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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> x <- array(list(2
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+ ,2)
+ ,dim=c(9
+ ,156)
+ ,dimnames=list(c('Y'
+ ,'month'
+ ,'X1t'
+ ,'X2t'
+ ,'X3t'
+ ,'X4t'
+ ,'X5t'
+ ,'X6t'
+ ,'X7t')
+ ,1:156))
> y <- array(NA,dim=c(9,156),dimnames=list(c('Y','month','X1t','X2t','X3t','X4t','X5t','X6t','X7t'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y month X1t X2t X3t X4t X5t X6t X7t
1 2 9 2 1 4 3 3 3 3
2 3 9 2 3 4 3 3 4 3
3 3 9 4 2 3 4 4 4 3
4 3 9 3 3 2 3 3 3 3
5 3 9 3 2 3 3 2 2 2
6 3 9 1 2 4 3 3 2 2
7 2 9 4 4 5 4 4 5 4
8 3 9 2 2 4 2 2 3 2
9 3 9 2 2 4 4 3 2 3
10 4 9 2 2 2 2 2 2 2
11 3 9 4 2 2 3 2 4 4
12 3 9 3 3 4 3 2 3 3
13 2 9 3 2 4 4 4 3 3
14 3 9 2 2 5 3 4 2 3
15 9 3 3 5 3 3 4 3 3
16 9 2 2 4 3 2 2 2 3
17 9 3 3 3 3 3 3 3 3
18 9 3 3 4 4 4 4 3 2
19 9 2 2 4 2 2 2 2 4
20 9 2 2 2 3 2 2 3 3
21 9 1 1 4 3 3 3 2 2
22 9 4 3 4 4 4 4 3 3
23 9 3 2 4 3 3 2 3 3
24 9 2 2 4 3 3 2 2 2
25 9 3 3 4 3 4 3 3 2
26 9 3 3 4 4 4 4 3 4
27 9 4 3 4 4 2 4 4 2
28 9 3 2 3 4 3 3 3 3
29 9 3 3 3 4 3 3 3 2
30 9 2 2 4 4 4 4 2 4
31 9 2 2 3 2 4 2 2 3
32 9 4 3 4 3 3 3 4 2
33 9 4 3 4 4 3 4 4 3
34 9 2 2 4 3 2 3 3 3
35 9 2 2 4 3 2 2 3 1
36 9 3 3 4 4 4 4 4 3
37 9 3 3 4 3 3 4 3 3
38 9 3 2 3 2 2 2 2 3
39 9 3 3 4 3 3 3 3 2
40 9 4 3 4 4 4 4 4 3
41 9 3 3 4 3 4 4 3 9
42 1 2 3 2 2 3 3 5 9
43 2 1 5 2 1 4 2 4 9
44 2 2 4 3 2 3 2 3 9
45 3 3 4 3 2 3 3 2 9
46 4 3 4 4 4 3 4 2 9
47 3 2 4 4 4 3 4 3 9
48 2 2 5 2 2 2 2 4 9
49 2 3 4 3 3 4 3 2 9
50 3 3 4 4 3 4 3 3 9
51 3 3 4 3 2 4 3 4 10
52 4 2 3 3 1 2 2 3 10
53 3 2 4 4 3 3 4 4 10
54 2 2 4 3 2 3 3 3 10
55 2 3 5 3 4 3 4 3 10
56 2 3 4 3 3 3 3 4 10
57 2 2 3 3 4 2 3 2 10
58 3 3 3 4 4 4 4 4 10
59 1 1 4 3 4 4 1 2 10
60 5 3 4 4 4 4 4 4 10
61 2 1 4 3 1 3 2 2 10
62 3 3 4 4 4 4 3 3 10
63 4 2 3 3 4 3 3 2 10
64 4 2 3 4 4 4 3 3 10
65 2 3 3 3 1 3 3 3 10
66 3 2 4 3 4 3 4 3 10
67 3 3 4 3 3 3 2 3 10
68 3 2 4 3 3 3 2 2 10
69 3 3 4 3 4 4 4 4 10
70 1 1 5 2 1 1 1 2 10
71 3 2 3 3 4 4 4 3 10
72 3 2 4 3 3 4 4 4 10
73 3 2 3 4 3 3 3 2 10
74 4 2 2 4 2 5 2 3 10
75 3 3 4 3 3 3 3 3 10
76 4 2 4 3 3 3 3 3 10
77 3 2 5 3 3 3 3 3 10
78 3 2 2 3 4 4 3 2 10
79 2 2 4 4 3 4 4 4 10
80 1 1 4 2 1 3 2 4 10
81 2 2 4 3 3 3 2 10 3
82 3 3 3 3 3 2 10 3 3
83 4 4 4 4 3 4 10 2 3
84 3 3 3 2 2 3 10 2 1
85 4 3 4 4 2 3 10 3 3
86 5 3 3 3 3 3 10 2 2
87 2 3 2 2 2 3 10 3 2
88 2 4 3 4 3 2 10 4 4
89 4 4 4 4 3 4 10 2 2
90 4 3 3 3 3 3 10 3 3
91 3 4 4 4 3 4 10 3 3
92 4 4 3 4 4 2 10 4 3
93 4 4 4 4 4 2 10 3 3
94 4 3 4 3 4 3 10 2 3
95 4 3 3 3 3 3 10 2 2
96 4 2 2 4 2 3 10 3 3
97 2 3 1 5 3 4 10 2 1
98 4 3 2 3 2 2 10 3 2
99 4 4 2 4 3 3 10 3 3
100 4 3 3 4 3 2 10 4 3
101 4 4 4 4 4 2 10 4 3
102 4 3 4 4 3 3 10 3 3
103 5 3 5 5 3 3 10 1 2
104 4 3 2 4 2 5 10 1 1
105 4 3 1 3 1 2 10 4 4
106 4 4 3 4 3 4 10 2 1
107 3 3 3 4 3 3 10 4 4
108 4 4 4 4 4 4 10 2 1
109 4 3 2 4 2 4 10 2 2
110 4 3 2 4 3 10 3 2 4
111 3 2 4 3 3 10 4 3 3
112 3 3 3 4 3 10 3 3 4
113 3 3 4 3 3 10 3 3 4
114 3 4 4 4 2 10 4 3 4
115 4 4 4 4 2 10 2 4 5
116 3 4 4 3 2 10 3 3 4
117 3 4 4 3 2 10 3 3 4
118 4 2 4 3 1 10 3 3 4
119 3 3 4 3 2 10 2 2 4
120 3 3 3 3 3 10 2 2 4
121 3 3 4 3 3 10 2 3 4
122 3 4 3 3 4 10 2 2 4
123 3 3 3 3 3 10 4 2 4
124 3 4 4 4 2 10 3 3 4
125 4 4 4 3 3 10 2 3 4
126 3 3 4 2 2 10 4 4 4
127 4 4 4 4 3 10 3 3 4
128 3 3 3 3 3 10 2 3 3
129 4 3 3 3 5 10 1 3 1
130 1 1 1 1 2 10 4 4 4
131 4 4 4 4 2 10 3 3 4
132 3 4 3 3 3 10 2 2 4
133 4 2 4 2 1 10 2 4 4
134 4 2 4 2 3 10 3 3 4
135 4 4 3 4 3 10 2 2 4
136 3 3 4 3 3 10 3 3 4
137 3 4 4 4 4 10 2 1 4
138 3 2 2 2 1 10 3 3 4
