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 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(102.86
+ ,102.38
+ ,102.37
+ ,101.76
+ ,102.87
+ ,102.86
+ ,102.38
+ ,102.37
+ ,102.92
+ ,102.87
+ ,102.86
+ ,102.38
+ ,102.95
+ ,102.92
+ ,102.87
+ ,102.86
+ ,103.02
+ ,102.95
+ ,102.92
+ ,102.87
+ ,104.08
+ ,103.02
+ ,102.95
+ ,102.92
+ ,104.16
+ ,104.08
+ ,103.02
+ ,102.95
+ ,104.24
+ ,104.16
+ ,104.08
+ ,103.02
+ ,104.33
+ ,104.24
+ ,104.16
+ ,104.08
+ ,104.73
+ ,104.33
+ ,104.24
+ ,104.16
+ ,104.86
+ ,104.73
+ ,104.33
+ ,104.24
+ ,105.03
+ ,104.86
+ ,104.73
+ ,104.33
+ ,105.62
+ ,105.03
+ ,104.86
+ ,104.73
+ ,105.63
+ ,105.62
+ ,105.03
+ ,104.86
+ ,105.63
+ ,105.63
+ ,105.62
+ ,105.03
+ ,105.94
+ ,105.63
+ ,105.63
+ ,105.62
+ ,106.61
+ ,105.94
+ ,105.63
+ ,105.63
+ ,107.69
+ ,106.61
+ ,105.94
+ ,105.63
+ ,107.78
+ ,107.69
+ ,106.61
+ ,105.94
+ ,107.93
+ ,107.78
+ ,107.69
+ ,106.61
+ ,108.48
+ ,107.93
+ ,107.78
+ ,107.69
+ ,108.14
+ ,108.48
+ ,107.93
+ ,107.78
+ ,108.48
+ ,108.14
+ ,108.48
+ ,107.93
+ ,108.48
+ ,108.48
+ ,108.14
+ ,108.48
+ ,108.89
+ ,108.48
+ ,108.48
+ ,108.14
+ ,108.93
+ ,108.89
+ ,108.48
+ ,108.48
+ ,109.21
+ ,108.93
+ ,108.89
+ ,108.48
+ ,109.47
+ ,109.21
+ ,108.93
+ ,108.89
+ ,109.80
+ ,109.47
+ ,109.21
+ ,108.93
+ ,111.73
+ ,109.80
+ ,109.47
+ ,109.21
+ ,111.85
+ ,111.73
+ ,109.80
+ ,109.47
+ ,112.12
+ ,111.85
+ ,111.73
+ ,109.80
+ ,112.15
+ ,112.12
+ ,111.85
+ ,111.73
+ ,112.17
+ ,112.15
+ ,112.12
+ ,111.85
+ ,112.67
+ ,112.17
+ ,112.15
+ ,112.12
+ ,112.80
+ ,112.67
+ ,112.17
+ ,112.15
+ ,113.44
+ ,112.80
+ ,112.67
+ ,112.17
+ ,113.53
+ ,113.44
+ ,112.80
+ ,112.67
+ ,114.53
+ ,113.53
+ ,113.44
+ ,112.80
+ ,114.51
+ ,114.53
+ ,113.53
+ ,113.44
+ ,115.05
+ ,114.51
+ ,114.53
+ ,113.53
+ ,116.67
+ ,115.05
+ ,114.51
+ ,114.53
+ ,117.07
+ ,116.67
+ ,115.05
+ ,114.51
+ ,116.92
+ ,117.07
+ ,116.67
+ ,115.05
+ ,117.00
+ ,116.92
+ ,117.07
+ ,116.67
+ ,117.02
+ ,117.00
+ ,116.92
+ ,117.07
+ ,117.35
+ ,117.02
+ ,117.00
+ ,116.92
+ ,117.36
+ ,117.35
+ ,117.02
+ ,117.00
+ ,117.82
+ ,117.36
+ ,117.35
+ ,117.02
+ ,117.88
+ ,117.82
+ ,117.36
+ ,117.35
+ ,118.24
+ ,117.88
+ ,117.82
+ ,117.36
+ ,118.50
+ ,118.24
+ ,117.88
+ ,117.82
+ ,118.80
+ ,118.50
+ ,118.24
+ ,117.88
+ ,119.76
+ ,118.80
+ ,118.50
+ ,118.24
+ ,120.09
+ ,119.76
+ ,118.80
+ ,118.50)
+ ,dim=c(4
+ ,55)
+ ,dimnames=list(c('Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:55))
> y <- array(NA,dim=c(4,55),dimnames=list(c('Y1','Y2','Y3','Y4'),1:55))
> 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'
> 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
Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 102.86 102.38 102.37 101.76 1 0 0 0 0 0 0 0 0 0 0 1
2 102.87 102.86 102.38 102.37 0 1 0 0 0 0 0 0 0 0 0 2
3 102.92 102.87 102.86 102.38 0 0 1 0 0 0 0 0 0 0 0 3
4 102.95 102.92 102.87 102.86 0 0 0 1 0 0 0 0 0 0 0 4
5 103.02 102.95 102.92 102.87 0 0 0 0 1 0 0 0 0 0 0 5
6 104.08 103.02 102.95 102.92 0 0 0 0 0 1 0 0 0 0 0 6
7 104.16 104.08 103.02 102.95 0 0 0 0 0 0 1 0 0 0 0 7
8 104.24 104.16 104.08 103.02 0 0 0 0 0 0 0 1 0 0 0 8
