R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(104.37
+ ,1
+ ,1
+ ,167.16
+ ,101.56
+ ,100.93
+ ,104.89
+ ,2
+ ,2
+ ,179.84
+ ,102.13
+ ,101.18
+ ,105.15
+ ,3
+ ,3
+ ,174.44
+ ,102.39
+ ,101.11
+ ,105.72
+ ,4
+ ,4
+ ,180.35
+ ,102.42
+ ,102.42
+ ,106.38
+ ,5
+ ,5
+ ,193.17
+ ,103.87
+ ,102.37
+ ,106.40
+ ,6
+ ,6
+ ,195.16
+ ,104.44
+ ,101.95
+ ,106.47
+ ,7
+ ,7
+ ,202.43
+ ,104.97
+ ,102.20
+ ,106.59
+ ,8
+ ,8
+ ,189.91
+ ,105.17
+ ,103.35
+ ,106.76
+ ,9
+ ,9
+ ,195.98
+ ,105.35
+ ,103.65
+ ,107.35
+ ,10
+ ,10
+ ,212.09
+ ,104.65
+ ,102.06
+ ,107.81
+ ,11
+ ,11
+ ,205.81
+ ,106.62
+ ,102.66
+ ,108.03
+ ,12
+ ,12
+ ,204.31
+ ,107.05
+ ,102.32
+ ,109.08
+ ,1
+ ,13
+ ,196.07
+ ,112.30
+ ,102.21
+ ,109.86
+ ,2
+ ,14
+ ,199.98
+ ,114.70
+ ,102.33
+ ,110.29
+ ,3
+ ,15
+ ,199.1
+ ,115.40
+ ,104.41
+ ,110.34
+ ,4
+ ,16
+ ,198.31
+ ,115.64
+ ,104.33
+ ,110.59
+ ,5
+ ,17
+ ,195.72
+ ,115.66
+ ,105.27
+ ,110.64
+ ,6
+ ,18
+ ,223.04
+ ,114.50
+ ,105.34
+ ,110.83
+ ,7
+ ,19
+ ,238.41
+ ,115.14
+ ,104.88
+ ,111.51
+ ,8
+ ,20
+ ,259.73
+ ,115.41
+ ,105.49
+ ,113.32
+ ,9
+ ,21
+ ,326.54
+ ,119.32
+ ,105.90
+ ,115.89
+ ,10
+ ,22
+ ,335.15
+ ,124.77
+ ,105.39
+ ,116.51
+ ,11
+ ,23
+ ,321.81
+ ,130.96
+ ,104.40
+ ,117.44
+ ,12
+ ,24
+ ,368.62
+ ,141.02
+ ,106.19
+ ,118.25
+ ,1
+ ,25
+ ,369.59
+ ,150.60
+ ,106.54
+ ,118.65
+ ,2
+ ,26
+ ,425
+ ,151.10
+ ,108.26
+ ,118.52
+ ,3
+ ,27
+ ,439.72
+ ,157.19
+ ,106.95
+ ,119.07
+ ,4
+ ,28
+ ,362.23
+ ,157.28
+ ,108.32
+ ,119.12
+ ,5
+ ,29
+ ,328.76
+ ,156.54
+ ,108.35
+ ,119.28
+ ,6
+ ,30
+ ,348.55
+ ,159.62
+ ,109.29
+ ,119.30
+ ,7
+ ,31
+ ,328.18
+ ,163.77
+ ,109.46
+ ,119.44
+ ,8
+ ,32
+ ,329.34
+ ,165.08
+ ,109.50
+ ,119.57
+ ,9
+ ,33
+ ,295.55
+ ,164.75
+ ,109.84
+ ,119.93
+ ,10
+ ,34
+ ,237.38
+ ,163.93
+ ,108.73
+ ,120.03
+ ,11
+ ,35
+ ,226.85
+ ,157.51
+ ,109.38
+ ,119.66
+ ,12
+ ,36
+ ,220.14
+ ,153.36
+ ,109.97
+ ,119.46
+ ,1
+ ,37
+ ,239.36
+ ,156.83
+ ,111.10
+ ,119.48
+ ,2
+ ,38
+ ,224.69
+ ,154.98
+ ,110.53
+ ,119.56
+ ,3
+ ,39
+ ,230.98
+ ,155.02
+ ,110.23
+ ,119.43
+ ,4
+ ,40
+ ,233.47
+ ,153.34
+ ,109.41
+ ,119.57
+ ,5
+ ,41
+ ,256.7
+ ,153.19
+ ,108.94
+ ,119.59
+ ,6
+ ,42
+ ,253.41
+ ,152.80
+ ,109.81
+ ,119.50
+ ,7
+ ,43
+ ,224.95
+ ,152.97
+ ,109.20
+ ,119.54
+ ,8
+ ,44
+ ,210.37
+ ,152.96
+ ,109.45
+ ,119.56
+ ,9
+ ,45
+ ,191.09
+ ,152.35
+ ,110.61
+ ,119.61
+ ,10
+ ,46
+ ,198.85
+ ,151.88
+ ,109.44
+ ,119.64
+ ,11
+ ,47
+ ,211.04
+ ,150.27
+ ,109.77
+ ,119.60
+ ,12
+ ,48
+ ,206.25
+ ,148.80
+ ,108.04
+ ,119.71
+ ,1
+ ,49
+ ,201.19
+ ,149.28
+ ,109.65
+ ,119.72
+ ,2
+ ,50
+ ,194.37
+ ,148.64
+ ,111.69
+ ,119.66
+ ,3
+ ,51
+ ,191.08
+ ,150.36
+ ,111.65
+ ,119.76
+ ,4
+ ,52
+ ,192.87
+ ,149.69
+ ,112.04
+ ,119.80
+ ,5
+ ,53
+ ,181.61
+ ,152.94
+ ,111.42
+ ,119.88
+ ,6
+ ,54
+ ,157.67
+ ,155.18
+ ,112.25
+ ,119.78
+ ,7
+ ,55
+ ,196.14
+ ,156.32
+ ,111.46
+ ,120.08
+ ,8
+ ,56
+ ,246.35
+ ,156.25
+ ,111.62
+ ,120.22
+ ,9
+ ,57
+ ,271.9
+ ,155.52
+ ,111.77)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('Brood'
+ ,'Maand'
