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(1,8,1,14,4,2,8,3,82,1,3,8,2,14,3,4,8,1,16,5,5,8,5,140,7,6,8,8,173,2,7,8,3,9,8,8,8,8,13,6,1,12,12,17,4,2,12,3,16,9,3,12,8,21,7,4,12,3,14,2,5,12,3,15,12,6,12,3,10,8,7,12,3,14,1,8,12,1,16,6,9,12,2,14,10,10,12,20,17,3,11,12,2,10,5,12,12,1,23,11,1,9,1,21,2,2,9,6,14,4,3,9,8,14,7,4,9,5,14,11,5,9,1,16,5,6,9,7,14,1,7,9,7,14,9,8,9,5,7,3,9,9,8,17,10,1,14,2,14,3,2,14,5,21,4,3,14,2,24,7,4,14,5,7,6,5,14,1,30,13,6,14,2,93,16,7,14,6,14,9,8,14,3,14,1,9,14,6,107,10,10,14,6,231,5,11,14,1,385,2,12,14,2,14,11,13,14,10,29,14,14,14,1,16,15,1,13,2,7,10,2,13,1,21,3,3,13,1,14,2,4,13,1,17,13,5,13,6,14,4,6,13,4,21,1),dim=c(5,49),dimnames=list(c('position','starters','last','since','number'),1:49))
> y <- array(NA,dim=c(5,49),dimnames=list(c('position','starters','last','since','number'),1:49))
> 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'
> #'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
> 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
position starters last since number
1 1 8 1 14 4
2 2 8 3 82 1
3 3 8 2 14 3
4 4 8 1 16 5
5 5 8 5 140 7
6 6 8 8 173 2
7 7 8 3 9 8
8 8 8 8 13 6
9 1 12 12 17 4
10 2 12 3 16 9
11 3 12 8 21 7
12 4 12 3 14 2
13 5 12 3 15 12
14 6 12 3 10 8
15 7 12 3 14 1
16 8 12 1 16 6
17 9 12 2 14 10
18 10 12 20 17 3
19 11 12 2 10 5
20 12 12 1 23 11
21 1 9 1 21 2
22 2 9 6 14 4
23 3 9 8 14 7
24 4 9 5 14 11
25 5 9 1 16 5
26 6 9 7 14 1
27 7 9 7 14 9
28 8 9 5 7 3
29 9 9 8 17 10
30 1 14 2 14 3
31 2 14 5 21 4
32 3 14 2 24 7
33 4 14 5 7 6
34 5 14 1 30 13
35 6 14 2 93 16
36 7 14 6 14 9
37 8 14 3 14 1
38 9 14 6 107 10
39 10 14 6 231 5
40 11 14 1 385 2
41 12 14 2 14 11
42 13 14 10 29 14
43 14 14 1 16 15
44 1 13 2 7 10
45 2 13 1 21 3
46 3 13 1 14 2
47 4 13 1 17 13
48 5 13 6 14 4
49 6 13 4 21 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) starters last since number
0.06947 0.21935 0.18457 0.01461 0.29378
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.3401 -2.4026 -0.0964 2.2936 6.3141
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.069465 2.443209 0.028 0.9774
starters 0.219355 0.207818 1.056 0.2970
last 0.184571 0.127766 1.445 0.1557
since 0.014612 0.006915 2.113 0.0403 *
number 0.293779 0.116620 2.519 0.0155 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.166 on 44 degrees of freedom
Multiple R-squared: 0.249, Adjusted R-squared: 0.1807
F-statistic: 3.647 on 4 and 44 DF, p-value: 0.0119
> 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,] 0.08650524 0.17301048 0.9134948
[2,] 0.03244194 0.06488388 0.9675581
[3,] 0.03866910 0.07733820 0.9613309
[4,] 0.01864623 0.03729245 0.9813538
[5,] 0.14819422 0.29638844 0.8518058
[6,] 0.09391963 0.18783925 0.9060804
[7,] 0.08993113 0.17986227 0.9100689
[8,] 0.18443673 0.36887347 0.8155633
[9,] 0.20993444 0.41986888 0.7900656
[10,] 0.21517820 0.43035640 0.7848218
[11,] 0.26660382 0.53320765 0.7333962
[12,] 0.48971541 0.97943083 0.5102846
[13,] 0.66216393 0.67567215 0.3378361
[14,] 0.61212687 0.77574626 0.3878731
[15,] 0.57816601 0.84366799 0.4218340
[16,] 0.57439732 0.85120537 0.4256027
[17,] 0.55261469 0.89477062 0.4473853
[18,] 0.46661585 0.93323169 0.5333842
[19,] 0.40248279 0.80496558 0.5975172
[20,] 0.33680496 0.67360991 0.6631950
[21,] 0.34456825 0.68913650 0.6554317
[22,] 0.33018422 0.66036844 0.6698158
[23,] 0.37616313 0.75232625 0.6238369
[24,] 0.45521415 0.91042830 0.5447858
[25,] 0.49595389 0.99190778 0.5040461
[26,] 0.57689254 0.84621491 0.4231075
[27,] 0.61972003 0.76055993 0.3802800
[28,] 0.73618786 0.52762428 0.2638121
[29,] 0.79910949 0.40178102 0.2008905
[30,] 0.84160127 0.31679747 0.1583987
