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
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> x <- array(list(98.71,153.4,98.54,145,98.2,137.7,96.92,148.3,99.06,152.2,99.65,169.4,99.82,168.6,99.99,161.1,100.33,174.1,99.31,179,101.1,190.6,101.1,190,100.93,181.6,100.85,174.8,100.93,180.5,99.6,196.8,101.88,193.8,101.81,197,102.38,216.3,102.74,221.4,102.82,217.9,101.72,229.7,103.47,227.4,102.98,204.2,102.68,196.6,102.9,198.8,103.03,207.5,101.29,190.7,103.69,201.6,103.68,210.5,104.2,223.5,104.08,223.8,104.16,231.2,103.05,244,104.66,234.7,104.46,250.2,104.95,265.7,105.85,287.6,106.23,283.3,104.86,295.4,107.44,312.3,108.23,333.8,108.45,347.7,109.39,383.2,110.15,407.1,109.13,413.6,110.28,362.7,110.17,321.9,109.99,239.4,109.26,191,109.11,159.7,107.06,163.4,109.53,157.6,108.92,166.2,109.24,176.7,109.12,198.3,109,226.2,107.23,216.2,109.49,235.9,109.04,226.9),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> 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 = '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
1 98.71 153.4 1 0 0 0 0 0 0 0 0 0 0
2 98.54 145.0 0 1 0 0 0 0 0 0 0 0 0
3 98.20 137.7 0 0 1 0 0 0 0 0 0 0 0
4 96.92 148.3 0 0 0 1 0 0 0 0 0 0 0
5 99.06 152.2 0 0 0 0 1 0 0 0 0 0 0
6 99.65 169.4 0 0 0 0 0 1 0 0 0 0 0
7 99.82 168.6 0 0 0 0 0 0 1 0 0 0 0
8 99.99 161.1 0 0 0 0 0 0 0 1 0 0 0
9 100.33 174.1 0 0 0 0 0 0 0 0 1 0 0
10 99.31 179.0 0 0 0 0 0 0 0 0 0 1 0
11 101.10 190.6 0 0 0 0 0 0 0 0 0 0 1
12 101.10 190.0 0 0 0 0 0 0 0 0 0 0 0
13 100.93 181.6 1 0 0 0 0 0 0 0 0 0 0
14 100.85 174.8 0 1 0 0 0 0 0 0 0 0 0
15 100.93 180.5 0 0 1 0 0 0 0 0 0 0 0
16 99.60 196.8 0 0 0 1 0 0 0 0 0 0 0
17 101.88 193.8 0 0 0 0 1 0 0 0 0 0 0
18 101.81 197.0 0 0 0 0 0 1 0 0 0 0 0
19 102.38 216.3 0 0 0 0 0 0 1 0 0 0 0
20 102.74 221.4 0 0 0 0 0 0 0 1 0 0 0
21 102.82 217.9 0 0 0 0 0 0 0 0 1 0 0
22 101.72 229.7 0 0 0 0 0 0 0 0 0 1 0
23 103.47 227.4 0 0 0 0 0 0 0 0 0 0 1
24 102.98 204.2 0 0 0 0 0 0 0 0 0 0 0
25 102.68 196.6 1 0 0 0 0 0 0 0 0 0 0
26 102.90 198.8 0 1 0 0 0 0 0 0 0 0 0
27 103.03 207.5 0 0 1 0 0 0 0 0 0 0 0
28 101.29 190.7 0 0 0 1 0 0 0 0 0 0 0
29 103.69 201.6 0 0 0 0 1 0 0 0 0 0 0
30 103.68 210.5 0 0 0 0 0 1 0 0 0 0 0
31 104.20 223.5 0 0 0 0 0 0 1 0 0 0 0
32 104.08 223.8 0 0 0 0 0 0 0 1 0 0 0
33 104.16 231.2 0 0 0 0 0 0 0 0 1 0 0
34 103.05 244.0 0 0 0 0 0 0 0 0 0 1 0
35 104.66 234.7 0 0 0 0 0 0 0 0 0 0 1
36 104.46 250.2 0 0 0 0 0 0 0 0 0 0 0
37 104.95 265.7 1 0 0 0 0 0 0 0 0 0 0
38 105.85 287.6 0 1 0 0 0 0 0 0 0 0 0
39 106.23 283.3 0 0 1 0 0 0 0 0 0 0 0
40 104.86 295.4 0 0 0 1 0 0 0 0 0 0 0
41 107.44 312.3 0 0 0 0 1 0 0 0 0 0 0
