R version 2.6.0 (2007-10-03)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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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(1.1608,0,1.1208,0,1.0883,0,1.0704,0,1.0628,0,1.0378,0,1.0353,0,1.0604,0,1.0501,0,1.0706,0,1.0338,0,1.0110,0,1.0137,0,0.9834,0,0.9643,0,0.9470,0,0.9060,0,0.9492,0,0.9397,0,0.9041,0,0.8721,0,0.8552,0,0.8564,0,0.8973,0,0.9383,0,0.9217,0,0.9095,0,0.8920,0,0.8742,0,0.8532,0,0.8607,0,0.9005,0,0.9111,1,0.9059,1,0.8883,1,0.8924,1,0.8833,1,0.8700,1,0.8758,1,0.8858,1,0.9170,1,0.9554,1,0.9922,1,0.9778,1,0.9808,1,0.9811,1,1.0014,1,1.0183,1,1.0622,1,1.0773,1,1.0807,1,1.0848,1,1.1582,1,1.1663,1,1.1372,1,1.1139,1,1.1222,1,1.1692,1,1.1702,1,1.2286,1),dim=c(2,60),dimnames=list(c('y','x'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('y','x'),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 x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1.1608 0 1 0 0 0 0 0 0 0 0 0 0 1
2 1.1208 0 0 1 0 0 0 0 0 0 0 0 0 2
3 1.0883 0 0 0 1 0 0 0 0 0 0 0 0 3
4 1.0704 0 0 0 0 1 0 0 0 0 0 0 0 4
5 1.0628 0 0 0 0 0 1 0 0 0 0 0 0 5
6 1.0378 0 0 0 0 0 0 1 0 0 0 0 0 6
7 1.0353 0 0 0 0 0 0 0 1 0 0 0 0 7
8 1.0604 0 0 0 0 0 0 0 0 1 0 0 0 8
9 1.0501 0 0 0 0 0 0 0 0 0 1 0 0 9
10 1.0706 0 0 0 0 0 0 0 0 0 0 1 0 10
11 1.0338 0 0 0 0 0 0 0 0 0 0 0 1 11
12 1.0110 0 0 0 0 0 0 0 0 0 0 0 0 12
13 1.0137 0 1 0 0 0 0 0 0 0 0 0 0 13
14 0.9834 0 0 1 0 0 0 0 0 0 0 0 0 14
15 0.9643 0 0 0 1 0 0 0 0 0 0 0 0 15
16 0.9470 0 0 0 0 1 0 0 0 0 0 0 0 16
17 0.9060 0 0 0 0 0 1 0 0 0 0 0 0 17
18 0.9492 0 0 0 0 0 0 1 0 0 0 0 0 18
19 0.9397 0 0 0 0 0 0 0 1 0 0 0 0 19
20 0.9041 0 0 0 0 0 0 0 0 1 0 0 0 20
21 0.8721 0 0 0 0 0 0 0 0 0 1 0 0 21
22 0.8552 0 0 0 0 0 0 0 0 0 0 1 0 22
23 0.8564 0 0 0 0 0 0 0 0 0 0 0 1 23
24 0.8973 0 0 0 0 0 0 0 0 0 0 0 0 24
25 0.9383 0 1 0 0 0 0 0 0 0 0 0 0 25
26 0.9217 0 0 1 0 0 0 0 0 0 0 0 0 26
27 0.9095 0 0 0 1 0 0 0 0 0 0 0 0 27
28 0.8920 0 0 0 0 1 0 0 0 0 0 0 0 28
29 0.8742 0 0 0 0 0 1 0 0 0 0 0 0 29
30 0.8532 0 0 0 0 0 0 1 0 0 0 0 0 30
31 0.8607 0 0 0 0 0 0 0 1 0 0 0 0 31
32 0.9005 0 0 0 0 0 0 0 0 1 0 0 0 32
33 0.9111 1 0 0 0 0 0 0 0 0 1 0 0 33
34 0.9059 1 0 0 0 0 0 0 0 0 0 1 0 34
35 0.8883 1 0 0 0 0 0 0 0 0 0 0 1 35
36 0.8924 1 0 0 0 0 0 0 0 0 0 0 0 36
37 0.8833 1 1 0 0 0 0 0 0 0 0 0 0 37
38 0.8700 1 0 1 0 0 0 0 0 0 0 0 0 38
39 0.8758 1 0 0 1 0 0 0 0 0 0 0 0 39
40 0.8858 1 0 0 0 1 0 0 0 0 0 0 0 40
41 0.9170 1 0 0 0 0 1 0 0 0 0 0 0 41
42 0.9554 1 0 0 0 0 0 1 0 0 0 0 0 42
43 0.9922 1 0 0 0 0 0 0 1 0 0 0 0 43
44 0.9778 1 0 0 0 0 0 0 0 1 0 0 0 44
45 0.9808 1 0 0 0 0 0 0 0 0 1 0 0 45
46 0.9811 1 0 0 0 0 0 0 0 0 0 1 0 46
47 1.0014 1 0 0 0 0 0 0 0 0 0 0 1 47
48 1.0183 1 0 0 0 0 0 0 0 0 0 0 0 48
49 1.0622 1 1 0 0 0 0 0 0 0 0 0 0 49
50 1.0773 1 0 1 0 0 0 0 0 0 0 0 0 50
51 1.0807 1 0 0 1 0 0 0 0 0 0 0 0 51
52 1.0848 1 0 0 0 1 0 0 0 0 0 0 0 52
53 1.1582 1 0 0 0 0 1 0 0 0 0 0 0 53
54 1.1663 1 0 0 0 0 0 1 0 0 0 0 0 54
55 1.1372 1 0 0 0 0 0 0 1 0 0 0 0 55
56 1.1139 1 0 0 0 0 0 0 0 1 0 0 0 56
57 1.1222 1 0 0 0 0 0 0 0 0 1 0 0 57
58 1.1692 1 0 0 0 0 0 0 0 0 0 1 0 58
59 1.1702 1 0 0 0 0 0 0 0 0 0 0 1 59