+ ,'Trend'
+ ,'Tarwe'
+ ,'Meel'
+ ,'Water')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('Brood','Maand','Trend','Tarwe','Meel','Water'),1:57))
> 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
Brood Maand Trend Tarwe Meel Water
1 104.37 1 1 167.16 101.56 100.93
2 104.89 2 2 179.84 102.13 101.18
3 105.15 3 3 174.44 102.39 101.11
4 105.72 4 4 180.35 102.42 102.42
5 106.38 5 5 193.17 103.87 102.37
6 106.40 6 6 195.16 104.44 101.95
7 106.47 7 7 202.43 104.97 102.20
8 106.59 8 8 189.91 105.17 103.35
9 106.76 9 9 195.98 105.35 103.65
10 107.35 10 10 212.09 104.65 102.06
11 107.81 11 11 205.81 106.62 102.66
12 108.03 12 12 204.31 107.05 102.32
13 109.08 1 13 196.07 112.30 102.21
14 109.86 2 14 199.98 114.70 102.33
15 110.29 3 15 199.10 115.40 104.41
16 110.34 4 16 198.31 115.64 104.33
17 110.59 5 17 195.72 115.66 105.27
18 110.64 6 18 223.04 114.50 105.34
19 110.83 7 19 238.41 115.14 104.88
20 111.51 8 20 259.73 115.41 105.49
21 113.32 9 21 326.54 119.32 105.90
22 115.89 10 22 335.15 124.77 105.39
23 116.51 11 23 321.81 130.96 104.40
24 117.44 12 24 368.62 141.02 106.19
25 118.25 1 25 369.59 150.60 106.54
26 118.65 2 26 425.00 151.10 108.26
27 118.52 3 27 439.72 157.19 106.95
28 119.07 4 28 362.23 157.28 108.32
29 119.12 5 29 328.76 156.54 108.35
30 119.28 6 30 348.55 159.62 109.29
31 119.30 7 31 328.18 163.77 109.46
32 119.44 8 32 329.34 165.08 109.50
33 119.57 9 33 295.55 164.75 109.84
34 119.93 10 34 237.38 163.93 108.73
35 120.03 11 35 226.85 157.51 109.38
36 119.66 12 36 220.14 153.36 109.97
37 119.46 1 37 239.36 156.83 111.10
38 119.48 2 38 224.69 154.98 110.53
39 119.56 3 39 230.98 155.02 110.23
40 119.43 4 40 233.47 153.34 109.41
41 119.57 5 41 256.70 153.19 108.94
42 119.59 6 42 253.41 152.80 109.81
43 119.50 7 43 224.95 152.97 109.20
44 119.54 8 44 210.37 152.96 109.45
45 119.56 9 45 191.09 152.35 110.61
46 119.61 10 46 198.85 151.88 109.44
47 119.64 11 47 211.04 150.27 109.77
48 119.60 12 48 206.25 148.80 108.04
49 119.71 1 49 201.19 149.28 109.65
50 119.72 2 50 194.37 148.64 111.69
51 119.66 3 51 191.08 150.36 111.65
52 119.76 4 52 192.87 149.69 112.04
53 119.80 5 53 181.61 152.94 111.42
54 119.88 6 54 157.67 155.18 112.25
55 119.78 7 55 196.14 156.32 111.46
56 120.08 8 56 246.35 156.25 111.62
57 120.22 9 57 271.90 155.52 111.77
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Maand Trend Tarwe Meel Water
86.27374 0.04693 0.14030 0.01195 0.13658 0.02789
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.0820 -0.4712 0.1567 0.5589 2.0734
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 86.273736 13.330912 6.472 3.72e-08 ***
Maand 0.046933 0.033617 1.396 0.169
Trend 0.140300 0.024151 5.809 4.07e-07 ***
Tarwe 0.011952 0.002367 5.049 6.04e-06 ***
Meel 0.136582 0.016335 8.361 3.98e-11 ***
Water 0.027895 0.139972 0.199 0.843
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8318 on 51 degrees of freedom
Multiple R-squared: 0.9802, Adjusted R-squared: 0.9782
F-statistic: 503.7 on 5 and 51 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,] 5.505756e-02 1.101151e-01 9.449424e-01
