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(100
+ ,0
+ ,108.1560276
+ ,0
+ ,114.0150276
+ ,0
+ ,102.1880309
+ ,0
+ ,110.3672031
+ ,0
+ ,96.8602511
+ ,0
+ ,94.1944583
+ ,0
+ ,99.51621961
+ ,0
+ ,94.06333487
+ ,0
+ ,97.5541476
+ ,0
+ ,78.15062422
+ ,0
+ ,81.2434643
+ ,0
+ ,92.36262465
+ ,0
+ ,96.06324371
+ ,0
+ ,114.0523777
+ ,0
+ ,110.6616666
+ ,0
+ ,104.9171949
+ ,0
+ ,90.00187193
+ ,0
+ ,95.7008067
+ ,0
+ ,86.02741157
+ ,0
+ ,84.85287668
+ ,0
+ ,100.04328
+ ,0
+ ,80.91713823
+ ,0
+ ,74.06539709
+ ,0
+ ,77.30281369
+ ,0
+ ,97.23043249
+ ,0
+ ,90.75515676
+ ,0
+ ,100.5614455
+ ,0
+ ,92.01293267
+ ,0
+ ,99.24012138
+ ,0
+ ,105.8672755
+ ,0
+ ,90.9920463
+ ,0
+ ,93.30624423
+ ,0
+ ,91.17419413
+ ,0
+ ,77.33295039
+ ,0
+ ,91.1277721
+ ,0
+ ,85.01249943
+ ,0
+ ,83.90390242
+ ,0
+ ,104.8626302
+ ,0
+ ,110.9039108
+ ,0
+ ,95.43714373
+ ,0
+ ,111.6238727
+ ,0
+ ,108.8925403
+ ,0
+ ,96.17511682
+ ,0
+ ,101.9740205
+ ,0
+ ,99.11953031
+ ,0
+ ,86.78158147
+ ,0
+ ,118.4195003
+ ,0
+ ,118.7441447
+ ,0
+ ,106.5296192
+ ,0
+ ,134.7772694
+ ,0
+ ,104.6778714
+ ,0
+ ,105.2954304
+ ,0
+ ,139.4139849
+ ,0
+ ,103.6060491
+ ,0
+ ,99.78182974
+ ,0
+ ,103.4610301
+ ,0
+ ,120.0594945
+ ,0
+ ,96.71377168
+ ,0
+ ,107.1308929
+ ,0
+ ,105.3608372
+ ,0
+ ,111.6942359
+ ,0
+ ,132.0519998
+ ,0
+ ,126.8037879
+ ,0
+ ,154.4824253
+ ,0
+ ,141.5570984
+ ,0
+ ,109.9506882
+ ,0
+ ,127.904198
+ ,0
+ ,133.0888617
+ ,0
+ ,120.0796299
+ ,0
+ ,117.5557142
+ ,0
+ ,143.0362309
+ ,0
+ ,159.982927
+ ,1
+ ,128.5991124
+ ,1
+ ,149.7373327
+ ,1
+ ,126.8169313
+ ,1
+ ,140.9639674
+ ,1
+ ,137.6691981
+ ,1
+ ,117.9402337
+ ,1
+ ,122.3095247
+ ,1
+ ,127.7804207
+ ,1
+ ,136.1677176
+ ,1
+ ,116.2405856
+ ,1
+ ,123.1576893
+ ,1
+ ,116.3400234
+ ,1
+ ,108.6119282
+ ,1
+ ,125.8982264
+ ,1
+ ,112.8003105
+ ,1
+ ,107.5182447
+ ,1
+ ,135.0955413
+ ,1
+ ,115.5096488
+ ,1
+ ,115.8640759
+ ,1
+ ,104.5883906
+ ,1
+ ,163.7213386
+ ,1
+ ,113.4482275
+ ,1
+ ,98.0428844
+ ,1
+ ,116.7868521
+ ,1
+ ,126.5330444
+ ,1
+ ,113.0336597
+ ,1
+ ,124.3392163
+ ,1
+ ,109.8298759
+ ,1
+ ,124.4434777
+ ,1
+ ,111.5039454
+ ,1
+ ,102.0350019
+ ,1
+ ,116.8726598
+ ,1
+ ,112.2073122
+ ,1
+ ,101.1513902
+ ,1
+ ,124.4255108
+ ,1)
+ ,dim=c(2
+ ,108)
+ ,dimnames=list(c('Y'
+ ,'X')
+ ,1:108))
> y <- array(NA,dim=c(2,108),dimnames=list(c('Y','X'),1:108))
> 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)
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 100.00000 0 1 0 0 0 0 0 0 0 0 0 0 1
2 108.15603 0 0 1 0 0 0 0 0 0 0 0 0 2
3 114.01503 0 0 0 1 0 0 0 0 0 0 0 0 3
4 102.18803 0 0 0 0 1 0 0 0 0 0 0 0 4
5 110.36720 0 0 0 0 0 1 0 0 0 0 0 0 5
6 96.86025 0 0 0 0 0 0 1 0 0 0 0 0 6
7 94.19446 0 0 0 0 0 0 0 1 0 0 0 0 7
8 99.51622 0 0 0 0 0 0 0 0 1 0 0 0 8
9 94.06333 0 0 0 0 0 0 0 0 0 1 0 0 9
10 97.55415 0 0 0 0 0 0 0 0 0 0 1 0 10
11 78.15062 0 0 0 0 0 0 0 0 0 0 0 1 11
12 81.24346 0 0 0 0 0 0 0 0 0 0 0 0 12
