R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(645,3,1.194,42,3,2.116,60,1,2.526,25,3,2.803,624,4,1.361,180,4,2.282,35,1,2.981,392,4,1.825,63,1,2.674,230,1,2.272,112,4,2.526,281,5,1.361,0,2,2.332,365,5,1.131,42,1,2.128,28,2,2.152,42,2,2.370,120,2,2.370,0,1,1.808,0,1,2.896,400,5,0,148,5,1.335,16,2,2.667,252,1,2.485,310,1,1.825,63,1,2.565,28,3,2.625,68,4,2.104,336,5,1.065,100,1,2.380,33,4,0,21.5,4,2.208,50,1,2.991,267,1,2.079,30,1,2.361,45,3,2.416,19,3,2.580,30,3,2.549,12,1,2.965,120,1,2.856,440,5,0,140,2,2.833,170,4,2.389,17,2,2.617,115,4,2.128,31,5,2.128,63,2,2.526,21,3,2.580,52,1,2.282,164,2,2.262,225,2,1.887,225,3,1.686,150,5,0.956,151,5,1.335,90,2,2.398,0,2,2.332,60,2,2.588,200,3,1.686,46,2,2.760,210,4,2.332,14,1,2.965,38,1,0),dim=c(3,62),dimnames=list(c('aantal-dagen-dat-baby-in-buik-is','danger-high-voltage','slaap'),1:62))
> y <- array(NA,dim=c(3,62),dimnames=list(c('aantal-dagen-dat-baby-in-buik-is','danger-high-voltage','slaap'),1:62))
> 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 = '3'
> #'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
slaap aantal-dagen-dat-baby-in-buik-is danger-high-voltage
1 1.194 645.0 3
2 2.116 42.0 3
3 2.526 60.0 1
4 2.803 25.0 3
5 1.361 624.0 4
6 2.282 180.0 4
7 2.981 35.0 1
8 1.825 392.0 4
9 2.674 63.0 1
10 2.272 230.0 1
11 2.526 112.0 4
12 1.361 281.0 5
13 2.332 0.0 2
14 1.131 365.0 5
15 2.128 42.0 1
16 2.152 28.0 2
17 2.370 42.0 2
18 2.370 120.0 2
19 1.808 0.0 1
20 2.896 0.0 1
21 0.000 400.0 5
22 1.335 148.0 5
23 2.667 16.0 2
24 2.485 252.0 1
25 1.825 310.0 1
26 2.565 63.0 1
27 2.625 28.0 3
28 2.104 68.0 4
29 1.065 336.0 5
30 2.380 100.0 1
31 0.000 33.0 4
32 2.208 21.5 4
33 2.991 50.0 1
34 2.079 267.0 1
35 2.361 30.0 1
36 2.416 45.0 3
37 2.580 19.0 3
38 2.549 30.0 3
39 2.965 12.0 1
40 2.856 120.0 1
41 0.000 440.0 5
42 2.833 140.0 2
43 2.389 170.0 4
44 2.617 17.0 2
45 2.128 115.0 4
46 2.128 31.0 5
47 2.526 63.0 2
48 2.580 21.0 3
49 2.282 52.0 1
50 2.262 164.0 2
51 1.887 225.0 2
52 1.686 225.0 3
53 0.956 150.0 5
54 1.335 151.0 5
55 2.398 90.0 2
56 2.332 0.0 2
57 2.588 60.0 2
58 1.686 200.0 3
59 2.760 46.0 2
60 2.332 210.0 4
61 2.965 14.0 1
62 0.000 38.0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `aantal-dagen-dat-baby-in-buik-is`
2.888165 -0.001944
`danger-high-voltage`
-0.204810
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.6095 -0.1493 0.1190 0.3312 0.6748
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.8881647 0.1541285 18.739 < 2e-16 ***
`aantal-dagen-dat-baby-in-buik-is` -0.0019443 0.0005562 -3.495 0.000905 ***
`danger-high-voltage` -0.2048102 0.0564330 -3.629 0.000595 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5801 on 59 degrees of freedom
Multiple R-squared: 0.4207, Adjusted R-squared: 0.401
F-statistic: 21.42 on 2 and 59 DF, p-value: 1.014e-07
> 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,] 0.1194065343 0.238813069 0.8805935
[2,] 0.0899117499 0.179823500 0.9100883
[3,] 0.0400325494 0.080065099 0.9599675
[4,] 0.0152709440 0.030541888 0.9847291
[5,] 0.0055005057 0.011001011 0.9944995
[6,] 0.0025342871 0.005068574 0.9974657
[7,] 0.0064323170 0.012864634 0.9935677
[8,] 0.0052318360 0.010463672 0.9947682
[9,] 0.0056161975 0.011232395 0.9943838
[10,] 0.0086622158 0.017324432 0.9913378
[11,] 0.0066476306 0.013295261 0.9933524
[12,] 0.0032364969 0.006472994 0.9967635
[13,] 0.0014986287 0.002997257 0.9985014
[14,] 0.0075200361 0.015040072 0.9924800
