R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(98.1
+ ,102.8
+ ,104.7
+ ,95.9
+ ,94.6
+ ,15607.4
+ ,-7.5
+ ,15172.6
+ ,113.9
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.9
+ ,17160.9
+ ,-7.8
+ ,16858.9
+ ,80.9
+ ,113.9
+ ,98.1
+ ,102.8
+ ,104.7
+ ,14915.8
+ ,-7.7
+ ,14143.5
+ ,95.7
+ ,80.9
+ ,113.9
+ ,98.1
+ ,102.8
+ ,13768
+ ,-6.6
+ ,14731.8
+ ,113.2
+ ,95.7
+ ,80.9
+ ,113.9
+ ,98.1
+ ,17487.5
+ ,-4.2
+ ,16471.6
+ ,105.9
+ ,113.2
+ ,95.7
+ ,80.9
+ ,113.9
+ ,16198.1
+ ,-2.0
+ ,15214
+ ,108.8
+ ,105.9
+ ,113.2
+ ,95.7
+ ,80.9
+ ,17535.2
+ ,-0.7
+ ,17637.4
+ ,102.3
+ ,108.8
+ ,105.9
+ ,113.2
+ ,95.7
+ ,16571.8
+ ,0.1
+ ,17972.4
+ ,99
+ ,102.3
+ ,108.8
+ ,105.9
+ ,113.2
+ ,16198.9
+ ,0.9
+ ,16896.2
+ ,100.7
+ ,99
+ ,102.3
+ ,108.8
+ ,105.9
+ ,16554.2
+ ,2.1
+ ,16698
+ ,115.5
+ ,100.7
+ ,99
+ ,102.3
+ ,108.8
+ ,19554.2
+ ,3.5
+ ,19691.6
+ ,100.7
+ ,115.5
+ ,100.7
+ ,99
+ ,102.3
+ ,15903.8
+ ,4.9
+ ,15930.7
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.7
+ ,99
+ ,18003.8
+ ,5.7
+ ,17444.6
+ ,114.6
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.7
+ ,18329.6
+ ,6.2
+ ,17699.4
+ ,85.4
+ ,114.6
+ ,109.9
+ ,100.7
+ ,115.5
+ ,16260.7
+ ,6.5
+ ,15189.8
+ ,100.5
+ ,85.4
+ ,114.6
+ ,109.9
+ ,100.7
+ ,14851.9
+ ,6.5
+ ,15672.7
+ ,114.8
+ ,100.5
+ ,85.4
+ ,114.6
+ ,109.9
+ ,18174.1
+ ,6.3
+ ,17180.8
+ ,116.5
+ ,114.8
+ ,100.5
+ ,85.4
+ ,114.6
+ ,18406.6
+ ,6.2
+ ,17664.9
+ ,112.9
+ ,116.5
+ ,114.8
+ ,100.5
+ ,85.4
+ ,18466.5
+ ,6.4
+ ,17862.9
+ ,102
+ ,112.9
+ ,116.5
+ ,114.8
+ ,100.5
+ ,16016.5
+ ,6.3
+ ,16162.3
+ ,106
+ ,102
+ ,112.9
+ ,116.5
+ ,114.8
+ ,17428.5
+ ,5.8
+ ,17463.6
+ ,105.3
+ ,106
+ ,102
+ ,112.9
+ ,116.5
+ ,17167.2
+ ,5.1
+ ,16772.1
+ ,118.8
+ ,105.3
+ ,106
+ ,102
+ ,112.9
+ ,19630
+ ,5.1
+ ,19106.9
+ ,106.1
+ ,118.8
+ ,105.3
+ ,106
+ ,102
+ ,17183.6
+ ,5.8
+ ,16721.3
+ ,109.3
+ ,106.1
+ ,118.8
+ ,105.3
+ ,106
+ ,18344.7
+ ,6.7
+ ,18161.3
+ ,117.2
+ ,109.3
+ ,106.1
+ ,118.8
+ ,105.3
+ ,19301.4
+ ,7.1
+ ,18509.9
+ ,92.5
+ ,117.2
+ ,109.3
+ ,106.1
+ ,118.8
+ ,18147.5
+ ,6.7
+ ,17802.7
+ ,104.2
+ ,92.5
+ ,117.2
+ ,109.3
+ ,106.1
+ ,16192.9
+ ,5.5
+ ,16409.9
+ ,112.5
+ ,104.2
+ ,92.5
+ ,117.2
+ ,109.3
+ ,18374.4
+ ,4.2
+ ,17967.7
+ ,122.4
+ ,112.5
+ ,104.2
+ ,92.5
+ ,117.2
+ ,20515.2
+ ,3.0
+ ,20286.6
+ ,113.3
+ ,122.4
+ ,112.5
+ ,104.2
+ ,92.5
+ ,18957.2
+ ,2.2
+ ,19537.3
+ ,100
+ ,113.3
+ ,122.4
+ ,112.5
+ ,104.2
+ ,16471.5
+ ,2.0
+ ,18021.9
+ ,110.7
+ ,100
+ ,113.3
+ ,122.4
+ ,112.5
+ ,18746.8
+ ,1.8
+ ,20194.3
+ ,112.8
+ ,110.7
+ ,100
+ ,113.3
+ ,122.4
+ ,19009.5
+ ,1.8
+ ,19049.6
+ ,109.8
+ ,112.8
+ ,110.7
