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(4
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+ ,dim=c(7
+ ,195)
+ ,dimnames=list(c('Teamwork33'
+ ,'geslacht'
+ ,'leeftijd'
+ ,'opleiding'
+ ,'Neuroticisme'
+ ,'Extraversie'
+ ,'Openheid')
+ ,1:195))
> y <- array(NA,dim=c(7,195),dimnames=list(c('Teamwork33','geslacht','leeftijd','opleiding','Neuroticisme','Extraversie','Openheid'),1:195))
> 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
Teamwork33 geslacht leeftijd opleiding Neuroticisme Extraversie Openheid
1 4 1 27 5 26 49 35
2 4 1 36 4 25 45 34
3 5 1 25 4 17 54 13
4 2 1 27 3 37 36 35
5 3 2 25 3 35 36 28
6 5 2 44 3 15 53 32
7 4 1 50 4 27 46 35
8 4 1 41 4 36 42 36
9 4 1 48 5 25 41 27
10 4 2 43 4 30 45 29
11 5 2 47 2 27 47 27
12 4 2 41 3 33 42 28
13 3 1 44 2 29 45 29
14 4 2 47 5 30 40 28
15 3 2 40 3 25 45 30
16 3 2 46 3 23 40 25
17 4 1 28 3 26 42 15
18 3 1 56 3 24 45 33
19 4 2 49 4 35 47 31
20 2 2 25 4 39 31 37
21 4 2 41 4 23 46 37
22 3 2 26 3 32 34 34
23 4 1 50 5 29 43 32
24 4 1 47 4 26 45 21
25 3 1 52 2 21 42 25
26 3 2 37 5 35 51 32
27 2 2 41 3 23 44 28
28 4 1 45 4 21 47 22
29 5 2 26 4 28 47 25
30 4 1 3 30 41 26 2
31 1 52 4 21 44 34 5
32 1 46 2 29 51 34 4
33 1 58 3 28 46 36 3
34 1 54 5 19 47 36 4
35 1 29 3 26 46 26 2
36 2 50 3 33 38 26 3
37 1 43 2 34 50 34 3
38 2 30 3 33 48 33 3
39 2 47 2 40 36 31 5
40 1 45 3 24 51 33 2
41 48 1 35 35 22 4 2
42 48 3 35 49 29 4 2
43 26 4 32 38 24 4 1
44 46 5 20 47 37 2 2
45 3 35 36 32 4 2 50
46 3 35 47 23 3 1 25
47 4 21 46 29 4 1 47
48 2 33 43 35 1 2 47
49 2 40 53 20 2 1 41
50 3 22 55 28 2 2 45
51 2 35 39 26 4 2 41
52 4 20 55 36 3 2 45
53 5 28 41 26 4 2 40
54 3 46 33 33 3 1 29
55 4 18 52 25 3 2 34
56 5 22 42 29 5 1 45
57 5 20 56 32 3 2 52
58 3 25 46 35 2 2 41
59 4 31 33 24 1 2 48
60 3 21 51 31 2 2 45
61 3 23 46 29 5 1 54
62 2 26 46 27 4 2 25
63 3 34 50 29 4 2 26
64 4 31 46 29 3 1 28
65 4 23 51 27 4 2 50
66 4 31 48 34 4 2 48
67 4 26 44 32 2 2 51
68 3 36 38 31 3 2 53
69 3 28 42 31 4 1 37
70 3 34 39 31 3 1 56
71 2 25 45 16 2 1 43
72 3 33 31 25 4 1 34
73 3 46 29 27 4 1 42
74 3 24 48 32 3 2 32
75 3 32 38 28 5 2 31
76 5 33 55 25 1 1 46
77 3 42 32 25 3 2 30
78 5 17 51 36 3 2 47
79 4 36 53 36 5 2 33
80 4 40 47 36 2 1 25
81 4 30 45 27 3 1 25
82 5 19 33 29 3 2 21
83 4 33 49 32 4 2 36
84 5 35 46 29 2 2 50
85 3 23 42 31 4 2 48
86 3 15 56 34 3 2 48
87 2 38 35 27 3 1 25
88 3 37 40 28 3 1 48
89 4 23 44 32 2 2 49
90 5 41 46 33 3 1 27
91 5 34 46 29 2 1 28
92 3 38 39 32 4 2 43
93 2 45 35 35 4 2 48
94 3 27 48 33 2 2 48
95 4 46 42 27 1 1 25
96 1 26 39 16 5 2 49
97 4 44 39 32 4 1 26
98 3 36 41 26 4 1 51
99 3 20 52 32 4 2 25
100 4 44 45 38 3 1 29
101 3 27 42 24 3 1 29
102 4 27 44 26 1 1 43
103 2 41 33 19 5 2 46
104 3 30 42 37 3 1 44
105 3 33 46 25 3 1 25
106 3 37 45 24 2 1 51
107 2 30 40 23 4 1 42
108 5 20 48 28 4 2 53
109 5 44 32 38 3 1 25
110 4 20 53 28 4 2 49
111 2 33 39 28 4 1 51
112 3 31 45 26 2 2 20
113 3 23 36 21 3 2 44
114 3 33 38 35 3 2 38
115 4 33 49 31 3 1 46
116 5 32 46 34 4 2 42
117 4 25 43 30 5 1 29
118 22 37 30 3 2 46 4
119 16 48 24 3 2 49 2
120 36 45 27 2 2 51 3
121 35 32 26 3 1 38 3
122 25 46 30 1 1 41 1
123 27 20 15 4 2 47 3
124 32 42 28 4 2 44 3
125 36 45 34 4 2 47 3
126 51 29 29 3 2 46 3
127 30 51 26 5 1 44 4
128 20 55 31 2 2 28 3
129 29 50 28 2 2 47 4
130 26 44 33 3 2 28 4
131 20 41 32 3 1 41 5
132 40 40 33 2 2 45 4
133 29 47 31 1 2 46 4
134 32 42 37 3 1 46 4
135 33 40 27 5 2 22 3
136 32 51 19 4 2 33 3
137 34 43 27 4 1 41 4
138 24 45 31 4 2 47 5
139 25 41 38 3 1 25 3
140 41 41 22 5 2 42 3
141 39 37 35 3 2 47 3
142 21 46 35 3 2 50 3
143 38 38 30 3 1 55 5
144 28 39 41 3 1 21 3
145 37 45 25 4 1 3 26
146 46 28 2 1 52 3 30
147 39 45 2 2 49 4 25
148 21 21 4 2 46 4 38
149 31 33 3 1 4 31 35
150 25 3 2 45 3 31 49
151 29 2 2 52 3 27 40
152 31 3 1 3 21 45 29
153 3 2 40 4 26 46 31
154 4 2 49 4 37 45 31
155 1 1 38 5 28 34 25
156 1 1 32 5 29 41 27
157 5 2 46 4 33 43 26
158 4 2 32 3 41 45 26
159 3 2 41 3 19 48 23
160 3 2 43 3 37 43 27
161 4 1 44 4 36 45 24
162 3 1 47 5 27 45 35
163 2 2 28 3 33 34 24
164 1 1 52 1 29 40 32
165 1 1 27 2 42 40 24
166 5 2 45 5 27 55 24
167 4 1 27 4 47 44 38
168 3 1 25 4 17 44 36
169 4 1 28 4 34 48 24
170 5 1 25 3 32 51 18
171 4 1 52 4 25 49 34
172 4 1 44 3 27 33 23
