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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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(110.3672031
+ ,0
+ ,102.1880309
+ ,114.0150276
+ ,108.1560276
+ ,100
+ ,96.8602511
+ ,0
+ ,110.3672031
+ ,102.1880309
+ ,114.0150276
+ ,108.1560276
+ ,94.1944583
+ ,0
+ ,96.8602511
+ ,110.3672031
+ ,102.1880309
+ ,114.0150276
+ ,99.51621961
+ ,0
+ ,94.1944583
+ ,96.8602511
+ ,110.3672031
+ ,102.1880309
+ ,94.06333487
+ ,0
+ ,99.51621961
+ ,94.1944583
+ ,96.8602511
+ ,110.3672031
+ ,97.5541476
+ ,0
+ ,94.06333487
+ ,99.51621961
+ ,94.1944583
+ ,96.8602511
+ ,78.15062422
+ ,0
+ ,97.5541476
+ ,94.06333487
+ ,99.51621961
+ ,94.1944583
+ ,81.2434643
+ ,0
+ ,78.15062422
+ ,97.5541476
+ ,94.06333487
+ ,99.51621961
+ ,92.36262465
+ ,0
+ ,81.2434643
+ ,78.15062422
+ ,97.5541476
+ ,94.06333487
+ ,96.06324371
+ ,0
+ ,92.36262465
+ ,81.2434643
+ ,78.15062422
+ ,97.5541476
+ ,114.0523777
+ ,0
+ ,96.06324371
+ ,92.36262465
+ ,81.2434643
+ ,78.15062422
+ ,110.6616666
+ ,0
+ ,114.0523777
+ ,96.06324371
+ ,92.36262465
+ ,81.2434643
+ ,104.9171949
+ ,0
+ ,110.6616666
+ ,114.0523777
+ ,96.06324371
+ ,92.36262465
+ ,90.00187193
+ ,0
+ ,104.9171949
+ ,110.6616666
+ ,114.0523777
+ ,96.06324371
+ ,95.7008067
+ ,0
+ ,90.00187193
+ ,104.9171949
+ ,110.6616666
+ ,114.0523777
+ ,86.02741157
+ ,0
+ ,95.7008067
+ ,90.00187193
+ ,104.9171949
+ ,110.6616666
+ ,84.85287668
+ ,0
+ ,86.02741157
+ ,95.7008067
+ ,90.00187193
+ ,104.9171949
+ ,100.04328
+ ,0
+ ,84.85287668
+ ,86.02741157
+ ,95.7008067
+ ,90.00187193
+ ,80.91713823
+ ,0
+ ,100.04328
+ ,84.85287668
+ ,86.02741157
+ ,95.7008067
+ ,74.06539709
+ ,0
+ ,80.91713823
+ ,100.04328
+ ,84.85287668
+ ,86.02741157
+ ,77.30281369
+ ,0
+ ,74.06539709
+ ,80.91713823
+ ,100.04328
+ ,84.85287668
+ ,97.23043249
+ ,0
+ ,77.30281369
+ ,74.06539709
+ ,80.91713823
+ ,100.04328
+ ,90.75515676
+ ,0
+ ,97.23043249
+ ,77.30281369
+ ,74.06539709
+ ,80.91713823
+ ,100.5614455
+ ,0
+ ,90.75515676
+ ,97.23043249
+ ,77.30281369
+ ,74.06539709
+ ,92.01293267
+ ,0
+ ,100.5614455
+ ,90.75515676
+ ,97.23043249
+ ,77.30281369
+ ,99.24012138
+ ,0
+ ,92.01293267
+ ,100.5614455
+ ,90.75515676
+ ,97.23043249
+ ,105.8672755
+ ,0
+ ,99.24012138
+ ,92.01293267
+ ,100.5614455
+ ,90.75515676
+ ,90.9920463
+ ,0
+ ,105.8672755
+ ,99.24012138
+ ,92.01293267
+ ,100.5614455
+ ,93.30624423
+ ,0
+ ,90.9920463
+ ,105.8672755
+ ,99.24012138
+ ,92.01293267
+ ,91.17419413
+ ,0
+ ,93.30624423
+ ,90.9920463
+ ,105.8672755
+ ,99.24012138
+ ,77.33295039
+ ,0
+ ,91.17419413
+ ,93.30624423
+ ,90.9920463
+ ,105.8672755
+ ,91.1277721
+ ,0
+ ,77.33295039
+ ,91.17419413
+ ,93.30624423
+ ,90.9920463
+ ,85.01249943
+ ,0
+ ,91.1277721
+ ,77.33295039
+ ,91.17419413
+ ,93.30624423
+ ,83.90390242
+ ,0
+ ,85.01249943
+ ,91.1277721
+ ,77.33295039
+ ,91.17419413
+ ,104.8626302
+ ,0
+ ,83.90390242
+ ,85.01249943
+ ,91.1277721
+ ,77.33295039
+ ,110.9039108
+ ,0
+ ,104.8626302
+ ,83.90390242
+ ,85.01249943
+ ,91.1277721
+ ,95.43714373
+ ,0
+ ,110.9039108
+ ,104.8626302
+ ,83.90390242
+ ,85.01249943
+ ,111.6238727
+ ,0
+ ,95.43714373
+ ,110.9039108
+ ,104.8626302
+ ,83.90390242
+ ,108.8925403
+ ,0
+ ,111.6238727
+ ,95.43714373
+ ,110.9039108
+ ,104.8626302
+ ,96.17511682
+ ,0
+ ,108.8925403
+ ,111.6238727
+ ,95.43714373
+ ,110.9039108
+ ,101.9740205
+ ,0
+ ,96.17511682
+ ,108.8925403
+ ,111.6238727
+ ,95.43714373
+ ,99.11953031
+ ,0
+ ,101.9740205
+ ,96.17511682
+ ,108.8925403
+ ,111.6238727
+ ,86.78158147
+ ,0
+ ,99.11953031
+ ,101.9740205
+ ,96.17511682
+ ,108.8925403
+ ,118.4195003
+ ,0
+ ,86.78158147
+ ,99.11953031
+ ,101.9740205
+ ,96.17511682
