R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(0,210907,0,2,0,149061,0,0,0,237213,1,0,0,133131,1,4,0,324799,1,0,0,230964,0,-1,0,236785,1,0,0,344297,1,1,0,174724,1,0,0,174415,1,3,0,223632,1,-1,0,294424,0,4,0,325107,1,3,0,106408,0,1,0,96560,0,0,0,265769,1,-2,0,149112,0,-4,0,152871,0,2,0,362301,1,2,0,183167,0,-4,0,218946,1,2,0,244052,1,2,0,341570,1,0,0,196553,1,-3,0,143246,0,2,0,143756,0,4,0,152299,1,2,0,193339,1,2,0,130585,0,-4,0,112611,1,3,0,148446,1,3,0,182079,0,2,0,243060,1,-1,0,162765,1,-3,0,85574,1,0,0,225060,0,1,0,133328,1,-3,0,100750,1,3,0,101523,1,0,0,243511,1,0,0,152474,1,0,0,132487,1,3,0,317394,0,-3,0,244749,1,0,0,128423,0,2,0,97839,0,-1,1,229242,1,2,1,324598,0,2,1,195838,0,-2,1,254488,0,0,1,92499,1,-2,1,224330,0,0,1,181633,1,6,1,271856,1,-3,1,95227,1,3,1,98146,0,0,1,118612,0,-2,1,65475,1,1,1,108446,0,0,1,121848,0,2,1,76302,1,2,1,98104,0,-3,1,30989,1,-2,1,31774,0,1,1,150580,1,-4,1,59382,0,1,1,84105,0,0),dim=c(4,67),dimnames=list(c('pop','time_in_rfc','gender','total_tests'),1:67))
> y <- array(NA,dim=c(4,67),dimnames=list(c('pop','time_in_rfc','gender','total_tests'),1:67))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '2'
> 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
time_in_rfc pop gender total_tests
1 210907 0 0 2
2 149061 0 0 0
3 237213 0 1 0
4 133131 0 1 4
5 324799 0 1 0
6 230964 0 0 -1
7 236785 0 1 0
8 344297 0 1 1
9 174724 0 1 0
10 174415 0 1 3
11 223632 0 1 -1
12 294424 0 0 4
13 325107 0 1 3
14 106408 0 0 1
15 96560 0 0 0
16 265769 0 1 -2
17 149112 0 0 -4
18 152871 0 0 2
19 362301 0 1 2
20 183167 0 0 -4
21 218946 0 1 2
22 244052 0 1 2
23 341570 0 1 0
24 196553 0 1 -3
25 143246 0 0 2
26 143756 0 0 4
27 152299 0 1 2
28 193339 0 1 2
29 130585 0 0 -4
30 112611 0 1 3
31 148446 0 1 3
32 182079 0 0 2
33 243060 0 1 -1
34 162765 0 1 -3
35 85574 0 1 0
36 225060 0 0 1
37 133328 0 1 -3
38 100750 0 1 3
39 101523 0 1 0
40 243511 0 1 0
41 152474 0 1 0
42 132487 0 1 3
43 317394 0 0 -3
44 244749 0 1 0
45 128423 0 0 2
46 97839 0 0 -1
47 229242 1 1 2
48 324598 1 0 2
49 195838 1 0 -2
50 254488 1 0 0
51 92499 1 1 -2
52 224330 1 0 0
53 181633 1 1 6
54 271856 1 1 -3
55 95227 1 1 3
56 98146 1 0 0
57 118612 1 0 -2
58 65475 1 1 1
59 108446 1 0 0
60 121848 1 0 2
61 76302 1 1 2
62 98104 1 0 -3
63 30989 1 1 -2
64 31774 1 0 1
65 150580 1 1 -4
66 59382 1 0 1
67 84105 1 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pop gender total_tests
182305.5 -52039.0 19860.2 -431.8
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-120001 -53526 -21820 42667 195195
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 182305.5 16950.7 10.755 6.78e-16 ***
pop -52039.0 21029.9 -2.475 0.0160 *
gender 19860.2 19758.6 1.005 0.3187
total_tests -431.8 4271.8 -0.101 0.9198
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 78260 on 63 degrees of freedom
Multiple R-squared: 0.1171, Adjusted R-squared: 0.0751
F-statistic: 2.786 on 3 and 63 DF, p-value: 0.04789
> 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,] 0.4049993 0.80999850 0.59500075
[2,] 0.5326049 0.93479013 0.46739507
[3,] 0.5688047 0.86239064 0.43119532
[4,] 0.4438595 0.88771905 0.55614047
[5,] 0.3644864 0.72897276 0.63551362
[6,] 0.5139003 0.97219932 0.48609966
[7,] 0.5799116 0.84017681 0.42008840
[8,] 0.6239093 0.75218138 0.37609069
[9,] 0.6408047 0.71839051 0.35919525
[10,] 0.5765855 0.84682908 0.42341454
[11,] 0.4934521 0.98690416 0.50654792
[12,] 0.4137590 0.82751808 0.58624096
[13,] 0.5762880 0.84742409 0.42371204