+ ,100
+ ,113.3
+ ,19211.2
+ ,1.5
+ ,20244.7
+ ,117.3
+ ,109.8
+ ,112.8
+ ,110.7
+ ,100
+ ,20547.7
+ ,0.4
+ ,21473.3
+ ,109.1
+ ,117.3
+ ,109.8
+ ,112.8
+ ,110.7
+ ,19325.8
+ ,-0.9
+ ,19673.6
+ ,115.9
+ ,109.1
+ ,117.3
+ ,109.8
+ ,112.8
+ ,20605.5
+ ,-1.7
+ ,21053.2
+ ,96
+ ,115.9
+ ,109.1
+ ,117.3
+ ,109.8
+ ,20056.9
+ ,-2.6
+ ,20159.5
+ ,99.8
+ ,96
+ ,115.9
+ ,109.1
+ ,117.3
+ ,16141.4
+ ,-4.4
+ ,18203.6
+ ,116.8
+ ,99.8
+ ,96
+ ,115.9
+ ,109.1
+ ,20359.8
+ ,-8.3
+ ,21289.5
+ ,115.7
+ ,116.8
+ ,99.8
+ ,96
+ ,115.9
+ ,19711.6
+ ,-14.4
+ ,20432.3
+ ,99.4
+ ,115.7
+ ,116.8
+ ,99.8
+ ,96
+ ,15638.6
+ ,-21.3
+ ,17180.4
+ ,94.3
+ ,99.4
+ ,115.7
+ ,116.8
+ ,99.8
+ ,14384.5
+ ,-26.5
+ ,15816.8
+ ,91
+ ,94.3
+ ,99.4
+ ,115.7
+ ,116.8
+ ,13855.6
+ ,-29.2
+ ,15071.8
+ ,93.2
+ ,91
+ ,94.3
+ ,99.4
+ ,115.7
+ ,14308.3
+ ,-30.8
+ ,14521.1
+ ,103.1
+ ,93.2
+ ,91
+ ,94.3
+ ,99.4
+ ,15290.6
+ ,-30.9
+ ,15668.8
+ ,94.1
+ ,103.1
+ ,93.2
+ ,91
+ ,94.3
+ ,14423.8
+ ,-29.5
+ ,14346.9
+ ,91.8
+ ,94.1
+ ,103.1
+ ,93.2
+ ,91
+ ,13779.7
+ ,-27.1
+ ,13881
+ ,102.7
+ ,91.8
+ ,94.1
+ ,103.1
+ ,93.2
+ ,15686.3
+ ,-24.4
+ ,15465.9
+ ,82.6
+ ,102.7
+ ,91.8
+ ,94.1
+ ,103.1
+ ,14733.8
+ ,-21.9
+ ,14238.2
+ ,89.1
+ ,82.6
+ ,102.7
+ ,91.8
+ ,94.1
+ ,12522.5
+ ,-19.3
+ ,13557.7
+ ,104.5
+ ,89.1
+ ,82.6
+ ,102.7
+ ,91.8
+ ,16189.4
+ ,-17.0
+ ,16127.6
+ ,105.1
+ ,104.5
+ ,89.1
+ ,82.6
+ ,102.7
+ ,16059.1
+ ,-13.8
+ ,16793.9
+ ,95.1
+ ,105.1
+ ,104.5
+ ,89.1
+ ,82.6
+ ,16007.1
+ ,-9.9
+ ,16014
+ ,88.7
+ ,95.1
+ ,105.1
+ ,104.5
+ ,89.1
+ ,15806.8
+ ,-7.9
+ ,16867.9
+ ,86.3
+ ,88.7
+ ,95.1
+ ,105.1
+ ,104.5
+ ,15160
+ ,-7.2
+ ,16014.6
+ ,91.8
+ ,86.3
+ ,88.7
+ ,95.1
+ ,105.1
+ ,15692.1
+ ,-6.2
+ ,15878.6
+ ,111.5
+ ,91.8
+ ,86.3
+ ,88.7
+ ,95.1
+ ,18908.9
+ ,-4.5
+ ,18664.9
+ ,99.7
+ ,111.5
+ ,91.8
+ ,86.3
+ ,88.7
+ ,16969.9
+ ,-3.9
+ ,17962.5
+ ,97.5
+ ,99.7
+ ,111.5
+ ,91.8
+ ,86.3
+ ,16997.5
+ ,-5.0
+ ,17332.7
+ ,111.7
+ ,97.5
+ ,99.7
+ ,111.5
+ ,91.8
+ ,19858.9
+ ,-6.2
+ ,19542.1
+ ,86.2
+ ,111.7
+ ,97.5
+ ,99.7
+ ,111.5
+ ,17681.2
+ ,-6.1
+ ,17203.6
+ ,95.4
+ ,86.2
+ ,111.7
+ ,97.5
+ ,99.7
+ ,16006.9
+ ,-5.0
+ ,16579)
+ ,dim=c(8
+ ,64)
+ ,dimnames=list(c('y'
+ ,'y1'
+ ,'y2'
+ ,'y3'
+ ,'y4'
+ ,'uitvoer'
+ ,'ondernemersvertrouwen'
+ ,'invoer')
+ ,1:64))
> y <- array(NA,dim=c(8,64),dimnames=list(c('y','y1','y2','y3','y4','uitvoer','ondernemersvertrouwen','invoer'),1:64))
> 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 y1 y2 y3 y4 uitvoer ondernemersvertrouwen invoer M1 M2 M3
1 98.1 102.8 104.7 95.9 94.6 15607.4 -7.5 15172.6 1 0 0
2 113.9 98.1 102.8 104.7 95.9 17160.9 -7.8 16858.9 0 1 0
