R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(1
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+ ,dim=c(3
+ ,289)
+ ,dimnames=list(c('Beter'
+ ,'Fout'
+ ,'Ouders')
+ ,1:289))
> y <- array(NA,dim=c(3,289),dimnames=list(c('Beter','Fout','Ouders'),1:289))
> 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'
> 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, 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
Beter Fout Ouders
1 1 4 4
2 4 3 3
3 4 4 3
4 0 0 0
5 0 0 0
6 1 4 4
7 3 3 3
8 0 0 0
9 0 0 0
10 4 4 5
11 0 0 0
12 2 1 1
13 0 0 0
14 2 2 2
15 0 0 0
16 0 0 0
17 4 4 4
18 4 4 4
19 2 2 2
20 0 0 0
21 4 3 4
22 2 2 2
23 2 3 2
24 0 0 0
25 0 0 0
26 1 4 2
27 1 2 4
28 0 0 0
29 2 4 2
30 1 2 3
31 2 4 4
32 0 0 0
33 2 2 4
34 1 2 2
35 0 0 0
36 4 4 2
37 4 3 4
38 0 0 0
39 2 3 3
40 0 0 0
41 0 0 0
42 1 2 4
43 4 4 4
44 2 4 4
45 0 0 0
46 3 2 4
47 3 4 3
48 3 4 2
49 0 0 0
50 0 0 0
51 2 4 4
52 0 0 0
53 2 3 4
54 1 4 1
55 1 3 2
56 3 3 4
57 0 0 0
58 0 0 0
59 0 0 0
60 0 0 0
61 3 5 3
62 4 4 4
63 4 4 4
64 2 4 3
65 0 0 0
66 0 0 0
67 0 0 0
68 2 4 4
69 0 0 0
70 0 0 0
71 0 0 0
72 2 4 2
73 0 0 0
74 2 3 2
75 2 4 4
76 2 4 4
77 2 1 3
78 0 0 0
79 0 0 0
80 0 0 0
81 1 1 4
82 2 4 4
83 0 0 0
84 5 5 1
85 4 2 5
86 1 4 2
87 2 4 4
88 3 4 2
89 1 3 5
90 0 0 0
91 4 4 4
92 0 0 0
93 2 4 2
94 1 4 4
95 2 2 4
96 2 4 4
97 3 3 2
98 4 4 4
99 2 3 4
100 3 4 2
101 1 2 3
102 2 2 4
103 3 2 5
104 1 2 4
105 0 0 0
106 0 0 0
107 1 4 4
108 2 4 3
109 3 4 4
110 2 4 4
111 0 0 0
112 0 0 0
113 0 0 0
114 0 0 0
115 2 3 2
116 2 3 2
117 3 4 3
118 1 2 4
119 5 5 4
120 2 4 4
121 1 3 2
122 2 3 2
123 1 2 2
124 1 3 3
125 0 0 0
126 2 4 2
127 2 2 3
128 3 3 4
129 2 2 2
130 0 0 0
131 0 0 0
132 4 3 2
133 0 0 0
134 1 4 4
135 1 3 2
136 2 3 3
137 0 0 0
138 0 0 0
139 2 3 2
140 0 0 0
141 2 4 3
142 2 4 4
143 1 2 3
144 2 4 3
145 0 0 0
146 3 4 4
147 3 4 5
148 3 4 4
149 0 0 0
150 0 0 0
151 1 2 3
152 2 3 4
153 1 4 3
154 2 2 2
155 2 4 5
156 4 4 2
157 0 0 0
158 0 0 0
159 0 0 0
160 2 1 5
161 0 0 0
162 2 4 5
163 1 1 1
164 4 4 4
165 2 3 4
166 4 3 2
167 0 0 0
168 2 2 2
169 0 0 0
170 2 3 4
171 3 4 3
172 1 4 5
173 2 2 4
174 4 4 2
175 0 0 0
176 0 0 0
177 0 0 0
178 3 3 4
179 2 1 4
180 1 3 2
181 3 4 5
182 1 3 5
183 0 0 0
184 1 1 2
185 3 4 4
186 0 0 0
187 0 0 0
188 0 0 0
189 2 4 2
190 2 4 4
191 2 3 4
192 3 3 1
193 0 0 0
194 2 2 3
195 2 2 2
196 3 2 5
197 2 4 2
198 1 1 4
199 1 2 2
200 2 2 4
201 0 0 0
202 0 0 0
203 4 2 2
204 4 4 3
205 4 4 3
206 0 0 0
207 0 0 0
208 2 2 4
209 2 2 2
210 0 0 0
211 3 4 1
212 3 2 4
213 0 0 0
214 1 4 2
215 2 2 4
216 3 3 5
217 1 4 2
218 2 3 4
219 4 4 5
220 0 0 0
221 2 4 4
222 2 3 4
223 2 2 4
224 0 0 0
225 0 0 0
226 3 2 2
227 0 0 0
228 2 3 2
229 2 3 4
230 0 0 0
231 2 2 4
232 1 4 2
233 2 4 4
234 0 0 0
235 2 2 2
236 1 4 1
237 3 4 4
238 0 0 0
239 3 3 5
240 0 0 0
241 3 3 3
242 0 0 0
243 0 0 0
244 1 2 2
245 0 0 0
246 0 0 0
247 2 3 4
248 0 0 0
249 0 0 0
250 4 4 2
251 2 5 3
252 2 2 2
253 0 0 0
254 3 2 2
255 3 3 2
256 2 4 4
257 1 1 1
258 0 0 0
259 2 4 5
260 0 0 0
261 0 0 0
262 0 0 0
263 0 0 0
264 3 2 3
265 2 2 2
266 2 4 5
267 0 0 0
268 3 4 4
269 1 2 1
270 2 4 4
271 0 0 0
272 0 0 0
273 0 0 0
274 1 3 2
275 0 0 0
276 2 3 4
277 3 2 2
278 0 0 0
279 3 3 4
280 3 4 4
281 2 3 4
282 0 0 0
283 0 0 0
284 0 0 0
285 4 3 2
286 0 0 0
287 0 0 0
288 0 0 0
289 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Fout Ouders
0.1004 0.4787 0.1972
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.0014 -0.3255 -0.1004 0.1958 2.5477
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.10042 0.07171 1.400 0.162
Fout 0.47873 0.04548 10.527 < 2e-16 ***
Ouders 0.19721 0.04338 4.546 8.08e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7776 on 286 degrees of freedom
Multiple R-squared: 0.6745, Adjusted R-squared: 0.6722
F-statistic: 296.3 on 2 and 286 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,] 0.9966377 6.724556e-03 3.362278e-03
[2,] 0.9977302 4.539641e-03 2.269821e-03
[3,] 0.9951257 9.748608e-03 4.874304e-03
[4,] 0.9902724 1.945512e-02 9.727559e-03
[5,] 0.9980862 3.827572e-03 1.913786e-03
[6,] 0.9962314 7.537244e-03 3.768622e-03
[7,] 0.9968364 6.327236e-03 3.163618e-03
[8,] 0.9944414 1.111723e-02 5.558613e-03
[9,] 0.9911970 1.760593e-02 8.802965e-03
[10,] 0.9858756 2.824881e-02 1.412441e-02
[11,] 0.9781194 4.376126e-02 2.188063e-02
[12,] 0.9781476 4.370474e-02 2.185237e-02
[13,] 0.9762115 4.757709e-02 2.378855e-02
[14,] 0.9662870 6.742592e-02 3.371296e-02
[15,] 0.9522604 9.547912e-02 4.773956e-02
[16,] 0.9665202 6.695956e-02 3.347978e-02
[17,] 0.9542007 9.159851e-02 4.579926e-02
[18,] 0.9373989 1.252022e-01 6.260110e-02
[19,] 0.9169754 1.660491e-01 8.302457e-02
[20,] 0.8921434 2.157133e-01 1.078566e-01
[21,] 0.9060530 1.878940e-01 9.394699e-02
[22,] 0.9669841 6.603171e-02 3.301585e-02
[23,] 0.9553821 8.923574e-02 4.461787e-02
[24,] 0.9427493 1.145014e-01 5.725068e-02
[25,] 0.9489455 1.021090e-01 5.105450e-02
[26,] 0.9553929 8.921411e-02 4.460706e-02
[27,] 0.9415846 1.168308e-01 5.841540e-02
[28,] 0.9263913 1.472174e-01 7.360871e-02
[29,] 0.9149268 1.701464e-01 8.507320e-02
[30,] 0.8932055 2.135890e-01 1.067945e-01
[31,] 0.9396701 1.206598e-01 6.032989e-02
[32,] 0.9637841 7.243182e-02 3.621591e-02
[33,] 0.9529901 9.401982e-02 4.700991e-02
[34,] 0.9423256 1.153488e-01 5.767441e-02
[35,] 0.9271141 1.457718e-01 7.288590e-02
[36,] 0.9091425 1.817150e-01 9.085752e-02
[37,] 0.9203757 1.592486e-01 7.962429e-02
[38,] 0.9259002 1.481996e-01 7.409979e-02
[39,] 0.9362652 1.274696e-01 6.373481e-02
[40,] 0.9207044 1.585912e-01 7.929562e-02
[41,] 0.9283273 1.433453e-01 7.167266e-02
[42,] 0.9129635 1.740730e-01 8.703650e-02
[43,] 0.8994764 2.010471e-01 1.005236e-01
[44,] 0.8785307 2.429385e-01 1.214693e-01
[45,] 0.8548294 2.903412e-01 1.451706e-01
[46,] 0.8716593 2.566815e-01 1.283407e-01
[47,] 0.8475039 3.049922e-01 1.524961e-01
[48,] 0.8316126 3.367748e-01 1.683874e-01
[49,] 0.8660121 2.679758e-01 1.339879e-01
[50,] 0.8748117 2.503767e-01 1.251883e-01
[51,] 0.8606256 2.787488e-01 1.393744e-01
[52,] 0.8360131 3.279738e-01 1.639869e-01
[53,] 0.8088735 3.822529e-01 1.911265e-01
[54,] 0.7792826 4.414347e-01 2.207174e-01
[55,] 0.7473754 5.052492e-01 2.526246e-01
