R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(612613,0,611324,0,594167,0,595454,0,590865,0,589379,0,584428,0,573100,0,567456,0,569028,0,620735,0,628884,0,628232,0,612117,0,595404,0,597141,0,593408,0,590072,0,579799,0,574205,0,572775,0,572942,0,619567,0,625809,0,619916,0,587625,0,565742,0,557274,0,560576,1,548854,1,531673,1,525919,1,511038,1,498662,1,555362,1,564591,1,541657,1,527070,1,509846,1,514258,1,516922,1,507561,1,492622,1,490243,1,469357,1,477580,1,528379,1,533590,1,517945,1,506174,1,501866,1,516141,1,528222,1,532638,1,536322,1,536535,1,523597,1,536214,1,586570,1,596594,1),dim=c(2,60),dimnames=list(c('wlh','dummies'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('wlh','dummies'),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 = 'Do not include Seasonal 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)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> 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
wlh dummies t
1 612613 0 1
2 611324 0 2
3 594167 0 3
4 595454 0 4
5 590865 0 5
6 589379 0 6
7 584428 0 7
8 573100 0 8
9 567456 0 9
10 569028 0 10
11 620735 0 11
12 628884 0 12
13 628232 0 13
14 612117 0 14
15 595404 0 15
16 597141 0 16
17 593408 0 17
18 590072 0 18
19 579799 0 19
20 574205 0 20
21 572775 0 21
22 572942 0 22
23 619567 0 23
24 625809 0 24
25 619916 0 25
26 587625 0 26
27 565742 0 27
28 557274 0 28
29 560576 1 29
30 548854 1 30
31 531673 1 31
32 525919 1 32
33 511038 1 33
34 498662 1 34
35 555362 1 35
36 564591 1 36
37 541657 1 37
38 527070 1 38
39 509846 1 39
40 514258 1 40
41 516922 1 41
42 507561 1 42
43 492622 1 43
44 490243 1 44
45 469357 1 45
46 477580 1 46
47 528379 1 47
48 533590 1 48
49 517945 1 49
50 506174 1 50
51 501866 1 51
52 516141 1 52
53 528222 1 53
54 532638 1 54
55 536322 1 55
56 536535 1 56
57 523597 1 57
58 536214 1 58
59 586570 1 59
60 596594 1 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummies t
593211.20 -69585.66 48.15
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-56435.14 -18529.74 65.54 16691.66 70079.67
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 593211.20 7221.91 82.140 < 2e-16 ***
dummies -69585.66 12981.86 -5.360 1.56e-06 ***
t 48.15 373.97 0.129 0.898
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 25240 on 57 degrees of freedom
Multiple R-squared: 0.6563, Adjusted R-squared: 0.6443
F-statistic: 54.43 on 2 and 57 DF, p-value: 6.021e-14
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 1.168816e-02 2.337633e-02 0.9883118
[2,] 2.145462e-03 4.290924e-03 0.9978545
[3,] 4.733131e-04 9.466261e-04 0.9995267
[4,] 9.067516e-05 1.813503e-04 0.9999093
[5,] 2.130261e-05 4.260522e-05 0.9999787
[6,] 1.150228e-01 2.300457e-01 0.8849772
[7,] 2.931810e-01 5.863621e-01 0.7068190
[8,] 3.517148e-01 7.034296e-01 0.6482852
[9,] 2.808145e-01 5.616291e-01 0.7191855
[10,] 2.135492e-01 4.270983e-01 0.7864508
[11,] 1.547522e-01 3.095044e-01 0.8452478
[12,] 1.105949e-01 2.211897e-01 0.8894051
[13,] 7.811422e-02 1.562284e-01 0.9218858
[14,] 6.246773e-02 1.249355e-01 0.9375323
[15,] 5.264022e-02 1.052804e-01 0.9473598
