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 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(29.837,0,29.571,0,30.167,0,30.524,0,30.996,0,31.033,0,31.198,0,30.937,0,31.649,0,33.115,0,34.106,0,33.926,0,33.382,0,32.851,0,32.948,0,36.112,0,36.113,0,35.210,0,35.193,0,34.383,0,35.349,0,37.058,0,38.076,0,36.630,0,36.045,0,35.638,0,35.114,0,35.465,0,35.254,0,35.299,0,35.916,0,36.683,0,37.288,0,38.536,0,38.977,0,36.407,0,34.955,0,34.951,0,32.680,0,34.791,0,34.178,0,35.213,0,34.871,0,35.299,0,35.443,0,37.108,0,36.419,0,34.471,0,33.868,0,34.385,0,33.643,1,34.627,1,32.919,1,35.500,1,36.110,1,37.086,1,37.711,1,40.427,1,39.884,1,38.512,1,38.767,1),dim=c(2,61),dimnames=list(c('saldo_zichtrek','crisis'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('saldo_zichtrek','crisis'),1:61))
> 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 = '1'
> #'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
saldo_zichtrek crisis
1 29.837 0
2 29.571 0
3 30.167 0
4 30.524 0
5 30.996 0
6 31.033 0
7 31.198 0
8 30.937 0
9 31.649 0
10 33.115 0
11 34.106 0
12 33.926 0
13 33.382 0
14 32.851 0
15 32.948 0
16 36.112 0
17 36.113 0
18 35.210 0
19 35.193 0
20 34.383 0
21 35.349 0
22 37.058 0
23 38.076 0
24 36.630 0
25 36.045 0
26 35.638 0
27 35.114 0
28 35.465 0
29 35.254 0
30 35.299 0
31 35.916 0
32 36.683 0
33 37.288 0
34 38.536 0
35 38.977 0
36 36.407 0
37 34.955 0
38 34.951 0
39 32.680 0
40 34.791 0
41 34.178 0
42 35.213 0
43 34.871 0
44 35.299 0
45 35.443 0
46 37.108 0
47 36.419 0
48 34.471 0
49 33.868 0
50 34.385 0
51 33.643 1
52 34.627 1
53 32.919 1
54 35.500 1
55 36.110 1
56 37.086 1
57 37.711 1
58 40.427 1
59 39.884 1
60 38.512 1
61 38.767 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) crisis
34.512 2.323
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.9414 -1.3974 0.4426 1.5996 4.4646
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.5124 0.3274 105.419 < 2e-16 ***
crisis 2.3227 0.7709 3.013 0.00381 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.315 on 59 degrees of freedom
Multiple R-squared: 0.1333, Adjusted R-squared: 0.1186
F-statistic: 9.077 on 1 and 59 DF, p-value: 0.003809
> 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,] 0.044451418 0.08890284 0.95554858
[2,] 0.026213453 0.05242691 0.97378655
[3,] 0.017698916 0.03539783 0.98230108
[4,] 0.009157742 0.01831548 0.99084226
[5,] 0.011330020 0.02266004 0.98866998
[6,] 0.078768910 0.15753782 0.92123109
[7,] 0.287849469 0.57569894 0.71215053
[8,] 0.415816595 0.83163319 0.58418340
[9,] 0.444830891 0.88966178 0.55516911
[10,] 0.437354127 0.87470825 0.56264587
[11,] 0.436457974 0.87291595 0.56354203
[12,] 0.734640439 0.53071912 0.26535956
[13,] 0.860160446 0.27967911 0.13983955
[14,] 0.879216608 0.24156678 0.12078339
[15,] 0.886703953 0.22659209 0.11329605
[16,] 0.873435534 0.25312893 0.12656447
[17,] 0.874882625 0.25023475 0.12511737
[18,] 0.926159606 0.14768079 0.07384039