+ ,118.7441447
+ ,0
+ ,118.4195003
+ ,86.78158147
+ ,99.11953031
+ ,101.9740205
+ ,106.5296192
+ ,0
+ ,118.7441447
+ ,118.4195003
+ ,86.78158147
+ ,99.11953031
+ ,134.7772694
+ ,0
+ ,106.5296192
+ ,118.7441447
+ ,118.4195003
+ ,86.78158147
+ ,104.6778714
+ ,0
+ ,134.7772694
+ ,106.5296192
+ ,118.7441447
+ ,118.4195003
+ ,105.2954304
+ ,0
+ ,104.6778714
+ ,134.7772694
+ ,106.5296192
+ ,118.7441447
+ ,139.4139849
+ ,0
+ ,105.2954304
+ ,104.6778714
+ ,134.7772694
+ ,106.5296192
+ ,103.6060491
+ ,0
+ ,139.4139849
+ ,105.2954304
+ ,104.6778714
+ ,134.7772694
+ ,99.78182974
+ ,0
+ ,103.6060491
+ ,139.4139849
+ ,105.2954304
+ ,104.6778714
+ ,103.4610301
+ ,0
+ ,99.78182974
+ ,103.6060491
+ ,139.4139849
+ ,105.2954304
+ ,120.0594945
+ ,0
+ ,103.4610301
+ ,99.78182974
+ ,103.6060491
+ ,139.4139849
+ ,96.71377168
+ ,0
+ ,120.0594945
+ ,103.4610301
+ ,99.78182974
+ ,103.6060491
+ ,107.1308929
+ ,0
+ ,96.71377168
+ ,120.0594945
+ ,103.4610301
+ ,99.78182974
+ ,105.3608372
+ ,0
+ ,107.1308929
+ ,96.71377168
+ ,120.0594945
+ ,103.4610301
+ ,111.6942359
+ ,0
+ ,105.3608372
+ ,107.1308929
+ ,96.71377168
+ ,120.0594945
+ ,132.0519998
+ ,0
+ ,111.6942359
+ ,105.3608372
+ ,107.1308929
+ ,96.71377168
+ ,126.8037879
+ ,0
+ ,132.0519998
+ ,111.6942359
+ ,105.3608372
+ ,107.1308929
+ ,154.4824253
+ ,0
+ ,126.8037879
+ ,132.0519998
+ ,111.6942359
+ ,105.3608372
+ ,141.5570984
+ ,0
+ ,154.4824253
+ ,126.8037879
+ ,132.0519998
+ ,111.6942359
+ ,109.9506882
+ ,0
+ ,141.5570984
+ ,154.4824253
+ ,126.8037879
+ ,132.0519998
+ ,127.904198
+ ,0
+ ,109.9506882
+ ,141.5570984
+ ,154.4824253
+ ,126.8037879
+ ,133.0888617
+ ,0
+ ,127.904198
+ ,109.9506882
+ ,141.5570984
+ ,154.4824253
+ ,120.0796299
+ ,0
+ ,133.0888617
+ ,127.904198
+ ,109.9506882
+ ,141.5570984
+ ,117.5557142
+ ,0
+ ,120.0796299
+ ,133.0888617
+ ,127.904198
+ ,109.9506882
+ ,143.0362309
+ ,0
+ ,117.5557142
+ ,120.0796299
+ ,133.0888617
+ ,127.904198
+ ,159.982927
+ ,1
+ ,143.0362309
+ ,117.5557142
+ ,120.0796299
+ ,133.0888617
+ ,128.5991124
+ ,1
+ ,159.982927
+ ,143.0362309
+ ,117.5557142
+ ,120.0796299
+ ,149.7373327
+ ,1
+ ,128.5991124
+ ,159.982927
+ ,143.0362309
+ ,117.5557142
+ ,126.8169313
+ ,1
+ ,149.7373327
+ ,128.5991124
+ ,159.982927
+ ,143.0362309
+ ,140.9639674
+ ,1
+ ,126.8169313
+ ,149.7373327
+ ,128.5991124
+ ,159.982927
+ ,137.6691981
+ ,1
+ ,140.9639674
+ ,126.8169313
+ ,149.7373327
+ ,128.5991124
+ ,117.9402337
+ ,1
+ ,137.6691981
+ ,140.9639674
+ ,126.8169313
+ ,149.7373327
+ ,122.3095247
+ ,1
+ ,117.9402337
+ ,137.6691981
+ ,140.9639674
+ ,126.8169313
+ ,127.7804207
+ ,1
+ ,122.3095247
+ ,117.9402337
+ ,137.6691981
+ ,140.9639674
+ ,136.1677176
+ ,1
+ ,127.7804207
+ ,122.3095247
+ ,117.9402337
+ ,137.6691981
+ ,116.2405856
+ ,1
+ ,136.1677176
+ ,127.7804207
+ ,122.3095247
+ ,117.9402337
+ ,123.1576893
+ ,1
+ ,116.2405856
+ ,136.1677176
+ ,127.7804207
+ ,122.3095247
+ ,116.3400234
+ ,1
+ ,123.1576893
+ ,116.2405856
+ ,136.1677176
+ ,127.7804207
+ ,108.6119282
+ ,1
+ ,116.3400234
+ ,123.1576893
+ ,116.2405856
+ ,136.1677176
+ ,125.8982264
+ ,1
+ ,108.6119282
+ ,116.3400234
+ ,123.1576893
+ ,116.2405856
+ ,112.8003105
+ ,1
+ ,125.8982264
+ ,108.6119282
+ ,116.3400234
+ ,123.1576893
+ ,107.5182447
+ ,1
+ ,112.8003105
+ ,125.8982264
+ ,108.6119282
+ ,116.3400234
+ ,135.0955413
+ ,1
+ ,107.5182447
+ ,112.8003105
+ ,125.8982264
+ ,108.6119282
+ ,115.5096488
+ ,1
+ ,135.0955413
+ ,107.5182447
+ ,112.8003105
+ ,125.8982264
+ ,115.8640759
+ ,1
+ ,115.5096488
+ ,135.0955413
+ ,107.5182447
+ ,112.8003105
+ ,104.5883906
+ ,1
+ ,115.8640759
+ ,115.5096488
+ ,135.0955413
+ ,107.5182447
+ ,163.7213386
+ ,1
+ ,104.5883906
+ ,115.8640759