[2,] 1.697017e-02 3.394034e-02 9.830298e-01
[3,] 1.255367e-02 2.510735e-02 9.874463e-01
[4,] 6.257984e-03 1.251597e-02 9.937420e-01
[5,] 2.033485e-03 4.066971e-03 9.979665e-01
[6,] 7.559689e-04 1.511938e-03 9.992440e-01
[7,] 2.843852e-04 5.687703e-04 9.997156e-01
[8,] 9.882150e-05 1.976430e-04 9.999012e-01
[9,] 3.786053e-05 7.572105e-05 9.999621e-01
[10,] 3.250764e-05 6.501528e-05 9.999675e-01
[11,] 1.820385e-04 3.640771e-04 9.998180e-01
[12,] 3.900350e-03 7.800699e-03 9.960997e-01
[13,] 1.831936e-01 3.663872e-01 8.168064e-01
[14,] 3.918706e-01 7.837412e-01 6.081294e-01
[15,] 8.603613e-01 2.792774e-01 1.396387e-01
[16,] 9.999884e-01 2.315680e-05 1.157840e-05
[17,] 9.999998e-01 4.868224e-07 2.434112e-07
[18,] 9.999998e-01 3.539711e-07 1.769855e-07
[19,] 1.000000e+00 1.635407e-09 8.177036e-10
[20,] 1.000000e+00 6.582337e-09 3.291169e-09
[21,] 1.000000e+00 2.252722e-08 1.126361e-08
[22,] 1.000000e+00 7.113891e-08 3.556946e-08
[23,] 1.000000e+00 7.161776e-08 3.580888e-08
[24,] 1.000000e+00 5.114634e-08 2.557317e-08
[25,] 1.000000e+00 3.083497e-08 1.541749e-08
[26,] 1.000000e+00 6.467498e-08 3.233749e-08
[27,] 1.000000e+00 2.345534e-11 1.172767e-11
[28,] 1.000000e+00 2.328673e-12 1.164336e-12
[29,] 1.000000e+00 2.027902e-11 1.013951e-11
[30,] 1.000000e+00 1.680899e-10 8.404495e-11
[31,] 1.000000e+00 3.942946e-10 1.971473e-10
[32,] 1.000000e+00 2.107714e-09 1.053857e-09
[33,] 1.000000e+00 1.059561e-08 5.297805e-09
[34,] 1.000000e+00 7.280366e-08 3.640183e-08
[35,] 9.999998e-01 4.603414e-07 2.301707e-07
[36,] 9.999982e-01 3.653384e-06 1.826692e-06
[37,] 9.999842e-01 3.160472e-05 1.580236e-05
[38,] 9.998772e-01 2.456642e-04 1.228321e-04
[39,] 9.991132e-01 1.773646e-03 8.868230e-04
[40,] 9.936496e-01 1.270086e-02 6.350432e-03
> postscript(file="/var/www/html/freestat/rcomp/tmp/19g9l1292775699.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/29g9l1292775699.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3k89p1292775699.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4k89p1292775699.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5k89p1292775699.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 = 57
Frequency = 1
1 2 3 4 5 6 7
-0.7755658 -0.6791762 -0.5754274 -0.3039370 -0.1810443 -0.4381981 -0.7216848
8 9 10 11 12 13 14
-0.6986745 -0.8214099 -0.4712300 -0.4092080 -0.4077594 0.4027010 0.6175912
15 16 17 18 19 20 21
0.7172467 0.5589073 0.6236768 0.3163967 0.0608800 0.2449366 0.5237166
22 23 24 25 26 27 28
2.0734313 1.8478118 0.6071583 0.4633092 -0.1024558 -1.3908644 -0.1524444
29 30 31 32 33 34 35
0.2105900 -0.5000674 -0.9953953 -1.2365312 -0.8543181 0.1566579 1.0540034
36 37 38 39 40 41 42
1.1273250 0.5681090 0.8447884 0.6652820 0.5706196 0.2793387 0.1804258
43 44 45 46 47 48 49
0.2371438 0.2585629 0.3727212 0.2395708 0.1473339 0.2263843 0.6623537
50 51 52 53 54 55 56
0.5971398 0.1554234 0.1274267 -0.3118233 -0.4620217 -1.3427156 -1.8249623
57
-2.0820491