[19,] 0.973201456 0.05359709 0.02679854
[20,] 0.975935224 0.04812955 0.02406478
[21,] 0.972201235 0.05559753 0.02779877
[22,] 0.964193537 0.07161293 0.03580646
[23,] 0.951124146 0.09775171 0.04887585
[24,] 0.935847397 0.12830521 0.06415260
[25,] 0.915035568 0.16992886 0.08496443
[26,] 0.889227010 0.22154598 0.11077299
[27,] 0.864911054 0.27017789 0.13508895
[28,] 0.854817443 0.29036511 0.14518256
[29,] 0.863731718 0.27253656 0.13626828
[30,] 0.921760050 0.15647990 0.07823995
[31,] 0.971203169 0.05759366 0.02879683
[32,] 0.964808797 0.07038241 0.03519120
[33,] 0.946507393 0.10698521 0.05349261
[34,] 0.921094353 0.15781129 0.07890565
[35,] 0.920090649 0.15981870 0.07990935
[36,] 0.885531542 0.22893692 0.11446846
[37,] 0.847495517 0.30500897 0.15250448
[38,] 0.793827855 0.41234429 0.20617215
[39,] 0.729444767 0.54111047 0.27055523
[40,] 0.655338533 0.68932293 0.34466147
[41,] 0.574501101 0.85099780 0.42549890
[42,] 0.564425653 0.87114869 0.43557435
[43,] 0.531164530 0.93767094 0.46883547
[44,] 0.436770354 0.87354071 0.56322965
[45,] 0.345530143 0.69106029 0.65446986
[46,] 0.257647147 0.51529429 0.74235285
[47,] 0.300845667 0.60169133 0.69915433
[48,] 0.301886593 0.60377319 0.69811341
[49,] 0.650793003 0.69841399 0.34920700
[50,] 0.729664735 0.54067053 0.27033527
[51,] 0.803036901 0.39392620 0.19696310
[52,] 0.809690198 0.38061960 0.19030980
> postscript(file="/var/www/html/rcomp/tmp/1k5zb1258735685.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2m2hg1258735685.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31wr31258735685.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hci91258735685.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5mdan1258735685.ps",horizontal=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 = 61
Frequency = 1
1 2 3 4 5 6 7
-4.6753600 -4.9413600 -4.3453600 -3.9883600 -3.5163600 -3.4793600 -3.3143600
8 9 10 11 12 13 14
-3.5753600 -2.8633600 -1.3973600 -0.4063600 -0.5863600 -1.1303600 -1.6613600
15 16 17 18 19 20 21
-1.5643600 1.5996400 1.6006400 0.6976400 0.6806400 -0.1293600 0.8366400
22 23 24 25 26 27 28
2.5456400 3.5636400 2.1176400 1.5326400 1.1256400 0.6016400 0.9526400
29 30 31 32 33 34 35
0.7416400 0.7866400 1.4036400 2.1706400 2.7756400 4.0236400 4.4646400
36 37 38 39 40 41 42
1.8946400 0.4426400 0.4386400 -1.8323600 0.2786400 -0.3343600 0.7006400
43 44 45 46 47 48 49
0.3586400 0.7866400 0.9306400 2.5956400 1.9066400 -0.0413600 -0.6443600
50 51 52 53 54 55 56
-0.1273600 -3.1920909 -2.2080909 -3.9160909 -1.3350909 -0.7250909 0.2509091
57 58 59 60 61
0.8759091 3.5919091 3.0489091 1.6769091 1.9319091
> postscript(file="/var/www/html/rcomp/tmp/6yv8u1258735685.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -4.6753600 NA
1 -4.9413600 -4.6753600
2 -4.3453600 -4.9413600
3 -3.9883600 -4.3453600
4 -3.5163600 -3.9883600
5 -3.4793600 -3.5163600
6 -3.3143600 -3.4793600
7 -3.5753600 -3.3143600
8 -2.8633600 -3.5753600
