R version 2.13.0 (2011-04-13)
Copyright (C) 2011 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(96
+ ,6.08
+ ,54.7
+ ,1914
+ ,1005
+ ,2
+ ,89
+ ,5.73
+ ,54.2
+ ,1684
+ ,963
+ ,2
+ ,87
+ ,6.22
+ ,53
+ ,1902
+ ,1035
+ ,2
+ ,87
+ ,5.8
+ ,52.9
+ ,1860
+ ,1027
+ ,2
+ ,101
+ ,7.99
+ ,57.8
+ ,2264
+ ,1281
+ ,2
+ ,103
+ ,8.42
+ ,56.9
+ ,2216
+ ,1272
+ ,2
+ ,103
+ ,7.44
+ ,56.6
+ ,1866
+ ,1051
+ ,2
+ ,96
+ ,6.84
+ ,55.3
+ ,1850
+ ,1079
+ ,2
+ ,127
+ ,6.48
+ ,53.1
+ ,1743
+ ,1034
+ ,2
+ ,126
+ ,6.43
+ ,54.8
+ ,1709
+ ,1070
+ ,2
+ ,101
+ ,7.99
+ ,57.2
+ ,1689
+ ,1173
+ ,1
+ ,96
+ ,8.76
+ ,57.2
+ ,1806
+ ,1079
+ ,1
+ ,93
+ ,6.32
+ ,57.2
+ ,2136
+ ,1067
+ ,1
+ ,88
+ ,6.32
+ ,57.2
+ ,2018
+ ,1104
+ ,1
+ ,94
+ ,7.6
+ ,55.8
+ ,1966
+ ,1347
+ ,1
+ ,85
+ ,7.62
+ ,57.2
+ ,2154
+ ,1439
+ ,1
+ ,97
+ ,6.03
+ ,57.2
+ ,1767
+ ,1029
+ ,1
+ ,114
+ ,6.59
+ ,56.5
+ ,1827
+ ,1100
+ ,1
+ ,113
+ ,7.52
+ ,59.2
+ ,1773
+ ,1204
+ ,1
+ ,124
+ ,7.67
+ ,58.5
+ ,1971
+ ,1160
+ ,1
+ ,129
+ ,7.57
+ ,57.3
+ ,1867
+ ,1401
+ ,1
+ ,110
+ ,6.45
+ ,53.7
+ ,1993
+ ,1142
+ ,1
+ ,102
+ ,7.99
+ ,56.6
+ ,1910
+ ,1288
+ ,1
+ ,134
+ ,8.43
+ ,57.5
+ ,1688
+ ,979
+ ,1
+ ,119
+ ,7.02
+ ,55.5
+ ,1696
+ ,1104
+ ,2
+ ,139
+ ,5.21
+ ,55.7
+ ,2107
+ ,956
+ ,2
+ ,75
+ ,6.21
+ ,53.1
+ ,2060
+ ,1153
+ ,1
+ ,138
+ ,5.39
+ ,55.9
+ ,1870
+ ,1001
+ ,2
+ ,132
+ ,5.59
+ ,57.8
+ ,1808
+ ,1230
+ ,1
+ ,122
+ ,7.72
+ ,59
+ ,1846
+ ,1014
+ ,2
+ ,102
+ ,6.69
+ ,58.4
+ ,2227
+ ,1287
+ ,1
+ ,78
+ ,5.96
+ ,55.4
+ ,2177
+ ,1198
+ ,2
+ ,119
+ ,8.49
+ ,59.5
+ ,2295
+ ,1125
+ ,2
+ ,136
+ ,6.64
+ ,53
+ ,1788
+ ,1142
+ ,1
+ ,109
+ ,5.23
+ ,54.6
+ ,2337
+ ,1379
+ ,2
+ ,85
+ ,6.2
+ ,58.4
+ ,1678
+ ,1148
+ ,2
+ ,119
+ ,7.36
+ ,58.2
+ ,2103
+ ,1318
+ ,2
+ ,136
+ ,6.67
+ ,53.2
+ ,2018
+ ,1041
+ ,2
+ ,72
+ ,6.36
+ ,54.2
+ ,1697
+ ,1253
+ ,2
+ ,125
+ ,7.43
+ ,53.8
+ ,2158
+ ,1264
+ ,1
+ ,87
+ ,8.41
+ ,53.8
+ ,1964
+ ,953
+ ,1
+ ,106
+ ,7.15
+ ,57.3
+ ,1936
+ ,1049
+ ,1
+ ,99
+ ,5.36
+ ,53
+ ,2016
+ ,1392
+ ,2
+ ,123
+ ,7.39
+ ,52.1
+ ,2275
+ ,1135
+ ,1
+ ,99
+ ,5.63
+ ,52.7
+ ,2265
+ ,1450
+ ,1
+ ,88
+ ,8.47
+ ,55.5
+ ,2095
+ ,958
+ ,1
+ ,97
+ ,7.75
+ ,57.8
+ ,2070
+ ,1209
+ ,2
+ ,119
+ ,8.33
+ ,55.4
+ ,2135
+ ,1441
+ ,2
+ ,77
+ ,6
+ ,57.9
+ ,1882
+ ,994
+ ,1
+ ,128
+ ,5.45
+ ,55.2
+ ,1931
+ ,1149
+ ,1
+ ,100
+ ,8.28
+ ,58.5
+ ,2163
+ ,1204
+ ,1
+ ,116
+ ,5.6
+ ,53.4
+ ,2317
+ ,1414
+ ,2
+ ,76
+ ,7.38
+ ,58.6
+ ,1793
+ ,1339
+ ,2
+ ,76
+ ,7.99
+ ,53.5
+ ,2322
+ ,1255
+ ,1
+ ,100
+ ,6.83
+ ,53.3
+ ,2127
+ ,1189
+ ,2
+ ,105
+ ,5.64
+ ,53.4
+ ,1885
+ ,1298
+ ,2
+ ,120
+ ,8.43
+ ,57.2
+ ,1747
