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(1
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+ ,0)
+ ,dim=c(10
+ ,143)
+ ,dimnames=list(c('gender'
+ ,'popularity'
+ ,'popularity_g'
+ ,'hapiness'
+ ,'hapiness_g'
+ ,'doubsaboutactions'
+ ,'doubtsaboutactions_g'
+ ,'belonging'
+ ,'parentalexpectations'
+ ,'parentalexpectations_g')
+ ,1:143))
> y <- array(NA,dim=c(10,143),dimnames=list(c('gender','popularity','popularity_g','hapiness','hapiness_g','doubsaboutactions','doubtsaboutactions_g','belonging','parentalexpectations','parentalexpectations_g'),1:143))
> 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 = '8'
> #'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
belonging gender popularity popularity_g hapiness hapiness_g
1 51 1 12 12 18 18
2 42 1 15 15 11 11
3 46 1 12 12 16 16
4 47 1 15 15 15 15
5 33 1 9 9 19 19
6 47 1 11 11 18 18
7 32 1 11 11 14 14
8 53 1 15 15 18 18
9 33 1 11 11 14 14
10 37 1 10 10 12 12
11 49 1 11 11 16 16
12 43 1 11 11 9 9
13 43 1 14 14 17 17
14 46 1 13 13 17 17
15 42 1 16 16 12 12
16 40 1 13 13 11 11
17 42 1 14 14 17 17
18 44 1 9 9 16 16
19 46 1 12 12 12 12
20 45 1 13 13 16 16
21 49 1 16 16 14 14
22 43 1 15 15 12 12
23 37 1 5 5 14 14
24 45 1 11 11 15 15
25 45 1 17 17 11 11
26 31 1 9 9 14 14
27 33 1 13 13 15 15
28 44 1 10 10 16 16
29 38 1 12 12 15 15
30 33 1 11 11 16 16
31 47 1 16 16 9 9
32 48 1 15 15 15 15
33 54 1 14 14 17 17
34 43 1 16 16 17 17
35 54 1 9 9 15 15
36 44 1 14 14 13 13
37 45 1 15 15 15 15
38 44 1 15 15 15 15
39 47 1 13 13 14 14
40 43 1 12 12 7 7
41 33 1 12 12 13 13
42 46 1 12 12 15 15
43 47 1 14 14 13 13
44 47 1 6 6 16 16
45 43 1 14 14 12 12
46 44 1 12 12 14 14
47 47 1 16 16 15 15
48 47 1 14 14 15 15
49 46 1 10 10 17 17
50 47 1 16 16 16 16
51 46 1 15 15 14 14
52 36 1 10 10 16 16
53 30 1 8 8 10 10
54 49 1 13 13 15 15
55 55 1 16 16 13 13
56 52 1 11 11 16 16
57 47 1 14 14 18 18
58 33 1 9 9 14 14
59 44 1 14 14 14 14
60 42 1 8 8 14 14
61 55 1 8 8 14 14
62 42 1 11 11 15 15
63 46 1 12 12 14 14
64 46 1 14 14 15 15
65 33 1 16 16 12 12
66 53 1 16 16 19 19
67 44 1 12 12 15 15
68 53 1 12 12 16 16
69 44 1 12 12 17 17
70 35 1 11 11 11 11
71 40 1 4 4 15 15
72 44 1 16 16 11 11
73 46 1 15 15 15 15
74 45 1 10 10 17 17
75 53 1 13 13 14 14
76 48 1 12 12 14 14
77 55 1 7 7 16 16
78 47 1 19 19 16 16
79 43 1 12 12 14 14
80 47 1 12 12 13 13
81 47 1 10 10 13 13
82 44 1 16 16 12 12
83 42 1 13 13 11 11
84 51 1 16 16 13 13
85 54 1 9 9 15 15
86 51 1 12 12 13 13
87 42 0 13 0 15 0
88 41 0 10 0 12 0
89 49 0 12 0 17 0
90 42 0 11 0 10 0
91 41 0 7 0 18 0
92 41 0 11 0 14 0
93 43 0 14 0 16 0
94 33 0 6 0 13 0
95 42 0 15 0 14 0
96 37 0 12 0 9 0
97 42 0 15 0 13 0
98 43 0 9 0 15 0
99 33 0 13 0 16 0
100 44 0 12 0 16 0
101 52 0 11 0 17 0
102 45 0 16 0 13 0
103 36 0 10 0 12 0
104 43 0 14 0 12 0
105 32 0 8 0 8 0
106 45 0 16 0 14 0
107 45 0 9 0 13 0
108 49 0 6 0 10 0
109 44 0 12 0 11 0
110 41 0 8 0 12 0
111 44 0 14 0 14 0
112 37 0 8 0 11 0
113 40 0 7 0 15 0
114 50 0 16 0 13 0
115 47 0 11 0 15 0
116 33 0 13 0 13 0
117 33 0 5 0 10 0
118 45 0 11 0 15 0
119 43 0 11 0 16 0
120 0 0 7 0 16 0
121 46 0 13 0 15 0
122 36 0 12 0 14 0
123 42 0 9 0 11 0
124 41 0 10 0 9 0
125 46 0 12 0 15 0
126 48 0 8 0 17 0
127 45 0 11 0 15 0
128 11 0 14 0 14 0
129 33 0 4 0 11 0
130 47 0 15 0 15 0
131 42 0 14 0 13 0
132 55 0 14 0 17 0
133 40 0 8 0 9 0
134 46 0 16 0 15 0
135 45 0 15 0 12 0
136 46 0 14 0 15 0
137 38 0 12 0 11 0
138 40 0 8 0 14 0
139 42 0 8 0 14 0
140 53 0 10 0 16 0
141 43 0 14 0 16 0
142 41 0 14 0 13 0
143 51 0 14 0 16 0
doubsaboutactions doubtsaboutactions_g parentalexpectations