[15,] 0.0052386753 0.010477351 0.9947613
[16,] 0.0848809163 0.169761833 0.9151191
[17,] 0.0637599545 0.127519909 0.9362400
[18,] 0.0445051691 0.089010338 0.9554948
[19,] 0.0318842252 0.063768450 0.9681158
[20,] 0.0226122560 0.045224512 0.9773877
[21,] 0.0136726275 0.027345255 0.9863274
[22,] 0.0104179505 0.020835901 0.9895820
[23,] 0.0061785201 0.012357040 0.9938215
[24,] 0.0038382880 0.007676576 0.9961617
[25,] 0.0021471189 0.004294238 0.9978529
[26,] 0.2633359532 0.526671906 0.7366640
[27,] 0.2149343693 0.429868739 0.7850656
[28,] 0.1868308771 0.373661754 0.8131691
[29,] 0.1472947550 0.294589510 0.8527052
[30,] 0.1142954155 0.228590831 0.8857046
[31,] 0.0857337053 0.171467411 0.9142663
[32,] 0.0661490540 0.132298108 0.9338509
[33,] 0.0494810401 0.098962080 0.9505190
[34,] 0.0361135338 0.072227068 0.9638865
[35,] 0.0315055327 0.063011065 0.9684945
[36,] 0.0602582473 0.120516495 0.9397418
[37,] 0.0662556635 0.132511327 0.9337443
[38,] 0.0665967637 0.133193527 0.9334032
[39,] 0.0471131296 0.094226259 0.9528869
[40,] 0.0323039295 0.064607859 0.9676961
[41,] 0.0215127697 0.043025539 0.9784872
[42,] 0.0141512029 0.028302406 0.9858488
[43,] 0.0108126013 0.021625203 0.9891874
[44,] 0.0063929435 0.012785887 0.9936071
[45,] 0.0035336291 0.007067258 0.9964664
[46,] 0.0017302684 0.003460537 0.9982697
[47,] 0.0007881051 0.001576210 0.9992119
[48,] 0.0009083103 0.001816621 0.9990917
[49,] 0.0048812801 0.009762560 0.9951187
[50,] 0.0028675300 0.005735060 0.9971325
[51,] 0.0043164911 0.008632982 0.9956835
> postscript(file="/var/www/html/rcomp/tmp/1rodz1292354159.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21fvk1292354159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/31fvk1292354159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4c6u51292354159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5c6u51292354159.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 = 62
Frequency = 1
1 2 3 4 5 6
0.174326988 -0.076074201 -0.040697610 0.577873029 0.505307326 0.563046749
7 8 9 10 11 12
0.365695375 0.518234232 0.113135231 0.035830088 0.674835670 0.043229317
13 14 15 16 17 18
-0.146544215 -0.023451114 -0.473694661 -0.272104359 -0.026884431 0.124769454
19 20 21 22 23 24
-0.875354445 0.212645555 -1.086401294 -0.241360000 0.219564274 0.291604261
25 26 27 28 29 30
-0.255627466 0.004135231 0.405705871 0.167287324 -0.145835251 -0.108926387
31 32 33 34 35 36
-2.004762496 0.180878277 0.404859584 -0.085231531 -0.264026028 0.229758641
37 38 39 40 41 42
0.343207346 0.333594432 0.304976922 0.405959224 -1.008630071 0.626655066
43 44 45 46 47 48
0.650603943 0.171508555 0.282668511 0.324159173 0.169945461 0.347095907
49 50 51 52 53 54
-0.300251855 0.102317800 -0.154081085 -0.150270855 -0.616471438 -0.235527158
55 56 57 58 59 60
0.094441037 -0.146544215 0.226112620 -0.198877869 0.370892692 0.671375166
61 62
0.308865483 -2.609471783
> postscript(file="/var/www/html/rcomp/tmp/6c6u51292354159.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 = 62
Frequency = 1
lag(myerror, k = 1) myerror
0 0.174326988 NA
1 -0.076074201 0.174326988
2 -0.040697610 -0.076074201
3 0.577873029 -0.040697610
4 0.505307326 0.577873029
5 0.563046749 0.505307326
6 0.365695375 0.563046749
7 0.518234232 0.365695375
8 0.113135231 0.518234232
9 0.035830088 0.113135231
10 0.674835670 0.035830088
11 0.043229317 0.674835670
12 -0.146544215 0.043229317
