# Tools for Environmental Analyses, Ecotoxicology and Various R Functions 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("http://marc.girondot.free.fr/CRAN/HelpersMG.tar.gz", repos=NULL, type="source") ## Details Helpers functions for several packages ||| |:--|:--| |Package:|HelpersMG| |Type:|Package| |Version:|6.6 build 1714| |Date:|2025-06-16| |License:|GPL (>= 2)| |LazyLoad:|yes| ## Examples ```r ## 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) ``` ## Author(s) Marc Girondot marc.girondot@gmail.com

HelpersMG-package