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.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(15561600
+ ,15.73
+ ,3.56
+ ,142.86
+ ,14917500
+ ,16.17
+ ,1.33
+ ,380.71
+ ,14805920
+ ,12.00
+ ,0.00
+ ,460.00
+ ,16958000
+ ,12.86
+ ,0.69
+ ,361.43
+ ,17605000
+ ,10.30
+ ,10.05
+ ,140.00
+ ,17131200
+ ,12.97
+ ,0.51
+ ,275.00
+ ,18474600
+ ,12.06
+ ,0.91
+ ,274.29
+ ,17286700
+ ,10.49
+ ,2.67
+ ,212.86
+ ,18574400
+ ,5.97
+ ,1.39
+ ,172.86
+ ,18056000
+ ,9.26
+ ,1.24
+ ,186.43
+ ,19701600
+ ,9.74
+ ,2.79
+ ,77.14
+ ,19061700
+ ,5.46
+ ,3.37
+ ,17.86
+ ,19681900
+ ,2.71
+ ,1.60
+ ,37.14
+ ,34521200
+ ,3.90
+ ,4.73
+ ,42.86
+ ,19922700
+ ,1.51
+ ,0.79
+ ,85.00
+ ,20177900
+ ,5.01
+ ,0.67
+ ,45.00
+ ,19759900
+ ,2.96
+ ,0.00
+ ,206.43
+ ,23076700
+ ,-1.97
+ ,0.60
+ ,178.57
+ ,22532000
+ ,-4.61
+ ,0.40
+ ,285.71
+ ,22029400
+ ,4.27
+ ,2.24
+ ,58.57
+ ,22587000
+ ,4.01
+ ,5.74
+ ,88.57
+ ,23256600
+ ,0.04
+ ,0.06
+ ,309.29
+ ,22680300
+ ,3.04
+ ,0.87
+ ,58.57
+ ,21916400
+ ,2.29
+ ,4.91
+ ,132.14
+ ,19640200
+ ,4.37
+ ,1.93
+ ,3.57
+ ,18813100
+ ,6.39
+ ,0.41
+ ,102.86
+ ,18730000
+ ,5.74
+ ,1.21
+ ,185.71
+ ,18154700
+ ,7.64
+ ,2.01
+ ,177.14
+ ,17848800
+ ,7.07
+ ,0.00
+ ,530.00
+ ,18077500
+ ,6.23
+ ,6.49
+ ,162.86
+ ,17133100
+ ,10.20
+ ,0.00
+ ,553.57
+ ,16602600
+ ,14.07
+ ,0.31
+ ,258.57
+ ,15878900
+ ,12.83
+ ,4.87
+ ,326.43
+ ,15789100
+ ,12.04
+ ,1.37
+ ,580.00
+ ,15422000
+ ,11.97
+ ,0.19
+ ,286.43
+ ,14661400
+ ,12.63
+ ,0.34
+ ,310.71
+ ,15879200
+ ,13.56
+ ,3.60
+ ,148.57
+ ,14339300
+ ,15.66
+ ,0.10
+ ,627.14
+ ,13169600
+ ,16.34
+ ,2.10
+ ,477.86
+ ,14528900
+ ,14.09
+ ,0.10
+ ,385.71
+ ,13375800
+ ,15.03
+ ,7.27
+ ,327.86
+ ,12309900
+ ,16.09
+ ,0.76
+ ,402.14
+ ,11933900
+ ,19.27
+ ,1.09
+ ,567.86
+ ,10061900
+ ,22.50
+ ,0.34
+ ,678.57
+ ,12609600
+ ,16.07
+ ,4.13
+ ,253.57
+ ,11156500
+ ,19.11
+ ,1.89
+ ,459.29
+ ,12187200
+ ,18.66
+ ,3.80
+ ,331.43
+ ,11284300
+ ,18.29
+ ,2.47
+ ,421.43
+ ,10177000
+ ,20.26
+ ,0.00
+ ,595.00
+ ,10970720
+ ,19.20
+ ,1.01
+ ,425.71
+ ,10820680
+ ,20.10
+ ,1.21
+ ,603.57
+ ,11492390
+ ,17.93
+ ,0.54
+ ,420.00
+ ,14573750
+ ,16.11
+ ,2.86
+ ,308.57
+ ,13992820
+ ,16.90
+ ,0.04
+ ,325.00
+ ,14727070
+ ,16.14
+ ,1.03
+ ,319.29
+ ,15685360
+ ,15.04
+ ,0.23
+ ,452.86
+ ,16736210
+ ,13.41
+ ,0.20
+ ,83.57
+ ,17950180
+ ,14.14
+ ,13.87
+ ,99.43
+ ,17002730
+ ,9.59
+ ,0.36
+ ,312.71
+ ,17415160
+ ,10.74
+ ,0.56
+ ,128.00
+ ,17929810
+ ,11.67
+ ,1.98
+ ,152.67
+ ,17865790
+ ,8.09
+ ,3.83
+ ,135.00
+ ,19202360
+ ,10.07
+ ,1.46
+ ,57.71
+ ,19085000
+ ,11.80
+ ,2.00
+ ,190.43
+ ,18188880
+ ,12.01
+ ,4.96
+ ,12.86
+ ,18466410
+ ,6.61
+ ,2.76
+ ,32.43
+ ,18520400
+ ,6.47
+ ,2.10
+ ,38.29
+ ,20025500
+ ,-3.11
+ ,2.09
+ ,210.14
+ ,20636100
+ ,1.94
+ ,2.21
+ ,109.14
+ ,20672000
+ ,1.10
+ ,2.90
+ ,71.43
+ ,22589100
+ ,-3.40
+ ,0.57
+ ,102.29
+ ,21864800
+ ,1.64
+ ,1.79
+ ,48.43
+ ,22750100
+ ,3.11
+ ,0.80
+ ,70.43
+ ,22548746
+ ,-0.16
+ ,2.66
+ ,139.86
+ ,21325495
+ ,3.80
+ ,1.70
+ ,83.14
+ ,21556563
+ ,-2.39
+ ,0.79
+ ,27.71
+ ,21415269
+ ,1.51
+ ,0.30
+ ,96.14
+ ,20401054
+ ,7.24
+ ,8.09
