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(126.51,0,131.02,0,136.51,0,138.04,0,132.92,0,129.61,0,122.96,0,124.04,0,121.29,0,124.56,0,118.53,0,113.14,0,114.15,0,122.17,0,129.23,0,131.19,0,129.12,0,128.28,0,126.83,0,138.13,0,140.52,0,146.83,0,135.14,0,131.84,0,125.7,0,128.98,0,133.25,0,136.76,0,133.24,0,128.54,0,121.08,0,120.23,0,119.08,0,125.75,0,126.89,0,126.6,0,121.89,0,123.44,0,126.46,0,129.49,0,127.78,0,125.29,0,119.02,0,119.96,0,122.86,0,131.89,0,132.73,0,135.01,0,136.71,1,142.73,1,144.43,1,144.93,1,138.75,1,130.22,1,122.19,1,128.4,1,140.43,1,153.5,1,149.33,1,142.97,1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = '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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 126.51 0 1 0 0 0 0 0 0 0 0 0 0
2 131.02 0 0 1 0 0 0 0 0 0 0 0 0
3 136.51 0 0 0 1 0 0 0 0 0 0 0 0
4 138.04 0 0 0 0 1 0 0 0 0 0 0 0
5 132.92 0 0 0 0 0 1 0 0 0 0 0 0
6 129.61 0 0 0 0 0 0 1 0 0 0 0 0
7 122.96 0 0 0 0 0 0 0 1 0 0 0 0
8 124.04 0 0 0 0 0 0 0 0 1 0 0 0
9 121.29 0 0 0 0 0 0 0 0 0 1 0 0
10 124.56 0 0 0 0 0 0 0 0 0 0 1 0
11 118.53 0 0 0 0 0 0 0 0 0 0 0 1
12 113.14 0 0 0 0 0 0 0 0 0 0 0 0
13 114.15 0 1 0 0 0 0 0 0 0 0 0 0
14 122.17 0 0 1 0 0 0 0 0 0 0 0 0
15 129.23 0 0 0 1 0 0 0 0 0 0 0 0
16 131.19 0 0 0 0 1 0 0 0 0 0 0 0
17 129.12 0 0 0 0 0 1 0 0 0 0 0 0
18 128.28 0 0 0 0 0 0 1 0 0 0 0 0
19 126.83 0 0 0 0 0 0 0 1 0 0 0 0
20 138.13 0 0 0 0 0 0 0 0 1 0 0 0
21 140.52 0 0 0 0 0 0 0 0 0 1 0 0
22 146.83 0 0 0 0 0 0 0 0 0 0 1 0
23 135.14 0 0 0 0 0 0 0 0 0 0 0 1
24 131.84 0 0 0 0 0 0 0 0 0 0 0 0
25 125.70 0 1 0 0 0 0 0 0 0 0 0 0
26 128.98 0 0 1 0 0 0 0 0 0 0 0 0
27 133.25 0 0 0 1 0 0 0 0 0 0 0 0
28 136.76 0 0 0 0 1 0 0 0 0 0 0 0
29 133.24 0 0 0 0 0 1 0 0 0 0 0 0
30 128.54 0 0 0 0 0 0 1 0 0 0 0 0
31 121.08 0 0 0 0 0 0 0 1 0 0 0 0
32 120.23 0 0 0 0 0 0 0 0 1 0 0 0
33 119.08 0 0 0 0 0 0 0 0 0 1 0 0
34 125.75 0 0 0 0 0 0 0 0 0 0 1 0
35 126.89 0 0 0 0 0 0 0 0 0 0 0 1
36 126.60 0 0 0 0 0 0 0 0 0 0 0 0
37 121.89 0 1 0 0 0 0 0 0 0 0 0 0
38 123.44 0 0 1 0 0 0 0 0 0 0 0 0
39 126.46 0 0 0 1 0 0 0 0 0 0 0 0
40 129.49 0 0 0 0 1 0 0 0 0 0 0 0
41 127.78 0 0 0 0 0 1 0 0 0 0 0 0
42 125.29 0 0 0 0 0 0 1 0 0 0 0 0
43 119.02 0 0 0 0 0 0 0 1 0 0 0 0
44 119.96 0 0 0 0 0 0 0 0 1 0 0 0
45 122.86 0 0 0 0 0 0 0 0 0 1 0 0
46 131.89 0 0 0 0 0 0 0 0 0 0 1 0
47 132.73 0 0 0 0 0 0 0 0 0 0 0 1
48 135.01 0 0 0 0 0 0 0 0 0 0 0 0
49 136.71 1 1 0 0 0 0 0 0 0 0 0 0
50 142.73 1 0 1 0 0 0 0 0 0 0 0 0
51 144.43 1 0 0 1 0 0 0 0 0 0 0 0
52 144.93 1 0 0 0 1 0 0 0 0 0 0 0
53 138.75 1 0 0 0 0 1 0 0 0 0 0 0
54 130.22 1 0 0 0 0 0 1 0 0 0 0 0
55 122.19 1 0 0 0 0 0 0 1 0 0 0 0
56 128.40 1 0 0 0 0 0 0 0 1 0 0 0
57 140.43 1 0 0 0 0 0 0 0 0 1 0 0
58 153.50 1 0 0 0 0 0 0 0 0 0 1 0
