R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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
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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(3499
+ ,1
+ ,4164
+ ,3186
+ ,4145
+ ,1
+ ,3499
+ ,3902
+ ,3796
+ ,1
+ ,4145
+ ,4164
+ ,3711
+ ,1
+ ,3796
+ ,3499
+ ,3949
+ ,1
+ ,3711
+ ,4145
+ ,3740
+ ,1
+ ,3949
+ ,3796
+ ,3243
+ ,1
+ ,3740
+ ,3711
+ ,4407
+ ,1
+ ,3243
+ ,3949
+ ,4814
+ ,1
+ ,4407
+ ,3740
+ ,3908
+ ,1
+ ,4814
+ ,3243
+ ,5250
+ ,1
+ ,3908
+ ,4407
+ ,3937
+ ,1
+ ,5250
+ ,4814
+ ,4004
+ ,1
+ ,3937
+ ,3908
+ ,5560
+ ,1
+ ,4004
+ ,5250
+ ,3922
+ ,1
+ ,5560
+ ,3937
+ ,3759
+ ,1
+ ,3922
+ ,4004
+ ,4138
+ ,1
+ ,3759
+ ,5560
+ ,4634
+ ,1
+ ,4138
+ ,3922
+ ,3996
+ ,1
+ ,4634
+ ,3759
+ ,4308
+ ,1
+ ,3996
+ ,4138
+ ,4143
+ ,0
+ ,4308
+ ,4634
+ ,4429
+ ,0
+ ,4143
+ ,3996
+ ,5219
+ ,0
+ ,4429
+ ,4308
+ ,4929
+ ,0
+ ,5219
+ ,4143
+ ,5755
+ ,0
+ ,4929
+ ,4429
+ ,5592
+ ,0
+ ,5755
+ ,5219
+ ,4163
+ ,0
+ ,5592
+ ,4929
+ ,4962
+ ,0
+ ,4163
+ ,5755
+ ,5208
+ ,0
+ ,4962
+ ,5592
+ ,4755
+ ,0
+ ,5208
+ ,4163
+ ,4491
+ ,0
+ ,4755
+ ,4962
+ ,5732
+ ,0
+ ,4491
+ ,5208
+ ,5731
+ ,0
+ ,5732
+ ,4755
+ ,5040
+ ,0
+ ,5731
+ ,4491
+ ,6102
+ ,0
+ ,5040
+ ,5732
+ ,4904
+ ,0
+ ,6102
+ ,5731
+ ,5369
+ ,0
+ ,4904
+ ,5040
+ ,5578
+ ,0
+ ,5369
+ ,6102
+ ,4619
+ ,0
+ ,5578
+ ,4904
+ ,4731
+ ,0
+ ,4619
+ ,5369
+ ,5011
+ ,0
+ ,4731
+ ,5578
+ ,5299
+ ,0
+ ,5011
+ ,4619
+ ,4146
+ ,0
+ ,5299
+ ,4731
+ ,4625
+ ,0
+ ,4146
+ ,5011
+ ,4736
+ ,0
+ ,4625
+ ,5299
+ ,4219
+ ,0
+ ,4736
+ ,4146
+ ,5116
+ ,0
+ ,4219
+ ,4625
+ ,4205
+ ,0
+ ,5116
+ ,4736
+ ,4121
+ ,0
+ ,4205
+ ,4219
+ ,5103
+ ,1
+ ,4121
+ ,5116
+ ,4300
+ ,1
+ ,5103
+ ,4205
+ ,4578
+ ,1
+ ,4300
+ ,4121
+ ,3809
+ ,1
+ ,4578
+ ,5103
+ ,5526
+ ,1
+ ,3809
+ ,4300
+ ,4247
+ ,1
+ ,5526
+ ,4578
+ ,3830
+ ,1
+ ,4247
+ ,3809
+ ,4394
+ ,1
+ ,3830
+ ,5526)
+ ,dim=c(4
+ ,57)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y3')
+ ,1:57))
> y <- array(NA,dim=c(4,57),dimnames=list(c('Y','X','Y1','Y3'),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 = '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
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
Y X Y1 Y3 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 3499 1 4164 3186 1 0 0 0 0 0 0 0 0 0 0 1
2 4145 1 3499 3902 0 1 0 0 0 0 0 0 0 0 0 2
3 3796 1 4145 4164 0 0 1 0 0 0 0 0 0 0 0 3
4 3711 1 3796 3499 0 0 0 1 0 0 0 0 0 0 0 4
5 3949 1 3711 4145 0 0 0 0 1 0 0 0 0 0 0 5
6 3740 1 3949 3796 0 0 0 0 0 1 0 0 0 0 0 6
7 3243 1 3740 3711 0 0 0 0 0 0 1 0 0 0 0 7
8 4407 1 3243 3949 0 0 0 0 0 0 0 1 0 0 0 8
9 4814 1 4407 3740 0 0 0 0 0 0 0 0 1 0 0 9
10 3908 1 4814 3243 0 0 0 0 0 0 0 0 0 1 0 10
11 5250 1 3908 4407 0 0 0 0 0 0 0 0 0 0 1 11
12 3937 1 5250 4814 0 0 0 0 0 0 0 0 0 0 0 12