[56,] 0.7135544 5.728911e-01 2.864456e-01
[57,] 0.7379365 5.241271e-01 2.620635e-01
[58,] 0.7587870 4.824260e-01 2.412130e-01
[59,] 0.7513788 4.972425e-01 2.486212e-01
[60,] 0.7184514 5.630972e-01 2.815486e-01
[61,] 0.6836954 6.326093e-01 3.163046e-01
[62,] 0.6473803 7.052393e-01 3.526197e-01
[63,] 0.6683511 6.632977e-01 3.316489e-01
[64,] 0.6317962 7.364076e-01 3.682038e-01
[65,] 0.5941462 8.117076e-01 4.058538e-01
[66,] 0.5557434 8.885131e-01 4.442566e-01
[67,] 0.5241601 9.516798e-01 4.758399e-01
[68,] 0.4853474 9.706949e-01 5.146526e-01
[69,] 0.4469226 8.938453e-01 5.530774e-01
[70,] 0.4662090 9.324180e-01 5.337910e-01
[71,] 0.4819625 9.639250e-01 5.180375e-01
[72,] 0.4691913 9.383825e-01 5.308087e-01
[73,] 0.4315231 8.630462e-01 5.684769e-01
[74,] 0.3945774 7.891549e-01 6.054226e-01
[75,] 0.3586615 7.173229e-01 6.413385e-01
[76,] 0.3524607 7.049214e-01 6.475393e-01
[77,] 0.3640869 7.281739e-01 6.359131e-01
[78,] 0.3295590 6.591180e-01 6.704410e-01
[79,] 0.6285837 7.428327e-01 3.714163e-01
[80,] 0.7700516 4.598968e-01 2.299484e-01
[81,] 0.8270039 3.459923e-01 1.729961e-01
[82,] 0.8338778 3.322444e-01 1.661222e-01
[83,] 0.8242741 3.514519e-01 1.757259e-01
[84,] 0.8908751 2.182498e-01 1.091249e-01
[85,] 0.8732403 2.535193e-01 1.267597e-01
[86,] 0.8918660 2.162680e-01 1.081340e-01
[87,] 0.8744558 2.510885e-01 1.255442e-01
[88,] 0.8606436 2.787128e-01 1.393564e-01
[89,] 0.9306252 1.387496e-01 6.937482e-02
[90,] 0.9183046 1.633907e-01 8.169535e-02
[91,] 0.9197207 1.605585e-01 8.027927e-02
[92,] 0.9298420 1.403160e-01 7.015798e-02
[93,] 0.9423374 1.153252e-01 5.766258e-02
[94,] 0.9339958 1.320083e-01 6.600417e-02
[95,] 0.9286634 1.426732e-01 7.133658e-02
[96,] 0.9252361 1.495278e-01 7.476392e-02
[97,] 0.9124668 1.750664e-01 8.753319e-02
[98,] 0.9167985 1.664029e-01 8.320146e-02
[99,] 0.9201523 1.596954e-01 7.984768e-02
[100,] 0.9066167 1.867666e-01 9.338329e-02
[101,] 0.8914809 2.170383e-01 1.085191e-01
[102,] 0.9463341 1.073318e-01 5.366588e-02
[103,] 0.9424421 1.151157e-01 5.755786e-02
[104,] 0.9324182 1.351636e-01 6.758179e-02
[105,] 0.9330900 1.338200e-01 6.690998e-02
[106,] 0.9213624 1.572752e-01 7.863760e-02
[107,] 0.9081564 1.836872e-01 9.184359e-02
[108,] 0.8933942 2.132116e-01 1.066058e-01
[109,] 0.8770123 2.459754e-01 1.229877e-01
[110,] 0.8588509 2.822983e-01 1.411491e-01
[111,] 0.8389689 3.220622e-01 1.610311e-01
[112,] 0.8227103 3.545794e-01 1.772897e-01
[113,] 0.8266269 3.467463e-01 1.733731e-01
[114,] 0.8957852 2.084295e-01 1.042148e-01
[115,] 0.8968009 2.063981e-01 1.031991e-01
[116,] 0.9026908 1.946185e-01 9.730924e-02
[117,] 0.8873791 2.252418e-01 1.126209e-01
[118,] 0.8760228 2.479545e-01 1.239772e-01
[119,] 0.8934293 2.131415e-01 1.065707e-01
[120,] 0.8773077 2.453847e-01 1.226923e-01
[121,] 0.8642501 2.714997e-01 1.357499e-01
[122,] 0.8489691 3.020618e-01 1.510309e-01
[123,] 0.8436228 3.127545e-01 1.563772e-01
[124,] 0.8331372 3.337256e-01 1.668628e-01
[125,] 0.8115273 3.769453e-01 1.884727e-01
[126,] 0.7882932 4.234136e-01 2.117068e-01
[127,] 0.9043916 1.912168e-01 9.560842e-02
[128,] 0.8895467 2.209066e-01 1.104533e-01
[129,] 0.9438369 1.123262e-01 5.616312e-02
[130,] 0.9476594 1.046812e-01 5.234058e-02
[131,] 0.9382760 1.234481e-01 6.172404e-02
[132,] 0.9275444 1.449113e-01 7.245564e-02
[133,] 0.9154462 1.691077e-01 8.455383e-02
[134,] 0.9017212 1.965575e-01 9.827875e-02
[135,] 0.8866319 2.267363e-01 1.133681e-01
[136,] 0.8801022 2.397957e-01 1.198978e-01
[137,] 0.8812184 2.375632e-01 1.187816e-01
[138,] 0.8760243 2.479514e-01 1.239757e-01
[139,] 0.8693535 2.612930e-01 1.306465e-01
[140,] 0.8510040 2.979921e-01 1.489960e-01
[141,] 0.8318391 3.363217e-01 1.681609e-01
[142,] 0.8097886 3.804229e-01 1.902114e-01
[143,] 0.7874952 4.250096e-01 2.125048e-01
[144,] 0.7627508 4.744984e-01 2.372492e-01
[145,] 0.7365133 5.269734e-01 2.634867e-01
[146,] 0.7281931 5.436138e-01 2.718069e-01
[147,] 0.7047570 5.904860e-01 2.952430e-01
[148,] 0.7940912 4.118177e-01 2.059088e-01
[149,] 0.7812892 4.374217e-01 2.187108e-01
[150,] 0.7988867 4.022266e-01 2.011133e-01
[151,] 0.8636627 2.726746e-01 1.363373e-01
[152,] 0.8446981 3.106038e-01 1.553019e-01
[153,] 0.8240729 3.518541e-01 1.759271e-01
[154,] 0.8017961 3.964077e-01 1.982039e-01
[155,] 0.7850949 4.298101e-01 2.149051e-01
[156,] 0.7600614 4.798773e-01 2.399386e-01
[157,] 0.7790884 4.418231e-01 2.209116e-01
[158,] 0.7550308 4.899383e-01 2.449692e-01
[159,] 0.7881783 4.236435e-01 2.118217e-01
[160,] 0.7679125 4.641750e-01 2.320875e-01
[161,] 0.8945079 2.109842e-01 1.054921e-01
[162,] 0.8784026 2.431947e-01 1.215974e-01
[163,] 0.8691829 2.616341e-01 1.308171e-01
[164,] 0.8504879 2.990243e-01 1.495121e-01
[165,] 0.8339317 3.321366e-01 1.660683e-01
[166,] 0.8175038 3.649924e-01 1.824962e-01
[167,] 0.9224882 1.550235e-01 7.751176e-02
[168,] 0.9096790 1.806421e-01 9.032104e-02
[169,] 0.9494226 1.011547e-01 5.057735e-02
[170,] 0.9399100 1.201800e-01 6.009002e-02
[171,] 0.9290436 1.419127e-01 7.095636e-02
[172,] 0.9167203 1.665595e-01 8.327973e-02
[173,] 0.9125049 1.749902e-01 8.749509e-02
[174,] 0.9057658 1.884684e-01 9.423421e-02
[175,] 0.9120771 1.758458e-01 8.792292e-02
[176,] 0.8973229 2.053542e-01 1.026771e-01
[177,] 0.9425077 1.149845e-01 5.749227e-02
[178,] 0.9318357 1.363285e-01 6.816427e-02
[179,] 0.9195763 1.608473e-01 8.042366e-02
[180,] 0.9061241 1.877518e-01 9.387592e-02
[181,] 0.8907217 2.185567e-01 1.092783e-01
[182,] 0.8735683 2.528633e-01 1.264317e-01
[183,] 0.8546080 2.907841e-01 1.453920e-01
[184,] 0.8384423 3.231154e-01 1.615577e-01
[185,] 0.8439915 3.120170e-01 1.560085e-01
[186,] 0.8290158 3.419683e-01 1.709842e-01
[187,] 0.8707024 2.585952e-01 1.292976e-01
[188,] 0.8511113 2.977774e-01 1.488887e-01
[189,] 0.8320965 3.358069e-01 1.679035e-01
[190,] 0.8193007 3.613987e-01 1.806993e-01
[191,] 0.8209023 3.581953e-01 1.790977e-01
[192,] 0.8016595 3.966810e-01 1.983405e-01
[193,] 0.7910815 4.178370e-01 2.089185e-01
[194,] 0.7751652 4.496697e-01 2.248348e-01
[195,] 0.7478827 5.042346e-01 2.521173e-01
[196,] 0.7182824 5.634352e-01 2.817176e-01
[197,] 0.6870597 6.258806e-01 3.129403e-01
[198,] 0.9202323 1.595355e-01 7.976775e-02