173 2 2 43 3 37 43 35
174 3 2 47 4 34 44 22
175 4 2 52 4 27 44 34
176 3 2 40 2 37 41 28
177 4 1 42 3 32 45 34
178 3 1 45 5 26 44 32
179 4 1 45 2 29 44 24
180 1 1 50 5 28 40 34
181 2 1 49 3 19 48 33
182 3 1 52 2 46 49 33
183 3 2 48 3 31 46 29
184 5 2 51 3 42 49 38
185 4 2 49 4 33 55 24
186 3 2 31 4 39 51 25
187 3 2 43 3 27 46 37
188 3 2 31 3 35 37 33
189 3 2 28 4 23 43 30
190 4 2 43 4 32 41 22
191 3 2 31 3 22 45 28
192 2 2 51 3 17 39 24
193 4 2 58 4 35 38 33
194 2 2 25 5 34 41 37
195 4 1 27 5 26 49 35
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) geslacht leeftijd opleiding Neuroticisme
55.6998 -0.1896 -0.3626 -0.3126 -0.4376
Extraversie Openheid
-0.1934 -0.4518
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-17.170 -5.666 -0.449 3.983 35.245
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 55.69981 5.40411 10.307 < 2e-16 ***
geslacht -0.18960 0.05682 -3.337 0.00102 **
leeftijd -0.36258 0.05963 -6.081 6.55e-09 ***
opleiding -0.31259 0.07648 -4.087 6.47e-05 ***
Neuroticisme -0.43763 0.05765 -7.591 1.44e-12 ***
Extraversie -0.19341 0.06540 -2.957 0.00350 **
Openheid -0.45176 0.05779 -7.817 3.74e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 8.511 on 188 degrees of freedom
Multiple R-squared: 0.5272, Adjusted R-squared: 0.5121
F-statistic: 34.94 on 6 and 188 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,] 2.159836e-04 4.319671e-04 9.997840e-01
[2,] 1.468988e-05 2.937976e-05 9.999853e-01
[3,] 6.473910e-07 1.294782e-06 9.999994e-01
[4,] 1.429399e-07 2.858798e-07 9.999999e-01
[5,] 8.767379e-09 1.753476e-08 1.000000e+00
[6,] 3.487406e-09 6.974813e-09 1.000000e+00
[7,] 2.580452e-10 5.160904e-10 1.000000e+00
[8,] 2.709985e-11 5.419970e-11 1.000000e+00
[9,] 3.046041e-12 6.092081e-12 1.000000e+00
[10,] 4.595789e-13 9.191578e-13 1.000000e+00
[11,] 2.943923e-14 5.887846e-14 1.000000e+00
[12,] 1.812930e-15 3.625860e-15 1.000000e+00
[13,] 2.121783e-16 4.243567e-16 1.000000e+00
[14,] 1.319784e-17 2.639568e-17 1.000000e+00
[15,] 7.892696e-19 1.578539e-18 1.000000e+00
[16,] 4.635041e-20 9.270083e-20 1.000000e+00
[17,] 2.030027e-19 4.060053e-19 1.000000e+00
[18,] 4.844753e-19 9.689505e-19 1.000000e+00
[19,] 4.062324e-20 8.124649e-20 1.000000e+00
[20,] 7.973499e-21 1.594700e-20 1.000000e+00
[21,] 1.359759e-21 2.719518e-21 1.000000e+00
[22,] 1.308373e-22 2.616746e-22 1.000000e+00
[23,] 1.545937e-23 3.091875e-23 1.000000e+00
[24,] 1.258816e-24 2.517632e-24 1.000000e+00
[25,] 1.108943e-25 2.217887e-25 1.000000e+00
[26,] 2.367390e-26 4.734780e-26 1.000000e+00
[27,] 7.624347e-27 1.524869e-26 1.000000e+00
[28,] 1.846826e-27 3.693652e-27 1.000000e+00
[29,] 2.864791e-28 5.729583e-28 1.000000e+00
[30,] 5.552772e-29 1.110554e-28 1.000000e+00
[31,] 2.719549e-29 5.439099e-29 1.000000e+00
[32,] 6.364321e-05 1.272864e-04 9.999364e-01
[33,] 4.105907e-04 8.211814e-04 9.995894e-01
[34,] 1.567633e-03 3.135266e-03 9.984324e-01
[35,] 1.563059e-02 3.126118e-02 9.843694e-01
[36,] 5.484950e-02 1.096990e-01 9.451505e-01
[37,] 7.839548e-02 1.567910e-01 9.216045e-01
[38,] 8.206500e-02 1.641300e-01 9.179350e-01
[39,] 7.033835e-02 1.406767e-01 9.296616e-01
[40,] 5.511016e-02 1.102203e-01 9.448898e-01
[41,] 5.815670e-02 1.163134e-01 9.418433e-01
[42,] 4.655360e-02 9.310720e-02 9.534464e-01
[43,] 6.229025e-02 1.245805e-01 9.377097e-01
[44,] 4.842828e-02 9.685656e-02 9.515717e-01
[45,] 4.257442e-02 8.514883e-02 9.574256e-01
[46,] 4.862830e-02 9.725660e-02 9.513717e-01
[47,] 3.801173e-02 7.602345e-02 9.619883e-01
[48,] 3.600425e-02 7.200851e-02 9.639957e-01
[49,] 3.244355e-02 6.488709e-02 9.675565e-01
[50,] 3.506153e-02 7.012306e-02 9.649385e-01
[51,] 3.197972e-02 6.395944e-02 9.680203e-01
[52,] 2.518368e-02 5.036736e-02 9.748163e-01
[53,] 3.076672e-02 6.153344e-02 9.692333e-01
[54,] 2.695020e-02 5.390040e-02 9.730498e-01
[55,] 2.200097e-02 4.400195e-02 9.779990e-01
[56,] 1.791336e-02 3.582671e-02 9.820866e-01
[57,] 1.398847e-02 2.797693e-02 9.860115e-01
[58,] 1.077713e-02 2.155426e-02 9.892229e-01
[59,] 1.003770e-02 2.007540e-02 9.899623e-01
[60,] 7.895195e-03 1.579039e-02 9.921048e-01
[61,] 6.921850e-03 1.384370e-02 9.930781e-01
[62,] 5.151738e-03 1.030348e-02 9.948483e-01
[63,] 4.431586e-03 8.863172e-03 9.955684e-01
[64,] 6.512863e-03 1.302573e-02 9.934871e-01
