R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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+ ,10
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+ ,8
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+ ,2
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+ ,13
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+ ,40
+ ,10
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+ ,22
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+ ,2
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+ ,8
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+ ,9
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+ ,2
+ ,21
+ ,42
+ ,15
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+ ,14
+ ,5
+ ,10
+ ,21
+ ,24
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+ ,2
+ ,28
+ ,56
+ ,14
+ ,28
+ ,11
+ ,22
+ ,8
+ ,16
+ ,24
+ ,24
+ ,48)
+ ,dim=c(12
+ ,159)
+ ,dimnames=list(c('Gender'
+ ,'CM'
+ ,'CM_G'
+ ,'D'
+ ,'D_G'
+ ,'PE'
+ ,'PE_G'
+ ,'PC'
+ ,'PC_G'
+ ,'PS'
+ ,'O'
+ ,'O_G')
+ ,1:159))
> y <- array(NA,dim=c(12,159),dimnames=list(c('Gender','CM','CM_G','D','D_G','PE','PE_G','PC','PC_G','PS','O','O_G'),1:159))
> 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 = '10'
> #'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
PS Gender CM CM_G D D_G PE PE_G PC PC_G O O_G
1 25 1 25 25 11 11 7 7 8 8 23 23
2 30 1 17 17 6 6 17 17 8 8 25 25
3 22 1 18 18 8 8 12 12 9 9 19 19
4 22 1 16 16 10 10 12 12 7 7 29 29
5 25 1 20 20 10 10 11 11 4 4 25 25
6 23 1 16 16 11 11 11 11 11 11 21 21
7 17 1 18 18 16 16 12 12 7 7 22 22
8 21 1 17 17 11 11 13 13 7 7 25 25
9 19 1 30 30 12 12 16 16 10 10 18 18
10 15 1 23 23 8 8 11 11 10 10 22 22
11 16 1 18 18 12 12 10 10 8 8 15 15
12 22 1 21 21 9 9 9 9 9 9 20 20
13 23 1 31 31 14 14 17 17 11 11 20 20
14 23 1 27 27 15 15 11 11 9 9 21 21
15 19 1 21 21 9 9 14 14 13 13 21 21
16 23 1 16 16 8 8 15 15 9 9 24 24
17 25 1 20 20 9 9 15 15 6 6 24 24
18 22 1 17 17 9 9 13 13 6 6 23 23
19 26 1 25 25 16 16 18 18 16 16 24 24
20 29 1 26 26 11 11 18 18 5 5 18 18
21 32 1 25 25 8 8 12 12 7 7 25 25
22 25 1 17 17 9 9 17 17 9 9 21 21
23 28 1 32 32 12 12 18 18 12 12 22 22
24 25 1 22 22 9 9 14 14 9 9 23 23
25 25 1 17 17 9 9 16 16 5 5 23 23
26 18 1 20 20 14 14 14 14 10 10 24 24
27 25 1 29 29 10 10 12 12 8 8 23 23
28 25 1 23 23 14 14 17 17 7 7 21 21
29 20 1 20 20 10 10 12 12 8 8 28 28
30 15 1 11 11 6 6 6 6 4 4 16 16
31 24 1 26 26 13 13 12 12 8 8 29 29
32 26 1 22 22 10 10 12 12 8 8 27 27
33 14 1 14 14 15 15 13 13 8 8 16 16
34 24 1 19 19 12 12 14 14 7 7 28 28
35 25 1 20 20 11 11 11 11 8 8 25 25
36 20 1 28 28 8 8 12 12 7 7 22 22
37 21 1 19 19 9 9 9 9 7 7 23 23
38 27 1 30 30 9 9 15 15 9 9 26 26
39 23 1 29 29 15 15 18 18 11 11 23 23
40 25 1 26 26 9 9 15 15 6 6 25 25
41 20 1 23 23 10 10 12 12 8 8 21 21
42 22 1 21 21 12 12 14 14 9 9 24 24
43 25 1 28 28 11 11 13 13 6 6 22 22
44 25 1 23 23 14 14 13 13 10 10 27 27
45 17 1 18 18 6 6 11 11 8 8 26 26
46 25 1 20 20 8 8 16 16 10 10 24 24
47 26 1 21 21 10 10 11 11 5 5 24 24
48 27 1 28 28 12 12 16 16 14 14 22 22
49 19 1 10 10 5 5 8 8 6 6 24 24
50 22 1 22 22 10 10 15 15 6 6 20 20
51 32 1 31 31 10 10 21 21 12 12 26 26
52 21 1 29 29 13 13 18 18 12 12 21 21
53 18 1 22 22 10 10 13 13 8 8 19 19
54 23 1 23 23 10 10 15 15 10 10 21 21
55 20 1 20 20 9 9 19 19 10 10 16 16
56 21 1 18 18 8 8 15 15 10 10 22 22
57 17 1 25 25 14 14 11 11 5 5 15 15
58 18 1 21 21 8 8 10 10 7 7 17 17
59 19 1 24 24 9 9 13 13 10 10 15 15
60 22 1 25 25 14 14 15 15 11 11 21 21
61 14 1 13 13 8 8 12 12 7 7 19 19
62 18 1 28 28 8 8 16 16 12 12 24 24
63 35 1 25 25 7 7 18 18 11 11 17 17
64 29 1 9 9 6 6 8 8 11 11 23 23
65 21 1 16 16 8 8 13 13 5 5 24 24
66 25 1 19 19 6 6 17 17 8 8 14 14
67 26 1 29 29 11 11 7 7 4 4 22 22
68 17 1 14 14 11 11 12 12 7 7 16 16
69 25 1 22 22 14 14 14 14 11 11 19 19
70 20 1 15 15 8 8 6 6 6 6 25 25
71 22 1 15 15 8 8 10 10 4 4 24 24