59 149.33 1 0 0 0 0 0 0 0 0 0 0 1
60 142.97 1 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
127.562 11.748 -4.920 -0.244 4.064 6.170
M5 M6 M7 M8 M9 M10
2.450 -1.524 -7.496 -3.760 -1.076 6.594
M11
2.612
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.4225 -3.6805 0.6255 3.5936 14.3275
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 127.562 2.981 42.786 < 2e-16 ***
X 11.748 2.130 5.516 1.44e-06 ***
M1 -4.920 4.173 -1.179 0.2443
M2 -0.244 4.173 -0.058 0.9536
M3 4.064 4.173 0.974 0.3351
M4 6.170 4.173 1.479 0.1459
M5 2.450 4.173 0.587 0.5600
M6 -1.524 4.173 -0.365 0.7166
M7 -7.496 4.173 -1.796 0.0789 .
M8 -3.760 4.173 -0.901 0.3722
M9 -1.076 4.173 -0.258 0.7977
M10 6.594 4.173 1.580 0.1208
M11 2.612 4.173 0.626 0.5344
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.598 on 47 degrees of freedom
Multiple R-squared: 0.5363, Adjusted R-squared: 0.4179
F-statistic: 4.529 on 12 and 47 DF, p-value: 8.29e-05
> 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,] 0.7358964 0.528207201 0.264103601
[2,] 0.6035398 0.792920351 0.396460175
[3,] 0.4587530 0.917505963 0.541247019
[4,] 0.3755412 0.751082455 0.624458773
[5,] 0.6823680 0.635264080 0.317632040
[6,] 0.9486833 0.102633354 0.051316677
[7,] 0.9965595 0.006881097 0.003440548
[8,] 0.9976291 0.004741859 0.002370930
[9,] 0.9983878 0.003224490 0.001612245
[10,] 0.9970193 0.005961440 0.002980720
[11,] 0.9941342 0.011731608 0.005865804
[12,] 0.9898525 0.020294936 0.010147468
[13,] 0.9855475 0.028905023 0.014452511
[14,] 0.9810901 0.037819731 0.018909866
[15,] 0.9781791 0.043641881 0.021820941
[16,] 0.9763589 0.047282297 0.023641148
[17,] 0.9712314 0.057537186 0.028768593
[18,] 0.9701077 0.059784617 0.029892308
[19,] 0.9821640 0.035671981 0.017835990
[20,] 0.9808790 0.038242020 0.019121010
[21,] 0.9754311 0.049137866 0.024568933
[22,] 0.9539043 0.092191330 0.046095665
[23,] 0.9414536 0.117092715 0.058546357
[24,] 0.9237918 0.152416428 0.076208214
[25,] 0.8826864 0.234627105 0.117313553
[26,] 0.8023236 0.395352703 0.197676352
[27,] 0.7624560 0.475087951 0.237543976
[28,] 0.8212976 0.357404794 0.178702397
[29,] 0.7898589 0.420282119 0.210141059
> postscript(file="/var/www/html/rcomp/tmp/19lut1258718588.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2tfjg1258718588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/368on1258718588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45bsz1258718588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5esew1258718588.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
3.8675 3.7015 4.8835 4.3075 2.9075 3.5715 2.8935 0.2375
9 10 11 12 13 14 15 16
-5.1965 -9.5965 -11.6445 -14.4225 -8.4925 -5.1485 -2.3965 -2.5425
17 18 19 20 21 22 23 24
-0.8925 2.2415 6.7635 14.3275 14.0335 12.6735 4.9655 4.2775
