R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(2
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+ ,3)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('standards'
+ ,'organization'
+ ,'punished'
+ ,'secondrate'
+ ,'mistakes'
+ ,'competent'
+ ,'neat')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('standards','organization','punished','secondrate','mistakes','competent','neat'),1:159))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '7'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
neat standards organization punished secondrate mistakes competent
1 4 2 5 2 3 3 4
2 4 2 4 2 4 3 4
3 4 4 4 2 4 2 5
4 4 2 4 2 2 2 2
5 4 3 2 2 2 3 2
6 5 4 5 1 3 2 4
7 4 3 5 1 2 1 4
8 3 3 4 3 3 3 4
9 4 3 3 2 3 2 4
10 4 2 4 1 3 2 2
11 4 4 4 4 3 3 3
12 4 4 2 2 4 2 4
13 4 3 3 3 2 2 3
14 2 3 3 2 2 2 4
15 3 4 4 1 1 3 4
16 4 4 5 1 1 1 4
17 3 3 4 2 3 3 4
18 2 3 2 2 2 2 2
19 4 3 4 2 2 3 4
20 3 4 4 2 3 4 4
21 3 2 4 1 4 2 4
22 4 5 4 2 4 3 3
23 3 4 4 4 3 5 2
24 3 2 4 2 2 2 4
25 4 3 5 2 3 2 2
26 4 4 4 2 4 3 3
27 4 4 4 2 3 2 4
28 4 3 4 2 2 2 3
29 4 4 4 3 1 2 4
30 4 4 4 2 3 2 4
31 5 1 4 1 2 3 4
32 4 4 4 4 4 4 4
33 4 5 2 1 4 1 4
34 4 2 4 2 5 3 4
35 3 4 4 2 2 3 4
36 4 3 5 2 4 2 5
37 3 2 5 2 4 1 4
38 4 4 4 2 2 1 2
39 4 5 3 2 4 2 4
40 3 4 4 2 4 2 4
41 5 4 5 2 2 2 5
42 4 4 4 2 3 1 4
43 3 3 4 2 2 2 2
44 3 4 5 2 4 1 4
45 3 2 4 2 3 2 4
46 4 2 5 1 1 2 4
47 4 4 4 2 2 4 2
48 4 2 4 1 5 2 5
49 4 4 4 2 2 2 4
50 4 4 3 1 4 2 4
51 4 1 4 1 4 1 4
52 4 4 4 2 2 2 4
53 5 2 4 2 2 2 4
54 3 1 2 1 2 1 3
55 3 4 3 5 4 5 5
56 5 3 5 2 3 2 4
57 5 2 4 2 4 2 4
58 4 4 4 1 2 2 4
59 4 3 5 1 3 1 4
60 3 2 3 2 2 3 2
61 4 2 5 2 2 1 4
62 4 3 4 1 3 1 4
63 5 2 5 1 2 2 4
64 4 1 4 2 3 3 4
65 4 3 4 1 2 2 3
66 5 2 5 1 4 2 4
67 4 3 4 2 2 2 2
68 3 3 4 1 5 4 4
69 4 3 5 1 1 1 4
70 4 2 4 2 3 2 4
71 4 3 3 1 2 2 4
72 4 2 4 1 2 2 4
73 4 4 5 3 3 2 4
74 4 4 5 3 4 2 3
75 4 4 5 2 4 1 4
76 3 2 4 2 2 2 4
77 4 3 4 1 3 2 4
78 3 4 5 3 4 2 4
79 5 3 5 2 2 2 4
80 4 4 4 2 2 1 4
81 5 2 5 2 4 4 4
82 5 3 3 2 2 2 2
83 4 3 4 1 4 3 3
84 4 4 4 4 2 2 5
85 4 2 4 1 3 1 3
86 4 4 4 1 4 2 3
87 4 2 4 1 3 2 4
88 5 2 5 1 1 1 4
89 4 4 4 4 3 2 4
90 3 3 4 2 2 1 4
91 4 4 4 2 2 2 4
92 3 2 5 1 1 1 3
93 4 2 3 1 3 2 4
94 4 3 3 1 2 2 4
95 4 3 5 3 3 3 4
96 4 5 5 4 5 4 5
97 4 2 4 4 3 1 4
98 3 3 4 3 4 3 4
99 3 4 4 2 2 1 2
100 3 3 4 2 2 1 3
101 3 4 4 3 3 2 3
102 3 3 4 1 2 1 3
103 2 3 4 3 2 3 4
104 3 2 4 2 2 2 4
105 5 3 5 2 3 2 2
106 2 2 2 2 5 1 3
107 2 3 4 2 2 2 3
108 3 2 2 4 3 2 4
109 3 4 4 3 3 1 4
110 3 2 5 1 1 2 2
111 4 4 3 1 1 2 3
112 4 4 4 2 3 4 4
113 3 1 3 1 4 3 4
114 2 5 4 3 5 2 5
115 3 2 4 2 3 5 3
116 4 3 4 2 3 1 3
117 2 4 2 2 3 2 4
118 4 1 1 1 2 1 3
119 4 5 4 3 3 2 3
120 2 3 3 1 2 1 2
121 3 3 4 1 3 1 4
122 3 3 3 2 2 2 3
123 3 3 3 3 4 2 4
124 4 2 5 2 2 2 5
125 4 2 4 1 2 3 4
126 4 4 3 2 4 2 3
127 3 4 4 1 4 1 3
128 4 3 4 2 3 2 3
129 4 3 4 1 3 2 3
130 4 3 4 2 3 3 4
131 2 4 3 3 4 2 4
132 4 3 4 2 2 2 3
133 5 4 4 1 1 2 2
134 4 4 4 1 3 1 3
135 4 2 4 2 2 2 2
136 4 4 4 2 3 2 4
137 3 2 3 1 2 2 4
138 1 4 4 2 2 3 4
139 4 3 4 3 3 1 4
140 3 3 2 4 2 3 4
141 3 2 2 2 4 4 4
142 3 2 4 4 4 2 5
143 1 5 2 5 2 5 3
144 4 2 4 1 2 1 4
145 5 4 3 3 3 2 4
146 4 3 4 2 4 3 4
147 3 3 3 2 4 2 5
148 4 3 2 2 4 2 3
149 3 3 2 1 1 3 2
150 4 4 4 4 4 2 4
151 4 4 3 2 4 1 3
152 4 4 4 2 3 2 4
153 5 4 4 3 1 1 5
154 2 4 2 1 2 2 3
155 3 5 5 4 2 3 3
156 3 3 4 2 2 2 3
157 4 3 4 2 3 2 5
158 4 4 4 4 3 2 4
159 3 4 3 4 3 4 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) standards organization punished secondrate