3 80.9 113.9 98.1 102.8 104.7 14915.8 -7.7 14143.5 0 0 1
4 95.7 80.9 113.9 98.1 102.8 13768.0 -6.6 14731.8 0 0 0
5 113.2 95.7 80.9 113.9 98.1 17487.5 -4.2 16471.6 0 0 0
6 105.9 113.2 95.7 80.9 113.9 16198.1 -2.0 15214.0 0 0 0
7 108.8 105.9 113.2 95.7 80.9 17535.2 -0.7 17637.4 0 0 0
8 102.3 108.8 105.9 113.2 95.7 16571.8 0.1 17972.4 0 0 0
9 99.0 102.3 108.8 105.9 113.2 16198.9 0.9 16896.2 0 0 0
10 100.7 99.0 102.3 108.8 105.9 16554.2 2.1 16698.0 0 0 0
11 115.5 100.7 99.0 102.3 108.8 19554.2 3.5 19691.6 0 0 0
12 100.7 115.5 100.7 99.0 102.3 15903.8 4.9 15930.7 0 0 0
13 109.9 100.7 115.5 100.7 99.0 18003.8 5.7 17444.6 1 0 0
14 114.6 109.9 100.7 115.5 100.7 18329.6 6.2 17699.4 0 1 0
15 85.4 114.6 109.9 100.7 115.5 16260.7 6.5 15189.8 0 0 1
16 100.5 85.4 114.6 109.9 100.7 14851.9 6.5 15672.7 0 0 0
17 114.8 100.5 85.4 114.6 109.9 18174.1 6.3 17180.8 0 0 0
18 116.5 114.8 100.5 85.4 114.6 18406.6 6.2 17664.9 0 0 0
19 112.9 116.5 114.8 100.5 85.4 18466.5 6.4 17862.9 0 0 0
20 102.0 112.9 116.5 114.8 100.5 16016.5 6.3 16162.3 0 0 0
21 106.0 102.0 112.9 116.5 114.8 17428.5 5.8 17463.6 0 0 0
22 105.3 106.0 102.0 112.9 116.5 17167.2 5.1 16772.1 0 0 0
23 118.8 105.3 106.0 102.0 112.9 19630.0 5.1 19106.9 0 0 0
24 106.1 118.8 105.3 106.0 102.0 17183.6 5.8 16721.3 0 0 0
25 109.3 106.1 118.8 105.3 106.0 18344.7 6.7 18161.3 1 0 0
26 117.2 109.3 106.1 118.8 105.3 19301.4 7.1 18509.9 0 1 0
27 92.5 117.2 109.3 106.1 118.8 18147.5 6.7 17802.7 0 0 1
28 104.2 92.5 117.2 109.3 106.1 16192.9 5.5 16409.9 0 0 0
29 112.5 104.2 92.5 117.2 109.3 18374.4 4.2 17967.7 0 0 0
30 122.4 112.5 104.2 92.5 117.2 20515.2 3.0 20286.6 0 0 0
31 113.3 122.4 112.5 104.2 92.5 18957.2 2.2 19537.3 0 0 0
32 100.0 113.3 122.4 112.5 104.2 16471.5 2.0 18021.9 0 0 0
33 110.7 100.0 113.3 122.4 112.5 18746.8 1.8 20194.3 0 0 0
34 112.8 110.7 100.0 113.3 122.4 19009.5 1.8 19049.6 0 0 0
35 109.8 112.8 110.7 100.0 113.3 19211.2 1.5 20244.7 0 0 0
36 117.3 109.8 112.8 110.7 100.0 20547.7 0.4 21473.3 0 0 0
37 109.1 117.3 109.8 112.8 110.7 19325.8 -0.9 19673.6 1 0 0
38 115.9 109.1 117.3 109.8 112.8 20605.5 -1.7 21053.2 0 1 0
39 96.0 115.9 109.1 117.3 109.8 20056.9 -2.6 20159.5 0 0 1
40 99.8 96.0 115.9 109.1 117.3 16141.4 -4.4 18203.6 0 0 0
41 116.8 99.8 96.0 115.9 109.1 20359.8 -8.3 21289.5 0 0 0
42 115.7 116.8 99.8 96.0 115.9 19711.6 -14.4 20432.3 0 0 0
43 99.4 115.7 116.8 99.8 96.0 15638.6 -21.3 17180.4 0 0 0
44 94.3 99.4 115.7 116.8 99.8 14384.5 -26.5 15816.8 0 0 0
45 91.0 94.3 99.4 115.7 116.8 13855.6 -29.2 15071.8 0 0 0
46 93.2 91.0 94.3 99.4 115.7 14308.3 -30.8 14521.1 0 0 0
