R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(1775,2197,2920,4240,5415,6136,6719,6234,7152,3646,2165,2803,1615,2350,3350,3536,5834,6767,5993,7276,5641,3477,2247,2466,1567,2237,2598,3729,5715,5776,5852,6878,5488,3583,2054,2282,1552,2261,2446,3519,5161,5085,5711,6057,5224,3363,1899,2115,1491,2061,2419,3430,4778,4862,6176,5664,5529,3418,1941,2402,1579,2146,2462,3695,4831,5134,6250,5760,6249,2917,1741,2359,1511,2059,2635,2867,4403,5720,4502,5749,5627,2846,1762,2429,1169,2154,2249,2687,4359,5382,4459,6398,4596,3024,1887,2070,1351,2218,2461,3028,4784,4975,4607,6249,4809,3157,1910,2228,1594,2467,2222,3607,4685,4962,5770,5480,5000,3228,1993,2288,1588,2105,2191,3591,4668,4885,5822,5599,5340,3082,2010,2301),dim=c(1,132),dimnames=list(c('marriages'),1:132))
> y <- array(NA,dim=c(1,132),dimnames=list(c('marriages'),1:132))
> 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 = '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
> 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
marriages M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1775 1 0 0 0 0 0 0 0 0 0 0
2 2197 0 1 0 0 0 0 0 0 0 0 0
3 2920 0 0 1 0 0 0 0 0 0 0 0
4 4240 0 0 0 1 0 0 0 0 0 0 0
5 5415 0 0 0 0 1 0 0 0 0 0 0
6 6136 0 0 0 0 0 1 0 0 0 0 0
7 6719 0 0 0 0 0 0 1 0 0 0 0
8 6234 0 0 0 0 0 0 0 1 0 0 0
9 7152 0 0 0 0 0 0 0 0 1 0 0
10 3646 0 0 0 0 0 0 0 0 0 1 0
11 2165 0 0 0 0 0 0 0 0 0 0 1
12 2803 0 0 0 0 0 0 0 0 0 0 0
13 1615 1 0 0 0 0 0 0 0 0 0 0
14 2350 0 1 0 0 0 0 0 0 0 0 0
15 3350 0 0 1 0 0 0 0 0 0 0 0
16 3536 0 0 0 1 0 0 0 0 0 0 0
17 5834 0 0 0 0 1 0 0 0 0 0 0
18 6767 0 0 0 0 0 1 0 0 0 0 0
19 5993 0 0 0 0 0 0 1 0 0 0 0
20 7276 0 0 0 0 0 0 0 1 0 0 0
21 5641 0 0 0 0 0 0 0 0 1 0 0
22 3477 0 0 0 0 0 0 0 0 0 1 0
23 2247 0 0 0 0 0 0 0 0 0 0 1
24 2466 0 0 0 0 0 0 0 0 0 0 0
25 1567 1 0 0 0 0 0 0 0 0 0 0
26 2237 0 1 0 0 0 0 0 0 0 0 0
27 2598 0 0 1 0 0 0 0 0 0 0 0
28 3729 0 0 0 1 0 0 0 0 0 0 0
29 5715 0 0 0 0 1 0 0 0 0 0 0
30 5776 0 0 0 0 0 1 0 0 0 0 0
31 5852 0 0 0 0 0 0 1 0 0 0 0
32 6878 0 0 0 0 0 0 0 1 0 0 0
33 5488 0 0 0 0 0 0 0 0 1 0 0
34 3583 0 0 0 0 0 0 0 0 0 1 0
35 2054 0 0 0 0 0 0 0 0 0 0 1
36 2282 0 0 0 0 0 0 0 0 0 0 0
37 1552 1 0 0 0 0 0 0 0 0 0 0
38 2261 0 1 0 0 0 0 0 0 0 0 0
39 2446 0 0 1 0 0 0 0 0 0 0 0
40 3519 0 0 0 1 0 0 0 0 0 0 0
41 5161 0 0 0 0 1 0 0 0 0 0 0
42 5085 0 0 0 0 0 1 0 0 0 0 0
43 5711 0 0 0 0 0 0 1 0 0 0 0
44 6057 0 0 0 0 0 0 0 1 0 0 0
45 5224 0 0 0 0 0 0 0 0 1 0 0
46 3363 0 0 0 0 0 0 0 0 0 1 0
47 1899 0 0 0 0 0 0 0 0 0 0 1
48 2115 0 0 0 0 0 0 0 0 0 0 0
49 1491 1 0 0 0 0 0 0 0 0 0 0
50 2061 0 1 0 0 0 0 0 0 0 0 0
51 2419 0 0 1 0 0 0 0 0 0 0 0
52 3430 0 0 0 1 0 0 0 0 0 0 0
53 4778 0 0 0 0 1 0 0 0 0 0 0
54 4862 0 0 0 0 0 1 0 0 0 0 0
55 6176 0 0 0 0 0 0 1 0 0 0 0
56 5664 0 0 0 0 0 0 0 1 0 0 0
57 5529 0 0 0 0 0 0 0 0 1 0 0
58 3418 0 0 0 0 0 0 0 0 0 1 0
59 1941 0 0 0 0 0 0 0 0 0 0 1
60 2402 0 0 0 0 0 0 0 0 0 0 0
61 1579 1 0 0 0 0 0 0 0 0 0 0
62 2146 0 1 0 0 0 0 0 0 0 0 0
63 2462 0 0 1 0 0 0 0 0 0 0 0
64 3695 0 0 0 1 0 0 0 0 0 0 0
65 4831 0 0 0 0 1 0 0 0 0 0 0
66 5134 0 0 0 0 0 1 0 0 0 0 0
