R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-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.
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(6.80
+ ,225.00
+ ,0.44
+ ,0.67
+ ,9.20
+ ,6.30
+ ,180.00
+ ,0.44
+ ,0.80
+ ,11.70
+ ,6.40
+ ,190.00
+ ,0.46
+ ,0.76
+ ,15.80
+ ,6.20
+ ,180.00
+ ,0.42
+ ,0.65
+ ,8.60
+ ,6.90
+ ,205.00
+ ,0.45
+ ,0.90
+ ,23.20
+ ,6.40
+ ,225.00
+ ,0.43
+ ,0.78
+ ,27.40
+ ,6.30
+ ,185.00
+ ,0.49
+ ,0.77
+ ,9.30
+ ,6.80
+ ,235.00
+ ,0.47
+ ,0.75
+ ,16.00
+ ,6.90
+ ,235.00
+ ,0.44
+ ,0.82
+ ,4.70
+ ,6.70
+ ,210.00
+ ,0.48
+ ,0.83
+ ,12.50
+ ,6.90
+ ,245.00
+ ,0.52
+ ,0.63
+ ,20.10
+ ,6.90
+ ,245.00
+ ,0.49
+ ,0.76
+ ,9.10
+ ,6.30
+ ,185.00
+ ,0.37
+ ,0.71
+ ,8.10
+ ,6.10
+ ,185.00
+ ,0.42
+ ,0.78
+ ,8.60
+ ,6.20
+ ,180.00
+ ,0.44
+ ,0.78
+ ,20.30
+ ,6.80
+ ,220.00
+ ,0.50
+ ,0.88
+ ,25.00
+ ,6.50
+ ,194.00
+ ,0.50
+ ,0.83
+ ,19.20
+ ,7.60
+ ,225.00
+ ,0.43
+ ,0.57
+ ,3.30
+ ,6.30
+ ,210.00
+ ,0.37
+ ,0.82
+ ,11.20
+ ,7.10
+ ,240.00
+ ,0.50
+ ,0.71
+ ,10.50
+ ,6.80
+ ,225.00
+ ,0.40
+ ,0.77
+ ,10.10
+ ,7.30
+ ,263.00
+ ,0.48
+ ,0.66
+ ,7.20
+ ,6.40
+ ,210.00
+ ,0.48
+ ,0.24
+ ,13.60
+ ,6.80
+ ,235.00
+ ,0.43
+ ,0.73
+ ,9.00
+ ,7.20
+ ,230.00
+ ,0.56
+ ,0.72
+ ,24.60
+ ,6.40
+ ,190.00
+ ,0.44
+ ,0.76
+ ,12.60
+ ,6.60
+ ,220.00
+ ,0.49
+ ,0.75
+ ,5.60
+ ,6.80
+ ,210.00
+ ,0.40
+ ,0.74
+ ,8.70
+ ,6.10
+ ,180.00
+ ,0.42
+ ,0.71
+ ,7.70
+ ,6.50
+ ,235.00
+ ,0.49
+ ,0.74
+ ,24.10
+ ,6.40
+ ,185.00
+ ,0.48
+ ,0.86
+ ,11.70
+ ,6.00
+ ,175.00
+ ,0.39
+ ,0.72
+ ,7.70
+ ,6.00
+ ,192.00
+ ,0.44
+ ,0.79
+ ,9.60
+ ,7.30
+ ,263.00
+ ,0.48
+ ,0.66
+ ,7.20
+ ,6.10
+ ,180.00
+ ,0.34
+ ,0.82
+ ,12.30
+ ,6.70
+ ,240.00
+ ,0.52
+ ,0.73
+ ,8.90
+ ,6.40
+ ,210.00
+ ,0.48
+ ,0.85
+ ,13.60
+ ,5.80
+ ,160.00
+ ,0.41
+ ,0.81
+ ,11.20
+ ,6.90
+ ,230.00
+ ,0.41
+ ,0.60
+ ,2.80
+ ,7.00
+ ,245.00
+ ,0.41
+ ,0.57
+ ,3.20
+ ,7.30
+ ,228.00
+ ,0.45
+ ,0.73
+ ,9.40
+ ,5.90
+ ,155.00
+ ,0.29
+ ,0.71
+ ,11.90
+ ,6.20
+ ,200.00
+ ,0.45
+ ,0.80
+ ,15.40
+ ,6.80
+ ,235.00
+ ,0.55
+ ,0.78
+ ,7.40
+ ,7.00
+ ,235.00
+ ,0.48
+ ,0.74
+ ,18.90
+ ,5.90
+ ,105.00
+ ,0.36
+ ,0.84
+ ,7.90
+ ,6.10
+ ,180.00
+ ,0.53
+ ,0.79
+ ,12.20
+ ,5.70
+ ,185.00
+ ,0.35
+ ,0.70
+ ,11.00
+ ,7.10
+ ,245.00
+ ,0.41
+ ,0.78
+ ,2.80
+ ,5.80
+ ,180.00
+ ,0.43
+ ,0.87
+ ,11.80
+ ,7.40
+ ,240.00
+ ,0.60
+ ,0.71
+ ,17.10
+ ,6.80
+ ,225.00
+ ,0.48
+ ,0.70
+ ,11.60
+ ,6.80
+ ,215.00
+ ,0.46
+ ,0.73
+ ,5.80
+ ,7.00
+ ,230.00
+ ,0.44
+ ,0.76
+ ,8.30)
+ ,dim=c(5
+ ,54)
+ ,dimnames=list(c('X1'
+ ,'X2'
+ ,'X3'
+ ,'X4'
+ ,'X5
')
+ ,1:54))
> y <- array(NA,dim=c(5,54),dimnames=list(c('X1','X2','X3','X4','X5
'),1:54))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
X1 X2 X3 X4 X5\r
1 6.8 225 0.44 0.67 9.2
2 6.3 180 0.44 0.80 11.7
3 6.4 190 0.46 0.76 15.8
4 6.2 180 0.42 0.65 8.6
5 6.9 205 0.45 0.90 23.2
