R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(4
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+ ,dim=c(5
+ ,159)
+ ,dimnames=list(c('neat'
+ ,'fail'
+ ,'performance'
+ ,'goals'
+ ,'organized
')
+ ,1:159))
> y <- array(NA,dim=c(5,159),dimnames=list(c('neat','fail','performance','goals','organized
'),1:159))
> 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 = 'Include Monthly 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
neat fail performance goals organized\r\r M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 4 4 1 4 5 1 0 0 0 0 0 0 0 0 0
2 4 2 1 4 4 0 1 0 0 0 0 0 0 0 0
3 4 3 2 5 5 0 0 1 0 0 0 0 0 0 0
4 4 2 1 3 4 0 0 0 1 0 0 0 0 0 0
5 4 2 2 4 3 0 0 0 0 1 0 0 0 0 0
6 5 2 1 3 5 0 0 0 0 0 1 0 0 0 0
7 4 1 3 4 4 0 0 0 0 0 0 1 0 0 0
8 3 1 1 3 4 0 0 0 0 0 0 0 1 0 0
9 4 1 1 2 4 0 0 0 0 0 0 0 0 1 0
10 4 2 1 4 4 0 0 0 0 0 0 0 0 0 1
11 4 2 2 2 4 0 0 0 0 0 0 0 0 0 0
12 4 2 4 2 4 0 0 0 0 0 0 0 0 0 0
13 4 2 2 2 4 1 0 0 0 0 0 0 0 0 0
14 2 2 1 1 3 0 1 0 0 0 0 0 0 0 0
15 3 1 1 4 4 0 0 1 0 0 0 0 0 0 0
16 4 3 3 4 5 0 0 0 1 0 0 0 0 0 0
17 3 2 2 2 4 0 0 0 0 1 0 0 0 0 0
18 2 2 2 2 2 0 0 0 0 0 1 0 0 0 0
19 4 2 3 3 4 0 0 0 0 0 0 1 0 0 0
20 3 2 3 3 4 0 0 0 0 0 0 0 1 0 0
21 3 3 1 3 4 0 0 0 0 0 0 0 0 1 0
22 4 4 2 4 4 0 0 0 0 0 0 0 0 0 1
23 3 2 2 3 4 0 0 0 0 0 0 0 0 0 0
24 3 2 2 2 4 0 0 0 0 0 0 0 0 0 0
25 4 2 2 2 5 1 0 0 0 0 0 0 0 0 0
26 4 1 3 4 4 0 1 0 0 0 0 0 0 0 0
27 4 2 2 4 4 0 0 1 0 0 0 0 0 0 0
28 4 2 2 3 4 0 0 0 1 0 0 0 0 0 0
29 4 2 2 4 4 0 0 0 0 1 0 0 0 0 0
30 4 2 2 2 4 0 0 0 0 0 1 0 0 0 0
31 5 4 2 4 5 0 0 0 0 0 0 1 0 0 0
32 4 2 3 4 4 0 0 0 0 0 0 0 1 0 0
33 4 4 2 5 2 0 0 0 0 0 0 0 0 1 0
34 4 3 2 5 5 0 0 0 0 0 0 0 0 0 1
35 3 1 2 4 4 0 0 0 0 0 0 0 0 0 0
36 4 4 2 4 5 0 0 0 0 0 0 0 0 0 0
37 3 3 2 4 4 1 0 0 0 0 0 0 0 0 0
38 4 2 1 2 4 0 1 0 0 0 0 0 0 0 0
39 4 4 2 4 4 0 0 1 0 0 0 0 0 0 0
40 3 2 1 4 4 0 0 0 1 0 0 0 0 0 0
41 5 3 2 4 5 0 0 0 0 1 0 0 0 0 0
42 4 3 2 3 4 0 0 0 0 0 1 0 0 0 0
43 3 2 2 2 4 0 0 0 0 0 0 1 0 0 0
44 3 1 2 3 5 0 0 0 0 0 0 0 1 0 0
45 3 2 2 4 4 0 0 0 0 0 0 0 0 1 0
46 4 1 3 3 4 0 0 0 0 0 0 0 0 0 1
47 4 2 2 2 4 0 0 0 0 0 0 0 0 0 0
48 4 4 2 4 4 0 0 0 0 0 0 0 0 0 0
49 4 2 2 4 4 1 0 0 0 0 0 0 0 0 0
50 4 2 4 3 4 0 1 0 0 0 0 0 0 0 0
51 4 2 1 4 4 0 0 1 0 0 0 0 0 0 0
52 4 2 2 3 4 0 0 0 1 0 0 0 0 0 0
53 5 2 2 4 5 0 0 0 0 1 0 0 0 0 0
54 3 1 1 2 3 0 0 0 0 0 1 0 0 0 0
55 3 2 5 4 4 0 0 0 0 0 0 1 0 0 0
56 5 3 2 4 5 0 0 0 0 0 0 0 1 0 0
57 5 2 2 4 5 0 0 0 0 0 0 0 0 1 0
58 4 2 2 4 4 0 0 0 0 0 0 0 0 0 1
59 4 1 1 3 5 0 0 0 0 0 0 0 0 0 0
60 3 1 2 1 2 0 0 0 0 0 0 0 0 0 0
61 4 2 2 3 4 1 0 0 0 0 0 0 0 0 0
62 4 2 2 3 4 0 1 0 0 0 0 0 0 0 0
63 5 1 2 4 4 0 0 1 0 0 0 0 0 0 0
64 4 2 2 2 4 0 0 0 1 0 0 0 0 0 0
65 4 1 1 3 4 0 0 0 0 1 0 0 0 0 0
66 5 4 1 5 5 0 0 0 0 0 1 0 0 0 0
67 4 4 2 4 4 0 0 0 0 0 0 1 0 0 0
68 3 1 2 4 4 0 0 0 0 0 0 0 1 0 0
69 4 1 1 3 4 0 0 0 0 0 0 0 0 1 0
70 4 3 2 4 4 0 0 0 0 0 0 0 0 0 1
71 4 4 2 2 3 0 0 0 0 0 0 0 0 0 0
72 4 2 1 3 4 0 0 0 0 0 0 0 0 0 0
73 4 4 3 4 5 1 0 0 0 0 0 0 0 0 0
74 4 4 3 3 5 0 1 0 0 0 0 0 0 0 0
75 4 3 3 4 4 0 0 1 0 0 0 0 0 0 0
76 3 4 2 4 4 0 0 0 1 0 0 0 0 0 0
77 4 2 2 3 5 0 0 0 0 1 0 0 0 0 0
78 3 2 2 3 4 0 0 0 0 0 1 0 0 0 0
79 5 2 1 2 5 0 0 0 0 0 0 1 0 0 0
80 4 2 4 4 3 0 0 0 0 0 0 0 1 0 0
81 5 2 3 3 4 0 0 0 0 0 0 0 0 1 0
82 5 2 2 2 4 0 0 0 0 0 0 0 0 0 1
83 4 2 2 2 4 0 0 0 0 0 0 0 0 0 0
84 4 1 2 3 4 0 0 0 0 0 0 0 0 0 0
85 4 3 1 2 5 1 0 0 0 0 0 0 0 0 0
86 4 3 2 2 4 0 1 0 0 0 0 0 0 0 0
87 4 2 3 4 4 0 0 1 0 0 0 0 0 0 0
88 5 4 1 4 5 0 0 0 1 0 0 0 0 0 0
89 4 4 2 4 3 0 0 0 0 1 0 0 0 0 0
90 3 2 2 2 4 0 0 0 0 0 1 0 0 0 0
91 4 2 2 2 4 0 0 0 0 0 0 1 0 0 0
92 3 1 1 4 5 0 0 0 0 0 0 0 1 0 0
93 4 1 1 2 4 0 0 0 0 0 0 0 0 1 0
94 4 1 2 3 3 0 0 0 0 0 0 0 0 0 1
95 4 2 2 3 5 0 0 0 0 0 0 0 0 0 0
96 4 2 4 5 5 0 0 0 0 0 0 0 0 0 0
97 4 3 2 3 5 1 0 0 0 0 0 0 0 0 0
98 3 4 4 4 4 0 1 0 0 0 0 0 0 0 0
99 3 2 1 3 4 0 0 1 0 0 0 0 0 0 0
100 3 2 3 2 4 0 0 0 1 0 0 0 0 0 0
