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.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> 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 = '4'
> 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
total_tests pop time_in_rfc gender
1 2 0 210907 0
2 0 0 149061 0
3 0 0 237213 1
4 4 0 133131 1
5 0 0 324799 1
6 -1 0 230964 0
7 0 0 236785 1
8 1 0 344297 1
9 0 0 174724 1
10 3 0 174415 1
11 -1 0 223632 1
12 4 0 294424 0
13 3 0 325107 1
14 1 0 106408 0
15 0 0 96560 0
16 -2 0 265769 1
17 -4 0 149112 0
18 2 0 152871 0
19 2 0 362301 1
20 -4 0 183167 0
21 2 0 218946 1
22 2 0 244052 1
23 0 0 341570 1
24 -3 0 196553 1
25 2 0 143246 0
26 4 0 143756 0
27 2 0 152299 1
28 2 0 193339 1
29 -4 0 130585 0
30 3 0 112611 1
31 3 0 148446 1
32 2 0 182079 0
33 -1 0 243060 1
34 -3 0 162765 1
35 0 0 85574 1
36 1 0 225060 0
37 -3 0 133328 1
38 3 0 100750 1
39 0 0 101523 1
40 0 0 243511 1
41 0 0 152474 1
42 3 0 132487 1
43 -3 0 317394 0
44 0 0 244749 1
45 2 0 128423 0
46 -1 0 97839 0
47 2 1 229242 1
48 2 1 324598 0
49 -2 1 195838 0
50 0 1 254488 0
51 -2 1 92499 1
52 0 1 224330 0
53 6 1 181633 1
54 -3 1 271856 1
55 3 1 95227 1
56 0 1 98146 0
57 -2 1 118612 0
58 1 1 65475 1
59 0 1 108446 0
60 2 1 121848 0
61 2 1 76302 1
62 -3 1 98104 0
63 -2 1 30989 1
64 1 1 31774 0
65 -4 1 150580 1
66 1 1 59382 0
67 0 1 84105 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pop time_in_rfc gender
2.646e-01 -3.270e-01 -3.755e-07 4.893e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.3704 -1.4453 0.1031 1.7864 5.6413
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.646e-01 8.412e-01 0.315 0.754
pop -3.270e-01 6.483e-01 -0.504 0.616
time_in_rfc -3.755e-07 3.715e-06 -0.101 0.920
gender 4.893e-01 5.841e-01 0.838 0.405
Residual standard error: 2.308 on 63 degrees of freedom
Multiple R-squared: 0.01785, Adjusted R-squared: -0.02892
F-statistic: 0.3817 on 3 and 63 DF, p-value: 0.7665
> 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,] 0.3435247 0.6870494 0.6564753
[2,] 0.2627954 0.5255908 0.7372046
[3,] 0.2181812 0.4363624 0.7818188
[4,] 0.1667827 0.3335654 0.8332173
[5,] 0.1674944 0.3349887 0.8325056
[6,] 0.3598532 0.7197064 0.6401468
[7,] 0.3542154 0.7084307 0.6457846
[8,] 0.2632217 0.5264434 0.7367783
[9,] 0.1987435 0.3974870 0.8012565
[10,] 0.2507412 0.5014824 0.7492588
[11,] 0.4847548 0.9695096 0.5152452
[12,] 0.4410157 0.8820314 0.5589843
[13,] 0.3747969 0.7495939 0.6252031
[14,] 0.5561450 0.8877100 0.4438550
[15,] 0.4960401 0.9920801 0.5039599
[16,] 0.4363595 0.8727189 0.5636405
[17,] 0.3717722 0.7435444 0.6282278
[18,] 0.4772726 0.9545452 0.5227274
[19,] 0.4477474 0.8954949 0.5522526
[20,] 0.5624136 0.8751727 0.4375864
[21,] 0.5124406 0.9751188 0.4875594
[22,] 0.4623314 0.9246629 0.5376686
[23,] 0.6164447 0.7671105 0.3835553
[24,] 0.6102757 0.7794486 0.3897243
[25,] 0.6078760 0.7842480 0.3921240
[26,] 0.5875509 0.8248981 0.4124491
[27,] 0.5454743 0.9090513 0.4545257
[28,] 0.6344846 0.7310308 0.3655154
[29,] 0.5670462 0.8659076 0.4329538
[30,] 0.5073485 0.9853029 0.4926515
[31,] 0.5958777 0.8082446 0.4041223
[32,] 0.5969534 0.8060931 0.4030466
[33,] 0.5262255 0.9475490 0.4737745