[199,] 0.9516371 9.672576e-02 4.836288e-02
[200,] 0.9743150 5.136997e-02 2.568499e-02
[201,] 0.9681938 6.361243e-02 3.180622e-02
[202,] 0.9609010 7.819800e-02 3.909900e-02
[203,] 0.9523790 9.524206e-02 4.762103e-02
[204,] 0.9477996 1.044008e-01 5.220038e-02
[205,] 0.9368994 1.262013e-01 6.310065e-02
[206,] 0.9549509 9.009826e-02 4.504913e-02
[207,] 0.9600232 7.995354e-02 3.997677e-02
[208,] 0.9510065 9.798707e-02 4.899354e-02
[209,] 0.9632266 7.354689e-02 3.677344e-02
[210,] 0.9546822 9.063556e-02 4.531778e-02
[211,] 0.9463927 1.072145e-01 5.360726e-02
[212,] 0.9625503 7.489931e-02 3.744966e-02
[213,] 0.9560877 8.782456e-02 4.391228e-02
[214,] 0.9623605 7.527903e-02 3.763952e-02
[215,] 0.9534397 9.312056e-02 4.656028e-02
[216,] 0.9542572 9.148550e-02 4.574275e-02
[217,] 0.9464283 1.071434e-01 5.357172e-02
[218,] 0.9341928 1.316143e-01 6.580715e-02
[219,] 0.9201693 1.596615e-01 7.983074e-02
[220,] 0.9039495 1.921010e-01 9.605048e-02
[221,] 0.9512081 9.758386e-02 4.879193e-02
[222,] 0.9397202 1.205595e-01 6.027976e-02
[223,] 0.9264234 1.471533e-01 7.357664e-02
[224,] 0.9144891 1.710218e-01 8.551092e-02
[225,] 0.8967477 2.065046e-01 1.032523e-01
[226,] 0.8759172 2.481657e-01 1.240828e-01
[227,] 0.9167013 1.665974e-01 8.329872e-02
[228,] 0.9213815 1.572371e-01 7.861853e-02
[229,] 0.9040835 1.918330e-01 9.591652e-02
[230,] 0.8935278 2.129444e-01 1.064722e-01
[231,] 0.9384163 1.231674e-01 6.158370e-02
[232,] 0.9233839 1.532321e-01 7.661605e-02
[233,] 0.9057507 1.884985e-01 9.424927e-02
[234,] 0.9034413 1.931174e-01 9.655868e-02
[235,] 0.8823412 2.353176e-01 1.176588e-01
[236,] 0.8882576 2.234848e-01 1.117424e-01
[237,] 0.8645116 2.709767e-01 1.354884e-01
[238,] 0.8373863 3.252275e-01 1.626137e-01
[239,] 0.8195959 3.608082e-01 1.804041e-01
[240,] 0.7868896 4.262208e-01 2.131104e-01
[241,] 0.7507980 4.984040e-01 2.492020e-01
[242,] 0.7126933 5.746134e-01 2.873067e-01
[243,] 0.6704400 6.591200e-01 3.295600e-01
[244,] 0.6256964 7.486073e-01 3.743036e-01
[245,] 0.7276808 5.446383e-01 2.723192e-01
[246,] 0.8204712 3.590577e-01 1.795288e-01
[247,] 0.7984851 4.030298e-01 2.015149e-01
[248,] 0.7598808 4.802384e-01 2.401192e-01
[249,] 0.8695533 2.608933e-01 1.304467e-01
[250,] 0.8804259 2.391483e-01 1.195741e-01
[251,] 0.8880428 2.239143e-01 1.119572e-01
[252,] 0.8604428 2.791145e-01 1.395572e-01
[253,] 0.8258956 3.482087e-01 1.741044e-01
[254,] 0.8526779 2.946443e-01 1.473221e-01
[255,] 0.8155345 3.689309e-01 1.844655e-01
[256,] 0.7725752 4.548495e-01 2.274248e-01
[257,] 0.7239353 5.521294e-01 2.760647e-01
[258,] 0.6700643 6.598714e-01 3.299357e-01
[259,] 0.8178251 3.643498e-01 1.821749e-01
[260,] 0.7951129 4.097742e-01 2.048871e-01
[261,] 0.8213198 3.573603e-01 1.786802e-01
[262,] 0.7719170 4.561661e-01 2.280830e-01
[263,] 0.7144574 5.710852e-01 2.855426e-01
[264,] 0.6834274 6.331453e-01 3.165726e-01
[265,] 0.7765907 4.468187e-01 2.234093e-01
[266,] 0.7140029 5.719942e-01 2.859971e-01
[267,] 0.6424815 7.150370e-01 3.575185e-01
[268,] 0.5636678 8.726645e-01 4.363322e-01
[269,] 0.9735128 5.297435e-02 2.648717e-02
[270,] 0.9543540 9.129195e-02 4.564598e-02
[271,] 0.9307880 1.384240e-01 6.921199e-02
[272,] 0.9791534 4.169318e-02 2.084659e-02
[273,] 0.9586311 8.273782e-02 4.136891e-02
[274,] 0.9966993 6.601461e-03 3.300730e-03
[275,] 1.0000000 4.392488e-95 2.196244e-95
[276,] 1.0000000 8.047249e-80 4.023624e-80
[277,] 1.0000000 1.004010e-60 5.020049e-61
[278,] 1.0000000 2.355905e-49 1.177953e-49
> postscript(file="/var/fisher/rcomp/tmp/1u5d61353330689.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/28qvr1353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3nuaj1353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4j4d21353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5qy3i1353330689.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 = 289
Frequency = 1
1 2 3 4 5 6
-1.804191602 1.871750230 1.393015249 -0.100424276 -0.100424276 -1.804191602
7 8 9 10 11 12
0.871750230 -0.100424276 -0.100424276 0.998601548 -0.100424276 1.223633893
13 14 15 16 17 18
-0.100424276 0.547692061 -0.100424276 -0.100424276 1.195808398 1.195808398
19 20 21 22 23 24
0.547692061 -0.100424276 1.674543379 0.547692061 0.068957080 -0.100424276
25 26 27 28 29 30
-0.100424276 -1.409777901 -0.846721639 -0.100424276 -0.409777901 -0.649514789
31 32 33 34 35 36
-0.804191602 -0.100424276 0.153278361 -0.452307939 -0.100424276 1.590222099
37 38 39 40 41 42
1.674543379 -0.100424276 -0.128249770 -0.100424276 -0.100424276 -0.846721639
43 44 45 46 47 48
1.195808398 -0.804191602 -0.100424276 1.153278361 0.393015249 0.590222099
49 50 51 52 53 54
-0.100424276 -0.100424276 -0.804191602 -0.100424276 -0.325456621 -1.212571051
55 56 57 58 59 60
-0.931042920 0.674543379 -0.100424276 -0.100424276 -0.100424276 -0.100424276
61 62 63 64 65 66
-0.085719732 1.195808398 1.195808398 -0.606984751 -0.100424276 -0.100424276
67 68 69 70 71 72
-0.100424276 -0.804191602 -0.100424276 -0.100424276 -0.100424276 -0.409777901
73 74 75 76 77 78
-0.100424276 0.068957080 -0.804191602 -0.804191602 0.829220192 -0.100424276
79 80 81 82 83 84
-0.100424276 -0.100424276 -0.367986658 -0.804191602 -0.100424276 2.308693968
85 86 87 88 89 90
1.956071510 -1.409777901 -0.804191602 0.590222099 -1.522663471 -0.100424276
91 92 93 94 95 96
1.195808398 -0.100424276 -0.409777901 -1.804191602 0.153278361 -0.804191602
97 98 99 100 101 102
1.068957080 1.195808398 -0.325456621 0.590222099 -0.649514789 0.153278361
103 104 105 106 107 108
0.956071510 -0.846721639 -0.100424276 -0.100424276 -1.804191602 -0.606984751
109 110 111 112 113 114
0.195808398 -0.804191602 -0.100424276 -0.100424276 -0.100424276 -0.100424276
115 116 117 118 119 120
0.068957080 0.068957080 0.393015249 -0.846721639 1.717073417 -0.804191602
121 122 123 124 125 126
-0.931042920 0.068957080 -0.452307939 -1.128249770 -0.100424276 -0.409777901
127 128 129 130 131 132
0.350485211 0.674543379 0.547692061 -0.100424276 -0.100424276 2.068957080
133 134 135 136 137 138
-0.100424276 -1.804191602 -0.931042920 -0.128249770 -0.100424276 -0.100424276
139 140 141 142 143 144
