R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(27.72
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+ ,11299240
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+ ,78808770
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+ ,73.1
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+ ,25.94
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+ ,123513900
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+ ,2976.78
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,26.15
+ ,-0.3
+ ,85687430
+ ,107.59
+ ,2967.79
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,26.36
+ ,-0.3
+ ,49113040
+ ,107.85
+ ,2991.78
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,27.32
+ ,-0.3
+ ,88572990
+ ,107.11
+ ,3012.03
+ ,0.0176
+ ,73.1
+ ,0.16
+ ,28.00
+ ,-0.3
+ ,126867400
+ ,108.14
+ ,3010.24
+ ,0.0176
+ ,73.1
+ ,0.16)
+ ,dim=c(8
+ ,126)
+ ,dimnames=list(c('FACEBOOK'
+ ,'REV.GROWTH'
+ ,'VOLUME'
+ ,'LINKEDIN'
+ ,'NASDAQ'
+ ,'INF.CONS.CONF'
+ ,'FED'
+ ,'FUNDS.RATE')
+ ,1:126))
> y <- array(NA,dim=c(8,126),dimnames=list(c('FACEBOOK','REV.GROWTH','VOLUME','LINKEDIN','NASDAQ','INF.CONS.CONF','FED','FUNDS.RATE'),1:126))
> 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'
> 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, 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
FACEBOOK REV.GROWTH VOLUME LINKEDIN NASDAQ INF.CONS.CONF FED
1 27.72 0.02 41837160 91.51 2747.48 0.0160 62.7
2 26.90 0.02 35204750 91.09 2760.01 0.0160 62.7
3 25.86 0.02 42367740 93.00 2778.11 0.0160 62.7
4 26.81 0.02 61427940 93.08 2844.72 0.0160 62.7
5 26.31 0.02 26132090 94.13 2831.02 0.0160 62.7
6 27.10 0.02 3799718 96.26 2858.42 0.0160 62.7
7 27.00 0.02 28202230 94.29 2809.73 0.0160 62.7
8 27.40 0.02 15809640 94.46 2843.07 0.0160 62.7
9 27.27 0.02 17110160 95.53 2818.61 0.0160 62.7
10 28.29 0.02 16835510 98.29 2836.33 0.0160 62.7
11 30.01 0.02 43517670 102.01 2872.80 0.0160 62.7
12 31.41 0.02 42958450 105.16 2895.33 0.0160 62.7
13 31.91 0.02 30826830 105.34 2929.76 0.0160 62.7
14 31.60 0.01 15549740 105.27 2930.45 0.0160 62.7
15 31.84 0.01 21843070 102.19 2859.09 0.0160 62.7
16 33.05 0.01 73424890 106.85 2892.42 0.0160 62.7
17 32.06 0.01 24330740 103.05 2836.16 0.0160 62.7
18 33.10 0.01 24785970 106.42 2854.06 0.0160 62.7
19 32.23 0.01 28553940 105.17 2875.32 0.0160 62.7
20 31.36 0.01 17659080 102.74 2849.49 0.0160 62.7
21 31.09 0.01 19508980 106.27 2935.05 0.0160 62.7
22 30.77 0.01 14110230 107.63 2951.23 0.0141 65.4
23 31.20 0.01 8765498 108.54 2976.08 0.0141 65.4
24 31.47 0.01 10027250 108.24 2976.12 0.0141 65.4
25 31.73 0.01 10943350 108.86 2937.33 0.0141 65.4
26 32.17 0.01 17755740 102.98 2931.77 0.0141 65.4
27 31.47 0.00 14238190 99.53 2902.33 0.0141 65.4
28 30.97 0.00 12997760 101.08 2887.98 0.0141 65.4
29 30.81 0.00 11299240 104.64 2866.19 0.0141 65.4
30 30.72 0.00 8102653 105.59 2908.47 0.0141 65.4
31 28.24 0.00 24549800 103.21 2896.94 0.0141 65.4
32 28.09 0.00 30410530 103.84 2910.04 0.0141 65.4
33 29.11 0.00 16807730 104.61 2942.60 0.0141 65.4
34 29.00 0.00 13671200 108.65 2965.90 0.0141 65.4
35 28.76 0.00 11854290 106.26 2925.30 0.0141 65.4
36 28.75 0.00 12383610 104.20 2890.15 0.0141 65.4