+ ,40.57
+ ,19062253
+ ,2.00
+ ,0.97
+ ,364.71
+ ,19085706
+ ,2.11
+ ,0.07
+ ,207.43
+ ,19279967
+ ,10.54
+ ,1.47
+ ,156.29
+ ,18552045
+ ,11.10
+ ,2.74
+ ,229.00
+ ,17800733
+ ,7.34
+ ,3.14
+ ,160.43
+ ,17142490
+ ,9.53
+ ,0.96
+ ,357.43
+ ,17593173
+ ,9.71
+ ,0.00
+ ,542.00
+ ,17633859
+ ,10.14
+ ,0.00
+ ,578.43
+ ,17336613
+ ,13.93
+ ,2.80
+ ,427.43
+ ,17008347
+ ,8.33
+ ,0.23
+ ,130.29
+ ,17951965
+ ,8.31
+ ,2.69
+ ,174.29
+ ,14520929
+ ,13.83
+ ,0.23
+ ,679.14
+ ,16941217
+ ,14.50
+ ,3.60
+ ,389.43
+ ,15436824
+ ,16.71
+ ,0.93
+ ,532.57
+ ,14744261
+ ,16.49
+ ,2.56
+ ,253.71
+ ,14248004
+ ,14.57
+ ,0.74
+ ,414.14
+ ,11540953
+ ,19.04
+ ,0.07
+ ,719.71
+ ,12881661
+ ,22.84
+ ,0.76
+ ,639.86
+ ,15185757
+ ,22.23
+ ,2.73
+ ,619.71
+ ,13554339
+ ,19.56
+ ,4.30
+ ,507.14
+ ,13575106
+ ,19.76
+ ,0.19
+ ,463.86
+ ,12238400
+ ,18.36
+ ,1.19
+ ,254.14
+ ,13303614
+ ,16.99
+ ,1.43
+ ,226.29
+ ,14151478
+ ,16.87
+ ,9.63
+ ,299.57
+ ,14172009
+ ,18.50
+ ,10.44
+ ,274.00
+ ,14022320
+ ,16.51
+ ,4.36
+ ,253.29)
+ ,dim=c(4
+ ,104)
+ ,dimnames=list(c('Kijkcijfers'
+ ,'Temperatuur'
+ ,'Neerslag'
+ ,'Zonneschijnduur')
+ ,1:104))
> y <- array(NA,dim=c(4,104),dimnames=list(c('Kijkcijfers','Temperatuur','Neerslag','Zonneschijnduur'),1:104))
> 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
Kijkcijfers Temperatuur Neerslag Zonneschijnduur
1 15561600 15.73 3.56 142.86
2 14917500 16.17 1.33 380.71
3 14805920 12.00 0.00 460.00
4 16958000 12.86 0.69 361.43
5 17605000 10.30 10.05 140.00
6 17131200 12.97 0.51 275.00
7 18474600 12.06 0.91 274.29
8 17286700 10.49 2.67 212.86
9 18574400 5.97 1.39 172.86
10 18056000 9.26 1.24 186.43
11 19701600 9.74 2.79 77.14
12 19061700 5.46 3.37 17.86
13 19681900 2.71 1.60 37.14
14 34521200 3.90 4.73 42.86
15 19922700 1.51 0.79 85.00
16 20177900 5.01 0.67 45.00
17 19759900 2.96 0.00 206.43
18 23076700 -1.97 0.60 178.57
19 22532000 -4.61 0.40 285.71
20 22029400 4.27 2.24 58.57
21 22587000 4.01 5.74 88.57
22 23256600 0.04 0.06 309.29
23 22680300 3.04 0.87 58.57
24 21916400 2.29 4.91 132.14
25 19640200 4.37 1.93 3.57
26 18813100 6.39 0.41 102.86
27 18730000 5.74 1.21 185.71
28 18154700 7.64 2.01 177.14
29 17848800 7.07 0.00 530.00
30 18077500 6.23 6.49 162.86
31 17133100 10.20 0.00 553.57
32 16602600 14.07 0.31 258.57
33 15878900 12.83 4.87 326.43
34 15789100 12.04 1.37 580.00
35 15422000 11.97 0.19 286.43
36 14661400 12.63 0.34 310.71
37 15879200 13.56 3.60 148.57
38 14339300 15.66 0.10 627.14
39 13169600 16.34 2.10 477.86
40 14528900 14.09 0.10 385.71
41 13375800 15.03 7.27 327.86
42 12309900 16.09 0.76 402.14
43 11933900 19.27 1.09 567.86
44 10061900 22.50 0.34 678.57
45 12609600 16.07 4.13 253.57
46 11156500 19.11 1.89 459.29
47 12187200 18.66 3.80 331.43
48 11284300 18.29 2.47 421.43
49 10177000 20.26 0.00 595.00
50 10970720 19.20 1.01 425.71
51 10820680 20.10 1.21 603.57
52 11492390 17.93 0.54 420.00
53 14573750 16.11 2.86 308.57
54 13992820 16.90 0.04 325.00
55 14727070 16.14 1.03 319.29
56 15685360 15.04 0.23 452.86
57 16736210 13.41 0.20 83.57
58 17950180 14.14 13.87 99.43
59 17002730 9.59 0.36 312.71
