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
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(12103,1,12989,1,11610,1,10206,1,11356,1,11307,1,12649,1,11947,1,11714,0,12193,1,11269,1,9097,1,12640,1,13040,1,11687,1,11192,1,11392,1,11793,1,13933,1,12778,1,11810,2,13698,2,11957,2,10724,2,13939,1,13980,2,13807,2,12974,1,12510,2,12934,2,14908,2,13772,2,13013,2,14050,2,11817,2,11593,2,14466,2,13616,2,14734,2,13881,2,13528,2,13584,2,16170,2,13261,2,14742,2,15487,2,13155,2,12621,2,15032,1,15452,1,15428,2,13106,2,14717,1,14180,1,16202,1,15036,1,15915,1,16468,1,14730,1,13705,1),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 12103 1 1 0 0 0 0 0 0 0 0 0 0 1
2 12989 1 0 1 0 0 0 0 0 0 0 0 0 2
3 11610 1 0 0 1 0 0 0 0 0 0 0 0 3
4 10206 1 0 0 0 1 0 0 0 0 0 0 0 4
5 11356 1 0 0 0 0 1 0 0 0 0 0 0 5
6 11307 1 0 0 0 0 0 1 0 0 0 0 0 6
7 12649 1 0 0 0 0 0 0 1 0 0 0 0 7
8 11947 1 0 0 0 0 0 0 0 1 0 0 0 8
9 11714 0 0 0 0 0 0 0 0 0 1 0 0 9
10 12193 1 0 0 0 0 0 0 0 0 0 1 0 10
11 11269 1 0 0 0 0 0 0 0 0 0 0 1 11
12 9097 1 0 0 0 0 0 0 0 0 0 0 0 12
13 12640 1 1 0 0 0 0 0 0 0 0 0 0 13
14 13040 1 0 1 0 0 0 0 0 0 0 0 0 14
15 11687 1 0 0 1 0 0 0 0 0 0 0 0 15
16 11192 1 0 0 0 1 0 0 0 0 0 0 0 16
17 11392 1 0 0 0 0 1 0 0 0 0 0 0 17
18 11793 1 0 0 0 0 0 1 0 0 0 0 0 18
19 13933 1 0 0 0 0 0 0 1 0 0 0 0 19
20 12778 1 0 0 0 0 0 0 0 1 0 0 0 20
21 11810 2 0 0 0 0 0 0 0 0 1 0 0 21
22 13698 2 0 0 0 0 0 0 0 0 0 1 0 22
23 11957 2 0 0 0 0 0 0 0 0 0 0 1 23
24 10724 2 0 0 0 0 0 0 0 0 0 0 0 24
25 13939 1 1 0 0 0 0 0 0 0 0 0 0 25
26 13980 2 0 1 0 0 0 0 0 0 0 0 0 26
27 13807 2 0 0 1 0 0 0 0 0 0 0 0 27
28 12974 1 0 0 0 1 0 0 0 0 0 0 0 28
29 12510 2 0 0 0 0 1 0 0 0 0 0 0 29
30 12934 2 0 0 0 0 0 1 0 0 0 0 0 30
31 14908 2 0 0 0 0 0 0 1 0 0 0 0 31
32 13772 2 0 0 0 0 0 0 0 1 0 0 0 32
33 13013 2 0 0 0 0 0 0 0 0 1 0 0 33
34 14050 2 0 0 0 0 0 0 0 0 0 1 0 34
35 11817 2 0 0 0 0 0 0 0 0 0 0 1 35
36 11593 2 0 0 0 0 0 0 0 0 0 0 0 36
37 14466 2 1 0 0 0 0 0 0 0 0 0 0 37
38 13616 2 0 1 0 0 0 0 0 0 0 0 0 38
39 14734 2 0 0 1 0 0 0 0 0 0 0 0 39
40 13881 2 0 0 0 1 0 0 0 0 0 0 0 40
41 13528 2 0 0 0 0 1 0 0 0 0 0 0 41
42 13584 2 0 0 0 0 0 1 0 0 0 0 0 42
43 16170 2 0 0 0 0 0 0 1 0 0 0 0 43
44 13261 2 0 0 0 0 0 0 0 1 0 0 0 44
45 14742 2 0 0 0 0 0 0 0 0 1 0 0 45
46 15487 2 0 0 0 0 0 0 0 0 0 1 0 46
47 13155 2 0 0 0 0 0 0 0 0 0 0 1 47
48 12621 2 0 0 0 0 0 0 0 0 0 0 0 48
49 15032 1 1 0 0 0 0 0 0 0 0 0 0 49
50 15452 1 0 1 0 0 0 0 0 0 0 0 0 50
51 15428 2 0 0 1 0 0 0 0 0 0 0 0 51
52 13106 2 0 0 0 1 0 0 0 0 0 0 0 52
53 14717 1 0 0 0 0 1 0 0 0 0 0 0 53
54 14180 1 0 0 0 0 0 1 0 0 0 0 0 54
55 16202 1 0 0 0 0 0 0 1 0 0 0 0 55
56 15036 1 0 0 0 0 0 0 0 1 0 0 0 56
57 15915 1 0 0 0 0 0 0 0 0 1 0 0 57
58 16468 1 0 0 0 0 0 0 0 0 0 1 0 58
59 14730 1 0 0 0 0 0 0 0 0 0 0 1 59
60 13705 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
9033.29 -101.50 2865.40 2990.74 2574.47 1298.41
M5 M6 M7 M8 M9 M10
1652.85 1637.48 3575.92 2087.96 2093.59 2979.93
M11 t
1111.96 74.36
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1030.87 -296.87 80.98 293.34 917.73
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9033.294 311.835 28.968 < 2e-16 ***
x -101.495 132.806 -0.764 0.448628
M1 2865.404 323.489 8.858 1.66e-11 ***
M2 2990.739 320.950 9.318 3.66e-12 ***
M3 2574.475 320.436 8.034 2.63e-10 ***
M4 1298.412 320.247 4.054 0.000192 ***
M5 1652.848 319.972 5.166 5.03e-06 ***
M6 1637.484 319.747 5.121 5.85e-06 ***
M7 3575.920 319.573 11.190 1.01e-14 ***
M8 2087.956 319.451 6.536 4.53e-08 ***
M9 2093.592 319.379 6.555 4.24e-08 ***
M10 2979.928 318.468 9.357 3.22e-12 ***
M11 1111.964 318.391 3.492 0.001069 **
t 74.364 4.041 18.402 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 503.4 on 46 degrees of freedom
