R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
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
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Type 'q()' to quit R.
> x <- array(list(117,0,103.8,0,100.8,0,110.6,0,104,0,112.6,0,107.3,0,98.9,0,109.8,0,104.9,0,102.2,0,123.9,0,124.9,0,112.7,0,121.9,0,100.6,0,104.3,0,120.4,0,107.5,0,102.9,0,125.6,0,107.5,0,108.8,0,128.4,1,121.1,1,119.5,1,128.7,1,108.7,1,105.5,1,119.8,1,111.3,1,110.6,1,120.1,1,97.5,1,107.7,1,127.3,1,117.2,1,119.8,1,116.2,1,111,1,112.4,1,130.6,1,109.1,1,118.8,1,123.9,1,101.6,1,112.8,1,128,1,129.6,1,125.8,1,119.5,1,115.7,1,113.6,1,129.7,1,112,1,116.8,1,127,1,112.9,1,113.3,1,121.7,1),dim=c(2,60),dimnames=list(c('Cons','Reg'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Cons','Reg'),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)
> 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
Cons Reg M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 117.0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 103.8 0 0 1 0 0 0 0 0 0 0 0 0 2
3 100.8 0 0 0 1 0 0 0 0 0 0 0 0 3
4 110.6 0 0 0 0 1 0 0 0 0 0 0 0 4
5 104.0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 112.6 0 0 0 0 0 0 1 0 0 0 0 0 6
7 107.3 0 0 0 0 0 0 0 1 0 0 0 0 7
8 98.9 0 0 0 0 0 0 0 0 1 0 0 0 8
9 109.8 0 0 0 0 0 0 0 0 0 1 0 0 9
10 104.9 0 0 0 0 0 0 0 0 0 0 1 0 10
11 102.2 0 0 0 0 0 0 0 0 0 0 0 1 11
12 123.9 0 0 0 0 0 0 0 0 0 0 0 0 12
13 124.9 0 1 0 0 0 0 0 0 0 0 0 0 13
14 112.7 0 0 1 0 0 0 0 0 0 0 0 0 14
15 121.9 0 0 0 1 0 0 0 0 0 0 0 0 15
16 100.6 0 0 0 0 1 0 0 0 0 0 0 0 16
17 104.3 0 0 0 0 0 1 0 0 0 0 0 0 17
18 120.4 0 0 0 0 0 0 1 0 0 0 0 0 18
19 107.5 0 0 0 0 0 0 0 1 0 0 0 0 19
20 102.9 0 0 0 0 0 0 0 0 1 0 0 0 20
21 125.6 0 0 0 0 0 0 0 0 0 1 0 0 21
22 107.5 0 0 0 0 0 0 0 0 0 0 1 0 22
23 108.8 0 0 0 0 0 0 0 0 0 0 0 1 23
24 128.4 1 0 0 0 0 0 0 0 0 0 0 0 24
25 121.1 1 1 0 0 0 0 0 0 0 0 0 0 25
26 119.5 1 0 1 0 0 0 0 0 0 0 0 0 26
27 128.7 1 0 0 1 0 0 0 0 0 0 0 0 27
28 108.7 1 0 0 0 1 0 0 0 0 0 0 0 28
29 105.5 1 0 0 0 0 1 0 0 0 0 0 0 29
30 119.8 1 0 0 0 0 0 1 0 0 0 0 0 30
31 111.3 1 0 0 0 0 0 0 1 0 0 0 0 31
32 110.6 1 0 0 0 0 0 0 0 1 0 0 0 32
33 120.1 1 0 0 0 0 0 0 0 0 1 0 0 33
34 97.5 1 0 0 0 0 0 0 0 0 0 1 0 34
35 107.7 1 0 0 0 0 0 0 0 0 0 0 1 35
36 127.3 1 0 0 0 0 0 0 0 0 0 0 0 36
37 117.2 1 1 0 0 0 0 0 0 0 0 0 0 37
38 119.8 1 0 1 0 0 0 0 0 0 0 0 0 38
39 116.2 1 0 0 1 0 0 0 0 0 0 0 0 39
40 111.0 1 0 0 0 1 0 0 0 0 0 0 0 40
41 112.4 1 0 0 0 0 1 0 0 0 0 0 0 41
42 130.6 1 0 0 0 0 0 1 0 0 0 0 0 42
43 109.1 1 0 0 0 0 0 0 1 0 0 0 0 43
44 118.8 1 0 0 0 0 0 0 0 1 0 0 0 44
45 123.9 1 0 0 0 0 0 0 0 0 1 0 0 45
46 101.6 1 0 0 0 0 0 0 0 0 0 1 0 46
47 112.8 1 0 0 0 0 0 0 0 0 0 0 1 47
48 128.0 1 0 0 0 0 0 0 0 0 0 0 0 48
49 129.6 1 1 0 0 0 0 0 0 0 0 0 0 49
50 125.8 1 0 1 0 0 0 0 0 0 0 0 0 50
51 119.5 1 0 0 1 0 0 0 0 0 0 0 0 51
52 115.7 1 0 0 0 1 0 0 0 0 0 0 0 52
53 113.6 1 0 0 0 0 1 0 0 0 0 0 0 53
54 129.7 1 0 0 0 0 0 1 0 0 0 0 0 54