+ ,115.5096488
+ ,135.0955413
+ ,113.4482275
+ ,1
+ ,163.7213386
+ ,104.5883906
+ ,115.8640759
+ ,115.5096488
+ ,98.0428844
+ ,1
+ ,113.4482275
+ ,163.7213386
+ ,104.5883906
+ ,115.8640759
+ ,116.7868521
+ ,1
+ ,98.0428844
+ ,113.4482275
+ ,163.7213386
+ ,104.5883906
+ ,126.5330444
+ ,1
+ ,116.7868521
+ ,98.0428844
+ ,113.4482275
+ ,163.7213386
+ ,113.0336597
+ ,1
+ ,126.5330444
+ ,116.7868521
+ ,98.0428844
+ ,113.4482275
+ ,124.3392163
+ ,1
+ ,113.0336597
+ ,126.5330444
+ ,116.7868521
+ ,98.0428844
+ ,109.8298759
+ ,1
+ ,124.3392163
+ ,113.0336597
+ ,126.5330444
+ ,116.7868521
+ ,124.4434777
+ ,1
+ ,109.8298759
+ ,124.3392163
+ ,113.0336597
+ ,126.5330444
+ ,111.5039454
+ ,1
+ ,124.4434777
+ ,109.8298759
+ ,124.3392163
+ ,113.0336597
+ ,102.0350019
+ ,1
+ ,111.5039454
+ ,124.4434777
+ ,109.8298759
+ ,124.3392163
+ ,116.8726598
+ ,1
+ ,102.0350019
+ ,111.5039454
+ ,124.4434777
+ ,109.8298759
+ ,112.2073122
+ ,1
+ ,116.8726598
+ ,102.0350019
+ ,111.5039454
+ ,124.4434777
+ ,101.1513902
+ ,1
+ ,112.2073122
+ ,116.8726598
+ ,102.0350019
+ ,111.5039454
+ ,124.4255108
+ ,1
+ ,101.1513902
+ ,112.2073122
+ ,116.8726598
+ ,102.0350019)
+ ,dim=c(6
+ ,104)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:104))
> y <- array(NA,dim=c(6,104),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:104))
> 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 Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8
1 110.36720 0 102.18803 114.01503 108.15603 100.00000 1 0 0 0 0 0 0 0
2 96.86025 0 110.36720 102.18803 114.01503 108.15603 0 1 0 0 0 0 0 0
3 94.19446 0 96.86025 110.36720 102.18803 114.01503 0 0 1 0 0 0 0 0
4 99.51622 0 94.19446 96.86025 110.36720 102.18803 0 0 0 1 0 0 0 0
5 94.06333 0 99.51622 94.19446 96.86025 110.36720 0 0 0 0 1 0 0 0
6 97.55415 0 94.06333 99.51622 94.19446 96.86025 0 0 0 0 0 1 0 0
7 78.15062 0 97.55415 94.06333 99.51622 94.19446 0 0 0 0 0 0 1 0
8 81.24346 0 78.15062 97.55415 94.06333 99.51622 0 0 0 0 0 0 0 1
9 92.36262 0 81.24346 78.15062 97.55415 94.06333 0 0 0 0 0 0 0 0
10 96.06324 0 92.36262 81.24346 78.15062 97.55415 0 0 0 0 0 0 0 0
11 114.05238 0 96.06324 92.36262 81.24346 78.15062 0 0 0 0 0 0 0 0
12 110.66167 0 114.05238 96.06324 92.36262 81.24346 0 0 0 0 0 0 0 0
13 104.91719 0 110.66167 114.05238 96.06324 92.36262 1 0 0 0 0 0 0 0
14 90.00187 0 104.91719 110.66167 114.05238 96.06324 0 1 0 0 0 0 0 0
15 95.70081 0 90.00187 104.91719 110.66167 114.05238 0 0 1 0 0 0 0 0
16 86.02741 0 95.70081 90.00187 104.91719 110.66167 0 0 0 1 0 0 0 0
17 84.85288 0 86.02741 95.70081 90.00187 104.91719 0 0 0 0 1 0 0 0
18 100.04328 0 84.85288 86.02741 95.70081 90.00187 0 0 0 0 0 1 0 0
19 80.91714 0 100.04328 84.85288 86.02741 95.70081 0 0 0 0 0 0 1 0
20 74.06540 0 80.91714 100.04328 84.85288 86.02741 0 0 0 0 0 0 0 1
21 77.30281 0 74.06540 80.91714 100.04328 84.85288 0 0 0 0 0 0 0 0
22 97.23043 0 77.30281 74.06540 80.91714 100.04328 0 0 0 0 0 0 0 0
23 90.75516 0 97.23043 77.30281 74.06540 80.91714 0 0 0 0 0 0 0 0
24 100.56145 0 90.75516 97.23043 77.30281 74.06540 0 0 0 0 0 0 0 0
25 92.01293 0 100.56145 90.75516 97.23043 77.30281 1 0 0 0 0 0 0 0
26 99.24012 0 92.01293 100.56145 90.75516 97.23043 0 1 0 0 0 0 0 0
27 105.86728 0 99.24012 92.01293 100.56145 90.75516 0 0 1 0 0 0 0 0
28 90.99205 0 105.86728 99.24012 92.01293 100.56145 0 0 0 1 0 0 0 0
29 93.30624 0 90.99205 105.86728 99.24012 92.01293 0 0 0 0 1 0 0 0
30 91.17419 0 93.30624 90.99205 105.86728 99.24012 0 0 0 0 0 1 0 0
31 77.33295 0 91.17419 93.30624 90.99205 105.86728 0 0 0 0 0 0 1 0
32 91.12777 0 77.33295 91.17419 93.30624 90.99205 0 0 0 0 0 0 0 1
33 85.01250 0 91.12777 77.33295 91.17419 93.30624 0 0 0 0 0 0 0 0