72 24 1 20 20 11 11 11 11 8 8 26 26
73 21 1 18 18 10 10 14 14 9 9 26 26
74 26 1 33 33 14 14 11 11 8 8 25 25
75 24 1 22 22 11 11 13 13 11 11 18 18
76 16 1 16 16 9 9 12 12 8 8 21 21
77 18 1 16 16 8 8 9 9 4 4 23 23
78 19 1 18 18 13 13 12 12 6 6 20 20
79 21 1 18 18 12 12 13 13 9 9 13 13
80 22 1 22 22 13 13 12 12 13 13 15 15
81 23 1 30 30 14 14 9 9 9 9 14 14
82 29 1 30 30 12 12 15 15 10 10 22 22
83 21 1 24 24 14 14 24 24 20 20 10 10
84 23 1 21 21 13 13 17 17 11 11 22 22
85 27 1 29 29 16 16 11 11 6 6 24 24
86 25 1 31 31 9 9 17 17 9 9 19 19
87 21 1 20 20 9 9 11 11 7 7 20 20
88 10 1 16 16 9 9 12 12 9 9 13 13
89 20 1 22 22 8 8 14 14 10 10 20 20
90 26 1 20 20 7 7 11 11 9 9 22 22
91 24 1 28 28 16 16 16 16 8 8 24 24
92 29 1 38 38 11 11 21 21 7 7 29 29
93 19 1 22 22 9 9 14 14 6 6 12 12
94 24 1 20 20 11 11 20 20 13 13 20 20
95 19 1 17 17 9 9 13 13 6 6 21 21
96 22 1 22 22 13 13 15 15 10 10 22 22
97 17 1 31 31 16 16 19 19 16 16 20 20
98 24 2 24 48 14 28 11 22 12 24 26 52
99 19 2 18 36 12 24 10 20 8 16 23 46
100 19 2 23 46 13 26 14 28 12 24 24 48
101 23 2 15 30 11 22 11 22 8 16 22 44
102 27 2 12 24 4 8 15 30 4 8 28 56
103 14 2 15 30 8 16 11 22 8 16 12 24
104 22 2 20 40 8 16 17 34 7 14 24 48
105 21 2 34 68 16 32 18 36 11 22 20 40
106 18 2 31 62 14 28 10 20 8 16 23 46
107 20 2 19 38 11 22 11 22 8 16 28 56
108 19 2 21 42 9 18 13 26 9 18 24 48
109 24 2 22 44 9 18 16 32 9 18 23 46
110 25 2 24 48 10 20 9 18 6 12 29 58
111 29 2 32 64 16 32 9 18 6 12 26 52
112 28 2 33 66 11 22 9 18 6 12 22 44
113 17 2 13 26 16 32 12 24 5 10 22 44
114 29 2 25 50 12 24 12 24 7 14 23 46
115 26 2 29 58 14 28 18 36 10 20 30 60
116 14 2 18 36 10 20 15 30 8 16 17 34
117 26 2 20 40 10 20 10 20 8 16 23 46
118 20 2 15 30 12 24 11 22 8 16 25 50
119 32 2 33 66 14 28 9 18 6 12 24 48
120 23 2 26 52 16 32 5 10 4 8 24 48
121 21 2 18 36 9 18 12 24 8 16 24 48
122 30 2 28 56 8 16 24 48 20 40 20 40
123 24 2 17 34 8 16 14 28 6 12 22 44
124 22 2 12 24 7 14 7 14 4 8 28 56
125 24 2 17 34 9 18 12 24 9 18 25 50
126 24 2 21 42 10 20 13 26 6 12 24 48
127 24 2 18 36 13 26 8 16 9 18 24 48
128 19 2 10 20 10 20 11 22 5 10 23 46
129 31 2 29 58 11 22 9 18 5 10 30 60
130 22 2 31 62 8 16 11 22 8 16 24 48
131 27 2 19 38 9 18 13 26 8 16 21 42
132 19 2 9 18 13 26 10 20 6 12 25 50
133 21 2 13 26 14 28 13 26 6 12 25 50
134 23 2 19 38 12 24 10 20 8 16 29 58
135 19 2 21 42 12 24 13 26 8 16 22 44
136 19 2 23 46 14 28 8 16 5 10 27 54
137 20 2 21 42 11 22 16 32 7 14 24 48
138 23 2 15 30 14 28 9 18 8 16 29 58
139 17 2 19 38 10 20 12 24 7 14 21 42
140 17 2 26 52 14 28 14 28 8 16 24 48
141 17 2 16 32 11 22 9 18 5 10 23 46
142 21 2 19 38 9 18 11 22 10 20 27 54
143 21 2 31 62 16 32 14 28 9 18 25 50
144 18 2 19 38 9 18 12 24 7 14 21 42
145 19 2 15 30 7 14 12 24 6 12 21 42
146 20 2 23 46 14 28 11 22 10 20 29 58
147 15 2 17 34 14 28 12 24 6 12 21 42
148 24 2 21 42 8 16 9 18 11 22 20 40
149 20 2 17 34 11 22 9 18 6 12 19 38
150 22 2 25 50 14 28 15 30 9 18 24 48
151 13 2 20 40 11 22 8 16 4 8 13 26
152 19 2 19 38 20 40 8 16 7 14 25 50
153 21 2 20 40 11 22 17 34 8 16 23 46
154 23 2 17 34 9 18 11 22 5 10 26 52
155 16 2 21 42 10 20 12 24 8 16 23 46
156 26 2 26 52 13 26 20 40 10 20 22 44
157 21 2 17 34 8 16 12 24 9 18 24 48
158 21 2 21 42 15 30 7 14 5 10 24 48
159 24 2 28 56 14 28 11 22 8 16 24 48
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender CM CM_G D D_G
8.8811 -1.2316 0.2524 0.0448 -0.1833 -0.1306
PE PE_G PC PC_G O O_G
0.5732 -0.2847 -0.1443 0.1103 0.2079 0.1620
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.5470 -2.2015 -0.2332 2.2144 11.0089
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.8811 6.8471 1.297 0.197
Gender -1.2316 4.8774 -0.253 0.801
CM 0.2524 0.1750 1.443 0.151
CM_G 0.0448 0.1139 0.393 0.695
D -0.1833 0.3458 -0.530 0.597
D_G -0.1306 0.2261 -0.578 0.564
PE 0.5732 0.3151 1.819 0.071 .