13 -0.023451114 -0.146544215
14 -0.473694661 -0.023451114
15 -0.272104359 -0.473694661
16 -0.026884431 -0.272104359
17 0.124769454 -0.026884431
18 -0.875354445 0.124769454
19 0.212645555 -0.875354445
20 -1.086401294 0.212645555
21 -0.241360000 -1.086401294
22 0.219564274 -0.241360000
23 0.291604261 0.219564274
24 -0.255627466 0.291604261
25 0.004135231 -0.255627466
26 0.405705871 0.004135231
27 0.167287324 0.405705871
28 -0.145835251 0.167287324
29 -0.108926387 -0.145835251
30 -2.004762496 -0.108926387
31 0.180878277 -2.004762496
32 0.404859584 0.180878277
33 -0.085231531 0.404859584
34 -0.264026028 -0.085231531
35 0.229758641 -0.264026028
36 0.343207346 0.229758641
37 0.333594432 0.343207346
38 0.304976922 0.333594432
39 0.405959224 0.304976922
40 -1.008630071 0.405959224
41 0.626655066 -1.008630071
42 0.650603943 0.626655066
43 0.171508555 0.650603943
44 0.282668511 0.171508555
45 0.324159173 0.282668511
46 0.169945461 0.324159173
47 0.347095907 0.169945461
48 -0.300251855 0.347095907
49 0.102317800 -0.300251855
50 -0.154081085 0.102317800
51 -0.150270855 -0.154081085
52 -0.616471438 -0.150270855
53 -0.235527158 -0.616471438
54 0.094441037 -0.235527158
55 -0.146544215 0.094441037
56 0.226112620 -0.146544215
57 -0.198877869 0.226112620
58 0.370892692 -0.198877869
59 0.671375166 0.370892692
60 0.308865483 0.671375166
61 -2.609471783 0.308865483
62 NA -2.609471783
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.076074201 0.174326988
[2,] -0.040697610 -0.076074201
[3,] 0.577873029 -0.040697610
[4,] 0.505307326 0.577873029
[5,] 0.563046749 0.505307326
[6,] 0.365695375 0.563046749
[7,] 0.518234232 0.365695375
[8,] 0.113135231 0.518234232
[9,] 0.035830088 0.113135231
[10,] 0.674835670 0.035830088
[11,] 0.043229317 0.674835670
[12,] -0.146544215 0.043229317
[13,] -0.023451114 -0.146544215
[14,] -0.473694661 -0.023451114
[15,] -0.272104359 -0.473694661
[16,] -0.026884431 -0.272104359
[17,] 0.124769454 -0.026884431
[18,] -0.875354445 0.124769454
[19,] 0.212645555 -0.875354445
[20,] -1.086401294 0.212645555
[21,] -0.241360000 -1.086401294
[22,] 0.219564274 -0.241360000
[23,] 0.291604261 0.219564274
[24,] -0.255627466 0.291604261
[25,] 0.004135231 -0.255627466
[26,] 0.405705871 0.004135231
[27,] 0.167287324 0.405705871
[28,] -0.145835251 0.167287324
[29,] -0.108926387 -0.145835251
[30,] -2.004762496 -0.108926387
[31,] 0.180878277 -2.004762496
[32,] 0.404859584 0.180878277
[33,] -0.085231531 0.404859584
[34,] -0.264026028 -0.085231531
[35,] 0.229758641 -0.264026028
[36,] 0.343207346 0.229758641
[37,] 0.333594432 0.343207346
[38,] 0.304976922 0.333594432
[39,] 0.405959224 0.304976922
[40,] -1.008630071 0.405959224
[41,] 0.626655066 -1.008630071
[42,] 0.650603943 0.626655066
[43,] 0.171508555 0.650603943
[44,] 0.282668511 0.171508555
[45,] 0.324159173 0.282668511
[46,] 0.169945461 0.324159173
[47,] 0.347095907 0.169945461
[48,] -0.300251855 0.347095907
[49,] 0.102317800 -0.300251855
[50,] -0.154081085 0.102317800
[51,] -0.150270855 -0.154081085
[52,] -0.616471438 -0.150270855
[53,] -0.235527158 -0.616471438
[54,] 0.094441037 -0.235527158
[55,] -0.146544215 0.094441037
[56,] 0.226112620 -0.146544215
[57,] -0.198877869 0.226112620
[58,] 0.370892692 -0.198877869
[59,] 0.671375166 0.370892692
[60,] 0.308865483 0.671375166
[61,] -2.609471783 0.308865483
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.076074201 0.174326988
2 -0.040697610 -0.076074201
