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acf lambda -0.3

*The author of this computation has been verified*
R Software Module: rwasp_autocorrelation.wasp (opens new window with default values)
Title produced by software: (Partial) Autocorrelation Function
Date of computation: Fri, 05 Dec 2008 11:09:06 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/05/t12285012301niur70ma8ehj2e.htm/, Retrieved Fri, 05 Dec 2008 18:20:32 +0000
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/05/t12285012301niur70ma8ehj2e.htm/},
    year = {2008},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
 
Feedback Forum:
2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc] [reply] 
test

Post a new message
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
2150.3 2425.7 2642.0 2291.5 2570.7 2526.6 2266.2 1981.9 2630.3 2942.6 2713.4 2437.5 2678.9 2582.0 2780.0 2512.4 2658.4 2708.7 2518.7 2018.3 2579.3 2693.5 2468.8 2122.8 2412.8 2370.6 2642.5 2634.2 2457.5 2579.1 2505.9 1903.2 2660.2 2844.1 2607.1 2356.0 2659.9 2531.4 2845.7 2654.3 2588.2 2789.6 2533.1 1846.5 2796.3 2895.6 2472.2 2584.4 2630.4 2663.1 3176.2 2856.7 2551.4 3088.7 2628.3 2226.2 3023.6 3077.9 3084.1 2990.3 2949.6 3014.7 3517.7 3121.2 3067.4 3174.6 2676.3 2424.0 3195.1 3146.6 3506.7 3528.5
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ 72.249.76.132


Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.5098283.94910.000104
20.4427763.42970.000549
30.5156273.9949e-05
40.2767852.1440.018049
50.2365571.83240.03593
60.267522.07220.021276
70.0677880.52510.30073
80.0830290.64310.261291
90.0555720.43050.334203
10-0.141031-1.09240.139507
11-0.122091-0.94570.174044
12-0.144378-1.11830.133939
13-0.150551-1.16620.124082
14-0.061726-0.47810.317147
15-0.010566-0.08180.467522
16-0.135023-1.04590.149904
170.0429050.33230.370395
180.0107310.08310.467015
19-0.058464-0.45290.326141
200.1529051.18440.120463
210.0783090.60660.273208
220.0690630.5350.297328
230.1845781.42970.07899
240.0376830.29190.38569
25-0.054177-0.41970.33812
260.0017990.01390.494464
27-0.142919-1.1070.136347
28-0.127517-0.98770.163624
29-0.105648-0.81830.208199
30-0.215562-1.66970.05009
31-0.216111-1.6740.04967
32-0.195751-1.51630.06735
33-0.257801-1.99690.025188
34-0.279198-2.16270.017282
35-0.191612-1.48420.071492
36-0.251628-1.94910.027981
37-0.161862-1.25380.107394
38-0.143225-1.10940.135838
39-0.196791-1.52430.066339
40-0.14085-1.0910.139813
41-0.080028-0.61990.268838
42-0.110647-0.85710.197409
430.0272150.21080.416878
44-0.002163-0.01680.493345
45-0.028643-0.22190.412586
460.0544650.42190.337307
47-0.01931-0.14960.4408
48-0.048281-0.3740.354867
49-0.000935-0.00720.497124
50-0.020898-0.16190.435975
51-0.008164-0.06320.474892
520.0728130.5640.287427
530.0145820.1130.455222
54-0.010097-0.07820.46896
550.0267380.20710.418312
560.0173180.13410.446868
57-0.007216-0.05590.477807
580.0411680.31890.37546
590.0531080.41140.341132
60NANANA


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.5098283.94910.000104
20.2470711.91380.03021
30.3161582.44890.008633
4-0.156235-1.21020.115476
5-0.029783-0.23070.409167
60.0511350.39610.346721
7-0.143959-1.11510.134626
80.0145650.11280.455275
9-0.055794-0.43220.33358
10-0.180595-1.39890.083498
11-0.06095-0.47210.319278
12-0.043385-0.33610.369
130.1378551.06780.14494
140.1186730.91920.180826
150.1399181.08380.141394
16-0.180441-1.39770.083676
170.1435621.1120.135282
18-0.045851-0.35520.361858
19-0.032126-0.24880.402164
200.1740331.34810.091354
21-0.101783-0.78840.216781
220.0315360.24430.403926
23-0.049606-0.38420.351078
24-0.098564-0.76350.224086
25-0.11675-0.90430.184716
26-0.100033-0.77490.220735
27-0.046265-0.35840.360663
28-0.017265-0.13370.44703
290.0153240.11870.452954
30-0.050594-0.39190.34826
31-0.008209-0.06360.474754
32-0.006282-0.04870.480676
330.0506860.39260.347997
34-0.094311-0.73050.233955
35-0.012116-0.09390.462769
36-0.054049-0.41870.338478
37-0.037712-0.29210.385603
38-0.059088-0.45770.324413
39-0.03948-0.30580.380403
40-0.072851-0.56430.287327
410.0396530.30720.379896
420.0158950.12310.451211
430.1088690.84330.201206
44-0.048727-0.37740.35359
45-0.002916-0.02260.491027
46-0.039163-0.30340.381334
47-0.003739-0.0290.488496
48-0.02754-0.21330.415899
490.0022010.0170.493227
50-0.014485-0.11220.45552
510.0515870.39960.345439
520.0738480.5720.284722
530.0319350.24740.402736
54-0.027883-0.2160.414867
55-0.028602-0.22160.412707
560.0138350.10720.457508
57-0.035648-0.27610.3917
58-0.032499-0.25170.401052
590.0685190.53070.298777
60NANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t12285012301niur70ma8ehj2e/1wugr1228500544.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t12285012301niur70ma8ehj2e/1wugr1228500544.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t12285012301niur70ma8ehj2e/2nihm1228500544.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t12285012301niur70ma8ehj2e/2nihm1228500544.ps (open in new window)


 
Parameters (Session):
par1 = 60 ; par2 = -0.3 ; par3 = 0 ; par4 = 1 ; par5 = 12 ;
 
Parameters (R input):
par1 = 60 ; par2 = -0.3 ; par3 = 0 ; par4 = 1 ; par5 = 12 ;
 
R code (references can be found in the software module):
if (par1 == 'Default') {
par1 = 10*log10(length(x))
} else {
par1 <- as.numeric(par1)
}
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
if (par2 == 0) {
x <- log(x)
} else {
x <- (x ^ par2 - 1) / par2
}
if (par3 > 0) x <- diff(x,lag=1,difference=par3)
if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
bitmap(file='pic1.png')
racf <- acf(x,par1,main='Autocorrelation',xlab='lags',ylab='ACF')
dev.off()
bitmap(file='pic2.png')
rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF')
dev.off()
(myacf <- c(racf$acf))
(mypacf <- c(rpacf$acf))
lengthx <- length(x)
sqrtn <- sqrt(lengthx)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 2:(par1+1)) {
a<-table.row.start(a)
a<-table.element(a,i-1,header=TRUE)
a<-table.element(a,round(myacf[i],6))
mytstat <- myacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Partial Autocorrelation Function',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Time lag k',header=TRUE)
a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,'P-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:par1) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,round(mypacf[i],6))
mytstat <- mypacf[i]*sqrtn
a<-table.element(a,round(mytstat,4))
a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
 




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