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.
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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
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(9,2,3,2,14,9,2,4,1,18,9,4,2,2,11,9,3,2,2,12,9,3,4,1,16,9,2,4,1,18,9,4,4,2,14,9,3,4,3,14,9,2,3,2,15,9,2,3,2,15,9,2,5,2,17,9,1,4,1,19,9,2,2,4,10,9,1,3,2,16,9,2,5,2,18,9,3,4,3,14,9,2,3,3,14,9,2,4,1,17,9,3,2,1,14,9,2,3,2,16,9,1,4,1,18,9,3,2,3,11,9,4,5,2,14,9,3,3,3,12,9,2,4,2,17,9,4,3,4,9,9,2,4,2,16,9,4,4,2,14,9,3,4,2,15,9,4,2,2,11,9,2,4,2,16,9,3,4,3,13,9,1,4,2,17,9,2,3,2,15,9,3,4,3,14,9,2,4,2,16,9,4,3,4,9,9,2,3,2,15,9,2,4,2,17,9,2,4,4,13,9,2,4,3,15,9,2,4,2,16,9,2,4,3,16,9,3,4,4,12,9,2,2,12,9,4,3,3,11,9,2,4,3,15,9,2,3,2,15,9,3,4,1,17,9,4,3,2,13,9,2,4,1,16,9,2,3,2,14,9,4,2,3,11,9,2,3,4,12,9,3,4,5,12,9,2,4,3,15,9,2,4,2,16,9,2,4,2,15,9,3,3,3,12,9,4,3,2,12,9,5,2,4,8,9,3,3,3,13,9,5,2,2,11,9,3,3,2,14,9,3,4,2,15,10,4,2,3,10),dim=c(5,66),dimnames=list(c('month','IDT','TGYW','POP','PSS
'),1:66))
> y <- array(NA,dim=c(5,66),dimnames=list(c('month','IDT','TGYW','POP','PSS
'),1:66))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '5'
> ylab = ''
> xlab = ''
> main = ''
> #'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
PSS\r month IDT TGYW POP t
1 14 9 2 3 2 1
2 18 9 2 4 1 2
3 11 9 4 2 2 3
4 12 9 3 2 2 4
5 16 9 3 4 1 5
6 18 9 2 4 1 6
7 14 9 4 4 2 7
8 14 9 3 4 3 8
9 15 9 2 3 2 9
10 15 9 2 3 2 10
11 17 9 2 5 2 11
12 19 9 1 4 1 12
13 10 9 2 2 4 13
14 16 9 1 3 2 14
15 18 9 2 5 2 15
16 14 9 3 4 3 16
17 14 9 2 3 3 17
18 17 9 2 4 1 18
19 14 9 3 2 1 19
20 16 9 2 3 2 20
21 18 9 1 4 1 21
22 11 9 3 2 3 22
23 14 9 4 5 2 23
24 12 9 3 3 3 24
25 17 9 2 4 2 25
26 9 9 4 3 4 26
27 16 9 2 4 2 27
28 14 9 4 4 2 28
29 15 9 3 4 2 29
30 11 9 4 2 2 30
31 16 9 2 4 2 31
32 13 9 3 4 3 32
33 17 9 1 4 2 33
34 15 9 2 3 2 34
35 14 9 3 4 3 35
36 16 9 2 4 2 36
37 9 9 4 3 4 37
38 15 9 2 3 2 38
39 17 9 2 4 2 39
40 13 9 2 4 4 40
41 15 9 2 4 3 41
42 16 9 2 4 2 42
43 16 9 2 4 3 43
44 12 9 3 4 4 44
45 9 9 2 2 12 45
46 9 4 3 3 11 46
47 9 2 4 3 15 47
48 9 2 3 2 15 48
49 9 3 4 1 17 49
50 9 4 3 2 13 50
51 9 2 4 1 16 51
52 9 2 3 2 14 52
53 9 4 2 3 11 53
54 9 2 3 4 12 54
55 9 3 4 5 12 55
56 9 2 4 3 15 56
57 9 2 4 2 16 57
58 9 2 4 2 15 58
59 9 3 3 3 12 59
60 9 4 3 2 12 60
61 9 5 2 4 8 61
62 9 3 3 3 13 62
63 9 5 2 2 11 63
64 9 3 3 2 14 64
65 10 3 4 2 15 65
66 9 4 2 3 10 66
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month IDT TGYW POP t
17.50562 -0.17151 -1.15793 0.87447 -0.28441 -0.04594
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.75244 -0.99172 0.05891 1.01204 3.14394
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.50562 2.87998 6.078 9.09e-08 ***
month -0.17151 0.23394 -0.733 0.46633
IDT -1.15793 0.24640 -4.699 1.57e-05 ***
TGYW 0.87447 0.25474 3.433 0.00109 **
POP -0.28441 0.14842 -1.916 0.06010 .
