R version 2.12.0 (2010-10-15)
Copyright (C) 2010 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 '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(-999
+ ,-999
+ ,38.6
+ ,6.654
+ ,5.712
+ ,645
+ ,3
+ ,5
+ ,3
+ ,6.3
+ ,2
+ ,4.5
+ ,1
+ ,6.6
+ ,42
+ ,3
+ ,1
+ ,3
+ ,-999
+ ,-999
+ ,14
+ ,3.385
+ ,44.5
+ ,60
+ ,1
+ ,1
+ ,1
+ ,-999
+ ,-999
+ ,-999
+ ,0.92
+ ,5.7
+ ,25
+ ,5
+ ,2
+ ,3
+ ,2.1
+ ,1.8
+ ,69
+ ,2547
+ ,4603
+ ,624
+ ,3
+ ,5
+ ,4
+ ,0.1
+ ,0.7
+ ,27
+ ,10.55
+ ,0.5
+ ,180
+ ,4
+ ,4
+ ,4
+ ,15.8
+ ,3.9
+ ,19
+ ,0.023
+ ,0.3
+ ,35
+ ,1
+ ,1
+ ,1
+ ,5.2
+ ,1
+ ,30.4
+ ,160
+ ,169
+ ,392
+ ,4
+ ,5
+ ,4
+ ,10.9
+ ,3.6
+ ,28
+ ,3.3
+ ,25.6
+ ,63
+ ,1
+ ,2
+ ,1
+ ,8.3
+ ,1.4
+ ,50
+ ,52.16
+ ,440
+ ,230
+ ,1
+ ,1
+ ,1
+ ,11
+ ,1.5
+ ,7
+ ,0.425
+ ,6.4
+ ,112
+ ,5
+ ,4
+ ,4
+ ,3.2
+ ,0.7
+ ,30
+ ,465
+ ,423
+ ,281
+ ,5
+ ,5
+ ,5
+ ,7.6
+ ,2.7
+ ,-999
+ ,0.55
+ ,2.4
+ ,-999
+ ,2
+ ,1
+ ,2
+ ,-999
+ ,-999
+ ,40
+ ,187.1
+ ,419
+ ,365
+ ,5
+ ,5
+ ,5
+ ,6.3
+ ,2.1
+ ,3.5
+ ,0.075
+ ,1.2
+ ,42
+ ,1
+ ,1
+ ,1
+ ,8.6
+ ,0
+ ,50
+ ,3
+ ,25
+ ,28
+ ,2
+ ,2
+ ,2
+ ,6.6
+ ,4.1
+ ,6
+ ,0.785
+ ,3.5
+ ,42
+ ,2
+ ,2
+ ,2
+ ,9.5
+ ,1.2
+ ,10.4
+ ,0.2
+ ,5
+ ,120
+ ,2
+ ,2
+ ,2
+ ,4.8
+ ,1.3
+ ,34
+ ,1.41
+ ,17.5
+ ,-999
+ ,1
+ ,2
+ ,1
+ ,12
+ ,6.1
+ ,7
+ ,60
+ ,81
+ ,-999
+ ,1
+ ,1
+ ,1
+ ,-999
+ ,0.3
+ ,28
+ ,529
+ ,680
+ ,400
+ ,5
+ ,5
+ ,5
+ ,3.3
+ ,0.5
+ ,20
+ ,27.66
+ ,115
+ ,148
+ ,5
+ ,5
+ ,5
+ ,11
+ ,3.4
+ ,3.9
+ ,0.12
+ ,1
+ ,16
+ ,3
+ ,1
+ ,2
+ ,-999
+ ,-999
+ ,39.3
+ ,207
+ ,406
+ ,252
+ ,1
+ ,4
+ ,1
+ ,4.7
+ ,1.5
+ ,41
+ ,85
+ ,325
+ ,310
+ ,1
+ ,3
+ ,1
+ ,-999
+ ,-999
+ ,16.2
+ ,36.33
+ ,119.5
+ ,63
+ ,1
+ ,1
+ ,1
+ ,10.4
+ ,3.4
+ ,9
+ ,0.101
+ ,4
+ ,28
+ ,5
+ ,1
+ ,3
+ ,7.4
+ ,0.8
+ ,7.6
+ ,1.04
+ ,5.5
+ ,68
+ ,5
+ ,3
+ ,4
+ ,2.1
+ ,0.8
+ ,46
+ ,521
+ ,655
+ ,336
+ ,5
+ ,5
+ ,5
+ ,2.1
+ ,-999
+ ,22.4
+ ,100
+ ,157
+ ,100
+ ,1
+ ,1
+ ,1
+ ,-999
+ ,-999
+ ,16.3
+ ,35
+ ,56
+ ,33
+ ,3
+ ,5
+ ,4
+ ,7.7
+ ,1.4
+ ,2.6
+ ,0.005
+ ,0.14
+ ,21.5
+ ,5
+ ,2
+ ,4
+ ,17.9
+ ,2
+ ,24
+ ,0.01
+ ,0.25
+ ,50
+ ,1
+ ,1
+ ,1
+ ,6.1
+ ,1.9
+ ,100
+ ,62
+ ,1320
+ ,267
+ ,1
+ ,1
+ ,1
+ ,8.2
+ ,2.4
+ ,-999
+ ,0.122
+ ,3
+ ,30
+ ,2
+ ,1
+ ,1
+ ,8.4
+ ,2.8
+ ,-999
+ ,1.35
+ ,8.1
+ ,45
+ ,3
+ ,1
+ ,3
+ ,11.9
+ ,1.3
+ ,3.2
+ ,0.23
+ ,0.4
+ ,19
+ ,4
+ ,1
+ ,3
+ ,10.8
+ ,2
+ ,2
+ ,0.048
+ ,0.33
+ ,30
+ ,4
+ ,1
+ ,3
+ ,13.8
+ ,5.6
+ ,5
+ ,1.7
+ ,6.3
+ ,12
+ ,2
+ ,1
+ ,1
+ ,14.3
+ ,3.1
+ ,6.5
+ ,3.5
+ ,10.8
+ ,120
+ ,2
+ ,1
+ ,1
+ ,-999
+ ,1
+ ,23.6
