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.
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(267413,294912,267366,293488,264777,290555,258863,284736,254844,281818,254868,287854,277267,316263,285351,325412,286602,326011,283042,328282,276687,317480,277915,317539,277128,313737,277103,312276,275037,309391,270150,302950,267140,300316,264993,304035,287259,333476,291186,337698,292300,335932,288186,323931,281477,313927,282656,314485,280190,313218,280408,309664,276836,302963,275216,298989,274352,298423,271311,301631,289802,329765,290726,335083,292300,327616,278506,309119,269826,295916,265861,291413,269034,291542,264176,284678,255198,276475,253353,272566,246057,264981,235372,263290,258556,296806,260993,303598,254663,286994,250643,276427,243422,266424,247105,267153,248541,268381,245039,262522,237080,255542,237085,253158,225554,243803,226839,250741,247934,280445,248333,285257,246969,270976,245098,261076,246263,255603,255765,260376,264319,263903,268347,264291,273046,263276,273963,262572,267430,256167,271993,264221,292710,293860),dim=c(2,67),dimnames=list(c('Y','X'),1:67))
> y <- array(NA,dim=c(2,67),dimnames=list(c('Y','X'),1:67))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No 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
Y X
1 267413 294912
2 267366 293488
3 264777 290555
4 258863 284736
5 254844 281818
6 254868 287854
7 277267 316263
8 285351 325412
9 286602 326011
10 283042 328282
11 276687 317480
12 277915 317539
13 277128 313737
14 277103 312276
15 275037 309391
16 270150 302950
17 267140 300316
18 264993 304035
19 287259 333476
20 291186 337698
21 292300 335932
22 288186 323931
23 281477 313927
24 282656 314485
25 280190 313218
26 280408 309664
27 276836 302963
28 275216 298989
29 274352 298423
30 271311 301631
31 289802 329765
32 290726 335083
33 292300 327616
34 278506 309119
35 269826 295916
36 265861 291413
37 269034 291542
38 264176 284678
39 255198 276475
40 253353 272566
41 246057 264981
42 235372 263290
43 258556 296806
44 260993 303598
45 254663 286994
46 250643 276427
47 243422 266424
48 247105 267153
49 248541 268381
50 245039 262522
51 237080 255542
52 237085 253158
53 225554 243803
54 226839 250741
55 247934 280445
56 248333 285257
57 246969 270976
58 245098 261076
59 246263 255603
60 255765 260376
61 264319 263903
62 268347 264291
63 273046 263276
64 273963 262572
65 267430 256167
66 271993 264221
67 292710 293860
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
1.007e+05 5.664e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-15840 -4779 -745 2886 25608
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.007e+05 1.301e+04 7.735 8.65e-11 ***
X 5.664e-01 4.444e-02 12.744 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9138 on 65 degrees of freedom
Multiple R-squared: 0.7142, Adjusted R-squared: 0.7098
F-statistic: 162.4 on 1 and 65 DF, p-value: < 2.2e-16
> 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,] 1.040600e-03 2.081199e-03 0.99895940
[2,] 1.962890e-02 3.925780e-02 0.98037110
[3,] 1.524936e-02 3.049872e-02 0.98475064
[4,] 4.711795e-03 9.423590e-03 0.99528820
[5,] 1.389705e-03 2.779411e-03 0.99861029
[6,] 8.136701e-04 1.627340e-03 0.99918633
[7,] 3.148230e-04 6.296460e-04 0.99968518
[8,] 9.189306e-05 1.837861e-04 0.99990811
[9,] 2.471065e-05 4.942130e-05 0.99997529
[10,] 7.076561e-06 1.415312e-05 0.99999292