139 4 5 4 4 2 10 3 2 3
140 3 3 3 4 4 10 2 2 4
141 3 4 3 3 2 10 3 3 4
142 3 3 4 3 2 10 2 2 4
143 4 4 4 3 4 10 2 2 4
144 2 2 2 2 3 10 2 2 4
145 3 3 3 3 3 10 3 3 4
146 3 1 3 3 4 10 2 3 4
147 3 2 3 3 1 10 3 3 5
148 3 5 5 4 2 10 3 3 4
149 3 4 4 4 2 10 4 4 4
150 4 4 4 3 3 10 4 3 4
151 3 3 3 3 2 10 4 3 4
152 4 4 4 4 3 10 2 2 3
153 3 2 3 2 3 10 3 3 3
154 4 4 4 3 3 10 3 4 3
155 4 4 4 1 10 1 3 3 3
156 4 2 2 9 2 1 4 3 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month X1t X2t X3t X4t
10.12806 -0.47094 -0.09304 0.69345 0.19420 -0.38008
X5t X6t X7t
-0.44658 -0.10972 -0.57576
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.40669 -0.67964 -0.04216 0.72314 6.02514
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.12806 1.01914 9.938 < 2e-16 ***
month -0.47094 0.07340 -6.416 1.81e-09 ***
X1t -0.09304 0.15768 -0.590 0.556
X2t 0.69345 0.14050 4.936 2.14e-06 ***
X3t 0.19420 0.11860 1.637 0.104
X4t -0.38008 0.04746 -8.009 3.25e-13 ***
X5t -0.44658 0.05289 -8.444 2.71e-14 ***
X6t -0.10972 0.12953 -0.847 0.398
X7t -0.57576 0.05745 -10.022 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.513 on 147 degrees of freedom
Multiple R-squared: 0.6059, Adjusted R-squared: 0.5844
F-statistic: 28.24 on 8 and 147 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,] 0.0371083522 7.421670e-02 9.628916e-01
[2,] 0.0109387865 2.187757e-02 9.890612e-01
[3,] 0.0148915450 2.978309e-02 9.851085e-01
[4,] 0.0047530385 9.506077e-03 9.952470e-01
[5,] 0.0027575745 5.515149e-03 9.972424e-01
[6,] 0.0028625104 5.725021e-03 9.971375e-01
[7,] 0.0011678697 2.335739e-03 9.988321e-01
[8,] 0.0004717998 9.435997e-04 9.995282e-01
[9,] 0.0001591638 3.183275e-04 9.998408e-01
[10,] 0.0006998011 1.399602e-03 9.993002e-01
[11,] 0.0036965689 7.393138e-03 9.963034e-01
[12,] 0.0029143690 5.828738e-03 9.970856e-01
[13,] 0.0019302183 3.860437e-03 9.980698e-01
[14,] 0.0010086252 2.017250e-03 9.989914e-01
[15,] 0.0008993388 1.798678e-03 9.991007e-01
[16,] 0.0006767910 1.353582e-03 9.993232e-01
[17,] 0.0005642029 1.128406e-03 9.994358e-01
[18,] 0.0003072810 6.145620e-04 9.996927e-01
[19,] 0.0002229973 4.459945e-04 9.997770e-01
[20,] 0.0001441033 2.882065e-04 9.998559e-01
[21,] 0.0001357103 2.714206e-04 9.998643e-01
[22,] 0.0001727735 3.455469e-04 9.998272e-01
[23,] 0.0001875596 3.751193e-04 9.998124e-01
[24,] 0.0002891141 5.782282e-04 9.997109e-01
[25,] 0.0003611364 7.222728e-04 9.996389e-01
[26,] 0.0004815540 9.631079e-04 9.995184e-01
[27,] 0.0009403805 1.880761e-03 9.990596e-01
[28,] 0.0028672527 5.734505e-03 9.971327e-01
[29,] 0.0302459000 6.049180e-02 9.697541e-01
[30,] 0.3901926746 7.803853e-01 6.098073e-01
[31,] 0.9997272097 5.455807e-04 2.727903e-04
[32,] 0.9999809072 3.818558e-05 1.909279e-05
[33,] 0.9999955489 8.902239e-06 4.451120e-06
[34,] 0.9999940595 1.188093e-05 5.940465e-06
[35,] 0.9999924086 1.518284e-05 7.591421e-06
[36,] 0.9999952912 9.417520e-06 4.708760e-06
[37,] 0.9999918187 1.636264e-05 8.181319e-06
[38,] 0.9999922354 1.552930e-05 7.764649e-06
[39,] 0.9999873946 2.521077e-05 1.260539e-05
[40,] 0.9999828899 3.422017e-05 1.711009e-05
[41,] 0.9999930418 1.391639e-05 6.958193e-06
[42,] 0.9999907380 1.852409e-05 9.262047e-06
[43,] 0.9999885423 2.291549e-05 1.145775e-05
[44,] 0.9999885711 2.285788e-05 1.142894e-05
[45,] 0.9999848855 3.022901e-05 1.511451e-05
[46,] 0.9999821360 3.572807e-05 1.786403e-05
[47,] 0.9999710671 5.786584e-05 2.893292e-05
[48,] 0.9999902180 1.956395e-05 9.781973e-06
[49,] 0.9999986215 2.757016e-06 1.378508e-06
[50,] 0.9999986065 2.787087e-06 1.393543e-06