0.068957080 -0.100424276 -0.606984751 -0.804191602 -0.649514789 -0.606984751
145 146 147 148 149 150
-0.100424276 0.195808398 -0.001398452 0.195808398 -0.100424276 -0.100424276
151 152 153 154 155 156
-0.649514789 -0.325456621 -1.606984751 0.547692061 -1.001398452 1.590222099
157 158 159 160 161 162
-0.100424276 -0.100424276 -0.100424276 0.434806491 -0.100424276 -1.001398452
163 164 165 166 167 168
0.223633893 1.195808398 -0.325456621 2.068957080 -0.100424276 0.547692061
169 170 171 172 173 174
-0.100424276 -0.325456621 0.393015249 -2.001398452 0.153278361 1.590222099
175 176 177 178 179 180
-0.100424276 -0.100424276 -0.100424276 0.674543379 0.632013342 -0.931042920
181 182 183 184 185 186
-0.001398452 -1.522663471 -0.100424276 0.026427042 0.195808398 -0.100424276
187 188 189 190 191 192
-0.100424276 -0.100424276 -0.409777901 -0.804191602 -0.325456621 1.266163931
193 194 195 196 197 198
-0.100424276 0.350485211 0.547692061 0.956071510 -0.409777901 -0.367986658
199 200 201 202 203 204
-0.452307939 0.153278361 -0.100424276 -0.100424276 2.547692061 1.393015249
205 206 207 208 209 210
1.393015249 -0.100424276 -0.100424276 0.153278361 0.547692061 -0.100424276
211 212 213 214 215 216
0.787428949 1.153278361 -0.100424276 -1.409777901 0.153278361 0.477336529
217 218 219 220 221 222
-1.409777901 -0.325456621 0.998601548 -0.100424276 -0.804191602 -0.325456621
223 224 225 226 227 228
0.153278361 -0.100424276 -0.100424276 1.547692061 -0.100424276 0.068957080
229 230 231 232 233 234
-0.325456621 -0.100424276 0.153278361 -1.409777901 -0.804191602 -0.100424276
235 236 237 238 239 240
0.547692061 -1.212571051 0.195808398 -0.100424276 0.477336529 -0.100424276
241 242 243 244 245 246
0.871750230 -0.100424276 -0.100424276 -0.452307939 -0.100424276 -0.100424276
247 248 249 250 251 252
-0.325456621 -0.100424276 -0.100424276 1.590222099 -1.085719732 0.547692061
253 254 255 256 257 258
-0.100424276 1.547692061 1.068957080 -0.804191602 0.223633893 -0.100424276
259 260 261 262 263 264
-1.001398452 -0.100424276 -0.100424276 -0.100424276 -0.100424276 1.350485211
265 266 267 268 269 270
0.547692061 -1.001398452 -0.100424276 0.195808398 -0.255101088 -0.804191602
271 272 273 274 275 276
-0.100424276 -0.100424276 -0.100424276 -0.931042920 -0.100424276 -0.325456621
277 278 279 280 281 282
1.547692061 -0.100424276 0.674543379 0.195808398 -0.325456621 -0.100424276
283 284 285 286 287 288
-0.100424276 -0.100424276 2.068957080 -0.100424276 -0.100424276 -0.100424276
289
-0.100424276
> postscript(file="/var/fisher/rcomp/tmp/6v4wg1353330689.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 = 289
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.804191602 NA
1 1.871750230 -1.804191602
2 1.393015249 1.871750230
3 -0.100424276 1.393015249
4 -0.100424276 -0.100424276
5 -1.804191602 -0.100424276
6 0.871750230 -1.804191602
7 -0.100424276 0.871750230
8 -0.100424276 -0.100424276
9 0.998601548 -0.100424276
10 -0.100424276 0.998601548
11 1.223633893 -0.100424276
12 -0.100424276 1.223633893
13 0.547692061 -0.100424276
14 -0.100424276 0.547692061
15 -0.100424276 -0.100424276
16 1.195808398 -0.100424276
17 1.195808398 1.195808398
18 0.547692061 1.195808398
19 -0.100424276 0.547692061
20 1.674543379 -0.100424276
21 0.547692061 1.674543379
22 0.068957080 0.547692061
23 -0.100424276 0.068957080
24 -0.100424276 -0.100424276
25 -1.409777901 -0.100424276
26 -0.846721639 -1.409777901
27 -0.100424276 -0.846721639
28 -0.409777901 -0.100424276
29 -0.649514789 -0.409777901
30 -0.804191602 -0.649514789
31 -0.100424276 -0.804191602
32 0.153278361 -0.100424276
33 -0.452307939 0.153278361
34 -0.100424276 -0.452307939
35 1.590222099 -0.100424276
36 1.674543379 1.590222099
37 -0.100424276 1.674543379
38 -0.128249770 -0.100424276
39 -0.100424276 -0.128249770
40 -0.100424276 -0.100424276
41 -0.846721639 -0.100424276
42 1.195808398 -0.846721639
43 -0.804191602 1.195808398
44 -0.100424276 -0.804191602
45 1.153278361 -0.100424276
46 0.393015249 1.153278361
47 0.590222099 0.393015249
48 -0.100424276 0.590222099
49 -0.100424276 -0.100424276
50 -0.804191602 -0.100424276
51 -0.100424276 -0.804191602
52 -0.325456621 -0.100424276
53 -1.212571051 -0.325456621
54 -0.931042920 -1.212571051
55 0.674543379 -0.931042920
56 -0.100424276 0.674543379
57 -0.100424276 -0.100424276
58 -0.100424276 -0.100424276
59 -0.100424276 -0.100424276
60 -0.085719732 -0.100424276
61 1.195808398 -0.085719732
62 1.195808398 1.195808398
63 -0.606984751 1.195808398
64 -0.100424276 -0.606984751
65 -0.100424276 -0.100424276
66 -0.100424276 -0.100424276
67 -0.804191602 -0.100424276
68 -0.100424276 -0.804191602
69 -0.100424276 -0.100424276
70 -0.100424276 -0.100424276
71 -0.409777901 -0.100424276
72 -0.100424276 -0.409777901
73 0.068957080 -0.100424276
74 -0.804191602 0.068957080
75 -0.804191602 -0.804191602
76 0.829220192 -0.804191602
77 -0.100424276 0.829220192
78 -0.100424276 -0.100424276
79 -0.100424276 -0.100424276
80 -0.367986658 -0.100424276
81 -0.804191602 -0.367986658
82 -0.100424276 -0.804191602
83 2.308693968 -0.100424276
84 1.956071510 2.308693968
85 -1.409777901 1.956071510
86 -0.804191602 -1.409777901
87 0.590222099 -0.804191602
88 -1.522663471 0.590222099
89 -0.100424276 -1.522663471
90 1.195808398 -0.100424276
91 -0.100424276 1.195808398
92 -0.409777901 -0.100424276
93 -1.804191602 -0.409777901
94 0.153278361 -1.804191602
95 -0.804191602 0.153278361
96 1.068957080 -0.804191602
97 1.195808398 1.068957080
98 -0.325456621 1.195808398
99 0.590222099 -0.325456621
100 -0.649514789 0.590222099
101 0.153278361 -0.649514789
102 0.956071510 0.153278361
103 -0.846721639 0.956071510
104 -0.100424276 -0.846721639
105 -0.100424276 -0.100424276
106 -1.804191602 -0.100424276
107 -0.606984751 -1.804191602
108 0.195808398 -0.606984751
109 -0.804191602 0.195808398
110 -0.100424276 -0.804191602
111 -0.100424276 -0.100424276
112 -0.100424276 -0.100424276
113 -0.100424276 -0.100424276
114 0.068957080 -0.100424276
115 0.068957080 0.068957080
116 0.393015249 0.068957080
117 -0.846721639 0.393015249
118 1.717073417 -0.846721639
119 -0.804191602 1.717073417
120 -0.931042920 -0.804191602
121 0.068957080 -0.931042920