[11,] 1.854324e-06 3.708648e-06 0.99999815
[12,] 4.347606e-07 8.695213e-07 0.99999957
[13,] 1.104062e-07 2.208125e-07 0.99999989
[14,] 2.405855e-07 4.811710e-07 0.99999976
[15,] 6.839342e-08 1.367868e-07 0.99999993
[16,] 1.628462e-08 3.256924e-08 0.99999998
[17,] 5.030933e-09 1.006187e-08 0.99999999
[18,] 6.963242e-09 1.392648e-08 0.99999999
[19,] 5.890224e-09 1.178045e-08 0.99999999
[20,] 6.297469e-09 1.259494e-08 0.99999999
[21,] 2.845345e-09 5.690690e-09 1.00000000
[22,] 3.495676e-09 6.991351e-09 1.00000000
[23,] 4.906475e-09 9.812950e-09 1.00000000
[24,] 7.975854e-09 1.595171e-08 0.99999999
[25,] 7.809777e-09 1.561955e-08 0.99999999
[26,] 2.323161e-09 4.646322e-09 1.00000000
[27,] 7.388970e-10 1.477794e-09 1.00000000
[28,] 2.068825e-10 4.137649e-10 1.00000000
[29,] 1.751289e-10 3.502579e-10 1.00000000
[30,] 6.865047e-11 1.373009e-10 1.00000000
[31,] 2.350754e-11 4.701509e-11 1.00000000
[32,] 6.512153e-12 1.302431e-11 1.00000000
[33,] 3.332070e-12 6.664141e-12 1.00000000
[34,] 1.268174e-12 2.536348e-12 1.00000000
[35,] 3.210863e-13 6.421725e-13 1.00000000
[36,] 7.678337e-14 1.535667e-13 1.00000000
[37,] 2.559179e-14 5.118357e-14 1.00000000
[38,] 9.744846e-13 1.948969e-12 1.00000000
[39,] 2.947692e-12 5.895384e-12 1.00000000
[40,] 2.223227e-11 4.446455e-11 1.00000000
[41,] 2.491868e-11 4.983736e-11 1.00000000
[42,] 1.349630e-11 2.699259e-11 1.00000000
[43,] 7.876046e-12 1.575209e-11 1.00000000
[44,] 3.041710e-12 6.083419e-12 1.00000000
[45,] 1.182160e-12 2.364320e-12 1.00000000
[46,] 4.319347e-13 8.638694e-13 1.00000000
[47,] 2.221102e-13 4.442204e-13 1.00000000
[48,] 9.667429e-14 1.933486e-13 1.00000000
[49,] 3.067308e-13 6.134616e-13 1.00000000
[50,] 3.426399e-11 6.852798e-11 1.00000000
[51,] 3.643105e-10 7.286210e-10 1.00000000
[52,] 4.227032e-07 8.454065e-07 0.99999958
[53,] 1.000306e-04 2.000612e-04 0.99989997
[54,] 5.736431e-03 1.147286e-02 0.99426357
[55,] 1.172143e-01 2.344287e-01 0.88278567
[56,] 6.408759e-01 7.182482e-01 0.35912411
[57,] 9.252955e-01 1.494090e-01 0.07470449
[58,] 9.865461e-01 2.690774e-02 0.01345387
> postscript(file="/var/www/html/rcomp/tmp/1tmca1259050672.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2wy1y1259050672.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hvlj1259050672.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/459h51259050672.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5g2gk1259050672.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 = 67
Frequency = 1
1 2 3 4 5 6
-284.4132 475.1274 -452.6481 -3070.8195 -5437.0909 -8831.8263
7 8 9 10 11 12
-2523.4251 378.6647 1290.3965 -3555.8772 -3792.7228 -2598.1399
13 14 15 16 17 18
-1231.7217 -429.2246 -861.1869 -2100.0632 -3618.1896 -7871.5973
19 20 21 22 23 24
-2280.7115 -745.0138 1369.2325 4052.4896 3009.6640 3872.6179
25 26 27 28 29 30
2124.2352 4355.1884 4578.5737 5209.4111 4665.9884 -191.9936
31 32 33 34 35 36
2364.1651 276.0984 6079.3391 2761.8714 1559.9303 145.3885
37 38 39 40 41 42
3245.3241 2275.0311 -2056.8644 -1687.8424 -4687.7675 -14415.0005
43 44 45 46 47 48
-10214.1576 -11624.0845 -8549.7302 -6584.6776 -8140.0696 -4869.9685
49 50 51 52 53 54
-4129.4965 -4313.0123 -8318.6039 -6963.3280 -13195.7413 -15840.3613
55 56 57 58 59 60
-11569.4362 -13895.9092 -7171.2809 -3435.0111 829.8462 7628.4625
61 62 63 64 65 66
14184.8019 17993.0422 23266.9289 24582.6681 21677.4018 21678.6896
67
25608.4300