67 6250 0 0 0 0 0 0 1 0 0 0 0
68 5760 0 0 0 0 0 0 0 1 0 0 0
69 6249 0 0 0 0 0 0 0 0 1 0 0
70 2917 0 0 0 0 0 0 0 0 0 1 0
71 1741 0 0 0 0 0 0 0 0 0 0 1
72 2359 0 0 0 0 0 0 0 0 0 0 0
73 1511 1 0 0 0 0 0 0 0 0 0 0
74 2059 0 1 0 0 0 0 0 0 0 0 0
75 2635 0 0 1 0 0 0 0 0 0 0 0
76 2867 0 0 0 1 0 0 0 0 0 0 0
77 4403 0 0 0 0 1 0 0 0 0 0 0
78 5720 0 0 0 0 0 1 0 0 0 0 0
79 4502 0 0 0 0 0 0 1 0 0 0 0
80 5749 0 0 0 0 0 0 0 1 0 0 0
81 5627 0 0 0 0 0 0 0 0 1 0 0
82 2846 0 0 0 0 0 0 0 0 0 1 0
83 1762 0 0 0 0 0 0 0 0 0 0 1
84 2429 0 0 0 0 0 0 0 0 0 0 0
85 1169 1 0 0 0 0 0 0 0 0 0 0
86 2154 0 1 0 0 0 0 0 0 0 0 0
87 2249 0 0 1 0 0 0 0 0 0 0 0
88 2687 0 0 0 1 0 0 0 0 0 0 0
89 4359 0 0 0 0 1 0 0 0 0 0 0
90 5382 0 0 0 0 0 1 0 0 0 0 0
91 4459 0 0 0 0 0 0 1 0 0 0 0
92 6398 0 0 0 0 0 0 0 1 0 0 0
93 4596 0 0 0 0 0 0 0 0 1 0 0
94 3024 0 0 0 0 0 0 0 0 0 1 0
95 1887 0 0 0 0 0 0 0 0 0 0 1
96 2070 0 0 0 0 0 0 0 0 0 0 0
97 1351 1 0 0 0 0 0 0 0 0 0 0
98 2218 0 1 0 0 0 0 0 0 0 0 0
99 2461 0 0 1 0 0 0 0 0 0 0 0
100 3028 0 0 0 1 0 0 0 0 0 0 0
101 4784 0 0 0 0 1 0 0 0 0 0 0
102 4975 0 0 0 0 0 1 0 0 0 0 0
103 4607 0 0 0 0 0 0 1 0 0 0 0
104 6249 0 0 0 0 0 0 0 1 0 0 0
105 4809 0 0 0 0 0 0 0 0 1 0 0
106 3157 0 0 0 0 0 0 0 0 0 1 0
107 1910 0 0 0 0 0 0 0 0 0 0 1
108 2228 0 0 0 0 0 0 0 0 0 0 0
109 1594 1 0 0 0 0 0 0 0 0 0 0
110 2467 0 1 0 0 0 0 0 0 0 0 0
111 2222 0 0 1 0 0 0 0 0 0 0 0
112 3607 0 0 0 1 0 0 0 0 0 0 0
113 4685 0 0 0 0 1 0 0 0 0 0 0
114 4962 0 0 0 0 0 1 0 0 0 0 0
115 5770 0 0 0 0 0 0 1 0 0 0 0
116 5480 0 0 0 0 0 0 0 1 0 0 0
117 5000 0 0 0 0 0 0 0 0 1 0 0
118 3228 0 0 0 0 0 0 0 0 0 1 0
119 1993 0 0 0 0 0 0 0 0 0 0 1
120 2288 0 0 0 0 0 0 0 0 0 0 0
121 1588 1 0 0 0 0 0 0 0 0 0 0
122 2105 0 1 0 0 0 0 0 0 0 0 0
123 2191 0 0 1 0 0 0 0 0 0 0 0
124 3591 0 0 0 1 0 0 0 0 0 0 0
125 4668 0 0 0 0 1 0 0 0 0 0 0
126 4885 0 0 0 0 0 1 0 0 0 0 0
127 5822 0 0 0 0 0 0 1 0 0 0 0
128 5599 0 0 0 0 0 0 0 1 0 0 0
129 5340 0 0 0 0 0 0 0 0 1 0 0
130 3082 0 0 0 0 0 0 0 0 0 1 0
131 2010 0 0 0 0 0 0 0 0 0 0 1
132 2301 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
2340.3 -813.7 -135.3 200.9 1107.8 2626.4
M6 M7 M8 M9 M10 M11
3085.5 3283.5 3781.9 3173.8 908.9 -375.8
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1164.73 -225.20 -16.82 161.39 1637.91
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2340.3 137.6 17.004 < 2e-16 ***
M1 -813.7 194.6 -4.181 5.55e-05 ***
M2 -135.3 194.6 -0.695 0.4884
M3 200.9 194.6 1.032 0.3041
M4 1107.8 194.6 5.692 9.08e-08 ***
M5 2626.4 194.6 13.493 < 2e-16 ***
M6 3085.5 194.6 15.852 < 2e-16 ***
M7 3283.5 194.6 16.869 < 2e-16 ***
M8 3781.9 194.6 19.430 < 2e-16 ***
M9 3173.8 194.6 16.306 < 2e-16 ***
M10 908.9 194.6 4.670 7.94e-06 ***
M11 -375.8 194.6 -1.931 0.0559 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 456.5 on 120 degrees of freedom
Multiple R-squared: 0.9314, Adjusted R-squared: 0.9251
F-statistic: 148 on 11 and 120 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,] 0.1251862 2.503724e-01 8.748138e-01
[2,] 0.2900446 5.800891e-01 7.099554e-01
[3,] 0.2660760 5.321520e-01 7.339240e-01