2.66021 -0.02165 0.31710 -0.12757 0.01454
mistakes competent
-0.05518 0.04891
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.6461 -0.5612 0.1712 0.4357 1.7289
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.66021 0.42574 6.248 3.97e-09 ***
standards -0.02165 0.06571 -0.329 0.7423
organization 0.31710 0.07124 4.451 1.64e-05 ***
punished -0.12757 0.07323 -1.742 0.0835 .
secondrate 0.01454 0.06292 0.231 0.8175
mistakes -0.05518 0.07060 -0.782 0.4356
competent 0.04891 0.07842 0.624 0.5338
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7397 on 152 degrees of freedom
Multiple R-squared: 0.1682, Adjusted R-squared: 0.1353
F-statistic: 5.121 on 6 and 152 DF, p-value: 8.09e-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,] 0.213016950 0.426033899 0.7869831
[2,] 0.165618609 0.331237219 0.8343814
[3,] 0.083382563 0.166765125 0.9166174
[4,] 0.068499889 0.136999777 0.9315001
[5,] 0.363035208 0.726070416 0.6369648
[6,] 0.276900227 0.553800454 0.7230998
[7,] 0.201012611 0.402025222 0.7989874
[8,] 0.185781790 0.371563579 0.8142182
[9,] 0.458396568 0.916793135 0.5416034
[10,] 0.469548848 0.939097695 0.5304512
[11,] 0.463855219 0.927710438 0.5361448
[12,] 0.502276917 0.995446165 0.4977231
[13,] 0.463507108 0.927014216 0.5364929
[14,] 0.430016193 0.860032386 0.5699838
[15,] 0.379501237 0.759002474 0.6204988
[16,] 0.332624173 0.665248346 0.6673758
[17,] 0.271262294 0.542524589 0.7287377
[18,] 0.216419125 0.432838251 0.7835809
[19,] 0.178269678 0.356539355 0.8217303
[20,] 0.152116145 0.304232290 0.8478839
[21,] 0.115853621 0.231707241 0.8841464
[22,] 0.333053111 0.666106221 0.6669469
[23,] 0.297145369 0.594290738 0.7028546
[24,] 0.263083999 0.526167998 0.7369160
[25,] 0.215835583 0.431671167 0.7841644
[26,] 0.198192297 0.396384594 0.8018077
[27,] 0.161758586 0.323517172 0.8382414
[28,] 0.239199265 0.478398529 0.7608007
[29,] 0.198900722 0.397801445 0.8010993
[30,] 0.169660483 0.339320965 0.8303395
[31,] 0.186961771 0.373923541 0.8130382
[32,] 0.233162258 0.466324516 0.7668377
[33,] 0.193673143 0.387346286 0.8063269
[34,] 0.187300188 0.374600376 0.8126998
[35,] 0.236482932 0.472965863 0.7635171
[36,] 0.229200782 0.458401564 0.7707992
[37,] 0.192629506 0.385259012 0.8073705
[38,] 0.166216996 0.332433991 0.8337830
[39,] 0.136366294 0.272732589 0.8636337
[40,] 0.112691693 0.225383385 0.8873083
[41,] 0.094225281 0.188450563 0.9057747
[42,] 0.076788372 0.153576744 0.9232116
[43,] 0.061831580 0.123663159 0.9381684
[44,] 0.108726505 0.217453011 0.8912735
[45,] 0.091093473 0.182186946 0.9089065
[46,] 0.074410219 0.148820439 0.9255898
[47,] 0.092774292 0.185548584 0.9072257
[48,] 0.137933341 0.275866683 0.8620667
[49,] 0.113329043 0.226658086 0.8866710
[50,] 0.092541003 0.185082005 0.9074590
[51,] 0.077476339 0.154952678 0.9225237
[52,] 0.061446474 0.122892947 0.9385535
[53,] 0.047862306 0.095724613 0.9521377
[54,] 0.052996404 0.105992808 0.9470036
[55,] 0.042233094 0.084466189 0.9577669
[56,] 0.032823881 0.065647761 0.9671761
[57,] 0.034086279 0.068172557 0.9659137
[58,] 0.027537323 0.055074645 0.9724627
[59,] 0.030680820 0.061361640 0.9693192
[60,] 0.023743279 0.047486558 0.9762567
[61,] 0.018166875 0.036333750 0.9818331
[62,] 0.014946466 0.029892932 0.9850535
[63,] 0.011056920 0.022113841 0.9889431
[64,] 0.008042514 0.016085028 0.9919575
[65,] 0.005794113 0.011588226 0.9942059
[66,] 0.004139347 0.008278694 0.9958607
[67,] 0.004382960 0.008765920 0.9956170
