R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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> x <- array(list(4754,4531,4690,4716,4824,5270,5172,5150,5245,5300,4836,4663,4592,4553,4217,4366,4532,4743,4776,4949,5069,4980,5213,5394,6075,5919,5758,5916,6474,6704,7553,7891,7840,7007,6680,6102,5238,4237,3983,3879,3733,3940,3945,4324,4233,4550,4344,4388,4561,4512,4756,4704,5107,5472,5537,5539,5313,5371,5459,5461),dim=c(1,60),dimnames=list(c('Y'),1:60))
> y <- array(NA,dim=c(1,60),dimnames=list(c('Y'),1:60))
> 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 = 'Include Monthly 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
> 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
Y M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 4754 1 0 0 0 0 0 0 0 0 0 0
2 4531 0 1 0 0 0 0 0 0 0 0 0
3 4690 0 0 1 0 0 0 0 0 0 0 0
4 4716 0 0 0 1 0 0 0 0 0 0 0
5 4824 0 0 0 0 1 0 0 0 0 0 0
6 5270 0 0 0 0 0 1 0 0 0 0 0
7 5172 0 0 0 0 0 0 1 0 0 0 0
8 5150 0 0 0 0 0 0 0 1 0 0 0
9 5245 0 0 0 0 0 0 0 0 1 0 0
10 5300 0 0 0 0 0 0 0 0 0 1 0
11 4836 0 0 0 0 0 0 0 0 0 0 1
12 4663 0 0 0 0 0 0 0 0 0 0 0
13 4592 1 0 0 0 0 0 0 0 0 0 0
14 4553 0 1 0 0 0 0 0 0 0 0 0
15 4217 0 0 1 0 0 0 0 0 0 0 0
16 4366 0 0 0 1 0 0 0 0 0 0 0
17 4532 0 0 0 0 1 0 0 0 0 0 0
18 4743 0 0 0 0 0 1 0 0 0 0 0
19 4776 0 0 0 0 0 0 1 0 0 0 0
20 4949 0 0 0 0 0 0 0 1 0 0 0
21 5069 0 0 0 0 0 0 0 0 1 0 0
22 4980 0 0 0 0 0 0 0 0 0 1 0
23 5213 0 0 0 0 0 0 0 0 0 0 1
24 5394 0 0 0 0 0 0 0 0 0 0 0
25 6075 1 0 0 0 0 0 0 0 0 0 0
26 5919 0 1 0 0 0 0 0 0 0 0 0
27 5758 0 0 1 0 0 0 0 0 0 0 0
28 5916 0 0 0 1 0 0 0 0 0 0 0
29 6474 0 0 0 0 1 0 0 0 0 0 0
30 6704 0 0 0 0 0 1 0 0 0 0 0
31 7553 0 0 0 0 0 0 1 0 0 0 0
32 7891 0 0 0 0 0 0 0 1 0 0 0
33 7840 0 0 0 0 0 0 0 0 1 0 0
34 7007 0 0 0 0 0 0 0 0 0 1 0
35 6680 0 0 0 0 0 0 0 0 0 0 1
36 6102 0 0 0 0 0 0 0 0 0 0 0
37 5238 1 0 0 0 0 0 0 0 0 0 0
38 4237 0 1 0 0 0 0 0 0 0 0 0
39 3983 0 0 1 0 0 0 0 0 0 0 0
40 3879 0 0 0 1 0 0 0 0 0 0 0
41 3733 0 0 0 0 1 0 0 0 0 0 0
42 3940 0 0 0 0 0 1 0 0 0 0 0
43 3945 0 0 0 0 0 0 1 0 0 0 0
44 4324 0 0 0 0 0 0 0 1 0 0 0
45 4233 0 0 0 0 0 0 0 0 1 0 0
46 4550 0 0 0 0 0 0 0 0 0 1 0
47 4344 0 0 0 0 0 0 0 0 0 0 1
48 4388 0 0 0 0 0 0 0 0 0 0 0
49 4561 1 0 0 0 0 0 0 0 0 0 0
50 4512 0 1 0 0 0 0 0 0 0 0 0
51 4756 0 0 1 0 0 0 0 0 0 0 0
52 4704 0 0 0 1 0 0 0 0 0 0 0
53 5107 0 0 0 0 1 0 0 0 0 0 0
54 5472 0 0 0 0 0 1 0 0 0 0 0
55 5537 0 0 0 0 0 0 1 0 0 0 0
56 5539 0 0 0 0 0 0 0 1 0 0 0
57 5313 0 0 0 0 0 0 0 0 1 0 0
58 5371 0 0 0 0 0 0 0 0 0 1 0
59 5459 0 0 0 0 0 0 0 0 0 0 1
60 5461 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
5201.6 -157.6 -451.2 -520.8 -485.4 -267.6
M6 M7 M8 M9 M10 M11
24.2 195.0 369.0 338.4 240.0 104.8
