#--------------------------------------------
#' @title null.lg.fit
#'
#' @description Gaussian fit for null similarity scores.
#'
#' @details The gaussian distribution is a distribution which often gives
#' a good fit to the null log similarity scores when these scores are on a scale of 0 to 1. Other than
#' standardization, this function assumes that if the user wants the scores to be transformed, they've
#' transformed them. This routine calls the \code{fitdistr} function from the MASS package.
#'
#' @param score.null.vec Vector of null (non-match) similarity scores or transformed similarity scores
#' @param standardizeQ Whether or not to standardize the null scores
#' @param plotQ Diagnostic plots?
#'
#' @return list with the fitted parameters, fit info and chi-square goodness of fit test results
#'
#' @references XXXX
#'
#' @examples
#' XXXX
#--------------------------------------------
null.lg.fit <- function(score.null.vec, standardizeQ=FALSE, plotQ=FALSE) {
#Take the log:
lgs <- score.null.vec
if(standardizeQ==TRUE){
lgs <- (lgs - mean(lgs))/sd(lgs)
}
lgs <- sort(lgs)
normfit <- MASS::fitdistr(lgs,"normal")
#If data was standardized, these should be pretty close to 0 and 1:
mu <- normfit$estimate[1]
sig <- normfit$estimate[2]
dens <- dnorm(lgs, mean=mu, sd=sig)
#Compute AIC and BIC for comparison to other fits
llk <- sum(log(dens))
N<-length(lgs)
k<-2
aic <- -2* llk + (2*N*k/(N-k-1))
bic <- -2* llk + k*log(N)
#This is needed for both plots and fit diagnostics:
lgs.hist.info <- hist(lgs,plot=F)
if(plotQ==TRUE){
print("Rendering diagnostic plots...")
split.screen( figs = c( 1, 2 ) )
screen(1)
ylim.max <- max(dens,lgs.hist.info$density)
xlim.max <- max(lgs,lgs.hist.info$breaks)
xlim.min <- min(lgs,lgs.hist.info$breaks)
if(standardizeQ==TRUE){
fittitle <- "Gaussian fit to standardized log(null)"
} else {
fittitle <- "Gaussian fit to log(null)"
}
plot(lgs, dens, xlim=c(xlim.min,xlim.max), ylim=c(0,ylim.max), typ="l", col="blue", lwd=3, xlab="", ylab="")
par(new=T)
hist(lgs, probability=T, xlim=c(xlim.min,xlim.max), ylim=c(0,ylim.max), xlab="STD log(KNM)", main=fittitle)
#Q-Q Plot:
#t-axis:
tmax<-1000
tax<-seq(1,tmax,1)/(tmax+1)
cemp<-ecdf(lgs)(lgs)
#Empirical quantile function (inverse CDF):
qemp<-splinefun(cemp,lgs)
#Empirical quantiles:
Zt<-qemp(tax)
#Quantiles from fit:
Zt.hat<-qnorm(tax, mean=mu, sd=sig)
if(standardizeQ==TRUE){
QQtitle <- "Q-Q plot for Gaussian fit to standardized log(null) hist"
} else {
QQtitle <- "Q-Q plot for Gaussian fit to log(null) hist"
}
#Q-Q plot:
screen(2)
plot(Zt,Zt.hat, xlab="empirical quantiles", ylab="fit quantiles",main=QQtitle)
abline(0,1)
close.screen( all = TRUE )
}
freq.obs <- lgs.hist.info$counts
freq.expec <- rep(-1,length(lgs.hist.info$mids))
fit.probs <- rep(-1,length(lgs.hist.info$mids))
print("Computing fit interquantile probabilities...")
for(i in 1:(length(lgs.hist.info$breaks)-1)){
upi <- lgs.hist.info$breaks[i+1]
loi <- lgs.hist.info$breaks[i]
# print(paste("Bin:",i,
# "Upper:",upi,
# "Lower:",loi,
# "p-upper:",pssd(upi, ssdfit),
# "p-lower:",pssd(loi, ssdfit),
# "prob:", pssd(upi, ssdfit) - pssd(loi, ssdfit)
# )
# )
fit.probs[i] <- pnorm(upi, mean=mu, sd=sig) - pnorm(loi, mean=mu, sd=sig)
freq.expec[i] <- fit.probs[i] * length(score.null.vec)
}
plt <- pnorm(lgs.hist.info$breaks[1], mean=mu, sd=sig)
prt <- 1-sum(c(plt,fit.probs))
fit.probs <- c(plt,fit.probs,prt)
fit.info <- cbind(fit.probs, c(plt*length(score.null.vec), freq.expec, prt*length(score.null.vec)), c(0,freq.obs,0))
colnames(fit.info) <- c("interquant.probs", "interquant.exp.cts", "interquant.obs.cts")
chisq.results <- chisq.test(c(0,freq.obs,0), p = fit.probs)
fit.params <- c(mu,sig)
names(fit.params) <- c("mu.est","sig.est")
info.list <- list(fit.params, fit.info, chisq.results, normfit, aic, bic)
names(info.list) <- c("parameters", "fit.info", "chi.square.test", "fit.obj", "AIC", "BIC")
return(info.list)
}
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