PE_G -0.2847 0.2152 -1.323 0.188
PC -0.1443 0.3922 -0.368 0.714
PC_G 0.1103 0.2775 0.397 0.692
O 0.2079 0.2244 0.927 0.356
O_G 0.1620 0.1595 1.015 0.312
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.424 on 147 degrees of freedom
Multiple R-squared: 0.3865, Adjusted R-squared: 0.3406
F-statistic: 8.418 on 11 and 147 DF, p-value: 2.193e-11
> 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,] 0.99108993 0.01782014 0.008910072
[2,] 0.97899308 0.04201385 0.021006924
[3,] 0.95823234 0.08353532 0.041767659
[4,] 0.92847997 0.14304005 0.071520026
[5,] 0.92191489 0.15617021 0.078085105
[6,] 0.94087275 0.11825451 0.059127254
[7,] 0.96765162 0.06469677 0.032348383
[8,] 0.95410712 0.09178575 0.045892877
[9,] 0.93153299 0.13693403 0.068467015
[10,] 0.90082869 0.19834262 0.099171311
[11,] 0.86662761 0.26674477 0.133372387
[12,] 0.86450669 0.27098663 0.135493313
[13,] 0.82166555 0.35666890 0.178334452
[14,] 0.78178417 0.43643167 0.218215833
[15,] 0.80695676 0.38608647 0.193043236
[16,] 0.76459403 0.47081193 0.235405966
[17,] 0.71500370 0.56999261 0.284996304
[18,] 0.66522336 0.66955328 0.334776641
[19,] 0.61429273 0.77141455 0.385707273
[20,] 0.55060939 0.89878123 0.449390613
[21,] 0.52831697 0.94336606 0.471683031
[22,] 0.62544708 0.74910584 0.374552922
[23,] 0.56439064 0.87121871 0.435609356
[24,] 0.50883956 0.98232089 0.491160443
[25,] 0.46694829 0.93389659 0.533051706
[26,] 0.42742388 0.85484776 0.572576120
[27,] 0.39087524 0.78175047 0.609124765
[28,] 0.33695800 0.67391601 0.663041997
[29,] 0.28650779 0.57301557 0.713492214
[30,] 0.25195171 0.50390343 0.748048286
[31,] 0.39047418 0.78094837 0.609525816
[32,] 0.33860307 0.67720614 0.661396932
[33,] 0.33040181 0.66080361 0.669598194
[34,] 0.32028074 0.64056148 0.679719261
[35,] 0.27429162 0.54858325 0.725708376
[36,] 0.23692998 0.47385996 0.763070022
[37,] 0.21782868 0.43565735 0.782171323
[38,] 0.22858005 0.45716010 0.771419952
[39,] 0.22576129 0.45152257 0.774238714
[40,] 0.18733045 0.37466089 0.812669555
[41,] 0.16189069 0.32378138 0.838109310
[42,] 0.13581845 0.27163690 0.864181551
[43,] 0.11568956 0.23137913 0.884310437
[44,] 0.09954430 0.19908859 0.900455705
[45,] 0.08452768 0.16905536 0.915472321
[46,] 0.06613969 0.13227937 0.933860314
[47,] 0.08447735 0.16895470 0.915522650
[48,] 0.25378019 0.50756037 0.746219813
[49,] 0.70212923 0.59574155 0.297870774
[50,] 0.92393450 0.15213101 0.076065503
[51,] 0.90820296 0.18359408 0.091797042
[52,] 0.91921244 0.16157512 0.080787560
[53,] 0.91842337 0.16315325 0.081576625
[54,] 0.89828556 0.20342889 0.101714443
[55,] 0.91000670 0.17998660 0.089993299
[56,] 0.88909901 0.22180198 0.110900991
[57,] 0.86865680 0.26268640 0.131343202
[58,] 0.84584342 0.30831317 0.154156584
[59,] 0.82426934 0.35146133 0.175730663
[60,] 0.79675760 0.40648480 0.203242402
[61,] 0.78983341 0.42033318 0.210166589
[62,] 0.80241005 0.39517991 0.197589953
[63,] 0.78838462 0.42323076 0.211615379
[64,] 0.75338393 0.49323214 0.246616068
[65,] 0.75390148 0.49219704 0.246098518
[66,] 0.73806175 0.52387651 0.261938253
[67,] 0.72442218 0.55115564 0.275577820
[68,] 0.74486934 0.51026132 0.255130661
[69,] 0.77248842 0.45502316 0.227511579
[70,] 0.73885238 0.52229525 0.261147625
[71,] 0.74770661 0.50458679 0.252293394
[72,] 0.72367134 0.55265733 0.276328663
[73,] 0.68172991 0.63654018 0.318270090
[74,] 0.78495550 0.43008900 0.215044502
[75,] 0.76943384 0.46113232 0.230566161
[76,] 0.79904655 0.40190691 0.200953454
[77,] 0.77723359 0.44553283 0.222766414
[78,] 0.75319644 0.49360713 0.246803564
[79,] 0.71232586 0.57534829 0.287674143
[80,] 0.66877667 0.66244666 0.331223331
[81,] 0.62779186 0.74441628 0.372208140
[82,] 0.57943612 0.84112776 0.420563878
[83,] 0.61080319 0.77839362 0.389196808
[84,] 0.56305988 0.87388023 0.436940117
[85,] 0.51541973 0.96916055 0.484580274
[86,] 0.49330808 0.98661615 0.506691924
[87,] 0.48315620 0.96631241 0.516843795
[88,] 0.46795335 0.93590670 0.532046652