t -0.04594 0.01774 -2.590 0.01202 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.537 on 60 degrees of freedom
Multiple R-squared: 0.8025, Adjusted R-squared: 0.786
F-statistic: 48.76 on 5 and 60 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 5.085538e-02 1.017108e-01 9.491446e-01
[2,] 1.422307e-02 2.844614e-02 9.857769e-01
[3,] 1.427934e-02 2.855869e-02 9.857207e-01
[4,] 4.556482e-03 9.112963e-03 9.954435e-01
[5,] 2.657376e-03 5.314752e-03 9.973426e-01
[6,] 8.071271e-04 1.614254e-03 9.991929e-01
[7,] 2.596935e-04 5.193870e-04 9.997403e-01
[8,] 7.694754e-05 1.538951e-04 9.999231e-01
[9,] 3.466113e-05 6.932226e-05 9.999653e-01
[10,] 8.069054e-05 1.613811e-04 9.999193e-01
[11,] 2.664156e-05 5.328311e-05 9.999734e-01
[12,] 3.361453e-05 6.722907e-05 9.999664e-01
[13,] 5.018067e-05 1.003613e-04 9.999498e-01
[14,] 2.768275e-05 5.536549e-05 9.999723e-01
[15,] 7.162504e-05 1.432501e-04 9.999284e-01
[16,] 3.916669e-05 7.833339e-05 9.999608e-01
[17,] 3.307017e-05 6.614035e-05 9.999669e-01
[18,] 7.138597e-05 1.427719e-04 9.999286e-01
[19,] 2.957262e-05 5.914524e-05 9.999704e-01
[20,] 1.302110e-05 2.604221e-05 9.999870e-01
[21,] 4.943484e-06 9.886968e-06 9.999951e-01
[22,] 5.726310e-06 1.145262e-05 9.999943e-01
[23,] 2.379670e-06 4.759339e-06 9.999976e-01
[24,] 1.435504e-06 2.871007e-06 9.999986e-01
[25,] 8.932724e-07 1.786545e-06 9.999991e-01
[26,] 3.285487e-07 6.570973e-07 9.999997e-01
[27,] 1.705805e-07 3.411610e-07 9.999998e-01
[28,] 8.231421e-08 1.646284e-07 9.999999e-01
[29,] 3.259600e-04 6.519200e-04 9.996740e-01
[30,] 2.457200e-04 4.914400e-04 9.997543e-01
[31,] 9.334678e-04 1.866936e-03 9.990665e-01
[32,] 5.513274e-04 1.102655e-03 9.994487e-01
[33,] 4.250378e-04 8.500755e-04 9.995750e-01
[34,] 1.062767e-03 2.125535e-03 9.989372e-01
[35,] 9.759583e-01 4.808330e-02 2.404165e-02
[36,] 9.999240e-01 1.519047e-04 7.595236e-05
[37,] 9.999998e-01 3.220090e-07 1.610045e-07
[38,] 9.999994e-01 1.179890e-06 5.899451e-07
[39,] 9.999988e-01 2.467523e-06 1.233762e-06
[40,] 9.999962e-01 7.534349e-06 3.767174e-06
[41,] 9.999985e-01 3.053835e-06 1.526918e-06
[42,] 9.999925e-01 1.505387e-05 7.526936e-06
[43,] 9.999755e-01 4.901141e-05 2.450570e-05
[44,] 9.999220e-01 1.559320e-04 7.796601e-05
[45,] 9.999465e-01 1.070446e-04 5.352230e-05
[46,] 9.999909e-01 1.814035e-05 9.070177e-06
[47,] 9.999859e-01 2.825021e-05 1.412510e-05
[48,] 9.998315e-01 3.369784e-04 1.684892e-04
[49,] 9.983155e-01 3.369096e-03 1.684548e-03
> postscript(file="/var/www/html/rcomp/tmp/1bnry1292969056.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2me811292969056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3me811292969056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4e6p41292969056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5e6p41292969056.ps",horizontal=F,onefile=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 = 66
