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.
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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(1
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+ ,0)
+ ,dim=c(6
+ ,86)
+ ,dimnames=list(c('UseLimit'
+ ,'T40'
+ ,'Used'
+ ,'CorrectAnalysis'
+ ,'Useful'
+ ,'Outcome
')
+ ,1:86))
> y <- array(NA,dim=c(6,86),dimnames=list(c('UseLimit','T40','Used','CorrectAnalysis','Useful','Outcome
'),1:86))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> 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
CorrectAnalysis UseLimit T40 Used Useful Outcome\r
1 0 1 1 0 0 1
2 0 0 0 0 0 0
3 0 0 0 0 0 0
4 0 0 0 0 0 0
5 0 0 0 0 0 0
6 0 1 0 0 1 1
7 0 0 0 0 0 0
8 0 0 1 0 0 0
9 0 0 0 0 0 1
10 0 1 0 0 0 0
11 0 1 1 0 0 0
12 0 0 0 0 0 0
13 0 0 0 1 1 0
14 0 1 1 0 0 0
15 0 0 0 1 1 1
16 0 0 1 1 1 1
17 1 1 1 1 1 0
18 0 1 1 0 0 0
19 0 0 0 0 0 1
20 1 0 1 1 1 1
21 0 1 0 0 1 0
22 0 1 0 1 1 1
23 0 0 0 0 1 1
24 0 1 0 0 1 1
25 0 0 1 1 0 1
26 0 0 0 1 1 0
27 0 1 0 0 0 1
28 0 0 0 1 0 0
29 0 0 0 0 0 1
30 0 0 0 0 1 0
31 0 0 0 0 0 0
32 0 1 0 0 0 0
33 0 1 0 0 1 0
34 0 0 1 0 0 1
35 0 0 0 0 0 0
36 0 0 0 0 0 0
37 0 1 1 1 1 0
38 0 0 0 1 0 1
39 0 0 0 0 1 1
40 0 0 1 0 1 0
41 1 0 0 1 1 1
42 0 0 0 1 0 1
43 0 1 0 0 1 1
44 0 1 1 0 0 0
45 0 0 0 0 1 0
46 0 0 0 0 1 1
47 0 0 0 0 0 0
48 0 0 0 0 0 1
49 0 0 0 0 1 1
50 0 0 0 0 0 0
51 0 0 1 1 0 0
52 1 1 1 1 1 0
53 0 0 0 0 0 1
54 1 0 0 1 0 0
55 0 0 0 0 0 0
56 0 0 1 1 0 1
57 0 0 0 1 1 1
58 0 0 0 0 0 1
59 0 0 0 0 0 1
60 1 1 1 1 1 1
61 0 1 1 0 0 1
62 0 0 0 1 1 0
63 0 0 0 0 0 0
64 0 1 1 0 0 1
65 0 0 0 0 0 0
66 0 0 0 0 0 0
67 1 0 1 1 1 0
68 0 1 0 0 0 0
69 0 0 0 0 0 1
70 0 0 0 1 0 0
71 0 0 0 0 0 0
72 0 0 0 0 0 1
73 0 0 0 1 0 1
74 0 1 0 1 0 0
75 0 0 0 0 0 1
76 0 0 1 0 1 1
77 0 0 0 0 0 1
78 0 0 0 1 1 1
79 1 0 1 1 0 1
80 0 0 1 0 1 0
81 0 0 0 0 0 0
82 0 1 0 1 0 1
83 0 0 0 0 0 0
84 1 0 0 1 0 0
85 0 0 0 0 1 1
86 0 1 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) UseLimit T40 Used Useful
-0.025501 -0.007484 0.150191 0.280285 0.063988
`Outcome\\r`
-0.046232
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.46148 -0.13801 0.02037 0.03299 0.74522
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.025501 0.049180 -0.519 0.6055
UseLimit -0.007484 0.067009 -0.112 0.9113
T40 0.150191 0.068758 2.184 0.0319 *
Used 0.280285 0.065026 4.310 4.6e-05 ***
Useful 0.063988 0.063324 1.010 0.3153
`Outcome\\r` -0.046232 0.058251 -0.794 0.4297
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2653 on 80 degrees of freedom
Multiple R-squared: 0.3014, Adjusted R-squared: 0.2578
F-statistic: 6.904 on 5 and 80 DF, p-value: 2.104e-05
> 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,] 0.00000000 0.00000000 1.0000000
[2,] 0.00000000 0.00000000 1.0000000
[3,] 0.00000000 0.00000000 1.0000000
[4,] 0.00000000 0.00000000 1.0000000
[5,] 0.00000000 0.00000000 1.0000000
[6,] 0.00000000 0.00000000 1.0000000
[7,] 0.00000000 0.00000000 1.0000000
[8,] 0.00000000 0.00000000 1.0000000
[9,] 0.16651077 0.33302153 0.8334892
[10,] 0.13262392 0.26524783 0.8673761
[11,] 0.10973609 0.21947219 0.8902639
[12,] 0.51022835 0.97954331 0.4897717
[13,] 0.42708628 0.85417256 0.5729137
[14,] 0.40479099 0.80958198 0.5952090
[15,] 0.32809553 0.65619106 0.6719045
[16,] 0.25996285 0.51992570 0.7400372
[17,] 0.25944367 0.51888735 0.7405563