42 108.23 333.8 0 0 0 0 0 1 0 0 0 0 0
43 108.45 347.7 0 0 0 0 0 0 1 0 0 0 0
44 109.39 383.2 0 0 0 0 0 0 0 1 0 0 0
45 110.15 407.1 0 0 0 0 0 0 0 0 1 0 0
46 109.13 413.6 0 0 0 0 0 0 0 0 0 1 0
47 110.28 362.7 0 0 0 0 0 0 0 0 0 0 1
48 110.17 321.9 0 0 0 0 0 0 0 0 0 0 0
49 109.99 239.4 1 0 0 0 0 0 0 0 0 0 0
50 109.26 191.0 0 1 0 0 0 0 0 0 0 0 0
51 109.11 159.7 0 0 1 0 0 0 0 0 0 0 0
52 107.06 163.4 0 0 0 1 0 0 0 0 0 0 0
53 109.53 157.6 0 0 0 0 1 0 0 0 0 0 0
54 108.92 166.2 0 0 0 0 0 1 0 0 0 0 0
55 109.24 176.7 0 0 0 0 0 0 1 0 0 0 0
56 109.12 198.3 0 0 0 0 0 0 0 1 0 0 0
57 109.00 226.2 0 0 0 0 0 0 0 0 1 0 0
58 107.23 216.2 0 0 0 0 0 0 0 0 0 1 0
59 109.49 235.9 0 0 0 0 0 0 0 0 0 0 1
60 109.04 226.9 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
97.133637 0.035268 -0.994111 -0.687493 -0.466465 -2.203154
M5 M6 M7 M8 M9 M10
0.009319 -0.271666 -0.305962 -0.447911 -0.704493 -2.091887
M11
-0.159815
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.4507 -2.0526 -0.6386 -0.3864 6.8288
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 97.133637 2.264442 42.895 < 2e-16 ***
X 0.035268 0.007098 4.969 9.34e-06 ***
M1 -0.994111 2.137059 -0.465 0.644
M2 -0.687493 2.143613 -0.321 0.750
M3 -0.466465 2.149238 -0.217 0.829
M4 -2.203154 2.144095 -1.028 0.309
M5 0.009319 2.140064 0.004 0.997
M6 -0.271666 2.131882 -0.127 0.899
M7 -0.305962 2.127209 -0.144 0.886
M8 -0.447911 2.125494 -0.211 0.834
M9 -0.704493 2.127379 -0.331 0.742
M10 -2.091887 2.129257 -0.982 0.331
M11 -0.159815 2.127080 -0.075 0.940
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.361 on 47 degrees of freedom
Multiple R-squared: 0.3941, Adjusted R-squared: 0.2394
F-statistic: 2.548 on 12 and 47 DF, p-value: 0.01110
> 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,] 1.069995e-03 2.139989e-03 0.9989300
[2,] 9.037226e-05 1.807445e-04 0.9999096
[3,] 1.133709e-05 2.267419e-05 0.9999887
[4,] 2.752002e-06 5.504003e-06 0.9999972
[5,] 2.527303e-06 5.054606e-06 0.9999975
[6,] 3.152596e-07 6.305193e-07 0.9999997
[7,] 7.178286e-08 1.435657e-07 0.9999999
[8,] 1.081938e-08 2.163876e-08 1.0000000
[9,] 1.372387e-08 2.744774e-08 1.0000000
[10,] 2.547953e-08 5.095907e-08 1.0000000
[11,] 1.455136e-08 2.910273e-08 1.0000000
[12,] 4.564973e-09 9.129945e-09 1.0000000
[13,] 6.428190e-08 1.285638e-07 0.9999999
[14,] 1.036801e-07 2.073603e-07 0.9999999
[15,] 1.176271e-07 2.352541e-07 0.9999999
[16,] 1.130724e-07 2.261449e-07 0.9999999
[17,] 9.790510e-08 1.958102e-07 0.9999999
[18,] 1.243470e-07 2.486941e-07 0.9999999
[19,] 2.466773e-07 4.933546e-07 0.9999998
[20,] 1.560850e-06 3.121701e-06 0.9999984
