R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
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.
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(1.66
+ ,0.67
+ ,0.38
+ ,0.25
+ ,0.07
+ ,0.16
+ ,0.34
+ ,0.10
+ ,0.07
+ ,0.63
+ ,0.45
+ ,0.08
+ ,0.27
+ ,0.24
+ ,0.91
+ ,0.46
+ ,0.76
+ ,0.75
+ ,1.55
+ ,0.18
+ ,0.72
+ ,0.18
+ ,0.27
+ ,0.81
+ ,0.39
+ ,0.60
+ ,0.58
+ ,0.44
+ ,0.06
+ ,0.02
+ ,0.28
+ ,0.77
+ ,0.05
+ ,0.79
+ ,0.80
+ ,0.66
+ ,-0.06
+ ,0.25
+ ,0.87
+ ,0.23
+ ,0.22
+ ,0.03
+ ,0.24
+ ,0.88
+ ,0.89
+ ,0.92
+ ,0.82
+ ,0.22
+ ,2.03
+ ,0.22
+ ,0.30
+ ,0.32
+ ,0.60
+ ,0.31
+ ,1.80
+ ,0.10
+ ,0.44
+ ,0.28
+ ,0.60
+ ,0.87
+ ,0.39
+ ,0.08
+ ,0.65
+ ,0.40
+ ,0.78
+ ,0.28
+ ,-0.68
+ ,0.85
+ ,0.28
+ ,0.91
+ ,0.90
+ ,0.73
+ ,1.00
+ ,0.66
+ ,0.43
+ ,0.87
+ ,0.70
+ ,0.20
+ ,1.69
+ ,0.81
+ ,0.72
+ ,0.73
+ ,0.38
+ ,0.36
+ ,1.25
+ ,0.45
+ ,0.96
+ ,0.46
+ ,0.51
+ ,0.08
+ ,0.93
+ ,0.41
+ ,0.75
+ ,0.86
+ ,0.62
+ ,0.24
+ ,0.22
+ ,0.56
+ ,0.86
+ ,0.48
+ ,0.81
+ ,0.94
+ ,0.46
+ ,0.38
+ ,0.47
+ ,0.27
+ ,0.96
+ ,0.48
+ ,-1.05
+ ,0.32
+ ,0.05
+ ,0.37
+ ,0.07
+ ,0.58
+ ,1.25
+ ,0.74
+ ,0.01
+ ,0.95
+ ,0.31
+ ,0.03
+ ,1.88
+ ,0.32
+ ,0.05
+ ,0.02
+ ,0.56
+ ,0.15
+ ,1.31
+ ,0.88
+ ,0.82
+ ,0.60
+ ,0.22
+ ,0.22
+ ,1.37
+ ,0.85
+ ,0.99
+ ,0.57
+ ,0.50
+ ,0.48
+ ,-1.22
+ ,0.03
+ ,0.10
+ ,0.73
+ ,0.85
+ ,0.27
+ ,-0.14
+ ,0.11
+ ,0.18
+ ,0.21
+ ,0.47
+ ,0.68
+ ,0.29
+ ,0.16
+ ,0.83
+ ,0.70
+ ,0.83
+ ,0.16
+ ,0.60
+ ,0.63
+ ,0.06
+ ,0.02
+ ,0.93
+ ,0.67
+ ,0.84
+ ,0.90
+ ,0.39
+ ,0.67
+ ,0.32
+ ,0.70
+ ,-2.13
+ ,0.30
+ ,0.17
+ ,0.80
+ ,0.39
+ ,0.35
+ ,1.30
+ ,0.54
+ ,0.39
+ ,0.67
+ ,0.64
+ ,0.09
+ ,1.25
+ ,0.53
+ ,0.31
+ ,0.64
+ ,0.86
+ ,0.93
+ ,1.54
+ ,0.15
+ ,0.30
+ ,0.85
+ ,0.68
+ ,0.20
+ ,1.58
+ ,0.38
+ ,0.26
+ ,0.88
+ ,0.89
+ ,0.18
+ ,1.20
+ ,0.94
+ ,0.01
+ ,0.22
+ ,0.63
+ ,0.41
+ ,1.81
+ ,0.86
+ ,0.95
+ ,0.88
+ ,0.42
+ ,0.39
+ ,-0.63
+ ,0.53
+ ,0.05
+ ,0.89
+ ,0.87
+ ,0.89
+ ,0.74
+ ,1.00
+ ,0.47
+ ,0.84
+ ,0.64
+ ,0.03
+ ,1.69
+ ,0.74
+ ,0.65
+ ,0.31
+ ,0.33
+ ,0.14
+ ,0.80
+ ,0.03
+ ,0.17
+ ,0.13
+ ,0.99
+ ,0.77)
+ ,dim=c(6
+ ,39)
+ ,dimnames=list(c('succes'
+ ,'kleding'
+ ,'socialevaardigheden'