[18,] 0.26173034 0.52346068 0.7382697
[19,] 0.22043269 0.44086537 0.7795673
[20,] 0.18498959 0.36997919 0.8150104
[21,] 0.14656524 0.29313049 0.8534348
[22,] 0.11178880 0.22357759 0.8882112
[23,] 0.08223682 0.16447364 0.9177632
[24,] 0.05934000 0.11868001 0.9406600
[25,] 0.04186241 0.08372483 0.9581376
[26,] 0.02981090 0.05962180 0.9701891
[27,] 0.02005172 0.04010344 0.9799483
[28,] 0.01314609 0.02629218 0.9868539
[29,] 0.02602167 0.05204335 0.9739783
[30,] 0.01974426 0.03948852 0.9802557
[31,] 0.01294759 0.02589517 0.9870524
[32,] 0.01150242 0.02300485 0.9884976
[33,] 0.16774887 0.33549774 0.8322511
[34,] 0.14556430 0.29112860 0.8544357
[35,] 0.11203573 0.22407145 0.8879643
[36,] 0.09104755 0.18209511 0.9089524
[37,] 0.06757171 0.13514342 0.9324283
[38,] 0.04917167 0.09834333 0.9508283
[39,] 0.03478304 0.06956608 0.9652170
[40,] 0.02449390 0.04898779 0.9755061
[41,] 0.01673536 0.03347072 0.9832646
[42,] 0.01102497 0.02204994 0.9889750
[43,] 0.02868874 0.05737748 0.9713113
[44,] 0.08836239 0.17672479 0.9116376
[45,] 0.06672859 0.13345718 0.9332714
[46,] 0.31273849 0.62547699 0.6872615
[47,] 0.25438687 0.50877375 0.7456131
[48,] 0.46735662 0.93471324 0.5326434
[49,] 0.43849668 0.87699336 0.5615033
[50,] 0.37497772 0.74995545 0.6250223
[51,] 0.31486692 0.62973383 0.6851331
[52,] 0.64220226 0.71559549 0.3577977
[53,] 0.58486880 0.83026239 0.4151312
[54,] 0.56855298 0.86289404 0.4314470
[55,] 0.49390554 0.98781108 0.5060945
[56,] 0.43862266 0.87724532 0.5613773
[57,] 0.36336336 0.72672673 0.6366366
[58,] 0.29270936 0.58541873 0.7072906
[59,] 0.43773313 0.87546626 0.5622669
[60,] 0.37581049 0.75162099 0.6241895
[61,] 0.29440446 0.58880892 0.7055955
[62,] 0.40628887 0.81257773 0.5937111
[63,] 0.33158864 0.66317729 0.6684114
[64,] 0.24470714 0.48941428 0.7552929
[65,] 0.40854741 0.81709482 0.5914526
[66,] 0.39865097 0.79730194 0.6013490
[67,] 0.28350223 0.56700447 0.7164978
[68,] 0.18226298 0.36452596 0.8177370
[69,] 0.10190187 0.20380374 0.8980981
> postscript(file="/var/fisher/rcomp/tmp/1c44l1356107007.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/fisher/rcomp/tmp/2ilb41356107007.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3i2jf1356107007.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4mkcy1356107007.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5xl501356107007.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 86
Frequency = 1
1 2 3 4 5 6
-0.070973338 0.025501350 0.025501350 0.025501350 0.025501350 0.015229741
7 8 9 10 11 12
0.025501350 -0.124689670 0.071733275 0.032985758 -0.117205263 0.025501350
13 14 15 16 17 18
-0.318771848 -0.117205263 -0.272539923 -0.422730944 0.538521539 -0.117205263
19 20 21 22 23 24
0.071733275 0.577269056 -0.031002184 -0.265055515 0.007745333 0.015229741
25 26 27 28 29 30
-0.358743002 -0.318771848 0.079217683 -0.254783906 0.071733275 -0.038486591
31 32 33 34 35 36
0.025501350 0.032985758 -0.031002184 -0.078457745 0.025501350 0.025501350
37 38 39 40 41 42
-0.461478461 -0.208551981 0.007745333 -0.188677612 0.727460077 -0.208551981
43 44 45 46 47 48
0.015229741 -0.117205263 -0.038486591 0.007745333 0.025501350 0.071733275
49 50 51 52 53 54
0.007745333 0.025501350 -0.404974927 0.538521539 0.071733275 0.745216094
55 56 57 58 59 60