[21,] 6.953257e-05 1.390651e-04 0.9999305
[22,] 4.248437e-03 8.496875e-03 0.9957516
[23,] 3.663969e-02 7.327939e-02 0.9633603
[24,] 9.721957e-02 1.944391e-01 0.9027804
[25,] 1.978305e-01 3.956611e-01 0.8021695
[26,] 5.134307e-01 9.731386e-01 0.4865693
[27,] 5.816472e-01 8.367056e-01 0.4183528
[28,] 8.618143e-01 2.763714e-01 0.1381857
[29,] 9.616492e-01 7.670160e-02 0.0383508
> postscript(file="/var/www/html/rcomp/tmp/1tahm1258718394.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zlfj1258718394.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hvzf1258718394.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/42idt1258718394.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55qim1258718394.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 = 60
Frequency = 1
1 2 3 4 5 6 7
-2.8396424 -3.0200084 -3.3235795 -3.2407323 -3.4507500 -3.1863759 -2.9538649
8 9 10 11 12 13 14
-2.3774063 -2.2393080 -2.0447276 -2.5959093 -2.7345630 -1.6142009 -1.7609957
15 16 17 18 19 20 21
-2.1030513 -2.2712318 -2.0979001 -1.9997736 -2.0761500 -1.7540686 -1.2940478
22 23 24 25 26 27 28
-1.4228168 -1.5237728 -1.3553690 -0.3932213 -0.5574285 -0.9552881 -0.3660968
29 30 31 32 33 34 35
-0.5629907 -0.6058920 -0.5100798 -0.4987119 -0.4231126 -0.5971496 -0.5912294
36 37 38 39 40 41 42
-1.4976984 -0.5602423 -0.7392296 -0.4286049 -0.4886597 -0.7171618 -0.4044403
43 44 45 46 47 48 49
-0.6403693 -0.8104361 -0.6367593 -0.4986077 0.5144626 1.6835837 5.4073069
50 51 52 53 54 55 56
6.0776622 6.8105238 6.3667205 6.8288026 6.1964818 6.1804640 5.4406229
57 58 59 60
4.5932276 4.5633017 4.1964489 3.9040467
> postscript(file="/var/www/html/rcomp/tmp/63hth1258718394.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.8396424 NA
1 -3.0200084 -2.8396424
2 -3.3235795 -3.0200084
3 -3.2407323 -3.3235795
4 -3.4507500 -3.2407323
5 -3.1863759 -3.4507500
6 -2.9538649 -3.1863759
7 -2.3774063 -2.9538649
8 -2.2393080 -2.3774063
9 -2.0447276 -2.2393080
10 -2.5959093 -2.0447276
11 -2.7345630 -2.5959093
12 -1.6142009 -2.7345630
13 -1.7609957 -1.6142009
14 -2.1030513 -1.7609957
15 -2.2712318 -2.1030513
16 -2.0979001 -2.2712318
17 -1.9997736 -2.0979001
18 -2.0761500 -1.9997736
19 -1.7540686 -2.0761500
20 -1.2940478 -1.7540686
21 -1.4228168 -1.2940478
22 -1.5237728 -1.4228168
23 -1.3553690 -1.5237728
24 -0.3932213 -1.3553690
25 -0.5574285 -0.3932213
26 -0.9552881 -0.5574285
27 -0.3660968 -0.9552881
28 -0.5629907 -0.3660968
29 -0.6058920 -0.5629907
30 -0.5100798 -0.6058920
31 -0.4987119 -0.5100798
32 -0.4231126 -0.4987119
33 -0.5971496 -0.4231126
34 -0.5912294 -0.5971496
35 -1.4976984 -0.5912294