Frequency = 1
1 2 3 4 5 6
-1.65484462 1.23222470 -1.37264255 -1.48462768 0.52797488 1.41598975
7 8 9 10 11 12
0.06218897 -0.76539099 -0.28731453 -0.24137327 0.05563446 1.53371092
13 14 15 16 17 18
-3.66027237 -0.21553462 1.23939950 -0.39786090 -0.63537927 0.96728487
19 20 21 22 23 24
0.92008606 1.21803934 0.94718226 -1.37327981 -0.07721762 -1.15586405
25 26 27 28 29 30
1.57327888 -2.62164996 0.66516140 1.02695544 0.91497031 -0.13222851
31 32 33 34 35 36
0.84892644 -0.66280073 0.78288257 0.86121699 0.47502305 1.07863274
37 38 39 40 41 42
-2.11629609 1.04498203 2.21645653 -1.16879186 0.59274422 1.35428031
43 44 45 46 47 48
1.68462674 -0.82710043 -0.91491062 -1.72745367 0.27101918 0.03350081
49 50 51 52 53 54
2.85215333 -0.10041151 2.48812293 -0.06713932 -2.56379124 -2.29300068
55 56 57 58 59 60
-1.79209204 0.68449053 1.88930373 1.65083981 -1.01731986 0.07459592
61 62 63 64 65 66
-3.75243569 -0.59509090 -1.05840411 0.65566356 3.14393639 -2.25096003
> postscript(file="/var/www/html/rcomp/tmp/6e6p41292969056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 66
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.65484462 NA
1 1.23222470 -1.65484462
2 -1.37264255 1.23222470
3 -1.48462768 -1.37264255
4 0.52797488 -1.48462768
5 1.41598975 0.52797488
6 0.06218897 1.41598975
7 -0.76539099 0.06218897
8 -0.28731453 -0.76539099
9 -0.24137327 -0.28731453
10 0.05563446 -0.24137327
11 1.53371092 0.05563446
12 -3.66027237 1.53371092
13 -0.21553462 -3.66027237
14 1.23939950 -0.21553462
15 -0.39786090 1.23939950
16 -0.63537927 -0.39786090
17 0.96728487 -0.63537927
18 0.92008606 0.96728487
19 1.21803934 0.92008606
20 0.94718226 1.21803934
21 -1.37327981 0.94718226
22 -0.07721762 -1.37327981
23 -1.15586405 -0.07721762
24 1.57327888 -1.15586405
25 -2.62164996 1.57327888
26 0.66516140 -2.62164996
27 1.02695544 0.66516140
28 0.91497031 1.02695544
29 -0.13222851 0.91497031
30 0.84892644 -0.13222851
31 -0.66280073 0.84892644
32 0.78288257 -0.66280073
33 0.86121699 0.78288257
34 0.47502305 0.86121699
35 1.07863274 0.47502305
36 -2.11629609 1.07863274
37 1.04498203 -2.11629609
38 2.21645653 1.04498203
39 -1.16879186 2.21645653
40 0.59274422 -1.16879186
41 1.35428031 0.59274422
42 1.68462674 1.35428031
43 -0.82710043 1.68462674
44 -0.91491062 -0.82710043
45 -1.72745367 -0.91491062
46 0.27101918 -1.72745367
47 0.03350081 0.27101918
48 2.85215333 0.03350081
49 -0.10041151 2.85215333
50 2.48812293 -0.10041151
51 -0.06713932 2.48812293
52 -2.56379124 -0.06713932
53 -2.29300068 -2.56379124
54 -1.79209204 -2.29300068
55 0.68449053 -1.79209204
56 1.88930373 0.68449053
57 1.65083981 1.88930373
58 -1.01731986 1.65083981
59 0.07459592 -1.01731986