60 1.2286 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) x M1 M2 M3 M4
9.789e-01 4.684e-02 1.228e-02 -4.806e-03 -1.580e-02 -2.359e-02
M5 M6 M7 M8 M9 M10
-1.602e-02 -7.348e-03 -6.779e-03 -8.529e-03 -2.205e-02 -1.298e-02
M11 t
-1.943e-02 7.056e-05
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.15731 -0.08400 -0.01720 0.09028 0.19865
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.789e-01 6.073e-02 16.119 <2e-16 ***
x 4.684e-02 5.843e-02 0.802 0.427
M1 1.228e-02 7.087e-02 0.173 0.863
M2 -4.806e-03 7.069e-02 -0.068 0.946
M3 -1.580e-02 7.055e-02 -0.224 0.824
M4 -2.359e-02 7.045e-02 -0.335 0.739
M5 -1.602e-02 7.038e-02 -0.228 0.821
M6 -7.348e-03 7.036e-02 -0.104 0.917
M7 -6.779e-03 7.038e-02 -0.096 0.924
M8 -8.529e-03 7.045e-02 -0.121 0.904
M9 -2.205e-02 7.030e-02 -0.314 0.755
M10 -1.298e-02 7.020e-02 -0.185 0.854
M11 -1.943e-02 7.014e-02 -0.277 0.783
t 7.056e-05 1.687e-03 0.042 0.967
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1109 on 46 degrees of freedom
Multiple R-Squared: 0.06643, Adjusted R-squared: -0.1974
F-statistic: 0.2518 on 13 and 46 DF, p-value: 0.9953
> postscript(file="/var/www/html/rcomp/tmp/1u0691197027688.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/2fs141197027688.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3ibwu1197027688.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4rqze1197027688.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vro91197027688.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
0.1695700000 0.1465900000 0.1250100000 0.1148300000 0.0995900000
6 7 8 9 10
0.0658500000 0.0627100000 0.0894900000 0.0926383333 0.1039983333
11 12 13 14 15
0.0735783333 0.0312783333 0.0216233333 0.0083433333 0.0001633333
16 17 18 19 20
-0.0094166667 -0.0580566667 -0.0235966667 -0.0337366667 -0.0676566667
21 22 23 24 25
-0.0862083333 -0.1122483333 -0.1046683333 -0.0832683333 -0.0546233333
26 27 28 29 30
-0.0542033333 -0.0554833333 -0.0652633333 -0.0907033333 -0.1204433333
31 32 33 34 35
-0.1135833333 -0.0721033333 -0.0948966667 -0.1092366667 -0.1204566667
36 37 38 39 40
-0.1358566667 -0.1573116667 -0.1535916667 -0.1368716667 -0.1191516667
41 42 43 44 45
-0.0955916667 -0.0659316667 -0.0297716667 -0.0424916667 -0.0260433333
46 47 48 49 50
-0.0348833333 -0.0082033333 -0.0108033333 0.0207416667 0.0528616667
51 52 53 54 55
0.0671816667 0.0790016667 0.1447616667 0.1441216667 0.1143816667
56 57 58 59 60
0.0927616667 0.1145100000 0.1523700000 0.1597500000 0.1986500000
> postscript(file="/var/www/html/rcomp/tmp/6czqp1197027689.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 0.1695700000 NA
1 0.1465900000 0.1695700000
2 0.1250100000 0.1465900000
3 0.1148300000 0.1250100000
4 0.0995900000 0.1148300000
5 0.0658500000 0.0995900000
6 0.0627100000 0.0658500000
7 0.0894900000 0.0627100000
8 0.0926383333 0.0894900000
9 0.1039983333 0.0926383333
10 0.0735783333 0.1039983333
11 0.0312783333 0.0735783333
12 0.0216233333 0.0312783333
13 0.0083433333 0.0216233333
14 0.0001633333 0.0083433333
15 -0.0094166667 0.0001633333
16 -0.0580566667 -0.0094166667
17 -0.0235966667 -0.0580566667
18 -0.0337366667 -0.0235966667