37 28.45 0.00 11512350 102.99 2862.99 0.0141 65.4
38 29.34 0.00 16749990 102.19 2854.24 0.0141 65.4
39 26.84 0.00 61009290 100.82 2893.25 0.0141 65.4
40 23.70 0.00 123011300 103.42 2958.09 0.0141 65.4
41 23.15 0.00 29253590 104.18 2945.84 0.0141 65.4
42 21.71 0.00 55998620 102.65 2939.52 0.0141 65.4
43 20.88 0.00 44488370 95.64 2920.21 0.0169 61.3
44 20.04 0.00 56264460 93.51 2909.77 0.0169 61.3
45 21.09 0.00 80626220 108.51 2967.90 0.0169 61.3
46 21.92 0.00 27733830 111.55 2989.91 0.0169 61.3
47 20.72 0.00 36699380 106.70 3015.86 0.0169 61.3
48 20.72 0.00 29514550 104.93 3011.25 0.0169 61.3
49 21.01 -0.01 15605960 105.23 3018.64 0.0169 61.3
50 21.80 -0.01 25714310 104.92 3020.86 0.0169 61.3
51 21.60 -0.01 24904700 104.60 3022.52 0.0169 61.3
52 20.38 -0.01 38971320 101.76 3016.98 0.0169 61.3
53 21.20 -0.01 47682050 102.23 3030.93 0.0169 61.3
54 19.87 -0.01 157188200 103.99 3062.39 0.0169 61.3
55 19.05 -0.01 129057400 101.36 3076.59 0.0169 61.3
56 20.01 -0.01 100818300 102.92 3076.21 0.0169 61.3
57 19.15 -0.01 70483330 105.25 3067.26 0.0169 61.3
58 19.43 -0.01 49779450 105.71 3073.67 0.0169 61.3
59 19.44 -0.01 32747000 105.42 3053.40 0.0169 61.3
60 19.40 -0.01 29588690 105.11 3069.79 0.0169 61.3
61 19.15 -0.01 20663220 104.67 3073.19 0.0169 61.3
62 19.34 -0.01 25402980 107.51 3077.14 0.0169 61.3
63 19.10 -0.01 16071190 109.00 3081.19 0.0169 61.3
64 19.08 -0.01 30571430 107.37 3048.71 0.0169 61.3
65 18.05 -0.01 58612440 107.30 3066.96 0.0169 61.3
66 17.72 -0.01 46177000 107.37 3075.06 0.0199 70.3
67 18.58 -0.01 60657900 113.28 3069.27 0.0199 70.3
68 18.96 -0.01 46028860 119.10 3135.81 0.0199 70.3
69 18.98 -0.01 36325880 119.04 3136.42 0.0199 70.3
70 18.81 -0.01 24752340 117.80 3104.02 0.0199 70.3
71 19.43 -0.01 47343020 117.90 3104.53 0.0199 70.3
72 20.93 -0.01 121399400 119.55 3114.31 0.0199 70.3
73 20.71 -0.01 64896660 119.47 3155.83 0.0199 70.3
74 22.00 -0.01 72707430 123.23 3183.95 0.0199 70.3
75 21.52 -0.02 50593510 121.40 3178.67 0.0199 70.3
76 21.87 -0.02 36696330 121.43 3177.80 0.0199 70.3
77 23.29 -0.02 78525460 122.51 3182.62 0.0199 70.3
78 22.59 -0.02 57115160 122.78 3175.96 0.0199 70.3
79 22.86 -0.02 51163120 122.84 3179.96 0.0199 70.3
80 20.79 -0.02 78968380 122.70 3160.78 0.0199 70.3
81 20.28 -0.02 46169460 119.89 3117.73 0.0199 70.3
82 20.62 -0.02 38212360 118.00 3093.70 0.0199 70.3
83 20.32 -0.02 30061050 119.61 3136.60 0.0199 70.3
84 21.66 -0.02 65415370 120.40 3116.23 0.0199 70.3
85 21.99 -0.02 51198150 117.94 3113.53 0.0216 73.1
86 22.27 -0.02 29276680 118.77 3120.04 0.0216 73.1
87 21.83 -0.02 31940720 121.68 3135.23 0.0216 73.1
88 21.94 -0.02 46549400 121.98 3149.46 0.0216 73.1
89 20.91 -0.02 40483780 118.83 3136.19 0.0216 73.1
90 20.40 -0.02 32190200 117.97 3112.35 0.0216 73.1
91 20.22 -0.02 27125670 113.07 3065.02 0.0216 73.1
92 19.64 -0.30 39282420 111.98 3051.78 0.0216 73.1
93 19.75 -0.30 21803710 113.77 3049.41 0.0216 73.1
94 19.51 -0.30 18743920 110.41 3044.11 0.0216 73.1
95 19.52 -0.30 20154860 110.85 3064.18 0.0216 73.1
96 19.48 -0.30 21816100 111.18 3101.17 0.0216 73.1
97 19.88 -0.30 44020450 109.42 3104.12 0.0216 73.1
98 18.97 -0.30 52059860 108.87 3072.87 0.0216 73.1
