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.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(6.3,2,6.2,1.8,6.1,2.7,6.3,2.3,6.5,1.9,6.6,2,6.5,2.3,6.2,2.8,6.2,2.4,5.9,2.3,6.1,2.7,6.1,2.7,6.1,2.9,6.1,3,6.1,2.2,6.4,2.3,6.7,2.8,6.9,2.8,7,2.8,7,2.2,6.8,2.6,6.4,2.8,5.9,2.5,5.5,2.4,5.5,2.3,5.6,1.9,5.8,1.7,5.9,2,6.1,2.1,6.1,1.7,6,1.8,6,1.8,5.9,1.8,5.5,1.3,5.6,1.3,5.4,1.3,5.2,1.2,5.2,1.4,5.2,2.2,5.5,2.9,5.8,3.1,5.8,3.5,5.5,3.6,5.3,4.4,5.1,4.1,5.2,5.1,5.8,5.8,5.8,5.9,5.5,5.4,5,5.5,4.9,4.8,5.3,3.2,6.1,2.7,6.5,2.1,6.8,1.9,6.6,0.6,6.4,0.7,6.4,-0.2,6.6,-1,6.7,-1.7),dim=c(2,60),dimnames=list(c('WMan>25','Infl'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('WMan>25','Infl'),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 = 'Do not include Seasonal 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
WMan>25 Infl
1 6.3 2.0
2 6.2 1.8
3 6.1 2.7
4 6.3 2.3
5 6.5 1.9
6 6.6 2.0
7 6.5 2.3
8 6.2 2.8
9 6.2 2.4
10 5.9 2.3
11 6.1 2.7
12 6.1 2.7
13 6.1 2.9
14 6.1 3.0
15 6.1 2.2
16 6.4 2.3
17 6.7 2.8
18 6.9 2.8
19 7.0 2.8
20 7.0 2.2
21 6.8 2.6
22 6.4 2.8
23 5.9 2.5
24 5.5 2.4
25 5.5 2.3
26 5.6 1.9
27 5.8 1.7
28 5.9 2.0
29 6.1 2.1
30 6.1 1.7
31 6.0 1.8
32 6.0 1.8
33 5.9 1.8
34 5.5 1.3
35 5.6 1.3
36 5.4 1.3
37 5.2 1.2
38 5.2 1.4
39 5.2 2.2
40 5.5 2.9
41 5.8 3.1
42 5.8 3.5
43 5.5 3.6
44 5.3 4.4
45 5.1 4.1
46 5.2 5.1
47 5.8 5.8
48 5.8 5.9
49 5.5 5.4
50 5.0 5.5
51 4.9 4.8
52 5.3 3.2
53 6.1 2.7
54 6.5 2.1
55 6.8 1.9
56 6.6 0.6
57 6.4 0.7
58 6.4 -0.2
59 6.6 -1.0
60 6.7 -1.7
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Infl
6.3859 -0.1605
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.99332 -0.37057 0.04621 0.28999 1.06343
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.3859 0.1266 50.43 < 2e-16 ***
Infl -0.1605 0.0447 -3.59 0.000682 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4884 on 58 degrees of freedom
Multiple R-squared: 0.1818, Adjusted R-squared: 0.1677
F-statistic: 12.89 on 1 and 58 DF, p-value: 0.0006817
> 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,] 2.749808e-02 5.499616e-02 0.972501918
[2,] 2.652796e-02 5.305592e-02 0.973472040
[3,] 1.525265e-02 3.050530e-02 0.984747352
[4,] 4.784753e-03 9.569506e-03 0.995215247
[5,] 1.613789e-03 3.227579e-03 0.998386211
[6,] 4.148491e-03 8.296983e-03 0.995851509
[7,] 1.502208e-03 3.004417e-03 0.998497792
[8,] 5.121215e-04 1.024243e-03 0.999487878
[9,] 1.650699e-04 3.301399e-04 0.999834930
[10,] 5.220670e-05 1.044134e-04 0.999947793
[11,] 2.622171e-05 5.244342e-05 0.999973778
[12,] 1.210962e-05 2.421923e-05 0.999987890
[13,] 2.043705e-04 4.087411e-04 0.999795629
[14,] 3.296052e-03 6.592103e-03 0.996703948
[15,] 2.565855e-02 5.131710e-02 0.974341452
[16,] 9.426790e-02 1.885358e-01 0.905732102
[17,] 1.629260e-01 3.258520e-01 0.837073981
[18,] 1.555467e-01 3.110935e-01 0.844453257
[19,] 1.653525e-01 3.307051e-01 0.834647469
[20,] 3.075060e-01 6.150120e-01 0.692494000
[21,] 4.263207e-01 8.526414e-01 0.573679279
[22,] 4.582096e-01 9.164192e-01 0.541790421
