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(235.1
+ ,1
+ ,280.7
+ ,1
+ ,264.6
+ ,2
+ ,240.7
+ ,0
+ ,201.4
+ ,1
+ ,240.8
+ ,0
+ ,241.1
+ ,-1
+ ,223.8
+ ,-3
+ ,206.1
+ ,-3
+ ,174.7
+ ,-3
+ ,203.3
+ ,-4
+ ,220.5
+ ,-8
+ ,299.5
+ ,-9
+ ,347.4
+ ,-13
+ ,338.3
+ ,-18
+ ,327.7
+ ,-11
+ ,351.6
+ ,-9
+ ,396.6
+ ,-10
+ ,438.8
+ ,-13
+ ,395.6
+ ,-11
+ ,363.5
+ ,-5
+ ,378.8
+ ,-15
+ ,357
+ ,-6
+ ,369
+ ,-6
+ ,464.8
+ ,-3
+ ,479.1
+ ,-1
+ ,431.3
+ ,-3
+ ,366.5
+ ,-4
+ ,326.3
+ ,-6
+ ,355.1
+ ,0
+ ,331.6
+ ,-4
+ ,261.3
+ ,-2
+ ,249
+ ,-2
+ ,205.5
+ ,-6
+ ,235.6
+ ,-7
+ ,240.9
+ ,-6
+ ,264.9
+ ,-6
+ ,253.8
+ ,-3
+ ,232.3
+ ,-2
+ ,193.8
+ ,-5
+ ,177
+ ,-11
+ ,213.2
+ ,-11
+ ,207.2
+ ,-11
+ ,180.6
+ ,-10
+ ,188.6
+ ,-14
+ ,175.4
+ ,-8
+ ,199
+ ,-9
+ ,179.6
+ ,-5
+ ,225.8
+ ,-1
+ ,234
+ ,-2
+ ,200.2
+ ,-5
+ ,183.6
+ ,-4
+ ,178.2
+ ,-6
+ ,203.2
+ ,-2
+ ,208.5
+ ,-2
+ ,191.8
+ ,-2
+ ,172.8
+ ,-2
+ ,148
+ ,2
+ ,159.4
+ ,1
+ ,154.5
+ ,-8
+ ,213.2
+ ,-1
+ ,196.4
+ ,1
+ ,182.8
+ ,-1
+ ,176.4
+ ,2
+ ,153.6
+ ,2
+ ,173.2
+ ,1
+ ,171
+ ,-1
+ ,151.2
+ ,-2
+ ,161.9
+ ,-2
+ ,157.2
+ ,-1
+ ,201.7
+ ,-8
+ ,236.4
+ ,-4
+ ,356.1
+ ,-6
+ ,398.3
+ ,-3
+ ,403.7
+ ,-3
+ ,384.6
+ ,-7
+ ,365.8
+ ,-9
+ ,368.1
+ ,-11
+ ,367.9
+ ,-13
+ ,347
+ ,-11
+ ,343.3
+ ,-9
+ ,292.9
+ ,-17
+ ,311.5
+ ,-22
+ ,300.9
+ ,-25
+ ,366.9
+ ,-20
+ ,356.9
+ ,-24
+ ,329.7
+ ,-24
+ ,316.2
+ ,-22
+ ,269
+ ,-19
+ ,289.3
+ ,-18
+ ,266.2
+ ,-17
+ ,253.6
+ ,-11
+ ,233.8
+ ,-11
+ ,228.4
+ ,-12
+ ,253.6
+ ,-10
+ ,260.1
+ ,-15
+ ,306.6
+ ,-15
+ ,309.2
+ ,-15
+ ,309.5
+ ,-13
+ ,271
+ ,-8
+ ,279.9
+ ,-13
+ ,317.9
+ ,-9
+ ,298.4
+ ,-7
+ ,246.7
+ ,-4
+ ,227.3
+ ,-4
+ ,209.1
+ ,-2)
+ ,dim=c(2
+ ,106)
+ ,dimnames=list(c('Y'
+ ,'X')
+ ,1:106))
> y <- array(NA,dim=c(2,106),dimnames=list(c('Y','X'),1:106))
> 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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 235.1 1 1 0 0 0 0 0 0 0 0 0 0
2 280.7 1 0 1 0 0 0 0 0 0 0 0 0
3 264.6 2 0 0 1 0 0 0 0 0 0 0 0
4 240.7 0 0 0 0 1 0 0 0 0 0 0 0