13 4004 1 3937 3908 1 0 0 0 0 0 0 0 0 0 0 13
14 5560 1 4004 5250 0 1 0 0 0 0 0 0 0 0 0 14
15 3922 1 5560 3937 0 0 1 0 0 0 0 0 0 0 0 15
16 3759 1 3922 4004 0 0 0 1 0 0 0 0 0 0 0 16
17 4138 1 3759 5560 0 0 0 0 1 0 0 0 0 0 0 17
18 4634 1 4138 3922 0 0 0 0 0 1 0 0 0 0 0 18
19 3996 1 4634 3759 0 0 0 0 0 0 1 0 0 0 0 19
20 4308 1 3996 4138 0 0 0 0 0 0 0 1 0 0 0 20
21 4143 0 4308 4634 0 0 0 0 0 0 0 0 1 0 0 21
22 4429 0 4143 3996 0 0 0 0 0 0 0 0 0 1 0 22
23 5219 0 4429 4308 0 0 0 0 0 0 0 0 0 0 1 23
24 4929 0 5219 4143 0 0 0 0 0 0 0 0 0 0 0 24
25 5755 0 4929 4429 1 0 0 0 0 0 0 0 0 0 0 25
26 5592 0 5755 5219 0 1 0 0 0 0 0 0 0 0 0 26
27 4163 0 5592 4929 0 0 1 0 0 0 0 0 0 0 0 27
28 4962 0 4163 5755 0 0 0 1 0 0 0 0 0 0 0 28
29 5208 0 4962 5592 0 0 0 0 1 0 0 0 0 0 0 29
30 4755 0 5208 4163 0 0 0 0 0 1 0 0 0 0 0 30
31 4491 0 4755 4962 0 0 0 0 0 0 1 0 0 0 0 31
32 5732 0 4491 5208 0 0 0 0 0 0 0 1 0 0 0 32
33 5731 0 5732 4755 0 0 0 0 0 0 0 0 1 0 0 33
34 5040 0 5731 4491 0 0 0 0 0 0 0 0 0 1 0 34
35 6102 0 5040 5732 0 0 0 0 0 0 0 0 0 0 1 35
36 4904 0 6102 5731 0 0 0 0 0 0 0 0 0 0 0 36
37 5369 0 4904 5040 1 0 0 0 0 0 0 0 0 0 0 37
38 5578 0 5369 6102 0 1 0 0 0 0 0 0 0 0 0 38
39 4619 0 5578 4904 0 0 1 0 0 0 0 0 0 0 0 39
40 4731 0 4619 5369 0 0 0 1 0 0 0 0 0 0 0 40
41 5011 0 4731 5578 0 0 0 0 1 0 0 0 0 0 0 41
42 5299 0 5011 4619 0 0 0 0 0 1 0 0 0 0 0 42
43 4146 0 5299 4731 0 0 0 0 0 0 1 0 0 0 0 43
44 4625 0 4146 5011 0 0 0 0 0 0 0 1 0 0 0 44
45 4736 0 4625 5299 0 0 0 0 0 0 0 0 1 0 0 45
46 4219 0 4736 4146 0 0 0 0 0 0 0 0 0 1 0 46
47 5116 0 4219 4625 0 0 0 0 0 0 0 0 0 0 1 47
48 4205 0 5116 4736 0 0 0 0 0 0 0 0 0 0 0 48
49 4121 0 4205 4219 1 0 0 0 0 0 0 0 0 0 0 49
50 5103 1 4121 5116 0 1 0 0 0 0 0 0 0 0 0 50
51 4300 1 5103 4205 0 0 1 0 0 0 0 0 0 0 0 51
52 4578 1 4300 4121 0 0 0 1 0 0 0 0 0 0 0 52
53 3809 1 4578 5103 0 0 0 0 1 0 0 0 0 0 0 53
54 5526 1 3809 4300 0 0 0 0 0 1 0 0 0 0 0 54
55 4247 1 5526 4578 0 0 0 0 0 0 1 0 0 0 0 55
56 3830 1 4247 3809 0 0 0 0 0 0 0 1 0 0 0 56
57 4394 1 3830 5526 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y3 M1 M2
873.5520 -166.5097 0.3365 0.3866 679.0994 947.0511
M3 M4 M5 M6 M7 M8
-37.8229 453.0974 216.1444 960.4946 -0.8976 785.1415
M9 M10 M11 t
607.1484 435.8897 1304.8417 -1.3340
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-715.98 -247.58 -84.63 227.45 986.30
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 873.5520 990.0459 0.882 0.382740
X -166.5097 175.6586 -0.948 0.348726
Y1 0.3365 0.1379 2.441 0.019051 *
Y3 0.3866 0.1447 2.673 0.010747 *
M1 679.0994 322.0127 2.109 0.041104 *
M2 947.0511 317.2347 2.985 0.004760 **
M3 -37.8229 294.3958 -0.128 0.898400
M4 453.0974 326.2729 1.389 0.172420
M5 216.1444 329.5252 0.656 0.515536
M6 960.4946 318.8983 3.012 0.004433 **
