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(4
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+ ,3)
+ ,dim=c(8
+ ,195)
+ ,dimnames=list(c('Teamwork33'
+ ,'geslacht'
+ ,'leeftijd'
+ ,'opleiding'
+ ,'Neuroticisme'
+ ,'Extraversie'
+ ,'Openheid'
+ ,'beroep')
+ ,1:195))
> y <- array(NA,dim=c(8,195),dimnames=list(c('Teamwork33','geslacht','leeftijd','opleiding','Neuroticisme','Extraversie','Openheid','beroep'),1:195))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Teamwork33 geslacht leeftijd opleiding Neuroticisme Extraversie Openheid
1 4 1 27 5 26 49 35
2 4 1 36 4 25 45 34
3 5 1 25 4 17 54 13
4 2 1 27 3 37 36 35
5 3 2 25 3 35 36 28
6 5 2 44 3 15 53 32
7 4 1 50 4 27 46 35
8 4 1 41 4 36 42 36
9 4 1 48 5 25 41 27
10 4 2 43 4 30 45 29
11 5 2 47 2 27 47 27
12 4 2 41 3 33 42 28
13 3 1 44 2 29 45 29
14 4 2 47 5 30 40 28
15 3 2 40 3 25 45 30
16 3 2 46 3 23 40 25
17 4 1 28 3 26 42 15
18 3 1 56 3 24 45 33
19 4 2 49 4 35 47 31
20 2 2 25 4 39 31 37
21 4 2 41 4 23 46 37
22 3 2 26 3 32 34 34
23 4 1 50 5 29 43 32
24 4 1 47 4 26 45 21
25 3 1 52 2 21 42 25
26 3 2 37 5 35 51 32
27 2 2 41 3 23 44 28
28 4 1 45 4 21 47 22
29 5 2 26 4 28 47 25
30 4 1 3 30 41 26 1
31 1 52 4 21 44 34 4
32 1 46 2 29 51 34 1
33 1 58 3 28 46 36 2
34 1 54 5 19 47 36 2
35 1 29 3 26 46 26 2
36 2 50 3 33 38 26 1
37 1 43 2 34 50 34 4
38 2 30 3 33 48 33 1
39 2 47 2 40 36 31 2
40 1 45 3 24 51 33 2
41 48 1 35 35 22 1 4
42 48 3 35 49 29 2 4
43 26 4 32 38 24 2 4
44 46 5 20 47 37 2 2
45 3 35 36 32 2 4 2
46 3 35 47 23 2 3 1
47 4 21 46 29 2 4 1
48 2 33 43 35 2 1 2
49 2 40 53 20 4 2 1
50 3 22 55 28 1 2 2
51 2 35 39 26 1 4 2
52 4 20 55 36 2 3 2
53 5 28 41 26 2 4 2
54 3 46 33 33 1 3 1
55 4 18 52 25 2 3 2
56 5 22 42 29 2 5 1
57 5 20 56 32 2 3 2
58 3 25 46 35 2 2 2
59 4 31 33 24 2 1 2
60 3 21 51 31 1 2 2
61 3 23 46 29 4 5 1
62 2 26 46 27 1 4 2
63 3 34 50 29 2 4 2
64 4 31 46 29 3 3 1
65 4 23 51 27 2 4 2
66 4 31 48 34 2 4 2
67 4 26 44 32 3 2 2
68 3 36 38 31 2 3 2
69 3 28 42 31 2 4 1
70 3 34 39 31 2 3 1
71 2 25 45 16 1 2 1
72 3 33 31 25 1 4 1
73 3 46 29 27 4 4 1
74 3 24 48 32 4 3 2
75 3 32 38 28 1 5 2
76 5 33 55 25 4 1 1
77 3 42 32 25 2 3 2
78 5 17 51 36 5 3 2
79 4 36 53 36 1 5 2
80 4 40 47 36 2 2 1
81 4 30 45 27 4 3 1
82 5 19 33 29 6 3 2
83 4 33 49 32 1 4 2
84 5 35 46 29 2 2 2
85 3 23 42 31 1 4 2
86 3 15 56 34 3 3 2
87 2 38 35 27 1 3 1
88 3 37 40 28 3 3 1
89 4 23 44 32 4 2 2
90 5 41 46 33 3 3 1
91 5 34 46 29 5 2 1
92 3 38 39 32 1 4 2
93 2 45 35 35 1 4 2
94 3 27 48 33 2 2 2
95 4 46 42 27 3 1 1
96 1 26 39 16 1 5 2
97 4 44 39 32 2 4 1
98 3 36 41 26 2 4 1
99 3 20 52 32 2 4 2
100 4 44 45 38 2 3 1
101 3 27 42 24 1 3 1
102 4 27 44 26 2 1 1
103 2 41 33 19 3 5 2
104 3 30 42 37 2 3 1
105 3 33 46 25 1 3 1
106 3 37 45 24 1 2 1
107 2 30 40 23 4 4 1
108 5 20 48 28 4 4 2
109 5 44 32 38 2 3 1
110 4 20 53 28 2 4 2
111 2 33 39 28 4 4 1
112 3 31 45 26 1 2 2
113 3 23 36 21 2 3 2
114 3 33 38 35 2 3 2
115 4 33 49 31 2 3 1
116 5 32 46 34 4 4 2
117 4 25 43 30 2 5 1
118 22 37 30 1 3 2 46
119 16 48 24 3 3 2 49
120 36 45 27 2 2 2 51
121 35 32 26 2 3 1 38
122 25 46 30 1 1 1 41
123 27 20 15 1 4 2 47
124 32 42 28 1 4 2 44
125 36 45 34 2 4 2 47
126 51 29 29 2 3 2 46
127 30 51 26 2 5 1 44
128 20 55 31 2 2 2 28
129 29 50 28 2 2 2 47
130 26 44 33 4 3 2 28
131 20 41 32 3 3 1 41
132 40 40 33 2 2 2 45
133 29 47 31 3 1 2 46
134 32 42 37 2 3 1 46
135 33 40 27 4 5 2 22
136 32 51 19 2 4 2 33
137 34 43 27 2 4 1 41
138 24 45 31 3 4 2 47
139 25 41 38 2 3 1 25
140 41 41 22 1 5 2 42
141 39 37 35 2 3 2 47
142 21 46 35 6 3 2 50
143 38 38 30 6 3 1 55
144 28 39 41 6 3 1 21
145 37 45 25 1 4 1 3
146 46 28 1 2 1 52 3
147 39 45 5 2 2 49 4
148 21 21 3 4 2 46 4
149 31 33 2 3 1 4 31
150 25 2 3 2 45 3 31
151 29 1 2 2 52 3 27
152 31 2 3 1 3 21 45
153 3 3 2 40 4 26 46
154 2 4 2 49 4 37 45
155 2 1 1 38 5 28 34
156 5 1 1 32 5 29 41
157 4 5 2 46 4 33 43
158 2 4 2 32 3 41 45
159 2 3 2 41 3 19 48
160 2 3 2 43 3 37 43
161 1 4 1 44 4 36 45
162 2 3 1 47 5 27 45
163 2 2 2 28 3 33 34
164 2 1 1 52 1 29 40
165 1 1 1 27 2 42 40
166 2 5 2 45 5 27 55
167 2 4 1 27 4 47 44
168 3 3 1 25 4 17 44
169 2 4 1 28 4 34 48
170 2 5 1 25 3 32 51
171 1 4 1 52 4 25 49
172 2 4 1 44 3 27 33
173 2 2 2 43 3 37 43
174 2 3 2 47 4 34 44
175 2 4 2 52 4 27 44
176 2 3 2 40 2 37 41
177 1 4 1 42 3 32 45
178 2 3 1 45 5 26 44
179 2 4 1 45 2 29 44
180 4 1 1 50 5 28 40
181 5 2 1 49 3 19 48
182 4 3 1 52 2 46 49
183 1 3 2 48 3 31 46
184 2 5 2 51 3 42 49
185 2 4 2 49 4 33 55
186 2 3 2 31 4 39 51