19 -0.0676566667 -0.0337366667
20 -0.0862083333 -0.0676566667
21 -0.1122483333 -0.0862083333
22 -0.1046683333 -0.1122483333
23 -0.0832683333 -0.1046683333
24 -0.0546233333 -0.0832683333
25 -0.0542033333 -0.0546233333
26 -0.0554833333 -0.0542033333
27 -0.0652633333 -0.0554833333
28 -0.0907033333 -0.0652633333
29 -0.1204433333 -0.0907033333
30 -0.1135833333 -0.1204433333
31 -0.0721033333 -0.1135833333
32 -0.0948966667 -0.0721033333
33 -0.1092366667 -0.0948966667
34 -0.1204566667 -0.1092366667
35 -0.1358566667 -0.1204566667
36 -0.1573116667 -0.1358566667
37 -0.1535916667 -0.1573116667
38 -0.1368716667 -0.1535916667
39 -0.1191516667 -0.1368716667
40 -0.0955916667 -0.1191516667
41 -0.0659316667 -0.0955916667
42 -0.0297716667 -0.0659316667
43 -0.0424916667 -0.0297716667
44 -0.0260433333 -0.0424916667
45 -0.0348833333 -0.0260433333
46 -0.0082033333 -0.0348833333
47 -0.0108033333 -0.0082033333
48 0.0207416667 -0.0108033333
49 0.0528616667 0.0207416667
50 0.0671816667 0.0528616667
51 0.0790016667 0.0671816667
52 0.1447616667 0.0790016667
53 0.1441216667 0.1447616667
54 0.1143816667 0.1441216667
55 0.0927616667 0.1143816667
56 0.1145100000 0.0927616667
57 0.1523700000 0.1145100000
58 0.1597500000 0.1523700000
59 0.1986500000 0.1597500000
60 NA 0.1986500000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.1465900000 0.1695700000
[2,] 0.1250100000 0.1465900000
[3,] 0.1148300000 0.1250100000
[4,] 0.0995900000 0.1148300000
[5,] 0.0658500000 0.0995900000
[6,] 0.0627100000 0.0658500000
[7,] 0.0894900000 0.0627100000
[8,] 0.0926383333 0.0894900000
[9,] 0.1039983333 0.0926383333
[10,] 0.0735783333 0.1039983333
[11,] 0.0312783333 0.0735783333
[12,] 0.0216233333 0.0312783333
[13,] 0.0083433333 0.0216233333
[14,] 0.0001633333 0.0083433333
[15,] -0.0094166667 0.0001633333
[16,] -0.0580566667 -0.0094166667
[17,] -0.0235966667 -0.0580566667
[18,] -0.0337366667 -0.0235966667
[19,] -0.0676566667 -0.0337366667
[20,] -0.0862083333 -0.0676566667
[21,] -0.1122483333 -0.0862083333
[22,] -0.1046683333 -0.1122483333
[23,] -0.0832683333 -0.1046683333
[24,] -0.0546233333 -0.0832683333
[25,] -0.0542033333 -0.0546233333
[26,] -0.0554833333 -0.0542033333
[27,] -0.0652633333 -0.0554833333
[28,] -0.0907033333 -0.0652633333
[29,] -0.1204433333 -0.0907033333
[30,] -0.1135833333 -0.1204433333
[31,] -0.0721033333 -0.1135833333
[32,] -0.0948966667 -0.0721033333
[33,] -0.1092366667 -0.0948966667
[34,] -0.1204566667 -0.1092366667
[35,] -0.1358566667 -0.1204566667
[36,] -0.1573116667 -0.1358566667
[37,] -0.1535916667 -0.1573116667
[38,] -0.1368716667 -0.1535916667
[39,] -0.1191516667 -0.1368716667
[40,] -0.0955916667 -0.1191516667
[41,] -0.0659316667 -0.0955916667
[42,] -0.0297716667 -0.0659316667
[43,] -0.0424916667 -0.0297716667
[44,] -0.0260433333 -0.0424916667
[45,] -0.0348833333 -0.0260433333
[46,] -0.0082033333 -0.0348833333
[47,] -0.0108033333 -0.0082033333
[48,] 0.0207416667 -0.0108033333
[49,] 0.0528616667 0.0207416667
[50,] 0.0671816667 0.0528616667
[51,] 0.0790016667 0.0671816667
[52,] 0.1447616667 0.0790016667
[53,] 0.1441216667 0.1447616667
[54,] 0.1143816667 0.1441216667
[55,] 0.0927616667 0.1143816667