+ ,250
+ ,490
+ ,440
+ ,5
+ ,5
+ ,5
+ ,15.2
+ ,1.8
+ ,12
+ ,0.48
+ ,15.5
+ ,140
+ ,2
+ ,2
+ ,2
+ ,10
+ ,0.9
+ ,20.2
+ ,10
+ ,115
+ ,170
+ ,4
+ ,4
+ ,4
+ ,11.9
+ ,1.8
+ ,13
+ ,1.62
+ ,11.4
+ ,17
+ ,2
+ ,1
+ ,2
+ ,6.5
+ ,1.9
+ ,27
+ ,192
+ ,180
+ ,115
+ ,4
+ ,4
+ ,4
+ ,7.5
+ ,0.9
+ ,18
+ ,2.5
+ ,12.1
+ ,31
+ ,5
+ ,5
+ ,5
+ ,-999
+ ,-999
+ ,13.7
+ ,4.288
+ ,39.2
+ ,63
+ ,2
+ ,2
+ ,2
+ ,10.6
+ ,2.6
+ ,4.7
+ ,0.28
+ ,1.9
+ ,21
+ ,3
+ ,1
+ ,3
+ ,7.4
+ ,2.4
+ ,9.8
+ ,4.235
+ ,50.4
+ ,52
+ ,1
+ ,1
+ ,1
+ ,8.4
+ ,1.2
+ ,29
+ ,6.8
+ ,179
+ ,164
+ ,2
+ ,3
+ ,2
+ ,5.7
+ ,0.9
+ ,7
+ ,0.75
+ ,12.3
+ ,225
+ ,2
+ ,2
+ ,2
+ ,4.9
+ ,0.5
+ ,6
+ ,3.6
+ ,21
+ ,150
+ ,3
+ ,2
+ ,3
+ ,-999
+ ,-999
+ ,17
+ ,14.83
+ ,98.2
+ ,151
+ ,5
+ ,5
+ ,5
+ ,3.2
+ ,0.6
+ ,20
+ ,55.5
+ ,175
+ ,150
+ ,5
+ ,5
+ ,5
+ ,-999
+ ,-999
+ ,12.7
+ ,1.4
+ ,12.5
+ ,90
+ ,2
+ ,2
+ ,2
+ ,8.1
+ ,2.2
+ ,3.5
+ ,0.06
+ ,1
+ ,-999
+ ,3
+ ,1
+ ,2
+ ,11
+ ,2.3
+ ,4.5
+ ,0.9
+ ,2.6
+ ,60
+ ,2
+ ,1
+ ,2
+ ,4.9
+ ,0.5
+ ,7.5
+ ,2
+ ,12.3
+ ,200
+ ,3
+ ,1
+ ,3
+ ,13.2
+ ,2.6
+ ,2.3
+ ,0.104
+ ,2.5
+ ,46
+ ,3
+ ,2
+ ,2
+ ,9.7
+ ,0.6
+ ,24
+ ,4.19
+ ,58
+ ,210
+ ,4
+ ,3
+ ,4
+ ,12.8
+ ,6.6
+ ,3
+ ,3.5
+ ,3.9
+ ,14
+ ,1
+ ,1
+ ,2
+ ,-999
+ ,-999
+ ,13
+ ,4.05
+ ,17
+ ,38
+ ,3
+ ,1
+ ,1)
+ ,dim=c(9
+ ,62)
+ ,dimnames=list(c('SWS'
+ ,'PS'
+ ,'L'
+ ,'WB'
+ ,'WBR'
+ ,'TG'
+ ,'P'
+ ,'S'
+ ,'D')
+ ,1:62))
> y <- array(NA,dim=c(9,62),dimnames=list(c('SWS','PS','L','WB','WBR','TG','P','S','D'),1:62))
> 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 = '2'
> #'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
> 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
PS SWS L WB WBR TG P S D
1 -999.0 -999.0 38.6 6.654 5.712 645.0 3 5 3
2 2.0 6.3 4.5 1.000 6.600 42.0 3 1 3
3 -999.0 -999.0 14.0 3.385 44.500 60.0 1 1 1
4 -999.0 -999.0 -999.0 0.920 5.700 25.0 5 2 3
5 1.8 2.1 69.0 2547.000 4603.000 624.0 3 5 4
6 0.7 0.1 27.0 10.550 0.500 180.0 4 4 4
7 3.9 15.8 19.0 0.023 0.300 35.0 1 1 1
8 1.0 5.2 30.4 160.000 169.000 392.0 4 5 4
9 3.6 10.9 28.0 3.300 25.600 63.0 1 2 1
10 1.4 8.3 50.0 52.160 440.000 230.0 1 1 1
11 1.5 11.0 7.0 0.425 6.400 112.0 5 4 4
12 0.7 3.2 30.0 465.000 423.000 281.0 5 5 5
13 2.7 7.6 -999.0 0.550 2.400 -999.0 2 1 2
14 -999.0 -999.0 40.0 187.100 419.000 365.0 5 5 5
15 2.1 6.3 3.5 0.075 1.200 42.0 1 1 1
16 0.0 8.6 50.0 3.000 25.000 28.0 2 2 2
17 4.1 6.6 6.0 0.785 3.500 42.0 2 2 2
18 1.2 9.5 10.4 0.200 5.000 120.0 2 2 2
19 1.3 4.8 34.0 1.410 17.500 -999.0 1 2 1
20 6.1 12.0 7.0 60.000 81.000 -999.0 1 1 1