[51,] 0.9999978662 4.267638e-06 2.133819e-06
[52,] 0.9999986096 2.780766e-06 1.390383e-06
[53,] 0.9999984774 3.045193e-06 1.522596e-06
[54,] 0.9999979943 4.011316e-06 2.005658e-06
[55,] 0.9999964199 7.160274e-06 3.580137e-06
[56,] 0.9999967287 6.542546e-06 3.271273e-06
[57,] 0.9999951760 9.648003e-06 4.824001e-06
[58,] 0.9999922055 1.558895e-05 7.794475e-06
[59,] 0.9999974287 5.142680e-06 2.571340e-06
[60,] 0.9999957688 8.462475e-06 4.231237e-06
[61,] 0.9999929054 1.418912e-05 7.094562e-06
[62,] 0.9999896022 2.079569e-05 1.039784e-05
[63,] 0.9999949108 1.017846e-05 5.089231e-06
[64,] 0.9999929878 1.402443e-05 7.012217e-06
[65,] 0.9999970910 5.818074e-06 2.909037e-06
[66,] 0.9999954023 9.195381e-06 4.597690e-06
[67,] 0.9999958889 8.222214e-06 4.111107e-06
[68,] 0.9999970684 5.863205e-06 2.931602e-06
[69,] 0.9999989799 2.040276e-06 1.020138e-06
[70,] 0.9999999093 1.813466e-07 9.067328e-08
[71,] 0.9999999993 1.303456e-09 6.517278e-10
[72,] 0.9999999996 8.746093e-10 4.373047e-10
[73,] 0.9999999998 4.231535e-10 2.115767e-10
[74,] 0.9999999996 8.086659e-10 4.043329e-10
[75,] 0.9999999998 4.943255e-10 2.471628e-10
[76,] 1.0000000000 7.156464e-11 3.578232e-11
[77,] 1.0000000000 4.483865e-12 2.241933e-12
[78,] 1.0000000000 8.178270e-12 4.089135e-12
[79,] 1.0000000000 1.822102e-11 9.110511e-12
[80,] 1.0000000000 1.047753e-11 5.238766e-12
[81,] 1.0000000000 2.610004e-11 1.305002e-11
[82,] 1.0000000000 6.239162e-11 3.119581e-11
[83,] 0.9999999999 1.563383e-10 7.816916e-11
[84,] 0.9999999998 3.741913e-10 1.870957e-10
[85,] 0.9999999997 5.149402e-10 2.574701e-10
[86,] 1.0000000000 1.179201e-12 5.896007e-13
[87,] 1.0000000000 3.156758e-12 1.578379e-12
[88,] 1.0000000000 7.256907e-12 3.628454e-12
[89,] 1.0000000000 1.936027e-11 9.680137e-12
[90,] 1.0000000000 5.175260e-11 2.587630e-11
[91,] 0.9999999999 1.391243e-10 6.956213e-11
[92,] 0.9999999999 2.119497e-10 1.059749e-10
[93,] 0.9999999998 3.686803e-10 1.843401e-10
[94,] 0.9999999999 1.381403e-10 6.907014e-11
[95,] 0.9999999998 3.358595e-10 1.679297e-10
[96,] 0.9999999996 8.443807e-10 4.221904e-10
[97,] 0.9999999991 1.728190e-09 8.640951e-10
[98,] 0.9999999993 1.358091e-09 6.790453e-10
[99,] 0.9999999999 1.188068e-10 5.940341e-11
[100,] 0.9999999999 2.540996e-10 1.270498e-10
[101,] 0.9999999997 6.128279e-10 3.064140e-10
[102,] 0.9999999993 1.372922e-09 6.864611e-10
[103,] 0.9999999985 3.081250e-09 1.540625e-09
[104,] 0.9999999971 5.736212e-09 2.868106e-09
[105,] 0.9999999941 1.177735e-08 5.888674e-09
[106,] 0.9999999885 2.307390e-08 1.153695e-08
[107,] 0.9999999817 3.663795e-08 1.831897e-08
[108,] 0.9999999675 6.495456e-08 3.247728e-08
[109,] 0.9999999169 1.661631e-07 8.308154e-08
[110,] 0.9999998707 2.586179e-07 1.293090e-07
[111,] 0.9999996607 6.785704e-07 3.392852e-07
[112,] 0.9999992083 1.583477e-06 7.917384e-07
[113,] 0.9999988424 2.315290e-06 1.157645e-06
[114,] 0.9999973671 5.265759e-06 2.632880e-06
[115,] 0.9999947719 1.045628e-05 5.228141e-06
[116,] 0.9999896370 2.072592e-05 1.036296e-05
[117,] 0.9999804189 3.916217e-05 1.958108e-05
[118,] 0.9999620401 7.591973e-05 3.795987e-05
[119,] 0.9999749066 5.018682e-05 2.509341e-05
[120,] 0.9999519302 9.613962e-05 4.806981e-05
[121,] 0.9998758783 2.482434e-04 1.241217e-04
[122,] 0.9997968690 4.062619e-04 2.031310e-04
[123,] 0.9998669846 2.660307e-04 1.330154e-04
[124,] 0.9998517598 2.964803e-04 1.482402e-04
[125,] 0.9996162518 7.674964e-04 3.837482e-04
[126,] 0.9992316883 1.536623e-03 7.683117e-04
[127,] 0.9986865830 2.626834e-03 1.313417e-03
[128,] 0.9964167789 7.166442e-03 3.583221e-03