[4,] 0.3972123 7.944246e-01 6.027877e-01
[5,] 0.5074042 9.851916e-01 4.925958e-01
[6,] 0.8038609 3.922781e-01 1.961391e-01
[7,] 0.9752541 4.949180e-02 2.474590e-02
[8,] 0.9617380 7.652406e-02 3.826203e-02
[9,] 0.9428689 1.142621e-01 5.713106e-02
[10,] 0.9239696 1.520608e-01 7.603041e-02
[11,] 0.8925500 2.149000e-01 1.074500e-01
[12,] 0.8512749 2.974502e-01 1.487251e-01
[13,] 0.8515878 2.968244e-01 1.484122e-01
[14,] 0.8173448 3.653104e-01 1.826552e-01
[15,] 0.8260836 3.478328e-01 1.739164e-01
[16,] 0.8737459 2.525082e-01 1.262541e-01
[17,] 0.8786246 2.427508e-01 1.213754e-01
[18,] 0.8978459 2.043081e-01 1.021541e-01
[19,] 0.9431567 1.136865e-01 5.684326e-02
[20,] 0.9300899 1.398203e-01 6.991013e-02
[21,] 0.9088274 1.823451e-01 9.117255e-02
[22,] 0.8923931 2.152138e-01 1.076069e-01
[23,] 0.8616892 2.766216e-01 1.383108e-01
[24,] 0.8243782 3.512436e-01 1.756218e-01
[25,] 0.8243378 3.513243e-01 1.756622e-01
[26,] 0.8033485 3.933030e-01 1.966515e-01
[27,] 0.8216509 3.566981e-01 1.783491e-01
[28,] 0.9254234 1.491532e-01 7.457661e-02
[29,] 0.9248198 1.503605e-01 7.518023e-02
[30,] 0.9395528 1.208943e-01 6.044717e-02
[31,] 0.9576048 8.479038e-02 4.239519e-02
[32,] 0.9473684 1.052631e-01 5.263157e-02
[33,] 0.9334775 1.330449e-01 6.652246e-02
[34,] 0.9242388 1.515224e-01 7.576120e-02
[35,] 0.9031337 1.937326e-01 9.686629e-02
[36,] 0.8810233 2.379535e-01 1.189767e-01
[37,] 0.8662767 2.674466e-01 1.337233e-01
[38,] 0.8468473 3.063054e-01 1.531527e-01
[39,] 0.8676156 2.647687e-01 1.323844e-01
[40,] 0.9239554 1.520891e-01 7.604457e-02
[41,] 0.9471804 1.056393e-01 5.281963e-02
[42,] 0.9634899 7.302021e-02 3.651011e-02
[43,] 0.9571368 8.572635e-02 4.286317e-02
[44,] 0.9493180 1.013639e-01 5.068195e-02
[45,] 0.9343483 1.313033e-01 6.565166e-02
[46,] 0.9158103 1.683794e-01 8.418971e-02
[47,] 0.8938483 2.123035e-01 1.061517e-01
[48,] 0.8671314 2.657372e-01 1.328686e-01
[49,] 0.8429566 3.140867e-01 1.570434e-01
[50,] 0.8369392 3.261216e-01 1.630608e-01
[51,] 0.8330012 3.339977e-01 1.669988e-01
[52,] 0.8259450 3.481100e-01 1.740550e-01
[53,] 0.9257579 1.484843e-01 7.424214e-02
[54,] 0.9258229 1.483542e-01 7.417711e-02
[55,] 0.9806063 3.878737e-02 1.939369e-02
[56,] 0.9789888 4.202232e-02 2.101116e-02
[57,] 0.9736909 5.261823e-02 2.630912e-02
[58,] 0.9643939 7.121218e-02 3.560609e-02
[59,] 0.9525299 9.494020e-02 4.747010e-02
[60,] 0.9397149 1.205702e-01 6.028509e-02
[61,] 0.9306453 1.387093e-01 6.935467e-02
[62,] 0.9442760 1.114481e-01 5.572404e-02
[63,] 0.9527529 9.449424e-02 4.724712e-02
[64,] 0.9629199 7.416017e-02 3.708008e-02
[65,] 0.9931512 1.369764e-02 6.848821e-03
[66,] 0.9917778 1.644446e-02 8.222231e-03
[67,] 0.9948612 1.027763e-02 5.138817e-03
[68,] 0.9941893 1.162145e-02 5.810725e-03
[69,] 0.9919532 1.609370e-02 8.046850e-03
[70,] 0.9888021 2.239578e-02 1.119789e-02
[71,] 0.9873083 2.538342e-02 1.269171e-02
[72,] 0.9816430 3.671392e-02 1.835696e-02
[73,] 0.9755093 4.898135e-02 2.449068e-02
[74,] 0.9888885 2.222300e-02 1.111150e-02
[75,] 0.9896684 2.066319e-02 1.033160e-02
[76,] 0.9892632 2.147358e-02 1.073679e-02
