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(286602,0,283042,0,276687,0,277915,0,277128,0,277103,0,275037,0,270150,0,267140,0,264993,0,287259,0,291186,0,292300,0,288186,0,281477,0,282656,0,280190,0,280408,0,276836,0,275216,0,274352,0,271311,0,289802,0,290726,0,292300,0,278506,0,269826,0,265861,0,269034,0,264176,0,255198,0,253353,0,246057,0,235372,0,258556,0,260993,0,254663,0,250643,0,243422,0,247105,0,248541,0,245039,0,237080,0,237085,0,225554,0,226839,1,247934,1,248333,1,246969,1,245098,1,246263,1,255765,1,264319,1,268347,1,273046,1,273963,1,267430,1,271993,1,292710,1,295881,1),dim=c(2,60),dimnames=list(c('nwwmb','dummy_variable'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('nwwmb','dummy_variable'),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 = '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
nwwmb dummy_variable M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 286602 0 1 0 0 0 0 0 0 0 0 0 0 1
2 283042 0 0 1 0 0 0 0 0 0 0 0 0 2
3 276687 0 0 0 1 0 0 0 0 0 0 0 0 3
4 277915 0 0 0 0 1 0 0 0 0 0 0 0 4
5 277128 0 0 0 0 0 1 0 0 0 0 0 0 5
6 277103 0 0 0 0 0 0 1 0 0 0 0 0 6
7 275037 0 0 0 0 0 0 0 1 0 0 0 0 7
8 270150 0 0 0 0 0 0 0 0 1 0 0 0 8
9 267140 0 0 0 0 0 0 0 0 0 1 0 0 9
10 264993 0 0 0 0 0 0 0 0 0 0 1 0 10
11 287259 0 0 0 0 0 0 0 0 0 0 0 1 11
12 291186 0 0 0 0 0 0 0 0 0 0 0 0 12
13 292300 0 1 0 0 0 0 0 0 0 0 0 0 13
14 288186 0 0 1 0 0 0 0 0 0 0 0 0 14
15 281477 0 0 0 1 0 0 0 0 0 0 0 0 15
16 282656 0 0 0 0 1 0 0 0 0 0 0 0 16
17 280190 0 0 0 0 0 1 0 0 0 0 0 0 17
18 280408 0 0 0 0 0 0 1 0 0 0 0 0 18
19 276836 0 0 0 0 0 0 0 1 0 0 0 0 19
20 275216 0 0 0 0 0 0 0 0 1 0 0 0 20
21 274352 0 0 0 0 0 0 0 0 0 1 0 0 21
22 271311 0 0 0 0 0 0 0 0 0 0 1 0 22
23 289802 0 0 0 0 0 0 0 0 0 0 0 1 23
24 290726 0 0 0 0 0 0 0 0 0 0 0 0 24
25 292300 0 1 0 0 0 0 0 0 0 0 0 0 25
26 278506 0 0 1 0 0 0 0 0 0 0 0 0 26
27 269826 0 0 0 1 0 0 0 0 0 0 0 0 27
28 265861 0 0 0 0 1 0 0 0 0 0 0 0 28
29 269034 0 0 0 0 0 1 0 0 0 0 0 0 29
30 264176 0 0 0 0 0 0 1 0 0 0 0 0 30
31 255198 0 0 0 0 0 0 0 1 0 0 0 0 31
32 253353 0 0 0 0 0 0 0 0 1 0 0 0 32
33 246057 0 0 0 0 0 0 0 0 0 1 0 0 33
34 235372 0 0 0 0 0 0 0 0 0 0 1 0 34
35 258556 0 0 0 0 0 0 0 0 0 0 0 1 35
36 260993 0 0 0 0 0 0 0 0 0 0 0 0 36
37 254663 0 1 0 0 0 0 0 0 0 0 0 0 37
38 250643 0 0 1 0 0 0 0 0 0 0 0 0 38
39 243422 0 0 0 1 0 0 0 0 0 0 0 0 39
40 247105 0 0 0 0 1 0 0 0 0 0 0 0 40
41 248541 0 0 0 0 0 1 0 0 0 0 0 0 41
42 245039 0 0 0 0 0 0 1 0 0 0 0 0 42
43 237080 0 0 0 0 0 0 0 1 0 0 0 0 43
44 237085 0 0 0 0 0 0 0 0 1 0 0 0 44
45 225554 0 0 0 0 0 0 0 0 0 1 0 0 45
46 226839 1 0 0 0 0 0 0 0 0 0 1 0 46
47 247934 1 0 0 0 0 0 0 0 0 0 0 1 47
48 248333 1 0 0 0 0 0 0 0 0 0 0 0 48
49 246969 1 1 0 0 0 0 0 0 0 0 0 0 49
50 245098 1 0 1 0 0 0 0 0 0 0 0 0 50
51 246263 1 0 0 1 0 0 0 0 0 0 0 0 51
52 255765 1 0 0 0 1 0 0 0 0 0 0 0 52
53 264319 1 0 0 0 0 1 0 0 0 0 0 0 53
54 268347 1 0 0 0 0 0 1 0 0 0 0 0 54
55 273046 1 0 0 0 0 0 0 1 0 0 0 0 55
56 273963 1 0 0 0 0 0 0 0 1 0 0 0 56
57 267430 1 0 0 0 0 0 0 0 0 1 0 0 57
58 271993 1 0 0 0 0 0 0 0 0 0 1 0 58
59 292710 1 0 0 0 0 0 0 0 0 0 0 1 59
60 295881 1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummy_variable M1 M2 M3
300922.9 18529.6 -8596.1 -13209.2 -17910.6