55 112.0 1 0 0 0 0 0 0 1 0 0 0 0 55
56 116.8 1 0 0 0 0 0 0 0 1 0 0 0 56
57 127.0 1 0 0 0 0 0 0 0 0 1 0 0 57
58 112.9 1 0 0 0 0 0 0 0 0 0 1 0 58
59 113.3 1 0 0 0 0 0 0 0 0 0 0 1 59
60 121.7 1 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) Reg M1 M2 M3 M4
117.8585 -0.1866 -1.4468 -7.3132 -6.4396 -14.7660
M5 M6 M7 M8 M9 M10
-16.3524 -1.9189 -15.3253 -15.3917 -3.9381 -20.5645
M11 t
-16.7109 0.2264
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.2981 -2.7588 0.3613 2.4989 11.3546
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 117.85851 2.65088 44.460 < 2e-16 ***
Reg -0.18659 2.58905 -0.072 0.94286
M1 -1.44681 3.22101 -0.449 0.65541
M2 -7.31322 3.21341 -2.276 0.02756 *
M3 -6.43963 3.20749 -2.008 0.05057 .
M4 -14.76604 3.20325 -4.610 3.21e-05 ***
M5 -16.35245 3.20071 -5.109 6.09e-06 ***
M6 -1.91886 3.19986 -0.600 0.55167
M7 -15.32527 3.20071 -4.788 1.78e-05 ***
M8 -15.39168 3.20325 -4.805 1.69e-05 ***
M9 -3.93809 3.20749 -1.228 0.22578
M10 -20.56450 3.21341 -6.400 7.27e-08 ***
M11 -16.71091 3.22101 -5.188 4.66e-06 ***
t 0.22641 0.07369 3.072 0.00356 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.041 on 46 degrees of freedom
Multiple R-squared: 0.7551, Adjusted R-squared: 0.6858
F-statistic: 10.91 on 13 and 46 DF, p-value: 4.485e-10
> postscript(file="/var/www/html/freestat/rcomp/tmp/1t4hs1229420177.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/freestat/rcomp/tmp/2tsu61229420177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3fsbz1229420177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4nmwp1229420177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5nlab1229420177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> 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
0.36189011 -7.19810989 -11.29810989 6.60189011 1.36189011 -4.69810989
7 8 9 10 11 12
3.18189011 -5.37810989 -6.15810989 5.34189011 -1.43810989 3.32457143
13 14 15 16 17 18
5.54496703 -1.01503297 7.08496703 -6.11503297 -1.05503297 0.38496703
19 20 21 22 23 24
0.66496703 -4.09503297 6.92496703 5.22496703 2.44496703 5.29424176
25 26 27 28 29 30
-0.78536264 3.25463736 11.35463736 -0.54536264 -2.38536264 -2.74536264
31 32 33 34 35 36
1.93463736 1.07463736 -1.10536264 -7.30536264 -1.18536264 1.47731868
37 38 39 40 41 42
-7.40228571 0.83771429 -3.86228571 -0.96228571 1.79771429 5.33771429
43 44 45 46 47 48
-2.98228571 6.55771429 -0.02228571 -5.92228571 1.19771429 -0.53960440
49 50 51 52 53 54
2.28079121 4.12079121 -3.27920879 1.02079121 0.28079121 1.72079121
55 56 57 58 59 60
-2.79920879 1.84079121 0.36079121 2.66079121 -1.01920879 -9.55652747
> postscript(file="/var/www/html/freestat/rcomp/tmp/6lb9z1229420177.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 0.36189011 NA
1 -7.19810989 0.36189011
2 -11.29810989 -7.19810989
3 6.60189011 -11.29810989
4 1.36189011 6.60189011
5 -4.69810989 1.36189011
6 3.18189011 -4.69810989
7 -5.37810989 3.18189011
8 -6.15810989 -5.37810989
9 5.34189011 -6.15810989
