R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(27
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+ ,dim=c(6
+ ,195)
+ ,dimnames=list(c('leeftijd'
+ ,'opleiding'
+ ,'Intrinsieke_waarden'
+ ,'Neuroticisme'
+ ,'Extraversie'
+ ,'Openheid
')
+ ,1:195))
> y <- array(NA,dim=c(6,195),dimnames=list(c('leeftijd','opleiding','Intrinsieke_waarden','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 = '3'
> #'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
> 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
Intrinsieke_waarden leeftijd opleiding Neuroticisme Extraversie
1 40 27 5 26 49
2 45 36 4 25 45
3 38 25 4 17 54
4 28 27 3 37 36
5 35 25 3 36 28
6 15 3 38 53 32
7 27 4 39 46 35
8 36 4 37 42 36
9 25 5 30 41 27
10 30 4 30 45 29
11 27 2 30 47 27
12 33 3 26 42 28
13 29 2 29 45 29
14 30 5 31 40 28
15 25 3 27 45 30
16 23 3 25 40 25
17 26 3 39 42 15
18 24 3 35 45 33
19 35 4 27 47 31
20 39 4 40 31 37
21 23 4 34 46 37
22 32 3 32 34 34
23 29 5 34 43 32
24 26 4 38 45 21
25 21 2 21 42 25
26 35 5 33 51 32
27 23 3 27 44 28
28 21 4 35 47 22
29 28 4 33 47 25
30 41 36 30 26 52
31 34 21 44 46 2
32 34 29 51 58 3
33 36 28 46 54 5
34 36 19 47 29 3
35 26 26 46 50 3
36 26 33 38 43 2
37 34 34 50 30 3
38 33 33 48 47 2
39 31 40 36 45 3
40 33 24 51 48 1
41 22 35 35 48 3
42 29 35 49 26 4
43 24 32 38 46 5
44 37 20 47 3 29
45 50 36 32 3 35
46 25 47 23 4 44
47 47 46 29 2 35
48 47 43 35 2 30
49 41 53 20 3 32
50 45 55 28 2 24
51 41 39 26 4 34
52 45 55 36 5 27
53 40 41 26 3 31
54 29 33 33 4 38
55 34 52 25 5 41
56 45 42 29 5 40
57 52 56 32 3 25
58 41 46 35 4 19
59 48 33 24 3 33
60 45 51 31 3 27
61 54 46 29 2 45
62 25 46 27 3 27
63 26 50 29 4 30
64 28 46 29 4 42
65 50 51 27 4 21
66 48 48 34 4 32
67 51 44 32 3 31
68 53 38 31 3 36
69 37 42 31 3 34
70 56 39 31 2 11
71 43 45 16 3 35
72 34 31 25 3 39
73 42 29 27 3 32
74 32 48 32 3 28
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77 30 32 25 5 35
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79 33 53 36 4 36
80 25 47 36 4 34
81 25 45 27 5 34
82 21 33 29 4 38
83 36 49 32 5 28
84 50 46 29 3 23
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86 48 56 34 2 29
87 25 35 27 3 28
88 48 40 28 4 30
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90 27 46 33 5 36
91 28 46 29 3 40
92 43 39 32 2 37
93 48 35 35 3 27
94 48 48 33 4 25
95 25 42 27 1 22
96 49 39 16 4 21
97 26 39 32 3 28
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99 25 52 32 4 32
100 29 45 38 3 23
101 29 42 24 4 29
102 43 44 26 2 35
103 46 33 19 3 31
104 44 42 37 3 36
105 25 46 25 3 32
106 51 45 24 2 35
107 42 40 23 5 45
108 53 48 28 5 29
109 25 32 38 4 41
110 49 53 28 2 36
111 51 39 28 3 37
112 20 45 26 3 25
113 44 36 21 3 36
114 38 38 35 4 34
115 46 49 31 5 33
116 42 46 34 4 32
117 29 43 30 40 22
118 4 30 46 27 16
119 2 24 49 24 36
120 3 27 51 26 35
121 3 26 38 25 46
122 13 41 1 27 20
123 22 47 3 32 42
124 29 44 3 36 45
125 30 47 3 51 29
126 24 46 3 30 51
127 20 44 4 55 31
128 29 3 26 50 28
129 26 4 37 44 33
130 20 4 36 41 32
131 40 5 38 40 33
132 29 4 34 47 31
133 32 4 35 42 37
134 33 4 32 40 27
135 32 3 44 51 19
136 34 3 40 43 27
137 24 4 24 45 31
138 25 5 36 41 38
139 41 3 20 41 22
140 39 3 28 37 35
141 21 3 18 46 35
142 38 3 23 38 30
143 28 5 28 39 41
144 37 3 30 45 25
145 46 30 26 28 52
146 39 43 30 45 49
147 21 20 25 21 46
148 31 37 38 33 4
149 25 31 35 45 3
150 29 31 49 52 3
151 31 27 40 3 27
152 40 45 29 4 22
153 49 46 31 4 28
154 38 45 31 5 18
155 32 34 25 5 38
156 46 41 27 4 23
157 32 43 26 3 38
158 41 45 26 3 21
159 43 48 23 3 25
160 44 43 27 4 36
161 5 24 47 30 27
162 3 35 28 25 33
163 1 24 52 17 29
164 2 32 27 26 42
165 5 24 45 39 27
166 4 24 27 27 47
167 4 38 25 33 17
168 4 36 28 47 34
169 3 24 25 37 32
170 4 18 52 34 25
171 3 34 44 24 27
172 3 23 43 25 37
173 4 35 47 20 34
174 4 22 52 34 27
175 2 34 40 22 37
176 3 28 42 39 32
177 5 34 45 33 26
178 2 32 45 35 29
179 5 24 50 26 28
180 3 34 49 32 19
181 2 33 52 22 46
182 3 33 48 39 31
183 3 29 51 35 42
184 4 38 49 21 33
185 4 24 31 27 39
186 3 25 43 31 27
187 3 37 31 20 35
188 4 33 28 28 23
189 4 30 43 26 32
190 3 22 31 36 22
191 3 28 51 16 17
192 4 24 58 34 35
193 5 33 25 30 34
194 5 37 27 40 26
195 4 35 36 45 25
Openheid\r\r
1 35
2 34
3 13
4 35
5 44
6 50
7 41
8 48
9 43
10 47
11 41
12 44
13 47
14 40
15 46
16 28
17 56
18 49
19 25
20 41
21 26
22 50
23 47
24 52
25 37
26 41
27 45
28 26
29 3
30 4
31 37
32 37
33 37
34 32
35 25
36 31
37 33
38 18
39 42
40 26
41 26
42 32
43 31
44 35
45 35
46 21
47 33
48 40
49 22
50 35
51 20
52 28
53 46
54 18
55 22
56 20
57 25
58 31
59 21
