R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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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(6654
+ ,5712
+ ,-999
+ ,-999
+ ,3.3
+ ,38.6
+ ,645
+ ,3
+ ,5
+ ,3
+ ,1
+ ,6.6
+ ,6.3
+ ,2
+ ,8.3
+ ,4.5
+ ,42
+ ,3
+ ,1
+ ,3
+ ,3.385
+ ,44.5
+ ,-999
+ ,-999
+ ,12.5
+ ,14
+ ,60
+ ,1
+ ,1
+ ,1
+ ,0.92
+ ,5.7
+ ,-999
+ ,-999
+ ,16.5
+ ,-999
+ ,25
+ ,5
+ ,2
+ ,3
+ ,2547
+ ,4603
+ ,2.1
+ ,1.8
+ ,3.9
+ ,69
+ ,624
+ ,3
+ ,5
+ ,4
+ ,10.55
+ ,179.5
+ ,9.1
+ ,0.7
+ ,9.8
+ ,27
+ ,180
+ ,4
+ ,4
+ ,4
+ ,0.023
+ ,0.3
+ ,15.8
+ ,3.9
+ ,19.7
+ ,19
+ ,35
+ ,1
+ ,1
+ ,1
+ ,160
+ ,169
+ ,5.2
+ ,1
+ ,6.2
+ ,30.4
+ ,392
+ ,4
+ ,5
+ ,4
+ ,3.3
+ ,25.6
+ ,10.9
+ ,3.6
+ ,14.5
+ ,28
+ ,63
+ ,1
+ ,2
+ ,1
+ ,52.16
+ ,440
+ ,8.3
+ ,1.4
+ ,9.7
+ ,50
+ ,230
+ ,1
+ ,1
+ ,1
+ ,0.425
+ ,6.4
+ ,11
+ ,1.5
+ ,12.5
+ ,7
+ ,112
+ ,5
+ ,4
+ ,4
+ ,465
+ ,423
+ ,3.2
+ ,0.7
+ ,3.9
+ ,30
+ ,281
+ ,5
+ ,5
+ ,5
+ ,0.55
+ ,2.4
+ ,7.6
+ ,2.7
+ ,10.3
+ ,-999
+ ,-999
+ ,2
+ ,1
+ ,2
+ ,187.1
+ ,419
+ ,-999
+ ,-999
+ ,3.1
+ ,40
+ ,365
+ ,5
+ ,5
+ ,5
+ ,0.075
+ ,1.2
+ ,6.3
+ ,2.1
+ ,8.4
+ ,3.5
+ ,42
+ ,1
+ ,1
+ ,1
+ ,3
+ ,25
+ ,8.6
+ ,0
+ ,8.6
+ ,50
+ ,28
+ ,2
+ ,2
+ ,2
+ ,0.785
+ ,3.5
+ ,6.6
+ ,4.1
+ ,10.7
+ ,6
+ ,42
+ ,2
+ ,2
+ ,2
+ ,0.2
+ ,5
+ ,9.5
+ ,1.2
+ ,10.7
+ ,10.4
+ ,120
+ ,2
+ ,2
+ ,2
+ ,1.41
+ ,17.5
+ ,4.8
+ ,1.3
+ ,6.1
+ ,34
+ ,-999
+ ,1
+ ,2
+ ,1
+ ,60
+ ,81
+ ,12
+ ,6.1
+ ,18.1
+ ,7
+ ,-999
+ ,1
+ ,1
+ ,1
+ ,529
+ ,680
+ ,-999
+ ,0.3
+ ,-999
+ ,28
+ ,400
+ ,5
+ ,5
+ ,5
+ ,27.66
+ ,115
+ ,3.3
+ ,0.5
+ ,3.8
+ ,20
+ ,148
+ ,5
+ ,5
+ ,5
+ ,0.12
+ ,1
+ ,11
+ ,3.4
+ ,14.4
+ ,3.9
+ ,16
+ ,3
+ ,1
+ ,2
+ ,207
+ ,406
+ ,-999
+ ,-999
+ ,12
+ ,39.3
+ ,252
+ ,1
+ ,4
+ ,1
+ ,85
+ ,325
+ ,4.7
+ ,1.5
+ ,6.2
+ ,41
+ ,310
+ ,1
+ ,3
+ ,1
+ ,36.33
+ ,119.5
+ ,-999
+ ,-999
+ ,13
+ ,16.2
+ ,63
+ ,1
+ ,1
+ ,1
+ ,0.101
+ ,4
+ ,10.4
+ ,3.4
+ ,13.8
+ ,9
+ ,28
+ ,5
+ ,1
+ ,3
+ ,1.04
+ ,5.5
+ ,7.4
+ ,0.8
+ ,8.2
+ ,7.6
+ ,68
+ ,5
+ ,3
+ ,4
+ ,521
+ ,655
+ ,2.1
+ ,0.8
+ ,2.9
+ ,46
+ ,336
+ ,5
+ ,5
+ ,5
+ ,100
+ ,157
+ ,-999
+ ,-999
+ ,10.8
+ ,22.4
+ ,100
+ ,1
+ ,1
+ ,1
+ ,35
+ ,56
+ ,-999
+ ,-999
+ ,-999
+ ,16.3
+ ,33
+ ,3
+ ,5
+ ,4
+ ,0.005
+ ,0.14
+ ,7.7
+ ,1.4
+ ,9.1
+ ,2.6
+ ,21.5
+ ,5
+ ,2
+ ,4
+ ,0.01
+ ,0.25
+ ,17.9
+ ,2
+ ,19.9
+ ,24
+ ,50
+ ,1
+ ,1
+ ,1