101 3 4 2 4 4 0 0 0 0 1 0 0 0 0 0
102 3 2 2 3 4 0 0 0 0 0 1 0 0 0 0
103 2 3 4 3 2 0 0 0 0 0 0 1 0 0 0
104 3 2 3 3 4 0 0 0 0 0 0 0 1 0 0
105 5 2 2 4 5 0 0 0 0 0 0 0 0 1 0
106 2 4 1 1 2 0 0 0 0 0 0 0 0 0 1
107 2 2 1 3 3 0 0 0 0 0 0 0 0 0 0
108 3 3 2 2 2 0 0 0 0 0 0 0 0 0 0
109 3 2 3 3 4 1 0 0 0 0 0 0 0 0 0
110 2 2 2 4 4 0 1 0 0 0 0 0 0 0 0
111 2 1 2 3 4 0 0 1 0 0 0 0 0 0 0
112 4 2 2 4 3 0 0 0 1 0 0 0 0 0 0
113 3 2 4 4 2 0 0 0 0 1 0 0 0 0 0
114 1 2 5 3 3 0 0 0 0 0 1 0 0 0 0
115 1 1 5 5 4 0 0 0 0 0 0 1 0 0 0
116 1 2 3 4 2 0 0 0 0 0 0 0 1 0 0
117 2 3 4 2 4 0 0 0 0 0 0 0 0 1 0
118 2 2 4 3 4 0 0 0 0 0 0 0 0 0 1
119 3 2 3 4 2 0 0 0 0 0 0 0 0 0 0
120 1 1 2 2 3 0 0 0 0 0 0 0 0 0 0
121 3 1 4 4 3 1 0 0 0 0 0 0 0 0 0
122 1 2 2 3 3 0 1 0 0 0 0 0 0 0 0
123 2 2 3 4 4 0 0 1 0 0 0 0 0 0 0
124 1 1 2 4 4 0 0 0 1 0 0 0 0 0 0
125 2 2 2 4 4 0 0 0 0 1 0 0 0 0 0
126 2 3 2 4 3 0 0 0 0 0 1 0 0 0 0
127 3 1 4 5 4 0 0 0 0 0 0 1 0 0 0
128 2 2 4 5 4 0 0 0 0 0 0 0 1 0 0
129 2 2 4 5 4 0 0 0 0 0 0 0 0 1 0
130 4 2 4 5 2 0 0 0 0 0 0 0 0 0 1
131 2 2 3 4 4 0 0 0 0 0 0 0 0 0 0
132 3 2 2 4 5 0 0 0 0 0 0 0 0 0 0
133 2 1 4 5 4 1 0 0 0 0 0 0 0 0 0
134 1 1 3 4 4 0 1 0 0 0 0 0 0 0 0
135 2 2 2 4 4 0 0 1 0 0 0 0 0 0 0
136 3 4 4 4 3 0 0 0 1 0 0 0 0 0 0
137 1 2 3 4 1 0 0 0 0 1 0 0 0 0 0
138 2 2 3 2 4 0 0 0 0 0 1 0 0 0 0
139 2 2 3 4 3 0 0 0 0 0 0 1 0 0 0
140 3 2 3 2 3 0 0 0 0 0 0 0 1 0 0
141 3 2 3 3 3 0 0 0 0 0 0 0 0 1 0
142 3 2 4 4 1 0 0 0 0 0 0 0 0 0 1
143 4 5 5 1 4 0 0 0 0 0 0 0 0 0 0
144 4 1 2 4 5 0 0 0 0 0 0 0 0 0 0
145 2 4 2 3 4 1 0 0 0 0 0 0 0 0 0
146 3 2 4 4 3 0 1 0 0 0 0 0 0 0 0
147 3 3 3 4 4 0 0 1 0 0 0 0 0 0 0
148 2 2 3 4 3 0 0 0 1 0 0 0 0 0 0
149 1 2 1 1 4 0 0 0 0 1 0 0 0 0 0
150 2 2 2 2 4 0 0 0 0 0 1 0 0 0 0
151 2 1 4 4 4 0 0 0 0 0 0 1 0 0 0
152 4 2 4 4 5 0 0 0 0 0 0 0 1 0 0
153 4 5 5 5 2 0 0 0 0 0 0 0 0 1 0
154 2 2 2 2 3 0 0 0 0 0 0 0 0 0 1
155 3 3 4 2 3 0 0 0 0 0 0 0 0 0 0
156 2 2 3 4 4 0 0 0 0 0 0 0 0 0 0
157 4 2 2 4 4 1 0 0 0 0 0 0 0 0 0
158 2 2 4 4 3 0 1 0 0 0 0 0 0 0 0
159 4 4 2 4 4 0 0 1 0 0 0 0 0 0 0
M11
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 1
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 1
24 0
25 0
26 0
27 0
28 0
29 0
30 0
31 0
32 0
33 0
34 0
35 1
36 0
37 0
38 0
39 0
40 0
41 0
42 0
43 0
44 0
45 0
46 0
47 1
48 0
49 0
50 0
51 0
52 0
53 0
54 0
55 0
56 0
57 0
58 0
59 1
60 0
61 0
62 0
63 0
64 0
65 0
66 0
67 0
68 0
69 0
70 0
71 1
72 0
73 0
74 0
75 0
76 0
77 0
78 0
79 0
80 0
81 0
82 0
83 1
84 0
85 0
86 0
87 0
88 0
89 0
90 0
91 0
92 0
93 0
94 0
95 1
96 0
97 0
98 0
99 0
100 0
101 0
102 0
103 0
104 0
105 0
106 0
107 1
108 0
109 0
110 0
111 0
112 0
113 0
114 0
115 0
116 0
117 0
118 0
119 1
120 0
121 0
122 0
123 0
124 0
125 0
126 0
127 0
128 0
129 0
130 0
131 1
132 0
133 0
134 0
135 0
136 0
137 0
138 0
139 0
140 0
141 0
142 0
143 1
144 0
145 0
146 0
147 0
148 0
149 0
150 0
151 0
152 0
153 0
154 0
155 1
156 0
157 0
158 0
159 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) fail performance goals
1.164406 0.233140 -0.187500 0.036873
`organized\r\r` M1 M2 M3
0.504276 -0.103651 -0.226063 -0.078311
M4 M5 M6 M7
-0.055774 0.005119 -0.297903 0.050290
M8 M9 M10 M11
-0.068640 0.408636 0.451578 0.129307
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.50228 -0.56895 0.09193 0.66959 1.89117
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.164406 0.538332 2.163 0.03220 *
fail 0.233140 0.077657 3.002 0.00316 **
performance -0.187500 0.080182 -2.338 0.02075 *
goals 0.036873 0.081926 0.450 0.65334
`organized\r\r` 0.504276 0.093793 5.376 3.02e-07 ***
M1 -0.103651 0.348300 -0.298 0.76645
M2 -0.226063 0.345762 -0.654 0.51428
M3 -0.078311 0.353381 -0.222 0.82494
M4 -0.055774 0.354427 -0.157 0.87518
M5 0.005119 0.355562 0.014 0.98853
M6 -0.297903 0.353007 -0.844 0.40013
M7 0.050290 0.356624 0.141 0.88806
M8 -0.068640 0.355554 -0.193 0.84719
M9 0.408636 0.353382 1.156 0.24946
M10 0.451578 0.356492 1.267 0.20731