[77,] 0.9990531 1.893735e-03 9.468673e-04
[78,] 0.9994911 1.017746e-03 5.088731e-04
[79,] 0.9997559 4.882646e-04 2.441323e-04
[80,] 0.9995720 8.560639e-04 4.280319e-04
[81,] 0.9992106 1.578790e-03 7.893952e-04
[82,] 0.9987677 2.464611e-03 1.232306e-03
[83,] 0.9981013 3.797320e-03 1.898660e-03
[84,] 0.9966252 6.749666e-03 3.374833e-03
[85,] 0.9950052 9.989538e-03 4.994769e-03
[86,] 0.9965157 6.968655e-03 3.484327e-03
[87,] 0.9940454 1.190915e-02 5.954575e-03
[88,] 0.9905950 1.881002e-02 9.405010e-03
[89,] 0.9999769 4.622298e-05 2.311149e-05
[90,] 0.9999999 2.446363e-07 1.223181e-07
[91,] 1.0000000 3.386254e-08 1.693127e-08
[92,] 0.9999999 1.665557e-07 8.327783e-08
[93,] 0.9999997 6.308601e-07 3.154300e-07
[94,] 0.9999987 2.607969e-06 1.303985e-06
[95,] 0.9999941 1.171443e-05 5.857215e-06
[96,] 0.9999985 2.902721e-06 1.451361e-06
[97,] 0.9999924 1.514181e-05 7.570907e-06
[98,] 0.9999608 7.839556e-05 3.919778e-05
[99,] 0.9998105 3.790798e-04 1.895399e-04
[100,] 0.9992392 1.521546e-03 7.607731e-04
[101,] 0.9967886 6.422877e-03 3.211439e-03
[102,] 0.9895472 2.090551e-02 1.045276e-02
[103,] 0.9972551 5.489745e-03 2.744872e-03
> postscript(file="/var/www/rcomp/tmp/1khye1293616226.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2khye1293616226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3khye1293616226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4vrfz1293616226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5vrfz1293616226.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 = 132
Frequency = 1
1 2 3 4 5 6
248.45455 -8.00000 378.81818 791.90909 448.36364 710.18182
7 8 9 10 11 12
1095.27273 111.81818 1637.90909 396.81818 200.54545 462.72727
13 14 15 16 17 18
88.45455 145.00000 808.81818 87.90909 867.36364 1341.18182
19 20 21 22 23 24
369.27273 1153.81818 126.90909 227.81818 282.54545 125.72727
25 26 27 28 29 30
40.45455 32.00000 56.81818 280.90909 748.36364 350.18182
31 32 33 34 35 36
228.27273 755.81818 -26.09091 333.81818 89.54545 -58.27273
37 38 39 40 41 42
25.45455 56.00000 -95.18182 70.90909 194.36364 -340.81818
43 44 45 46 47 48
87.27273 -65.18182 -290.09091 113.81818 -65.45455 -225.27273
49 50 51 52 53 54
-35.54545 -144.00000 -122.18182 -18.09091 -188.63636 -563.81818
55 56 57 58 59 60
552.27273 -458.18182 14.90909 168.81818 -23.45455 61.72727
61 62 63 64 65 66
52.45455 -59.00000 -79.18182 246.90909 -135.63636 -291.81818
67 68 69 70 71 72
626.27273 -362.18182 734.90909 -332.18182 -223.45455 18.72727
73 74 75 76 77 78
-15.54545 -146.00000 93.81818 -581.09091 -563.63636 294.18182
79 80 81 82 83 84
-1121.72727 -373.18182 112.90909 -403.18182 -202.45455 88.72727
85 86 87 88 89 90
-357.54545 -51.00000 -292.18182 -761.09091 -607.63636 -43.81818
91 92 93 94 95 96
-1164.72727 275.81818 -918.09091 -225.18182 -77.45455 -270.27273
97 98 99 100 101 102
-175.54545 13.00000 -80.18182 -420.09091 -182.63636 -450.81818
103 104 105 106 107 108
-1016.72727 126.81818 -705.09091 -92.18182 -54.45455 -112.27273
109 110 111 112 113 114
67.45455 262.00000 -319.18182 158.90909 -281.63636 -463.81818
