R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(47.54,45.31,46.9,47.16,48.24,52.7,51.72,51.5,52.45,53,48.36,46.63,45.92,45.53,42.17,43.66,45.32,47.43,47.76,49.49,50.69,49.8,52.13,53.94,60.75,59.19,57.58,59.16,64.74,67.04,75.53,78.91,78.4,70.07,66.8,61.02,52.38,42.37,39.83,38.79,37.33,39.4,39.45,43.24,42.33,45.5,43.44,43.88,45.61,45.12,47.56,47.04,51.07,54.72,55.37,55.39,53.13,53.71,54.59,54.61),dim=c(1,60),dimnames=list(c('Y'),1:60))
> y <- array(NA,dim=c(1,60),dimnames=list(c('Y'),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
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 47.54 1 0 0 0 0 0 0 0 0 0 0 1
2 45.31 0 1 0 0 0 0 0 0 0 0 0 2
3 46.90 0 0 1 0 0 0 0 0 0 0 0 3
4 47.16 0 0 0 1 0 0 0 0 0 0 0 4
5 48.24 0 0 0 0 1 0 0 0 0 0 0 5
6 52.70 0 0 0 0 0 1 0 0 0 0 0 6
7 51.72 0 0 0 0 0 0 1 0 0 0 0 7
8 51.50 0 0 0 0 0 0 0 1 0 0 0 8
9 52.45 0 0 0 0 0 0 0 0 1 0 0 9
10 53.00 0 0 0 0 0 0 0 0 0 1 0 10
11 48.36 0 0 0 0 0 0 0 0 0 0 1 11
12 46.63 0 0 0 0 0 0 0 0 0 0 0 12
13 45.92 1 0 0 0 0 0 0 0 0 0 0 13
14 45.53 0 1 0 0 0 0 0 0 0 0 0 14
15 42.17 0 0 1 0 0 0 0 0 0 0 0 15
16 43.66 0 0 0 1 0 0 0 0 0 0 0 16
17 45.32 0 0 0 0 1 0 0 0 0 0 0 17
18 47.43 0 0 0 0 0 1 0 0 0 0 0 18
19 47.76 0 0 0 0 0 0 1 0 0 0 0 19
20 49.49 0 0 0 0 0 0 0 1 0 0 0 20
21 50.69 0 0 0 0 0 0 0 0 1 0 0 21
22 49.80 0 0 0 0 0 0 0 0 0 1 0 22
23 52.13 0 0 0 0 0 0 0 0 0 0 1 23
24 53.94 0 0 0 0 0 0 0 0 0 0 0 24
25 60.75 1 0 0 0 0 0 0 0 0 0 0 25
26 59.19 0 1 0 0 0 0 0 0 0 0 0 26
27 57.58 0 0 1 0 0 0 0 0 0 0 0 27
28 59.16 0 0 0 1 0 0 0 0 0 0 0 28
29 64.74 0 0 0 0 1 0 0 0 0 0 0 29
30 67.04 0 0 0 0 0 1 0 0 0 0 0 30
31 75.53 0 0 0 0 0 0 1 0 0 0 0 31
32 78.91 0 0 0 0 0 0 0 1 0 0 0 32
33 78.40 0 0 0 0 0 0 0 0 1 0 0 33
34 70.07 0 0 0 0 0 0 0 0 0 1 0 34
35 66.80 0 0 0 0 0 0 0 0 0 0 1 35
36 61.02 0 0 0 0 0 0 0 0 0 0 0 36
37 52.38 1 0 0 0 0 0 0 0 0 0 0 37
38 42.37 0 1 0 0 0 0 0 0 0 0 0 38
39 39.83 0 0 1 0 0 0 0 0 0 0 0 39
40 38.79 0 0 0 1 0 0 0 0 0 0 0 40
41 37.33 0 0 0 0 1 0 0 0 0 0 0 41
42 39.40 0 0 0 0 0 1 0 0 0 0 0 42
43 39.45 0 0 0 0 0 0 1 0 0 0 0 43
44 43.24 0 0 0 0 0 0 0 1 0 0 0 44
45 42.33 0 0 0 0 0 0 0 0 1 0 0 45
46 45.50 0 0 0 0 0 0 0 0 0 1 0 46
47 43.44 0 0 0 0 0 0 0 0 0 0 1 47
48 43.88 0 0 0 0 0 0 0 0 0 0 0 48
49 45.61 1 0 0 0 0 0 0 0 0 0 0 49
50 45.12 0 1 0 0 0 0 0 0 0 0 0 50
51 47.56 0 0 1 0 0 0 0 0 0 0 0 51
52 47.04 0 0 0 1 0 0 0 0 0 0 0 52
53 51.07 0 0 0 0 1 0 0 0 0 0 0 53
54 54.72 0 0 0 0 0 1 0 0 0 0 0 54
55 55.37 0 0 0 0 0 0 1 0 0 0 0 55
56 55.39 0 0 0 0 0 0 0 1 0 0 0 56
57 53.13 0 0 0 0 0 0 0 0 1 0 0 57
58 53.71 0 0 0 0 0 0 0 0 0 1 0 58
59 54.59 0 0 0 0 0 0 0 0 0 0 1 59
60 54.61 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) M1 M2 M3 M4 M5
52.343000 -1.675917 -4.602833 -5.289750 -4.926667 -2.739583
M6 M7 M8 M9 M10 M11