47 103.1 93.2 91.0 94.3 99.4 15290.6 -30.9 15668.8 0 0 0
48 94.1 103.1 93.2 91.0 94.3 14423.8 -29.5 14346.9 0 0 0
49 91.8 94.1 103.1 93.2 91.0 13779.7 -27.1 13881.0 1 0 0
50 102.7 91.8 94.1 103.1 93.2 15686.3 -24.4 15465.9 0 1 0
51 82.6 102.7 91.8 94.1 103.1 14733.8 -21.9 14238.2 0 0 1
52 89.1 82.6 102.7 91.8 94.1 12522.5 -19.3 13557.7 0 0 0
53 104.5 89.1 82.6 102.7 91.8 16189.4 -17.0 16127.6 0 0 0
54 105.1 104.5 89.1 82.6 102.7 16059.1 -13.8 16793.9 0 0 0
55 95.1 105.1 104.5 89.1 82.6 16007.1 -9.9 16014.0 0 0 0
56 88.7 95.1 105.1 104.5 89.1 15806.8 -7.9 16867.9 0 0 0
57 86.3 88.7 95.1 105.1 104.5 15160.0 -7.2 16014.6 0 0 0
58 91.8 86.3 88.7 95.1 105.1 15692.1 -6.2 15878.6 0 0 0
59 111.5 91.8 86.3 88.7 95.1 18908.9 -4.5 18664.9 0 0 0
60 99.7 111.5 91.8 86.3 88.7 16969.9 -3.9 17962.5 0 0 0
61 97.5 99.7 111.5 91.8 86.3 16997.5 -5.0 17332.7 1 0 0
62 111.7 97.5 99.7 111.5 91.8 19858.9 -6.2 19542.1 0 1 0
63 86.2 111.7 97.5 99.7 111.5 17681.2 -6.1 17203.6 0 0 1
64 95.4 86.2 111.7 97.5 99.7 16006.9 -5.0 16579.0 0 0 0
M4 M5 M6 M7 M8 M9 M10 M11
1 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0
4 1 0 0 0 0 0 0 0
5 0 1 0 0 0 0 0 0
6 0 0 1 0 0 0 0 0
7 0 0 0 1 0 0 0 0
8 0 0 0 0 1 0 0 0
9 0 0 0 0 0 1 0 0
10 0 0 0 0 0 0 1 0
11 0 0 0 0 0 0 0 1
12 0 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0 0
15 0 0 0 0 0 0 0 0
16 1 0 0 0 0 0 0 0
17 0 1 0 0 0 0 0 0
18 0 0 1 0 0 0 0 0
19 0 0 0 1 0 0 0 0
20 0 0 0 0 1 0 0 0
21 0 0 0 0 0 1 0 0
22 0 0 0 0 0 0 1 0
23 0 0 0 0 0 0 0 1
24 0 0 0 0 0 0 0 0
25 0 0 0 0 0 0 0 0
26 0 0 0 0 0 0 0 0
27 0 0 0 0 0 0 0 0
28 1 0 0 0 0 0 0 0
29 0 1 0 0 0 0 0 0
30 0 0 1 0 0 0 0 0
31 0 0 0 1 0 0 0 0
32 0 0 0 0 1 0 0 0
33 0 0 0 0 0 1 0 0
34 0 0 0 0 0 0 1 0
35 0 0 0 0 0 0 0 1
36 0 0 0 0 0 0 0 0
37 0 0 0 0 0 0 0 0
38 0 0 0 0 0 0 0 0
39 0 0 0 0 0 0 0 0
40 1 0 0 0 0 0 0 0
41 0 1 0 0 0 0 0 0
42 0 0 1 0 0 0 0 0
43 0 0 0 1 0 0 0 0
44 0 0 0 0 1 0 0 0
45 0 0 0 0 0 1 0 0
46 0 0 0 0 0 0 1 0
47 0 0 0 0 0 0 0 1
48 0 0 0 0 0 0 0 0
49 0 0 0 0 0 0 0 0
50 0 0 0 0 0 0 0 0
51 0 0 0 0 0 0 0 0
52 1 0 0 0 0 0 0 0
53 0 1 0 0 0 0 0 0
54 0 0 1 0 0 0 0 0
55 0 0 0 1 0 0 0 0
56 0 0 0 0 1 0 0 0
57 0 0 0 0 0 1 0 0
58 0 0 0 0 0 0 1 0
59 0 0 0 0 0 0 0 1
60 0 0 0 0 0 0 0 0
61 0 0 0 0 0 0 0 0
62 0 0 0 0 0 0 0 0
63 0 0 0 0 0 0 0 0
64 1 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) y1 y2
22.672926 0.017469 0.131438
y3 y4 uitvoer
0.300292 -0.031989 0.004513
ondernemersvertrouwen invoer M1
0.008792 -0.002168 -3.257003
M2 M3 M4
0.633034 -19.234837 -1.943181
M5 M6 M7
2.791387 10.165372 0.033113
M8 M9 M10
-7.231640 -6.339951 -3.288692
M11
5.435076