115 116 117 118 119 120
146.27273 -642.18182 -514.09091 -21.18182 28.54545 -52.27273
121 122 123 124 125 126
61.45455 -100.00000 -350.18182 142.90909 -298.63636 -540.81818
127 128 129 130 131 132
198.27273 -523.18182 -174.09091 -167.18182 45.54545 -39.27273
> postscript(file="/var/www/rcomp/tmp/6vrfz1293616226.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 = 132
Frequency = 1
lag(myerror, k = 1) myerror
0 248.45455 NA
1 -8.00000 248.45455
2 378.81818 -8.00000
3 791.90909 378.81818
4 448.36364 791.90909
5 710.18182 448.36364
6 1095.27273 710.18182
7 111.81818 1095.27273
8 1637.90909 111.81818
9 396.81818 1637.90909
10 200.54545 396.81818
11 462.72727 200.54545
12 88.45455 462.72727
13 145.00000 88.45455
14 808.81818 145.00000
15 87.90909 808.81818
16 867.36364 87.90909
17 1341.18182 867.36364
18 369.27273 1341.18182
19 1153.81818 369.27273
20 126.90909 1153.81818
21 227.81818 126.90909
22 282.54545 227.81818
23 125.72727 282.54545
24 40.45455 125.72727
25 32.00000 40.45455
26 56.81818 32.00000
27 280.90909 56.81818
28 748.36364 280.90909
29 350.18182 748.36364
30 228.27273 350.18182
31 755.81818 228.27273
32 -26.09091 755.81818
33 333.81818 -26.09091
34 89.54545 333.81818
35 -58.27273 89.54545
36 25.45455 -58.27273
37 56.00000 25.45455
38 -95.18182 56.00000
39 70.90909 -95.18182
40 194.36364 70.90909
41 -340.81818 194.36364
42 87.27273 -340.81818
43 -65.18182 87.27273
44 -290.09091 -65.18182
45 113.81818 -290.09091
46 -65.45455 113.81818
47 -225.27273 -65.45455
48 -35.54545 -225.27273
49 -144.00000 -35.54545
50 -122.18182 -144.00000
51 -18.09091 -122.18182
52 -188.63636 -18.09091
53 -563.81818 -188.63636
54 552.27273 -563.81818
55 -458.18182 552.27273
56 14.90909 -458.18182
57 168.81818 14.90909
58 -23.45455 168.81818
59 61.72727 -23.45455
60 52.45455 61.72727
61 -59.00000 52.45455
62 -79.18182 -59.00000
63 246.90909 -79.18182
64 -135.63636 246.90909
65 -291.81818 -135.63636
66 626.27273 -291.81818
67 -362.18182 626.27273
68 734.90909 -362.18182
69 -332.18182 734.90909
70 -223.45455 -332.18182
71 18.72727 -223.45455
72 -15.54545 18.72727
73 -146.00000 -15.54545
74 93.81818 -146.00000
75 -581.09091 93.81818
76 -563.63636 -581.09091
77 294.18182 -563.63636
78 -1121.72727 294.18182
79 -373.18182 -1121.72727
80 112.90909 -373.18182
81 -403.18182 112.90909
82 -202.45455 -403.18182
83 88.72727 -202.45455
84 -357.54545 88.72727
85 -51.00000 -357.54545
86 -292.18182 -51.00000
87 -761.09091 -292.18182
88 -607.63636 -761.09091
89 -43.81818 -607.63636
90 -1164.72727 -43.81818
91 275.81818 -1164.72727
92 -918.09091 275.81818
93 -225.18182 -918.09091
94 -77.45455 -225.18182
95 -270.27273 -77.45455
96 -175.54545 -270.27273
97 13.00000 -175.54545
98 -80.18182 13.00000
99 -420.09091 -80.18182
100 -182.63636 -420.09091
101 -450.81818 -182.63636
102 -1016.72727 -450.81818
103 126.81818 -1016.72727
104 -705.09091 126.81818
105 -92.18182 -705.09091
106 -54.45455 -92.18182
107 -112.27273 -54.45455
108 67.45455 -112.27273
109 262.00000 67.45455
110 -319.18182 262.00000