3 0.577873029 -0.040697610
4 0.505307326 0.577873029
5 0.563046749 0.505307326
6 0.365695375 0.563046749
7 0.518234232 0.365695375
8 0.113135231 0.518234232
9 0.035830088 0.113135231
10 0.674835670 0.035830088
11 0.043229317 0.674835670
12 -0.146544215 0.043229317
13 -0.023451114 -0.146544215
14 -0.473694661 -0.023451114
15 -0.272104359 -0.473694661
16 -0.026884431 -0.272104359
17 0.124769454 -0.026884431
18 -0.875354445 0.124769454
19 0.212645555 -0.875354445
20 -1.086401294 0.212645555
21 -0.241360000 -1.086401294
22 0.219564274 -0.241360000
23 0.291604261 0.219564274
24 -0.255627466 0.291604261
25 0.004135231 -0.255627466
26 0.405705871 0.004135231
27 0.167287324 0.405705871
28 -0.145835251 0.167287324
29 -0.108926387 -0.145835251
30 -2.004762496 -0.108926387
31 0.180878277 -2.004762496
32 0.404859584 0.180878277
33 -0.085231531 0.404859584
34 -0.264026028 -0.085231531
35 0.229758641 -0.264026028
36 0.343207346 0.229758641
37 0.333594432 0.343207346
38 0.304976922 0.333594432
39 0.405959224 0.304976922
40 -1.008630071 0.405959224
41 0.626655066 -1.008630071
42 0.650603943 0.626655066
43 0.171508555 0.650603943
44 0.282668511 0.171508555
45 0.324159173 0.282668511
46 0.169945461 0.324159173
47 0.347095907 0.169945461
48 -0.300251855 0.347095907
49 0.102317800 -0.300251855
50 -0.154081085 0.102317800
51 -0.150270855 -0.154081085
52 -0.616471438 -0.150270855
53 -0.235527158 -0.616471438
54 0.094441037 -0.235527158
55 -0.146544215 0.094441037
56 0.226112620 -0.146544215
57 -0.198877869 0.226112620
58 0.370892692 -0.198877869
59 0.671375166 0.370892692
60 0.308865483 0.671375166
61 -2.609471783 0.308865483
> 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/rcomp/tmp/75yb81292354159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/85yb81292354159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9gpbb1292354159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gpbb1292354159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11j79z1292354159.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/rcomp/tmp/1248741292354159.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/rcomp/tmp/13tr4g1292354159.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/rcomp/tmp/14m0mj1292354159.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/rcomp/tmp/15ht5k1292354160.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/rcomp/tmp/16e33b1292354160.tab")
+ }
>
> try(system("convert tmp/1rodz1292354159.ps tmp/1rodz1292354159.png",intern=TRUE))
character(0)
> try(system("convert tmp/21fvk1292354159.ps tmp/21fvk1292354159.png",intern=TRUE))
character(0)
> try(system("convert tmp/31fvk1292354159.ps tmp/31fvk1292354159.png",intern=TRUE))
character(0)
> try(system("convert tmp/4c6u51292354159.ps tmp/4c6u51292354159.png",intern=TRUE))
character(0)
> try(system("convert tmp/5c6u51292354159.ps tmp/5c6u51292354159.png",intern=TRUE))
character(0)
> try(system("convert tmp/6c6u51292354159.ps tmp/6c6u51292354159.png",intern=TRUE))
character(0)
> try(system("convert tmp/75yb81292354159.ps tmp/75yb81292354159.png",intern=TRUE))
character(0)
> try(system("convert tmp/85yb81292354159.ps tmp/85yb81292354159.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gpbb1292354159.ps tmp/9gpbb1292354159.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gpbb1292354159.ps tmp/10gpbb1292354159.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.729 1.718 8.224