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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> x <- array(list(99.9,98.8,98.6,100.5,107.2,110.4,95.7,96.4,93.7,101.9,106.7,106.2,86.7,81,95.3,94.7,99.3,101,101.8,109.4,96,102.3,91.7,90.7,95.3,96.2,96.6,96.1,107.2,106,108,103.1,98.4,102,103.1,104.7,81.1,86,96.6,92.1,103.7,106.9,106.6,112.6,97.6,101.7,87.6,92,99.4,97.4,98.5,97,105.2,105.4,104.6,102.7,97.5,98.1,108.9,104.5,86.8,87.4,88.9,89.9,110.3,109.8,114.8,111.7,94.6,98.6,92,96.9,93.8,95.1,93.8,97,107.6,112.7,101,102.9,95.4,97.4,96.5,111.4,89.2,87.4,87.1,96.8,110.5,114.1,110.8,110.3,104.2,103.9,88.9,101.6,89.8,94.6,90,95.9,93.9,104.7,91.3,102.8,87.8,98.1,99.7,113.9,73.5,80.9,79.2,95.7,96.9,113.2,95.2,105.9,95.6,108.8,89.7,102.3),dim=c(2,60),dimnames=list(c('ProdMetal','TotProd'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('ProdMetal','TotProd'),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 = '2'
> #'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
TotProd ProdMetal M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 98.8 99.9 1 0 0 0 0 0 0 0 0 0 0 1
2 100.5 98.6 0 1 0 0 0 0 0 0 0 0 0 2
3 110.4 107.2 0 0 1 0 0 0 0 0 0 0 0 3
4 96.4 95.7 0 0 0 1 0 0 0 0 0 0 0 4
5 101.9 93.7 0 0 0 0 1 0 0 0 0 0 0 5
6 106.2 106.7 0 0 0 0 0 1 0 0 0 0 0 6
7 81.0 86.7 0 0 0 0 0 0 1 0 0 0 0 7
8 94.7 95.3 0 0 0 0 0 0 0 1 0 0 0 8
9 101.0 99.3 0 0 0 0 0 0 0 0 1 0 0 9
10 109.4 101.8 0 0 0 0 0 0 0 0 0 1 0 10
11 102.3 96.0 0 0 0 0 0 0 0 0 0 0 1 11
12 90.7 91.7 0 0 0 0 0 0 0 0 0 0 0 12
13 96.2 95.3 1 0 0 0 0 0 0 0 0 0 0 13
14 96.1 96.6 0 1 0 0 0 0 0 0 0 0 0 14
15 106.0 107.2 0 0 1 0 0 0 0 0 0 0 0 15
16 103.1 108.0 0 0 0 1 0 0 0 0 0 0 0 16
17 102.0 98.4 0 0 0 0 1 0 0 0 0 0 0 17
18 104.7 103.1 0 0 0 0 0 1 0 0 0 0 0 18
19 86.0 81.1 0 0 0 0 0 0 1 0 0 0 0 19
20 92.1 96.6 0 0 0 0 0 0 0 1 0 0 0 20
21 106.9 103.7 0 0 0 0 0 0 0 0 1 0 0 21
22 112.6 106.6 0 0 0 0 0 0 0 0 0 1 0 22
23 101.7 97.6 0 0 0 0 0 0 0 0 0 0 1 23
24 92.0 87.6 0 0 0 0 0 0 0 0 0 0 0 24
25 97.4 99.4 1 0 0 0 0 0 0 0 0 0 0 25
26 97.0 98.5 0 1 0 0 0 0 0 0 0 0 0 26
27 105.4 105.2 0 0 1 0 0 0 0 0 0 0 0 27
28 102.7 104.6 0 0 0 1 0 0 0 0 0 0 0 28
29 98.1 97.5 0 0 0 0 1 0 0 0 0 0 0 29
30 104.5 108.9 0 0 0 0 0 1 0 0 0 0 0 30
31 87.4 86.8 0 0 0 0 0 0 1 0 0 0 0 31
32 89.9 88.9 0 0 0 0 0 0 0 1 0 0 0 32
33 109.8 110.3 0 0 0 0 0 0 0 0 1 0 0 33
34 111.7 114.8 0 0 0 0 0 0 0 0 0 1 0 34
35 98.6 94.6 0 0 0 0 0 0 0 0 0 0 1 35
36 96.9 92.0 0 0 0 0 0 0 0 0 0 0 0 36
37 95.1 93.8 1 0 0 0 0 0 0 0 0 0 0 37
38 97.0 93.8 0 1 0 0 0 0 0 0 0 0 0 38
39 112.7 107.6 0 0 1 0 0 0 0 0 0 0 0 39
40 102.9 101.0 0 0 0 1 0 0 0 0 0 0 0 40
41 97.4 95.4 0 0 0 0 1 0 0 0 0 0 0 41
42 111.4 96.5 0 0 0 0 0 1 0 0 0 0 0 42