34 83.90390 0 85.01250 91.12777 77.33295 91.17419 0 0 0 0 0 0 0 0
35 104.86263 0 83.90390 85.01250 91.12777 77.33295 0 0 0 0 0 0 0 0
36 110.90391 0 104.86263 83.90390 85.01250 91.12777 0 0 0 0 0 0 0 0
37 95.43714 0 110.90391 104.86263 83.90390 85.01250 1 0 0 0 0 0 0 0
38 111.62387 0 95.43714 110.90391 104.86263 83.90390 0 1 0 0 0 0 0 0
39 108.89254 0 111.62387 95.43714 110.90391 104.86263 0 0 1 0 0 0 0 0
40 96.17512 0 108.89254 111.62387 95.43714 110.90391 0 0 0 1 0 0 0 0
41 101.97402 0 96.17512 108.89254 111.62387 95.43714 0 0 0 0 1 0 0 0
42 99.11953 0 101.97402 96.17512 108.89254 111.62387 0 0 0 0 0 1 0 0
43 86.78158 0 99.11953 101.97402 96.17512 108.89254 0 0 0 0 0 0 1 0
44 118.41950 0 86.78158 99.11953 101.97402 96.17512 0 0 0 0 0 0 0 1
45 118.74414 0 118.41950 86.78158 99.11953 101.97402 0 0 0 0 0 0 0 0
46 106.52962 0 118.74414 118.41950 86.78158 99.11953 0 0 0 0 0 0 0 0
47 134.77727 0 106.52962 118.74414 118.41950 86.78158 0 0 0 0 0 0 0 0
48 104.67787 0 134.77727 106.52962 118.74414 118.41950 0 0 0 0 0 0 0 0
49 105.29543 0 104.67787 134.77727 106.52962 118.74414 1 0 0 0 0 0 0 0
50 139.41398 0 105.29543 104.67787 134.77727 106.52962 0 1 0 0 0 0 0 0
51 103.60605 0 139.41398 105.29543 104.67787 134.77727 0 0 1 0 0 0 0 0
52 99.78183 0 103.60605 139.41398 105.29543 104.67787 0 0 0 1 0 0 0 0
53 103.46103 0 99.78183 103.60605 139.41398 105.29543 0 0 0 0 1 0 0 0
54 120.05949 0 103.46103 99.78183 103.60605 139.41398 0 0 0 0 0 1 0 0
55 96.71377 0 120.05949 103.46103 99.78183 103.60605 0 0 0 0 0 0 1 0
56 107.13089 0 96.71377 120.05949 103.46103 99.78183 0 0 0 0 0 0 0 1
57 105.36084 0 107.13089 96.71377 120.05949 103.46103 0 0 0 0 0 0 0 0
58 111.69424 0 105.36084 107.13089 96.71377 120.05949 0 0 0 0 0 0 0 0
59 132.05200 0 111.69424 105.36084 107.13089 96.71377 0 0 0 0 0 0 0 0
60 126.80379 0 132.05200 111.69424 105.36084 107.13089 0 0 0 0 0 0 0 0
61 154.48243 0 126.80379 132.05200 111.69424 105.36084 1 0 0 0 0 0 0 0
62 141.55710 0 154.48243 126.80379 132.05200 111.69424 0 1 0 0 0 0 0 0
63 109.95069 0 141.55710 154.48243 126.80379 132.05200 0 0 1 0 0 0 0 0
64 127.90420 0 109.95069 141.55710 154.48243 126.80379 0 0 0 1 0 0 0 0
65 133.08886 0 127.90420 109.95069 141.55710 154.48243 0 0 0 0 1 0 0 0
66 120.07963 0 133.08886 127.90420 109.95069 141.55710 0 0 0 0 0 1 0 0
67 117.55571 0 120.07963 133.08886 127.90420 109.95069 0 0 0 0 0 0 1 0
68 143.03623 0 117.55571 120.07963 133.08886 127.90420 0 0 0 0 0 0 0 1
69 159.98293 1 143.03623 117.55571 120.07963 133.08886 0 0 0 0 0 0 0 0
70 128.59911 1 159.98293 143.03623 117.55571 120.07963 0 0 0 0 0 0 0 0
71 149.73733 1 128.59911 159.98293 143.03623 117.55571 0 0 0 0 0 0 0 0
72 126.81693 1 149.73733 128.59911 159.98293 143.03623 0 0 0 0 0 0 0 0
73 140.96397 1 126.81693 149.73733 128.59911 159.98293 1 0 0 0 0 0 0 0
74 137.66920 1 140.96397 126.81693 149.73733 128.59911 0 1 0 0 0 0 0 0
75 117.94023 1 137.66920 140.96397 126.81693 149.73733 0 0 1 0 0 0 0 0
76 122.30952 1 117.94023 137.66920 140.96397 126.81693 0 0 0 1 0 0 0 0
77 127.78042 1 122.30952 117.94023 137.66920 140.96397 0 0 0 0 1 0 0 0
78 136.16772 1 127.78042 122.30952 117.94023 137.66920 0 0 0 0 0 1 0 0
79 116.24059 1 136.16772 127.78042 122.30952 117.94023 0 0 0 0 0 0 1 0
80 123.15769 1 116.24059 136.16772 127.78042 122.30952 0 0 0 0 0 0 0 1
81 116.34002 1 123.15769 116.24059 136.16772 127.78042 0 0 0 0 0 0 0 0
82 108.61193 1 116.34002 123.15769 116.24059 136.16772 0 0 0 0 0 0 0 0
83 125.89823 1 108.61193 116.34002 123.15769 116.24059 0 0 0 0 0 0 0 0
84 112.80031 1 125.89823 108.61193 116.34002 123.15769 0 0 0 0 0 0 0 0
85 107.51824 1 112.80031 125.89823 108.61193 116.34002 1 0 0 0 0 0 0 0