[129,] 0.9925665723 1.486686e-02 7.433428e-03
[130,] 0.9809034969 3.819301e-02 1.909650e-02
[131,] 0.9537312598 9.253748e-02 4.626874e-02
[132,] 0.9229089799 1.541820e-01 7.709102e-02
[133,] 0.8768981144 2.462038e-01 1.231019e-01
> postscript(file="/var/www/html/freestat/rcomp/tmp/1df1o1291332373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2df1o1291332373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3df1o1291332373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/456i91291332373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/556i91291332373.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 = 156
Frequency = 1
1 2 3 4 5 6
-0.63735520 -0.91453191 0.98585117 -0.54282244 -1.17563969 -1.10932748
7 8 9 10 11 12
-1.10396127 -1.73323005 -0.06044986 -0.45455629 0.48256730 -1.37780282
13 14 15 16 17 18
-0.41110753 -0.18814604 1.49704088 0.24355000 2.43735912 1.80061005
19 20 21 22 23 24
1.01351102 1.74017486 -0.06952205 2.84730875 1.20429047 0.04786770
25 26 27 28 29 30
1.54822643 2.95213426 1.62111110 2.15012449 1.66739809 2.27843789
31 32 33 34 35 36
1.89135893 1.74880728 2.57695301 0.79985662 -0.79825014 2.48609623
37 38 39 40 41 42
2.19049127 1.60213590 1.16814662 2.95703282 6.02514371 -1.47190738
43 44 45 46 47 48
-0.73880045 -1.73835276 0.06944230 0.43417661 -0.92703590 -1.22222240
49 50 51 52 53 54
-0.74467681 -0.32840313 1.24473236 0.55849275 -0.04735080 -0.71600811
55 56 57 58 59 60
-0.09385111 -0.32954637 -1.68724554 0.51643097 -3.19815190 2.60946668
61 62 63 64 65 66
-1.54905240 0.05316005 0.69283427 0.48918776 -0.14390831 0.34217660
67 68 69 70 71 72
0.11414701 -0.46651365 1.30291707 -2.96930846 0.62922070 1.02617940
73 74 75 76 77 78
-0.80641721 0.71804715 0.56072956 1.08979297 0.18282868 -0.02012163
79 80 81 82 83 84
-0.66727099 -1.63615386 -4.61905578 -0.81664287 0.70431457 -0.81016203
85 86 87 88 89 90
0.15722117 0.87795076 -1.21771156 -1.35367050 0.12855247 0.56343694
91 92 93 94 95 96
-0.18596135 -0.12363152 -0.14031989 0.35254965 -0.12204924 -0.49978683
97 98 99 100 101 102
-3.89070374 -0.29124176 0.24788743 -0.40036919 -0.03059581 -0.03697775
103 104 105 106 107 108
-0.43260269 -0.63966298 1.07116973 -0.54024534 -0.44452727 -0.64140855
109 110 111 112 113 114
-0.33425661 -0.22253029 -0.83340055 -1.01977051 -0.23328441 0.18498325
115 116 117 118 119 120
0.97730434 0.43185110 0.43185110 0.68417684 -0.59539211 -0.88262674
121 122 123 124 125 126
-0.67986695 -0.60588907 0.01053836 -0.26159929 0.79106963 1.21067153
127 128 129 130 131 132
0.54420179 -1.34866477 -2.33516936 -1.31685838 0.73840071 -0.41169015
133 134 135 136 137 138
1.04076877 0.98922940 -0.10514054 -0.23328441 -1.31602783 0.19155582
139 140 141 142 143 144
0.52385111 -1.77027605 0.33881539 -0.59539211 0.48714664 -1.75314864
145 146 147 148 149 150
-0.32632011 -1.90897476 0.16690324 0.30237300 0.29470733 1.68423473
151 152 153 154 155 156
0.31446135 -0.58786694 -0.67956841 0.77161415 -2.73131986 -7.40669369
> postscript(file="/var/www/html/freestat/rcomp/tmp/6yyzc1291332373.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.63735520 NA
1 -0.91453191 -0.63735520
2 0.98585117 -0.91453191
3 -0.54282244 0.98585117
4 -1.17563969 -0.54282244
5 -1.10932748 -1.17563969
6 -1.10396127 -1.10932748
7 -1.73323005 -1.10396127
8 -0.06044986 -1.73323005
9 -0.45455629 -0.06044986
10 0.48256730 -0.45455629
11 -1.37780282 0.48256730
12 -0.41110753 -1.37780282
13 -0.18814604 -0.41110753
14 1.49704088 -0.18814604
15 0.24355000 1.49704088
16 2.43735912 0.24355000
17 1.80061005 2.43735912
