R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(2
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+ ,41)
+ ,dim=c(7
+ ,195)
+ ,dimnames=list(c('Teamwork33rec'
+ ,'geslacht'
+ ,'leeftijd'
+ ,'opleiding'
+ ,'Openheid'
+ ,'Neuroticisme'
+ ,'Extraversie
')
+ ,1:195))
> y <- array(NA,dim=c(7,195),dimnames=list(c('Teamwork33rec','geslacht','leeftijd','opleiding','Openheid','Neuroticisme','Extraversie
'),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
Teamwork33rec geslacht leeftijd opleiding Openheid Neuroticisme
1 2 1 27 5 35 26
2 2 1 36 4 34 25
3 1 1 25 4 13 17
4 4 1 27 3 35 37
5 3 2 25 3 28 35
6 1 2 44 3 32 15
7 2 1 50 4 35 27
8 2 1 41 4 36 36
9 2 1 48 5 27 25
10 2 2 43 4 29 30
11 1 2 47 2 27 27
12 2 2 41 3 28 33
13 3 1 44 2 29 29
14 2 2 47 5 28 30
15 3 2 40 3 30 25
16 3 2 46 3 25 23
17 2 1 28 3 15 26
18 3 1 56 3 33 24
19 2 2 49 4 31 35
20 4 2 25 4 37 39
21 2 2 41 4 37 23
22 3 2 26 3 34 32
23 2 1 50 5 32 29
24 2 1 47 4 21 26
25 3 1 52 2 25 21
26 3 2 37 5 32 35
27 4 2 41 3 28 23
28 2 1 45 4 22 21
29 1 2 26 4 25 28
30 2 1 3 26 30 41
31 1 52 4 34 21 44
32 1 46 2 34 29 51
33 1 58 3 36 28 46
34 1 54 5 36 19 47
35 1 29 3 26 26 46
36 2 50 3 26 33 38
37 1 43 2 34 34 50
38 2 30 3 33 33 48
39 2 47 2 31 40 36
40 1 45 3 33 24 51
41 48 1 22 35 35 2
42 48 3 29 35 49 2
43 26 4 24 32 38 2
44 46 5 37 20 47 4
45 3 32 35 36 2 2
46 3 23 35 47 3 1
47 4 29 21 46 2 1
48 2 35 33 43 5 2
49 2 20 40 53 4 1
50 3 28 22 55 4 2
51 2 26 35 39 2 2
52 4 36 20 55 3 2
53 5 26 28 41 2 2
54 3 33 46 33 3 1
55 4 25 18 52 3 2
56 5 29 22 42 1 1
57 5 32 20 56 3 2
58 3 35 25 46 4 2
59 4 24 31 33 5 2
60 3 31 21 51 4 2
61 3 29 23 46 1 1
62 2 27 26 46 2 2
63 3 29 34 50 2 2
64 4 29 31 46 3 1
65 4 27 23 51 2 2
66 4 34 31 48 2 2
67 4 32 26 44 4 2
68 3 31 36 38 3 2
69 3 31 28 42 2 1
70 3 31 34 39 3 1
71 2 16 25 45 4 1
72 3 25 33 31 2 1
73 3 27 46 29 2 1
74 3 32 24 48 3 2
75 3 28 32 38 1 2
76 5 25 33 55 5 1
77 3 25 42 32 3 2
78 5 36 17 51 3 2
79 4 36 36 53 1 2
80 4 36 40 47 4 1
81 4 27 30 45 3 1
82 5 29 19 33 3 2
83 4 32 33 49 2 2
84 5 29 35 46 4 2
85 3 31 23 42 2 2
86 3 34 15 56 3 2
87 2 27 38 35 3 1
88 3 28 37 40 3 1
89 4 32 23 44 4 2
90 5 33 41 46 3 1
91 5 29 34 46 4 1
92 3 32 38 39 2 2
93 2 35 45 35 2 2
94 3 33 27 48 4 2
95 4 27 46 42 5 1
96 1 16 26 39 1 2
97 4 32 44 39 2 1
98 3 26 36 41 2 1
99 3 32 20 52 2 2
100 4 38 44 45 3 1
101 3 24 27 42 3 1
102 4 26 27 44 5 1
103 2 19 41 33 1 2
104 3 37 30 42 3 1
105 3 25 33 46 3 1
106 3 24 37 45 4 1
107 2 23 30 40 2 1
108 5 28 20 48 2 2
109 5 38 44 32 3 1
110 4 28 20 53 2 2
111 2 28 33 39 2 1
112 3 26 31 45 4 2
113 3 21 23 36 3 2
114 3 35 33 38 3 2
115 4 31 33 49 3 1
116 5 34 32 46 2 2
117 4 30 25 43 1 1
118 30 22 37 3 2 46
119 24 16 48 3 2 49
120 27 36 45 4 2 51
121 26 35 32 3 1 38
122 30 25 46 5 1 41
123 15 27 20 2 2 47
124 28 32 42 2 2 44
125 34 36 45 2 2 47
126 29 51 29 3 2 46
127 26 30 51 1 1 44
128 31 20 55 4 2 28
129 28 29 50 4 2 47
130 33 26 44 3 2 28
131 32 20 41 3 1 41
132 33 40 40 4 2 45
133 31 29 47 5 2 46
134 37 32 42 3 1 46
135 27 33 40 1 2 22
136 19 32 51 2 2 33
137 27 34 43 2 1 41
138 31 24 45 2 2 47
139 38 25 41 3 1 25
140 22 41 41 1 2 42
141 35 39 37 3 2 47
142 35 21 46 3 2 50
143 30 38 38 3 1 55
144 41 28 39 3 1 21
145 25 37 45 2 1 3
146 26 46 4 1 52 3
147 30 39 4 2 49 4
148 25 21 2 2 46 4
149 38 31 3 1 4 25
150 35 3 2 45 3 29
151 49 4 2 52 3 31
152 40 3 1 3 29 21
153 3 2 40 4 31 26
154 2 2 49 4 31 37
155 5 1 38 5 25 28
156 5 1 32 5 27 29
157 1 2 46 4 26 33
158 2 2 32 3 26 41
159 3 2 41 3 23 19
160 3 2 43 3 27 37
161 2 1 44 4 24 36
162 3 1 47 5 35 27
163 4 2 28 3 24 33
164 5 1 52 1 32 29
165 5 1 27 2 24 42
166 1 2 45 5 24 27
167 2 1 27 4 38 47
168 3 1 25 4 36 17
169 2 1 28 4 24 34
170 1 1 25 3 18 32
171 2 1 52 4 34 25
172 2 1 44 3 23 27
173 4 2 43 3 35 37
174 3 2 47 4 22 34
175 2 2 52 4 34 27
176 3 2 40 2 28 37
177 2 1 42 3 34 32
178 3 1 45 5 32 26
179 2 1 45 2 24 29
180 5 1 50 5 34 28
181 4 1 49 3 33 19
182 3 1 52 2 33 46
183 3 2 48 3 29 31
184 1 2 51 3 38 42
185 2 2 49 4 24 33
186 3 2 31 4 25 39
187 3 2 43 3 37 27
188 3 2 31 3 33 35
189 3 2 28 4 30 23
190 2 2 43 4 22 32
191 3 2 31 3 28 22
192 4 2 51 3 24 17
193 2 2 58 4 33 35
194 4 2 25 5 37 34
195 2 1 27 5 35 26
Extraversie\r
1 49
2 45
3 54
4 36
5 36
6 53
7 46
8 42
9 41
10 45
11 47
12 42
13 45
14 40
15 45
16 40
17 42
18 45
19 47
20 31
21 46
22 34
23 43
24 45
25 42
26 51
27 44
28 47
29 47
30 4
31 1
32 2
33 3
34 2
35 4
36 3
37 3
38 3
39 1
40 2
41 2
42 2
43 1
44 2
45 50
46 25
47 47
48 47
49 41
50 45
51 41
