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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(100.00
+ ,0
+ ,100.21
+ ,100.42
+ ,0
+ ,100.00
+ ,100.50
+ ,0
+ ,100.42
+ ,101.14
+ ,0
+ ,100.50
+ ,101.98
+ ,0
+ ,101.14
+ ,102.31
+ ,0
+ ,101.98
+ ,103.27
+ ,0
+ ,102.31
+ ,103.80
+ ,0
+ ,103.27
+ ,103.46
+ ,0
+ ,103.80
+ ,105.06
+ ,0
+ ,103.46
+ ,106.08
+ ,0
+ ,105.06
+ ,106.74
+ ,0
+ ,106.08
+ ,107.35
+ ,0
+ ,106.74
+ ,108.96
+ ,0
+ ,107.35
+ ,109.85
+ ,0
+ ,108.96
+ ,109.81
+ ,0
+ ,109.85
+ ,109.99
+ ,0
+ ,109.81
+ ,111.60
+ ,0
+ ,109.99
+ ,112.74
+ ,0
+ ,111.60
+ ,112.78
+ ,0
+ ,112.74
+ ,113.66
+ ,0
+ ,112.78
+ ,115.37
+ ,0
+ ,113.66
+ ,116.26
+ ,0
+ ,115.37
+ ,116.24
+ ,0
+ ,116.26
+ ,116.73
+ ,0
+ ,116.24
+ ,118.76
+ ,0
+ ,116.73
+ ,119.78
+ ,0
+ ,118.76
+ ,120.23
+ ,0
+ ,119.78
+ ,121.48
+ ,0
+ ,120.23
+ ,124.07
+ ,0
+ ,121.48
+ ,125.82
+ ,0
+ ,124.07
+ ,126.92
+ ,0
+ ,125.82
+ ,128.48
+ ,0
+ ,126.92
+ ,131.44
+ ,0
+ ,128.48
+ ,133.51
+ ,0
+ ,131.44
+ ,134.58
+ ,0
+ ,133.51
+ ,136.68
+ ,0
+ ,134.58
+ ,140.10
+ ,0
+ ,136.68
+ ,142.45
+ ,0
+ ,140.10
+ ,143.91
+ ,0
+ ,142.45
+ ,146.19
+ ,0
+ ,143.91
+ ,149.84
+ ,0
+ ,146.19
+ ,152.31
+ ,0
+ ,149.84
+ ,153.62
+ ,0
+ ,152.31
+ ,155.79
+ ,0
+ ,153.62
+ ,159.89
+ ,0
+ ,155.79
+ ,163.21
+ ,0
+ ,159.89
+ ,165.32
+ ,0
+ ,163.21
+ ,167.68
+ ,0
+ ,165.32
+ ,171.79
+ ,0
+ ,167.68
+ ,175.38
+ ,0
+ ,171.79
+ ,177.81
+ ,0
+ ,175.38
+ ,181.09
+ ,0
+ ,177.81
+ ,186.48
+ ,0
+ ,181.09
+ ,191.07
+ ,0
+ ,186.48
+ ,194.23
+ ,0
+ ,191.07
+ ,197.82
+ ,0
+ ,194.23
+ ,204.41
+ ,0
+ ,197.82
+ ,209.26
+ ,0
+ ,204.41
+ ,212.24
+ ,0
+ ,209.26
+ ,214.88
+ ,0
+ ,212.24
+ ,218.87
+ ,0
+ ,214.88
+ ,219.86
+ ,0
+ ,218.87
+ ,219.75
+ ,0
+ ,219.86
+ ,220.89
+ ,0
+ ,219.75
+ ,224.02
+ ,0
+ ,220.89
+ ,222.27
+ ,0
+ ,224.02
+ ,217.27
+ ,1
+ ,222.27
+ ,213.23
+ ,1
+ ,217.27
+ ,212.44
+ ,1
+ ,213.23
+ ,207.87
+ ,1
+ ,212.44
+ ,199.46
+ ,1
+ ,207.87
+ ,198.19
+ ,1
+ ,199.46
+ ,199.77
+ ,1
+ ,198.19
+ ,200.10
+ ,1
+ ,199.77
+ ,195.76
+ ,1
+ ,200.10
+ ,191.27
+ ,1
+ ,195.76
+ ,195.79
+ ,1
+ ,191.27
+ ,192.7
+ ,1
+ ,195.79)
+ ,dim=c(3
+ ,79)
+ ,dimnames=list(c('woningprijsindex_us'
+ ,'Dummy_'
+ ,'Y1')
+ ,1:79))
> y <- array(NA,dim=c(3,79),dimnames=list(c('woningprijsindex_us','Dummy_','Y1'),1:79))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
> 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
woningprijsindex_us Dummy_ Y1 t
1 100.00 0 100.21 1
2 100.42 0 100.00 2
3 100.50 0 100.42 3
4 101.14 0 100.50 4
5 101.98 0 101.14 5
6 102.31 0 101.98 6
7 103.27 0 102.31 7
8 103.80 0 103.27 8
9 103.46 0 103.80 9
10 105.06 0 103.46 10
11 106.08 0 105.06 11
12 106.74 0 106.08 12
13 107.35 0 106.74 13
14 108.96 0 107.35 14
15 109.85 0 108.96 15
16 109.81 0 109.85 16
17 109.99 0 109.81 17
18 111.60 0 109.99 18
19 112.74 0 111.60 19
20 112.78 0 112.74 20
21 113.66 0 112.78 21
22 115.37 0 113.66 22
23 116.26 0 115.37 23