13 92.36262 0 1 0 0 0 0 0 0 0 0 0 0 13
14 96.06324 0 0 1 0 0 0 0 0 0 0 0 0 14
15 114.05238 0 0 0 1 0 0 0 0 0 0 0 0 15
16 110.66167 0 0 0 0 1 0 0 0 0 0 0 0 16
17 104.91719 0 0 0 0 0 1 0 0 0 0 0 0 17
18 90.00187 0 0 0 0 0 0 1 0 0 0 0 0 18
19 95.70081 0 0 0 0 0 0 0 1 0 0 0 0 19
20 86.02741 0 0 0 0 0 0 0 0 1 0 0 0 20
21 84.85288 0 0 0 0 0 0 0 0 0 1 0 0 21
22 100.04328 0 0 0 0 0 0 0 0 0 0 1 0 22
23 80.91714 0 0 0 0 0 0 0 0 0 0 0 1 23
24 74.06540 0 0 0 0 0 0 0 0 0 0 0 0 24
25 77.30281 0 1 0 0 0 0 0 0 0 0 0 0 25
26 97.23043 0 0 1 0 0 0 0 0 0 0 0 0 26
27 90.75516 0 0 0 1 0 0 0 0 0 0 0 0 27
28 100.56145 0 0 0 0 1 0 0 0 0 0 0 0 28
29 92.01293 0 0 0 0 0 1 0 0 0 0 0 0 29
30 99.24012 0 0 0 0 0 0 1 0 0 0 0 0 30
31 105.86728 0 0 0 0 0 0 0 1 0 0 0 0 31
32 90.99205 0 0 0 0 0 0 0 0 1 0 0 0 32
33 93.30624 0 0 0 0 0 0 0 0 0 1 0 0 33
34 91.17419 0 0 0 0 0 0 0 0 0 0 1 0 34
35 77.33295 0 0 0 0 0 0 0 0 0 0 0 1 35
36 91.12777 0 0 0 0 0 0 0 0 0 0 0 0 36
37 85.01250 0 1 0 0 0 0 0 0 0 0 0 0 37
38 83.90390 0 0 1 0 0 0 0 0 0 0 0 0 38
39 104.86263 0 0 0 1 0 0 0 0 0 0 0 0 39
40 110.90391 0 0 0 0 1 0 0 0 0 0 0 0 40
41 95.43714 0 0 0 0 0 1 0 0 0 0 0 0 41
42 111.62387 0 0 0 0 0 0 1 0 0 0 0 0 42
43 108.89254 0 0 0 0 0 0 0 1 0 0 0 0 43
44 96.17512 0 0 0 0 0 0 0 0 1 0 0 0 44
45 101.97402 0 0 0 0 0 0 0 0 0 1 0 0 45
46 99.11953 0 0 0 0 0 0 0 0 0 0 1 0 46
47 86.78158 0 0 0 0 0 0 0 0 0 0 0 1 47
48 118.41950 0 0 0 0 0 0 0 0 0 0 0 0 48
49 118.74414 0 1 0 0 0 0 0 0 0 0 0 0 49
50 106.52962 0 0 1 0 0 0 0 0 0 0 0 0 50
51 134.77727 0 0 0 1 0 0 0 0 0 0 0 0 51
52 104.67787 0 0 0 0 1 0 0 0 0 0 0 0 52
53 105.29543 0 0 0 0 0 1 0 0 0 0 0 0 53
54 139.41398 0 0 0 0 0 0 1 0 0 0 0 0 54
55 103.60605 0 0 0 0 0 0 0 1 0 0 0 0 55
56 99.78183 0 0 0 0 0 0 0 0 1 0 0 0 56
57 103.46103 0 0 0 0 0 0 0 0 0 1 0 0 57
58 120.05949 0 0 0 0 0 0 0 0 0 0 1 0 58
59 96.71377 0 0 0 0 0 0 0 0 0 0 0 1 59
60 107.13089 0 0 0 0 0 0 0 0 0 0 0 0 60
61 105.36084 0 1 0 0 0 0 0 0 0 0 0 0 61
62 111.69424 0 0 1 0 0 0 0 0 0 0 0 0 62
63 132.05200 0 0 0 1 0 0 0 0 0 0 0 0 63
64 126.80379 0 0 0 0 1 0 0 0 0 0 0 0 64
65 154.48243 0 0 0 0 0 1 0 0 0 0 0 0 65
66 141.55710 0 0 0 0 0 0 1 0 0 0 0 0 66
67 109.95069 0 0 0 0 0 0 0 1 0 0 0 0 67
68 127.90420 0 0 0 0 0 0 0 0 1 0 0 0 68
69 133.08886 0 0 0 0 0 0 0 0 0 1 0 0 69
70 120.07963 0 0 0 0 0 0 0 0 0 0 1 0 70
71 117.55571 0 0 0 0 0 0 0 0 0 0 0 1 71
72 143.03623 0 0 0 0 0 0 0 0 0 0 0 0 72
73 159.98293 1 1 0 0 0 0 0 0 0 0 0 0 73
74 128.59911 1 0 1 0 0 0 0 0 0 0 0 0 74
75 149.73733 1 0 0 1 0 0 0 0 0 0 0 0 75
76 126.81693 1 0 0 0 1 0 0 0 0 0 0 0 76
77 140.96397 1 0 0 0 0 1 0 0 0 0 0 0 77
78 137.66920 1 0 0 0 0 0 1 0 0 0 0 0 78
79 117.94023 1 0 0 0 0 0 0 1 0 0 0 0 79
80 122.30952 1 0 0 0 0 0 0 0 1 0 0 0 80
81 127.78042 1 0 0 0 0 0 0 0 0 1 0 0 81
82 136.16772 1 0 0 0 0 0 0 0 0 0 1 0 82
83 116.24059 1 0 0 0 0 0 0 0 0 0 0 1 83
84 123.15769 1 0 0 0 0 0 0 0 0 0 0 0 84