> postscript(file="/var/www/html/rcomp/tmp/6lbn51259050672.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 = 67
Frequency = 1
lag(myerror, k = 1) myerror
0 -284.4132 NA
1 475.1274 -284.4132
2 -452.6481 475.1274
3 -3070.8195 -452.6481
4 -5437.0909 -3070.8195
5 -8831.8263 -5437.0909
6 -2523.4251 -8831.8263
7 378.6647 -2523.4251
8 1290.3965 378.6647
9 -3555.8772 1290.3965
10 -3792.7228 -3555.8772
11 -2598.1399 -3792.7228
12 -1231.7217 -2598.1399
13 -429.2246 -1231.7217
14 -861.1869 -429.2246
15 -2100.0632 -861.1869
16 -3618.1896 -2100.0632
17 -7871.5973 -3618.1896
18 -2280.7115 -7871.5973
19 -745.0138 -2280.7115
20 1369.2325 -745.0138
21 4052.4896 1369.2325
22 3009.6640 4052.4896
23 3872.6179 3009.6640
24 2124.2352 3872.6179
25 4355.1884 2124.2352
26 4578.5737 4355.1884
27 5209.4111 4578.5737
28 4665.9884 5209.4111
29 -191.9936 4665.9884
30 2364.1651 -191.9936
31 276.0984 2364.1651
32 6079.3391 276.0984
33 2761.8714 6079.3391
34 1559.9303 2761.8714
35 145.3885 1559.9303
36 3245.3241 145.3885
37 2275.0311 3245.3241
38 -2056.8644 2275.0311
39 -1687.8424 -2056.8644
40 -4687.7675 -1687.8424
41 -14415.0005 -4687.7675
42 -10214.1576 -14415.0005
43 -11624.0845 -10214.1576
44 -8549.7302 -11624.0845
45 -6584.6776 -8549.7302
46 -8140.0696 -6584.6776
47 -4869.9685 -8140.0696
48 -4129.4965 -4869.9685
49 -4313.0123 -4129.4965
50 -8318.6039 -4313.0123
51 -6963.3280 -8318.6039
52 -13195.7413 -6963.3280
53 -15840.3613 -13195.7413
54 -11569.4362 -15840.3613
55 -13895.9092 -11569.4362
56 -7171.2809 -13895.9092
57 -3435.0111 -7171.2809
58 829.8462 -3435.0111
59 7628.4625 829.8462
60 14184.8019 7628.4625
61 17993.0422 14184.8019
62 23266.9289 17993.0422
63 24582.6681 23266.9289
64 21677.4018 24582.6681
65 21678.6896 21677.4018
66 25608.4300 21678.6896
67 NA 25608.4300
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 475.1274 -284.4132
[2,] -452.6481 475.1274
[3,] -3070.8195 -452.6481
[4,] -5437.0909 -3070.8195
[5,] -8831.8263 -5437.0909
[6,] -2523.4251 -8831.8263
[7,] 378.6647 -2523.4251
[8,] 1290.3965 378.6647
[9,] -3555.8772 1290.3965
[10,] -3792.7228 -3555.8772
[11,] -2598.1399 -3792.7228
[12,] -1231.7217 -2598.1399
[13,] -429.2246 -1231.7217
[14,] -861.1869 -429.2246
[15,] -2100.0632 -861.1869
[16,] -3618.1896 -2100.0632
[17,] -7871.5973 -3618.1896
[18,] -2280.7115 -7871.5973
[19,] -745.0138 -2280.7115
[20,] 1369.2325 -745.0138
[21,] 4052.4896 1369.2325
[22,] 3009.6640 4052.4896
[23,] 3872.6179 3009.6640
[24,] 2124.2352 3872.6179
[25,] 4355.1884 2124.2352
[26,] 4578.5737 4355.1884
[27,] 5209.4111 4578.5737
[28,] 4665.9884 5209.4111
[29,] -191.9936 4665.9884
[30,] 2364.1651 -191.9936
[31,] 276.0984 2364.1651
[32,] 6079.3391 276.0984
[33,] 2761.8714 6079.3391
[34,] 1559.9303 2761.8714
[35,] 145.3885 1559.9303
[36,] 3245.3241 145.3885
[37,] 2275.0311 3245.3241
[38,] -2056.8644 2275.0311
[39,] -1687.8424 -2056.8644
[40,] -4687.7675 -1687.8424
[41,] -14415.0005 -4687.7675
[42,] -10214.1576 -14415.0005
[43,] -11624.0845 -10214.1576
[44,] -8549.7302 -11624.0845
[45,] -6584.6776 -8549.7302
[46,] -8140.0696 -6584.6776
[47,] -4869.9685 -8140.0696
[48,] -4129.4965 -4869.9685
[49,] -4313.0123 -4129.4965
[50,] -8318.6039 -4313.0123
[51,] -6963.3280 -8318.6039
[52,] -13195.7413 -6963.3280
[53,] -15840.3613 -13195.7413
[54,] -11569.4362 -15840.3613
[55,] -13895.9092 -11569.4362
[56,] -7171.2809 -13895.9092