Multiple R-squared: 0.9222, Adjusted R-squared: 0.9002
F-statistic: 41.92 on 13 and 46 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/freestat/rcomp/tmp/1b38w1227534289.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/27kly1227534289.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/35kjs1227534289.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/490d71227534289.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5zd811227534289.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
231.433179 917.734107 -119.364965 -321.665893 399.534107 291.534107
7 8 9 10 11 12
-379.265893 332.334107 -82.161253 -462.364965 407.235035 -727.164965
13 14 15 16 17 18
-123.932947 76.367981 -934.731090 -228.032019 -456.832019 -114.832019
19 20 21 22 23 24
12.367981 270.967981 -675.536659 251.764269 304.364269 108.964269
25 26 27 28 29 30
282.700928 225.497216 394.398144 661.601856 -129.702784 235.297216
31 32 33 34 35 36
196.497216 474.097216 -364.902784 -288.601856 -728.001856 85.598144
37 38 39 40 41 42
18.830162 -1030.868910 429.032019 777.731090 -4.068910 -7.068910
43 44 45 46 47 48
566.131090 -929.268910 471.731090 256.032019 -282.367981 221.232019
49 50 51 52 53 54
-409.031323 -188.730394 230.665893 -889.635035 191.069606 -404.930394
55 56 57 58 59 60
-395.730394 -148.130394 650.869606 243.170534 298.770534 311.370534
> postscript(file="/var/www/html/freestat/rcomp/tmp/6typ51227534289.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
0 231.433179 NA
1 917.734107 231.433179
2 -119.364965 917.734107
3 -321.665893 -119.364965
4 399.534107 -321.665893
5 291.534107 399.534107
6 -379.265893 291.534107
7 332.334107 -379.265893
8 -82.161253 332.334107
9 -462.364965 -82.161253
10 407.235035 -462.364965
11 -727.164965 407.235035
12 -123.932947 -727.164965
13 76.367981 -123.932947
14 -934.731090 76.367981
15 -228.032019 -934.731090
16 -456.832019 -228.032019
17 -114.832019 -456.832019
18 12.367981 -114.832019
19 270.967981 12.367981
20 -675.536659 270.967981
21 251.764269 -675.536659
22 304.364269 251.764269
23 108.964269 304.364269
24 282.700928 108.964269
25 225.497216 282.700928
26 394.398144 225.497216
27 661.601856 394.398144
28 -129.702784 661.601856
29 235.297216 -129.702784
30 196.497216 235.297216
31 474.097216 196.497216
32 -364.902784 474.097216
33 -288.601856 -364.902784
34 -728.001856 -288.601856
35 85.598144 -728.001856
36 18.830162 85.598144
37 -1030.868910 18.830162
38 429.032019 -1030.868910
39 777.731090 429.032019
40 -4.068910 777.731090
41 -7.068910 -4.068910
42 566.131090 -7.068910
43 -929.268910 566.131090
44 471.731090 -929.268910
45 256.032019 471.731090
46 -282.367981 256.032019
47 221.232019 -282.367981
48 -409.031323 221.232019
49 -188.730394 -409.031323
50 230.665893 -188.730394
51 -889.635035 230.665893
52 191.069606 -889.635035
53 -404.930394 191.069606
54 -395.730394 -404.930394
55 -148.130394 -395.730394
56 650.869606 -148.130394
57 243.170534 650.869606
58 298.770534 243.170534
59 311.370534 298.770534
60 NA 311.370534
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 917.734107 231.433179
[2,] -119.364965 917.734107
[3,] -321.665893 -119.364965
[4,] 399.534107 -321.665893
[5,] 291.534107 399.534107
[6,] -379.265893 291.534107
[7,] 332.334107 -379.265893
[8,] -82.161253 332.334107
[9,] -462.364965 -82.161253
[10,] 407.235035 -462.364965
[11,] -727.164965 407.235035
[12,] -123.932947 -727.164965
[13,] 76.367981 -123.932947
[14,] -934.731090 76.367981
[15,] -228.032019 -934.731090
[16,] -456.832019 -228.032019
[17,] -114.832019 -456.832019
[18,] 12.367981 -114.832019
[19,] 270.967981 12.367981
[20,] -675.536659 270.967981
[21,] 251.764269 -675.536659
[22,] 304.364269 251.764269
[23,] 108.964269 304.364269
[24,] 282.700928 108.964269
