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WS 8 review

*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, 04 Dec 2009 02:15:08 -0700
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/04/t1259918174bosjmszitmktg1f.htm/, Retrieved Fri, 04 Dec 2009 10:16:16 +0100
 
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/2009/Dec/04/t1259918174bosjmszitmktg1f.htm/},
    year = {2009},
}
@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 = {2009},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
11.1 10.9 10 9.2 9.2 9.5 9.6 9.5 9.1 8.9 9 10.1 10.3 10.2 9.6 9.2 9.3 9.4 9.4 9.2 9 9 9 9.8 10 9.8 9.3 9 9 9.1 9.1 9.1 9.2 8.8 8.3 8.4 8.1 7.7 7.9 7.9 8 7.9 7.6 7.1 6.8 6.5 6.9 8.2 8.7 8.3 7.9 7.5 7.8 8.3 8.4 8.2 7.7 7.2 7.3 8.1
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Autocorrelation Function
Time lag kACF(k)T-STATP-value
10.3969343.04890.001718
2-0.248066-1.90540.030801
3-0.499029-3.83310.000155
4-0.334539-2.56960.006364
50.100240.770.222199
60.3903282.99820.001986
70.2328381.78850.039418
8-0.14038-1.07830.142649
9-0.327823-2.51810.007267
10-0.255465-1.96230.027227
110.0711270.54630.293448
120.3596712.76270.003817
130.0539270.41420.340107
14-0.127542-0.97970.165624
15-0.096202-0.73890.231436
16-0.05339-0.41010.34161
170.0765480.5880.279396
180.1078540.82840.205379
190.0160060.12290.451284
20-0.151955-1.16720.123915
21-0.198271-1.5230.066557
22-0.115443-0.88670.189411
230.1205610.9260.179098
240.2509741.92780.029351
250.0129250.09930.460625
26-0.138438-1.06340.145975
27-0.087073-0.66880.253109
28-0.045788-0.35170.363156
290.0966230.74220.230462
300.1574411.20930.11568
310.0795760.61120.271695
32-0.155485-1.19430.118569
33-0.228276-1.75340.042362
34-0.049225-0.37810.353354
350.1833161.40810.082177
360.2937052.2560.013896
370.0801440.61560.270263
38-0.136403-1.04770.149518
39-0.169703-1.30350.098732
40-0.036286-0.27870.390719
410.1822551.39990.083385
420.252611.94030.028561
430.0771660.59270.277816
44-0.231463-1.77790.040287
45-0.318271-2.44470.008752
46-0.114257-0.87760.191854
470.1096640.84230.201499
480.2079691.59740.057755
490.0429440.32990.371336
50-0.092959-0.7140.239011
51-0.117197-0.90020.185834
52-0.029838-0.22920.409758
530.0599820.46070.323342
540.1047630.80470.212112
550.0379120.29120.385956
56-0.069939-0.53720.29657
57-0.074593-0.5730.284424
58-0.012707-0.09760.46129
59NANANA
60NANANA


Partial Autocorrelation Function
Time lag kPACF(k)T-STATP-value
10.3969343.04890.001718
2-0.481483-3.69830.000239
3-0.250387-1.92330.029639
4-0.139757-1.07350.143711
50.1082680.83160.204487
60.1083660.83240.204277
7-0.082347-0.63250.264745
8-0.116812-0.89730.186616
9-0.044054-0.33840.368137
10-0.099681-0.76570.223464
110.062450.47970.316611
120.1165260.89510.187197
13-0.399154-3.0660.001636
140.224391.72360.045011
150.1134910.87170.193442
16-0.11814-0.90750.18393
170.0261110.20060.420866
18-0.085823-0.65920.256161
190.0743920.57140.284944
20-0.150819-1.15850.125671
21-0.190946-1.46670.073886
22-0.031214-0.23980.405674
230.0673820.51760.303348
24-0.028238-0.21690.414518
25-0.064587-0.49610.310832
26-0.109314-0.83970.202245
270.1225490.94130.17519
28-0.007789-0.05980.476247
29-0.055368-0.42530.336086
30-0.003246-0.02490.490097
31-0.025825-0.19840.42172
32-0.137068-1.05280.148354
33-0.019108-0.14680.441905
340.0883220.67840.25008
35-0.028738-0.22070.413027
360.1509391.15940.125485
370.0330170.25360.400339
38-0.024718-0.18990.425035
390.0110380.08480.46636
400.1901021.46020.074769
410.0814670.62580.266942
42-0.011182-0.08590.465923
43-0.069445-0.53340.297874
440.0404980.31110.378423
450.0173020.13290.447362
46-0.131536-1.01030.158227
47-0.040328-0.30980.378914
48-0.043697-0.33560.369166
490.0116920.08980.464371
500.0918260.70530.24169
51-0.095393-0.73270.233313
520.0002980.00230.499092
53-0.030913-0.23750.406565
540.0039020.030.488096
550.0159560.12260.451435
560.021510.16520.434666
57-0.021965-0.16870.4333
58-0.005897-0.04530.482011
59NANANA
60NANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Dec/04/t1259918174bosjmszitmktg1f/101wm1259918104.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/04/t1259918174bosjmszitmktg1f/101wm1259918104.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Dec/04/t1259918174bosjmszitmktg1f/2mnhx1259918104.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Dec/04/t1259918174bosjmszitmktg1f/2mnhx1259918104.ps (open in new window)


 
Parameters (Session):
par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
 
Parameters (R input):
par1 = 60 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = MA ; par7 = 0.95 ;
 
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 (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma'
par7 <- as.numeric(par7)
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='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep=''))
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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