+ ,62
+ ,1320
+ ,6.1
+ ,1.9
+ ,8
+ ,100
+ ,267
+ ,1
+ ,1
+ ,1
+ ,0.122
+ ,3
+ ,8.2
+ ,2.4
+ ,10.6
+ ,-999
+ ,30
+ ,2
+ ,1
+ ,1
+ ,1.35
+ ,8.1
+ ,8.4
+ ,2.8
+ ,11.2
+ ,-999
+ ,45
+ ,3
+ ,1
+ ,3
+ ,0.023
+ ,0.4
+ ,11.9
+ ,1.3
+ ,13.2
+ ,3.2
+ ,19
+ ,4
+ ,1
+ ,3
+ ,0.048
+ ,0.33
+ ,10.8
+ ,2
+ ,12.8
+ ,2
+ ,30
+ ,4
+ ,1
+ ,3
+ ,1.7
+ ,6.3
+ ,13.8
+ ,5.6
+ ,19.4
+ ,5
+ ,12
+ ,2
+ ,1
+ ,1
+ ,3.5
+ ,10.8
+ ,14.3
+ ,3.1
+ ,17.4
+ ,6.5
+ ,120
+ ,2
+ ,1
+ ,1
+ ,250
+ ,490
+ ,-999
+ ,1
+ ,-999
+ ,23.6
+ ,440
+ ,5
+ ,5
+ ,5
+ ,0.48
+ ,15.5
+ ,15.2
+ ,1.8
+ ,17
+ ,12
+ ,140
+ ,2
+ ,2
+ ,2
+ ,10
+ ,115
+ ,10
+ ,0.9
+ ,10.9
+ ,20.2
+ ,170
+ ,4
+ ,4
+ ,4
+ ,1.62
+ ,11.4
+ ,11.9
+ ,1.8
+ ,13.7
+ ,13
+ ,17
+ ,2
+ ,1
+ ,2
+ ,192
+ ,180
+ ,6.5
+ ,1.9
+ ,8.4
+ ,27
+ ,115
+ ,4
+ ,4
+ ,4
+ ,2.5
+ ,12.1
+ ,7.5
+ ,0.9
+ ,8.4
+ ,18
+ ,31
+ ,5
+ ,5
+ ,5
+ ,4.288
+ ,39.2
+ ,-999
+ ,-999
+ ,12.5
+ ,13.7
+ ,63
+ ,2
+ ,2
+ ,2
+ ,0.28
+ ,1.9
+ ,10.6
+ ,2.6
+ ,13.2
+ ,4.7
+ ,21
+ ,3
+ ,1
+ ,3
+ ,4.235
+ ,50.4
+ ,7.4
+ ,2.4
+ ,9.8
+ ,9.8
+ ,52
+ ,1
+ ,1
+ ,1
+ ,6.8
+ ,179
+ ,8.4
+ ,1.2
+ ,9.6
+ ,29
+ ,164
+ ,2
+ ,3
+ ,2
+ ,0.75
+ ,12.3
+ ,5.7
+ ,0.9
+ ,6.6
+ ,7
+ ,225
+ ,2
+ ,2
+ ,2
+ ,3.6
+ ,21
+ ,4.9
+ ,0.5
+ ,5.4
+ ,6
+ ,225
+ ,3
+ ,2
+ ,3
+ ,14.83
+ ,98.2
+ ,-999
+ ,-999
+ ,2.6
+ ,17
+ ,150
+ ,5
+ ,5
+ ,5
+ ,55.5
+ ,175
+ ,3.2
+ ,0.6
+ ,3.8
+ ,20
+ ,151
+ ,5
+ ,5
+ ,5
+ ,1.4
+ ,12.5
+ ,-999
+ ,-999
+ ,11
+ ,12.7
+ ,90
+ ,2
+ ,2
+ ,2
+ ,0.06
+ ,1
+ ,8.1
+ ,2.2
+ ,10.3
+ ,3.5
+ ,-999
+ ,3
+ ,1
+ ,2
+ ,0.9
+ ,2.6
+ ,11
+ ,2.3
+ ,13.3
+ ,4.5
+ ,60
+ ,2
+ ,1
+ ,2
+ ,2
+ ,12.3
+ ,4.9
+ ,0.5
+ ,5.4
+ ,7.5
+ ,200
+ ,3
+ ,1
+ ,3
+ ,0.104
+ ,2.5
+ ,13.2
+ ,2.6
+ ,15.8
+ ,2.3
+ ,46
+ ,3
+ ,2
+ ,2
+ ,4.19
+ ,58
+ ,9.7
+ ,0.6
+ ,10.3
+ ,24
+ ,210
+ ,4
+ ,3
+ ,4
+ ,3.5
+ ,3.9
+ ,12.8
+ ,6.6
+ ,19.4
+ ,3
+ ,14
+ ,2
+ ,1
+ ,1
+ ,4.05
+ ,17
+ ,-999
+ ,-999
+ ,-999
+ ,13
+ ,38
+ ,3
+ ,1
+ ,1)
+ ,dim=c(10
+ ,62)
+ ,dimnames=list(c('bodyweight'
+ ,'brainweight'
+ ,'sws'
+ ,'ps'
+ ,'total'
+ ,'lifespan'
+ ,'gesttime'
+ ,'pindex'
+ ,'expindex'
+ ,'dangindex')
+ ,1:62))
> y <- array(NA,dim=c(10,62),dimnames=list(c('bodyweight','brainweight','sws','ps','total','lifespan','gesttime','pindex','expindex','dangindex'),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 = '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