52 45
53 40
54 29
55 34
56 45
57 52
58 41
59 48
60 45
61 54
62 25
63 26
64 28
65 50
66 48
67 51
68 53
69 37
70 56
71 43
72 34
73 42
74 32
75 31
76 46
77 30
78 47
79 33
80 25
81 25
82 21
83 36
84 50
85 48
86 48
87 25
88 48
89 49
90 27
91 28
92 43
93 48
94 48
95 25
96 49
97 26
98 51
99 25
100 29
101 29
102 43
103 46
104 44
105 25
106 51
107 42
108 53
109 25
110 49
111 51
112 20
113 44
114 38
115 46
116 42
117 29
118 4
119 2
120 3
121 3
122 1
123 3
124 3
125 3
126 3
127 4
128 3
129 4
130 4
131 5
132 4
133 4
134 4
135 3
136 3
137 4
138 5
139 3
140 3
141 3
142 3
143 5
144 3
145 28
146 45
147 21
148 33
149 31
150 31
151 27
152 45
153 46
154 45
155 34
156 41
157 43
158 45
159 48
160 43
161 45
162 45
163 34
164 40
165 40
166 55
167 44
168 44
169 48
170 51
171 49
172 33
173 43
174 44
175 44
176 41
177 45
178 44
179 44
180 40
181 48
182 49
183 46
184 49
185 55
186 51
187 46
188 37
189 43
190 41
191 45
192 39
193 38
194 41
195 49
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) geslacht leeftijd opleiding
56.3579 -0.1811 -0.2179 -0.4031
Openheid Neuroticisme `Extraversie\r`
-0.3093 -0.3084 -0.5261
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-19.0145 -4.9637 -0.7055 3.6658 39.4557
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 56.35787 4.90696 11.485 < 2e-16 ***
geslacht -0.18112 0.06400 -2.830 0.005162 **
leeftijd -0.21786 0.05728 -3.803 0.000193 ***
opleiding -0.40310 0.05910 -6.820 1.21e-10 ***
Openheid -0.30935 0.06065 -5.100 8.24e-07 ***
Neuroticisme -0.30837 0.05773 -5.341 2.65e-07 ***
`Extraversie\r` -0.52609 0.04701 -11.192 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.091 on 188 degrees of freedom
Multiple R-squared: 0.5643, Adjusted R-squared: 0.5504
F-statistic: 40.58 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,] 2.468159e-04 4.936318e-04 9.997532e-01
[2,] 1.757496e-05 3.514992e-05 9.999824e-01
[3,] 8.119771e-07 1.623954e-06 9.999992e-01
[4,] 1.875504e-07 3.751008e-07 9.999998e-01
[5,] 1.207084e-08 2.414168e-08 1.000000e+00
[6,] 5.012737e-09 1.002547e-08 1.000000e+00
[7,] 3.894667e-10 7.789335e-10 1.000000e+00
[8,] 4.225687e-11 8.451374e-11 1.000000e+00
[9,] 4.954545e-12 9.909090e-12 1.000000e+00
[10,] 7.714893e-13 1.542979e-12 1.000000e+00
[11,] 5.224552e-14 1.044910e-13 1.000000e+00
[12,] 3.367380e-15 6.734759e-15 1.000000e+00
[13,] 4.216525e-16 8.433049e-16 1.000000e+00
[14,] 2.726491e-17 5.452981e-17 1.000000e+00
[15,] 1.720471e-18 3.440942e-18 1.000000e+00
[16,] 1.065936e-19 2.131872e-19 1.000000e+00
[17,] 4.917157e-19 9.834313e-19 1.000000e+00
[18,] 1.195655e-18 2.391310e-18 1.000000e+00
[19,] 1.045398e-19 2.090795e-19 1.000000e+00
[20,] 2.161042e-20 4.322083e-20 1.000000e+00
[21,] 2.841173e-21 5.682346e-21 1.000000e+00
[22,] 2.832758e-22 5.665516e-22 1.000000e+00
[23,] 2.432197e-23 4.864394e-23 1.000000e+00
[24,] 2.125235e-24 4.250471e-24 1.000000e+00
[25,] 2.175329e-25 4.350658e-25 1.000000e+00
[26,] 1.077270e-25 2.154540e-25 1.000000e+00
[27,] 1.445615e-26 2.891230e-26 1.000000e+00
[28,] 1.657822e-27 3.315643e-27 1.000000e+00
[29,] 5.382295e-28 1.076459e-27 1.000000e+00
[30,] 1.461219e-28 2.922439e-28 1.000000e+00
[31,] 1.410766e-28 2.821532e-28 1.000000e+00
[32,] 2.578452e-05 5.156905e-05 9.999742e-01
[33,] 7.867040e-05 1.573408e-04 9.999213e-01
[34,] 7.634279e-04 1.526856e-03 9.992366e-01
[35,] 1.510418e-02 3.020835e-02 9.848958e-01
[36,] 1.180130e-02 2.360260e-02 9.881987e-01
[37,] 3.834421e-02 7.668841e-02 9.616558e-01
[38,] 2.991140e-02 5.982280e-02 9.700886e-01
[39,] 2.191749e-02 4.383497e-02 9.780825e-01
[40,] 2.042098e-02 4.084196e-02 9.795790e-01
[41,] 1.486707e-02 2.973414e-02 9.851329e-01
[42,] 1.099536e-02 2.199072e-02 9.890046e-01
[43,] 8.592158e-03 1.718432e-02 9.914078e-01
[44,] 6.033917e-03 1.206783e-02 9.939661e-01
[45,] 5.429301e-03 1.085860e-02 9.945707e-01
[46,] 4.315703e-03 8.631406e-03 9.956843e-01
[47,] 3.337969e-03 6.675937e-03 9.966620e-01
[48,] 3.072102e-03 6.144204e-03 9.969279e-01
[49,] 2.136456e-03 4.272912e-03 9.978635e-01
[50,] 1.463406e-03 2.926812e-03 9.985366e-01
[51,] 9.765776e-04 1.953155e-03 9.990234e-01
[52,] 7.339411e-04 1.467882e-03 9.992661e-01
[53,] 1.081085e-03 2.162169e-03 9.989189e-01
[54,] 9.593946e-04 1.918789e-03 9.990406e-01
[55,] 7.777550e-04 1.555510e-03 9.992222e-01
[56,] 5.603180e-04 1.120636e-03 9.994397e-01
[57,] 4.368328e-04 8.736656e-04 9.995632e-01
[58,] 3.142931e-04 6.285862e-04 9.996857e-01
[59,] 2.463690e-04 4.927381e-04 9.997536e-01
[60,] 1.718964e-04 3.437928e-04 9.998281e-01
