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(6.5
+ ,8.9
+ ,9
+ ,6.3
+ ,8.4
+ ,11
+ ,5.9
+ ,8.1
+ ,13
+ ,5.5
+ ,8.3
+ ,12
+ ,5.2
+ ,8.1
+ ,13
+ ,4.9
+ ,8
+ ,15
+ ,5.4
+ ,8.7
+ ,13
+ ,5.8
+ ,9.2
+ ,16
+ ,5.7
+ ,9
+ ,10
+ ,5.6
+ ,8.9
+ ,14
+ ,5.5
+ ,8.5
+ ,14
+ ,5.4
+ ,8.1
+ ,15
+ ,5.4
+ ,7.5
+ ,13
+ ,5.4
+ ,7.1
+ ,8
+ ,5.5
+ ,6.9
+ ,7
+ ,5.8
+ ,7.1
+ ,3
+ ,5.7
+ ,7
+ ,3
+ ,5.4
+ ,6.7
+ ,4
+ ,5.6
+ ,7
+ ,4
+ ,5.8
+ ,7.3
+ ,0
+ ,6.2
+ ,7.7
+ ,-4
+ ,6.8
+ ,8.4
+ ,-14
+ ,6.7
+ ,8.4
+ ,-18
+ ,6.7
+ ,8.8
+ ,-8
+ ,6.4
+ ,9.1
+ ,-1
+ ,6.3
+ ,9
+ ,1
+ ,6.3
+ ,8.6
+ ,2
+ ,6.4
+ ,7.9
+ ,0
+ ,6.3
+ ,7.7
+ ,1
+ ,6
+ ,7.8
+ ,0
+ ,6.3
+ ,9.2
+ ,-1
+ ,6.3
+ ,9.4
+ ,-3
+ ,6.6
+ ,9.2
+ ,-3
+ ,7.5
+ ,8.7
+ ,-3
+ ,7.8
+ ,8.4
+ ,-4
+ ,7.9
+ ,8.6
+ ,-8
+ ,7.8
+ ,9
+ ,-9
+ ,7.6
+ ,9.1
+ ,-13
+ ,7.5
+ ,8.7
+ ,-18
+ ,7.6
+ ,8.2
+ ,-11
+ ,7.5
+ ,7.9
+ ,-9
+ ,7.3
+ ,7.9
+ ,-10
+ ,7.6
+ ,9.1
+ ,-13
+ ,7.5
+ ,9.4
+ ,-11
+ ,7.6
+ ,9.4
+ ,-5
+ ,7.9
+ ,9.1
+ ,-15
+ ,7.9
+ ,9
+ ,-6
+ ,8.1
+ ,9.3
+ ,-6
+ ,8.2
+ ,9.9
+ ,-3
+ ,8
+ ,9.8
+ ,-1
+ ,7.5
+ ,9.3
+ ,-3
+ ,6.8
+ ,8.3
+ ,-4
+ ,6.5
+ ,8
+ ,-6
+ ,6.6
+ ,8.5
+ ,0
+ ,7.6
+ ,10.4
+ ,-4
+ ,8
+ ,11.1
+ ,-2
+ ,8.1
+ ,10.9
+ ,-2
+ ,7.7
+ ,10
+ ,-6
+ ,7.5
+ ,9.2
+ ,-7
+ ,7.6
+ ,9.2
+ ,-6
+ ,7.8
+ ,9.5
+ ,-6
+ ,7.8
+ ,9.6
+ ,-3
+ ,7.8
+ ,9.5
+ ,-2
+ ,7.5
+ ,9.1
+ ,-5
+ ,7.5
+ ,8.9
+ ,-11
+ ,7.1
+ ,9
+ ,-11
+ ,7.5
+ ,10.1
+ ,-11
+ ,7.5
+ ,10.3
+ ,-10
+ ,7.6
+ ,10.2
+ ,-14
+ ,7.7
+ ,9.6
+ ,-8
+ ,7.7
+ ,9.2
+ ,-9
+ ,7.9
+ ,9.3
+ ,-5
+ ,8.1
+ ,9.4
+ ,-1
+ ,8.2
+ ,9.4
+ ,-2
+ ,8.2
+ ,9.2
+ ,-5
+ ,8.2
+ ,9
+ ,-4
+ ,7.9
+ ,9
+ ,-6
+ ,7.3
+ ,9
+ ,-2
+ ,6.9
+ ,9.8
+ ,-2
+ ,6.6
+ ,10
+ ,-2
+ ,6.7
+ ,9.8
+ ,-2
+ ,6.9
+ ,9.3
+ ,2
+ ,7
+ ,9
+ ,1
+ ,7.1
+ ,9
+ ,-8
+ ,7.2
+ ,9.1
+ ,-1
+ ,7.1
+ ,9.1
+ ,1
+ ,6.9
+ ,9.1
+ ,-1
+ ,7
+ ,9.2
+ ,2
+ ,6.8
+ ,8.8
+ ,2
+ ,6.4
+ ,8.3
+ ,1
+ ,6.7
+ ,8.4
+ ,-1
+ ,6.6
+ ,8.1
+ ,-2
+ ,6.4
+ ,7.7
+ ,-2
+ ,6.3
+ ,7.9
+ ,-1
+ ,6.2
+ ,7.9
+ ,-8
+ ,6.5
+ ,8
+ ,-4
+ ,6.8
+ ,7.9
+ ,-6
+ ,6.8
+ ,7.6
+ ,-3
+ ,6.4
+ ,7.1
+ ,-3
+ ,6.1
+ ,6.8
+ ,-7
+ ,5.8
+ ,6.5
+ ,-9
+ ,6.1
+ ,6.9
+ ,-11
+ ,7.2
+ ,8.2
+ ,-13
+ ,7.3
+ ,8.7
+ ,-11
+ ,6.9
+ ,8.3
+ ,-9
+ ,6.1
+ ,7.9
+ ,-17
+ ,5.8
+ ,7.5
+ ,-22
+ ,6.2
+ ,7.8
+ ,-25
+ ,7.1
+ ,8.3
+ ,-20
+ ,7.7
+ ,8.4
+ ,-24
+ ,8
+ ,8.2
+ ,-24
+ ,7.8
+ ,7.6
+ ,-22
+ ,7.4
+ ,7.2
+ ,-19
+ ,7.4
+ ,7.5
+ ,-18
+ ,7.7
+ ,8.7
+ ,-17
+ ,7.8
+ ,9
+ ,-11
+ ,7.8
+ ,8.6
+ ,-11
+ ,8
+ ,7.9
+ ,-12
+ ,8.1
+ ,7.8
+ ,-10
+ ,8.4
+ ,8.2
+ ,-15)
+ ,dim=c(3
+ ,120)
+ ,dimnames=list(c('Mannen'
+ ,'Vrouwen'
+ ,'Consumvertr')
+ ,1:120))
> y <- array(NA,dim=c(3,120),dimnames=list(c('Mannen','Vrouwen','Consumvertr'),1:120))
> 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
Mannen Vrouwen Consumvertr
1 6.5 8.9 9
2 6.3 8.4 11
3 5.9 8.1 13
4 5.5 8.3 12
5 5.2 8.1 13
6 4.9 8.0 15
7 5.4 8.7 13
8 5.8 9.2 16
9 5.7 9.0 10
10 5.6 8.9 14
11 5.5 8.5 14
12 5.4 8.1 15
13 5.4 7.5 13
14 5.4 7.1 8
15 5.5 6.9 7
16 5.8 7.1 3
17 5.7 7.0 3
18 5.4 6.7 4
19 5.6 7.0 4
20 5.8 7.3 0
21 6.2 7.7 -4
22 6.8 8.4 -14
23 6.7 8.4 -18
24 6.7 8.8 -8
25 6.4 9.1 -1
26 6.3 9.0 1
27 6.3 8.6 2