ps bodyweight brainweight sws total lifespan gesttime pindex
1 -999.0 6654.000 5712.00 -999.0 3.3 38.6 645.0 3
2 2.0 1.000 6.60 6.3 8.3 4.5 42.0 3
3 -999.0 3.385 44.50 -999.0 12.5 14.0 60.0 1
4 -999.0 0.920 5.70 -999.0 16.5 -999.0 25.0 5
5 1.8 2547.000 4603.00 2.1 3.9 69.0 624.0 3
6 0.7 10.550 179.50 9.1 9.8 27.0 180.0 4
7 3.9 0.023 0.30 15.8 19.7 19.0 35.0 1
8 1.0 160.000 169.00 5.2 6.2 30.4 392.0 4
9 3.6 3.300 25.60 10.9 14.5 28.0 63.0 1
10 1.4 52.160 440.00 8.3 9.7 50.0 230.0 1
11 1.5 0.425 6.40 11.0 12.5 7.0 112.0 5
12 0.7 465.000 423.00 3.2 3.9 30.0 281.0 5
13 2.7 0.550 2.40 7.6 10.3 -999.0 -999.0 2
14 -999.0 187.100 419.00 -999.0 3.1 40.0 365.0 5
15 2.1 0.075 1.20 6.3 8.4 3.5 42.0 1
16 0.0 3.000 25.00 8.6 8.6 50.0 28.0 2
17 4.1 0.785 3.50 6.6 10.7 6.0 42.0 2
18 1.2 0.200 5.00 9.5 10.7 10.4 120.0 2
19 1.3 1.410 17.50 4.8 6.1 34.0 -999.0 1
20 6.1 60.000 81.00 12.0 18.1 7.0 -999.0 1
21 0.3 529.000 680.00 -999.0 -999.0 28.0 400.0 5
22 0.5 27.660 115.00 3.3 3.8 20.0 148.0 5
23 3.4 0.120 1.00 11.0 14.4 3.9 16.0 3
24 -999.0 207.000 406.00 -999.0 12.0 39.3 252.0 1
25 1.5 85.000 325.00 4.7 6.2 41.0 310.0 1
26 -999.0 36.330 119.50 -999.0 13.0 16.2 63.0 1
27 3.4 0.101 4.00 10.4 13.8 9.0 28.0 5
28 0.8 1.040 5.50 7.4 8.2 7.6 68.0 5
29 0.8 521.000 655.00 2.1 2.9 46.0 336.0 5
30 -999.0 100.000 157.00 -999.0 10.8 22.4 100.0 1
31 -999.0 35.000 56.00 -999.0 -999.0 16.3 33.0 3
32 1.4 0.005 0.14 7.7 9.1 2.6 21.5 5
33 2.0 0.010 0.25 17.9 19.9 24.0 50.0 1
34 1.9 62.000 1320.00 6.1 8.0 100.0 267.0 1
35 2.4 0.122 3.00 8.2 10.6 -999.0 30.0 2
36 2.8 1.350 8.10 8.4 11.2 -999.0 45.0 3
37 1.3 0.023 0.40 11.9 13.2 3.2 19.0 4
38 2.0 0.048 0.33 10.8 12.8 2.0 30.0 4
39 5.6 1.700 6.30 13.8 19.4 5.0 12.0 2
40 3.1 3.500 10.80 14.3 17.4 6.5 120.0 2
41 1.0 250.000 490.00 -999.0 -999.0 23.6 440.0 5
42 1.8 0.480 15.50 15.2 17.0 12.0 140.0 2
43 0.9 10.000 115.00 10.0 10.9 20.2 170.0 4
44 1.8 1.620 11.40 11.9 13.7 13.0 17.0 2
45 1.9 192.000 180.00 6.5 8.4 27.0 115.0 4
46 0.9 2.500 12.10 7.5 8.4 18.0 31.0 5
47 -999.0 4.288 39.20 -999.0 12.5 13.7 63.0 2
48 2.6 0.280 1.90 10.6 13.2 4.7 21.0 3
49 2.4 4.235 50.40 7.4 9.8 9.8 52.0 1
50 1.2 6.800 179.00 8.4 9.6 29.0 164.0 2
51 0.9 0.750 12.30 5.7 6.6 7.0 225.0 2
52 0.5 3.600 21.00 4.9 5.4 6.0 225.0 3
53 -999.0 14.830 98.20 -999.0 2.6 17.0 150.0 5