43 87.4 89.2 0 0 0 0 0 0 1 0 0 0 0 43
44 96.8 87.1 0 0 0 0 0 0 0 1 0 0 0 44
45 114.1 110.5 0 0 0 0 0 0 0 0 1 0 0 45
46 110.3 110.8 0 0 0 0 0 0 0 0 0 1 0 46
47 103.9 104.2 0 0 0 0 0 0 0 0 0 0 1 47
48 101.6 88.9 0 0 0 0 0 0 0 0 0 0 0 48
49 94.6 89.8 1 0 0 0 0 0 0 0 0 0 0 49
50 95.9 90.0 0 1 0 0 0 0 0 0 0 0 0 50
51 104.7 93.9 0 0 1 0 0 0 0 0 0 0 0 51
52 102.8 91.3 0 0 0 1 0 0 0 0 0 0 0 52
53 98.1 87.8 0 0 0 0 1 0 0 0 0 0 0 53
54 113.9 99.7 0 0 0 0 0 1 0 0 0 0 0 54
55 80.9 73.5 0 0 0 0 0 0 1 0 0 0 0 55
56 95.7 79.2 0 0 0 0 0 0 0 1 0 0 0 56
57 113.2 96.9 0 0 0 0 0 0 0 0 1 0 0 57
58 105.9 95.2 0 0 0 0 0 0 0 0 0 1 0 58
59 108.8 95.6 0 0 0 0 0 0 0 0 0 0 1 59
60 102.3 89.7 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) ProdMetal M1 M2 M3 M4
68.99987 0.27228 -0.84311 -0.01389 8.06295 2.83037
M5 M6 M7 M8 M9 M10
2.17532 8.43385 -9.94022 -2.35190 8.71128 9.13951
M11 t
4.37416 0.08891
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.2519 -2.2225 -0.3913 2.5222 4.7679
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.99987 8.87139 7.778 6.30e-10 ***
ProdMetal 0.27228 0.09220 2.953 0.004944 **
M1 -0.84311 2.03397 -0.415 0.680425
M2 -0.01389 2.03081 -0.007 0.994571
M3 8.06295 2.32768 3.464 0.001163 **
M4 2.83037 2.15823 1.311 0.196220
M5 2.17532 2.01435 1.080 0.285810
M6 8.43385 2.28202 3.696 0.000582 ***
M7 -9.94022 2.08890 -4.759 1.97e-05 ***
M8 -2.35190 1.98314 -1.186 0.241733
M9 8.71128 2.35228 3.703 0.000569 ***
M10 9.13951 2.44680 3.735 0.000516 ***
M11 4.37416 2.09570 2.087 0.042438 *
t 0.08891 0.02652 3.352 0.001613 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.128 on 46 degrees of freedom
Multiple R-squared: 0.8784, Adjusted R-squared: 0.8441
F-statistic: 25.57 on 13 and 46 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,] 0.1579669 0.3159337 0.8420331
[2,] 0.1261745 0.2523489 0.8738255
[3,] 0.6308980 0.7382040 0.3691020
[4,] 0.5452802 0.9094396 0.4547198
[5,] 0.5406471 0.9187057 0.4593529
[6,] 0.6007877 0.7984246 0.3992123
[7,] 0.5439438 0.9121125 0.4560562
[8,] 0.4796128 0.9592257 0.5203872
[9,] 0.4216590 0.8433180 0.5783410
[10,] 0.3543926 0.7087852 0.6456074
[11,] 0.2802862 0.5605723 0.7197138
[12,] 0.2257361 0.4514723 0.7742639
[13,] 0.2477925 0.4955851 0.7522075
[14,] 0.4770925 0.9541850 0.5229075
[15,] 0.6440802 0.7118397 0.3559198
[16,] 0.6857903 0.6284193 0.3142097
[17,] 0.6788238 0.6423524 0.3211762
[18,] 0.6826839 0.6346323 0.3173161
[19,] 0.6072250 0.7855499 0.3927750
[20,] 0.6738931 0.6522138 0.3261069
[21,] 0.5718394 0.8563212 0.4281606
[22,] 0.4869593 0.9739187 0.5130407
[23,] 0.7081829 0.5836343 0.2918171
[24,] 0.6113612 0.7772777 0.3886388
[25,] 0.5095565 0.9808869 0.4904435
