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.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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> x <- array(list(9,911,8,915,9,452,9,112,8,472,8,230,8,384,8,625,8,221,8,649,8,625,10,443,10,357,8,586,8,892,8,329,8,101,7,922,8,120,7,838,7,735,8,406,8,209,9,451,10,041,9,411,10,405,8,467,8,464,8,102,7,627,7,513,7,510,8,291,8,064,9,383,9,706,8,579,9,474,8,318,8,213,8,059,9,111,7,708,7,680,8,014,8,007,8,718,9,486,9,113,9,025,8,476,7,952,7,759,7,835,7,600,7,651,8,319,8,812,8,630),dim=c(2,60),dimnames=list(c('y',''),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('y',''),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
y M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9 911 1 0 0 0 0 0 0 0 0 0 0 1
2 8 915 0 1 0 0 0 0 0 0 0 0 0 2
3 9 452 0 0 1 0 0 0 0 0 0 0 0 3
4 9 112 0 0 0 1 0 0 0 0 0 0 0 4
5 8 472 0 0 0 0 1 0 0 0 0 0 0 5
6 8 230 0 0 0 0 0 1 0 0 0 0 0 6
7 8 384 0 0 0 0 0 0 1 0 0 0 0 7
8 8 625 0 0 0 0 0 0 0 1 0 0 0 8
9 8 221 0 0 0 0 0 0 0 0 1 0 0 9
10 8 649 0 0 0 0 0 0 0 0 0 1 0 10
11 8 625 0 0 0 0 0 0 0 0 0 0 1 11
12 10 443 0 0 0 0 0 0 0 0 0 0 0 12
13 10 357 1 0 0 0 0 0 0 0 0 0 0 13
14 8 586 0 1 0 0 0 0 0 0 0 0 0 14
15 8 892 0 0 1 0 0 0 0 0 0 0 0 15
16 8 329 0 0 0 1 0 0 0 0 0 0 0 16
17 8 101 0 0 0 0 1 0 0 0 0 0 0 17
18 7 922 0 0 0 0 0 1 0 0 0 0 0 18
19 8 120 0 0 0 0 0 0 1 0 0 0 0 19
20 7 838 0 0 0 0 0 0 0 1 0 0 0 20
21 7 735 0 0 0 0 0 0 0 0 1 0 0 21
22 8 406 0 0 0 0 0 0 0 0 0 1 0 22
23 8 209 0 0 0 0 0 0 0 0 0 0 1 23
24 9 451 0 0 0 0 0 0 0 0 0 0 0 24
25 10 41 1 0 0 0 0 0 0 0 0 0 0 25
26 9 411 0 1 0 0 0 0 0 0 0 0 0 26
27 10 405 0 0 1 0 0 0 0 0 0 0 0 27
28 8 467 0 0 0 1 0 0 0 0 0 0 0 28
29 8 464 0 0 0 0 1 0 0 0 0 0 0 29
30 8 102 0 0 0 0 0 1 0 0 0 0 0 30
31 7 627 0 0 0 0 0 0 1 0 0 0 0 31
32 7 513 0 0 0 0 0 0 0 1 0 0 0 32
33 7 510 0 0 0 0 0 0 0 0 1 0 0 33
34 8 291 0 0 0 0 0 0 0 0 0 1 0 34
35 8 64 0 0 0 0 0 0 0 0 0 0 1 35
36 9 383 0 0 0 0 0 0 0 0 0 0 0 36
37 9 706 1 0 0 0 0 0 0 0 0 0 0 37
38 8 579 0 1 0 0 0 0 0 0 0 0 0 38
39 9 474 0 0 1 0 0 0 0 0 0 0 0 39
40 8 318 0 0 0 1 0 0 0 0 0 0 0 40
41 8 213 0 0 0 0 1 0 0 0 0 0 0 41
42 8 59 0 0 0 0 0 1 0 0 0 0 0 42
43 9 111 0 0 0 0 0 0 1 0 0 0 0 43
44 7 708 0 0 0 0 0 0 0 1 0 0 0 44
45 7 680 0 0 0 0 0 0 0 0 1 0 0 45
46 8 14 0 0 0 0 0 0 0 0 0 1 0 46
47 8 7 0 0 0 0 0 0 0 0 0 0 1 47
48 8 718 0 0 0 0 0 0 0 0 0 0 0 48
49 9 486 1 0 0 0 0 0 0 0 0 0 0 49
50 9 113 0 1 0 0 0 0 0 0 0 0 0 50
51 9 25 0 0 1 0 0 0 0 0 0 0 0 51
52 8 476 0 0 0 1 0 0 0 0 0 0 0 52
53 7 952 0 0 0 0 1 0 0 0 0 0 0 53
54 7 759 0 0 0 0 0 1 0 0 0 0 0 54
55 7 835 0 0 0 0 0 0 1 0 0 0 0 55
56 7 600 0 0 0 0 0 0 0 1 0 0 0 56
57 7 651 0 0 0 0 0 0 0 0 1 0 0 57
58 8 319 0 0 0 0 0 0 0 0 0 1 0 58
59 8 812 0 0 0 0 0 0 0 0 0 0 1 59
60 8 630 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) V2 M1 M2 M3 M4
9.815647 -0.001308 0.467048 -0.496870 0.019139 -0.914555
M5 M6 M7 M8 M9 M10
-1.174619 -1.399489 -1.189043 -1.464159 -1.582419 -1.065745
M11 t
-1.046667 -0.009138
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.53100 -0.22828 -0.02762 0.13973 0.94166