28 6.4 7.9 0
29 6.3 7.7 1
30 6.0 7.8 0
31 6.3 9.2 -1
32 6.3 9.4 -3
33 6.6 9.2 -3
34 7.5 8.7 -3
35 7.8 8.4 -4
36 7.9 8.6 -8
37 7.8 9.0 -9
38 7.6 9.1 -13
39 7.5 8.7 -18
40 7.6 8.2 -11
41 7.5 7.9 -9
42 7.3 7.9 -10
43 7.6 9.1 -13
44 7.5 9.4 -11
45 7.6 9.4 -5
46 7.9 9.1 -15
47 7.9 9.0 -6
48 8.1 9.3 -6
49 8.2 9.9 -3
50 8.0 9.8 -1
51 7.5 9.3 -3
52 6.8 8.3 -4
53 6.5 8.0 -6
54 6.6 8.5 0
55 7.6 10.4 -4
56 8.0 11.1 -2
57 8.1 10.9 -2
58 7.7 10.0 -6
59 7.5 9.2 -7
60 7.6 9.2 -6
61 7.8 9.5 -6
62 7.8 9.6 -3
63 7.8 9.5 -2
64 7.5 9.1 -5
65 7.5 8.9 -11
66 7.1 9.0 -11
67 7.5 10.1 -11
68 7.5 10.3 -10
69 7.6 10.2 -14
70 7.7 9.6 -8
71 7.7 9.2 -9
72 7.9 9.3 -5
73 8.1 9.4 -1
74 8.2 9.4 -2
75 8.2 9.2 -5
76 8.2 9.0 -4
77 7.9 9.0 -6
78 7.3 9.0 -2
79 6.9 9.8 -2
80 6.6 10.0 -2
81 6.7 9.8 -2
82 6.9 9.3 2
83 7.0 9.0 1
84 7.1 9.0 -8
85 7.2 9.1 -1
86 7.1 9.1 1
87 6.9 9.1 -1
88 7.0 9.2 2
89 6.8 8.8 2
90 6.4 8.3 1
91 6.7 8.4 -1
92 6.6 8.1 -2
93 6.4 7.7 -2
94 6.3 7.9 -1
95 6.2 7.9 -8
96 6.5 8.0 -4
97 6.8 7.9 -6
98 6.8 7.6 -3
99 6.4 7.1 -3
100 6.1 6.8 -7
101 5.8 6.5 -9
102 6.1 6.9 -11
103 7.2 8.2 -13
104 7.3 8.7 -11
105 6.9 8.3 -9
106 6.1 7.9 -17
107 5.8 7.5 -22
108 6.2 7.8 -25
109 7.1 8.3 -20
110 7.7 8.4 -24
111 8.0 8.2 -24
112 7.8 7.6 -22
113 7.4 7.2 -19
114 7.4 7.5 -18
115 7.7 8.7 -17
116 7.8 9.0 -11
117 7.8 8.6 -11
118 8.0 7.9 -12
119 8.1 7.8 -10
120 8.4 8.2 -15
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Vrouwen Consumvertr
2.18741 0.52370 -0.05579
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.54259 -0.28314 0.01210 0.28611 1.26981
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.187408 0.457705 4.779 5.16e-06 ***
Vrouwen 0.523701 0.052830 9.913 < 2e-16 ***
Consumvertr -0.055792 0.005352 -10.424 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5233 on 117 degrees of freedom
Multiple R-squared: 0.6395, Adjusted R-squared: 0.6333
F-statistic: 103.8 on 2 and 117 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,] 2.758647e-01 5.517295e-01 0.72413527
[2,] 1.541029e-01 3.082058e-01 0.84589712
[3,] 2.087798e-01 4.175597e-01 0.79122017
[4,] 2.923135e-01 5.846269e-01 0.70768655
[5,] 1.990430e-01 3.980861e-01 0.80095696
[6,] 1.287411e-01 2.574822e-01 0.87125888
[7,] 8.627102e-02 1.725420e-01 0.91372898
[8,] 5.264767e-02 1.052953e-01 0.94735233
[9,] 3.658724e-02 7.317449e-02 0.96341276
[10,] 2.052216e-02 4.104433e-02 0.97947784
[11,] 1.204385e-02 2.408770e-02 0.98795615
[12,] 6.828356e-03 1.365671e-02 0.99317164
[13,] 4.075756e-03 8.151512e-03 0.99592424
[14,] 2.123982e-03 4.247965e-03 0.99787602
[15,] 1.504161e-03 3.008322e-03 0.99849584
[16,] 8.903104e-04 1.780621e-03 0.99910969
[17,] 5.933872e-04 1.186774e-03 0.99940661
[18,] 5.558598e-04 1.111720e-03 0.99944414
[19,] 3.000247e-04 6.000494e-04 0.99969998
[20,] 1.685925e-04 3.371851e-04 0.99983141
[21,] 9.396279e-05 1.879256e-04 0.99990604
[22,] 5.463614e-05 1.092723e-04 0.99994536
[23,] 5.464552e-05 1.092910e-04 0.99994535
[24,] 5.035416e-05 1.007083e-04 0.99994965
[25,] 2.707067e-05 5.414134e-05 0.99997293
[26,] 2.206380e-05 4.412759e-05 0.99997794