> postscript(file="/var/www/html/freestat/rcomp/tmp/6dhq91292775699.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.7755658 NA
1 -0.6791762 -0.7755658
2 -0.5754274 -0.6791762
3 -0.3039370 -0.5754274
4 -0.1810443 -0.3039370
5 -0.4381981 -0.1810443
6 -0.7216848 -0.4381981
7 -0.6986745 -0.7216848
8 -0.8214099 -0.6986745
9 -0.4712300 -0.8214099
10 -0.4092080 -0.4712300
11 -0.4077594 -0.4092080
12 0.4027010 -0.4077594
13 0.6175912 0.4027010
14 0.7172467 0.6175912
15 0.5589073 0.7172467
16 0.6236768 0.5589073
17 0.3163967 0.6236768
18 0.0608800 0.3163967
19 0.2449366 0.0608800
20 0.5237166 0.2449366
21 2.0734313 0.5237166
22 1.8478118 2.0734313
23 0.6071583 1.8478118
24 0.4633092 0.6071583
25 -0.1024558 0.4633092
26 -1.3908644 -0.1024558
27 -0.1524444 -1.3908644
28 0.2105900 -0.1524444
29 -0.5000674 0.2105900
30 -0.9953953 -0.5000674
31 -1.2365312 -0.9953953
32 -0.8543181 -1.2365312
33 0.1566579 -0.8543181
34 1.0540034 0.1566579
35 1.1273250 1.0540034
36 0.5681090 1.1273250
37 0.8447884 0.5681090
38 0.6652820 0.8447884
39 0.5706196 0.6652820
40 0.2793387 0.5706196
41 0.1804258 0.2793387
42 0.2371438 0.1804258
43 0.2585629 0.2371438
44 0.3727212 0.2585629
45 0.2395708 0.3727212
46 0.1473339 0.2395708
47 0.2263843 0.1473339
48 0.6623537 0.2263843
49 0.5971398 0.6623537
50 0.1554234 0.5971398
51 0.1274267 0.1554234
52 -0.3118233 0.1274267
53 -0.4620217 -0.3118233
54 -1.3427156 -0.4620217
55 -1.8249623 -1.3427156
56 -2.0820491 -1.8249623
57 NA -2.0820491
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.6791762 -0.7755658
[2,] -0.5754274 -0.6791762
[3,] -0.3039370 -0.5754274
[4,] -0.1810443 -0.3039370
[5,] -0.4381981 -0.1810443
[6,] -0.7216848 -0.4381981
[7,] -0.6986745 -0.7216848
[8,] -0.8214099 -0.6986745
[9,] -0.4712300 -0.8214099
[10,] -0.4092080 -0.4712300
[11,] -0.4077594 -0.4092080
[12,] 0.4027010 -0.4077594
[13,] 0.6175912 0.4027010
[14,] 0.7172467 0.6175912
[15,] 0.5589073 0.7172467
[16,] 0.6236768 0.5589073
[17,] 0.3163967 0.6236768
[18,] 0.0608800 0.3163967
[19,] 0.2449366 0.0608800
[20,] 0.5237166 0.2449366
[21,] 2.0734313 0.5237166
[22,] 1.8478118 2.0734313
[23,] 0.6071583 1.8478118
[24,] 0.4633092 0.6071583
[25,] -0.1024558 0.4633092
[26,] -1.3908644 -0.1024558
[27,] -0.1524444 -1.3908644
[28,] 0.2105900 -0.1524444
[29,] -0.5000674 0.2105900
[30,] -0.9953953 -0.5000674
[31,] -1.2365312 -0.9953953
[32,] -0.8543181 -1.2365312
[33,] 0.1566579 -0.8543181
[34,] 1.0540034 0.1566579
[35,] 1.1273250 1.0540034
[36,] 0.5681090 1.1273250
[37,] 0.8447884 0.5681090
[38,] 0.6652820 0.8447884
[39,] 0.5706196 0.6652820
[40,] 0.2793387 0.5706196
[41,] 0.1804258 0.2793387
[42,] 0.2371438 0.1804258
[43,] 0.2585629 0.2371438
[44,] 0.3727212 0.2585629
[45,] 0.2395708 0.3727212
[46,] 0.1473339 0.2395708
[47,] 0.2263843 0.1473339
[48,] 0.6623537 0.2263843
[49,] 0.5971398 0.6623537
[50,] 0.1554234 0.5971398
[51,] 0.1274267 0.1554234
[52,] -0.3118233 0.1274267
[53,] -0.4620217 -0.3118233
[54,] -1.3427156 -0.4620217
[55,] -1.8249623 -1.3427156
[56,] -2.0820491 -1.8249623
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.6791762 -0.7755658
