R version 2.13.0 (2011-04-13)
Copyright (C) 2011 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.
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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 = '1'
> 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
pop time_in_rfc gender total_tests
1 0 210907 0 2
2 0 149061 0 0
3 0 237213 1 0
4 0 133131 1 4
5 0 324799 1 0
6 0 230964 0 -1
7 0 236785 1 0
8 0 344297 1 1
9 0 174724 1 0
10 0 174415 1 3
11 0 223632 1 -1
12 0 294424 0 4
13 0 325107 1 3
14 0 106408 0 1
15 0 96560 0 0
16 0 265769 1 -2
17 0 149112 0 -4
18 0 152871 0 2
19 0 362301 1 2
20 0 183167 0 -4
21 0 218946 1 2
22 0 244052 1 2
23 0 341570 1 0
24 0 196553 1 -3
25 0 143246 0 2
26 0 143756 0 4
27 0 152299 1 2
28 0 193339 1 2
29 0 130585 0 -4
30 0 112611 1 3
31 0 148446 1 3
32 0 182079 0 2
33 0 243060 1 -1
34 0 162765 1 -3
35 0 85574 1 0
36 0 225060 0 1
37 0 133328 1 -3
38 0 100750 1 3
39 0 101523 1 0
40 0 243511 1 0
41 0 152474 1 0
42 0 132487 1 3
43 0 317394 0 -3
44 0 244749 1 0
45 0 128423 0 2
46 0 97839 0 -1
47 1 229242 1 2
48 1 324598 0 2
49 1 195838 0 -2
50 1 254488 0 0
51 1 92499 1 -2
52 1 224330 0 0
53 1 181633 1 6
54 1 271856 1 -3
55 1 95227 1 3
56 1 98146 0 0
57 1 118612 0 -2
58 1 65475 1 1
59 1 108446 0 0
60 1 121848 0 2
61 1 76302 1 2
62 1 98104 0 -3
63 1 30989 1 -2
64 1 31774 0 1
65 1 150580 1 -4
66 1 59382 0 1
67 1 84105 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) time_in_rfc gender total_tests
6.883e-01 -1.702e-06 -1.213e-01 -1.230e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.5340 -0.3326 -0.1656 0.4580 0.8889
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.883e-01 1.383e-01 4.975 5.32e-06 ***
time_in_rfc -1.702e-06 6.879e-07 -2.475 0.016 *
gender -1.213e-01 1.129e-01 -1.074 0.287
total_tests -1.230e-02 2.439e-02 -0.504 0.616
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4476 on 63 degrees of freedom
Multiple R-squared: 0.1246, Adjusted R-squared: 0.08288
F-statistic: 2.988 on 3 and 63 DF, p-value: 0.03763
> 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 0.000000e+00 1.000000e+00
[2,] 0 0.000000e+00 1.000000e+00
[3,] 0 0.000000e+00 1.000000e+00
[4,] 0 0.000000e+00 1.000000e+00
[5,] 0 0.000000e+00 1.000000e+00
[6,] 0 0.000000e+00 1.000000e+00
[7,] 0 0.000000e+00 1.000000e+00
[8,] 0 0.000000e+00 1.000000e+00
[9,] 0 0.000000e+00 1.000000e+00
[10,] 0 0.000000e+00 1.000000e+00
[11,] 0 0.000000e+00 1.000000e+00
[12,] 0 0.000000e+00 1.000000e+00
[13,] 0 0.000000e+00 1.000000e+00
[14,] 0 0.000000e+00 1.000000e+00
[15,] 0 0.000000e+00 1.000000e+00
[16,] 0 0.000000e+00 1.000000e+00
[17,] 0 0.000000e+00 1.000000e+00
[18,] 0 0.000000e+00 1.000000e+00
[19,] 0 0.000000e+00 1.000000e+00
[20,] 0 0.000000e+00 1.000000e+00
[21,] 0 0.000000e+00 1.000000e+00
[22,] 0 0.000000e+00 1.000000e+00
[23,] 0 0.000000e+00 1.000000e+00
[24,] 0 0.000000e+00 1.000000e+00
[25,] 0 0.000000e+00 1.000000e+00
[26,] 0 0.000000e+00 1.000000e+00
[27,] 0 0.000000e+00 1.000000e+00
[28,] 0 0.000000e+00 1.000000e+00
[29,] 0 0.000000e+00 1.000000e+00
[30,] 0 0.000000e+00 1.000000e+00
[31,] 0 0.000000e+00 1.000000e+00
[32,] 0 0.000000e+00 1.000000e+00
[33,] 0 0.000000e+00 1.000000e+00