1 9 9 15
2 9 9 14
3 8 8 10
4 15 15 18
5 11 11 11
6 8 8 12
7 9 9 15
8 6 6 17
9 11 11 7
10 16 16 18
11 7 7 18
12 15 15 11
13 10 10 12
14 6 6 11
15 12 12 16
16 14 14 14
17 9 9 13
18 14 14 17
19 14 14 13
20 8 8 12
21 10 10 12
22 9 9 9
23 11 11 18
24 9 9 14
25 10 10 12
26 8 8 12
27 14 14 9
28 10 10 12
29 14 14 11
30 15 15 13
31 11 11 13
32 8 8 6
33 10 10 21
34 10 10 11
35 9 9 9
36 13 13 18
37 10 10 15
38 11 11 11
39 10 10 14
40 16 16 12
41 6 6 8
42 11 11 11
43 14 14 17
44 9 9 16
45 11 11 13
46 12 12 13
47 9 9 13
48 14 14 15
49 8 8 12
50 10 10 12
51 8 8 15
52 11 11 21
53 14 14 24
54 10 10 15
55 9 9 17
56 8 8 16
57 8 8 15
58 16 16 11
59 13 13 15
60 13 13 12
61 8 8 14
62 9 9 12
63 11 11 20
64 9 9 17
65 14 14 11
66 7 7 11
67 11 11 12
68 9 9 15
69 8 8 10
70 14 14 14
71 12 12 16
72 12 12 18
73 6 6 6
74 16 16 16
75 8 8 11
76 12 12 10
77 12 12 15
78 9 9 14
79 11 11 7
80 13 13 12
81 11 11 13
82 12 12 14
83 10 10 13
84 13 13 12
85 9 9 11
86 8 8 13
87 9 0 12
88 14 0 10
89 14 0 9
90 14 0 11
91 14 0 14
92 8 0 24
93 11 0 11
94 13 0 14
95 9 0 12
96 16 0 5
97 14 0 11
98 12 0 10
99 4 0 15
100 13 0 8
101 14 0 18
102 10 0 10
103 8 0 11
104 9 0 12
105 15 0 7
106 9 0 16
107 8 0 17
108 11 0 9
109 9 0 13
110 12 0 10
111 13 0 10
112 9 0 13
113 7 0 7
114 10 0 13
115 11 0 9
116 8 0 9
117 14 0 9
118 16 0 14
119 11 0 8
120 9 0 11
121 12 0 11
122 20 0 8
123 11 0 11
124 10 0 15
125 7 0 12
126 8 0 11
127 14 0 12
128 16 0 12
129 12 0 13
130 8 0 12
131 11 0 9
132 10 0 11
133 14 0 8
134 10 0 12
135 13 0 20
136 11 0 16
137 16 0 9
138 10 0 12
139 11 0 17
140 9 0 11
141 11 0 11
142 14 0 15
143 14 0 11
parentalexpectations_g
1 15
2 14
3 10
4 18
5 11
6 12
7 15
8 17
9 7
10 18
11 18
12 11
13 12
14 11
15 16
16 14
17 13
18 17
19 13
20 12
21 12
22 9
23 18
24 14
25 12
26 12
27 9
28 12
29 11
30 13
31 13
32 6
33 21
34 11
35 9
36 18
37 15
38 11
39 14
40 12
41 8
42 11
43 17
44 16
45 13
46 13
47 13
48 15
49 12
50 12
51 15
52 21
53 24
54 15
55 17
56 16
57 15
58 11
59 15
60 12
61 14
62 12
63 20
64 17
65 11
66 11
67 12
68 15
69 10
70 14
71 16
72 18
73 6
74 16
75 11
76 10
77 15
78 14
79 7
80 12
81 13
82 14
83 13
84 12
85 11
86 13
87 0
88 0
89 0
90 0
91 0
92 0
93 0
94 0
95 0
96 0
97 0
98 0
99 0
100 0
101 0
102 0
103 0
104 0
105 0
106 0
107 0
108 0
109 0
110 0
111 0
112 0
113 0
114 0
115 0
116 0
117 0
118 0
119 0
120 0
121 0
122 0
123 0
124 0
125 0
126 0
127 0
128 0
129 0
130 0
131 0
132 0
133 0
134 0
135 0
136 0
137 0
138 0
139 0
140 0
141 0
142 0
143 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) gender popularity
27.1549 11.0419 0.7119
popularity_g hapiness hapiness_g
-0.2623 0.4457 -0.1072
doubsaboutactions doubtsaboutactions_g parentalexpectations
-0.1214 -0.6561 0.1232
parentalexpectations_g
0.1764
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-39.5311 -2.0011 0.3838 3.2887 13.3440
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 27.1549 8.1717 3.323 0.00115 **
gender 11.0419 12.4089 0.890 0.37516
popularity 0.7119 0.3070 2.319 0.02190 *
popularity_g -0.2623 0.4088 -0.642 0.52228
hapiness 0.4457 0.4070 1.095 0.27555
hapiness_g -0.1072 0.5486 -0.195 0.84539
doubsaboutactions -0.1214 0.3287 -0.369 0.71241
doubtsaboutactions_g -0.6561 0.4677 -1.403 0.16304
parentalexpectations 0.1232 0.2868 0.429 0.66834
parentalexpectations_g 0.1764 0.3678 0.479 0.63240
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.842 on 133 degrees of freedom
Multiple R-squared: 0.1901, Adjusted R-squared: 0.1353
F-statistic: 3.468 on 9 and 133 DF, p-value: 0.0007115
> 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,] 9.050054e-01 1.899893e-01 0.09499463