60 17415160 10.74 0.56 128.00
61 17929810 11.67 1.98 152.67
62 17865790 8.09 3.83 135.00
63 19202360 10.07 1.46 57.71
64 19085000 11.80 2.00 190.43
65 18188880 12.01 4.96 12.86
66 18466410 6.61 2.76 32.43
67 18520400 6.47 2.10 38.29
68 20025500 -3.11 2.09 210.14
69 20636100 1.94 2.21 109.14
70 20672000 1.10 2.90 71.43
71 22589100 -3.40 0.57 102.29
72 21864800 1.64 1.79 48.43
73 22750100 3.11 0.80 70.43
74 22548746 -0.16 2.66 139.86
75 21325495 3.80 1.70 83.14
76 21556563 -2.39 0.79 27.71
77 21415269 1.51 0.30 96.14
78 20401054 7.24 8.09 40.57
79 19062253 2.00 0.97 364.71
80 19085706 2.11 0.07 207.43
81 19279967 10.54 1.47 156.29
82 18552045 11.10 2.74 229.00
83 17800733 7.34 3.14 160.43
84 17142490 9.53 0.96 357.43
85 17593173 9.71 0.00 542.00
86 17633859 10.14 0.00 578.43
87 17336613 13.93 2.80 427.43
88 17008347 8.33 0.23 130.29
89 17951965 8.31 2.69 174.29
90 14520929 13.83 0.23 679.14
91 16941217 14.50 3.60 389.43
92 15436824 16.71 0.93 532.57
93 14744261 16.49 2.56 253.71
94 14248004 14.57 0.74 414.14
95 11540953 19.04 0.07 719.71
96 12881661 22.84 0.76 639.86
97 15185757 22.23 2.73 619.71
98 13554339 19.56 4.30 507.14
99 13575106 19.76 0.19 463.86
100 12238400 18.36 1.19 254.14
101 13303614 16.99 1.43 226.29
102 14151478 16.87 9.63 299.57
103 14172009 18.50 10.44 274.00
104 14022320 16.51 4.36 253.29
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Temperatuur Neerslag Zonneschijnduur
22305689 -434868 126331 -2972
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3272114 -1107822 -209545 764685 13441333
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22305689 416071 53.610 <2e-16 ***
Temperatuur -434868 39674 -10.961 <2e-16 ***
Neerslag 126331 84185 1.501 0.1366
Zonneschijnduur -2972 1458 -2.039 0.0441 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1879000 on 100 degrees of freedom
Multiple R-squared: 0.7568, Adjusted R-squared: 0.7495
F-statistic: 103.7 on 3 and 100 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,] 0.18877934 3.775587e-01 8.112207e-01
[2,] 0.12988477 2.597695e-01 8.701152e-01
[3,] 0.08998904 1.799781e-01 9.100110e-01
[4,] 0.04191620 8.383240e-02 9.580838e-01
[5,] 0.02814624 5.629249e-02 9.718538e-01
[6,] 0.02074560 4.149121e-02 9.792544e-01
[7,] 0.01154751 2.309501e-02 9.884525e-01
[8,] 1.00000000 2.470736e-10 1.235368e-10
[9,] 1.00000000 1.651556e-10 8.257778e-11
[10,] 1.00000000 4.817509e-10 2.408754e-10
[11,] 1.00000000 1.215620e-09 6.078098e-10
[12,] 1.00000000 3.366733e-09 1.683366e-09
[13,] 1.00000000 7.702886e-09 3.851443e-09
[14,] 0.99999999 1.324445e-08 6.622223e-09
[15,] 0.99999999 1.959771e-08 9.798853e-09
[16,] 0.99999999 1.839999e-08 9.199996e-09
[17,] 0.99999999 2.114037e-08 1.057018e-08
[18,] 0.99999998 4.047474e-08 2.023737e-08
[19,] 0.99999997 6.282262e-08 3.141131e-08
[20,] 0.99999993 1.381662e-07 6.908308e-08
[21,] 0.99999986 2.801694e-07 1.400847e-07
[22,] 0.99999972 5.602632e-07 2.801316e-07
[23,] 0.99999940 1.201468e-06 6.007342e-07
[24,] 0.99999954 9.270266e-07 4.635133e-07
[25,] 0.99999920 1.604156e-06 8.020780e-07
[26,] 0.99999871 2.575513e-06 1.287756e-06