9 104.33 104.24 104.16 104.08 0 0 0 0 0 0 0 0 1 0 0 9
10 104.73 104.33 104.24 104.16 0 0 0 0 0 0 0 0 0 1 0 10
11 104.86 104.73 104.33 104.24 0 0 0 0 0 0 0 0 0 0 1 11
12 105.03 104.86 104.73 104.33 0 0 0 0 0 0 0 0 0 0 0 12
13 105.62 105.03 104.86 104.73 1 0 0 0 0 0 0 0 0 0 0 13
14 105.63 105.62 105.03 104.86 0 1 0 0 0 0 0 0 0 0 0 14
15 105.63 105.63 105.62 105.03 0 0 1 0 0 0 0 0 0 0 0 15
16 105.94 105.63 105.63 105.62 0 0 0 1 0 0 0 0 0 0 0 16
17 106.61 105.94 105.63 105.63 0 0 0 0 1 0 0 0 0 0 0 17
18 107.69 106.61 105.94 105.63 0 0 0 0 0 1 0 0 0 0 0 18
19 107.78 107.69 106.61 105.94 0 0 0 0 0 0 1 0 0 0 0 19
20 107.93 107.78 107.69 106.61 0 0 0 0 0 0 0 1 0 0 0 20
21 108.48 107.93 107.78 107.69 0 0 0 0 0 0 0 0 1 0 0 21
22 108.14 108.48 107.93 107.78 0 0 0 0 0 0 0 0 0 1 0 22
23 108.48 108.14 108.48 107.93 0 0 0 0 0 0 0 0 0 0 1 23
24 108.48 108.48 108.14 108.48 0 0 0 0 0 0 0 0 0 0 0 24
25 108.89 108.48 108.48 108.14 1 0 0 0 0 0 0 0 0 0 0 25
26 108.93 108.89 108.48 108.48 0 1 0 0 0 0 0 0 0 0 0 26
27 109.21 108.93 108.89 108.48 0 0 1 0 0 0 0 0 0 0 0 27
28 109.47 109.21 108.93 108.89 0 0 0 1 0 0 0 0 0 0 0 28
29 109.80 109.47 109.21 108.93 0 0 0 0 1 0 0 0 0 0 0 29
30 111.73 109.80 109.47 109.21 0 0 0 0 0 1 0 0 0 0 0 30
31 111.85 111.73 109.80 109.47 0 0 0 0 0 0 1 0 0 0 0 31
32 112.12 111.85 111.73 109.80 0 0 0 0 0 0 0 1 0 0 0 32
33 112.15 112.12 111.85 111.73 0 0 0 0 0 0 0 0 1 0 0 33
34 112.17 112.15 112.12 111.85 0 0 0 0 0 0 0 0 0 1 0 34
35 112.67 112.17 112.15 112.12 0 0 0 0 0 0 0 0 0 0 1 35
36 112.80 112.67 112.17 112.15 0 0 0 0 0 0 0 0 0 0 0 36
37 113.44 112.80 112.67 112.17 1 0 0 0 0 0 0 0 0 0 0 37
38 113.53 113.44 112.80 112.67 0 1 0 0 0 0 0 0 0 0 0 38
39 114.53 113.53 113.44 112.80 0 0 1 0 0 0 0 0 0 0 0 39
40 114.51 114.53 113.53 113.44 0 0 0 1 0 0 0 0 0 0 0 40
41 115.05 114.51 114.53 113.53 0 0 0 0 1 0 0 0 0 0 0 41
42 116.67 115.05 114.51 114.53 0 0 0 0 0 1 0 0 0 0 0 42
43 117.07 116.67 115.05 114.51 0 0 0 0 0 0 1 0 0 0 0 43
44 116.92 117.07 116.67 115.05 0 0 0 0 0 0 0 1 0 0 0 44
45 117.00 116.92 117.07 116.67 0 0 0 0 0 0 0 0 1 0 0 45
46 117.02 117.00 116.92 117.07 0 0 0 0 0 0 0 0 0 1 0 46
47 117.35 117.02 117.00 116.92 0 0 0 0 0 0 0 0 0 0 1 47
48 117.36 117.35 117.02 117.00 0 0 0 0 0 0 0 0 0 0 0 48
49 117.82 117.36 117.35 117.02 1 0 0 0 0 0 0 0 0 0 0 49
50 117.88 117.82 117.36 117.35 0 1 0 0 0 0 0 0 0 0 0 50
51 118.24 117.88 117.82 117.36 0 0 1 0 0 0 0 0 0 0 0 51
52 118.50 118.24 117.88 117.82 0 0 0 1 0 0 0 0 0 0 0 52
53 118.80 118.50 118.24 117.88 0 0 0 0 1 0 0 0 0 0 0 53
54 119.76 118.80 118.50 118.24 0 0 0 0 0 1 0 0 0 0 0 54
55 120.09 119.76 118.80 118.50 0 0 0 0 0 0 1 0 0 0 0 55
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Y2 Y3 Y4 M1 M2
14.24778 0.75735 0.16233 -0.06052 0.40608 0.02095
M3 M4 M5 M6 M7 M8
0.19819 0.08555 0.23892 1.22373 0.31955 0.01813
M9 M10 M11 t
0.14839 -0.02145 0.21041 0.04906
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.41448 -0.09479 -0.02204 0.07512 0.60044
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.24778 6.17467 2.307 0.0264 *
Y2 0.75735 0.15831 4.784 2.47e-05 ***
Y3 0.16233 0.20230 0.802 0.4272