[31,] 0.87634304 0.24731393 0.1236570
[32,] 0.85825440 0.28349121 0.1417456
[33,] 0.76963444 0.46073112 0.2303656
[34,] 0.65729709 0.68540582 0.3427029
> postscript(file="/var/wessaorg/rcomp/tmp/1l3dt1322053572.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2nmyj1322053572.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30ylq1322053572.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4iag11322053572.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5bdnr1322053572.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 = 49
Frequency = 1
1 2 3 4 5 6
-2.38855138 -1.86994680 -0.27934389 0.28844647 -1.84923658 -0.41623930
7 8 9 10 11 12
2.14024802 2.74650223 -5.34009044 -4.13323050 -3.54158790 -0.04755472
13 14 15 16 17 18
-1.99995570 0.24821809 3.24622422 3.11724921 2.78678523 2.47711692
19 20 21 22 23 24
6.31412635 5.54607327 -2.12262934 -2.53076319 -2.78124290 -2.40264431
25 26 27 28 29 30
1.06909189 2.16600217 0.81577067 4.04986845 2.29358546 -3.59547137
31 32 33 34 35 36
-3.54524590 -2.91670320 -1.92824127 -2.58247502 -3.56891459 -0.09643078
37 38 39 40 41 42
3.80751506 0.25091074 0.90796603 1.46197245 5.05429713 3.47721462
43 44 45 46 47 48
6.03452961 -5.33028809 -2.29382660 -0.89776640 -3.17316954 -0.40818152
49
1.74001694
> postscript(file="/var/wessaorg/rcomp/tmp/633yl1322053572.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 = 49
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.38855138 NA
1 -1.86994680 -2.38855138
2 -0.27934389 -1.86994680
3 0.28844647 -0.27934389
4 -1.84923658 0.28844647
5 -0.41623930 -1.84923658
6 2.14024802 -0.41623930
7 2.74650223 2.14024802
8 -5.34009044 2.74650223
9 -4.13323050 -5.34009044
10 -3.54158790 -4.13323050
11 -0.04755472 -3.54158790
12 -1.99995570 -0.04755472
13 0.24821809 -1.99995570
14 3.24622422 0.24821809
15 3.11724921 3.24622422
16 2.78678523 3.11724921
17 2.47711692 2.78678523
18 6.31412635 2.47711692
19 5.54607327 6.31412635
20 -2.12262934 5.54607327
21 -2.53076319 -2.12262934
22 -2.78124290 -2.53076319
23 -2.40264431 -2.78124290
24 1.06909189 -2.40264431
25 2.16600217 1.06909189
26 0.81577067 2.16600217
27 4.04986845 0.81577067
28 2.29358546 4.04986845
29 -3.59547137 2.29358546
30 -3.54524590 -3.59547137
31 -2.91670320 -3.54524590
32 -1.92824127 -2.91670320
33 -2.58247502 -1.92824127
34 -3.56891459 -2.58247502
35 -0.09643078 -3.56891459
36 3.80751506 -0.09643078
37 0.25091074 3.80751506
38 0.90796603 0.25091074
39 1.46197245 0.90796603
40 5.05429713 1.46197245
41 3.47721462 5.05429713
42 6.03452961 3.47721462
43 -5.33028809 6.03452961
44 -2.29382660 -5.33028809
45 -0.89776640 -2.29382660
46 -3.17316954 -0.89776640
47 -0.40818152 -3.17316954
48 1.74001694 -0.40818152
49 NA 1.74001694
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.86994680 -2.38855138
[2,] -0.27934389 -1.86994680
[3,] 0.28844647 -0.27934389
[4,] -1.84923658 0.28844647
[5,] -0.41623930 -1.84923658
[6,] 2.14024802 -0.41623930
[7,] 2.74650223 2.14024802
[8,] -5.34009044 2.74650223
[9,] -4.13323050 -5.34009044
[10,] -3.54158790 -4.13323050
[11,] -0.04755472 -3.54158790
[12,] -1.99995570 -0.04755472
[13,] 0.24821809 -1.99995570
[14,] 3.24622422 0.24821809
[15,] 3.11724921 3.24622422
[16,] 2.78678523 3.11724921
[17,] 2.47711692 2.78678523
[18,] 6.31412635 2.47711692
[19,] 5.54607327 6.31412635
[20,] -2.12262934 5.54607327
[21,] -2.53076319 -2.12262934
[22,] -2.78124290 -2.53076319
[23,] -2.40264431 -2.78124290
[24,] 1.06909189 -2.40264431
[25,] 2.16600217 1.06909189
[26,] 0.81577067 2.16600217
[27,] 4.04986845 0.81577067
[28,] 2.29358546 4.04986845
