R version 2.7.0 (2008-04-22)
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
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(11857.9,0,14616,0,15643.4,0,14077.2,0,14887.5,0,14159.9,0,14643,0,17192.5,0,15386.1,0,14287.1,0,17526.6,0,14497,0,14398.3,0,16629.6,0,16670.7,0,16614.8,0,16869.2,0,15663.9,0,16359.9,0,18447.7,0,16889,0,16505,0,18320.9,0,15052.1,0,15699.8,0,18135.3,0,16768.7,0,18883,0,19021,0,18101.9,0,17776.1,0,21489.9,0,17065.3,0,18690,0,18953.1,0,16398.9,0,16895.7,0,18553,0,19270,0,19422.1,0,17579.4,0,18637.3,0,18076.7,0,20438.6,0,18075.2,0,19563,0,19899.2,0,19227.5,0,17789.6,0,19220.8,0,21968.9,0,21131.5,1,19484.6,1,22404.1,1,21099,1,22486.5,1,23707.5,1,21897.5,1,23326.4,1,23765.4,1,20444,1),dim=c(2,61),dimnames=list(c('y','x'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('y','x'),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'
> #'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
y x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 11857.9 0 1 0 0 0 0 0 0 0 0 0 0 1
2 14616.0 0 0 1 0 0 0 0 0 0 0 0 0 2
3 15643.4 0 0 0 1 0 0 0 0 0 0 0 0 3
4 14077.2 0 0 0 0 1 0 0 0 0 0 0 0 4
5 14887.5 0 0 0 0 0 1 0 0 0 0 0 0 5
6 14159.9 0 0 0 0 0 0 1 0 0 0 0 0 6
7 14643.0 0 0 0 0 0 0 0 1 0 0 0 0 7
8 17192.5 0 0 0 0 0 0 0 0 1 0 0 0 8
9 15386.1 0 0 0 0 0 0 0 0 0 1 0 0 9
10 14287.1 0 0 0 0 0 0 0 0 0 0 1 0 10
11 17526.6 0 0 0 0 0 0 0 0 0 0 0 1 11
12 14497.0 0 0 0 0 0 0 0 0 0 0 0 0 12
13 14398.3 0 1 0 0 0 0 0 0 0 0 0 0 13
14 16629.6 0 0 1 0 0 0 0 0 0 0 0 0 14
15 16670.7 0 0 0 1 0 0 0 0 0 0 0 0 15
16 16614.8 0 0 0 0 1 0 0 0 0 0 0 0 16
17 16869.2 0 0 0 0 0 1 0 0 0 0 0 0 17
18 15663.9 0 0 0 0 0 0 1 0 0 0 0 0 18
19 16359.9 0 0 0 0 0 0 0 1 0 0 0 0 19
20 18447.7 0 0 0 0 0 0 0 0 1 0 0 0 20
21 16889.0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 16505.0 0 0 0 0 0 0 0 0 0 0 1 0 22
23 18320.9 0 0 0 0 0 0 0 0 0 0 0 1 23
24 15052.1 0 0 0 0 0 0 0 0 0 0 0 0 24
25 15699.8 0 1 0 0 0 0 0 0 0 0 0 0 25
26 18135.3 0 0 1 0 0 0 0 0 0 0 0 0 26
27 16768.7 0 0 0 1 0 0 0 0 0 0 0 0 27
28 18883.0 0 0 0 0 1 0 0 0 0 0 0 0 28
29 19021.0 0 0 0 0 0 1 0 0 0 0 0 0 29
30 18101.9 0 0 0 0 0 0 1 0 0 0 0 0 30
31 17776.1 0 0 0 0 0 0 0 1 0 0 0 0 31
32 21489.9 0 0 0 0 0 0 0 0 1 0 0 0 32
33 17065.3 0 0 0 0 0 0 0 0 0 1 0 0 33
34 18690.0 0 0 0 0 0 0 0 0 0 0 1 0 34
35 18953.1 0 0 0 0 0 0 0 0 0 0 0 1 35
36 16398.9 0 0 0 0 0 0 0 0 0 0 0 0 36
37 16895.7 0 1 0 0 0 0 0 0 0 0 0 0 37
38 18553.0 0 0 1 0 0 0 0 0 0 0 0 0 38
39 19270.0 0 0 0 1 0 0 0 0 0 0 0 0 39
40 19422.1 0 0 0 0 1 0 0 0 0 0 0 0 40
41 17579.4 0 0 0 0 0 1 0 0 0 0 0 0 41
42 18637.3 0 0 0 0 0 0 1 0 0 0 0 0 42
43 18076.7 0 0 0 0 0 0 0 1 0 0 0 0 43
44 20438.6 0 0 0 0 0 0 0 0 1 0 0 0 44
45 18075.2 0 0 0 0 0 0 0 0 0 1 0 0 45
46 19563.0 0 0 0 0 0 0 0 0 0 0 1 0 46
47 19899.2 0 0 0 0 0 0 0 0 0 0 0 1 47
48 19227.5 0 0 0 0 0 0 0 0 0 0 0 0 48
49 17789.6 0 1 0 0 0 0 0 0 0 0 0 0 49
50 19220.8 0 0 1 0 0 0 0 0 0 0 0 0 50
51 21968.9 0 0 0 1 0 0 0 0 0 0 0 0 51
52 21131.5 1 0 0 0 1 0 0 0 0 0 0 0 52
53 19484.6 1 0 0 0 0 1 0 0 0 0 0 0 53
54 22404.1 1 0 0 0 0 0 1 0 0 0 0 0 54