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.9256 -1.5219 0.2736 1.3305 6.1766
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.267e+01 1.089e+01 2.082 0.043030 *
y1 1.747e-02 1.163e-01 0.150 0.881294
y2 1.314e-01 9.390e-02 1.400 0.168462
y3 3.003e-01 9.281e-02 3.236 0.002279 **
y4 -3.199e-02 1.045e-01 -0.306 0.760849
uitvoer 4.513e-03 1.168e-03 3.864 0.000355 ***
ondernemersvertrouwen 8.792e-03 6.927e-02 0.127 0.899569
invoer -2.168e-03 9.112e-04 -2.380 0.021629 *
M1 -3.257e+00 2.152e+00 -1.514 0.137141
M2 6.330e-01 2.429e+00 0.261 0.795550
M3 -1.923e+01 2.185e+00 -8.805 2.40e-11 ***
M4 -1.943e+00 4.195e+00 -0.463 0.645465
M5 2.791e+00 3.585e+00 0.779 0.440230
M6 1.017e+01 2.919e+00 3.483 0.001116 **
M7 3.311e-02 2.595e+00 0.013 0.989875
M8 -7.232e+00 2.897e+00 -2.496 0.016298 *
M9 -6.340e+00 3.823e+00 -1.658 0.104212
M10 -3.289e+00 3.524e+00 -0.933 0.355692
M11 5.435e+00 2.719e+00 1.999 0.051652 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.827 on 45 degrees of freedom
Multiple R-squared: 0.9447, Adjusted R-squared: 0.9225
F-statistic: 42.68 on 18 and 45 DF, p-value: < 2.2e-16
> 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,] 0.073211397 0.146422794 0.9267886
[2,] 0.022556593 0.045113185 0.9774434
[3,] 0.043531183 0.087062367 0.9564688
[4,] 0.019683231 0.039366462 0.9803168
[5,] 0.034714978 0.069429956 0.9652850
[6,] 0.015086897 0.030173793 0.9849131
[7,] 0.008917007 0.017834014 0.9910830
[8,] 0.005135392 0.010270783 0.9948646
[9,] 0.002990417 0.005980834 0.9970096
[10,] 0.003707646 0.007415291 0.9962924
[11,] 0.002216540 0.004433079 0.9977835
[12,] 0.009319906 0.018639812 0.9906801
[13,] 0.066483824 0.132967647 0.9335162
[14,] 0.053041864 0.106083727 0.9469581
[15,] 0.145631554 0.291263108 0.8543684
[16,] 0.125610752 0.251221504 0.8743892
[17,] 0.228472459 0.456944918 0.7715275
[18,] 0.175710305 0.351420611 0.8242897
[19,] 0.102237990 0.204475980 0.8977620
[20,] 0.057865679 0.115731358 0.9421343
[21,] 0.031411001 0.062822002 0.9685890
> postscript(file="/var/www/rcomp/tmp/18rq21293197823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/28rq21293197823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/38rq21293197823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4i07n1293197823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5i07n1293197823.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 = 64
Frequency = 1
1 2 3 4 5 6
-0.11268585 6.17659595 -1.51878252 2.28547618 1.20108752 -2.23626668
7 8 9 10 11 12
2.33254632 4.29132547 1.92624807 -1.66104304 -0.19565376 -0.95321844
13 14 15 16 17 18
2.99978681 0.28221438 -1.53121790 0.33785274 0.64002139 1.65171729