5 201.4 1 0 0 0 0 1 0 0 0 0 0 0
6 240.8 0 0 0 0 0 0 1 0 0 0 0 0
7 241.1 -1 0 0 0 0 0 0 1 0 0 0 0
8 223.8 -3 0 0 0 0 0 0 0 1 0 0 0
9 206.1 -3 0 0 0 0 0 0 0 0 1 0 0
10 174.7 -3 0 0 0 0 0 0 0 0 0 1 0
11 203.3 -4 0 0 0 0 0 0 0 0 0 0 1
12 220.5 -8 0 0 0 0 0 0 0 0 0 0 0
13 299.5 -9 1 0 0 0 0 0 0 0 0 0 0
14 347.4 -13 0 1 0 0 0 0 0 0 0 0 0
15 338.3 -18 0 0 1 0 0 0 0 0 0 0 0
16 327.7 -11 0 0 0 1 0 0 0 0 0 0 0
17 351.6 -9 0 0 0 0 1 0 0 0 0 0 0
18 396.6 -10 0 0 0 0 0 1 0 0 0 0 0
19 438.8 -13 0 0 0 0 0 0 1 0 0 0 0
20 395.6 -11 0 0 0 0 0 0 0 1 0 0 0
21 363.5 -5 0 0 0 0 0 0 0 0 1 0 0
22 378.8 -15 0 0 0 0 0 0 0 0 0 1 0
23 357.0 -6 0 0 0 0 0 0 0 0 0 0 1
24 369.0 -6 0 0 0 0 0 0 0 0 0 0 0
25 464.8 -3 1 0 0 0 0 0 0 0 0 0 0
26 479.1 -1 0 1 0 0 0 0 0 0 0 0 0
27 431.3 -3 0 0 1 0 0 0 0 0 0 0 0
28 366.5 -4 0 0 0 1 0 0 0 0 0 0 0
29 326.3 -6 0 0 0 0 1 0 0 0 0 0 0
30 355.1 0 0 0 0 0 0 1 0 0 0 0 0
31 331.6 -4 0 0 0 0 0 0 1 0 0 0 0
32 261.3 -2 0 0 0 0 0 0 0 1 0 0 0
33 249.0 -2 0 0 0 0 0 0 0 0 1 0 0
34 205.5 -6 0 0 0 0 0 0 0 0 0 1 0
35 235.6 -7 0 0 0 0 0 0 0 0 0 0 1
36 240.9 -6 0 0 0 0 0 0 0 0 0 0 0
37 264.9 -6 1 0 0 0 0 0 0 0 0 0 0
38 253.8 -3 0 1 0 0 0 0 0 0 0 0 0
39 232.3 -2 0 0 1 0 0 0 0 0 0 0 0
40 193.8 -5 0 0 0 1 0 0 0 0 0 0 0
41 177.0 -11 0 0 0 0 1 0 0 0 0 0 0
42 213.2 -11 0 0 0 0 0 1 0 0 0 0 0
43 207.2 -11 0 0 0 0 0 0 1 0 0 0 0
44 180.6 -10 0 0 0 0 0 0 0 1 0 0 0
45 188.6 -14 0 0 0 0 0 0 0 0 1 0 0
46 175.4 -8 0 0 0 0 0 0 0 0 0 1 0
47 199.0 -9 0 0 0 0 0 0 0 0 0 0 1
48 179.6 -5 0 0 0 0 0 0 0 0 0 0 0
49 225.8 -1 1 0 0 0 0 0 0 0 0 0 0
50 234.0 -2 0 1 0 0 0 0 0 0 0 0 0
51 200.2 -5 0 0 1 0 0 0 0 0 0 0 0
52 183.6 -4 0 0 0 1 0 0 0 0 0 0 0
53 178.2 -6 0 0 0 0 1 0 0 0 0 0 0
54 203.2 -2 0 0 0 0 0 1 0 0 0 0 0
55 208.5 -2 0 0 0 0 0 0 1 0 0 0 0
56 191.8 -2 0 0 0 0 0 0 0 1 0 0 0
57 172.8 -2 0 0 0 0 0 0 0 0 1 0 0
58 148.0 2 0 0 0 0 0 0 0 0 0 1 0
59 159.4 1 0 0 0 0 0 0 0 0 0 0 1
60 154.5 -8 0 0 0 0 0 0 0 0 0 0 0
61 213.2 -1 1 0 0 0 0 0 0 0 0 0 0
62 196.4 1 0 1 0 0 0 0 0 0 0 0 0
