ptestg {ptest} | R Documentation |
This function is used to test the existence of the periodicity for a short time series (length<=100). Several methods based on periodograms are provided with the response surface method implemented for efficiently obtaining accurate p-values.
ptestg(z, method = c("Fisher", "robust", "extended", "extendedRobust", "FisherRSR"), multiple = FALSE)
z |
A series or a matrix containg series as columns |
method |
The statistical test to be used. See details for more information. |
multiple |
Indicating whether z contains multiple series. |
The null hypothesis is set as no peridicities, H0: f=0. Discriptions of different test statistics (methods) are as follow:
Fisher
: The Fisher's g test statistic. The p-value is computed
directly from the exact distribution.
robust
: The robust g test proprosed in Ahdesmaki et al. (2005),
where the p-value is computed by the response surface regression method.
extended
: The extended Fisher's g test statistic, which
extend the Fisher's g test by enlarging the searching region
of the frequency from the fourier frequencies to be
En = {j/101 | j=1,…,50 and j/101 ≥ 1/n}.
The p-value is computed by the response surface regression method.
extendedRobust
: Extend the frequency searching region of the robust
En = {j/101 | j=1,…,50 and j/101 ≥ 1/n}.
The p-value is computed by the response surface regression method.
FisherRSR
: Only for experimental purposes, the Fisher;s g test with
p-value computed form the response surface regression method.
Object of class "Htest" produced.
An object of class "Htest" is a list containing the following components:
obsStat |
Vector containing the observed test statistics. |
pvalue |
Vector containing the p-values of the selected tests. |
freq |
Vector containing the estimated frequencies. |
Yuanhao Lai and A.I. McLeod
Fisher, R.A. (1929). Tests of significance in harmonic analysis. Proc. Roy. Soc. A, 125, 54-59.
Ahdesmaki, M., Lahdesmaki, H., Pearson, R., Huttunen, H., and Yli-Harja O.(2005). BMC Bioinformatics 6:117. http://www.biomedcentral.com/1471-2105/6/117.
MacKinnon, James (2001) : Computing numerical distribution functions in econometrics, Queen's Economics Department Working Paper, No. 1037.
# Simulate the harmonic regression model with standard Gaussian error terms set.seed(193) ## Non-Fourier frequency z <- simHReg(n = 14, f=2/10, A = 2, B = 1, model="Gaussian",sig=1) ptestg(z,method="Fisher") ptestg(z,method="robust") ptestg(z,method="extended") ptestg(z,method="extendedRobust") ptestg(z,method="FisherRSR") # Performe tests on the alpha factor experiment data(alpha) ## Eliminate genes with missing observations alpha.nonNA <- alpha[complete.cases(alpha),] ## Using the multiple option to do the test for all the genes ## Transpose the data set so that each column stands for a gene alpha.nonNA <- t(alpha.nonNA) result <- ptestg(alpha.nonNA, method = "extended",multiple=TRUE) str(result) # The movtivating example: gene ORF06806 in Cc data(Cc) x <- Cc[which(rownames(Cc)=="ORF06806"),] plot(1:length(x),x,type="b", main="ORF06806", xlab="time",ylab="Gene expression") ptestg(x,method="Fisher") #Fail to detect the periodicity ptestg(x,method="robust") ptestg(x,method="extended")