111 158.90909 -319.18182
112 -281.63636 158.90909
113 -463.81818 -281.63636
114 146.27273 -463.81818
115 -642.18182 146.27273
116 -514.09091 -642.18182
117 -21.18182 -514.09091
118 28.54545 -21.18182
119 -52.27273 28.54545
120 61.45455 -52.27273
121 -100.00000 61.45455
122 -350.18182 -100.00000
123 142.90909 -350.18182
124 -298.63636 142.90909
125 -540.81818 -298.63636
126 198.27273 -540.81818
127 -523.18182 198.27273
128 -174.09091 -523.18182
129 -167.18182 -174.09091
130 45.54545 -167.18182
131 -39.27273 45.54545
132 NA -39.27273
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -8.00000 248.45455
[2,] 378.81818 -8.00000
[3,] 791.90909 378.81818
[4,] 448.36364 791.90909
[5,] 710.18182 448.36364
[6,] 1095.27273 710.18182
[7,] 111.81818 1095.27273
[8,] 1637.90909 111.81818
[9,] 396.81818 1637.90909
[10,] 200.54545 396.81818
[11,] 462.72727 200.54545
[12,] 88.45455 462.72727
[13,] 145.00000 88.45455
[14,] 808.81818 145.00000
[15,] 87.90909 808.81818
[16,] 867.36364 87.90909
[17,] 1341.18182 867.36364
[18,] 369.27273 1341.18182
[19,] 1153.81818 369.27273
[20,] 126.90909 1153.81818
[21,] 227.81818 126.90909
[22,] 282.54545 227.81818
[23,] 125.72727 282.54545
[24,] 40.45455 125.72727
[25,] 32.00000 40.45455
[26,] 56.81818 32.00000
[27,] 280.90909 56.81818
[28,] 748.36364 280.90909
[29,] 350.18182 748.36364
[30,] 228.27273 350.18182
[31,] 755.81818 228.27273
[32,] -26.09091 755.81818
[33,] 333.81818 -26.09091
[34,] 89.54545 333.81818
[35,] -58.27273 89.54545
[36,] 25.45455 -58.27273
[37,] 56.00000 25.45455
[38,] -95.18182 56.00000
[39,] 70.90909 -95.18182
[40,] 194.36364 70.90909
[41,] -340.81818 194.36364
[42,] 87.27273 -340.81818
[43,] -65.18182 87.27273
[44,] -290.09091 -65.18182
[45,] 113.81818 -290.09091
[46,] -65.45455 113.81818
[47,] -225.27273 -65.45455
[48,] -35.54545 -225.27273
[49,] -144.00000 -35.54545
[50,] -122.18182 -144.00000
[51,] -18.09091 -122.18182
[52,] -188.63636 -18.09091
[53,] -563.81818 -188.63636
[54,] 552.27273 -563.81818
[55,] -458.18182 552.27273
[56,] 14.90909 -458.18182
[57,] 168.81818 14.90909
[58,] -23.45455 168.81818
[59,] 61.72727 -23.45455
[60,] 52.45455 61.72727
[61,] -59.00000 52.45455
[62,] -79.18182 -59.00000
[63,] 246.90909 -79.18182
[64,] -135.63636 246.90909
[65,] -291.81818 -135.63636
[66,] 626.27273 -291.81818
[67,] -362.18182 626.27273
[68,] 734.90909 -362.18182
[69,] -332.18182 734.90909
[70,] -223.45455 -332.18182
[71,] 18.72727 -223.45455
[72,] -15.54545 18.72727
[73,] -146.00000 -15.54545
[74,] 93.81818 -146.00000
[75,] -581.09091 93.81818
[76,] -563.63636 -581.09091
[77,] 294.18182 -563.63636
[78,] -1121.72727 294.18182
[79,] -373.18182 -1121.72727
[80,] 112.90909 -373.18182
[81,] -403.18182 112.90909
[82,] -202.45455 -403.18182
[83,] 88.72727 -202.45455
[84,] -357.54545 88.72727
[85,] -51.00000 -357.54545
[86,] -292.18182 -51.00000
[87,] -761.09091 -292.18182
[88,] -607.63636 -761.09091
[89,] -43.81818 -607.63636
[90,] -1164.72727 -43.81818
[91,] 275.81818 -1164.72727
[92,] -918.09091 275.81818
[93,] -225.18182 -918.09091
[94,] -77.45455 -225.18182