36 -0.5602423 -1.4976984
37 -0.7392296 -0.5602423
38 -0.4286049 -0.7392296
39 -0.4886597 -0.4286049
40 -0.7171618 -0.4886597
41 -0.4044403 -0.7171618
42 -0.6403693 -0.4044403
43 -0.8104361 -0.6403693
44 -0.6367593 -0.8104361
45 -0.4986077 -0.6367593
46 0.5144626 -0.4986077
47 1.6835837 0.5144626
48 5.4073069 1.6835837
49 6.0776622 5.4073069
50 6.8105238 6.0776622
51 6.3667205 6.8105238
52 6.8288026 6.3667205
53 6.1964818 6.8288026
54 6.1804640 6.1964818
55 5.4406229 6.1804640
56 4.5932276 5.4406229
57 4.5633017 4.5932276
58 4.1964489 4.5633017
59 3.9040467 4.1964489
60 NA 3.9040467
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.0200084 -2.8396424
[2,] -3.3235795 -3.0200084
[3,] -3.2407323 -3.3235795
[4,] -3.4507500 -3.2407323
[5,] -3.1863759 -3.4507500
[6,] -2.9538649 -3.1863759
[7,] -2.3774063 -2.9538649
[8,] -2.2393080 -2.3774063
[9,] -2.0447276 -2.2393080
[10,] -2.5959093 -2.0447276
[11,] -2.7345630 -2.5959093
[12,] -1.6142009 -2.7345630
[13,] -1.7609957 -1.6142009
[14,] -2.1030513 -1.7609957
[15,] -2.2712318 -2.1030513
[16,] -2.0979001 -2.2712318
[17,] -1.9997736 -2.0979001
[18,] -2.0761500 -1.9997736
[19,] -1.7540686 -2.0761500
[20,] -1.2940478 -1.7540686
[21,] -1.4228168 -1.2940478
[22,] -1.5237728 -1.4228168
[23,] -1.3553690 -1.5237728
[24,] -0.3932213 -1.3553690
[25,] -0.5574285 -0.3932213
[26,] -0.9552881 -0.5574285
[27,] -0.3660968 -0.9552881
[28,] -0.5629907 -0.3660968
[29,] -0.6058920 -0.5629907
[30,] -0.5100798 -0.6058920
[31,] -0.4987119 -0.5100798
[32,] -0.4231126 -0.4987119
[33,] -0.5971496 -0.4231126
[34,] -0.5912294 -0.5971496
[35,] -1.4976984 -0.5912294
[36,] -0.5602423 -1.4976984
[37,] -0.7392296 -0.5602423
[38,] -0.4286049 -0.7392296
[39,] -0.4886597 -0.4286049
[40,] -0.7171618 -0.4886597
[41,] -0.4044403 -0.7171618
[42,] -0.6403693 -0.4044403
[43,] -0.8104361 -0.6403693
[44,] -0.6367593 -0.8104361
[45,] -0.4986077 -0.6367593
[46,] 0.5144626 -0.4986077
[47,] 1.6835837 0.5144626
[48,] 5.4073069 1.6835837
[49,] 6.0776622 5.4073069
[50,] 6.8105238 6.0776622
[51,] 6.3667205 6.8105238
[52,] 6.8288026 6.3667205
[53,] 6.1964818 6.8288026
[54,] 6.1804640 6.1964818
[55,] 5.4406229 6.1804640
[56,] 4.5932276 5.4406229
[57,] 4.5633017 4.5932276
[58,] 4.1964489 4.5633017
[59,] 3.9040467 4.1964489
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.0200084 -2.8396424
2 -3.3235795 -3.0200084
3 -3.2407323 -3.3235795
4 -3.4507500 -3.2407323
5 -3.1863759 -3.4507500
6 -2.9538649 -3.1863759
7 -2.3774063 -2.9538649
8 -2.2393080 -2.3774063
9 -2.0447276 -2.2393080
10 -2.5959093 -2.0447276
11 -2.7345630 -2.5959093
12 -1.6142009 -2.7345630
