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.
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(20995,17382,9367,31124,26551,30651,25859,25100,25778,20418,18688,20424,24776,19814,12738,31566,30111,30019,31934,25826,26835,20205,17789,20520,22518,15572,11509,25447,24090,27786,26195,20516,22759,19028,16971,20036,22485,18730,14538,27561,25985,34670,32066,27186,29586,21359,21553,19573,24256,22380,16167,27297,28287,33474,28229,28785,25597,18130,20198,22849,23118),dim=c(1,61),dimnames=list(c('Inschrijvingen'),1:61))
> y <- array(NA,dim=c(1,61),dimnames=list(c('Inschrijvingen'),1:61))
> 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 = 'Include Monthly 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
Inschrijvingen M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 20995 1 0 0 0 0 0 0 0 0 0 0 1
2 17382 0 1 0 0 0 0 0 0 0 0 0 2
3 9367 0 0 1 0 0 0 0 0 0 0 0 3
4 31124 0 0 0 1 0 0 0 0 0 0 0 4
5 26551 0 0 0 0 1 0 0 0 0 0 0 5
6 30651 0 0 0 0 0 1 0 0 0 0 0 6
7 25859 0 0 0 0 0 0 1 0 0 0 0 7
8 25100 0 0 0 0 0 0 0 1 0 0 0 8
9 25778 0 0 0 0 0 0 0 0 1 0 0 9
10 20418 0 0 0 0 0 0 0 0 0 1 0 10
11 18688 0 0 0 0 0 0 0 0 0 0 1 11
12 20424 0 0 0 0 0 0 0 0 0 0 0 12
13 24776 1 0 0 0 0 0 0 0 0 0 0 13
14 19814 0 1 0 0 0 0 0 0 0 0 0 14
15 12738 0 0 1 0 0 0 0 0 0 0 0 15
16 31566 0 0 0 1 0 0 0 0 0 0 0 16
17 30111 0 0 0 0 1 0 0 0 0 0 0 17
18 30019 0 0 0 0 0 1 0 0 0 0 0 18
19 31934 0 0 0 0 0 0 1 0 0 0 0 19
20 25826 0 0 0 0 0 0 0 1 0 0 0 20
21 26835 0 0 0 0 0 0 0 0 1 0 0 21
22 20205 0 0 0 0 0 0 0 0 0 1 0 22
23 17789 0 0 0 0 0 0 0 0 0 0 1 23
24 20520 0 0 0 0 0 0 0 0 0 0 0 24
25 22518 1 0 0 0 0 0 0 0 0 0 0 25
26 15572 0 1 0 0 0 0 0 0 0 0 0 26
27 11509 0 0 1 0 0 0 0 0 0 0 0 27
28 25447 0 0 0 1 0 0 0 0 0 0 0 28
29 24090 0 0 0 0 1 0 0 0 0 0 0 29
30 27786 0 0 0 0 0 1 0 0 0 0 0 30
31 26195 0 0 0 0 0 0 1 0 0 0 0 31
32 20516 0 0 0 0 0 0 0 1 0 0 0 32
33 22759 0 0 0 0 0 0 0 0 1 0 0 33
34 19028 0 0 0 0 0 0 0 0 0 1 0 34
35 16971 0 0 0 0 0 0 0 0 0 0 1 35
36 20036 0 0 0 0 0 0 0 0 0 0 0 36
37 22485 1 0 0 0 0 0 0 0 0 0 0 37
38 18730 0 1 0 0 0 0 0 0 0 0 0 38
39 14538 0 0 1 0 0 0 0 0 0 0 0 39
40 27561 0 0 0 1 0 0 0 0 0 0 0 40
41 25985 0 0 0 0 1 0 0 0 0 0 0 41
42 34670 0 0 0 0 0 1 0 0 0 0 0 42
43 32066 0 0 0 0 0 0 1 0 0 0 0 43
44 27186 0 0 0 0 0 0 0 1 0 0 0 44
45 29586 0 0 0 0 0 0 0 0 1 0 0 45
46 21359 0 0 0 0 0 0 0 0 0 1 0 46
47 21553 0 0 0 0 0 0 0 0 0 0 1 47
48 19573 0 0 0 0 0 0 0 0 0 0 0 48
49 24256 1 0 0 0 0 0 0 0 0 0 0 49
50 22380 0 1 0 0 0 0 0 0 0 0 0 50
51 16167 0 0 1 0 0 0 0 0 0 0 0 51
52 27297 0 0 0 1 0 0 0 0 0 0 0 52
53 28287 0 0 0 0 1 0 0 0 0 0 0 53
54 33474 0 0 0 0 0 1 0 0 0 0 0 54
55 28229 0 0 0 0 0 0 1 0 0 0 0 55
56 28785 0 0 0 0 0 0 0 1 0 0 0 56
57 25597 0 0 0 0 0 0 0 0 1 0 0 57
58 18130 0 0 0 0 0 0 0 0 0 1 0 58
59 20198 0 0 0 0 0 0 0 0 0 0 1 59
60 22849 0 0 0 0 0 0 0 0 0 0 0 60
61 23118 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
19502.45 2507.87 -1577.59 -7522.11 8180.37 6553.45
M6 M7 M8 M9 M10 M11
10835.93 8339.80 4933.08 5528.76 -786.96 -1607.88
t
32.72
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4966.6 -1412.5 266.9 1368.7 3498.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19502.45 1184.03 16.471 < 2e-16 ***
M1 2507.87 1380.85 1.816 0.075591 .