60 23
61 26
62 34
63 31
64 23
65 31
66 26
67 36
68 28
69 34
70 25
71 33
72 46
73 24
74 32
75 33
76 42
77 17
78 36
79 40
80 30
81 19
82 33
83 35
84 23
85 15
86 38
87 37
88 23
89 41
90 34
91 38
92 45
93 27
94 46
95 26
96 44
97 36
98 20
99 44
100 27
101 27
102 41
103 30
104 33
105 37
106 30
107 20
108 44
109 20
110 33
111 31
112 23
113 33
114 33
115 32
116 25
117 37
118 48
119 45
120 32
121 30
122 15
123 28
124 34
125 29
126 26
127 28
128 47
129 28
130 41
131 45
132 46
133 46
134 22
135 33
136 41
137 47
138 25
139 42
140 47
141 50
142 55
143 21
144 3
145 3
146 4
147 4
148 24
149 33
150 43
151 21
152 26
153 37
154 28
155 29
156 33
157 41
158 19
159 37
160 45
161 45
162 34
163 40
164 40
165 55
166 44
167 44
168 48
169 51
170 49
171 33
172 43
173 44
174 44
175 41
176 45
177 44
178 44
179 40
180 48
181 49
182 46
183 49
184 55
185 51
186 46
187 37
188 43
189 41
190 45
191 39
192 38
193 41
194 49
195 45
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) leeftijd opleiding Neuroticisme Extraversie
92.3772 -0.3040 -0.5166 -0.5411 -0.4763
`Openheid\r\r`
-0.3326
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-37.273 -8.713 0.190 8.712 24.209
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 92.37720 7.26090 12.723 < 2e-16 ***
leeftijd -0.30401 0.08122 -3.743 0.000241 ***
opleiding -0.51664 0.08762 -5.896 1.68e-08 ***
Neuroticisme -0.54106 0.07374 -7.337 6.27e-12 ***
Extraversie -0.47626 0.09155 -5.202 5.10e-07 ***
`Openheid\r\r` -0.33263 0.08260 -4.027 8.17e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.88 on 189 degrees of freedom
Multiple R-squared: 0.4188, Adjusted R-squared: 0.4034
F-statistic: 27.23 on 5 and 189 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,] 6.721371e-02 1.344274e-01 9.327863e-01
[2,] 3.275321e-02 6.550642e-02 9.672468e-01
[3,] 2.480132e-02 4.960263e-02 9.751987e-01
[4,] 1.079721e-02 2.159443e-02 9.892028e-01
[5,] 3.696940e-03 7.393880e-03 9.963031e-01
[6,] 1.250739e-03 2.501479e-03 9.987493e-01
[7,] 4.489996e-04 8.979993e-04 9.995510e-01
[8,] 1.357266e-04 2.714531e-04 9.998643e-01
[9,] 2.604367e-04 5.208734e-04 9.997396e-01
[10,] 1.511948e-04 3.023896e-04 9.998488e-01
[11,] 1.096844e-03 2.193687e-03 9.989032e-01
[12,] 6.238799e-04 1.247760e-03 9.993761e-01
[13,] 3.012965e-04 6.025930e-04 9.996987e-01
[14,] 1.354427e-04 2.708855e-04 9.998646e-01
[15,] 5.637083e-05 1.127417e-04 9.999436e-01
[16,] 2.285570e-05 4.571140e-05 9.999771e-01
[17,] 1.529323e-05 3.058647e-05 9.999847e-01
[18,] 3.643380e-05 7.286760e-05 9.999636e-01
[19,] 1.795710e-05 3.591420e-05 9.999820e-01
[20,] 9.010069e-06 1.802014e-05 9.999910e-01
[21,] 4.794257e-06 9.588515e-06 9.999952e-01
[22,] 2.485742e-06 4.971483e-06 9.999975e-01
[23,] 1.358791e-06 2.717582e-06 9.999986e-01
[24,] 7.448950e-07 1.489790e-06 9.999993e-01
[25,] 4.170583e-07 8.341166e-07 9.999996e-01
[26,] 1.778089e-07 3.556178e-07 9.999998e-01
[27,] 1.503709e-07 3.007418e-07 9.999998e-01
[28,] 1.957340e-07 3.914680e-07 9.999998e-01
[29,] 1.198367e-07 2.396734e-07 9.999999e-01
[30,] 5.145572e-08 1.029114e-07 9.999999e-01
[31,] 2.717378e-08 5.434756e-08 1.000000e+00
[32,] 1.452966e-08 2.905931e-08 1.000000e+00
[33,] 2.043675e-08 4.087350e-08 1.000000e+00
[34,] 2.812208e-08 5.624417e-08 1.000000e+00
[35,] 2.102226e-08 4.204451e-08 1.000000e+00
[36,] 1.112902e-08 2.225804e-08 1.000000e+00
[37,] 8.306643e-09 1.661329e-08 1.000000e+00
[38,] 6.914093e-07 1.382819e-06 9.999993e-01
[39,] 4.253460e-07 8.506920e-07 9.999996e-01
[40,] 2.701335e-07 5.402671e-07 9.999997e-01
[41,] 1.333866e-07 2.667733e-07 9.999999e-01
[42,] 6.565223e-08 1.313045e-07 9.999999e-01
[43,] 3.003674e-08 6.007348e-08 1.000000e+00
[44,] 1.581735e-08 3.163470e-08 1.000000e+00
[45,] 7.780548e-09 1.556110e-08 1.000000e+00
[46,] 1.584509e-08 3.169018e-08 1.000000e+00
[47,] 1.777036e-08 3.554072e-08 1.000000e+00
[48,] 1.047529e-08 2.095058e-08 1.000000e+00
[49,] 1.421725e-08 2.843450e-08 1.000000e+00
[50,] 6.709331e-09 1.341866e-08 1.000000e+00
[51,] 7.094231e-09 1.418846e-08 1.000000e+00
[52,] 3.603747e-09 7.207494e-09 1.000000e+00
[53,] 6.286490e-09 1.257298e-08 1.000000e+00
[54,] 5.493671e-08 1.098734e-07 9.999999e-01
[55,] 2.536121e-07 5.072243e-07 9.999997e-01
[56,] 5.897166e-07 1.179433e-06 9.999994e-01
[57,] 6.359114e-07 1.271823e-06 9.999994e-01
[58,] 5.432014e-07 1.086403e-06 9.999995e-01
[59,] 6.879156e-07 1.375831e-06 9.999993e-01
[60,] 1.158817e-06 2.317633e-06 9.999988e-01
[61,] 7.741718e-07 1.548344e-06 9.999992e-01
[62,] 1.886741e-06 3.773482e-06 9.999981e-01
[63,] 1.065352e-06 2.130703e-06 9.999989e-01