0.025501350 -0.358743002 -0.272539923 0.071733275 0.071733275 0.584753464
61 62 63 64 65 66
-0.070973338 -0.318771848 0.025501350 -0.070973338 0.025501350 0.025501350
67 68 69 70 71 72
0.531037131 0.032985758 0.071733275 -0.254783906 0.025501350 0.071733275
73 74 75 76 77 78
-0.208551981 -0.247299498 0.071733275 -0.142445687 0.071733275 -0.272539923
79 80 81 82 83 84
0.641256998 -0.188677612 0.025501350 -0.201067574 0.025501350 0.745216094
85 86
0.007745333 0.032985758
> postscript(file="/var/fisher/rcomp/tmp/6iamf1356107008.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 = 86
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.070973338 NA
1 0.025501350 -0.070973338
2 0.025501350 0.025501350
3 0.025501350 0.025501350
4 0.025501350 0.025501350
5 0.015229741 0.025501350
6 0.025501350 0.015229741
7 -0.124689670 0.025501350
8 0.071733275 -0.124689670
9 0.032985758 0.071733275
10 -0.117205263 0.032985758
11 0.025501350 -0.117205263
12 -0.318771848 0.025501350
13 -0.117205263 -0.318771848
14 -0.272539923 -0.117205263
15 -0.422730944 -0.272539923
16 0.538521539 -0.422730944
17 -0.117205263 0.538521539
18 0.071733275 -0.117205263
19 0.577269056 0.071733275
20 -0.031002184 0.577269056
21 -0.265055515 -0.031002184
22 0.007745333 -0.265055515
23 0.015229741 0.007745333
24 -0.358743002 0.015229741
25 -0.318771848 -0.358743002
26 0.079217683 -0.318771848
27 -0.254783906 0.079217683
28 0.071733275 -0.254783906
29 -0.038486591 0.071733275
30 0.025501350 -0.038486591
31 0.032985758 0.025501350
32 -0.031002184 0.032985758
33 -0.078457745 -0.031002184
34 0.025501350 -0.078457745
35 0.025501350 0.025501350
36 -0.461478461 0.025501350
37 -0.208551981 -0.461478461
38 0.007745333 -0.208551981
39 -0.188677612 0.007745333
40 0.727460077 -0.188677612
41 -0.208551981 0.727460077
42 0.015229741 -0.208551981
43 -0.117205263 0.015229741
44 -0.038486591 -0.117205263
45 0.007745333 -0.038486591
46 0.025501350 0.007745333
47 0.071733275 0.025501350
48 0.007745333 0.071733275
49 0.025501350 0.007745333
50 -0.404974927 0.025501350
51 0.538521539 -0.404974927
52 0.071733275 0.538521539
53 0.745216094 0.071733275
54 0.025501350 0.745216094
55 -0.358743002 0.025501350
56 -0.272539923 -0.358743002
57 0.071733275 -0.272539923
58 0.071733275 0.071733275
59 0.584753464 0.071733275
60 -0.070973338 0.584753464
61 -0.318771848 -0.070973338
62 0.025501350 -0.318771848
63 -0.070973338 0.025501350
64 0.025501350 -0.070973338
65 0.025501350 0.025501350
66 0.531037131 0.025501350
67 0.032985758 0.531037131
68 0.071733275 0.032985758
69 -0.254783906 0.071733275
70 0.025501350 -0.254783906
71 0.071733275 0.025501350
72 -0.208551981 0.071733275
73 -0.247299498 -0.208551981
74 0.071733275 -0.247299498
75 -0.142445687 0.071733275
76 0.071733275 -0.142445687
77 -0.272539923 0.071733275
78 0.641256998 -0.272539923
79 -0.188677612 0.641256998
80 0.025501350 -0.188677612
81 -0.201067574 0.025501350
82 0.025501350 -0.201067574
83 0.745216094 0.025501350
84 0.007745333 0.745216094
85 0.032985758 0.007745333
86 NA 0.032985758
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.025501350 -0.070973338
[2,] 0.025501350 0.025501350
[3,] 0.025501350 0.025501350
[4,] 0.025501350 0.025501350
[5,] 0.015229741 0.025501350
[6,] 0.025501350 0.015229741
[7,] -0.124689670 0.025501350
[8,] 0.071733275 -0.124689670