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.8156467 0.2236632 43.886 < 2e-16 ***
V2 -0.0013080 0.0001955 -6.690 2.66e-08 ***
M1 0.4670475 0.2414309 1.934 0.059216 .
M2 -0.4968704 0.2410199 -2.062 0.044929 *
M3 0.0191391 0.2411512 0.079 0.937086
M4 -0.9145548 0.2431122 -3.762 0.000476 ***
M5 -1.1746190 0.2407167 -4.880 1.31e-05 ***
M6 -1.3994888 0.2408968 -5.809 5.58e-07 ***
M7 -1.1890431 0.2406886 -4.940 1.07e-05 ***
M8 -1.4641588 0.2409448 -6.077 2.22e-07 ***
M9 -1.5824185 0.2395419 -6.606 3.56e-08 ***
M10 -1.0657454 0.2422089 -4.400 6.37e-05 ***
M11 -1.0466670 0.2419324 -4.326 8.08e-05 ***
t -0.0091377 0.0028783 -3.175 0.002676 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3784 on 46 degrees of freedom
Multiple R-squared: 0.8361, Adjusted R-squared: 0.7898
F-statistic: 18.05 on 13 and 46 DF, p-value: 7.375e-14
> postscript(file="/var/www/html/rcomp/tmp/1u6n61291063578.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/www/html/rcomp/tmp/2u6n61291063578.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3mf491291063578.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4mf491291063578.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5mf491291063578.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-0.0819858412 -0.1036983254 -0.2161652740 0.2819527155 0.0220277707
6 7 8 9 10
-0.0604961213 -0.0603750311 0.5391017765 0.1380748076 0.1903552151
11 12 13 14 15
0.1490230161 0.8734411312 0.3030449093 -0.4243718542 -0.5310014280
16 17 18 19 20
-0.3245631973 -0.3535809592 -0.0457210680 -0.2960297961 -0.0726460603
21 22 23 24 25
-0.0799707538 -0.0178319493 -0.2854448581 -0.0064428068 -0.0006248667
26 27 28 29 30
0.4563836882 0.9416640482 -0.0344096078 0.2308683512 -0.0086132586
31 32 33 34 35
-0.5232312243 -0.3880876651 -0.2646142605 -0.0585975480 -0.3654498862
36 37 38 39 40
0.0142667006 -0.0211652999 -0.2142232927 0.1415669501 -0.1196465598
41 42 43 44 45
0.0122173390 0.0447957734 0.9115028034 -0.0233791596 0.0673947205
46 47 48 49 50
-0.3112560657 -0.3303525880 -0.4379074564 -0.1992689016 0.2859097841
51 52 53 54 55
-0.3360642964 0.1966666494 0.0884674984 0.0700346745 -0.0318667519
56 57 58 59 60
-0.0549888914 0.1391154862 0.1973303478 0.8322243162 -0.4433575686
> postscript(file="/var/www/html/rcomp/tmp/6xp4c1291063578.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0819858412 NA
1 -0.1036983254 -0.0819858412
2 -0.2161652740 -0.1036983254
3 0.2819527155 -0.2161652740
4 0.0220277707 0.2819527155
5 -0.0604961213 0.0220277707
6 -0.0603750311 -0.0604961213
7 0.5391017765 -0.0603750311
8 0.1380748076 0.5391017765
9 0.1903552151 0.1380748076
10 0.1490230161 0.1903552151
11 0.8734411312 0.1490230161
12 0.3030449093 0.8734411312
13 -0.4243718542 0.3030449093
14 -0.5310014280 -0.4243718542
15 -0.3245631973 -0.5310014280
16 -0.3535809592 -0.3245631973
17 -0.0457210680 -0.3535809592
18 -0.2960297961 -0.0457210680
19 -0.0726460603 -0.2960297961
20 -0.0799707538 -0.0726460603
21 -0.0178319493 -0.0799707538
22 -0.2854448581 -0.0178319493
23 -0.0064428068 -0.2854448581
24 -0.0006248667 -0.0064428068
25 0.4563836882 -0.0006248667
26 0.9416640482 0.4563836882