[61,] 1.351190e-04 2.702380e-04 9.998649e-01
[62,] 1.102349e-04 2.204697e-04 9.998898e-01
[63,] 8.123220e-05 1.624644e-04 9.999188e-01
[64,] 5.216386e-05 1.043277e-04 9.999478e-01
[65,] 4.236004e-05 8.472007e-05 9.999576e-01
[66,] 3.093463e-05 6.186926e-05 9.999691e-01
[67,] 2.291613e-05 4.583226e-05 9.999771e-01
[68,] 1.561637e-05 3.123275e-05 9.999844e-01
[69,] 1.031377e-05 2.062754e-05 9.999897e-01
[70,] 6.173929e-06 1.234786e-05 9.999938e-01
[71,] 4.290476e-06 8.580952e-06 9.999957e-01
[72,] 3.754548e-06 7.509096e-06 9.999962e-01
[73,] 6.248826e-06 1.249765e-05 9.999938e-01
[74,] 3.733305e-06 7.466609e-06 9.999963e-01
[75,] 3.154072e-06 6.308145e-06 9.999968e-01
[76,] 1.973372e-06 3.946744e-06 9.999980e-01
[77,] 1.177158e-06 2.354315e-06 9.999988e-01
[78,] 1.246014e-06 2.492028e-06 9.999988e-01
[79,] 7.755323e-07 1.551065e-06 9.999992e-01
[80,] 4.603694e-07 9.207389e-07 9.999995e-01
[81,] 2.646453e-07 5.292905e-07 9.999997e-01
[82,] 1.680748e-07 3.361495e-07 9.999998e-01
[83,] 1.007442e-07 2.014884e-07 9.999999e-01
[84,] 7.140015e-08 1.428003e-07 9.999999e-01
[85,] 3.948407e-08 7.896815e-08 1.000000e+00
[86,] 2.589029e-08 5.178058e-08 1.000000e+00
[87,] 1.485338e-08 2.970676e-08 1.000000e+00
[88,] 8.501873e-09 1.700375e-08 1.000000e+00
[89,] 5.420716e-09 1.084143e-08 1.000000e+00
[90,] 9.460177e-09 1.892035e-08 1.000000e+00
[91,] 5.103925e-09 1.020785e-08 1.000000e+00
[92,] 6.602923e-09 1.320585e-08 1.000000e+00
[93,] 3.675094e-09 7.350187e-09 1.000000e+00
[94,] 2.041828e-09 4.083655e-09 1.000000e+00
[95,] 1.094648e-09 2.189297e-09 1.000000e+00
[96,] 1.570290e-09 3.140581e-09 1.000000e+00
[97,] 9.501308e-10 1.900262e-09 1.000000e+00
[98,] 5.923491e-10 1.184698e-09 1.000000e+00
[99,] 3.753929e-10 7.507857e-10 1.000000e+00
[100,] 2.764921e-10 5.529842e-10 1.000000e+00
[101,] 1.463512e-10 2.927025e-10 1.000000e+00
[102,] 7.441062e-11 1.488212e-10 1.000000e+00
[103,] 5.421912e-10 1.084382e-09 1.000000e+00
[104,] 5.214303e-10 1.042861e-09 1.000000e+00
[105,] 5.890588e-10 1.178118e-09 1.000000e+00
[106,] 4.159970e-10 8.319940e-10 1.000000e+00
[107,] 7.856660e-10 1.571332e-09 1.000000e+00
[108,] 4.749581e-06 9.499162e-06 9.999953e-01
[109,] 1.823390e-01 3.646779e-01 8.176610e-01
[110,] 3.902691e-01 7.805382e-01 6.097309e-01
[111,] 6.285240e-01 7.429519e-01 3.714760e-01
[112,] 7.299668e-01 5.400665e-01 2.700332e-01
[113,] 7.684815e-01 4.630370e-01 2.315185e-01
[114,] 9.141663e-01 1.716675e-01 8.583373e-02
[115,] 9.254911e-01 1.490178e-01 7.450892e-02
[116,] 9.538711e-01 9.225781e-02 4.612890e-02
[117,] 9.761605e-01 4.767903e-02 2.383951e-02
[118,] 9.713770e-01 5.724607e-02 2.862304e-02
[119,] 9.685975e-01 6.280504e-02 3.140252e-02
[120,] 9.643374e-01 7.132523e-02 3.566261e-02
[121,] 9.605409e-01 7.891820e-02 3.945910e-02
[122,] 9.610902e-01 7.781965e-02 3.890983e-02
[123,] 9.621636e-01 7.567275e-02 3.783637e-02
[124,] 9.586253e-01 8.274945e-02 4.137473e-02
[125,] 9.704250e-01 5.915001e-02 2.957501e-02
[126,] 9.676114e-01 6.477725e-02 3.238862e-02
[127,] 9.783550e-01 4.328995e-02 2.164498e-02
[128,] 9.751687e-01 4.966259e-02 2.483130e-02
[129,] 9.728817e-01 5.423669e-02 2.711835e-02
[130,] 9.749498e-01 5.010040e-02 2.505020e-02
[131,] 9.878974e-01 2.420524e-02 1.210262e-02
[132,] 9.872792e-01 2.544155e-02 1.272077e-02
[133,] 9.916374e-01 1.672525e-02 8.362625e-03
[134,] 9.903773e-01 1.924534e-02 9.622672e-03
[135,] 9.955660e-01 8.868005e-03 4.434003e-03
[136,] 9.936689e-01 1.266222e-02 6.331110e-03
[137,] 9.968596e-01 6.280855e-03 3.140427e-03
[138,] 9.976826e-01 4.634880e-03 2.317440e-03
[139,] 9.981417e-01 3.716575e-03 1.858287e-03
[140,] 9.994196e-01 1.160703e-03 5.803517e-04
[141,] 9.998322e-01 3.355796e-04 1.677898e-04
[142,] 9.999917e-01 1.658156e-05 8.290782e-06
[143,] 1.000000e+00 7.016826e-27 3.508413e-27
[144,] 1.000000e+00 5.845014e-26 2.922507e-26
[145,] 1.000000e+00 4.755238e-25 2.377619e-25
[146,] 1.000000e+00 1.889395e-24 9.446975e-25
[147,] 1.000000e+00 1.753219e-24 8.766096e-25
[148,] 1.000000e+00 4.886139e-24 2.443069e-24
[149,] 1.000000e+00 3.828930e-23 1.914465e-23
[150,] 1.000000e+00 3.166701e-22 1.583350e-22
[151,] 1.000000e+00 2.789425e-21 1.394712e-21
[152,] 1.000000e+00 2.352048e-20 1.176024e-20
[153,] 1.000000e+00 1.892910e-19 9.464551e-20
[154,] 1.000000e+00 1.353547e-18 6.767734e-19
[155,] 1.000000e+00 4.726031e-18 2.363015e-18
[156,] 1.000000e+00 2.524445e-18 1.262222e-18
[157,] 1.000000e+00 1.188483e-17 5.942417e-18
[158,] 1.000000e+00 1.025818e-16 5.129092e-17