[9,] 0.032985758 0.071733275
[10,] -0.117205263 0.032985758
[11,] 0.025501350 -0.117205263
[12,] -0.318771848 0.025501350
[13,] -0.117205263 -0.318771848
[14,] -0.272539923 -0.117205263
[15,] -0.422730944 -0.272539923
[16,] 0.538521539 -0.422730944
[17,] -0.117205263 0.538521539
[18,] 0.071733275 -0.117205263
[19,] 0.577269056 0.071733275
[20,] -0.031002184 0.577269056
[21,] -0.265055515 -0.031002184
[22,] 0.007745333 -0.265055515
[23,] 0.015229741 0.007745333
[24,] -0.358743002 0.015229741
[25,] -0.318771848 -0.358743002
[26,] 0.079217683 -0.318771848
[27,] -0.254783906 0.079217683
[28,] 0.071733275 -0.254783906
[29,] -0.038486591 0.071733275
[30,] 0.025501350 -0.038486591
[31,] 0.032985758 0.025501350
[32,] -0.031002184 0.032985758
[33,] -0.078457745 -0.031002184
[34,] 0.025501350 -0.078457745
[35,] 0.025501350 0.025501350
[36,] -0.461478461 0.025501350
[37,] -0.208551981 -0.461478461
[38,] 0.007745333 -0.208551981
[39,] -0.188677612 0.007745333
[40,] 0.727460077 -0.188677612
[41,] -0.208551981 0.727460077
[42,] 0.015229741 -0.208551981
[43,] -0.117205263 0.015229741
[44,] -0.038486591 -0.117205263
[45,] 0.007745333 -0.038486591
[46,] 0.025501350 0.007745333
[47,] 0.071733275 0.025501350
[48,] 0.007745333 0.071733275
[49,] 0.025501350 0.007745333
[50,] -0.404974927 0.025501350
[51,] 0.538521539 -0.404974927
[52,] 0.071733275 0.538521539
[53,] 0.745216094 0.071733275
[54,] 0.025501350 0.745216094
[55,] -0.358743002 0.025501350
[56,] -0.272539923 -0.358743002
[57,] 0.071733275 -0.272539923
[58,] 0.071733275 0.071733275
[59,] 0.584753464 0.071733275
[60,] -0.070973338 0.584753464
[61,] -0.318771848 -0.070973338
[62,] 0.025501350 -0.318771848
[63,] -0.070973338 0.025501350
[64,] 0.025501350 -0.070973338
[65,] 0.025501350 0.025501350
[66,] 0.531037131 0.025501350
[67,] 0.032985758 0.531037131
[68,] 0.071733275 0.032985758
[69,] -0.254783906 0.071733275
[70,] 0.025501350 -0.254783906
[71,] 0.071733275 0.025501350
[72,] -0.208551981 0.071733275
[73,] -0.247299498 -0.208551981
[74,] 0.071733275 -0.247299498
[75,] -0.142445687 0.071733275
[76,] 0.071733275 -0.142445687
[77,] -0.272539923 0.071733275
[78,] 0.641256998 -0.272539923
[79,] -0.188677612 0.641256998
[80,] 0.025501350 -0.188677612
[81,] -0.201067574 0.025501350
[82,] 0.025501350 -0.201067574
[83,] 0.745216094 0.025501350
[84,] 0.007745333 0.745216094
[85,] 0.032985758 0.007745333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.025501350 -0.070973338
2 0.025501350 0.025501350
3 0.025501350 0.025501350
4 0.025501350 0.025501350
5 0.015229741 0.025501350
6 0.025501350 0.015229741
7 -0.124689670 0.025501350
8 0.071733275 -0.124689670
9 0.032985758 0.071733275
10 -0.117205263 0.032985758
11 0.025501350 -0.117205263
12 -0.318771848 0.025501350
13 -0.117205263 -0.318771848
14 -0.272539923 -0.117205263
15 -0.422730944 -0.272539923
16 0.538521539 -0.422730944
17 -0.117205263 0.538521539
18 0.071733275 -0.117205263
19 0.577269056 0.071733275
20 -0.031002184 0.577269056
21 -0.265055515 -0.031002184
22 0.007745333 -0.265055515
23 0.015229741 0.007745333
24 -0.358743002 0.015229741
25 -0.318771848 -0.358743002
26 0.079217683 -0.318771848
27 -0.254783906 0.079217683
28 0.071733275 -0.254783906
29 -0.038486591 0.071733275
30 0.025501350 -0.038486591
31 0.032985758 0.025501350
32 -0.031002184 0.032985758