[26,] 0.5251239 0.9497521 0.4748761
[27,] 0.5052147 0.9895706 0.4947853
> postscript(file="/var/www/html/rcomp/tmp/1suz51260712375.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/28eyn1260712375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35cka1260712375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4q9mv1260712375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52nhh1260712375.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
3.35387848 4.48971950 3.88239271 -1.84275941 4.76793539 -0.81909208
7 8 9 10 11 12
-2.28839925 1.39279015 -4.54840317 2.65377543 1.80942018 -4.33453860
13 14 15 16 17 18
0.93946279 -0.43261572 -1.58449599 0.44134797 2.52134601 -2.40578451
19 20 21 22 23 24
3.16946181 -2.62805831 -0.91330954 3.47995837 -0.29311130 -2.98509266
25 26 27 28 29 30
-0.04376055 -1.11683023 -2.70683121 -0.09979980 -2.20049362 -5.25187831
31 32 33 34 35 36
1.95059568 -3.79841609 -0.87722473 -0.71959961 -3.64316977 -0.34999902
37 38 39 40 41 42
-1.88589949 -0.90401824 2.87281592 0.01350777 -3.39560116 3.95746459
43 44 45 46 47 48
0.23024280 2.52479334 2.30143123 -2.09738134 -2.02391519 4.12717018
49 50 51 52 53 54
-2.36368122 -2.03625532 -2.46388142 1.48770347 -1.69318662 4.51929032
55 56 57 58 59 60
-3.06190105 2.50889090 4.03750621 -3.31675286 4.15077609 3.54246009
> postscript(file="/var/www/html/rcomp/tmp/6cc141260712375.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 3.35387848 NA
1 4.48971950 3.35387848
2 3.88239271 4.48971950
3 -1.84275941 3.88239271
4 4.76793539 -1.84275941
5 -0.81909208 4.76793539
6 -2.28839925 -0.81909208
7 1.39279015 -2.28839925
8 -4.54840317 1.39279015
9 2.65377543 -4.54840317
10 1.80942018 2.65377543
11 -4.33453860 1.80942018
12 0.93946279 -4.33453860
13 -0.43261572 0.93946279
14 -1.58449599 -0.43261572
15 0.44134797 -1.58449599
16 2.52134601 0.44134797
17 -2.40578451 2.52134601
18 3.16946181 -2.40578451
19 -2.62805831 3.16946181
20 -0.91330954 -2.62805831
21 3.47995837 -0.91330954
22 -0.29311130 3.47995837
23 -2.98509266 -0.29311130
24 -0.04376055 -2.98509266
25 -1.11683023 -0.04376055
26 -2.70683121 -1.11683023
27 -0.09979980 -2.70683121
28 -2.20049362 -0.09979980
29 -5.25187831 -2.20049362
30 1.95059568 -5.25187831
31 -3.79841609 1.95059568
32 -0.87722473 -3.79841609
33 -0.71959961 -0.87722473
34 -3.64316977 -0.71959961
35 -0.34999902 -3.64316977
36 -1.88589949 -0.34999902
37 -0.90401824 -1.88589949
38 2.87281592 -0.90401824
39 0.01350777 2.87281592
40 -3.39560116 0.01350777
41 3.95746459 -3.39560116
42 0.23024280 3.95746459
43 2.52479334 0.23024280
44 2.30143123 2.52479334
45 -2.09738134 2.30143123
46 -2.02391519 -2.09738134
47 4.12717018 -2.02391519
48 -2.36368122 4.12717018