27 -0.0344096078 0.9416640482
28 0.2308683512 -0.0344096078
29 -0.0086132586 0.2308683512
30 -0.5232312243 -0.0086132586
31 -0.3880876651 -0.5232312243
32 -0.2646142605 -0.3880876651
33 -0.0585975480 -0.2646142605
34 -0.3654498862 -0.0585975480
35 0.0142667006 -0.3654498862
36 -0.0211652999 0.0142667006
37 -0.2142232927 -0.0211652999
38 0.1415669501 -0.2142232927
39 -0.1196465598 0.1415669501
40 0.0122173390 -0.1196465598
41 0.0447957734 0.0122173390
42 0.9115028034 0.0447957734
43 -0.0233791596 0.9115028034
44 0.0673947205 -0.0233791596
45 -0.3112560657 0.0673947205
46 -0.3303525880 -0.3112560657
47 -0.4379074564 -0.3303525880
48 -0.1992689016 -0.4379074564
49 0.2859097841 -0.1992689016
50 -0.3360642964 0.2859097841
51 0.1966666494 -0.3360642964
52 0.0884674984 0.1966666494
53 0.0700346745 0.0884674984
54 -0.0318667519 0.0700346745
55 -0.0549888914 -0.0318667519
56 0.1391154862 -0.0549888914
57 0.1973303478 0.1391154862
58 0.8322243162 0.1973303478
59 -0.4433575686 0.8322243162
60 NA -0.4433575686
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1036983254 -0.0819858412
[2,] -0.2161652740 -0.1036983254
[3,] 0.2819527155 -0.2161652740
[4,] 0.0220277707 0.2819527155
[5,] -0.0604961213 0.0220277707
[6,] -0.0603750311 -0.0604961213
[7,] 0.5391017765 -0.0603750311
[8,] 0.1380748076 0.5391017765
[9,] 0.1903552151 0.1380748076
[10,] 0.1490230161 0.1903552151
[11,] 0.8734411312 0.1490230161
[12,] 0.3030449093 0.8734411312
[13,] -0.4243718542 0.3030449093
[14,] -0.5310014280 -0.4243718542
[15,] -0.3245631973 -0.5310014280
[16,] -0.3535809592 -0.3245631973
[17,] -0.0457210680 -0.3535809592
[18,] -0.2960297961 -0.0457210680
[19,] -0.0726460603 -0.2960297961
[20,] -0.0799707538 -0.0726460603
[21,] -0.0178319493 -0.0799707538
[22,] -0.2854448581 -0.0178319493
[23,] -0.0064428068 -0.2854448581
[24,] -0.0006248667 -0.0064428068
[25,] 0.4563836882 -0.0006248667
[26,] 0.9416640482 0.4563836882
[27,] -0.0344096078 0.9416640482
[28,] 0.2308683512 -0.0344096078
[29,] -0.0086132586 0.2308683512
[30,] -0.5232312243 -0.0086132586
[31,] -0.3880876651 -0.5232312243
[32,] -0.2646142605 -0.3880876651
[33,] -0.0585975480 -0.2646142605
[34,] -0.3654498862 -0.0585975480
[35,] 0.0142667006 -0.3654498862
[36,] -0.0211652999 0.0142667006
[37,] -0.2142232927 -0.0211652999
[38,] 0.1415669501 -0.2142232927
[39,] -0.1196465598 0.1415669501
[40,] 0.0122173390 -0.1196465598
[41,] 0.0447957734 0.0122173390
[42,] 0.9115028034 0.0447957734
[43,] -0.0233791596 0.9115028034
[44,] 0.0673947205 -0.0233791596
[45,] -0.3112560657 0.0673947205
[46,] -0.3303525880 -0.3112560657
[47,] -0.4379074564 -0.3303525880
[48,] -0.1992689016 -0.4379074564
[49,] 0.2859097841 -0.1992689016
[50,] -0.3360642964 0.2859097841
[51,] 0.1966666494 -0.3360642964
[52,] 0.0884674984 0.1966666494
[53,] 0.0700346745 0.0884674984
[54,] -0.0318667519 0.0700346745
[55,] -0.0549888914 -0.0318667519
[56,] 0.1391154862 -0.0549888914
[57,] 0.1973303478 0.1391154862
[58,] 0.8322243162 0.1973303478
[59,] -0.4433575686 0.8322243162
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1036983254 -0.0819858412
2 -0.2161652740 -0.1036983254