63 182.8 -1 0 0 1 0 0 0 0 0 0 0 0
64 176.4 2 0 0 0 1 0 0 0 0 0 0 0
65 153.6 2 0 0 0 0 1 0 0 0 0 0 0
66 173.2 1 0 0 0 0 0 1 0 0 0 0 0
67 171.0 -1 0 0 0 0 0 0 1 0 0 0 0
68 151.2 -2 0 0 0 0 0 0 0 1 0 0 0
69 161.9 -2 0 0 0 0 0 0 0 0 1 0 0
70 157.2 -1 0 0 0 0 0 0 0 0 0 1 0
71 201.7 -8 0 0 0 0 0 0 0 0 0 0 1
72 236.4 -4 0 0 0 0 0 0 0 0 0 0 0
73 356.1 -6 1 0 0 0 0 0 0 0 0 0 0
74 398.3 -3 0 1 0 0 0 0 0 0 0 0 0
75 403.7 -3 0 0 1 0 0 0 0 0 0 0 0
76 384.6 -7 0 0 0 1 0 0 0 0 0 0 0
77 365.8 -9 0 0 0 0 1 0 0 0 0 0 0
78 368.1 -11 0 0 0 0 0 1 0 0 0 0 0
79 367.9 -13 0 0 0 0 0 0 1 0 0 0 0
80 347.0 -11 0 0 0 0 0 0 0 1 0 0 0
81 343.3 -9 0 0 0 0 0 0 0 0 1 0 0
82 292.9 -17 0 0 0 0 0 0 0 0 0 1 0
83 311.5 -22 0 0 0 0 0 0 0 0 0 0 1
84 300.9 -25 0 0 0 0 0 0 0 0 0 0 0
85 366.9 -20 1 0 0 0 0 0 0 0 0 0 0
86 356.9 -24 0 1 0 0 0 0 0 0 0 0 0
87 329.7 -24 0 0 1 0 0 0 0 0 0 0 0
88 316.2 -22 0 0 0 1 0 0 0 0 0 0 0
89 269.0 -19 0 0 0 0 1 0 0 0 0 0 0
90 289.3 -18 0 0 0 0 0 1 0 0 0 0 0
91 266.2 -17 0 0 0 0 0 0 1 0 0 0 0
92 253.6 -11 0 0 0 0 0 0 0 1 0 0 0
93 233.8 -11 0 0 0 0 0 0 0 0 1 0 0
94 228.4 -12 0 0 0 0 0 0 0 0 0 1 0
95 253.6 -10 0 0 0 0 0 0 0 0 0 0 1
96 260.1 -15 0 0 0 0 0 0 0 0 0 0 0
97 306.6 -15 1 0 0 0 0 0 0 0 0 0 0
98 309.2 -15 0 1 0 0 0 0 0 0 0 0 0
99 309.5 -13 0 0 1 0 0 0 0 0 0 0 0
100 271.0 -8 0 0 0 1 0 0 0 0 0 0 0
101 279.9 -13 0 0 0 0 1 0 0 0 0 0 0
102 317.9 -9 0 0 0 0 0 1 0 0 0 0 0
103 298.4 -7 0 0 0 0 0 0 1 0 0 0 0
104 246.7 -4 0 0 0 0 0 0 0 1 0 0 0
105 227.3 -4 0 0 0 0 0 0 0 0 1 0 0
106 209.1 -2 0 0 0 0 0 0 0 0 0 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
199.795 -4.721 72.385 86.565 64.213 42.643
M5 M6 M7 M8 M9 M10
19.350 52.885 45.197 21.006 11.404 -13.431
M11
1.982
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-94.08 -47.79 -14.90 27.84 188.02
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 199.795 27.784 7.191 1.60e-10 ***
X -4.721 1.105 -4.271 4.69e-05 ***
M1 72.385 35.426 2.043 0.0439 *
M2 86.565 35.438 2.443 0.0165 *
M3 64.213 35.357 1.816 0.0726 .