21 0.3 -999.0 28.0 529.000 680.000 400.0 5 5 5
22 0.5 3.3 20.0 27.660 115.000 148.0 5 5 5
23 3.4 11.0 3.9 0.120 1.000 16.0 3 1 2
24 -999.0 -999.0 39.3 207.000 406.000 252.0 1 4 1
25 1.5 4.7 41.0 85.000 325.000 310.0 1 3 1
26 -999.0 -999.0 16.2 36.330 119.500 63.0 1 1 1
27 3.4 10.4 9.0 0.101 4.000 28.0 5 1 3
28 0.8 7.4 7.6 1.040 5.500 68.0 5 3 4
29 0.8 2.1 46.0 521.000 655.000 336.0 5 5 5
30 -999.0 2.1 22.4 100.000 157.000 100.0 1 1 1
31 -999.0 -999.0 16.3 35.000 56.000 33.0 3 5 4
32 1.4 7.7 2.6 0.005 0.140 21.5 5 2 4
33 2.0 17.9 24.0 0.010 0.250 50.0 1 1 1
34 1.9 6.1 100.0 62.000 1320.000 267.0 1 1 1
35 2.4 8.2 -999.0 0.122 3.000 30.0 2 1 1
36 2.8 8.4 -999.0 1.350 8.100 45.0 3 1 3
37 1.3 11.9 3.2 0.230 0.400 19.0 4 1 3
38 2.0 10.8 2.0 0.048 0.330 30.0 4 1 3
39 5.6 13.8 5.0 1.700 6.300 12.0 2 1 1
40 3.1 14.3 6.5 3.500 10.800 120.0 2 1 1
41 1.0 -999.0 23.6 250.000 490.000 440.0 5 5 5
42 1.8 15.2 12.0 0.480 15.500 140.0 2 2 2
43 0.9 10.0 20.2 10.000 115.000 170.0 4 4 4
44 1.8 11.9 13.0 1.620 11.400 17.0 2 1 2
45 1.9 6.5 27.0 192.000 180.000 115.0 4 4 4
46 0.9 7.5 18.0 2.500 12.100 31.0 5 5 5
47 -999.0 -999.0 13.7 4.288 39.200 63.0 2 2 2
48 2.6 10.6 4.7 0.280 1.900 21.0 3 1 3
49 2.4 7.4 9.8 4.235 50.400 52.0 1 1 1
50 1.2 8.4 29.0 6.800 179.000 164.0 2 3 2
51 0.9 5.7 7.0 0.750 12.300 225.0 2 2 2
52 0.5 4.9 6.0 3.600 21.000 150.0 3 2 3
53 -999.0 -999.0 17.0 14.830 98.200 151.0 5 5 5
54 0.6 3.2 20.0 55.500 175.000 150.0 5 5 5
55 -999.0 -999.0 12.7 1.400 12.500 90.0 2 2 2
56 2.2 8.1 3.5 0.060 1.000 -999.0 3 1 2
57 2.3 11.0 4.5 0.900 2.600 60.0 2 1 2
58 0.5 4.9 7.5 2.000 12.300 200.0 3 1 3
59 2.6 13.2 2.3 0.104 2.500 46.0 3 2 2
60 0.6 9.7 24.0 4.190 58.000 210.0 4 3 4
61 6.6 12.8 3.0 3.500 3.900 14.0 1 1 2
62 -999.0 -999.0 13.0 4.050 17.000 38.0 3 1 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) SWS L WB WBR TG
-1.474e+02 8.279e-01 1.530e-02 -1.467e-04 2.701e-02 -2.097e-02
P S D
3.160e+00 -9.021e+00 5.077e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-900.67 -71.09 -6.52 58.82 746.60
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.474e+02 6.619e+01 -2.228 0.0302 *
SWS 8.279e-01 7.248e-02 11.422 6.57e-16 ***
L 1.530e-02 1.163e-01 0.132 0.8958
WB -1.467e-04 2.945e-01 0.000 0.9996
WBR 2.701e-02 1.609e-01 0.168 0.8674
TG -2.097e-02 1.028e-01 -0.204 0.8392
P 3.160e+00 5.031e+01 0.063 0.9502
S -9.021e+00 3.342e+01 -0.270 0.7882
D 5.077e+01 6.574e+01 0.772 0.4434