24 116.24 0 116.26 24
25 116.73 0 116.24 25
26 118.76 0 116.73 26
27 119.78 0 118.76 27
28 120.23 0 119.78 28
29 121.48 0 120.23 29
30 124.07 0 121.48 30
31 125.82 0 124.07 31
32 126.92 0 125.82 32
33 128.48 0 126.92 33
34 131.44 0 128.48 34
35 133.51 0 131.44 35
36 134.58 0 133.51 36
37 136.68 0 134.58 37
38 140.10 0 136.68 38
39 142.45 0 140.10 39
40 143.91 0 142.45 40
41 146.19 0 143.91 41
42 149.84 0 146.19 42
43 152.31 0 149.84 43
44 153.62 0 152.31 44
45 155.79 0 153.62 45
46 159.89 0 155.79 46
47 163.21 0 159.89 47
48 165.32 0 163.21 48
49 167.68 0 165.32 49
50 171.79 0 167.68 50
51 175.38 0 171.79 51
52 177.81 0 175.38 52
53 181.09 0 177.81 53
54 186.48 0 181.09 54
55 191.07 0 186.48 55
56 194.23 0 191.07 56
57 197.82 0 194.23 57
58 204.41 0 197.82 58
59 209.26 0 204.41 59
60 212.24 0 209.26 60
61 214.88 0 212.24 61
62 218.87 0 214.88 62
63 219.86 0 218.87 63
64 219.75 0 219.86 64
65 220.89 0 219.75 65
66 224.02 0 220.89 66
67 222.27 0 224.02 67
68 217.27 1 222.27 68
69 213.23 1 217.27 69
70 212.44 1 213.23 70
71 207.87 1 212.44 71
72 199.46 1 207.87 72
73 198.19 1 199.46 73
74 199.77 1 198.19 74
75 200.10 1 199.77 75
76 195.76 1 200.10 76
77 191.27 1 195.76 77
78 195.79 1 191.27 78
79 192.70 1 195.79 79
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy_ Y1 t
3.2681 -6.7293 0.9606 0.1232
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.6262 -0.8735 0.0692 0.8447 5.9099
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.26808 1.28961 2.534 0.013357 *
Dummy_ -6.72930 0.70942 -9.486 1.78e-14 ***
Y1 0.96057 0.01559 61.601 < 2e-16 ***
t 0.12323 0.03153 3.909 0.000202 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.691 on 75 degrees of freedom
Multiple R-squared: 0.9985, Adjusted R-squared: 0.9984
F-statistic: 1.634e+04 on 3 and 75 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,] 5.889857e-03 1.177971e-02 0.9941101
[2,] 7.814208e-04 1.562842e-03 0.9992186
[3,] 1.280059e-03 2.560118e-03 0.9987199
[4,] 6.131279e-04 1.226256e-03 0.9993869
[5,] 4.417590e-04 8.835181e-04 0.9995582
[6,] 1.270043e-04 2.540086e-04 0.9998730
[7,] 2.947365e-05 5.894731e-05 0.9999705
[8,] 4.404691e-05 8.809382e-05 0.9999560
[9,] 1.291163e-05 2.582327e-05 0.9999871
[10,] 6.074255e-06 1.214851e-05 0.9999939
[11,] 2.582035e-06 5.164069e-06 0.9999974
[12,] 1.530271e-06 3.060543e-06 0.9999985
[13,] 6.170173e-07 1.234035e-06 0.9999994
[14,] 2.232331e-07 4.464663e-07 0.9999998
[15,] 5.559842e-08 1.111968e-07 0.9999999
[16,] 6.870533e-08 1.374107e-07 0.9999999
[17,] 2.249996e-08 4.499993e-08 1.0000000
[18,] 8.689745e-09 1.737949e-08 1.0000000
[19,] 2.448054e-09 4.896107e-09 1.0000000
[20,] 4.914147e-09 9.828295e-09 1.0000000
[21,] 1.968784e-09 3.937569e-09 1.0000000
[22,] 5.249397e-10 1.049879e-09 1.0000000
[23,] 2.295584e-10 4.591168e-10 1.0000000
[24,] 6.160627e-09 1.232125e-08 1.0000000
[25,] 5.224038e-09 1.044808e-08 1.0000000