[65,] 6.786686e-03 1.357337e-02 9.932133e-01
[66,] 5.421815e-03 1.084363e-02 9.945782e-01
[67,] 4.724941e-03 9.449882e-03 9.952751e-01
[68,] 5.125033e-03 1.025007e-02 9.948750e-01
[69,] 5.727813e-03 1.145563e-02 9.942722e-01
[70,] 4.553458e-03 9.106916e-03 9.954465e-01
[71,] 3.485571e-03 6.971143e-03 9.965144e-01
[72,] 2.784784e-03 5.569568e-03 9.972152e-01
[73,] 3.058917e-03 6.117834e-03 9.969411e-01
[74,] 2.220686e-03 4.441373e-03 9.977793e-01
[75,] 2.018723e-03 4.037446e-03 9.979813e-01
[76,] 1.486115e-03 2.972229e-03 9.985139e-01
[77,] 2.167880e-03 4.335761e-03 9.978321e-01
[78,] 2.213870e-03 4.427739e-03 9.977861e-01
[79,] 1.832082e-03 3.664164e-03 9.981679e-01
[80,] 1.382070e-03 2.764140e-03 9.986179e-01
[81,] 9.792418e-04 1.958484e-03 9.990208e-01
[82,] 6.813993e-04 1.362799e-03 9.993186e-01
[83,] 5.080995e-04 1.016199e-03 9.994919e-01
[84,] 4.782806e-04 9.565612e-04 9.995217e-01
[85,] 3.509631e-04 7.019263e-04 9.996490e-01
[86,] 3.316340e-04 6.632680e-04 9.996684e-01
[87,] 2.451274e-04 4.902547e-04 9.997549e-01
[88,] 2.188548e-04 4.377097e-04 9.997811e-01
[89,] 1.781248e-04 3.562497e-04 9.998219e-01
[90,] 2.597578e-04 5.195156e-04 9.997402e-01
[91,] 1.915553e-04 3.831105e-04 9.998084e-01
[92,] 1.515960e-04 3.031921e-04 9.998484e-01
[93,] 1.003509e-04 2.007019e-04 9.998996e-01
[94,] 1.611107e-04 3.222213e-04 9.998389e-01
[95,] 1.122435e-04 2.244869e-04 9.998878e-01
[96,] 9.065569e-05 1.813114e-04 9.999093e-01
[97,] 7.706312e-05 1.541262e-04 9.999229e-01
[98,] 6.163983e-05 1.232797e-04 9.999384e-01
[99,] 4.540952e-05 9.081903e-05 9.999546e-01
[100,] 7.250403e-05 1.450081e-04 9.999275e-01
[101,] 5.808196e-05 1.161639e-04 9.999419e-01
[102,] 4.605056e-05 9.210112e-05 9.999539e-01
[103,] 5.331112e-05 1.066222e-04 9.999467e-01
[104,] 4.394288e-05 8.788576e-05 9.999561e-01
[105,] 5.760352e-05 1.152070e-04 9.999424e-01
[106,] 4.666131e-05 9.332261e-05 9.999533e-01
[107,] 5.095515e-05 1.019103e-04 9.999490e-01
[108,] 1.632231e-04 3.264462e-04 9.998368e-01
[109,] 7.129520e-03 1.425904e-02 9.928705e-01
[110,] 4.937992e-02 9.875983e-02 9.506201e-01
[111,] 2.605406e-01 5.210812e-01 7.394594e-01
[112,] 3.949850e-01 7.899701e-01 6.050150e-01
[113,] 4.365479e-01 8.730958e-01 5.634521e-01
[114,] 3.994669e-01 7.989337e-01 6.005331e-01
[115,] 4.425910e-01 8.851821e-01 5.574090e-01
[116,] 5.381545e-01 9.236909e-01 4.618455e-01
[117,] 9.678969e-01 6.420613e-02 3.210306e-02
[118,] 9.687629e-01 6.247429e-02 3.123715e-02
[119,] 9.916636e-01 1.667288e-02 8.336441e-03
[120,] 9.921908e-01 1.561842e-02 7.809208e-03
[121,] 9.920177e-01 1.596453e-02 7.982263e-03
[122,] 9.946702e-01 1.065966e-02 5.329830e-03
[123,] 9.981541e-01 3.691816e-03 1.845908e-03
[124,] 9.978597e-01 4.280533e-03 2.140266e-03
[125,] 9.973343e-01 5.331321e-03 2.665660e-03
[126,] 9.967765e-01 6.447002e-03 3.223501e-03
[127,] 9.977191e-01 4.561885e-03 2.280943e-03
[128,] 9.970084e-01 5.983103e-03 2.991552e-03
[129,] 9.984271e-01 3.145728e-03 1.572864e-03
[130,] 9.984180e-01 3.163992e-03 1.581996e-03
[131,] 9.985852e-01 2.829534e-03 1.414767e-03
[132,] 9.995186e-01 9.628831e-04 4.814416e-04
[133,] 9.999495e-01 1.010988e-04 5.054938e-05
[134,] 9.999507e-01 9.853959e-05 4.926980e-05
[135,] 9.999197e-01 1.606663e-04 8.033314e-05
[136,] 9.999551e-01 8.988646e-05 4.494323e-05
[137,] 1.000000e+00 6.686463e-09 3.343232e-09
[138,] 1.000000e+00 5.093454e-09 2.546727e-09
[139,] 1.000000e+00 9.761599e-09 4.880800e-09
[140,] 1.000000e+00 4.180830e-09 2.090415e-09
[141,] 1.000000e+00 4.619951e-09 2.309976e-09
[142,] 1.000000e+00 3.809748e-09 1.904874e-09
[143,] 1.000000e+00 3.306349e-27 1.653174e-27
[144,] 1.000000e+00 2.796303e-26 1.398152e-26
[145,] 1.000000e+00 2.158715e-25 1.079358e-25
[146,] 1.000000e+00 8.308315e-25 4.154157e-25
[147,] 1.000000e+00 7.199694e-25 3.599847e-25
[148,] 1.000000e+00 2.058209e-24 1.029105e-24
[149,] 1.000000e+00 1.667596e-23 8.337981e-24
[150,] 1.000000e+00 1.335977e-22 6.679883e-23
[151,] 1.000000e+00 1.208251e-21 6.041253e-22
[152,] 1.000000e+00 1.046780e-20 5.233898e-21
[153,] 1.000000e+00 8.648898e-20 4.324449e-20
[154,] 1.000000e+00 6.251972e-19 3.125986e-19
[155,] 1.000000e+00 2.251716e-18 1.125858e-18
[156,] 1.000000e+00 1.194022e-18 5.970111e-19