85 116.34002 1 1 0 0 0 0 0 0 0 0 0 0 85
86 108.61193 1 0 1 0 0 0 0 0 0 0 0 0 86
87 125.89823 1 0 0 1 0 0 0 0 0 0 0 0 87
88 112.80031 1 0 0 0 1 0 0 0 0 0 0 0 88
89 107.51824 1 0 0 0 0 1 0 0 0 0 0 0 89
90 135.09554 1 0 0 0 0 0 1 0 0 0 0 0 90
91 115.50965 1 0 0 0 0 0 0 1 0 0 0 0 91
92 115.86408 1 0 0 0 0 0 0 0 1 0 0 0 92
93 104.58839 1 0 0 0 0 0 0 0 0 1 0 0 93
94 163.72134 1 0 0 0 0 0 0 0 0 0 1 0 94
95 113.44823 1 0 0 0 0 0 0 0 0 0 0 1 95
96 98.04288 1 0 0 0 0 0 0 0 0 0 0 0 96
97 116.78685 1 1 0 0 0 0 0 0 0 0 0 0 97
98 126.53304 1 0 1 0 0 0 0 0 0 0 0 0 98
99 113.03366 1 0 0 1 0 0 0 0 0 0 0 0 99
100 124.33922 1 0 0 0 1 0 0 0 0 0 0 0 100
101 109.82988 1 0 0 0 0 1 0 0 0 0 0 0 101
102 124.44348 1 0 0 0 0 0 1 0 0 0 0 0 102
103 111.50395 1 0 0 0 0 0 0 1 0 0 0 0 103
104 102.03500 1 0 0 0 0 0 0 0 1 0 0 0 104
105 116.87266 1 0 0 0 0 0 0 0 0 1 0 0 105
106 112.20731 1 0 0 0 0 0 0 0 0 0 1 0 106
107 101.15139 1 0 0 0 0 0 0 0 0 0 0 1 107
108 124.42551 1 0 0 0 0 0 0 0 0 0 0 0 108
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
85.364 -1.763 5.276 4.402 16.465 9.495
M5 M6 M7 M8 M9 M10
9.248 15.002 2.110 -0.763 1.025 9.563
M11 t
-9.896 0.366
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-23.270 -8.842 -1.441 8.591 44.386
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 85.36443 5.81858 14.671 < 2e-16 ***
X -1.76327 5.08157 -0.347 0.7294
M1 5.27566 6.74403 0.782 0.4360
M2 4.40171 6.73472 0.654 0.5150
M3 16.46480 6.72628 2.448 0.0162 *
M4 9.49538 6.71872 1.413 0.1609
M5 9.24837 6.71205 1.378 0.1715
M6 15.00244 6.70625 2.237 0.0276 *
M7 2.10977 6.70135 0.315 0.7536
M8 -0.76296 6.69734 -0.114 0.9095
M9 1.02461 6.69421 0.153 0.8787
M10 9.56288 6.69198 1.429 0.1563
M11 -9.89589 6.69064 -1.479 0.1425
t 0.36604 0.07731 4.734 7.75e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 14.19 on 94 degrees of freedom
Multiple R-squared: 0.4696, Adjusted R-squared: 0.3963
F-statistic: 6.403 on 13 and 94 DF, p-value: 1.6e-08
> 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,] 9.698373e-02 0.1939674653 0.9030163
[2,] 3.667026e-02 0.0733405146 0.9633297
[3,] 1.453258e-02 0.0290651605 0.9854674
[4,] 9.608570e-03 0.0192171391 0.9903914
[5,] 3.712354e-03 0.0074247084 0.9962876
[6,] 1.782931e-03 0.0035658624 0.9982171
[7,] 8.174710e-04 0.0016349419 0.9991825
[8,] 3.094032e-04 0.0006188065 0.9996906
[9,] 4.797155e-04 0.0009594311 0.9995203
[10,] 1.810066e-04 0.0003620131 0.9998190
[11,] 4.221239e-04 0.0008442478 0.9995779
[12,] 1.778692e-04 0.0003557385 0.9998221
[13,] 8.913318e-05 0.0001782664 0.9999109
[14,] 2.041066e-04 0.0004082133 0.9997959
[15,] 6.093317e-04 0.0012186634 0.9993907
[16,] 3.060315e-04 0.0006120629 0.9996940
[17,] 2.227161e-04 0.0004454322 0.9997773
[18,] 1.201547e-04 0.0002403094 0.9998798
[19,] 6.214728e-05 0.0001242946 0.9999379
[20,] 1.691715e-04 0.0003383430 0.9998308
[21,] 1.253107e-04 0.0002506214 0.9998747
