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.
R is a collaborative project with many contributors.
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(2921.44
+ ,0
+ ,2849.27
+ ,2756.76
+ ,2981.85
+ ,0
+ ,2921.44
+ ,2849.27
+ ,3080.58
+ ,0
+ ,2981.85
+ ,2921.44
+ ,3106.22
+ ,0
+ ,3080.58
+ ,2981.85
+ ,3119.31
+ ,0
+ ,3106.22
+ ,3080.58
+ ,3061.26
+ ,0
+ ,3119.31
+ ,3106.22
+ ,3097.31
+ ,0
+ ,3061.26
+ ,3119.31
+ ,3161.69
+ ,0
+ ,3097.31
+ ,3061.26
+ ,3257.16
+ ,0
+ ,3161.69
+ ,3097.31
+ ,3277.01
+ ,0
+ ,3257.16
+ ,3161.69
+ ,3295.32
+ ,0
+ ,3277.01
+ ,3257.16
+ ,3363.99
+ ,0
+ ,3295.32
+ ,3277.01
+ ,3494.17
+ ,0
+ ,3363.99
+ ,3295.32
+ ,3667.03
+ ,1
+ ,3494.17
+ ,3363.99
+ ,3813.06
+ ,1
+ ,3667.03
+ ,3494.17
+ ,3917.96
+ ,1
+ ,3813.06
+ ,3667.03
+ ,3895.51
+ ,1
+ ,3917.96
+ ,3813.06
+ ,3801.06
+ ,1
+ ,3895.51
+ ,3917.96
+ ,3570.12
+ ,0
+ ,3801.06
+ ,3895.51
+ ,3701.61
+ ,1
+ ,3570.12
+ ,3801.06
+ ,3862.27
+ ,1
+ ,3701.61
+ ,3570.12
+ ,3970.1
+ ,1
+ ,3862.27
+ ,3701.61
+ ,4138.52
+ ,1
+ ,3970.1
+ ,3862.27
+ ,4199.75
+ ,1
+ ,4138.52
+ ,3970.1
+ ,4290.89
+ ,1
+ ,4199.75
+ ,4138.52
+ ,4443.91
+ ,1
+ ,4290.89
+ ,4199.75
+ ,4502.64
+ ,1
+ ,4443.91
+ ,4290.89
+ ,4356.98
+ ,1
+ ,4502.64
+ ,4443.91
+ ,4591.27
+ ,1
+ ,4356.98
+ ,4502.64
+ ,4696.96
+ ,1
+ ,4591.27
+ ,4356.98
+ ,4621.4
+ ,1
+ ,4696.96
+ ,4591.27
+ ,4562.84
+ ,1
+ ,4621.4
+ ,4696.96
+ ,4202.52
+ ,1
+ ,4562.84
+ ,4621.4
+ ,4296.49
+ ,1
+ ,4202.52
+ ,4562.84
+ ,4435.23
+ ,1
+ ,4296.49
+ ,4202.52
+ ,4105.18
+ ,1
+ ,4435.23
+ ,4296.49
+ ,4116.68
+ ,1
+ ,4105.18
+ ,4435.23
+ ,3844.49
+ ,1
+ ,4116.68
+ ,4105.18
+ ,3720.98
+ ,1
+ ,3844.49
+ ,4116.68
+ ,3674.4
+ ,1
+ ,3720.98
+ ,3844.49
+ ,3857.62
+ ,1
+ ,3674.4
+ ,3720.98
+ ,3801.06
+ ,1
+ ,3857.62
+ ,3674.4
+ ,3504.37
+ ,1
+ ,3801.06
+ ,3857.62
+ ,3032.6
+ ,1
+ ,3504.37
+ ,3801.06
+ ,3047.03
+ ,0
+ ,3032.6
+ ,3504.37
+ ,2962.34
+ ,1
+ ,3047.03
+ ,3032.6
+ ,2197.82
+ ,1
+ ,2962.34
+ ,3047.03
+ ,2014.45
+ ,1
+ ,2197.82
+ ,2962.34
+ ,1862.83
+ ,0
+ ,2014.45
+ ,2197.82
+ ,1905.41
+ ,0
+ ,1862.83
+ ,2014.45
+ ,1810.99
+ ,0
+ ,1905.41
+ ,1862.83
+ ,1670.07
+ ,0
+ ,1810.99
+ ,1905.41
+ ,1864.44
+ ,0
+ ,1670.07
+ ,1810.99
+ ,2052.02
+ ,0
+ ,1864.44
+ ,1670.07
+ ,2029.6
+ ,0
+ ,2052.02
+ ,1864.44
+ ,2070.83
+ ,0
+ ,2029.6
+ ,2052.02
+ ,2293.41
+ ,0
+ ,2070.83
+ ,2029.6
+ ,2443.27
+ ,0
+ ,2293.41
+ ,2070.83)
+ ,dim=c(4
+ ,58)
+ ,dimnames=list(c('Yt'
+ ,'X'
+ ,'Yt-1'
+ ,'Yt-2
')
+ ,1:58))