[89,] 0.42090422 0.84180845 0.579095775
[90,] 0.38947912 0.77895824 0.610520881
[91,] 0.36252535 0.72505070 0.637474648
[92,] 0.43332425 0.86664850 0.566675750
[93,] 0.42889567 0.85779135 0.571104326
[94,] 0.43141143 0.86282286 0.568588571
[95,] 0.39037007 0.78074014 0.609629929
[96,] 0.35274073 0.70548146 0.647259268
[97,] 0.36819093 0.73638185 0.631809073
[98,] 0.36632494 0.73264988 0.633675062
[99,] 0.32570388 0.65140776 0.674296121
[100,] 0.44854404 0.89708808 0.551455962
[101,] 0.39479667 0.78959334 0.605203329
[102,] 0.40619515 0.81239031 0.593804847
[103,] 0.42856377 0.85712754 0.571436229
[104,] 0.37211672 0.74423344 0.627883280
[105,] 0.62121122 0.75757757 0.378788783
[106,] 0.64055886 0.71888227 0.359441137
[107,] 0.58710406 0.82579187 0.412895937
[108,] 0.62510511 0.74978979 0.374894894
[109,] 0.61744445 0.76511110 0.382555550
[110,] 0.55548693 0.88902615 0.444513073
[111,] 0.50427777 0.99144446 0.495722231
[112,] 0.47054992 0.94109984 0.529450082
[113,] 0.48003251 0.96006503 0.519967487
[114,] 0.41266492 0.82532984 0.587335078
[115,] 0.63514124 0.72971753 0.364858765
[116,] 0.59430100 0.81139799 0.405698997
[117,] 0.79940146 0.40119707 0.200598535
[118,] 0.73820363 0.52359275 0.261796373
[119,] 0.68865210 0.62269580 0.311347899
[120,] 0.61932478 0.76135044 0.380675218
[121,] 0.54299860 0.91400280 0.457001402
[122,] 0.46803679 0.93607359 0.531963206
[123,] 0.38223554 0.76447108 0.617764460
[124,] 0.32610718 0.65221437 0.673892816
[125,] 0.27225777 0.54451553 0.727742234
[126,] 0.30833798 0.61667597 0.691662016
[127,] 0.22511896 0.45023793 0.774881037
[128,] 0.15891998 0.31783996 0.841080018
[129,] 0.11619229 0.23238457 0.883807714
[130,] 0.06795274 0.13590549 0.932047257
> postscript(file="/var/www/html/freestat/rcomp/tmp/1xork1290474312.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2xork1290474312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3xork1290474312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4qx8n1290474312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5qx8n1290474312.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 = 159
Frequency = 1
1 2 3 4 5 6
3.11771681 5.30030204 1.32716052 -1.21724770 2.26003903 3.48030491
7 8 9 10 11 12
-2.33853505 -1.00948171 -4.73411693 -7.94618710 -1.39415618 1.24521807
13 14 15 16 17 18
-1.39775779 1.39871375 -3.43161123 0.20649365 1.22963728 0.06836235
19 20 21 22 23 24
2.41533880 5.39390698 6.95920466 2.75580755 1.68264466 1.39550632
25 26 27 28 29 30
2.16872190 -3.77600062 0.17198180 2.47439606 -4.00234231 -1.54894073
31 32 33 34 35 36
-1.21369678 1.77307489 -2.49880573 0.31179238 2.70988575 -4.82282944
37 38 39 40 41 42
-0.33791471 -0.38063736 -1.88753130 -0.92368472 -2.30480281 -0.73518145
43 44 45 46 47 48
0.79661501 1.51116358 -6.63547733 0.76294695 3.36665872 2.51664321
49 50 51 52 53 54
-1.03402761 -0.57127615 3.00665474 -3.74175847 -3.55633660 -0.10255010
55 56 57 58 59 60
-1.82959274 -1.61423136 -2.23729637 -2.31557837 -1.91729960 -0.40710521
61 62 63 64 65 66
-5.25457775 -8.54702907 11.00886809 10.11691499 -1.35224651 3.77465324
67 68 69 70 71 72
3.16280027 -0.50016813 4.51295457 -0.37102661 0.77670327 1.33999235
73 74 75 76 77 78
-2.21123230 0.78775799 3.22943913 -4.53812529 -2.86208274 -0.57468016
79 80 81 82 83 84
3.51391893 3.32358515 3.35938145 4.07486815 0.66761156 0.52084723
85 86 87 88 89 90
3.90665337 -0.66573903 -0.10258998 -7.54501370 -2.77483827 3.59757364
91 92 93 94 95 96
-0.17097321 -3.03952490 -0.63755943 1.13214648 -2.19185085 -0.23323788
97 98 99 100 101 102
-7.17707488 0.81084742 -1.12137196 -3.23975459 3.98804710 3.00030153
103 104 105 106 107 108
-1.02727404 -1.06683477 -1.48007167 -5.67854154 -3.57123689 -4.10129373
109 110 111 112 113 114
1.07681299 -1.09740289 4.42970806 2.99193536 1.12040525 6.55292184
115 116 117 118 119 120
-0.90141206 -3.83902111 4.30529191 -0.16289312 7.26212203 1.71393366
121 122 123 124 125 126