[2,] 8.263227e-01 3.473546e-01 0.17367730
[3,] 7.620001e-01 4.759997e-01 0.23799985
[4,] 6.552054e-01 6.895892e-01 0.34479459
[5,] 5.735103e-01 8.529795e-01 0.42648974
[6,] 4.911597e-01 9.823194e-01 0.50884032
[7,] 4.797081e-01 9.594162e-01 0.52029192
[8,] 3.845470e-01 7.690940e-01 0.61545302
[9,] 3.190843e-01 6.381685e-01 0.68091573
[10,] 2.413315e-01 4.826630e-01 0.75866852
[11,] 1.798576e-01 3.597152e-01 0.82014239
[12,] 1.340051e-01 2.680102e-01 0.86599490
[13,] 9.306789e-02 1.861358e-01 0.90693211
[14,] 1.252179e-01 2.504359e-01 0.87478206
[15,] 1.310978e-01 2.621957e-01 0.86890216
[16,] 1.095625e-01 2.191250e-01 0.89043752
[17,] 7.955894e-02 1.591179e-01 0.92044106
[18,] 8.073196e-02 1.614639e-01 0.91926804
[19,] 6.116148e-02 1.223230e-01 0.93883852
[20,] 5.698382e-02 1.139676e-01 0.94301618
[21,] 4.437603e-02 8.875206e-02 0.95562397
[22,] 3.466512e-02 6.933024e-02 0.96533488
[23,] 1.565422e-01 3.130845e-01 0.84345776
[24,] 1.205621e-01 2.411242e-01 0.87943791
[25,] 9.272410e-02 1.854482e-01 0.90727590
[26,] 6.907076e-02 1.381415e-01 0.93092924
[27,] 5.278372e-02 1.055674e-01 0.94721628
[28,] 5.082789e-02 1.016558e-01 0.94917211
[29,] 8.811495e-02 1.762299e-01 0.91188505
[30,] 7.552319e-02 1.510464e-01 0.92447681
[31,] 6.080234e-02 1.216047e-01 0.93919766
[32,] 5.676726e-02 1.135345e-01 0.94323274
[33,] 4.185869e-02 8.371737e-02 0.95814131
[34,] 3.109041e-02 6.218083e-02 0.96890959
[35,] 2.218197e-02 4.436394e-02 0.97781803
[36,] 1.738961e-02 3.477922e-02 0.98261039
[37,] 1.355127e-02 2.710253e-02 0.98644873
[38,] 9.364255e-03 1.872851e-02 0.99063574
[39,] 6.590942e-03 1.318188e-02 0.99340906
[40,] 1.153419e-02 2.306839e-02 0.98846581
[41,] 2.149643e-02 4.299287e-02 0.97850357
[42,] 1.730057e-02 3.460113e-02 0.98269943
[43,] 1.785875e-02 3.571751e-02 0.98214125
[44,] 1.729339e-02 3.458678e-02 0.98270661
[45,] 1.307991e-02 2.615983e-02 0.98692009
[46,] 1.047251e-02 2.094502e-02 0.98952749
[47,] 7.255556e-03 1.451111e-02 0.99274444
[48,] 5.978482e-03 1.195696e-02 0.99402152
[49,] 1.332870e-02 2.665740e-02 0.98667130
[50,] 1.040936e-02 2.081872e-02 0.98959064
[51,] 7.323779e-03 1.464756e-02 0.99267622
[52,] 5.344175e-03 1.068835e-02 0.99465583
[53,] 7.031118e-03 1.406224e-02 0.99296888
[54,] 5.299547e-03 1.059909e-02 0.99470045
[55,] 3.767359e-03 7.534718e-03 0.99623264
[56,] 3.610677e-03 7.221354e-03 0.99638932
[57,] 2.841942e-03 5.683885e-03 0.99715806
[58,] 2.438617e-03 4.877234e-03 0.99756138
[59,] 2.322364e-03 4.644728e-03 0.99767764
[60,] 1.577203e-03 3.154406e-03 0.99842280
[61,] 1.071591e-03 2.143181e-03 0.99892841
[62,] 9.347823e-04 1.869565e-03 0.99906522
[63,] 9.975795e-04 1.995159e-03 0.99900242
[64,] 8.982382e-04 1.796476e-03 0.99910176
[65,] 1.995678e-03 3.991356e-03 0.99800432
[66,] 1.604442e-03 3.208883e-03 0.99839556
[67,] 1.132680e-03 2.265359e-03 0.99886732
[68,] 9.068941e-04 1.813788e-03 0.99909311
[69,] 6.954841e-04 1.390968e-03 0.99930452
[70,] 4.412371e-04 8.824743e-04 0.99955876
[71,] 2.789960e-04 5.579921e-04 0.99972100
[72,] 2.509913e-04 5.019825e-04 0.99974901
[73,] 3.129160e-04 6.258320e-04 0.99968708
[74,] 2.302483e-04 4.604966e-04 0.99976975
[75,] 1.416266e-04 2.832531e-04 0.99985837
[76,] 8.616433e-05 1.723287e-04 0.99991384
[77,] 6.131248e-05 1.226250e-04 0.99993869
[78,] 3.720377e-05 7.440753e-05 0.99996280
[79,] 2.155874e-05 4.311748e-05 0.99997844
[80,] 1.265919e-05 2.531837e-05 0.99998734
[81,] 7.756781e-06 1.551356e-05 0.99999224
[82,] 4.767585e-06 9.535169e-06 0.99999523
[83,] 2.675252e-06 5.350504e-06 0.99999732