[27,] 0.99999760 4.803352e-06 2.401676e-06
[28,] 0.99999524 9.525554e-06 4.762777e-06
[29,] 0.99999184 1.631341e-05 8.156704e-06
[30,] 0.99998822 2.355382e-05 1.177691e-05
[31,] 0.99997929 4.142018e-05 2.071009e-05
[32,] 0.99996500 6.999114e-05 3.499557e-05
[33,] 0.99994358 1.128304e-04 5.641519e-05
[34,] 0.99990155 1.968981e-04 9.844906e-05
[35,] 0.99993011 1.397728e-04 6.988642e-05
[36,] 0.99993026 1.394800e-04 6.974002e-05
[37,] 0.99988114 2.377126e-04 1.188563e-04
[38,] 0.99981065 3.787060e-04 1.893530e-04
[39,] 0.99988028 2.394390e-04 1.197195e-04
[40,] 0.99987967 2.406592e-04 1.203296e-04
[41,] 0.99986348 2.730405e-04 1.365203e-04
[42,] 0.99990691 1.861818e-04 9.309089e-05
[43,] 0.99992106 1.578750e-04 7.893751e-05
[44,] 0.99994951 1.009805e-04 5.049025e-05
[45,] 0.99995481 9.037753e-05 4.518877e-05
[46,] 0.99997868 4.263387e-05 2.131693e-05
[47,] 0.99996421 7.158741e-05 3.579370e-05
[48,] 0.99994338 1.132362e-04 5.661809e-05
[49,] 0.99990434 1.913273e-04 9.566364e-05
[50,] 0.99985755 2.848932e-04 1.424466e-04
[51,] 0.99974944 5.011123e-04 2.505561e-04
[52,] 0.99961800 7.640062e-04 3.820031e-04
[53,] 0.99937167 1.256656e-03 6.283281e-04
[54,] 0.99894307 2.113865e-03 1.056932e-03
[55,] 0.99840972 3.180565e-03 1.590283e-03
[56,] 0.99773161 4.536778e-03 2.268389e-03
[57,] 0.99716151 5.676981e-03 2.838490e-03
[58,] 0.99799931 4.001371e-03 2.000685e-03
[59,] 0.99712520 5.749600e-03 2.874800e-03
[60,] 0.99605397 7.892066e-03 3.946033e-03
[61,] 0.99451198 1.097604e-02 5.488019e-03
[62,] 0.99799621 4.007576e-03 2.003788e-03
[63,] 0.99682437 6.351259e-03 3.175630e-03
[64,] 0.99556060 8.878796e-03 4.439398e-03
[65,] 0.99333113 1.333773e-02 6.668867e-03
[66,] 0.99024182 1.951637e-02 9.758185e-03
[67,] 0.99460709 1.078581e-02 5.392906e-03
[68,] 0.99254836 1.490328e-02 7.451641e-03
[69,] 0.99218819 1.562363e-02 7.811813e-03
[70,] 0.98903253 2.193493e-02 1.096747e-02
[71,] 0.98557128 2.885743e-02 1.442872e-02
[72,] 0.98521646 2.956708e-02 1.478354e-02
[73,] 0.98066022 3.867955e-02 1.933978e-02
[74,] 0.97623854 4.752291e-02 2.376146e-02
[75,] 0.98829286 2.341427e-02 1.170714e-02
[76,] 0.99207833 1.584333e-02 7.921667e-03
[77,] 0.98652810 2.694381e-02 1.347190e-02
[78,] 0.97706978 4.586043e-02 2.293022e-02
[79,] 0.96447632 7.104737e-02 3.552368e-02
[80,] 0.94876068 1.024786e-01 5.123932e-02
[81,] 0.95760065 8.479870e-02 4.239935e-02
[82,] 0.93358941 1.328212e-01 6.641059e-02
[83,] 0.92672935 1.465413e-01 7.327065e-02
[84,] 0.89292775 2.141445e-01 1.070723e-01
[85,] 0.93552426 1.289515e-01 6.447574e-02
[86,] 0.95422638 9.154724e-02 4.577362e-02
[87,] 0.94178871 1.164226e-01 5.821129e-02
[88,] 0.95876481 8.247039e-02 4.123519e-02
[89,] 0.96816364 6.367272e-02 3.183636e-02
[90,] 0.96341901 7.316197e-02 3.658099e-02
[91,] 0.97245371 5.509257e-02 2.754629e-02
> postscript(file="/var/www/html/rcomp/tmp/19x411292759197.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/29x411292759197.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3263m1292759197.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4263m1292759197.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5263m1292759197.ps",horizontal=F,onefile=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 = 104