M7 -0.8976 302.3943 -0.003 0.997646
M8 785.1415 338.2139 2.321 0.025312 *
M9 607.1484 310.3111 1.957 0.057231 .
M10 435.8897 336.8809 1.294 0.202942
M11 1304.8417 336.3071 3.880 0.000371 ***
t -1.3340 4.3023 -0.310 0.758085
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 432.7 on 41 degrees of freedom
Multiple R-squared: 0.6941, Adjusted R-squared: 0.5821
F-statistic: 6.201 on 15 and 41 DF, p-value: 1.604e-06
> 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,] 0.6216030 0.7567941 0.37839705
[2,] 0.5804285 0.8391429 0.41957145
[3,] 0.5006035 0.9987929 0.49939646
[4,] 0.5733220 0.8533561 0.42667803
[5,] 0.4961340 0.9922681 0.50386595
[6,] 0.5314902 0.9370196 0.46850979
[7,] 0.7911493 0.4177014 0.20885072
[8,] 0.7153929 0.5692141 0.28460706
[9,] 0.7494804 0.5010393 0.25051965
[10,] 0.7065689 0.5868621 0.29343105
[11,] 0.6141123 0.7717754 0.38588768
[12,] 0.8724651 0.2550699 0.12753495
[13,] 0.9450838 0.1098325 0.05491623
[14,] 0.9123285 0.1753431 0.08767154
[15,] 0.8735029 0.2529941 0.12649707
[16,] 0.7899137 0.4201726 0.21008632
[17,] 0.6765582 0.6468836 0.32344181
[18,] 0.6062704 0.7874591 0.39372956
[19,] 0.4490041 0.8980083 0.55099586
[20,] 0.3395778 0.6791555 0.66042224
> postscript(file="/var/www/html/rcomp/tmp/1iekg1258624682.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/256xl1258624682.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3qv851258624682.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4hlu81258624682.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55c681258624682.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 57
Frequency = 1
1 2 3 4 5 6 7
-518.76308 -192.43156 126.10379 -73.94083 181.19031 -715.98113 -147.06405
8 9 10 11 12 13 14
307.45400 582.90199 -95.30847 233.91171 -381.84772 -200.51187 547.47772
15 16 17 18 19 20 21
-120.26923 -247.57618 -177.02474 81.71412 302.55696 -101.99260 -550.92724
22 23 24 25 26 27 28
209.85268 -84.62984 729.50547 864.75025 -148.24618 -424.06866 46.84990
29 30 31 32 33 34 35
325.29481 -401.01627 141.23190 591.25304 527.12662 111.12411 58.22738
36 37 38 39 40 41 42
-190.57074 266.94404 -357.73809 62.31560 -172.34897 227.44619 48.98160
43 44 45 46 47 48 49
-281.50525 -307.48294 -289.68551 -225.66831 -207.50925 -157.08700 -412.41933
50 51 52 53 54 55 56
150.93810 355.91849 447.01608 -556.90658 986.30167 -15.21955 -489.23151
57
-269.41586
> postscript(file="/var/www/html/rcomp/tmp/652lb1258624682.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 -518.76308 NA
1 -192.43156 -518.76308
2 126.10379 -192.43156
3 -73.94083 126.10379
4 181.19031 -73.94083
5 -715.98113 181.19031
6 -147.06405 -715.98113
7 307.45400 -147.06405
8 582.90199 307.45400
9 -95.30847 582.90199
10 233.91171 -95.30847
11 -381.84772 233.91171