[84,] 1.619873e-06 3.239745e-06 0.99999838
[85,] 9.976274e-07 1.995255e-06 0.99999900
[86,] 5.849296e-07 1.169859e-06 0.99999942
[87,] 1.027873e-06 2.055746e-06 0.99999897
[88,] 5.515638e-07 1.103128e-06 0.99999945
[89,] 5.245982e-07 1.049196e-06 0.99999948
[90,] 2.790646e-07 5.581293e-07 0.99999972
[91,] 1.894044e-07 3.788087e-07 0.99999981
[92,] 1.051847e-07 2.103694e-07 0.99999989
[93,] 5.520179e-08 1.104036e-07 0.99999994
[94,] 2.690021e-08 5.380041e-08 0.99999997
[95,] 2.381823e-08 4.763645e-08 0.99999998
[96,] 1.721654e-07 3.443308e-07 0.99999983
[97,] 9.095699e-08 1.819140e-07 0.99999991
[98,] 4.783555e-08 9.567110e-08 0.99999995
[99,] 2.143889e-08 4.287779e-08 0.99999998
[100,] 9.547399e-09 1.909480e-08 0.99999999
[101,] 4.152819e-09 8.305639e-09 1.00000000
[102,] 2.486981e-09 4.973962e-09 1.00000000
[103,] 1.620822e-09 3.241645e-09 1.00000000
[104,] 2.864916e-09 5.729832e-09 1.00000000
[105,] 1.330508e-09 2.661015e-09 1.00000000
[106,] 1.018248e-09 2.036496e-09 1.00000000
[107,] 3.915242e-10 7.830485e-10 1.00000000
[108,] 2.579014e-02 5.158027e-02 0.97420986
[109,] 1.683669e-02 3.367338e-02 0.98316331
[110,] 1.482452e-02 2.964905e-02 0.98517548
[111,] 8.923879e-03 1.784776e-02 0.99107612
[112,] 4.906527e-03 9.813054e-03 0.99509347
[113,] 3.554808e-03 7.109616e-03 0.99644519
[114,] 2.321405e-03 4.642810e-03 0.99767859
[115,] 1.715309e-03 3.430618e-03 0.99828469
[116,] 6.491850e-01 7.016300e-01 0.35081498
[117,] 5.585690e-01 8.828621e-01 0.44143105
[118,] 3.912831e-01 7.825661e-01 0.60871693
> postscript(file="/var/www/html/rcomp/tmp/1w3yx1292320405.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2w3yx1292320405.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37cxi1292320405.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/47cxi1292320405.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57cxi1292320405.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 = 143
Frequency = 1
1 2 3 4 5 6
3.81926229 -3.86080403 0.21633037 3.25205557 -10.41725913 0.38998526
7 8 9 10 11 12
-13.37716843 1.53886184 -8.42608865 -2.70678827 0.49237315 5.17814465
13 14 15 16 17 18
-3.06553032 -2.42626321 -2.91554216 -1.07413580 -5.14252107 2.13350649
19 20 21 22 23 24
5.33654851 -1.83235059 3.05061410 -1.70170239 -5.02287967 -0.41613232
25 26 27 28 29 30
-0.38359329 -13.35679726 -7.93047497 0.07154273 -3.07985954 -7.79025001
31 32 33 34 35 36
3.22097343 2.40393158 5.23882225 -3.66531033 10.98074598 -0.17628443
37 38 39 40 41 42
-1.73676712 -0.76122723 1.80052592 5.88341498 -12.72414479 2.58771736
43 44 45 46 47 48
3.90070632 2.89459563 -0.89517099 1.10463923 -0.36485692 3.82277854
49 50 51 52 53 54
0.17811373 0.37365356 -1.95323559 -9.84663033 -11.48257836 3.16252927
55 56 57 58 59 60
7.11403809 4.86888028 -1.85750847 -4.83748594 0.38378445 1.98022277
61 62 63 64 65 66
10.49381817 -2.81709956 0.23055020 -1.66362606 -8.86301151 3.32530603
67 68 69 70 71 72
0.28820098 6.49622283 -2.12214990 -5.17483941 -0.53520461 -1.17609465
73 74 75 76 77 78
-1.15101716 4.19984314 7.14412633 6.00318838 13.07688691 -2.35179816
79 80 81 82 83 84
1.12426316 5.52011025 4.56494153 -0.31650939 -1.88451689 7.72151747
85 86 87 88 89 90
10.38171321 5.33322204 -1.48015262 1.84601057 6.31701005 2.90223626
91 92 93 94 95 96
0.81519895 -2.20998698 -1.27175647 -4.36598311 -2.45835647 -1.38223762
97 98 99 100 101 102
-1.28247039 2.97813733 -11.90236286 1.76441908 8.92048316 0.64310851
103 104 105 106 107 108
-4.00562856 0.14489494 -3.45658755 -0.66293660 4.52167100 13.34398098
109 110 111 112 113 114