Y4 -0.06052 0.15490 -0.391 0.6982
M1 0.40608 0.16653 2.438 0.0194 *
M2 0.02095 0.15700 0.133 0.8945
M3 0.19819 0.17456 1.135 0.2632
M4 0.08555 0.15342 0.558 0.5803
M5 0.23892 0.16251 1.470 0.1495
M6 1.22373 0.15579 7.855 1.48e-09 ***
M7 0.31955 0.22858 1.398 0.1700
M8 0.01813 0.27090 0.067 0.9470
M9 0.14839 0.16884 0.879 0.3849
M10 -0.02145 0.16416 -0.131 0.8967
M11 0.21041 0.16874 1.247 0.2198
t 0.04906 0.02031 2.415 0.0205 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2284 on 39 degrees of freedom
Multiple R-squared: 0.9987, Adjusted R-squared: 0.9982
F-statistic: 1999 on 15 and 39 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.4754059 0.95081178 0.52459411
[2,] 0.3068296 0.61365925 0.69317038
[3,] 0.3046061 0.60921227 0.69539386
[4,] 0.7020003 0.59599938 0.29799969
[5,] 0.6330234 0.73395319 0.36697660
[6,] 0.5177163 0.96456743 0.48228372
[7,] 0.4934903 0.98698059 0.50650971
[8,] 0.4063018 0.81260355 0.59369823
[9,] 0.5727274 0.85454524 0.42727262
[10,] 0.6459133 0.70817336 0.35408668
[11,] 0.9043951 0.19120985 0.09560493
[12,] 0.9683468 0.06330638 0.03165319
[13,] 0.9606356 0.07872889 0.03936444
[14,] 0.9424105 0.11517910 0.05758955
[15,] 0.8934441 0.21311182 0.10655591
[16,] 0.8037779 0.39244421 0.19622211
[17,] 0.7323086 0.53538270 0.26769135
[18,] 0.5675184 0.86496320 0.43248160
> postscript(file="/var/wessaorg/rcomp/tmp/17o3z1322609431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ewg71322609431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3brzf1322609431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4q24y1322609431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/56iio1322609431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 55
Frequency = 1
1 2 3 4 5 6
0.160328614 0.178157059 -0.083031874 0.000100102 -0.162571208 -0.191306304
7 8 9 10 11 12
-0.068526716 0.035405596 -0.063336397 0.381124549 -0.082507303 0.090900804
13 14 15 16 17 18
0.100112761 -0.020388523 -0.339751013 0.067905291 0.301293284 -0.210328775
19 20 21 22 23 24
-0.173149746 0.026273175 0.334103917 -0.320572893 -0.084203752 -0.091875426
25 26 27 28 29 30
-0.212787138 -0.126660888 -0.169812247 -0.039976332 -0.152358122 0.468579451
31 32 33 34 35 36
-0.055820082 0.082324993 -0.174155711 -0.092676105 0.122722271 0.033966587
37 38 39 40 41 42
0.040413183 -0.009070625 0.600441835 -0.089209762 0.106613258 0.347533422
43 44 45 46 47 48
0.286877048 -0.144003764 -0.096611810 0.032124450 0.043988784 -0.032991965
49 50 51 52 53 54
-0.088067420 -0.022037023 -0.007846701 0.061180701 -0.092977213 -0.414477794
55
0.010619495
> postscript(file="/var/wessaorg/rcomp/tmp/6hfef1322609431.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 = 55
Frequency = 1
lag(myerror, k = 1) myerror
0 0.160328614 NA
1 0.178157059 0.160328614
2 -0.083031874 0.178157059
3 0.000100102 -0.083031874
4 -0.162571208 0.000100102
5 -0.191306304 -0.162571208
6 -0.068526716 -0.191306304
7 0.035405596 -0.068526716
8 -0.063336397 0.035405596
9 0.381124549 -0.063336397
10 -0.082507303 0.381124549
11 0.090900804 -0.082507303
12 0.100112761 0.090900804
13 -0.020388523 0.100112761