[27,] 2.952864e-05 5.905729e-05 0.99997047
[28,] 1.926116e-05 3.852233e-05 0.99998074
[29,] 1.308504e-03 2.617008e-03 0.99869150
[30,] 4.204034e-02 8.408068e-02 0.95795966
[31,] 1.304342e-01 2.608683e-01 0.86956584
[32,] 1.619763e-01 3.239526e-01 0.83802371
[33,] 1.322110e-01 2.644220e-01 0.86778898
[34,] 1.038289e-01 2.076577e-01 0.89617113
[35,] 1.160114e-01 2.320227e-01 0.88398864
[36,] 1.424949e-01 2.849899e-01 0.85750506
[37,] 1.298781e-01 2.597563e-01 0.87012185
[38,] 1.026362e-01 2.052723e-01 0.89736384
[39,] 8.022743e-02 1.604549e-01 0.91977257
[40,] 7.618160e-02 1.523632e-01 0.92381840
[41,] 6.143784e-02 1.228757e-01 0.93856216
[42,] 9.061509e-02 1.812302e-01 0.90938491
[43,] 1.361808e-01 2.723616e-01 0.86381919
[44,] 1.869413e-01 3.738826e-01 0.81305870
[45,] 2.213764e-01 4.427528e-01 0.77862362
[46,] 1.949603e-01 3.899206e-01 0.80503970
[47,] 1.604637e-01 3.209274e-01 0.83953631
[48,] 1.391942e-01 2.783883e-01 0.86080583
[49,] 1.147671e-01 2.295343e-01 0.88523287
[50,] 9.442892e-02 1.888578e-01 0.90557108
[51,] 7.431226e-02 1.486245e-01 0.92568774
[52,] 5.916205e-02 1.183241e-01 0.94083795
[53,] 4.499448e-02 8.998896e-02 0.95500552
[54,] 3.399564e-02 6.799128e-02 0.96600436
[55,] 2.699788e-02 5.399575e-02 0.97300212
[56,] 2.203855e-02 4.407709e-02 0.97796145
[57,] 1.980320e-02 3.960640e-02 0.98019680
[58,] 1.987999e-02 3.975999e-02 0.98012001
[59,] 1.533477e-02 3.066954e-02 0.98466523
[60,] 1.097527e-02 2.195055e-02 0.98902473
[61,] 1.007207e-02 2.014415e-02 0.98992793
[62,] 1.094676e-02 2.189352e-02 0.98905324
[63,] 1.247224e-02 2.494448e-02 0.98752776
[64,] 1.574758e-02 3.149517e-02 0.98425242
[65,] 1.122459e-02 2.244919e-02 0.98877541
[66,] 8.186630e-03 1.637326e-02 0.99181337
[67,] 8.491151e-03 1.698230e-02 0.99150885
[68,] 1.707337e-02 3.414673e-02 0.98292663
[69,] 3.512347e-02 7.024695e-02 0.96487653
[70,] 6.118614e-02 1.223723e-01 0.93881386
[71,] 1.303008e-01 2.606016e-01 0.86969920
[72,] 1.520441e-01 3.040882e-01 0.84795592
[73,] 1.309367e-01 2.618734e-01 0.86906332
[74,] 1.234845e-01 2.469691e-01 0.87651545
[75,] 1.808884e-01 3.617768e-01 0.81911162
[76,] 2.234110e-01 4.468219e-01 0.77658904
[77,] 1.894568e-01 3.789136e-01 0.81054321
[78,] 1.550616e-01 3.101233e-01 0.84493837
[79,] 1.333435e-01 2.666869e-01 0.86665653
[80,] 1.061816e-01 2.123632e-01 0.89381840
[81,] 8.356785e-02 1.671357e-01 0.91643215
[82,] 6.868840e-02 1.373768e-01 0.93131160
[83,] 5.410581e-02 1.082116e-01 0.94589419
[84,] 4.206425e-02 8.412849e-02 0.95793575
[85,] 3.497514e-02 6.995027e-02 0.96502486
[86,] 2.692123e-02 5.384246e-02 0.97307877
[87,] 2.003436e-02 4.006872e-02 0.97996564
[88,] 1.433487e-02 2.866974e-02 0.98566513
[89,] 1.216504e-02 2.433009e-02 0.98783496
[90,] 1.749243e-02 3.498486e-02 0.98250757
[91,] 1.639544e-02 3.279088e-02 0.98360456
[92,] 1.183488e-02 2.366975e-02 0.98816512
[93,] 8.259712e-03 1.651942e-02 0.99174029
[94,] 5.413625e-03 1.082725e-02 0.99458637
[95,] 3.630536e-03 7.261071e-03 0.99636946
[96,] 3.240127e-03 6.480253e-03 0.99675987
[97,] 4.320034e-03 8.640067e-03 0.99567997