[22,] 1.448495e-04 0.0002896989 0.9998552
[23,] 9.953452e-05 0.0001990690 0.9999005
[24,] 9.778741e-05 0.0001955748 0.9999022
[25,] 6.777086e-05 0.0001355417 0.9999322
[26,] 3.025073e-04 0.0006050146 0.9996975
[27,] 3.138058e-04 0.0006276116 0.9996862
[28,] 2.250280e-04 0.0004500561 0.9997750
[29,] 2.549666e-04 0.0005099332 0.9997450
[30,] 3.121854e-04 0.0006243708 0.9996878
[31,] 3.699812e-04 0.0007399623 0.9996300
[32,] 6.508426e-03 0.0130168521 0.9934916
[33,] 1.715660e-02 0.0343132003 0.9828434
[34,] 1.353166e-02 0.0270633139 0.9864683
[35,] 2.144029e-02 0.0428805856 0.9785597
[36,] 1.956628e-02 0.0391325623 0.9804337
[37,] 2.035059e-02 0.0407011770 0.9796494
[38,] 5.373739e-02 0.1074747730 0.9462626
[39,] 4.403773e-02 0.0880754576 0.9559623
[40,] 4.256495e-02 0.0851299008 0.9574350
[41,] 4.393706e-02 0.0878741111 0.9560629
[42,] 5.384233e-02 0.1076846567 0.9461577
[43,] 6.361329e-02 0.1272265871 0.9363867
[44,] 8.132977e-02 0.1626595483 0.9186702
[45,] 1.253832e-01 0.2507664143 0.8746168
[46,] 1.228437e-01 0.2456874231 0.8771563
[47,] 1.051931e-01 0.2103861522 0.8948069
[48,] 8.736869e-02 0.1747373817 0.9126313
[49,] 2.923763e-01 0.5847525338 0.7076237
[50,] 2.861229e-01 0.5722458268 0.7138771
[51,] 2.639720e-01 0.5279439338 0.7360280
[52,] 2.528139e-01 0.5056277132 0.7471861
[53,] 2.622545e-01 0.5245090805 0.7377455
[54,] 3.511960e-01 0.7023920209 0.6488040
[55,] 3.638143e-01 0.7276285250 0.6361857
[56,] 3.990987e-01 0.7981974678 0.6009013
[57,] 5.427716e-01 0.9144568159 0.4572284
[58,] 5.307575e-01 0.9384849306 0.4692425
[59,] 5.618987e-01 0.8762026990 0.4381013
[60,] 5.260565e-01 0.9478870277 0.4739435
[61,] 5.980668e-01 0.8038664497 0.4019332
[62,] 5.264268e-01 0.9471464091 0.4735732
[63,] 4.699527e-01 0.9399053748 0.5300473
[64,] 4.030104e-01 0.8060207972 0.5969896
[65,] 3.543427e-01 0.7086854063 0.6456573
[66,] 2.901721e-01 0.5803442787 0.7098279
[67,] 2.204804e-01 0.4409607665 0.7795196
[68,] 1.690748e-01 0.3381495207 0.8309252
[69,] 1.300049e-01 0.2600098178 0.8699951
[70,] 1.521286e-01 0.3042571345 0.8478714
[71,] 1.173238e-01 0.2346475258 0.8826762
[72,] 1.093743e-01 0.2187485231 0.8906257
[73,] 8.748179e-02 0.1749635769 0.9125182
[74,] 4.731592e-02 0.0946318440 0.9526841
[75,] 2.268138e-02 0.0453627597 0.9773186
> postscript(file="/var/www/html/rcomp/tmp/1ihhg1258618159.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2kt9v1258618159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3adhp1258618159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46c2e1258618159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5kprj1258618159.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 = 108
Frequency = 1
1 2 3 4 5 6
8.9938775 17.6578135 11.0876875 5.8640807 13.9242255 -5.7028376
7 8 9 10 11 12
4.1580109 11.9864635 4.3799771 -1.0335219 -1.3443051 -8.5133938
13 14 15 16 17 18
