fitGOProfile(pn, p0, n = ngenes(pn), method = "lcombChisq", ab.approx = "asymptotic", confidence = 0.95, nsims = 10000, simplify = T)
In the i-th comparison (i from 1 to max(ncol(pn),ncol(p0))), if p stands for the profile originating the sample profile pn[,i] and d(,) for the squared euclidean distance, if p != p0[,i], the distribution of sqrt(n)(d(pn[,i],p0[,i]) - d(p,p0[,i]))/se is approximately standard normal, N(0,1). This provides the basis for the confidence interval in the result field conf.int. When p==p0[,i], the asymptotic distribution of n d(pn[,i],p0[,i]) is the distribution of a linear combination of independent chi-square random variables, each one with one degree of freedom. This sampling distribution may be directly computed (approximating it by simulation, method="lcombChisq") or approximated by a chi-square distribution, based on two correcting constants a and b (method="chi-square"). These constants are chosen to equate the first two moments of both distributions (the distribution of a linear combination of chi square variables and the approximating chi-square distribution). When method="chi-square", the returned test statistic value is the chi-square approximation (n d(pn,p0) - b) / a. Then, the result field 'parameter' is a vector containing the 'a' and 'b' values and the number of degrees of freedom, 'df'. Otherwise, the returned test statistic value is n d(pn,p0) and 'parameter' contains the coefficients of the linear combination of chi-squares
#data(sampleProfiles)
#comparedMF <-fitGOProfile(pn=expandedWelsh01[['MF']],
# p0 = expandedSingh01[['MF']])
#print(comparedMF)
#print(compSummary(comparedMF))
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