M4 42.643 35.438 1.203 0.2319
M5 19.350 35.334 0.548 0.5853
M6 52.885 35.426 1.493 0.1389
M7 45.197 35.342 1.279 0.2041
M8 21.006 35.475 0.592 0.5552
M9 11.404 35.531 0.321 0.7490
M10 -13.431 35.405 -0.379 0.7053
M11 1.982 36.336 0.055 0.9566
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 72.6 on 93 degrees of freedom
Multiple R-squared: 0.2756, Adjusted R-squared: 0.1821
F-statistic: 2.948 on 12 and 93 DF, p-value: 0.001633
> 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,] 0.01534525 3.069049e-02 9.846548e-01
[2,] 0.07380011 1.476002e-01 9.261999e-01
[3,] 0.09524235 1.904847e-01 9.047577e-01
[4,] 0.14073458 2.814692e-01 8.592654e-01
[5,] 0.17360136 3.472027e-01 8.263986e-01
[6,] 0.28464715 5.692943e-01 7.153529e-01
[7,] 0.29034373 5.806875e-01 7.096563e-01
[8,] 0.38407819 7.681564e-01 6.159218e-01
[9,] 0.56659811 8.668038e-01 4.334019e-01
[10,] 0.86593801 2.681240e-01 1.340620e-01
[11,] 0.97787592 4.424816e-02 2.212408e-02
[12,] 0.99430840 1.138320e-02 5.691600e-03
[13,] 0.99570021 8.599577e-03 4.299788e-03
[14,] 0.99501369 9.972611e-03 4.986306e-03
[15,] 0.99640552 7.188961e-03 3.594481e-03
[16,] 0.99587090 8.258195e-03 4.129098e-03
[17,] 0.99395390 1.209219e-02 6.046097e-03
[18,] 0.99135145 1.729710e-02 8.648551e-03
[19,] 0.98742229 2.515543e-02 1.257771e-02
[20,] 0.98313037 3.373927e-02 1.686963e-02
[21,] 0.97768725 4.462551e-02 2.231275e-02
[22,] 0.97442630 5.114740e-02 2.557370e-02
[23,] 0.97400897 5.198206e-02 2.599103e-02
[24,] 0.96892197 6.215605e-02 3.107803e-02
[25,] 0.97301289 5.397422e-02 2.698711e-02
[26,] 0.98552560 2.894880e-02 1.447440e-02
[27,] 0.99200549 1.598901e-02 7.994507e-03
[28,] 0.99499653 1.000695e-02 5.003473e-03
[29,] 0.99639733 7.205339e-03 3.602670e-03
[30,] 0.99742040 5.159208e-03 2.579604e-03
[31,] 0.99672963 6.540737e-03 3.270369e-03
[32,] 0.99563235 8.735293e-03 4.367647e-03
[33,] 0.99435157 1.129685e-02 5.648427e-03
[34,] 0.99262733 1.474533e-02 7.372666e-03
[35,] 0.99126795 1.746410e-02 8.732049e-03
[36,] 0.99210820 1.578360e-02 7.891799e-03
[37,] 0.99215410 1.569180e-02 7.845902e-03
[38,] 0.99144898 1.710203e-02 8.551017e-03
[39,] 0.98950416 2.099169e-02 1.049584e-02
[40,] 0.98599364 2.801272e-02 1.400636e-02
[41,] 0.98078551 3.842898e-02 1.921449e-02
[42,] 0.97533342 4.933315e-02 2.466658e-02
[43,] 0.96546112 6.907775e-02 3.453888e-02
[44,] 0.95328849 9.342302e-02 4.671151e-02
[45,] 0.95394507 9.210985e-02 4.605493e-02
[46,] 0.94788752 1.042250e-01 5.211248e-02
[47,] 0.95066522 9.866955e-02 4.933478e-02
[48,] 0.96020769 7.958462e-02 3.979231e-02
[49,] 0.96071141 7.857718e-02 3.928859e-02
[50,] 0.96411712 7.176576e-02 3.588288e-02
[51,] 0.97449186 5.101627e-02 2.550814e-02
[52,] 0.98384265 3.231470e-02 1.615735e-02
[53,] 0.99120208 1.759584e-02 8.797919e-03
[54,] 0.99360490 1.279021e-02 6.395104e-03
[55,] 0.99391962 1.216077e-02 6.080385e-03
[56,] 0.99397748 1.204504e-02 6.022522e-03
[57,] 0.99157418 1.685164e-02 8.425820e-03
[58,] 0.98639922 2.720155e-02 1.360078e-02
[59,] 0.98488523 3.022955e-02 1.511477e-02
[60,] 0.99032030 1.935940e-02 9.679700e-03
[61,] 0.99487849 1.024301e-02 5.121507e-03
[62,] 0.99799743 4.005131e-03 2.002566e-03
[63,] 0.99810718 3.785632e-03 1.892816e-03
[64,] 0.99869337 2.613257e-03 1.306629e-03