0.187500 1.904583 3.653667 3.356750 2.381833 1.038917
t
-0.009083
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.407 -4.926 -2.067 1.973 23.204
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 52.343000 5.196676 10.072 2.54e-13 ***
M1 -1.675917 6.322048 -0.265 0.792
M2 -4.602833 6.312602 -0.729 0.470
M3 -5.289750 6.304044 -0.839 0.406
M4 -4.926667 6.296376 -0.782 0.438
M5 -2.739583 6.289604 -0.436 0.665
M6 0.187500 6.283728 0.030 0.976
M7 1.904583 6.278752 0.303 0.763
M8 3.653667 6.274677 0.582 0.563
M9 3.356750 6.271507 0.535 0.595
M10 2.381833 6.269241 0.380 0.706
M11 1.038917 6.267881 0.166 0.869
t -0.009083 0.075385 -0.120 0.905
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.91 on 47 degrees of freedom
Multiple R-squared: 0.1096, Adjusted R-squared: -0.1177
F-statistic: 0.4822 on 12 and 47 DF, p-value: 0.9151
> 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,] 3.244523e-03 6.489047e-03 9.967555e-01
[2,] 3.660844e-04 7.321688e-04 9.996339e-01
[3,] 9.244892e-05 1.848978e-04 9.999076e-01
[4,] 1.259200e-05 2.518400e-05 9.999874e-01
[5,] 1.864048e-06 3.728097e-06 9.999981e-01
[6,] 2.901150e-07 5.802300e-07 9.999997e-01
[7,] 3.943677e-08 7.887354e-08 1.000000e+00
[8,] 6.119424e-07 1.223885e-06 9.999994e-01
[9,] 9.412305e-06 1.882461e-05 9.999906e-01
[10,] 5.892899e-04 1.178580e-03 9.994107e-01
[11,] 1.292313e-03 2.584626e-03 9.987077e-01
[12,] 1.235225e-03 2.470451e-03 9.987648e-01
[13,] 1.037408e-03 2.074816e-03 9.989626e-01
[14,] 1.513521e-03 3.027042e-03 9.984865e-01
[15,] 1.467777e-03 2.935555e-03 9.985322e-01
[16,] 6.819739e-03 1.363948e-02 9.931803e-01
[17,] 3.304088e-02 6.608177e-02 9.669591e-01
[18,] 1.452240e-01 2.904480e-01 8.547760e-01
[19,] 2.638977e-01 5.277954e-01 7.361023e-01
[20,] 5.656680e-01 8.686640e-01 4.343320e-01
[21,] 9.492414e-01 1.015172e-01 5.075862e-02
[22,] 9.990094e-01 1.981207e-03 9.906033e-04
[23,] 9.999158e-01 1.684649e-04 8.423245e-05
[24,] 9.999218e-01 1.563275e-04 7.816374e-05
[25,] 9.999096e-01 1.808142e-04 9.040708e-05
[26,] 9.997428e-01 5.143836e-04 2.571918e-04
[27,] 9.995406e-01 9.187748e-04 4.593874e-04
[28,] 9.998365e-01 3.270180e-04 1.635090e-04
[29,] 9.991289e-01 1.742130e-03 8.710648e-04
> postscript(file="/var/www/rcomp/tmp/1sy9y1290878886.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/23pqj1290878886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/33pqj1290878886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/43pqj1290878886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/53pqj1290878886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6 7 8 9 10
-3.118 -2.412 -0.126 -0.220 -1.318 0.224 -2.464 -4.424 -3.168 -1.634
11 12 13 14 15 16 17 18 19 20
-4.922 -5.604 -4.629 -2.083 -4.747 -3.611 -4.129 -4.937 -6.315 -6.325