[26,] 1.675410e-09 3.350820e-09 1.0000000
[27,] 6.549321e-10 1.309864e-09 1.0000000
[28,] 2.224622e-09 4.449243e-09 1.0000000
[29,] 7.176728e-10 1.435346e-09 1.0000000
[30,] 4.112384e-10 8.224768e-10 1.0000000
[31,] 1.423597e-10 2.847194e-10 1.0000000
[32,] 2.295410e-10 4.590820e-10 1.0000000
[33,] 7.002619e-11 1.400524e-10 1.0000000
[34,] 5.422153e-11 1.084431e-10 1.0000000
[35,] 1.735740e-11 3.471481e-11 1.0000000
[36,] 1.553172e-11 3.106343e-11 1.0000000
[37,] 5.268510e-12 1.053702e-11 1.0000000
[38,] 1.217504e-11 2.435008e-11 1.0000000
[39,] 5.630124e-12 1.126025e-11 1.0000000
[40,] 8.181728e-12 1.636346e-11 1.0000000
[41,] 2.788961e-12 5.577922e-12 1.0000000
[42,] 2.831231e-12 5.662462e-12 1.0000000
[43,] 2.191407e-12 4.382814e-12 1.0000000
[44,] 1.623249e-12 3.246497e-12 1.0000000
[45,] 6.662847e-13 1.332569e-12 1.0000000
[46,] 1.266666e-12 2.533332e-12 1.0000000
[47,] 1.152750e-12 2.305500e-12 1.0000000
[48,] 3.494040e-12 6.988080e-12 1.0000000
[49,] 1.732960e-12 3.465921e-12 1.0000000
[50,] 3.988737e-12 7.977473e-12 1.0000000
[51,] 8.942740e-12 1.788548e-11 1.0000000
[52,] 4.461278e-11 8.922557e-11 1.0000000
[53,] 1.433176e-11 2.866352e-11 1.0000000
[54,] 4.458506e-11 8.917013e-11 1.0000000
[55,] 1.674631e-10 3.349263e-10 1.0000000
[56,] 5.506883e-11 1.101377e-10 1.0000000
[57,] 1.610514e-09 3.221027e-09 1.0000000
[58,] 1.281627e-07 2.563255e-07 0.9999999
[59,] 2.795468e-07 5.590936e-07 0.9999997
[60,] 1.444213e-07 2.888427e-07 0.9999999
[61,] 2.573453e-06 5.146906e-06 0.9999974
[62,] 1.408025e-06 2.816051e-06 0.9999986
[63,] 7.566871e-07 1.513374e-06 0.9999992
[64,] 5.676201e-05 1.135240e-04 0.9999432
[65,] 2.756242e-03 5.512484e-03 0.9972438
[66,] 3.250482e-03 6.500963e-03 0.9967495
> postscript(file="/var/www/rcomp/tmp/102m51292596344.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/202m51292596344.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3atmp1292596344.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4atmp1292596344.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5atmp1292596344.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 = 79
Frequency = 1
1 2 3 4 5 6
0.34954827 0.84803443 0.40135888 0.84127853 0.94327667 0.34315997
7 8 9 10 11 12
0.86293609 0.34755049 -0.62478822 1.17857258 0.53841953 0.09539949
13 14 15 16 17 18
-0.05181386 0.84900150 0.06924270 -0.94890270 -0.85371415 0.46014809
19 20 21 22 23 24
-0.06961070 -1.24789964 -0.52955702 0.21190332 -0.66391289 -1.66205829
25 26 27 28 29 30
-1.27608122 0.16000304 -0.89319690 -1.54621694 -0.85170972 0.41433818
31 32 33 34 35 36
-0.44678328 -1.15102245 -0.77088843 0.56738149 -0.32915241 -1.37077531
37 38 39 40 41 42
-0.42182406 0.85773581 -0.20066219 -1.12124585 -0.36691852 0.96973801
43 44 45 46 47 48
-0.18959205 -1.37544461 -0.58703116 1.30528852 0.56370010 -0.63864049
49 50 51 52 53 54
-0.42868636 1.29112424 0.80993007 -0.33176554 0.49080487 2.60688725