M11 0.129307 0.353546 0.366 0.71510
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8957 on 143 degrees of freedom
Multiple R-squared: 0.3129, Adjusted R-squared: 0.2409
F-statistic: 4.342 on 15 and 143 DF, p-value: 1.205e-06
> 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,] 3.894867e-01 7.789734e-01 0.6105133
[2,] 2.491947e-01 4.983894e-01 0.7508053
[3,] 1.688757e-01 3.377515e-01 0.8311243
[4,] 1.276670e-01 2.553341e-01 0.8723330
[5,] 1.631412e-01 3.262825e-01 0.8368588
[6,] 1.231554e-01 2.463109e-01 0.8768446
[7,] 8.544258e-02 1.708852e-01 0.9145574
[8,] 5.753612e-02 1.150722e-01 0.9424639
[9,] 5.783268e-02 1.156654e-01 0.9421673
[10,] 3.715978e-02 7.431956e-02 0.9628402
[11,] 2.181744e-02 4.363489e-02 0.9781826
[12,] 1.338975e-02 2.677950e-02 0.9866103
[13,] 1.300514e-02 2.601029e-02 0.9869949
[14,] 1.111931e-02 2.223862e-02 0.9888807
[15,] 1.189077e-02 2.378153e-02 0.9881092
[16,] 1.180692e-02 2.361384e-02 0.9881931
[17,] 1.272387e-02 2.544774e-02 0.9872761
[18,] 8.051703e-03 1.610341e-02 0.9919483
[19,] 9.653077e-03 1.930615e-02 0.9903469
[20,] 8.817440e-03 1.763488e-02 0.9911826
[21,] 6.096886e-03 1.219377e-02 0.9939031
[22,] 5.245892e-03 1.049178e-02 0.9947541
[23,] 3.821217e-03 7.642434e-03 0.9961788
[24,] 2.403013e-03 4.806027e-03 0.9975970
[25,] 2.046876e-03 4.093753e-03 0.9979531
[26,] 1.726033e-03 3.452066e-03 0.9982740
[27,] 2.610821e-03 5.221641e-03 0.9973892
[28,] 1.709654e-03 3.419308e-03 0.9982903
[29,] 1.477628e-03 2.955256e-03 0.9985224
[30,] 9.209088e-04 1.841818e-03 0.9990791
[31,] 6.265626e-04 1.253125e-03 0.9993734
[32,] 4.770377e-04 9.540754e-04 0.9995230
[33,] 3.535014e-04 7.070028e-04 0.9996465
[34,] 2.480062e-04 4.960123e-04 0.9997520
[35,] 2.182081e-04 4.364163e-04 0.9997818
[36,] 1.360660e-04 2.721320e-04 0.9998639
[37,] 2.370914e-04 4.741829e-04 0.9997629
[38,] 3.466060e-04 6.932121e-04 0.9996534
[39,] 2.787658e-04 5.575316e-04 0.9997212
[40,] 1.661192e-04 3.322384e-04 0.9998339
[41,] 9.548009e-05 1.909602e-04 0.9999045
[42,] 1.166421e-04 2.332842e-04 0.9998834
[43,] 8.710073e-05 1.742015e-04 0.9999129
[44,] 6.801917e-05 1.360383e-04 0.9999320
[45,] 3.204479e-04 6.408958e-04 0.9996796
[46,] 2.725026e-04 5.450052e-04 0.9997275
[47,] 2.311996e-04 4.623991e-04 0.9997688
[48,] 1.686457e-04 3.372914e-04 0.9998314
[49,] 1.019174e-04 2.038348e-04 0.9998981
[50,] 6.630923e-05 1.326185e-04 0.9999337
[51,] 4.232607e-05 8.465215e-05 0.9999577
[52,] 2.432216e-05 4.864431e-05 0.9999757
[53,] 2.405795e-05 4.811589e-05 0.9999759
[54,] 1.678662e-05 3.357324e-05 0.9999832
[55,] 1.075952e-05 2.151904e-05 0.9999892
[56,] 7.212270e-06 1.442454e-05 0.9999928
[57,] 4.779828e-06 9.559657e-06 0.9999952
[58,] 5.924778e-06 1.184956e-05 0.9999941
[59,] 4.924311e-06 9.848622e-06 0.9999951
[60,] 5.338930e-06 1.067786e-05 0.9999947
[61,] 1.035370e-05 2.070741e-05 0.9999896
[62,] 2.255534e-05 4.511068e-05 0.9999774
[63,] 5.240868e-05 1.048174e-04 0.9999476
[64,] 1.302599e-04 2.605199e-04 0.9998697
[65,] 1.077041e-04 2.154082e-04 0.9998923
[66,] 1.181306e-04 2.362611e-04 0.9998819
[67,] 7.305379e-05 1.461076e-04 0.9999269
[68,] 9.103978e-05 1.820796e-04 0.9999090
[69,] 9.900436e-05 1.980087e-04 0.9999010
[70,] 1.015187e-04 2.030373e-04 0.9998985
[71,] 9.977265e-05 1.995453e-04 0.9999002
[72,] 1.053577e-04 2.107153e-04 0.9998946
[73,] 1.881224e-04 3.762448e-04 0.9998119
[74,] 1.651114e-04 3.302229e-04 0.9998349
[75,] 2.097585e-04 4.195170e-04 0.9997902
[76,] 3.679325e-04 7.358649e-04 0.9996321
[77,] 3.175993e-04 6.351986e-04 0.9996824
[78,] 2.335671e-04 4.671343e-04 0.9997664
[79,] 1.715566e-04 3.431132e-04 0.9998284
[80,] 1.708339e-04 3.416679e-04 0.9998292
[81,] 1.822127e-04 3.644254e-04 0.9998178
[82,] 1.840987e-04 3.681973e-04 0.9998159
[83,] 2.183592e-04 4.367185e-04 0.9997816
[84,] 2.613964e-04 5.227928e-04 0.9997386
[85,] 2.456584e-04 4.913168e-04 0.9997543