[157,] 1.000000e+00 5.806730e-18 2.903365e-18
[158,] 1.000000e+00 5.049390e-17 2.524695e-17
[159,] 1.000000e+00 4.156167e-16 2.078083e-16
[160,] 1.000000e+00 3.789617e-15 1.894809e-15
[161,] 1.000000e+00 2.053552e-14 1.026776e-14
[162,] 1.000000e+00 1.329908e-13 6.649540e-14
[163,] 1.000000e+00 4.690819e-13 2.345410e-13
[164,] 1.000000e+00 1.600570e-12 8.002852e-13
[165,] 1.000000e+00 1.425140e-11 7.125699e-12
[166,] 1.000000e+00 8.722536e-11 4.361268e-11
[167,] 1.000000e+00 7.098450e-10 3.549225e-10
[168,] 1.000000e+00 4.052294e-09 2.026147e-09
[169,] 1.000000e+00 3.086685e-08 1.543343e-08
[170,] 9.999999e-01 1.095219e-07 5.476093e-08
[171,] 9.999999e-01 2.573016e-07 1.286508e-07
[172,] 9.999992e-01 1.684658e-06 8.423288e-07
[173,] 9.999982e-01 3.651976e-06 1.825988e-06
[174,] 9.999854e-01 2.918468e-05 1.459234e-05
[175,] 9.998702e-01 2.596401e-04 1.298200e-04
[176,] 9.985038e-01 2.992458e-03 1.496229e-03
> postscript(file="/var/www/html/freestat/rcomp/tmp/10fbz1293201353.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2gsz81293201353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3gsz81293201353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4gsz81293201353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/53y9m1293201353.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 = 195
Frequency = 1
1 2 3 4 5 6
-3.4904391 -2.2028772 -16.4384419 -3.8160175 -7.3891312 -2.1577886
7 8 9 10 11 12
4.3936443 4.7472583 -1.4753094 0.4541584 0.4497087 -0.3026773
13 14 15 16 17 18
-1.4356706 0.7982267 -3.6825775 -5.6082324 -14.1420458 2.8466925
19 20 21 22 23 24
6.1081353 -3.2272782 0.4730201 -6.0157532 3.6459902 -3.6497142
25 26 27 28 29 30
-4.4233944 2.2952069 -6.2921949 -5.7244577 -7.0051186 -17.1695795
31 32 33 34 35 36
-8.7352434 -5.4856145 -5.4135352 -7.3706637 -13.9229789 -9.8026164
37 38 39 40 41 42
-5.3808700 -7.8643501 -7.5505727 -7.9724934 27.4256523 35.2445017
43 44 45 46 47 48
6.2679902 30.6741157 1.7168514 -9.0330128 1.2017981 1.1452831
49 50 51 52 53 54
-1.0568588 1.7566406 -4.1367346 5.3157680 -2.1905287 -7.0906202
55 56 57 58 59 60
-4.5589228 0.4752114 8.5902887 -0.5566758 -3.8463824 1.0544914
61 62 63 64 65 66
4.1809196 -10.2205870 -5.1765573 -5.9232015 4.3173910 6.0310447
67 68 69 70 71 72
3.4875649 3.2366507 -3.8137007 4.3818822 -7.1482971 -10.0848407
73 74 75 76 77 78
-4.1059909 -4.5870445 -7.5228571 4.7251774 -10.0671145 5.2001727
79 80 81 82 83 84
3.0784006 -3.4590298 -8.4558172 -14.8807694 0.7265809 5.5295962
85 86 87 88 89 90
0.4010295 4.4604441 -12.5647981 0.7614533 2.0152568 -2.2286214
91 92 93 94 95 96
-4.7920391 0.2110917 2.2845124 3.0847929 -7.3852339 -5.9173104
97 98 99 100 101 102
-5.5245727 2.1021601 -6.6197856 -0.5559588 -10.2430831 -2.4434475
103 104 105 106 107 108
-4.6662964 1.1656697 -9.1496155 2.2416264 -5.4015703 5.3287150
109 110 111 112 113 114
-6.0764860 4.3345794 0.4333811 -12.0818025 -7.1449547 -2.8581341
115 116 117 118 119 120
4.3005009 3.7849558 -6.5089165 -3.2902108 -9.7033534 11.3415794
121 122 123 124 125 126
4.8747797 -1.9689653 -6.8978089 6.4066318 13.7311358 23.3786641
127 128 129 130 131 132
5.7145757 -5.7606514 5.3302499 -0.3567442 -4.7596110 15.8603175
133 134 135 136 137 138
5.3431841 9.7581909 2.7223268 2.7222692 7.6675313 1.5469150
139 140 141 142 143 144
-1.5822880 12.9673265 15.2643345 -0.4490311 14.6542383 1.3525884
145 146 147 148 149 150
12.9104509 33.5366186 26.6941213 9.4288173 6.5151085 14.1053027
151 152 153 154 155 156
15.2643508 8.1642123 -2.2871866 6.5965758 -9.0455902 -8.5260078
157 158 159 160 161 162
2.1126989 -0.3880640 -8.5278537 0.9146747 0.9941668 2.4250844
163 164 165 166 167 168
-10.3705194 -0.4594436 -7.1360842 0.8543614 5.7754967 -9.9822080
169 170 171 172 173 174
-5.1020964 -8.5079741 4.3720172 -6.0298591 3.5287191 -0.7006969
175 176 177 178 179 180
4.4698137 -0.4207167 2.7234427 -0.2863859 -2.5252854 0.5316236
181 182 183 184 185 186
-2.2992788 11.4854163 1.5855077 14.1332635 3.6178921 -1.6045936
187 188 189 190 191 192
1.6361269 -2.7614752 -8.9830169 -2.6065185 -9.1621883 -8.0663235
193 194 195
8.5341130 -3.1687214 -3.4904391
> postscript(file="/var/www/html/freestat/rcomp/tmp/63y9m1293201353.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 = 195