86 135.09554 1 107.51824 112.80031 125.89823 108.61193 0 1 0 0 0 0 0 0
87 115.50965 1 135.09554 107.51824 112.80031 125.89823 0 0 1 0 0 0 0 0
88 115.86408 1 115.50965 135.09554 107.51824 112.80031 0 0 0 1 0 0 0 0
89 104.58839 1 115.86408 115.50965 135.09554 107.51824 0 0 0 0 1 0 0 0
90 163.72134 1 104.58839 115.86408 115.50965 135.09554 0 0 0 0 0 1 0 0
91 113.44823 1 163.72134 104.58839 115.86408 115.50965 0 0 0 0 0 0 1 0
92 98.04288 1 113.44823 163.72134 104.58839 115.86408 0 0 0 0 0 0 0 1
93 116.78685 1 98.04288 113.44823 163.72134 104.58839 0 0 0 0 0 0 0 0
94 126.53304 1 116.78685 98.04288 113.44823 163.72134 0 0 0 0 0 0 0 0
95 113.03366 1 126.53304 116.78685 98.04288 113.44823 0 0 0 0 0 0 0 0
96 124.33922 1 113.03366 126.53304 116.78685 98.04288 0 0 0 0 0 0 0 0
97 109.82988 1 124.33922 113.03366 126.53304 116.78685 1 0 0 0 0 0 0 0
98 124.44348 1 109.82988 124.33922 113.03366 126.53304 0 1 0 0 0 0 0 0
99 111.50395 1 124.44348 109.82988 124.33922 113.03366 0 0 1 0 0 0 0 0
100 102.03500 1 111.50395 124.44348 109.82988 124.33922 0 0 0 1 0 0 0 0
101 116.87266 1 102.03500 111.50395 124.44348 109.82988 0 0 0 0 1 0 0 0
102 112.20731 1 116.87266 102.03500 111.50395 124.44348 0 0 0 0 0 1 0 0
103 101.15139 1 112.20731 116.87266 102.03500 111.50395 0 0 0 0 0 0 1 0
104 124.42551 1 101.15139 112.20731 116.87266 102.03500 0 0 0 0 0 0 0 1
M9 M10 M11 t
1 0 0 0 1
2 0 0 0 2
3 0 0 0 3
4 0 0 0 4
5 0 0 0 5
6 0 0 0 6
7 0 0 0 7
8 0 0 0 8
9 1 0 0 9
10 0 1 0 10
11 0 0 1 11
12 0 0 0 12
13 0 0 0 13
14 0 0 0 14
15 0 0 0 15
16 0 0 0 16
17 0 0 0 17
18 0 0 0 18
19 0 0 0 19
20 0 0 0 20
21 1 0 0 21
22 0 1 0 22
23 0 0 1 23
24 0 0 0 24
25 0 0 0 25
26 0 0 0 26
27 0 0 0 27
28 0 0 0 28
29 0 0 0 29
30 0 0 0 30
31 0 0 0 31
32 0 0 0 32
33 1 0 0 33
34 0 1 0 34
35 0 0 1 35
36 0 0 0 36
37 0 0 0 37
38 0 0 0 38
39 0 0 0 39
40 0 0 0 40
41 0 0 0 41
42 0 0 0 42
43 0 0 0 43
44 0 0 0 44
45 1 0 0 45
46 0 1 0 46
47 0 0 1 47
48 0 0 0 48
49 0 0 0 49
50 0 0 0 50
51 0 0 0 51
52 0 0 0 52
53 0 0 0 53
54 0 0 0 54
55 0 0 0 55
56 0 0 0 56
57 1 0 0 57
58 0 1 0 58
59 0 0 1 59
60 0 0 0 60
61 0 0 0 61
62 0 0 0 62
63 0 0 0 63
64 0 0 0 64
65 0 0 0 65
66 0 0 0 66
67 0 0 0 67
68 0 0 0 68
69 1 0 0 69
70 0 1 0 70
71 0 0 1 71
72 0 0 0 72
73 0 0 0 73
74 0 0 0 74
75 0 0 0 75
76 0 0 0 76
77 0 0 0 77
78 0 0 0 78
79 0 0 0 79
80 0 0 0 80
81 1 0 0 81
82 0 1 0 82
83 0 0 1 83
84 0 0 0 84
85 0 0 0 85
86 0 0 0 86
87 0 0 0 87
88 0 0 0 88
89 0 0 0 89
90 0 0 0 90
91 0 0 0 91
92 0 0 0 92
93 1 0 0 93
94 0 1 0 94
95 0 0 1 95
96 0 0 0 96
97 0 0 0 97
98 0 0 0 98
99 0 0 0 99
100 0 0 0 100
101 0 0 0 101
102 0 0 0 102
103 0 0 0 103
104 0 0 0 104
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
23.66160 -6.28343 0.25913 0.06786 0.30057 0.11954
M1 M2 M3 M4 M5 M6
0.44712 3.13191 -10.66731 -9.76718 -8.14522 3.22707
M7 M8 M9 M10 M11 t
-16.24578 -2.16575 -2.42716 -1.16358 11.82600 0.17752
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-19.737656 -8.726920 -0.000861 6.327707 41.306614
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.66160 11.84488 1.998 0.04892 *
X -6.28343 4.41128 -1.424 0.15795
Y1 0.25913 0.10664 2.430 0.01718 *
Y2 0.06786 0.10574 0.642 0.52272
Y3 0.30057 0.10604 2.834 0.00572 **
Y4 0.11954 0.10777 1.109 0.27041
M1 0.44712 6.06951 0.074 0.94145
M2 3.13191 5.98013 0.524 0.60182
M3 -10.66731 6.02045 -1.772 0.07996 .
M4 -9.76718 6.29317 -1.552 0.12433
M5 -8.14522 6.26395 -1.300 0.19696
M6 3.22707 6.31597 0.511 0.61070
M7 -16.24578 5.83511 -2.784 0.00660 **
M8 -2.16575 6.51221 -0.333 0.74027
M9 -2.42716 6.29611 -0.386 0.70082
M10 -1.16358 6.44509 -0.181 0.85715
M11 11.82600 6.10335 1.938 0.05595 .