Frequency = 1
1 2 3 4 5 6
71238.829 607102.747 -914203.602 1231739.182 -1075085.425 1218639.536
7 8 9 10 11 12
2113666.724 -161892.752 -796976.097 174621.253 1508328.858 -1242263.210
13 14 15 16 17 18
-1537043.858 13441333.216 -1573515.089 100001.662 -645057.671 369241.197
19 20 21 22 23 24
-979818.994 1471690.006 1563226.960 1879952.509 1760775.573 379000.689
25 26 27 28 29 30
-998323.569 -459870.778 -680465.301 -556050.845 192819.305 -1854819.626
31 32 33 34 35 36
908308.605 1144830.078 -492492.923 269944.164 -851032.262 -1271407.306
37 38 39 40 41 42
-542908.308 694910.839 -875409.396 -515775.867 -2337827.208 -1899586.827
43 44 45 46 47 48
-441865.674 -485456.266 -2475878.395 -1712585.591 -1498875.834 -2127171.721
49 50 51 52 53 54
-1549883.486 -1847857.676 -1103172.569 -1836065.294 -170430.071 -2729.420
55 56 57 58 59 60
258982.378 1236959.559 485213.653 336827.856 -248659.615 89634.373
61 62 63 64 65 66
902642.355 -1004435.160 1262868.772 2224063.439 517577.742 -1217089.765
67 68 69 70 71 72
-1123186.545 -3272114.309 -780766.473 -1309400.586 -963139.019 190098.597
73 74 75 76 77 78
1905108.116 253108.525 704639.094 -1805907.738 14064.863 342368.928
79 80 81 82 83 84
-1412302.519 -1694760.895 1836585.831 1407847.743 -1232895.574 -77880.064
85 86 87 88 89 90
1120909.428 1456860.806 2005258.316 -1316716.082 -561799.573 218854.597
91 92 93 94 95 96
1643734.263 1863124.476 40183.270 -584291.436 -354668.000 2314052.628
97 98 99 100 101 102
4044119.692 718699.016 1217030.048 -1478122.280 -1121769.145 -1144212.347
103 104
-593169.605 -901704.653
> postscript(file="/var/www/html/rcomp/tmp/6dg3p1292759197.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 104
Frequency = 1
lag(myerror, k = 1) myerror
0 71238.829 NA
1 607102.747 71238.829
2 -914203.602 607102.747
3 1231739.182 -914203.602
4 -1075085.425 1231739.182
5 1218639.536 -1075085.425
6 2113666.724 1218639.536
7 -161892.752 2113666.724
8 -796976.097 -161892.752
9 174621.253 -796976.097
10 1508328.858 174621.253
11 -1242263.210 1508328.858
12 -1537043.858 -1242263.210
13 13441333.216 -1537043.858
14 -1573515.089 13441333.216
15 100001.662 -1573515.089
16 -645057.671 100001.662
17 369241.197 -645057.671
18 -979818.994 369241.197
19 1471690.006 -979818.994
20 1563226.960 1471690.006
21 1879952.509 1563226.960
22 1760775.573 1879952.509
23 379000.689 1760775.573
24 -998323.569 379000.689
25 -459870.778 -998323.569
26 -680465.301 -459870.778
27 -556050.845 -680465.301
28 192819.305 -556050.845
29 -1854819.626 192819.305
30 908308.605 -1854819.626
31 1144830.078 908308.605
32 -492492.923 1144830.078
33 269944.164 -492492.923
34 -851032.262 269944.164
35 -1271407.306 -851032.262
36 -542908.308 -1271407.306
37 694910.839 -542908.308
38 -875409.396 694910.839
39 -515775.867 -875409.396
40 -2337827.208 -515775.867
41 -1899586.827 -2337827.208
42 -441865.674 -1899586.827
43 -485456.266 -441865.674
44 -2475878.395 -485456.266
45 -1712585.591 -2475878.395