+ ,1167
+ ,2
+ ,97
+ ,7.38
+ ,54.2
+ ,1998
+ ,1290
+ ,2
+ ,95
+ ,6.55
+ ,55.7
+ ,2296
+ ,1057
+ ,2
+ ,101
+ ,5.71
+ ,59.2
+ ,2199
+ ,1018
+ ,1)
+ ,dim=c(6
+ ,60)
+ ,dimnames=list(c('IQ'
+ ,'CCMIDSA'
+ ,'HC'
+ ,'TOTSA'
+ ,'TOTVOL'
+ ,'SEX')
+ ,1:60))
> y <- array(NA,dim=c(6,60),dimnames=list(c('IQ','CCMIDSA','HC','TOTSA','TOTVOL','SEX'),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 = '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
IQ CCMIDSA HC TOTSA TOTVOL SEX t
1 96 6.08 54.7 1914 1005 2 1
2 89 5.73 54.2 1684 963 2 2
3 87 6.22 53.0 1902 1035 2 3
4 87 5.80 52.9 1860 1027 2 4
5 101 7.99 57.8 2264 1281 2 5
6 103 8.42 56.9 2216 1272 2 6
7 103 7.44 56.6 1866 1051 2 7
8 96 6.84 55.3 1850 1079 2 8
9 127 6.48 53.1 1743 1034 2 9
10 126 6.43 54.8 1709 1070 2 10
11 101 7.99 57.2 1689 1173 1 11
12 96 8.76 57.2 1806 1079 1 12
13 93 6.32 57.2 2136 1067 1 13
14 88 6.32 57.2 2018 1104 1 14
15 94 7.60 55.8 1966 1347 1 15
16 85 7.62 57.2 2154 1439 1 16
17 97 6.03 57.2 1767 1029 1 17
18 114 6.59 56.5 1827 1100 1 18
19 113 7.52 59.2 1773 1204 1 19
20 124 7.67 58.5 1971 1160 1 20
21 129 7.57 57.3 1867 1401 1 21
22 110 6.45 53.7 1993 1142 1 22
23 102 7.99 56.6 1910 1288 1 23
24 134 8.43 57.5 1688 979 1 24
25 119 7.02 55.5 1696 1104 2 25
26 139 5.21 55.7 2107 956 2 26
27 75 6.21 53.1 2060 1153 1 27
28 138 5.39 55.9 1870 1001 2 28
29 132 5.59 57.8 1808 1230 1 29
30 122 7.72 59.0 1846 1014 2 30
31 102 6.69 58.4 2227 1287 1 31
32 78 5.96 55.4 2177 1198 2 32
33 119 8.49 59.5 2295 1125 2 33
34 136 6.64 53.0 1788 1142 1 34
35 109 5.23 54.6 2337 1379 2 35
36 85 6.20 58.4 1678 1148 2 36
37 119 7.36 58.2 2103 1318 2 37
38 136 6.67 53.2 2018 1041 2 38
39 72 6.36 54.2 1697 1253 2 39
40 125 7.43 53.8 2158 1264 1 40
41 87 8.41 53.8 1964 953 1 41
42 106 7.15 57.3 1936 1049 1 42
43 99 5.36 53.0 2016 1392 2 43
44 123 7.39 52.1 2275 1135 1 44
45 99 5.63 52.7 2265 1450 1 45
46 88 8.47 55.5 2095 958 1 46
47 97 7.75 57.8 2070 1209 2 47
48 119 8.33 55.4 2135 1441 2 48
49 77 6.00 57.9 1882 994 1 49
50 128 5.45 55.2 1931 1149 1 50
51 100 8.28 58.5 2163 1204 1 51
52 116 5.60 53.4 2317 1414 2 52
53 76 7.38 58.6 1793 1339 2 53
54 76 7.99 53.5 2322 1255 1 54
55 100 6.83 53.3 2127 1189 2 55
56 105 5.64 53.4 1885 1298 2 56
57 120 8.43 57.2 1747 1167 2 57
58 97 7.38 54.2 1998 1290 2 58
59 95 6.55 55.7 2296 1057 2 59
60 101 5.71 59.2 2199 1018 1 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CCMIDSA HC TOTSA TOTVOL SEX
110.681614 -0.159876 0.266203 -0.007993 -0.005144 1.031391
t
0.032283
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-35.404 -10.415 -2.041 14.462 33.182
