R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-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 '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(0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,1,1,0,0,1,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,1,0,1,0),dim=c(3,68),dimnames=list(c('T20','Used','Useful'),1:68))
> y <- array(NA,dim=c(3,68),dimnames=list(c('T20','Used','Useful'),1:68))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
T20 Used Useful t
1 0 0 0 1
2 1 1 0 2
3 0 0 0 3
4 0 0 0 4
5 0 0 1 5
6 1 0 0 6
7 0 0 1 7
8 0 0 0 8
9 1 0 0 9
10 0 0 0 10
11 1 0 0 11
12 0 0 0 12
13 0 0 0 13
14 0 0 0 14
15 0 0 0 15
16 0 0 0 16
17 0 0 0 17
18 0 0 0 18
19 1 1 0 19
20 0 0 0 20
21 0 0 0 21
22 1 1 0 22
23 0 0 0 23
24 0 0 0 24
25 1 1 1 25
26 1 0 0 26
27 0 1 0 27
28 1 1 0 28
29 0 0 0 29
30 0 0 0 30
31 0 0 0 31
32 0 0 0 32
33 0 0 0 33
34 0 0 0 34
35 0 0 0 35
36 0 0 0 36
37 1 1 0 37
38 0 1 1 38
39 0 0 0 39
40 1 0 0 40
41 0 0 1 41
42 0 0 0 42
43 0 0 0 43
44 0 0 0 44
45 0 0 0 45
46 0 0 0 46
47 0 1 0 47
48 0 0 0 48
49 0 0 0 49
50 0 0 0 50
51 0 1 1 51
52 1 1 1 52
53 1 0 0 53
54 0 0 0 54
55 0 1 0 55
56 1 1 0 56
57 0 0 0 57
58 0 0 1 58
59 0 0 1 59
60 1 0 0 60
61 1 1 0 61
62 1 0 0 62
63 0 0 0 63
64 0 0 1 64
65 0 0 0 65
66 0 1 0 66
67 0 1 1 67
68 0 1 0 68
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Used Useful t
0.249624 0.425720 -0.163319 -0.002308
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6130 -0.2133 -0.1515 0.1284 0.8935
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.249624 0.100124 2.493 0.015258 *
Used 0.425720 0.118801 3.583 0.000655 ***
Useful -0.163319 0.138070 -1.183 0.241235
t -0.002308 0.002605 -0.886 0.378977
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4063 on 64 degrees of freedom
Multiple R-squared: 0.1714, Adjusted R-squared: 0.1325
F-statistic: 4.412 on 3 and 64 DF, p-value: 0.006973
> 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,] 0.41694956 0.8338991 0.5830504
[2,] 0.60786920 0.7842616 0.3921308
[3,] 0.62157786 0.7568443 0.3784221
[4,] 0.71725227 0.5654955 0.2827477
[5,] 0.73100011 0.5379998 0.2689999
[6,] 0.79536764 0.4092647 0.2046324
[7,] 0.79155451 0.4168910 0.2084455
[8,] 0.75812623 0.4837475 0.2418738
[9,] 0.70553612 0.5889278 0.2944639
[10,] 0.63914412 0.7217118 0.3608559
[11,] 0.56376479 0.8724704 0.4362352
[12,] 0.48437950 0.9687590 0.5156205
[13,] 0.42598095 0.8519619 0.5740190
[14,] 0.35137272 0.7027454 0.6486273
[15,] 0.28251431 0.5650286 0.7174857
[16,] 0.24067073 0.4813415 0.7593293
[17,] 0.18525951 0.3705190 0.8147405
[18,] 0.13935454 0.2787091 0.8606455
[19,] 0.14308616 0.2861723 0.8569138
[20,] 0.39229979 0.7845996 0.6077002
[21,] 0.57247569 0.8550486 0.4275243
[22,] 0.57836347 0.8432731 0.4216365
[23,] 0.50814582 0.9837084 0.4918542
[24,] 0.43728291 0.8745658 0.5627171
[25,] 0.36849527 0.7369905 0.6315047
[26,] 0.30414391 0.6082878 0.6958561
[27,] 0.24604462 0.4920892 0.7539554
[28,] 0.19536232 0.3907246 0.8046377
[29,] 0.15260106 0.3052021 0.8473989
[30,] 0.11768084 0.2353617 0.8823192
[31,] 0.12860002 0.2572000 0.8714000
[32,] 0.13125306 0.2625061 0.8687469
[33,] 0.09945481 0.1989096 0.9005452
[34,] 0.29734662 0.5946932 0.7026534
[35,] 0.23970216 0.4794043 0.7602978
[36,] 0.18774513 0.3754903 0.8122549
[37,] 0.14383722 0.2876744 0.8561628
[38,] 0.10816126 0.2163225 0.8918387