9 -1.3973600 -2.8633600
10 -0.4063600 -1.3973600
11 -0.5863600 -0.4063600
12 -1.1303600 -0.5863600
13 -1.6613600 -1.1303600
14 -1.5643600 -1.6613600
15 1.5996400 -1.5643600
16 1.6006400 1.5996400
17 0.6976400 1.6006400
18 0.6806400 0.6976400
19 -0.1293600 0.6806400
20 0.8366400 -0.1293600
21 2.5456400 0.8366400
22 3.5636400 2.5456400
23 2.1176400 3.5636400
24 1.5326400 2.1176400
25 1.1256400 1.5326400
26 0.6016400 1.1256400
27 0.9526400 0.6016400
28 0.7416400 0.9526400
29 0.7866400 0.7416400
30 1.4036400 0.7866400
31 2.1706400 1.4036400
32 2.7756400 2.1706400
33 4.0236400 2.7756400
34 4.4646400 4.0236400
35 1.8946400 4.4646400
36 0.4426400 1.8946400
37 0.4386400 0.4426400
38 -1.8323600 0.4386400
39 0.2786400 -1.8323600
40 -0.3343600 0.2786400
41 0.7006400 -0.3343600
42 0.3586400 0.7006400
43 0.7866400 0.3586400
44 0.9306400 0.7866400
45 2.5956400 0.9306400
46 1.9066400 2.5956400
47 -0.0413600 1.9066400
48 -0.6443600 -0.0413600
49 -0.1273600 -0.6443600
50 -3.1920909 -0.1273600
51 -2.2080909 -3.1920909
52 -3.9160909 -2.2080909
53 -1.3350909 -3.9160909
54 -0.7250909 -1.3350909
55 0.2509091 -0.7250909
56 0.8759091 0.2509091
57 3.5919091 0.8759091
58 3.0489091 3.5919091
59 1.6769091 3.0489091
60 1.9319091 1.6769091
61 NA 1.9319091
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.9413600 -4.6753600
[2,] -4.3453600 -4.9413600
[3,] -3.9883600 -4.3453600
[4,] -3.5163600 -3.9883600
[5,] -3.4793600 -3.5163600
[6,] -3.3143600 -3.4793600
[7,] -3.5753600 -3.3143600
[8,] -2.8633600 -3.5753600
[9,] -1.3973600 -2.8633600
[10,] -0.4063600 -1.3973600
[11,] -0.5863600 -0.4063600
[12,] -1.1303600 -0.5863600
[13,] -1.6613600 -1.1303600
[14,] -1.5643600 -1.6613600
[15,] 1.5996400 -1.5643600
[16,] 1.6006400 1.5996400
[17,] 0.6976400 1.6006400
[18,] 0.6806400 0.6976400
[19,] -0.1293600 0.6806400
[20,] 0.8366400 -0.1293600
[21,] 2.5456400 0.8366400
[22,] 3.5636400 2.5456400
[23,] 2.1176400 3.5636400
[24,] 1.5326400 2.1176400
[25,] 1.1256400 1.5326400
[26,] 0.6016400 1.1256400
[27,] 0.9526400 0.6016400
[28,] 0.7416400 0.9526400
[29,] 0.7866400 0.7416400
[30,] 1.4036400 0.7866400
[31,] 2.1706400 1.4036400
[32,] 2.7756400 2.1706400
[33,] 4.0236400 2.7756400
[34,] 4.4646400 4.0236400
[35,] 1.8946400 4.4646400
[36,] 0.4426400 1.8946400
[37,] 0.4386400 0.4426400
[38,] -1.8323600 0.4386400
[39,] 0.2786400 -1.8323600
[40,] -0.3343600 0.2786400
[41,] 0.7006400 -0.3343600
[42,] 0.3586400 0.7006400
[43,] 0.7866400 0.3586400
[44,] 0.9306400 0.7866400
[45,] 2.5956400 0.9306400
[46,] 1.9066400 2.5956400
[47,] -0.0413600 1.9066400
[48,] -0.6443600 -0.0413600
[49,] -0.1273600 -0.6443600
[50,] -3.1920909 -0.1273600
[51,] -2.2080909 -3.1920909
[52,] -3.9160909 -2.2080909
[53,] -1.3350909 -3.9160909
[54,] -0.7250909 -1.3350909
[55,] 0.2509091 -0.7250909
[56,] 0.8759091 0.2509091
[57,] 3.5919091 0.8759091
[58,] 3.0489091 3.5919091
[59,] 1.6769091 3.0489091
[60,] 1.9319091 1.6769091