46 -1498875.834 -1712585.591
47 -2127171.721 -1498875.834
48 -1549883.486 -2127171.721
49 -1847857.676 -1549883.486
50 -1103172.569 -1847857.676
51 -1836065.294 -1103172.569
52 -170430.071 -1836065.294
53 -2729.420 -170430.071
54 258982.378 -2729.420
55 1236959.559 258982.378
56 485213.653 1236959.559
57 336827.856 485213.653
58 -248659.615 336827.856
59 89634.373 -248659.615
60 902642.355 89634.373
61 -1004435.160 902642.355
62 1262868.772 -1004435.160
63 2224063.439 1262868.772
64 517577.742 2224063.439
65 -1217089.765 517577.742
66 -1123186.545 -1217089.765
67 -3272114.309 -1123186.545
68 -780766.473 -3272114.309
69 -1309400.586 -780766.473
70 -963139.019 -1309400.586
71 190098.597 -963139.019
72 1905108.116 190098.597
73 253108.525 1905108.116
74 704639.094 253108.525
75 -1805907.738 704639.094
76 14064.863 -1805907.738
77 342368.928 14064.863
78 -1412302.519 342368.928
79 -1694760.895 -1412302.519
80 1836585.831 -1694760.895
81 1407847.743 1836585.831
82 -1232895.574 1407847.743
83 -77880.064 -1232895.574
84 1120909.428 -77880.064
85 1456860.806 1120909.428
86 2005258.316 1456860.806
87 -1316716.082 2005258.316
88 -561799.573 -1316716.082
89 218854.597 -561799.573
90 1643734.263 218854.597
91 1863124.476 1643734.263
92 40183.270 1863124.476
93 -584291.436 40183.270
94 -354668.000 -584291.436
95 2314052.628 -354668.000
96 4044119.692 2314052.628
97 718699.016 4044119.692
98 1217030.048 718699.016
99 -1478122.280 1217030.048
100 -1121769.145 -1478122.280
101 -1144212.347 -1121769.145
102 -593169.605 -1144212.347
103 -901704.653 -593169.605
104 NA -901704.653
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 607102.747 71238.829
[2,] -914203.602 607102.747
[3,] 1231739.182 -914203.602
[4,] -1075085.425 1231739.182
[5,] 1218639.536 -1075085.425
[6,] 2113666.724 1218639.536
[7,] -161892.752 2113666.724
[8,] -796976.097 -161892.752
[9,] 174621.253 -796976.097
[10,] 1508328.858 174621.253
[11,] -1242263.210 1508328.858
[12,] -1537043.858 -1242263.210
[13,] 13441333.216 -1537043.858
[14,] -1573515.089 13441333.216
[15,] 100001.662 -1573515.089
[16,] -645057.671 100001.662
[17,] 369241.197 -645057.671
[18,] -979818.994 369241.197
[19,] 1471690.006 -979818.994
[20,] 1563226.960 1471690.006
[21,] 1879952.509 1563226.960
[22,] 1760775.573 1879952.509
[23,] 379000.689 1760775.573
[24,] -998323.569 379000.689
[25,] -459870.778 -998323.569
[26,] -680465.301 -459870.778
[27,] -556050.845 -680465.301
[28,] 192819.305 -556050.845
[29,] -1854819.626 192819.305
[30,] 908308.605 -1854819.626
[31,] 1144830.078 908308.605
[32,] -492492.923 1144830.078
[33,] 269944.164 -492492.923
[34,] -851032.262 269944.164
[35,] -1271407.306 -851032.262
[36,] -542908.308 -1271407.306
[37,] 694910.839 -542908.308
[38,] -875409.396 694910.839
[39,] -515775.867 -875409.396
[40,] -2337827.208 -515775.867
[41,] -1899586.827 -2337827.208
[42,] -441865.674 -1899586.827
[43,] -485456.266 -441865.674
[44,] -2475878.395 -485456.266
[45,] -1712585.591 -2475878.395
[46,] -1498875.834 -1712585.591
[47,] -2127171.721 -1498875.834
[48,] -1549883.486 -2127171.721
[49,] -1847857.676 -1549883.486