99 19.00 -0.30 34769600 106.72 3005.62 0.0216 73.1
100 19.32 -0.30 32269470 107.28 3016.96 0.0216 73.1
101 19.50 -0.30 72281000 104.13 2990.46 0.0216 73.1
102 23.22 -0.30 228364700 107.55 2981.70 0.0216 73.1
103 22.56 -0.30 76050080 105.72 2986.12 0.0216 73.1
104 21.94 -0.30 9999999 104.55 2987.95 0.0216 73.1
105 21.11 -0.02 99311480 106.93 2977.23 0.0216 73.1
106 21.21 -0.30 37631000 106.85 3020.06 0.0176 73.1
107 21.18 -0.30 38308550 106.78 2982.13 0.0176 73.1
108 21.25 -0.30 31752420 107.29 2999.66 0.0176 73.1
109 21.17 -0.30 29030780 104.14 3011.93 0.0176 73.1
110 20.47 -0.30 33352920 101.21 2937.29 0.0176 73.1
111 19.99 -0.30 34106840 96.35 2895.58 0.0176 73.1
112 19.21 -0.30 42257790 95.62 2904.87 0.0176 73.1
113 20.07 -0.30 67220540 99.00 2904.26 0.0176 73.1
114 19.86 -0.30 71524510 99.26 2883.89 0.0176 73.1
115 22.36 -0.30 229081600 98.77 2846.81 0.0176 73.1
116 22.17 -0.30 78808770 100.65 2836.94 0.0176 73.1
117 23.56 -0.30 107091400 103.13 2853.13 0.0176 73.1
118 22.92 -0.30 84944370 105.53 2916.07 0.0176 73.1
119 23.10 -0.30 46515660 106.76 2916.68 0.0176 73.1
120 24.32 -0.30 89720920 107.59 2926.55 0.0176 73.1
121 23.99 -0.30 29520310 107.62 2966.85 0.0176 73.1
122 25.94 -0.30 123513900 108.82 2976.78 0.0176 73.1
123 26.15 -0.30 85687430 107.59 2967.79 0.0176 73.1
124 26.36 -0.30 49113040 107.85 2991.78 0.0176 73.1
125 27.32 -0.30 88572990 107.11 3012.03 0.0176 73.1
126 28.00 -0.30 126867400 108.14 3010.24 0.0176 73.1
FUNDS.RATE
1 0.16
2 0.17
3 0.17
4 0.16
5 0.16
6 0.17
7 0.17
8 0.16
9 0.17
10 0.17
11 0.18
12 0.17
13 0.17
14 0.16
15 0.17
16 0.17
17 0.17
18 0.16
19 0.15
20 0.15
21 0.09
22 0.18
23 0.17
24 0.17
25 0.17
26 0.17
27 0.17
28 0.17
29 0.18
30 0.19
31 0.18
32 0.17
33 0.16
34 0.13
35 0.13
36 0.14
37 0.15
38 0.15
39 0.14
40 0.14
41 0.14
42 0.13
43 0.14
44 0.14
45 0.14
46 0.14
47 0.13
48 0.13
49 0.13
50 0.13
51 0.13
52 0.13
53 0.13
54 0.13
55 0.13
56 0.13
57 0.13
58 0.13
59 0.13
60 0.13
61 0.13
62 0.13
63 0.13
64 0.14
65 0.13
66 0.14
67 0.16
68 0.16
69 0.15
70 0.15
71 0.15
72 0.15
73 0.15
74 0.16
75 0.16
76 0.16
77 0.15
78 0.16
79 0.15
80 0.16
81 0.15
82 0.16
83 0.14
84 0.09
85 0.15
86 0.16
87 0.16
88 0.15
89 0.15
90 0.15
91 0.16
92 0.16
93 0.16
94 0.16
95 0.16
96 0.16
97 0.15
98 0.15
99 0.16
100 0.15
101 0.15
102 0.17
103 0.16
104 0.16
105 0.18
106 0.17
107 0.16
108 0.17
109 0.16
110 0.16
111 0.16
112 0.16
113 0.16
114 0.16
115 0.16
116 0.16
117 0.16
118 0.16
119 0.16
120 0.16
121 0.16
122 0.16
123 0.16
124 0.16
125 0.16
126 0.16
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) REV.GROWTH VOLUME LINKEDIN NASDAQ
1.066e+02 -7.240e-02 -3.840e-09 5.006e-01 -4.019e-02
INF.CONS.CONF FED FUNDS.RATE
-7.355e+02 -1.865e-01 6.090e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.9759 -1.4282 -0.0244 1.6106 7.2458
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.066e+02 1.135e+01 9.398 5.35e-16 ***
REV.GROWTH -7.240e-02 3.057e+00 -0.024 0.981146
VOLUME -3.840e-09 5.675e-09 -0.677 0.499952
LINKEDIN 5.006e-01 6.762e-02 7.403 2.15e-11 ***
NASDAQ -4.019e-02 4.803e-03 -8.367 1.38e-13 ***
INF.CONS.CONF -7.355e+02 1.432e+02 -5.135 1.13e-06 ***
FED -1.865e-01 1.069e-01 -1.744 0.083751 .