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 110.681614 77.822491 1.422 0.161
CCMIDSA -0.159876 2.632704 -0.061 0.952
HC 0.266203 1.293318 0.206 0.838
TOTSA -0.007993 0.014089 -0.567 0.573
TOTVOL -0.005144 0.019150 -0.269 0.789
SEX 1.031391 5.018969 0.205 0.838
t 0.032283 0.152684 0.211 0.833
Residual standard error: 18.94 on 53 degrees of freedom
Multiple R-squared: 0.01241, Adjusted R-squared: -0.0994
F-statistic: 0.111 on 6 and 53 DF, p-value: 0.9948
> 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,] 0.3690441813 0.738088363 0.6309558
[2,] 0.2119188118 0.423837624 0.7880812
[3,] 0.1138677849 0.227735570 0.8861322
[4,] 0.0582647096 0.116529419 0.9417353
[5,] 0.0416240206 0.083248041 0.9583760
[6,] 0.0269604158 0.053920832 0.9730396
[7,] 0.0208158473 0.041631695 0.9791842
[8,] 0.0129413365 0.025882673 0.9870587
[9,] 0.0078005407 0.015601081 0.9921995
[10,] 0.0034692696 0.006938539 0.9965307
[11,] 0.0019783019 0.003956604 0.9980217
[12,] 0.0018852188 0.003770438 0.9981148
[13,] 0.0009762503 0.001952501 0.9990237
[14,] 0.0025375623 0.005075125 0.9974624
[15,] 0.0013928501 0.002785700 0.9986071
[16,] 0.0055367067 0.011073413 0.9944633
[17,] 0.0041216649 0.008243330 0.9958783
[18,] 0.0266407000 0.053281400 0.9733593
[19,] 0.0209059644 0.041811929 0.9790940
[20,] 0.0187166681 0.037433336 0.9812833
[21,] 0.0389397787 0.077879557 0.9610602
[22,] 0.0292315617 0.058463123 0.9707684
[23,] 0.1390561261 0.278112252 0.8609439
[24,] 0.1011196019 0.202239204 0.8988804
[25,] 0.1625368771 0.325073754 0.8374631
[26,] 0.1217635681 0.243527136 0.8782364
[27,] 0.3095589369 0.619117874 0.6904411
[28,] 0.2458770210 0.491754042 0.7541230
[29,] 0.3160681762 0.632136352 0.6839318
[30,] 0.5483510199 0.903297960 0.4516490
[31,] 0.5456125367 0.908774927 0.4543875
[32,] 0.5931870247 0.813625951 0.4068130
[33,] 0.5172846018 0.965430796 0.4827154
[34,] 0.4511335277 0.902267055 0.5488665
[35,] 0.4581113388 0.916222678 0.5418887
[36,] 0.3649088473 0.729817695 0.6350912
[37,] 0.3057245510 0.611449102 0.6942754
[38,] 0.2221613492 0.444322698 0.7778387
[39,] 0.2010417067 0.402083413 0.7989583
[40,] 0.4128004644 0.825600929 0.5871995
[41,] 0.2789422980 0.557884596 0.7210577
> postscript(file="/var/wessaorg/rcomp/tmp/1lsex1321788470.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2a6m31321788470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/383g01321788470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4a44d1321788470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/567tj1321788470.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 = 60