[86,] 1.792371e-04 3.584741e-04 0.9998208
[87,] 9.721174e-04 1.944235e-03 0.9990279
[88,] 1.114365e-03 2.228730e-03 0.9988856
[89,] 1.202886e-03 2.405772e-03 0.9987971
[90,] 8.520162e-04 1.704032e-03 0.9991480
[91,] 6.466585e-04 1.293317e-03 0.9993533
[92,] 1.048727e-03 2.097454e-03 0.9989513
[93,] 1.663819e-03 3.327637e-03 0.9983362
[94,] 6.563123e-03 1.312625e-02 0.9934369
[95,] 8.469647e-03 1.693929e-02 0.9915304
[96,] 1.846632e-02 3.693263e-02 0.9815337
[97,] 6.871817e-02 1.374363e-01 0.9312818
[98,] 1.199037e-01 2.398074e-01 0.8800963
[99,] 1.414702e-01 2.829404e-01 0.8585298
[100,] 1.917307e-01 3.834614e-01 0.8082693
[101,] 1.911035e-01 3.822069e-01 0.8088965
[102,] 2.717765e-01 5.435529e-01 0.7282235
[103,] 2.424042e-01 4.848083e-01 0.7575958
[104,] 2.653281e-01 5.306562e-01 0.7346719
[105,] 2.657700e-01 5.315400e-01 0.7342300
[106,] 3.089560e-01 6.179120e-01 0.6910440
[107,] 3.232354e-01 6.464708e-01 0.6767646
[108,] 2.727715e-01 5.455429e-01 0.7272285
[109,] 2.616201e-01 5.232403e-01 0.7383799
[110,] 3.421053e-01 6.842107e-01 0.6578947
[111,] 3.830693e-01 7.661386e-01 0.6169307
[112,] 4.125526e-01 8.251053e-01 0.5874474
[113,] 4.602244e-01 9.204488e-01 0.5397756
[114,] 3.938393e-01 7.876786e-01 0.6061607
[115,] 3.666411e-01 7.332821e-01 0.6333589
[116,] 5.021308e-01 9.957384e-01 0.4978692
[117,] 5.597587e-01 8.804826e-01 0.4402413
[118,] 5.131883e-01 9.736234e-01 0.4868117
[119,] 4.189324e-01 8.378648e-01 0.5810676
[120,] 3.088805e-01 6.177610e-01 0.6911195
[121,] 2.027365e-01 4.054730e-01 0.7972635
[122,] 3.850209e-01 7.700418e-01 0.6149791
> postscript(file="/var/www/html/freestat/rcomp/tmp/15tey1291387202.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2y2d11291387202.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3y2d11291387202.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4y2d11291387202.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5y2d11291387202.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 = 159
Frequency = 1
1 2 3 4 5 6
-0.47468205 0.61828468 -0.11625496 0.48486872 1.07887921 1.22272209
7 8 9 10 11 12
0.95007257 -0.26912534 0.29047094 -0.05935641 0.52416033 1.02846838
13 14 15 16 17 18
0.75711852 -0.76682175 -0.29632773 0.08558149 -0.35165102 -0.04007820
19 20 21 22 23 24
0.75380557 -0.12726433 -1.21268106 -0.33813540 -0.51271234 -0.34653230
25 26 27 28 29 30
0.25284295 1.22642502 0.65803294 0.67236906 0.57460364 0.95137066
31 32 33 34 35 36
0.55887767 0.83586300 0.67648542 -0.64614397 -0.31644535 -0.39083253
37 38 39 40 41 42
-0.54976648 0.69203002 0.19175362 -0.55200395 0.83718841 0.68135833
43 44 45 46 47 48
-0.39682210 -0.58590057 -0.82891373 0.58565660 0.52416033 0.11344304
49 50 51 52 53 54
0.68337318 1.21765836 0.47053261 0.67236906 1.07032807 0.50128556
55 56 57 58 59 60
0.09193358 0.91094743 0.66681071 0.12814393 0.02865142 0.93203117
61 62 63 64 65 66
0.72024585 0.84265768 1.89117261 0.70924173 0.65711564 0.68269742
67 68 69 70 71 72
0.06315324 -0.11849767 0.25359827 -0.10499574 0.56215657 0.42909469
73 74 75 76 77 78
-0.09968138 0.05960313 0.61239362 -0.83078294 0.10720074 -0.08550200
79 80 81 82 83 84
0.91140200 1.52763891 1.39545928 1.20188927 0.52416033 0.84973470
85 86 87 88 89 90
-0.16779705 0.64639069 0.84553328 0.47744115 0.61259989 -0.04862934
91 92 93 94 95 96
0.60317790 -0.81027358 0.29047094 0.90243183 -0.01698791 0.41357480
97 98 99 100 101 102
-0.01716938 -0.28549364 -0.49259473 -0.10325794 -0.89167568 -0.08550200
103 104 105 106 107 108
-0.28328261 -0.12726433 0.66681071 -1.40646659 -1.19593711 0.42887918
109 110 111 112 113 114
-0.09225381 -1.19421498 -1.07195472 1.13977196 0.95815546 -1.01872542
115 116 117 118 119 120
-1.71179943 -1.15558586 -1.61330737 -1.45998273 0.64646647 -1.60911707
121 122 123 124 125 126
0.79578909 -1.65306675 -1.15446672 -2.13136395 -1.42539636 -0.85123877
127 128 129 130 131 132
