R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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
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> x <- array(list(2253,14.9,2218,18.6,1855,19.1,2187,18.8,1852,18.2,1570,18,1851,19,1954,20.7,1828,21.2,2251,20.7,2277,19.6,2085,18.6,2282,18.7,2266,23.8,1878,24.9,2267,24.8,2069,23.8,1746,22.3,2299,21.7,2360,20.7,2214,19.7,2825,18.4,2355,17.4,2333,17,3016,18,2155,23.8,2172,25.5,2150,25.6,2533,23.7,2058,22,2160,21.3,2260,20.7,2498,20.4,2695,20.3,2799,20.4,2946,19.8,2930,19.5,2318,23.1,2540,23.5,2570,23.5,2669,22.9,2450,21.9,2842,21.5,3440,20.5,2678,20.2,2981,19.4,2260,19.2,2844,18.8,2546,18.8,2456,22.6,2295,23.3,2379,23,2479,21.4,2057,19.9,2280,18.8,2351,18.6,2276,18.4,2548,18.6,2311,19.9,2201,19.2),dim=c(2,60),dimnames=list(c('wngb','<25'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('wngb','<25'),1:60))
> 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'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
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
wngb <25 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 2253 14.9 1 0 0 0 0 0 0 0 0 0 0 1
2 2218 18.6 0 1 0 0 0 0 0 0 0 0 0 2
3 1855 19.1 0 0 1 0 0 0 0 0 0 0 0 3
4 2187 18.8 0 0 0 1 0 0 0 0 0 0 0 4
5 1852 18.2 0 0 0 0 1 0 0 0 0 0 0 5
6 1570 18.0 0 0 0 0 0 1 0 0 0 0 0 6
7 1851 19.0 0 0 0 0 0 0 1 0 0 0 0 7
8 1954 20.7 0 0 0 0 0 0 0 1 0 0 0 8
9 1828 21.2 0 0 0 0 0 0 0 0 1 0 0 9
10 2251 20.7 0 0 0 0 0 0 0 0 0 1 0 10
11 2277 19.6 0 0 0 0 0 0 0 0 0 0 1 11
12 2085 18.6 0 0 0 0 0 0 0 0 0 0 0 12
13 2282 18.7 1 0 0 0 0 0 0 0 0 0 0 13
14 2266 23.8 0 1 0 0 0 0 0 0 0 0 0 14
15 1878 24.9 0 0 1 0 0 0 0 0 0 0 0 15
16 2267 24.8 0 0 0 1 0 0 0 0 0 0 0 16
17 2069 23.8 0 0 0 0 1 0 0 0 0 0 0 17
18 1746 22.3 0 0 0 0 0 1 0 0 0 0 0 18
19 2299 21.7 0 0 0 0 0 0 1 0 0 0 0 19
20 2360 20.7 0 0 0 0 0 0 0 1 0 0 0 20
21 2214 19.7 0 0 0 0 0 0 0 0 1 0 0 21
22 2825 18.4 0 0 0 0 0 0 0 0 0 1 0 22
23 2355 17.4 0 0 0 0 0 0 0 0 0 0 1 23
24 2333 17.0 0 0 0 0 0 0 0 0 0 0 0 24
25 3016 18.0 1 0 0 0 0 0 0 0 0 0 0 25
26 2155 23.8 0 1 0 0 0 0 0 0 0 0 0 26
27 2172 25.5 0 0 1 0 0 0 0 0 0 0 0 27
28 2150 25.6 0 0 0 1 0 0 0 0 0 0 0 28
29 2533 23.7 0 0 0 0 1 0 0 0 0 0 0 29
30 2058 22.0 0 0 0 0 0 1 0 0 0 0 0 30
31 2160 21.3 0 0 0 0 0 0 1 0 0 0 0 31
32 2260 20.7 0 0 0 0 0 0 0 1 0 0 0 32
33 2498 20.4 0 0 0 0 0 0 0 0 1 0 0 33
34 2695 20.3 0 0 0 0 0 0 0 0 0 1 0 34
35 2799 20.4 0 0 0 0 0 0 0 0 0 0 1 35
36 2946 19.8 0 0 0 0 0 0 0 0 0 0 0 36
37 2930 19.5 1 0 0 0 0 0 0 0 0 0 0 37
38 2318 23.1 0 1 0 0 0 0 0 0 0 0 0 38
39 2540 23.5 0 0 1 0 0 0 0 0 0 0 0 39
40 2570 23.5 0 0 0 1 0 0 0 0 0 0 0 40
41 2669 22.9 0 0 0 0 1 0 0 0 0 0 0 41
42 2450 21.9 0 0 0 0 0 1 0 0 0 0 0 42
43 2842 21.5 0 0 0 0 0 0 1 0 0 0 0 43
44 3440 20.5 0 0 0 0 0 0 0 1 0 0 0 44
45 2678 20.2 0 0 0 0 0 0 0 0 1 0 0 45
46 2981 19.4 0 0 0 0 0 0 0 0 0 1 0 46
47 2260 19.2 0 0 0 0 0 0 0 0 0 0 1 47
48 2844 18.8 0 0 0 0 0 0 0 0 0 0 0 48
49 2546 18.8 1 0 0 0 0 0 0 0 0 0 0 49
50 2456 22.6 0 1 0 0 0 0 0 0 0 0 0 50
51 2295 23.3 0 0 1 0 0 0 0 0 0 0 0 51
52 2379 23.0 0 0 0 1 0 0 0 0 0 0 0 52
53 2479 21.4 0 0 0 0 1 0 0 0 0 0 0 53
54 2057 19.9 0 0 0 0 0 1 0 0 0 0 0 54
55 2280 18.8 0 0 0 0 0 0 1 0 0 0 0 55
56 2351 18.6 0 0 0 0 0 0 0 1 0 0 0 56
57 2276 18.4 0 0 0 0 0 0 0 0 1 0 0 57
58 2548 18.6 0 0 0 0 0 0 0 0 0 1 0 58
59 2311 19.9 0 0 0 0 0 0 0 0 0 0 1 59