M4 M5 M6 M7 M8
-14726.6 -11885.9 -11855.1 -14571.7 -15199.0
M9 M10 M11 t
-20187.2 -25039.5 -3030.2 -858.6
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-29905 -7014 -3352 10251 27947
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 300922.9 7984.9 37.686 < 2e-16 ***
dummy_variable 18529.6 6809.6 2.721 0.00916 **
M1 -8596.1 9443.3 -0.910 0.36742
M2 -13209.2 9429.5 -1.401 0.16797
M3 -17910.6 9418.7 -1.902 0.06350 .
M4 -14726.6 9411.0 -1.565 0.12448
M5 -11885.9 9406.4 -1.264 0.21274
M6 -11855.1 9404.9 -1.261 0.21384
M7 -14571.7 9406.4 -1.549 0.12821
M8 -15199.0 9411.0 -1.615 0.11315
M9 -20187.2 9418.7 -2.143 0.03741 *
M10 -25039.5 9367.8 -2.673 0.01037 *
M11 -3030.2 9363.2 -0.324 0.74768
t -858.6 170.2 -5.044 7.59e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 14800 on 46 degrees of freedom
Multiple R-squared: 0.4715, Adjusted R-squared: 0.3221
F-statistic: 3.157 on 13 and 46 DF, p-value: 0.001977
> 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,] 1.361087e-04 2.722175e-04 0.999863891
[2,] 9.824008e-06 1.964802e-05 0.999990176
[3,] 2.418653e-06 4.837307e-06 0.999997581
[4,] 1.507601e-07 3.015203e-07 0.999999849
[5,] 4.273719e-08 8.547437e-08 0.999999957
[6,] 4.610359e-09 9.220718e-09 0.999999995
[7,] 6.357887e-10 1.271577e-09 0.999999999
[8,] 8.762449e-10 1.752490e-09 0.999999999
[9,] 4.981622e-10 9.963244e-10 1.000000000
[10,] 2.424283e-07 4.848565e-07 0.999999758
[11,] 2.888504e-06 5.777008e-06 0.999997111
[12,] 3.185508e-05 6.371016e-05 0.999968145
[13,] 4.172252e-05 8.344504e-05 0.999958277
[14,] 1.128143e-04 2.256286e-04 0.999887186
[15,] 5.573028e-04 1.114606e-03 0.999442697
[16,] 1.442998e-03 2.885997e-03 0.998557002
[17,] 1.285208e-02 2.570416e-02 0.987147922
[18,] 4.174051e-02 8.348101e-02 0.958259495
[19,] 8.242884e-02 1.648577e-01 0.917571164
[20,] 1.881639e-01 3.763278e-01 0.811836099
[21,] 3.428504e-01 6.857008e-01 0.657149595
[22,] 5.640787e-01 8.718425e-01 0.435921273
[23,] 7.361205e-01 5.277591e-01 0.263879546
[24,] 8.748366e-01 2.503269e-01 0.125163443
[25,] 9.602690e-01 7.946197e-02 0.039730987
[26,] 9.978147e-01 4.370659e-03 0.002185329
[27,] 9.927583e-01 1.448349e-02 0.007241745
> postscript(file="/var/www/html/rcomp/tmp/1fucy1258743334.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2rg8f1258743334.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ga5h1258743334.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ecv71258743334.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ssrj1258743334.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
-4866.1608 -2954.3608 -3749.3608 -4846.7608 -7615.7608 -6812.9608
7 8 9 10 11 12
-5303.7608 -8704.7608 -5867.9608 -2304.0455 -1188.6455 566.7545
13 14 15 16 17 18
11135.4772 12493.2772 11344.2772 10197.8772 5749.8772 6795.6772
19 20 21 22 23 24
6798.8772 6664.8772 11647.6772 14317.5926 11657.9926 10410.3926
25 26 27 28 29 30
21439.1153 13116.9153 9996.9153 3706.5153 4897.5153 867.3153
31 32 33 34 35 36
-4535.4847 -4894.4847 -6343.6847 -11317.7693 -9284.3693 -9018.9693
37 38 39 40 41 42
-5894.2466 -4442.4466 -6103.4466 -4745.8466 -5291.8466 -7966.0466
43 44 45 46 47 48
-12349.8466 -10858.8466 -16543.0466 -28076.7079 -28132.3079 -29904.9079