[64,] 8.073173e-07 1.614635e-06 9.999992e-01
[65,] 4.512216e-07 9.024433e-07 9.999995e-01
[66,] 4.825295e-07 9.650590e-07 9.999995e-01
[67,] 4.585403e-07 9.170806e-07 9.999995e-01
[68,] 3.023546e-07 6.047091e-07 9.999997e-01
[69,] 3.654918e-07 7.309835e-07 9.999996e-01
[70,] 3.950077e-07 7.900154e-07 9.999996e-01
[71,] 4.157314e-07 8.314628e-07 9.999996e-01
[72,] 9.259686e-07 1.851937e-06 9.999991e-01
[73,] 2.046904e-06 4.093808e-06 9.999980e-01
[74,] 6.580646e-06 1.316129e-05 9.999934e-01
[75,] 4.439503e-06 8.879006e-06 9.999956e-01
[76,] 4.152525e-06 8.305051e-06 9.999958e-01
[77,] 3.631298e-06 7.262596e-06 9.999964e-01
[78,] 4.560782e-06 9.121564e-06 9.999954e-01
[79,] 8.407164e-06 1.681433e-05 9.999916e-01
[80,] 7.151507e-06 1.430301e-05 9.999928e-01
[81,] 8.920395e-06 1.784079e-05 9.999911e-01
[82,] 1.023662e-05 2.047324e-05 9.999898e-01
[83,] 1.036041e-05 2.072082e-05 9.999896e-01
[84,] 8.962250e-06 1.792450e-05 9.999910e-01
[85,] 8.250098e-06 1.650020e-05 9.999917e-01
[86,] 1.227870e-05 2.455739e-05 9.999877e-01
[87,] 2.814233e-05 5.628466e-05 9.999719e-01
[88,] 2.371312e-05 4.742624e-05 9.999763e-01
[89,] 2.993299e-05 5.986598e-05 9.999701e-01
[90,] 3.265386e-05 6.530773e-05 9.999673e-01
[91,] 4.150287e-05 8.300574e-05 9.999585e-01
[92,] 3.911371e-05 7.822742e-05 9.999609e-01
[93,] 4.099538e-05 8.199076e-05 9.999590e-01
[94,] 3.366394e-05 6.732787e-05 9.999663e-01
[95,] 2.400671e-05 4.801342e-05 9.999760e-01
[96,] 2.610810e-05 5.221621e-05 9.999739e-01
[97,] 3.338960e-05 6.677920e-05 9.999666e-01
[98,] 4.353162e-05 8.706323e-05 9.999565e-01
[99,] 3.046817e-05 6.093634e-05 9.999695e-01
[100,] 1.043276e-04 2.086553e-04 9.998957e-01
[101,] 1.076195e-04 2.152391e-04 9.998924e-01
[102,] 2.035126e-04 4.070252e-04 9.997965e-01
[103,] 3.693497e-04 7.386993e-04 9.996307e-01
[104,] 9.142253e-04 1.828451e-03 9.990858e-01
[105,] 7.569305e-04 1.513861e-03 9.992431e-01
[106,] 6.729903e-04 1.345981e-03 9.993270e-01
[107,] 1.199289e-03 2.398577e-03 9.988007e-01
[108,] 1.382979e-03 2.765957e-03 9.986170e-01
[109,] 1.530870e-03 3.061740e-03 9.984691e-01
[110,] 4.914987e-03 9.829974e-03 9.950850e-01
[111,] 1.094727e-02 2.189454e-02 9.890527e-01
[112,] 1.801815e-02 3.603631e-02 9.819818e-01
[113,] 3.130839e-02 6.261677e-02 9.686916e-01
[114,] 1.586562e-01 3.173124e-01 8.413438e-01
[115,] 1.492704e-01 2.985409e-01 8.507296e-01
[116,] 1.290272e-01 2.580544e-01 8.709728e-01
[117,] 1.106503e-01 2.213007e-01 8.893497e-01
[118,] 9.440416e-02 1.888083e-01 9.055958e-01
[119,] 8.004924e-02 1.600985e-01 9.199508e-01
[120,] 6.788670e-02 1.357734e-01 9.321133e-01
[121,] 5.474360e-02 1.094872e-01 9.452564e-01
[122,] 4.485694e-02 8.971387e-02 9.551431e-01
[123,] 7.662501e-02 1.532500e-01 9.233750e-01
[124,] 7.507208e-02 1.501442e-01 9.249279e-01
[125,] 9.063181e-02 1.812636e-01 9.093682e-01
[126,] 7.471403e-02 1.494281e-01 9.252860e-01
[127,] 7.643316e-02 1.528663e-01 9.235668e-01
[128,] 9.691893e-02 1.938379e-01 9.030811e-01
[129,] 8.215699e-02 1.643140e-01 9.178430e-01
[130,] 6.791237e-02 1.358247e-01 9.320876e-01
[131,] 7.233938e-02 1.446788e-01 9.276606e-01
[132,] 1.416199e-01 2.832398e-01 8.583801e-01
[133,] 1.390164e-01 2.780327e-01 8.609836e-01
[134,] 4.857951e-01 9.715902e-01 5.142049e-01
[135,] 5.434353e-01 9.131293e-01 4.565647e-01
[136,] 6.180265e-01 7.639470e-01 3.819735e-01
[137,] 7.325483e-01 5.349035e-01 2.674517e-01
[138,] 8.272323e-01 3.455354e-01 1.727677e-01
[139,] 8.339142e-01 3.321715e-01 1.660858e-01
[140,] 8.053819e-01 3.892363e-01 1.946181e-01
[141,] 8.031041e-01 3.937917e-01 1.968959e-01
[142,] 9.672956e-01 6.540880e-02 3.270440e-02
[143,] 9.624560e-01 7.508807e-02 3.754403e-02
[144,] 9.525891e-01 9.482175e-02 4.741088e-02
[145,] 9.739318e-01 5.213631e-02 2.606815e-02
[146,] 9.670584e-01 6.588329e-02 3.294165e-02
[147,] 9.637118e-01 7.257639e-02 3.628820e-02
[148,] 9.830883e-01 3.382339e-02 1.691169e-02
[149,] 9.782424e-01 4.351523e-02 2.175761e-02
[150,] 9.911270e-01 1.774591e-02 8.872955e-03
[151,] 9.988893e-01 2.221473e-03 1.110737e-03
[152,] 1.000000e+00 3.929175e-23 1.964588e-23
[153,] 1.000000e+00 7.571363e-23 3.785681e-23
[154,] 1.000000e+00 3.534801e-22 1.767400e-22
[155,] 1.000000e+00 1.900487e-22 9.502434e-23
[156,] 1.000000e+00 4.797915e-22 2.398957e-22
[157,] 1.000000e+00 1.973697e-21 9.868487e-22
[158,] 1.000000e+00 1.331229e-20 6.656147e-21
[159,] 1.000000e+00 9.933095e-20 4.966547e-20
[160,] 1.000000e+00 1.005507e-18 5.027533e-19
[161,] 1.000000e+00 5.643787e-18 2.821893e-18
[162,] 1.000000e+00 5.219188e-17 2.609594e-17