49 -2.03625532 -2.36368122
50 -2.46388142 -2.03625532
51 1.48770347 -2.46388142
52 -1.69318662 1.48770347
53 4.51929032 -1.69318662
54 -3.06190105 4.51929032
55 2.50889090 -3.06190105
56 4.03750621 2.50889090
57 -3.31675286 4.03750621
58 4.15077609 -3.31675286
59 3.54246009 4.15077609
60 NA 3.54246009
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.48971950 3.35387848
[2,] 3.88239271 4.48971950
[3,] -1.84275941 3.88239271
[4,] 4.76793539 -1.84275941
[5,] -0.81909208 4.76793539
[6,] -2.28839925 -0.81909208
[7,] 1.39279015 -2.28839925
[8,] -4.54840317 1.39279015
[9,] 2.65377543 -4.54840317
[10,] 1.80942018 2.65377543
[11,] -4.33453860 1.80942018
[12,] 0.93946279 -4.33453860
[13,] -0.43261572 0.93946279
[14,] -1.58449599 -0.43261572
[15,] 0.44134797 -1.58449599
[16,] 2.52134601 0.44134797
[17,] -2.40578451 2.52134601
[18,] 3.16946181 -2.40578451
[19,] -2.62805831 3.16946181
[20,] -0.91330954 -2.62805831
[21,] 3.47995837 -0.91330954
[22,] -0.29311130 3.47995837
[23,] -2.98509266 -0.29311130
[24,] -0.04376055 -2.98509266
[25,] -1.11683023 -0.04376055
[26,] -2.70683121 -1.11683023
[27,] -0.09979980 -2.70683121
[28,] -2.20049362 -0.09979980
[29,] -5.25187831 -2.20049362
[30,] 1.95059568 -5.25187831
[31,] -3.79841609 1.95059568
[32,] -0.87722473 -3.79841609
[33,] -0.71959961 -0.87722473
[34,] -3.64316977 -0.71959961
[35,] -0.34999902 -3.64316977
[36,] -1.88589949 -0.34999902
[37,] -0.90401824 -1.88589949
[38,] 2.87281592 -0.90401824
[39,] 0.01350777 2.87281592
[40,] -3.39560116 0.01350777
[41,] 3.95746459 -3.39560116
[42,] 0.23024280 3.95746459
[43,] 2.52479334 0.23024280
[44,] 2.30143123 2.52479334
[45,] -2.09738134 2.30143123
[46,] -2.02391519 -2.09738134
[47,] 4.12717018 -2.02391519
[48,] -2.36368122 4.12717018
[49,] -2.03625532 -2.36368122
[50,] -2.46388142 -2.03625532
[51,] 1.48770347 -2.46388142
[52,] -1.69318662 1.48770347
[53,] 4.51929032 -1.69318662
[54,] -3.06190105 4.51929032
[55,] 2.50889090 -3.06190105
[56,] 4.03750621 2.50889090
[57,] -3.31675286 4.03750621
[58,] 4.15077609 -3.31675286
[59,] 3.54246009 4.15077609
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.48971950 3.35387848
2 3.88239271 4.48971950
3 -1.84275941 3.88239271
4 4.76793539 -1.84275941
5 -0.81909208 4.76793539
6 -2.28839925 -0.81909208
7 1.39279015 -2.28839925
8 -4.54840317 1.39279015
9 2.65377543 -4.54840317
10 1.80942018 2.65377543
11 -4.33453860 1.80942018
12 0.93946279 -4.33453860
13 -0.43261572 0.93946279
14 -1.58449599 -0.43261572
15 0.44134797 -1.58449599
16 2.52134601 0.44134797
17 -2.40578451 2.52134601
18 3.16946181 -2.40578451
19 -2.62805831 3.16946181
20 -0.91330954 -2.62805831
21 3.47995837 -0.91330954
22 -0.29311130 3.47995837