18 1.01351102 1.80061005
19 1.74017486 1.01351102
20 -0.06952205 1.74017486
21 2.84730875 -0.06952205
22 1.20429047 2.84730875
23 0.04786770 1.20429047
24 1.54822643 0.04786770
25 2.95213426 1.54822643
26 1.62111110 2.95213426
27 2.15012449 1.62111110
28 1.66739809 2.15012449
29 2.27843789 1.66739809
30 1.89135893 2.27843789
31 1.74880728 1.89135893
32 2.57695301 1.74880728
33 0.79985662 2.57695301
34 -0.79825014 0.79985662
35 2.48609623 -0.79825014
36 2.19049127 2.48609623
37 1.60213590 2.19049127
38 1.16814662 1.60213590
39 2.95703282 1.16814662
40 6.02514371 2.95703282
41 -1.47190738 6.02514371
42 -0.73880045 -1.47190738
43 -1.73835276 -0.73880045
44 0.06944230 -1.73835276
45 0.43417661 0.06944230
46 -0.92703590 0.43417661
47 -1.22222240 -0.92703590
48 -0.74467681 -1.22222240
49 -0.32840313 -0.74467681
50 1.24473236 -0.32840313
51 0.55849275 1.24473236
52 -0.04735080 0.55849275
53 -0.71600811 -0.04735080
54 -0.09385111 -0.71600811
55 -0.32954637 -0.09385111
56 -1.68724554 -0.32954637
57 0.51643097 -1.68724554
58 -3.19815190 0.51643097
59 2.60946668 -3.19815190
60 -1.54905240 2.60946668
61 0.05316005 -1.54905240
62 0.69283427 0.05316005
63 0.48918776 0.69283427
64 -0.14390831 0.48918776
65 0.34217660 -0.14390831
66 0.11414701 0.34217660
67 -0.46651365 0.11414701
68 1.30291707 -0.46651365
69 -2.96930846 1.30291707
70 0.62922070 -2.96930846
71 1.02617940 0.62922070
72 -0.80641721 1.02617940
73 0.71804715 -0.80641721
74 0.56072956 0.71804715
75 1.08979297 0.56072956
76 0.18282868 1.08979297
77 -0.02012163 0.18282868
78 -0.66727099 -0.02012163
79 -1.63615386 -0.66727099
80 -4.61905578 -1.63615386
81 -0.81664287 -4.61905578
82 0.70431457 -0.81664287
83 -0.81016203 0.70431457
84 0.15722117 -0.81016203
85 0.87795076 0.15722117
86 -1.21771156 0.87795076
87 -1.35367050 -1.21771156
88 0.12855247 -1.35367050
89 0.56343694 0.12855247
90 -0.18596135 0.56343694
91 -0.12363152 -0.18596135
92 -0.14031989 -0.12363152
93 0.35254965 -0.14031989
94 -0.12204924 0.35254965
95 -0.49978683 -0.12204924
96 -3.89070374 -0.49978683
97 -0.29124176 -3.89070374
98 0.24788743 -0.29124176
99 -0.40036919 0.24788743
100 -0.03059581 -0.40036919
101 -0.03697775 -0.03059581
102 -0.43260269 -0.03697775
103 -0.63966298 -0.43260269
104 1.07116973 -0.63966298
105 -0.54024534 1.07116973
106 -0.44452727 -0.54024534
107 -0.64140855 -0.44452727
108 -0.33425661 -0.64140855
109 -0.22253029 -0.33425661
110 -0.83340055 -0.22253029
111 -1.01977051 -0.83340055
112 -0.23328441 -1.01977051
113 0.18498325 -0.23328441
114 0.97730434 0.18498325
115 0.43185110 0.97730434
116 0.43185110 0.43185110
117 0.68417684 0.43185110
118 -0.59539211 0.68417684
119 -0.88262674 -0.59539211
120 -0.67986695 -0.88262674
121 -0.60588907 -0.67986695
122 0.01053836 -0.60588907
123 -0.26159929 0.01053836
124 0.79106963 -0.26159929
125 1.21067153 0.79106963
126 0.54420179 1.21067153
127 -1.34866477 0.54420179
128 -2.33516936 -1.34866477
129 -1.31685838 -2.33516936
130 0.73840071 -1.31685838
131 -0.41169015 0.73840071
132 1.04076877 -0.41169015
133 0.98922940 1.04076877
134 -0.10514054 0.98922940
135 -0.23328441 -0.10514054
136 -1.31602783 -0.23328441
137 0.19155582 -1.31602783
138 0.52385111 0.19155582
139 -1.77027605 0.52385111
140 0.33881539 -1.77027605
141 -0.59539211 0.33881539
142 0.48714664 -0.59539211
143 -1.75314864 0.48714664
144 -0.32632011 -1.75314864
145 -1.90897476 -0.32632011
146 0.16690324 -1.90897476
147 0.30237300 0.16690324
148 0.29470733 0.30237300
149 1.68423473 0.29470733
150 0.31446135 1.68423473