[159,] 1.000000e+00 8.643420e-16 4.321710e-16
[160,] 1.000000e+00 7.664929e-15 3.832465e-15
[161,] 1.000000e+00 4.080331e-14 2.040166e-14
[162,] 1.000000e+00 2.575683e-13 1.287842e-13
[163,] 1.000000e+00 8.267170e-13 4.133585e-13
[164,] 1.000000e+00 2.730483e-12 1.365241e-12
[165,] 1.000000e+00 2.345513e-11 1.172756e-11
[166,] 1.000000e+00 1.404808e-10 7.024039e-11
[167,] 1.000000e+00 1.100500e-09 5.502498e-10
[168,] 1.000000e+00 6.083974e-09 3.041987e-09
[169,] 1.000000e+00 4.465175e-08 2.232587e-08
[170,] 9.999999e-01 1.521337e-07 7.606684e-08
[171,] 9.999998e-01 3.443371e-07 1.721685e-07
[172,] 9.999989e-01 2.175324e-06 1.087662e-06
[173,] 9.999977e-01 4.614681e-06 2.307340e-06
[174,] 9.999823e-01 3.547911e-05 1.773955e-05
[175,] 9.998449e-01 3.102920e-04 1.551460e-04
[176,] 9.982921e-01 3.415750e-03 1.707875e-03
> postscript(file="/var/www/html/rcomp/tmp/1kaux1291198364.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2kaux1291198364.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3cku11291198364.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4cku11291198364.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5cku11291198364.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
-1.65569161 -2.82015594 -10.44504384 -3.90900623 -7.94577860 -1.79294437
7 8 9 10 11 12
1.68204801 0.70164637 -4.07255225 -1.11890241 -2.54530007 -2.92023118
13 14 15 16 17 18
-1.19674308 -2.78417435 -2.40809310 -5.89488483 -12.11363624 1.51619344
19 20 21 22 23 24
3.40098850 -5.15552152 -0.71235024 -7.84913503 0.19557508 -4.13685790
25 26 27 28 29 30
-4.73651701 4.60350514 -2.95176581 -4.75290252 -6.62444787 -19.01445074
31 32 33 34 35 36
-10.77185635 -7.13482687 -5.26241811 -8.55303585 -14.63857625 -10.66265718
37 38 39 40 41 42
-5.91373349 -8.37965013 -8.91186183 -9.04793058 23.22083619 29.43895543
43 44 45 46 47 48
1.39256000 23.49557319 2.11483211 -8.23246318 1.66582678 3.39396553
49 50 51 52 53 54
2.45890668 4.20537191 -3.49741982 5.90928007 -1.74231652 -7.56387338
55 56 57 58 59 60
-3.51509887 -0.09025827 10.27054268 0.39450396 -0.53902564 2.91846672
61 62 63 64 65 66
4.47484259 -10.87278251 -5.62916684 -5.84199377 5.64146390 6.39069066
67 68 69 70 71 72
5.52371514 4.84539931 -4.32025448 6.08269001 -3.70609681 -10.33009961
73 74 75 76 77 78
-3.73316196 -4.60469214 -8.76212593 8.58551949 -9.45291923 5.69547750
79 80 81 82 83 84
1.65700883 -3.47924173 -8.40347538 -16.07090826 0.55415969 8.22119099
85 86 87 88 89 90
0.68583998 5.43912371 -12.69163528 2.38726528 3.81795475 -2.46501377
91 92 93 94 95 96
-3.87907045 0.29507024 2.38149532 4.95682983 -5.50835144 -4.36997226
97 98 99 100 101 102
-6.64971952 3.47919571 -7.85570882 -1.25674818 -9.70535530 0.44708118
103 104 105 106 107 108
-3.55562272 1.19418613 -8.70903999 5.56591764 -4.50925434 6.53797811
109 110 111 112 113 114
-7.60145461 5.44912315 1.38165745 -11.37948049 -5.33901498 -2.97507473
115 116 117 118 119 120
5.63493687 3.64578964 -8.26993507 3.80505710 -1.01229696 6.50249853
121 122 123 124 125 126
-2.23207017 3.68587079 -14.61375406 3.15962712 11.46280511 5.78862283
127 128 129 130 131 132
2.57175307 2.69058431 6.61654480 3.50385898 4.98913334 10.81355280
133 134 135 136 137 138
9.05769997 13.39621859 -5.28225368 -7.27173679 2.03135790 7.34153040
139 140 141 142 143 144
5.90860595 -2.44798367 11.66640411 11.29205816 9.91295356 7.78276590
145 146 147 148 149 150
1.91852226 19.93360701 9.82297226 6.51213116 13.56913111 23.94053334
151 152 153 154 155 156
39.45574956 24.73370836 -0.86117844 2.96554772 -6.62736368 -3.32480194
157 158 159 160 161 162
-2.52043820 -1.45440123 -4.62762208 -0.03428155 -0.77867170 1.90548279
163 164 165 166 167 168
-9.19852158 0.44060387 -3.06870341 1.50898716 2.71459711 -6.59097299
169 170 171 172 173 174
-3.30287600 -6.25410692 2.76995071 -10.57957724 3.44049897 -0.70550563
175 176 177 178 179 180
0.93735460 -1.83379723 0.24249621 -0.29274132 -4.05171464 1.92762134
181 182 183 184 185 186
1.02760065 9.13020788 1.40175190 7.80982388 4.82755021 1.96130551
187 188 189 190 191 192
1.55375178 -4.56579694 -6.28822177 -4.77196077 -5.91263129 -6.49126334
193 194 195
1.24558008 -1.03335515 -1.65569161
> postscript(file="/var/www/html/rcomp/tmp/65tb31291198364.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 -1.65569161 NA
1 -2.82015594 -1.65569161
2 -10.44504384 -2.82015594
3 -3.90900623 -10.44504384
4 -7.94577860 -3.90900623
5 -1.79294437 -7.94577860
6 1.68204801 -1.79294437
7 0.70164637 1.68204801