[95,] -270.27273 -77.45455
[96,] -175.54545 -270.27273
[97,] 13.00000 -175.54545
[98,] -80.18182 13.00000
[99,] -420.09091 -80.18182
[100,] -182.63636 -420.09091
[101,] -450.81818 -182.63636
[102,] -1016.72727 -450.81818
[103,] 126.81818 -1016.72727
[104,] -705.09091 126.81818
[105,] -92.18182 -705.09091
[106,] -54.45455 -92.18182
[107,] -112.27273 -54.45455
[108,] 67.45455 -112.27273
[109,] 262.00000 67.45455
[110,] -319.18182 262.00000
[111,] 158.90909 -319.18182
[112,] -281.63636 158.90909
[113,] -463.81818 -281.63636
[114,] 146.27273 -463.81818
[115,] -642.18182 146.27273
[116,] -514.09091 -642.18182
[117,] -21.18182 -514.09091
[118,] 28.54545 -21.18182
[119,] -52.27273 28.54545
[120,] 61.45455 -52.27273
[121,] -100.00000 61.45455
[122,] -350.18182 -100.00000
[123,] 142.90909 -350.18182
[124,] -298.63636 142.90909
[125,] -540.81818 -298.63636
[126,] 198.27273 -540.81818
[127,] -523.18182 198.27273
[128,] -174.09091 -523.18182
[129,] -167.18182 -174.09091
[130,] 45.54545 -167.18182
[131,] -39.27273 45.54545
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -8.00000 248.45455
2 378.81818 -8.00000
3 791.90909 378.81818
4 448.36364 791.90909
5 710.18182 448.36364
6 1095.27273 710.18182
7 111.81818 1095.27273
8 1637.90909 111.81818
9 396.81818 1637.90909
10 200.54545 396.81818
11 462.72727 200.54545
12 88.45455 462.72727
13 145.00000 88.45455
14 808.81818 145.00000
15 87.90909 808.81818
16 867.36364 87.90909
17 1341.18182 867.36364
18 369.27273 1341.18182
19 1153.81818 369.27273
20 126.90909 1153.81818
21 227.81818 126.90909
22 282.54545 227.81818
23 125.72727 282.54545
24 40.45455 125.72727
25 32.00000 40.45455
26 56.81818 32.00000
27 280.90909 56.81818
28 748.36364 280.90909
29 350.18182 748.36364
30 228.27273 350.18182
31 755.81818 228.27273
32 -26.09091 755.81818
33 333.81818 -26.09091
34 89.54545 333.81818
35 -58.27273 89.54545
36 25.45455 -58.27273
37 56.00000 25.45455
38 -95.18182 56.00000
39 70.90909 -95.18182
40 194.36364 70.90909
41 -340.81818 194.36364
42 87.27273 -340.81818
43 -65.18182 87.27273
44 -290.09091 -65.18182
45 113.81818 -290.09091
46 -65.45455 113.81818
47 -225.27273 -65.45455
48 -35.54545 -225.27273
49 -144.00000 -35.54545
50 -122.18182 -144.00000
51 -18.09091 -122.18182
52 -188.63636 -18.09091
53 -563.81818 -188.63636
54 552.27273 -563.81818
55 -458.18182 552.27273
56 14.90909 -458.18182
57 168.81818 14.90909
58 -23.45455 168.81818
59 61.72727 -23.45455
60 52.45455 61.72727
61 -59.00000 52.45455
62 -79.18182 -59.00000
63 246.90909 -79.18182
64 -135.63636 246.90909
65 -291.81818 -135.63636
66 626.27273 -291.81818
67 -362.18182 626.27273
68 734.90909 -362.18182
69 -332.18182 734.90909
70 -223.45455 -332.18182
71 18.72727 -223.45455
72 -15.54545 18.72727
73 -146.00000 -15.54545
74 93.81818 -146.00000
75 -581.09091 93.81818
76 -563.63636 -581.09091
77 294.18182 -563.63636
78 -1121.72727 294.18182
79 -373.18182 -1121.72727
80 112.90909 -373.18182
81 -403.18182 112.90909
82 -202.45455 -403.18182
83 88.72727 -202.45455
84 -357.54545 88.72727
85 -51.00000 -357.54545