0.10070024 -1.01350933 -1.49078572 1.47482307 -1.36208467 -0.92455321
133 134 135 136 137 138
-0.74535915 -1.77357498 -1.34196706 0.04849331 -0.72506931 -0.86112900
139 140 141 142 143 144
-0.77879153 0.41388391 -0.10026515 1.01597131 0.42411502 0.30858646
145 146 147 148 149 150
-1.74603348 0.68506126 -0.38760638 -0.67272770 -2.50227869 -1.04862934
151 152 153 154 155 156
-0.86242710 0.51908777 1.00584677 -1.29383517 0.17029691 -1.23277730
157 158 159
0.68337318 -0.31493874 0.19175362
> postscript(file="/var/www/html/freestat/rcomp/tmp/69bcm1291387202.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.47468205 NA
1 0.61828468 -0.47468205
2 -0.11625496 0.61828468
3 0.48486872 -0.11625496
4 1.07887921 0.48486872
5 1.22272209 1.07887921
6 0.95007257 1.22272209
7 -0.26912534 0.95007257
8 0.29047094 -0.26912534
9 -0.05935641 0.29047094
10 0.52416033 -0.05935641
11 1.02846838 0.52416033
12 0.75711852 1.02846838
13 -0.76682175 0.75711852
14 -0.29632773 -0.76682175
15 0.08558149 -0.29632773
16 -0.35165102 0.08558149
17 -0.04007820 -0.35165102
18 0.75380557 -0.04007820
19 -0.12726433 0.75380557
20 -1.21268106 -0.12726433
21 -0.33813540 -1.21268106
22 -0.51271234 -0.33813540
23 -0.34653230 -0.51271234
24 0.25284295 -0.34653230
25 1.22642502 0.25284295
26 0.65803294 1.22642502
27 0.67236906 0.65803294
28 0.57460364 0.67236906
29 0.95137066 0.57460364
30 0.55887767 0.95137066
31 0.83586300 0.55887767
32 0.67648542 0.83586300
33 -0.64614397 0.67648542
34 -0.31644535 -0.64614397
35 -0.39083253 -0.31644535
36 -0.54976648 -0.39083253
37 0.69203002 -0.54976648
38 0.19175362 0.69203002
39 -0.55200395 0.19175362
40 0.83718841 -0.55200395
41 0.68135833 0.83718841
42 -0.39682210 0.68135833
43 -0.58590057 -0.39682210
44 -0.82891373 -0.58590057
45 0.58565660 -0.82891373
46 0.52416033 0.58565660
47 0.11344304 0.52416033
48 0.68337318 0.11344304
49 1.21765836 0.68337318
50 0.47053261 1.21765836
51 0.67236906 0.47053261
52 1.07032807 0.67236906
53 0.50128556 1.07032807
54 0.09193358 0.50128556
55 0.91094743 0.09193358
56 0.66681071 0.91094743
57 0.12814393 0.66681071
58 0.02865142 0.12814393
59 0.93203117 0.02865142
60 0.72024585 0.93203117
61 0.84265768 0.72024585
62 1.89117261 0.84265768
63 0.70924173 1.89117261
64 0.65711564 0.70924173
65 0.68269742 0.65711564
66 0.06315324 0.68269742
67 -0.11849767 0.06315324
68 0.25359827 -0.11849767
69 -0.10499574 0.25359827
70 0.56215657 -0.10499574
71 0.42909469 0.56215657
72 -0.09968138 0.42909469
73 0.05960313 -0.09968138
74 0.61239362 0.05960313
75 -0.83078294 0.61239362
76 0.10720074 -0.83078294
77 -0.08550200 0.10720074
78 0.91140200 -0.08550200
79 1.52763891 0.91140200
80 1.39545928 1.52763891
81 1.20188927 1.39545928
82 0.52416033 1.20188927
83 0.84973470 0.52416033
84 -0.16779705 0.84973470
85 0.64639069 -0.16779705
86 0.84553328 0.64639069
87 0.47744115 0.84553328
88 0.61259989 0.47744115
89 -0.04862934 0.61259989
90 0.60317790 -0.04862934
91 -0.81027358 0.60317790
92 0.29047094 -0.81027358
93 0.90243183 0.29047094
94 -0.01698791 0.90243183
95 0.41357480 -0.01698791
96 -0.01716938 0.41357480
97 -0.28549364 -0.01716938
98 -0.49259473 -0.28549364
99 -0.10325794 -0.49259473
100 -0.89167568 -0.10325794
101 -0.08550200 -0.89167568
102 -0.28328261 -0.08550200
103 -0.12726433 -0.28328261
104 0.66681071 -0.12726433
105 -1.40646659 0.66681071
106 -1.19593711 -1.40646659
107 0.42887918 -1.19593711
108 -0.09225381 0.42887918
109 -1.19421498 -0.09225381
110 -1.07195472 -1.19421498
111 1.13977196 -1.07195472
112 0.95815546 1.13977196
113 -1.01872542 0.95815546
114 -1.71179943 -1.01872542
115 -1.15558586 -1.71179943
116 -1.61330737 -1.15558586
117 -1.45998273 -1.61330737
118 0.64646647 -1.45998273
119 -1.60911707 0.64646647
120 0.79578909 -1.60911707
121 -1.65306675 0.79578909
122 -1.15446672 -1.65306675
123 -2.13136395 -1.15446672
124 -1.42539636 -2.13136395