6 6.4 225 0.43 0.78 27.4
7 6.3 185 0.49 0.77 9.3
8 6.8 235 0.47 0.75 16.0
9 6.9 235 0.44 0.82 4.7
10 6.7 210 0.48 0.83 12.5
11 6.9 245 0.52 0.63 20.1
12 6.9 245 0.49 0.76 9.1
13 6.3 185 0.37 0.71 8.1
14 6.1 185 0.42 0.78 8.6
15 6.2 180 0.44 0.78 20.3
16 6.8 220 0.50 0.88 25.0
17 6.5 194 0.50 0.83 19.2
18 7.6 225 0.43 0.57 3.3
19 6.3 210 0.37 0.82 11.2
20 7.1 240 0.50 0.71 10.5
21 6.8 225 0.40 0.77 10.1
22 7.3 263 0.48 0.66 7.2
23 6.4 210 0.48 0.24 13.6
24 6.8 235 0.43 0.73 9.0
25 7.2 230 0.56 0.72 24.6
26 6.4 190 0.44 0.76 12.6
27 6.6 220 0.49 0.75 5.6
28 6.8 210 0.40 0.74 8.7
29 6.1 180 0.42 0.71 7.7
30 6.5 235 0.49 0.74 24.1
31 6.4 185 0.48 0.86 11.7
32 6.0 175 0.39 0.72 7.7
33 6.0 192 0.44 0.79 9.6
34 7.3 263 0.48 0.66 7.2
35 6.1 180 0.34 0.82 12.3
36 6.7 240 0.52 0.73 8.9
37 6.4 210 0.48 0.85 13.6
38 5.8 160 0.41 0.81 11.2
39 6.9 230 0.41 0.60 2.8
40 7.0 245 0.41 0.57 3.2
41 7.3 228 0.45 0.73 9.4
42 5.9 155 0.29 0.71 11.9
43 6.2 200 0.45 0.80 15.4
44 6.8 235 0.55 0.78 7.4
45 7.0 235 0.48 0.74 18.9
46 5.9 105 0.36 0.84 7.9
47 6.1 180 0.53 0.79 12.2
48 5.7 185 0.35 0.70 11.0
49 7.1 245 0.41 0.78 2.8
50 5.8 180 0.43 0.87 11.8
51 7.4 240 0.60 0.71 17.1
52 6.8 225 0.48 0.70 11.6
53 6.8 215 0.46 0.73 5.8
54 7.0 230 0.44 0.76 8.3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X2 X3 X4 `X5\\r`
3.788406 0.011522 1.122540 -0.038042 -0.008192
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.49609 -0.18586 0.01407 0.09178 0.78521
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.788406 0.448754 8.442 4.06e-11 ***
X2 0.011522 0.001441 7.998 1.92e-10 ***
X3 1.122540 0.783126 1.433 0.158
X4 -0.038042 0.377025 -0.101 0.920
`X5\\r` -0.008192 0.006628 -1.236 0.222
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2562 on 49 degrees of freedom
Multiple R-squared: 0.7119, Adjusted R-squared: 0.6883
F-statistic: 30.27 on 4 and 49 DF, p-value: 1.061e-12
> 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,] 0.298835572 0.59767114 0.70116443
[2,] 0.310980190 0.62196038 0.68901981
[3,] 0.184277409 0.36855482 0.81572259
[4,] 0.121527816 0.24305563 0.87847218
[5,] 0.075764989 0.15152998 0.92423501
[6,] 0.040089126 0.08017825 0.95991087
[7,] 0.050393278 0.10078656 0.94960672
[8,] 0.027102168 0.05420434 0.97289783
[9,] 0.014397579 0.02879516 0.98560242
[10,] 0.007126953 0.01425391 0.99287305
[11,] 0.554378620 0.89124276 0.44562138
[12,] 0.519853133 0.96029373 0.48014687
[13,] 0.434075521 0.86815104 0.56592448
[14,] 0.353339667 0.70667933 0.64666033
[15,] 0.276374237 0.55274847 0.72362576
[16,] 0.284443985 0.56888797 0.71555602
[17,] 0.222953399 0.44590680 0.77704660
[18,] 0.269881591 0.53976318 0.73011841
[19,] 0.206577781 0.41315556 0.79342222
[20,] 0.197699479 0.39539896 0.80230052
[21,] 0.191362525 0.38272505 0.80863747
[22,] 0.157571049 0.31514210 0.84242895
[23,] 0.168225289 0.33645058 0.83177471