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 211.8 on 53 degrees of freedom
Multiple R-squared: 0.7547, Adjusted R-squared: 0.7177
F-statistic: 20.38 on 8 and 53 DF, p-value: 1.150e-13
> 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,] 9.468045e-07 1.893609e-06 9.999991e-01
[2,] 7.981994e-08 1.596399e-07 9.999999e-01
[3,] 2.659348e-09 5.318696e-09 1.000000e+00
[4,] 5.382179e-11 1.076436e-10 1.000000e+00
[5,] 8.443153e-13 1.688631e-12 1.000000e+00
[6,] 2.209340e-14 4.418680e-14 1.000000e+00
[7,] 4.172943e-16 8.345886e-16 1.000000e+00
[8,] 1.400354e-17 2.800707e-17 1.000000e+00
[9,] 2.052811e-19 4.105623e-19 1.000000e+00
[10,] 5.388272e-01 9.223457e-01 4.611728e-01
[11,] 4.467303e-01 8.934606e-01 5.532697e-01
[12,] 3.560187e-01 7.120375e-01 6.439813e-01
[13,] 2.751682e-01 5.503363e-01 7.248318e-01
[14,] 2.541807e-01 5.083614e-01 7.458193e-01
[15,] 2.083462e-01 4.166925e-01 7.916538e-01
[16,] 1.513315e-01 3.026630e-01 8.486685e-01
[17,] 1.066048e-01 2.132095e-01 8.933952e-01
[18,] 1.002713e-01 2.005425e-01 8.997287e-01
[19,] 9.997758e-01 4.483668e-04 2.241834e-04
[20,] 9.996623e-01 6.753151e-04 3.376575e-04
[21,] 9.992490e-01 1.502063e-03 7.510313e-04
[22,] 9.985279e-01 2.944105e-03 1.472053e-03
[23,] 9.999580e-01 8.405278e-05 4.202639e-05
[24,] 9.999125e-01 1.749815e-04 8.749073e-05
[25,] 9.999859e-01 2.811107e-05 1.405554e-05
[26,] 9.999604e-01 7.911161e-05 3.955580e-05
[27,] 9.999090e-01 1.819322e-04 9.096608e-05
[28,] 9.997521e-01 4.957232e-04 2.478616e-04
[29,] 9.993507e-01 1.298605e-03 6.493027e-04
[30,] 1.000000e+00 3.770521e-22 1.885261e-22
[31,] 1.000000e+00 5.462646e-21 2.731323e-21
[32,] 1.000000e+00 3.217240e-19 1.608620e-19
[33,] 1.000000e+00 2.097169e-17 1.048584e-17
[34,] 1.000000e+00 1.046984e-15 5.234920e-16
[35,] 1.000000e+00 8.413096e-14 4.206548e-14
[36,] 1.000000e+00 8.912705e-12 4.456353e-12
[37,] 1.000000e+00 6.855345e-10 3.427673e-10
[38,] 1.000000e+00 6.690099e-08 3.345050e-08
[39,] 9.999968e-01 6.334754e-06 3.167377e-06
> postscript(file="/var/www/rcomp/tmp/139aj1292867664.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/239aj1292867664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/339aj1292867664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4v0sm1292867664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5v0sm1292867664.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 = 62
Frequency = 1
1 2 3 4 5 6
-128.430241 -7.904456 -69.592111 -158.939113 -131.852980 -28.223580
7 8 9 10 11 12
