.41667237 4.15017439
72 -2.68879090 -4.41667237
73 0.30583901 -2.68879090
74 1.21877357 0.30583901
75 4.77219329 1.21877357
76 1.43770007 4.77219329
77 -0.05681806 1.43770007
78 -5.33134243 -0.05681806
79 4.21877357 -5.33134243
80 -2.47233318 4.21877357
81 -0.64238899 -2.47233318
82 -0.54249153 -0.64238899
83 2.18769187 -0.54249153
84 -2.25232288 2.18769187
85 -0.57638772 -2.25232288
86 1.06632213 -0.57638772
87 -2.07729828 1.06632213
88 -4.60023088 -2.07729828
89 -0.16505554 -4.60023088
90 -4.03042144 -0.16505554
91 1.75974680 -4.03042144
92 -2.66172126 1.75974680
93 0.10349547 -2.66172126
94 -3.53476857 0.10349547
95 2.79615981 -3.53476857
96 1.84381035 2.79615981
97 0.60851561 1.84381035
98 2.09497253 0.60851561
99 -3.04305276 2.09497253
100 2.24092258 -3.04305276
101 1.98084133 2.24092258
102 -0.60581383 1.98084133
103 -0.09019063 -0.60581383
104 -3.38941533 -0.09019063
105 6.75928785 -3.38941533
106 2.06993393 6.75928785
107 4.45213839 2.06993393
108 1.13055459 4.45213839
109 6.08270838 1.13055459
110 -5.15215491 6.08270838
111 -3.23440922 -5.15215491
112 -5.30126609 -3.23440922
113 -1.41821779 -5.30126609
114 2.74164574 -1.41821779
115 2.32766285 2.74164574
116 -2.72416837 2.32766285
117 -2.37369262 -2.72416837
118 -0.84688562 -2.37369262
119 2.89087458 -0.84688562
120 -2.09938314 2.89087458
121 -1.43782708 -2.09938314
122 2.08893287 -1.43782708
123 1.35880869 2.08893287
124 0.08301525 1.35880869
125 -3.25421763 0.08301525
126 4.21153502 -3.25421763
127 7.11632636 4.21153502
128 -3.21376986 7.11632636
129 1.45462995 -3.21376986
130 -3.19209300 1.45462995
131 -4.33698183 -3.19209300
132 3.93788908 -4.33698183
133 -5.07300093 3.93788908
134 0.81454616 -5.07300093
135 -1.08991908 0.81454616
136 -2.19504516 -1.08991908
137 -0.82432686 -2.19504516
138 -3.06313047 -0.82432686
139 1.39159774 -3.06313047
140 -2.28265636 1.39159774
141 -3.67177430 -2.28265636
142 -4.87534034 -3.67177430
143 -4.70544645 -4.87534034
144 -0.62680662 -4.70544645
145 7.39616785 -0.62680662
146 -1.27663045 7.39616785
147 1.17553668 -1.27663045
148 -2.14040772 1.17553668
149 2.05476512 -2.14040772
150 3.08337898 2.05476512
151 7.08167163 3.08337898
152 -3.33911513 7.08167163
153 -2.49956151 -3.33911513
154 1.15164739 -2.49956151
155 1.30592954 1.15164739
156 1.75974680 1.30592954
157 -3.21342882 1.75974680
158 -2.52857805 -3.21342882
159 0.26986427 -2.52857805
160 -1.74234186 0.26986427
161 -0.52025302 -1.74234186
> 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/7zy1h1323961823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8fhj71323961823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9xdpv1323961823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/1007gx1323961823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11w6jr1323961823.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/12scug1323961823.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/13goon1323961823.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/14ecke1323961823.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/15blvq1323961823.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/163fx11323961823.tab")
+ }
>
> try(system("convert tmp/1sfth1323961823.ps tmp/1sfth1323961823.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xmhl1323961823.ps tmp/2xmhl1323961823.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xpqm1323961823.ps tmp/3xpqm1323961823.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ult31323961823.ps tmp/4ult31323961823.png",intern=TRUE))
character(0)
> try(system("convert tmp/5n3t81323961823.ps tmp/5n3t81323961823.png",intern=TRUE))
character(0)
> try(system("convert tmp/67qow1323961823.ps tmp/67qow1323961823.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zy1h1323961823.ps tmp/7zy1h1323961823.png",intern=TRUE))
character(0)
> try(system("convert tmp/8fhj71323961823.ps tmp/8fhj71323961823.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xdpv1323961823.ps tmp/9xdpv1323961823.png",intern=TRUE))
character(0)
> try(system("convert tmp/1007gx1323961823.ps tmp/1007gx1323961823.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.380 0.350 5.742