122 -0.452307939 0.068957080
123 -1.128249770 -0.452307939
124 -0.100424276 -1.128249770
125 -0.409777901 -0.100424276
126 0.350485211 -0.409777901
127 0.674543379 0.350485211
128 0.547692061 0.674543379
129 -0.100424276 0.547692061
130 -0.100424276 -0.100424276
131 2.068957080 -0.100424276
132 -0.100424276 2.068957080
133 -1.804191602 -0.100424276
134 -0.931042920 -1.804191602
135 -0.128249770 -0.931042920
136 -0.100424276 -0.128249770
137 -0.100424276 -0.100424276
138 0.068957080 -0.100424276
139 -0.100424276 0.068957080
140 -0.606984751 -0.100424276
141 -0.804191602 -0.606984751
142 -0.649514789 -0.804191602
143 -0.606984751 -0.649514789
144 -0.100424276 -0.606984751
145 0.195808398 -0.100424276
146 -0.001398452 0.195808398
147 0.195808398 -0.001398452
148 -0.100424276 0.195808398
149 -0.100424276 -0.100424276
150 -0.649514789 -0.100424276
151 -0.325456621 -0.649514789
152 -1.606984751 -0.325456621
153 0.547692061 -1.606984751
154 -1.001398452 0.547692061
155 1.590222099 -1.001398452
156 -0.100424276 1.590222099
157 -0.100424276 -0.100424276
158 -0.100424276 -0.100424276
159 0.434806491 -0.100424276
160 -0.100424276 0.434806491
161 -1.001398452 -0.100424276
162 0.223633893 -1.001398452
163 1.195808398 0.223633893
164 -0.325456621 1.195808398
165 2.068957080 -0.325456621
166 -0.100424276 2.068957080
167 0.547692061 -0.100424276
168 -0.100424276 0.547692061
169 -0.325456621 -0.100424276
170 0.393015249 -0.325456621
171 -2.001398452 0.393015249
172 0.153278361 -2.001398452
173 1.590222099 0.153278361
174 -0.100424276 1.590222099
175 -0.100424276 -0.100424276
176 -0.100424276 -0.100424276
177 0.674543379 -0.100424276
178 0.632013342 0.674543379
179 -0.931042920 0.632013342
180 -0.001398452 -0.931042920
181 -1.522663471 -0.001398452
182 -0.100424276 -1.522663471
183 0.026427042 -0.100424276
184 0.195808398 0.026427042
185 -0.100424276 0.195808398
186 -0.100424276 -0.100424276
187 -0.100424276 -0.100424276
188 -0.409777901 -0.100424276
189 -0.804191602 -0.409777901
190 -0.325456621 -0.804191602
191 1.266163931 -0.325456621
192 -0.100424276 1.266163931
193 0.350485211 -0.100424276
194 0.547692061 0.350485211
195 0.956071510 0.547692061
196 -0.409777901 0.956071510
197 -0.367986658 -0.409777901
198 -0.452307939 -0.367986658
199 0.153278361 -0.452307939
200 -0.100424276 0.153278361
201 -0.100424276 -0.100424276
202 2.547692061 -0.100424276
203 1.393015249 2.547692061
204 1.393015249 1.393015249
205 -0.100424276 1.393015249
206 -0.100424276 -0.100424276
207 0.153278361 -0.100424276
208 0.547692061 0.153278361
209 -0.100424276 0.547692061
210 0.787428949 -0.100424276
211 1.153278361 0.787428949
212 -0.100424276 1.153278361
213 -1.409777901 -0.100424276
214 0.153278361 -1.409777901
215 0.477336529 0.153278361
216 -1.409777901 0.477336529
217 -0.325456621 -1.409777901
218 0.998601548 -0.325456621
219 -0.100424276 0.998601548
220 -0.804191602 -0.100424276
221 -0.325456621 -0.804191602
222 0.153278361 -0.325456621
223 -0.100424276 0.153278361
224 -0.100424276 -0.100424276
225 1.547692061 -0.100424276
226 -0.100424276 1.547692061
227 0.068957080 -0.100424276
228 -0.325456621 0.068957080
229 -0.100424276 -0.325456621
230 0.153278361 -0.100424276
231 -1.409777901 0.153278361
232 -0.804191602 -1.409777901
233 -0.100424276 -0.804191602
234 0.547692061 -0.100424276
235 -1.212571051 0.547692061
236 0.195808398 -1.212571051
237 -0.100424276 0.195808398
238 0.477336529 -0.100424276
239 -0.100424276 0.477336529
240 0.871750230 -0.100424276
241 -0.100424276 0.871750230
242 -0.100424276 -0.100424276
243 -0.452307939 -0.100424276
244 -0.100424276 -0.452307939
245 -0.100424276 -0.100424276
246 -0.325456621 -0.100424276
247 -0.100424276 -0.325456621
248 -0.100424276 -0.100424276
249 1.590222099 -0.100424276
250 -1.085719732 1.590222099
251 0.547692061 -1.085719732
252 -0.100424276 0.547692061
253 1.547692061 -0.100424276
254 1.068957080 1.547692061
255 -0.804191602 1.068957080
256 0.223633893 -0.804191602
257 -0.100424276 0.223633893
258 -1.001398452 -0.100424276
259 -0.100424276 -1.001398452
260 -0.100424276 -0.100424276
261 -0.100424276 -0.100424276
262 -0.100424276 -0.100424276
263 1.350485211 -0.100424276
264 0.547692061 1.350485211
265 -1.001398452 0.547692061
266 -0.100424276 -1.001398452
267 0.195808398 -0.100424276
268 -0.255101088 0.195808398
269 -0.804191602 -0.255101088
270 -0.100424276 -0.804191602
271 -0.100424276 -0.100424276
272 -0.100424276 -0.100424276
273 -0.931042920 -0.100424276
274 -0.100424276 -0.931042920
275 -0.325456621 -0.100424276
276 1.547692061 -0.325456621
277 -0.100424276 1.547692061
278 0.674543379 -0.100424276
279 0.195808398 0.674543379
280 -0.325456621 0.195808398
281 -0.100424276 -0.325456621
282 -0.100424276 -0.100424276
283 -0.100424276 -0.100424276
284 2.068957080 -0.100424276
285 -0.100424276 2.068957080
286 -0.100424276 -0.100424276
287 -0.100424276 -0.100424276
288 -0.100424276 -0.100424276
289 NA -0.100424276
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.871750230 -1.804191602
[2,] 1.393015249 1.871750230
[3,] -0.100424276 1.393015249
[4,] -0.100424276 -0.100424276
[5,] -1.804191602 -0.100424276
[6,] 0.871750230 -1.804191602
[7,] -0.100424276 0.871750230
[8,] -0.100424276 -0.100424276
[9,] 0.998601548 -0.100424276
[10,] -0.100424276 0.998601548
[11,] 1.223633893 -0.100424276
[12,] -0.100424276 1.223633893
[13,] 0.547692061 -0.100424276
[14,] -0.100424276 0.547692061
[15,] -0.100424276 -0.100424276
[16,] 1.195808398 -0.100424276
[17,] 1.195808398 1.195808398
[18,] 0.547692061 1.195808398
[19,] -0.100424276 0.547692061
[20,] 1.674543379 -0.100424276
[21,] 0.547692061 1.674543379
[22,] 0.068957080 0.547692061
[23,] -0.100424276 0.068957080
[24,] -0.100424276 -0.100424276
[25,] -1.409777901 -0.100424276
[26,] -0.846721639 -1.409777901
[27,] -0.100424276 -0.846721639
[28,] -0.409777901 -0.100424276
[29,] -0.649514789 -0.409777901
[30,] -0.804191602 -0.649514789
[31,] -0.100424276 -0.804191602
[32,] 0.153278361 -0.100424276
[33,] -0.452307939 0.153278361
[34,] -0.100424276 -0.452307939
[35,] 1.590222099 -0.100424276
[36,] 1.674543379 1.590222099