19 20 21 22 23 24
0.96347340 0.72621033 0.89905393 -0.66799268 0.70127349 -2.39616673
25 26 27 28 29 30
0.72110165 -1.29687193 1.23529332 -0.51930126 -2.63701492 1.25381206
31 32 33 34 35 36
2.13180313 0.76935604 3.74300836 3.73483758 -4.04553082 1.66970521
37 38 39 40 41 42
-1.67500722 -1.41609154 -2.29155229 -0.18262341 0.01664364 -1.94029233
43 44 45 46 47 48
-0.71097500 -0.35182416 -0.64268728 0.87072091 1.50887500 -0.65723173
49 50 51 52 53 54
0.26489441 0.40426052 4.91776524 1.92790197 0.77926238 1.27102966
55 56 57 58 59 60
-4.71684785 -5.43506768 -5.92562309 -2.27652277 2.03103608 2.33691168
61 62 63 64
-2.19808981 -4.15010738 -0.81150586 -3.84930621
> postscript(file="/var/www/rcomp/tmp/6i07n1293197823.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 = 64
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.11268585 NA
1 6.17659595 -0.11268585
2 -1.51878252 6.17659595
3 2.28547618 -1.51878252
4 1.20108752 2.28547618
5 -2.23626668 1.20108752
6 2.33254632 -2.23626668
7 4.29132547 2.33254632
8 1.92624807 4.29132547
9 -1.66104304 1.92624807
10 -0.19565376 -1.66104304
11 -0.95321844 -0.19565376
12 2.99978681 -0.95321844
13 0.28221438 2.99978681
14 -1.53121790 0.28221438
15 0.33785274 -1.53121790
16 0.64002139 0.33785274
17 1.65171729 0.64002139
18 0.96347340 1.65171729
19 0.72621033 0.96347340
20 0.89905393 0.72621033
21 -0.66799268 0.89905393
22 0.70127349 -0.66799268
23 -2.39616673 0.70127349
24 0.72110165 -2.39616673
25 -1.29687193 0.72110165
26 1.23529332 -1.29687193
27 -0.51930126 1.23529332
28 -2.63701492 -0.51930126
29 1.25381206 -2.63701492
30 2.13180313 1.25381206
31 0.76935604 2.13180313
32 3.74300836 0.76935604
33 3.73483758 3.74300836
34 -4.04553082 3.73483758
35 1.66970521 -4.04553082
36 -1.67500722 1.66970521
37 -1.41609154 -1.67500722
38 -2.29155229 -1.41609154
39 -0.18262341 -2.29155229
40 0.01664364 -0.18262341
41 -1.94029233 0.01664364
42 -0.71097500 -1.94029233
43 -0.35182416 -0.71097500
44 -0.64268728 -0.35182416
45 0.87072091 -0.64268728
46 1.50887500 0.87072091
47 -0.65723173 1.50887500
48 0.26489441 -0.65723173
49 0.40426052 0.26489441
50 4.91776524 0.40426052
51 1.92790197 4.91776524
52 0.77926238 1.92790197
53 1.27102966 0.77926238
54 -4.71684785 1.27102966
55 -5.43506768 -4.71684785
56 -5.92562309 -5.43506768
57 -2.27652277 -5.92562309
58 2.03103608 -2.27652277
59 2.33691168 2.03103608
60 -2.19808981 2.33691168
61 -4.15010738 -2.19808981
62 -0.81150586 -4.15010738
63 -3.84930621 -0.81150586
64 NA -3.84930621
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 6.17659595 -0.11268585