187 3 3 2 43 3 27 46
188 2 3 2 31 3 35 37
189 2 3 2 28 4 23 43
190 2 4 2 43 4 32 41
191 3 3 2 31 3 22 45
192 2 2 2 51 3 17 39
193 3 4 2 58 4 35 38
194 2 2 2 25 5 34 41
195 3 4 1 27 5 26 49
beroep
1 5
2 4
3 2
4 3
5 1
6 3
7 2
8 2
9 2
10 2
11 4
12 1
13 6
14 2
15 2
16 2
17 2
18 1
19 2
20 4
21 4
22 2
23 2
24 5
25 1
26 2
27 1
28 2
29 2
30 2
31 5
32 4
33 3
34 4
35 2
36 3
37 3
38 3
39 5
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 26
146 30
147 25
148 38
149 35
150 49
151 40
152 29
153 31
154 31
155 25
156 27
157 26
158 26
159 23
160 27
161 24
162 35
163 24
164 32
165 24
166 24
167 38
168 36
169 24
170 18
171 34
172 23
173 35
174 22
175 34
176 28
177 34
178 32
179 24
180 34
181 33
182 33
183 29
184 38
185 24
186 25
187 37
188 33
189 30
190 22
191 28
192 24
193 33
194 37
195 35
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) geslacht leeftijd opleiding Neuroticisme
49.60859 -0.13937 -0.30885 -0.39780 -0.19780
Extraversie Openheid beroep
-0.49837 -0.06861 -0.32732
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.7064 -4.4001 -0.2794 2.8410 37.5366
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 49.60859 5.27232 9.409 < 2e-16 ***
geslacht -0.13937 0.05386 -2.588 0.01043 *
leeftijd -0.30885 0.04880 -6.328 1.79e-09 ***
opleiding -0.39780 0.05217 -7.625 1.19e-12 ***
Neuroticisme -0.19780 0.06838 -2.893 0.00427 **
Extraversie -0.49837 0.05429 -9.179 < 2e-16 ***
Openheid -0.06861 0.05616 -1.222 0.22337
beroep -0.32732 0.05581 -5.865 2.00e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.956 on 187 degrees of freedom
Multiple R-squared: 0.5953, Adjusted R-squared: 0.5801
F-statistic: 39.29 on 7 and 187 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,] 3.328752e-04 6.657504e-04 9.996671e-01
[2,] 1.684029e-05 3.368059e-05 9.999832e-01
[3,] 3.070375e-06 6.140751e-06 9.999969e-01
[4,] 1.759053e-07 3.518107e-07 9.999998e-01
[5,] 7.815301e-08 1.563060e-07 9.999999e-01
[6,] 6.200559e-09 1.240112e-08 1.000000e+00
[7,] 6.373185e-10 1.274637e-09 1.000000e+00
[8,] 8.307293e-11 1.661459e-10 1.000000e+00
[9,] 1.254242e-11 2.508485e-11 1.000000e+00
[10,] 9.653061e-13 1.930612e-12 1.000000e+00
[11,] 6.518343e-14 1.303669e-13 1.000000e+00
[12,] 8.813239e-15 1.762648e-14 1.000000e+00
[13,] 6.001992e-16 1.200398e-15 1.000000e+00
[14,] 3.835281e-17 7.670563e-17 1.000000e+00
[15,] 2.571069e-18 5.142139e-18 1.000000e+00
[16,] 9.596384e-18 1.919277e-17 1.000000e+00
[17,] 1.832852e-17 3.665703e-17 1.000000e+00
[18,] 1.800288e-18 3.600576e-18 1.000000e+00
[19,] 5.182102e-19 1.036420e-18 1.000000e+00
[20,] 9.489197e-20 1.897839e-19 1.000000e+00
[21,] 1.073454e-20 2.146908e-20 1.000000e+00
[22,] 1.403609e-21 2.807218e-21 1.000000e+00
[23,] 1.281165e-22 2.562330e-22 1.000000e+00
[24,] 1.283834e-23 2.567667e-23 1.000000e+00
[25,] 2.793898e-24 5.587795e-24 1.000000e+00
[26,] 8.239289e-25 1.647858e-24 1.000000e+00
[27,] 1.827666e-25 3.655332e-25 1.000000e+00
[28,] 3.185559e-26 6.371117e-26 1.000000e+00
[29,] 3.755873e-27 7.511746e-27 1.000000e+00
[30,] 7.358664e-27 1.471733e-26 1.000000e+00
[31,] 4.956164e-04 9.912328e-04 9.995044e-01
[32,] 3.179906e-03 6.359812e-03 9.968201e-01
[33,] 1.043203e-02 2.086407e-02 9.895680e-01
[34,] 1.080558e-01 2.161116e-01 8.919442e-01
[35,] 1.479950e-01 2.959900e-01 8.520050e-01
[36,] 2.303677e-01 4.607354e-01 7.696323e-01
[37,] 1.940962e-01 3.881925e-01 8.059038e-01
[38,] 1.702535e-01 3.405070e-01 8.297465e-01
[39,] 1.583480e-01 3.166960e-01 8.416520e-01
[40,] 1.376080e-01 2.752159e-01 8.623920e-01
[41,] 1.144609e-01 2.289218e-01 8.855391e-01
[42,] 1.178593e-01 2.357186e-01 8.821407e-01
[43,] 9.646970e-02 1.929394e-01 9.035303e-01
[44,] 9.565625e-02 1.913125e-01 9.043438e-01
[45,] 9.451457e-02 1.890291e-01 9.054854e-01
[46,] 7.968578e-02 1.593716e-01 9.203142e-01
[47,] 6.842522e-02 1.368504e-01 9.315748e-01
[48,] 6.822604e-02 1.364521e-01 9.317740e-01
[49,] 7.623578e-02 1.524716e-01 9.237642e-01
[50,] 6.568461e-02 1.313692e-01 9.343154e-01
[51,] 6.044966e-02 1.208993e-01 9.395503e-01
[52,] 8.803649e-02 1.760730e-01 9.119635e-01
[53,] 9.071567e-02 1.814313e-01 9.092843e-01
[54,] 8.305954e-02 1.661191e-01 9.169405e-01
[55,] 7.054551e-02 1.410910e-01 9.294545e-01
[56,] 5.822167e-02 1.164433e-01 9.417783e-01
[57,] 4.814454e-02 9.628908e-02 9.518555e-01
[58,] 4.551146e-02 9.102292e-02 9.544885e-01
[59,] 3.809036e-02 7.618071e-02 9.619096e-01
[60,] 3.627789e-02 7.255577e-02 9.637221e-01
[61,] 3.311729e-02 6.623458e-02 9.668827e-01