25 26 27 28 29 30 31 32
3.0575 1.6615 1.6235 3.0275 3.2275 2.5015 1.0135 -3.5725
33 34 35 36 37 38 39 40
-7.4065 -8.4065 -3.2845 -0.9625 -0.7525 -3.8785 -5.1665 -4.2425
41 42 43 44 45 46 47 48
-2.2325 -0.7485 -1.0465 -3.8425 -3.6265 -2.2665 2.5555 7.4475
49 50 51 52 53 54 55 56
2.3200 3.6640 1.0560 -0.5500 -3.0100 -7.5660 -9.6240 -7.1500
57 58 59 60
2.1960 7.5960 7.4080 3.6600
> postscript(file="/var/www/html/rcomp/tmp/6np6w1258718588.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 3.8675 NA
1 3.7015 3.8675
2 4.8835 3.7015
3 4.3075 4.8835
4 2.9075 4.3075
5 3.5715 2.9075
6 2.8935 3.5715
7 0.2375 2.8935
8 -5.1965 0.2375
9 -9.5965 -5.1965
10 -11.6445 -9.5965
11 -14.4225 -11.6445
12 -8.4925 -14.4225
13 -5.1485 -8.4925
14 -2.3965 -5.1485
15 -2.5425 -2.3965
16 -0.8925 -2.5425
17 2.2415 -0.8925
18 6.7635 2.2415
19 14.3275 6.7635
20 14.0335 14.3275
21 12.6735 14.0335
22 4.9655 12.6735
23 4.2775 4.9655
24 3.0575 4.2775
25 1.6615 3.0575
26 1.6235 1.6615
27 3.0275 1.6235
28 3.2275 3.0275
29 2.5015 3.2275
30 1.0135 2.5015
31 -3.5725 1.0135
32 -7.4065 -3.5725
33 -8.4065 -7.4065
34 -3.2845 -8.4065
35 -0.9625 -3.2845
36 -0.7525 -0.9625
37 -3.8785 -0.7525
38 -5.1665 -3.8785
39 -4.2425 -5.1665
40 -2.2325 -4.2425
41 -0.7485 -2.2325
42 -1.0465 -0.7485
43 -3.8425 -1.0465
44 -3.6265 -3.8425
45 -2.2665 -3.6265
46 2.5555 -2.2665
47 7.4475 2.5555
48 2.3200 7.4475
49 3.6640 2.3200
50 1.0560 3.6640
51 -0.5500 1.0560
52 -3.0100 -0.5500
53 -7.5660 -3.0100
54 -9.6240 -7.5660
55 -7.1500 -9.6240
56 2.1960 -7.1500
57 7.5960 2.1960
58 7.4080 7.5960
59 3.6600 7.4080
60 NA 3.6600
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.7015 3.8675
[2,] 4.8835 3.7015
[3,] 4.3075 4.8835
[4,] 2.9075 4.3075
[5,] 3.5715 2.9075
[6,] 2.8935 3.5715
[7,] 0.2375 2.8935
[8,] -5.1965 0.2375
[9,] -9.5965 -5.1965
[10,] -11.6445 -9.5965
[11,] -14.4225 -11.6445
[12,] -8.4925 -14.4225
[13,] -5.1485 -8.4925
[14,] -2.3965 -5.1485
[15,] -2.5425 -2.3965
[16,] -0.8925 -2.5425
[17,] 2.2415 -0.8925
[18,] 6.7635 2.2415
[19,] 14.3275 6.7635
[20,] 14.0335 14.3275
[21,] 12.6735 14.0335
[22,] 4.9655 12.6735
[23,] 4.2775 4.9655
[24,] 3.0575 4.2775
[25,] 1.6615 3.0575
[26,] 1.6235 1.6615
[27,] 3.0275 1.6235
[28,] 3.2275 3.0275
[29,] 2.5015 3.2275
[30,] 1.0135 2.5015
[31,] -3.5725 1.0135
[32,] -7.4065 -3.5725
[33,] -8.4065 -7.4065
[34,] -3.2845 -8.4065
[35,] -0.9625 -3.2845
[36,] -0.7525 -0.9625
[37,] -3.8785 -0.7525
[38,] -5.1665 -3.8785
[39,] -4.2425 -5.1665
[40,] -2.2325 -4.2425
[41,] -0.7485 -2.2325
[42,] -1.0465 -0.7485
[43,] -3.8425 -1.0465
[44,] -3.6265 -3.8425
[45,] -2.2665 -3.6265
[46,] 2.5555 -2.2665
[47,] 7.4475 2.5555
[48,] 2.3200 7.4475
[49,] 3.6640 2.3200
[50,] 1.0560 3.6640
[51,] -0.5500 1.0560