2.89125628 3.02704852 0.98555087 -1.26101645 1.16442299 5.27362227
115 116 117 118 119 120
5.55602293 -9.34075965 -1.57984868 3.54727674 1.23352522 -39.53105943
121 122 123 124 125 126
3.00724798 -4.49437145 3.51620157 2.08152819 2.98895351 7.18993612
127 128 129 130 131 132
3.55077522 -31.89653476 -2.04905065 1.97457090 -0.68845294 10.16117089
133 134 135 136 137 138
3.85317774 0.50546477 0.93331785 1.55809293 -1.76620551 0.64657910
139 140 141 142 143
2.15218154 11.33314511 -1.27175647 -2.06318689 7.09248205
> postscript(file="/var/www/html/rcomp/tmp/6z3w31292320405.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 = 143
Frequency = 1
lag(myerror, k = 1) myerror
0 3.81926229 NA
1 -3.86080403 3.81926229
2 0.21633037 -3.86080403
3 3.25205557 0.21633037
4 -10.41725913 3.25205557
5 0.38998526 -10.41725913
6 -13.37716843 0.38998526
7 1.53886184 -13.37716843
8 -8.42608865 1.53886184
9 -2.70678827 -8.42608865
10 0.49237315 -2.70678827
11 5.17814465 0.49237315
12 -3.06553032 5.17814465
13 -2.42626321 -3.06553032
14 -2.91554216 -2.42626321
15 -1.07413580 -2.91554216
16 -5.14252107 -1.07413580
17 2.13350649 -5.14252107
18 5.33654851 2.13350649
19 -1.83235059 5.33654851
20 3.05061410 -1.83235059
21 -1.70170239 3.05061410
22 -5.02287967 -1.70170239
23 -0.41613232 -5.02287967
24 -0.38359329 -0.41613232
25 -13.35679726 -0.38359329
26 -7.93047497 -13.35679726
27 0.07154273 -7.93047497
28 -3.07985954 0.07154273
29 -7.79025001 -3.07985954
30 3.22097343 -7.79025001
31 2.40393158 3.22097343
32 5.23882225 2.40393158
33 -3.66531033 5.23882225
34 10.98074598 -3.66531033
35 -0.17628443 10.98074598
36 -1.73676712 -0.17628443
37 -0.76122723 -1.73676712
38 1.80052592 -0.76122723
39 5.88341498 1.80052592
40 -12.72414479 5.88341498
41 2.58771736 -12.72414479
42 3.90070632 2.58771736
43 2.89459563 3.90070632
44 -0.89517099 2.89459563
45 1.10463923 -0.89517099
46 -0.36485692 1.10463923
47 3.82277854 -0.36485692
48 0.17811373 3.82277854
49 0.37365356 0.17811373
50 -1.95323559 0.37365356
51 -9.84663033 -1.95323559
52 -11.48257836 -9.84663033
53 3.16252927 -11.48257836
54 7.11403809 3.16252927
55 4.86888028 7.11403809
56 -1.85750847 4.86888028
57 -4.83748594 -1.85750847
58 0.38378445 -4.83748594
59 1.98022277 0.38378445
60 10.49381817 1.98022277
61 -2.81709956 10.49381817
62 0.23055020 -2.81709956
63 -1.66362606 0.23055020
64 -8.86301151 -1.66362606
65 3.32530603 -8.86301151
66 0.28820098 3.32530603
67 6.49622283 0.28820098
68 -2.12214990 6.49622283
69 -5.17483941 -2.12214990
70 -0.53520461 -5.17483941
71 -1.17609465 -0.53520461
72 -1.15101716 -1.17609465
73 4.19984314 -1.15101716
74 7.14412633 4.19984314
75 6.00318838 7.14412633
76 13.07688691 6.00318838
77 -2.35179816 13.07688691
78 1.12426316 -2.35179816
79 5.52011025 1.12426316
80 4.56494153 5.52011025
81 -0.31650939 4.56494153
82 -1.88451689 -0.31650939
83 7.72151747 -1.88451689
84 10.38171321 7.72151747
85 5.33322204 10.38171321
86 -1.48015262 5.33322204
87 1.84601057 -1.48015262
88 6.31701005 1.84601057
89 2.90223626 6.31701005
90 0.81519895 2.90223626
91 -2.20998698 0.81519895
92 -1.27175647 -2.20998698
93 -4.36598311 -1.27175647
94 -2.45835647 -4.36598311
95 -1.38223762 -2.45835647
96 -1.28247039 -1.38223762
97 2.97813733 -1.28247039
98 -11.90236286 2.97813733
99 1.76441908 -11.90236286
100 8.92048316 1.76441908
101 0.64310851 8.92048316
102 -4.00562856 0.64310851
103 0.14489494 -4.00562856
104 -3.45658755 0.14489494
105 -0.66293660 -3.45658755
106 4.52167100 -0.66293660
107 13.34398098 4.52167100
108 2.89125628 13.34398098
109 3.02704852 2.89125628