12 -200.51187 -381.84772
13 547.47772 -200.51187
14 -120.26923 547.47772
15 -247.57618 -120.26923
16 -177.02474 -247.57618
17 81.71412 -177.02474
18 302.55696 81.71412
19 -101.99260 302.55696
20 -550.92724 -101.99260
21 209.85268 -550.92724
22 -84.62984 209.85268
23 729.50547 -84.62984
24 864.75025 729.50547
25 -148.24618 864.75025
26 -424.06866 -148.24618
27 46.84990 -424.06866
28 325.29481 46.84990
29 -401.01627 325.29481
30 141.23190 -401.01627
31 591.25304 141.23190
32 527.12662 591.25304
33 111.12411 527.12662
34 58.22738 111.12411
35 -190.57074 58.22738
36 266.94404 -190.57074
37 -357.73809 266.94404
38 62.31560 -357.73809
39 -172.34897 62.31560
40 227.44619 -172.34897
41 48.98160 227.44619
42 -281.50525 48.98160
43 -307.48294 -281.50525
44 -289.68551 -307.48294
45 -225.66831 -289.68551
46 -207.50925 -225.66831
47 -157.08700 -207.50925
48 -412.41933 -157.08700
49 150.93810 -412.41933
50 355.91849 150.93810
51 447.01608 355.91849
52 -556.90658 447.01608
53 986.30167 -556.90658
54 -15.21955 986.30167
55 -489.23151 -15.21955
56 -269.41586 -489.23151
57 NA -269.41586
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -192.43156 -518.76308
[2,] 126.10379 -192.43156
[3,] -73.94083 126.10379
[4,] 181.19031 -73.94083
[5,] -715.98113 181.19031
[6,] -147.06405 -715.98113
[7,] 307.45400 -147.06405
[8,] 582.90199 307.45400
[9,] -95.30847 582.90199
[10,] 233.91171 -95.30847
[11,] -381.84772 233.91171
[12,] -200.51187 -381.84772
[13,] 547.47772 -200.51187
[14,] -120.26923 547.47772
[15,] -247.57618 -120.26923
[16,] -177.02474 -247.57618
[17,] 81.71412 -177.02474
[18,] 302.55696 81.71412
[19,] -101.99260 302.55696
[20,] -550.92724 -101.99260
[21,] 209.85268 -550.92724
[22,] -84.62984 209.85268
[23,] 729.50547 -84.62984
[24,] 864.75025 729.50547
[25,] -148.24618 864.75025
[26,] -424.06866 -148.24618
[27,] 46.84990 -424.06866
[28,] 325.29481 46.84990
[29,] -401.01627 325.29481
[30,] 141.23190 -401.01627
[31,] 591.25304 141.23190
[32,] 527.12662 591.25304
[33,] 111.12411 527.12662
[34,] 58.22738 111.12411
[35,] -190.57074 58.22738
[36,] 266.94404 -190.57074
[37,] -357.73809 266.94404
[38,] 62.31560 -357.73809
[39,] -172.34897 62.31560
[40,] 227.44619 -172.34897
[41,] 48.98160 227.44619
[42,] -281.50525 48.98160
[43,] -307.48294 -281.50525
[44,] -289.68551 -307.48294
[45,] -225.66831 -289.68551
[46,] -207.50925 -225.66831
[47,] -157.08700 -207.50925
[48,] -412.41933 -157.08700
[49,] 150.93810 -412.41933
[50,] 355.91849 150.93810
[51,] 447.01608 355.91849
[52,] -556.90658 447.01608
[53,] 986.30167 -556.90658
[54,] -15.21955 986.30167
[55,] -489.23151 -15.21955
[56,] -269.41586 -489.23151
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -192.43156 -518.76308
2 126.10379 -192.43156
3 -73.94083 126.10379