+ ,'zelfzekerheid'
+ ,'testosteron'
+ ,'verzorgdheid')
+ ,1:39))
> y <- array(NA,dim=c(6,39),dimnames=list(c('succes','kleding','socialevaardigheden','zelfzekerheid','testosteron','verzorgdheid'),1:39))
> 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'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects 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
succes kleding socialevaardigheden zelfzekerheid testosteron verzorgdheid
1 1.66 0.67 0.38 0.25 0.07 0.16
2 0.34 0.10 0.07 0.63 0.45 0.08
3 0.27 0.24 0.91 0.46 0.76 0.75
4 1.55 0.18 0.72 0.18 0.27 0.81
5 0.39 0.60 0.58 0.44 0.06 0.02
6 0.28 0.77 0.05 0.79 0.80 0.66
7 -0.06 0.25 0.87 0.23 0.22 0.03
8 0.24 0.88 0.89 0.92 0.82 0.22
9 2.03 0.22 0.30 0.32 0.60 0.31
10 1.80 0.10 0.44 0.28 0.60 0.87
11 0.39 0.08 0.65 0.40 0.78 0.28
12 -0.68 0.85 0.28 0.91 0.90 0.73
13 1.00 0.66 0.43 0.87 0.70 0.20
14 1.69 0.81 0.72 0.73 0.38 0.36
15 1.25 0.45 0.96 0.46 0.51 0.08
16 0.93 0.41 0.75 0.86 0.62 0.24
17 0.22 0.56 0.86 0.48 0.81 0.94
18 0.46 0.38 0.47 0.27 0.96 0.48
19 -1.05 0.32 0.05 0.37 0.07 0.58
20 1.25 0.74 0.01 0.95 0.31 0.03
21 1.88 0.32 0.05 0.02 0.56 0.15
22 1.31 0.88 0.82 0.60 0.22 0.22
23 1.37 0.85 0.99 0.57 0.50 0.48
24 -1.22 0.03 0.10 0.73 0.85 0.27
25 -0.14 0.11 0.18 0.21 0.47 0.68
26 0.29 0.16 0.83 0.70 0.83 0.16
27 0.60 0.63 0.06 0.02 0.93 0.67
28 0.84 0.90 0.39 0.67 0.32 0.70
29 -2.13 0.30 0.17 0.80 0.39 0.35
30 1.30 0.54 0.39 0.67 0.64 0.09
31 1.25 0.53 0.31 0.64 0.86 0.93
32 1.54 0.15 0.30 0.85 0.68 0.20
33 1.58 0.38 0.26 0.88 0.89 0.18
34 1.20 0.94 0.01 0.22 0.63 0.41
35 1.81 0.86 0.95 0.88 0.42 0.39
36 -0.63 0.53 0.05 0.89 0.87 0.89
37 0.74 1.00 0.47 0.84 0.64 0.03
38 1.69 0.74 0.65 0.31 0.33 0.14
39 0.80 0.03 0.17 0.13 0.99 0.77
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) kleding socialevaardigheden
0.7539 0.9443 0.6088
zelfzekerheid testosteron verzorgdheid
-1.2189 0.3260 -0.7341
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.16583 -0.53792 0.04696 0.60910 1.46812
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.7539 0.5473 1.377 0.1776
kleding 0.9443 0.5419 1.743 0.0907 .