55 56 57 58 59 60
1.89615817 0.52388841 0.95523969 3.97354408 2.37012602 0.56810698
61 62 63 64 65 66
0.22236161 1.55321144 -1.41271383 -2.59691665 -1.47448790 0.43722316
67 68 69 70 71 72
-4.44260834 -1.15554113 -0.51590479 2.45158036 -1.48280047 -5.62621102
73 74 75 76 77 78
1.05898317 3.73567793 2.42473636 -2.35548752 -2.79983012 5.90991340
79
-1.64511617
> postscript(file="/var/www/rcomp/tmp/63k3a1292596344.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 = 79
Frequency = 1
lag(myerror, k = 1) myerror
0 0.34954827 NA
1 0.84803443 0.34954827
2 0.40135888 0.84803443
3 0.84127853 0.40135888
4 0.94327667 0.84127853
5 0.34315997 0.94327667
6 0.86293609 0.34315997
7 0.34755049 0.86293609
8 -0.62478822 0.34755049
9 1.17857258 -0.62478822
10 0.53841953 1.17857258
11 0.09539949 0.53841953
12 -0.05181386 0.09539949
13 0.84900150 -0.05181386
14 0.06924270 0.84900150
15 -0.94890270 0.06924270
16 -0.85371415 -0.94890270
17 0.46014809 -0.85371415
18 -0.06961070 0.46014809
19 -1.24789964 -0.06961070
20 -0.52955702 -1.24789964
21 0.21190332 -0.52955702
22 -0.66391289 0.21190332
23 -1.66205829 -0.66391289
24 -1.27608122 -1.66205829
25 0.16000304 -1.27608122
26 -0.89319690 0.16000304
27 -1.54621694 -0.89319690
28 -0.85170972 -1.54621694
29 0.41433818 -0.85170972
30 -0.44678328 0.41433818
31 -1.15102245 -0.44678328
32 -0.77088843 -1.15102245
33 0.56738149 -0.77088843
34 -0.32915241 0.56738149
35 -1.37077531 -0.32915241
36 -0.42182406 -1.37077531
37 0.85773581 -0.42182406
38 -0.20066219 0.85773581
39 -1.12124585 -0.20066219
40 -0.36691852 -1.12124585
41 0.96973801 -0.36691852
42 -0.18959205 0.96973801
43 -1.37544461 -0.18959205
44 -0.58703116 -1.37544461
45 1.30528852 -0.58703116
46 0.56370010 1.30528852
47 -0.63864049 0.56370010
48 -0.42868636 -0.63864049
49 1.29112424 -0.42868636
50 0.80993007 1.29112424
51 -0.33176554 0.80993007
52 0.49080487 -0.33176554
53 2.60688725 0.49080487
54 1.89615817 2.60688725
55 0.52388841 1.89615817
56 0.95523969 0.52388841
57 3.97354408 0.95523969
58 2.37012602 3.97354408
59 0.56810698 2.37012602
60 0.22236161 0.56810698
61 1.55321144 0.22236161
62 -1.41271383 1.55321144
63 -2.59691665 -1.41271383
64 -1.47448790 -2.59691665
65 0.43722316 -1.47448790
66 -4.44260834 0.43722316
67 -1.15554113 -4.44260834
68 -0.51590479 -1.15554113
69 2.45158036 -0.51590479
70 -1.48280047 2.45158036
71 -5.62621102 -1.48280047
72 1.05898317 -5.62621102
73 3.73567793 1.05898317
74 2.42473636 3.73567793
75 -2.35548752 2.42473636
76 -2.79983012 -2.35548752
77 5.90991340 -2.79983012
78 -1.64511617 5.90991340
79 NA -1.64511617
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.84803443 0.34954827
[2,] 0.40135888 0.84803443
[3,] 0.84127853 0.40135888
[4,] 0.94327667 0.84127853
[5,] 0.34315997 0.94327667
[6,] 0.86293609 0.34315997
[7,] 0.34755049 0.86293609
[8,] -0.62478822 0.34755049
[9,] 1.17857258 -0.62478822
[10,] 0.53841953 1.17857258