[23,] 4.062623e-01 8.125246e-01 0.593737699
[24,] 3.464102e-01 6.928204e-01 0.653589778
[25,] 2.893742e-01 5.787484e-01 0.710625807
[26,] 2.335841e-01 4.671682e-01 0.766415899
[27,] 1.817166e-01 3.634332e-01 0.818283380
[28,] 1.377458e-01 2.754915e-01 0.862254247
[29,] 1.024892e-01 2.049784e-01 0.897510796
[30,] 9.939228e-02 1.987846e-01 0.900607724
[31,] 8.342542e-02 1.668508e-01 0.916574584
[32,] 9.496714e-02 1.899343e-01 0.905032865
[33,] 1.711961e-01 3.423922e-01 0.828803876
[34,] 3.215667e-01 6.431334e-01 0.678433295
[35,] 5.814748e-01 8.370503e-01 0.418525157
[36,] 6.722023e-01 6.555954e-01 0.327797705
[37,] 6.484051e-01 7.031898e-01 0.351594895
[38,] 6.311899e-01 7.376202e-01 0.368810119
[39,] 6.560701e-01 6.878597e-01 0.343929863
[40,] 7.000703e-01 5.998594e-01 0.299929695
[41,] 7.871815e-01 4.256369e-01 0.212818472
[42,] 7.714537e-01 4.570926e-01 0.228546324
[43,] 7.549472e-01 4.901056e-01 0.245052778
[44,] 7.923006e-01 4.153989e-01 0.207699432
[45,] 7.461193e-01 5.077614e-01 0.253880680
[46,] 6.842346e-01 6.315308e-01 0.315765419
[47,] 7.811639e-01 4.376723e-01 0.218836134
[48,] 9.825313e-01 3.493741e-02 0.017468706
[49,] 9.904194e-01 1.916123e-02 0.009580616
[50,] 9.725846e-01 5.483079e-02 0.027415394
[51,] 9.839967e-01 3.200657e-02 0.016003284
> postscript(file="/var/www/html/rcomp/tmp/1hcv51258810796.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23cir1258810796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35sfb1258810796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4iruh1258810796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5n4ue1258810796.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
0.23505203 0.10295803 0.14738103 0.28319303 0.41900503 0.53505203
7 8 9 10 11 12
0.48319303 0.26342803 0.19924003 -0.11680697 0.14738103 0.14738103
13 14 15 16 17 18
0.17947503 0.19552203 0.06714603 0.38319303 0.76342803 0.96342803
19 20 21 22 23 24
1.06342803 0.96714603 0.83133403 0.46342803 -0.08471297 -0.50075997
25 26 27 28 29 30
-0.51680697 -0.48099497 -0.31308897 -0.16494797 0.05109903 -0.01308897
31 32 33 34 35 36
-0.09704197 -0.09704197 -0.19704197 -0.67727697 -0.57727697 -0.77727697
37 38 39 40 41 42
-0.99332397 -0.96122997 -0.83285397 -0.42052497 -0.08843097 -0.02424297
43 44 45 46 47 48
-0.30819597 -0.37981997 -0.62796097 -0.36749096 0.34483804 0.36088504
49 50 51 52 53 54
-0.01934996 -0.50330296 -0.71563196 -0.57238397 0.14738103 0.45109903
55 56 57 58 59 60
0.71900503 0.31039403 0.12644103 -0.01798197 0.05364203 0.04131303
> postscript(file="/var/www/html/rcomp/tmp/6munp1258810796.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 0.23505203 NA
1 0.10295803 0.23505203
2 0.14738103 0.10295803
3 0.28319303 0.14738103
4 0.41900503 0.28319303
5 0.53505203 0.41900503
6 0.48319303 0.53505203
7 0.26342803 0.48319303
8 0.19924003 0.26342803
9 -0.11680697 0.19924003
10 0.14738103 -0.11680697
11 0.14738103 0.14738103
12 0.17947503 0.14738103
13 0.19552203 0.17947503
14 0.06714603 0.19552203