8 -4.07255225 0.70164637
9 -1.11890241 -4.07255225
10 -2.54530007 -1.11890241
11 -2.92023118 -2.54530007
12 -1.19674308 -2.92023118
13 -2.78417435 -1.19674308
14 -2.40809310 -2.78417435
15 -5.89488483 -2.40809310
16 -12.11363624 -5.89488483
17 1.51619344 -12.11363624
18 3.40098850 1.51619344
19 -5.15552152 3.40098850
20 -0.71235024 -5.15552152
21 -7.84913503 -0.71235024
22 0.19557508 -7.84913503
23 -4.13685790 0.19557508
24 -4.73651701 -4.13685790
25 4.60350514 -4.73651701
26 -2.95176581 4.60350514
27 -4.75290252 -2.95176581
28 -6.62444787 -4.75290252
29 -19.01445074 -6.62444787
30 -10.77185635 -19.01445074
31 -7.13482687 -10.77185635
32 -5.26241811 -7.13482687
33 -8.55303585 -5.26241811
34 -14.63857625 -8.55303585
35 -10.66265718 -14.63857625
36 -5.91373349 -10.66265718
37 -8.37965013 -5.91373349
38 -8.91186183 -8.37965013
39 -9.04793058 -8.91186183
40 23.22083619 -9.04793058
41 29.43895543 23.22083619
42 1.39256000 29.43895543
43 23.49557319 1.39256000
44 2.11483211 23.49557319
45 -8.23246318 2.11483211
46 1.66582678 -8.23246318
47 3.39396553 1.66582678
48 2.45890668 3.39396553
49 4.20537191 2.45890668
50 -3.49741982 4.20537191
51 5.90928007 -3.49741982
52 -1.74231652 5.90928007
53 -7.56387338 -1.74231652
54 -3.51509887 -7.56387338
55 -0.09025827 -3.51509887
56 10.27054268 -0.09025827
57 0.39450396 10.27054268
58 -0.53902564 0.39450396
59 2.91846672 -0.53902564
60 4.47484259 2.91846672
61 -10.87278251 4.47484259
62 -5.62916684 -10.87278251
63 -5.84199377 -5.62916684
64 5.64146390 -5.84199377
65 6.39069066 5.64146390
66 5.52371514 6.39069066
67 4.84539931 5.52371514
68 -4.32025448 4.84539931
69 6.08269001 -4.32025448
70 -3.70609681 6.08269001
71 -10.33009961 -3.70609681
72 -3.73316196 -10.33009961
73 -4.60469214 -3.73316196
74 -8.76212593 -4.60469214
75 8.58551949 -8.76212593
76 -9.45291923 8.58551949
77 5.69547750 -9.45291923
78 1.65700883 5.69547750
79 -3.47924173 1.65700883
80 -8.40347538 -3.47924173
81 -16.07090826 -8.40347538
82 0.55415969 -16.07090826
83 8.22119099 0.55415969
84 0.68583998 8.22119099
85 5.43912371 0.68583998
86 -12.69163528 5.43912371
87 2.38726528 -12.69163528
88 3.81795475 2.38726528
89 -2.46501377 3.81795475
90 -3.87907045 -2.46501377
91 0.29507024 -3.87907045
92 2.38149532 0.29507024
93 4.95682983 2.38149532
94 -5.50835144 4.95682983
95 -4.36997226 -5.50835144
96 -6.64971952 -4.36997226
97 3.47919571 -6.64971952
98 -7.85570882 3.47919571
99 -1.25674818 -7.85570882
100 -9.70535530 -1.25674818
101 0.44708118 -9.70535530
102 -3.55562272 0.44708118
103 1.19418613 -3.55562272
104 -8.70903999 1.19418613
105 5.56591764 -8.70903999
106 -4.50925434 5.56591764
107 6.53797811 -4.50925434
108 -7.60145461 6.53797811
109 5.44912315 -7.60145461
110 1.38165745 5.44912315
111 -11.37948049 1.38165745
112 -5.33901498 -11.37948049
113 -2.97507473 -5.33901498
114 5.63493687 -2.97507473
115 3.64578964 5.63493687
116 -8.26993507 3.64578964
117 3.80505710 -8.26993507
118 -1.01229696 3.80505710
119 6.50249853 -1.01229696
120 -2.23207017 6.50249853
121 3.68587079 -2.23207017
122 -14.61375406 3.68587079
123 3.15962712 -14.61375406
124 11.46280511 3.15962712
125 5.78862283 11.46280511
126 2.57175307 5.78862283
127 2.69058431 2.57175307
128 6.61654480 2.69058431
129 3.50385898 6.61654480
130 4.98913334 3.50385898
131 10.81355280 4.98913334
132 9.05769997 10.81355280
133 13.39621859 9.05769997
134 -5.28225368 13.39621859
135 -7.27173679 -5.28225368
136 2.03135790 -7.27173679
137 7.34153040 2.03135790
138 5.90860595 7.34153040
139 -2.44798367 5.90860595
140 11.66640411 -2.44798367
141 11.29205816 11.66640411
142 9.91295356 11.29205816
143 7.78276590 9.91295356
144 1.91852226 7.78276590
145 19.93360701 1.91852226
146 9.82297226 19.93360701
147 6.51213116 9.82297226
148 13.56913111 6.51213116
149 23.94053334 13.56913111
150 39.45574956 23.94053334
151 24.73370836 39.45574956
152 -0.86117844 24.73370836
153 2.96554772 -0.86117844
154 -6.62736368 2.96554772
155 -3.32480194 -6.62736368
156 -2.52043820 -3.32480194
157 -1.45440123 -2.52043820
158 -4.62762208 -1.45440123
159 -0.03428155 -4.62762208
160 -0.77867170 -0.03428155
161 1.90548279 -0.77867170
162 -9.19852158 1.90548279
163 0.44060387 -9.19852158
164 -3.06870341 0.44060387
165 1.50898716 -3.06870341
166 2.71459711 1.50898716
167 -6.59097299 2.71459711
168 -3.30287600 -6.59097299
169 -6.25410692 -3.30287600
170 2.76995071 -6.25410692
171 -10.57957724 2.76995071
172 3.44049897 -10.57957724
173 -0.70550563 3.44049897