[57,] -3435.0111 -7171.2809
[58,] 829.8462 -3435.0111
[59,] 7628.4625 829.8462
[60,] 14184.8019 7628.4625
[61,] 17993.0422 14184.8019
[62,] 23266.9289 17993.0422
[63,] 24582.6681 23266.9289
[64,] 21677.4018 24582.6681
[65,] 21678.6896 21677.4018
[66,] 25608.4300 21678.6896
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 475.1274 -284.4132
2 -452.6481 475.1274
3 -3070.8195 -452.6481
4 -5437.0909 -3070.8195
5 -8831.8263 -5437.0909
6 -2523.4251 -8831.8263
7 378.6647 -2523.4251
8 1290.3965 378.6647
9 -3555.8772 1290.3965
10 -3792.7228 -3555.8772
11 -2598.1399 -3792.7228
12 -1231.7217 -2598.1399
13 -429.2246 -1231.7217
14 -861.1869 -429.2246
15 -2100.0632 -861.1869
16 -3618.1896 -2100.0632
17 -7871.5973 -3618.1896
18 -2280.7115 -7871.5973
19 -745.0138 -2280.7115
20 1369.2325 -745.0138
21 4052.4896 1369.2325
22 3009.6640 4052.4896
23 3872.6179 3009.6640
24 2124.2352 3872.6179
25 4355.1884 2124.2352
26 4578.5737 4355.1884
27 5209.4111 4578.5737
28 4665.9884 5209.4111
29 -191.9936 4665.9884
30 2364.1651 -191.9936
31 276.0984 2364.1651
32 6079.3391 276.0984
33 2761.8714 6079.3391
34 1559.9303 2761.8714
35 145.3885 1559.9303
36 3245.3241 145.3885
37 2275.0311 3245.3241
38 -2056.8644 2275.0311
39 -1687.8424 -2056.8644
40 -4687.7675 -1687.8424
41 -14415.0005 -4687.7675
42 -10214.1576 -14415.0005
43 -11624.0845 -10214.1576
44 -8549.7302 -11624.0845
45 -6584.6776 -8549.7302
46 -8140.0696 -6584.6776
47 -4869.9685 -8140.0696
48 -4129.4965 -4869.9685
49 -4313.0123 -4129.4965
50 -8318.6039 -4313.0123
51 -6963.3280 -8318.6039
52 -13195.7413 -6963.3280
53 -15840.3613 -13195.7413
54 -11569.4362 -15840.3613
55 -13895.9092 -11569.4362
56 -7171.2809 -13895.9092
57 -3435.0111 -7171.2809
58 829.8462 -3435.0111
59 7628.4625 829.8462
60 14184.8019 7628.4625
61 17993.0422 14184.8019
62 23266.9289 17993.0422
63 24582.6681 23266.9289
64 21677.4018 24582.6681
65 21678.6896 21677.4018
66 25608.4300 21678.6896
> 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/722pc1259050672.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/835zp1259050672.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9l6gx1259050672.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107aqm1259050672.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11dxok1259050672.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/12rpbw1259050672.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/13adgq1259050672.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/148maf1259050673.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/158joo1259050673.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/1664qy1259050673.tab")
+ }
> system("convert tmp/1tmca1259050672.ps tmp/1tmca1259050672.png")
> system("convert tmp/2wy1y1259050672.ps tmp/2wy1y1259050672.png")
> system("convert tmp/3hvlj1259050672.ps tmp/3hvlj1259050672.png")
> system("convert tmp/459h51259050672.ps tmp/459h51259050672.png")
> system("convert tmp/5g2gk1259050672.ps tmp/5g2gk1259050672.png")
> system("convert tmp/6lbn51259050672.ps tmp/6lbn51259050672.png")
> system("convert tmp/722pc1259050672.ps tmp/722pc1259050672.png")
> system("convert tmp/835zp1259050672.ps tmp/835zp1259050672.png")
> system("convert tmp/9l6gx1259050672.ps tmp/9l6gx1259050672.png")
> system("convert tmp/107aqm1259050672.ps tmp/107aqm1259050672.png")
>
>
> proc.time()
user system elapsed
2.498 1.545 3.291