[68,] 0.003115216 0.006230431 0.9968848
[69,] 0.003907930 0.007815861 0.9960921
[70,] 0.005132935 0.010265870 0.9948671
[71,] 0.003755723 0.007511446 0.9962443
[72,] 0.005433648 0.010867296 0.9945664
[73,] 0.018643742 0.037287485 0.9813563
[74,] 0.014687143 0.029374286 0.9853129
[75,] 0.012249702 0.024499404 0.9877503
[76,] 0.009036238 0.018072477 0.9909638
[77,] 0.006840936 0.013681873 0.9931591
[78,] 0.004996704 0.009993408 0.9950033
[79,] 0.005393404 0.010786807 0.9946066
[80,] 0.004489372 0.008978743 0.9955106
[81,] 0.004832156 0.009664313 0.9951678
[82,] 0.003687177 0.007374354 0.9963128
[83,] 0.005981770 0.011963540 0.9940182
[84,] 0.004843134 0.009686267 0.9951569
[85,] 0.004066602 0.008133204 0.9959334
[86,] 0.002942427 0.005884853 0.9970576
[87,] 0.002410013 0.004820026 0.9975900
[88,] 0.001803618 0.003607237 0.9981964
[89,] 0.001549449 0.003098898 0.9984506
[90,] 0.001476344 0.002952689 0.9985237
[91,] 0.001495372 0.002990744 0.9985046
[92,] 0.001243085 0.002486171 0.9987569
[93,] 0.001394538 0.002789077 0.9986055
[94,] 0.004504534 0.009009067 0.9954955
[95,] 0.004491441 0.008982881 0.9955086
[96,] 0.006568228 0.013136456 0.9934318
[97,] 0.010838915 0.021677830 0.9891611
[98,] 0.033899725 0.067799451 0.9661003
[99,] 0.025954124 0.051908249 0.9740459
[100,] 0.024602568 0.049205135 0.9753974
[101,] 0.037714870 0.075429739 0.9622851
[102,] 0.034421406 0.068842813 0.9655786
[103,] 0.040945275 0.081890550 0.9590547
[104,] 0.034592776 0.069185551 0.9654072
[105,] 0.063350438 0.126700876 0.9366496
[106,] 0.053513968 0.107027936 0.9464860
[107,] 0.041691563 0.083383126 0.9583084
[108,] 0.048597607 0.097195214 0.9514024
[109,] 0.050173804 0.100347608 0.9498262
[110,] 0.042040135 0.084080271 0.9579599
[111,] 0.116879782 0.233759564 0.8831202
[112,] 0.140559791 0.281119582 0.8594402
[113,] 0.124850587 0.249701175 0.8751494
[114,] 0.107226722 0.214453444 0.8927733
[115,] 0.083390625 0.166781249 0.9166094
[116,] 0.072183923 0.144367847 0.9278161
[117,] 0.064357644 0.128715287 0.9356424
[118,] 0.085410609 0.170821218 0.9145894
[119,] 0.065107521 0.130215042 0.9348925
[120,] 0.048266723 0.096533447 0.9517333
[121,] 0.047648469 0.095296938 0.9523515
[122,] 0.107825061 0.215650123 0.8921749
[123,] 0.083244035 0.166488070 0.9167560
[124,] 0.180012178 0.360024356 0.8199878
[125,] 0.140349078 0.280698157 0.8596509
[126,] 0.111340365 0.222680731 0.8886596
[127,] 0.090448528 0.180897056 0.9095515
[128,] 0.068860729 0.137721457 0.9311393
[129,] 0.352747394 0.705494789 0.6472526
[130,] 0.292214523 0.584429046 0.7077855
[131,] 0.230094865 0.460189731 0.7699051
[132,] 0.206722873 0.413445746 0.7932771
[133,] 0.299433174 0.598866347 0.7005668
[134,] 0.424388631 0.848777262 0.5756114
[135,] 0.329578448 0.659156896 0.6704216
[136,] 0.500783291 0.998433418 0.4992167
[137,] 0.516710522 0.966578955 0.4832895
[138,] 0.566502083 0.866995833 0.4334979
[139,] 0.453492302 0.906984603 0.5465077
[140,] 0.470471187 0.940942374 0.5295288
> postscript(file="/var/www/html/freestat/rcomp/tmp/1ey321291223514.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2ey321291223514.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3pqk51291223514.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4pqk51291223514.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5pqk51291223514.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 159
Frequency = 1
1 2 3 4 5 6
