1d:T7f9,# Adaptive Predicted Mean Votes Function to calculate adaptive Predicted Mean Vote (aPMV) adjusted through the adaptive coefficient. ```r calcaPMV(ta, tr, vel, rh, clo = 0.5, met = 1, wme = 0, apCoeff) ``` ## Arguments - `ta`: a numeric value presenting air temperature in [degree C] - `tr`: a numeric value presenting mean radiant temperature in [degree C] - `vel`: a numeric value presenting air velocity in [m/s] - `rh`: a numeric value presenting relative humidity [%] - `clo`: a numeric value presenting clothing insulation level in [clo] - `met`: a numeric value presenting metabolic rate in [met] - `wme`: a numeric value presenting external work in [met] - `apCoeff`: adaptive coefficient lambda ## Returns `calcaPMV` returns the predicted mean vote adjusted through the adaptive coefficients. ## Details `apCoeff` can be derived using `calcapCoeff`. ## Note In case one of `apCoeff` is not given, a standard value will be taken from a list (see `createCond` for details. ## Examples ```r ## Note. Due to random generated asv values. The values for the coefficients will not be meaningful. ## Create sample data ta <- 20:24 # vector with air temperature values tr <- ta # vector with radiant temperature values vel <- rep(.1,5) # vector with air velocities rh <- rep(50,5) # vector with relative humidity values clo <- rep(1.0,5) # vector with clo values met <- rep(1.1,5) # vector with metabolic rates asv <- rnorm(5) # vector with actual sensation votes lsCond <- as.list(data.frame(ta,tr,vel,rh,clo,met,asv)) ## Calculate coefficient apCoeff for data set apCoeff <- calcapCoeff(lsCond) ## calculate apmv apmv <- NULL for (i in 1:length(ta)){ apmv[i] <- calcaPMV(ta[i], tr[i], vel[i], rh[i], clo[i], met[i], apCoeff = apCoeff)$apmv} apmv ``` ## References aPMV is based on Yao, Li and Liu (2009) ## See Also `calcComfInd`, `calcapCoeff` ## Author(s) Code implemented in to R by Marcel Schweiker. Further contribution by Sophia Mueller and Shoaib Sarwar

calcaPMV function