FUNDS.RATE 6.090e+01 1.559e+01 3.906 0.000157 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.242 on 118 degrees of freedom
Multiple R-squared: 0.7636, Adjusted R-squared: 0.7496
F-statistic: 54.45 on 7 and 118 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,] 9.480842e-02 1.896168e-01 0.905191581
[2,] 3.274037e-02 6.548073e-02 0.967259634
[3,] 1.745390e-02 3.490781e-02 0.982546097
[4,] 6.057917e-03 1.211583e-02 0.993942083
[5,] 3.372488e-03 6.744976e-03 0.996627512
[6,] 1.433514e-03 2.867028e-03 0.998566486
[7,] 4.574831e-04 9.149663e-04 0.999542517
[8,] 1.968111e-04 3.936222e-04 0.999803189
[9,] 8.671161e-05 1.734232e-04 0.999913288
[10,] 3.255674e-05 6.511349e-05 0.999967443
[11,] 3.755428e-05 7.510857e-05 0.999962446
[12,] 1.164017e-05 2.328035e-05 0.999988360
[13,] 4.260958e-06 8.521917e-06 0.999995739
[14,] 1.970096e-06 3.940193e-06 0.999998030
[15,] 6.428012e-07 1.285602e-06 0.999999357
[16,] 7.004282e-05 1.400856e-04 0.999929957
[17,] 6.150641e-05 1.230128e-04 0.999938494
[18,] 4.498290e-05 8.996579e-05 0.999955017
[19,] 1.094975e-04 2.189951e-04 0.999890502
[20,] 2.296277e-04 4.592553e-04 0.999770372
[21,] 2.983195e-03 5.966390e-03 0.997016805
[22,] 1.151733e-02 2.303467e-02 0.988482666
[23,] 1.601891e-02 3.203782e-02 0.983981091
[24,] 3.544072e-02 7.088144e-02 0.964559280
[25,] 4.269799e-02 8.539599e-02 0.957302007
[26,] 4.568498e-02 9.136995e-02 0.954315025
[27,] 5.051171e-02 1.010234e-01 0.949488295
[28,] 8.767204e-02 1.753441e-01 0.912327957
[29,] 1.186786e-01 2.373573e-01 0.881321374
[30,] 1.822255e-01 3.644511e-01 0.817774462
[31,] 5.804053e-01 8.391895e-01 0.419594731
[32,] 7.547013e-01 4.905974e-01 0.245298676
[33,] 7.371865e-01 5.256271e-01 0.262813546
[34,] 7.142135e-01 5.715729e-01 0.285786459
[35,] 7.864977e-01 4.270046e-01 0.213502302
[36,] 8.577076e-01 2.845849e-01 0.142292439
[37,] 8.380567e-01 3.238867e-01 0.161943338
[38,] 8.126901e-01 3.746199e-01 0.187309943
[39,] 8.058880e-01 3.882241e-01 0.194112027
[40,] 8.086624e-01 3.826752e-01 0.191337614
[41,] 8.190029e-01 3.619941e-01 0.180997057
[42,] 8.117267e-01 3.765466e-01 0.188273302
[43,] 8.429095e-01 3.141810e-01 0.157090509
[44,] 8.424011e-01 3.151978e-01 0.157598892
[45,] 8.141010e-01 3.717980e-01 0.185899018
[46,] 7.810977e-01 4.378045e-01 0.218902258
[47,] 7.679022e-01 4.641957e-01 0.232097832
[48,] 7.597750e-01 4.804500e-01 0.240225014
[49,] 7.627369e-01 4.745261e-01 0.237263070
[50,] 7.612897e-01 4.774205e-01 0.238710268
[51,] 7.692669e-01 4.614662e-01 0.230733105
[52,] 7.877515e-01 4.244970e-01 0.212248517
[53,] 8.264381e-01 3.471238e-01 0.173561881
[54,] 8.846455e-01 2.307090e-01 0.115354490
[55,] 9.621303e-01 7.573936e-02 0.037869679
[56,] 9.982285e-01 3.543097e-03 0.001771548
[57,] 9.976921e-01 4.615799e-03 0.002307900
[58,] 9.973844e-01 5.231270e-03 0.002615635
[59,] 9.966543e-01 6.691346e-03 0.003345673
[60,] 9.955212e-01 8.957650e-03 0.004478825
[61,] 9.937307e-01 1.253867e-02 0.006269337
[62,] 9.928861e-01 1.422777e-02 0.007113886
[63,] 9.906700e-01 1.865993e-02 0.009329967
[64,] 9.876869e-01 2.462614e-02 0.012313070
[65,] 9.861282e-01 2.774366e-02 0.013871831
[66,] 9.843059e-01 3.138819e-02 0.015694094
[67,] 9.866547e-01 2.669066e-02 0.013345332
[68,] 9.836261e-01 3.274771e-02 0.016373855
[69,] 9.832073e-01 3.358539e-02 0.016792696
[70,] 9.798902e-01 4.021969e-02 0.020109846
[71,] 9.730573e-01 5.388546e-02 0.026942729
[72,] 9.623661e-01 7.526784e-02 0.037633919
[73,] 9.505872e-01 9.882565e-02 0.049412826
[74,] 9.431919e-01 1.136162e-01 0.056808106
[75,] 9.422069e-01 1.155863e-01 0.057793138
[76,] 9.363479e-01 1.273041e-01 0.063652068
[77,] 9.178504e-01 1.642992e-01 0.082149594
[78,] 9.005324e-01 1.989352e-01 0.099467595
[79,] 8.820279e-01 2.359442e-01 0.117972087
[80,] 8.738717e-01 2.522567e-01 0.126128341
[81,] 8.564751e-01 2.870498e-01 0.143524893
[82,] 8.812782e-01 2.374435e-01 0.118721764
[83,] 8.748175e-01 2.503650e-01 0.125182508