-0.02099306 0.28156872 0.22076921 0.35328813 1.06431865 0.83954399
7 8 9 10 11 12
-0.22274372 -0.55467132 0.57967491 0.21117435 0.64345949 0.90388287
13 14 15 16 17 18
0.77069928 -1.40578534 -0.75909370 -0.18655779 -0.68224535 -0.99086262
19 20 21 22 23 24
0.33229440 -0.60541789 -0.90118659 0.39541785 -0.19726736 -0.74453306
25 26 27 28 29 30
0.04329303 0.37377167 0.28421955 0.32602372 0.44087309 0.28421955
31 32 33 34 35 36
1.16142801 0.63519042 0.74277374 0.26702897 -0.64605942 -0.11797850
37 38 39 40 41 42
-1.14589537 0.34139921 0.60842751 -0.73032020 0.93274718 0.22903827
43 44 45 46 47 48
-0.62506569 -1.10260301 -0.75907281 -0.17466887 0.50694305 0.03536307
49 50 51 52 53 54
0.29875930 0.45920730 0.02198595 0.29875930 1.25546694 -0.26582088
55 56 57 58 59 60
0.08613667 0.94547184 1.22638744 0.17118527 -0.23728347 -0.27442906
61 62 63 64 65 66
-0.11681587 0.07981806 0.81079138 0.27446229 0.19844969 0.78171188
67 68 69 70 71 72
0.37493431 -0.78371761 -0.20820397 0.24092719 0.46664063 0.12789291
73 74 75 76 77 78
0.09469205 0.12906289 -0.10260301 -0.74453306 0.13499934 -0.91984770
79 80 81 82 83 84
0.96001159 0.24357803 1.01964846 1.69203584 0.22455146 0.50499677
85 86 87 88 89 90
0.10708248 0.19101636 0.11335316 0.77014985 0.53936762 -0.77806815
91 92 93 94 95 96
0.29875930 -1.18093955 0.43045469 0.46664063 0.12822715 0.27628472
97 98 99 100 101 102
0.44089398 -0.56921107 -0.65860079 -0.72915756 -0.53929582 -0.85673159
103 104 105 106 107 108
-1.54013157 -0.74453306 1.04329303 -1.16021993 -1.67397628 0.13027832
109 110 111 112 113 114
-0.64338769 -1.07684768 0.55173715 0.39458211 -0.55054996 -1.64455033
115 116 117 118 119 120
-0.54461838 0.25630269 -1.08157738 1.05128065 0.48235036 -1.49071947
121 122 123 124 125 126
-0.92018194 -0.35687475 -0.30729081 -0.11054518 0.18307419 0.63569193
127 128 129 130 131 132
-0.86416491 0.31148397 0.18390993 0.31775465 -1.28564463 0.32602372
133 134 135 136 137 138
1.28354621 0.15037484 0.35328813 0.28421955 -0.55500555 -2.64605942
139 140 141 142 143 144
0.33496613 0.22164553 -0.02904694 -0.56737509 -1.44821493 0.07271164
145 146 147 148 149 150
1.72889512 0.30321490 -0.48377544 0.93114728 -0.04871563 0.52482786
151 152 153 154 155 156
0.58051065 0.28421955 1.33678122 -1.14570107 -0.63745612 -0.67397628
157 158 159
0.21366278 0.53936762 0.06465289
> postscript(file="/var/www/html/freestat/rcomp/tmp/6zh181291223514.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.02099306 NA
1 0.28156872 -0.02099306
2 0.22076921 0.28156872
3 0.35328813 0.22076921
4 1.06431865 0.35328813
5 0.83954399 1.06431865
6 -0.22274372 0.83954399
7 -0.55467132 -0.22274372
8 0.57967491 -0.55467132
9 0.21117435 0.57967491
10 0.64345949 0.21117435
11 0.90388287 0.64345949
12 0.77069928 0.90388287
13 -1.40578534 0.77069928
14 -0.75909370 -1.40578534
15 -0.18655779 -0.75909370
16 -0.68224535 -0.18655779
17 -0.99086262 -0.68224535
18 0.33229440 -0.99086262
19 -0.60541789 0.33229440
20 -0.90118659 -0.60541789
21 0.39541785 -0.90118659
22 -0.19726736 0.39541785
23 -0.74453306 -0.19726736
24 0.04329303 -0.74453306
25 0.37377167 0.04329303
26 0.28421955 0.37377167
27 0.32602372 0.28421955
28 0.44087309 0.32602372
29 0.28421955 0.44087309
30 1.16142801 0.28421955
31 0.63519042 1.16142801
32 0.74277374 0.63519042
33 0.26702897 0.74277374
34 -0.64605942 0.26702897
35 -0.11797850 -0.64605942
36 -1.14589537 -0.11797850
37 0.34139921 -1.14589537
38 0.60842751 0.34139921
39 -0.73032020 0.60842751