[98,] 2.846542e-03 5.693084e-03 0.99715346
[99,] 2.211285e-03 4.422570e-03 0.99778871
[100,] 4.448877e-03 8.897754e-03 0.99555112
[101,] 4.609364e-02 9.218728e-02 0.95390636
[102,] 3.810177e-01 7.620354e-01 0.61898232
[103,] 8.603002e-01 2.793996e-01 0.13969979
[104,] 9.340426e-01 1.319147e-01 0.06595737
[105,] 8.853984e-01 2.292032e-01 0.11460160
[106,] 8.611088e-01 2.777824e-01 0.13889122
[107,] 8.235063e-01 3.529875e-01 0.17649374
[108,] 7.357653e-01 5.284694e-01 0.26423468
[109,] 8.611924e-01 2.776151e-01 0.13880756
> postscript(file="/var/www/html/rcomp/tmp/1um241292698995.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2um241292698995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35v1p1292698995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45v1p1292698995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55v1p1292698995.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 = 120
Frequency = 1
1 2 3 4 5 6
0.153781409 0.327215558 0.195909546 -0.364622488 -0.504090454 -0.640136627
7 8 9 10 11 12
-0.618310937 -0.312785721 -0.642796798 -0.467259225 -0.357778903 -0.192506708
13 14 15 16 17 18
0.010130029 -0.059349014 0.089599274 0.061691621 0.014061702 -0.073036184
19 20 21 22 23 24
-0.030146425 -0.210424159 -0.243071973 -0.567581268 -0.890748760 -0.542310352
25 26 27 28 29 30
-0.608877482 -0.544923655 -0.279651460 0.075355358 0.135887392 -0.272274562
31 32 33 34 35 36
-0.761247563 -0.977571470 -0.572831309 0.589019094 0.990337463 0.762429810
37 38 39 40 41 42
0.397157614 -0.078379958 -0.247859001 0.504534513 0.673228500 0.417436627
43 44 45 46 47 48
-0.078379958 -0.223906454 0.210844784 0.110036296 0.664533233 0.707422992
49 50 51 52 53 54
0.660578128 0.624531954 0.274798611 0.042707543 -0.211765961 -0.038865126
55 56 57 58 59 60
-0.257064148 -0.112070966 0.092669195 -0.059167572 0.104001199 0.259793072
61 62 63 64 65 66
0.302682831 0.417688369 0.525850323 0.267955026 0.037943949 -0.414426132
67 68 69 70 71 72
-0.590497018 -0.639445306 -0.710242717 0.038729004 0.192417453 0.563214865
73 74 75 76 77 78
0.934012276 0.978220403 0.915584945 1.076116979 0.664533233 0.287700726
79 80 81 82 83 84
-0.531259919 -0.936000080 -0.731259919 -0.046242024 0.155076345 -0.247050513
85 86 87 88 89 90
0.191122518 0.202706264 -0.108877482 0.106128057 0.115608379 -0.078333092
91 92 93 94 95 96
0.057713082 0.059031450 0.068511773 -0.080436515 -0.570979627 -0.100182215
97 98 99 100 101 102
0.140604119 0.465089980 0.326940383 -0.039116868 -0.293590372 -0.314654440
103 104 105 106 107 108
-0.007049233 -0.057315890 -0.136251822 -1.173106484 -1.542585527 -1.467071388
109 110 111 112 113 114
-0.549962425 -0.225499998 0.179240163 0.405044393 0.381900334 0.280581965
115 116 117 118 119 120
0.007932872 0.285573868 0.495054190 1.005852881 1.269806708 1.081367020
> postscript(file="/var/www/html/rcomp/tmp/6ym0r1292698995.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 = 120
Frequency = 1
lag(myerror, k = 1) myerror
0 0.153781409 NA
1 0.327215558 0.153781409
2 0.195909546 0.327215558