[65,] 0.99936971 1.260576e-03 6.302880e-04
[66,] 0.99997081 5.838360e-05 2.919180e-05
[67,] 0.99997361 5.277255e-05 2.638628e-05
[68,] 0.99994323 1.135330e-04 5.676650e-05
[69,] 0.99984356 3.128796e-04 1.564398e-04
[70,] 0.99986565 2.686966e-04 1.343483e-04
[71,] 0.99979802 4.039578e-04 2.019789e-04
[72,] 0.99920610 1.587799e-03 7.938993e-04
[73,] 0.99938501 1.229973e-03 6.149865e-04
[74,] 0.99661096 6.778084e-03 3.389042e-03
[75,] 0.98919027 2.161945e-02 1.080973e-02
> postscript(file="/var/www/html/rcomp/tmp/1djed1291026816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2djed1291026816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3obvg1291026816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4obvg1291026816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5obvg1291026816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 106
Frequency = 1
1 2 3 4 5 6
-32.3588525 -0.9389979 10.0345859 -1.7380848 -13.0240646 -11.8801616
7 8 9 10 11 12
-8.6134949 -11.1646707 -19.2630303 -25.8282424 -17.3621000 -17.0653727
13 14 15 16 17 18
-15.1719435 -0.3373253 -10.6915961 33.3275151 89.9628444 96.7067474
19 20 21 22 23 24
132.4307959 122.8648565 128.6943515 121.6160484 126.8952818 140.8772455
25 26 27 28 29 30
178.4559111 188.0183839 153.1280404 105.1766788 78.8267717 102.4198384
31 32 33 34 35 36
67.7225778 31.0566384 28.3582788 -9.1921697 0.7739727 12.7772455
37 38 39 40 41 42
-35.6080162 -46.7242343 -41.1506505 -72.2446303 -94.0797738 -91.4145617
43 44 45 46 47 48
-89.7265859 -87.4138344 -88.6974304 -48.7347879 -45.2686455 -43.8014454
49 50 51 52 53 54
-51.1014707 -61.8029252 -87.4145778 -77.7233212 -69.2732283 -58.9227798
55 56 57 58 59 60
-45.9348040 -38.4433616 -47.8417212 -28.9216969 -37.6555545 -83.0653727
61 62 63 64 65 66
-63.7014707 -85.2389979 -85.9293414 -56.5954666 -56.1027555 -74.7588525
67 68 69 70 71 72
-78.7134949 -79.0433616 -58.7417212 -33.8856242 -37.8473364 17.7198637
73 74 75 76 77 78
55.5919838 97.7757657 125.5280404 109.1127515 104.1628444 63.4854383
79 80 81 82 83 84
61.5307959 74.2648565 89.6091151 26.2734302 5.8543362 -16.9276274
85 86 87 88 89 90
0.2936564 -42.7717254 -47.6194507 -30.1068850 -39.8502466 -48.3637254
91 92 93 94 95 96
-59.0544405 -19.1351435 -29.3335031 -14.6200243 4.6100454 -10.5145364
97 98 99 100 101 102
-36.3997981 -47.9799435 -15.8850506 -9.2085576 -0.6223920 22.7280565
103 104 105 106
20.3586505 7.0140202 -2.7843394 13.2930667
> postscript(file="/var/www/html/rcomp/tmp/6ykuj1291026816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 106
Frequency = 1
lag(myerror, k = 1) myerror
0 -32.3588525 NA
1 -0.9389979 -32.3588525
2 10.0345859 -0.9389979
3 -1.7380848 10.0345859
4 -13.0240646 -1.7380848
5 -11.8801616 -13.0240646
6 -8.6134949 -11.8801616
7 -11.1646707 -8.6134949
8 -19.2630303 -11.1646707
9 -25.8282424 -19.2630303
10 -17.3621000 -25.8282424
11 -17.0653727 -17.3621000
12 -15.1719435 -17.0653727
13 -0.3373253 -15.1719435
14 -10.6915961 -0.3373253
15 33.3275151 -10.6915961
16 89.9628444 33.3275151
17 96.7067474 89.9628444
18 132.4307959 96.7067474
19 122.8648565 132.4307959
20 128.6943515 122.8648565
21 121.6160484 128.6943515
22 126.8952818 121.6160484
23 140.8772455 126.8952818
24 178.4559111 140.8772455
25 188.0183839 178.4559111