86 -292.18182 -51.00000
87 -761.09091 -292.18182
88 -607.63636 -761.09091
89 -43.81818 -607.63636
90 -1164.72727 -43.81818
91 275.81818 -1164.72727
92 -918.09091 275.81818
93 -225.18182 -918.09091
94 -77.45455 -225.18182
95 -270.27273 -77.45455
96 -175.54545 -270.27273
97 13.00000 -175.54545
98 -80.18182 13.00000
99 -420.09091 -80.18182
100 -182.63636 -420.09091
101 -450.81818 -182.63636
102 -1016.72727 -450.81818
103 126.81818 -1016.72727
104 -705.09091 126.81818
105 -92.18182 -705.09091
106 -54.45455 -92.18182
107 -112.27273 -54.45455
108 67.45455 -112.27273
109 262.00000 67.45455
110 -319.18182 262.00000
111 158.90909 -319.18182
112 -281.63636 158.90909
113 -463.81818 -281.63636
114 146.27273 -463.81818
115 -642.18182 146.27273
116 -514.09091 -642.18182
117 -21.18182 -514.09091
118 28.54545 -21.18182
119 -52.27273 28.54545
120 61.45455 -52.27273
121 -100.00000 61.45455
122 -350.18182 -100.00000
123 142.90909 -350.18182
124 -298.63636 142.90909
125 -540.81818 -298.63636
126 198.27273 -540.81818
127 -523.18182 198.27273
128 -174.09091 -523.18182
129 -167.18182 -174.09091
130 45.54545 -167.18182
131 -39.27273 45.54545
> 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/rcomp/tmp/7ifqw1293616226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8t6pg1293616226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9t6pg1293616226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
hat values (leverages) are all = 0.0909091
and there are no factor predictors; no plot no. 5
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10mf6j1293616226.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/117y471293616226.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/rcomp/tmp/12sgld1293616226.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/rcomp/tmp/13781m1293616226.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/rcomp/tmp/14zhip1293616226.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/rcomp/tmp/1530zd1293616226.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/rcomp/tmp/16zael1293616226.tab")
+ }
> try(system("convert tmp/1khye1293616226.ps tmp/1khye1293616226.png",intern=TRUE))
character(0)
> try(system("convert tmp/2khye1293616226.ps tmp/2khye1293616226.png",intern=TRUE))
character(0)
> try(system("convert tmp/3khye1293616226.ps tmp/3khye1293616226.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vrfz1293616226.ps tmp/4vrfz1293616226.png",intern=TRUE))
character(0)
> try(system("convert tmp/5vrfz1293616226.ps tmp/5vrfz1293616226.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vrfz1293616226.ps tmp/6vrfz1293616226.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ifqw1293616226.ps tmp/7ifqw1293616226.png",intern=TRUE))
character(0)
> try(system("convert tmp/8t6pg1293616226.ps tmp/8t6pg1293616226.png",intern=TRUE))
character(0)
> try(system("convert tmp/9t6pg1293616226.ps tmp/9t6pg1293616226.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mf6j1293616226.ps tmp/10mf6j1293616226.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.970 1.760 5.724