3 -0.364622488 0.195909546
4 -0.504090454 -0.364622488
5 -0.640136627 -0.504090454
6 -0.618310937 -0.640136627
7 -0.312785721 -0.618310937
8 -0.642796798 -0.312785721
9 -0.467259225 -0.642796798
10 -0.357778903 -0.467259225
11 -0.192506708 -0.357778903
12 0.010130029 -0.192506708
13 -0.059349014 0.010130029
14 0.089599274 -0.059349014
15 0.061691621 0.089599274
16 0.014061702 0.061691621
17 -0.073036184 0.014061702
18 -0.030146425 -0.073036184
19 -0.210424159 -0.030146425
20 -0.243071973 -0.210424159
21 -0.567581268 -0.243071973
22 -0.890748760 -0.567581268
23 -0.542310352 -0.890748760
24 -0.608877482 -0.542310352
25 -0.544923655 -0.608877482
26 -0.279651460 -0.544923655
27 0.075355358 -0.279651460
28 0.135887392 0.075355358
29 -0.272274562 0.135887392
30 -0.761247563 -0.272274562
31 -0.977571470 -0.761247563
32 -0.572831309 -0.977571470
33 0.589019094 -0.572831309
34 0.990337463 0.589019094
35 0.762429810 0.990337463
36 0.397157614 0.762429810
37 -0.078379958 0.397157614
38 -0.247859001 -0.078379958
39 0.504534513 -0.247859001
40 0.673228500 0.504534513
41 0.417436627 0.673228500
42 -0.078379958 0.417436627
43 -0.223906454 -0.078379958
44 0.210844784 -0.223906454
45 0.110036296 0.210844784
46 0.664533233 0.110036296
47 0.707422992 0.664533233
48 0.660578128 0.707422992
49 0.624531954 0.660578128
50 0.274798611 0.624531954
51 0.042707543 0.274798611
52 -0.211765961 0.042707543
53 -0.038865126 -0.211765961
54 -0.257064148 -0.038865126
55 -0.112070966 -0.257064148
56 0.092669195 -0.112070966
57 -0.059167572 0.092669195
58 0.104001199 -0.059167572
59 0.259793072 0.104001199
60 0.302682831 0.259793072
61 0.417688369 0.302682831
62 0.525850323 0.417688369
63 0.267955026 0.525850323
64 0.037943949 0.267955026
65 -0.414426132 0.037943949
66 -0.590497018 -0.414426132
67 -0.639445306 -0.590497018
68 -0.710242717 -0.639445306
69 0.038729004 -0.710242717
70 0.192417453 0.038729004
71 0.563214865 0.192417453
72 0.934012276 0.563214865
73 0.978220403 0.934012276
74 0.915584945 0.978220403
75 1.076116979 0.915584945
76 0.664533233 1.076116979
77 0.287700726 0.664533233
78 -0.531259919 0.287700726
79 -0.936000080 -0.531259919
80 -0.731259919 -0.936000080
81 -0.046242024 -0.731259919
82 0.155076345 -0.046242024
83 -0.247050513 0.155076345
84 0.191122518 -0.247050513
85 0.202706264 0.191122518
86 -0.108877482 0.202706264
87 0.106128057 -0.108877482
88 0.115608379 0.106128057
89 -0.078333092 0.115608379
90 0.057713082 -0.078333092
91 0.059031450 0.057713082
92 0.068511773 0.059031450
93 -0.080436515 0.068511773
94 -0.570979627 -0.080436515
95 -0.100182215 -0.570979627
96 0.140604119 -0.100182215
97 0.465089980 0.140604119
98 0.326940383 0.465089980
99 -0.039116868 0.326940383
100 -0.293590372 -0.039116868
101 -0.314654440 -0.293590372
102 -0.007049233 -0.314654440
103 -0.057315890 -0.007049233
104 -0.136251822 -0.057315890
105 -1.173106484 -0.136251822
106 -1.542585527 -1.173106484
107 -1.467071388 -1.542585527
108 -0.549962425 -1.467071388
109 -0.225499998 -0.549962425
110 0.179240163 -0.225499998