54 0.6 55.500 175.00 3.2 3.8 20.0 151.0 5
55 -999.0 1.400 12.50 -999.0 11.0 12.7 90.0 2
56 2.2 0.060 1.00 8.1 10.3 3.5 -999.0 3
57 2.3 0.900 2.60 11.0 13.3 4.5 60.0 2
58 0.5 2.000 12.30 4.9 5.4 7.5 200.0 3
59 2.6 0.104 2.50 13.2 15.8 2.3 46.0 3
60 0.6 4.190 58.00 9.7 10.3 24.0 210.0 4
61 6.6 3.500 3.90 12.8 19.4 3.0 14.0 2
62 -999.0 4.050 17.00 -999.0 -999.0 13.0 38.0 3
expindex dangindex
1 5 3
2 1 3
3 1 1
4 2 3
5 5 4
6 4 4
7 1 1
8 5 4
9 2 1
10 1 1
11 4 4
12 5 5
13 1 2
14 5 5
15 1 1
16 2 2
17 2 2
18 2 2
19 2 1
20 1 1
21 5 5
22 5 5
23 1 2
24 4 1
25 3 1
26 1 1
27 1 3
28 3 4
29 5 5
30 1 1
31 5 4
32 2 4
33 1 1
34 1 1
35 1 1
36 1 3
37 1 3
38 1 3
39 1 1
40 1 1
41 5 5
42 2 2
43 4 4
44 1 2
45 4 4
46 5 5
47 2 2
48 1 3
49 1 1
50 3 2
51 2 2
52 2 3
53 5 5
54 5 5
55 2 2
56 1 2
57 1 2
58 1 3
59 2 2
60 3 4
61 1 1
62 1 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) bodyweight brainweight sws total lifespan
-40.39612 -0.01193 0.01398 0.99041 -0.47358 -0.01973
gesttime pindex expindex dangindex
0.04068 -20.30790 -11.55873 46.12661
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-509.463 -27.827 2.508 22.316 466.800
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -40.39612 41.68929 -0.969 0.337
bodyweight -0.01193 0.05644 -0.211 0.833
brainweight 0.01398 0.05623 0.249 0.805
sws 0.99041 0.05038 19.657 < 2e-16 ***
total -0.47358 0.08203 -5.773 4.38e-07 ***
lifespan -0.01973 0.07347 -0.269 0.789
gesttime 0.04068 0.06601 0.616 0.540
pindex -20.30790 33.35860 -0.609 0.545
expindex -11.55873 21.87674 -0.528 0.599
dangindex 46.12661 43.01496 1.072 0.289
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 133.8 on 52 degrees of freedom
Multiple R-squared: 0.9041, Adjusted R-squared: 0.8875
F-statistic: 54.44 on 9 and 52 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,] 4.581277e-06 9.162555e-06 9.999954e-01
[2,] 1.880891e-07 3.761781e-07 9.999998e-01
[3,] 2.818324e-09 5.636649e-09 1.000000e+00
[4,] 8.056453e-11 1.611291e-10 1.000000e+00
[5,] 6.198892e-12 1.239778e-11 1.000000e+00
[6,] 1.457939e-13 2.915879e-13 1.000000e+00
[7,] 3.649468e-15 7.298936e-15 1.000000e+00
[8,] 1.315725e-16 2.631450e-16 1.000000e+00
[9,] 2.040736e-16 4.081473e-16 1.000000e+00
[10,] 4.861810e-18 9.723619e-18 1.000000e+00
[11,] 1.101260e-19 2.202520e-19 1.000000e+00