[14,] 0.4971522 0.99430444 0.50284778
[15,] 0.4370291 0.87405818 0.56297091
[16,] 0.3806092 0.76121849 0.61939076
[17,] 0.5148961 0.97020780 0.48510390
[18,] 0.4648058 0.92961151 0.53519424
[19,] 0.4021889 0.80437785 0.59781108
[20,] 0.3412172 0.68243447 0.65878277
[21,] 0.3560936 0.71218710 0.64390645
[22,] 0.3153997 0.63079950 0.68460025
[23,] 0.2731629 0.54632581 0.72683709
[24,] 0.3349493 0.66989854 0.66505073
[25,] 0.3161429 0.63228577 0.68385712
[26,] 0.2547459 0.50949184 0.74525408
[27,] 0.2238676 0.44773529 0.77613236
[28,] 0.1969630 0.39392606 0.80303697
[29,] 0.2669936 0.53398728 0.73300636
[30,] 0.2342691 0.46853825 0.76573087
[31,] 0.2162299 0.43245988 0.78377006
[32,] 0.2393431 0.47868629 0.76065685
[33,] 0.2613976 0.52279519 0.73860241
[34,] 0.2254224 0.45084484 0.77457758
[35,] 0.1860021 0.37200416 0.81399792
[36,] 0.1655815 0.33116291 0.83441855
[37,] 0.2695021 0.53900428 0.73049786
[38,] 0.2830287 0.56605748 0.71697126
[39,] 0.2289705 0.45794100 0.77102950
[40,] 0.1905836 0.38116722 0.80941639
[41,] 0.1883069 0.37661385 0.81169307
[42,] 0.4375377 0.87507539 0.56246231
[43,] 0.4112934 0.82258685 0.58870658
[44,] 0.5675805 0.86483908 0.43241954
[45,] 0.5738998 0.85220037 0.42610018
[46,] 0.7088297 0.58234066 0.29117033
[47,] 0.7991311 0.40173781 0.20086891
[48,] 0.9830898 0.03382045 0.01691023
[49,] 0.9772145 0.04557108 0.02278554
[50,] 0.9564813 0.08703731 0.04351865
[51,] 0.9202262 0.15954767 0.07977384
[52,] 0.8614940 0.27701210 0.13850605
[53,] 0.7726161 0.45476780 0.22738390
[54,] 0.7706995 0.45860100 0.22930050
> postscript(file="/var/www/rcomp/tmp/1zvmk1323614130.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/rcomp/tmp/2j6v61323614130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3wmtr1323614130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4djv41323614130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5yhif1323614130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 67
Frequency = 1
1 2 3 4 5 6
29465.0949 -33244.4665 35047.3439 -67307.5332 122633.3439 48226.7528
7 8 9 10 11 12
34619.3439 142563.1246 -27441.6561 -26455.3140 21034.5632 113845.6564
13 14 15 16 17 18
124236.6860 -75465.6858 -85745.4665 62739.7825 -34920.5893 -28570.9051
19 20 21 22 23 24
160998.9053 -865.5893 17643.9053 42749.9053 139404.3439 -6907.9982
25 26 27 28 29 30
-38195.9051 -36822.3436 -49003.0947 -7963.0947 -53447.5893 -88259.3140
31 32 33 34 35 36
-52424.3140 637.0949 40462.5632 -40695.9982 -116591.6561 43186.3142
37 38 39 40 41 42
-70132.9982 -100120.3140 -100642.6561 41345.3439 -49691.6561 -68383.3140
43 44 45 46 47 48
133793.1914 42583.3439 -53018.9051 -84898.2472 79978.9025 195195.0921
49 50 51 52 53 54
64707.9693 124221.5307 -58491.2203 94063.5307 34097.0254 120433.9990
55 56 57 58 59 60
-53604.3168 -32120.4693 -12518.0307 -84219.8782 -21820.4693 -7554.9079
61 62 63 64 65 66
-72961.0975 -33457.8114 -120001.2203 -98060.6886 -1273.7817 -70452.6886
67
-46161.4693
> postscript(file="/var/www/rcomp/tmp/6gpyg1323614130.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 = 67
Frequency = 1
lag(myerror, k = 1) myerror
0 29465.0949 NA
1 -33244.4665 29465.0949
2 35047.3439 -33244.4665
3 -67307.5332 35047.3439
4 122633.3439 -67307.5332
5 48226.7528 122633.3439
6 34619.3439 48226.7528
7 142563.1246 34619.3439
8 -27441.6561 142563.1246
9 -26455.3140 -27441.6561
10 21034.5632 -26455.3140
11 113845.6564 21034.5632
12 124236.6860 113845.6564
13 -75465.6858 124236.6860
14 -85745.4665 -75465.6858