23 -2.98509266 -0.29311130
24 -0.04376055 -2.98509266
25 -1.11683023 -0.04376055
26 -2.70683121 -1.11683023
27 -0.09979980 -2.70683121
28 -2.20049362 -0.09979980
29 -5.25187831 -2.20049362
30 1.95059568 -5.25187831
31 -3.79841609 1.95059568
32 -0.87722473 -3.79841609
33 -0.71959961 -0.87722473
34 -3.64316977 -0.71959961
35 -0.34999902 -3.64316977
36 -1.88589949 -0.34999902
37 -0.90401824 -1.88589949
38 2.87281592 -0.90401824
39 0.01350777 2.87281592
40 -3.39560116 0.01350777
41 3.95746459 -3.39560116
42 0.23024280 3.95746459
43 2.52479334 0.23024280
44 2.30143123 2.52479334
45 -2.09738134 2.30143123
46 -2.02391519 -2.09738134
47 4.12717018 -2.02391519
48 -2.36368122 4.12717018
49 -2.03625532 -2.36368122
50 -2.46388142 -2.03625532
51 1.48770347 -2.46388142
52 -1.69318662 1.48770347
53 4.51929032 -1.69318662
54 -3.06190105 4.51929032
55 2.50889090 -3.06190105
56 4.03750621 2.50889090
57 -3.31675286 4.03750621
58 4.15077609 -3.31675286
59 3.54246009 4.15077609
> 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/7eugo1260712375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/851261260712375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9va9s1260712375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10fq011260712375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11184h1260712375.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/121z4n1260712376.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/13dk1g1260712376.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/14cx1z1260712376.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/150qig1260712376.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/16rpf31260712376.tab")
+ }
> try(system("convert tmp/1suz51260712375.ps tmp/1suz51260712375.png",intern=TRUE))
character(0)
> try(system("convert tmp/28eyn1260712375.ps tmp/28eyn1260712375.png",intern=TRUE))
character(0)
> try(system("convert tmp/35cka1260712375.ps tmp/35cka1260712375.png",intern=TRUE))
character(0)
> try(system("convert tmp/4q9mv1260712375.ps tmp/4q9mv1260712375.png",intern=TRUE))
character(0)
> try(system("convert tmp/52nhh1260712375.ps tmp/52nhh1260712375.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cc141260712375.ps tmp/6cc141260712375.png",intern=TRUE))
character(0)
> try(system("convert tmp/7eugo1260712375.ps tmp/7eugo1260712375.png",intern=TRUE))
character(0)
> try(system("convert tmp/851261260712375.ps tmp/851261260712375.png",intern=TRUE))
character(0)
> try(system("convert tmp/9va9s1260712375.ps tmp/9va9s1260712375.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fq011260712375.ps tmp/10fq011260712375.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.384 1.560 3.105