R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(9487,1169,8700,2154,9627,2249,8947,2687,9283,4359,8829,5382,9947,4459,9628,6398,9318,4596,9605,3024,8640,1887,9214,2070,9567,1351,8547,2218,9185,2461,9470,3028,9123,4784,9278,4975,10170,4607,9434,6249,9655,4809,9429,3157,8739,1910,9552,2228,9784,1594,9089,2467,9763,2222,9330,3607,9144,4685,9895,4962,10404,5770,10195,5480,9987,5000,9789,3228,9437,1993,10096,2288,9776,1580,9106,2111,10258,2192,9766,3601,9826,4665,9957,4876,10036,5813,10508,5589,10146,5331,10166,3075,9365,2002,9968,2306,10123,1507,9144,1992,10447,2487,9699,3490,10451,4647,10192,5594,10404,5611,10597,5788,10633,6204,10727,3013,9784,1931,9667,2549,10297,1504,9426,2090,10274,2702,9598,2939,10400,4500,9985,6208,10761,6415,11081,5657,10297,5964,10751,3163,9760,1997,10133,2422),dim=c(2,72),dimnames=list(c('Y','X'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('Y','X'),1:72))
> 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'
> #'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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9487 1169 1 0 0 0 0 0 0 0 0 0 0 1
2 8700 2154 0 1 0 0 0 0 0 0 0 0 0 2
3 9627 2249 0 0 1 0 0 0 0 0 0 0 0 3
4 8947 2687 0 0 0 1 0 0 0 0 0 0 0 4
5 9283 4359 0 0 0 0 1 0 0 0 0 0 0 5
6 8829 5382 0 0 0 0 0 1 0 0 0 0 0 6
7 9947 4459 0 0 0 0 0 0 1 0 0 0 0 7
8 9628 6398 0 0 0 0 0 0 0 1 0 0 0 8
9 9318 4596 0 0 0 0 0 0 0 0 1 0 0 9
10 9605 3024 0 0 0 0 0 0 0 0 0 1 0 10
11 8640 1887 0 0 0 0 0 0 0 0 0 0 1 11
12 9214 2070 0 0 0 0 0 0 0 0 0 0 0 12
13 9567 1351 1 0 0 0 0 0 0 0 0 0 0 13
14 8547 2218 0 1 0 0 0 0 0 0 0 0 0 14
15 9185 2461 0 0 1 0 0 0 0 0 0 0 0 15
16 9470 3028 0 0 0 1 0 0 0 0 0 0 0 16
17 9123 4784 0 0 0 0 1 0 0 0 0 0 0 17
18 9278 4975 0 0 0 0 0 1 0 0 0 0 0 18
19 10170 4607 0 0 0 0 0 0 1 0 0 0 0 19
20 9434 6249 0 0 0 0 0 0 0 1 0 0 0 20
21 9655 4809 0 0 0 0 0 0 0 0 1 0 0 21
22 9429 3157 0 0 0 0 0 0 0 0 0 1 0 22
23 8739 1910 0 0 0 0 0 0 0 0 0 0 1 23
24 9552 2228 0 0 0 0 0 0 0 0 0 0 0 24
25 9784 1594 1 0 0 0 0 0 0 0 0 0 0 25
26 9089 2467 0 1 0 0 0 0 0 0 0 0 0 26
27 9763 2222 0 0 1 0 0 0 0 0 0 0 0 27
28 9330 3607 0 0 0 1 0 0 0 0 0 0 0 28
29 9144 4685 0 0 0 0 1 0 0 0 0 0 0 29
30 9895 4962 0 0 0 0 0 1 0 0 0 0 0 30
31 10404 5770 0 0 0 0 0 0 1 0 0 0 0 31