93.793599 -23.259551 106.337807 89.250488 -40.887817 -84.970816
13 14 15 16 17 18
39.287676 -253.319349 100.217867 49.656804 56.959621 53.186588
19 20 21 22 23 24
86.944214 75.469462 739.899824 -79.635949 39.990502 -48.622636
25 26 27 28 29 30
115.298640 -71.583474 -16.510714 -48.536042 -89.308793 -900.670926
31 32 33 34 35 36
-193.047423 -57.959436 90.394500 67.818857 110.823670 6.533625
37 38 39 40 41 42
-16.695390 -14.833855 93.559563 92.766186 746.596140 49.179217
43 44 45 46 47 48
-39.417255 40.406379 -38.505843 -82.360611 -114.290976 -11.180839
49 50 51 52 53 54
98.392453 59.058290 58.089248 2.628883 -248.816795 -81.027494
55 56 57 58 59 60
-112.988850 19.912811 42.920761 -5.131884 47.003328 -46.170789
61 62
47.914078 -75.615096
> postscript(file="/var/www/rcomp/tmp/6o99p1292867664.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 = 62
Frequency = 1
lag(myerror, k = 1) myerror
0 -128.430241 NA
1 -7.904456 -128.430241
2 -69.592111 -7.904456
3 -158.939113 -69.592111
4 -131.852980 -158.939113
5 -28.223580 -131.852980
6 93.793599 -28.223580
7 -23.259551 93.793599
8 106.337807 -23.259551
9 89.250488 106.337807
10 -40.887817 89.250488
11 -84.970816 -40.887817
12 39.287676 -84.970816
13 -253.319349 39.287676
14 100.217867 -253.319349
15 49.656804 100.217867
16 56.959621 49.656804
17 53.186588 56.959621
18 86.944214 53.186588
19 75.469462 86.944214
20 739.899824 75.469462
21 -79.635949 739.899824
22 39.990502 -79.635949
23 -48.622636 39.990502
24 115.298640 -48.622636
25 -71.583474 115.298640
26 -16.510714 -71.583474
27 -48.536042 -16.510714
28 -89.308793 -48.536042
29 -900.670926 -89.308793
30 -193.047423 -900.670926
31 -57.959436 -193.047423
32 90.394500 -57.959436
33 67.818857 90.394500
34 110.823670 67.818857
35 6.533625 110.823670
36 -16.695390 6.533625
37 -14.833855 -16.695390
38 93.559563 -14.833855
39 92.766186 93.559563
40 746.596140 92.766186
41 49.179217 746.596140
42 -39.417255 49.179217
43 40.406379 -39.417255
44 -38.505843 40.406379
45 -82.360611 -38.505843
46 -114.290976 -82.360611
47 -11.180839 -114.290976
48 98.392453 -11.180839
49 59.058290 98.392453
50 58.089248 59.058290
51 2.628883 58.089248
52 -248.816795 2.628883
53 -81.027494 -248.816795
54 -112.988850 -81.027494
55 19.912811 -112.988850
56 42.920761 19.912811
57 -5.131884 42.920761
58 47.003328 -5.131884
59 -46.170789 47.003328
60 47.914078 -46.170789
61 -75.615096 47.914078
62 NA -75.615096
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -7.904456 -128.430241