[84,] 8.469706e-01 3.060587e-01 0.153029367
[85,] 8.233647e-01 3.532706e-01 0.176635304
[86,] 8.138592e-01 3.722815e-01 0.186140751
[87,] 8.110558e-01 3.778885e-01 0.188944234
[88,] 8.659248e-01 2.681505e-01 0.134075250
[89,] 8.467800e-01 3.064400e-01 0.153219989
[90,] 8.962632e-01 2.074736e-01 0.103736802
[91,] 9.816481e-01 3.670381e-02 0.018351905
[92,] 9.721378e-01 5.572444e-02 0.027862221
[93,] 9.619998e-01 7.600035e-02 0.038000175
[94,] 9.411217e-01 1.177566e-01 0.058878307
[95,] 9.096018e-01 1.807963e-01 0.090398150
[96,] 8.721123e-01 2.557754e-01 0.127887697
[97,] 9.456363e-01 1.087274e-01 0.054363715
[98,] 9.153807e-01 1.692385e-01 0.084619271
[99,] 9.524249e-01 9.515014e-02 0.047575071
[100,] 9.434934e-01 1.130132e-01 0.056506577
[101,] 9.120210e-01 1.759581e-01 0.087979031
[102,] 8.489241e-01 3.021518e-01 0.151075887
[103,] 7.862893e-01 4.274214e-01 0.213710721
[104,] 8.309772e-01 3.380457e-01 0.169022826
[105,] 8.644813e-01 2.710374e-01 0.135518685
> postscript(file="/var/wessaorg/rcomp/tmp/1u0jz1356078203.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2s0kl1356078203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3q3e31356078203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4anmb1356078203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5do8o1356078203.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 = 126
Frequency = 1
1 2 3 4 5 6
-0.44970318 -1.19039118 -2.43163507 1.83726224 0.12557667 0.25568194
7 8 9 10 11 12
-0.72110888 1.49498456 -0.75757303 -0.40814083 0.40873632 1.74413208
13 14 15 16 17 18
3.49103429 3.79340624 2.12272427 2.53747513 1.00032102 1.68342501
19 20 21 22 23 24
2.91695566 2.18353654 7.24581172 0.49967724 2.06122824 2.48785486
25 26 27 28 29 30
0.88221226 4.06835304 3.89806025 2.04073348 -1.39249021 -0.88025805
31 32 33 34 35 36
-1.96007106 -1.26751060 1.23224260 1.85114766 1.16902216 0.17073616
37 38 39 40 41 42
-1.22733831 -0.26838598 0.26399225 -1.33379925 -3.11653560 -3.33293205
43 44 45 46 47 48
-0.78826728 -0.93634941 -4.96552098 -4.97590242 -2.06185282 -1.38867001
49 50 51 52 53 54
-1.00600414 0.06720309 0.09098818 0.12402327 1.30278827 0.77649997
55 56 57 58 59 60
1.73564304 1.79103117 -0.71146651 -0.48364478 -1.20844209 -0.44674736
61 62 63 64 65 66
-0.37413378 -1.42884919 -2.28779573 -3.35038869 -2.89529557 0.29294539
67 68 69 70 71 72
-3.20053505 -3.11613686 -2.46985750 -3.36559315 -2.68841035 -1.33698306
73 74 75 76 77 78
-0.06539495 -0.10655543 0.03168750 0.27834467 2.12102231 0.32702580
79 80 81 82 83 84
1.31386802 -1.95903113 -2.30934584 -2.62845349 -0.82374552 2.48292140
85 86 87 88 89 90
1.99967961 1.43263979 0.15659957 1.35335272 1.34362691 0.27425433
91 92 93 94 95 96
0.01667643 -0.52333902 -1.47173489 -0.25451723 0.34717067 1.63482434
97 98 99 100 101 102
3.72864635 1.86903553 -0.40258235 0.69218709 1.53773760 2.57509359
103 104 105 106 107 108
3.03289191 2.81848451 -0.48844739 -1.21718607 -2.12479456 -2.23980149
109 110 111 112 113 114
0.34864400 -1.86751323 -1.58793896 -1.59789176 -2.35851267 -3.50071793
115 116 117 118 119 120
-1.64051129 -3.74526911 -2.83750096 -2.23465380 -2.79341750 -1.42636295
121 122 123 124 125 126
-0.38306465 1.72620885 2.04540396 2.94886375 5.24457495 5.48409178
> postscript(file="/var/wessaorg/rcomp/tmp/6e6zy1356078203.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 = 126
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.44970318 NA
1 -1.19039118 -0.44970318
2 -2.43163507 -1.19039118
3 1.83726224 -2.43163507
4 0.12557667 1.83726224
5 0.25568194 0.12557667
6 -0.72110888 0.25568194
7 1.49498456 -0.72110888
8 -0.75757303 1.49498456
9 -0.40814083 -0.75757303
10 0.40873632 -0.40814083
11 1.74413208 0.40873632
12 3.49103429 1.74413208
13 3.79340624 3.49103429
14 2.12272427 3.79340624
15 2.53747513 2.12272427
16 1.00032102 2.53747513
17 1.68342501 1.00032102
18 2.91695566 1.68342501