60 2201 19.2 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `<25` M1 M2 M3 M4
1681.394 25.680 239.567 -205.134 -371.241 -214.468
M5 M6 M7 M8 M9 M10
-184.301 -507.106 -196.570 -13.228 -189.660 175.472
M11 t
-88.414 8.908
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-507.95 -185.76 -36.66 181.10 853.42
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1681.394 447.085 3.761 0.000477 ***
`<25` 25.680 23.578 1.089 0.281760
M1 239.567 179.371 1.336 0.188252
M2 -205.134 201.199 -1.020 0.313270
M3 -371.241 211.048 -1.759 0.085222 .
M4 -214.468 209.102 -1.026 0.310416
M5 -184.301 196.018 -0.940 0.352015
M6 -507.106 185.822 -2.729 0.008969 **
M7 -196.570 183.298 -1.072 0.289132
M8 -13.228 181.886 -0.073 0.942338
M9 -189.660 180.498 -1.051 0.298858
M10 175.472 178.652 0.982 0.331138
M11 -88.414 178.147 -0.496 0.622050
t 8.908 2.194 4.060 0.000189 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 280.6 on 46 degrees of freedom
Multiple R-squared: 0.4979, Adjusted R-squared: 0.356
F-statistic: 3.508 on 13 and 46 DF, p-value: 0.0007974
> 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,] 0.0122714685 0.0245429369 0.9877285
[2,] 0.0046756261 0.0093512522 0.9953244
[3,] 0.0073077722 0.0146155445 0.9926922
[4,] 0.0023839815 0.0047679629 0.9976160
[5,] 0.0008932019 0.0017864038 0.9991068
[6,] 0.0002559738 0.0005119477 0.9997440
[7,] 0.0020221451 0.0040442903 0.9979779
[8,] 0.0009001603 0.0018003206 0.9990998
[9,] 0.0020043946 0.0040087892 0.9979956
[10,] 0.0116893276 0.0233786553 0.9883107
[11,] 0.0068224127 0.0136448254 0.9931776
[12,] 0.0149191901 0.0298383803 0.9850808
[13,] 0.0134278247 0.0268556494 0.9865722
[14,] 0.0086482811 0.0172965622 0.9913517
[15,] 0.0179797123 0.0359594246 0.9820203
[16,] 0.1483374226 0.2966748453 0.8516626
[17,] 0.1534668594 0.3069337187 0.8465331
[18,] 0.3133359118 0.6266718236 0.6866641
[19,] 0.2499999855 0.4999999710 0.7500000
[20,] 0.2683081609 0.5366163218 0.7316918
[21,] 0.1885217130 0.3770434260 0.8114783
[22,] 0.3169947316 0.6339894632 0.6830053
[23,] 0.2273747267 0.4547494535 0.7726253
[24,] 0.1666818835 0.3333637670 0.8333181
[25,] 0.1567952553 0.3135905105 0.8432047
[26,] 0.1243098534 0.2486197068 0.8756901
[27,] 0.1040586994 0.2081173989 0.8959413
> postscript(file="/var/www/html/rcomp/tmp/10nbc1258737461.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2a9zh1258737461.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3f52i1258737461.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zav51258737461.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/56pxt1258737461.ps",horizontal=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 = 60
Frequency = 1
1 2 3 4 5 6
-59.506192 246.270011 27.628521 201.650973 -157.016383 -119.983067
7 8 9 10 11 12
-184.108260 -317.014416 -288.331436 -226.531436 82.694430 -180.947060
13 14 15 16 17 18
-234.990550 53.833248 -205.216416 20.669978 -190.725262 -161.307570
19 20 21 22 23 24
87.655700 -17.913674 29.289740 299.633971 110.291808 1.242145
25 26 27 28 29 30
410.086394 -164.066011 -33.523848 -223.773512 168.943508 51.497259
31 32 33 34 35 36
-147.971443 -224.812933 188.414278 13.942163 370.351682 435.438076
37 38 39 40 41 42
178.666702 -89.989067 278.937472 143.255837 218.588481 339.166029
43 44 45 46 47 48
421.993241 853.423866 266.651078 216.155165 -244.731230 252.219107