111 0.405044393 0.179240163
112 0.381900334 0.405044393
113 0.280581965 0.381900334
114 0.007932872 0.280581965
115 0.285573868 0.007932872
116 0.495054190 0.285573868
117 1.005852881 0.495054190
118 1.269806708 1.005852881
119 1.081367020 1.269806708
120 NA 1.081367020
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.327215558 0.153781409
[2,] 0.195909546 0.327215558
[3,] -0.364622488 0.195909546
[4,] -0.504090454 -0.364622488
[5,] -0.640136627 -0.504090454
[6,] -0.618310937 -0.640136627
[7,] -0.312785721 -0.618310937
[8,] -0.642796798 -0.312785721
[9,] -0.467259225 -0.642796798
[10,] -0.357778903 -0.467259225
[11,] -0.192506708 -0.357778903
[12,] 0.010130029 -0.192506708
[13,] -0.059349014 0.010130029
[14,] 0.089599274 -0.059349014
[15,] 0.061691621 0.089599274
[16,] 0.014061702 0.061691621
[17,] -0.073036184 0.014061702
[18,] -0.030146425 -0.073036184
[19,] -0.210424159 -0.030146425
[20,] -0.243071973 -0.210424159
[21,] -0.567581268 -0.243071973
[22,] -0.890748760 -0.567581268
[23,] -0.542310352 -0.890748760
[24,] -0.608877482 -0.542310352
[25,] -0.544923655 -0.608877482
[26,] -0.279651460 -0.544923655
[27,] 0.075355358 -0.279651460
[28,] 0.135887392 0.075355358
[29,] -0.272274562 0.135887392
[30,] -0.761247563 -0.272274562
[31,] -0.977571470 -0.761247563
[32,] -0.572831309 -0.977571470
[33,] 0.589019094 -0.572831309
[34,] 0.990337463 0.589019094
[35,] 0.762429810 0.990337463
[36,] 0.397157614 0.762429810
[37,] -0.078379958 0.397157614
[38,] -0.247859001 -0.078379958
[39,] 0.504534513 -0.247859001
[40,] 0.673228500 0.504534513
[41,] 0.417436627 0.673228500
[42,] -0.078379958 0.417436627
[43,] -0.223906454 -0.078379958
[44,] 0.210844784 -0.223906454
[45,] 0.110036296 0.210844784
[46,] 0.664533233 0.110036296
[47,] 0.707422992 0.664533233
[48,] 0.660578128 0.707422992
[49,] 0.624531954 0.660578128
[50,] 0.274798611 0.624531954
[51,] 0.042707543 0.274798611
[52,] -0.211765961 0.042707543
[53,] -0.038865126 -0.211765961
[54,] -0.257064148 -0.038865126
[55,] -0.112070966 -0.257064148
[56,] 0.092669195 -0.112070966
[57,] -0.059167572 0.092669195
[58,] 0.104001199 -0.059167572
[59,] 0.259793072 0.104001199
[60,] 0.302682831 0.259793072
[61,] 0.417688369 0.302682831
[62,] 0.525850323 0.417688369
[63,] 0.267955026 0.525850323
[64,] 0.037943949 0.267955026
[65,] -0.414426132 0.037943949
[66,] -0.590497018 -0.414426132
[67,] -0.639445306 -0.590497018
[68,] -0.710242717 -0.639445306
[69,] 0.038729004 -0.710242717
[70,] 0.192417453 0.038729004
[71,] 0.563214865 0.192417453
[72,] 0.934012276 0.563214865
[73,] 0.978220403 0.934012276
[74,] 0.915584945 0.978220403
[75,] 1.076116979 0.915584945
[76,] 0.664533233 1.076116979
[77,] 0.287700726 0.664533233
[78,] -0.531259919 0.287700726
[79,] -0.936000080 -0.531259919
[80,] -0.731259919 -0.936000080
[81,] -0.046242024 -0.731259919
[82,] 0.155076345 -0.046242024
[83,] -0.247050513 0.155076345
[84,] 0.191122518 -0.247050513
[85,] 0.202706264 0.191122518
[86,] -0.108877482 0.202706264
[87,] 0.106128057 -0.108877482
[88,] 0.115608379 0.106128057