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.4904391 NA
1 -2.2028772 -3.4904391
2 -16.4384419 -2.2028772
3 -3.8160175 -16.4384419
4 -7.3891312 -3.8160175
5 -2.1577886 -7.3891312
6 4.3936443 -2.1577886
7 4.7472583 4.3936443
8 -1.4753094 4.7472583
9 0.4541584 -1.4753094
10 0.4497087 0.4541584
11 -0.3026773 0.4497087
12 -1.4356706 -0.3026773
13 0.7982267 -1.4356706
14 -3.6825775 0.7982267
15 -5.6082324 -3.6825775
16 -14.1420458 -5.6082324
17 2.8466925 -14.1420458
18 6.1081353 2.8466925
19 -3.2272782 6.1081353
20 0.4730201 -3.2272782
21 -6.0157532 0.4730201
22 3.6459902 -6.0157532
23 -3.6497142 3.6459902
24 -4.4233944 -3.6497142
25 2.2952069 -4.4233944
26 -6.2921949 2.2952069
27 -5.7244577 -6.2921949
28 -7.0051186 -5.7244577
29 -17.1695795 -7.0051186
30 -8.7352434 -17.1695795
31 -5.4856145 -8.7352434
32 -5.4135352 -5.4856145
33 -7.3706637 -5.4135352
34 -13.9229789 -7.3706637
35 -9.8026164 -13.9229789
36 -5.3808700 -9.8026164
37 -7.8643501 -5.3808700
38 -7.5505727 -7.8643501
39 -7.9724934 -7.5505727
40 27.4256523 -7.9724934
41 35.2445017 27.4256523
42 6.2679902 35.2445017
43 30.6741157 6.2679902
44 1.7168514 30.6741157
45 -9.0330128 1.7168514
46 1.2017981 -9.0330128
47 1.1452831 1.2017981
48 -1.0568588 1.1452831
49 1.7566406 -1.0568588
50 -4.1367346 1.7566406
51 5.3157680 -4.1367346
52 -2.1905287 5.3157680
53 -7.0906202 -2.1905287
54 -4.5589228 -7.0906202
55 0.4752114 -4.5589228
56 8.5902887 0.4752114
57 -0.5566758 8.5902887
58 -3.8463824 -0.5566758
59 1.0544914 -3.8463824
60 4.1809196 1.0544914
61 -10.2205870 4.1809196
62 -5.1765573 -10.2205870
63 -5.9232015 -5.1765573
64 4.3173910 -5.9232015
65 6.0310447 4.3173910
66 3.4875649 6.0310447
67 3.2366507 3.4875649
68 -3.8137007 3.2366507
69 4.3818822 -3.8137007
70 -7.1482971 4.3818822
71 -10.0848407 -7.1482971
72 -4.1059909 -10.0848407
73 -4.5870445 -4.1059909
74 -7.5228571 -4.5870445
75 4.7251774 -7.5228571
76 -10.0671145 4.7251774
77 5.2001727 -10.0671145
78 3.0784006 5.2001727
79 -3.4590298 3.0784006
80 -8.4558172 -3.4590298
81 -14.8807694 -8.4558172
82 0.7265809 -14.8807694
83 5.5295962 0.7265809
84 0.4010295 5.5295962
85 4.4604441 0.4010295
86 -12.5647981 4.4604441
87 0.7614533 -12.5647981
88 2.0152568 0.7614533
89 -2.2286214 2.0152568
90 -4.7920391 -2.2286214
91 0.2110917 -4.7920391
92 2.2845124 0.2110917
93 3.0847929 2.2845124
94 -7.3852339 3.0847929
95 -5.9173104 -7.3852339
96 -5.5245727 -5.9173104
97 2.1021601 -5.5245727
98 -6.6197856 2.1021601
99 -0.5559588 -6.6197856
100 -10.2430831 -0.5559588
101 -2.4434475 -10.2430831
102 -4.6662964 -2.4434475
103 1.1656697 -4.6662964
104 -9.1496155 1.1656697
105 2.2416264 -9.1496155
106 -5.4015703 2.2416264
107 5.3287150 -5.4015703
108 -6.0764860 5.3287150
109 4.3345794 -6.0764860
110 0.4333811 4.3345794
111 -12.0818025 0.4333811
112 -7.1449547 -12.0818025
113 -2.8581341 -7.1449547
114 4.3005009 -2.8581341
115 3.7849558 4.3005009
116 -6.5089165 3.7849558
117 -3.2902108 -6.5089165
118 -9.7033534 -3.2902108
119 11.3415794 -9.7033534
120 4.8747797 11.3415794
121 -1.9689653 4.8747797
122 -6.8978089 -1.9689653
123 6.4066318 -6.8978089
124 13.7311358 6.4066318
125 23.3786641 13.7311358
126 5.7145757 23.3786641
127 -5.7606514 5.7145757
128 5.3302499 -5.7606514
129 -0.3567442 5.3302499
130 -4.7596110 -0.3567442
131 15.8603175 -4.7596110
132 5.3431841 15.8603175
133 9.7581909 5.3431841
134 2.7223268 9.7581909
135 2.7222692 2.7223268
136 7.6675313 2.7222692
137 1.5469150 7.6675313
138 -1.5822880 1.5469150
139 12.9673265 -1.5822880
140 15.2643345 12.9673265
141 -0.4490311 15.2643345
142 14.6542383 -0.4490311
143 1.3525884 14.6542383
144 12.9104509 1.3525884
145 33.5366186 12.9104509
146 26.6941213 33.5366186
147 9.4288173 26.6941213
148 6.5151085 9.4288173
149 14.1053027 6.5151085
150 15.2643508 14.1053027
151 8.1642123 15.2643508
152 -2.2871866 8.1642123
153 6.5965758 -2.2871866
154 -9.0455902 6.5965758
155 -8.5260078 -9.0455902
156 2.1126989 -8.5260078
157 -0.3880640 2.1126989
158 -8.5278537 -0.3880640
159 0.9146747 -8.5278537
160 0.9941668 0.9146747
161 2.4250844 0.9941668
162 -10.3705194 2.4250844
163 -0.4594436 -10.3705194
164 -7.1360842 -0.4594436
165 0.8543614 -7.1360842
166 5.7754967 0.8543614
167 -9.9822080 5.7754967
168 -5.1020964 -9.9822080
169 -8.5079741 -5.1020964