[37,] -0.100424276 1.674543379
[38,] -0.128249770 -0.100424276
[39,] -0.100424276 -0.128249770
[40,] -0.100424276 -0.100424276
[41,] -0.846721639 -0.100424276
[42,] 1.195808398 -0.846721639
[43,] -0.804191602 1.195808398
[44,] -0.100424276 -0.804191602
[45,] 1.153278361 -0.100424276
[46,] 0.393015249 1.153278361
[47,] 0.590222099 0.393015249
[48,] -0.100424276 0.590222099
[49,] -0.100424276 -0.100424276
[50,] -0.804191602 -0.100424276
[51,] -0.100424276 -0.804191602
[52,] -0.325456621 -0.100424276
[53,] -1.212571051 -0.325456621
[54,] -0.931042920 -1.212571051
[55,] 0.674543379 -0.931042920
[56,] -0.100424276 0.674543379
[57,] -0.100424276 -0.100424276
[58,] -0.100424276 -0.100424276
[59,] -0.100424276 -0.100424276
[60,] -0.085719732 -0.100424276
[61,] 1.195808398 -0.085719732
[62,] 1.195808398 1.195808398
[63,] -0.606984751 1.195808398
[64,] -0.100424276 -0.606984751
[65,] -0.100424276 -0.100424276
[66,] -0.100424276 -0.100424276
[67,] -0.804191602 -0.100424276
[68,] -0.100424276 -0.804191602
[69,] -0.100424276 -0.100424276
[70,] -0.100424276 -0.100424276
[71,] -0.409777901 -0.100424276
[72,] -0.100424276 -0.409777901
[73,] 0.068957080 -0.100424276
[74,] -0.804191602 0.068957080
[75,] -0.804191602 -0.804191602
[76,] 0.829220192 -0.804191602
[77,] -0.100424276 0.829220192
[78,] -0.100424276 -0.100424276
[79,] -0.100424276 -0.100424276
[80,] -0.367986658 -0.100424276
[81,] -0.804191602 -0.367986658
[82,] -0.100424276 -0.804191602
[83,] 2.308693968 -0.100424276
[84,] 1.956071510 2.308693968
[85,] -1.409777901 1.956071510
[86,] -0.804191602 -1.409777901
[87,] 0.590222099 -0.804191602
[88,] -1.522663471 0.590222099
[89,] -0.100424276 -1.522663471
[90,] 1.195808398 -0.100424276
[91,] -0.100424276 1.195808398
[92,] -0.409777901 -0.100424276
[93,] -1.804191602 -0.409777901
[94,] 0.153278361 -1.804191602
[95,] -0.804191602 0.153278361
[96,] 1.068957080 -0.804191602
[97,] 1.195808398 1.068957080
[98,] -0.325456621 1.195808398
[99,] 0.590222099 -0.325456621
[100,] -0.649514789 0.590222099
[101,] 0.153278361 -0.649514789
[102,] 0.956071510 0.153278361
[103,] -0.846721639 0.956071510
[104,] -0.100424276 -0.846721639
[105,] -0.100424276 -0.100424276
[106,] -1.804191602 -0.100424276
[107,] -0.606984751 -1.804191602
[108,] 0.195808398 -0.606984751
[109,] -0.804191602 0.195808398
[110,] -0.100424276 -0.804191602
[111,] -0.100424276 -0.100424276
[112,] -0.100424276 -0.100424276
[113,] -0.100424276 -0.100424276
[114,] 0.068957080 -0.100424276
[115,] 0.068957080 0.068957080
[116,] 0.393015249 0.068957080
[117,] -0.846721639 0.393015249
[118,] 1.717073417 -0.846721639
[119,] -0.804191602 1.717073417
[120,] -0.931042920 -0.804191602
[121,] 0.068957080 -0.931042920
[122,] -0.452307939 0.068957080
[123,] -1.128249770 -0.452307939
[124,] -0.100424276 -1.128249770
[125,] -0.409777901 -0.100424276
[126,] 0.350485211 -0.409777901
[127,] 0.674543379 0.350485211
[128,] 0.547692061 0.674543379
[129,] -0.100424276 0.547692061
[130,] -0.100424276 -0.100424276
[131,] 2.068957080 -0.100424276
[132,] -0.100424276 2.068957080
[133,] -1.804191602 -0.100424276
[134,] -0.931042920 -1.804191602
[135,] -0.128249770 -0.931042920
[136,] -0.100424276 -0.128249770
[137,] -0.100424276 -0.100424276
[138,] 0.068957080 -0.100424276
[139,] -0.100424276 0.068957080
[140,] -0.606984751 -0.100424276
[141,] -0.804191602 -0.606984751
[142,] -0.649514789 -0.804191602
[143,] -0.606984751 -0.649514789
[144,] -0.100424276 -0.606984751
[145,] 0.195808398 -0.100424276
[146,] -0.001398452 0.195808398
[147,] 0.195808398 -0.001398452
[148,] -0.100424276 0.195808398
[149,] -0.100424276 -0.100424276
[150,] -0.649514789 -0.100424276
[151,] -0.325456621 -0.649514789
[152,] -1.606984751 -0.325456621
[153,] 0.547692061 -1.606984751
[154,] -1.001398452 0.547692061
[155,] 1.590222099 -1.001398452
[156,] -0.100424276 1.590222099
[157,] -0.100424276 -0.100424276
[158,] -0.100424276 -0.100424276
[159,] 0.434806491 -0.100424276
[160,] -0.100424276 0.434806491
[161,] -1.001398452 -0.100424276
[162,] 0.223633893 -1.001398452
[163,] 1.195808398 0.223633893
[164,] -0.325456621 1.195808398
[165,] 2.068957080 -0.325456621
[166,] -0.100424276 2.068957080
[167,] 0.547692061 -0.100424276
[168,] -0.100424276 0.547692061
[169,] -0.325456621 -0.100424276
[170,] 0.393015249 -0.325456621
[171,] -2.001398452 0.393015249
[172,] 0.153278361 -2.001398452
[173,] 1.590222099 0.153278361
[174,] -0.100424276 1.590222099
[175,] -0.100424276 -0.100424276
[176,] -0.100424276 -0.100424276
[177,] 0.674543379 -0.100424276
[178,] 0.632013342 0.674543379
[179,] -0.931042920 0.632013342
[180,] -0.001398452 -0.931042920
[181,] -1.522663471 -0.001398452
[182,] -0.100424276 -1.522663471
[183,] 0.026427042 -0.100424276
[184,] 0.195808398 0.026427042
[185,] -0.100424276 0.195808398
[186,] -0.100424276 -0.100424276
[187,] -0.100424276 -0.100424276
[188,] -0.409777901 -0.100424276
[189,] -0.804191602 -0.409777901
[190,] -0.325456621 -0.804191602
[191,] 1.266163931 -0.325456621
[192,] -0.100424276 1.266163931
[193,] 0.350485211 -0.100424276
[194,] 0.547692061 0.350485211
[195,] 0.956071510 0.547692061
[196,] -0.409777901 0.956071510
[197,] -0.367986658 -0.409777901
[198,] -0.452307939 -0.367986658
[199,] 0.153278361 -0.452307939
[200,] -0.100424276 0.153278361
[201,] -0.100424276 -0.100424276
[202,] 2.547692061 -0.100424276
[203,] 1.393015249 2.547692061
[204,] 1.393015249 1.393015249
[205,] -0.100424276 1.393015249
[206,] -0.100424276 -0.100424276
[207,] 0.153278361 -0.100424276
[208,] 0.547692061 0.153278361
[209,] -0.100424276 0.547692061
[210,] 0.787428949 -0.100424276
[211,] 1.153278361 0.787428949
[212,] -0.100424276 1.153278361
[213,] -1.409777901 -0.100424276
[214,] 0.153278361 -1.409777901
[215,] 0.477336529 0.153278361
[216,] -1.409777901 0.477336529
[217,] -0.325456621 -1.409777901
[218,] 0.998601548 -0.325456621
[219,] -0.100424276 0.998601548
[220,] -0.804191602 -0.100424276
[221,] -0.325456621 -0.804191602
[222,] 0.153278361 -0.325456621
[223,] -0.100424276 0.153278361
[224,] -0.100424276 -0.100424276
[225,] 1.547692061 -0.100424276
[226,] -0.100424276 1.547692061