[34,] 0.4537392 0.9074783 0.5462608
[35,] 0.3835601 0.7671201 0.6164399
[36,] 0.3915867 0.7831734 0.6084133
[37,] 0.4261274 0.8522548 0.5738726
[38,] 0.3582015 0.7164029 0.6417985
[39,] 0.3325500 0.6651000 0.6674500
[40,] 0.2692523 0.5385047 0.7307477
[41,] 0.2247357 0.4494714 0.7752643
[42,] 0.2085715 0.4171431 0.7914285
[43,] 0.1934529 0.3869059 0.8065471
[44,] 0.1421310 0.2842620 0.8578690
[45,] 0.1392597 0.2785193 0.8607403
[46,] 0.0987560 0.1975120 0.9012440
[47,] 0.5539054 0.8921893 0.4460946
[48,] 0.4946343 0.9892686 0.5053657
[49,] 0.6235548 0.7528904 0.3764452
[50,] 0.5109381 0.9781238 0.4890619
[51,] 0.4347389 0.8694778 0.5652611
[52,] 0.3634154 0.7268307 0.6365846
[53,] 0.2407826 0.4815651 0.7592174
[54,] 0.2915011 0.5830022 0.7084989
> postscript(file="/var/www/rcomp/tmp/1pb1n1323612886.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/2fj1b1323612886.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3g0vr1323612886.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/47bbb1323612886.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/50mz01323612886.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
1.81457084 -0.20865329 -0.66481813 3.29609746 -0.63192823 -1.17789745
7 8 9 10 11 12
-0.66497885 0.37539357 -0.68828372 2.31160025 -1.66991801 3.84593277
13 14 15 16 17 18
2.36818743 0.77532985 -0.22836823 -2.65409491 -4.20863414 1.79277742
19 20 21 22 23 24
1.38215435 -4.19584596 1.32832233 1.33775002 -0.62563046 -3.68008659
25 26 27 28 29 30
1.78916308 3.78935460 1.30329535 1.31870650 -4.21559132 2.28839189
31 32 33 34 35 36
2.30184848 1.80374548 -1.66262249 -3.69277451 -0.72176093 0.81988551
37 38 39 40 41 42
-3.70382856 2.28393790 -0.71577183 -0.66245313 -0.69663894 2.29585563
43 44 45 46 47 48
-3.14544164 -0.66198825 1.78359682 -1.22788795 1.65916669 2.18424162
49 50 51 52 53 54
-1.86410975 0.15791423 -2.39218243 0.14658943 5.64128877 -3.32483110
55 56 57 58 59 60
2.60884198 0.09920539 -1.89310931 0.59766964 0.10307320 2.10810585
61 62 63 64 65 66
1.60173535 -2.90081039 -2.41528039 1.07428167 -4.37037211 1.08464890
67
0.09393277
> postscript(file="/var/www/rcomp/tmp/67sd51323612886.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 1.81457084 NA
1 -0.20865329 1.81457084
2 -0.66481813 -0.20865329
3 3.29609746 -0.66481813
4 -0.63192823 3.29609746
5 -1.17789745 -0.63192823
6 -0.66497885 -1.17789745
7 0.37539357 -0.66497885
8 -0.68828372 0.37539357
9 2.31160025 -0.68828372
10 -1.66991801 2.31160025
11 3.84593277 -1.66991801
12 2.36818743 3.84593277
13 0.77532985 2.36818743
14 -0.22836823 0.77532985
15 -2.65409491 -0.22836823
16 -4.20863414 -2.65409491
17 1.79277742 -4.20863414
18 1.38215435 1.79277742
19 -4.19584596 1.38215435
20 1.32832233 -4.19584596
21 1.33775002 1.32832233
22 -0.62563046 1.33775002
23 -3.68008659 -0.62563046
24 1.78916308 -3.68008659
25 3.78935460 1.78916308
26 1.30329535 3.78935460
27 1.31870650 1.30329535
28 -4.21559132 1.31870650
29 2.28839189 -4.21559132
30 2.30184848 2.28839189
31 1.80374548 2.30184848
32 -1.66262249 1.80374548
33 -3.69277451 -1.66262249
34 -0.72176093 -3.69277451
35 0.81988551 -0.72176093