> y <- array(NA,dim=c(4,58),dimnames=list(c('Yt','X','Yt-1','Yt-2
'),1:58))
> 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
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
Yt X Yt-1 Yt-2\r\r\r\r t
1 2921.44 0 2849.27 2756.76 1
2 2981.85 0 2921.44 2849.27 2
3 3080.58 0 2981.85 2921.44 3
4 3106.22 0 3080.58 2981.85 4
5 3119.31 0 3106.22 3080.58 5
6 3061.26 0 3119.31 3106.22 6
7 3097.31 0 3061.26 3119.31 7
8 3161.69 0 3097.31 3061.26 8
9 3257.16 0 3161.69 3097.31 9
10 3277.01 0 3257.16 3161.69 10
11 3295.32 0 3277.01 3257.16 11
12 3363.99 0 3295.32 3277.01 12
13 3494.17 0 3363.99 3295.32 13
14 3667.03 1 3494.17 3363.99 14
15 3813.06 1 3667.03 3494.17 15
16 3917.96 1 3813.06 3667.03 16
17 3895.51 1 3917.96 3813.06 17
18 3801.06 1 3895.51 3917.96 18
19 3570.12 0 3801.06 3895.51 19
20 3701.61 1 3570.12 3801.06 20
21 3862.27 1 3701.61 3570.12 21
22 3970.10 1 3862.27 3701.61 22
23 4138.52 1 3970.10 3862.27 23
24 4199.75 1 4138.52 3970.10 24
25 4290.89 1 4199.75 4138.52 25
26 4443.91 1 4290.89 4199.75 26
27 4502.64 1 4443.91 4290.89 27
28 4356.98 1 4502.64 4443.91 28
29 4591.27 1 4356.98 4502.64 29
30 4696.96 1 4591.27 4356.98 30
31 4621.40 1 4696.96 4591.27 31
32 4562.84 1 4621.40 4696.96 32
33 4202.52 1 4562.84 4621.40 33
34 4296.49 1 4202.52 4562.84 34
35 4435.23 1 4296.49 4202.52 35
36 4105.18 1 4435.23 4296.49 36
37 4116.68 1 4105.18 4435.23 37
38 3844.49 1 4116.68 4105.18 38
39 3720.98 1 3844.49 4116.68 39
40 3674.40 1 3720.98 3844.49 40
41 3857.62 1 3674.40 3720.98 41
42 3801.06 1 3857.62 3674.40 42
43 3504.37 1 3801.06 3857.62 43
44 3032.60 1 3504.37 3801.06 44
45 3047.03 0 3032.60 3504.37 45
46 2962.34 1 3047.03 3032.60 46
47 2197.82 1 2962.34 3047.03 47
48 2014.45 1 2197.82 2962.34 48
49 1862.83 0 2014.45 2197.82 49
50 1905.41 0 1862.83 2014.45 50
51 1810.99 0 1905.41 1862.83 51
52 1670.07 0 1810.99 1905.41 52
53 1864.44 0 1670.07 1810.99 53
54 2052.02 0 1864.44 1670.07 54
55 2029.60 0 2052.02 1864.44 55
56 2070.83 0 2029.60 2052.02 56
57 2293.41 0 2070.83 2029.60 57
58 2443.27 0 2293.41 2070.83 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X `Yt-1` `Yt-2\r\r\r\r` t
201.7308 -11.4119 1.2136 -0.2507 -2.5144
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-705.51 -65.38 16.17 112.11 315.08
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 201.7308 182.9190 1.103 0.2751
X -11.4119 86.1990 -0.132 0.8952
`Yt-1` 1.2136 0.1368 8.869 4.71e-12 ***
`Yt-2\r\r\r\r` -0.2507 0.1347 -1.861 0.0684 .
