HelpersMG-package: Tools for Environmental Analyses, Ecotoxicology and Various R...

In HelpersMG: Tools for Environmental Analyses, Ecotoxicology and Various R Functions

Tools for Environmental Analyses, Ecotoxicology and Various R Functions

Description

Contains miscellaneous functions useful for managing
'NetCDF' files (see http://en.wikipedia.org/wiki/NetCDF),
get tide levels on any point of the globe,
get moon phase and time for sun rise and fall,
analyse and reconstruct daily time series of temperature
with irregular sinusoidal pattern,
show scales and wind rose in plot with change of color of text,
Metropolis-Hastings algorithm for Bayesian MCMC analysis,
plot graphs or boxplot with error bars,
search files in disk by there names or their content,
read the contents of all files from a folder at one time,
calculate IC50 for ecotoxicological studies,
calculate the probability mass function of the sum of negative binomial
distributions, calculate distribution of unobserved values in censored or truncated distributions.
The latest version of this package can always be installed using:
install.packages("https://hebergement.universite-paris-saclay.fr/marcgirondot/CRAN/HelpersMG.tar.gz", repos=NULL, type="source")

Details

Helpers functions for several packages

Package:	HelpersMG
Type:	Package
Version:	6.0.3 build 1495
Date:	2023-10-05
License:	GPL (>= 2)
LazyLoad:	yes

Author(s)

Marc Girondot [e-mail]

Examples

## Not run: 
library(HelpersMG)
print('----------------------------------')
print('Examples for mcmcComposite objects')
print('----------------------------------')
require(coda)
x <- rnorm(30, 10, 2)
dnormx <- function(x, par) return(-sum(dnorm(x, mean=par['mean'], sd=par['sd'], log=TRUE)))
parameters_mcmc <- data.frame(Density=c('dnorm', 'dlnorm'), 
Prior1=c(10, 0.5), Prior2=c(2, 0.5), SDProp=c(0.35, 0.2), 
Min=c(-3, 0), Max=c(100, 10), Init=c(10, 2), stringsAsFactors = FALSE, 
row.names=c('mean', 'sd'))
mcmc_run <- MHalgoGen(n.iter=100000, parameters=parameters_mcmc, data=x, 
likelihood=dnormx, n.chains=1, n.adapt=100, thin=1, trace=1)
plot(mcmc_run, xlim=c(0, 20))
plot(mcmc_run, xlim=c(0, 10), parameters="sd")
mcmcforcoda <- as.mcmc(mcmc_run)
# Optimal rejection rate should be 0.234
rejectionRate(mcmcforcoda)
heidel.diag(mcmcforcoda)
raftery.diag(mcmcforcoda)
autocorr.diag(mcmcforcoda)
acf(mcmcforcoda[[1]][,"mean"], lag.max=20, bty="n", las=1)
acf(mcmcforcoda[[1]][,"sd"], lag.max=20, bty="n", las=1)
batchSE(mcmcforcoda, batchSize=100)
# The batch standard error procedure is usually thought to 
# be not as accurate as the time series methods used in summary
summary(mcmcforcoda)$statistics[,"Time-series SE"]
summary(mcmc_run)
as.parameters(mcmc_run)
lastp <- as.parameters(mcmc_run, index="last")
parameters_mcmc[,"Init"] <- lastp
# The n.adapt set to 1 is used to not record the first set of parameters
# then it is not duplicated (as it is also the last one for 
# the object mcmc_run)
mcmc_run2 <- MHalgoGen(n.iter=10000, parameters=parameters_mcmc, data=x, 
likelihood=dnormx, n.chains=1, n.adapt=1, thin=1, trace=1)
mcmc_run3 <- merge(mcmc_run, mcmc_run2)
####### no adaptation, n.adapt must be 0
parameters_mcmc[,"Init"] <- c(mean(x), sd(x))
mcmc_run3 <- MHalgoGen(n.iter=10000, parameters=parameters_mcmc, data=x, 
likelihood=dnormx, n.chains=1, n.adapt=0, thin=1, trace=1)
print('------------------------------------------')
print('Examples for Daily patterns of temperature')
print('------------------------------------------')
# Generate a timeserie of time
time.obs <- NULL
for (i in 0:9) time.obs <- c(time.obs, c(0, 6, 12, 18)+i*24)
# For these time, generate a timeseries of temperatures
temp.obs <- rep(NA, length(time.obs))
temp.obs[3+(0:9)*4] <- rnorm(10, 25, 3)
temp.obs[1+(0:9)*4] <- rnorm(10, 10, 3)
for (i in 1:(length(time.obs)-1)) 
  if (is.na(temp.obs[i])) 
  temp.obs[i] <- mean(c(temp.obs[i-1], temp.obs[i+1]))
  if (is.na(temp.obs[length(time.obs)])) 
  temp.obs[length(time.obs)] <- temp.obs[length(time.obs)-1]/2
observed <- data.frame(time=time.obs, temperature=temp.obs)
# Search for the minimum and maximum values
r <- minmax.periodic(time.minmax.daily=c(Min=2, Max=15), 
observed=observed, period=24)

# Estimate all the temperatures for these values
t <- temperature.periodic(minmax=r)

plot_errbar(x=t[,"time"], y=t[,"temperature"],
errbar.y=ifelse(is.na(t[,"sd"]), 0, 2*t[,"sd"]),
type="l", las=1, bty="n", errbar.y.polygon = TRUE, 
xlab="hours", ylab="Temperatures", ylim=c(0, 35), 
errbar.y.polygon.list = list(col="grey"))

plot_add(x=t[,"time"], y=t[,"temperature"], type="l")

# How many times this package has been download
library(cranlogs)
HelpersMG <- cran_downloads("HelpersMG", from = "2015-04-07", 
                            to = Sys.Date() - 1) 
sum(HelpersMG$count)
plot(HelpersMG$date, HelpersMG$count, type="l", bty="n")

## End(Not run)

HelpersMG documentation built on Oct. 5, 2023, 5:08 p.m.