40 0.93274718 -0.73032020
41 0.22903827 0.93274718
42 -0.62506569 0.22903827
43 -1.10260301 -0.62506569
44 -0.75907281 -1.10260301
45 -0.17466887 -0.75907281
46 0.50694305 -0.17466887
47 0.03536307 0.50694305
48 0.29875930 0.03536307
49 0.45920730 0.29875930
50 0.02198595 0.45920730
51 0.29875930 0.02198595
52 1.25546694 0.29875930
53 -0.26582088 1.25546694
54 0.08613667 -0.26582088
55 0.94547184 0.08613667
56 1.22638744 0.94547184
57 0.17118527 1.22638744
58 -0.23728347 0.17118527
59 -0.27442906 -0.23728347
60 -0.11681587 -0.27442906
61 0.07981806 -0.11681587
62 0.81079138 0.07981806
63 0.27446229 0.81079138
64 0.19844969 0.27446229
65 0.78171188 0.19844969
66 0.37493431 0.78171188
67 -0.78371761 0.37493431
68 -0.20820397 -0.78371761
69 0.24092719 -0.20820397
70 0.46664063 0.24092719
71 0.12789291 0.46664063
72 0.09469205 0.12789291
73 0.12906289 0.09469205
74 -0.10260301 0.12906289
75 -0.74453306 -0.10260301
76 0.13499934 -0.74453306
77 -0.91984770 0.13499934
78 0.96001159 -0.91984770
79 0.24357803 0.96001159
80 1.01964846 0.24357803
81 1.69203584 1.01964846
82 0.22455146 1.69203584
83 0.50499677 0.22455146
84 0.10708248 0.50499677
85 0.19101636 0.10708248
86 0.11335316 0.19101636
87 0.77014985 0.11335316
88 0.53936762 0.77014985
89 -0.77806815 0.53936762
90 0.29875930 -0.77806815
91 -1.18093955 0.29875930
92 0.43045469 -1.18093955
93 0.46664063 0.43045469
94 0.12822715 0.46664063
95 0.27628472 0.12822715
96 0.44089398 0.27628472
97 -0.56921107 0.44089398
98 -0.65860079 -0.56921107
99 -0.72915756 -0.65860079
100 -0.53929582 -0.72915756
101 -0.85673159 -0.53929582
102 -1.54013157 -0.85673159
103 -0.74453306 -1.54013157
104 1.04329303 -0.74453306
105 -1.16021993 1.04329303
106 -1.67397628 -1.16021993
107 0.13027832 -1.67397628
108 -0.64338769 0.13027832
109 -1.07684768 -0.64338769
110 0.55173715 -1.07684768
111 0.39458211 0.55173715
112 -0.55054996 0.39458211
113 -1.64455033 -0.55054996
114 -0.54461838 -1.64455033
115 0.25630269 -0.54461838
116 -1.08157738 0.25630269
117 1.05128065 -1.08157738
118 0.48235036 1.05128065
119 -1.49071947 0.48235036
120 -0.92018194 -1.49071947
121 -0.35687475 -0.92018194
122 -0.30729081 -0.35687475
123 -0.11054518 -0.30729081
124 0.18307419 -0.11054518
125 0.63569193 0.18307419
126 -0.86416491 0.63569193
127 0.31148397 -0.86416491
128 0.18390993 0.31148397
129 0.31775465 0.18390993
130 -1.28564463 0.31775465
131 0.32602372 -1.28564463
132 1.28354621 0.32602372
133 0.15037484 1.28354621
134 0.35328813 0.15037484
135 0.28421955 0.35328813
136 -0.55500555 0.28421955
137 -2.64605942 -0.55500555
138 0.33496613 -2.64605942
139 0.22164553 0.33496613
140 -0.02904694 0.22164553
141 -0.56737509 -0.02904694
142 -1.44821493 -0.56737509
143 0.07271164 -1.44821493
144 1.72889512 0.07271164
145 0.30321490 1.72889512
146 -0.48377544 0.30321490
147 0.93114728 -0.48377544
148 -0.04871563 0.93114728
149 0.52482786 -0.04871563
150 0.58051065 0.52482786
151 0.28421955 0.58051065
152 1.33678122 0.28421955
153 -1.14570107 1.33678122
154 -0.63745612 -1.14570107
155 -0.67397628 -0.63745612
156 0.21366278 -0.67397628
157 0.53936762 0.21366278
158 0.06465289 0.53936762
159 NA 0.06465289
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.28156872 -0.02099306
[2,] 0.22076921 0.28156872
[3,] 0.35328813 0.22076921
[4,] 1.06431865 0.35328813
[5,] 0.83954399 1.06431865
[6,] -0.22274372 0.83954399
[7,] -0.55467132 -0.22274372
[8,] 0.57967491 -0.55467132