[163,] 1.000000e+00 4.046680e-16 2.023340e-16
[164,] 1.000000e+00 3.620513e-15 1.810257e-15
[165,] 1.000000e+00 2.631250e-14 1.315625e-14
[166,] 1.000000e+00 2.379781e-13 1.189891e-13
[167,] 1.000000e+00 8.643229e-13 4.321615e-13
[168,] 1.000000e+00 7.747166e-12 3.873583e-12
[169,] 1.000000e+00 3.138956e-11 1.569478e-11
[170,] 1.000000e+00 9.967758e-11 4.983879e-11
[171,] 1.000000e+00 1.739545e-10 8.697725e-11
[172,] 1.000000e+00 2.121542e-09 1.060771e-09
[173,] 1.000000e+00 9.270112e-09 4.635056e-09
[174,] 1.000000e+00 8.214891e-08 4.107446e-08
[175,] 9.999998e-01 4.911887e-07 2.455943e-07
[176,] 9.999960e-01 7.940934e-06 3.970467e-06
[177,] 9.999388e-01 1.223585e-04 6.117927e-05
[178,] 9.995062e-01 9.875120e-04 4.937560e-04
> postscript(file="/var/www/rcomp/tmp/1wi431293379145.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2wi431293379145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/37rlo1293379145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/47rlo1293379145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/57rlo1293379145.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
7.46060897 11.90128000 -5.47009099 -5.81248485 -0.77799252 3.71496855
7 8 9 10 11 12
11.18333861 19.79051789 -1.01252192 8.13074636 2.65653584 5.66286915
13 14 15 16 17 18
6.00609790 3.44143216 1.42046339 -12.68675921 3.17928024 6.98024364
19 20 21 22 23 24
6.29760654 16.53666480 0.56322572 8.28761170 9.84798104 6.11688066
25 26 27 28 29 30
-12.98152738 17.66400917 -2.40575149 -7.52304509 -7.77802596 15.22983602
31 32 33 34 35 36
8.88737891 21.90481958 19.80593339 1.44443947 2.08960463 -2.18332505
37 38 39 40 41 42
6.42812267 7.82307367 9.12875165 9.36276835 -5.60683275 -0.80509863
43 44 45 46 47 48
-1.43537014 2.06175215 15.03387367 -11.10118831 12.31770295 14.45259963
49 50 51 52 53 54
-0.75066169 7.96347141 -1.07859590 12.82011537 5.20795764 -10.04637480
55 56 57 58 59 60
-0.10299206 8.78197382 15.02504130 2.21414816 2.37940595 6.27564336
61 62 63 64 65 66
21.75193257 -13.65199876 -9.43074437 -5.59263952 9.55360901 13.83380577
67 68 69 70 71 72
16.89349280 16.27309941 2.53237627 6.13155159 1.83847180 -0.53862252
73 74 75 76 77 78
-2.76507883 -3.64979876 -0.24516940 7.42544367 -14.70384398 19.53419390
79 80 81 82 83 84
7.94899125 -6.15387571 -14.52949903 -15.12346668 2.73421012 6.81728371
85 86 87 88 89 90
8.64119112 17.74651182 -15.52190613 6.35151811 14.30490702 -3.18368639
91 92 93 94 95 96
-2.09676774 12.68366753 7.80862169 16.63592729 -20.99249813 3.54672850
97 98 99 100 101 102
-11.05532854 8.98835958 -2.99609793 -8.50646707 -13.25276116 8.82082376
103 104 105 106 107 108
-0.16263396 13.25209654 -11.30606031 12.43262084 4.45546147 20.83359428
109 110 111 112 113 114
-9.67314178 16.40537019 14.50134954 -24.08410459 3.16186976 4.59132707
115 116 117 118 119 120
13.60100311 6.89316393 9.62153556 -17.29672464 -12.66662190 -13.43967845
121 122 123 124 125 126
-16.42737555 -37.27307484 -7.90847771 3.76830422 4.51278059 -3.67347790
127 128 129 130 131 132
-3.09847761 6.98920806 2.79122872 -1.50064502 21.10236471 10.89930449
133 134 135 136 137 138
14.56824725 0.19045173 10.88652645 12.96268803 -0.01654731 1.33885955
139 140 141 142 143 144
6.49914467 14.32260445 -2.97637099 11.56019732 -0.77807750 -1.71405468
145 146 147 148 149 150
17.08873510 24.20914645 -17.78030589 -2.75374616 -3.11761057 15.22900371
151 152 153 154 155 156
-11.03602138 -2.42403906 14.42976082 -4.08950522 -6.67547479 4.13134119
157 158 159 160 161 162
-0.51333324 -6.31968327 2.93482729 12.92235291 -11.73993850 -23.71860804
163 164 165 166 167 168
-20.90110436 -19.32404548 -4.57740479 -15.50319232 -23.32197822 -5.37828119
169 170 171 172 173 174
-16.94145770 -9.43857066 -19.48769317 -14.71839042 -11.80521584 -8.93317137
175 176 177 178 179 180
-16.21266734 -7.85627591 -8.91888220 -10.01598896 -13.54113907 -11.39671815
181 182 183 184 185 186
-3.36960709 -3.38005899 1.02640344 -6.13619835 -14.91834531 -14.62879453
187 188 189 190 191 192
-22.31554554 -23.47242039 -14.09587655 -21.74912794 -24.79057594 -3.41100623
193 194 195
-18.36657361 -11.85578781 -7.91557211
> postscript(file="/var/www/rcomp/tmp/60i2r1293379145.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 7.46060897 NA
1 11.90128000 7.46060897
2 -5.47009099 11.90128000
3 -5.81248485 -5.47009099
4 -0.77799252 -5.81248485
5 3.71496855 -0.77799252
6 11.18333861 3.71496855
7 19.79051789 11.18333861
8 -1.01252192 19.79051789
9 8.13074636 -1.01252192
10 2.65653584 8.13074636
11 5.66286915 2.65653584