[12,] 2.541823e-21 5.083645e-21 1.000000e+00
[13,] 6.699594e-23 1.339919e-22 1.000000e+00
[14,] 1.390372e-24 2.780744e-24 1.000000e+00
[15,] 2.888417e-26 5.776834e-26 1.000000e+00
[16,] 6.792269e-28 1.358454e-27 1.000000e+00
[17,] 1.132285e-27 2.264569e-27 1.000000e+00
[18,] 5.564161e-29 1.112832e-28 1.000000e+00
[19,] 9.138830e-01 1.722339e-01 8.611697e-02
[20,] 8.794716e-01 2.410568e-01 1.205284e-01
[21,] 8.281840e-01 3.436321e-01 1.718160e-01
[22,] 9.448179e-01 1.103642e-01 5.518212e-02
[23,] 9.214685e-01 1.570629e-01 7.853147e-02
[24,] 9.925005e-01 1.499896e-02 7.499478e-03
[25,] 9.861702e-01 2.765955e-02 1.382978e-02
[26,] 9.750909e-01 4.981814e-02 2.490907e-02
[27,] 9.617099e-01 7.658013e-02 3.829007e-02
[28,] 9.543007e-01 9.139855e-02 4.569928e-02
[29,] 1.000000e+00 2.553918e-17 1.276959e-17
[30,] 1.000000e+00 1.250282e-16 6.251411e-17
[31,] 1.000000e+00 3.592726e-15 1.796363e-15
[32,] 1.000000e+00 1.236958e-13 6.184789e-14
[33,] 1.000000e+00 4.400427e-12 2.200214e-12
[34,] 1.000000e+00 2.631475e-10 1.315737e-10
[35,] 1.000000e+00 1.580808e-08 7.904040e-09
[36,] 9.999997e-01 5.977458e-07 2.988729e-07
[37,] 9.999835e-01 3.292537e-05 1.646268e-05
> postscript(file="/var/www/html/rcomp/tmp/1i73m1292671137.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2i73m1292671137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3i73m1292671137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4bykp1292671137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5bykp1292671137.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
-13.2074744 -27.5102057 19.7293863 4.1145396 -81.8046826 -29.4884931
7 8 9 10 11 12
22.6642988 -22.0982893 35.0373917 10.0108467 -4.3129237 -42.7292558
13 14 15 16 17 18
21.2713572 -56.6992873 24.3193023 6.9650314 12.8770587 3.9908516
19 20 21 22 23 24
78.2096276 69.2817047 466.7996458 -38.7760083 19.3638760 44.2336778
25 26 27 28 29 30
33.7042050 19.2321558 13.7334721 -13.2209796 -46.5102509 17.0430859
31 32 33 34 35 36
-509.4631236 -22.1951444 18.2681639 -0.8197061 24.7749288 -47.3511415
37 38 39 40 41 42
-10.1431069 -9.0129365 47.1064928 38.7590974 465.1124458 1.0036033
43 44 45 46 47 48
-28.4910962 -3.7555641 -21.5739165 -34.4990799 5.4263271 -27.9331457
49 50 51 52 53 54
23.2721531 12.3413049 1.0790163 -25.0230051 -46.2149552 -39.2055977