32 10195 5480 0 0 0 0 0 0 0 1 0 0 0 32
33 9987 5000 0 0 0 0 0 0 0 0 1 0 0 33
34 9789 3228 0 0 0 0 0 0 0 0 0 1 0 34
35 9437 1993 0 0 0 0 0 0 0 0 0 0 1 35
36 10096 2288 0 0 0 0 0 0 0 0 0 0 0 36
37 9776 1580 1 0 0 0 0 0 0 0 0 0 0 37
38 9106 2111 0 1 0 0 0 0 0 0 0 0 0 38
39 10258 2192 0 0 1 0 0 0 0 0 0 0 0 39
40 9766 3601 0 0 0 1 0 0 0 0 0 0 0 40
41 9826 4665 0 0 0 0 1 0 0 0 0 0 0 41
42 9957 4876 0 0 0 0 0 1 0 0 0 0 0 42
43 10036 5813 0 0 0 0 0 0 1 0 0 0 0 43
44 10508 5589 0 0 0 0 0 0 0 1 0 0 0 44
45 10146 5331 0 0 0 0 0 0 0 0 1 0 0 45
46 10166 3075 0 0 0 0 0 0 0 0 0 1 0 46
47 9365 2002 0 0 0 0 0 0 0 0 0 0 1 47
48 9968 2306 0 0 0 0 0 0 0 0 0 0 0 48
49 10123 1507 1 0 0 0 0 0 0 0 0 0 0 49
50 9144 1992 0 1 0 0 0 0 0 0 0 0 0 50
51 10447 2487 0 0 1 0 0 0 0 0 0 0 0 51
52 9699 3490 0 0 0 1 0 0 0 0 0 0 0 52
53 10451 4647 0 0 0 0 1 0 0 0 0 0 0 53
54 10192 5594 0 0 0 0 0 1 0 0 0 0 0 54
55 10404 5611 0 0 0 0 0 0 1 0 0 0 0 55
56 10597 5788 0 0 0 0 0 0 0 1 0 0 0 56
57 10633 6204 0 0 0 0 0 0 0 0 1 0 0 57
58 10727 3013 0 0 0 0 0 0 0 0 0 1 0 58
59 9784 1931 0 0 0 0 0 0 0 0 0 0 1 59
60 9667 2549 0 0 0 0 0 0 0 0 0 0 0 60
61 10297 1504 1 0 0 0 0 0 0 0 0 0 0 61
62 9426 2090 0 1 0 0 0 0 0 0 0 0 0 62
63 10274 2702 0 0 1 0 0 0 0 0 0 0 0 63
64 9598 2939 0 0 0 1 0 0 0 0 0 0 0 64
65 10400 4500 0 0 0 0 1 0 0 0 0 0 0 65
66 9985 6208 0 0 0 0 0 1 0 0 0 0 0 66
67 10761 6415 0 0 0 0 0 0 1 0 0 0 0 67
68 11081 5657 0 0 0 0 0 0 0 1 0 0 0 68
69 10297 5964 0 0 0 0 0 0 0 0 1 0 0 69
70 10751 3163 0 0 0 0 0 0 0 0 0 1 0 70
71 9760 1997 0 0 0 0 0 0 0 0 0 0 1 71
72 10133 2422 0 0 0 0 0 0 0 0 0 0 0 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
9399.8468 -0.1821 118.3606 -606.1771 337.4994 14.2390
M5 M6 M7 M8 M9 M10
483.0926 581.2988 1180.6730 1190.7571 838.5291 489.5081
M11 t
-530.3400 18.8712
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-488.61 -153.06 11.29 125.70 448.79
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9399.84684 210.24830 44.708 < 2e-16 ***
X -0.18211 0.08818 -2.065 0.043380 *
M1 118.36057 152.90451 0.774 0.442027
M2 -606.17714 136.37407 -4.445 4.02e-05 ***
M3 337.49935 136.64086 2.470 0.016475 *
M4 14.23901 160.64404 0.089 0.929676
M5 483.09262 247.38044 1.953 0.055670 .
M6 581.29882 302.35653 1.923 0.059451 .