socialevaardigheden 0.6088 0.4647 1.310 0.1992
zelfzekerheid -1.2189 0.5935 -2.054 0.0480 *
testosteron 0.3260 0.6305 0.517 0.6086
verzorgdheid -0.7341 0.5556 -1.321 0.1956
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9054 on 33 degrees of freedom
Multiple R-squared: 0.2104, Adjusted R-squared: 0.09072
F-statistic: 1.758 on 5 and 33 DF, p-value: 0.1489
> 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,] 0.4896069 0.9792138 0.5103931
[2,] 0.4172174 0.8344348 0.5827826
[3,] 0.3072659 0.6145317 0.6927341
[4,] 0.3828555 0.7657110 0.6171445
[5,] 0.4575708 0.9151417 0.5424292
[6,] 0.5619927 0.8760147 0.4380073
[7,] 0.4794562 0.9589123 0.5205438
[8,] 0.4053629 0.8107259 0.5946371
[9,] 0.3323848 0.6647696 0.6676152
[10,] 0.2613108 0.5226216 0.7386892
[11,] 0.4452342 0.8904683 0.5547658
[12,] 0.4464775 0.8929550 0.5535225
[13,] 0.4466395 0.8932790 0.5533605
[14,] 0.3471759 0.6943518 0.6528241
[15,] 0.2826441 0.5652882 0.7173559
[16,] 0.3153751 0.6307502 0.6846249
[17,] 0.2346724 0.4693449 0.7653276
[18,] 0.3445643 0.6891287 0.6554357
[19,] 0.3015989 0.6031978 0.6984011
[20,] 0.3278360 0.6556720 0.6721640
[21,] 0.6829013 0.6341974 0.3170987
[22,] 0.5218016 0.9563969 0.4781984
> postscript(file="/var/fisher/rcomp/tmp/191ik1384719625.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2vil41384719625.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3n5ff1384719625.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4kwfg1384719625.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/59n1g1384719625.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 = 39
Frequency = 1
1 2 3 4 5 6
0.44138193 0.12894602 -0.40106030 0.91374630 -0.75219844 -0.04487525
7 8 9 10 11 12
-1.34900727 -0.87118477 1.30770358 1.46811700 -0.39635013 -1.05538715
13 14 15 16 17 18
0.34009447 0.76301513 -0.06014508 0.35461391 -0.57524972 -0.57037423
19 20 21 22 23 24
-1.28263571 0.87007612 0.74539502 0.04696493 0.09482721 -1.25223641
25 26 27 28 29 30
-0.50546287 -0.42020317 -0.57231697 0.22493387 -2.16583265 0.47280045
31 32 33 34 35 36
0.98928984 1.42297975 1.22357256 -0.08391604 0.88759299 -0.46033318
37 38 39
-0.40712544 0.21461138 0.31523234
> postscript(file="/var/fisher/rcomp/tmp/6j9t11384719625.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 = 39
Frequency = 1
lag(myerror, k = 1) myerror
0 0.44138193 NA
1 0.12894602 0.44138193
2 -0.40106030 0.12894602
3 0.91374630 -0.40106030
4 -0.75219844 0.91374630
5 -0.04487525 -0.75219844
6 -1.34900727 -0.04487525
7 -0.87118477 -1.34900727
8 1.30770358 -0.87118477
9 1.46811700 1.30770358
10 -0.39635013 1.46811700
11 -1.05538715 -0.39635013
12 0.34009447 -1.05538715
13 0.76301513 0.34009447
14 -0.06014508 0.76301513
15 0.35461391 -0.06014508
16 -0.57524972 0.35461391
17 -0.57037423 -0.57524972
18 -1.28263571 -0.57037423
19 0.87007612 -1.28263571
20 0.74539502 0.87007612
21 0.04696493 0.74539502
22 0.09482721 0.04696493
23 -1.25223641 0.09482721
24 -0.50546287 -1.25223641
25 -0.42020317 -0.50546287
26 -0.57231697 -0.42020317
27 0.22493387 -0.57231697
28 -2.16583265 0.22493387
29 0.47280045 -2.16583265
30 0.98928984 0.47280045
31 1.42297975 0.98928984
32 1.22357256 1.42297975
33 -0.08391604 1.22357256
34 0.88759299 -0.08391604
35 -0.46033318 0.88759299
36 -0.40712544 -0.46033318
37 0.21461138 -0.40712544
38 0.31523234 0.21461138
39 NA 0.31523234
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.12894602 0.44138193
[2,] -0.40106030 0.12894602
[3,] 0.91374630 -0.40106030
[4,] -0.75219844 0.91374630
[5,] -0.04487525 -0.75219844
[6,] -1.34900727 -0.04487525
[7,] -0.87118477 -1.34900727
[8,] 1.30770358 -0.87118477
[9,] 1.46811700 1.30770358
[10,] -0.39635013 1.46811700