49 50 51 52 53 54
-294.256354 -46.048181 -67.825729 -141.803277 -39.790344 -109.372651
55 56 57 58 59 60
-177.569237 -293.682843 -196.023660 -303.199863 -318.606691 -507.952268
> postscript(file="/var/www/html/rcomp/tmp/68zgw1258737461.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -59.506192 NA
1 246.270011 -59.506192
2 27.628521 246.270011
3 201.650973 27.628521
4 -157.016383 201.650973
5 -119.983067 -157.016383
6 -184.108260 -119.983067
7 -317.014416 -184.108260
8 -288.331436 -317.014416
9 -226.531436 -288.331436
10 82.694430 -226.531436
11 -180.947060 82.694430
12 -234.990550 -180.947060
13 53.833248 -234.990550
14 -205.216416 53.833248
15 20.669978 -205.216416
16 -190.725262 20.669978
17 -161.307570 -190.725262
18 87.655700 -161.307570
19 -17.913674 87.655700
20 29.289740 -17.913674
21 299.633971 29.289740
22 110.291808 299.633971
23 1.242145 110.291808
24 410.086394 1.242145
25 -164.066011 410.086394
26 -33.523848 -164.066011
27 -223.773512 -33.523848
28 168.943508 -223.773512
29 51.497259 168.943508
30 -147.971443 51.497259
31 -224.812933 -147.971443
32 188.414278 -224.812933
33 13.942163 188.414278
34 370.351682 13.942163
35 435.438076 370.351682
36 178.666702 435.438076
37 -89.989067 178.666702
38 278.937472 -89.989067
39 143.255837 278.937472
40 218.588481 143.255837
41 339.166029 218.588481
42 421.993241 339.166029
43 853.423866 421.993241
44 266.651078 853.423866
45 216.155165 266.651078
46 -244.731230 216.155165
47 252.219107 -244.731230
48 -294.256354 252.219107
49 -46.048181 -294.256354
50 -67.825729 -46.048181
51 -141.803277 -67.825729
52 -39.790344 -141.803277
53 -109.372651 -39.790344
54 -177.569237 -109.372651
55 -293.682843 -177.569237
56 -196.023660 -293.682843
57 -303.199863 -196.023660
58 -318.606691 -303.199863
59 -507.952268 -318.606691
60 NA -507.952268
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 246.270011 -59.506192
[2,] 27.628521 246.270011
[3,] 201.650973 27.628521
[4,] -157.016383 201.650973
[5,] -119.983067 -157.016383
[6,] -184.108260 -119.983067
[7,] -317.014416 -184.108260
[8,] -288.331436 -317.014416
[9,] -226.531436 -288.331436
[10,] 82.694430 -226.531436
[11,] -180.947060 82.694430
[12,] -234.990550 -180.947060
[13,] 53.833248 -234.990550
[14,] -205.216416 53.833248
[15,] 20.669978 -205.216416
[16,] -190.725262 20.669978
[17,] -161.307570 -190.725262
[18,] 87.655700 -161.307570
[19,] -17.913674 87.655700
[20,] 29.289740 -17.913674
[21,] 299.633971 29.289740
[22,] 110.291808 299.633971
[23,] 1.242145 110.291808
[24,] 410.086394 1.242145
[25,] -164.066011 410.086394
[26,] -33.523848 -164.066011
[27,] -223.773512 -33.523848
[28,] 168.943508 -223.773512
[29,] 51.497259 168.943508
[30,] -147.971443 51.497259
[31,] -224.812933 -147.971443
[32,] 188.414278 -224.812933
[33,] 13.942163 188.414278
[34,] 370.351682 13.942163
[35,] 435.438076 370.351682
[36,] 178.666702 435.438076
[37,] -89.989067 178.666702
[38,] 278.937472 -89.989067
[39,] 143.255837 278.937472
[40,] 218.588481 143.255837
[41,] 339.166029 218.588481
[42,] 421.993241 339.166029
[43,] 853.423866 421.993241
[44,] 266.651078 853.423866
[45,] 216.155165 266.651078
[46,] -244.731230 216.155165
[47,] 252.219107 -244.731230
[48,] -294.256354 252.219107
[49,] -46.048181 -294.256354
[50,] -67.825729 -46.048181
[51,] -141.803277 -67.825729