19 2.18353654 2.91695566
20 7.24581172 2.18353654
21 0.49967724 7.24581172
22 2.06122824 0.49967724
23 2.48785486 2.06122824
24 0.88221226 2.48785486
25 4.06835304 0.88221226
26 3.89806025 4.06835304
27 2.04073348 3.89806025
28 -1.39249021 2.04073348
29 -0.88025805 -1.39249021
30 -1.96007106 -0.88025805
31 -1.26751060 -1.96007106
32 1.23224260 -1.26751060
33 1.85114766 1.23224260
34 1.16902216 1.85114766
35 0.17073616 1.16902216
36 -1.22733831 0.17073616
37 -0.26838598 -1.22733831
38 0.26399225 -0.26838598
39 -1.33379925 0.26399225
40 -3.11653560 -1.33379925
41 -3.33293205 -3.11653560
42 -0.78826728 -3.33293205
43 -0.93634941 -0.78826728
44 -4.96552098 -0.93634941
45 -4.97590242 -4.96552098
46 -2.06185282 -4.97590242
47 -1.38867001 -2.06185282
48 -1.00600414 -1.38867001
49 0.06720309 -1.00600414
50 0.09098818 0.06720309
51 0.12402327 0.09098818
52 1.30278827 0.12402327
53 0.77649997 1.30278827
54 1.73564304 0.77649997
55 1.79103117 1.73564304
56 -0.71146651 1.79103117
57 -0.48364478 -0.71146651
58 -1.20844209 -0.48364478
59 -0.44674736 -1.20844209
60 -0.37413378 -0.44674736
61 -1.42884919 -0.37413378
62 -2.28779573 -1.42884919
63 -3.35038869 -2.28779573
64 -2.89529557 -3.35038869
65 0.29294539 -2.89529557
66 -3.20053505 0.29294539
67 -3.11613686 -3.20053505
68 -2.46985750 -3.11613686
69 -3.36559315 -2.46985750
70 -2.68841035 -3.36559315
71 -1.33698306 -2.68841035
72 -0.06539495 -1.33698306
73 -0.10655543 -0.06539495
74 0.03168750 -0.10655543
75 0.27834467 0.03168750
76 2.12102231 0.27834467
77 0.32702580 2.12102231
78 1.31386802 0.32702580
79 -1.95903113 1.31386802
80 -2.30934584 -1.95903113
81 -2.62845349 -2.30934584
82 -0.82374552 -2.62845349
83 2.48292140 -0.82374552
84 1.99967961 2.48292140
85 1.43263979 1.99967961
86 0.15659957 1.43263979
87 1.35335272 0.15659957
88 1.34362691 1.35335272
89 0.27425433 1.34362691
90 0.01667643 0.27425433
91 -0.52333902 0.01667643
92 -1.47173489 -0.52333902
93 -0.25451723 -1.47173489
94 0.34717067 -0.25451723
95 1.63482434 0.34717067
96 3.72864635 1.63482434
97 1.86903553 3.72864635
98 -0.40258235 1.86903553
99 0.69218709 -0.40258235
100 1.53773760 0.69218709
101 2.57509359 1.53773760
102 3.03289191 2.57509359
103 2.81848451 3.03289191
104 -0.48844739 2.81848451
105 -1.21718607 -0.48844739
106 -2.12479456 -1.21718607
107 -2.23980149 -2.12479456
108 0.34864400 -2.23980149
109 -1.86751323 0.34864400
110 -1.58793896 -1.86751323
111 -1.59789176 -1.58793896
112 -2.35851267 -1.59789176
113 -3.50071793 -2.35851267
114 -1.64051129 -3.50071793
115 -3.74526911 -1.64051129
116 -2.83750096 -3.74526911
117 -2.23465380 -2.83750096
118 -2.79341750 -2.23465380
119 -1.42636295 -2.79341750
120 -0.38306465 -1.42636295
121 1.72620885 -0.38306465
122 2.04540396 1.72620885
123 2.94886375 2.04540396
124 5.24457495 2.94886375
125 5.48409178 5.24457495
126 NA 5.48409178
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.19039118 -0.44970318
[2,] -2.43163507 -1.19039118
[3,] 1.83726224 -2.43163507
[4,] 0.12557667 1.83726224
[5,] 0.25568194 0.12557667
[6,] -0.72110888 0.25568194
[7,] 1.49498456 -0.72110888
[8,] -0.75757303 1.49498456
[9,] -0.40814083 -0.75757303
[10,] 0.40873632 -0.40814083
[11,] 1.74413208 0.40873632
[12,] 3.49103429 1.74413208
[13,] 3.79340624 3.49103429
[14,] 2.12272427 3.79340624
[15,] 2.53747513 2.12272427
[16,] 1.00032102 2.53747513
[17,] 1.68342501 1.00032102
[18,] 2.91695566 1.68342501
[19,] 2.18353654 2.91695566
[20,] 7.24581172 2.18353654
[21,] 0.49967724 7.24581172
[22,] 2.06122824 0.49967724
[23,] 2.48785486 2.06122824
[24,] 0.88221226 2.48785486
[25,] 4.06835304 0.88221226
[26,] 3.89806025 4.06835304
[27,] 2.04073348 3.89806025
[28,] -1.39249021 2.04073348
[29,] -0.88025805 -1.39249021
[30,] -1.96007106 -0.88025805
[31,] -1.26751060 -1.96007106
[32,] 1.23224260 -1.26751060
[33,] 1.85114766 1.23224260
[34,] 1.16902216 1.85114766
[35,] 0.17073616 1.16902216
[36,] -1.22733831 0.17073616