[16,] 4.215437e-02 8.430874e-02 0.9578456
[17,] 3.168145e-02 6.336289e-02 0.9683186
[18,] 4.500090e-02 9.000179e-02 0.9549991
[19,] 7.097286e-02 1.419457e-01 0.9290271
[20,] 8.660276e-02 1.732055e-01 0.9133972
[21,] 6.811620e-02 1.362324e-01 0.9318838
[22,] 6.950569e-02 1.390114e-01 0.9304943
[23,] 7.546460e-02 1.509292e-01 0.9245354
[24,] 7.619426e-02 1.523885e-01 0.9238057
[25,] 7.186965e-02 1.437393e-01 0.9281304
[26,] 6.318355e-02 1.263671e-01 0.9368165
[27,] 5.251188e-02 1.050238e-01 0.9474881
[28,] 4.688053e-02 9.376105e-02 0.9531195
[29,] 4.839476e-02 9.678952e-02 0.9516052
[30,] 7.659627e-02 1.531925e-01 0.9234037
[31,] 2.187569e-01 4.375138e-01 0.7812431
[32,] 3.153332e-01 6.306664e-01 0.6846668
[33,] 3.798554e-01 7.597109e-01 0.6201446
[34,] 3.923518e-01 7.847035e-01 0.6076482
[35,] 4.288390e-01 8.576780e-01 0.5711610
[36,] 5.153146e-01 9.693707e-01 0.4846854
[37,] 5.673326e-01 8.653347e-01 0.4326674
[38,] 5.547208e-01 8.905583e-01 0.4452792
[39,] 5.163552e-01 9.672895e-01 0.4836448
[40,] 5.667162e-01 8.665676e-01 0.4332838
[41,] 5.799042e-01 8.401915e-01 0.4200958
[42,] 6.087143e-01 7.825714e-01 0.3912857
[43,] 7.588532e-01 4.822937e-01 0.2411468
[44,] 7.750503e-01 4.498995e-01 0.2249497
[45,] 6.972812e-01 6.054376e-01 0.3027188
[46,] 5.783092e-01 8.433815e-01 0.4216908
[47,] 4.562163e-01 9.124327e-01 0.5437837
[48,] 3.826353e-01 7.652706e-01 0.6173647
[49,] 3.361381e-01 6.722761e-01 0.6638619
> postscript(file="/var/www/html/rcomp/tmp/105wt1262208238.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2eowy1262208238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/383xe1262208238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ivig1262208238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xiu91262208238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> 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
19353.6569 18016.5103 811.3638 2050.2173 -2586.9293 -4121.0758
7 8 9 10 11 12
-9120.2224 -20496.3689 -26188.5154 -24664.6620 26994.1915 35095.0449
13 14 15 16 17 18
34394.8984 18231.7518 1470.6053 3159.4588 -621.6878 -4005.8343
19 20 21 22 23 24
-14326.9809 -19969.1274 -21447.2740 -21328.4205 25248.4330 31442.2864
25 26 27 28 29 30
25501.1399 -6838.0067 -28769.1532 -37285.2997 35554.2089 23784.0624
31 32 33 34 35 36
6554.9158 752.7693 -14176.3773 -26600.5238 30051.3297 39232.1831
37 38 39 40 41 42
16250.0366 1614.8900 -15657.2565 -11293.4031 -8677.5496 -18086.6961
43 44 45 46 47 48
-33073.8427 -35500.9892 -56435.1358 -48260.2823 2490.5711 7653.4246
49 50 51 52 53 54
-8039.7219 -19858.8685 -24215.0150 -9988.1616 2044.6919 6412.5453
55 56 57 58 59 60
10048.3988 10213.2523 -2772.8943 9795.9592 60103.8126 70079.6661
> postscript(file="/var/www/html/rcomp/tmp/6q20x1262208238.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 19353.6569 NA
1 18016.5103 19353.6569
2 811.3638 18016.5103
3 2050.2173 811.3638
4 -2586.9293 2050.2173
5 -4121.0758 -2586.9293
6 -9120.2224 -4121.0758
7 -20496.3689 -9120.2224
8 -26188.5154 -20496.3689
9 -24664.6620 -26188.5154
10 26994.1915 -24664.6620
11 35095.0449 26994.1915