14 -0.339751013 -0.020388523
15 0.067905291 -0.339751013
16 0.301293284 0.067905291
17 -0.210328775 0.301293284
18 -0.173149746 -0.210328775
19 0.026273175 -0.173149746
20 0.334103917 0.026273175
21 -0.320572893 0.334103917
22 -0.084203752 -0.320572893
23 -0.091875426 -0.084203752
24 -0.212787138 -0.091875426
25 -0.126660888 -0.212787138
26 -0.169812247 -0.126660888
27 -0.039976332 -0.169812247
28 -0.152358122 -0.039976332
29 0.468579451 -0.152358122
30 -0.055820082 0.468579451
31 0.082324993 -0.055820082
32 -0.174155711 0.082324993
33 -0.092676105 -0.174155711
34 0.122722271 -0.092676105
35 0.033966587 0.122722271
36 0.040413183 0.033966587
37 -0.009070625 0.040413183
38 0.600441835 -0.009070625
39 -0.089209762 0.600441835
40 0.106613258 -0.089209762
41 0.347533422 0.106613258
42 0.286877048 0.347533422
43 -0.144003764 0.286877048
44 -0.096611810 -0.144003764
45 0.032124450 -0.096611810
46 0.043988784 0.032124450
47 -0.032991965 0.043988784
48 -0.088067420 -0.032991965
49 -0.022037023 -0.088067420
50 -0.007846701 -0.022037023
51 0.061180701 -0.007846701
52 -0.092977213 0.061180701
53 -0.414477794 -0.092977213
54 0.010619495 -0.414477794
55 NA 0.010619495
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.178157059 0.160328614
[2,] -0.083031874 0.178157059
[3,] 0.000100102 -0.083031874
[4,] -0.162571208 0.000100102
[5,] -0.191306304 -0.162571208
[6,] -0.068526716 -0.191306304
[7,] 0.035405596 -0.068526716
[8,] -0.063336397 0.035405596
[9,] 0.381124549 -0.063336397
[10,] -0.082507303 0.381124549
[11,] 0.090900804 -0.082507303
[12,] 0.100112761 0.090900804
[13,] -0.020388523 0.100112761
[14,] -0.339751013 -0.020388523
[15,] 0.067905291 -0.339751013
[16,] 0.301293284 0.067905291
[17,] -0.210328775 0.301293284
[18,] -0.173149746 -0.210328775
[19,] 0.026273175 -0.173149746
[20,] 0.334103917 0.026273175
[21,] -0.320572893 0.334103917
[22,] -0.084203752 -0.320572893
[23,] -0.091875426 -0.084203752
[24,] -0.212787138 -0.091875426
[25,] -0.126660888 -0.212787138
[26,] -0.169812247 -0.126660888
[27,] -0.039976332 -0.169812247
[28,] -0.152358122 -0.039976332
[29,] 0.468579451 -0.152358122
[30,] -0.055820082 0.468579451
[31,] 0.082324993 -0.055820082
[32,] -0.174155711 0.082324993
[33,] -0.092676105 -0.174155711
[34,] 0.122722271 -0.092676105
[35,] 0.033966587 0.122722271
[36,] 0.040413183 0.033966587
[37,] -0.009070625 0.040413183
[38,] 0.600441835 -0.009070625
[39,] -0.089209762 0.600441835
[40,] 0.106613258 -0.089209762
[41,] 0.347533422 0.106613258
[42,] 0.286877048 0.347533422
[43,] -0.144003764 0.286877048
[44,] -0.096611810 -0.144003764
[45,] 0.032124450 -0.096611810
[46,] 0.043988784 0.032124450
[47,] -0.032991965 0.043988784
[48,] -0.088067420 -0.032991965
[49,] -0.022037023 -0.088067420
[50,] -0.007846701 -0.022037023
[51,] 0.061180701 -0.007846701
[52,] -0.092977213 0.061180701
[53,] -0.414477794 -0.092977213
[54,] 0.010619495 -0.414477794
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.178157059 0.160328614
2 -0.083031874 0.178157059
3 0.000100102 -0.083031874
4 -0.162571208 0.000100102
5 -0.191306304 -0.162571208