174 0.93735460 -0.70550563
175 -1.83379723 0.93735460
176 0.24249621 -1.83379723
177 -0.29274132 0.24249621
178 -4.05171464 -0.29274132
179 1.92762134 -4.05171464
180 1.02760065 1.92762134
181 9.13020788 1.02760065
182 1.40175190 9.13020788
183 7.80982388 1.40175190
184 4.82755021 7.80982388
185 1.96130551 4.82755021
186 1.55375178 1.96130551
187 -4.56579694 1.55375178
188 -6.28822177 -4.56579694
189 -4.77196077 -6.28822177
190 -5.91263129 -4.77196077
191 -6.49126334 -5.91263129
192 1.24558008 -6.49126334
193 -1.03335515 1.24558008
194 -1.65569161 -1.03335515
195 NA -1.65569161
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.82015594 -1.65569161
[2,] -10.44504384 -2.82015594
[3,] -3.90900623 -10.44504384
[4,] -7.94577860 -3.90900623
[5,] -1.79294437 -7.94577860
[6,] 1.68204801 -1.79294437
[7,] 0.70164637 1.68204801
[8,] -4.07255225 0.70164637
[9,] -1.11890241 -4.07255225
[10,] -2.54530007 -1.11890241
[11,] -2.92023118 -2.54530007
[12,] -1.19674308 -2.92023118
[13,] -2.78417435 -1.19674308
[14,] -2.40809310 -2.78417435
[15,] -5.89488483 -2.40809310
[16,] -12.11363624 -5.89488483
[17,] 1.51619344 -12.11363624
[18,] 3.40098850 1.51619344
[19,] -5.15552152 3.40098850
[20,] -0.71235024 -5.15552152
[21,] -7.84913503 -0.71235024
[22,] 0.19557508 -7.84913503
[23,] -4.13685790 0.19557508
[24,] -4.73651701 -4.13685790
[25,] 4.60350514 -4.73651701
[26,] -2.95176581 4.60350514
[27,] -4.75290252 -2.95176581
[28,] -6.62444787 -4.75290252
[29,] -19.01445074 -6.62444787
[30,] -10.77185635 -19.01445074
[31,] -7.13482687 -10.77185635
[32,] -5.26241811 -7.13482687
[33,] -8.55303585 -5.26241811
[34,] -14.63857625 -8.55303585
[35,] -10.66265718 -14.63857625
[36,] -5.91373349 -10.66265718
[37,] -8.37965013 -5.91373349
[38,] -8.91186183 -8.37965013
[39,] -9.04793058 -8.91186183
[40,] 23.22083619 -9.04793058
[41,] 29.43895543 23.22083619
[42,] 1.39256000 29.43895543
[43,] 23.49557319 1.39256000
[44,] 2.11483211 23.49557319
[45,] -8.23246318 2.11483211
[46,] 1.66582678 -8.23246318
[47,] 3.39396553 1.66582678
[48,] 2.45890668 3.39396553
[49,] 4.20537191 2.45890668
[50,] -3.49741982 4.20537191
[51,] 5.90928007 -3.49741982
[52,] -1.74231652 5.90928007
[53,] -7.56387338 -1.74231652
[54,] -3.51509887 -7.56387338
[55,] -0.09025827 -3.51509887
[56,] 10.27054268 -0.09025827
[57,] 0.39450396 10.27054268
[58,] -0.53902564 0.39450396
[59,] 2.91846672 -0.53902564
[60,] 4.47484259 2.91846672
[61,] -10.87278251 4.47484259
[62,] -5.62916684 -10.87278251
[63,] -5.84199377 -5.62916684
[64,] 5.64146390 -5.84199377
[65,] 6.39069066 5.64146390
[66,] 5.52371514 6.39069066
[67,] 4.84539931 5.52371514
[68,] -4.32025448 4.84539931
[69,] 6.08269001 -4.32025448
[70,] -3.70609681 6.08269001
[71,] -10.33009961 -3.70609681
[72,] -3.73316196 -10.33009961
[73,] -4.60469214 -3.73316196
[74,] -8.76212593 -4.60469214
[75,] 8.58551949 -8.76212593
[76,] -9.45291923 8.58551949
[77,] 5.69547750 -9.45291923
[78,] 1.65700883 5.69547750
[79,] -3.47924173 1.65700883
[80,] -8.40347538 -3.47924173
[81,] -16.07090826 -8.40347538
[82,] 0.55415969 -16.07090826
[83,] 8.22119099 0.55415969
[84,] 0.68583998 8.22119099
[85,] 5.43912371 0.68583998
[86,] -12.69163528 5.43912371
[87,] 2.38726528 -12.69163528
[88,] 3.81795475 2.38726528
[89,] -2.46501377 3.81795475
[90,] -3.87907045 -2.46501377
[91,] 0.29507024 -3.87907045
[92,] 2.38149532 0.29507024
[93,] 4.95682983 2.38149532
[94,] -5.50835144 4.95682983
[95,] -4.36997226 -5.50835144
[96,] -6.64971952 -4.36997226
[97,] 3.47919571 -6.64971952
[98,] -7.85570882 3.47919571
[99,] -1.25674818 -7.85570882
[100,] -9.70535530 -1.25674818
[101,] 0.44708118 -9.70535530
[102,] -3.55562272 0.44708118
[103,] 1.19418613 -3.55562272
[104,] -8.70903999 1.19418613
[105,] 5.56591764 -8.70903999
[106,] -4.50925434 5.56591764
[107,] 6.53797811 -4.50925434
[108,] -7.60145461 6.53797811
[109,] 5.44912315 -7.60145461
[110,] 1.38165745 5.44912315
[111,] -11.37948049 1.38165745
[112,] -5.33901498 -11.37948049
[113,] -2.97507473 -5.33901498
[114,] 5.63493687 -2.97507473
[115,] 3.64578964 5.63493687
[116,] -8.26993507 3.64578964
[117,] 3.80505710 -8.26993507
[118,] -1.01229696 3.80505710
[119,] 6.50249853 -1.01229696
[120,] -2.23207017 6.50249853
[121,] 3.68587079 -2.23207017
[122,] -14.61375406 3.68587079
[123,] 3.15962712 -14.61375406
[124,] 11.46280511 3.15962712
[125,] 5.78862283 11.46280511
[126,] 2.57175307 5.78862283
[127,] 2.69058431 2.57175307
[128,] 6.61654480 2.69058431
[129,] 3.50385898 6.61654480
[130,] 4.98913334 3.50385898