[11,] -1.05538715 -0.39635013
[12,] 0.34009447 -1.05538715
[13,] 0.76301513 0.34009447
[14,] -0.06014508 0.76301513
[15,] 0.35461391 -0.06014508
[16,] -0.57524972 0.35461391
[17,] -0.57037423 -0.57524972
[18,] -1.28263571 -0.57037423
[19,] 0.87007612 -1.28263571
[20,] 0.74539502 0.87007612
[21,] 0.04696493 0.74539502
[22,] 0.09482721 0.04696493
[23,] -1.25223641 0.09482721
[24,] -0.50546287 -1.25223641
[25,] -0.42020317 -0.50546287
[26,] -0.57231697 -0.42020317
[27,] 0.22493387 -0.57231697
[28,] -2.16583265 0.22493387
[29,] 0.47280045 -2.16583265
[30,] 0.98928984 0.47280045
[31,] 1.42297975 0.98928984
[32,] 1.22357256 1.42297975
[33,] -0.08391604 1.22357256
[34,] 0.88759299 -0.08391604
[35,] -0.46033318 0.88759299
[36,] -0.40712544 -0.46033318
[37,] 0.21461138 -0.40712544
[38,] 0.31523234 0.21461138
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.12894602 0.44138193
2 -0.40106030 0.12894602
3 0.91374630 -0.40106030
4 -0.75219844 0.91374630
5 -0.04487525 -0.75219844
6 -1.34900727 -0.04487525
7 -0.87118477 -1.34900727
8 1.30770358 -0.87118477
9 1.46811700 1.30770358
10 -0.39635013 1.46811700
11 -1.05538715 -0.39635013
12 0.34009447 -1.05538715
13 0.76301513 0.34009447
14 -0.06014508 0.76301513
15 0.35461391 -0.06014508
16 -0.57524972 0.35461391
17 -0.57037423 -0.57524972
18 -1.28263571 -0.57037423
19 0.87007612 -1.28263571
20 0.74539502 0.87007612
21 0.04696493 0.74539502
22 0.09482721 0.04696493
23 -1.25223641 0.09482721
24 -0.50546287 -1.25223641
25 -0.42020317 -0.50546287
26 -0.57231697 -0.42020317
27 0.22493387 -0.57231697
28 -2.16583265 0.22493387
29 0.47280045 -2.16583265
30 0.98928984 0.47280045
31 1.42297975 0.98928984
32 1.22357256 1.42297975
33 -0.08391604 1.22357256
34 0.88759299 -0.08391604
35 -0.46033318 0.88759299
36 -0.40712544 -0.46033318
37 0.21461138 -0.40712544
38 0.31523234 0.21461138
> 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/fisher/rcomp/tmp/7j7x91384719625.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8eu8o1384719625.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9ca4t1384719625.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10u16l1384719625.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$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/fisher/rcomp/tmp/11zi6z1384719625.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12wv4e1384719625.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13kwvq1384719625.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14ccjm1384719625.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15ne0r1384719625.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/fisher/rcomp/tmp/16xs8q1384719626.tab")
+ }
>
> try(system("convert tmp/191ik1384719625.ps tmp/191ik1384719625.png",intern=TRUE))
character(0)
> try(system("convert tmp/2vil41384719625.ps tmp/2vil41384719625.png",intern=TRUE))
character(0)
> try(system("convert tmp/3n5ff1384719625.ps tmp/3n5ff1384719625.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kwfg1384719625.ps tmp/4kwfg1384719625.png",intern=TRUE))
character(0)
> try(system("convert tmp/59n1g1384719625.ps tmp/59n1g1384719625.png",intern=TRUE))
character(0)
> try(system("convert tmp/6j9t11384719625.ps tmp/6j9t11384719625.png",intern=TRUE))
character(0)
> try(system("convert tmp/7j7x91384719625.ps tmp/7j7x91384719625.png",intern=TRUE))
character(0)
> try(system("convert tmp/8eu8o1384719625.ps tmp/8eu8o1384719625.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ca4t1384719625.ps tmp/9ca4t1384719625.png",intern=TRUE))
character(0)
> try(system("convert tmp/10u16l1384719625.ps tmp/10u16l1384719625.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.500 1.212 6.717