151 -0.58786694 0.31446135
152 -0.67956841 -0.58786694
153 0.77161415 -0.67956841
154 -2.73131986 0.77161415
155 -7.40669369 -2.73131986
156 NA -7.40669369
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.91453191 -0.63735520
[2,] 0.98585117 -0.91453191
[3,] -0.54282244 0.98585117
[4,] -1.17563969 -0.54282244
[5,] -1.10932748 -1.17563969
[6,] -1.10396127 -1.10932748
[7,] -1.73323005 -1.10396127
[8,] -0.06044986 -1.73323005
[9,] -0.45455629 -0.06044986
[10,] 0.48256730 -0.45455629
[11,] -1.37780282 0.48256730
[12,] -0.41110753 -1.37780282
[13,] -0.18814604 -0.41110753
[14,] 1.49704088 -0.18814604
[15,] 0.24355000 1.49704088
[16,] 2.43735912 0.24355000
[17,] 1.80061005 2.43735912
[18,] 1.01351102 1.80061005
[19,] 1.74017486 1.01351102
[20,] -0.06952205 1.74017486
[21,] 2.84730875 -0.06952205
[22,] 1.20429047 2.84730875
[23,] 0.04786770 1.20429047
[24,] 1.54822643 0.04786770
[25,] 2.95213426 1.54822643
[26,] 1.62111110 2.95213426
[27,] 2.15012449 1.62111110
[28,] 1.66739809 2.15012449
[29,] 2.27843789 1.66739809
[30,] 1.89135893 2.27843789
[31,] 1.74880728 1.89135893
[32,] 2.57695301 1.74880728
[33,] 0.79985662 2.57695301
[34,] -0.79825014 0.79985662
[35,] 2.48609623 -0.79825014
[36,] 2.19049127 2.48609623
[37,] 1.60213590 2.19049127
[38,] 1.16814662 1.60213590
[39,] 2.95703282 1.16814662
[40,] 6.02514371 2.95703282
[41,] -1.47190738 6.02514371
[42,] -0.73880045 -1.47190738
[43,] -1.73835276 -0.73880045
[44,] 0.06944230 -1.73835276
[45,] 0.43417661 0.06944230
[46,] -0.92703590 0.43417661
[47,] -1.22222240 -0.92703590
[48,] -0.74467681 -1.22222240
[49,] -0.32840313 -0.74467681
[50,] 1.24473236 -0.32840313
[51,] 0.55849275 1.24473236
[52,] -0.04735080 0.55849275
[53,] -0.71600811 -0.04735080
[54,] -0.09385111 -0.71600811
[55,] -0.32954637 -0.09385111
[56,] -1.68724554 -0.32954637
[57,] 0.51643097 -1.68724554
[58,] -3.19815190 0.51643097
[59,] 2.60946668 -3.19815190
[60,] -1.54905240 2.60946668
[61,] 0.05316005 -1.54905240
[62,] 0.69283427 0.05316005
[63,] 0.48918776 0.69283427
[64,] -0.14390831 0.48918776
[65,] 0.34217660 -0.14390831
[66,] 0.11414701 0.34217660
[67,] -0.46651365 0.11414701
[68,] 1.30291707 -0.46651365
[69,] -2.96930846 1.30291707
[70,] 0.62922070 -2.96930846
[71,] 1.02617940 0.62922070
[72,] -0.80641721 1.02617940
[73,] 0.71804715 -0.80641721
[74,] 0.56072956 0.71804715
[75,] 1.08979297 0.56072956
[76,] 0.18282868 1.08979297
[77,] -0.02012163 0.18282868
[78,] -0.66727099 -0.02012163
[79,] -1.63615386 -0.66727099
[80,] -4.61905578 -1.63615386
[81,] -0.81664287 -4.61905578
[82,] 0.70431457 -0.81664287
[83,] -0.81016203 0.70431457
[84,] 0.15722117 -0.81016203
[85,] 0.87795076 0.15722117
[86,] -1.21771156 0.87795076
[87,] -1.35367050 -1.21771156
[88,] 0.12855247 -1.35367050
[89,] 0.56343694 0.12855247
[90,] -0.18596135 0.56343694
[91,] -0.12363152 -0.18596135
[92,] -0.14031989 -0.12363152
[93,] 0.35254965 -0.14031989
[94,] -0.12204924 0.35254965
[95,] -0.49978683 -0.12204924
[96,] -3.89070374 -0.49978683
[97,] -0.29124176 -3.89070374
[98,] 0.24788743 -0.29124176
[99,] -0.40036919 0.24788743
[100,] -0.03059581 -0.40036919
[101,] -0.03697775 -0.03059581
[102,] -0.43260269 -0.03697775
[103,] -0.63966298 -0.43260269
[104,] 1.07116973 -0.63966298
[105,] -0.54024534 1.07116973
[106,] -0.44452727 -0.54024534
[107,] -0.64140855 -0.44452727
[108,] -0.33425661 -0.64140855
[109,] -0.22253029 -0.33425661
[110,] -0.83340055 -0.22253029
[111,] -1.01977051 -0.83340055
[112,] -0.23328441 -1.01977051
[113,] 0.18498325 -0.23328441
[114,] 0.97730434 0.18498325