4 181.19031 -73.94083
5 -715.98113 181.19031
6 -147.06405 -715.98113
7 307.45400 -147.06405
8 582.90199 307.45400
9 -95.30847 582.90199
10 233.91171 -95.30847
11 -381.84772 233.91171
12 -200.51187 -381.84772
13 547.47772 -200.51187
14 -120.26923 547.47772
15 -247.57618 -120.26923
16 -177.02474 -247.57618
17 81.71412 -177.02474
18 302.55696 81.71412
19 -101.99260 302.55696
20 -550.92724 -101.99260
21 209.85268 -550.92724
22 -84.62984 209.85268
23 729.50547 -84.62984
24 864.75025 729.50547
25 -148.24618 864.75025
26 -424.06866 -148.24618
27 46.84990 -424.06866
28 325.29481 46.84990
29 -401.01627 325.29481
30 141.23190 -401.01627
31 591.25304 141.23190
32 527.12662 591.25304
33 111.12411 527.12662
34 58.22738 111.12411
35 -190.57074 58.22738
36 266.94404 -190.57074
37 -357.73809 266.94404
38 62.31560 -357.73809
39 -172.34897 62.31560
40 227.44619 -172.34897
41 48.98160 227.44619
42 -281.50525 48.98160
43 -307.48294 -281.50525
44 -289.68551 -307.48294
45 -225.66831 -289.68551
46 -207.50925 -225.66831
47 -157.08700 -207.50925
48 -412.41933 -157.08700
49 150.93810 -412.41933
50 355.91849 150.93810
51 447.01608 355.91849
52 -556.90658 447.01608
53 986.30167 -556.90658
54 -15.21955 986.30167
55 -489.23151 -15.21955
56 -269.41586 -489.23151
> 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/rcomp/tmp/76j4d1258624682.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8dc1w1258624682.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vnz11258624682.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/102rqd1258624682.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11ck3g1258624682.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/rcomp/tmp/12wg371258624682.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/rcomp/tmp/13wq3n1258624682.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/rcomp/tmp/14xnx51258624682.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/rcomp/tmp/15u8fq1258624682.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/rcomp/tmp/16oqkr1258624682.tab")
+ }
>
> system("convert tmp/1iekg1258624682.ps tmp/1iekg1258624682.png")
> system("convert tmp/256xl1258624682.ps tmp/256xl1258624682.png")
> system("convert tmp/3qv851258624682.ps tmp/3qv851258624682.png")
> system("convert tmp/4hlu81258624682.ps tmp/4hlu81258624682.png")
> system("convert tmp/55c681258624682.ps tmp/55c681258624682.png")
> system("convert tmp/652lb1258624682.ps tmp/652lb1258624682.png")
> system("convert tmp/76j4d1258624682.ps tmp/76j4d1258624682.png")
> system("convert tmp/8dc1w1258624682.ps tmp/8dc1w1258624682.png")
> system("convert tmp/9vnz11258624682.ps tmp/9vnz11258624682.png")
> system("convert tmp/102rqd1258624682.ps tmp/102rqd1258624682.png")
>
>
> proc.time()
user system elapsed
2.338 1.553 3.329