-0.99494503 5.30454560 3.11104214 -0.63454807 1.73888886 1.57236552
127 128 129 130 131 132
3.72287440 0.95074804 3.18154633 -4.88211174 6.25468990 1.49053270
133 134 135 136 137 138
2.55531444 -0.65455481 -1.62733267 -3.83286785 -2.07105952 1.60675465
139 140 141 142 143 144
-3.22042819 -5.51585484 -2.64900806 -3.08133067 -2.94495454 -2.66506254
145 146 147 148 149 150
-1.10985475 -4.29001016 -2.68148174 2.44444509 2.06004935 -0.25406868
151 152 153 154 155 156
-2.61831346 1.11410620 -0.27741517 0.51630312 -6.04455364 3.92710841
157 158 159
-1.17388730 0.89531441 0.81179549
> postscript(file="/var/www/html/freestat/rcomp/tmp/6qx8n1290474312.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 3.11771681 NA
1 5.30030204 3.11771681
2 1.32716052 5.30030204
3 -1.21724770 1.32716052
4 2.26003903 -1.21724770
5 3.48030491 2.26003903
6 -2.33853505 3.48030491
7 -1.00948171 -2.33853505
8 -4.73411693 -1.00948171
9 -7.94618710 -4.73411693
10 -1.39415618 -7.94618710
11 1.24521807 -1.39415618
12 -1.39775779 1.24521807
13 1.39871375 -1.39775779
14 -3.43161123 1.39871375
15 0.20649365 -3.43161123
16 1.22963728 0.20649365
17 0.06836235 1.22963728
18 2.41533880 0.06836235
19 5.39390698 2.41533880
20 6.95920466 5.39390698
21 2.75580755 6.95920466
22 1.68264466 2.75580755
23 1.39550632 1.68264466
24 2.16872190 1.39550632
25 -3.77600062 2.16872190
26 0.17198180 -3.77600062
27 2.47439606 0.17198180
28 -4.00234231 2.47439606
29 -1.54894073 -4.00234231
30 -1.21369678 -1.54894073
31 1.77307489 -1.21369678
32 -2.49880573 1.77307489
33 0.31179238 -2.49880573
34 2.70988575 0.31179238
35 -4.82282944 2.70988575
36 -0.33791471 -4.82282944
37 -0.38063736 -0.33791471
38 -1.88753130 -0.38063736
39 -0.92368472 -1.88753130
40 -2.30480281 -0.92368472
41 -0.73518145 -2.30480281
42 0.79661501 -0.73518145
43 1.51116358 0.79661501
44 -6.63547733 1.51116358
45 0.76294695 -6.63547733
46 3.36665872 0.76294695
47 2.51664321 3.36665872
48 -1.03402761 2.51664321
49 -0.57127615 -1.03402761
50 3.00665474 -0.57127615
51 -3.74175847 3.00665474
52 -3.55633660 -3.74175847
53 -0.10255010 -3.55633660
54 -1.82959274 -0.10255010
55 -1.61423136 -1.82959274
56 -2.23729637 -1.61423136
57 -2.31557837 -2.23729637
58 -1.91729960 -2.31557837
59 -0.40710521 -1.91729960
60 -5.25457775 -0.40710521
61 -8.54702907 -5.25457775
62 11.00886809 -8.54702907
63 10.11691499 11.00886809
64 -1.35224651 10.11691499
65 3.77465324 -1.35224651
66 3.16280027 3.77465324
67 -0.50016813 3.16280027
68 4.51295457 -0.50016813
69 -0.37102661 4.51295457
70 0.77670327 -0.37102661
71 1.33999235 0.77670327
72 -2.21123230 1.33999235
73 0.78775799 -2.21123230
74 3.22943913 0.78775799
75 -4.53812529 3.22943913
76 -2.86208274 -4.53812529
77 -0.57468016 -2.86208274
78 3.51391893 -0.57468016
79 3.32358515 3.51391893
80 3.35938145 3.32358515
81 4.07486815 3.35938145
82 0.66761156 4.07486815
83 0.52084723 0.66761156
84 3.90665337 0.52084723
85 -0.66573903 3.90665337
86 -0.10258998 -0.66573903
87 -7.54501370 -0.10258998
88 -2.77483827 -7.54501370
89 3.59757364 -2.77483827
90 -0.17097321 3.59757364
91 -3.03952490 -0.17097321
92 -0.63755943 -3.03952490
93 1.13214648 -0.63755943
94 -2.19185085 1.13214648
95 -0.23323788 -2.19185085
96 -7.17707488 -0.23323788
97 0.81084742 -7.17707488
98 -1.12137196 0.81084742
99 -3.23975459 -1.12137196
100 3.98804710 -3.23975459
101 3.00030153 3.98804710
102 -1.02727404 3.00030153
103 -1.06683477 -1.02727404
104 -1.48007167 -1.06683477
105 -5.67854154 -1.48007167
106 -3.57123689 -5.67854154
107 -4.10129373 -3.57123689
108 1.07681299 -4.10129373
109 -1.09740289 1.07681299
110 4.42970806 -1.09740289
111 2.99193536 4.42970806
112 1.12040525 2.99193536
113 6.55292184 1.12040525
114 -0.90141206 6.55292184
115 -3.83902111 -0.90141206
116 4.30529191 -3.83902111
117 -0.16289312 4.30529191
118 7.26212203 -0.16289312
119 1.71393366 7.26212203
120 -0.99494503 1.71393366
121 5.30454560 -0.99494503