[2,] -69.592111 -7.904456
[3,] -158.939113 -69.592111
[4,] -131.852980 -158.939113
[5,] -28.223580 -131.852980
[6,] 93.793599 -28.223580
[7,] -23.259551 93.793599
[8,] 106.337807 -23.259551
[9,] 89.250488 106.337807
[10,] -40.887817 89.250488
[11,] -84.970816 -40.887817
[12,] 39.287676 -84.970816
[13,] -253.319349 39.287676
[14,] 100.217867 -253.319349
[15,] 49.656804 100.217867
[16,] 56.959621 49.656804
[17,] 53.186588 56.959621
[18,] 86.944214 53.186588
[19,] 75.469462 86.944214
[20,] 739.899824 75.469462
[21,] -79.635949 739.899824
[22,] 39.990502 -79.635949
[23,] -48.622636 39.990502
[24,] 115.298640 -48.622636
[25,] -71.583474 115.298640
[26,] -16.510714 -71.583474
[27,] -48.536042 -16.510714
[28,] -89.308793 -48.536042
[29,] -900.670926 -89.308793
[30,] -193.047423 -900.670926
[31,] -57.959436 -193.047423
[32,] 90.394500 -57.959436
[33,] 67.818857 90.394500
[34,] 110.823670 67.818857
[35,] 6.533625 110.823670
[36,] -16.695390 6.533625
[37,] -14.833855 -16.695390
[38,] 93.559563 -14.833855
[39,] 92.766186 93.559563
[40,] 746.596140 92.766186
[41,] 49.179217 746.596140
[42,] -39.417255 49.179217
[43,] 40.406379 -39.417255
[44,] -38.505843 40.406379
[45,] -82.360611 -38.505843
[46,] -114.290976 -82.360611
[47,] -11.180839 -114.290976
[48,] 98.392453 -11.180839
[49,] 59.058290 98.392453
[50,] 58.089248 59.058290
[51,] 2.628883 58.089248
[52,] -248.816795 2.628883
[53,] -81.027494 -248.816795
[54,] -112.988850 -81.027494
[55,] 19.912811 -112.988850
[56,] 42.920761 19.912811
[57,] -5.131884 42.920761
[58,] 47.003328 -5.131884
[59,] -46.170789 47.003328
[60,] 47.914078 -46.170789
[61,] -75.615096 47.914078
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -7.904456 -128.430241
2 -69.592111 -7.904456
3 -158.939113 -69.592111
4 -131.852980 -158.939113
5 -28.223580 -131.852980
6 93.793599 -28.223580
7 -23.259551 93.793599
8 106.337807 -23.259551
9 89.250488 106.337807
10 -40.887817 89.250488
11 -84.970816 -40.887817
12 39.287676 -84.970816
13 -253.319349 39.287676
14 100.217867 -253.319349
15 49.656804 100.217867
16 56.959621 49.656804
17 53.186588 56.959621
18 86.944214 53.186588
19 75.469462 86.944214
20 739.899824 75.469462
21 -79.635949 739.899824
22 39.990502 -79.635949
23 -48.622636 39.990502
24 115.298640 -48.622636
25 -71.583474 115.298640
26 -16.510714 -71.583474
27 -48.536042 -16.510714
28 -89.308793 -48.536042
29 -900.670926 -89.308793
30 -193.047423 -900.670926
31 -57.959436 -193.047423
32 90.394500 -57.959436