[9,] 0.21117435 0.57967491
[10,] 0.64345949 0.21117435
[11,] 0.90388287 0.64345949
[12,] 0.77069928 0.90388287
[13,] -1.40578534 0.77069928
[14,] -0.75909370 -1.40578534
[15,] -0.18655779 -0.75909370
[16,] -0.68224535 -0.18655779
[17,] -0.99086262 -0.68224535
[18,] 0.33229440 -0.99086262
[19,] -0.60541789 0.33229440
[20,] -0.90118659 -0.60541789
[21,] 0.39541785 -0.90118659
[22,] -0.19726736 0.39541785
[23,] -0.74453306 -0.19726736
[24,] 0.04329303 -0.74453306
[25,] 0.37377167 0.04329303
[26,] 0.28421955 0.37377167
[27,] 0.32602372 0.28421955
[28,] 0.44087309 0.32602372
[29,] 0.28421955 0.44087309
[30,] 1.16142801 0.28421955
[31,] 0.63519042 1.16142801
[32,] 0.74277374 0.63519042
[33,] 0.26702897 0.74277374
[34,] -0.64605942 0.26702897
[35,] -0.11797850 -0.64605942
[36,] -1.14589537 -0.11797850
[37,] 0.34139921 -1.14589537
[38,] 0.60842751 0.34139921
[39,] -0.73032020 0.60842751
[40,] 0.93274718 -0.73032020
[41,] 0.22903827 0.93274718
[42,] -0.62506569 0.22903827
[43,] -1.10260301 -0.62506569
[44,] -0.75907281 -1.10260301
[45,] -0.17466887 -0.75907281
[46,] 0.50694305 -0.17466887
[47,] 0.03536307 0.50694305
[48,] 0.29875930 0.03536307
[49,] 0.45920730 0.29875930
[50,] 0.02198595 0.45920730
[51,] 0.29875930 0.02198595
[52,] 1.25546694 0.29875930
[53,] -0.26582088 1.25546694
[54,] 0.08613667 -0.26582088
[55,] 0.94547184 0.08613667
[56,] 1.22638744 0.94547184
[57,] 0.17118527 1.22638744
[58,] -0.23728347 0.17118527
[59,] -0.27442906 -0.23728347
[60,] -0.11681587 -0.27442906
[61,] 0.07981806 -0.11681587
[62,] 0.81079138 0.07981806
[63,] 0.27446229 0.81079138
[64,] 0.19844969 0.27446229
[65,] 0.78171188 0.19844969
[66,] 0.37493431 0.78171188
[67,] -0.78371761 0.37493431
[68,] -0.20820397 -0.78371761
[69,] 0.24092719 -0.20820397
[70,] 0.46664063 0.24092719
[71,] 0.12789291 0.46664063
[72,] 0.09469205 0.12789291
[73,] 0.12906289 0.09469205
[74,] -0.10260301 0.12906289
[75,] -0.74453306 -0.10260301
[76,] 0.13499934 -0.74453306
[77,] -0.91984770 0.13499934
[78,] 0.96001159 -0.91984770
[79,] 0.24357803 0.96001159
[80,] 1.01964846 0.24357803
[81,] 1.69203584 1.01964846
[82,] 0.22455146 1.69203584
[83,] 0.50499677 0.22455146
[84,] 0.10708248 0.50499677
[85,] 0.19101636 0.10708248
[86,] 0.11335316 0.19101636
[87,] 0.77014985 0.11335316
[88,] 0.53936762 0.77014985
[89,] -0.77806815 0.53936762
[90,] 0.29875930 -0.77806815
[91,] -1.18093955 0.29875930
[92,] 0.43045469 -1.18093955
[93,] 0.46664063 0.43045469
[94,] 0.12822715 0.46664063
[95,] 0.27628472 0.12822715
[96,] 0.44089398 0.27628472
[97,] -0.56921107 0.44089398
[98,] -0.65860079 -0.56921107
[99,] -0.72915756 -0.65860079
[100,] -0.53929582 -0.72915756
[101,] -0.85673159 -0.53929582
[102,] -1.54013157 -0.85673159
[103,] -0.74453306 -1.54013157
[104,] 1.04329303 -0.74453306
[105,] -1.16021993 1.04329303
[106,] -1.67397628 -1.16021993
[107,] 0.13027832 -1.67397628
[108,] -0.64338769 0.13027832
[109,] -1.07684768 -0.64338769
[110,] 0.55173715 -1.07684768
[111,] 0.39458211 0.55173715
[112,] -0.55054996 0.39458211
[113,] -1.64455033 -0.55054996
[114,] -0.54461838 -1.64455033
[115,] 0.25630269 -0.54461838
[116,] -1.08157738 0.25630269
[117,] 1.05128065 -1.08157738
[118,] 0.48235036 1.05128065
[119,] -1.49071947 0.48235036
[120,] -0.92018194 -1.49071947
[121,] -0.35687475 -0.92018194
[122,] -0.30729081 -0.35687475
[123,] -0.11054518 -0.30729081
[124,] 0.18307419 -0.11054518
[125,] 0.63569193 0.18307419
[126,] -0.86416491 0.63569193