[52,] -3.0100 -0.5500
[53,] -7.5660 -3.0100
[54,] -9.6240 -7.5660
[55,] -7.1500 -9.6240
[56,] 2.1960 -7.1500
[57,] 7.5960 2.1960
[58,] 7.4080 7.5960
[59,] 3.6600 7.4080
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.7015 3.8675
2 4.8835 3.7015
3 4.3075 4.8835
4 2.9075 4.3075
5 3.5715 2.9075
6 2.8935 3.5715
7 0.2375 2.8935
8 -5.1965 0.2375
9 -9.5965 -5.1965
10 -11.6445 -9.5965
11 -14.4225 -11.6445
12 -8.4925 -14.4225
13 -5.1485 -8.4925
14 -2.3965 -5.1485
15 -2.5425 -2.3965
16 -0.8925 -2.5425
17 2.2415 -0.8925
18 6.7635 2.2415
19 14.3275 6.7635
20 14.0335 14.3275
21 12.6735 14.0335
22 4.9655 12.6735
23 4.2775 4.9655
24 3.0575 4.2775
25 1.6615 3.0575
26 1.6235 1.6615
27 3.0275 1.6235
28 3.2275 3.0275
29 2.5015 3.2275
30 1.0135 2.5015
31 -3.5725 1.0135
32 -7.4065 -3.5725
33 -8.4065 -7.4065
34 -3.2845 -8.4065
35 -0.9625 -3.2845
36 -0.7525 -0.9625
37 -3.8785 -0.7525
38 -5.1665 -3.8785
39 -4.2425 -5.1665
40 -2.2325 -4.2425
41 -0.7485 -2.2325
42 -1.0465 -0.7485
43 -3.8425 -1.0465
44 -3.6265 -3.8425
45 -2.2665 -3.6265
46 2.5555 -2.2665
47 7.4475 2.5555
48 2.3200 7.4475
49 3.6640 2.3200
50 1.0560 3.6640
51 -0.5500 1.0560
52 -3.0100 -0.5500
53 -7.5660 -3.0100
54 -9.6240 -7.5660
55 -7.1500 -9.6240
56 2.1960 -7.1500
57 7.5960 2.1960
58 7.4080 7.5960
59 3.6600 7.4080
> 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/7hvrv1258718588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8bh7d1258718588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jtpq1258718588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1003uf1258718588.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ilss1258718588.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/12cxbn1258718588.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/13z7ql1258718588.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/14pbfz1258718588.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/15q3x51258718588.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/16i2fn1258718588.tab")
+ }
>
> system("convert tmp/19lut1258718588.ps tmp/19lut1258718588.png")
> system("convert tmp/2tfjg1258718588.ps tmp/2tfjg1258718588.png")
> system("convert tmp/368on1258718588.ps tmp/368on1258718588.png")
> system("convert tmp/45bsz1258718588.ps tmp/45bsz1258718588.png")
> system("convert tmp/5esew1258718588.ps tmp/5esew1258718588.png")
> system("convert tmp/6np6w1258718588.ps tmp/6np6w1258718588.png")
> system("convert tmp/7hvrv1258718588.ps tmp/7hvrv1258718588.png")
> system("convert tmp/8bh7d1258718588.ps tmp/8bh7d1258718588.png")
> system("convert tmp/9jtpq1258718588.ps tmp/9jtpq1258718588.png")
> system("convert tmp/1003uf1258718588.ps tmp/1003uf1258718588.png")
>
>
> proc.time()
user system elapsed
2.387 1.553 2.896