[11,] 0.09539949 0.53841953
[12,] -0.05181386 0.09539949
[13,] 0.84900150 -0.05181386
[14,] 0.06924270 0.84900150
[15,] -0.94890270 0.06924270
[16,] -0.85371415 -0.94890270
[17,] 0.46014809 -0.85371415
[18,] -0.06961070 0.46014809
[19,] -1.24789964 -0.06961070
[20,] -0.52955702 -1.24789964
[21,] 0.21190332 -0.52955702
[22,] -0.66391289 0.21190332
[23,] -1.66205829 -0.66391289
[24,] -1.27608122 -1.66205829
[25,] 0.16000304 -1.27608122
[26,] -0.89319690 0.16000304
[27,] -1.54621694 -0.89319690
[28,] -0.85170972 -1.54621694
[29,] 0.41433818 -0.85170972
[30,] -0.44678328 0.41433818
[31,] -1.15102245 -0.44678328
[32,] -0.77088843 -1.15102245
[33,] 0.56738149 -0.77088843
[34,] -0.32915241 0.56738149
[35,] -1.37077531 -0.32915241
[36,] -0.42182406 -1.37077531
[37,] 0.85773581 -0.42182406
[38,] -0.20066219 0.85773581
[39,] -1.12124585 -0.20066219
[40,] -0.36691852 -1.12124585
[41,] 0.96973801 -0.36691852
[42,] -0.18959205 0.96973801
[43,] -1.37544461 -0.18959205
[44,] -0.58703116 -1.37544461
[45,] 1.30528852 -0.58703116
[46,] 0.56370010 1.30528852
[47,] -0.63864049 0.56370010
[48,] -0.42868636 -0.63864049
[49,] 1.29112424 -0.42868636
[50,] 0.80993007 1.29112424
[51,] -0.33176554 0.80993007
[52,] 0.49080487 -0.33176554
[53,] 2.60688725 0.49080487
[54,] 1.89615817 2.60688725
[55,] 0.52388841 1.89615817
[56,] 0.95523969 0.52388841
[57,] 3.97354408 0.95523969
[58,] 2.37012602 3.97354408
[59,] 0.56810698 2.37012602
[60,] 0.22236161 0.56810698
[61,] 1.55321144 0.22236161
[62,] -1.41271383 1.55321144
[63,] -2.59691665 -1.41271383
[64,] -1.47448790 -2.59691665
[65,] 0.43722316 -1.47448790
[66,] -4.44260834 0.43722316
[67,] -1.15554113 -4.44260834
[68,] -0.51590479 -1.15554113
[69,] 2.45158036 -0.51590479
[70,] -1.48280047 2.45158036
[71,] -5.62621102 -1.48280047
[72,] 1.05898317 -5.62621102
[73,] 3.73567793 1.05898317
[74,] 2.42473636 3.73567793
[75,] -2.35548752 2.42473636
[76,] -2.79983012 -2.35548752
[77,] 5.90991340 -2.79983012
[78,] -1.64511617 5.90991340
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.84803443 0.34954827
2 0.40135888 0.84803443
3 0.84127853 0.40135888
4 0.94327667 0.84127853
5 0.34315997 0.94327667
6 0.86293609 0.34315997
7 0.34755049 0.86293609
8 -0.62478822 0.34755049
9 1.17857258 -0.62478822
10 0.53841953 1.17857258
11 0.09539949 0.53841953
12 -0.05181386 0.09539949
13 0.84900150 -0.05181386
14 0.06924270 0.84900150
15 -0.94890270 0.06924270
16 -0.85371415 -0.94890270
17 0.46014809 -0.85371415
18 -0.06961070 0.46014809
19 -1.24789964 -0.06961070
20 -0.52955702 -1.24789964
21 0.21190332 -0.52955702
22 -0.66391289 0.21190332
23 -1.66205829 -0.66391289
24 -1.27608122 -1.66205829
25 0.16000304 -1.27608122
26 -0.89319690 0.16000304
27 -1.54621694 -0.89319690
28 -0.85170972 -1.54621694
29 0.41433818 -0.85170972
30 -0.44678328 0.41433818
31 -1.15102245 -0.44678328
32 -0.77088843 -1.15102245
33 0.56738149 -0.77088843