12 6.00609790 5.66286915
13 3.44143216 6.00609790
14 1.42046339 3.44143216
15 -12.68675921 1.42046339
16 3.17928024 -12.68675921
17 6.98024364 3.17928024
18 6.29760654 6.98024364
19 16.53666480 6.29760654
20 0.56322572 16.53666480
21 8.28761170 0.56322572
22 9.84798104 8.28761170
23 6.11688066 9.84798104
24 -12.98152738 6.11688066
25 17.66400917 -12.98152738
26 -2.40575149 17.66400917
27 -7.52304509 -2.40575149
28 -7.77802596 -7.52304509
29 15.22983602 -7.77802596
30 8.88737891 15.22983602
31 21.90481958 8.88737891
32 19.80593339 21.90481958
33 1.44443947 19.80593339
34 2.08960463 1.44443947
35 -2.18332505 2.08960463
36 6.42812267 -2.18332505
37 7.82307367 6.42812267
38 9.12875165 7.82307367
39 9.36276835 9.12875165
40 -5.60683275 9.36276835
41 -0.80509863 -5.60683275
42 -1.43537014 -0.80509863
43 2.06175215 -1.43537014
44 15.03387367 2.06175215
45 -11.10118831 15.03387367
46 12.31770295 -11.10118831
47 14.45259963 12.31770295
48 -0.75066169 14.45259963
49 7.96347141 -0.75066169
50 -1.07859590 7.96347141
51 12.82011537 -1.07859590
52 5.20795764 12.82011537
53 -10.04637480 5.20795764
54 -0.10299206 -10.04637480
55 8.78197382 -0.10299206
56 15.02504130 8.78197382
57 2.21414816 15.02504130
58 2.37940595 2.21414816
59 6.27564336 2.37940595
60 21.75193257 6.27564336
61 -13.65199876 21.75193257
62 -9.43074437 -13.65199876
63 -5.59263952 -9.43074437
64 9.55360901 -5.59263952
65 13.83380577 9.55360901
66 16.89349280 13.83380577
67 16.27309941 16.89349280
68 2.53237627 16.27309941
69 6.13155159 2.53237627
70 1.83847180 6.13155159
71 -0.53862252 1.83847180
72 -2.76507883 -0.53862252
73 -3.64979876 -2.76507883
74 -0.24516940 -3.64979876
75 7.42544367 -0.24516940
76 -14.70384398 7.42544367
77 19.53419390 -14.70384398
78 7.94899125 19.53419390
79 -6.15387571 7.94899125
80 -14.52949903 -6.15387571
81 -15.12346668 -14.52949903
82 2.73421012 -15.12346668
83 6.81728371 2.73421012
84 8.64119112 6.81728371
85 17.74651182 8.64119112
86 -15.52190613 17.74651182
87 6.35151811 -15.52190613
88 14.30490702 6.35151811
89 -3.18368639 14.30490702
90 -2.09676774 -3.18368639
91 12.68366753 -2.09676774
92 7.80862169 12.68366753
93 16.63592729 7.80862169
94 -20.99249813 16.63592729
95 3.54672850 -20.99249813
96 -11.05532854 3.54672850
97 8.98835958 -11.05532854
98 -2.99609793 8.98835958
99 -8.50646707 -2.99609793
100 -13.25276116 -8.50646707
101 8.82082376 -13.25276116
102 -0.16263396 8.82082376
103 13.25209654 -0.16263396
104 -11.30606031 13.25209654
105 12.43262084 -11.30606031
106 4.45546147 12.43262084
107 20.83359428 4.45546147
108 -9.67314178 20.83359428
109 16.40537019 -9.67314178
110 14.50134954 16.40537019
111 -24.08410459 14.50134954
112 3.16186976 -24.08410459
113 4.59132707 3.16186976
114 13.60100311 4.59132707
115 6.89316393 13.60100311
116 9.62153556 6.89316393
117 -17.29672464 9.62153556
118 -12.66662190 -17.29672464
119 -13.43967845 -12.66662190
120 -16.42737555 -13.43967845
121 -37.27307484 -16.42737555
122 -7.90847771 -37.27307484
123 3.76830422 -7.90847771
124 4.51278059 3.76830422
125 -3.67347790 4.51278059
126 -3.09847761 -3.67347790
127 6.98920806 -3.09847761
128 2.79122872 6.98920806
129 -1.50064502 2.79122872
130 21.10236471 -1.50064502
131 10.89930449 21.10236471
132 14.56824725 10.89930449
133 0.19045173 14.56824725
134 10.88652645 0.19045173
135 12.96268803 10.88652645
136 -0.01654731 12.96268803
137 1.33885955 -0.01654731
138 6.49914467 1.33885955
139 14.32260445 6.49914467
140 -2.97637099 14.32260445
141 11.56019732 -2.97637099
142 -0.77807750 11.56019732
143 -1.71405468 -0.77807750
144 17.08873510 -1.71405468
145 24.20914645 17.08873510
146 -17.78030589 24.20914645
147 -2.75374616 -17.78030589
148 -3.11761057 -2.75374616
149 15.22900371 -3.11761057
150 -11.03602138 15.22900371
151 -2.42403906 -11.03602138
152 14.42976082 -2.42403906
153 -4.08950522 14.42976082
154 -6.67547479 -4.08950522
155 4.13134119 -6.67547479
156 -0.51333324 4.13134119
157 -6.31968327 -0.51333324
158 2.93482729 -6.31968327
159 12.92235291 2.93482729
160 -11.73993850 12.92235291
161 -23.71860804 -11.73993850
162 -20.90110436 -23.71860804
163 -19.32404548 -20.90110436
164 -4.57740479 -19.32404548
165 -15.50319232 -4.57740479
166 -23.32197822 -15.50319232
167 -5.37828119 -23.32197822
168 -16.94145770 -5.37828119
169 -9.43857066 -16.94145770
170 -19.48769317 -9.43857066
171 -14.71839042 -19.48769317
172 -11.80521584 -14.71839042
173 -8.93317137 -11.80521584
174 -16.21266734 -8.93317137
175 -7.85627591 -16.21266734
176 -8.91888220 -7.85627591
177 -10.01598896 -8.91888220