[227,] 0.068957080 -0.100424276
[228,] -0.325456621 0.068957080
[229,] -0.100424276 -0.325456621
[230,] 0.153278361 -0.100424276
[231,] -1.409777901 0.153278361
[232,] -0.804191602 -1.409777901
[233,] -0.100424276 -0.804191602
[234,] 0.547692061 -0.100424276
[235,] -1.212571051 0.547692061
[236,] 0.195808398 -1.212571051
[237,] -0.100424276 0.195808398
[238,] 0.477336529 -0.100424276
[239,] -0.100424276 0.477336529
[240,] 0.871750230 -0.100424276
[241,] -0.100424276 0.871750230
[242,] -0.100424276 -0.100424276
[243,] -0.452307939 -0.100424276
[244,] -0.100424276 -0.452307939
[245,] -0.100424276 -0.100424276
[246,] -0.325456621 -0.100424276
[247,] -0.100424276 -0.325456621
[248,] -0.100424276 -0.100424276
[249,] 1.590222099 -0.100424276
[250,] -1.085719732 1.590222099
[251,] 0.547692061 -1.085719732
[252,] -0.100424276 0.547692061
[253,] 1.547692061 -0.100424276
[254,] 1.068957080 1.547692061
[255,] -0.804191602 1.068957080
[256,] 0.223633893 -0.804191602
[257,] -0.100424276 0.223633893
[258,] -1.001398452 -0.100424276
[259,] -0.100424276 -1.001398452
[260,] -0.100424276 -0.100424276
[261,] -0.100424276 -0.100424276
[262,] -0.100424276 -0.100424276
[263,] 1.350485211 -0.100424276
[264,] 0.547692061 1.350485211
[265,] -1.001398452 0.547692061
[266,] -0.100424276 -1.001398452
[267,] 0.195808398 -0.100424276
[268,] -0.255101088 0.195808398
[269,] -0.804191602 -0.255101088
[270,] -0.100424276 -0.804191602
[271,] -0.100424276 -0.100424276
[272,] -0.100424276 -0.100424276
[273,] -0.931042920 -0.100424276
[274,] -0.100424276 -0.931042920
[275,] -0.325456621 -0.100424276
[276,] 1.547692061 -0.325456621
[277,] -0.100424276 1.547692061
[278,] 0.674543379 -0.100424276
[279,] 0.195808398 0.674543379
[280,] -0.325456621 0.195808398
[281,] -0.100424276 -0.325456621
[282,] -0.100424276 -0.100424276
[283,] -0.100424276 -0.100424276
[284,] 2.068957080 -0.100424276
[285,] -0.100424276 2.068957080
[286,] -0.100424276 -0.100424276
[287,] -0.100424276 -0.100424276
[288,] -0.100424276 -0.100424276
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.871750230 -1.804191602
2 1.393015249 1.871750230
3 -0.100424276 1.393015249
4 -0.100424276 -0.100424276
5 -1.804191602 -0.100424276
6 0.871750230 -1.804191602
7 -0.100424276 0.871750230
8 -0.100424276 -0.100424276
9 0.998601548 -0.100424276
10 -0.100424276 0.998601548
11 1.223633893 -0.100424276
12 -0.100424276 1.223633893
13 0.547692061 -0.100424276
14 -0.100424276 0.547692061
15 -0.100424276 -0.100424276
16 1.195808398 -0.100424276
17 1.195808398 1.195808398
18 0.547692061 1.195808398
19 -0.100424276 0.547692061
20 1.674543379 -0.100424276
21 0.547692061 1.674543379
22 0.068957080 0.547692061
23 -0.100424276 0.068957080
24 -0.100424276 -0.100424276
25 -1.409777901 -0.100424276
26 -0.846721639 -1.409777901
27 -0.100424276 -0.846721639
28 -0.409777901 -0.100424276
29 -0.649514789 -0.409777901
30 -0.804191602 -0.649514789
31 -0.100424276 -0.804191602
32 0.153278361 -0.100424276
33 -0.452307939 0.153278361
34 -0.100424276 -0.452307939
35 1.590222099 -0.100424276
36 1.674543379 1.590222099
37 -0.100424276 1.674543379
38 -0.128249770 -0.100424276
39 -0.100424276 -0.128249770
40 -0.100424276 -0.100424276
41 -0.846721639 -0.100424276
42 1.195808398 -0.846721639
43 -0.804191602 1.195808398
44 -0.100424276 -0.804191602
45 1.153278361 -0.100424276
46 0.393015249 1.153278361
47 0.590222099 0.393015249
48 -0.100424276 0.590222099
49 -0.100424276 -0.100424276
50 -0.804191602 -0.100424276
51 -0.100424276 -0.804191602
52 -0.325456621 -0.100424276
53 -1.212571051 -0.325456621
54 -0.931042920 -1.212571051
55 0.674543379 -0.931042920
56 -0.100424276 0.674543379
57 -0.100424276 -0.100424276
58 -0.100424276 -0.100424276
59 -0.100424276 -0.100424276
60 -0.085719732 -0.100424276
61 1.195808398 -0.085719732
62 1.195808398 1.195808398
63 -0.606984751 1.195808398
64 -0.100424276 -0.606984751
65 -0.100424276 -0.100424276
66 -0.100424276 -0.100424276
67 -0.804191602 -0.100424276
68 -0.100424276 -0.804191602
69 -0.100424276 -0.100424276
70 -0.100424276 -0.100424276
71 -0.409777901 -0.100424276
72 -0.100424276 -0.409777901
73 0.068957080 -0.100424276
74 -0.804191602 0.068957080
75 -0.804191602 -0.804191602
76 0.829220192 -0.804191602
77 -0.100424276 0.829220192
78 -0.100424276 -0.100424276
79 -0.100424276 -0.100424276
80 -0.367986658 -0.100424276
81 -0.804191602 -0.367986658
82 -0.100424276 -0.804191602
83 2.308693968 -0.100424276
84 1.956071510 2.308693968
85 -1.409777901 1.956071510
86 -0.804191602 -1.409777901
87 0.590222099 -0.804191602
88 -1.522663471 0.590222099
89 -0.100424276 -1.522663471
90 1.195808398 -0.100424276
91 -0.100424276 1.195808398
92 -0.409777901 -0.100424276
93 -1.804191602 -0.409777901
94 0.153278361 -1.804191602
95 -0.804191602 0.153278361
96 1.068957080 -0.804191602
97 1.195808398 1.068957080
98 -0.325456621 1.195808398
99 0.590222099 -0.325456621
100 -0.649514789 0.590222099
101 0.153278361 -0.649514789
102 0.956071510 0.153278361
103 -0.846721639 0.956071510
104 -0.100424276 -0.846721639
105 -0.100424276 -0.100424276
106 -1.804191602 -0.100424276
107 -0.606984751 -1.804191602
108 0.195808398 -0.606984751
109 -0.804191602 0.195808398
110 -0.100424276 -0.804191602
111 -0.100424276 -0.100424276
112 -0.100424276 -0.100424276
113 -0.100424276 -0.100424276
114 0.068957080 -0.100424276
115 0.068957080 0.068957080
116 0.393015249 0.068957080
117 -0.846721639 0.393015249
118 1.717073417 -0.846721639
119 -0.804191602 1.717073417
120 -0.931042920 -0.804191602
121 0.068957080 -0.931042920
122 -0.452307939 0.068957080
123 -1.128249770 -0.452307939
124 -0.100424276 -1.128249770
125 -0.409777901 -0.100424276
126 0.350485211 -0.409777901
127 0.674543379 0.350485211
128 0.547692061 0.674543379
129 -0.100424276 0.547692061
130 -0.100424276 -0.100424276
131 2.068957080 -0.100424276
132 -0.100424276 2.068957080
133 -1.804191602 -0.100424276
134 -0.931042920 -1.804191602
135 -0.128249770 -0.931042920
136 -0.100424276 -0.128249770
137 -0.100424276 -0.100424276
138 0.068957080 -0.100424276
139 -0.100424276 0.068957080
140 -0.606984751 -0.100424276
141 -0.804191602 -0.606984751