33 -0.078457745 -0.031002184
34 0.025501350 -0.078457745
35 0.025501350 0.025501350
36 -0.461478461 0.025501350
37 -0.208551981 -0.461478461
38 0.007745333 -0.208551981
39 -0.188677612 0.007745333
40 0.727460077 -0.188677612
41 -0.208551981 0.727460077
42 0.015229741 -0.208551981
43 -0.117205263 0.015229741
44 -0.038486591 -0.117205263
45 0.007745333 -0.038486591
46 0.025501350 0.007745333
47 0.071733275 0.025501350
48 0.007745333 0.071733275
49 0.025501350 0.007745333
50 -0.404974927 0.025501350
51 0.538521539 -0.404974927
52 0.071733275 0.538521539
53 0.745216094 0.071733275
54 0.025501350 0.745216094
55 -0.358743002 0.025501350
56 -0.272539923 -0.358743002
57 0.071733275 -0.272539923
58 0.071733275 0.071733275
59 0.584753464 0.071733275
60 -0.070973338 0.584753464
61 -0.318771848 -0.070973338
62 0.025501350 -0.318771848
63 -0.070973338 0.025501350
64 0.025501350 -0.070973338
65 0.025501350 0.025501350
66 0.531037131 0.025501350
67 0.032985758 0.531037131
68 0.071733275 0.032985758
69 -0.254783906 0.071733275
70 0.025501350 -0.254783906
71 0.071733275 0.025501350
72 -0.208551981 0.071733275
73 -0.247299498 -0.208551981
74 0.071733275 -0.247299498
75 -0.142445687 0.071733275
76 0.071733275 -0.142445687
77 -0.272539923 0.071733275
78 0.641256998 -0.272539923
79 -0.188677612 0.641256998
80 0.025501350 -0.188677612
81 -0.201067574 0.025501350
82 0.025501350 -0.201067574
83 0.745216094 0.025501350
84 0.007745333 0.745216094
85 0.032985758 0.007745333
> 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/fisher/rcomp/tmp/7wfev1356107008.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/fisher/rcomp/tmp/87hzv1356107008.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/fisher/rcomp/tmp/94pg91356107008.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/fisher/rcomp/tmp/107brq1356107008.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11ck3g1356107008.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12ggaq1356107008.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13r3al1356107008.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14fmgh1356107008.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/fisher/rcomp/tmp/155pa01356107008.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/fisher/rcomp/tmp/168uuz1356107008.tab")
+ }
>
> try(system("convert tmp/1c44l1356107007.ps tmp/1c44l1356107007.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ilb41356107007.ps tmp/2ilb41356107007.png",intern=TRUE))
character(0)
> try(system("convert tmp/3i2jf1356107007.ps tmp/3i2jf1356107007.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mkcy1356107007.ps tmp/4mkcy1356107007.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xl501356107007.ps tmp/5xl501356107007.png",intern=TRUE))
character(0)
> try(system("convert tmp/6iamf1356107008.ps tmp/6iamf1356107008.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wfev1356107008.ps tmp/7wfev1356107008.png",intern=TRUE))
character(0)
> try(system("convert tmp/87hzv1356107008.ps tmp/87hzv1356107008.png",intern=TRUE))
character(0)
> try(system("convert tmp/94pg91356107008.ps tmp/94pg91356107008.png",intern=TRUE))
character(0)
> try(system("convert tmp/107brq1356107008.ps tmp/107brq1356107008.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.778 1.795 8.652