6 -0.068526716 -0.191306304
7 0.035405596 -0.068526716
8 -0.063336397 0.035405596
9 0.381124549 -0.063336397
10 -0.082507303 0.381124549
11 0.090900804 -0.082507303
12 0.100112761 0.090900804
13 -0.020388523 0.100112761
14 -0.339751013 -0.020388523
15 0.067905291 -0.339751013
16 0.301293284 0.067905291
17 -0.210328775 0.301293284
18 -0.173149746 -0.210328775
19 0.026273175 -0.173149746
20 0.334103917 0.026273175
21 -0.320572893 0.334103917
22 -0.084203752 -0.320572893
23 -0.091875426 -0.084203752
24 -0.212787138 -0.091875426
25 -0.126660888 -0.212787138
26 -0.169812247 -0.126660888
27 -0.039976332 -0.169812247
28 -0.152358122 -0.039976332
29 0.468579451 -0.152358122
30 -0.055820082 0.468579451
31 0.082324993 -0.055820082
32 -0.174155711 0.082324993
33 -0.092676105 -0.174155711
34 0.122722271 -0.092676105
35 0.033966587 0.122722271
36 0.040413183 0.033966587
37 -0.009070625 0.040413183
38 0.600441835 -0.009070625
39 -0.089209762 0.600441835
40 0.106613258 -0.089209762
41 0.347533422 0.106613258
42 0.286877048 0.347533422
43 -0.144003764 0.286877048
44 -0.096611810 -0.144003764
45 0.032124450 -0.096611810
46 0.043988784 0.032124450
47 -0.032991965 0.043988784
48 -0.088067420 -0.032991965
49 -0.022037023 -0.088067420
50 -0.007846701 -0.022037023
51 0.061180701 -0.007846701
52 -0.092977213 0.061180701
53 -0.414477794 -0.092977213
54 0.010619495 -0.414477794
> 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/7e3iy1322609431.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/8sp8z1322609431.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/9ebyt1322609431.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/107e1j1322609431.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1160yw1322609431.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1222u91322609431.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13ldx21322609431.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14sbh71322609431.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/15fz671322609431.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/16pe631322609431.tab")
+ }
>
> try(system("convert tmp/17o3z1322609431.ps tmp/17o3z1322609431.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ewg71322609431.ps tmp/2ewg71322609431.png",intern=TRUE))
character(0)
> try(system("convert tmp/3brzf1322609431.ps tmp/3brzf1322609431.png",intern=TRUE))
character(0)
> try(system("convert tmp/4q24y1322609431.ps tmp/4q24y1322609431.png",intern=TRUE))
character(0)
> try(system("convert tmp/56iio1322609431.ps tmp/56iio1322609431.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hfef1322609431.ps tmp/6hfef1322609431.png",intern=TRUE))
character(0)
> try(system("convert tmp/7e3iy1322609431.ps tmp/7e3iy1322609431.png",intern=TRUE))
character(0)
> try(system("convert tmp/8sp8z1322609431.ps tmp/8sp8z1322609431.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ebyt1322609431.ps tmp/9ebyt1322609431.png",intern=TRUE))
character(0)
> try(system("convert tmp/107e1j1322609431.ps tmp/107e1j1322609431.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.110 0.522 3.648