-3.0359264 1.1726010 6.7326090 9.9452878 4.0817887 -16.9536453
19 20 21 22 23 24
1.2719307 -5.8947731 -9.2229096 -2.9368181 -2.9702197 -20.0838895
25 26 27 28 29 30
-22.4881660 -2.0526387 -20.9570404 -4.5473618 -13.2149021 -12.1078244
31 32 33 34 35 36
7.0459710 -5.3225669 -5.1619706 -16.1983325 -10.9468361 -7.4139431
37 38 39 40 41 42
-19.1709088 -19.7715973 -11.2419956 1.4026749 -14.1831196 -4.1165016
43 44 45 46 47 48
5.6788072 -4.5319250 -0.8866229 -12.6454248 -5.8906335 15.4853566
49 50 51 52 53 54
10.1683080 -1.5383091 14.2802151 -9.2157930 -8.7172614 19.2811820
55 56 57 58 59 60
-4.0001125 -5.3176406 -3.7920419 3.9021108 -0.3508719 -0.1956794
61 62 63 64 65 66
-7.6074281 -0.7661210 7.1625169 8.5176949 36.0773049 17.0318670
67 68 69 70 71 72
-2.0479020 18.4122991 21.4433612 -0.4701824 16.0986421 31.3172301
73 74 75 76 77 78
44.3855027 13.5095965 22.2186908 5.9016793 19.9296880 10.5148076
79 80 81 82 83 84
3.3124845 10.1884668 13.5057611 12.9887463 12.1543544 8.8095294
85 86 87 88 89 90
-3.6498295 -10.8700163 -6.0128441 -12.5073701 -17.9084633 3.5487223
91 92 93 94 95 96
-3.5105290 -0.6494106 -14.0786975 36.1499387 4.9695678 -20.6977040
97 98 99 100 101 102
-7.5954293 2.6586714 -23.2698393 -5.3608928 -19.9892606 -11.4957699
103 104 105 106 107 108
-11.9086609 -18.8709131 -6.1868569 -19.7565162 -11.7196981 1.2924938
> postscript(file="/var/www/html/rcomp/tmp/6a6dk1258618159.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 = 108
Frequency = 1
lag(myerror, k = 1) myerror
0 8.9938775 NA
1 17.6578135 8.9938775
2 11.0876875 17.6578135
3 5.8640807 11.0876875
4 13.9242255 5.8640807
5 -5.7028376 13.9242255
6 4.1580109 -5.7028376
7 11.9864635 4.1580109
8 4.3799771 11.9864635
9 -1.0335219 4.3799771
10 -1.3443051 -1.0335219
11 -8.5133938 -1.3443051
12 -3.0359264 -8.5133938
13 1.1726010 -3.0359264
14 6.7326090 1.1726010
15 9.9452878 6.7326090
16 4.0817887 9.9452878
17 -16.9536453 4.0817887
18 1.2719307 -16.9536453
19 -5.8947731 1.2719307
20 -9.2229096 -5.8947731
21 -2.9368181 -9.2229096
22 -2.9702197 -2.9368181
23 -20.0838895 -2.9702197
24 -22.4881660 -20.0838895
25 -2.0526387 -22.4881660
26 -20.9570404 -2.0526387
27 -4.5473618 -20.9570404
28 -13.2149021 -4.5473618
29 -12.1078244 -13.2149021
30 7.0459710 -12.1078244
31 -5.3225669 7.0459710
32 -5.1619706 -5.3225669
33 -16.1983325 -5.1619706
34 -10.9468361 -16.1983325
35 -7.4139431 -10.9468361
36 -19.1709088 -7.4139431
37 -19.7715973 -19.1709088
38 -11.2419956 -19.7715973
39 1.4026749 -11.2419956
40 -14.1831196 1.4026749
41 -4.1165016 -14.1831196
42 5.6788072 -4.1165016
43 -4.5319250 5.6788072
44 -0.8866229 -4.5319250
45 -12.6454248 -0.8866229
46 -5.8906335 -12.6454248
47 15.4853566 -5.8906335
48 10.1683080 15.4853566
49 -1.5383091 10.1683080
50 14.2802151 -1.5383091
51 -9.2157930 14.2802151
52 -8.7172614 -9.2157930
53 19.2811820 -8.7172614
54 -4.0001125 19.2811820