15 0.38319303 0.06714603
16 0.76342803 0.38319303
17 0.96342803 0.76342803
18 1.06342803 0.96342803
19 0.96714603 1.06342803
20 0.83133403 0.96714603
21 0.46342803 0.83133403
22 -0.08471297 0.46342803
23 -0.50075997 -0.08471297
24 -0.51680697 -0.50075997
25 -0.48099497 -0.51680697
26 -0.31308897 -0.48099497
27 -0.16494797 -0.31308897
28 0.05109903 -0.16494797
29 -0.01308897 0.05109903
30 -0.09704197 -0.01308897
31 -0.09704197 -0.09704197
32 -0.19704197 -0.09704197
33 -0.67727697 -0.19704197
34 -0.57727697 -0.67727697
35 -0.77727697 -0.57727697
36 -0.99332397 -0.77727697
37 -0.96122997 -0.99332397
38 -0.83285397 -0.96122997
39 -0.42052497 -0.83285397
40 -0.08843097 -0.42052497
41 -0.02424297 -0.08843097
42 -0.30819597 -0.02424297
43 -0.37981997 -0.30819597
44 -0.62796097 -0.37981997
45 -0.36749096 -0.62796097
46 0.34483804 -0.36749096
47 0.36088504 0.34483804
48 -0.01934996 0.36088504
49 -0.50330296 -0.01934996
50 -0.71563196 -0.50330296
51 -0.57238397 -0.71563196
52 0.14738103 -0.57238397
53 0.45109903 0.14738103
54 0.71900503 0.45109903
55 0.31039403 0.71900503
56 0.12644103 0.31039403
57 -0.01798197 0.12644103
58 0.05364203 -0.01798197
59 0.04131303 0.05364203
60 NA 0.04131303
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.10295803 0.23505203
[2,] 0.14738103 0.10295803
[3,] 0.28319303 0.14738103
[4,] 0.41900503 0.28319303
[5,] 0.53505203 0.41900503
[6,] 0.48319303 0.53505203
[7,] 0.26342803 0.48319303
[8,] 0.19924003 0.26342803
[9,] -0.11680697 0.19924003
[10,] 0.14738103 -0.11680697
[11,] 0.14738103 0.14738103
[12,] 0.17947503 0.14738103
[13,] 0.19552203 0.17947503
[14,] 0.06714603 0.19552203
[15,] 0.38319303 0.06714603
[16,] 0.76342803 0.38319303
[17,] 0.96342803 0.76342803
[18,] 1.06342803 0.96342803
[19,] 0.96714603 1.06342803
[20,] 0.83133403 0.96714603
[21,] 0.46342803 0.83133403
[22,] -0.08471297 0.46342803
[23,] -0.50075997 -0.08471297
[24,] -0.51680697 -0.50075997
[25,] -0.48099497 -0.51680697
[26,] -0.31308897 -0.48099497
[27,] -0.16494797 -0.31308897
[28,] 0.05109903 -0.16494797
[29,] -0.01308897 0.05109903
[30,] -0.09704197 -0.01308897
[31,] -0.09704197 -0.09704197
[32,] -0.19704197 -0.09704197
[33,] -0.67727697 -0.19704197
[34,] -0.57727697 -0.67727697
[35,] -0.77727697 -0.57727697
[36,] -0.99332397 -0.77727697
[37,] -0.96122997 -0.99332397
[38,] -0.83285397 -0.96122997
[39,] -0.42052497 -0.83285397
[40,] -0.08843097 -0.42052497
[41,] -0.02424297 -0.08843097
[42,] -0.30819597 -0.02424297
[43,] -0.37981997 -0.30819597
[44,] -0.62796097 -0.37981997
[45,] -0.36749096 -0.62796097
[46,] 0.34483804 -0.36749096
[47,] 0.36088504 0.34483804
[48,] -0.01934996 0.36088504
[49,] -0.50330296 -0.01934996
[50,] -0.71563196 -0.50330296
[51,] -0.57238397 -0.71563196
[52,] 0.14738103 -0.57238397
[53,] 0.45109903 0.14738103
[54,] 0.71900503 0.45109903
[55,] 0.31039403 0.71900503
[56,] 0.12644103 0.31039403
[57,] -0.01798197 0.12644103
[58,] 0.05364203 -0.01798197
[59,] 0.04131303 0.05364203