[62,] 2.693416e-02 5.386831e-02 9.730658e-01
[63,] 2.894060e-02 5.788120e-02 9.710594e-01
[64,] 3.260670e-02 6.521341e-02 9.673933e-01
[65,] 2.700211e-02 5.400422e-02 9.729979e-01
[66,] 2.312411e-02 4.624823e-02 9.768759e-01
[67,] 2.064824e-02 4.129649e-02 9.793518e-01
[68,] 2.109149e-02 4.218298e-02 9.789085e-01
[69,] 1.787931e-02 3.575862e-02 9.821207e-01
[70,] 1.647810e-02 3.295621e-02 9.835219e-01
[71,] 1.451071e-02 2.902141e-02 9.854893e-01
[72,] 1.700359e-02 3.400719e-02 9.829964e-01
[73,] 1.304905e-02 2.609809e-02 9.869510e-01
[74,] 1.188983e-02 2.377967e-02 9.881102e-01
[75,] 9.189117e-03 1.837823e-02 9.908109e-01
[76,] 1.016963e-02 2.033926e-02 9.898304e-01
[77,] 9.523632e-03 1.904726e-02 9.904764e-01
[78,] 8.085219e-03 1.617044e-02 9.919148e-01
[79,] 6.512406e-03 1.302481e-02 9.934876e-01
[80,] 4.835253e-03 9.670506e-03 9.951647e-01
[81,] 3.629606e-03 7.259211e-03 9.963704e-01
[82,] 2.697685e-03 5.395369e-03 9.973023e-01
[83,] 2.124435e-03 4.248870e-03 9.978756e-01
[84,] 1.660587e-03 3.321173e-03 9.983394e-01
[85,] 1.446519e-03 2.893037e-03 9.985535e-01
[86,] 1.415352e-03 2.830704e-03 9.985846e-01
[87,] 1.107408e-03 2.214815e-03 9.988926e-01
[88,] 1.002092e-03 2.004184e-03 9.989979e-01
[89,] 1.330191e-03 2.660381e-03 9.986698e-01
[90,] 9.566409e-04 1.913282e-03 9.990434e-01
[91,] 7.737511e-04 1.547502e-03 9.992262e-01
[92,] 5.340335e-04 1.068067e-03 9.994660e-01
[93,] 9.474798e-04 1.894960e-03 9.990525e-01
[94,] 7.073793e-04 1.414759e-03 9.992926e-01
[95,] 6.060264e-04 1.212053e-03 9.993940e-01
[96,] 5.574265e-04 1.114853e-03 9.994426e-01
[97,] 4.894292e-04 9.788584e-04 9.995106e-01
[98,] 3.637011e-04 7.274022e-04 9.996363e-01
[99,] 2.890127e-04 5.780253e-04 9.997110e-01
[100,] 2.189573e-04 4.379146e-04 9.997810e-01
[101,] 1.680134e-04 3.360267e-04 9.998320e-01
[102,] 1.809021e-04 3.618041e-04 9.998191e-01
[103,] 1.535784e-04 3.071567e-04 9.998464e-01
[104,] 1.235495e-04 2.470991e-04 9.998765e-01
[105,] 9.015970e-05 1.803194e-04 9.999098e-01
[106,] 6.660647e-05 1.332129e-04 9.999334e-01
[107,] 1.039870e-04 2.079741e-04 9.998960e-01
[108,] 3.081625e-04 6.163249e-04 9.996918e-01
[109,] 7.677491e-04 1.535498e-03 9.992323e-01
[110,] 4.066058e-03 8.132116e-03 9.959339e-01
[111,] 8.390559e-03 1.678112e-02 9.916094e-01
[112,] 8.660627e-03 1.732125e-02 9.913394e-01
[113,] 6.731476e-03 1.346295e-02 9.932685e-01
[114,] 7.307205e-03 1.461441e-02 9.926928e-01
[115,] 1.042897e-02 2.085793e-02 9.895710e-01
[116,] 2.878605e-01 5.757211e-01 7.121395e-01
[117,] 2.677380e-01 5.354760e-01 7.322620e-01
[118,] 4.700930e-01 9.401860e-01 5.299070e-01
[119,] 4.546902e-01 9.093805e-01 5.453098e-01
[120,] 4.637055e-01 9.274110e-01 5.362945e-01
[121,] 5.497057e-01 9.005886e-01 4.502943e-01
[122,] 6.781616e-01 6.436768e-01 3.218384e-01
[123,] 6.492758e-01 7.014484e-01 3.507242e-01
[124,] 6.140594e-01 7.718812e-01 3.859406e-01
[125,] 6.338668e-01 7.322664e-01 3.661332e-01
[126,] 6.858591e-01 6.282817e-01 3.141409e-01
[127,] 6.529903e-01 6.940195e-01 3.470097e-01
[128,] 7.212654e-01 5.574692e-01 2.787346e-01
[129,] 7.393643e-01 5.212715e-01 2.606357e-01
[130,] 7.644375e-01 4.711250e-01 2.355625e-01
[131,] 8.556285e-01 2.887429e-01 1.443715e-01
[132,] 9.327070e-01 1.345860e-01 6.729298e-02
[133,] 9.613447e-01 7.731068e-02 3.865534e-02
[134,] 9.557143e-01 8.857149e-02 4.428575e-02
[135,] 9.830259e-01 3.394828e-02 1.697414e-02
[136,] 9.999997e-01 5.987927e-07 2.993964e-07
[137,] 9.999999e-01 1.899915e-07 9.499577e-08
[138,] 9.999998e-01 4.026855e-07 2.013427e-07
[139,] 9.999999e-01 2.140843e-07 1.070421e-07
[140,] 9.999998e-01 3.160574e-07 1.580287e-07
[141,] 9.999998e-01 3.359768e-07 1.679884e-07
[142,] 1.000000e+00 1.064444e-25 5.322218e-26
[143,] 1.000000e+00 3.850329e-25 1.925164e-25
[144,] 1.000000e+00 3.065130e-24 1.532565e-24
[145,] 1.000000e+00 1.277140e-23 6.385700e-24
[146,] 1.000000e+00 5.159103e-24 2.579552e-24
[147,] 1.000000e+00 1.993738e-24 9.968691e-25
[148,] 1.000000e+00 1.718687e-23 8.593436e-24
[149,] 1.000000e+00 1.261252e-22 6.306259e-23
[150,] 1.000000e+00 1.240530e-21 6.202650e-22
[151,] 1.000000e+00 7.360999e-21 3.680499e-21
[152,] 1.000000e+00 4.716927e-20 2.358463e-20
[153,] 1.000000e+00 3.646059e-19 1.823030e-19
[154,] 1.000000e+00 1.927025e-18 9.635127e-19
[155,] 1.000000e+00 3.202422e-18 1.601211e-18
[156,] 1.000000e+00 2.730934e-17 1.365467e-17
[157,] 1.000000e+00 2.604982e-16 1.302491e-16
[158,] 1.000000e+00 2.027429e-15 1.013714e-15