21 22 23 24 25 26 27 28 29 30
-4.819 -4.725 -1.043 1.815 10.310 11.686 10.772 11.998 15.400 14.782
31 32 33 34 35 36 37 38 39 40
21.564 23.204 23.000 15.654 13.736 9.004 2.049 -5.025 -6.869 -8.263
41 42 43 44 45 46 47 48 49 50
-11.901 -12.749 -14.407 -12.357 -12.961 -8.807 -9.515 -8.027 -4.612 -2.166
51 52 53 54 55 56 57 58 59 60
0.970 0.096 1.948 2.680 1.622 -0.098 -2.052 -0.488 1.744 2.812
> postscript(file="/var/www/rcomp/tmp/6vz741290878886.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 -3.118 NA
1 -2.412 -3.118
2 -0.126 -2.412
3 -0.220 -0.126
4 -1.318 -0.220
5 0.224 -1.318
6 -2.464 0.224
7 -4.424 -2.464
8 -3.168 -4.424
9 -1.634 -3.168
10 -4.922 -1.634
11 -5.604 -4.922
12 -4.629 -5.604
13 -2.083 -4.629
14 -4.747 -2.083
15 -3.611 -4.747
16 -4.129 -3.611
17 -4.937 -4.129
18 -6.315 -4.937
19 -6.325 -6.315
20 -4.819 -6.325
21 -4.725 -4.819
22 -1.043 -4.725
23 1.815 -1.043
24 10.310 1.815
25 11.686 10.310
26 10.772 11.686
27 11.998 10.772
28 15.400 11.998
29 14.782 15.400
30 21.564 14.782
31 23.204 21.564
32 23.000 23.204
33 15.654 23.000
34 13.736 15.654
35 9.004 13.736
36 2.049 9.004
37 -5.025 2.049
38 -6.869 -5.025
39 -8.263 -6.869
40 -11.901 -8.263
41 -12.749 -11.901
42 -14.407 -12.749
43 -12.357 -14.407
44 -12.961 -12.357
45 -8.807 -12.961
46 -9.515 -8.807
47 -8.027 -9.515
48 -4.612 -8.027
49 -2.166 -4.612
50 0.970 -2.166
51 0.096 0.970
52 1.948 0.096
53 2.680 1.948
54 1.622 2.680
55 -0.098 1.622
56 -2.052 -0.098
57 -0.488 -2.052
58 1.744 -0.488
59 2.812 1.744
60 NA 2.812
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.412 -3.118
[2,] -0.126 -2.412
[3,] -0.220 -0.126
[4,] -1.318 -0.220
[5,] 0.224 -1.318
[6,] -2.464 0.224
[7,] -4.424 -2.464
[8,] -3.168 -4.424
[9,] -1.634 -3.168
[10,] -4.922 -1.634
[11,] -5.604 -4.922
[12,] -4.629 -5.604
[13,] -2.083 -4.629
[14,] -4.747 -2.083
[15,] -3.611 -4.747
[16,] -4.129 -3.611
[17,] -4.937 -4.129
[18,] -6.315 -4.937
[19,] -6.325 -6.315
[20,] -4.819 -6.325
[21,] -4.725 -4.819
[22,] -1.043 -4.725
[23,] 1.815 -1.043
[24,] 10.310 1.815
[25,] 11.686 10.310
[26,] 10.772 11.686
[27,] 11.998 10.772
[28,] 15.400 11.998
[29,] 14.782 15.400
[30,] 21.564 14.782
[31,] 23.204 21.564
[32,] 23.000 23.204
[33,] 15.654 23.000
[34,] 13.736 15.654
[35,] 9.004 13.736
[36,] 2.049 9.004
[37,] -5.025 2.049
[38,] -6.869 -5.025
[39,] -8.263 -6.869
[40,] -11.901 -8.263
[41,] -12.749 -11.901
[42,] -14.407 -12.749
[43,] -12.357 -14.407
[44,] -12.961 -12.357
[45,] -8.807 -12.961
[46,] -9.515 -8.807
[47,] -8.027 -9.515
[48,] -4.612 -8.027
[49,] -2.166 -4.612
[50,] 0.970 -2.166
[51,] 0.096 0.970
[52,] 1.948 0.096
[53,] 2.680 1.948
[54,] 1.622 2.680
[55,] -0.098 1.622
[56,] -2.052 -0.098
[57,] -0.488 -2.052
[58,] 1.744 -0.488
[59,] 2.812 1.744
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.412 -3.118