170 4.3720172 -8.5079741
171 -6.0298591 4.3720172
172 3.5287191 -6.0298591
173 -0.7006969 3.5287191
174 4.4698137 -0.7006969
175 -0.4207167 4.4698137
176 2.7234427 -0.4207167
177 -0.2863859 2.7234427
178 -2.5252854 -0.2863859
179 0.5316236 -2.5252854
180 -2.2992788 0.5316236
181 11.4854163 -2.2992788
182 1.5855077 11.4854163
183 14.1332635 1.5855077
184 3.6178921 14.1332635
185 -1.6045936 3.6178921
186 1.6361269 -1.6045936
187 -2.7614752 1.6361269
188 -8.9830169 -2.7614752
189 -2.6065185 -8.9830169
190 -9.1621883 -2.6065185
191 -8.0663235 -9.1621883
192 8.5341130 -8.0663235
193 -3.1687214 8.5341130
194 -3.4904391 -3.1687214
195 NA -3.4904391
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.2028772 -3.4904391
[2,] -16.4384419 -2.2028772
[3,] -3.8160175 -16.4384419
[4,] -7.3891312 -3.8160175
[5,] -2.1577886 -7.3891312
[6,] 4.3936443 -2.1577886
[7,] 4.7472583 4.3936443
[8,] -1.4753094 4.7472583
[9,] 0.4541584 -1.4753094
[10,] 0.4497087 0.4541584
[11,] -0.3026773 0.4497087
[12,] -1.4356706 -0.3026773
[13,] 0.7982267 -1.4356706
[14,] -3.6825775 0.7982267
[15,] -5.6082324 -3.6825775
[16,] -14.1420458 -5.6082324
[17,] 2.8466925 -14.1420458
[18,] 6.1081353 2.8466925
[19,] -3.2272782 6.1081353
[20,] 0.4730201 -3.2272782
[21,] -6.0157532 0.4730201
[22,] 3.6459902 -6.0157532
[23,] -3.6497142 3.6459902
[24,] -4.4233944 -3.6497142
[25,] 2.2952069 -4.4233944
[26,] -6.2921949 2.2952069
[27,] -5.7244577 -6.2921949
[28,] -7.0051186 -5.7244577
[29,] -17.1695795 -7.0051186
[30,] -8.7352434 -17.1695795
[31,] -5.4856145 -8.7352434
[32,] -5.4135352 -5.4856145
[33,] -7.3706637 -5.4135352
[34,] -13.9229789 -7.3706637
[35,] -9.8026164 -13.9229789
[36,] -5.3808700 -9.8026164
[37,] -7.8643501 -5.3808700
[38,] -7.5505727 -7.8643501
[39,] -7.9724934 -7.5505727
[40,] 27.4256523 -7.9724934
[41,] 35.2445017 27.4256523
[42,] 6.2679902 35.2445017
[43,] 30.6741157 6.2679902
[44,] 1.7168514 30.6741157
[45,] -9.0330128 1.7168514
[46,] 1.2017981 -9.0330128
[47,] 1.1452831 1.2017981
[48,] -1.0568588 1.1452831
[49,] 1.7566406 -1.0568588
[50,] -4.1367346 1.7566406
[51,] 5.3157680 -4.1367346
[52,] -2.1905287 5.3157680
[53,] -7.0906202 -2.1905287
[54,] -4.5589228 -7.0906202
[55,] 0.4752114 -4.5589228
[56,] 8.5902887 0.4752114
[57,] -0.5566758 8.5902887
[58,] -3.8463824 -0.5566758
[59,] 1.0544914 -3.8463824
[60,] 4.1809196 1.0544914
[61,] -10.2205870 4.1809196
[62,] -5.1765573 -10.2205870
[63,] -5.9232015 -5.1765573
[64,] 4.3173910 -5.9232015
[65,] 6.0310447 4.3173910
[66,] 3.4875649 6.0310447
[67,] 3.2366507 3.4875649
[68,] -3.8137007 3.2366507
[69,] 4.3818822 -3.8137007
[70,] -7.1482971 4.3818822
[71,] -10.0848407 -7.1482971
[72,] -4.1059909 -10.0848407
[73,] -4.5870445 -4.1059909
[74,] -7.5228571 -4.5870445
[75,] 4.7251774 -7.5228571
[76,] -10.0671145 4.7251774
[77,] 5.2001727 -10.0671145
[78,] 3.0784006 5.2001727
[79,] -3.4590298 3.0784006
[80,] -8.4558172 -3.4590298
[81,] -14.8807694 -8.4558172
[82,] 0.7265809 -14.8807694
[83,] 5.5295962 0.7265809
[84,] 0.4010295 5.5295962
[85,] 4.4604441 0.4010295
[86,] -12.5647981 4.4604441
[87,] 0.7614533 -12.5647981
[88,] 2.0152568 0.7614533
[89,] -2.2286214 2.0152568
[90,] -4.7920391 -2.2286214
[91,] 0.2110917 -4.7920391
[92,] 2.2845124 0.2110917
[93,] 3.0847929 2.2845124
[94,] -7.3852339 3.0847929
[95,] -5.9173104 -7.3852339
[96,] -5.5245727 -5.9173104
[97,] 2.1021601 -5.5245727
[98,] -6.6197856 2.1021601
[99,] -0.5559588 -6.6197856
[100,] -10.2430831 -0.5559588
[101,] -2.4434475 -10.2430831
[102,] -4.6662964 -2.4434475
[103,] 1.1656697 -4.6662964
[104,] -9.1496155 1.1656697
[105,] 2.2416264 -9.1496155
[106,] -5.4015703 2.2416264
[107,] 5.3287150 -5.4015703
[108,] -6.0764860 5.3287150
[109,] 4.3345794 -6.0764860
[110,] 0.4333811 4.3345794
[111,] -12.0818025 0.4333811
[112,] -7.1449547 -12.0818025
[113,] -2.8581341 -7.1449547
[114,] 4.3005009 -2.8581341
[115,] 3.7849558 4.3005009
[116,] -6.5089165 3.7849558
[117,] -3.2902108 -6.5089165
[118,] -9.7033534 -3.2902108
[119,] 11.3415794 -9.7033534
[120,] 4.8747797 11.3415794
[121,] -1.9689653 4.8747797
[122,] -6.8978089 -1.9689653
[123,] 6.4066318 -6.8978089
[124,] 13.7311358 6.4066318
[125,] 23.3786641 13.7311358
[126,] 5.7145757 23.3786641
[127,] -5.7606514 5.7145757