[56,] 0.1145100000 0.0927616667
[57,] 0.1523700000 0.1145100000
[58,] 0.1597500000 0.1523700000
[59,] 0.1986500000 0.1597500000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.1465900000 0.1695700000
2 0.1250100000 0.1465900000
3 0.1148300000 0.1250100000
4 0.0995900000 0.1148300000
5 0.0658500000 0.0995900000
6 0.0627100000 0.0658500000
7 0.0894900000 0.0627100000
8 0.0926383333 0.0894900000
9 0.1039983333 0.0926383333
10 0.0735783333 0.1039983333
11 0.0312783333 0.0735783333
12 0.0216233333 0.0312783333
13 0.0083433333 0.0216233333
14 0.0001633333 0.0083433333
15 -0.0094166667 0.0001633333
16 -0.0580566667 -0.0094166667
17 -0.0235966667 -0.0580566667
18 -0.0337366667 -0.0235966667
19 -0.0676566667 -0.0337366667
20 -0.0862083333 -0.0676566667
21 -0.1122483333 -0.0862083333
22 -0.1046683333 -0.1122483333
23 -0.0832683333 -0.1046683333
24 -0.0546233333 -0.0832683333
25 -0.0542033333 -0.0546233333
26 -0.0554833333 -0.0542033333
27 -0.0652633333 -0.0554833333
28 -0.0907033333 -0.0652633333
29 -0.1204433333 -0.0907033333
30 -0.1135833333 -0.1204433333
31 -0.0721033333 -0.1135833333
32 -0.0948966667 -0.0721033333
33 -0.1092366667 -0.0948966667
34 -0.1204566667 -0.1092366667
35 -0.1358566667 -0.1204566667
36 -0.1573116667 -0.1358566667
37 -0.1535916667 -0.1573116667
38 -0.1368716667 -0.1535916667
39 -0.1191516667 -0.1368716667
40 -0.0955916667 -0.1191516667
41 -0.0659316667 -0.0955916667
42 -0.0297716667 -0.0659316667
43 -0.0424916667 -0.0297716667
44 -0.0260433333 -0.0424916667
45 -0.0348833333 -0.0260433333
46 -0.0082033333 -0.0348833333
47 -0.0108033333 -0.0082033333
48 0.0207416667 -0.0108033333
49 0.0528616667 0.0207416667
50 0.0671816667 0.0528616667
51 0.0790016667 0.0671816667
52 0.1447616667 0.0790016667
53 0.1441216667 0.1447616667
54 0.1143816667 0.1441216667
55 0.0927616667 0.1143816667
56 0.1145100000 0.0927616667
57 0.1523700000 0.1145100000
58 0.1597500000 0.1523700000
59 0.1986500000 0.1597500000
> 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/7jrc71197027689.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8lsjt1197027689.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9v4ap1197027689.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10xkye1197027689.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/11esr61197027689.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/12g99d1197027689.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/13r3dn1197027689.tab")
>
> system("convert tmp/1u0691197027688.ps tmp/1u0691197027688.png")
> system("convert tmp/2fs141197027688.ps tmp/2fs141197027688.png")
> system("convert tmp/3ibwu1197027688.ps tmp/3ibwu1197027688.png")
> system("convert tmp/4rqze1197027688.ps tmp/4rqze1197027688.png")
> system("convert tmp/5vro91197027688.ps tmp/5vro91197027688.png")
> system("convert tmp/6czqp1197027689.ps tmp/6czqp1197027689.png")
> system("convert tmp/7jrc71197027689.ps tmp/7jrc71197027689.png")
> system("convert tmp/8lsjt1197027689.ps tmp/8lsjt1197027689.png")
> system("convert tmp/9v4ap1197027689.ps tmp/9v4ap1197027689.png")
>
>
> proc.time()
user system elapsed
2.303 1.465 2.679