125 -0.85123877 -1.42539636
126 0.10070024 -0.85123877
127 -1.01350933 0.10070024
128 -1.49078572 -1.01350933
129 1.47482307 -1.49078572
130 -1.36208467 1.47482307
131 -0.92455321 -1.36208467
132 -0.74535915 -0.92455321
133 -1.77357498 -0.74535915
134 -1.34196706 -1.77357498
135 0.04849331 -1.34196706
136 -0.72506931 0.04849331
137 -0.86112900 -0.72506931
138 -0.77879153 -0.86112900
139 0.41388391 -0.77879153
140 -0.10026515 0.41388391
141 1.01597131 -0.10026515
142 0.42411502 1.01597131
143 0.30858646 0.42411502
144 -1.74603348 0.30858646
145 0.68506126 -1.74603348
146 -0.38760638 0.68506126
147 -0.67272770 -0.38760638
148 -2.50227869 -0.67272770
149 -1.04862934 -2.50227869
150 -0.86242710 -1.04862934
151 0.51908777 -0.86242710
152 1.00584677 0.51908777
153 -1.29383517 1.00584677
154 0.17029691 -1.29383517
155 -1.23277730 0.17029691
156 0.68337318 -1.23277730
157 -0.31493874 0.68337318
158 0.19175362 -0.31493874
159 NA 0.19175362
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.61828468 -0.47468205
[2,] -0.11625496 0.61828468
[3,] 0.48486872 -0.11625496
[4,] 1.07887921 0.48486872
[5,] 1.22272209 1.07887921
[6,] 0.95007257 1.22272209
[7,] -0.26912534 0.95007257
[8,] 0.29047094 -0.26912534
[9,] -0.05935641 0.29047094
[10,] 0.52416033 -0.05935641
[11,] 1.02846838 0.52416033
[12,] 0.75711852 1.02846838
[13,] -0.76682175 0.75711852
[14,] -0.29632773 -0.76682175
[15,] 0.08558149 -0.29632773
[16,] -0.35165102 0.08558149
[17,] -0.04007820 -0.35165102
[18,] 0.75380557 -0.04007820
[19,] -0.12726433 0.75380557
[20,] -1.21268106 -0.12726433
[21,] -0.33813540 -1.21268106
[22,] -0.51271234 -0.33813540
[23,] -0.34653230 -0.51271234
[24,] 0.25284295 -0.34653230
[25,] 1.22642502 0.25284295
[26,] 0.65803294 1.22642502
[27,] 0.67236906 0.65803294
[28,] 0.57460364 0.67236906
[29,] 0.95137066 0.57460364
[30,] 0.55887767 0.95137066
[31,] 0.83586300 0.55887767
[32,] 0.67648542 0.83586300
[33,] -0.64614397 0.67648542
[34,] -0.31644535 -0.64614397
[35,] -0.39083253 -0.31644535
[36,] -0.54976648 -0.39083253
[37,] 0.69203002 -0.54976648
[38,] 0.19175362 0.69203002
[39,] -0.55200395 0.19175362
[40,] 0.83718841 -0.55200395
[41,] 0.68135833 0.83718841
[42,] -0.39682210 0.68135833
[43,] -0.58590057 -0.39682210
[44,] -0.82891373 -0.58590057
[45,] 0.58565660 -0.82891373
[46,] 0.52416033 0.58565660
[47,] 0.11344304 0.52416033
[48,] 0.68337318 0.11344304
[49,] 1.21765836 0.68337318
[50,] 0.47053261 1.21765836
[51,] 0.67236906 0.47053261
[52,] 1.07032807 0.67236906
[53,] 0.50128556 1.07032807
[54,] 0.09193358 0.50128556
[55,] 0.91094743 0.09193358
[56,] 0.66681071 0.91094743
[57,] 0.12814393 0.66681071
[58,] 0.02865142 0.12814393
[59,] 0.93203117 0.02865142
[60,] 0.72024585 0.93203117
[61,] 0.84265768 0.72024585
[62,] 1.89117261 0.84265768
[63,] 0.70924173 1.89117261
[64,] 0.65711564 0.70924173
[65,] 0.68269742 0.65711564
[66,] 0.06315324 0.68269742
[67,] -0.11849767 0.06315324
[68,] 0.25359827 -0.11849767
[69,] -0.10499574 0.25359827
[70,] 0.56215657 -0.10499574
[71,] 0.42909469 0.56215657
[72,] -0.09968138 0.42909469
[73,] 0.05960313 -0.09968138
[74,] 0.61239362 0.05960313
[75,] -0.83078294 0.61239362
[76,] 0.10720074 -0.83078294
[77,] -0.08550200 0.10720074
[78,] 0.91140200 -0.08550200
[79,] 1.52763891 0.91140200
[80,] 1.39545928 1.52763891
[81,] 1.20188927 1.39545928
[82,] 0.52416033 1.20188927
[83,] 0.84973470 0.52416033
[84,] -0.16779705 0.84973470
[85,] 0.64639069 -0.16779705
[86,] 0.84553328 0.64639069
[87,] 0.47744115 0.84553328
[88,] 0.61259989 0.47744115
[89,] -0.04862934 0.61259989
[90,] 0.60317790 -0.04862934
[91,] -0.81027358 0.60317790
[92,] 0.29047094 -0.81027358
[93,] 0.90243183 0.29047094
[94,] -0.01698791 0.90243183
[95,] 0.41357480 -0.01698791
[96,] -0.01716938 0.41357480
[97,] -0.28549364 -0.01716938
[98,] -0.49259473 -0.28549364
[99,] -0.10325794 -0.49259473
[100,] -0.89167568 -0.10325794