2 -0.126 -2.412
3 -0.220 -0.126
4 -1.318 -0.220
5 0.224 -1.318
6 -2.464 0.224
7 -4.424 -2.464
8 -3.168 -4.424
9 -1.634 -3.168
10 -4.922 -1.634
11 -5.604 -4.922
12 -4.629 -5.604
13 -2.083 -4.629
14 -4.747 -2.083
15 -3.611 -4.747
16 -4.129 -3.611
17 -4.937 -4.129
18 -6.315 -4.937
19 -6.325 -6.315
20 -4.819 -6.325
21 -4.725 -4.819
22 -1.043 -4.725
23 1.815 -1.043
24 10.310 1.815
25 11.686 10.310
26 10.772 11.686
27 11.998 10.772
28 15.400 11.998
29 14.782 15.400
30 21.564 14.782
31 23.204 21.564
32 23.000 23.204
33 15.654 23.000
34 13.736 15.654
35 9.004 13.736
36 2.049 9.004
37 -5.025 2.049
38 -6.869 -5.025
39 -8.263 -6.869
40 -11.901 -8.263
41 -12.749 -11.901
42 -14.407 -12.749
43 -12.357 -14.407
44 -12.961 -12.357
45 -8.807 -12.961
46 -9.515 -8.807
47 -8.027 -9.515
48 -4.612 -8.027
49 -2.166 -4.612
50 0.970 -2.166
51 0.096 0.970
52 1.948 0.096
53 2.680 1.948
54 1.622 2.680
55 -0.098 1.622
56 -2.052 -0.098
57 -0.488 -2.052
58 1.744 -0.488
59 2.812 1.744
> 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/rcomp/tmp/7oq7p1290878886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8oq7p1290878886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9oq7p1290878886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10zhos1290878886.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11khmy1290878886.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/rcomp/tmp/1250341290878886.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/rcomp/tmp/13jaiv1290878886.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/rcomp/tmp/14nsz11290878886.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/rcomp/tmp/15qtg61290878886.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/rcomp/tmp/16utwc1290878886.tab")
+ }
>
> try(system("convert tmp/1sy9y1290878886.ps tmp/1sy9y1290878886.png",intern=TRUE))
character(0)
> try(system("convert tmp/23pqj1290878886.ps tmp/23pqj1290878886.png",intern=TRUE))
character(0)
> try(system("convert tmp/33pqj1290878886.ps tmp/33pqj1290878886.png",intern=TRUE))
character(0)
> try(system("convert tmp/43pqj1290878886.ps tmp/43pqj1290878886.png",intern=TRUE))
character(0)
> try(system("convert tmp/53pqj1290878886.ps tmp/53pqj1290878886.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vz741290878886.ps tmp/6vz741290878886.png",intern=TRUE))
character(0)
> try(system("convert tmp/7oq7p1290878886.ps tmp/7oq7p1290878886.png",intern=TRUE))
character(0)
> try(system("convert tmp/8oq7p1290878886.ps tmp/8oq7p1290878886.png",intern=TRUE))
character(0)
> try(system("convert tmp/9oq7p1290878886.ps tmp/9oq7p1290878886.png",intern=TRUE))
character(0)
> try(system("convert tmp/10zhos1290878886.ps tmp/10zhos1290878886.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.700 1.770 5.509