[159,] 1.000000e+00 1.824709e-14 9.123546e-15
[160,] 1.000000e+00 1.368854e-13 6.844270e-14
[161,] 1.000000e+00 1.807478e-13 9.037391e-14
[162,] 1.000000e+00 1.693224e-12 8.466119e-13
[163,] 1.000000e+00 1.007974e-11 5.039871e-12
[164,] 1.000000e+00 9.571948e-11 4.785974e-11
[165,] 1.000000e+00 8.413592e-10 4.206796e-10
[166,] 1.000000e+00 7.425467e-09 3.712734e-09
[167,] 1.000000e+00 1.050212e-08 5.251060e-09
[168,] 1.000000e+00 3.099176e-08 1.549588e-08
[169,] 9.999999e-01 1.057890e-07 5.289452e-08
[170,] 9.999994e-01 1.185778e-06 5.928891e-07
[171,] 9.999963e-01 7.356448e-06 3.678224e-06
[172,] 9.999818e-01 3.636114e-05 1.818057e-05
[173,] 9.999300e-01 1.399662e-04 6.998309e-05
[174,] 9.996363e-01 7.274164e-04 3.637082e-04
> postscript(file="/var/www/html/rcomp/tmp/1uah51291207453.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2uah51291207453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35khp1291207453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45khp1291207453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55khp1291207453.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
-1.54053272 -1.74589719 -3.33570182 -9.29378807 -10.30260121 3.01090427
7 8 9 10 11 12
2.88592849 -0.03839278 -0.77028288 0.54673597 2.90728967 -1.76639963
13 14 15 16 17 18
0.03207748 -0.38052696 -2.69799555 -4.07539213 -7.86998948 1.78492177
19 20 21 22 23 24
4.52277850 -11.00603210 0.24631659 -10.84492212 1.97839118 1.28464462
25 26 27 28 29 30
-2.48564619 2.27648200 -4.74764702 -0.23864647 -3.37698343 -10.81805920
31 32 33 34 35 36
-4.21328217 -1.63336413 -0.30080629 -3.29568543 -10.44922602 -5.06148343
37 38 39 40 41 42
-0.38177084 -2.38235803 -0.18435628 -4.53728230 29.04250211 36.77340998
43 44 45 46 47 48
8.29412529 31.06899704 1.00970794 -7.92303758 0.90302230 0.60924626
49 50 51 52 53 54
-2.43219801 0.64367090 -4.59419011 5.24349543 -2.08161795 -5.62435465
55 56 57 58 59 60
-3.93807414 0.65073773 7.25235621 -0.04471463 -4.80647139 0.46230383
61 62 63 64 65 66
3.36694735 -8.52585927 -3.85476884 -4.22282882 2.98096740 5.29937045
67 68 69 70 71 72
2.75451964 1.85255614 -2.83432522 2.79599961 -8.52354070 -9.10134608
73 74 75 76 77 78
-3.89967517 -2.81166750 -6.30034716 3.33710264 -9.07937145 5.83801119
79 80 81 82 83 84
3.72690709 -1.55314305 -6.25079988 -10.53954095 0.96577234 3.90805494
85 86 87 88 89 90
-0.05990147 4.23962806 -10.81770278 -0.09116430 1.87956852 -0.56521879
91 92 93 94 95 96
-2.90748361 -0.13463951 1.43555432 2.34734025 -6.14192362 -7.70964586
97 98 99 100 101 102
-3.73358718 0.56523104 -4.32220295 0.98988425 -9.07299595 -2.87621422
103 104 105 106 107 108
-5.86510020 1.62406332 -7.91282990 -0.05014031 -5.32350071 4.41164937
109 110 111 112 113 114
-3.33441346 3.25103121 -0.27938638 -10.16896555 -7.50083059 -1.88410224
115 116 117 118 119 120
3.47195345 4.25274652 -4.46155304 -6.73315532 -12.70635625 7.67100622
121 122 123 124 125 126
3.35783408 -4.69775978 -8.79612865 3.07927592 9.95411120 20.91349952
127 128 129 130 131 132
2.14047654 -7.27786602 1.72960031 -0.87255169 -7.27646311 12.74290155
133 134 135 136 137 138
2.36942182 6.02507875 3.37349682 1.19709842 4.93072091 -1.92000268
139 140 141 142 143 144
-2.57353715 10.14739299 11.95018071 -2.99844060 11.84163946 2.39103088
145 146 147 148 149 150
11.78780381 37.53663484 31.27608021 12.86904088 3.57600568 5.95370389
151 152 153 154 155 156
7.66979541 6.63321158 -2.60917080 5.52386357 -6.58502339 -4.33856342
157 158 159 160 161 162
2.70255893 -1.07963806 -9.37907068 1.35341044 -0.56356827 0.80340594
163 164 165 166 167 168
-8.34586734 1.39421625 -5.49270797 -2.31899356 3.66971537 -11.87098701
169 170 171 172 173 174
-6.71928499 -10.72592270 0.68435803 -5.39731285 3.83256653 0.07932791
175 176 177 178 179 180
2.64690468 0.15231066 -0.27728609 -1.54112085 -3.11856336 3.54607314
181 182 183 184 185 186
-0.37173014 13.28784013 0.21264890 11.31853331 1.92525515 -2.33141766
187 188 189 190 191 192
0.84869725 -2.86468470 -10.41102329 -2.57506501 -9.43120694 -6.82734114
193 194 195
9.28169839 -3.90993265 -6.23716043
> postscript(file="/var/www/html/rcomp/tmp/6ytgs1291207453.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 -1.54053272 NA
1 -1.74589719 -1.54053272
2 -3.33570182 -1.74589719
3 -9.29378807 -3.33570182
4 -10.30260121 -9.29378807
5 3.01090427 -10.30260121
6 2.88592849 3.01090427
7 -0.03839278 2.88592849
8 -0.77028288 -0.03839278
9 0.54673597 -0.77028288
10 2.90728967 0.54673597