[37,] -0.26838598 -1.22733831
[38,] 0.26399225 -0.26838598
[39,] -1.33379925 0.26399225
[40,] -3.11653560 -1.33379925
[41,] -3.33293205 -3.11653560
[42,] -0.78826728 -3.33293205
[43,] -0.93634941 -0.78826728
[44,] -4.96552098 -0.93634941
[45,] -4.97590242 -4.96552098
[46,] -2.06185282 -4.97590242
[47,] -1.38867001 -2.06185282
[48,] -1.00600414 -1.38867001
[49,] 0.06720309 -1.00600414
[50,] 0.09098818 0.06720309
[51,] 0.12402327 0.09098818
[52,] 1.30278827 0.12402327
[53,] 0.77649997 1.30278827
[54,] 1.73564304 0.77649997
[55,] 1.79103117 1.73564304
[56,] -0.71146651 1.79103117
[57,] -0.48364478 -0.71146651
[58,] -1.20844209 -0.48364478
[59,] -0.44674736 -1.20844209
[60,] -0.37413378 -0.44674736
[61,] -1.42884919 -0.37413378
[62,] -2.28779573 -1.42884919
[63,] -3.35038869 -2.28779573
[64,] -2.89529557 -3.35038869
[65,] 0.29294539 -2.89529557
[66,] -3.20053505 0.29294539
[67,] -3.11613686 -3.20053505
[68,] -2.46985750 -3.11613686
[69,] -3.36559315 -2.46985750
[70,] -2.68841035 -3.36559315
[71,] -1.33698306 -2.68841035
[72,] -0.06539495 -1.33698306
[73,] -0.10655543 -0.06539495
[74,] 0.03168750 -0.10655543
[75,] 0.27834467 0.03168750
[76,] 2.12102231 0.27834467
[77,] 0.32702580 2.12102231
[78,] 1.31386802 0.32702580
[79,] -1.95903113 1.31386802
[80,] -2.30934584 -1.95903113
[81,] -2.62845349 -2.30934584
[82,] -0.82374552 -2.62845349
[83,] 2.48292140 -0.82374552
[84,] 1.99967961 2.48292140
[85,] 1.43263979 1.99967961
[86,] 0.15659957 1.43263979
[87,] 1.35335272 0.15659957
[88,] 1.34362691 1.35335272
[89,] 0.27425433 1.34362691
[90,] 0.01667643 0.27425433
[91,] -0.52333902 0.01667643
[92,] -1.47173489 -0.52333902
[93,] -0.25451723 -1.47173489
[94,] 0.34717067 -0.25451723
[95,] 1.63482434 0.34717067
[96,] 3.72864635 1.63482434
[97,] 1.86903553 3.72864635
[98,] -0.40258235 1.86903553
[99,] 0.69218709 -0.40258235
[100,] 1.53773760 0.69218709
[101,] 2.57509359 1.53773760
[102,] 3.03289191 2.57509359
[103,] 2.81848451 3.03289191
[104,] -0.48844739 2.81848451
[105,] -1.21718607 -0.48844739
[106,] -2.12479456 -1.21718607
[107,] -2.23980149 -2.12479456
[108,] 0.34864400 -2.23980149
[109,] -1.86751323 0.34864400
[110,] -1.58793896 -1.86751323
[111,] -1.59789176 -1.58793896
[112,] -2.35851267 -1.59789176
[113,] -3.50071793 -2.35851267
[114,] -1.64051129 -3.50071793
[115,] -3.74526911 -1.64051129
[116,] -2.83750096 -3.74526911
[117,] -2.23465380 -2.83750096
[118,] -2.79341750 -2.23465380
[119,] -1.42636295 -2.79341750
[120,] -0.38306465 -1.42636295
[121,] 1.72620885 -0.38306465
[122,] 2.04540396 1.72620885
[123,] 2.94886375 2.04540396
[124,] 5.24457495 2.94886375
[125,] 5.48409178 5.24457495
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.19039118 -0.44970318
2 -2.43163507 -1.19039118
3 1.83726224 -2.43163507
4 0.12557667 1.83726224
5 0.25568194 0.12557667
6 -0.72110888 0.25568194
7 1.49498456 -0.72110888
8 -0.75757303 1.49498456
9 -0.40814083 -0.75757303
10 0.40873632 -0.40814083
11 1.74413208 0.40873632
12 3.49103429 1.74413208
13 3.79340624 3.49103429
14 2.12272427 3.79340624
15 2.53747513 2.12272427
16 1.00032102 2.53747513
17 1.68342501 1.00032102
18 2.91695566 1.68342501
19 2.18353654 2.91695566
20 7.24581172 2.18353654
21 0.49967724 7.24581172
22 2.06122824 0.49967724
23 2.48785486 2.06122824
24 0.88221226 2.48785486
25 4.06835304 0.88221226
26 3.89806025 4.06835304
27 2.04073348 3.89806025
28 -1.39249021 2.04073348
29 -0.88025805 -1.39249021
30 -1.96007106 -0.88025805
31 -1.26751060 -1.96007106
32 1.23224260 -1.26751060
33 1.85114766 1.23224260
34 1.16902216 1.85114766
35 0.17073616 1.16902216
36 -1.22733831 0.17073616
37 -0.26838598 -1.22733831
38 0.26399225 -0.26838598
39 -1.33379925 0.26399225
40 -3.11653560 -1.33379925
41 -3.33293205 -3.11653560
42 -0.78826728 -3.33293205
43 -0.93634941 -0.78826728
44 -4.96552098 -0.93634941
45 -4.97590242 -4.96552098
46 -2.06185282 -4.97590242
47 -1.38867001 -2.06185282