55 -5.3176406 -4.0001125
56 -3.7920419 -5.3176406
57 3.9021108 -3.7920419
58 -0.3508719 3.9021108
59 -0.1956794 -0.3508719
60 -7.6074281 -0.1956794
61 -0.7661210 -7.6074281
62 7.1625169 -0.7661210
63 8.5176949 7.1625169
64 36.0773049 8.5176949
65 17.0318670 36.0773049
66 -2.0479020 17.0318670
67 18.4122991 -2.0479020
68 21.4433612 18.4122991
69 -0.4701824 21.4433612
70 16.0986421 -0.4701824
71 31.3172301 16.0986421
72 44.3855027 31.3172301
73 13.5095965 44.3855027
74 22.2186908 13.5095965
75 5.9016793 22.2186908
76 19.9296880 5.9016793
77 10.5148076 19.9296880
78 3.3124845 10.5148076
79 10.1884668 3.3124845
80 13.5057611 10.1884668
81 12.9887463 13.5057611
82 12.1543544 12.9887463
83 8.8095294 12.1543544
84 -3.6498295 8.8095294
85 -10.8700163 -3.6498295
86 -6.0128441 -10.8700163
87 -12.5073701 -6.0128441
88 -17.9084633 -12.5073701
89 3.5487223 -17.9084633
90 -3.5105290 3.5487223
91 -0.6494106 -3.5105290
92 -14.0786975 -0.6494106
93 36.1499387 -14.0786975
94 4.9695678 36.1499387
95 -20.6977040 4.9695678
96 -7.5954293 -20.6977040
97 2.6586714 -7.5954293
98 -23.2698393 2.6586714
99 -5.3608928 -23.2698393
100 -19.9892606 -5.3608928
101 -11.4957699 -19.9892606
102 -11.9086609 -11.4957699
103 -18.8709131 -11.9086609
104 -6.1868569 -18.8709131
105 -19.7565162 -6.1868569
106 -11.7196981 -19.7565162
107 1.2924938 -11.7196981
108 NA 1.2924938
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 17.6578135 8.9938775
[2,] 11.0876875 17.6578135
[3,] 5.8640807 11.0876875
[4,] 13.9242255 5.8640807
[5,] -5.7028376 13.9242255
[6,] 4.1580109 -5.7028376
[7,] 11.9864635 4.1580109
[8,] 4.3799771 11.9864635
[9,] -1.0335219 4.3799771
[10,] -1.3443051 -1.0335219
[11,] -8.5133938 -1.3443051
[12,] -3.0359264 -8.5133938
[13,] 1.1726010 -3.0359264
[14,] 6.7326090 1.1726010
[15,] 9.9452878 6.7326090
[16,] 4.0817887 9.9452878
[17,] -16.9536453 4.0817887
[18,] 1.2719307 -16.9536453
[19,] -5.8947731 1.2719307
[20,] -9.2229096 -5.8947731
[21,] -2.9368181 -9.2229096
[22,] -2.9702197 -2.9368181
[23,] -20.0838895 -2.9702197
[24,] -22.4881660 -20.0838895
[25,] -2.0526387 -22.4881660
[26,] -20.9570404 -2.0526387
[27,] -4.5473618 -20.9570404
[28,] -13.2149021 -4.5473618
[29,] -12.1078244 -13.2149021
[30,] 7.0459710 -12.1078244
[31,] -5.3225669 7.0459710
[32,] -5.1619706 -5.3225669
[33,] -16.1983325 -5.1619706
[34,] -10.9468361 -16.1983325
[35,] -7.4139431 -10.9468361
[36,] -19.1709088 -7.4139431
[37,] -19.7715973 -19.1709088
[38,] -11.2419956 -19.7715973
[39,] 1.4026749 -11.2419956
[40,] -14.1831196 1.4026749
[41,] -4.1165016 -14.1831196
[42,] 5.6788072 -4.1165016
[43,] -4.5319250 5.6788072
[44,] -0.8866229 -4.5319250
[45,] -12.6454248 -0.8866229
[46,] -5.8906335 -12.6454248
[47,] 15.4853566 -5.8906335
[48,] 10.1683080 15.4853566
[49,] -1.5383091 10.1683080
[50,] 14.2802151 -1.5383091
[51,] -9.2157930 14.2802151
[52,] -8.7172614 -9.2157930