11 -1.76639963 2.90728967
12 0.03207748 -1.76639963
13 -0.38052696 0.03207748
14 -2.69799555 -0.38052696
15 -4.07539213 -2.69799555
16 -7.86998948 -4.07539213
17 1.78492177 -7.86998948
18 4.52277850 1.78492177
19 -11.00603210 4.52277850
20 0.24631659 -11.00603210
21 -10.84492212 0.24631659
22 1.97839118 -10.84492212
23 1.28464462 1.97839118
24 -2.48564619 1.28464462
25 2.27648200 -2.48564619
26 -4.74764702 2.27648200
27 -0.23864647 -4.74764702
28 -3.37698343 -0.23864647
29 -10.81805920 -3.37698343
30 -4.21328217 -10.81805920
31 -1.63336413 -4.21328217
32 -0.30080629 -1.63336413
33 -3.29568543 -0.30080629
34 -10.44922602 -3.29568543
35 -5.06148343 -10.44922602
36 -0.38177084 -5.06148343
37 -2.38235803 -0.38177084
38 -0.18435628 -2.38235803
39 -4.53728230 -0.18435628
40 29.04250211 -4.53728230
41 36.77340998 29.04250211
42 8.29412529 36.77340998
43 31.06899704 8.29412529
44 1.00970794 31.06899704
45 -7.92303758 1.00970794
46 0.90302230 -7.92303758
47 0.60924626 0.90302230
48 -2.43219801 0.60924626
49 0.64367090 -2.43219801
50 -4.59419011 0.64367090
51 5.24349543 -4.59419011
52 -2.08161795 5.24349543
53 -5.62435465 -2.08161795
54 -3.93807414 -5.62435465
55 0.65073773 -3.93807414
56 7.25235621 0.65073773
57 -0.04471463 7.25235621
58 -4.80647139 -0.04471463
59 0.46230383 -4.80647139
60 3.36694735 0.46230383
61 -8.52585927 3.36694735
62 -3.85476884 -8.52585927
63 -4.22282882 -3.85476884
64 2.98096740 -4.22282882
65 5.29937045 2.98096740
66 2.75451964 5.29937045
67 1.85255614 2.75451964
68 -2.83432522 1.85255614
69 2.79599961 -2.83432522
70 -8.52354070 2.79599961
71 -9.10134608 -8.52354070
72 -3.89967517 -9.10134608
73 -2.81166750 -3.89967517
74 -6.30034716 -2.81166750
75 3.33710264 -6.30034716
76 -9.07937145 3.33710264
77 5.83801119 -9.07937145
78 3.72690709 5.83801119
79 -1.55314305 3.72690709
80 -6.25079988 -1.55314305
81 -10.53954095 -6.25079988
82 0.96577234 -10.53954095
83 3.90805494 0.96577234
84 -0.05990147 3.90805494
85 4.23962806 -0.05990147
86 -10.81770278 4.23962806
87 -0.09116430 -10.81770278
88 1.87956852 -0.09116430
89 -0.56521879 1.87956852
90 -2.90748361 -0.56521879
91 -0.13463951 -2.90748361
92 1.43555432 -0.13463951
93 2.34734025 1.43555432
94 -6.14192362 2.34734025
95 -7.70964586 -6.14192362
96 -3.73358718 -7.70964586
97 0.56523104 -3.73358718
98 -4.32220295 0.56523104
99 0.98988425 -4.32220295
100 -9.07299595 0.98988425
101 -2.87621422 -9.07299595
102 -5.86510020 -2.87621422
103 1.62406332 -5.86510020
104 -7.91282990 1.62406332
105 -0.05014031 -7.91282990
106 -5.32350071 -0.05014031
107 4.41164937 -5.32350071
108 -3.33441346 4.41164937
109 3.25103121 -3.33441346
110 -0.27938638 3.25103121
111 -10.16896555 -0.27938638
112 -7.50083059 -10.16896555
113 -1.88410224 -7.50083059
114 3.47195345 -1.88410224
115 4.25274652 3.47195345
116 -4.46155304 4.25274652
117 -6.73315532 -4.46155304
118 -12.70635625 -6.73315532
119 7.67100622 -12.70635625
120 3.35783408 7.67100622
121 -4.69775978 3.35783408
122 -8.79612865 -4.69775978
123 3.07927592 -8.79612865
124 9.95411120 3.07927592
125 20.91349952 9.95411120
126 2.14047654 20.91349952
127 -7.27786602 2.14047654
128 1.72960031 -7.27786602
129 -0.87255169 1.72960031
130 -7.27646311 -0.87255169
131 12.74290155 -7.27646311
132 2.36942182 12.74290155
133 6.02507875 2.36942182
134 3.37349682 6.02507875
135 1.19709842 3.37349682
136 4.93072091 1.19709842
137 -1.92000268 4.93072091
138 -2.57353715 -1.92000268
139 10.14739299 -2.57353715
140 11.95018071 10.14739299
141 -2.99844060 11.95018071
142 11.84163946 -2.99844060
143 2.39103088 11.84163946
144 11.78780381 2.39103088
145 37.53663484 11.78780381
146 31.27608021 37.53663484
147 12.86904088 31.27608021
148 3.57600568 12.86904088
149 5.95370389 3.57600568
150 7.66979541 5.95370389
151 6.63321158 7.66979541
152 -2.60917080 6.63321158
153 5.52386357 -2.60917080
154 -6.58502339 5.52386357
155 -4.33856342 -6.58502339
156 2.70255893 -4.33856342
157 -1.07963806 2.70255893
158 -9.37907068 -1.07963806
159 1.35341044 -9.37907068
160 -0.56356827 1.35341044
161 0.80340594 -0.56356827
162 -8.34586734 0.80340594
163 1.39421625 -8.34586734
164 -5.49270797 1.39421625
165 -2.31899356 -5.49270797
166 3.66971537 -2.31899356
167 -11.87098701 3.66971537
168 -6.71928499 -11.87098701
169 -10.72592270 -6.71928499
170 0.68435803 -10.72592270
171 -5.39731285 0.68435803
172 3.83256653 -5.39731285
173 0.07932791 3.83256653
174 2.64690468 0.07932791
175 0.15231066 2.64690468
176 -0.27728609 0.15231066