[101,] -0.08550200 -0.89167568
[102,] -0.28328261 -0.08550200
[103,] -0.12726433 -0.28328261
[104,] 0.66681071 -0.12726433
[105,] -1.40646659 0.66681071
[106,] -1.19593711 -1.40646659
[107,] 0.42887918 -1.19593711
[108,] -0.09225381 0.42887918
[109,] -1.19421498 -0.09225381
[110,] -1.07195472 -1.19421498
[111,] 1.13977196 -1.07195472
[112,] 0.95815546 1.13977196
[113,] -1.01872542 0.95815546
[114,] -1.71179943 -1.01872542
[115,] -1.15558586 -1.71179943
[116,] -1.61330737 -1.15558586
[117,] -1.45998273 -1.61330737
[118,] 0.64646647 -1.45998273
[119,] -1.60911707 0.64646647
[120,] 0.79578909 -1.60911707
[121,] -1.65306675 0.79578909
[122,] -1.15446672 -1.65306675
[123,] -2.13136395 -1.15446672
[124,] -1.42539636 -2.13136395
[125,] -0.85123877 -1.42539636
[126,] 0.10070024 -0.85123877
[127,] -1.01350933 0.10070024
[128,] -1.49078572 -1.01350933
[129,] 1.47482307 -1.49078572
[130,] -1.36208467 1.47482307
[131,] -0.92455321 -1.36208467
[132,] -0.74535915 -0.92455321
[133,] -1.77357498 -0.74535915
[134,] -1.34196706 -1.77357498
[135,] 0.04849331 -1.34196706
[136,] -0.72506931 0.04849331
[137,] -0.86112900 -0.72506931
[138,] -0.77879153 -0.86112900
[139,] 0.41388391 -0.77879153
[140,] -0.10026515 0.41388391
[141,] 1.01597131 -0.10026515
[142,] 0.42411502 1.01597131
[143,] 0.30858646 0.42411502
[144,] -1.74603348 0.30858646
[145,] 0.68506126 -1.74603348
[146,] -0.38760638 0.68506126
[147,] -0.67272770 -0.38760638
[148,] -2.50227869 -0.67272770
[149,] -1.04862934 -2.50227869
[150,] -0.86242710 -1.04862934
[151,] 0.51908777 -0.86242710
[152,] 1.00584677 0.51908777
[153,] -1.29383517 1.00584677
[154,] 0.17029691 -1.29383517
[155,] -1.23277730 0.17029691
[156,] 0.68337318 -1.23277730
[157,] -0.31493874 0.68337318
[158,] 0.19175362 -0.31493874
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.61828468 -0.47468205
2 -0.11625496 0.61828468
3 0.48486872 -0.11625496
4 1.07887921 0.48486872
5 1.22272209 1.07887921
6 0.95007257 1.22272209
7 -0.26912534 0.95007257
8 0.29047094 -0.26912534
9 -0.05935641 0.29047094
10 0.52416033 -0.05935641
11 1.02846838 0.52416033
12 0.75711852 1.02846838
13 -0.76682175 0.75711852
14 -0.29632773 -0.76682175
15 0.08558149 -0.29632773
16 -0.35165102 0.08558149
17 -0.04007820 -0.35165102
18 0.75380557 -0.04007820
19 -0.12726433 0.75380557
20 -1.21268106 -0.12726433
21 -0.33813540 -1.21268106
22 -0.51271234 -0.33813540
23 -0.34653230 -0.51271234
24 0.25284295 -0.34653230
25 1.22642502 0.25284295
26 0.65803294 1.22642502
27 0.67236906 0.65803294
28 0.57460364 0.67236906
29 0.95137066 0.57460364
30 0.55887767 0.95137066
31 0.83586300 0.55887767
32 0.67648542 0.83586300
33 -0.64614397 0.67648542
34 -0.31644535 -0.64614397
35 -0.39083253 -0.31644535
36 -0.54976648 -0.39083253
37 0.69203002 -0.54976648
38 0.19175362 0.69203002
39 -0.55200395 0.19175362
40 0.83718841 -0.55200395
41 0.68135833 0.83718841
42 -0.39682210 0.68135833
43 -0.58590057 -0.39682210
44 -0.82891373 -0.58590057
45 0.58565660 -0.82891373
46 0.52416033 0.58565660
47 0.11344304 0.52416033
48 0.68337318 0.11344304
49 1.21765836 0.68337318
50 0.47053261 1.21765836
51 0.67236906 0.47053261
52 1.07032807 0.67236906
53 0.50128556 1.07032807
54 0.09193358 0.50128556
55 0.91094743 0.09193358
56 0.66681071 0.91094743
57 0.12814393 0.66681071
58 0.02865142 0.12814393
59 0.93203117 0.02865142
60 0.72024585 0.93203117
61 0.84265768 0.72024585
62 1.89117261 0.84265768
63 0.70924173 1.89117261
64 0.65711564 0.70924173
65 0.68269742 0.65711564
66 0.06315324 0.68269742
67 -0.11849767 0.06315324
68 0.25359827 -0.11849767
69 -0.10499574 0.25359827
70 0.56215657 -0.10499574
71 0.42909469 0.56215657
72 -0.09968138 0.42909469
73 0.05960313 -0.09968138
74 0.61239362 0.05960313
75 -0.83078294 0.61239362
76 0.10720074 -0.83078294
77 -0.08550200 0.10720074
78 0.91140200 -0.08550200
79 1.52763891 0.91140200
80 1.39545928 1.52763891