178 -13.54113907 -10.01598896
179 -11.39671815 -13.54113907
180 -3.36960709 -11.39671815
181 -3.38005899 -3.36960709
182 1.02640344 -3.38005899
183 -6.13619835 1.02640344
184 -14.91834531 -6.13619835
185 -14.62879453 -14.91834531
186 -22.31554554 -14.62879453
187 -23.47242039 -22.31554554
188 -14.09587655 -23.47242039
189 -21.74912794 -14.09587655
190 -24.79057594 -21.74912794
191 -3.41100623 -24.79057594
192 -18.36657361 -3.41100623
193 -11.85578781 -18.36657361
194 -7.91557211 -11.85578781
195 NA -7.91557211
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 11.90128000 7.46060897
[2,] -5.47009099 11.90128000
[3,] -5.81248485 -5.47009099
[4,] -0.77799252 -5.81248485
[5,] 3.71496855 -0.77799252
[6,] 11.18333861 3.71496855
[7,] 19.79051789 11.18333861
[8,] -1.01252192 19.79051789
[9,] 8.13074636 -1.01252192
[10,] 2.65653584 8.13074636
[11,] 5.66286915 2.65653584
[12,] 6.00609790 5.66286915
[13,] 3.44143216 6.00609790
[14,] 1.42046339 3.44143216
[15,] -12.68675921 1.42046339
[16,] 3.17928024 -12.68675921
[17,] 6.98024364 3.17928024
[18,] 6.29760654 6.98024364
[19,] 16.53666480 6.29760654
[20,] 0.56322572 16.53666480
[21,] 8.28761170 0.56322572
[22,] 9.84798104 8.28761170
[23,] 6.11688066 9.84798104
[24,] -12.98152738 6.11688066
[25,] 17.66400917 -12.98152738
[26,] -2.40575149 17.66400917
[27,] -7.52304509 -2.40575149
[28,] -7.77802596 -7.52304509
[29,] 15.22983602 -7.77802596
[30,] 8.88737891 15.22983602
[31,] 21.90481958 8.88737891
[32,] 19.80593339 21.90481958
[33,] 1.44443947 19.80593339
[34,] 2.08960463 1.44443947
[35,] -2.18332505 2.08960463
[36,] 6.42812267 -2.18332505
[37,] 7.82307367 6.42812267
[38,] 9.12875165 7.82307367
[39,] 9.36276835 9.12875165
[40,] -5.60683275 9.36276835
[41,] -0.80509863 -5.60683275
[42,] -1.43537014 -0.80509863
[43,] 2.06175215 -1.43537014
[44,] 15.03387367 2.06175215
[45,] -11.10118831 15.03387367
[46,] 12.31770295 -11.10118831
[47,] 14.45259963 12.31770295
[48,] -0.75066169 14.45259963
[49,] 7.96347141 -0.75066169
[50,] -1.07859590 7.96347141
[51,] 12.82011537 -1.07859590
[52,] 5.20795764 12.82011537
[53,] -10.04637480 5.20795764
[54,] -0.10299206 -10.04637480
[55,] 8.78197382 -0.10299206
[56,] 15.02504130 8.78197382
[57,] 2.21414816 15.02504130
[58,] 2.37940595 2.21414816
[59,] 6.27564336 2.37940595
[60,] 21.75193257 6.27564336
[61,] -13.65199876 21.75193257
[62,] -9.43074437 -13.65199876
[63,] -5.59263952 -9.43074437
[64,] 9.55360901 -5.59263952
[65,] 13.83380577 9.55360901
[66,] 16.89349280 13.83380577
[67,] 16.27309941 16.89349280
[68,] 2.53237627 16.27309941
[69,] 6.13155159 2.53237627
[70,] 1.83847180 6.13155159
[71,] -0.53862252 1.83847180
[72,] -2.76507883 -0.53862252
[73,] -3.64979876 -2.76507883
[74,] -0.24516940 -3.64979876
[75,] 7.42544367 -0.24516940
[76,] -14.70384398 7.42544367
[77,] 19.53419390 -14.70384398
[78,] 7.94899125 19.53419390
[79,] -6.15387571 7.94899125
[80,] -14.52949903 -6.15387571
[81,] -15.12346668 -14.52949903
[82,] 2.73421012 -15.12346668
[83,] 6.81728371 2.73421012
[84,] 8.64119112 6.81728371
[85,] 17.74651182 8.64119112
[86,] -15.52190613 17.74651182
[87,] 6.35151811 -15.52190613
[88,] 14.30490702 6.35151811
[89,] -3.18368639 14.30490702
[90,] -2.09676774 -3.18368639
[91,] 12.68366753 -2.09676774
[92,] 7.80862169 12.68366753
[93,] 16.63592729 7.80862169
[94,] -20.99249813 16.63592729
[95,] 3.54672850 -20.99249813
[96,] -11.05532854 3.54672850
[97,] 8.98835958 -11.05532854
[98,] -2.99609793 8.98835958
[99,] -8.50646707 -2.99609793
[100,] -13.25276116 -8.50646707
[101,] 8.82082376 -13.25276116
[102,] -0.16263396 8.82082376
[103,] 13.25209654 -0.16263396
[104,] -11.30606031 13.25209654
[105,] 12.43262084 -11.30606031
[106,] 4.45546147 12.43262084
[107,] 20.83359428 4.45546147
[108,] -9.67314178 20.83359428
[109,] 16.40537019 -9.67314178
[110,] 14.50134954 16.40537019
[111,] -24.08410459 14.50134954
[112,] 3.16186976 -24.08410459
[113,] 4.59132707 3.16186976
[114,] 13.60100311 4.59132707
[115,] 6.89316393 13.60100311
[116,] 9.62153556 6.89316393
[117,] -17.29672464 9.62153556
[118,] -12.66662190 -17.29672464
[119,] -13.43967845 -12.66662190
[120,] -16.42737555 -13.43967845
[121,] -37.27307484 -16.42737555
[122,] -7.90847771 -37.27307484
[123,] 3.76830422 -7.90847771
[124,] 4.51278059 3.76830422
[125,] -3.67347790 4.51278059
[126,] -3.09847761 -3.67347790
[127,] 6.98920806 -3.09847761
[128,] 2.79122872 6.98920806
[129,] -1.50064502 2.79122872
[130,] 21.10236471 -1.50064502
[131,] 10.89930449 21.10236471
[132,] 14.56824725 10.89930449
[133,] 0.19045173 14.56824725