15 62739.7825 -85745.4665
16 -34920.5893 62739.7825
17 -28570.9051 -34920.5893
18 160998.9053 -28570.9051
19 -865.5893 160998.9053
20 17643.9053 -865.5893
21 42749.9053 17643.9053
22 139404.3439 42749.9053
23 -6907.9982 139404.3439
24 -38195.9051 -6907.9982
25 -36822.3436 -38195.9051
26 -49003.0947 -36822.3436
27 -7963.0947 -49003.0947
28 -53447.5893 -7963.0947
29 -88259.3140 -53447.5893
30 -52424.3140 -88259.3140
31 637.0949 -52424.3140
32 40462.5632 637.0949
33 -40695.9982 40462.5632
34 -116591.6561 -40695.9982
35 43186.3142 -116591.6561
36 -70132.9982 43186.3142
37 -100120.3140 -70132.9982
38 -100642.6561 -100120.3140
39 41345.3439 -100642.6561
40 -49691.6561 41345.3439
41 -68383.3140 -49691.6561
42 133793.1914 -68383.3140
43 42583.3439 133793.1914
44 -53018.9051 42583.3439
45 -84898.2472 -53018.9051
46 79978.9025 -84898.2472
47 195195.0921 79978.9025
48 64707.9693 195195.0921
49 124221.5307 64707.9693
50 -58491.2203 124221.5307
51 94063.5307 -58491.2203
52 34097.0254 94063.5307
53 120433.9990 34097.0254
54 -53604.3168 120433.9990
55 -32120.4693 -53604.3168
56 -12518.0307 -32120.4693
57 -84219.8782 -12518.0307
58 -21820.4693 -84219.8782
59 -7554.9079 -21820.4693
60 -72961.0975 -7554.9079
61 -33457.8114 -72961.0975
62 -120001.2203 -33457.8114
63 -98060.6886 -120001.2203
64 -1273.7817 -98060.6886
65 -70452.6886 -1273.7817
66 -46161.4693 -70452.6886
67 NA -46161.4693
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -33244.4665 29465.0949
[2,] 35047.3439 -33244.4665
[3,] -67307.5332 35047.3439
[4,] 122633.3439 -67307.5332
[5,] 48226.7528 122633.3439
[6,] 34619.3439 48226.7528
[7,] 142563.1246 34619.3439
[8,] -27441.6561 142563.1246
[9,] -26455.3140 -27441.6561
[10,] 21034.5632 -26455.3140
[11,] 113845.6564 21034.5632
[12,] 124236.6860 113845.6564
[13,] -75465.6858 124236.6860
[14,] -85745.4665 -75465.6858
[15,] 62739.7825 -85745.4665
[16,] -34920.5893 62739.7825
[17,] -28570.9051 -34920.5893
[18,] 160998.9053 -28570.9051
[19,] -865.5893 160998.9053
[20,] 17643.9053 -865.5893
[21,] 42749.9053 17643.9053
[22,] 139404.3439 42749.9053
[23,] -6907.9982 139404.3439
[24,] -38195.9051 -6907.9982
[25,] -36822.3436 -38195.9051
[26,] -49003.0947 -36822.3436
[27,] -7963.0947 -49003.0947
[28,] -53447.5893 -7963.0947
[29,] -88259.3140 -53447.5893
[30,] -52424.3140 -88259.3140
[31,] 637.0949 -52424.3140
[32,] 40462.5632 637.0949
[33,] -40695.9982 40462.5632
[34,] -116591.6561 -40695.9982
[35,] 43186.3142 -116591.6561
[36,] -70132.9982 43186.3142
[37,] -100120.3140 -70132.9982
[38,] -100642.6561 -100120.3140
[39,] 41345.3439 -100642.6561
[40,] -49691.6561 41345.3439
[41,] -68383.3140 -49691.6561
[42,] 133793.1914 -68383.3140
[43,] 42583.3439 133793.1914
[44,] -53018.9051 42583.3439
[45,] -84898.2472 -53018.9051
[46,] 79978.9025 -84898.2472
[47,] 195195.0921 79978.9025
[48,] 64707.9693 195195.0921
[49,] 124221.5307 64707.9693
[50,] -58491.2203 124221.5307
[51,] 94063.5307 -58491.2203
[52,] 34097.0254 94063.5307
[53,] 120433.9990 34097.0254
[54,] -53604.3168 120433.9990
[55,] -32120.4693 -53604.3168
[56,] -12518.0307 -32120.4693
[57,] -84219.8782 -12518.0307
[58,] -21820.4693 -84219.8782
[59,] -7554.9079 -21820.4693
[60,] -72961.0975 -7554.9079
[61,] -33457.8114 -72961.0975
[62,] -120001.2203 -33457.8114
[63,] -98060.6886 -120001.2203
[64,] -1273.7817 -98060.6886
[65,] -70452.6886 -1273.7817
[66,] -46161.4693 -70452.6886
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -33244.4665 29465.0949
2 35047.3439 -33244.4665