49 50 51 52 53 54
-21814.1852 -18213.3852 -11488.3852 -4311.7852 2260.2148 7116.0148
55 56 57 58 59 60
15390.2148 17793.2148 17107.0148 27380.9302 26947.3302 27946.7302
> postscript(file="/var/www/html/rcomp/tmp/6tgo11258743334.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 -4866.1608 NA
1 -2954.3608 -4866.1608
2 -3749.3608 -2954.3608
3 -4846.7608 -3749.3608
4 -7615.7608 -4846.7608
5 -6812.9608 -7615.7608
6 -5303.7608 -6812.9608
7 -8704.7608 -5303.7608
8 -5867.9608 -8704.7608
9 -2304.0455 -5867.9608
10 -1188.6455 -2304.0455
11 566.7545 -1188.6455
12 11135.4772 566.7545
13 12493.2772 11135.4772
14 11344.2772 12493.2772
15 10197.8772 11344.2772
16 5749.8772 10197.8772
17 6795.6772 5749.8772
18 6798.8772 6795.6772
19 6664.8772 6798.8772
20 11647.6772 6664.8772
21 14317.5926 11647.6772
22 11657.9926 14317.5926
23 10410.3926 11657.9926
24 21439.1153 10410.3926
25 13116.9153 21439.1153
26 9996.9153 13116.9153
27 3706.5153 9996.9153
28 4897.5153 3706.5153
29 867.3153 4897.5153
30 -4535.4847 867.3153
31 -4894.4847 -4535.4847
32 -6343.6847 -4894.4847
33 -11317.7693 -6343.6847
34 -9284.3693 -11317.7693
35 -9018.9693 -9284.3693
36 -5894.2466 -9018.9693
37 -4442.4466 -5894.2466
38 -6103.4466 -4442.4466
39 -4745.8466 -6103.4466
40 -5291.8466 -4745.8466
41 -7966.0466 -5291.8466
42 -12349.8466 -7966.0466
43 -10858.8466 -12349.8466
44 -16543.0466 -10858.8466
45 -28076.7079 -16543.0466
46 -28132.3079 -28076.7079
47 -29904.9079 -28132.3079
48 -21814.1852 -29904.9079
49 -18213.3852 -21814.1852
50 -11488.3852 -18213.3852
51 -4311.7852 -11488.3852
52 2260.2148 -4311.7852
53 7116.0148 2260.2148
54 15390.2148 7116.0148
55 17793.2148 15390.2148
56 17107.0148 17793.2148
57 27380.9302 17107.0148
58 26947.3302 27380.9302
59 27946.7302 26947.3302
60 NA 27946.7302
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2954.3608 -4866.1608
[2,] -3749.3608 -2954.3608
[3,] -4846.7608 -3749.3608
[4,] -7615.7608 -4846.7608
[5,] -6812.9608 -7615.7608
[6,] -5303.7608 -6812.9608
[7,] -8704.7608 -5303.7608
[8,] -5867.9608 -8704.7608
[9,] -2304.0455 -5867.9608
[10,] -1188.6455 -2304.0455
[11,] 566.7545 -1188.6455
[12,] 11135.4772 566.7545
[13,] 12493.2772 11135.4772
[14,] 11344.2772 12493.2772
[15,] 10197.8772 11344.2772
[16,] 5749.8772 10197.8772
[17,] 6795.6772 5749.8772
[18,] 6798.8772 6795.6772
[19,] 6664.8772 6798.8772
[20,] 11647.6772 6664.8772
[21,] 14317.5926 11647.6772
[22,] 11657.9926 14317.5926
[23,] 10410.3926 11657.9926
[24,] 21439.1153 10410.3926
[25,] 13116.9153 21439.1153
[26,] 9996.9153 13116.9153
[27,] 3706.5153 9996.9153
[28,] 4897.5153 3706.5153
[29,] 867.3153 4897.5153
[30,] -4535.4847 867.3153
[31,] -4894.4847 -4535.4847
[32,] -6343.6847 -4894.4847
[33,] -11317.7693 -6343.6847
[34,] -9284.3693 -11317.7693
[35,] -9018.9693 -9284.3693
[36,] -5894.2466 -9018.9693
[37,] -4442.4466 -5894.2466
[38,] -6103.4466 -4442.4466
[39,] -4745.8466 -6103.4466
[40,] -5291.8466 -4745.8466
[41,] -7966.0466 -5291.8466
[42,] -12349.8466 -7966.0466
[43,] -10858.8466 -12349.8466
[44,] -16543.0466 -10858.8466
[45,] -28076.7079 -16543.0466
[46,] -28132.3079 -28076.7079
[47,] -29904.9079 -28132.3079
[48,] -21814.1852 -29904.9079
[49,] -18213.3852 -21814.1852
[50,] -11488.3852 -18213.3852