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.10295803 0.23505203
2 0.14738103 0.10295803
3 0.28319303 0.14738103
4 0.41900503 0.28319303
5 0.53505203 0.41900503
6 0.48319303 0.53505203
7 0.26342803 0.48319303
8 0.19924003 0.26342803
9 -0.11680697 0.19924003
10 0.14738103 -0.11680697
11 0.14738103 0.14738103
12 0.17947503 0.14738103
13 0.19552203 0.17947503
14 0.06714603 0.19552203
15 0.38319303 0.06714603
16 0.76342803 0.38319303
17 0.96342803 0.76342803
18 1.06342803 0.96342803
19 0.96714603 1.06342803
20 0.83133403 0.96714603
21 0.46342803 0.83133403
22 -0.08471297 0.46342803
23 -0.50075997 -0.08471297
24 -0.51680697 -0.50075997
25 -0.48099497 -0.51680697
26 -0.31308897 -0.48099497
27 -0.16494797 -0.31308897
28 0.05109903 -0.16494797
29 -0.01308897 0.05109903
30 -0.09704197 -0.01308897
31 -0.09704197 -0.09704197
32 -0.19704197 -0.09704197
33 -0.67727697 -0.19704197
34 -0.57727697 -0.67727697
35 -0.77727697 -0.57727697
36 -0.99332397 -0.77727697
37 -0.96122997 -0.99332397
38 -0.83285397 -0.96122997
39 -0.42052497 -0.83285397
40 -0.08843097 -0.42052497
41 -0.02424297 -0.08843097
42 -0.30819597 -0.02424297
43 -0.37981997 -0.30819597
44 -0.62796097 -0.37981997
45 -0.36749096 -0.62796097
46 0.34483804 -0.36749096
47 0.36088504 0.34483804
48 -0.01934996 0.36088504
49 -0.50330296 -0.01934996
50 -0.71563196 -0.50330296
51 -0.57238397 -0.71563196
52 0.14738103 -0.57238397
53 0.45109903 0.14738103
54 0.71900503 0.45109903
55 0.31039403 0.71900503
56 0.12644103 0.31039403
57 -0.01798197 0.12644103
58 0.05364203 -0.01798197
59 0.04131303 0.05364203
> 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/7ay7c1258810796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kbo21258810796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9v68k1258810796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107uex1258810796.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11fqfm1258810796.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/12whdh1258810796.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/13xyqi1258810796.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/14tbkn1258810796.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/15zamq1258810796.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/16pqu61258810796.tab")
+ }
>
> system("convert tmp/1hcv51258810796.ps tmp/1hcv51258810796.png")
> system("convert tmp/23cir1258810796.ps tmp/23cir1258810796.png")
> system("convert tmp/35sfb1258810796.ps tmp/35sfb1258810796.png")
> system("convert tmp/4iruh1258810796.ps tmp/4iruh1258810796.png")
> system("convert tmp/5n4ue1258810796.ps tmp/5n4ue1258810796.png")
> system("convert tmp/6munp1258810796.ps tmp/6munp1258810796.png")
> system("convert tmp/7ay7c1258810796.ps tmp/7ay7c1258810796.png")
> system("convert tmp/8kbo21258810796.ps tmp/8kbo21258810796.png")
> system("convert tmp/9v68k1258810796.ps tmp/9v68k1258810796.png")
> system("convert tmp/107uex1258810796.ps tmp/107uex1258810796.png")
>
>
> proc.time()
user system elapsed
2.446 1.557 3.476