M2 -1577.59 1449.35 -1.088 0.281818
M3 -7522.11 1447.50 -5.197 4.11e-06 ***
M4 8180.37 1445.84 5.658 8.32e-07 ***
M5 6553.45 1444.38 4.537 3.82e-05 ***
M6 10835.93 1443.11 7.509 1.23e-09 ***
M7 8339.80 1442.03 5.783 5.36e-07 ***
M8 4933.08 1441.15 3.423 0.001276 **
M9 5528.76 1440.46 3.838 0.000362 ***
M10 -786.96 1439.98 -0.547 0.587248
M11 -1607.88 1439.68 -1.117 0.269626
t 32.72 16.80 1.948 0.057294 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2276 on 48 degrees of freedom
Multiple R-squared: 0.8654, Adjusted R-squared: 0.8318
F-statistic: 25.72 on 12 and 48 DF, p-value: < 2.2e-16
> 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.10369082 0.2073816 0.8963092
[2,] 0.05738181 0.1147636 0.9426182
[3,] 0.10744509 0.2148902 0.8925549
[4,] 0.22186395 0.4437279 0.7781360
[5,] 0.18108621 0.3621724 0.8189138
[6,] 0.14211300 0.2842260 0.8578870
[7,] 0.15006759 0.3001352 0.8499324
[8,] 0.15234192 0.3046838 0.8476581
[9,] 0.12888181 0.2577636 0.8711182
[10,] 0.14760501 0.2952100 0.8523950
[11,] 0.29405129 0.5881026 0.7059487
[12,] 0.22325722 0.4465144 0.7767428
[13,] 0.48782379 0.9756476 0.5121762
[14,] 0.51198271 0.9760346 0.4880173
[15,] 0.54266242 0.9146752 0.4573376
[16,] 0.49808437 0.9961687 0.5019156
[17,] 0.74770492 0.5045902 0.2522951
[18,] 0.80452452 0.3909510 0.1954755
[19,] 0.72764519 0.5447096 0.2723548
[20,] 0.75358455 0.4928309 0.2464154
[21,] 0.68075835 0.6384833 0.3192416
[22,] 0.61205854 0.7758829 0.3879415
[23,] 0.69389302 0.6122140 0.3061070
[24,] 0.72011434 0.5597713 0.2798857
[25,] 0.61934014 0.7613197 0.3806599
[26,] 0.63085464 0.7382907 0.3691454
[27,] 0.61271945 0.7745611 0.3872806
[28,] 0.62182085 0.7563583 0.3781792
[29,] 0.58920902 0.8215820 0.4107910
[30,] 0.58904764 0.8219047 0.4109524
> postscript(file="/var/wessaorg/rcomp/tmp/1dytc1322500159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ubfe1322500159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3cbl11322500159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/434n71322500159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5u8qe1322500159.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 61
Frequency = 1
1 2 3 4 5 6 7
-1048.0392 -608.2980 -2711.4980 3310.3020 331.5020 116.3020 -2212.2980
8 9 10 11 12 13 14
402.7020 452.3020 1375.3020 433.5020 528.9020 2340.3098 1431.0510
15 16 17 18 19 20 21
266.8510 3359.6510 3498.8510 -908.3490 3470.0510 736.0510 1116.6510
22 23 24 25 26 27 28
769.6510 -858.1490 232.2510 -310.3412 -3203.6000 -1354.8000 -3152.0000
29 30 31 32 33 34 35
-2914.8000 -3534.0000 -2661.6000 -4966.6000 -3352.0000 -800.0000 -2068.8000
36 37 38 39 40 41 42
-644.4000 -735.9922 -438.2510 1281.5490 -1430.6510 -1412.4510 2957.3490
43 44 45 46 47 48 49
2816.7490 1310.7490 3082.3490 1138.3490 2120.5490 -1500.0510 642.3569
50 51 52 53 54 55 56
2819.0980 2517.8980 -2087.3020 496.8980 1368.6980 -1412.9020 2517.0980
57 58 59 60 61
-1299.3020 -2483.3020 372.8980 1383.2980 -888.2941
> postscript(file="/var/wessaorg/rcomp/tmp/6nrjt1322500159.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -1048.0392 NA