[25,] 225.497216 282.700928
[26,] 394.398144 225.497216
[27,] 661.601856 394.398144
[28,] -129.702784 661.601856
[29,] 235.297216 -129.702784
[30,] 196.497216 235.297216
[31,] 474.097216 196.497216
[32,] -364.902784 474.097216
[33,] -288.601856 -364.902784
[34,] -728.001856 -288.601856
[35,] 85.598144 -728.001856
[36,] 18.830162 85.598144
[37,] -1030.868910 18.830162
[38,] 429.032019 -1030.868910
[39,] 777.731090 429.032019
[40,] -4.068910 777.731090
[41,] -7.068910 -4.068910
[42,] 566.131090 -7.068910
[43,] -929.268910 566.131090
[44,] 471.731090 -929.268910
[45,] 256.032019 471.731090
[46,] -282.367981 256.032019
[47,] 221.232019 -282.367981
[48,] -409.031323 221.232019
[49,] -188.730394 -409.031323
[50,] 230.665893 -188.730394
[51,] -889.635035 230.665893
[52,] 191.069606 -889.635035
[53,] -404.930394 191.069606
[54,] -395.730394 -404.930394
[55,] -148.130394 -395.730394
[56,] 650.869606 -148.130394
[57,] 243.170534 650.869606
[58,] 298.770534 243.170534
[59,] 311.370534 298.770534
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 917.734107 231.433179
2 -119.364965 917.734107
3 -321.665893 -119.364965
4 399.534107 -321.665893
5 291.534107 399.534107
6 -379.265893 291.534107
7 332.334107 -379.265893
8 -82.161253 332.334107
9 -462.364965 -82.161253
10 407.235035 -462.364965
11 -727.164965 407.235035
12 -123.932947 -727.164965
13 76.367981 -123.932947
14 -934.731090 76.367981
15 -228.032019 -934.731090
16 -456.832019 -228.032019
17 -114.832019 -456.832019
18 12.367981 -114.832019
19 270.967981 12.367981
20 -675.536659 270.967981
21 251.764269 -675.536659
22 304.364269 251.764269
23 108.964269 304.364269
24 282.700928 108.964269
25 225.497216 282.700928
26 394.398144 225.497216
27 661.601856 394.398144
28 -129.702784 661.601856
29 235.297216 -129.702784
30 196.497216 235.297216
31 474.097216 196.497216
32 -364.902784 474.097216
33 -288.601856 -364.902784
34 -728.001856 -288.601856
35 85.598144 -728.001856
36 18.830162 85.598144
37 -1030.868910 18.830162
38 429.032019 -1030.868910
39 777.731090 429.032019
40 -4.068910 777.731090
41 -7.068910 -4.068910
42 566.131090 -7.068910
43 -929.268910 566.131090
44 471.731090 -929.268910
45 256.032019 471.731090
46 -282.367981 256.032019
47 221.232019 -282.367981
48 -409.031323 221.232019
49 -188.730394 -409.031323
50 230.665893 -188.730394
51 -889.635035 230.665893
52 191.069606 -889.635035
53 -404.930394 191.069606
54 -395.730394 -404.930394
55 -148.130394 -395.730394
56 650.869606 -148.130394
57 243.170534 650.869606
58 298.770534 243.170534
59 311.370534 298.770534
> 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/7p36h1227534289.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8gcx91227534289.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9431k1227534289.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10itp21227534289.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/1118tn1227534290.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/12ou8e1227534290.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/130qow1227534290.tab")
>
> system("convert tmp/1b38w1227534289.ps tmp/1b38w1227534289.png")
> system("convert tmp/27kly1227534289.ps tmp/27kly1227534289.png")
> system("convert tmp/35kjs1227534289.ps tmp/35kjs1227534289.png")
> system("convert tmp/490d71227534289.ps tmp/490d71227534289.png")
> system("convert tmp/5zd811227534289.ps tmp/5zd811227534289.png")
> system("convert tmp/6typ51227534289.ps tmp/6typ51227534289.png")
> system("convert tmp/7p36h1227534289.ps tmp/7p36h1227534289.png")
> system("convert tmp/8gcx91227534289.ps tmp/8gcx91227534289.png")
> system("convert tmp/9431k1227534289.ps tmp/9431k1227534289.png")
>
>
> proc.time()
user system elapsed
2.921 2.209 3.274