34 -0.32915241 0.56738149
35 -1.37077531 -0.32915241
36 -0.42182406 -1.37077531
37 0.85773581 -0.42182406
38 -0.20066219 0.85773581
39 -1.12124585 -0.20066219
40 -0.36691852 -1.12124585
41 0.96973801 -0.36691852
42 -0.18959205 0.96973801
43 -1.37544461 -0.18959205
44 -0.58703116 -1.37544461
45 1.30528852 -0.58703116
46 0.56370010 1.30528852
47 -0.63864049 0.56370010
48 -0.42868636 -0.63864049
49 1.29112424 -0.42868636
50 0.80993007 1.29112424
51 -0.33176554 0.80993007
52 0.49080487 -0.33176554
53 2.60688725 0.49080487
54 1.89615817 2.60688725
55 0.52388841 1.89615817
56 0.95523969 0.52388841
57 3.97354408 0.95523969
58 2.37012602 3.97354408
59 0.56810698 2.37012602
60 0.22236161 0.56810698
61 1.55321144 0.22236161
62 -1.41271383 1.55321144
63 -2.59691665 -1.41271383
64 -1.47448790 -2.59691665
65 0.43722316 -1.47448790
66 -4.44260834 0.43722316
67 -1.15554113 -4.44260834
68 -0.51590479 -1.15554113
69 2.45158036 -0.51590479
70 -1.48280047 2.45158036
71 -5.62621102 -1.48280047
72 1.05898317 -5.62621102
73 3.73567793 1.05898317
74 2.42473636 3.73567793
75 -2.35548752 2.42473636
76 -2.79983012 -2.35548752
77 5.90991340 -2.79983012
78 -1.64511617 5.90991340
> 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/7wu2v1292596344.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8wu2v1292596344.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9wu2v1292596344.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10631y1292596344.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11a3im1292596344.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/12d4za1292596344.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/139ww11292596344.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/14dwv71292596344.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/15yftd1292596344.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/161fai1292596344.tab")
+ }
>
> try(system("convert tmp/102m51292596344.ps tmp/102m51292596344.png",intern=TRUE))
character(0)
> try(system("convert tmp/202m51292596344.ps tmp/202m51292596344.png",intern=TRUE))
character(0)
> try(system("convert tmp/3atmp1292596344.ps tmp/3atmp1292596344.png",intern=TRUE))
character(0)
> try(system("convert tmp/4atmp1292596344.ps tmp/4atmp1292596344.png",intern=TRUE))
character(0)
> try(system("convert tmp/5atmp1292596344.ps tmp/5atmp1292596344.png",intern=TRUE))
character(0)
> try(system("convert tmp/63k3a1292596344.ps tmp/63k3a1292596344.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wu2v1292596344.ps tmp/7wu2v1292596344.png",intern=TRUE))
character(0)
> try(system("convert tmp/8wu2v1292596344.ps tmp/8wu2v1292596344.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wu2v1292596344.ps tmp/9wu2v1292596344.png",intern=TRUE))
character(0)
> try(system("convert tmp/10631y1292596344.ps tmp/10631y1292596344.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.410 1.670 5.056