[127,] 0.31148397 -0.86416491
[128,] 0.18390993 0.31148397
[129,] 0.31775465 0.18390993
[130,] -1.28564463 0.31775465
[131,] 0.32602372 -1.28564463
[132,] 1.28354621 0.32602372
[133,] 0.15037484 1.28354621
[134,] 0.35328813 0.15037484
[135,] 0.28421955 0.35328813
[136,] -0.55500555 0.28421955
[137,] -2.64605942 -0.55500555
[138,] 0.33496613 -2.64605942
[139,] 0.22164553 0.33496613
[140,] -0.02904694 0.22164553
[141,] -0.56737509 -0.02904694
[142,] -1.44821493 -0.56737509
[143,] 0.07271164 -1.44821493
[144,] 1.72889512 0.07271164
[145,] 0.30321490 1.72889512
[146,] -0.48377544 0.30321490
[147,] 0.93114728 -0.48377544
[148,] -0.04871563 0.93114728
[149,] 0.52482786 -0.04871563
[150,] 0.58051065 0.52482786
[151,] 0.28421955 0.58051065
[152,] 1.33678122 0.28421955
[153,] -1.14570107 1.33678122
[154,] -0.63745612 -1.14570107
[155,] -0.67397628 -0.63745612
[156,] 0.21366278 -0.67397628
[157,] 0.53936762 0.21366278
[158,] 0.06465289 0.53936762
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.28156872 -0.02099306
2 0.22076921 0.28156872
3 0.35328813 0.22076921
4 1.06431865 0.35328813
5 0.83954399 1.06431865
6 -0.22274372 0.83954399
7 -0.55467132 -0.22274372
8 0.57967491 -0.55467132
9 0.21117435 0.57967491
10 0.64345949 0.21117435
11 0.90388287 0.64345949
12 0.77069928 0.90388287
13 -1.40578534 0.77069928
14 -0.75909370 -1.40578534
15 -0.18655779 -0.75909370
16 -0.68224535 -0.18655779
17 -0.99086262 -0.68224535
18 0.33229440 -0.99086262
19 -0.60541789 0.33229440
20 -0.90118659 -0.60541789
21 0.39541785 -0.90118659
22 -0.19726736 0.39541785
23 -0.74453306 -0.19726736
24 0.04329303 -0.74453306
25 0.37377167 0.04329303
26 0.28421955 0.37377167
27 0.32602372 0.28421955
28 0.44087309 0.32602372
29 0.28421955 0.44087309
30 1.16142801 0.28421955
31 0.63519042 1.16142801
32 0.74277374 0.63519042
33 0.26702897 0.74277374
34 -0.64605942 0.26702897
35 -0.11797850 -0.64605942
36 -1.14589537 -0.11797850
37 0.34139921 -1.14589537
38 0.60842751 0.34139921
39 -0.73032020 0.60842751
40 0.93274718 -0.73032020
41 0.22903827 0.93274718
42 -0.62506569 0.22903827
43 -1.10260301 -0.62506569
44 -0.75907281 -1.10260301
45 -0.17466887 -0.75907281
46 0.50694305 -0.17466887
47 0.03536307 0.50694305
48 0.29875930 0.03536307
49 0.45920730 0.29875930
50 0.02198595 0.45920730
51 0.29875930 0.02198595
52 1.25546694 0.29875930
53 -0.26582088 1.25546694
54 0.08613667 -0.26582088
55 0.94547184 0.08613667
56 1.22638744 0.94547184
57 0.17118527 1.22638744
58 -0.23728347 0.17118527
59 -0.27442906 -0.23728347
60 -0.11681587 -0.27442906
61 0.07981806 -0.11681587
62 0.81079138 0.07981806
63 0.27446229 0.81079138
64 0.19844969 0.27446229
65 0.78171188 0.19844969
66 0.37493431 0.78171188
67 -0.78371761 0.37493431
68 -0.20820397 -0.78371761
69 0.24092719 -0.20820397
70 0.46664063 0.24092719
71 0.12789291 0.46664063
72 0.09469205 0.12789291
73 0.12906289 0.09469205
74 -0.10260301 0.12906289
75 -0.74453306 -0.10260301
76 0.13499934 -0.74453306
77 -0.91984770 0.13499934
78 0.96001159 -0.91984770
79 0.24357803 0.96001159
80 1.01964846 0.24357803
81 1.69203584 1.01964846
82 0.22455146 1.69203584
83 0.50499677 0.22455146
84 0.10708248 0.50499677
85 0.19101636 0.10708248
86 0.11335316 0.19101636
87 0.77014985 0.11335316
88 0.53936762 0.77014985
89 -0.77806815 0.53936762
90 0.29875930 -0.77806815
91 -1.18093955 0.29875930
92 0.43045469 -1.18093955
93 0.46664063 0.43045469
94 0.12822715 0.46664063
95 0.27628472 0.12822715