177 -1.54112085 -0.27728609
178 -3.11856336 -1.54112085
179 3.54607314 -3.11856336
180 -0.37173014 3.54607314
181 13.28784013 -0.37173014
182 0.21264890 13.28784013
183 11.31853331 0.21264890
184 1.92525515 11.31853331
185 -2.33141766 1.92525515
186 0.84869725 -2.33141766
187 -2.86468470 0.84869725
188 -10.41102329 -2.86468470
189 -2.57506501 -10.41102329
190 -9.43120694 -2.57506501
191 -6.82734114 -9.43120694
192 9.28169839 -6.82734114
193 -3.90993265 9.28169839
194 -6.23716043 -3.90993265
195 NA -6.23716043
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.74589719 -1.54053272
[2,] -3.33570182 -1.74589719
[3,] -9.29378807 -3.33570182
[4,] -10.30260121 -9.29378807
[5,] 3.01090427 -10.30260121
[6,] 2.88592849 3.01090427
[7,] -0.03839278 2.88592849
[8,] -0.77028288 -0.03839278
[9,] 0.54673597 -0.77028288
[10,] 2.90728967 0.54673597
[11,] -1.76639963 2.90728967
[12,] 0.03207748 -1.76639963
[13,] -0.38052696 0.03207748
[14,] -2.69799555 -0.38052696
[15,] -4.07539213 -2.69799555
[16,] -7.86998948 -4.07539213
[17,] 1.78492177 -7.86998948
[18,] 4.52277850 1.78492177
[19,] -11.00603210 4.52277850
[20,] 0.24631659 -11.00603210
[21,] -10.84492212 0.24631659
[22,] 1.97839118 -10.84492212
[23,] 1.28464462 1.97839118
[24,] -2.48564619 1.28464462
[25,] 2.27648200 -2.48564619
[26,] -4.74764702 2.27648200
[27,] -0.23864647 -4.74764702
[28,] -3.37698343 -0.23864647
[29,] -10.81805920 -3.37698343
[30,] -4.21328217 -10.81805920
[31,] -1.63336413 -4.21328217
[32,] -0.30080629 -1.63336413
[33,] -3.29568543 -0.30080629
[34,] -10.44922602 -3.29568543
[35,] -5.06148343 -10.44922602
[36,] -0.38177084 -5.06148343
[37,] -2.38235803 -0.38177084
[38,] -0.18435628 -2.38235803
[39,] -4.53728230 -0.18435628
[40,] 29.04250211 -4.53728230
[41,] 36.77340998 29.04250211
[42,] 8.29412529 36.77340998
[43,] 31.06899704 8.29412529
[44,] 1.00970794 31.06899704
[45,] -7.92303758 1.00970794
[46,] 0.90302230 -7.92303758
[47,] 0.60924626 0.90302230
[48,] -2.43219801 0.60924626
[49,] 0.64367090 -2.43219801
[50,] -4.59419011 0.64367090
[51,] 5.24349543 -4.59419011
[52,] -2.08161795 5.24349543
[53,] -5.62435465 -2.08161795
[54,] -3.93807414 -5.62435465
[55,] 0.65073773 -3.93807414
[56,] 7.25235621 0.65073773
[57,] -0.04471463 7.25235621
[58,] -4.80647139 -0.04471463
[59,] 0.46230383 -4.80647139
[60,] 3.36694735 0.46230383
[61,] -8.52585927 3.36694735
[62,] -3.85476884 -8.52585927
[63,] -4.22282882 -3.85476884
[64,] 2.98096740 -4.22282882
[65,] 5.29937045 2.98096740
[66,] 2.75451964 5.29937045
[67,] 1.85255614 2.75451964
[68,] -2.83432522 1.85255614
[69,] 2.79599961 -2.83432522
[70,] -8.52354070 2.79599961
[71,] -9.10134608 -8.52354070
[72,] -3.89967517 -9.10134608
[73,] -2.81166750 -3.89967517
[74,] -6.30034716 -2.81166750
[75,] 3.33710264 -6.30034716
[76,] -9.07937145 3.33710264
[77,] 5.83801119 -9.07937145
[78,] 3.72690709 5.83801119
[79,] -1.55314305 3.72690709
[80,] -6.25079988 -1.55314305
[81,] -10.53954095 -6.25079988
[82,] 0.96577234 -10.53954095
[83,] 3.90805494 0.96577234
[84,] -0.05990147 3.90805494
[85,] 4.23962806 -0.05990147
[86,] -10.81770278 4.23962806
[87,] -0.09116430 -10.81770278
[88,] 1.87956852 -0.09116430
[89,] -0.56521879 1.87956852
[90,] -2.90748361 -0.56521879
[91,] -0.13463951 -2.90748361
[92,] 1.43555432 -0.13463951
[93,] 2.34734025 1.43555432
[94,] -6.14192362 2.34734025
[95,] -7.70964586 -6.14192362
[96,] -3.73358718 -7.70964586
[97,] 0.56523104 -3.73358718
[98,] -4.32220295 0.56523104
[99,] 0.98988425 -4.32220295
[100,] -9.07299595 0.98988425
[101,] -2.87621422 -9.07299595
[102,] -5.86510020 -2.87621422
[103,] 1.62406332 -5.86510020
[104,] -7.91282990 1.62406332
[105,] -0.05014031 -7.91282990
[106,] -5.32350071 -0.05014031
[107,] 4.41164937 -5.32350071
[108,] -3.33441346 4.41164937
[109,] 3.25103121 -3.33441346
[110,] -0.27938638 3.25103121
[111,] -10.16896555 -0.27938638
[112,] -7.50083059 -10.16896555
[113,] -1.88410224 -7.50083059
[114,] 3.47195345 -1.88410224
[115,] 4.25274652 3.47195345
[116,] -4.46155304 4.25274652
[117,] -6.73315532 -4.46155304
[118,] -12.70635625 -6.73315532
[119,] 7.67100622 -12.70635625
[120,] 3.35783408 7.67100622
[121,] -4.69775978 3.35783408
[122,] -8.79612865 -4.69775978
[123,] 3.07927592 -8.79612865
[124,] 9.95411120 3.07927592
[125,] 20.91349952 9.95411120
[126,] 2.14047654 20.91349952
[127,] -7.27786602 2.14047654
[128,] 1.72960031 -7.27786602
[129,] -0.87255169 1.72960031
[130,] -7.27646311 -0.87255169
[131,] 12.74290155 -7.27646311