[115,] 0.43185110 0.97730434
[116,] 0.43185110 0.43185110
[117,] 0.68417684 0.43185110
[118,] -0.59539211 0.68417684
[119,] -0.88262674 -0.59539211
[120,] -0.67986695 -0.88262674
[121,] -0.60588907 -0.67986695
[122,] 0.01053836 -0.60588907
[123,] -0.26159929 0.01053836
[124,] 0.79106963 -0.26159929
[125,] 1.21067153 0.79106963
[126,] 0.54420179 1.21067153
[127,] -1.34866477 0.54420179
[128,] -2.33516936 -1.34866477
[129,] -1.31685838 -2.33516936
[130,] 0.73840071 -1.31685838
[131,] -0.41169015 0.73840071
[132,] 1.04076877 -0.41169015
[133,] 0.98922940 1.04076877
[134,] -0.10514054 0.98922940
[135,] -0.23328441 -0.10514054
[136,] -1.31602783 -0.23328441
[137,] 0.19155582 -1.31602783
[138,] 0.52385111 0.19155582
[139,] -1.77027605 0.52385111
[140,] 0.33881539 -1.77027605
[141,] -0.59539211 0.33881539
[142,] 0.48714664 -0.59539211
[143,] -1.75314864 0.48714664
[144,] -0.32632011 -1.75314864
[145,] -1.90897476 -0.32632011
[146,] 0.16690324 -1.90897476
[147,] 0.30237300 0.16690324
[148,] 0.29470733 0.30237300
[149,] 1.68423473 0.29470733
[150,] 0.31446135 1.68423473
[151,] -0.58786694 0.31446135
[152,] -0.67956841 -0.58786694
[153,] 0.77161415 -0.67956841
[154,] -2.73131986 0.77161415
[155,] -7.40669369 -2.73131986
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.91453191 -0.63735520
2 0.98585117 -0.91453191
3 -0.54282244 0.98585117
4 -1.17563969 -0.54282244
5 -1.10932748 -1.17563969
6 -1.10396127 -1.10932748
7 -1.73323005 -1.10396127
8 -0.06044986 -1.73323005
9 -0.45455629 -0.06044986
10 0.48256730 -0.45455629
11 -1.37780282 0.48256730
12 -0.41110753 -1.37780282
13 -0.18814604 -0.41110753
14 1.49704088 -0.18814604
15 0.24355000 1.49704088
16 2.43735912 0.24355000
17 1.80061005 2.43735912
18 1.01351102 1.80061005
19 1.74017486 1.01351102
20 -0.06952205 1.74017486
21 2.84730875 -0.06952205
22 1.20429047 2.84730875
23 0.04786770 1.20429047
24 1.54822643 0.04786770
25 2.95213426 1.54822643
26 1.62111110 2.95213426
27 2.15012449 1.62111110
28 1.66739809 2.15012449
29 2.27843789 1.66739809
30 1.89135893 2.27843789
31 1.74880728 1.89135893
32 2.57695301 1.74880728
33 0.79985662 2.57695301
34 -0.79825014 0.79985662
35 2.48609623 -0.79825014
36 2.19049127 2.48609623
37 1.60213590 2.19049127
38 1.16814662 1.60213590
39 2.95703282 1.16814662
40 6.02514371 2.95703282
41 -1.47190738 6.02514371
42 -0.73880045 -1.47190738
43 -1.73835276 -0.73880045
44 0.06944230 -1.73835276
45 0.43417661 0.06944230
46 -0.92703590 0.43417661
47 -1.22222240 -0.92703590
48 -0.74467681 -1.22222240
49 -0.32840313 -0.74467681
50 1.24473236 -0.32840313
51 0.55849275 1.24473236
52 -0.04735080 0.55849275
53 -0.71600811 -0.04735080
54 -0.09385111 -0.71600811
55 -0.32954637 -0.09385111
56 -1.68724554 -0.32954637
57 0.51643097 -1.68724554
58 -3.19815190 0.51643097
59 2.60946668 -3.19815190
60 -1.54905240 2.60946668
61 0.05316005 -1.54905240
62 0.69283427 0.05316005
63 0.48918776 0.69283427
64 -0.14390831 0.48918776
65 0.34217660 -0.14390831
66 0.11414701 0.34217660
67 -0.46651365 0.11414701
68 1.30291707 -0.46651365
69 -2.96930846 1.30291707
70 0.62922070 -2.96930846
71 1.02617940 0.62922070
72 -0.80641721 1.02617940
73 0.71804715 -0.80641721
74 0.56072956 0.71804715
75 1.08979297 0.56072956
76 0.18282868 1.08979297
77 -0.02012163 0.18282868
78 -0.66727099 -0.02012163
79 -1.63615386 -0.66727099
80 -4.61905578 -1.63615386
81 -0.81664287 -4.61905578
82 0.70431457 -0.81664287
83 -0.81016203 0.70431457
84 0.15722117 -0.81016203
85 0.87795076 0.15722117
86 -1.21771156 0.87795076
87 -1.35367050 -1.21771156