3 -67307.5332 35047.3439
4 122633.3439 -67307.5332
5 48226.7528 122633.3439
6 34619.3439 48226.7528
7 142563.1246 34619.3439
8 -27441.6561 142563.1246
9 -26455.3140 -27441.6561
10 21034.5632 -26455.3140
11 113845.6564 21034.5632
12 124236.6860 113845.6564
13 -75465.6858 124236.6860
14 -85745.4665 -75465.6858
15 62739.7825 -85745.4665
16 -34920.5893 62739.7825
17 -28570.9051 -34920.5893
18 160998.9053 -28570.9051
19 -865.5893 160998.9053
20 17643.9053 -865.5893
21 42749.9053 17643.9053
22 139404.3439 42749.9053
23 -6907.9982 139404.3439
24 -38195.9051 -6907.9982
25 -36822.3436 -38195.9051
26 -49003.0947 -36822.3436
27 -7963.0947 -49003.0947
28 -53447.5893 -7963.0947
29 -88259.3140 -53447.5893
30 -52424.3140 -88259.3140
31 637.0949 -52424.3140
32 40462.5632 637.0949
33 -40695.9982 40462.5632
34 -116591.6561 -40695.9982
35 43186.3142 -116591.6561
36 -70132.9982 43186.3142
37 -100120.3140 -70132.9982
38 -100642.6561 -100120.3140
39 41345.3439 -100642.6561
40 -49691.6561 41345.3439
41 -68383.3140 -49691.6561
42 133793.1914 -68383.3140
43 42583.3439 133793.1914
44 -53018.9051 42583.3439
45 -84898.2472 -53018.9051
46 79978.9025 -84898.2472
47 195195.0921 79978.9025
48 64707.9693 195195.0921
49 124221.5307 64707.9693
50 -58491.2203 124221.5307
51 94063.5307 -58491.2203
52 34097.0254 94063.5307
53 120433.9990 34097.0254
54 -53604.3168 120433.9990
55 -32120.4693 -53604.3168
56 -12518.0307 -32120.4693
57 -84219.8782 -12518.0307
58 -21820.4693 -84219.8782
59 -7554.9079 -21820.4693
60 -72961.0975 -7554.9079
61 -33457.8114 -72961.0975
62 -120001.2203 -33457.8114
63 -98060.6886 -120001.2203
64 -1273.7817 -98060.6886
65 -70452.6886 -1273.7817
66 -46161.4693 -70452.6886
> 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/75mdh1323614130.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/rcomp/tmp/8nyim1323614130.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/rcomp/tmp/9t57d1323614130.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/rcomp/tmp/10b2k81323614130.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/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11pez31323614130.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12xv7w1323614130.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13dqbt1323614130.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14gaal1323614130.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/15xn3k1323614130.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/16ml7f1323614130.tab")
+ }
>
> try(system("convert tmp/1zvmk1323614130.ps tmp/1zvmk1323614130.png",intern=TRUE))
character(0)
> try(system("convert tmp/2j6v61323614130.ps tmp/2j6v61323614130.png",intern=TRUE))
character(0)
> try(system("convert tmp/3wmtr1323614130.ps tmp/3wmtr1323614130.png",intern=TRUE))
character(0)
> try(system("convert tmp/4djv41323614130.ps tmp/4djv41323614130.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yhif1323614130.ps tmp/5yhif1323614130.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gpyg1323614130.ps tmp/6gpyg1323614130.png",intern=TRUE))
character(0)
> try(system("convert tmp/75mdh1323614130.ps tmp/75mdh1323614130.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nyim1323614130.ps tmp/8nyim1323614130.png",intern=TRUE))
character(0)
> try(system("convert tmp/9t57d1323614130.ps tmp/9t57d1323614130.png",intern=TRUE))
character(0)
> try(system("convert tmp/10b2k81323614130.ps tmp/10b2k81323614130.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.732 0.760 6.575