26 153.1280404 188.0183839
27 105.1766788 153.1280404
28 78.8267717 105.1766788
29 102.4198384 78.8267717
30 67.7225778 102.4198384
31 31.0566384 67.7225778
32 28.3582788 31.0566384
33 -9.1921697 28.3582788
34 0.7739727 -9.1921697
35 12.7772455 0.7739727
36 -35.6080162 12.7772455
37 -46.7242343 -35.6080162
38 -41.1506505 -46.7242343
39 -72.2446303 -41.1506505
40 -94.0797738 -72.2446303
41 -91.4145617 -94.0797738
42 -89.7265859 -91.4145617
43 -87.4138344 -89.7265859
44 -88.6974304 -87.4138344
45 -48.7347879 -88.6974304
46 -45.2686455 -48.7347879
47 -43.8014454 -45.2686455
48 -51.1014707 -43.8014454
49 -61.8029252 -51.1014707
50 -87.4145778 -61.8029252
51 -77.7233212 -87.4145778
52 -69.2732283 -77.7233212
53 -58.9227798 -69.2732283
54 -45.9348040 -58.9227798
55 -38.4433616 -45.9348040
56 -47.8417212 -38.4433616
57 -28.9216969 -47.8417212
58 -37.6555545 -28.9216969
59 -83.0653727 -37.6555545
60 -63.7014707 -83.0653727
61 -85.2389979 -63.7014707
62 -85.9293414 -85.2389979
63 -56.5954666 -85.9293414
64 -56.1027555 -56.5954666
65 -74.7588525 -56.1027555
66 -78.7134949 -74.7588525
67 -79.0433616 -78.7134949
68 -58.7417212 -79.0433616
69 -33.8856242 -58.7417212
70 -37.8473364 -33.8856242
71 17.7198637 -37.8473364
72 55.5919838 17.7198637
73 97.7757657 55.5919838
74 125.5280404 97.7757657
75 109.1127515 125.5280404
76 104.1628444 109.1127515
77 63.4854383 104.1628444
78 61.5307959 63.4854383
79 74.2648565 61.5307959
80 89.6091151 74.2648565
81 26.2734302 89.6091151
82 5.8543362 26.2734302
83 -16.9276274 5.8543362
84 0.2936564 -16.9276274
85 -42.7717254 0.2936564
86 -47.6194507 -42.7717254
87 -30.1068850 -47.6194507
88 -39.8502466 -30.1068850
89 -48.3637254 -39.8502466
90 -59.0544405 -48.3637254
91 -19.1351435 -59.0544405
92 -29.3335031 -19.1351435
93 -14.6200243 -29.3335031
94 4.6100454 -14.6200243
95 -10.5145364 4.6100454
96 -36.3997981 -10.5145364
97 -47.9799435 -36.3997981
98 -15.8850506 -47.9799435
99 -9.2085576 -15.8850506
100 -0.6223920 -9.2085576
101 22.7280565 -0.6223920
102 20.3586505 22.7280565
103 7.0140202 20.3586505
104 -2.7843394 7.0140202
105 13.2930667 -2.7843394
106 NA 13.2930667
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.9389979 -32.3588525
[2,] 10.0345859 -0.9389979
[3,] -1.7380848 10.0345859
[4,] -13.0240646 -1.7380848
[5,] -11.8801616 -13.0240646
[6,] -8.6134949 -11.8801616
[7,] -11.1646707 -8.6134949
[8,] -19.2630303 -11.1646707
[9,] -25.8282424 -19.2630303
[10,] -17.3621000 -25.8282424
[11,] -17.0653727 -17.3621000
[12,] -15.1719435 -17.0653727
[13,] -0.3373253 -15.1719435
[14,] -10.6915961 -0.3373253
[15,] 33.3275151 -10.6915961
[16,] 89.9628444 33.3275151
[17,] 96.7067474 89.9628444
[18,] 132.4307959 96.7067474
[19,] 122.8648565 132.4307959
[20,] 128.6943515 122.8648565
[21,] 121.6160484 128.6943515
[22,] 126.8952818 121.6160484
[23,] 140.8772455 126.8952818
[24,] 178.4559111 140.8772455
[25,] 188.0183839 178.4559111
[26,] 153.1280404 188.0183839
[27,] 105.1766788 153.1280404
[28,] 78.8267717 105.1766788
[29,] 102.4198384 78.8267717
[30,] 67.7225778 102.4198384
[31,] 31.0566384 67.7225778
[32,] 28.3582788 31.0566384
[33,] -9.1921697 28.3582788
[34,] 0.7739727 -9.1921697
[35,] 12.7772455 0.7739727
[36,] -35.6080162 12.7772455
[37,] -46.7242343 -35.6080162
[38,] -41.1506505 -46.7242343