t -2.5144 1.8241 -1.378 0.1738
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 176.2 on 53 degrees of freedom
Multiple R-squared: 0.9607, Adjusted R-squared: 0.9577
F-statistic: 323.9 on 4 and 53 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,] 2.523431e-02 5.046862e-02 0.9747657
[2,] 6.563683e-03 1.312737e-02 0.9934363
[3,] 1.663801e-03 3.327603e-03 0.9983362
[4,] 3.355143e-04 6.710286e-04 0.9996645
[5,] 9.940400e-05 1.988080e-04 0.9999006
[6,] 8.877304e-05 1.775461e-04 0.9999112
[7,] 1.832714e-05 3.665427e-05 0.9999817
[8,] 3.870072e-06 7.740145e-06 0.9999961
[9,] 8.850361e-07 1.770072e-06 0.9999991
[10,] 2.816526e-07 5.633052e-07 0.9999997
[11,] 8.360613e-08 1.672123e-07 0.9999999
[12,] 1.327318e-07 2.654637e-07 0.9999999
[13,] 6.099861e-08 1.219972e-07 0.9999999
[14,] 9.656166e-08 1.931233e-07 0.9999999
[15,] 3.278852e-08 6.557703e-08 1.0000000
[16,] 3.868341e-08 7.736682e-08 1.0000000
[17,] 9.833090e-09 1.966618e-08 1.0000000
[18,] 1.965364e-08 3.930728e-08 1.0000000
[19,] 1.444203e-07 2.888406e-07 0.9999999
[20,] 5.759529e-08 1.151906e-07 0.9999999
[21,] 4.785302e-08 9.570604e-08 1.0000000
[22,] 8.508539e-06 1.701708e-05 0.9999915
[23,] 4.942012e-06 9.884024e-06 0.9999951
[24,] 2.046597e-06 4.093194e-06 0.9999980
[25,] 7.367168e-07 1.473434e-06 0.9999993
[26,] 1.139827e-04 2.279653e-04 0.9998860
[27,] 8.613505e-05 1.722701e-04 0.9999139
[28,] 8.467507e-05 1.693501e-04 0.9999153
[29,] 2.320373e-03 4.640746e-03 0.9976796
[30,] 1.692333e-03 3.384667e-03 0.9983077
[31,] 3.376927e-03 6.753855e-03 0.9966231
[32,] 2.000726e-03 4.001452e-03 0.9979993
[33,] 1.278406e-03 2.556811e-03 0.9987216
[34,] 1.135895e-02 2.271791e-02 0.9886410
[35,] 2.301786e-02 4.603571e-02 0.9769821
[36,] 3.045334e-02 6.090667e-02 0.9695467
[37,] 4.586464e-02 9.172928e-02 0.9541354
[38,] 6.719608e-02 1.343922e-01 0.9328039
[39,] 7.588240e-01 4.823520e-01 0.2411760
[40,] 8.286692e-01 3.426617e-01 0.1713308
[41,] 7.205147e-01 5.589707e-01 0.2794853
[42,] 5.969943e-01 8.060115e-01 0.4030057
[43,] 7.688205e-01 4.623591e-01 0.2311795
> postscript(file="/var/www/html/rcomp/tmp/1bcq71258907368.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2c9lp1258907368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hisz1258907368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pzhe1258907368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59te61258907368.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 = 58
Frequency = 1
1 2 3 4 5
-44.51366249 -45.98215954 0.04200889 -76.47677023 -67.23760109
6 7 8 9 10
-132.23123205 -19.93630863 -11.34469249 17.54639297 -59.81082086
11 12 13 14 15
-39.14223433 14.79766690 68.74510878 114.76153579 86.16038013
16 17 18 19 20
59.68953702 -50.94252045 -89.33501966 -220.17701279 181.82750946
21 22 23 24 25
127.53131375 75.86436645 156.21416958 42.59830278 104.16679487
26 27 28 29 30
164.44473369 62.83403919 -113.22420872 315.07508145 102.43117780
31 32 33 34 35
-40.14320749 22.00610309 -283.67422220 235.41017186 172.29285151
36 37 38 39 40
-300.05831005 149.28299185 -217.09100779 -4.87654724 32.71148388
41 42 43 44 45
244.01148953 -44.06544101 -223.66789606 -347.04283647 156.64610878
46 47 48 49 50
-49.90045131 -705.50956035 20.21719512 -109.42618949 73.70265185
51 52 53 54 55
-107.88807685 -121.03187029 223.20094731 142.08185080 -56.74118718
56 57 58
61.23743176 230.67494227 123.26470798