12 34394.8984 35095.0449
13 18231.7518 34394.8984
14 1470.6053 18231.7518
15 3159.4588 1470.6053
16 -621.6878 3159.4588
17 -4005.8343 -621.6878
18 -14326.9809 -4005.8343
19 -19969.1274 -14326.9809
20 -21447.2740 -19969.1274
21 -21328.4205 -21447.2740
22 25248.4330 -21328.4205
23 31442.2864 25248.4330
24 25501.1399 31442.2864
25 -6838.0067 25501.1399
26 -28769.1532 -6838.0067
27 -37285.2997 -28769.1532
28 35554.2089 -37285.2997
29 23784.0624 35554.2089
30 6554.9158 23784.0624
31 752.7693 6554.9158
32 -14176.3773 752.7693
33 -26600.5238 -14176.3773
34 30051.3297 -26600.5238
35 39232.1831 30051.3297
36 16250.0366 39232.1831
37 1614.8900 16250.0366
38 -15657.2565 1614.8900
39 -11293.4031 -15657.2565
40 -8677.5496 -11293.4031
41 -18086.6961 -8677.5496
42 -33073.8427 -18086.6961
43 -35500.9892 -33073.8427
44 -56435.1358 -35500.9892
45 -48260.2823 -56435.1358
46 2490.5711 -48260.2823
47 7653.4246 2490.5711
48 -8039.7219 7653.4246
49 -19858.8685 -8039.7219
50 -24215.0150 -19858.8685
51 -9988.1616 -24215.0150
52 2044.6919 -9988.1616
53 6412.5453 2044.6919
54 10048.3988 6412.5453
55 10213.2523 10048.3988
56 -2772.8943 10213.2523
57 9795.9592 -2772.8943
58 60103.8126 9795.9592
59 70079.6661 60103.8126
60 NA 70079.6661
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 18016.5103 19353.6569
[2,] 811.3638 18016.5103
[3,] 2050.2173 811.3638
[4,] -2586.9293 2050.2173
[5,] -4121.0758 -2586.9293
[6,] -9120.2224 -4121.0758
[7,] -20496.3689 -9120.2224
[8,] -26188.5154 -20496.3689
[9,] -24664.6620 -26188.5154
[10,] 26994.1915 -24664.6620
[11,] 35095.0449 26994.1915
[12,] 34394.8984 35095.0449
[13,] 18231.7518 34394.8984
[14,] 1470.6053 18231.7518
[15,] 3159.4588 1470.6053
[16,] -621.6878 3159.4588
[17,] -4005.8343 -621.6878
[18,] -14326.9809 -4005.8343
[19,] -19969.1274 -14326.9809
[20,] -21447.2740 -19969.1274
[21,] -21328.4205 -21447.2740
[22,] 25248.4330 -21328.4205
[23,] 31442.2864 25248.4330
[24,] 25501.1399 31442.2864
[25,] -6838.0067 25501.1399
[26,] -28769.1532 -6838.0067
[27,] -37285.2997 -28769.1532
[28,] 35554.2089 -37285.2997
[29,] 23784.0624 35554.2089
[30,] 6554.9158 23784.0624
[31,] 752.7693 6554.9158
[32,] -14176.3773 752.7693
[33,] -26600.5238 -14176.3773
[34,] 30051.3297 -26600.5238
[35,] 39232.1831 30051.3297
[36,] 16250.0366 39232.1831
[37,] 1614.8900 16250.0366
[38,] -15657.2565 1614.8900
[39,] -11293.4031 -15657.2565
[40,] -8677.5496 -11293.4031
[41,] -18086.6961 -8677.5496
[42,] -33073.8427 -18086.6961
[43,] -35500.9892 -33073.8427
[44,] -56435.1358 -35500.9892
[45,] -48260.2823 -56435.1358
[46,] 2490.5711 -48260.2823
[47,] 7653.4246 2490.5711
[48,] -8039.7219 7653.4246
[49,] -19858.8685 -8039.7219
[50,] -24215.0150 -19858.8685
[51,] -9988.1616 -24215.0150
[52,] 2044.6919 -9988.1616
[53,] 6412.5453 2044.6919
[54,] 10048.3988 6412.5453
[55,] 10213.2523 10048.3988
[56,] -2772.8943 10213.2523
[57,] 9795.9592 -2772.8943
[58,] 60103.8126 9795.9592
[59,] 70079.6661 60103.8126
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 18016.5103 19353.6569
2 811.3638 18016.5103
3 2050.2173 811.3638
4 -2586.9293 2050.2173