t 0.17752 0.07839 2.265 0.02605 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.87 on 86 degrees of freedom
Multiple R-squared: 0.659, Adjusted R-squared: 0.5916
F-statistic: 9.775 on 17 and 86 DF, p-value: 9.968e-14
> 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,] 0.101298808 0.202597616 0.8987012
[2,] 0.045533126 0.091066251 0.9544669
[3,] 0.153679177 0.307358355 0.8463208
[4,] 0.087195888 0.174391775 0.9128041
[5,] 0.053211085 0.106422170 0.9467889
[6,] 0.066779215 0.133558429 0.9332208
[7,] 0.129966155 0.259932310 0.8700338
[8,] 0.083352523 0.166705047 0.9166475
[9,] 0.058059933 0.116119865 0.9419401
[10,] 0.037279531 0.074559062 0.9627205
[11,] 0.024008360 0.048016720 0.9759916
[12,] 0.038840575 0.077681150 0.9611594
[13,] 0.026084798 0.052169596 0.9739152
[14,] 0.022306206 0.044612413 0.9776938
[15,] 0.014133587 0.028267175 0.9858664
[16,] 0.010395603 0.020791205 0.9896044
[17,] 0.006241904 0.012483807 0.9937581
[18,] 0.011905684 0.023811368 0.9880943
[19,] 0.008939773 0.017879546 0.9910602
[20,] 0.005583473 0.011166946 0.9944165
[21,] 0.003310498 0.006620997 0.9966895
[22,] 0.002837717 0.005675433 0.9971623
[23,] 0.001939104 0.003878208 0.9980609
[24,] 0.013824004 0.027648009 0.9861760
[25,] 0.017019109 0.034038219 0.9829809
[26,] 0.012132680 0.024265360 0.9878673
[27,] 0.008539814 0.017079628 0.9914602
[28,] 0.026815028 0.053630056 0.9731850
[29,] 0.021839895 0.043679791 0.9781601
[30,] 0.039083187 0.078166374 0.9609168
[31,] 0.029819485 0.059638969 0.9701805
[32,] 0.021800068 0.043600136 0.9781999
[33,] 0.023975679 0.047951358 0.9760243
[34,] 0.031350330 0.062700661 0.9686497
[35,] 0.026797765 0.053595530 0.9732022
[36,] 0.024667492 0.049334983 0.9753325
[37,] 0.037504830 0.075009659 0.9624952
[38,] 0.035882960 0.071765920 0.9641170
[39,] 0.031004407 0.062008814 0.9689956
[40,] 0.023472024 0.046944049 0.9765280
[41,] 0.121438277 0.242876553 0.8785617
[42,] 0.095728673 0.191457345 0.9042713
[43,] 0.091307412 0.182614824 0.9086926
[44,] 0.067233026 0.134466053 0.9327670
[45,] 0.051631843 0.103263685 0.9483682
[46,] 0.067644726 0.135289451 0.9323553
[47,] 0.056157969 0.112315938 0.9438420
[48,] 0.047641200 0.095282401 0.9523588
[49,] 0.160187502 0.320375004 0.8398125
[50,] 0.284651138 0.569302277 0.7153489
[51,] 0.369721158 0.739442315 0.6302788
[52,] 0.421997812 0.843995625 0.5780022
[53,] 0.442636543 0.885273086 0.5573635
[54,] 0.363160191 0.726320383 0.6368398
[55,] 0.288064507 0.576129014 0.7119355
[56,] 0.214394161 0.428788323 0.7856058
[57,] 0.154686239 0.309372479 0.8453138
[58,] 0.107559446 0.215118893 0.8924406
[59,] 0.066592557 0.133185113 0.9334074
[60,] 0.039165046 0.078330092 0.9608350
[61,] 0.025003703 0.050007406 0.9749963
[62,] 0.020697248 0.041394497 0.9793028
[63,] 0.016252476 0.032504953 0.9837475
> postscript(file="/var/www/html/rcomp/tmp/1tptx1258618612.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2cw3t1258618612.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qrgd1258618612.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4n1yu1258618612.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5t6bt1258618612.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 = 104
Frequency = 1
1 2 3 4 5 6
7.4015059 -13.0206816 3.7346335 8.5415704 3.1730316 -1.4181589
7 8 9 10 11 12
-3.3417517 -8.7125486 2.6084011 7.1914762 11.6900126 11.3233607
13 14 15 16 17 18
2.1706373 -19.7376557 2.7064314 -6.3773052 -2.0616189 2.6099021
19 20 21 22 23 24
1.1487293 -14.5257884 -12.5564215 9.4888856 -11.1911947 10.4352685
25 26 27 28 29 30
-7.2161636 -1.7375502 15.0452673 -1.7182375 1.0510630 -15.0769221
31 32 33 34 35 36
-5.5486423 -1.1973273 -9.4999372 -6.9859992 -0.9837359 11.5391654
37 38 39 40 41 42
-6.4757071 4.2796984 7.7038575 -2.5552859 1.9087340 -14.2493307
43 44 45 46 47 48
-2.7968260 17.7517230 10.9639007 -0.8731595 9.3161550 -19.5054282
49 50 51 52 53 54
-9.9972561 16.1113748 -9.2878994 -3.8135203 -8.8416406 2.1970949
55 56 57 58 59 60
-0.9740402 -0.5399319 -8.7700321 0.9067259 6.2361506 6.2180399
61 62 63 64 65 66
31.5585386 2.0786983 -15.2911489 2.9601100 4.4138703 -11.6620130
67 68 69 70 71 72
6.5108212 15.5660253 35.7388841 -0.8927203 6.7040321 -16.0554166
73 74 75 76 77 78
9.3789422 -1.4903643 -3.3417247 3.7737485 6.9508352 8.3978843
79 80 81 82 83 84
6.2666691 1.3540910 -8.9948319 -11.8799840 -4.9924318 -9.1745866
85 86 87 88 89 90
-9.7224469 12.9782702 2.0967202 7.7308050 -11.7644795 41.3066135
91 92 93 94 95 96
-1.9942578 -19.2958823 -9.4899632 3.0447752 -16.7789879 5.2195969
97 98 99 100 101 102
-17.0980504 0.5382100 -3.3661368 -8.5418851 5.1702048 -12.1050701
103 104
0.7292985 9.5996393
> postscript(file="/var/www/html/rcomp/tmp/6flqj1258618612.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 = 104
Frequency = 1