[39,] -72.2446303 -41.1506505
[40,] -94.0797738 -72.2446303
[41,] -91.4145617 -94.0797738
[42,] -89.7265859 -91.4145617
[43,] -87.4138344 -89.7265859
[44,] -88.6974304 -87.4138344
[45,] -48.7347879 -88.6974304
[46,] -45.2686455 -48.7347879
[47,] -43.8014454 -45.2686455
[48,] -51.1014707 -43.8014454
[49,] -61.8029252 -51.1014707
[50,] -87.4145778 -61.8029252
[51,] -77.7233212 -87.4145778
[52,] -69.2732283 -77.7233212
[53,] -58.9227798 -69.2732283
[54,] -45.9348040 -58.9227798
[55,] -38.4433616 -45.9348040
[56,] -47.8417212 -38.4433616
[57,] -28.9216969 -47.8417212
[58,] -37.6555545 -28.9216969
[59,] -83.0653727 -37.6555545
[60,] -63.7014707 -83.0653727
[61,] -85.2389979 -63.7014707
[62,] -85.9293414 -85.2389979
[63,] -56.5954666 -85.9293414
[64,] -56.1027555 -56.5954666
[65,] -74.7588525 -56.1027555
[66,] -78.7134949 -74.7588525
[67,] -79.0433616 -78.7134949
[68,] -58.7417212 -79.0433616
[69,] -33.8856242 -58.7417212
[70,] -37.8473364 -33.8856242
[71,] 17.7198637 -37.8473364
[72,] 55.5919838 17.7198637
[73,] 97.7757657 55.5919838
[74,] 125.5280404 97.7757657
[75,] 109.1127515 125.5280404
[76,] 104.1628444 109.1127515
[77,] 63.4854383 104.1628444
[78,] 61.5307959 63.4854383
[79,] 74.2648565 61.5307959
[80,] 89.6091151 74.2648565
[81,] 26.2734302 89.6091151
[82,] 5.8543362 26.2734302
[83,] -16.9276274 5.8543362
[84,] 0.2936564 -16.9276274
[85,] -42.7717254 0.2936564
[86,] -47.6194507 -42.7717254
[87,] -30.1068850 -47.6194507
[88,] -39.8502466 -30.1068850
[89,] -48.3637254 -39.8502466
[90,] -59.0544405 -48.3637254
[91,] -19.1351435 -59.0544405
[92,] -29.3335031 -19.1351435
[93,] -14.6200243 -29.3335031
[94,] 4.6100454 -14.6200243
[95,] -10.5145364 4.6100454
[96,] -36.3997981 -10.5145364
[97,] -47.9799435 -36.3997981
[98,] -15.8850506 -47.9799435
[99,] -9.2085576 -15.8850506
[100,] -0.6223920 -9.2085576
[101,] 22.7280565 -0.6223920
[102,] 20.3586505 22.7280565
[103,] 7.0140202 20.3586505
[104,] -2.7843394 7.0140202
[105,] 13.2930667 -2.7843394
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.9389979 -32.3588525
2 10.0345859 -0.9389979
3 -1.7380848 10.0345859
4 -13.0240646 -1.7380848
5 -11.8801616 -13.0240646
6 -8.6134949 -11.8801616
7 -11.1646707 -8.6134949
8 -19.2630303 -11.1646707
9 -25.8282424 -19.2630303
10 -17.3621000 -25.8282424
11 -17.0653727 -17.3621000
12 -15.1719435 -17.0653727
13 -0.3373253 -15.1719435
14 -10.6915961 -0.3373253
15 33.3275151 -10.6915961
16 89.9628444 33.3275151
17 96.7067474 89.9628444
18 132.4307959 96.7067474
19 122.8648565 132.4307959
20 128.6943515 122.8648565
21 121.6160484 128.6943515
22 126.8952818 121.6160484
23 140.8772455 126.8952818
24 178.4559111 140.8772455
25 188.0183839 178.4559111
26 153.1280404 188.0183839
27 105.1766788 153.1280404
28 78.8267717 105.1766788
29 102.4198384 78.8267717
30 67.7225778 102.4198384
31 31.0566384 67.7225778
32 28.3582788 31.0566384
33 -9.1921697 28.3582788
34 0.7739727 -9.1921697
35 12.7772455 0.7739727
36 -35.6080162 12.7772455
37 -46.7242343 -35.6080162
38 -41.1506505 -46.7242343
39 -72.2446303 -41.1506505
40 -94.0797738 -72.2446303
41 -91.4145617 -94.0797738
42 -89.7265859 -91.4145617
43 -87.4138344 -89.7265859
44 -88.6974304 -87.4138344
45 -48.7347879 -88.6974304
46 -45.2686455 -48.7347879