[24,] 0.126026820 0.25205364 0.87397318
[25,] 0.100467840 0.20093568 0.89953216
[26,] 0.142233542 0.28446708 0.85776646
[27,] 0.098294413 0.19658883 0.90170559
[28,] 0.065454750 0.13090950 0.93454525
[29,] 0.082058109 0.16411622 0.91794189
[30,] 0.061071304 0.12214261 0.93892870
[31,] 0.045096573 0.09019315 0.95490343
[32,] 0.027167356 0.05433471 0.97283264
[33,] 0.018686534 0.03737307 0.98131347
[34,] 0.046176357 0.09235271 0.95382364
[35,] 0.027691180 0.05538236 0.97230882
[36,] 0.018112534 0.03622507 0.98188747
[37,] 0.016111359 0.03222272 0.98388864
[38,] 0.025054400 0.05010880 0.97494560
[39,] 0.917170041 0.16565992 0.08282996
> postscript(file="/var/wessaorg/rcomp/tmp/1cavj1355174877.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/2r2r61355174877.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3b99t1355174877.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4aypz1355174877.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5fufy1355174877.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 54
Frequency = 1
1 2 3 4 5 6
0.026121870 0.070030142 0.064427483 -0.038621277 0.468773816 -0.209369674
7 8 9 10 11 12
-0.064508589 -0.064022126 -0.020255060 0.087168766 -0.106344363 -0.157837097
13 14 15 16 17 18
0.058083009 -0.191284923 0.039722393 0.153804559 0.103955081 0.785208946
19 20 21 22 23 24
-0.200382146 0.098113637 0.082200690 0.026625968 -0.226264710 -0.077226914
25 26 27 28 29 30
0.361870242 0.060663178 -0.198844606 0.242417725 -0.143711741 -0.320496363
31 32 33 34 35 36
0.069801941 -0.152045983 -0.385815871 0.026625968 -0.012039684 -0.336683870
37 38 39 40 41 42
-0.203058947 -0.169572799 0.047095765 -0.023596024 0.484251966 0.124671465
43 44 45 46 47 48
-0.241320589 -0.223137151 0.148129491 0.594361644 -0.227282767 -0.496089175
49 50 51 52 53 54
0.081115957 -0.415262280 0.339928294 0.002022867 0.093318329 0.164563536
> postscript(file="/var/wessaorg/rcomp/tmp/6qyv41355174877.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 = 54
Frequency = 1
lag(myerror, k = 1) myerror
0 0.026121870 NA
1 0.070030142 0.026121870
2 0.064427483 0.070030142
3 -0.038621277 0.064427483
4 0.468773816 -0.038621277
5 -0.209369674 0.468773816
6 -0.064508589 -0.209369674
7 -0.064022126 -0.064508589
8 -0.020255060 -0.064022126
9 0.087168766 -0.020255060
10 -0.106344363 0.087168766
11 -0.157837097 -0.106344363
12 0.058083009 -0.157837097
13 -0.191284923 0.058083009
14 0.039722393 -0.191284923
15 0.153804559 0.039722393
16 0.103955081 0.153804559
17 0.785208946 0.103955081
18 -0.200382146 0.785208946
19 0.098113637 -0.200382146
20 0.082200690 0.098113637
21 0.026625968 0.082200690
22 -0.226264710 0.026625968
23 -0.077226914 -0.226264710
24 0.361870242 -0.077226914
25 0.060663178 0.361870242
26 -0.198844606 0.060663178
27 0.242417725 -0.198844606