[2,] -1.51878252 6.17659595
[3,] 2.28547618 -1.51878252
[4,] 1.20108752 2.28547618
[5,] -2.23626668 1.20108752
[6,] 2.33254632 -2.23626668
[7,] 4.29132547 2.33254632
[8,] 1.92624807 4.29132547
[9,] -1.66104304 1.92624807
[10,] -0.19565376 -1.66104304
[11,] -0.95321844 -0.19565376
[12,] 2.99978681 -0.95321844
[13,] 0.28221438 2.99978681
[14,] -1.53121790 0.28221438
[15,] 0.33785274 -1.53121790
[16,] 0.64002139 0.33785274
[17,] 1.65171729 0.64002139
[18,] 0.96347340 1.65171729
[19,] 0.72621033 0.96347340
[20,] 0.89905393 0.72621033
[21,] -0.66799268 0.89905393
[22,] 0.70127349 -0.66799268
[23,] -2.39616673 0.70127349
[24,] 0.72110165 -2.39616673
[25,] -1.29687193 0.72110165
[26,] 1.23529332 -1.29687193
[27,] -0.51930126 1.23529332
[28,] -2.63701492 -0.51930126
[29,] 1.25381206 -2.63701492
[30,] 2.13180313 1.25381206
[31,] 0.76935604 2.13180313
[32,] 3.74300836 0.76935604
[33,] 3.73483758 3.74300836
[34,] -4.04553082 3.73483758
[35,] 1.66970521 -4.04553082
[36,] -1.67500722 1.66970521
[37,] -1.41609154 -1.67500722
[38,] -2.29155229 -1.41609154
[39,] -0.18262341 -2.29155229
[40,] 0.01664364 -0.18262341
[41,] -1.94029233 0.01664364
[42,] -0.71097500 -1.94029233
[43,] -0.35182416 -0.71097500
[44,] -0.64268728 -0.35182416
[45,] 0.87072091 -0.64268728
[46,] 1.50887500 0.87072091
[47,] -0.65723173 1.50887500
[48,] 0.26489441 -0.65723173
[49,] 0.40426052 0.26489441
[50,] 4.91776524 0.40426052
[51,] 1.92790197 4.91776524
[52,] 0.77926238 1.92790197
[53,] 1.27102966 0.77926238
[54,] -4.71684785 1.27102966
[55,] -5.43506768 -4.71684785
[56,] -5.92562309 -5.43506768
[57,] -2.27652277 -5.92562309
[58,] 2.03103608 -2.27652277
[59,] 2.33691168 2.03103608
[60,] -2.19808981 2.33691168
[61,] -4.15010738 -2.19808981
[62,] -0.81150586 -4.15010738
[63,] -3.84930621 -0.81150586
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 6.17659595 -0.11268585
2 -1.51878252 6.17659595
3 2.28547618 -1.51878252
4 1.20108752 2.28547618
5 -2.23626668 1.20108752
6 2.33254632 -2.23626668
7 4.29132547 2.33254632
8 1.92624807 4.29132547
9 -1.66104304 1.92624807
10 -0.19565376 -1.66104304
11 -0.95321844 -0.19565376
12 2.99978681 -0.95321844
13 0.28221438 2.99978681
14 -1.53121790 0.28221438
15 0.33785274 -1.53121790
16 0.64002139 0.33785274
17 1.65171729 0.64002139
18 0.96347340 1.65171729
19 0.72621033 0.96347340
20 0.89905393 0.72621033
21 -0.66799268 0.89905393
22 0.70127349 -0.66799268
23 -2.39616673 0.70127349
24 0.72110165 -2.39616673
25 -1.29687193 0.72110165
26 1.23529332 -1.29687193
27 -0.51930126 1.23529332
28 -2.63701492 -0.51930126
29 1.25381206 -2.63701492
30 2.13180313 1.25381206