> postscript(file="/var/www/html/rcomp/tmp/61h7r1258907368.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 -44.51366249 NA
1 -45.98215954 -44.51366249
2 0.04200889 -45.98215954
3 -76.47677023 0.04200889
4 -67.23760109 -76.47677023
5 -132.23123205 -67.23760109
6 -19.93630863 -132.23123205
7 -11.34469249 -19.93630863
8 17.54639297 -11.34469249
9 -59.81082086 17.54639297
10 -39.14223433 -59.81082086
11 14.79766690 -39.14223433
12 68.74510878 14.79766690
13 114.76153579 68.74510878
14 86.16038013 114.76153579
15 59.68953702 86.16038013
16 -50.94252045 59.68953702
17 -89.33501966 -50.94252045
18 -220.17701279 -89.33501966
19 181.82750946 -220.17701279
20 127.53131375 181.82750946
21 75.86436645 127.53131375
22 156.21416958 75.86436645
23 42.59830278 156.21416958
24 104.16679487 42.59830278
25 164.44473369 104.16679487
26 62.83403919 164.44473369
27 -113.22420872 62.83403919
28 315.07508145 -113.22420872
29 102.43117780 315.07508145
30 -40.14320749 102.43117780
31 22.00610309 -40.14320749
32 -283.67422220 22.00610309
33 235.41017186 -283.67422220
34 172.29285151 235.41017186
35 -300.05831005 172.29285151
36 149.28299185 -300.05831005
37 -217.09100779 149.28299185
38 -4.87654724 -217.09100779
39 32.71148388 -4.87654724
40 244.01148953 32.71148388
41 -44.06544101 244.01148953
42 -223.66789606 -44.06544101
43 -347.04283647 -223.66789606
44 156.64610878 -347.04283647
45 -49.90045131 156.64610878
46 -705.50956035 -49.90045131
47 20.21719512 -705.50956035
48 -109.42618949 20.21719512
49 73.70265185 -109.42618949
50 -107.88807685 73.70265185
51 -121.03187029 -107.88807685
52 223.20094731 -121.03187029
53 142.08185080 223.20094731
54 -56.74118718 142.08185080
55 61.23743176 -56.74118718
56 230.67494227 61.23743176
57 123.26470798 230.67494227
58 NA 123.26470798
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -45.98215954 -44.51366249
[2,] 0.04200889 -45.98215954
[3,] -76.47677023 0.04200889
[4,] -67.23760109 -76.47677023
[5,] -132.23123205 -67.23760109
[6,] -19.93630863 -132.23123205
[7,] -11.34469249 -19.93630863
[8,] 17.54639297 -11.34469249
[9,] -59.81082086 17.54639297
[10,] -39.14223433 -59.81082086
[11,] 14.79766690 -39.14223433
[12,] 68.74510878 14.79766690
[13,] 114.76153579 68.74510878
[14,] 86.16038013 114.76153579
[15,] 59.68953702 86.16038013
[16,] -50.94252045 59.68953702
[17,] -89.33501966 -50.94252045
[18,] -220.17701279 -89.33501966
[19,] 181.82750946 -220.17701279
[20,] 127.53131375 181.82750946
[21,] 75.86436645 127.53131375
[22,] 156.21416958 75.86436645
[23,] 42.59830278 156.21416958
[24,] 104.16679487 42.59830278
[25,] 164.44473369 104.16679487
[26,] 62.83403919 164.44473369
[27,] -113.22420872 62.83403919
[28,] 315.07508145 -113.22420872
[29,] 102.43117780 315.07508145
[30,] -40.14320749 102.43117780
[31,] 22.00610309 -40.14320749
[32,] -283.67422220 22.00610309
[33,] 235.41017186 -283.67422220
[34,] 172.29285151 235.41017186
[35,] -300.05831005 172.29285151
[36,] 149.28299185 -300.05831005
[37,] -217.09100779 149.28299185
[38,] -4.87654724 -217.09100779
[39,] 32.71148388 -4.87654724
[40,] 244.01148953 32.71148388
[41,] -44.06544101 244.01148953
[42,] -223.66789606 -44.06544101
[43,] -347.04283647 -223.66789606
[44,] 156.64610878 -347.04283647
[45,] -49.90045131 156.64610878
[46,] -705.50956035 -49.90045131
[47,] 20.21719512 -705.50956035
[48,] -109.42618949 20.21719512
[49,] 73.70265185 -109.42618949