55 21099.0 1 0 0 0 0 0 0 1 0 0 0 0 55
56 22486.5 1 0 0 0 0 0 0 0 1 0 0 0 56
57 23707.5 1 0 0 0 0 0 0 0 0 1 0 0 57
58 21897.5 1 0 0 0 0 0 0 0 0 0 1 0 58
59 23326.4 1 0 0 0 0 0 0 0 0 0 0 1 59
60 23765.4 1 0 0 0 0 0 0 0 0 0 0 0 60
61 20444.0 1 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) x M1 M2 M3 M4
13508.8 1429.1 -1005.0 1037.9 1560.4 1125.0
M5 M6 M7 M8 M9 M10
556.7 670.8 357.4 2666.6 769.2 622.2
M11 t
1928.0 110.9
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1889.38 -617.79 -62.47 422.69 2171.58
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13508.846 501.813 26.920 < 2e-16 ***
x 1429.123 430.053 3.323 0.00173 **
M1 -1005.005 568.024 -1.769 0.08333 .
M2 1037.893 596.684 1.739 0.08850 .
M3 1560.362 596.226 2.617 0.01189 *
M4 1124.986 596.349 1.886 0.06542 .
M5 556.676 595.331 0.935 0.35453
M6 670.825 594.447 1.128 0.26484
M7 357.414 593.698 0.602 0.55006
M8 2666.583 593.084 4.496 4.51e-05 ***
M9 769.232 592.606 1.298 0.20060
M10 622.202 592.265 1.051 0.29884
M11 1927.991 592.060 3.256 0.00210 **
t 110.931 8.996 12.332 2.44e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 936 on 47 degrees of freedom
Multiple R-squared: 0.9017, Adjusted R-squared: 0.8745
F-statistic: 33.15 on 13 and 47 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/1u59x1229257640.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/29utw1229257640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3opfe1229257640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pwy51229257640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fwvu1229257640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5
-756.8719647 -152.6005740 241.3994260 -1000.3560706 267.3239294
6 7 8 9 10
-685.3560706 0.2239294 129.6239294 109.6439294 -953.2560706
11 12 13 14 15
869.5239294 -343.0160706 452.3583223 529.8297130 -62.4702870
16 17 18 19 20
206.0742163 917.8542163 -512.5257837 385.9542163 53.6542163
21 22 23 24 25
281.3742163 -66.5257837 332.6542163 -1119.0857837 422.6886093
26 27 28 29 30
704.3600000 -1295.6400000 1143.1045033 1738.4845033 594.3045033
31 32 33 34 35
470.9845033 1764.6845033 -873.4954967 787.3045033 -366.3154967
36 37 38 39 40
-1103.4554967 287.4188962 -209.1097130 -125.5097130 351.0347903
41 42 43 44 45
-1034.2852097 -201.4652097 -559.5852097 -617.7852097 -1194.7652097
46 47 48 49 50
329.1347903 -751.3852097 393.9747903 -149.8508168 -872.4794260
51 52 53 54 55
1242.2205740 -699.8574393 -1889.3774393 805.0425607 -297.5774393
56 57 58 59 60
-1330.1774393 1677.2425607 -96.6574393 -84.4774393 2171.5825607
61
-255.7430464
> postscript(file="/var/www/html/rcomp/tmp/6nh0k1229257640.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -756.8719647 NA
1 -152.6005740 -756.8719647
2 241.3994260 -152.6005740
3 -1000.3560706 241.3994260
4 267.3239294 -1000.3560706
5 -685.3560706 267.3239294
6 0.2239294 -685.3560706
7 129.6239294 0.2239294
8 109.6439294 129.6239294
9 -953.2560706 109.6439294
10 869.5239294 -953.2560706
11 -343.0160706 869.5239294
12 452.3583223 -343.0160706
13 529.8297130 452.3583223
14 -62.4702870 529.8297130