M7 1180.67299 310.71470 3.800 0.000349 ***
M8 1190.75709 343.37098 3.468 0.000995 ***
M9 838.52910 299.43455 2.800 0.006923 **
M10 489.50809 153.38607 3.191 0.002287 **
M11 -530.33999 139.04537 -3.814 0.000334 ***
t 18.87121 1.47966 12.754 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 234.8 on 58 degrees of freedom
Multiple R-squared: 0.8571, Adjusted R-squared: 0.8251
F-statistic: 26.76 on 13 and 58 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,] 0.7153409 0.5693181 0.2846591
[2,] 0.7000152 0.5999696 0.2999848
[3,] 0.5887669 0.8224662 0.4112331
[4,] 0.6269470 0.7461059 0.3730530
[5,] 0.5860488 0.8279025 0.4139512
[6,] 0.5651499 0.8697003 0.4348501
[7,] 0.5025828 0.9948344 0.4974172
[8,] 0.4578440 0.9156881 0.5421560
[9,] 0.3800396 0.7600792 0.6199604
[10,] 0.3871979 0.7743957 0.6128021
[11,] 0.3208439 0.6416878 0.6791561
[12,] 0.2399849 0.4799698 0.7600151
[13,] 0.4153268 0.8306536 0.5846732
[14,] 0.5601653 0.8796693 0.4398347
[15,] 0.5976891 0.8046219 0.4023109
[16,] 0.5617670 0.8764660 0.4382330
[17,] 0.5113268 0.9773465 0.4886732
[18,] 0.5600820 0.8798360 0.4399180
[19,] 0.5793767 0.8412465 0.4206233
[20,] 0.7094600 0.5810799 0.2905400
[21,] 0.7041835 0.5916330 0.2958165
[22,] 0.6445419 0.7109162 0.3554581
[23,] 0.6020056 0.7959889 0.3979944
[24,] 0.6276265 0.7447471 0.3723735
[25,] 0.6390554 0.7218892 0.3609446
[26,] 0.5696563 0.8606873 0.4303437
[27,] 0.6427699 0.7144601 0.3572301
[28,] 0.5696210 0.8607580 0.4303790
[29,] 0.4924631 0.9849263 0.5075369
[30,] 0.5658130 0.8683740 0.4341870
[31,] 0.5781598 0.8436803 0.4218402
[32,] 0.5163823 0.9672355 0.4836177
[33,] 0.4289270 0.8578539 0.5710730
[34,] 0.4240474 0.8480949 0.5759526
[35,] 0.3796785 0.7593570 0.6203215
[36,] 0.2961379 0.5922758 0.7038621
[37,] 0.2653762 0.5307524 0.7346238
[38,] 0.2886851 0.5773702 0.7113149
[39,] 0.1831978 0.3663957 0.8168022
> postscript(file="/var/www/html/rcomp/tmp/1g0tb1260911929.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xl301260911929.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36lus1260911929.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4r4de1260911929.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rpg01260911929.ps",horizontal=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 = 72
Frequency = 1
1 2 3 4 5 6
162.810974 260.858339 242.611334 -53.234232 99.533154 -285.243100
7 8 9 10 11 12
46.421615 51.582574 -253.227498 77.641352 -93.443768 -35.328373
13 14 15 16 17 18
49.500915 -106.941003 -387.235349 305.411606 -209.523552 -136.817460
19 20 21 22 23 24
69.919729 -396.006745 -103.892068 -300.592223 -216.709725 104.990866
25 26 27 28 29 30
84.299722 253.950480 -79.214798 44.400236 -433.007243 251.360530
31 32 33 34 35 36
289.262096 -1.505854 36.436886 -154.116777 269.951072 433.463074
37 38 39 40 41 42
-152.704400 -20.336140 183.867279 252.853014 18.895960 71.244303
43 44 45 46 47 48
-297.361609 104.889870 29.261599 -31.434546 -26.864462 82.286554
49 50 51 52 53 54
-45.453164 -230.462081 200.135939 -60.816027 414.163387 210.546579
55 56 57 58 59 60
-192.602894 3.675724 448.791322 291.819928 152.751000 -400.914639
61 62 63 64 65 66
-98.454048 -157.069596 -160.164405 -488.614597 109.938294 -111.090852
67 68 69 70 71 72
84.361063 237.364432 -157.370240 116.682267 -85.684117 -184.497481
> postscript(file="/var/www/html/rcomp/tmp/6uh3v1260911929.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 162.810974 NA
1 260.858339 162.810974
2 242.611334 260.858339
3 -53.234232 242.611334
4 99.533154 -53.234232
5 -285.243100 99.533154
6 46.421615 -285.243100
7 51.582574 46.421615
8 -253.227498 51.582574