[132,] 2.36942182 12.74290155
[133,] 6.02507875 2.36942182
[134,] 3.37349682 6.02507875
[135,] 1.19709842 3.37349682
[136,] 4.93072091 1.19709842
[137,] -1.92000268 4.93072091
[138,] -2.57353715 -1.92000268
[139,] 10.14739299 -2.57353715
[140,] 11.95018071 10.14739299
[141,] -2.99844060 11.95018071
[142,] 11.84163946 -2.99844060
[143,] 2.39103088 11.84163946
[144,] 11.78780381 2.39103088
[145,] 37.53663484 11.78780381
[146,] 31.27608021 37.53663484
[147,] 12.86904088 31.27608021
[148,] 3.57600568 12.86904088
[149,] 5.95370389 3.57600568
[150,] 7.66979541 5.95370389
[151,] 6.63321158 7.66979541
[152,] -2.60917080 6.63321158
[153,] 5.52386357 -2.60917080
[154,] -6.58502339 5.52386357
[155,] -4.33856342 -6.58502339
[156,] 2.70255893 -4.33856342
[157,] -1.07963806 2.70255893
[158,] -9.37907068 -1.07963806
[159,] 1.35341044 -9.37907068
[160,] -0.56356827 1.35341044
[161,] 0.80340594 -0.56356827
[162,] -8.34586734 0.80340594
[163,] 1.39421625 -8.34586734
[164,] -5.49270797 1.39421625
[165,] -2.31899356 -5.49270797
[166,] 3.66971537 -2.31899356
[167,] -11.87098701 3.66971537
[168,] -6.71928499 -11.87098701
[169,] -10.72592270 -6.71928499
[170,] 0.68435803 -10.72592270
[171,] -5.39731285 0.68435803
[172,] 3.83256653 -5.39731285
[173,] 0.07932791 3.83256653
[174,] 2.64690468 0.07932791
[175,] 0.15231066 2.64690468
[176,] -0.27728609 0.15231066
[177,] -1.54112085 -0.27728609
[178,] -3.11856336 -1.54112085
[179,] 3.54607314 -3.11856336
[180,] -0.37173014 3.54607314
[181,] 13.28784013 -0.37173014
[182,] 0.21264890 13.28784013
[183,] 11.31853331 0.21264890
[184,] 1.92525515 11.31853331
[185,] -2.33141766 1.92525515
[186,] 0.84869725 -2.33141766
[187,] -2.86468470 0.84869725
[188,] -10.41102329 -2.86468470
[189,] -2.57506501 -10.41102329
[190,] -9.43120694 -2.57506501
[191,] -6.82734114 -9.43120694
[192,] 9.28169839 -6.82734114
[193,] -3.90993265 9.28169839
[194,] -6.23716043 -3.90993265
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.74589719 -1.54053272
2 -3.33570182 -1.74589719
3 -9.29378807 -3.33570182
4 -10.30260121 -9.29378807
5 3.01090427 -10.30260121
6 2.88592849 3.01090427
7 -0.03839278 2.88592849
8 -0.77028288 -0.03839278
9 0.54673597 -0.77028288
10 2.90728967 0.54673597
11 -1.76639963 2.90728967
12 0.03207748 -1.76639963
13 -0.38052696 0.03207748
14 -2.69799555 -0.38052696
15 -4.07539213 -2.69799555
16 -7.86998948 -4.07539213
17 1.78492177 -7.86998948
18 4.52277850 1.78492177
19 -11.00603210 4.52277850
20 0.24631659 -11.00603210
21 -10.84492212 0.24631659
22 1.97839118 -10.84492212
23 1.28464462 1.97839118
24 -2.48564619 1.28464462
25 2.27648200 -2.48564619
26 -4.74764702 2.27648200
27 -0.23864647 -4.74764702
28 -3.37698343 -0.23864647
29 -10.81805920 -3.37698343
30 -4.21328217 -10.81805920
31 -1.63336413 -4.21328217
32 -0.30080629 -1.63336413
33 -3.29568543 -0.30080629
34 -10.44922602 -3.29568543
35 -5.06148343 -10.44922602
36 -0.38177084 -5.06148343
37 -2.38235803 -0.38177084
38 -0.18435628 -2.38235803
39 -4.53728230 -0.18435628
40 29.04250211 -4.53728230
41 36.77340998 29.04250211
42 8.29412529 36.77340998
43 31.06899704 8.29412529
44 1.00970794 31.06899704
45 -7.92303758 1.00970794
46 0.90302230 -7.92303758
47 0.60924626 0.90302230
48 -2.43219801 0.60924626
49 0.64367090 -2.43219801
50 -4.59419011 0.64367090
51 5.24349543 -4.59419011
52 -2.08161795 5.24349543
53 -5.62435465 -2.08161795
54 -3.93807414 -5.62435465
55 0.65073773 -3.93807414
56 7.25235621 0.65073773
57 -0.04471463 7.25235621
58 -4.80647139 -0.04471463
59 0.46230383 -4.80647139
60 3.36694735 0.46230383
61 -8.52585927 3.36694735
62 -3.85476884 -8.52585927
63 -4.22282882 -3.85476884
64 2.98096740 -4.22282882
65 5.29937045 2.98096740
66 2.75451964 5.29937045
67 1.85255614 2.75451964
68 -2.83432522 1.85255614
69 2.79599961 -2.83432522
70 -8.52354070 2.79599961
71 -9.10134608 -8.52354070
72 -3.89967517 -9.10134608
73 -2.81166750 -3.89967517
74 -6.30034716 -2.81166750
75 3.33710264 -6.30034716
76 -9.07937145 3.33710264
77 5.83801119 -9.07937145
78 3.72690709 5.83801119
79 -1.55314305 3.72690709
80 -6.25079988 -1.55314305
81 -10.53954095 -6.25079988
82 0.96577234 -10.53954095
83 3.90805494 0.96577234
84 -0.05990147 3.90805494
85 4.23962806 -0.05990147
86 -10.81770278 4.23962806
87 -0.09116430 -10.81770278
88 1.87956852 -0.09116430
89 -0.56521879 1.87956852
90 -2.90748361 -0.56521879
91 -0.13463951 -2.90748361
92 1.43555432 -0.13463951
93 2.34734025 1.43555432
94 -6.14192362 2.34734025