122 3.11104214 5.30454560
123 -0.63454807 3.11104214
124 1.73888886 -0.63454807
125 1.57236552 1.73888886
126 3.72287440 1.57236552
127 0.95074804 3.72287440
128 3.18154633 0.95074804
129 -4.88211174 3.18154633
130 6.25468990 -4.88211174
131 1.49053270 6.25468990
132 2.55531444 1.49053270
133 -0.65455481 2.55531444
134 -1.62733267 -0.65455481
135 -3.83286785 -1.62733267
136 -2.07105952 -3.83286785
137 1.60675465 -2.07105952
138 -3.22042819 1.60675465
139 -5.51585484 -3.22042819
140 -2.64900806 -5.51585484
141 -3.08133067 -2.64900806
142 -2.94495454 -3.08133067
143 -2.66506254 -2.94495454
144 -1.10985475 -2.66506254
145 -4.29001016 -1.10985475
146 -2.68148174 -4.29001016
147 2.44444509 -2.68148174
148 2.06004935 2.44444509
149 -0.25406868 2.06004935
150 -2.61831346 -0.25406868
151 1.11410620 -2.61831346
152 -0.27741517 1.11410620
153 0.51630312 -0.27741517
154 -6.04455364 0.51630312
155 3.92710841 -6.04455364
156 -1.17388730 3.92710841
157 0.89531441 -1.17388730
158 0.81179549 0.89531441
159 NA 0.81179549
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.30030204 3.11771681
[2,] 1.32716052 5.30030204
[3,] -1.21724770 1.32716052
[4,] 2.26003903 -1.21724770
[5,] 3.48030491 2.26003903
[6,] -2.33853505 3.48030491
[7,] -1.00948171 -2.33853505
[8,] -4.73411693 -1.00948171
[9,] -7.94618710 -4.73411693
[10,] -1.39415618 -7.94618710
[11,] 1.24521807 -1.39415618
[12,] -1.39775779 1.24521807
[13,] 1.39871375 -1.39775779
[14,] -3.43161123 1.39871375
[15,] 0.20649365 -3.43161123
[16,] 1.22963728 0.20649365
[17,] 0.06836235 1.22963728
[18,] 2.41533880 0.06836235
[19,] 5.39390698 2.41533880
[20,] 6.95920466 5.39390698
[21,] 2.75580755 6.95920466
[22,] 1.68264466 2.75580755
[23,] 1.39550632 1.68264466
[24,] 2.16872190 1.39550632
[25,] -3.77600062 2.16872190
[26,] 0.17198180 -3.77600062
[27,] 2.47439606 0.17198180
[28,] -4.00234231 2.47439606
[29,] -1.54894073 -4.00234231
[30,] -1.21369678 -1.54894073
[31,] 1.77307489 -1.21369678
[32,] -2.49880573 1.77307489
[33,] 0.31179238 -2.49880573
[34,] 2.70988575 0.31179238
[35,] -4.82282944 2.70988575
[36,] -0.33791471 -4.82282944
[37,] -0.38063736 -0.33791471
[38,] -1.88753130 -0.38063736
[39,] -0.92368472 -1.88753130
[40,] -2.30480281 -0.92368472
[41,] -0.73518145 -2.30480281
[42,] 0.79661501 -0.73518145
[43,] 1.51116358 0.79661501
[44,] -6.63547733 1.51116358
[45,] 0.76294695 -6.63547733
[46,] 3.36665872 0.76294695
[47,] 2.51664321 3.36665872
[48,] -1.03402761 2.51664321
[49,] -0.57127615 -1.03402761
[50,] 3.00665474 -0.57127615
[51,] -3.74175847 3.00665474
[52,] -3.55633660 -3.74175847
[53,] -0.10255010 -3.55633660
[54,] -1.82959274 -0.10255010
[55,] -1.61423136 -1.82959274
[56,] -2.23729637 -1.61423136
[57,] -2.31557837 -2.23729637
[58,] -1.91729960 -2.31557837
[59,] -0.40710521 -1.91729960
[60,] -5.25457775 -0.40710521
[61,] -8.54702907 -5.25457775
[62,] 11.00886809 -8.54702907
[63,] 10.11691499 11.00886809
[64,] -1.35224651 10.11691499
[65,] 3.77465324 -1.35224651
[66,] 3.16280027 3.77465324
[67,] -0.50016813 3.16280027
[68,] 4.51295457 -0.50016813
[69,] -0.37102661 4.51295457
[70,] 0.77670327 -0.37102661
[71,] 1.33999235 0.77670327
[72,] -2.21123230 1.33999235
[73,] 0.78775799 -2.21123230
[74,] 3.22943913 0.78775799
[75,] -4.53812529 3.22943913
[76,] -2.86208274 -4.53812529
[77,] -0.57468016 -2.86208274
[78,] 3.51391893 -0.57468016
[79,] 3.32358515 3.51391893
[80,] 3.35938145 3.32358515
[81,] 4.07486815 3.35938145
[82,] 0.66761156 4.07486815
[83,] 0.52084723 0.66761156
[84,] 3.90665337 0.52084723
[85,] -0.66573903 3.90665337
[86,] -0.10258998 -0.66573903
[87,] -7.54501370 -0.10258998
[88,] -2.77483827 -7.54501370
[89,] 3.59757364 -2.77483827
[90,] -0.17097321 3.59757364
[91,] -3.03952490 -0.17097321
[92,] -0.63755943 -3.03952490
[93,] 1.13214648 -0.63755943
[94,] -2.19185085 1.13214648
[95,] -0.23323788 -2.19185085
[96,] -7.17707488 -0.23323788
[97,] 0.81084742 -7.17707488
[98,] -1.12137196 0.81084742