47 -43.8014454 -45.2686455
48 -51.1014707 -43.8014454
49 -61.8029252 -51.1014707
50 -87.4145778 -61.8029252
51 -77.7233212 -87.4145778
52 -69.2732283 -77.7233212
53 -58.9227798 -69.2732283
54 -45.9348040 -58.9227798
55 -38.4433616 -45.9348040
56 -47.8417212 -38.4433616
57 -28.9216969 -47.8417212
58 -37.6555545 -28.9216969
59 -83.0653727 -37.6555545
60 -63.7014707 -83.0653727
61 -85.2389979 -63.7014707
62 -85.9293414 -85.2389979
63 -56.5954666 -85.9293414
64 -56.1027555 -56.5954666
65 -74.7588525 -56.1027555
66 -78.7134949 -74.7588525
67 -79.0433616 -78.7134949
68 -58.7417212 -79.0433616
69 -33.8856242 -58.7417212
70 -37.8473364 -33.8856242
71 17.7198637 -37.8473364
72 55.5919838 17.7198637
73 97.7757657 55.5919838
74 125.5280404 97.7757657
75 109.1127515 125.5280404
76 104.1628444 109.1127515
77 63.4854383 104.1628444
78 61.5307959 63.4854383
79 74.2648565 61.5307959
80 89.6091151 74.2648565
81 26.2734302 89.6091151
82 5.8543362 26.2734302
83 -16.9276274 5.8543362
84 0.2936564 -16.9276274
85 -42.7717254 0.2936564
86 -47.6194507 -42.7717254
87 -30.1068850 -47.6194507
88 -39.8502466 -30.1068850
89 -48.3637254 -39.8502466
90 -59.0544405 -48.3637254
91 -19.1351435 -59.0544405
92 -29.3335031 -19.1351435
93 -14.6200243 -29.3335031
94 4.6100454 -14.6200243
95 -10.5145364 4.6100454
96 -36.3997981 -10.5145364
97 -47.9799435 -36.3997981
98 -15.8850506 -47.9799435
99 -9.2085576 -15.8850506
100 -0.6223920 -9.2085576
101 22.7280565 -0.6223920
102 20.3586505 22.7280565
103 7.0140202 20.3586505
104 -2.7843394 7.0140202
105 13.2930667 -2.7843394
> 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/7ykuj1291026816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8rbtm1291026816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rbtm1291026816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/102lt71291026816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11n39c1291026816.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/12yuqx1291026816.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/13nvnr1291026816.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/14qwmx1291026816.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/15jn301291026816.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/16ff1r1291026816.tab")
+ }
>
> try(system("convert tmp/1djed1291026816.ps tmp/1djed1291026816.png",intern=TRUE))
character(0)
> try(system("convert tmp/2djed1291026816.ps tmp/2djed1291026816.png",intern=TRUE))
character(0)
> try(system("convert tmp/3obvg1291026816.ps tmp/3obvg1291026816.png",intern=TRUE))
character(0)
> try(system("convert tmp/4obvg1291026816.ps tmp/4obvg1291026816.png",intern=TRUE))
character(0)
> try(system("convert tmp/5obvg1291026816.ps tmp/5obvg1291026816.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ykuj1291026816.ps tmp/6ykuj1291026816.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ykuj1291026816.ps tmp/7ykuj1291026816.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rbtm1291026816.ps tmp/8rbtm1291026816.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rbtm1291026816.ps tmp/9rbtm1291026816.png",intern=TRUE))
character(0)
> try(system("convert tmp/102lt71291026816.ps tmp/102lt71291026816.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.134 1.674 7.497