81 1.20188927 1.39545928
82 0.52416033 1.20188927
83 0.84973470 0.52416033
84 -0.16779705 0.84973470
85 0.64639069 -0.16779705
86 0.84553328 0.64639069
87 0.47744115 0.84553328
88 0.61259989 0.47744115
89 -0.04862934 0.61259989
90 0.60317790 -0.04862934
91 -0.81027358 0.60317790
92 0.29047094 -0.81027358
93 0.90243183 0.29047094
94 -0.01698791 0.90243183
95 0.41357480 -0.01698791
96 -0.01716938 0.41357480
97 -0.28549364 -0.01716938
98 -0.49259473 -0.28549364
99 -0.10325794 -0.49259473
100 -0.89167568 -0.10325794
101 -0.08550200 -0.89167568
102 -0.28328261 -0.08550200
103 -0.12726433 -0.28328261
104 0.66681071 -0.12726433
105 -1.40646659 0.66681071
106 -1.19593711 -1.40646659
107 0.42887918 -1.19593711
108 -0.09225381 0.42887918
109 -1.19421498 -0.09225381
110 -1.07195472 -1.19421498
111 1.13977196 -1.07195472
112 0.95815546 1.13977196
113 -1.01872542 0.95815546
114 -1.71179943 -1.01872542
115 -1.15558586 -1.71179943
116 -1.61330737 -1.15558586
117 -1.45998273 -1.61330737
118 0.64646647 -1.45998273
119 -1.60911707 0.64646647
120 0.79578909 -1.60911707
121 -1.65306675 0.79578909
122 -1.15446672 -1.65306675
123 -2.13136395 -1.15446672
124 -1.42539636 -2.13136395
125 -0.85123877 -1.42539636
126 0.10070024 -0.85123877
127 -1.01350933 0.10070024
128 -1.49078572 -1.01350933
129 1.47482307 -1.49078572
130 -1.36208467 1.47482307
131 -0.92455321 -1.36208467
132 -0.74535915 -0.92455321
133 -1.77357498 -0.74535915
134 -1.34196706 -1.77357498
135 0.04849331 -1.34196706
136 -0.72506931 0.04849331
137 -0.86112900 -0.72506931
138 -0.77879153 -0.86112900
139 0.41388391 -0.77879153
140 -0.10026515 0.41388391
141 1.01597131 -0.10026515
142 0.42411502 1.01597131
143 0.30858646 0.42411502
144 -1.74603348 0.30858646
145 0.68506126 -1.74603348
146 -0.38760638 0.68506126
147 -0.67272770 -0.38760638
148 -2.50227869 -0.67272770
149 -1.04862934 -2.50227869
150 -0.86242710 -1.04862934
151 0.51908777 -0.86242710
152 1.00584677 0.51908777
153 -1.29383517 1.00584677
154 0.17029691 -1.29383517
155 -1.23277730 0.17029691
156 0.68337318 -1.23277730
157 -0.31493874 0.68337318
158 0.19175362 -0.31493874
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/7jlu71291387202.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8jlu71291387202.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9cubs1291387202.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10cubs1291387202.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/11yurg1291387202.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/121vqm1291387202.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/137enf1291387202.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/1405401291387202.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/freestat/rcomp/tmp/154o2o1291387202.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/freestat/rcomp/tmp/16ix0f1291387202.tab")
+ }
>
> try(system("convert tmp/15tey1291387202.ps tmp/15tey1291387202.png",intern=TRUE))
character(0)
> try(system("convert tmp/2y2d11291387202.ps tmp/2y2d11291387202.png",intern=TRUE))
character(0)
> try(system("convert tmp/3y2d11291387202.ps tmp/3y2d11291387202.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y2d11291387202.ps tmp/4y2d11291387202.png",intern=TRUE))
character(0)
> try(system("convert tmp/5y2d11291387202.ps tmp/5y2d11291387202.png",intern=TRUE))
character(0)
> try(system("convert tmp/69bcm1291387202.ps tmp/69bcm1291387202.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jlu71291387202.ps tmp/7jlu71291387202.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jlu71291387202.ps tmp/8jlu71291387202.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cubs1291387202.ps tmp/9cubs1291387202.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cubs1291387202.ps tmp/10cubs1291387202.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.779 2.641 6.125