[50,] -1103172.569 -1847857.676
[51,] -1836065.294 -1103172.569
[52,] -170430.071 -1836065.294
[53,] -2729.420 -170430.071
[54,] 258982.378 -2729.420
[55,] 1236959.559 258982.378
[56,] 485213.653 1236959.559
[57,] 336827.856 485213.653
[58,] -248659.615 336827.856
[59,] 89634.373 -248659.615
[60,] 902642.355 89634.373
[61,] -1004435.160 902642.355
[62,] 1262868.772 -1004435.160
[63,] 2224063.439 1262868.772
[64,] 517577.742 2224063.439
[65,] -1217089.765 517577.742
[66,] -1123186.545 -1217089.765
[67,] -3272114.309 -1123186.545
[68,] -780766.473 -3272114.309
[69,] -1309400.586 -780766.473
[70,] -963139.019 -1309400.586
[71,] 190098.597 -963139.019
[72,] 1905108.116 190098.597
[73,] 253108.525 1905108.116
[74,] 704639.094 253108.525
[75,] -1805907.738 704639.094
[76,] 14064.863 -1805907.738
[77,] 342368.928 14064.863
[78,] -1412302.519 342368.928
[79,] -1694760.895 -1412302.519
[80,] 1836585.831 -1694760.895
[81,] 1407847.743 1836585.831
[82,] -1232895.574 1407847.743
[83,] -77880.064 -1232895.574
[84,] 1120909.428 -77880.064
[85,] 1456860.806 1120909.428
[86,] 2005258.316 1456860.806
[87,] -1316716.082 2005258.316
[88,] -561799.573 -1316716.082
[89,] 218854.597 -561799.573
[90,] 1643734.263 218854.597
[91,] 1863124.476 1643734.263
[92,] 40183.270 1863124.476
[93,] -584291.436 40183.270
[94,] -354668.000 -584291.436
[95,] 2314052.628 -354668.000
[96,] 4044119.692 2314052.628
[97,] 718699.016 4044119.692
[98,] 1217030.048 718699.016
[99,] -1478122.280 1217030.048
[100,] -1121769.145 -1478122.280
[101,] -1144212.347 -1121769.145
[102,] -593169.605 -1144212.347
[103,] -901704.653 -593169.605
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 607102.747 71238.829
2 -914203.602 607102.747
3 1231739.182 -914203.602
4 -1075085.425 1231739.182
5 1218639.536 -1075085.425
6 2113666.724 1218639.536
7 -161892.752 2113666.724
8 -796976.097 -161892.752
9 174621.253 -796976.097
10 1508328.858 174621.253
11 -1242263.210 1508328.858
12 -1537043.858 -1242263.210
13 13441333.216 -1537043.858
14 -1573515.089 13441333.216
15 100001.662 -1573515.089
16 -645057.671 100001.662
17 369241.197 -645057.671
18 -979818.994 369241.197
19 1471690.006 -979818.994
20 1563226.960 1471690.006
21 1879952.509 1563226.960
22 1760775.573 1879952.509
23 379000.689 1760775.573
24 -998323.569 379000.689
25 -459870.778 -998323.569
26 -680465.301 -459870.778
27 -556050.845 -680465.301
28 192819.305 -556050.845
29 -1854819.626 192819.305
30 908308.605 -1854819.626
31 1144830.078 908308.605
32 -492492.923 1144830.078
33 269944.164 -492492.923
34 -851032.262 269944.164
35 -1271407.306 -851032.262
36 -542908.308 -1271407.306
37 694910.839 -542908.308
38 -875409.396 694910.839
39 -515775.867 -875409.396
40 -2337827.208 -515775.867
41 -1899586.827 -2337827.208
42 -441865.674 -1899586.827
43 -485456.266 -441865.674
44 -2475878.395 -485456.266
45 -1712585.591 -2475878.395
46 -1498875.834 -1712585.591
47 -2127171.721 -1498875.834
48 -1549883.486 -2127171.721
49 -1847857.676 -1549883.486
50 -1103172.569 -1847857.676
51 -1836065.294 -1103172.569
52 -170430.071 -1836065.294