10 -1.43810989 5.34189011
11 3.32457143 -1.43810989
12 5.54496703 3.32457143
13 -1.01503297 5.54496703
14 7.08496703 -1.01503297
15 -6.11503297 7.08496703
16 -1.05503297 -6.11503297
17 0.38496703 -1.05503297
18 0.66496703 0.38496703
19 -4.09503297 0.66496703
20 6.92496703 -4.09503297
21 5.22496703 6.92496703
22 2.44496703 5.22496703
23 5.29424176 2.44496703
24 -0.78536264 5.29424176
25 3.25463736 -0.78536264
26 11.35463736 3.25463736
27 -0.54536264 11.35463736
28 -2.38536264 -0.54536264
29 -2.74536264 -2.38536264
30 1.93463736 -2.74536264
31 1.07463736 1.93463736
32 -1.10536264 1.07463736
33 -7.30536264 -1.10536264
34 -1.18536264 -7.30536264
35 1.47731868 -1.18536264
36 -7.40228571 1.47731868
37 0.83771429 -7.40228571
38 -3.86228571 0.83771429
39 -0.96228571 -3.86228571
40 1.79771429 -0.96228571
41 5.33771429 1.79771429
42 -2.98228571 5.33771429
43 6.55771429 -2.98228571
44 -0.02228571 6.55771429
45 -5.92228571 -0.02228571
46 1.19771429 -5.92228571
47 -0.53960440 1.19771429
48 2.28079121 -0.53960440
49 4.12079121 2.28079121
50 -3.27920879 4.12079121
51 1.02079121 -3.27920879
52 0.28079121 1.02079121
53 1.72079121 0.28079121
54 -2.79920879 1.72079121
55 1.84079121 -2.79920879
56 0.36079121 1.84079121
57 2.66079121 0.36079121
58 -1.01920879 2.66079121
59 -9.55652747 -1.01920879
60 NA -9.55652747
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -7.19810989 0.36189011
[2,] -11.29810989 -7.19810989
[3,] 6.60189011 -11.29810989
[4,] 1.36189011 6.60189011
[5,] -4.69810989 1.36189011
[6,] 3.18189011 -4.69810989
[7,] -5.37810989 3.18189011
[8,] -6.15810989 -5.37810989
[9,] 5.34189011 -6.15810989
[10,] -1.43810989 5.34189011
[11,] 3.32457143 -1.43810989
[12,] 5.54496703 3.32457143
[13,] -1.01503297 5.54496703
[14,] 7.08496703 -1.01503297
[15,] -6.11503297 7.08496703
[16,] -1.05503297 -6.11503297
[17,] 0.38496703 -1.05503297
[18,] 0.66496703 0.38496703
[19,] -4.09503297 0.66496703
[20,] 6.92496703 -4.09503297
[21,] 5.22496703 6.92496703
[22,] 2.44496703 5.22496703
[23,] 5.29424176 2.44496703
[24,] -0.78536264 5.29424176
[25,] 3.25463736 -0.78536264
[26,] 11.35463736 3.25463736
[27,] -0.54536264 11.35463736
[28,] -2.38536264 -0.54536264
[29,] -2.74536264 -2.38536264
[30,] 1.93463736 -2.74536264
[31,] 1.07463736 1.93463736
[32,] -1.10536264 1.07463736
[33,] -7.30536264 -1.10536264
[34,] -1.18536264 -7.30536264
[35,] 1.47731868 -1.18536264
[36,] -7.40228571 1.47731868
[37,] 0.83771429 -7.40228571
[38,] -3.86228571 0.83771429
[39,] -0.96228571 -3.86228571
[40,] 1.79771429 -0.96228571
[41,] 5.33771429 1.79771429
[42,] -2.98228571 5.33771429
[43,] 6.55771429 -2.98228571
[44,] -0.02228571 6.55771429
[45,] -5.92228571 -0.02228571
[46,] 1.19771429 -5.92228571
[47,] -0.53960440 1.19771429
[48,] 2.28079121 -0.53960440
[49,] 4.12079121 2.28079121
[50,] -3.27920879 4.12079121
[51,] 1.02079121 -3.27920879
[52,] 0.28079121 1.02079121
[53,] 1.72079121 0.28079121
[54,] -2.79920879 1.72079121
[55,] 1.84079121 -2.79920879