28 -0.143711741 0.242417725
29 -0.320496363 -0.143711741
30 0.069801941 -0.320496363
31 -0.152045983 0.069801941
32 -0.385815871 -0.152045983
33 0.026625968 -0.385815871
34 -0.012039684 0.026625968
35 -0.336683870 -0.012039684
36 -0.203058947 -0.336683870
37 -0.169572799 -0.203058947
38 0.047095765 -0.169572799
39 -0.023596024 0.047095765
40 0.484251966 -0.023596024
41 0.124671465 0.484251966
42 -0.241320589 0.124671465
43 -0.223137151 -0.241320589
44 0.148129491 -0.223137151
45 0.594361644 0.148129491
46 -0.227282767 0.594361644
47 -0.496089175 -0.227282767
48 0.081115957 -0.496089175
49 -0.415262280 0.081115957
50 0.339928294 -0.415262280
51 0.002022867 0.339928294
52 0.093318329 0.002022867
53 0.164563536 0.093318329
54 NA 0.164563536
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.070030142 0.026121870
[2,] 0.064427483 0.070030142
[3,] -0.038621277 0.064427483
[4,] 0.468773816 -0.038621277
[5,] -0.209369674 0.468773816
[6,] -0.064508589 -0.209369674
[7,] -0.064022126 -0.064508589
[8,] -0.020255060 -0.064022126
[9,] 0.087168766 -0.020255060
[10,] -0.106344363 0.087168766
[11,] -0.157837097 -0.106344363
[12,] 0.058083009 -0.157837097
[13,] -0.191284923 0.058083009
[14,] 0.039722393 -0.191284923
[15,] 0.153804559 0.039722393
[16,] 0.103955081 0.153804559
[17,] 0.785208946 0.103955081
[18,] -0.200382146 0.785208946
[19,] 0.098113637 -0.200382146
[20,] 0.082200690 0.098113637
[21,] 0.026625968 0.082200690
[22,] -0.226264710 0.026625968
[23,] -0.077226914 -0.226264710
[24,] 0.361870242 -0.077226914
[25,] 0.060663178 0.361870242
[26,] -0.198844606 0.060663178
[27,] 0.242417725 -0.198844606
[28,] -0.143711741 0.242417725
[29,] -0.320496363 -0.143711741
[30,] 0.069801941 -0.320496363
[31,] -0.152045983 0.069801941
[32,] -0.385815871 -0.152045983
[33,] 0.026625968 -0.385815871
[34,] -0.012039684 0.026625968
[35,] -0.336683870 -0.012039684
[36,] -0.203058947 -0.336683870
[37,] -0.169572799 -0.203058947
[38,] 0.047095765 -0.169572799
[39,] -0.023596024 0.047095765
[40,] 0.484251966 -0.023596024
[41,] 0.124671465 0.484251966
[42,] -0.241320589 0.124671465
[43,] -0.223137151 -0.241320589
[44,] 0.148129491 -0.223137151
[45,] 0.594361644 0.148129491
[46,] -0.227282767 0.594361644
[47,] -0.496089175 -0.227282767
[48,] 0.081115957 -0.496089175
[49,] -0.415262280 0.081115957
[50,] 0.339928294 -0.415262280
[51,] 0.002022867 0.339928294
[52,] 0.093318329 0.002022867
[53,] 0.164563536 0.093318329
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.070030142 0.026121870
2 0.064427483 0.070030142
3 -0.038621277 0.064427483
4 0.468773816 -0.038621277
5 -0.209369674 0.468773816
6 -0.064508589 -0.209369674
7 -0.064022126 -0.064508589
8 -0.020255060 -0.064022126
9 0.087168766 -0.020255060
10 -0.106344363 0.087168766
11 -0.157837097 -0.106344363
12 0.058083009 -0.157837097
13 -0.191284923 0.058083009