[29,] -3.59547137 2.29358546
[30,] -3.54524590 -3.59547137
[31,] -2.91670320 -3.54524590
[32,] -1.92824127 -2.91670320
[33,] -2.58247502 -1.92824127
[34,] -3.56891459 -2.58247502
[35,] -0.09643078 -3.56891459
[36,] 3.80751506 -0.09643078
[37,] 0.25091074 3.80751506
[38,] 0.90796603 0.25091074
[39,] 1.46197245 0.90796603
[40,] 5.05429713 1.46197245
[41,] 3.47721462 5.05429713
[42,] 6.03452961 3.47721462
[43,] -5.33028809 6.03452961
[44,] -2.29382660 -5.33028809
[45,] -0.89776640 -2.29382660
[46,] -3.17316954 -0.89776640
[47,] -0.40818152 -3.17316954
[48,] 1.74001694 -0.40818152
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.86994680 -2.38855138
2 -0.27934389 -1.86994680
3 0.28844647 -0.27934389
4 -1.84923658 0.28844647
5 -0.41623930 -1.84923658
6 2.14024802 -0.41623930
7 2.74650223 2.14024802
8 -5.34009044 2.74650223
9 -4.13323050 -5.34009044
10 -3.54158790 -4.13323050
11 -0.04755472 -3.54158790
12 -1.99995570 -0.04755472
13 0.24821809 -1.99995570
14 3.24622422 0.24821809
15 3.11724921 3.24622422
16 2.78678523 3.11724921
17 2.47711692 2.78678523
18 6.31412635 2.47711692
19 5.54607327 6.31412635
20 -2.12262934 5.54607327
21 -2.53076319 -2.12262934
22 -2.78124290 -2.53076319
23 -2.40264431 -2.78124290
24 1.06909189 -2.40264431
25 2.16600217 1.06909189
26 0.81577067 2.16600217
27 4.04986845 0.81577067
28 2.29358546 4.04986845
29 -3.59547137 2.29358546
30 -3.54524590 -3.59547137
31 -2.91670320 -3.54524590
32 -1.92824127 -2.91670320
33 -2.58247502 -1.92824127
34 -3.56891459 -2.58247502
35 -0.09643078 -3.56891459
36 3.80751506 -0.09643078
37 0.25091074 3.80751506
38 0.90796603 0.25091074
39 1.46197245 0.90796603
40 5.05429713 1.46197245
41 3.47721462 5.05429713
42 6.03452961 3.47721462
43 -5.33028809 6.03452961
44 -2.29382660 -5.33028809
45 -0.89776640 -2.29382660
46 -3.17316954 -0.89776640
47 -0.40818152 -3.17316954
48 1.74001694 -0.40818152
> 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/7xu9y1322053572.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8a8vv1322053572.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9b9pz1322053572.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10vojv1322053572.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11m4m51322053572.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/12e37q1322053572.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/13hvik1322053572.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/14yg6v1322053572.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/15bgul1322053572.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/16oqh01322053572.tab")
+ }
>
> try(system("convert tmp/1l3dt1322053572.ps tmp/1l3dt1322053572.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nmyj1322053572.ps tmp/2nmyj1322053572.png",intern=TRUE))
character(0)
> try(system("convert tmp/30ylq1322053572.ps tmp/30ylq1322053572.png",intern=TRUE))
character(0)
> try(system("convert tmp/4iag11322053572.ps tmp/4iag11322053572.png",intern=TRUE))
character(0)
> try(system("convert tmp/5bdnr1322053572.ps tmp/5bdnr1322053572.png",intern=TRUE))
character(0)
> try(system("convert tmp/633yl1322053572.ps tmp/633yl1322053572.png",intern=TRUE))
character(0)
> try(system("convert tmp/7xu9y1322053572.ps tmp/7xu9y1322053572.png",intern=TRUE))
character(0)
> try(system("convert tmp/8a8vv1322053572.ps tmp/8a8vv1322053572.png",intern=TRUE))
character(0)
> try(system("convert tmp/9b9pz1322053572.ps tmp/9b9pz1322053572.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vojv1322053572.ps tmp/10vojv1322053572.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.134 0.612 3.847