Frequency = 1
1 2 3 4 5 6
-9.8964078 -18.9061025 -18.4276380 -18.8773284 -1.3278623 0.5181993
7 8 9 10 11 12
-3.5255063 -10.2915056 20.1175079 18.5381044 -5.4822714 -9.9397815
13 14 15 16 17 18
-10.7860536 -16.5712245 -9.1917715 -16.6174935 -10.1066192 7.9818248
19 20 21 22 23 24
6.4828418 19.0172365 24.6968714 6.1186535 -2.3517942 26.0825431
25 26 27 28 29 30
11.0328371 33.1818887 -28.3292607 30.4299052 25.6376585 13.7876553
31 32 33 34 35 36
-0.7682806 -26.0075732 14.8408914 30.3093180 7.2019058 -24.1428941
37 38 39 40 41 42
14.3352736 30.4192694 -35.4040814 22.6141464 -18.4120724 -0.3074713
43 44 45 46 47 48
-5.1086727 21.2027814 -1.7300787 -16.9435750 -8.6432381 15.7691513
49 50 51 52 53 54
-30.5916156 22.1959664 -4.1249166 14.0518754 -31.6544779 -25.4037953
55 56 57 58 59 60
-4.4979345 -1.1207758 11.5044220 -8.2580133 -9.6388747 -4.6817664
> postscript(file="/var/wessaorg/rcomp/tmp/6e9v51321788470.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -9.8964078 NA
1 -18.9061025 -9.8964078
2 -18.4276380 -18.9061025
3 -18.8773284 -18.4276380
4 -1.3278623 -18.8773284
5 0.5181993 -1.3278623
6 -3.5255063 0.5181993
7 -10.2915056 -3.5255063
8 20.1175079 -10.2915056
9 18.5381044 20.1175079
10 -5.4822714 18.5381044
11 -9.9397815 -5.4822714
12 -10.7860536 -9.9397815
13 -16.5712245 -10.7860536
14 -9.1917715 -16.5712245
15 -16.6174935 -9.1917715
16 -10.1066192 -16.6174935
17 7.9818248 -10.1066192
18 6.4828418 7.9818248
19 19.0172365 6.4828418
20 24.6968714 19.0172365
21 6.1186535 24.6968714
22 -2.3517942 6.1186535
23 26.0825431 -2.3517942
24 11.0328371 26.0825431
25 33.1818887 11.0328371
26 -28.3292607 33.1818887
27 30.4299052 -28.3292607
28 25.6376585 30.4299052
29 13.7876553 25.6376585
30 -0.7682806 13.7876553
31 -26.0075732 -0.7682806
32 14.8408914 -26.0075732
33 30.3093180 14.8408914
34 7.2019058 30.3093180
35 -24.1428941 7.2019058
36 14.3352736 -24.1428941
37 30.4192694 14.3352736
38 -35.4040814 30.4192694
39 22.6141464 -35.4040814
40 -18.4120724 22.6141464
41 -0.3074713 -18.4120724
42 -5.1086727 -0.3074713
43 21.2027814 -5.1086727
44 -1.7300787 21.2027814
45 -16.9435750 -1.7300787
46 -8.6432381 -16.9435750
47 15.7691513 -8.6432381
48 -30.5916156 15.7691513
49 22.1959664 -30.5916156
50 -4.1249166 22.1959664
51 14.0518754 -4.1249166
52 -31.6544779 14.0518754
53 -25.4037953 -31.6544779
54 -4.4979345 -25.4037953
55 -1.1207758 -4.4979345
56 11.5044220 -1.1207758
57 -8.2580133 11.5044220
58 -9.6388747 -8.2580133
59 -4.6817664 -9.6388747