2 -0.5754274 -0.6791762
3 -0.3039370 -0.5754274
4 -0.1810443 -0.3039370
5 -0.4381981 -0.1810443
6 -0.7216848 -0.4381981
7 -0.6986745 -0.7216848
8 -0.8214099 -0.6986745
9 -0.4712300 -0.8214099
10 -0.4092080 -0.4712300
11 -0.4077594 -0.4092080
12 0.4027010 -0.4077594
13 0.6175912 0.4027010
14 0.7172467 0.6175912
15 0.5589073 0.7172467
16 0.6236768 0.5589073
17 0.3163967 0.6236768
18 0.0608800 0.3163967
19 0.2449366 0.0608800
20 0.5237166 0.2449366
21 2.0734313 0.5237166
22 1.8478118 2.0734313
23 0.6071583 1.8478118
24 0.4633092 0.6071583
25 -0.1024558 0.4633092
26 -1.3908644 -0.1024558
27 -0.1524444 -1.3908644
28 0.2105900 -0.1524444
29 -0.5000674 0.2105900
30 -0.9953953 -0.5000674
31 -1.2365312 -0.9953953
32 -0.8543181 -1.2365312
33 0.1566579 -0.8543181
34 1.0540034 0.1566579
35 1.1273250 1.0540034
36 0.5681090 1.1273250
37 0.8447884 0.5681090
38 0.6652820 0.8447884
39 0.5706196 0.6652820
40 0.2793387 0.5706196
41 0.1804258 0.2793387
42 0.2371438 0.1804258
43 0.2585629 0.2371438
44 0.3727212 0.2585629
45 0.2395708 0.3727212
46 0.1473339 0.2395708
47 0.2263843 0.1473339
48 0.6623537 0.2263843
49 0.5971398 0.6623537
50 0.1554234 0.5971398
51 0.1274267 0.1554234
52 -0.3118233 0.1274267
53 -0.4620217 -0.3118233
54 -1.3427156 -0.4620217
55 -1.8249623 -1.3427156
56 -2.0820491 -1.8249623
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/7dhq91292775699.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8nqpc1292775699.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9nqpc1292775699.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10nqpc1292775699.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/11kinl1292775699.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/12u94o1292775699.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/13jsji1292775699.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/14ntio1292775699.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/freestat/rcomp/tmp/15fkzr1292775699.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/freestat/rcomp/tmp/16bcf01292775699.tab")
+ }
>
> try(system("convert tmp/19g9l1292775699.ps tmp/19g9l1292775699.png",intern=TRUE))
character(0)
> try(system("convert tmp/29g9l1292775699.ps tmp/29g9l1292775699.png",intern=TRUE))
character(0)
> try(system("convert tmp/3k89p1292775699.ps tmp/3k89p1292775699.png",intern=TRUE))
character(0)
> try(system("convert tmp/4k89p1292775699.ps tmp/4k89p1292775699.png",intern=TRUE))
character(0)
> try(system("convert tmp/5k89p1292775699.ps tmp/5k89p1292775699.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dhq91292775699.ps tmp/6dhq91292775699.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dhq91292775699.ps tmp/7dhq91292775699.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nqpc1292775699.ps tmp/8nqpc1292775699.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nqpc1292775699.ps tmp/9nqpc1292775699.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nqpc1292775699.ps tmp/10nqpc1292775699.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.770 2.398 4.105