36 -3.70382856 0.81988551
37 2.28393790 -3.70382856
38 -0.71577183 2.28393790
39 -0.66245313 -0.71577183
40 -0.69663894 -0.66245313
41 2.29585563 -0.69663894
42 -3.14544164 2.29585563
43 -0.66198825 -3.14544164
44 1.78359682 -0.66198825
45 -1.22788795 1.78359682
46 1.65916669 -1.22788795
47 2.18424162 1.65916669
48 -1.86410975 2.18424162
49 0.15791423 -1.86410975
50 -2.39218243 0.15791423
51 0.14658943 -2.39218243
52 5.64128877 0.14658943
53 -3.32483110 5.64128877
54 2.60884198 -3.32483110
55 0.09920539 2.60884198
56 -1.89310931 0.09920539
57 0.59766964 -1.89310931
58 0.10307320 0.59766964
59 2.10810585 0.10307320
60 1.60173535 2.10810585
61 -2.90081039 1.60173535
62 -2.41528039 -2.90081039
63 1.07428167 -2.41528039
64 -4.37037211 1.07428167
65 1.08464890 -4.37037211
66 0.09393277 1.08464890
67 NA 0.09393277
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.20865329 1.81457084
[2,] -0.66481813 -0.20865329
[3,] 3.29609746 -0.66481813
[4,] -0.63192823 3.29609746
[5,] -1.17789745 -0.63192823
[6,] -0.66497885 -1.17789745
[7,] 0.37539357 -0.66497885
[8,] -0.68828372 0.37539357
[9,] 2.31160025 -0.68828372
[10,] -1.66991801 2.31160025
[11,] 3.84593277 -1.66991801
[12,] 2.36818743 3.84593277
[13,] 0.77532985 2.36818743
[14,] -0.22836823 0.77532985
[15,] -2.65409491 -0.22836823
[16,] -4.20863414 -2.65409491
[17,] 1.79277742 -4.20863414
[18,] 1.38215435 1.79277742
[19,] -4.19584596 1.38215435
[20,] 1.32832233 -4.19584596
[21,] 1.33775002 1.32832233
[22,] -0.62563046 1.33775002
[23,] -3.68008659 -0.62563046
[24,] 1.78916308 -3.68008659
[25,] 3.78935460 1.78916308
[26,] 1.30329535 3.78935460
[27,] 1.31870650 1.30329535
[28,] -4.21559132 1.31870650
[29,] 2.28839189 -4.21559132
[30,] 2.30184848 2.28839189
[31,] 1.80374548 2.30184848
[32,] -1.66262249 1.80374548
[33,] -3.69277451 -1.66262249
[34,] -0.72176093 -3.69277451
[35,] 0.81988551 -0.72176093
[36,] -3.70382856 0.81988551
[37,] 2.28393790 -3.70382856
[38,] -0.71577183 2.28393790
[39,] -0.66245313 -0.71577183
[40,] -0.69663894 -0.66245313
[41,] 2.29585563 -0.69663894
[42,] -3.14544164 2.29585563
[43,] -0.66198825 -3.14544164
[44,] 1.78359682 -0.66198825
[45,] -1.22788795 1.78359682
[46,] 1.65916669 -1.22788795
[47,] 2.18424162 1.65916669
[48,] -1.86410975 2.18424162
[49,] 0.15791423 -1.86410975
[50,] -2.39218243 0.15791423
[51,] 0.14658943 -2.39218243
[52,] 5.64128877 0.14658943
[53,] -3.32483110 5.64128877
[54,] 2.60884198 -3.32483110
[55,] 0.09920539 2.60884198
[56,] -1.89310931 0.09920539
[57,] 0.59766964 -1.89310931
[58,] 0.10307320 0.59766964
[59,] 2.10810585 0.10307320
[60,] 1.60173535 2.10810585
[61,] -2.90081039 1.60173535
[62,] -2.41528039 -2.90081039
[63,] 1.07428167 -2.41528039
[64,] -4.37037211 1.07428167
[65,] 1.08464890 -4.37037211
[66,] 0.09393277 1.08464890
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.20865329 1.81457084
2 -0.66481813 -0.20865329
3 3.29609746 -0.66481813
4 -0.63192823 3.29609746
5 -1.17789745 -0.63192823
6 -0.66497885 -1.17789745
7 0.37539357 -0.66497885
8 -0.68828372 0.37539357
9 2.31160025 -0.68828372
10 -1.66991801 2.31160025
11 3.84593277 -1.66991801
12 2.36818743 3.84593277