[89,] -0.078333092 0.115608379
[90,] 0.057713082 -0.078333092
[91,] 0.059031450 0.057713082
[92,] 0.068511773 0.059031450
[93,] -0.080436515 0.068511773
[94,] -0.570979627 -0.080436515
[95,] -0.100182215 -0.570979627
[96,] 0.140604119 -0.100182215
[97,] 0.465089980 0.140604119
[98,] 0.326940383 0.465089980
[99,] -0.039116868 0.326940383
[100,] -0.293590372 -0.039116868
[101,] -0.314654440 -0.293590372
[102,] -0.007049233 -0.314654440
[103,] -0.057315890 -0.007049233
[104,] -0.136251822 -0.057315890
[105,] -1.173106484 -0.136251822
[106,] -1.542585527 -1.173106484
[107,] -1.467071388 -1.542585527
[108,] -0.549962425 -1.467071388
[109,] -0.225499998 -0.549962425
[110,] 0.179240163 -0.225499998
[111,] 0.405044393 0.179240163
[112,] 0.381900334 0.405044393
[113,] 0.280581965 0.381900334
[114,] 0.007932872 0.280581965
[115,] 0.285573868 0.007932872
[116,] 0.495054190 0.285573868
[117,] 1.005852881 0.495054190
[118,] 1.269806708 1.005852881
[119,] 1.081367020 1.269806708
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.327215558 0.153781409
2 0.195909546 0.327215558
3 -0.364622488 0.195909546
4 -0.504090454 -0.364622488
5 -0.640136627 -0.504090454
6 -0.618310937 -0.640136627
7 -0.312785721 -0.618310937
8 -0.642796798 -0.312785721
9 -0.467259225 -0.642796798
10 -0.357778903 -0.467259225
11 -0.192506708 -0.357778903
12 0.010130029 -0.192506708
13 -0.059349014 0.010130029
14 0.089599274 -0.059349014
15 0.061691621 0.089599274
16 0.014061702 0.061691621
17 -0.073036184 0.014061702
18 -0.030146425 -0.073036184
19 -0.210424159 -0.030146425
20 -0.243071973 -0.210424159
21 -0.567581268 -0.243071973
22 -0.890748760 -0.567581268
23 -0.542310352 -0.890748760
24 -0.608877482 -0.542310352
25 -0.544923655 -0.608877482
26 -0.279651460 -0.544923655
27 0.075355358 -0.279651460
28 0.135887392 0.075355358
29 -0.272274562 0.135887392
30 -0.761247563 -0.272274562
31 -0.977571470 -0.761247563
32 -0.572831309 -0.977571470
33 0.589019094 -0.572831309
34 0.990337463 0.589019094
35 0.762429810 0.990337463
36 0.397157614 0.762429810
37 -0.078379958 0.397157614
38 -0.247859001 -0.078379958
39 0.504534513 -0.247859001
40 0.673228500 0.504534513
41 0.417436627 0.673228500
42 -0.078379958 0.417436627
43 -0.223906454 -0.078379958
44 0.210844784 -0.223906454
45 0.110036296 0.210844784
46 0.664533233 0.110036296
47 0.707422992 0.664533233
48 0.660578128 0.707422992
49 0.624531954 0.660578128
50 0.274798611 0.624531954
51 0.042707543 0.274798611
52 -0.211765961 0.042707543
53 -0.038865126 -0.211765961
54 -0.257064148 -0.038865126
55 -0.112070966 -0.257064148
56 0.092669195 -0.112070966
57 -0.059167572 0.092669195
58 0.104001199 -0.059167572
59 0.259793072 0.104001199
60 0.302682831 0.259793072
61 0.417688369 0.302682831
62 0.525850323 0.417688369
63 0.267955026 0.525850323
64 0.037943949 0.267955026
65 -0.414426132 0.037943949
66 -0.590497018 -0.414426132
67 -0.639445306 -0.590497018
68 -0.710242717 -0.639445306
69 0.038729004 -0.710242717
70 0.192417453 0.038729004
71 0.563214865 0.192417453
72 0.934012276 0.563214865