[34,] 0 0.000000e+00 1.000000e+00
[35,] 0 0.000000e+00 1.000000e+00
[36,] 0 0.000000e+00 1.000000e+00
[37,] 0 0.000000e+00 1.000000e+00
[38,] 0 0.000000e+00 1.000000e+00
[39,] 0 0.000000e+00 1.000000e+00
[40,] 0 0.000000e+00 1.000000e+00
[41,] 1 2.684099e-251 1.342050e-251
[42,] 1 1.703577e-225 8.517884e-226
[43,] 1 1.115176e-213 5.575882e-214
[44,] 1 9.249760e-212 4.624880e-212
[45,] 1 0.000000e+00 0.000000e+00
[46,] 1 5.980538e-169 2.990269e-169
[47,] 1 1.314256e-154 6.571279e-155
[48,] 1 1.406342e-157 7.031709e-158
[49,] 1 5.475018e-123 2.737509e-123
[50,] 1 1.773026e-115 8.865131e-116
[51,] 1 8.516464e-95 4.258232e-95
[52,] 1 6.359993e-79 3.179997e-79
[53,] 1 3.435673e-62 1.717836e-62
[54,] 1 1.756747e-47 8.783734e-48
> postscript(file="/var/wessaorg/rcomp/tmp/1j7sf1323616265.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/wessaorg/rcomp/tmp/2ie2m1323616265.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3syhr1323616265.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4fqmq1323616265.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5r5h41323616265.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-0.30464953 -0.43452618 -0.16319996 -0.29118245 -0.01410381 -0.30740274
7 8 9 10 11 12
-0.16392854 0.03138593 -0.26957392 -0.23320403 -0.19861730 -0.13788271
13 14 15 16 17 18
0.02331638 -0.49483502 -0.52389773 -0.13918684 -0.48363389 -0.40344322
19 20 21 22 23 24
0.07433246 -0.42566264 -0.16969830 -0.12696079 0.01444517 -0.26931068
25 26 27 28 29 30
-0.41982769 -0.39436226 -0.28315035 -0.21328866 -0.51517208 -0.33841192
31 32 33 34 35 36
-0.27741061 -0.35372294 -0.16554535 -0.32682741 -0.42133244 -0.29285575
37 38 39 40 41 42
-0.37693752 -0.35860270 -0.39418273 -0.15247899 -0.30744971 -0.30457735
43 44 45 46 47 48
-0.18487169 -0.15037156 -0.44506063 -0.53401915 0.84782840 0.88888472
49 50 51 52 53 54
0.62050424 0.74494040 0.56585861 0.69360295 0.81597894 0.85887633
55 56 57 58 59 60
0.63199559 0.47880209 0.48904375 0.55675201 0.49633560 0.54374686
61 62 63 64 65 66
0.58748126 0.44183470 0.46115119 0.37811680 0.64013164 0.42511342
67
0.45490033
> postscript(file="/var/wessaorg/rcomp/tmp/66ypb1323616265.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 -0.30464953 NA
1 -0.43452618 -0.30464953
2 -0.16319996 -0.43452618
3 -0.29118245 -0.16319996
4 -0.01410381 -0.29118245
5 -0.30740274 -0.01410381
6 -0.16392854 -0.30740274
7 0.03138593 -0.16392854
8 -0.26957392 0.03138593
9 -0.23320403 -0.26957392
10 -0.19861730 -0.23320403
11 -0.13788271 -0.19861730
12 0.02331638 -0.13788271
13 -0.49483502 0.02331638
14 -0.52389773 -0.49483502
15 -0.13918684 -0.52389773
16 -0.48363389 -0.13918684
17 -0.40344322 -0.48363389
18 0.07433246 -0.40344322
19 -0.42566264 0.07433246
20 -0.16969830 -0.42566264
21 -0.12696079 -0.16969830
22 0.01444517 -0.12696079
23 -0.26931068 0.01444517
24 -0.41982769 -0.26931068
25 -0.39436226 -0.41982769
26 -0.28315035 -0.39436226
27 -0.21328866 -0.28315035
28 -0.51517208 -0.21328866
29 -0.33841192 -0.51517208
30 -0.27741061 -0.33841192
31 -0.35372294 -0.27741061
32 -0.16554535 -0.35372294
33 -0.32682741 -0.16554535
34 -0.42133244 -0.32682741
35 -0.29285575 -0.42133244
36 -0.37693752 -0.29285575
