#' Smoother function
#'
#' quickly estimates predicted values of an ARY model
#'
#' @param perf
#' @param trial
#' @return fitted values
#'
#' Use nls() to estimate an ARY model and returns fitted values.
#' With grouped mutation one can easily estimate a bunch of curves.
#'
#'
#' @author Martin Schmettow
#' @usage smoothary(Laptrain$ToT, Laptrain$trial)
#'
#' @export
smoothary <- function(x, perf, trial){
perf <- enquo(perf)
trial <- enquo(trial)
data = x %>%
select(perf = !!perf,
trial = !!trial)
model <-
nls(asymptote::ARY,
start = list(ampl = 6, rate = .5, asym = 2.5),
data = data)
x %>%
mutate(predict_ARY = predict(model))
}
# Laptrain %>%
# group_split(Part) %>%
# map_df(smoothary, perf = Duration, trial = trial) %>%
# ggplot(aes(x = trial, y = predict_ARY, group = Part)) +
# geom_smooth(se = F)
#' Quick estimation
#'
#' quickly estimates predicted values of an ARY model
#'
#' @param perf
#' @param trial
#' @return fitted values
#'
#' Use nls() to estimate an ARY model and returns coefficients.
#' Trial counter and performance variable are given as vectors.
#' This makes smoothary useful in conjunction with manipulation chains.
#' With grouped mutation one can easily estimate a bunch of curves.
#'
#'
#' @author Martin Schmettow
#' @usage smoothary(Laptrain$ToT, Laptrain$trial)
#'
#' @export
coefary <- function(x, perf, trial){
perf <- enquo(perf)
trial <- enquo(trial)
data = x %>%
select(perf = !!perf,
trial = !!trial)
model <-
nls(asymptote::ARY,
start = list(ampl = 6, rate = .5, asym = 2.5),
data = data)
coef(model)
}
coefery <- function(x, perf, trial){
perf <- enquo(perf)
trial <- enquo(trial)
data = x %>%
select(perf = !!perf,
trial = !!trial)
model <-
nls(asymptote::ERY,
start = list(pexp = 0.1, rate = .5, asym = 2.5),
data = data)
coef(model)
}
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