lag(myerror, k = 1) myerror
0 7.4015059 NA
1 -13.0206816 7.4015059
2 3.7346335 -13.0206816
3 8.5415704 3.7346335
4 3.1730316 8.5415704
5 -1.4181589 3.1730316
6 -3.3417517 -1.4181589
7 -8.7125486 -3.3417517
8 2.6084011 -8.7125486
9 7.1914762 2.6084011
10 11.6900126 7.1914762
11 11.3233607 11.6900126
12 2.1706373 11.3233607
13 -19.7376557 2.1706373
14 2.7064314 -19.7376557
15 -6.3773052 2.7064314
16 -2.0616189 -6.3773052
17 2.6099021 -2.0616189
18 1.1487293 2.6099021
19 -14.5257884 1.1487293
20 -12.5564215 -14.5257884
21 9.4888856 -12.5564215
22 -11.1911947 9.4888856
23 10.4352685 -11.1911947
24 -7.2161636 10.4352685
25 -1.7375502 -7.2161636
26 15.0452673 -1.7375502
27 -1.7182375 15.0452673
28 1.0510630 -1.7182375
29 -15.0769221 1.0510630
30 -5.5486423 -15.0769221
31 -1.1973273 -5.5486423
32 -9.4999372 -1.1973273
33 -6.9859992 -9.4999372
34 -0.9837359 -6.9859992
35 11.5391654 -0.9837359
36 -6.4757071 11.5391654
37 4.2796984 -6.4757071
38 7.7038575 4.2796984
39 -2.5552859 7.7038575
40 1.9087340 -2.5552859
41 -14.2493307 1.9087340
42 -2.7968260 -14.2493307
43 17.7517230 -2.7968260
44 10.9639007 17.7517230
45 -0.8731595 10.9639007
46 9.3161550 -0.8731595
47 -19.5054282 9.3161550
48 -9.9972561 -19.5054282
49 16.1113748 -9.9972561
50 -9.2878994 16.1113748
51 -3.8135203 -9.2878994
52 -8.8416406 -3.8135203
53 2.1970949 -8.8416406
54 -0.9740402 2.1970949
55 -0.5399319 -0.9740402
56 -8.7700321 -0.5399319
57 0.9067259 -8.7700321
58 6.2361506 0.9067259
59 6.2180399 6.2361506
60 31.5585386 6.2180399
61 2.0786983 31.5585386
62 -15.2911489 2.0786983
63 2.9601100 -15.2911489
64 4.4138703 2.9601100
65 -11.6620130 4.4138703
66 6.5108212 -11.6620130
67 15.5660253 6.5108212
68 35.7388841 15.5660253
69 -0.8927203 35.7388841
70 6.7040321 -0.8927203
71 -16.0554166 6.7040321
72 9.3789422 -16.0554166
73 -1.4903643 9.3789422
74 -3.3417247 -1.4903643
75 3.7737485 -3.3417247
76 6.9508352 3.7737485
77 8.3978843 6.9508352
78 6.2666691 8.3978843
79 1.3540910 6.2666691
80 -8.9948319 1.3540910
81 -11.8799840 -8.9948319
82 -4.9924318 -11.8799840
83 -9.1745866 -4.9924318
84 -9.7224469 -9.1745866
85 12.9782702 -9.7224469
86 2.0967202 12.9782702
87 7.7308050 2.0967202
88 -11.7644795 7.7308050
89 41.3066135 -11.7644795
90 -1.9942578 41.3066135
91 -19.2958823 -1.9942578
92 -9.4899632 -19.2958823
93 3.0447752 -9.4899632
94 -16.7789879 3.0447752
95 5.2195969 -16.7789879
96 -17.0980504 5.2195969
97 0.5382100 -17.0980504
98 -3.3661368 0.5382100
99 -8.5418851 -3.3661368
100 5.1702048 -8.5418851
101 -12.1050701 5.1702048
102 0.7292985 -12.1050701
103 9.5996393 0.7292985
104 NA 9.5996393
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -13.0206816 7.4015059
[2,] 3.7346335 -13.0206816
[3,] 8.5415704 3.7346335
[4,] 3.1730316 8.5415704
[5,] -1.4181589 3.1730316
[6,] -3.3417517 -1.4181589
[7,] -8.7125486 -3.3417517
[8,] 2.6084011 -8.7125486
[9,] 7.1914762 2.6084011
[10,] 11.6900126 7.1914762
[11,] 11.3233607 11.6900126
[12,] 2.1706373 11.3233607
[13,] -19.7376557 2.1706373
[14,] 2.7064314 -19.7376557
[15,] -6.3773052 2.7064314
[16,] -2.0616189 -6.3773052
[17,] 2.6099021 -2.0616189
[18,] 1.1487293 2.6099021
[19,] -14.5257884 1.1487293
[20,] -12.5564215 -14.5257884
[21,] 9.4888856 -12.5564215
[22,] -11.1911947 9.4888856
[23,] 10.4352685 -11.1911947
[24,] -7.2161636 10.4352685
[25,] -1.7375502 -7.2161636
[26,] 15.0452673 -1.7375502
[27,] -1.7182375 15.0452673
[28,] 1.0510630 -1.7182375
[29,] -15.0769221 1.0510630
[30,] -5.5486423 -15.0769221
[31,] -1.1973273 -5.5486423
[32,] -9.4999372 -1.1973273
[33,] -6.9859992 -9.4999372
[34,] -0.9837359 -6.9859992
[35,] 11.5391654 -0.9837359
[36,] -6.4757071 11.5391654
[37,] 4.2796984 -6.4757071
[38,] 7.7038575 4.2796984
[39,] -2.5552859 7.7038575
[40,] 1.9087340 -2.5552859
[41,] -14.2493307 1.9087340
[42,] -2.7968260 -14.2493307
[43,] 17.7517230 -2.7968260
[44,] 10.9639007 17.7517230
[45,] -0.8731595 10.9639007
[46,] 9.3161550 -0.8731595
[47,] -19.5054282 9.3161550
[48,] -9.9972561 -19.5054282
[49,] 16.1113748 -9.9972561
[50,] -9.2878994 16.1113748
[51,] -3.8135203 -9.2878994
[52,] -8.8416406 -3.8135203
[53,] 2.1970949 -8.8416406
[54,] -0.9740402 2.1970949
[55,] -0.5399319 -0.9740402
[56,] -8.7700321 -0.5399319
[57,] 0.9067259 -8.7700321
[58,] 6.2361506 0.9067259
[59,] 6.2180399 6.2361506
[60,] 31.5585386 6.2180399
[61,] 2.0786983 31.5585386
[62,] -15.2911489 2.0786983
[63,] 2.9601100 -15.2911489
[64,] 4.4138703 2.9601100
[65,] -11.6620130 4.4138703
[66,] 6.5108212 -11.6620130
[67,] 15.5660253 6.5108212
[68,] 35.7388841 15.5660253
[69,] -0.8927203 35.7388841
[70,] 6.7040321 -0.8927203
[71,] -16.0554166 6.7040321
[72,] 9.3789422 -16.0554166
[73,] -1.4903643 9.3789422
[74,] -3.3417247 -1.4903643
[75,] 3.7737485 -3.3417247
[76,] 6.9508352 3.7737485
[77,] 8.3978843 6.9508352