33 67.818857 90.394500
34 110.823670 67.818857
35 6.533625 110.823670
36 -16.695390 6.533625
37 -14.833855 -16.695390
38 93.559563 -14.833855
39 92.766186 93.559563
40 746.596140 92.766186
41 49.179217 746.596140
42 -39.417255 49.179217
43 40.406379 -39.417255
44 -38.505843 40.406379
45 -82.360611 -38.505843
46 -114.290976 -82.360611
47 -11.180839 -114.290976
48 98.392453 -11.180839
49 59.058290 98.392453
50 58.089248 59.058290
51 2.628883 58.089248
52 -248.816795 2.628883
53 -81.027494 -248.816795
54 -112.988850 -81.027494
55 19.912811 -112.988850
56 42.920761 19.912811
57 -5.131884 42.920761
58 47.003328 -5.131884
59 -46.170789 47.003328
60 47.914078 -46.170789
61 -75.615096 47.914078
> 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/rcomp/tmp/7o99p1292867664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8h1qs1292867664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9h1qs1292867664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10as7d1292867664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11va611292867664.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/rcomp/tmp/12zb471292867664.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/rcomp/tmp/13v3ky1292867664.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/rcomp/tmp/14ylj31292867664.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/rcomp/tmp/1514zr1292867664.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/rcomp/tmp/165myf1292867664.tab")
+ }
>
> try(system("convert tmp/139aj1292867664.ps tmp/139aj1292867664.png",intern=TRUE))
character(0)
> try(system("convert tmp/239aj1292867664.ps tmp/239aj1292867664.png",intern=TRUE))
character(0)
> try(system("convert tmp/339aj1292867664.ps tmp/339aj1292867664.png",intern=TRUE))
character(0)
> try(system("convert tmp/4v0sm1292867664.ps tmp/4v0sm1292867664.png",intern=TRUE))
character(0)
> try(system("convert tmp/5v0sm1292867664.ps tmp/5v0sm1292867664.png",intern=TRUE))
character(0)
> try(system("convert tmp/6o99p1292867664.ps tmp/6o99p1292867664.png",intern=TRUE))
character(0)
> try(system("convert tmp/7o99p1292867664.ps tmp/7o99p1292867664.png",intern=TRUE))
character(0)
> try(system("convert tmp/8h1qs1292867664.ps tmp/8h1qs1292867664.png",intern=TRUE))
character(0)
> try(system("convert tmp/9h1qs1292867664.ps tmp/9h1qs1292867664.png",intern=TRUE))
character(0)
> try(system("convert tmp/10as7d1292867664.ps tmp/10as7d1292867664.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.170 1.550 4.728