73 0.978220403 0.934012276
74 0.915584945 0.978220403
75 1.076116979 0.915584945
76 0.664533233 1.076116979
77 0.287700726 0.664533233
78 -0.531259919 0.287700726
79 -0.936000080 -0.531259919
80 -0.731259919 -0.936000080
81 -0.046242024 -0.731259919
82 0.155076345 -0.046242024
83 -0.247050513 0.155076345
84 0.191122518 -0.247050513
85 0.202706264 0.191122518
86 -0.108877482 0.202706264
87 0.106128057 -0.108877482
88 0.115608379 0.106128057
89 -0.078333092 0.115608379
90 0.057713082 -0.078333092
91 0.059031450 0.057713082
92 0.068511773 0.059031450
93 -0.080436515 0.068511773
94 -0.570979627 -0.080436515
95 -0.100182215 -0.570979627
96 0.140604119 -0.100182215
97 0.465089980 0.140604119
98 0.326940383 0.465089980
99 -0.039116868 0.326940383
100 -0.293590372 -0.039116868
101 -0.314654440 -0.293590372
102 -0.007049233 -0.314654440
103 -0.057315890 -0.007049233
104 -0.136251822 -0.057315890
105 -1.173106484 -0.136251822
106 -1.542585527 -1.173106484
107 -1.467071388 -1.542585527
108 -0.549962425 -1.467071388
109 -0.225499998 -0.549962425
110 0.179240163 -0.225499998
111 0.405044393 0.179240163
112 0.381900334 0.405044393
113 0.280581965 0.381900334
114 0.007932872 0.280581965
115 0.285573868 0.007932872
116 0.495054190 0.285573868
117 1.005852881 0.495054190
118 1.269806708 1.005852881
119 1.081367020 1.269806708
> 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/7reid1292698995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8reid1292698995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9reid1292698995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jnhf1292698995.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/115nx31292698995.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/12q6wr1292698995.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/134gc01292698995.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/147yao1292698995.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/15tz9c1292698995.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/16ehph1292698995.tab")
+ }
>
> try(system("convert tmp/1um241292698995.ps tmp/1um241292698995.png",intern=TRUE))
character(0)
> try(system("convert tmp/2um241292698995.ps tmp/2um241292698995.png",intern=TRUE))
character(0)
> try(system("convert tmp/35v1p1292698995.ps tmp/35v1p1292698995.png",intern=TRUE))
character(0)
> try(system("convert tmp/45v1p1292698995.ps tmp/45v1p1292698995.png",intern=TRUE))
character(0)
> try(system("convert tmp/55v1p1292698995.ps tmp/55v1p1292698995.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ym0r1292698995.ps tmp/6ym0r1292698995.png",intern=TRUE))
character(0)
> try(system("convert tmp/7reid1292698995.ps tmp/7reid1292698995.png",intern=TRUE))
character(0)
> try(system("convert tmp/8reid1292698995.ps tmp/8reid1292698995.png",intern=TRUE))
character(0)
> try(system("convert tmp/9reid1292698995.ps tmp/9reid1292698995.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jnhf1292698995.ps tmp/10jnhf1292698995.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.317 1.719 8.034