[131,] 10.81355280 4.98913334
[132,] 9.05769997 10.81355280
[133,] 13.39621859 9.05769997
[134,] -5.28225368 13.39621859
[135,] -7.27173679 -5.28225368
[136,] 2.03135790 -7.27173679
[137,] 7.34153040 2.03135790
[138,] 5.90860595 7.34153040
[139,] -2.44798367 5.90860595
[140,] 11.66640411 -2.44798367
[141,] 11.29205816 11.66640411
[142,] 9.91295356 11.29205816
[143,] 7.78276590 9.91295356
[144,] 1.91852226 7.78276590
[145,] 19.93360701 1.91852226
[146,] 9.82297226 19.93360701
[147,] 6.51213116 9.82297226
[148,] 13.56913111 6.51213116
[149,] 23.94053334 13.56913111
[150,] 39.45574956 23.94053334
[151,] 24.73370836 39.45574956
[152,] -0.86117844 24.73370836
[153,] 2.96554772 -0.86117844
[154,] -6.62736368 2.96554772
[155,] -3.32480194 -6.62736368
[156,] -2.52043820 -3.32480194
[157,] -1.45440123 -2.52043820
[158,] -4.62762208 -1.45440123
[159,] -0.03428155 -4.62762208
[160,] -0.77867170 -0.03428155
[161,] 1.90548279 -0.77867170
[162,] -9.19852158 1.90548279
[163,] 0.44060387 -9.19852158
[164,] -3.06870341 0.44060387
[165,] 1.50898716 -3.06870341
[166,] 2.71459711 1.50898716
[167,] -6.59097299 2.71459711
[168,] -3.30287600 -6.59097299
[169,] -6.25410692 -3.30287600
[170,] 2.76995071 -6.25410692
[171,] -10.57957724 2.76995071
[172,] 3.44049897 -10.57957724
[173,] -0.70550563 3.44049897
[174,] 0.93735460 -0.70550563
[175,] -1.83379723 0.93735460
[176,] 0.24249621 -1.83379723
[177,] -0.29274132 0.24249621
[178,] -4.05171464 -0.29274132
[179,] 1.92762134 -4.05171464
[180,] 1.02760065 1.92762134
[181,] 9.13020788 1.02760065
[182,] 1.40175190 9.13020788
[183,] 7.80982388 1.40175190
[184,] 4.82755021 7.80982388
[185,] 1.96130551 4.82755021
[186,] 1.55375178 1.96130551
[187,] -4.56579694 1.55375178
[188,] -6.28822177 -4.56579694
[189,] -4.77196077 -6.28822177
[190,] -5.91263129 -4.77196077
[191,] -6.49126334 -5.91263129
[192,] 1.24558008 -6.49126334
[193,] -1.03335515 1.24558008
[194,] -1.65569161 -1.03335515
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.82015594 -1.65569161
2 -10.44504384 -2.82015594
3 -3.90900623 -10.44504384
4 -7.94577860 -3.90900623
5 -1.79294437 -7.94577860
6 1.68204801 -1.79294437
7 0.70164637 1.68204801
8 -4.07255225 0.70164637
9 -1.11890241 -4.07255225
10 -2.54530007 -1.11890241
11 -2.92023118 -2.54530007
12 -1.19674308 -2.92023118
13 -2.78417435 -1.19674308
14 -2.40809310 -2.78417435
15 -5.89488483 -2.40809310
16 -12.11363624 -5.89488483
17 1.51619344 -12.11363624
18 3.40098850 1.51619344
19 -5.15552152 3.40098850
20 -0.71235024 -5.15552152
21 -7.84913503 -0.71235024
22 0.19557508 -7.84913503
23 -4.13685790 0.19557508
24 -4.73651701 -4.13685790
25 4.60350514 -4.73651701
26 -2.95176581 4.60350514
27 -4.75290252 -2.95176581
28 -6.62444787 -4.75290252
29 -19.01445074 -6.62444787
30 -10.77185635 -19.01445074
31 -7.13482687 -10.77185635
32 -5.26241811 -7.13482687
33 -8.55303585 -5.26241811
34 -14.63857625 -8.55303585
35 -10.66265718 -14.63857625
36 -5.91373349 -10.66265718
37 -8.37965013 -5.91373349
38 -8.91186183 -8.37965013
39 -9.04793058 -8.91186183
40 23.22083619 -9.04793058
41 29.43895543 23.22083619
42 1.39256000 29.43895543
43 23.49557319 1.39256000
44 2.11483211 23.49557319
45 -8.23246318 2.11483211
46 1.66582678 -8.23246318
47 3.39396553 1.66582678
48 2.45890668 3.39396553
49 4.20537191 2.45890668
50 -3.49741982 4.20537191
51 5.90928007 -3.49741982
52 -1.74231652 5.90928007
53 -7.56387338 -1.74231652
54 -3.51509887 -7.56387338
55 -0.09025827 -3.51509887
56 10.27054268 -0.09025827
57 0.39450396 10.27054268
58 -0.53902564 0.39450396
59 2.91846672 -0.53902564
60 4.47484259 2.91846672
61 -10.87278251 4.47484259
62 -5.62916684 -10.87278251
63 -5.84199377 -5.62916684
64 5.64146390 -5.84199377
65 6.39069066 5.64146390
66 5.52371514 6.39069066
67 4.84539931 5.52371514
68 -4.32025448 4.84539931
69 6.08269001 -4.32025448
70 -3.70609681 6.08269001
71 -10.33009961 -3.70609681
72 -3.73316196 -10.33009961
73 -4.60469214 -3.73316196
74 -8.76212593 -4.60469214
75 8.58551949 -8.76212593
76 -9.45291923 8.58551949
77 5.69547750 -9.45291923
78 1.65700883 5.69547750
79 -3.47924173 1.65700883
80 -8.40347538 -3.47924173
81 -16.07090826 -8.40347538
82 0.55415969 -16.07090826
83 8.22119099 0.55415969
84 0.68583998 8.22119099
85 5.43912371 0.68583998
86 -12.69163528 5.43912371
87 2.38726528 -12.69163528
88 3.81795475 2.38726528
89 -2.46501377 3.81795475
90 -3.87907045 -2.46501377
91 0.29507024 -3.87907045
92 2.38149532 0.29507024
93 4.95682983 2.38149532
94 -5.50835144 4.95682983