55 56 57 58 59 60
3.9367561 60.3736749 -4.3560057 -35.4326617 27.3336533 -41.1614295
61 62
49.0311297 -417.4107958
> postscript(file="/var/www/html/rcomp/tmp/6bykp1292671137.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 -13.2074744 NA
1 -27.5102057 -13.2074744
2 19.7293863 -27.5102057
3 4.1145396 19.7293863
4 -81.8046826 4.1145396
5 -29.4884931 -81.8046826
6 22.6642988 -29.4884931
7 -22.0982893 22.6642988
8 35.0373917 -22.0982893
9 10.0108467 35.0373917
10 -4.3129237 10.0108467
11 -42.7292558 -4.3129237
12 21.2713572 -42.7292558
13 -56.6992873 21.2713572
14 24.3193023 -56.6992873
15 6.9650314 24.3193023
16 12.8770587 6.9650314
17 3.9908516 12.8770587
18 78.2096276 3.9908516
19 69.2817047 78.2096276
20 466.7996458 69.2817047
21 -38.7760083 466.7996458
22 19.3638760 -38.7760083
23 44.2336778 19.3638760
24 33.7042050 44.2336778
25 19.2321558 33.7042050
26 13.7334721 19.2321558
27 -13.2209796 13.7334721
28 -46.5102509 -13.2209796
29 17.0430859 -46.5102509
30 -509.4631236 17.0430859
31 -22.1951444 -509.4631236
32 18.2681639 -22.1951444
33 -0.8197061 18.2681639
34 24.7749288 -0.8197061
35 -47.3511415 24.7749288
36 -10.1431069 -47.3511415
37 -9.0129365 -10.1431069
38 47.1064928 -9.0129365
39 38.7590974 47.1064928
40 465.1124458 38.7590974
41 1.0036033 465.1124458
42 -28.4910962 1.0036033
43 -3.7555641 -28.4910962
44 -21.5739165 -3.7555641
45 -34.4990799 -21.5739165
46 5.4263271 -34.4990799
47 -27.9331457 5.4263271
48 23.2721531 -27.9331457
49 12.3413049 23.2721531
50 1.0790163 12.3413049
51 -25.0230051 1.0790163
52 -46.2149552 -25.0230051
53 -39.2055977 -46.2149552
54 3.9367561 -39.2055977
55 60.3736749 3.9367561
56 -4.3560057 60.3736749
57 -35.4326617 -4.3560057
58 27.3336533 -35.4326617
59 -41.1614295 27.3336533
60 49.0311297 -41.1614295
61 -417.4107958 49.0311297
62 NA -417.4107958
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -27.5102057 -13.2074744
[2,] 19.7293863 -27.5102057
[3,] 4.1145396 19.7293863
[4,] -81.8046826 4.1145396
[5,] -29.4884931 -81.8046826
[6,] 22.6642988 -29.4884931
[7,] -22.0982893 22.6642988
[8,] 35.0373917 -22.0982893
[9,] 10.0108467 35.0373917
[10,] -4.3129237 10.0108467
[11,] -42.7292558 -4.3129237
[12,] 21.2713572 -42.7292558
[13,] -56.6992873 21.2713572
[14,] 24.3193023 -56.6992873
[15,] 6.9650314 24.3193023
[16,] 12.8770587 6.9650314