60 NA -4.6817664
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -18.9061025 -9.8964078
[2,] -18.4276380 -18.9061025
[3,] -18.8773284 -18.4276380
[4,] -1.3278623 -18.8773284
[5,] 0.5181993 -1.3278623
[6,] -3.5255063 0.5181993
[7,] -10.2915056 -3.5255063
[8,] 20.1175079 -10.2915056
[9,] 18.5381044 20.1175079
[10,] -5.4822714 18.5381044
[11,] -9.9397815 -5.4822714
[12,] -10.7860536 -9.9397815
[13,] -16.5712245 -10.7860536
[14,] -9.1917715 -16.5712245
[15,] -16.6174935 -9.1917715
[16,] -10.1066192 -16.6174935
[17,] 7.9818248 -10.1066192
[18,] 6.4828418 7.9818248
[19,] 19.0172365 6.4828418
[20,] 24.6968714 19.0172365
[21,] 6.1186535 24.6968714
[22,] -2.3517942 6.1186535
[23,] 26.0825431 -2.3517942
[24,] 11.0328371 26.0825431
[25,] 33.1818887 11.0328371
[26,] -28.3292607 33.1818887
[27,] 30.4299052 -28.3292607
[28,] 25.6376585 30.4299052
[29,] 13.7876553 25.6376585
[30,] -0.7682806 13.7876553
[31,] -26.0075732 -0.7682806
[32,] 14.8408914 -26.0075732
[33,] 30.3093180 14.8408914
[34,] 7.2019058 30.3093180
[35,] -24.1428941 7.2019058
[36,] 14.3352736 -24.1428941
[37,] 30.4192694 14.3352736
[38,] -35.4040814 30.4192694
[39,] 22.6141464 -35.4040814
[40,] -18.4120724 22.6141464
[41,] -0.3074713 -18.4120724
[42,] -5.1086727 -0.3074713
[43,] 21.2027814 -5.1086727
[44,] -1.7300787 21.2027814
[45,] -16.9435750 -1.7300787
[46,] -8.6432381 -16.9435750
[47,] 15.7691513 -8.6432381
[48,] -30.5916156 15.7691513
[49,] 22.1959664 -30.5916156
[50,] -4.1249166 22.1959664
[51,] 14.0518754 -4.1249166
[52,] -31.6544779 14.0518754
[53,] -25.4037953 -31.6544779
[54,] -4.4979345 -25.4037953
[55,] -1.1207758 -4.4979345
[56,] 11.5044220 -1.1207758
[57,] -8.2580133 11.5044220
[58,] -9.6388747 -8.2580133
[59,] -4.6817664 -9.6388747
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -18.9061025 -9.8964078
2 -18.4276380 -18.9061025
3 -18.8773284 -18.4276380
4 -1.3278623 -18.8773284
5 0.5181993 -1.3278623
6 -3.5255063 0.5181993
7 -10.2915056 -3.5255063
8 20.1175079 -10.2915056
9 18.5381044 20.1175079
10 -5.4822714 18.5381044
11 -9.9397815 -5.4822714
12 -10.7860536 -9.9397815
13 -16.5712245 -10.7860536
14 -9.1917715 -16.5712245
15 -16.6174935 -9.1917715
16 -10.1066192 -16.6174935
17 7.9818248 -10.1066192
18 6.4828418 7.9818248
19 19.0172365 6.4828418
20 24.6968714 19.0172365
21 6.1186535 24.6968714
22 -2.3517942 6.1186535
23 26.0825431 -2.3517942
24 11.0328371 26.0825431
25 33.1818887 11.0328371
26 -28.3292607 33.1818887
27 30.4299052 -28.3292607
28 25.6376585 30.4299052
29 13.7876553 25.6376585
30 -0.7682806 13.7876553
31 -26.0075732 -0.7682806