[134,] 10.88652645 0.19045173
[135,] 12.96268803 10.88652645
[136,] -0.01654731 12.96268803
[137,] 1.33885955 -0.01654731
[138,] 6.49914467 1.33885955
[139,] 14.32260445 6.49914467
[140,] -2.97637099 14.32260445
[141,] 11.56019732 -2.97637099
[142,] -0.77807750 11.56019732
[143,] -1.71405468 -0.77807750
[144,] 17.08873510 -1.71405468
[145,] 24.20914645 17.08873510
[146,] -17.78030589 24.20914645
[147,] -2.75374616 -17.78030589
[148,] -3.11761057 -2.75374616
[149,] 15.22900371 -3.11761057
[150,] -11.03602138 15.22900371
[151,] -2.42403906 -11.03602138
[152,] 14.42976082 -2.42403906
[153,] -4.08950522 14.42976082
[154,] -6.67547479 -4.08950522
[155,] 4.13134119 -6.67547479
[156,] -0.51333324 4.13134119
[157,] -6.31968327 -0.51333324
[158,] 2.93482729 -6.31968327
[159,] 12.92235291 2.93482729
[160,] -11.73993850 12.92235291
[161,] -23.71860804 -11.73993850
[162,] -20.90110436 -23.71860804
[163,] -19.32404548 -20.90110436
[164,] -4.57740479 -19.32404548
[165,] -15.50319232 -4.57740479
[166,] -23.32197822 -15.50319232
[167,] -5.37828119 -23.32197822
[168,] -16.94145770 -5.37828119
[169,] -9.43857066 -16.94145770
[170,] -19.48769317 -9.43857066
[171,] -14.71839042 -19.48769317
[172,] -11.80521584 -14.71839042
[173,] -8.93317137 -11.80521584
[174,] -16.21266734 -8.93317137
[175,] -7.85627591 -16.21266734
[176,] -8.91888220 -7.85627591
[177,] -10.01598896 -8.91888220
[178,] -13.54113907 -10.01598896
[179,] -11.39671815 -13.54113907
[180,] -3.36960709 -11.39671815
[181,] -3.38005899 -3.36960709
[182,] 1.02640344 -3.38005899
[183,] -6.13619835 1.02640344
[184,] -14.91834531 -6.13619835
[185,] -14.62879453 -14.91834531
[186,] -22.31554554 -14.62879453
[187,] -23.47242039 -22.31554554
[188,] -14.09587655 -23.47242039
[189,] -21.74912794 -14.09587655
[190,] -24.79057594 -21.74912794
[191,] -3.41100623 -24.79057594
[192,] -18.36657361 -3.41100623
[193,] -11.85578781 -18.36657361
[194,] -7.91557211 -11.85578781
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 11.90128000 7.46060897
2 -5.47009099 11.90128000
3 -5.81248485 -5.47009099
4 -0.77799252 -5.81248485
5 3.71496855 -0.77799252
6 11.18333861 3.71496855
7 19.79051789 11.18333861
8 -1.01252192 19.79051789
9 8.13074636 -1.01252192
10 2.65653584 8.13074636
11 5.66286915 2.65653584
12 6.00609790 5.66286915
13 3.44143216 6.00609790
14 1.42046339 3.44143216
15 -12.68675921 1.42046339
16 3.17928024 -12.68675921
17 6.98024364 3.17928024
18 6.29760654 6.98024364
19 16.53666480 6.29760654
20 0.56322572 16.53666480
21 8.28761170 0.56322572
22 9.84798104 8.28761170
23 6.11688066 9.84798104
24 -12.98152738 6.11688066
25 17.66400917 -12.98152738
26 -2.40575149 17.66400917
27 -7.52304509 -2.40575149
28 -7.77802596 -7.52304509
29 15.22983602 -7.77802596
30 8.88737891 15.22983602
31 21.90481958 8.88737891
32 19.80593339 21.90481958
33 1.44443947 19.80593339
34 2.08960463 1.44443947
35 -2.18332505 2.08960463
36 6.42812267 -2.18332505
37 7.82307367 6.42812267
38 9.12875165 7.82307367
39 9.36276835 9.12875165
40 -5.60683275 9.36276835
41 -0.80509863 -5.60683275
42 -1.43537014 -0.80509863
43 2.06175215 -1.43537014
44 15.03387367 2.06175215
45 -11.10118831 15.03387367
46 12.31770295 -11.10118831
47 14.45259963 12.31770295
48 -0.75066169 14.45259963
49 7.96347141 -0.75066169
50 -1.07859590 7.96347141
51 12.82011537 -1.07859590
52 5.20795764 12.82011537
53 -10.04637480 5.20795764
54 -0.10299206 -10.04637480
55 8.78197382 -0.10299206
56 15.02504130 8.78197382
57 2.21414816 15.02504130
58 2.37940595 2.21414816
59 6.27564336 2.37940595
60 21.75193257 6.27564336
61 -13.65199876 21.75193257
62 -9.43074437 -13.65199876
63 -5.59263952 -9.43074437
64 9.55360901 -5.59263952
65 13.83380577 9.55360901
66 16.89349280 13.83380577
67 16.27309941 16.89349280
68 2.53237627 16.27309941
69 6.13155159 2.53237627
70 1.83847180 6.13155159
71 -0.53862252 1.83847180
72 -2.76507883 -0.53862252
73 -3.64979876 -2.76507883
74 -0.24516940 -3.64979876
75 7.42544367 -0.24516940
76 -14.70384398 7.42544367
77 19.53419390 -14.70384398
78 7.94899125 19.53419390
79 -6.15387571 7.94899125
80 -14.52949903 -6.15387571
81 -15.12346668 -14.52949903
82 2.73421012 -15.12346668
83 6.81728371 2.73421012
84 8.64119112 6.81728371
85 17.74651182 8.64119112
86 -15.52190613 17.74651182
87 6.35151811 -15.52190613
88 14.30490702 6.35151811
89 -3.18368639 14.30490702
90 -2.09676774 -3.18368639
91 12.68366753 -2.09676774
92 7.80862169 12.68366753
93 16.63592729 7.80862169
94 -20.99249813 16.63592729
95 3.54672850 -20.99249813
96 -11.05532854 3.54672850
97 8.98835958 -11.05532854