[17,] 3.9908516 12.8770587
[18,] 78.2096276 3.9908516
[19,] 69.2817047 78.2096276
[20,] 466.7996458 69.2817047
[21,] -38.7760083 466.7996458
[22,] 19.3638760 -38.7760083
[23,] 44.2336778 19.3638760
[24,] 33.7042050 44.2336778
[25,] 19.2321558 33.7042050
[26,] 13.7334721 19.2321558
[27,] -13.2209796 13.7334721
[28,] -46.5102509 -13.2209796
[29,] 17.0430859 -46.5102509
[30,] -509.4631236 17.0430859
[31,] -22.1951444 -509.4631236
[32,] 18.2681639 -22.1951444
[33,] -0.8197061 18.2681639
[34,] 24.7749288 -0.8197061
[35,] -47.3511415 24.7749288
[36,] -10.1431069 -47.3511415
[37,] -9.0129365 -10.1431069
[38,] 47.1064928 -9.0129365
[39,] 38.7590974 47.1064928
[40,] 465.1124458 38.7590974
[41,] 1.0036033 465.1124458
[42,] -28.4910962 1.0036033
[43,] -3.7555641 -28.4910962
[44,] -21.5739165 -3.7555641
[45,] -34.4990799 -21.5739165
[46,] 5.4263271 -34.4990799
[47,] -27.9331457 5.4263271
[48,] 23.2721531 -27.9331457
[49,] 12.3413049 23.2721531
[50,] 1.0790163 12.3413049
[51,] -25.0230051 1.0790163
[52,] -46.2149552 -25.0230051
[53,] -39.2055977 -46.2149552
[54,] 3.9367561 -39.2055977
[55,] 60.3736749 3.9367561
[56,] -4.3560057 60.3736749
[57,] -35.4326617 -4.3560057
[58,] 27.3336533 -35.4326617
[59,] -41.1614295 27.3336533
[60,] 49.0311297 -41.1614295
[61,] -417.4107958 49.0311297
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -27.5102057 -13.2074744
2 19.7293863 -27.5102057
3 4.1145396 19.7293863
4 -81.8046826 4.1145396
5 -29.4884931 -81.8046826
6 22.6642988 -29.4884931
7 -22.0982893 22.6642988
8 35.0373917 -22.0982893
9 10.0108467 35.0373917
10 -4.3129237 10.0108467
11 -42.7292558 -4.3129237
12 21.2713572 -42.7292558
13 -56.6992873 21.2713572
14 24.3193023 -56.6992873
15 6.9650314 24.3193023
16 12.8770587 6.9650314
17 3.9908516 12.8770587
18 78.2096276 3.9908516
19 69.2817047 78.2096276
20 466.7996458 69.2817047
21 -38.7760083 466.7996458
22 19.3638760 -38.7760083
23 44.2336778 19.3638760
24 33.7042050 44.2336778
25 19.2321558 33.7042050
26 13.7334721 19.2321558
27 -13.2209796 13.7334721
28 -46.5102509 -13.2209796
29 17.0430859 -46.5102509
30 -509.4631236 17.0430859
31 -22.1951444 -509.4631236
32 18.2681639 -22.1951444
33 -0.8197061 18.2681639
34 24.7749288 -0.8197061
35 -47.3511415 24.7749288
36 -10.1431069 -47.3511415
37 -9.0129365 -10.1431069