[39,] 0.08033485 0.1606697 0.9196651
[40,] 0.05959880 0.1191976 0.9404012
[41,] 0.07589353 0.1517871 0.9241065
[42,] 0.06221606 0.1244321 0.9377839
[43,] 0.05621763 0.1124353 0.9437824
[44,] 0.06215910 0.1243182 0.9378409
[45,] 0.07162987 0.1432597 0.9283701
[46,] 0.09663455 0.1932691 0.9033655
[47,] 0.16229556 0.3245911 0.8377044
[48,] 0.16287951 0.3257590 0.8371205
[49,] 0.31324278 0.6264856 0.6867572
[50,] 0.24618104 0.4923621 0.7538190
[51,] 0.44217543 0.8843509 0.5578246
[52,] 0.44659343 0.8931869 0.5534066
[53,] 0.69543705 0.6091259 0.3045630
[54,] 0.60931190 0.7813762 0.3906881
[55,] 0.45540383 0.9108077 0.5445962
> postscript(file="/var/wessaorg/rcomp/tmp/1869n1355957901.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/2j23v1355957901.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3hso31355957901.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4chxd1355957901.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5k0w41355957901.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 68
Frequency = 1
1 2 3 4 5 6
-0.247315277 0.329273283 -0.242698804 -0.240390567 -0.074763186 0.764225906
7 8 9 10 11 12
-0.070146713 -0.231157621 0.771150615 -0.226541148 0.775767088 -0.221924675
13 14 15 16 17 18
-0.219616439 -0.217308202 -0.214999966 -0.212691729 -0.210383493 -0.208075256
19 20 21 22 23 24
0.368513303 -0.203458783 -0.201150547 0.375438013 -0.196534074 -0.194225837
25 26 27 28 29 30
0.545681866 0.810390636 -0.613020805 0.389287432 -0.182684655 -0.180376418
31 32 33 34 35 36
-0.178068182 -0.175759945 -0.173451709 -0.171143472 -0.168835236 -0.166526999
37 38 39 40 41 42
0.410061560 -0.424311059 -0.159602290 0.842705947 0.008333327 -0.152677580
43 44 45 46 47 48
-0.150369344 -0.148061107 -0.145752871 -0.143444634 -0.566856075 -0.138828161
49 50 51 52 53 54
-0.136519925 -0.134211688 -0.394303985 0.608004252 0.872713021 -0.124978742
55 56 57 58 59 60
-0.548390183 0.453918053 -0.118054033 0.047573348 0.049881584 0.888870677
61 62 63 64 65 66
0.465459236 0.893487150 -0.104204614 0.061422767 -0.099588141 -0.522999582
67 68
-0.357372201 -0.518383109
> postscript(file="/var/wessaorg/rcomp/tmp/6dj9f1355957901.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 = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.247315277 NA
1 0.329273283 -0.247315277
2 -0.242698804 0.329273283
3 -0.240390567 -0.242698804
4 -0.074763186 -0.240390567
5 0.764225906 -0.074763186
6 -0.070146713 0.764225906
7 -0.231157621 -0.070146713
8 0.771150615 -0.231157621
9 -0.226541148 0.771150615
10 0.775767088 -0.226541148
11 -0.221924675 0.775767088
12 -0.219616439 -0.221924675
13 -0.217308202 -0.219616439
14 -0.214999966 -0.217308202
15 -0.212691729 -0.214999966
16 -0.210383493 -0.212691729
17 -0.208075256 -0.210383493
18 0.368513303 -0.208075256
19 -0.203458783 0.368513303
20 -0.201150547 -0.203458783
21 0.375438013 -0.201150547
22 -0.196534074 0.375438013
23 -0.194225837 -0.196534074
24 0.545681866 -0.194225837
25 0.810390636 0.545681866
26 -0.613020805 0.810390636
27 0.389287432 -0.613020805
28 -0.182684655 0.389287432
29 -0.180376418 -0.182684655
30 -0.178068182 -0.180376418
31 -0.175759945 -0.178068182
32 -0.173451709 -0.175759945
33 -0.171143472 -0.173451709
34 -0.168835236 -0.171143472
35 -0.166526999 -0.168835236
36 0.410061560 -0.166526999
37 -0.424311059 0.410061560