1 -608.2980 -1048.0392
2 -2711.4980 -608.2980
3 3310.3020 -2711.4980
4 331.5020 3310.3020
5 116.3020 331.5020
6 -2212.2980 116.3020
7 402.7020 -2212.2980
8 452.3020 402.7020
9 1375.3020 452.3020
10 433.5020 1375.3020
11 528.9020 433.5020
12 2340.3098 528.9020
13 1431.0510 2340.3098
14 266.8510 1431.0510
15 3359.6510 266.8510
16 3498.8510 3359.6510
17 -908.3490 3498.8510
18 3470.0510 -908.3490
19 736.0510 3470.0510
20 1116.6510 736.0510
21 769.6510 1116.6510
22 -858.1490 769.6510
23 232.2510 -858.1490
24 -310.3412 232.2510
25 -3203.6000 -310.3412
26 -1354.8000 -3203.6000
27 -3152.0000 -1354.8000
28 -2914.8000 -3152.0000
29 -3534.0000 -2914.8000
30 -2661.6000 -3534.0000
31 -4966.6000 -2661.6000
32 -3352.0000 -4966.6000
33 -800.0000 -3352.0000
34 -2068.8000 -800.0000
35 -644.4000 -2068.8000
36 -735.9922 -644.4000
37 -438.2510 -735.9922
38 1281.5490 -438.2510
39 -1430.6510 1281.5490
40 -1412.4510 -1430.6510
41 2957.3490 -1412.4510
42 2816.7490 2957.3490
43 1310.7490 2816.7490
44 3082.3490 1310.7490
45 1138.3490 3082.3490
46 2120.5490 1138.3490
47 -1500.0510 2120.5490
48 642.3569 -1500.0510
49 2819.0980 642.3569
50 2517.8980 2819.0980
51 -2087.3020 2517.8980
52 496.8980 -2087.3020
53 1368.6980 496.8980
54 -1412.9020 1368.6980
55 2517.0980 -1412.9020
56 -1299.3020 2517.0980
57 -2483.3020 -1299.3020
58 372.8980 -2483.3020
59 1383.2980 372.8980
60 -888.2941 1383.2980
61 NA -888.2941
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -608.2980 -1048.0392
[2,] -2711.4980 -608.2980
[3,] 3310.3020 -2711.4980
[4,] 331.5020 3310.3020
[5,] 116.3020 331.5020
[6,] -2212.2980 116.3020
[7,] 402.7020 -2212.2980
[8,] 452.3020 402.7020
[9,] 1375.3020 452.3020
[10,] 433.5020 1375.3020
[11,] 528.9020 433.5020
[12,] 2340.3098 528.9020
[13,] 1431.0510 2340.3098
[14,] 266.8510 1431.0510
[15,] 3359.6510 266.8510
[16,] 3498.8510 3359.6510
[17,] -908.3490 3498.8510
[18,] 3470.0510 -908.3490
[19,] 736.0510 3470.0510
[20,] 1116.6510 736.0510
[21,] 769.6510 1116.6510
[22,] -858.1490 769.6510
[23,] 232.2510 -858.1490
[24,] -310.3412 232.2510
[25,] -3203.6000 -310.3412
[26,] -1354.8000 -3203.6000
[27,] -3152.0000 -1354.8000
[28,] -2914.8000 -3152.0000
[29,] -3534.0000 -2914.8000
[30,] -2661.6000 -3534.0000
[31,] -4966.6000 -2661.6000
[32,] -3352.0000 -4966.6000
[33,] -800.0000 -3352.0000
[34,] -2068.8000 -800.0000
[35,] -644.4000 -2068.8000
[36,] -735.9922 -644.4000
[37,] -438.2510 -735.9922
[38,] 1281.5490 -438.2510
[39,] -1430.6510 1281.5490
[40,] -1412.4510 -1430.6510
[41,] 2957.3490 -1412.4510
[42,] 2816.7490 2957.3490
[43,] 1310.7490 2816.7490
[44,] 3082.3490 1310.7490
[45,] 1138.3490 3082.3490
[46,] 2120.5490 1138.3490
[47,] -1500.0510 2120.5490
[48,] 642.3569 -1500.0510
[49,] 2819.0980 642.3569
[50,] 2517.8980 2819.0980
[51,] -2087.3020 2517.8980
[52,] 496.8980 -2087.3020
[53,] 1368.6980 496.8980
[54,] -1412.9020 1368.6980
[55,] 2517.0980 -1412.9020
[56,] -1299.3020 2517.0980
[57,] -2483.3020 -1299.3020
[58,] 372.8980 -2483.3020
[59,] 1383.2980 372.8980
[60,] -888.2941 1383.2980
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -608.2980 -1048.0392