37 -0.35860270 -0.37693752
38 -0.39418273 -0.35860270
39 -0.15247899 -0.39418273
40 -0.30744971 -0.15247899
41 -0.30457735 -0.30744971
42 -0.18487169 -0.30457735
43 -0.15037156 -0.18487169
44 -0.44506063 -0.15037156
45 -0.53401915 -0.44506063
46 0.84782840 -0.53401915
47 0.88888472 0.84782840
48 0.62050424 0.88888472
49 0.74494040 0.62050424
50 0.56585861 0.74494040
51 0.69360295 0.56585861
52 0.81597894 0.69360295
53 0.85887633 0.81597894
54 0.63199559 0.85887633
55 0.47880209 0.63199559
56 0.48904375 0.47880209
57 0.55675201 0.48904375
58 0.49633560 0.55675201
59 0.54374686 0.49633560
60 0.58748126 0.54374686
61 0.44183470 0.58748126
62 0.46115119 0.44183470
63 0.37811680 0.46115119
64 0.64013164 0.37811680
65 0.42511342 0.64013164
66 0.45490033 0.42511342
67 NA 0.45490033
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.43452618 -0.30464953
[2,] -0.16319996 -0.43452618
[3,] -0.29118245 -0.16319996
[4,] -0.01410381 -0.29118245
[5,] -0.30740274 -0.01410381
[6,] -0.16392854 -0.30740274
[7,] 0.03138593 -0.16392854
[8,] -0.26957392 0.03138593
[9,] -0.23320403 -0.26957392
[10,] -0.19861730 -0.23320403
[11,] -0.13788271 -0.19861730
[12,] 0.02331638 -0.13788271
[13,] -0.49483502 0.02331638
[14,] -0.52389773 -0.49483502
[15,] -0.13918684 -0.52389773
[16,] -0.48363389 -0.13918684
[17,] -0.40344322 -0.48363389
[18,] 0.07433246 -0.40344322
[19,] -0.42566264 0.07433246
[20,] -0.16969830 -0.42566264
[21,] -0.12696079 -0.16969830
[22,] 0.01444517 -0.12696079
[23,] -0.26931068 0.01444517
[24,] -0.41982769 -0.26931068
[25,] -0.39436226 -0.41982769
[26,] -0.28315035 -0.39436226
[27,] -0.21328866 -0.28315035
[28,] -0.51517208 -0.21328866
[29,] -0.33841192 -0.51517208
[30,] -0.27741061 -0.33841192
[31,] -0.35372294 -0.27741061
[32,] -0.16554535 -0.35372294
[33,] -0.32682741 -0.16554535
[34,] -0.42133244 -0.32682741
[35,] -0.29285575 -0.42133244
[36,] -0.37693752 -0.29285575
[37,] -0.35860270 -0.37693752
[38,] -0.39418273 -0.35860270
[39,] -0.15247899 -0.39418273
[40,] -0.30744971 -0.15247899
[41,] -0.30457735 -0.30744971
[42,] -0.18487169 -0.30457735
[43,] -0.15037156 -0.18487169
[44,] -0.44506063 -0.15037156
[45,] -0.53401915 -0.44506063
[46,] 0.84782840 -0.53401915
[47,] 0.88888472 0.84782840
[48,] 0.62050424 0.88888472
[49,] 0.74494040 0.62050424
[50,] 0.56585861 0.74494040
[51,] 0.69360295 0.56585861
[52,] 0.81597894 0.69360295
[53,] 0.85887633 0.81597894
[54,] 0.63199559 0.85887633
[55,] 0.47880209 0.63199559
[56,] 0.48904375 0.47880209
[57,] 0.55675201 0.48904375
[58,] 0.49633560 0.55675201
[59,] 0.54374686 0.49633560
[60,] 0.58748126 0.54374686
[61,] 0.44183470 0.58748126
[62,] 0.46115119 0.44183470
[63,] 0.37811680 0.46115119
[64,] 0.64013164 0.37811680
[65,] 0.42511342 0.64013164
[66,] 0.45490033 0.42511342
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.43452618 -0.30464953
2 -0.16319996 -0.43452618
3 -0.29118245 -0.16319996
4 -0.01410381 -0.29118245
5 -0.30740274 -0.01410381
6 -0.16392854 -0.30740274
7 0.03138593 -0.16392854
8 -0.26957392 0.03138593
9 -0.23320403 -0.26957392
10 -0.19861730 -0.23320403
11 -0.13788271 -0.19861730
12 0.02331638 -0.13788271
13 -0.49483502 0.02331638
14 -0.52389773 -0.49483502