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.9413600 -4.6753600
2 -4.3453600 -4.9413600
3 -3.9883600 -4.3453600
4 -3.5163600 -3.9883600
5 -3.4793600 -3.5163600
6 -3.3143600 -3.4793600
7 -3.5753600 -3.3143600
8 -2.8633600 -3.5753600
9 -1.3973600 -2.8633600
10 -0.4063600 -1.3973600
11 -0.5863600 -0.4063600
12 -1.1303600 -0.5863600
13 -1.6613600 -1.1303600
14 -1.5643600 -1.6613600
15 1.5996400 -1.5643600
16 1.6006400 1.5996400
17 0.6976400 1.6006400
18 0.6806400 0.6976400
19 -0.1293600 0.6806400
20 0.8366400 -0.1293600
21 2.5456400 0.8366400
22 3.5636400 2.5456400
23 2.1176400 3.5636400
24 1.5326400 2.1176400
25 1.1256400 1.5326400
26 0.6016400 1.1256400
27 0.9526400 0.6016400
28 0.7416400 0.9526400
29 0.7866400 0.7416400
30 1.4036400 0.7866400
31 2.1706400 1.4036400
32 2.7756400 2.1706400
33 4.0236400 2.7756400
34 4.4646400 4.0236400
35 1.8946400 4.4646400
36 0.4426400 1.8946400
37 0.4386400 0.4426400
38 -1.8323600 0.4386400
39 0.2786400 -1.8323600
40 -0.3343600 0.2786400
41 0.7006400 -0.3343600
42 0.3586400 0.7006400
43 0.7866400 0.3586400
44 0.9306400 0.7866400
45 2.5956400 0.9306400
46 1.9066400 2.5956400
47 -0.0413600 1.9066400
48 -0.6443600 -0.0413600
49 -0.1273600 -0.6443600
50 -3.1920909 -0.1273600
51 -2.2080909 -3.1920909
52 -3.9160909 -2.2080909
53 -1.3350909 -3.9160909
54 -0.7250909 -1.3350909
55 0.2509091 -0.7250909
56 0.8759091 0.2509091
57 3.5919091 0.8759091
58 3.0489091 3.5919091
59 1.6769091 3.0489091
60 1.9319091 1.6769091
> 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/7zs8p1258735685.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88l571258735685.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zj0n1258735685.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10888h1258735685.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ca9m1258735685.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/12yrzy1258735685.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/13gf651258735685.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/14he0o1258735685.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/15nujf1258735685.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/16jej91258735685.tab")
+ }
>
> system("convert tmp/1k5zb1258735685.ps tmp/1k5zb1258735685.png")
> system("convert tmp/2m2hg1258735685.ps tmp/2m2hg1258735685.png")
> system("convert tmp/31wr31258735685.ps tmp/31wr31258735685.png")
> system("convert tmp/4hci91258735685.ps tmp/4hci91258735685.png")
> system("convert tmp/5mdan1258735685.ps tmp/5mdan1258735685.png")
> system("convert tmp/6yv8u1258735685.ps tmp/6yv8u1258735685.png")
> system("convert tmp/7zs8p1258735685.ps tmp/7zs8p1258735685.png")
> system("convert tmp/88l571258735685.ps tmp/88l571258735685.png")
> system("convert tmp/9zj0n1258735685.ps tmp/9zj0n1258735685.png")
> system("convert tmp/10888h1258735685.ps tmp/10888h1258735685.png")
>
>
> proc.time()
user system elapsed
2.490 1.587 2.886