[51,] -4311.7852 -11488.3852
[52,] 2260.2148 -4311.7852
[53,] 7116.0148 2260.2148
[54,] 15390.2148 7116.0148
[55,] 17793.2148 15390.2148
[56,] 17107.0148 17793.2148
[57,] 27380.9302 17107.0148
[58,] 26947.3302 27380.9302
[59,] 27946.7302 26947.3302
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2954.3608 -4866.1608
2 -3749.3608 -2954.3608
3 -4846.7608 -3749.3608
4 -7615.7608 -4846.7608
5 -6812.9608 -7615.7608
6 -5303.7608 -6812.9608
7 -8704.7608 -5303.7608
8 -5867.9608 -8704.7608
9 -2304.0455 -5867.9608
10 -1188.6455 -2304.0455
11 566.7545 -1188.6455
12 11135.4772 566.7545
13 12493.2772 11135.4772
14 11344.2772 12493.2772
15 10197.8772 11344.2772
16 5749.8772 10197.8772
17 6795.6772 5749.8772
18 6798.8772 6795.6772
19 6664.8772 6798.8772
20 11647.6772 6664.8772
21 14317.5926 11647.6772
22 11657.9926 14317.5926
23 10410.3926 11657.9926
24 21439.1153 10410.3926
25 13116.9153 21439.1153
26 9996.9153 13116.9153
27 3706.5153 9996.9153
28 4897.5153 3706.5153
29 867.3153 4897.5153
30 -4535.4847 867.3153
31 -4894.4847 -4535.4847
32 -6343.6847 -4894.4847
33 -11317.7693 -6343.6847
34 -9284.3693 -11317.7693
35 -9018.9693 -9284.3693
36 -5894.2466 -9018.9693
37 -4442.4466 -5894.2466
38 -6103.4466 -4442.4466
39 -4745.8466 -6103.4466
40 -5291.8466 -4745.8466
41 -7966.0466 -5291.8466
42 -12349.8466 -7966.0466
43 -10858.8466 -12349.8466
44 -16543.0466 -10858.8466
45 -28076.7079 -16543.0466
46 -28132.3079 -28076.7079
47 -29904.9079 -28132.3079
48 -21814.1852 -29904.9079
49 -18213.3852 -21814.1852
50 -11488.3852 -18213.3852
51 -4311.7852 -11488.3852
52 2260.2148 -4311.7852
53 7116.0148 2260.2148
54 15390.2148 7116.0148
55 17793.2148 15390.2148
56 17107.0148 17793.2148
57 27380.9302 17107.0148
58 26947.3302 27380.9302
59 27946.7302 26947.3302
> 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/7vi7j1258743334.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qni21258743334.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9m5391258743334.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ihq81258743334.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11isyw1258743334.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/12ftqp1258743334.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/13653c1258743334.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/14qac91258743334.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/15h6001258743334.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/16w5gw1258743334.tab")
+ }
>
> system("convert tmp/1fucy1258743334.ps tmp/1fucy1258743334.png")
> system("convert tmp/2rg8f1258743334.ps tmp/2rg8f1258743334.png")
> system("convert tmp/3ga5h1258743334.ps tmp/3ga5h1258743334.png")
> system("convert tmp/4ecv71258743334.ps tmp/4ecv71258743334.png")
> system("convert tmp/5ssrj1258743334.ps tmp/5ssrj1258743334.png")
> system("convert tmp/6tgo11258743334.ps tmp/6tgo11258743334.png")
> system("convert tmp/7vi7j1258743334.ps tmp/7vi7j1258743334.png")
> system("convert tmp/8qni21258743334.ps tmp/8qni21258743334.png")
> system("convert tmp/9m5391258743334.ps tmp/9m5391258743334.png")
> system("convert tmp/10ihq81258743334.ps tmp/10ihq81258743334.png")
>
>
> proc.time()
user system elapsed
2.373 1.562 2.797