48 -1.00600414 -1.38867001
49 0.06720309 -1.00600414
50 0.09098818 0.06720309
51 0.12402327 0.09098818
52 1.30278827 0.12402327
53 0.77649997 1.30278827
54 1.73564304 0.77649997
55 1.79103117 1.73564304
56 -0.71146651 1.79103117
57 -0.48364478 -0.71146651
58 -1.20844209 -0.48364478
59 -0.44674736 -1.20844209
60 -0.37413378 -0.44674736
61 -1.42884919 -0.37413378
62 -2.28779573 -1.42884919
63 -3.35038869 -2.28779573
64 -2.89529557 -3.35038869
65 0.29294539 -2.89529557
66 -3.20053505 0.29294539
67 -3.11613686 -3.20053505
68 -2.46985750 -3.11613686
69 -3.36559315 -2.46985750
70 -2.68841035 -3.36559315
71 -1.33698306 -2.68841035
72 -0.06539495 -1.33698306
73 -0.10655543 -0.06539495
74 0.03168750 -0.10655543
75 0.27834467 0.03168750
76 2.12102231 0.27834467
77 0.32702580 2.12102231
78 1.31386802 0.32702580
79 -1.95903113 1.31386802
80 -2.30934584 -1.95903113
81 -2.62845349 -2.30934584
82 -0.82374552 -2.62845349
83 2.48292140 -0.82374552
84 1.99967961 2.48292140
85 1.43263979 1.99967961
86 0.15659957 1.43263979
87 1.35335272 0.15659957
88 1.34362691 1.35335272
89 0.27425433 1.34362691
90 0.01667643 0.27425433
91 -0.52333902 0.01667643
92 -1.47173489 -0.52333902
93 -0.25451723 -1.47173489
94 0.34717067 -0.25451723
95 1.63482434 0.34717067
96 3.72864635 1.63482434
97 1.86903553 3.72864635
98 -0.40258235 1.86903553
99 0.69218709 -0.40258235
100 1.53773760 0.69218709
101 2.57509359 1.53773760
102 3.03289191 2.57509359
103 2.81848451 3.03289191
104 -0.48844739 2.81848451
105 -1.21718607 -0.48844739
106 -2.12479456 -1.21718607
107 -2.23980149 -2.12479456
108 0.34864400 -2.23980149
109 -1.86751323 0.34864400
110 -1.58793896 -1.86751323
111 -1.59789176 -1.58793896
112 -2.35851267 -1.59789176
113 -3.50071793 -2.35851267
114 -1.64051129 -3.50071793
115 -3.74526911 -1.64051129
116 -2.83750096 -3.74526911
117 -2.23465380 -2.83750096
118 -2.79341750 -2.23465380
119 -1.42636295 -2.79341750
120 -0.38306465 -1.42636295
121 1.72620885 -0.38306465
122 2.04540396 1.72620885
123 2.94886375 2.04540396
124 5.24457495 2.94886375
125 5.48409178 5.24457495
> 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/wessaorg/rcomp/tmp/7fw2h1356078203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8t3gm1356078203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/920bn1356078203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10a5dl1356078203.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11on7q1356078203.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/wessaorg/rcomp/tmp/12opca1356078203.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/wessaorg/rcomp/tmp/132hji1356078203.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/wessaorg/rcomp/tmp/14lwd31356078203.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/wessaorg/rcomp/tmp/15hrfv1356078203.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/wessaorg/rcomp/tmp/16fh4v1356078204.tab")
+ }
>
> try(system("convert tmp/1u0jz1356078203.ps tmp/1u0jz1356078203.png",intern=TRUE))
character(0)
> try(system("convert tmp/2s0kl1356078203.ps tmp/2s0kl1356078203.png",intern=TRUE))
character(0)
> try(system("convert tmp/3q3e31356078203.ps tmp/3q3e31356078203.png",intern=TRUE))
character(0)
> try(system("convert tmp/4anmb1356078203.ps tmp/4anmb1356078203.png",intern=TRUE))
character(0)
> try(system("convert tmp/5do8o1356078203.ps tmp/5do8o1356078203.png",intern=TRUE))
character(0)
> try(system("convert tmp/6e6zy1356078203.ps tmp/6e6zy1356078203.png",intern=TRUE))
character(0)
> try(system("convert tmp/7fw2h1356078203.ps tmp/7fw2h1356078203.png",intern=TRUE))
character(0)
> try(system("convert tmp/8t3gm1356078203.ps tmp/8t3gm1356078203.png",intern=TRUE))
character(0)
> try(system("convert tmp/920bn1356078203.ps tmp/920bn1356078203.png",intern=TRUE))
character(0)
> try(system("convert tmp/10a5dl1356078203.ps tmp/10a5dl1356078203.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.022 1.085 8.224