53 -2729.420 -170430.071
54 258982.378 -2729.420
55 1236959.559 258982.378
56 485213.653 1236959.559
57 336827.856 485213.653
58 -248659.615 336827.856
59 89634.373 -248659.615
60 902642.355 89634.373
61 -1004435.160 902642.355
62 1262868.772 -1004435.160
63 2224063.439 1262868.772
64 517577.742 2224063.439
65 -1217089.765 517577.742
66 -1123186.545 -1217089.765
67 -3272114.309 -1123186.545
68 -780766.473 -3272114.309
69 -1309400.586 -780766.473
70 -963139.019 -1309400.586
71 190098.597 -963139.019
72 1905108.116 190098.597
73 253108.525 1905108.116
74 704639.094 253108.525
75 -1805907.738 704639.094
76 14064.863 -1805907.738
77 342368.928 14064.863
78 -1412302.519 342368.928
79 -1694760.895 -1412302.519
80 1836585.831 -1694760.895
81 1407847.743 1836585.831
82 -1232895.574 1407847.743
83 -77880.064 -1232895.574
84 1120909.428 -77880.064
85 1456860.806 1120909.428
86 2005258.316 1456860.806
87 -1316716.082 2005258.316
88 -561799.573 -1316716.082
89 218854.597 -561799.573
90 1643734.263 218854.597
91 1863124.476 1643734.263
92 40183.270 1863124.476
93 -584291.436 40183.270
94 -354668.000 -584291.436
95 2314052.628 -354668.000
96 4044119.692 2314052.628
97 718699.016 4044119.692
98 1217030.048 718699.016
99 -1478122.280 1217030.048
100 -1121769.145 -1478122.280
101 -1144212.347 -1121769.145
102 -593169.605 -1144212.347
103 -901704.653 -593169.605
> 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/75p2a1292759197.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/85p2a1292759197.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/95p2a1292759197.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gy1d1292759197.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jz001292759197.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/12mzy61292759197.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/13j9ef1292759197.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/1449v31292759197.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/15psbr1292759197.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/16tsrf1292759197.tab")
+ }
>
> try(system("convert tmp/19x411292759197.ps tmp/19x411292759197.png",intern=TRUE))
character(0)
> try(system("convert tmp/29x411292759197.ps tmp/29x411292759197.png",intern=TRUE))
character(0)
> try(system("convert tmp/3263m1292759197.ps tmp/3263m1292759197.png",intern=TRUE))
character(0)
> try(system("convert tmp/4263m1292759197.ps tmp/4263m1292759197.png",intern=TRUE))
character(0)
> try(system("convert tmp/5263m1292759197.ps tmp/5263m1292759197.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dg3p1292759197.ps tmp/6dg3p1292759197.png",intern=TRUE))
character(0)
> try(system("convert tmp/75p2a1292759197.ps tmp/75p2a1292759197.png",intern=TRUE))
character(0)
> try(system("convert tmp/85p2a1292759197.ps tmp/85p2a1292759197.png",intern=TRUE))
character(0)
> try(system("convert tmp/95p2a1292759197.ps tmp/95p2a1292759197.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gy1d1292759197.ps tmp/10gy1d1292759197.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.092 1.705 7.012