5 -4121.0758 -2586.9293
6 -9120.2224 -4121.0758
7 -20496.3689 -9120.2224
8 -26188.5154 -20496.3689
9 -24664.6620 -26188.5154
10 26994.1915 -24664.6620
11 35095.0449 26994.1915
12 34394.8984 35095.0449
13 18231.7518 34394.8984
14 1470.6053 18231.7518
15 3159.4588 1470.6053
16 -621.6878 3159.4588
17 -4005.8343 -621.6878
18 -14326.9809 -4005.8343
19 -19969.1274 -14326.9809
20 -21447.2740 -19969.1274
21 -21328.4205 -21447.2740
22 25248.4330 -21328.4205
23 31442.2864 25248.4330
24 25501.1399 31442.2864
25 -6838.0067 25501.1399
26 -28769.1532 -6838.0067
27 -37285.2997 -28769.1532
28 35554.2089 -37285.2997
29 23784.0624 35554.2089
30 6554.9158 23784.0624
31 752.7693 6554.9158
32 -14176.3773 752.7693
33 -26600.5238 -14176.3773
34 30051.3297 -26600.5238
35 39232.1831 30051.3297
36 16250.0366 39232.1831
37 1614.8900 16250.0366
38 -15657.2565 1614.8900
39 -11293.4031 -15657.2565
40 -8677.5496 -11293.4031
41 -18086.6961 -8677.5496
42 -33073.8427 -18086.6961
43 -35500.9892 -33073.8427
44 -56435.1358 -35500.9892
45 -48260.2823 -56435.1358
46 2490.5711 -48260.2823
47 7653.4246 2490.5711
48 -8039.7219 7653.4246
49 -19858.8685 -8039.7219
50 -24215.0150 -19858.8685
51 -9988.1616 -24215.0150
52 2044.6919 -9988.1616
53 6412.5453 2044.6919
54 10048.3988 6412.5453
55 10213.2523 10048.3988
56 -2772.8943 10213.2523
57 9795.9592 -2772.8943
58 60103.8126 9795.9592
59 70079.6661 60103.8126
> 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/7csn51262208238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cf041262208238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/96it01262208238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10nywu1262208238.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11e1sr1262208238.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/12o36d1262208238.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/137z0k1262208238.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/1448a91262208238.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/159e791262208238.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16q8jw1262208239.tab")
+ }
>
> try(system("convert tmp/105wt1262208238.ps tmp/105wt1262208238.png",intern=TRUE))
character(0)
> try(system("convert tmp/2eowy1262208238.ps tmp/2eowy1262208238.png",intern=TRUE))
character(0)
> try(system("convert tmp/383xe1262208238.ps tmp/383xe1262208238.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ivig1262208238.ps tmp/4ivig1262208238.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xiu91262208238.ps tmp/5xiu91262208238.png",intern=TRUE))
character(0)
> try(system("convert tmp/6q20x1262208238.ps tmp/6q20x1262208238.png",intern=TRUE))
character(0)
> try(system("convert tmp/7csn51262208238.ps tmp/7csn51262208238.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cf041262208238.ps tmp/8cf041262208238.png",intern=TRUE))
character(0)
> try(system("convert tmp/96it01262208238.ps tmp/96it01262208238.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nywu1262208238.ps tmp/10nywu1262208238.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.442 1.610 3.129