3 0.2819527155 -0.2161652740
4 0.0220277707 0.2819527155
5 -0.0604961213 0.0220277707
6 -0.0603750311 -0.0604961213
7 0.5391017765 -0.0603750311
8 0.1380748076 0.5391017765
9 0.1903552151 0.1380748076
10 0.1490230161 0.1903552151
11 0.8734411312 0.1490230161
12 0.3030449093 0.8734411312
13 -0.4243718542 0.3030449093
14 -0.5310014280 -0.4243718542
15 -0.3245631973 -0.5310014280
16 -0.3535809592 -0.3245631973
17 -0.0457210680 -0.3535809592
18 -0.2960297961 -0.0457210680
19 -0.0726460603 -0.2960297961
20 -0.0799707538 -0.0726460603
21 -0.0178319493 -0.0799707538
22 -0.2854448581 -0.0178319493
23 -0.0064428068 -0.2854448581
24 -0.0006248667 -0.0064428068
25 0.4563836882 -0.0006248667
26 0.9416640482 0.4563836882
27 -0.0344096078 0.9416640482
28 0.2308683512 -0.0344096078
29 -0.0086132586 0.2308683512
30 -0.5232312243 -0.0086132586
31 -0.3880876651 -0.5232312243
32 -0.2646142605 -0.3880876651
33 -0.0585975480 -0.2646142605
34 -0.3654498862 -0.0585975480
35 0.0142667006 -0.3654498862
36 -0.0211652999 0.0142667006
37 -0.2142232927 -0.0211652999
38 0.1415669501 -0.2142232927
39 -0.1196465598 0.1415669501
40 0.0122173390 -0.1196465598
41 0.0447957734 0.0122173390
42 0.9115028034 0.0447957734
43 -0.0233791596 0.9115028034
44 0.0673947205 -0.0233791596
45 -0.3112560657 0.0673947205
46 -0.3303525880 -0.3112560657
47 -0.4379074564 -0.3303525880
48 -0.1992689016 -0.4379074564
49 0.2859097841 -0.1992689016
50 -0.3360642964 0.2859097841
51 0.1966666494 -0.3360642964
52 0.0884674984 0.1966666494
53 0.0700346745 0.0884674984
54 -0.0318667519 0.0700346745
55 -0.0549888914 -0.0318667519
56 0.1391154862 -0.0549888914
57 0.1973303478 0.1391154862
58 0.8322243162 0.1973303478
59 -0.4433575686 0.8322243162
> 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/7qx2f1291063578.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8qx2f1291063578.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qx2f1291063578.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10tgjl1291063578.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/11pr2m1291063579.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/12wazy1291063579.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/13ojyi1291063579.tab")
>
> try(system("convert tmp/1u6n61291063578.ps tmp/1u6n61291063578.png",intern=TRUE))
character(0)
> try(system("convert tmp/2u6n61291063578.ps tmp/2u6n61291063578.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mf491291063578.ps tmp/3mf491291063578.png",intern=TRUE))
character(0)
> try(system("convert tmp/4mf491291063578.ps tmp/4mf491291063578.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mf491291063578.ps tmp/5mf491291063578.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xp4c1291063578.ps tmp/6xp4c1291063578.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qx2f1291063578.ps tmp/7qx2f1291063578.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qx2f1291063578.ps tmp/8qx2f1291063578.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qx2f1291063578.ps tmp/9qx2f1291063578.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
1.992 1.507 4.711