13 -1.7609957 -1.6142009
14 -2.1030513 -1.7609957
15 -2.2712318 -2.1030513
16 -2.0979001 -2.2712318
17 -1.9997736 -2.0979001
18 -2.0761500 -1.9997736
19 -1.7540686 -2.0761500
20 -1.2940478 -1.7540686
21 -1.4228168 -1.2940478
22 -1.5237728 -1.4228168
23 -1.3553690 -1.5237728
24 -0.3932213 -1.3553690
25 -0.5574285 -0.3932213
26 -0.9552881 -0.5574285
27 -0.3660968 -0.9552881
28 -0.5629907 -0.3660968
29 -0.6058920 -0.5629907
30 -0.5100798 -0.6058920
31 -0.4987119 -0.5100798
32 -0.4231126 -0.4987119
33 -0.5971496 -0.4231126
34 -0.5912294 -0.5971496
35 -1.4976984 -0.5912294
36 -0.5602423 -1.4976984
37 -0.7392296 -0.5602423
38 -0.4286049 -0.7392296
39 -0.4886597 -0.4286049
40 -0.7171618 -0.4886597
41 -0.4044403 -0.7171618
42 -0.6403693 -0.4044403
43 -0.8104361 -0.6403693
44 -0.6367593 -0.8104361
45 -0.4986077 -0.6367593
46 0.5144626 -0.4986077
47 1.6835837 0.5144626
48 5.4073069 1.6835837
49 6.0776622 5.4073069
50 6.8105238 6.0776622
51 6.3667205 6.8105238
52 6.8288026 6.3667205
53 6.1964818 6.8288026
54 6.1804640 6.1964818
55 5.4406229 6.1804640
56 4.5932276 5.4406229
57 4.5633017 4.5932276
58 4.1964489 4.5633017
59 3.9040467 4.1964489
> 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/75gik1258718394.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88hyx1258718394.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/967iv1258718394.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10y0f31258718394.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1189831258718394.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/12kim51258718395.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/1348pu1258718395.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/14blmq1258718395.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/150qkq1258718395.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/16uss61258718395.tab")
+ }
>
> system("convert tmp/1tahm1258718394.ps tmp/1tahm1258718394.png")
> system("convert tmp/2zlfj1258718394.ps tmp/2zlfj1258718394.png")
> system("convert tmp/3hvzf1258718394.ps tmp/3hvzf1258718394.png")
> system("convert tmp/42idt1258718394.ps tmp/42idt1258718394.png")
> system("convert tmp/55qim1258718394.ps tmp/55qim1258718394.png")
> system("convert tmp/63hth1258718394.ps tmp/63hth1258718394.png")
> system("convert tmp/75gik1258718394.ps tmp/75gik1258718394.png")
> system("convert tmp/88hyx1258718394.ps tmp/88hyx1258718394.png")
> system("convert tmp/967iv1258718394.ps tmp/967iv1258718394.png")
> system("convert tmp/10y0f31258718394.ps tmp/10y0f31258718394.png")
>
>
> proc.time()
user system elapsed
2.372 1.547 2.779