[56,] 0.36079121 1.84079121
[57,] 2.66079121 0.36079121
[58,] -1.01920879 2.66079121
[59,] -9.55652747 -1.01920879
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -7.19810989 0.36189011
2 -11.29810989 -7.19810989
3 6.60189011 -11.29810989
4 1.36189011 6.60189011
5 -4.69810989 1.36189011
6 3.18189011 -4.69810989
7 -5.37810989 3.18189011
8 -6.15810989 -5.37810989
9 5.34189011 -6.15810989
10 -1.43810989 5.34189011
11 3.32457143 -1.43810989
12 5.54496703 3.32457143
13 -1.01503297 5.54496703
14 7.08496703 -1.01503297
15 -6.11503297 7.08496703
16 -1.05503297 -6.11503297
17 0.38496703 -1.05503297
18 0.66496703 0.38496703
19 -4.09503297 0.66496703
20 6.92496703 -4.09503297
21 5.22496703 6.92496703
22 2.44496703 5.22496703
23 5.29424176 2.44496703
24 -0.78536264 5.29424176
25 3.25463736 -0.78536264
26 11.35463736 3.25463736
27 -0.54536264 11.35463736
28 -2.38536264 -0.54536264
29 -2.74536264 -2.38536264
30 1.93463736 -2.74536264
31 1.07463736 1.93463736
32 -1.10536264 1.07463736
33 -7.30536264 -1.10536264
34 -1.18536264 -7.30536264
35 1.47731868 -1.18536264
36 -7.40228571 1.47731868
37 0.83771429 -7.40228571
38 -3.86228571 0.83771429
39 -0.96228571 -3.86228571
40 1.79771429 -0.96228571
41 5.33771429 1.79771429
42 -2.98228571 5.33771429
43 6.55771429 -2.98228571
44 -0.02228571 6.55771429
45 -5.92228571 -0.02228571
46 1.19771429 -5.92228571
47 -0.53960440 1.19771429
48 2.28079121 -0.53960440
49 4.12079121 2.28079121
50 -3.27920879 4.12079121
51 1.02079121 -3.27920879
52 0.28079121 1.02079121
53 1.72079121 0.28079121
54 -2.79920879 1.72079121
55 1.84079121 -2.79920879
56 0.36079121 1.84079121
57 2.66079121 0.36079121
58 -1.01920879 2.66079121
59 -9.55652747 -1.01920879
> 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/freestat/rcomp/tmp/790pf1229420177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8sbkq1229420177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/99h221229420177.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/10xvli1229420177.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/freestat/rcomp/tmp/11vpaj1229420177.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/freestat/rcomp/tmp/1270iy1229420177.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/freestat/rcomp/tmp/13q3w21229420177.tab")
>
> system("convert tmp/1t4hs1229420177.ps tmp/1t4hs1229420177.png")
> system("convert tmp/2tsu61229420177.ps tmp/2tsu61229420177.png")
> system("convert tmp/3fsbz1229420177.ps tmp/3fsbz1229420177.png")
> system("convert tmp/4nmwp1229420177.ps tmp/4nmwp1229420177.png")
> system("convert tmp/5nlab1229420177.ps tmp/5nlab1229420177.png")
> system("convert tmp/6lb9z1229420177.ps tmp/6lb9z1229420177.png")
> system("convert tmp/790pf1229420177.ps tmp/790pf1229420177.png")
> system("convert tmp/8sbkq1229420177.ps tmp/8sbkq1229420177.png")
> system("convert tmp/99h221229420177.ps tmp/99h221229420177.png")
>
>
> proc.time()
user system elapsed
3.227 2.391 3.951