[128,] 5.3302499 -5.7606514
[129,] -0.3567442 5.3302499
[130,] -4.7596110 -0.3567442
[131,] 15.8603175 -4.7596110
[132,] 5.3431841 15.8603175
[133,] 9.7581909 5.3431841
[134,] 2.7223268 9.7581909
[135,] 2.7222692 2.7223268
[136,] 7.6675313 2.7222692
[137,] 1.5469150 7.6675313
[138,] -1.5822880 1.5469150
[139,] 12.9673265 -1.5822880
[140,] 15.2643345 12.9673265
[141,] -0.4490311 15.2643345
[142,] 14.6542383 -0.4490311
[143,] 1.3525884 14.6542383
[144,] 12.9104509 1.3525884
[145,] 33.5366186 12.9104509
[146,] 26.6941213 33.5366186
[147,] 9.4288173 26.6941213
[148,] 6.5151085 9.4288173
[149,] 14.1053027 6.5151085
[150,] 15.2643508 14.1053027
[151,] 8.1642123 15.2643508
[152,] -2.2871866 8.1642123
[153,] 6.5965758 -2.2871866
[154,] -9.0455902 6.5965758
[155,] -8.5260078 -9.0455902
[156,] 2.1126989 -8.5260078
[157,] -0.3880640 2.1126989
[158,] -8.5278537 -0.3880640
[159,] 0.9146747 -8.5278537
[160,] 0.9941668 0.9146747
[161,] 2.4250844 0.9941668
[162,] -10.3705194 2.4250844
[163,] -0.4594436 -10.3705194
[164,] -7.1360842 -0.4594436
[165,] 0.8543614 -7.1360842
[166,] 5.7754967 0.8543614
[167,] -9.9822080 5.7754967
[168,] -5.1020964 -9.9822080
[169,] -8.5079741 -5.1020964
[170,] 4.3720172 -8.5079741
[171,] -6.0298591 4.3720172
[172,] 3.5287191 -6.0298591
[173,] -0.7006969 3.5287191
[174,] 4.4698137 -0.7006969
[175,] -0.4207167 4.4698137
[176,] 2.7234427 -0.4207167
[177,] -0.2863859 2.7234427
[178,] -2.5252854 -0.2863859
[179,] 0.5316236 -2.5252854
[180,] -2.2992788 0.5316236
[181,] 11.4854163 -2.2992788
[182,] 1.5855077 11.4854163
[183,] 14.1332635 1.5855077
[184,] 3.6178921 14.1332635
[185,] -1.6045936 3.6178921
[186,] 1.6361269 -1.6045936
[187,] -2.7614752 1.6361269
[188,] -8.9830169 -2.7614752
[189,] -2.6065185 -8.9830169
[190,] -9.1621883 -2.6065185
[191,] -8.0663235 -9.1621883
[192,] 8.5341130 -8.0663235
[193,] -3.1687214 8.5341130
[194,] -3.4904391 -3.1687214
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.2028772 -3.4904391
2 -16.4384419 -2.2028772
3 -3.8160175 -16.4384419
4 -7.3891312 -3.8160175
5 -2.1577886 -7.3891312
6 4.3936443 -2.1577886
7 4.7472583 4.3936443
8 -1.4753094 4.7472583
9 0.4541584 -1.4753094
10 0.4497087 0.4541584
11 -0.3026773 0.4497087
12 -1.4356706 -0.3026773
13 0.7982267 -1.4356706
14 -3.6825775 0.7982267
15 -5.6082324 -3.6825775
16 -14.1420458 -5.6082324
17 2.8466925 -14.1420458
18 6.1081353 2.8466925
19 -3.2272782 6.1081353
20 0.4730201 -3.2272782
21 -6.0157532 0.4730201
22 3.6459902 -6.0157532
23 -3.6497142 3.6459902
24 -4.4233944 -3.6497142
25 2.2952069 -4.4233944
26 -6.2921949 2.2952069
27 -5.7244577 -6.2921949
28 -7.0051186 -5.7244577
29 -17.1695795 -7.0051186
30 -8.7352434 -17.1695795
31 -5.4856145 -8.7352434
32 -5.4135352 -5.4856145
33 -7.3706637 -5.4135352
34 -13.9229789 -7.3706637
35 -9.8026164 -13.9229789
36 -5.3808700 -9.8026164
37 -7.8643501 -5.3808700
38 -7.5505727 -7.8643501
39 -7.9724934 -7.5505727
40 27.4256523 -7.9724934
41 35.2445017 27.4256523
42 6.2679902 35.2445017
43 30.6741157 6.2679902
44 1.7168514 30.6741157
45 -9.0330128 1.7168514
46 1.2017981 -9.0330128
47 1.1452831 1.2017981
48 -1.0568588 1.1452831
49 1.7566406 -1.0568588
50 -4.1367346 1.7566406
51 5.3157680 -4.1367346
52 -2.1905287 5.3157680
53 -7.0906202 -2.1905287
54 -4.5589228 -7.0906202
55 0.4752114 -4.5589228
56 8.5902887 0.4752114
57 -0.5566758 8.5902887
58 -3.8463824 -0.5566758
59 1.0544914 -3.8463824
60 4.1809196 1.0544914
61 -10.2205870 4.1809196
62 -5.1765573 -10.2205870
63 -5.9232015 -5.1765573
64 4.3173910 -5.9232015
65 6.0310447 4.3173910
66 3.4875649 6.0310447
67 3.2366507 3.4875649
68 -3.8137007 3.2366507
69 4.3818822 -3.8137007
70 -7.1482971 4.3818822
71 -10.0848407 -7.1482971
72 -4.1059909 -10.0848407
73 -4.5870445 -4.1059909
74 -7.5228571 -4.5870445
75 4.7251774 -7.5228571
76 -10.0671145 4.7251774
77 5.2001727 -10.0671145
78 3.0784006 5.2001727
79 -3.4590298 3.0784006
80 -8.4558172 -3.4590298
81 -14.8807694 -8.4558172
82 0.7265809 -14.8807694
83 5.5295962 0.7265809
84 0.4010295 5.5295962
85 4.4604441 0.4010295
86 -12.5647981 4.4604441
87 0.7614533 -12.5647981
88 2.0152568 0.7614533
89 -2.2286214 2.0152568
90 -4.7920391 -2.2286214
91 0.2110917 -4.7920391
92 2.2845124 0.2110917
93 3.0847929 2.2845124
94 -7.3852339 3.0847929