96 0.44089398 0.27628472
97 -0.56921107 0.44089398
98 -0.65860079 -0.56921107
99 -0.72915756 -0.65860079
100 -0.53929582 -0.72915756
101 -0.85673159 -0.53929582
102 -1.54013157 -0.85673159
103 -0.74453306 -1.54013157
104 1.04329303 -0.74453306
105 -1.16021993 1.04329303
106 -1.67397628 -1.16021993
107 0.13027832 -1.67397628
108 -0.64338769 0.13027832
109 -1.07684768 -0.64338769
110 0.55173715 -1.07684768
111 0.39458211 0.55173715
112 -0.55054996 0.39458211
113 -1.64455033 -0.55054996
114 -0.54461838 -1.64455033
115 0.25630269 -0.54461838
116 -1.08157738 0.25630269
117 1.05128065 -1.08157738
118 0.48235036 1.05128065
119 -1.49071947 0.48235036
120 -0.92018194 -1.49071947
121 -0.35687475 -0.92018194
122 -0.30729081 -0.35687475
123 -0.11054518 -0.30729081
124 0.18307419 -0.11054518
125 0.63569193 0.18307419
126 -0.86416491 0.63569193
127 0.31148397 -0.86416491
128 0.18390993 0.31148397
129 0.31775465 0.18390993
130 -1.28564463 0.31775465
131 0.32602372 -1.28564463
132 1.28354621 0.32602372
133 0.15037484 1.28354621
134 0.35328813 0.15037484
135 0.28421955 0.35328813
136 -0.55500555 0.28421955
137 -2.64605942 -0.55500555
138 0.33496613 -2.64605942
139 0.22164553 0.33496613
140 -0.02904694 0.22164553
141 -0.56737509 -0.02904694
142 -1.44821493 -0.56737509
143 0.07271164 -1.44821493
144 1.72889512 0.07271164
145 0.30321490 1.72889512
146 -0.48377544 0.30321490
147 0.93114728 -0.48377544
148 -0.04871563 0.93114728
149 0.52482786 -0.04871563
150 0.58051065 0.52482786
151 0.28421955 0.58051065
152 1.33678122 0.28421955
153 -1.14570107 1.33678122
154 -0.63745612 -1.14570107
155 -0.67397628 -0.63745612
156 0.21366278 -0.67397628
157 0.53936762 0.21366278
158 0.06465289 0.53936762
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/7sqjb1291223514.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8sqjb1291223514.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9sqjb1291223514.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10vr3r1291223515.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/11ya1w1291223515.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/12ks031291223515.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/13y2fb1291223515.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/1412wh1291223515.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/freestat/rcomp/tmp/1553u51291223515.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/freestat/rcomp/tmp/1684bb1291223515.tab")
+ }
>
> try(system("convert tmp/1ey321291223514.ps tmp/1ey321291223514.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ey321291223514.ps tmp/2ey321291223514.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pqk51291223514.ps tmp/3pqk51291223514.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pqk51291223514.ps tmp/4pqk51291223514.png",intern=TRUE))
character(0)
> try(system("convert tmp/5pqk51291223514.ps tmp/5pqk51291223514.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zh181291223514.ps tmp/6zh181291223514.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sqjb1291223514.ps tmp/7sqjb1291223514.png",intern=TRUE))
character(0)
> try(system("convert tmp/8sqjb1291223514.ps tmp/8sqjb1291223514.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sqjb1291223514.ps tmp/9sqjb1291223514.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vr3r1291223515.ps tmp/10vr3r1291223515.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.759 2.630 6.090