[53,] 19.2811820 -8.7172614
[54,] -4.0001125 19.2811820
[55,] -5.3176406 -4.0001125
[56,] -3.7920419 -5.3176406
[57,] 3.9021108 -3.7920419
[58,] -0.3508719 3.9021108
[59,] -0.1956794 -0.3508719
[60,] -7.6074281 -0.1956794
[61,] -0.7661210 -7.6074281
[62,] 7.1625169 -0.7661210
[63,] 8.5176949 7.1625169
[64,] 36.0773049 8.5176949
[65,] 17.0318670 36.0773049
[66,] -2.0479020 17.0318670
[67,] 18.4122991 -2.0479020
[68,] 21.4433612 18.4122991
[69,] -0.4701824 21.4433612
[70,] 16.0986421 -0.4701824
[71,] 31.3172301 16.0986421
[72,] 44.3855027 31.3172301
[73,] 13.5095965 44.3855027
[74,] 22.2186908 13.5095965
[75,] 5.9016793 22.2186908
[76,] 19.9296880 5.9016793
[77,] 10.5148076 19.9296880
[78,] 3.3124845 10.5148076
[79,] 10.1884668 3.3124845
[80,] 13.5057611 10.1884668
[81,] 12.9887463 13.5057611
[82,] 12.1543544 12.9887463
[83,] 8.8095294 12.1543544
[84,] -3.6498295 8.8095294
[85,] -10.8700163 -3.6498295
[86,] -6.0128441 -10.8700163
[87,] -12.5073701 -6.0128441
[88,] -17.9084633 -12.5073701
[89,] 3.5487223 -17.9084633
[90,] -3.5105290 3.5487223
[91,] -0.6494106 -3.5105290
[92,] -14.0786975 -0.6494106
[93,] 36.1499387 -14.0786975
[94,] 4.9695678 36.1499387
[95,] -20.6977040 4.9695678
[96,] -7.5954293 -20.6977040
[97,] 2.6586714 -7.5954293
[98,] -23.2698393 2.6586714
[99,] -5.3608928 -23.2698393
[100,] -19.9892606 -5.3608928
[101,] -11.4957699 -19.9892606
[102,] -11.9086609 -11.4957699
[103,] -18.8709131 -11.9086609
[104,] -6.1868569 -18.8709131
[105,] -19.7565162 -6.1868569
[106,] -11.7196981 -19.7565162
[107,] 1.2924938 -11.7196981
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 17.6578135 8.9938775
2 11.0876875 17.6578135
3 5.8640807 11.0876875
4 13.9242255 5.8640807
5 -5.7028376 13.9242255
6 4.1580109 -5.7028376
7 11.9864635 4.1580109
8 4.3799771 11.9864635
9 -1.0335219 4.3799771
10 -1.3443051 -1.0335219
11 -8.5133938 -1.3443051
12 -3.0359264 -8.5133938
13 1.1726010 -3.0359264
14 6.7326090 1.1726010
15 9.9452878 6.7326090
16 4.0817887 9.9452878
17 -16.9536453 4.0817887
18 1.2719307 -16.9536453
19 -5.8947731 1.2719307
20 -9.2229096 -5.8947731
21 -2.9368181 -9.2229096
22 -2.9702197 -2.9368181
23 -20.0838895 -2.9702197
24 -22.4881660 -20.0838895
25 -2.0526387 -22.4881660
26 -20.9570404 -2.0526387
27 -4.5473618 -20.9570404
28 -13.2149021 -4.5473618
29 -12.1078244 -13.2149021
30 7.0459710 -12.1078244
31 -5.3225669 7.0459710
32 -5.1619706 -5.3225669
33 -16.1983325 -5.1619706
34 -10.9468361 -16.1983325
35 -7.4139431 -10.9468361
36 -19.1709088 -7.4139431
37 -19.7715973 -19.1709088
38 -11.2419956 -19.7715973
39 1.4026749 -11.2419956
40 -14.1831196 1.4026749
41 -4.1165016 -14.1831196
42 5.6788072 -4.1165016
43 -4.5319250 5.6788072
44 -0.8866229 -4.5319250
45 -12.6454248 -0.8866229
46 -5.8906335 -12.6454248
47 15.4853566 -5.8906335
48 10.1683080 15.4853566
49 -1.5383091 10.1683080
50 14.2802151 -1.5383091
51 -9.2157930 14.2802151