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1451.6 -482.8 -208.4 192.8 2320.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5201.6 438.6 11.860 7.15e-16 ***
M1 -157.6 620.3 -0.254 0.801
M2 -451.2 620.3 -0.727 0.471
M3 -520.8 620.3 -0.840 0.405
M4 -485.4 620.3 -0.783 0.438
M5 -267.6 620.3 -0.431 0.668
M6 24.2 620.3 0.039 0.969
M7 195.0 620.3 0.314 0.755
M8 369.0 620.3 0.595 0.555
M9 338.4 620.3 0.546 0.588
M10 240.0 620.3 0.387 0.701
M11 104.8 620.3 0.169 0.867
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 980.7 on 48 degrees of freedom
Multiple R-squared: 0.1093, Adjusted R-squared: -0.09476
F-statistic: 0.5357 on 11 and 48 DF, p-value: 0.8691
> 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,] 7.543067e-03 1.508613e-02 0.99245693
[2,] 2.319204e-03 4.638409e-03 0.99768080
[3,] 5.921295e-04 1.184259e-03 0.99940787
[4,] 3.678172e-04 7.356343e-04 0.99963218
[5,] 1.319412e-04 2.638824e-04 0.99986806
[6,] 3.044735e-05 6.089470e-05 0.99996955
[7,] 6.392289e-06 1.278458e-05 0.99999361
[8,] 1.801756e-06 3.603512e-06 0.99999820
[9,] 5.809571e-07 1.161914e-06 0.99999942
[10,] 7.833068e-07 1.566614e-06 0.99999922
[11,] 4.034770e-05 8.069539e-05 0.99995965
[12,] 2.214602e-04 4.429204e-04 0.99977854
[13,] 5.084716e-04 1.016943e-03 0.99949153
[14,] 1.104642e-03 2.209285e-03 0.99889536
[15,] 4.581366e-03 9.162732e-03 0.99541863
[16,] 1.115730e-02 2.231460e-02 0.98884270
[17,] 8.018148e-02 1.603630e-01 0.91981852
[18,] 3.332863e-01 6.665726e-01 0.66671372
[19,] 7.214057e-01 5.571887e-01 0.27859435
[20,] 8.402112e-01 3.195776e-01 0.15978879
[21,] 9.032656e-01 1.934687e-01 0.09673436
[22,] 9.036598e-01 1.926804e-01 0.09634020
[23,] 8.617110e-01 2.765780e-01 0.13828900
[24,] 8.006341e-01 3.987319e-01 0.19936594
[25,] 7.473308e-01 5.053385e-01 0.25266923
[26,] 6.932117e-01 6.135767e-01 0.30678833
[27,] 7.032644e-01 5.934711e-01 0.29673556
[28,] 7.393859e-01 5.212283e-01 0.26061415
[29,] 7.977555e-01 4.044889e-01 0.20224446
[30,] 7.905457e-01 4.189086e-01 0.20945431
[31,] 7.558496e-01 4.883008e-01 0.24415041
> postscript(file="/var/www/rcomp/tmp/1qtg61290878691.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2qtg61290878691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3qtg61290878691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4j2fr1290878691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5j2fr1290878691.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 = 60
Frequency = 1
1 2 3 4 5 6 7 8 9 10
-290.0 -219.4 9.2 -0.2 -110.0 44.2 -224.6 -420.6 -295.0 -141.6
11 12 13 14 15 16 17 18 19 20
-470.4 -538.6 -452.0 -197.4 -463.8 -350.2 -402.0 -482.8 -620.6 -621.6
21 22 23 24 25 26 27 28 29 30
-471.0 -461.6 -93.4 192.4 1031.0 1168.6 1077.2 1199.8 1540.0 1478.2