[99,] -3.23975459 -1.12137196
[100,] 3.98804710 -3.23975459
[101,] 3.00030153 3.98804710
[102,] -1.02727404 3.00030153
[103,] -1.06683477 -1.02727404
[104,] -1.48007167 -1.06683477
[105,] -5.67854154 -1.48007167
[106,] -3.57123689 -5.67854154
[107,] -4.10129373 -3.57123689
[108,] 1.07681299 -4.10129373
[109,] -1.09740289 1.07681299
[110,] 4.42970806 -1.09740289
[111,] 2.99193536 4.42970806
[112,] 1.12040525 2.99193536
[113,] 6.55292184 1.12040525
[114,] -0.90141206 6.55292184
[115,] -3.83902111 -0.90141206
[116,] 4.30529191 -3.83902111
[117,] -0.16289312 4.30529191
[118,] 7.26212203 -0.16289312
[119,] 1.71393366 7.26212203
[120,] -0.99494503 1.71393366
[121,] 5.30454560 -0.99494503
[122,] 3.11104214 5.30454560
[123,] -0.63454807 3.11104214
[124,] 1.73888886 -0.63454807
[125,] 1.57236552 1.73888886
[126,] 3.72287440 1.57236552
[127,] 0.95074804 3.72287440
[128,] 3.18154633 0.95074804
[129,] -4.88211174 3.18154633
[130,] 6.25468990 -4.88211174
[131,] 1.49053270 6.25468990
[132,] 2.55531444 1.49053270
[133,] -0.65455481 2.55531444
[134,] -1.62733267 -0.65455481
[135,] -3.83286785 -1.62733267
[136,] -2.07105952 -3.83286785
[137,] 1.60675465 -2.07105952
[138,] -3.22042819 1.60675465
[139,] -5.51585484 -3.22042819
[140,] -2.64900806 -5.51585484
[141,] -3.08133067 -2.64900806
[142,] -2.94495454 -3.08133067
[143,] -2.66506254 -2.94495454
[144,] -1.10985475 -2.66506254
[145,] -4.29001016 -1.10985475
[146,] -2.68148174 -4.29001016
[147,] 2.44444509 -2.68148174
[148,] 2.06004935 2.44444509
[149,] -0.25406868 2.06004935
[150,] -2.61831346 -0.25406868
[151,] 1.11410620 -2.61831346
[152,] -0.27741517 1.11410620
[153,] 0.51630312 -0.27741517
[154,] -6.04455364 0.51630312
[155,] 3.92710841 -6.04455364
[156,] -1.17388730 3.92710841
[157,] 0.89531441 -1.17388730
[158,] 0.81179549 0.89531441
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.30030204 3.11771681
2 1.32716052 5.30030204
3 -1.21724770 1.32716052
4 2.26003903 -1.21724770
5 3.48030491 2.26003903
6 -2.33853505 3.48030491
7 -1.00948171 -2.33853505
8 -4.73411693 -1.00948171
9 -7.94618710 -4.73411693
10 -1.39415618 -7.94618710
11 1.24521807 -1.39415618
12 -1.39775779 1.24521807
13 1.39871375 -1.39775779
14 -3.43161123 1.39871375
15 0.20649365 -3.43161123
16 1.22963728 0.20649365
17 0.06836235 1.22963728
18 2.41533880 0.06836235
19 5.39390698 2.41533880
20 6.95920466 5.39390698
21 2.75580755 6.95920466
22 1.68264466 2.75580755
23 1.39550632 1.68264466
24 2.16872190 1.39550632
25 -3.77600062 2.16872190
26 0.17198180 -3.77600062
27 2.47439606 0.17198180
28 -4.00234231 2.47439606
29 -1.54894073 -4.00234231
30 -1.21369678 -1.54894073
31 1.77307489 -1.21369678
32 -2.49880573 1.77307489
33 0.31179238 -2.49880573
34 2.70988575 0.31179238
35 -4.82282944 2.70988575
36 -0.33791471 -4.82282944
37 -0.38063736 -0.33791471
38 -1.88753130 -0.38063736
39 -0.92368472 -1.88753130
40 -2.30480281 -0.92368472
41 -0.73518145 -2.30480281
42 0.79661501 -0.73518145
43 1.51116358 0.79661501
44 -6.63547733 1.51116358
45 0.76294695 -6.63547733
46 3.36665872 0.76294695
47 2.51664321 3.36665872
48 -1.03402761 2.51664321
49 -0.57127615 -1.03402761
50 3.00665474 -0.57127615
51 -3.74175847 3.00665474
52 -3.55633660 -3.74175847
53 -0.10255010 -3.55633660
54 -1.82959274 -0.10255010
55 -1.61423136 -1.82959274
56 -2.23729637 -1.61423136
57 -2.31557837 -2.23729637
58 -1.91729960 -2.31557837
59 -0.40710521 -1.91729960
60 -5.25457775 -0.40710521
61 -8.54702907 -5.25457775
62 11.00886809 -8.54702907
63 10.11691499 11.00886809
64 -1.35224651 10.11691499
65 3.77465324 -1.35224651
66 3.16280027 3.77465324
67 -0.50016813 3.16280027
68 4.51295457 -0.50016813
69 -0.37102661 4.51295457
70 0.77670327 -0.37102661
71 1.33999235 0.77670327
72 -2.21123230 1.33999235
73 0.78775799 -2.21123230
74 3.22943913 0.78775799
75 -4.53812529 3.22943913
76 -2.86208274 -4.53812529
77 -0.57468016 -2.86208274
78 3.51391893 -0.57468016
79 3.32358515 3.51391893