15 206.0742163 -62.4702870
16 917.8542163 206.0742163
17 -512.5257837 917.8542163
18 385.9542163 -512.5257837
19 53.6542163 385.9542163
20 281.3742163 53.6542163
21 -66.5257837 281.3742163
22 332.6542163 -66.5257837
23 -1119.0857837 332.6542163
24 422.6886093 -1119.0857837
25 704.3600000 422.6886093
26 -1295.6400000 704.3600000
27 1143.1045033 -1295.6400000
28 1738.4845033 1143.1045033
29 594.3045033 1738.4845033
30 470.9845033 594.3045033
31 1764.6845033 470.9845033
32 -873.4954967 1764.6845033
33 787.3045033 -873.4954967
34 -366.3154967 787.3045033
35 -1103.4554967 -366.3154967
36 287.4188962 -1103.4554967
37 -209.1097130 287.4188962
38 -125.5097130 -209.1097130
39 351.0347903 -125.5097130
40 -1034.2852097 351.0347903
41 -201.4652097 -1034.2852097
42 -559.5852097 -201.4652097
43 -617.7852097 -559.5852097
44 -1194.7652097 -617.7852097
45 329.1347903 -1194.7652097
46 -751.3852097 329.1347903
47 393.9747903 -751.3852097
48 -149.8508168 393.9747903
49 -872.4794260 -149.8508168
50 1242.2205740 -872.4794260
51 -699.8574393 1242.2205740
52 -1889.3774393 -699.8574393
53 805.0425607 -1889.3774393
54 -297.5774393 805.0425607
55 -1330.1774393 -297.5774393
56 1677.2425607 -1330.1774393
57 -96.6574393 1677.2425607
58 -84.4774393 -96.6574393
59 2171.5825607 -84.4774393
60 -255.7430464 2171.5825607
61 NA -255.7430464
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -152.6005740 -756.8719647
[2,] 241.3994260 -152.6005740
[3,] -1000.3560706 241.3994260
[4,] 267.3239294 -1000.3560706
[5,] -685.3560706 267.3239294
[6,] 0.2239294 -685.3560706
[7,] 129.6239294 0.2239294
[8,] 109.6439294 129.6239294
[9,] -953.2560706 109.6439294
[10,] 869.5239294 -953.2560706
[11,] -343.0160706 869.5239294
[12,] 452.3583223 -343.0160706
[13,] 529.8297130 452.3583223
[14,] -62.4702870 529.8297130
[15,] 206.0742163 -62.4702870
[16,] 917.8542163 206.0742163
[17,] -512.5257837 917.8542163
[18,] 385.9542163 -512.5257837
[19,] 53.6542163 385.9542163
[20,] 281.3742163 53.6542163
[21,] -66.5257837 281.3742163
[22,] 332.6542163 -66.5257837
[23,] -1119.0857837 332.6542163
[24,] 422.6886093 -1119.0857837
[25,] 704.3600000 422.6886093
[26,] -1295.6400000 704.3600000
[27,] 1143.1045033 -1295.6400000
[28,] 1738.4845033 1143.1045033
[29,] 594.3045033 1738.4845033
[30,] 470.9845033 594.3045033
[31,] 1764.6845033 470.9845033
[32,] -873.4954967 1764.6845033
[33,] 787.3045033 -873.4954967
[34,] -366.3154967 787.3045033
[35,] -1103.4554967 -366.3154967
[36,] 287.4188962 -1103.4554967
[37,] -209.1097130 287.4188962
[38,] -125.5097130 -209.1097130
[39,] 351.0347903 -125.5097130
[40,] -1034.2852097 351.0347903
[41,] -201.4652097 -1034.2852097
[42,] -559.5852097 -201.4652097
[43,] -617.7852097 -559.5852097
[44,] -1194.7652097 -617.7852097
[45,] 329.1347903 -1194.7652097
[46,] -751.3852097 329.1347903
[47,] 393.9747903 -751.3852097
[48,] -149.8508168 393.9747903
[49,] -872.4794260 -149.8508168
[50,] 1242.2205740 -872.4794260
[51,] -699.8574393 1242.2205740
[52,] -1889.3774393 -699.8574393
[53,] 805.0425607 -1889.3774393
[54,] -297.5774393 805.0425607
[55,] -1330.1774393 -297.5774393