110 0.98555087 3.02704852
111 -1.26101645 0.98555087
112 1.16442299 -1.26101645
113 5.27362227 1.16442299
114 5.55602293 5.27362227
115 -9.34075965 5.55602293
116 -1.57984868 -9.34075965
117 3.54727674 -1.57984868
118 1.23352522 3.54727674
119 -39.53105943 1.23352522
120 3.00724798 -39.53105943
121 -4.49437145 3.00724798
122 3.51620157 -4.49437145
123 2.08152819 3.51620157
124 2.98895351 2.08152819
125 7.18993612 2.98895351
126 3.55077522 7.18993612
127 -31.89653476 3.55077522
128 -2.04905065 -31.89653476
129 1.97457090 -2.04905065
130 -0.68845294 1.97457090
131 10.16117089 -0.68845294
132 3.85317774 10.16117089
133 0.50546477 3.85317774
134 0.93331785 0.50546477
135 1.55809293 0.93331785
136 -1.76620551 1.55809293
137 0.64657910 -1.76620551
138 2.15218154 0.64657910
139 11.33314511 2.15218154
140 -1.27175647 11.33314511
141 -2.06318689 -1.27175647
142 7.09248205 -2.06318689
143 NA 7.09248205
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.86080403 3.81926229
[2,] 0.21633037 -3.86080403
[3,] 3.25205557 0.21633037
[4,] -10.41725913 3.25205557
[5,] 0.38998526 -10.41725913
[6,] -13.37716843 0.38998526
[7,] 1.53886184 -13.37716843
[8,] -8.42608865 1.53886184
[9,] -2.70678827 -8.42608865
[10,] 0.49237315 -2.70678827
[11,] 5.17814465 0.49237315
[12,] -3.06553032 5.17814465
[13,] -2.42626321 -3.06553032
[14,] -2.91554216 -2.42626321
[15,] -1.07413580 -2.91554216
[16,] -5.14252107 -1.07413580
[17,] 2.13350649 -5.14252107
[18,] 5.33654851 2.13350649
[19,] -1.83235059 5.33654851
[20,] 3.05061410 -1.83235059
[21,] -1.70170239 3.05061410
[22,] -5.02287967 -1.70170239
[23,] -0.41613232 -5.02287967
[24,] -0.38359329 -0.41613232
[25,] -13.35679726 -0.38359329
[26,] -7.93047497 -13.35679726
[27,] 0.07154273 -7.93047497
[28,] -3.07985954 0.07154273
[29,] -7.79025001 -3.07985954
[30,] 3.22097343 -7.79025001
[31,] 2.40393158 3.22097343
[32,] 5.23882225 2.40393158
[33,] -3.66531033 5.23882225
[34,] 10.98074598 -3.66531033
[35,] -0.17628443 10.98074598
[36,] -1.73676712 -0.17628443
[37,] -0.76122723 -1.73676712
[38,] 1.80052592 -0.76122723
[39,] 5.88341498 1.80052592
[40,] -12.72414479 5.88341498
[41,] 2.58771736 -12.72414479
[42,] 3.90070632 2.58771736
[43,] 2.89459563 3.90070632
[44,] -0.89517099 2.89459563
[45,] 1.10463923 -0.89517099
[46,] -0.36485692 1.10463923
[47,] 3.82277854 -0.36485692
[48,] 0.17811373 3.82277854
[49,] 0.37365356 0.17811373
[50,] -1.95323559 0.37365356
[51,] -9.84663033 -1.95323559
[52,] -11.48257836 -9.84663033
[53,] 3.16252927 -11.48257836
[54,] 7.11403809 3.16252927
[55,] 4.86888028 7.11403809
[56,] -1.85750847 4.86888028
[57,] -4.83748594 -1.85750847
[58,] 0.38378445 -4.83748594
[59,] 1.98022277 0.38378445
[60,] 10.49381817 1.98022277
[61,] -2.81709956 10.49381817
[62,] 0.23055020 -2.81709956
[63,] -1.66362606 0.23055020
[64,] -8.86301151 -1.66362606
[65,] 3.32530603 -8.86301151
[66,] 0.28820098 3.32530603
[67,] 6.49622283 0.28820098
[68,] -2.12214990 6.49622283
[69,] -5.17483941 -2.12214990
[70,] -0.53520461 -5.17483941
[71,] -1.17609465 -0.53520461
[72,] -1.15101716 -1.17609465
[73,] 4.19984314 -1.15101716
[74,] 7.14412633 4.19984314
[75,] 6.00318838 7.14412633
[76,] 13.07688691 6.00318838
[77,] -2.35179816 13.07688691
[78,] 1.12426316 -2.35179816
[79,] 5.52011025 1.12426316
[80,] 4.56494153 5.52011025
[81,] -0.31650939 4.56494153
[82,] -1.88451689 -0.31650939
[83,] 7.72151747 -1.88451689
[84,] 10.38171321 7.72151747
[85,] 5.33322204 10.38171321
[86,] -1.48015262 5.33322204
[87,] 1.84601057 -1.48015262
[88,] 6.31701005 1.84601057
[89,] 2.90223626 6.31701005