95 -7.70964586 -6.14192362
96 -3.73358718 -7.70964586
97 0.56523104 -3.73358718
98 -4.32220295 0.56523104
99 0.98988425 -4.32220295
100 -9.07299595 0.98988425
101 -2.87621422 -9.07299595
102 -5.86510020 -2.87621422
103 1.62406332 -5.86510020
104 -7.91282990 1.62406332
105 -0.05014031 -7.91282990
106 -5.32350071 -0.05014031
107 4.41164937 -5.32350071
108 -3.33441346 4.41164937
109 3.25103121 -3.33441346
110 -0.27938638 3.25103121
111 -10.16896555 -0.27938638
112 -7.50083059 -10.16896555
113 -1.88410224 -7.50083059
114 3.47195345 -1.88410224
115 4.25274652 3.47195345
116 -4.46155304 4.25274652
117 -6.73315532 -4.46155304
118 -12.70635625 -6.73315532
119 7.67100622 -12.70635625
120 3.35783408 7.67100622
121 -4.69775978 3.35783408
122 -8.79612865 -4.69775978
123 3.07927592 -8.79612865
124 9.95411120 3.07927592
125 20.91349952 9.95411120
126 2.14047654 20.91349952
127 -7.27786602 2.14047654
128 1.72960031 -7.27786602
129 -0.87255169 1.72960031
130 -7.27646311 -0.87255169
131 12.74290155 -7.27646311
132 2.36942182 12.74290155
133 6.02507875 2.36942182
134 3.37349682 6.02507875
135 1.19709842 3.37349682
136 4.93072091 1.19709842
137 -1.92000268 4.93072091
138 -2.57353715 -1.92000268
139 10.14739299 -2.57353715
140 11.95018071 10.14739299
141 -2.99844060 11.95018071
142 11.84163946 -2.99844060
143 2.39103088 11.84163946
144 11.78780381 2.39103088
145 37.53663484 11.78780381
146 31.27608021 37.53663484
147 12.86904088 31.27608021
148 3.57600568 12.86904088
149 5.95370389 3.57600568
150 7.66979541 5.95370389
151 6.63321158 7.66979541
152 -2.60917080 6.63321158
153 5.52386357 -2.60917080
154 -6.58502339 5.52386357
155 -4.33856342 -6.58502339
156 2.70255893 -4.33856342
157 -1.07963806 2.70255893
158 -9.37907068 -1.07963806
159 1.35341044 -9.37907068
160 -0.56356827 1.35341044
161 0.80340594 -0.56356827
162 -8.34586734 0.80340594
163 1.39421625 -8.34586734
164 -5.49270797 1.39421625
165 -2.31899356 -5.49270797
166 3.66971537 -2.31899356
167 -11.87098701 3.66971537
168 -6.71928499 -11.87098701
169 -10.72592270 -6.71928499
170 0.68435803 -10.72592270
171 -5.39731285 0.68435803
172 3.83256653 -5.39731285
173 0.07932791 3.83256653
174 2.64690468 0.07932791
175 0.15231066 2.64690468
176 -0.27728609 0.15231066
177 -1.54112085 -0.27728609
178 -3.11856336 -1.54112085
179 3.54607314 -3.11856336
180 -0.37173014 3.54607314
181 13.28784013 -0.37173014
182 0.21264890 13.28784013
183 11.31853331 0.21264890
184 1.92525515 11.31853331
185 -2.33141766 1.92525515
186 0.84869725 -2.33141766
187 -2.86468470 0.84869725
188 -10.41102329 -2.86468470
189 -2.57506501 -10.41102329
190 -9.43120694 -2.57506501
191 -6.82734114 -9.43120694
192 9.28169839 -6.82734114
193 -3.90993265 9.28169839
194 -6.23716043 -3.90993265
> 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/782fv1291207453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/882fv1291207453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/982fv1291207453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jtey1291207453.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mcvm1291207453.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/128uba1291207453.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/13m4r11291207453.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/1475q71291207453.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/15b56d1291207453.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/16eon01291207453.tab")
+ }
>
> try(system("convert tmp/1uah51291207453.ps tmp/1uah51291207453.png",intern=TRUE))
character(0)
> try(system("convert tmp/2uah51291207453.ps tmp/2uah51291207453.png",intern=TRUE))
character(0)
> try(system("convert tmp/35khp1291207453.ps tmp/35khp1291207453.png",intern=TRUE))
character(0)
> try(system("convert tmp/45khp1291207453.ps tmp/45khp1291207453.png",intern=TRUE))
character(0)
> try(system("convert tmp/55khp1291207453.ps tmp/55khp1291207453.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ytgs1291207453.ps tmp/6ytgs1291207453.png",intern=TRUE))
character(0)
> try(system("convert tmp/782fv1291207453.ps tmp/782fv1291207453.png",intern=TRUE))
character(0)
> try(system("convert tmp/882fv1291207453.ps tmp/882fv1291207453.png",intern=TRUE))
character(0)
> try(system("convert tmp/982fv1291207453.ps tmp/982fv1291207453.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jtey1291207453.ps tmp/10jtey1291207453.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.083 1.845 11.201