[50,] -107.88807685 73.70265185
[51,] -121.03187029 -107.88807685
[52,] 223.20094731 -121.03187029
[53,] 142.08185080 223.20094731
[54,] -56.74118718 142.08185080
[55,] 61.23743176 -56.74118718
[56,] 230.67494227 61.23743176
[57,] 123.26470798 230.67494227
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -45.98215954 -44.51366249
2 0.04200889 -45.98215954
3 -76.47677023 0.04200889
4 -67.23760109 -76.47677023
5 -132.23123205 -67.23760109
6 -19.93630863 -132.23123205
7 -11.34469249 -19.93630863
8 17.54639297 -11.34469249
9 -59.81082086 17.54639297
10 -39.14223433 -59.81082086
11 14.79766690 -39.14223433
12 68.74510878 14.79766690
13 114.76153579 68.74510878
14 86.16038013 114.76153579
15 59.68953702 86.16038013
16 -50.94252045 59.68953702
17 -89.33501966 -50.94252045
18 -220.17701279 -89.33501966
19 181.82750946 -220.17701279
20 127.53131375 181.82750946
21 75.86436645 127.53131375
22 156.21416958 75.86436645
23 42.59830278 156.21416958
24 104.16679487 42.59830278
25 164.44473369 104.16679487
26 62.83403919 164.44473369
27 -113.22420872 62.83403919
28 315.07508145 -113.22420872
29 102.43117780 315.07508145
30 -40.14320749 102.43117780
31 22.00610309 -40.14320749
32 -283.67422220 22.00610309
33 235.41017186 -283.67422220
34 172.29285151 235.41017186
35 -300.05831005 172.29285151
36 149.28299185 -300.05831005
37 -217.09100779 149.28299185
38 -4.87654724 -217.09100779
39 32.71148388 -4.87654724
40 244.01148953 32.71148388
41 -44.06544101 244.01148953
42 -223.66789606 -44.06544101
43 -347.04283647 -223.66789606
44 156.64610878 -347.04283647
45 -49.90045131 156.64610878
46 -705.50956035 -49.90045131
47 20.21719512 -705.50956035
48 -109.42618949 20.21719512
49 73.70265185 -109.42618949
50 -107.88807685 73.70265185
51 -121.03187029 -107.88807685
52 223.20094731 -121.03187029
53 142.08185080 223.20094731
54 -56.74118718 142.08185080
55 61.23743176 -56.74118718
56 230.67494227 61.23743176
57 123.26470798 230.67494227
> 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/79sdm1258907368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80yfu1258907368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97auw1258907368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ryuo1258907368.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11a5rx1258907368.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/1229j81258907368.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/13h6fg1258907369.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/147rwg1258907369.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/15dm9u1258907369.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/167oxm1258907369.tab")
+ }
>
> system("convert tmp/1bcq71258907368.ps tmp/1bcq71258907368.png")
> system("convert tmp/2c9lp1258907368.ps tmp/2c9lp1258907368.png")
> system("convert tmp/3hisz1258907368.ps tmp/3hisz1258907368.png")
> system("convert tmp/4pzhe1258907368.ps tmp/4pzhe1258907368.png")
> system("convert tmp/59te61258907368.ps tmp/59te61258907368.png")
> system("convert tmp/61h7r1258907368.ps tmp/61h7r1258907368.png")
> system("convert tmp/79sdm1258907368.ps tmp/79sdm1258907368.png")
> system("convert tmp/80yfu1258907368.ps tmp/80yfu1258907368.png")
> system("convert tmp/97auw1258907368.ps tmp/97auw1258907368.png")
> system("convert tmp/10ryuo1258907368.ps tmp/10ryuo1258907368.png")
>
>
> proc.time()
user system elapsed
2.436 1.575 2.898