[90,] 0.81519895 2.90223626
[91,] -2.20998698 0.81519895
[92,] -1.27175647 -2.20998698
[93,] -4.36598311 -1.27175647
[94,] -2.45835647 -4.36598311
[95,] -1.38223762 -2.45835647
[96,] -1.28247039 -1.38223762
[97,] 2.97813733 -1.28247039
[98,] -11.90236286 2.97813733
[99,] 1.76441908 -11.90236286
[100,] 8.92048316 1.76441908
[101,] 0.64310851 8.92048316
[102,] -4.00562856 0.64310851
[103,] 0.14489494 -4.00562856
[104,] -3.45658755 0.14489494
[105,] -0.66293660 -3.45658755
[106,] 4.52167100 -0.66293660
[107,] 13.34398098 4.52167100
[108,] 2.89125628 13.34398098
[109,] 3.02704852 2.89125628
[110,] 0.98555087 3.02704852
[111,] -1.26101645 0.98555087
[112,] 1.16442299 -1.26101645
[113,] 5.27362227 1.16442299
[114,] 5.55602293 5.27362227
[115,] -9.34075965 5.55602293
[116,] -1.57984868 -9.34075965
[117,] 3.54727674 -1.57984868
[118,] 1.23352522 3.54727674
[119,] -39.53105943 1.23352522
[120,] 3.00724798 -39.53105943
[121,] -4.49437145 3.00724798
[122,] 3.51620157 -4.49437145
[123,] 2.08152819 3.51620157
[124,] 2.98895351 2.08152819
[125,] 7.18993612 2.98895351
[126,] 3.55077522 7.18993612
[127,] -31.89653476 3.55077522
[128,] -2.04905065 -31.89653476
[129,] 1.97457090 -2.04905065
[130,] -0.68845294 1.97457090
[131,] 10.16117089 -0.68845294
[132,] 3.85317774 10.16117089
[133,] 0.50546477 3.85317774
[134,] 0.93331785 0.50546477
[135,] 1.55809293 0.93331785
[136,] -1.76620551 1.55809293
[137,] 0.64657910 -1.76620551
[138,] 2.15218154 0.64657910
[139,] 11.33314511 2.15218154
[140,] -1.27175647 11.33314511
[141,] -2.06318689 -1.27175647
[142,] 7.09248205 -2.06318689
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.86080403 3.81926229
2 0.21633037 -3.86080403
3 3.25205557 0.21633037
4 -10.41725913 3.25205557
5 0.38998526 -10.41725913
6 -13.37716843 0.38998526
7 1.53886184 -13.37716843
8 -8.42608865 1.53886184
9 -2.70678827 -8.42608865
10 0.49237315 -2.70678827
11 5.17814465 0.49237315
12 -3.06553032 5.17814465
13 -2.42626321 -3.06553032
14 -2.91554216 -2.42626321
15 -1.07413580 -2.91554216
16 -5.14252107 -1.07413580
17 2.13350649 -5.14252107
18 5.33654851 2.13350649
19 -1.83235059 5.33654851
20 3.05061410 -1.83235059
21 -1.70170239 3.05061410
22 -5.02287967 -1.70170239
23 -0.41613232 -5.02287967
24 -0.38359329 -0.41613232
25 -13.35679726 -0.38359329
26 -7.93047497 -13.35679726
27 0.07154273 -7.93047497
28 -3.07985954 0.07154273
29 -7.79025001 -3.07985954
30 3.22097343 -7.79025001
31 2.40393158 3.22097343
32 5.23882225 2.40393158
33 -3.66531033 5.23882225
34 10.98074598 -3.66531033
35 -0.17628443 10.98074598
36 -1.73676712 -0.17628443
37 -0.76122723 -1.73676712
38 1.80052592 -0.76122723
39 5.88341498 1.80052592
40 -12.72414479 5.88341498
41 2.58771736 -12.72414479
42 3.90070632 2.58771736
43 2.89459563 3.90070632
44 -0.89517099 2.89459563
45 1.10463923 -0.89517099
46 -0.36485692 1.10463923
47 3.82277854 -0.36485692
48 0.17811373 3.82277854
49 0.37365356 0.17811373
50 -1.95323559 0.37365356
51 -9.84663033 -1.95323559
52 -11.48257836 -9.84663033
53 3.16252927 -11.48257836
54 7.11403809 3.16252927
55 4.86888028 7.11403809
56 -1.85750847 4.86888028
57 -4.83748594 -1.85750847
58 0.38378445 -4.83748594
59 1.98022277 0.38378445
60 10.49381817 1.98022277
61 -2.81709956 10.49381817
62 0.23055020 -2.81709956
63 -1.66362606 0.23055020
64 -8.86301151 -1.66362606
65 3.32530603 -8.86301151
66 0.28820098 3.32530603
67 6.49622283 0.28820098
68 -2.12214990 6.49622283
69 -5.17483941 -2.12214990
70 -0.53520461 -5.17483941
71 -1.17609465 -0.53520461
72 -1.15101716 -1.17609465
73 4.19984314 -1.15101716