[56,] 1677.2425607 -1330.1774393
[57,] -96.6574393 1677.2425607
[58,] -84.4774393 -96.6574393
[59,] 2171.5825607 -84.4774393
[60,] -255.7430464 2171.5825607
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -152.6005740 -756.8719647
2 241.3994260 -152.6005740
3 -1000.3560706 241.3994260
4 267.3239294 -1000.3560706
5 -685.3560706 267.3239294
6 0.2239294 -685.3560706
7 129.6239294 0.2239294
8 109.6439294 129.6239294
9 -953.2560706 109.6439294
10 869.5239294 -953.2560706
11 -343.0160706 869.5239294
12 452.3583223 -343.0160706
13 529.8297130 452.3583223
14 -62.4702870 529.8297130
15 206.0742163 -62.4702870
16 917.8542163 206.0742163
17 -512.5257837 917.8542163
18 385.9542163 -512.5257837
19 53.6542163 385.9542163
20 281.3742163 53.6542163
21 -66.5257837 281.3742163
22 332.6542163 -66.5257837
23 -1119.0857837 332.6542163
24 422.6886093 -1119.0857837
25 704.3600000 422.6886093
26 -1295.6400000 704.3600000
27 1143.1045033 -1295.6400000
28 1738.4845033 1143.1045033
29 594.3045033 1738.4845033
30 470.9845033 594.3045033
31 1764.6845033 470.9845033
32 -873.4954967 1764.6845033
33 787.3045033 -873.4954967
34 -366.3154967 787.3045033
35 -1103.4554967 -366.3154967
36 287.4188962 -1103.4554967
37 -209.1097130 287.4188962
38 -125.5097130 -209.1097130
39 351.0347903 -125.5097130
40 -1034.2852097 351.0347903
41 -201.4652097 -1034.2852097
42 -559.5852097 -201.4652097
43 -617.7852097 -559.5852097
44 -1194.7652097 -617.7852097
45 329.1347903 -1194.7652097
46 -751.3852097 329.1347903
47 393.9747903 -751.3852097
48 -149.8508168 393.9747903
49 -872.4794260 -149.8508168
50 1242.2205740 -872.4794260
51 -699.8574393 1242.2205740
52 -1889.3774393 -699.8574393
53 805.0425607 -1889.3774393
54 -297.5774393 805.0425607
55 -1330.1774393 -297.5774393
56 1677.2425607 -1330.1774393
57 -96.6574393 1677.2425607
58 -84.4774393 -96.6574393
59 2171.5825607 -84.4774393
60 -255.7430464 2171.5825607
> 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/7rm5w1229257640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80x1a1229257640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9nl651229257640.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/107vtr1229257640.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/112d431229257640.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/127m431229257640.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/132ioj1229257640.tab")
>
> system("convert tmp/1u59x1229257640.ps tmp/1u59x1229257640.png")
> system("convert tmp/29utw1229257640.ps tmp/29utw1229257640.png")
> system("convert tmp/3opfe1229257640.ps tmp/3opfe1229257640.png")
> system("convert tmp/4pwy51229257640.ps tmp/4pwy51229257640.png")
> system("convert tmp/5fwvu1229257640.ps tmp/5fwvu1229257640.png")
> system("convert tmp/6nh0k1229257640.ps tmp/6nh0k1229257640.png")
> system("convert tmp/7rm5w1229257640.ps tmp/7rm5w1229257640.png")
> system("convert tmp/80x1a1229257640.ps tmp/80x1a1229257640.png")
> system("convert tmp/9nl651229257640.ps tmp/9nl651229257640.png")
>
>
> proc.time()
user system elapsed
4.551 3.012 4.934