31 32 33 34 35 36 37 38 39 40
2156.4 2320.4 2300.0 1565.4 1373.6 900.4 194.0 -513.4 -697.8 -837.2
41 42 43 44 45 46 47 48 49 50
-1201.0 -1285.8 -1451.6 -1246.6 -1307.0 -891.6 -962.4 -813.6 -483.0 -238.4
51 52 53 54 55 56 57 58 59 60
75.2 -12.2 173.0 246.2 140.4 -31.6 -227.0 -70.6 152.6 259.4
> postscript(file="/var/www/rcomp/tmp/6j2fr1290878691.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -290.0 NA
1 -219.4 -290.0
2 9.2 -219.4
3 -0.2 9.2
4 -110.0 -0.2
5 44.2 -110.0
6 -224.6 44.2
7 -420.6 -224.6
8 -295.0 -420.6
9 -141.6 -295.0
10 -470.4 -141.6
11 -538.6 -470.4
12 -452.0 -538.6
13 -197.4 -452.0
14 -463.8 -197.4
15 -350.2 -463.8
16 -402.0 -350.2
17 -482.8 -402.0
18 -620.6 -482.8
19 -621.6 -620.6
20 -471.0 -621.6
21 -461.6 -471.0
22 -93.4 -461.6
23 192.4 -93.4
24 1031.0 192.4
25 1168.6 1031.0
26 1077.2 1168.6
27 1199.8 1077.2
28 1540.0 1199.8
29 1478.2 1540.0
30 2156.4 1478.2
31 2320.4 2156.4
32 2300.0 2320.4
33 1565.4 2300.0
34 1373.6 1565.4
35 900.4 1373.6
36 194.0 900.4
37 -513.4 194.0
38 -697.8 -513.4
39 -837.2 -697.8
40 -1201.0 -837.2
41 -1285.8 -1201.0
42 -1451.6 -1285.8
43 -1246.6 -1451.6
44 -1307.0 -1246.6
45 -891.6 -1307.0
46 -962.4 -891.6
47 -813.6 -962.4
48 -483.0 -813.6
49 -238.4 -483.0
50 75.2 -238.4
51 -12.2 75.2
52 173.0 -12.2
53 246.2 173.0
54 140.4 246.2
55 -31.6 140.4
56 -227.0 -31.6
57 -70.6 -227.0
58 152.6 -70.6
59 259.4 152.6
60 NA 259.4
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -219.4 -290.0
[2,] 9.2 -219.4
[3,] -0.2 9.2
[4,] -110.0 -0.2
[5,] 44.2 -110.0
[6,] -224.6 44.2
[7,] -420.6 -224.6
[8,] -295.0 -420.6
[9,] -141.6 -295.0
[10,] -470.4 -141.6
[11,] -538.6 -470.4
[12,] -452.0 -538.6
[13,] -197.4 -452.0
[14,] -463.8 -197.4
[15,] -350.2 -463.8
[16,] -402.0 -350.2
[17,] -482.8 -402.0
[18,] -620.6 -482.8
[19,] -621.6 -620.6
[20,] -471.0 -621.6
[21,] -461.6 -471.0
[22,] -93.4 -461.6
[23,] 192.4 -93.4
[24,] 1031.0 192.4
[25,] 1168.6 1031.0
[26,] 1077.2 1168.6
[27,] 1199.8 1077.2
[28,] 1540.0 1199.8
[29,] 1478.2 1540.0
[30,] 2156.4 1478.2
[31,] 2320.4 2156.4
[32,] 2300.0 2320.4
[33,] 1565.4 2300.0
[34,] 1373.6 1565.4
[35,] 900.4 1373.6
[36,] 194.0 900.4
[37,] -513.4 194.0
[38,] -697.8 -513.4
[39,] -837.2 -697.8
[40,] -1201.0 -837.2
[41,] -1285.8 -1201.0
[42,] -1451.6 -1285.8
[43,] -1246.6 -1451.6
[44,] -1307.0 -1246.6
[45,] -891.6 -1307.0
[46,] -962.4 -891.6
[47,] -813.6 -962.4
[48,] -483.0 -813.6
[49,] -238.4 -483.0
[50,] 75.2 -238.4
[51,] -12.2 75.2
[52,] 173.0 -12.2
[53,] 246.2 173.0
[54,] 140.4 246.2
[55,] -31.6 140.4
[56,] -227.0 -31.6
[57,] -70.6 -227.0
[58,] 152.6 -70.6
[59,] 259.4 152.6
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -219.4 -290.0
2 9.2 -219.4
3 -0.2 9.2
4 -110.0 -0.2
5 44.2 -110.0
6 -224.6 44.2
7 -420.6 -224.6