15 -0.13918684 -0.52389773
16 -0.48363389 -0.13918684
17 -0.40344322 -0.48363389
18 0.07433246 -0.40344322
19 -0.42566264 0.07433246
20 -0.16969830 -0.42566264
21 -0.12696079 -0.16969830
22 0.01444517 -0.12696079
23 -0.26931068 0.01444517
24 -0.41982769 -0.26931068
25 -0.39436226 -0.41982769
26 -0.28315035 -0.39436226
27 -0.21328866 -0.28315035
28 -0.51517208 -0.21328866
29 -0.33841192 -0.51517208
30 -0.27741061 -0.33841192
31 -0.35372294 -0.27741061
32 -0.16554535 -0.35372294
33 -0.32682741 -0.16554535
34 -0.42133244 -0.32682741
35 -0.29285575 -0.42133244
36 -0.37693752 -0.29285575
37 -0.35860270 -0.37693752
38 -0.39418273 -0.35860270
39 -0.15247899 -0.39418273
40 -0.30744971 -0.15247899
41 -0.30457735 -0.30744971
42 -0.18487169 -0.30457735
43 -0.15037156 -0.18487169
44 -0.44506063 -0.15037156
45 -0.53401915 -0.44506063
46 0.84782840 -0.53401915
47 0.88888472 0.84782840
48 0.62050424 0.88888472
49 0.74494040 0.62050424
50 0.56585861 0.74494040
51 0.69360295 0.56585861
52 0.81597894 0.69360295
53 0.85887633 0.81597894
54 0.63199559 0.85887633
55 0.47880209 0.63199559
56 0.48904375 0.47880209
57 0.55675201 0.48904375
58 0.49633560 0.55675201
59 0.54374686 0.49633560
60 0.58748126 0.54374686
61 0.44183470 0.58748126
62 0.46115119 0.44183470
63 0.37811680 0.46115119
64 0.64013164 0.37811680
65 0.42511342 0.64013164
66 0.45490033 0.42511342
> 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/wessaorg/rcomp/tmp/7to8g1323616265.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/wessaorg/rcomp/tmp/8jfr21323616265.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/wessaorg/rcomp/tmp/9mjh51323616265.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/wessaorg/rcomp/tmp/10ykrr1323616265.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11j9iy1323616265.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12z4pq1323616265.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1396uf1323616266.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/145jrz1323616266.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/wessaorg/rcomp/tmp/1580fr1323616266.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/wessaorg/rcomp/tmp/16f7u21323616266.tab")
+ }
>
> try(system("convert tmp/1j7sf1323616265.ps tmp/1j7sf1323616265.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ie2m1323616265.ps tmp/2ie2m1323616265.png",intern=TRUE))
character(0)
> try(system("convert tmp/3syhr1323616265.ps tmp/3syhr1323616265.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fqmq1323616265.ps tmp/4fqmq1323616265.png",intern=TRUE))
character(0)
> try(system("convert tmp/5r5h41323616265.ps tmp/5r5h41323616265.png",intern=TRUE))
character(0)
> try(system("convert tmp/66ypb1323616265.ps tmp/66ypb1323616265.png",intern=TRUE))
character(0)
> try(system("convert tmp/7to8g1323616265.ps tmp/7to8g1323616265.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jfr21323616265.ps tmp/8jfr21323616265.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mjh51323616265.ps tmp/9mjh51323616265.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ykrr1323616265.ps tmp/10ykrr1323616265.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.367 0.481 3.887