[78,] 6.2666691 8.3978843
[79,] 1.3540910 6.2666691
[80,] -8.9948319 1.3540910
[81,] -11.8799840 -8.9948319
[82,] -4.9924318 -11.8799840
[83,] -9.1745866 -4.9924318
[84,] -9.7224469 -9.1745866
[85,] 12.9782702 -9.7224469
[86,] 2.0967202 12.9782702
[87,] 7.7308050 2.0967202
[88,] -11.7644795 7.7308050
[89,] 41.3066135 -11.7644795
[90,] -1.9942578 41.3066135
[91,] -19.2958823 -1.9942578
[92,] -9.4899632 -19.2958823
[93,] 3.0447752 -9.4899632
[94,] -16.7789879 3.0447752
[95,] 5.2195969 -16.7789879
[96,] -17.0980504 5.2195969
[97,] 0.5382100 -17.0980504
[98,] -3.3661368 0.5382100
[99,] -8.5418851 -3.3661368
[100,] 5.1702048 -8.5418851
[101,] -12.1050701 5.1702048
[102,] 0.7292985 -12.1050701
[103,] 9.5996393 0.7292985
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -13.0206816 7.4015059
2 3.7346335 -13.0206816
3 8.5415704 3.7346335
4 3.1730316 8.5415704
5 -1.4181589 3.1730316
6 -3.3417517 -1.4181589
7 -8.7125486 -3.3417517
8 2.6084011 -8.7125486
9 7.1914762 2.6084011
10 11.6900126 7.1914762
11 11.3233607 11.6900126
12 2.1706373 11.3233607
13 -19.7376557 2.1706373
14 2.7064314 -19.7376557
15 -6.3773052 2.7064314
16 -2.0616189 -6.3773052
17 2.6099021 -2.0616189
18 1.1487293 2.6099021
19 -14.5257884 1.1487293
20 -12.5564215 -14.5257884
21 9.4888856 -12.5564215
22 -11.1911947 9.4888856
23 10.4352685 -11.1911947
24 -7.2161636 10.4352685
25 -1.7375502 -7.2161636
26 15.0452673 -1.7375502
27 -1.7182375 15.0452673
28 1.0510630 -1.7182375
29 -15.0769221 1.0510630
30 -5.5486423 -15.0769221
31 -1.1973273 -5.5486423
32 -9.4999372 -1.1973273
33 -6.9859992 -9.4999372
34 -0.9837359 -6.9859992
35 11.5391654 -0.9837359
36 -6.4757071 11.5391654
37 4.2796984 -6.4757071
38 7.7038575 4.2796984
39 -2.5552859 7.7038575
40 1.9087340 -2.5552859
41 -14.2493307 1.9087340
42 -2.7968260 -14.2493307
43 17.7517230 -2.7968260
44 10.9639007 17.7517230
45 -0.8731595 10.9639007
46 9.3161550 -0.8731595
47 -19.5054282 9.3161550
48 -9.9972561 -19.5054282
49 16.1113748 -9.9972561
50 -9.2878994 16.1113748
51 -3.8135203 -9.2878994
52 -8.8416406 -3.8135203
53 2.1970949 -8.8416406
54 -0.9740402 2.1970949
55 -0.5399319 -0.9740402
56 -8.7700321 -0.5399319
57 0.9067259 -8.7700321
58 6.2361506 0.9067259
59 6.2180399 6.2361506
60 31.5585386 6.2180399
61 2.0786983 31.5585386
62 -15.2911489 2.0786983
63 2.9601100 -15.2911489
64 4.4138703 2.9601100
65 -11.6620130 4.4138703
66 6.5108212 -11.6620130
67 15.5660253 6.5108212
68 35.7388841 15.5660253
69 -0.8927203 35.7388841
70 6.7040321 -0.8927203
71 -16.0554166 6.7040321
72 9.3789422 -16.0554166
73 -1.4903643 9.3789422
74 -3.3417247 -1.4903643
75 3.7737485 -3.3417247
76 6.9508352 3.7737485
77 8.3978843 6.9508352
78 6.2666691 8.3978843
79 1.3540910 6.2666691
80 -8.9948319 1.3540910
81 -11.8799840 -8.9948319
82 -4.9924318 -11.8799840
83 -9.1745866 -4.9924318
84 -9.7224469 -9.1745866
85 12.9782702 -9.7224469
86 2.0967202 12.9782702
87 7.7308050 2.0967202
88 -11.7644795 7.7308050
89 41.3066135 -11.7644795
90 -1.9942578 41.3066135
91 -19.2958823 -1.9942578
92 -9.4899632 -19.2958823
93 3.0447752 -9.4899632
94 -16.7789879 3.0447752
95 5.2195969 -16.7789879
96 -17.0980504 5.2195969
97 0.5382100 -17.0980504
98 -3.3661368 0.5382100
99 -8.5418851 -3.3661368
100 5.1702048 -8.5418851
101 -12.1050701 5.1702048
102 0.7292985 -12.1050701
103 9.5996393 0.7292985
> 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/7yld41258618612.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8v5e91258618612.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/954991258618612.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10fkwl1258618612.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11lg3r1258618612.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/12a7a61258618612.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/136zqu1258618612.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/1459qg1258618612.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/15b5t41258618612.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/16k5yp1258618612.tab")
+ }
>
> system("convert tmp/1tptx1258618612.ps tmp/1tptx1258618612.png")
> system("convert tmp/2cw3t1258618612.ps tmp/2cw3t1258618612.png")
> system("convert tmp/3qrgd1258618612.ps tmp/3qrgd1258618612.png")
> system("convert tmp/4n1yu1258618612.ps tmp/4n1yu1258618612.png")
> system("convert tmp/5t6bt1258618612.ps tmp/5t6bt1258618612.png")
> system("convert tmp/6flqj1258618612.ps tmp/6flqj1258618612.png")
> system("convert tmp/7yld41258618612.ps tmp/7yld41258618612.png")
> system("convert tmp/8v5e91258618612.ps tmp/8v5e91258618612.png")
> system("convert tmp/954991258618612.ps tmp/954991258618612.png")
> system("convert tmp/10fkwl1258618612.ps tmp/10fkwl1258618612.png")
>
>
> proc.time()
user system elapsed
3.170 1.615 3.755