142 -0.649514789 -0.804191602
143 -0.606984751 -0.649514789
144 -0.100424276 -0.606984751
145 0.195808398 -0.100424276
146 -0.001398452 0.195808398
147 0.195808398 -0.001398452
148 -0.100424276 0.195808398
149 -0.100424276 -0.100424276
150 -0.649514789 -0.100424276
151 -0.325456621 -0.649514789
152 -1.606984751 -0.325456621
153 0.547692061 -1.606984751
154 -1.001398452 0.547692061
155 1.590222099 -1.001398452
156 -0.100424276 1.590222099
157 -0.100424276 -0.100424276
158 -0.100424276 -0.100424276
159 0.434806491 -0.100424276
160 -0.100424276 0.434806491
161 -1.001398452 -0.100424276
162 0.223633893 -1.001398452
163 1.195808398 0.223633893
164 -0.325456621 1.195808398
165 2.068957080 -0.325456621
166 -0.100424276 2.068957080
167 0.547692061 -0.100424276
168 -0.100424276 0.547692061
169 -0.325456621 -0.100424276
170 0.393015249 -0.325456621
171 -2.001398452 0.393015249
172 0.153278361 -2.001398452
173 1.590222099 0.153278361
174 -0.100424276 1.590222099
175 -0.100424276 -0.100424276
176 -0.100424276 -0.100424276
177 0.674543379 -0.100424276
178 0.632013342 0.674543379
179 -0.931042920 0.632013342
180 -0.001398452 -0.931042920
181 -1.522663471 -0.001398452
182 -0.100424276 -1.522663471
183 0.026427042 -0.100424276
184 0.195808398 0.026427042
185 -0.100424276 0.195808398
186 -0.100424276 -0.100424276
187 -0.100424276 -0.100424276
188 -0.409777901 -0.100424276
189 -0.804191602 -0.409777901
190 -0.325456621 -0.804191602
191 1.266163931 -0.325456621
192 -0.100424276 1.266163931
193 0.350485211 -0.100424276
194 0.547692061 0.350485211
195 0.956071510 0.547692061
196 -0.409777901 0.956071510
197 -0.367986658 -0.409777901
198 -0.452307939 -0.367986658
199 0.153278361 -0.452307939
200 -0.100424276 0.153278361
201 -0.100424276 -0.100424276
202 2.547692061 -0.100424276
203 1.393015249 2.547692061
204 1.393015249 1.393015249
205 -0.100424276 1.393015249
206 -0.100424276 -0.100424276
207 0.153278361 -0.100424276
208 0.547692061 0.153278361
209 -0.100424276 0.547692061
210 0.787428949 -0.100424276
211 1.153278361 0.787428949
212 -0.100424276 1.153278361
213 -1.409777901 -0.100424276
214 0.153278361 -1.409777901
215 0.477336529 0.153278361
216 -1.409777901 0.477336529
217 -0.325456621 -1.409777901
218 0.998601548 -0.325456621
219 -0.100424276 0.998601548
220 -0.804191602 -0.100424276
221 -0.325456621 -0.804191602
222 0.153278361 -0.325456621
223 -0.100424276 0.153278361
224 -0.100424276 -0.100424276
225 1.547692061 -0.100424276
226 -0.100424276 1.547692061
227 0.068957080 -0.100424276
228 -0.325456621 0.068957080
229 -0.100424276 -0.325456621
230 0.153278361 -0.100424276
231 -1.409777901 0.153278361
232 -0.804191602 -1.409777901
233 -0.100424276 -0.804191602
234 0.547692061 -0.100424276
235 -1.212571051 0.547692061
236 0.195808398 -1.212571051
237 -0.100424276 0.195808398
238 0.477336529 -0.100424276
239 -0.100424276 0.477336529
240 0.871750230 -0.100424276
241 -0.100424276 0.871750230
242 -0.100424276 -0.100424276
243 -0.452307939 -0.100424276
244 -0.100424276 -0.452307939
245 -0.100424276 -0.100424276
246 -0.325456621 -0.100424276
247 -0.100424276 -0.325456621
248 -0.100424276 -0.100424276
249 1.590222099 -0.100424276
250 -1.085719732 1.590222099
251 0.547692061 -1.085719732
252 -0.100424276 0.547692061
253 1.547692061 -0.100424276
254 1.068957080 1.547692061
255 -0.804191602 1.068957080
256 0.223633893 -0.804191602
257 -0.100424276 0.223633893
258 -1.001398452 -0.100424276
259 -0.100424276 -1.001398452
260 -0.100424276 -0.100424276
261 -0.100424276 -0.100424276
262 -0.100424276 -0.100424276
263 1.350485211 -0.100424276
264 0.547692061 1.350485211
265 -1.001398452 0.547692061
266 -0.100424276 -1.001398452
267 0.195808398 -0.100424276
268 -0.255101088 0.195808398
269 -0.804191602 -0.255101088
270 -0.100424276 -0.804191602
271 -0.100424276 -0.100424276
272 -0.100424276 -0.100424276
273 -0.931042920 -0.100424276
274 -0.100424276 -0.931042920
275 -0.325456621 -0.100424276
276 1.547692061 -0.325456621
277 -0.100424276 1.547692061
278 0.674543379 -0.100424276
279 0.195808398 0.674543379
280 -0.325456621 0.195808398
281 -0.100424276 -0.325456621
282 -0.100424276 -0.100424276
283 -0.100424276 -0.100424276
284 2.068957080 -0.100424276
285 -0.100424276 2.068957080
286 -0.100424276 -0.100424276
287 -0.100424276 -0.100424276
288 -0.100424276 -0.100424276
> 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/fisher/rcomp/tmp/7ojs91353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8zn5g1353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9i8p01353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10rvz21353330689.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11hit71353330689.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/fisher/rcomp/tmp/12jip71353330689.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/fisher/rcomp/tmp/1350vh1353330689.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/fisher/rcomp/tmp/14afu31353330689.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/fisher/rcomp/tmp/15duvb1353330689.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/fisher/rcomp/tmp/160m4m1353330689.tab")
+ }
>
> try(system("convert tmp/1u5d61353330689.ps tmp/1u5d61353330689.png",intern=TRUE))
character(0)
> try(system("convert tmp/28qvr1353330689.ps tmp/28qvr1353330689.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nuaj1353330689.ps tmp/3nuaj1353330689.png",intern=TRUE))
character(0)
> try(system("convert tmp/4j4d21353330689.ps tmp/4j4d21353330689.png",intern=TRUE))
character(0)
> try(system("convert tmp/5qy3i1353330689.ps tmp/5qy3i1353330689.png",intern=TRUE))
character(0)
> try(system("convert tmp/6v4wg1353330689.ps tmp/6v4wg1353330689.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ojs91353330689.ps tmp/7ojs91353330689.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zn5g1353330689.ps tmp/8zn5g1353330689.png",intern=TRUE))
character(0)
> try(system("convert tmp/9i8p01353330689.ps tmp/9i8p01353330689.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rvz21353330689.ps tmp/10rvz21353330689.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.945 1.370 12.310