32 14.8408914 -26.0075732
33 30.3093180 14.8408914
34 7.2019058 30.3093180
35 -24.1428941 7.2019058
36 14.3352736 -24.1428941
37 30.4192694 14.3352736
38 -35.4040814 30.4192694
39 22.6141464 -35.4040814
40 -18.4120724 22.6141464
41 -0.3074713 -18.4120724
42 -5.1086727 -0.3074713
43 21.2027814 -5.1086727
44 -1.7300787 21.2027814
45 -16.9435750 -1.7300787
46 -8.6432381 -16.9435750
47 15.7691513 -8.6432381
48 -30.5916156 15.7691513
49 22.1959664 -30.5916156
50 -4.1249166 22.1959664
51 14.0518754 -4.1249166
52 -31.6544779 14.0518754
53 -25.4037953 -31.6544779
54 -4.4979345 -25.4037953
55 -1.1207758 -4.4979345
56 11.5044220 -1.1207758
57 -8.2580133 11.5044220
58 -9.6388747 -8.2580133
59 -4.6817664 -9.6388747
> 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/wessaorg/rcomp/tmp/70p561321788470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8c1yx1321788470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9tdu31321788470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10kg7s1321788470.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11kntu1321788470.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/wessaorg/rcomp/tmp/12sx8z1321788471.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/wessaorg/rcomp/tmp/1351ik1321788471.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/wessaorg/rcomp/tmp/14bdqb1321788471.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/wessaorg/rcomp/tmp/153vif1321788471.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/wessaorg/rcomp/tmp/16whdq1321788471.tab")
+ }
>
> try(system("convert tmp/1lsex1321788470.ps tmp/1lsex1321788470.png",intern=TRUE))
character(0)
> try(system("convert tmp/2a6m31321788470.ps tmp/2a6m31321788470.png",intern=TRUE))
character(0)
> try(system("convert tmp/383g01321788470.ps tmp/383g01321788470.png",intern=TRUE))
character(0)
> try(system("convert tmp/4a44d1321788470.ps tmp/4a44d1321788470.png",intern=TRUE))
character(0)
> try(system("convert tmp/567tj1321788470.ps tmp/567tj1321788470.png",intern=TRUE))
character(0)
> try(system("convert tmp/6e9v51321788470.ps tmp/6e9v51321788470.png",intern=TRUE))
character(0)
> try(system("convert tmp/70p561321788470.ps tmp/70p561321788470.png",intern=TRUE))
character(0)
> try(system("convert tmp/8c1yx1321788470.ps tmp/8c1yx1321788470.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tdu31321788470.ps tmp/9tdu31321788470.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kg7s1321788470.ps tmp/10kg7s1321788470.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.351 0.572 4.057