38 47.1064928 -9.0129365
39 38.7590974 47.1064928
40 465.1124458 38.7590974
41 1.0036033 465.1124458
42 -28.4910962 1.0036033
43 -3.7555641 -28.4910962
44 -21.5739165 -3.7555641
45 -34.4990799 -21.5739165
46 5.4263271 -34.4990799
47 -27.9331457 5.4263271
48 23.2721531 -27.9331457
49 12.3413049 23.2721531
50 1.0790163 12.3413049
51 -25.0230051 1.0790163
52 -46.2149552 -25.0230051
53 -39.2055977 -46.2149552
54 3.9367561 -39.2055977
55 60.3736749 3.9367561
56 -4.3560057 60.3736749
57 -35.4326617 -4.3560057
58 27.3336533 -35.4326617
59 -41.1614295 27.3336533
60 49.0311297 -41.1614295
61 -417.4107958 49.0311297
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7m81a1292671137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ez1d1292671137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ez1d1292671137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ez1d1292671137.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/110hz11292671137.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12liy71292671137.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13ajv11292671137.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14lac41292671137.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/156tsa1292671137.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/162k801292671137.tab")
+ }
>
> try(system("convert tmp/1i73m1292671137.ps tmp/1i73m1292671137.png",intern=TRUE))
character(0)
> try(system("convert tmp/2i73m1292671137.ps tmp/2i73m1292671137.png",intern=TRUE))
character(0)
> try(system("convert tmp/3i73m1292671137.ps tmp/3i73m1292671137.png",intern=TRUE))
character(0)
> try(system("convert tmp/4bykp1292671137.ps tmp/4bykp1292671137.png",intern=TRUE))
character(0)
> try(system("convert tmp/5bykp1292671137.ps tmp/5bykp1292671137.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bykp1292671137.ps tmp/6bykp1292671137.png",intern=TRUE))
character(0)
> try(system("convert tmp/7m81a1292671137.ps tmp/7m81a1292671137.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ez1d1292671137.ps tmp/8ez1d1292671137.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ez1d1292671137.ps tmp/9ez1d1292671137.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ez1d1292671137.ps tmp/10ez1d1292671137.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.562 1.653 7.628