80 3.35938145 3.32358515
81 4.07486815 3.35938145
82 0.66761156 4.07486815
83 0.52084723 0.66761156
84 3.90665337 0.52084723
85 -0.66573903 3.90665337
86 -0.10258998 -0.66573903
87 -7.54501370 -0.10258998
88 -2.77483827 -7.54501370
89 3.59757364 -2.77483827
90 -0.17097321 3.59757364
91 -3.03952490 -0.17097321
92 -0.63755943 -3.03952490
93 1.13214648 -0.63755943
94 -2.19185085 1.13214648
95 -0.23323788 -2.19185085
96 -7.17707488 -0.23323788
97 0.81084742 -7.17707488
98 -1.12137196 0.81084742
99 -3.23975459 -1.12137196
100 3.98804710 -3.23975459
101 3.00030153 3.98804710
102 -1.02727404 3.00030153
103 -1.06683477 -1.02727404
104 -1.48007167 -1.06683477
105 -5.67854154 -1.48007167
106 -3.57123689 -5.67854154
107 -4.10129373 -3.57123689
108 1.07681299 -4.10129373
109 -1.09740289 1.07681299
110 4.42970806 -1.09740289
111 2.99193536 4.42970806
112 1.12040525 2.99193536
113 6.55292184 1.12040525
114 -0.90141206 6.55292184
115 -3.83902111 -0.90141206
116 4.30529191 -3.83902111
117 -0.16289312 4.30529191
118 7.26212203 -0.16289312
119 1.71393366 7.26212203
120 -0.99494503 1.71393366
121 5.30454560 -0.99494503
122 3.11104214 5.30454560
123 -0.63454807 3.11104214
124 1.73888886 -0.63454807
125 1.57236552 1.73888886
126 3.72287440 1.57236552
127 0.95074804 3.72287440
128 3.18154633 0.95074804
129 -4.88211174 3.18154633
130 6.25468990 -4.88211174
131 1.49053270 6.25468990
132 2.55531444 1.49053270
133 -0.65455481 2.55531444
134 -1.62733267 -0.65455481
135 -3.83286785 -1.62733267
136 -2.07105952 -3.83286785
137 1.60675465 -2.07105952
138 -3.22042819 1.60675465
139 -5.51585484 -3.22042819
140 -2.64900806 -5.51585484
141 -3.08133067 -2.64900806
142 -2.94495454 -3.08133067
143 -2.66506254 -2.94495454
144 -1.10985475 -2.66506254
145 -4.29001016 -1.10985475
146 -2.68148174 -4.29001016
147 2.44444509 -2.68148174
148 2.06004935 2.44444509
149 -0.25406868 2.06004935
150 -2.61831346 -0.25406868
151 1.11410620 -2.61831346
152 -0.27741517 1.11410620
153 0.51630312 -0.27741517
154 -6.04455364 0.51630312
155 3.92710841 -6.04455364
156 -1.17388730 3.92710841
157 0.89531441 -1.17388730
158 0.81179549 0.89531441
> 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/freestat/rcomp/tmp/7jop81290474312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8bfpt1290474312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9bfpt1290474312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10mp6e1290474312.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/118pnk1290474312.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/freestat/rcomp/tmp/12b8l81290474312.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/freestat/rcomp/tmp/137zjg1290474312.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/freestat/rcomp/tmp/14a0z41290474312.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/freestat/rcomp/tmp/15wjga1290474312.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/freestat/rcomp/tmp/16hjwg1290474312.tab")
+ }
>
> try(system("convert tmp/1xork1290474312.ps tmp/1xork1290474312.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xork1290474312.ps tmp/2xork1290474312.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xork1290474312.ps tmp/3xork1290474312.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qx8n1290474312.ps tmp/4qx8n1290474312.png",intern=TRUE))
character(0)
> try(system("convert tmp/5qx8n1290474312.ps tmp/5qx8n1290474312.png",intern=TRUE))
character(0)
> try(system("convert tmp/6qx8n1290474312.ps tmp/6qx8n1290474312.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jop81290474312.ps tmp/7jop81290474312.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bfpt1290474312.ps tmp/8bfpt1290474312.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bfpt1290474312.ps tmp/9bfpt1290474312.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mp6e1290474312.ps tmp/10mp6e1290474312.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.691 2.661 7.235