9 77.641352 -253.227498
10 -93.443768 77.641352
11 -35.328373 -93.443768
12 49.500915 -35.328373
13 -106.941003 49.500915
14 -387.235349 -106.941003
15 305.411606 -387.235349
16 -209.523552 305.411606
17 -136.817460 -209.523552
18 69.919729 -136.817460
19 -396.006745 69.919729
20 -103.892068 -396.006745
21 -300.592223 -103.892068
22 -216.709725 -300.592223
23 104.990866 -216.709725
24 84.299722 104.990866
25 253.950480 84.299722
26 -79.214798 253.950480
27 44.400236 -79.214798
28 -433.007243 44.400236
29 251.360530 -433.007243
30 289.262096 251.360530
31 -1.505854 289.262096
32 36.436886 -1.505854
33 -154.116777 36.436886
34 269.951072 -154.116777
35 433.463074 269.951072
36 -152.704400 433.463074
37 -20.336140 -152.704400
38 183.867279 -20.336140
39 252.853014 183.867279
40 18.895960 252.853014
41 71.244303 18.895960
42 -297.361609 71.244303
43 104.889870 -297.361609
44 29.261599 104.889870
45 -31.434546 29.261599
46 -26.864462 -31.434546
47 82.286554 -26.864462
48 -45.453164 82.286554
49 -230.462081 -45.453164
50 200.135939 -230.462081
51 -60.816027 200.135939
52 414.163387 -60.816027
53 210.546579 414.163387
54 -192.602894 210.546579
55 3.675724 -192.602894
56 448.791322 3.675724
57 291.819928 448.791322
58 152.751000 291.819928
59 -400.914639 152.751000
60 -98.454048 -400.914639
61 -157.069596 -98.454048
62 -160.164405 -157.069596
63 -488.614597 -160.164405
64 109.938294 -488.614597
65 -111.090852 109.938294
66 84.361063 -111.090852
67 237.364432 84.361063
68 -157.370240 237.364432
69 116.682267 -157.370240
70 -85.684117 116.682267
71 -184.497481 -85.684117
72 NA -184.497481
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 260.858339 162.810974
[2,] 242.611334 260.858339
[3,] -53.234232 242.611334
[4,] 99.533154 -53.234232
[5,] -285.243100 99.533154
[6,] 46.421615 -285.243100
[7,] 51.582574 46.421615
[8,] -253.227498 51.582574
[9,] 77.641352 -253.227498
[10,] -93.443768 77.641352
[11,] -35.328373 -93.443768
[12,] 49.500915 -35.328373
[13,] -106.941003 49.500915
[14,] -387.235349 -106.941003
[15,] 305.411606 -387.235349
[16,] -209.523552 305.411606
[17,] -136.817460 -209.523552
[18,] 69.919729 -136.817460
[19,] -396.006745 69.919729
[20,] -103.892068 -396.006745
[21,] -300.592223 -103.892068
[22,] -216.709725 -300.592223
[23,] 104.990866 -216.709725
[24,] 84.299722 104.990866
[25,] 253.950480 84.299722
[26,] -79.214798 253.950480
[27,] 44.400236 -79.214798
[28,] -433.007243 44.400236
[29,] 251.360530 -433.007243
[30,] 289.262096 251.360530
[31,] -1.505854 289.262096
[32,] 36.436886 -1.505854
[33,] -154.116777 36.436886
[34,] 269.951072 -154.116777
[35,] 433.463074 269.951072
[36,] -152.704400 433.463074
[37,] -20.336140 -152.704400
[38,] 183.867279 -20.336140
[39,] 252.853014 183.867279
[40,] 18.895960 252.853014
[41,] 71.244303 18.895960
[42,] -297.361609 71.244303
[43,] 104.889870 -297.361609
[44,] 29.261599 104.889870
[45,] -31.434546 29.261599
[46,] -26.864462 -31.434546
[47,] 82.286554 -26.864462
[48,] -45.453164 82.286554
[49,] -230.462081 -45.453164
[50,] 200.135939 -230.462081
[51,] -60.816027 200.135939
[52,] 414.163387 -60.816027
[53,] 210.546579 414.163387
[54,] -192.602894 210.546579
[55,] 3.675724 -192.602894
[56,] 448.791322 3.675724
[57,] 291.819928 448.791322
[58,] 152.751000 291.819928
[59,] -400.914639 152.751000
[60,] -98.454048 -400.914639
[61,] -157.069596 -98.454048
[62,] -160.164405 -157.069596
[63,] -488.614597 -160.164405
[64,] 109.938294 -488.614597
[65,] -111.090852 109.938294
[66,] 84.361063 -111.090852
[67,] 237.364432 84.361063
[68,] -157.370240 237.364432
[69,] 116.682267 -157.370240