13 0.77532985 2.36818743
14 -0.22836823 0.77532985
15 -2.65409491 -0.22836823
16 -4.20863414 -2.65409491
17 1.79277742 -4.20863414
18 1.38215435 1.79277742
19 -4.19584596 1.38215435
20 1.32832233 -4.19584596
21 1.33775002 1.32832233
22 -0.62563046 1.33775002
23 -3.68008659 -0.62563046
24 1.78916308 -3.68008659
25 3.78935460 1.78916308
26 1.30329535 3.78935460
27 1.31870650 1.30329535
28 -4.21559132 1.31870650
29 2.28839189 -4.21559132
30 2.30184848 2.28839189
31 1.80374548 2.30184848
32 -1.66262249 1.80374548
33 -3.69277451 -1.66262249
34 -0.72176093 -3.69277451
35 0.81988551 -0.72176093
36 -3.70382856 0.81988551
37 2.28393790 -3.70382856
38 -0.71577183 2.28393790
39 -0.66245313 -0.71577183
40 -0.69663894 -0.66245313
41 2.29585563 -0.69663894
42 -3.14544164 2.29585563
43 -0.66198825 -3.14544164
44 1.78359682 -0.66198825
45 -1.22788795 1.78359682
46 1.65916669 -1.22788795
47 2.18424162 1.65916669
48 -1.86410975 2.18424162
49 0.15791423 -1.86410975
50 -2.39218243 0.15791423
51 0.14658943 -2.39218243
52 5.64128877 0.14658943
53 -3.32483110 5.64128877
54 2.60884198 -3.32483110
55 0.09920539 2.60884198
56 -1.89310931 0.09920539
57 0.59766964 -1.89310931
58 0.10307320 0.59766964
59 2.10810585 0.10307320
60 1.60173535 2.10810585
61 -2.90081039 1.60173535
62 -2.41528039 -2.90081039
63 1.07428167 -2.41528039
64 -4.37037211 1.07428167
65 1.08464890 -4.37037211
66 0.09393277 1.08464890
> 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/7b9ja1323612886.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/8k43a1323612886.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/9f4yu1323612886.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/10oz551323612886.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11z1x71323612886.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12c8id1323612886.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13vhd31323612886.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14ar8m1323612886.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/151swl1323612886.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/16wzwb1323612886.tab")
+ }
>
> try(system("convert tmp/1pb1n1323612886.ps tmp/1pb1n1323612886.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fj1b1323612886.ps tmp/2fj1b1323612886.png",intern=TRUE))
character(0)
> try(system("convert tmp/3g0vr1323612886.ps tmp/3g0vr1323612886.png",intern=TRUE))
character(0)
> try(system("convert tmp/47bbb1323612886.ps tmp/47bbb1323612886.png",intern=TRUE))
character(0)
> try(system("convert tmp/50mz01323612886.ps tmp/50mz01323612886.png",intern=TRUE))
character(0)
> try(system("convert tmp/67sd51323612886.ps tmp/67sd51323612886.png",intern=TRUE))
character(0)
> try(system("convert tmp/7b9ja1323612886.ps tmp/7b9ja1323612886.png",intern=TRUE))
character(0)
> try(system("convert tmp/8k43a1323612886.ps tmp/8k43a1323612886.png",intern=TRUE))
character(0)
> try(system("convert tmp/9f4yu1323612886.ps tmp/9f4yu1323612886.png",intern=TRUE))
character(0)
> try(system("convert tmp/10oz551323612886.ps tmp/10oz551323612886.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.704 0.644 4.412