95 -4.36997226 -5.50835144
96 -6.64971952 -4.36997226
97 3.47919571 -6.64971952
98 -7.85570882 3.47919571
99 -1.25674818 -7.85570882
100 -9.70535530 -1.25674818
101 0.44708118 -9.70535530
102 -3.55562272 0.44708118
103 1.19418613 -3.55562272
104 -8.70903999 1.19418613
105 5.56591764 -8.70903999
106 -4.50925434 5.56591764
107 6.53797811 -4.50925434
108 -7.60145461 6.53797811
109 5.44912315 -7.60145461
110 1.38165745 5.44912315
111 -11.37948049 1.38165745
112 -5.33901498 -11.37948049
113 -2.97507473 -5.33901498
114 5.63493687 -2.97507473
115 3.64578964 5.63493687
116 -8.26993507 3.64578964
117 3.80505710 -8.26993507
118 -1.01229696 3.80505710
119 6.50249853 -1.01229696
120 -2.23207017 6.50249853
121 3.68587079 -2.23207017
122 -14.61375406 3.68587079
123 3.15962712 -14.61375406
124 11.46280511 3.15962712
125 5.78862283 11.46280511
126 2.57175307 5.78862283
127 2.69058431 2.57175307
128 6.61654480 2.69058431
129 3.50385898 6.61654480
130 4.98913334 3.50385898
131 10.81355280 4.98913334
132 9.05769997 10.81355280
133 13.39621859 9.05769997
134 -5.28225368 13.39621859
135 -7.27173679 -5.28225368
136 2.03135790 -7.27173679
137 7.34153040 2.03135790
138 5.90860595 7.34153040
139 -2.44798367 5.90860595
140 11.66640411 -2.44798367
141 11.29205816 11.66640411
142 9.91295356 11.29205816
143 7.78276590 9.91295356
144 1.91852226 7.78276590
145 19.93360701 1.91852226
146 9.82297226 19.93360701
147 6.51213116 9.82297226
148 13.56913111 6.51213116
149 23.94053334 13.56913111
150 39.45574956 23.94053334
151 24.73370836 39.45574956
152 -0.86117844 24.73370836
153 2.96554772 -0.86117844
154 -6.62736368 2.96554772
155 -3.32480194 -6.62736368
156 -2.52043820 -3.32480194
157 -1.45440123 -2.52043820
158 -4.62762208 -1.45440123
159 -0.03428155 -4.62762208
160 -0.77867170 -0.03428155
161 1.90548279 -0.77867170
162 -9.19852158 1.90548279
163 0.44060387 -9.19852158
164 -3.06870341 0.44060387
165 1.50898716 -3.06870341
166 2.71459711 1.50898716
167 -6.59097299 2.71459711
168 -3.30287600 -6.59097299
169 -6.25410692 -3.30287600
170 2.76995071 -6.25410692
171 -10.57957724 2.76995071
172 3.44049897 -10.57957724
173 -0.70550563 3.44049897
174 0.93735460 -0.70550563
175 -1.83379723 0.93735460
176 0.24249621 -1.83379723
177 -0.29274132 0.24249621
178 -4.05171464 -0.29274132
179 1.92762134 -4.05171464
180 1.02760065 1.92762134
181 9.13020788 1.02760065
182 1.40175190 9.13020788
183 7.80982388 1.40175190
184 4.82755021 7.80982388
185 1.96130551 4.82755021
186 1.55375178 1.96130551
187 -4.56579694 1.55375178
188 -6.28822177 -4.56579694
189 -4.77196077 -6.28822177
190 -5.91263129 -4.77196077
191 -6.49126334 -5.91263129
192 1.24558008 -6.49126334
193 -1.03335515 1.24558008
194 -1.65569161 -1.03335515
> 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/rcomp/tmp/7yka61291198364.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8yka61291198364.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yka61291198364.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1014cm1291198365.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11m4ta1291198365.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/rcomp/tmp/12859g1291198365.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/rcomp/tmp/13mwpp1291198365.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/rcomp/tmp/14wooa1291198365.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/rcomp/tmp/150onf1291198365.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/rcomp/tmp/16eyk61291198365.tab")
+ }
>
> try(system("convert tmp/1kaux1291198364.ps tmp/1kaux1291198364.png",intern=TRUE))
character(0)
> try(system("convert tmp/2kaux1291198364.ps tmp/2kaux1291198364.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cku11291198364.ps tmp/3cku11291198364.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cku11291198364.ps tmp/4cku11291198364.png",intern=TRUE))
character(0)
> try(system("convert tmp/5cku11291198364.ps tmp/5cku11291198364.png",intern=TRUE))
character(0)
> try(system("convert tmp/65tb31291198364.ps tmp/65tb31291198364.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yka61291198364.ps tmp/7yka61291198364.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yka61291198364.ps tmp/8yka61291198364.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yka61291198364.ps tmp/9yka61291198364.png",intern=TRUE))
character(0)
> try(system("convert tmp/1014cm1291198365.ps tmp/1014cm1291198365.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.032 1.820 11.006