8 -295.0 -420.6
9 -141.6 -295.0
10 -470.4 -141.6
11 -538.6 -470.4
12 -452.0 -538.6
13 -197.4 -452.0
14 -463.8 -197.4
15 -350.2 -463.8
16 -402.0 -350.2
17 -482.8 -402.0
18 -620.6 -482.8
19 -621.6 -620.6
20 -471.0 -621.6
21 -461.6 -471.0
22 -93.4 -461.6
23 192.4 -93.4
24 1031.0 192.4
25 1168.6 1031.0
26 1077.2 1168.6
27 1199.8 1077.2
28 1540.0 1199.8
29 1478.2 1540.0
30 2156.4 1478.2
31 2320.4 2156.4
32 2300.0 2320.4
33 1565.4 2300.0
34 1373.6 1565.4
35 900.4 1373.6
36 194.0 900.4
37 -513.4 194.0
38 -697.8 -513.4
39 -837.2 -697.8
40 -1201.0 -837.2
41 -1285.8 -1201.0
42 -1451.6 -1285.8
43 -1246.6 -1451.6
44 -1307.0 -1246.6
45 -891.6 -1307.0
46 -962.4 -891.6
47 -813.6 -962.4
48 -483.0 -813.6
49 -238.4 -483.0
50 75.2 -238.4
51 -12.2 75.2
52 173.0 -12.2
53 246.2 173.0
54 140.4 246.2
55 -31.6 140.4
56 -227.0 -31.6
57 -70.6 -227.0
58 152.6 -70.6
59 259.4 152.6
> 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/7utwc1290878691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8m2vf1290878691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9m2vf1290878691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
hat values (leverages) are all = 0.2
and there are no factor predictors; no plot no. 5
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10fcvi1290878691.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111ut61290878691.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/124dsu1290878691.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/130n721290878691.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/143n6q1290878691.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/15p64w1290878691.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/16sol21290878691.tab")
+ }
>
> try(system("convert tmp/1qtg61290878691.ps tmp/1qtg61290878691.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qtg61290878691.ps tmp/2qtg61290878691.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qtg61290878691.ps tmp/3qtg61290878691.png",intern=TRUE))
character(0)
> try(system("convert tmp/4j2fr1290878691.ps tmp/4j2fr1290878691.png",intern=TRUE))
character(0)
> try(system("convert tmp/5j2fr1290878691.ps tmp/5j2fr1290878691.png",intern=TRUE))
character(0)
> try(system("convert tmp/6j2fr1290878691.ps tmp/6j2fr1290878691.png",intern=TRUE))
character(0)
> try(system("convert tmp/7utwc1290878691.ps tmp/7utwc1290878691.png",intern=TRUE))
character(0)
> try(system("convert tmp/8m2vf1290878691.ps tmp/8m2vf1290878691.png",intern=TRUE))
character(0)
> try(system("convert tmp/9m2vf1290878691.ps tmp/9m2vf1290878691.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fcvi1290878691.ps tmp/10fcvi1290878691.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.700 1.720 5.382