2 -2711.4980 -608.2980
3 3310.3020 -2711.4980
4 331.5020 3310.3020
5 116.3020 331.5020
6 -2212.2980 116.3020
7 402.7020 -2212.2980
8 452.3020 402.7020
9 1375.3020 452.3020
10 433.5020 1375.3020
11 528.9020 433.5020
12 2340.3098 528.9020
13 1431.0510 2340.3098
14 266.8510 1431.0510
15 3359.6510 266.8510
16 3498.8510 3359.6510
17 -908.3490 3498.8510
18 3470.0510 -908.3490
19 736.0510 3470.0510
20 1116.6510 736.0510
21 769.6510 1116.6510
22 -858.1490 769.6510
23 232.2510 -858.1490
24 -310.3412 232.2510
25 -3203.6000 -310.3412
26 -1354.8000 -3203.6000
27 -3152.0000 -1354.8000
28 -2914.8000 -3152.0000
29 -3534.0000 -2914.8000
30 -2661.6000 -3534.0000
31 -4966.6000 -2661.6000
32 -3352.0000 -4966.6000
33 -800.0000 -3352.0000
34 -2068.8000 -800.0000
35 -644.4000 -2068.8000
36 -735.9922 -644.4000
37 -438.2510 -735.9922
38 1281.5490 -438.2510
39 -1430.6510 1281.5490
40 -1412.4510 -1430.6510
41 2957.3490 -1412.4510
42 2816.7490 2957.3490
43 1310.7490 2816.7490
44 3082.3490 1310.7490
45 1138.3490 3082.3490
46 2120.5490 1138.3490
47 -1500.0510 2120.5490
48 642.3569 -1500.0510
49 2819.0980 642.3569
50 2517.8980 2819.0980
51 -2087.3020 2517.8980
52 496.8980 -2087.3020
53 1368.6980 496.8980
54 -1412.9020 1368.6980
55 2517.0980 -1412.9020
56 -1299.3020 2517.0980
57 -2483.3020 -1299.3020
58 372.8980 -2483.3020
59 1383.2980 372.8980
60 -888.2941 1383.2980
> 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/78wq71322500159.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/8a3bo1322500159.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/9240x1322500159.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/10fgcs1322500159.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11rxtu1322500159.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/122h2w1322500159.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13psad1322500159.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14kt7s1322500159.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/15qy011322500159.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/162bp41322500159.tab")
+ }
>
> try(system("convert tmp/1dytc1322500159.ps tmp/1dytc1322500159.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ubfe1322500159.ps tmp/2ubfe1322500159.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cbl11322500159.ps tmp/3cbl11322500159.png",intern=TRUE))
character(0)
> try(system("convert tmp/434n71322500159.ps tmp/434n71322500159.png",intern=TRUE))
character(0)
> try(system("convert tmp/5u8qe1322500159.ps tmp/5u8qe1322500159.png",intern=TRUE))
character(0)
> try(system("convert tmp/6nrjt1322500159.ps tmp/6nrjt1322500159.png",intern=TRUE))
character(0)
> try(system("convert tmp/78wq71322500159.ps tmp/78wq71322500159.png",intern=TRUE))
character(0)
> try(system("convert tmp/8a3bo1322500159.ps tmp/8a3bo1322500159.png",intern=TRUE))
character(0)
> try(system("convert tmp/9240x1322500159.ps tmp/9240x1322500159.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fgcs1322500159.ps tmp/10fgcs1322500159.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.231 0.517 3.789