14 0.039722393 -0.191284923
15 0.153804559 0.039722393
16 0.103955081 0.153804559
17 0.785208946 0.103955081
18 -0.200382146 0.785208946
19 0.098113637 -0.200382146
20 0.082200690 0.098113637
21 0.026625968 0.082200690
22 -0.226264710 0.026625968
23 -0.077226914 -0.226264710
24 0.361870242 -0.077226914
25 0.060663178 0.361870242
26 -0.198844606 0.060663178
27 0.242417725 -0.198844606
28 -0.143711741 0.242417725
29 -0.320496363 -0.143711741
30 0.069801941 -0.320496363
31 -0.152045983 0.069801941
32 -0.385815871 -0.152045983
33 0.026625968 -0.385815871
34 -0.012039684 0.026625968
35 -0.336683870 -0.012039684
36 -0.203058947 -0.336683870
37 -0.169572799 -0.203058947
38 0.047095765 -0.169572799
39 -0.023596024 0.047095765
40 0.484251966 -0.023596024
41 0.124671465 0.484251966
42 -0.241320589 0.124671465
43 -0.223137151 -0.241320589
44 0.148129491 -0.223137151
45 0.594361644 0.148129491
46 -0.227282767 0.594361644
47 -0.496089175 -0.227282767
48 0.081115957 -0.496089175
49 -0.415262280 0.081115957
50 0.339928294 -0.415262280
51 0.002022867 0.339928294
52 0.093318329 0.002022867
53 0.164563536 0.093318329
> 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/7jxtq1355174877.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/84qdl1355174877.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/9ko5l1355174877.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/10788c1355174877.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11h8r41355174877.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12c14e1355174877.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13hyy91355174877.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14t7av1355174877.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/15ync41355174877.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/16bylm1355174877.tab")
+ }
>
> try(system("convert tmp/1cavj1355174877.ps tmp/1cavj1355174877.png",intern=TRUE))
character(0)
> try(system("convert tmp/2r2r61355174877.ps tmp/2r2r61355174877.png",intern=TRUE))
character(0)
> try(system("convert tmp/3b99t1355174877.ps tmp/3b99t1355174877.png",intern=TRUE))
character(0)
> try(system("convert tmp/4aypz1355174877.ps tmp/4aypz1355174877.png",intern=TRUE))
character(0)
> try(system("convert tmp/5fufy1355174877.ps tmp/5fufy1355174877.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qyv41355174877.ps tmp/6qyv41355174877.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jxtq1355174877.ps tmp/7jxtq1355174877.png",intern=TRUE))
character(0)
> try(system("convert tmp/84qdl1355174877.ps tmp/84qdl1355174877.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ko5l1355174877.ps tmp/9ko5l1355174877.png",intern=TRUE))
character(0)
> try(system("convert tmp/10788c1355174877.ps tmp/10788c1355174877.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.079 0.884 6.974