#*********************************************
#*********************************************
#' Plots a histogram of 'x' added a line of the maximum likelihood fitted Gaussian distribution to 'x'.
#'
#'
#' @return
#'
#' @examples
#' \dontrun{}
#'
#' @importFrom stats dnorm sd
#' @importFrom stats sd
#'
#' @export
#' @rdname histnorm
#'
histnorm<-function(x,mu=NULL,sigma=NULL,breaks="Sturges",coll="red",lengthx=1000,lwd=1.5,add=FALSE,plot=TRUE,...){
############ AUTHOR(S): ############
# Arne Johannes Holmin
############ LANGUAGE: #############
# English
############### LOG: ###############
# Start: 2009-04-04 - Finished.
# Last: 2009-05-05 - Corrected for differing ranges of the output from hist() and from the fitted Gaussian distribution, by adjusting ylim.
########### DESCRIPTION: ###########
# Plots a histogram of 'x' added a line of the maximum likelihood fitted Gaussian distribution to 'x'.
########## DEPENDENCIES: ###########
#
############ VARIABLES: ############
# - 'x' is the data.
# - 'mu' and 'sigma' are the parameters in the Gaussian distribution.
# - 'breaks' has the same effect as in hist().
# - 'coll' is the colour for the Gaussian line.
# - 'lengthx' is the length of the Gaussian line.
# - 'lwd' is the line width of the Gaussian line.
# - 'add' is TRUE if the line Gaussian is to be added to a plot.
# - '...' variables passed on to hist().
##################################################
##################################################
##### Preparation #####
# Fitting a Gaussian distribution to the data by :
if(is.null(mu) && is.null(sigma)){
mu=mean(x,na.rm=TRUE)
sigma=sd(x,na.rm=TRUE)
}
else if(is.null(mu)){
mu=mean(x,na.rm=TRUE)
}
else if(is.null(sigma)){
sigma=sqrt(sum((x-mu)^2,na.rm=TRUE)/length(x))
}
normx=seq(min(x,na.rm=TRUE),max(x,na.rm=TRUE),length.out=lengthx)
normy=dnorm(normx,mu,sigma)
##### Execution and output #####
if(!add){
hh=hist(x,plot=FALSE,breaks=breaks,...)
# Adjusting for the total area of the histogram:
area=sum(hh$counts*diff(hh$breaks))
hh$counts=hh$counts/area
if(plot){
plot(hh,ylim=range(c(hh$density,normy)))
}
# Reverting hh to its original scale:
hh$counts=hh$counts*area
}
else{
hh=hist(x,plot=FALSE,breaks=breaks,...)
}
if(plot){
lines(normx,normy,col=coll,lwd=lwd,...)
}
invisible(list(hist=hh,normx=normx,normy=normy,mu=mu,sigma=sigma))
##################################################
##################################################
}
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