95 -5.9173104 -7.3852339
96 -5.5245727 -5.9173104
97 2.1021601 -5.5245727
98 -6.6197856 2.1021601
99 -0.5559588 -6.6197856
100 -10.2430831 -0.5559588
101 -2.4434475 -10.2430831
102 -4.6662964 -2.4434475
103 1.1656697 -4.6662964
104 -9.1496155 1.1656697
105 2.2416264 -9.1496155
106 -5.4015703 2.2416264
107 5.3287150 -5.4015703
108 -6.0764860 5.3287150
109 4.3345794 -6.0764860
110 0.4333811 4.3345794
111 -12.0818025 0.4333811
112 -7.1449547 -12.0818025
113 -2.8581341 -7.1449547
114 4.3005009 -2.8581341
115 3.7849558 4.3005009
116 -6.5089165 3.7849558
117 -3.2902108 -6.5089165
118 -9.7033534 -3.2902108
119 11.3415794 -9.7033534
120 4.8747797 11.3415794
121 -1.9689653 4.8747797
122 -6.8978089 -1.9689653
123 6.4066318 -6.8978089
124 13.7311358 6.4066318
125 23.3786641 13.7311358
126 5.7145757 23.3786641
127 -5.7606514 5.7145757
128 5.3302499 -5.7606514
129 -0.3567442 5.3302499
130 -4.7596110 -0.3567442
131 15.8603175 -4.7596110
132 5.3431841 15.8603175
133 9.7581909 5.3431841
134 2.7223268 9.7581909
135 2.7222692 2.7223268
136 7.6675313 2.7222692
137 1.5469150 7.6675313
138 -1.5822880 1.5469150
139 12.9673265 -1.5822880
140 15.2643345 12.9673265
141 -0.4490311 15.2643345
142 14.6542383 -0.4490311
143 1.3525884 14.6542383
144 12.9104509 1.3525884
145 33.5366186 12.9104509
146 26.6941213 33.5366186
147 9.4288173 26.6941213
148 6.5151085 9.4288173
149 14.1053027 6.5151085
150 15.2643508 14.1053027
151 8.1642123 15.2643508
152 -2.2871866 8.1642123
153 6.5965758 -2.2871866
154 -9.0455902 6.5965758
155 -8.5260078 -9.0455902
156 2.1126989 -8.5260078
157 -0.3880640 2.1126989
158 -8.5278537 -0.3880640
159 0.9146747 -8.5278537
160 0.9941668 0.9146747
161 2.4250844 0.9941668
162 -10.3705194 2.4250844
163 -0.4594436 -10.3705194
164 -7.1360842 -0.4594436
165 0.8543614 -7.1360842
166 5.7754967 0.8543614
167 -9.9822080 5.7754967
168 -5.1020964 -9.9822080
169 -8.5079741 -5.1020964
170 4.3720172 -8.5079741
171 -6.0298591 4.3720172
172 3.5287191 -6.0298591
173 -0.7006969 3.5287191
174 4.4698137 -0.7006969
175 -0.4207167 4.4698137
176 2.7234427 -0.4207167
177 -0.2863859 2.7234427
178 -2.5252854 -0.2863859
179 0.5316236 -2.5252854
180 -2.2992788 0.5316236
181 11.4854163 -2.2992788
182 1.5855077 11.4854163
183 14.1332635 1.5855077
184 3.6178921 14.1332635
185 -1.6045936 3.6178921
186 1.6361269 -1.6045936
187 -2.7614752 1.6361269
188 -8.9830169 -2.7614752
189 -2.6065185 -8.9830169
190 -9.1621883 -2.6065185
191 -8.0663235 -9.1621883
192 8.5341130 -8.0663235
193 -3.1687214 8.5341130
194 -3.4904391 -3.1687214
> 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/7w7971293201353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8w7971293201353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9pyqs1293201353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10pyqs1293201353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11sh6y1293201353.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/12wz541293201353.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/1331kg1293201353.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/14vs111293201353.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/15hai71293201353.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/16kbgc1293201353.tab")
+ }
>
> try(system("convert tmp/10fbz1293201353.ps tmp/10fbz1293201353.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gsz81293201353.ps tmp/2gsz81293201353.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gsz81293201353.ps tmp/3gsz81293201353.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gsz81293201353.ps tmp/4gsz81293201353.png",intern=TRUE))
character(0)
> try(system("convert tmp/53y9m1293201353.ps tmp/53y9m1293201353.png",intern=TRUE))
character(0)
> try(system("convert tmp/63y9m1293201353.ps tmp/63y9m1293201353.png",intern=TRUE))
character(0)
> try(system("convert tmp/7w7971293201353.ps tmp/7w7971293201353.png",intern=TRUE))
character(0)
> try(system("convert tmp/8w7971293201353.ps tmp/8w7971293201353.png",intern=TRUE))
character(0)
> try(system("convert tmp/9pyqs1293201353.ps tmp/9pyqs1293201353.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pyqs1293201353.ps tmp/10pyqs1293201353.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.649 2.685 7.043