[52,] -39.790344 -141.803277
[53,] -109.372651 -39.790344
[54,] -177.569237 -109.372651
[55,] -293.682843 -177.569237
[56,] -196.023660 -293.682843
[57,] -303.199863 -196.023660
[58,] -318.606691 -303.199863
[59,] -507.952268 -318.606691
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 246.270011 -59.506192
2 27.628521 246.270011
3 201.650973 27.628521
4 -157.016383 201.650973
5 -119.983067 -157.016383
6 -184.108260 -119.983067
7 -317.014416 -184.108260
8 -288.331436 -317.014416
9 -226.531436 -288.331436
10 82.694430 -226.531436
11 -180.947060 82.694430
12 -234.990550 -180.947060
13 53.833248 -234.990550
14 -205.216416 53.833248
15 20.669978 -205.216416
16 -190.725262 20.669978
17 -161.307570 -190.725262
18 87.655700 -161.307570
19 -17.913674 87.655700
20 29.289740 -17.913674
21 299.633971 29.289740
22 110.291808 299.633971
23 1.242145 110.291808
24 410.086394 1.242145
25 -164.066011 410.086394
26 -33.523848 -164.066011
27 -223.773512 -33.523848
28 168.943508 -223.773512
29 51.497259 168.943508
30 -147.971443 51.497259
31 -224.812933 -147.971443
32 188.414278 -224.812933
33 13.942163 188.414278
34 370.351682 13.942163
35 435.438076 370.351682
36 178.666702 435.438076
37 -89.989067 178.666702
38 278.937472 -89.989067
39 143.255837 278.937472
40 218.588481 143.255837
41 339.166029 218.588481
42 421.993241 339.166029
43 853.423866 421.993241
44 266.651078 853.423866
45 216.155165 266.651078
46 -244.731230 216.155165
47 252.219107 -244.731230
48 -294.256354 252.219107
49 -46.048181 -294.256354
50 -67.825729 -46.048181
51 -141.803277 -67.825729
52 -39.790344 -141.803277
53 -109.372651 -39.790344
54 -177.569237 -109.372651
55 -293.682843 -177.569237
56 -196.023660 -293.682843
57 -303.199863 -196.023660
58 -318.606691 -303.199863
59 -507.952268 -318.606691
> 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/html/rcomp/tmp/78h6q1258737461.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8z6561258737461.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/94xkn1258737461.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10nrmz1258737461.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/115v291258737461.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/html/rcomp/tmp/12mnai1258737461.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/html/rcomp/tmp/13ubza1258737461.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/html/rcomp/tmp/14ih541258737461.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/html/rcomp/tmp/1549mg1258737461.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/html/rcomp/tmp/16vtn11258737461.tab")
+ }
> system("convert tmp/10nbc1258737461.ps tmp/10nbc1258737461.png")
> system("convert tmp/2a9zh1258737461.ps tmp/2a9zh1258737461.png")
> system("convert tmp/3f52i1258737461.ps tmp/3f52i1258737461.png")
> system("convert tmp/4zav51258737461.ps tmp/4zav51258737461.png")
> system("convert tmp/56pxt1258737461.ps tmp/56pxt1258737461.png")
> system("convert tmp/68zgw1258737461.ps tmp/68zgw1258737461.png")
> system("convert tmp/78h6q1258737461.ps tmp/78h6q1258737461.png")
> system("convert tmp/8z6561258737461.ps tmp/8z6561258737461.png")
> system("convert tmp/94xkn1258737461.ps tmp/94xkn1258737461.png")
> system("convert tmp/10nrmz1258737461.ps tmp/10nrmz1258737461.png")
>
>
> proc.time()
user system elapsed
2.399 1.584 4.087