60 -3.75243569 0.07459592
61 -0.59509090 -3.75243569
62 -1.05840411 -0.59509090
63 0.65566356 -1.05840411
64 3.14393639 0.65566356
65 -2.25096003 3.14393639
66 NA -2.25096003
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.23222470 -1.65484462
[2,] -1.37264255 1.23222470
[3,] -1.48462768 -1.37264255
[4,] 0.52797488 -1.48462768
[5,] 1.41598975 0.52797488
[6,] 0.06218897 1.41598975
[7,] -0.76539099 0.06218897
[8,] -0.28731453 -0.76539099
[9,] -0.24137327 -0.28731453
[10,] 0.05563446 -0.24137327
[11,] 1.53371092 0.05563446
[12,] -3.66027237 1.53371092
[13,] -0.21553462 -3.66027237
[14,] 1.23939950 -0.21553462
[15,] -0.39786090 1.23939950
[16,] -0.63537927 -0.39786090
[17,] 0.96728487 -0.63537927
[18,] 0.92008606 0.96728487
[19,] 1.21803934 0.92008606
[20,] 0.94718226 1.21803934
[21,] -1.37327981 0.94718226
[22,] -0.07721762 -1.37327981
[23,] -1.15586405 -0.07721762
[24,] 1.57327888 -1.15586405
[25,] -2.62164996 1.57327888
[26,] 0.66516140 -2.62164996
[27,] 1.02695544 0.66516140
[28,] 0.91497031 1.02695544
[29,] -0.13222851 0.91497031
[30,] 0.84892644 -0.13222851
[31,] -0.66280073 0.84892644
[32,] 0.78288257 -0.66280073
[33,] 0.86121699 0.78288257
[34,] 0.47502305 0.86121699
[35,] 1.07863274 0.47502305
[36,] -2.11629609 1.07863274
[37,] 1.04498203 -2.11629609
[38,] 2.21645653 1.04498203
[39,] -1.16879186 2.21645653
[40,] 0.59274422 -1.16879186
[41,] 1.35428031 0.59274422
[42,] 1.68462674 1.35428031
[43,] -0.82710043 1.68462674
[44,] -0.91491062 -0.82710043
[45,] -1.72745367 -0.91491062
[46,] 0.27101918 -1.72745367
[47,] 0.03350081 0.27101918
[48,] 2.85215333 0.03350081
[49,] -0.10041151 2.85215333
[50,] 2.48812293 -0.10041151
[51,] -0.06713932 2.48812293
[52,] -2.56379124 -0.06713932
[53,] -2.29300068 -2.56379124
[54,] -1.79209204 -2.29300068
[55,] 0.68449053 -1.79209204
[56,] 1.88930373 0.68449053
[57,] 1.65083981 1.88930373
[58,] -1.01731986 1.65083981
[59,] 0.07459592 -1.01731986
[60,] -3.75243569 0.07459592
[61,] -0.59509090 -3.75243569
[62,] -1.05840411 -0.59509090
[63,] 0.65566356 -1.05840411
[64,] 3.14393639 0.65566356
[65,] -2.25096003 3.14393639
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.23222470 -1.65484462
2 -1.37264255 1.23222470
3 -1.48462768 -1.37264255
4 0.52797488 -1.48462768
5 1.41598975 0.52797488
6 0.06218897 1.41598975
7 -0.76539099 0.06218897
8 -0.28731453 -0.76539099
9 -0.24137327 -0.28731453
10 0.05563446 -0.24137327
11 1.53371092 0.05563446
12 -3.66027237 1.53371092
13 -0.21553462 -3.66027237
14 1.23939950 -0.21553462
15 -0.39786090 1.23939950
16 -0.63537927 -0.39786090
17 0.96728487 -0.63537927
18 0.92008606 0.96728487
19 1.21803934 0.92008606
20 0.94718226 1.21803934
21 -1.37327981 0.94718226
22 -0.07721762 -1.37327981
23 -1.15586405 -0.07721762
24 1.57327888 -1.15586405
25 -2.62164996 1.57327888