[70,] -85.684117 116.682267
[71,] -184.497481 -85.684117
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 260.858339 162.810974
2 242.611334 260.858339
3 -53.234232 242.611334
4 99.533154 -53.234232
5 -285.243100 99.533154
6 46.421615 -285.243100
7 51.582574 46.421615
8 -253.227498 51.582574
9 77.641352 -253.227498
10 -93.443768 77.641352
11 -35.328373 -93.443768
12 49.500915 -35.328373
13 -106.941003 49.500915
14 -387.235349 -106.941003
15 305.411606 -387.235349
16 -209.523552 305.411606
17 -136.817460 -209.523552
18 69.919729 -136.817460
19 -396.006745 69.919729
20 -103.892068 -396.006745
21 -300.592223 -103.892068
22 -216.709725 -300.592223
23 104.990866 -216.709725
24 84.299722 104.990866
25 253.950480 84.299722
26 -79.214798 253.950480
27 44.400236 -79.214798
28 -433.007243 44.400236
29 251.360530 -433.007243
30 289.262096 251.360530
31 -1.505854 289.262096
32 36.436886 -1.505854
33 -154.116777 36.436886
34 269.951072 -154.116777
35 433.463074 269.951072
36 -152.704400 433.463074
37 -20.336140 -152.704400
38 183.867279 -20.336140
39 252.853014 183.867279
40 18.895960 252.853014
41 71.244303 18.895960
42 -297.361609 71.244303
43 104.889870 -297.361609
44 29.261599 104.889870
45 -31.434546 29.261599
46 -26.864462 -31.434546
47 82.286554 -26.864462
48 -45.453164 82.286554
49 -230.462081 -45.453164
50 200.135939 -230.462081
51 -60.816027 200.135939
52 414.163387 -60.816027
53 210.546579 414.163387
54 -192.602894 210.546579
55 3.675724 -192.602894
56 448.791322 3.675724
57 291.819928 448.791322
58 152.751000 291.819928
59 -400.914639 152.751000
60 -98.454048 -400.914639
61 -157.069596 -98.454048
62 -160.164405 -157.069596
63 -488.614597 -160.164405
64 109.938294 -488.614597
65 -111.090852 109.938294
66 84.361063 -111.090852
67 237.364432 84.361063
68 -157.370240 237.364432
69 116.682267 -157.370240
70 -85.684117 116.682267
71 -184.497481 -85.684117
> 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/760tq1260911929.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88hvw1260911929.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9bzwi1260911929.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ri1r1260911929.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11r66a1260911929.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/12267d1260911929.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/13ip6c1260911930.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/14s8s51260911930.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/15vbr41260911930.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/16f5j81260911930.tab")
+ }
>
> try(system("convert tmp/1g0tb1260911929.ps tmp/1g0tb1260911929.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xl301260911929.ps tmp/2xl301260911929.png",intern=TRUE))
character(0)
> try(system("convert tmp/36lus1260911929.ps tmp/36lus1260911929.png",intern=TRUE))
character(0)
> try(system("convert tmp/4r4de1260911929.ps tmp/4r4de1260911929.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rpg01260911929.ps tmp/5rpg01260911929.png",intern=TRUE))
character(0)
> try(system("convert tmp/6uh3v1260911929.ps tmp/6uh3v1260911929.png",intern=TRUE))
character(0)
> try(system("convert tmp/760tq1260911929.ps tmp/760tq1260911929.png",intern=TRUE))
character(0)
> try(system("convert tmp/88hvw1260911929.ps tmp/88hvw1260911929.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bzwi1260911929.ps tmp/9bzwi1260911929.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ri1r1260911929.ps tmp/10ri1r1260911929.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.551 1.596 3.551