38 -0.159602290 -0.424311059
39 0.842705947 -0.159602290
40 0.008333327 0.842705947
41 -0.152677580 0.008333327
42 -0.150369344 -0.152677580
43 -0.148061107 -0.150369344
44 -0.145752871 -0.148061107
45 -0.143444634 -0.145752871
46 -0.566856075 -0.143444634
47 -0.138828161 -0.566856075
48 -0.136519925 -0.138828161
49 -0.134211688 -0.136519925
50 -0.394303985 -0.134211688
51 0.608004252 -0.394303985
52 0.872713021 0.608004252
53 -0.124978742 0.872713021
54 -0.548390183 -0.124978742
55 0.453918053 -0.548390183
56 -0.118054033 0.453918053
57 0.047573348 -0.118054033
58 0.049881584 0.047573348
59 0.888870677 0.049881584
60 0.465459236 0.888870677
61 0.893487150 0.465459236
62 -0.104204614 0.893487150
63 0.061422767 -0.104204614
64 -0.099588141 0.061422767
65 -0.522999582 -0.099588141
66 -0.357372201 -0.522999582
67 -0.518383109 -0.357372201
68 NA -0.518383109
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.329273283 -0.247315277
[2,] -0.242698804 0.329273283
[3,] -0.240390567 -0.242698804
[4,] -0.074763186 -0.240390567
[5,] 0.764225906 -0.074763186
[6,] -0.070146713 0.764225906
[7,] -0.231157621 -0.070146713
[8,] 0.771150615 -0.231157621
[9,] -0.226541148 0.771150615
[10,] 0.775767088 -0.226541148
[11,] -0.221924675 0.775767088
[12,] -0.219616439 -0.221924675
[13,] -0.217308202 -0.219616439
[14,] -0.214999966 -0.217308202
[15,] -0.212691729 -0.214999966
[16,] -0.210383493 -0.212691729
[17,] -0.208075256 -0.210383493
[18,] 0.368513303 -0.208075256
[19,] -0.203458783 0.368513303
[20,] -0.201150547 -0.203458783
[21,] 0.375438013 -0.201150547
[22,] -0.196534074 0.375438013
[23,] -0.194225837 -0.196534074
[24,] 0.545681866 -0.194225837
[25,] 0.810390636 0.545681866
[26,] -0.613020805 0.810390636
[27,] 0.389287432 -0.613020805
[28,] -0.182684655 0.389287432
[29,] -0.180376418 -0.182684655
[30,] -0.178068182 -0.180376418
[31,] -0.175759945 -0.178068182
[32,] -0.173451709 -0.175759945
[33,] -0.171143472 -0.173451709
[34,] -0.168835236 -0.171143472
[35,] -0.166526999 -0.168835236
[36,] 0.410061560 -0.166526999
[37,] -0.424311059 0.410061560
[38,] -0.159602290 -0.424311059
[39,] 0.842705947 -0.159602290
[40,] 0.008333327 0.842705947
[41,] -0.152677580 0.008333327
[42,] -0.150369344 -0.152677580
[43,] -0.148061107 -0.150369344
[44,] -0.145752871 -0.148061107
[45,] -0.143444634 -0.145752871
[46,] -0.566856075 -0.143444634
[47,] -0.138828161 -0.566856075
[48,] -0.136519925 -0.138828161
[49,] -0.134211688 -0.136519925
[50,] -0.394303985 -0.134211688
[51,] 0.608004252 -0.394303985
[52,] 0.872713021 0.608004252
[53,] -0.124978742 0.872713021
[54,] -0.548390183 -0.124978742
[55,] 0.453918053 -0.548390183
[56,] -0.118054033 0.453918053
[57,] 0.047573348 -0.118054033
[58,] 0.049881584 0.047573348
[59,] 0.888870677 0.049881584
[60,] 0.465459236 0.888870677
[61,] 0.893487150 0.465459236
[62,] -0.104204614 0.893487150
[63,] 0.061422767 -0.104204614
[64,] -0.099588141 0.061422767
[65,] -0.522999582 -0.099588141
[66,] -0.357372201 -0.522999582
[67,] -0.518383109 -0.357372201
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.329273283 -0.247315277
2 -0.242698804 0.329273283
3 -0.240390567 -0.242698804
4 -0.074763186 -0.240390567
5 0.764225906 -0.074763186
6 -0.070146713 0.764225906
7 -0.231157621 -0.070146713
8 0.771150615 -0.231157621
9 -0.226541148 0.771150615
10 0.775767088 -0.226541148