52 -8.7172614 -9.2157930
53 19.2811820 -8.7172614
54 -4.0001125 19.2811820
55 -5.3176406 -4.0001125
56 -3.7920419 -5.3176406
57 3.9021108 -3.7920419
58 -0.3508719 3.9021108
59 -0.1956794 -0.3508719
60 -7.6074281 -0.1956794
61 -0.7661210 -7.6074281
62 7.1625169 -0.7661210
63 8.5176949 7.1625169
64 36.0773049 8.5176949
65 17.0318670 36.0773049
66 -2.0479020 17.0318670
67 18.4122991 -2.0479020
68 21.4433612 18.4122991
69 -0.4701824 21.4433612
70 16.0986421 -0.4701824
71 31.3172301 16.0986421
72 44.3855027 31.3172301
73 13.5095965 44.3855027
74 22.2186908 13.5095965
75 5.9016793 22.2186908
76 19.9296880 5.9016793
77 10.5148076 19.9296880
78 3.3124845 10.5148076
79 10.1884668 3.3124845
80 13.5057611 10.1884668
81 12.9887463 13.5057611
82 12.1543544 12.9887463
83 8.8095294 12.1543544
84 -3.6498295 8.8095294
85 -10.8700163 -3.6498295
86 -6.0128441 -10.8700163
87 -12.5073701 -6.0128441
88 -17.9084633 -12.5073701
89 3.5487223 -17.9084633
90 -3.5105290 3.5487223
91 -0.6494106 -3.5105290
92 -14.0786975 -0.6494106
93 36.1499387 -14.0786975
94 4.9695678 36.1499387
95 -20.6977040 4.9695678
96 -7.5954293 -20.6977040
97 2.6586714 -7.5954293
98 -23.2698393 2.6586714
99 -5.3608928 -23.2698393
100 -19.9892606 -5.3608928
101 -11.4957699 -19.9892606
102 -11.9086609 -11.4957699
103 -18.8709131 -11.9086609
104 -6.1868569 -18.8709131
105 -19.7565162 -6.1868569
106 -11.7196981 -19.7565162
107 1.2924938 -11.7196981
> 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/7hiit1258618159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8x8hh1258618159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9wm921258618159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10darg1258618159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11sl0b1258618159.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/12y4pb1258618159.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/137vk91258618159.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/14y63p1258618159.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/15szl81258618159.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/16g6901258618159.tab")
+ }
> system("convert tmp/1ihhg1258618159.ps tmp/1ihhg1258618159.png")
> system("convert tmp/2kt9v1258618159.ps tmp/2kt9v1258618159.png")
> system("convert tmp/3adhp1258618159.ps tmp/3adhp1258618159.png")
> system("convert tmp/46c2e1258618159.ps tmp/46c2e1258618159.png")
> system("convert tmp/5kprj1258618159.ps tmp/5kprj1258618159.png")
> system("convert tmp/6a6dk1258618159.ps tmp/6a6dk1258618159.png")
> system("convert tmp/7hiit1258618159.ps tmp/7hiit1258618159.png")
> system("convert tmp/8x8hh1258618159.ps tmp/8x8hh1258618159.png")
> system("convert tmp/9wm921258618159.ps tmp/9wm921258618159.png")
> system("convert tmp/10darg1258618159.ps tmp/10darg1258618159.png")
>
>
> proc.time()
user system elapsed
3.073 1.611 4.104