31 0.76935604 2.13180313
32 3.74300836 0.76935604
33 3.73483758 3.74300836
34 -4.04553082 3.73483758
35 1.66970521 -4.04553082
36 -1.67500722 1.66970521
37 -1.41609154 -1.67500722
38 -2.29155229 -1.41609154
39 -0.18262341 -2.29155229
40 0.01664364 -0.18262341
41 -1.94029233 0.01664364
42 -0.71097500 -1.94029233
43 -0.35182416 -0.71097500
44 -0.64268728 -0.35182416
45 0.87072091 -0.64268728
46 1.50887500 0.87072091
47 -0.65723173 1.50887500
48 0.26489441 -0.65723173
49 0.40426052 0.26489441
50 4.91776524 0.40426052
51 1.92790197 4.91776524
52 0.77926238 1.92790197
53 1.27102966 0.77926238
54 -4.71684785 1.27102966
55 -5.43506768 -4.71684785
56 -5.92562309 -5.43506768
57 -2.27652277 -5.92562309
58 2.03103608 -2.27652277
59 2.33691168 2.03103608
60 -2.19808981 2.33691168
61 -4.15010738 -2.19808981
62 -0.81150586 -4.15010738
63 -3.84930621 -0.81150586
> 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/7ta781293197823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/84joa1293197823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/94joa1293197823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/104joa1293197823.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11pjny1293197823.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/12b2341293197823.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/13iliy1293197823.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/14sczj1293197823.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/15edyp1293197823.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/162xyt1293197824.tab")
+ }
>
> try(system("convert tmp/18rq21293197823.ps tmp/18rq21293197823.png",intern=TRUE))
character(0)
> try(system("convert tmp/28rq21293197823.ps tmp/28rq21293197823.png",intern=TRUE))
character(0)
> try(system("convert tmp/38rq21293197823.ps tmp/38rq21293197823.png",intern=TRUE))
character(0)
> try(system("convert tmp/4i07n1293197823.ps tmp/4i07n1293197823.png",intern=TRUE))
character(0)
> try(system("convert tmp/5i07n1293197823.ps tmp/5i07n1293197823.png",intern=TRUE))
character(0)
> try(system("convert tmp/6i07n1293197823.ps tmp/6i07n1293197823.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ta781293197823.ps tmp/7ta781293197823.png",intern=TRUE))
character(0)
> try(system("convert tmp/84joa1293197823.ps tmp/84joa1293197823.png",intern=TRUE))
character(0)
> try(system("convert tmp/94joa1293197823.ps tmp/94joa1293197823.png",intern=TRUE))
character(0)
> try(system("convert tmp/104joa1293197823.ps tmp/104joa1293197823.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.150 1.670 4.817