26 0.66516140 -2.62164996
27 1.02695544 0.66516140
28 0.91497031 1.02695544
29 -0.13222851 0.91497031
30 0.84892644 -0.13222851
31 -0.66280073 0.84892644
32 0.78288257 -0.66280073
33 0.86121699 0.78288257
34 0.47502305 0.86121699
35 1.07863274 0.47502305
36 -2.11629609 1.07863274
37 1.04498203 -2.11629609
38 2.21645653 1.04498203
39 -1.16879186 2.21645653
40 0.59274422 -1.16879186
41 1.35428031 0.59274422
42 1.68462674 1.35428031
43 -0.82710043 1.68462674
44 -0.91491062 -0.82710043
45 -1.72745367 -0.91491062
46 0.27101918 -1.72745367
47 0.03350081 0.27101918
48 2.85215333 0.03350081
49 -0.10041151 2.85215333
50 2.48812293 -0.10041151
51 -0.06713932 2.48812293
52 -2.56379124 -0.06713932
53 -2.29300068 -2.56379124
54 -1.79209204 -2.29300068
55 0.68449053 -1.79209204
56 1.88930373 0.68449053
57 1.65083981 1.88930373
58 -1.01731986 1.65083981
59 0.07459592 -1.01731986
60 -3.75243569 0.07459592
61 -0.59509090 -3.75243569
62 -1.05840411 -0.59509090
63 0.65566356 -1.05840411
64 3.14393639 0.65566356
65 -2.25096003 3.14393639
> 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/77xo71292969056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80ooa1292969056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90ooa1292969056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/100ooa1292969056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wg3j1292969056.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/12pplm1292969056.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/13v80g1292969056.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/146hh11292969056.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/1590fo1292969056.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/166avx1292969056.tab")
+ }
>
> try(system("convert tmp/1bnry1292969056.ps tmp/1bnry1292969056.png",intern=TRUE))
character(0)
> try(system("convert tmp/2me811292969056.ps tmp/2me811292969056.png",intern=TRUE))
character(0)
> try(system("convert tmp/3me811292969056.ps tmp/3me811292969056.png",intern=TRUE))
character(0)
> try(system("convert tmp/4e6p41292969056.ps tmp/4e6p41292969056.png",intern=TRUE))
character(0)
> try(system("convert tmp/5e6p41292969056.ps tmp/5e6p41292969056.png",intern=TRUE))
character(0)
> try(system("convert tmp/6e6p41292969056.ps tmp/6e6p41292969056.png",intern=TRUE))
character(0)
> try(system("convert tmp/77xo71292969056.ps tmp/77xo71292969056.png",intern=TRUE))
character(0)
> try(system("convert tmp/80ooa1292969056.ps tmp/80ooa1292969056.png",intern=TRUE))
character(0)
> try(system("convert tmp/90ooa1292969056.ps tmp/90ooa1292969056.png",intern=TRUE))
character(0)
> try(system("convert tmp/100ooa1292969056.ps tmp/100ooa1292969056.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.624 1.681 5.979