88 0.12855247 -1.35367050
89 0.56343694 0.12855247
90 -0.18596135 0.56343694
91 -0.12363152 -0.18596135
92 -0.14031989 -0.12363152
93 0.35254965 -0.14031989
94 -0.12204924 0.35254965
95 -0.49978683 -0.12204924
96 -3.89070374 -0.49978683
97 -0.29124176 -3.89070374
98 0.24788743 -0.29124176
99 -0.40036919 0.24788743
100 -0.03059581 -0.40036919
101 -0.03697775 -0.03059581
102 -0.43260269 -0.03697775
103 -0.63966298 -0.43260269
104 1.07116973 -0.63966298
105 -0.54024534 1.07116973
106 -0.44452727 -0.54024534
107 -0.64140855 -0.44452727
108 -0.33425661 -0.64140855
109 -0.22253029 -0.33425661
110 -0.83340055 -0.22253029
111 -1.01977051 -0.83340055
112 -0.23328441 -1.01977051
113 0.18498325 -0.23328441
114 0.97730434 0.18498325
115 0.43185110 0.97730434
116 0.43185110 0.43185110
117 0.68417684 0.43185110
118 -0.59539211 0.68417684
119 -0.88262674 -0.59539211
120 -0.67986695 -0.88262674
121 -0.60588907 -0.67986695
122 0.01053836 -0.60588907
123 -0.26159929 0.01053836
124 0.79106963 -0.26159929
125 1.21067153 0.79106963
126 0.54420179 1.21067153
127 -1.34866477 0.54420179
128 -2.33516936 -1.34866477
129 -1.31685838 -2.33516936
130 0.73840071 -1.31685838
131 -0.41169015 0.73840071
132 1.04076877 -0.41169015
133 0.98922940 1.04076877
134 -0.10514054 0.98922940
135 -0.23328441 -0.10514054
136 -1.31602783 -0.23328441
137 0.19155582 -1.31602783
138 0.52385111 0.19155582
139 -1.77027605 0.52385111
140 0.33881539 -1.77027605
141 -0.59539211 0.33881539
142 0.48714664 -0.59539211
143 -1.75314864 0.48714664
144 -0.32632011 -1.75314864
145 -1.90897476 -0.32632011
146 0.16690324 -1.90897476
147 0.30237300 0.16690324
148 0.29470733 0.30237300
149 1.68423473 0.29470733
150 0.31446135 1.68423473
151 -0.58786694 0.31446135
152 -0.67956841 -0.58786694
153 0.77161415 -0.67956841
154 -2.73131986 0.77161415
155 -7.40669369 -2.73131986
> 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/7yyzc1291332373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8rpgw1291332373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9rpgw1291332373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10jgfz1291332373.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11nze51291332373.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/12qzvb1291332373.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/134rs21291332373.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/148s981291332373.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/15tspe1291332373.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/16fto21291332373.tab")
+ }
>
> try(system("convert tmp/1df1o1291332373.ps tmp/1df1o1291332373.png",intern=TRUE))
character(0)
> try(system("convert tmp/2df1o1291332373.ps tmp/2df1o1291332373.png",intern=TRUE))
character(0)
> try(system("convert tmp/3df1o1291332373.ps tmp/3df1o1291332373.png",intern=TRUE))
character(0)
> try(system("convert tmp/456i91291332373.ps tmp/456i91291332373.png",intern=TRUE))
character(0)
> try(system("convert tmp/556i91291332373.ps tmp/556i91291332373.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yyzc1291332373.ps tmp/6yyzc1291332373.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yyzc1291332373.ps tmp/7yyzc1291332373.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rpgw1291332373.ps tmp/8rpgw1291332373.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rpgw1291332373.ps tmp/9rpgw1291332373.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jgfz1291332373.ps tmp/10jgfz1291332373.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.292 2.736 7.434