98 -2.99609793 8.98835958
99 -8.50646707 -2.99609793
100 -13.25276116 -8.50646707
101 8.82082376 -13.25276116
102 -0.16263396 8.82082376
103 13.25209654 -0.16263396
104 -11.30606031 13.25209654
105 12.43262084 -11.30606031
106 4.45546147 12.43262084
107 20.83359428 4.45546147
108 -9.67314178 20.83359428
109 16.40537019 -9.67314178
110 14.50134954 16.40537019
111 -24.08410459 14.50134954
112 3.16186976 -24.08410459
113 4.59132707 3.16186976
114 13.60100311 4.59132707
115 6.89316393 13.60100311
116 9.62153556 6.89316393
117 -17.29672464 9.62153556
118 -12.66662190 -17.29672464
119 -13.43967845 -12.66662190
120 -16.42737555 -13.43967845
121 -37.27307484 -16.42737555
122 -7.90847771 -37.27307484
123 3.76830422 -7.90847771
124 4.51278059 3.76830422
125 -3.67347790 4.51278059
126 -3.09847761 -3.67347790
127 6.98920806 -3.09847761
128 2.79122872 6.98920806
129 -1.50064502 2.79122872
130 21.10236471 -1.50064502
131 10.89930449 21.10236471
132 14.56824725 10.89930449
133 0.19045173 14.56824725
134 10.88652645 0.19045173
135 12.96268803 10.88652645
136 -0.01654731 12.96268803
137 1.33885955 -0.01654731
138 6.49914467 1.33885955
139 14.32260445 6.49914467
140 -2.97637099 14.32260445
141 11.56019732 -2.97637099
142 -0.77807750 11.56019732
143 -1.71405468 -0.77807750
144 17.08873510 -1.71405468
145 24.20914645 17.08873510
146 -17.78030589 24.20914645
147 -2.75374616 -17.78030589
148 -3.11761057 -2.75374616
149 15.22900371 -3.11761057
150 -11.03602138 15.22900371
151 -2.42403906 -11.03602138
152 14.42976082 -2.42403906
153 -4.08950522 14.42976082
154 -6.67547479 -4.08950522
155 4.13134119 -6.67547479
156 -0.51333324 4.13134119
157 -6.31968327 -0.51333324
158 2.93482729 -6.31968327
159 12.92235291 2.93482729
160 -11.73993850 12.92235291
161 -23.71860804 -11.73993850
162 -20.90110436 -23.71860804
163 -19.32404548 -20.90110436
164 -4.57740479 -19.32404548
165 -15.50319232 -4.57740479
166 -23.32197822 -15.50319232
167 -5.37828119 -23.32197822
168 -16.94145770 -5.37828119
169 -9.43857066 -16.94145770
170 -19.48769317 -9.43857066
171 -14.71839042 -19.48769317
172 -11.80521584 -14.71839042
173 -8.93317137 -11.80521584
174 -16.21266734 -8.93317137
175 -7.85627591 -16.21266734
176 -8.91888220 -7.85627591
177 -10.01598896 -8.91888220
178 -13.54113907 -10.01598896
179 -11.39671815 -13.54113907
180 -3.36960709 -11.39671815
181 -3.38005899 -3.36960709
182 1.02640344 -3.38005899
183 -6.13619835 1.02640344
184 -14.91834531 -6.13619835
185 -14.62879453 -14.91834531
186 -22.31554554 -14.62879453
187 -23.47242039 -22.31554554
188 -14.09587655 -23.47242039
189 -21.74912794 -14.09587655
190 -24.79057594 -21.74912794
191 -3.41100623 -24.79057594
192 -18.36657361 -3.41100623
193 -11.85578781 -18.36657361
194 -7.91557211 -11.85578781
> 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/rcomp/tmp/7tsku1293379145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8tsku1293379145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9tsku1293379145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10311x1293379145.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11pjz31293379145.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/rcomp/tmp/12a2g91293379145.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/rcomp/tmp/13zldl1293379145.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/rcomp/tmp/14rccn1293379145.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/rcomp/tmp/15vvst1293379145.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/rcomp/tmp/1695qk1293379145.tab")
+ }
>
> try(system("convert tmp/1wi431293379145.ps tmp/1wi431293379145.png",intern=TRUE))
character(0)
> try(system("convert tmp/2wi431293379145.ps tmp/2wi431293379145.png",intern=TRUE))
character(0)
> try(system("convert tmp/37rlo1293379145.ps tmp/37rlo1293379145.png",intern=TRUE))
character(0)
> try(system("convert tmp/47rlo1293379145.ps tmp/47rlo1293379145.png",intern=TRUE))
character(0)
> try(system("convert tmp/57rlo1293379145.ps tmp/57rlo1293379145.png",intern=TRUE))
character(0)
> try(system("convert tmp/60i2r1293379145.ps tmp/60i2r1293379145.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tsku1293379145.ps tmp/7tsku1293379145.png",intern=TRUE))
character(0)
> try(system("convert tmp/8tsku1293379145.ps tmp/8tsku1293379145.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tsku1293379145.ps tmp/9tsku1293379145.png",intern=TRUE))
character(0)
> try(system("convert tmp/10311x1293379145.ps tmp/10311x1293379145.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.380 1.640 7.065