11 -0.221924675 0.775767088
12 -0.219616439 -0.221924675
13 -0.217308202 -0.219616439
14 -0.214999966 -0.217308202
15 -0.212691729 -0.214999966
16 -0.210383493 -0.212691729
17 -0.208075256 -0.210383493
18 0.368513303 -0.208075256
19 -0.203458783 0.368513303
20 -0.201150547 -0.203458783
21 0.375438013 -0.201150547
22 -0.196534074 0.375438013
23 -0.194225837 -0.196534074
24 0.545681866 -0.194225837
25 0.810390636 0.545681866
26 -0.613020805 0.810390636
27 0.389287432 -0.613020805
28 -0.182684655 0.389287432
29 -0.180376418 -0.182684655
30 -0.178068182 -0.180376418
31 -0.175759945 -0.178068182
32 -0.173451709 -0.175759945
33 -0.171143472 -0.173451709
34 -0.168835236 -0.171143472
35 -0.166526999 -0.168835236
36 0.410061560 -0.166526999
37 -0.424311059 0.410061560
38 -0.159602290 -0.424311059
39 0.842705947 -0.159602290
40 0.008333327 0.842705947
41 -0.152677580 0.008333327
42 -0.150369344 -0.152677580
43 -0.148061107 -0.150369344
44 -0.145752871 -0.148061107
45 -0.143444634 -0.145752871
46 -0.566856075 -0.143444634
47 -0.138828161 -0.566856075
48 -0.136519925 -0.138828161
49 -0.134211688 -0.136519925
50 -0.394303985 -0.134211688
51 0.608004252 -0.394303985
52 0.872713021 0.608004252
53 -0.124978742 0.872713021
54 -0.548390183 -0.124978742
55 0.453918053 -0.548390183
56 -0.118054033 0.453918053
57 0.047573348 -0.118054033
58 0.049881584 0.047573348
59 0.888870677 0.049881584
60 0.465459236 0.888870677
61 0.893487150 0.465459236
62 -0.104204614 0.893487150
63 0.061422767 -0.104204614
64 -0.099588141 0.061422767
65 -0.522999582 -0.099588141
66 -0.357372201 -0.522999582
67 -0.518383109 -0.357372201
> 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/7tzsx1355957901.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/8lpju1355957901.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/92bkv1355957901.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/103vui1355957901.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/110bjk1355957901.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12um9o1355957901.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/136t841355957901.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14fsfr1355957901.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/15i0zx1355957901.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/16fpc01355957901.tab")
+ }
>
> try(system("convert tmp/1869n1355957901.ps tmp/1869n1355957901.png",intern=TRUE))
character(0)
> try(system("convert tmp/2j23v1355957901.ps tmp/2j23v1355957901.png",intern=TRUE))
character(0)
> try(system("convert tmp/3hso31355957901.ps tmp/3hso31355957901.png",intern=TRUE))
character(0)
> try(system("convert tmp/4chxd1355957901.ps tmp/4chxd1355957901.png",intern=TRUE))
character(0)
> try(system("convert tmp/5k0w41355957901.ps tmp/5k0w41355957901.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dj9f1355957901.ps tmp/6dj9f1355957901.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tzsx1355957901.ps tmp/7tzsx1355957901.png",intern=TRUE))
character(0)
> try(system("convert tmp/8lpju1355957901.ps tmp/8lpju1355957901.png",intern=TRUE))
character(0)
> try(system("convert tmp/92bkv1355957901.ps tmp/92bkv1355957901.png",intern=TRUE))
character(0)
> try(system("convert tmp/103vui1355957901.ps tmp/103vui1355957901.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.270 1.119 7.380