74 7.14412633 4.19984314
75 6.00318838 7.14412633
76 13.07688691 6.00318838
77 -2.35179816 13.07688691
78 1.12426316 -2.35179816
79 5.52011025 1.12426316
80 4.56494153 5.52011025
81 -0.31650939 4.56494153
82 -1.88451689 -0.31650939
83 7.72151747 -1.88451689
84 10.38171321 7.72151747
85 5.33322204 10.38171321
86 -1.48015262 5.33322204
87 1.84601057 -1.48015262
88 6.31701005 1.84601057
89 2.90223626 6.31701005
90 0.81519895 2.90223626
91 -2.20998698 0.81519895
92 -1.27175647 -2.20998698
93 -4.36598311 -1.27175647
94 -2.45835647 -4.36598311
95 -1.38223762 -2.45835647
96 -1.28247039 -1.38223762
97 2.97813733 -1.28247039
98 -11.90236286 2.97813733
99 1.76441908 -11.90236286
100 8.92048316 1.76441908
101 0.64310851 8.92048316
102 -4.00562856 0.64310851
103 0.14489494 -4.00562856
104 -3.45658755 0.14489494
105 -0.66293660 -3.45658755
106 4.52167100 -0.66293660
107 13.34398098 4.52167100
108 2.89125628 13.34398098
109 3.02704852 2.89125628
110 0.98555087 3.02704852
111 -1.26101645 0.98555087
112 1.16442299 -1.26101645
113 5.27362227 1.16442299
114 5.55602293 5.27362227
115 -9.34075965 5.55602293
116 -1.57984868 -9.34075965
117 3.54727674 -1.57984868
118 1.23352522 3.54727674
119 -39.53105943 1.23352522
120 3.00724798 -39.53105943
121 -4.49437145 3.00724798
122 3.51620157 -4.49437145
123 2.08152819 3.51620157
124 2.98895351 2.08152819
125 7.18993612 2.98895351
126 3.55077522 7.18993612
127 -31.89653476 3.55077522
128 -2.04905065 -31.89653476
129 1.97457090 -2.04905065
130 -0.68845294 1.97457090
131 10.16117089 -0.68845294
132 3.85317774 10.16117089
133 0.50546477 3.85317774
134 0.93331785 0.50546477
135 1.55809293 0.93331785
136 -1.76620551 1.55809293
137 0.64657910 -1.76620551
138 2.15218154 0.64657910
139 11.33314511 2.15218154
140 -1.27175647 11.33314511
141 -2.06318689 -1.27175647
142 7.09248205 -2.06318689
> 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/7z3w31292320405.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8sceo1292320405.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9sceo1292320405.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10k4vq1292320405.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116mte1292320405.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/12zvth1292320405.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/135xqt1292320405.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/14yo7w1292320405.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/152oo21292320405.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/16yg3t1292320405.tab")
+ }
>
> try(system("convert tmp/1w3yx1292320405.ps tmp/1w3yx1292320405.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w3yx1292320405.ps tmp/2w3yx1292320405.png",intern=TRUE))
character(0)
> try(system("convert tmp/37cxi1292320405.ps tmp/37cxi1292320405.png",intern=TRUE))
character(0)
> try(system("convert tmp/47cxi1292320405.ps tmp/47cxi1292320405.png",intern=TRUE))
character(0)
> try(system("convert tmp/57cxi1292320405.ps tmp/57cxi1292320405.png",intern=TRUE))
character(0)
> try(system("convert tmp/6z3w31292320405.ps tmp/6z3w31292320405.png",intern=TRUE))
character(0)
> try(system("convert tmp/7z3w31292320405.ps tmp/7z3w31292320405.png",intern=TRUE))
character(0)
> try(system("convert tmp/8sceo1292320405.ps tmp/8sceo1292320405.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sceo1292320405.ps tmp/9sceo1292320405.png",intern=TRUE))
character(0)
> try(system("convert tmp/10k4vq1292320405.ps tmp/10k4vq1292320405.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.171 1.869 10.023