Helper functions to create nlme models from mmkin row objects

These functions facilitate setting up a nonlinear mixed effects model for an mmkin row object. An mmkin row object is essentially a list of mkinfit objects that have been obtained by fitting the same model to a list of datasets.

nlme_function(object)

mean_degparms(object, random = FALSE)

nlme_data(object)

Arguments

object	An mmkin row object containing several fits of the same model to different datasets
random	Should a list with fixed and random effects be returned?

Value

A function that can be used with nlme

If random is FALSE (default), a named vector containing mean values of the fitted degradation model parameters. If random is TRUE, a list with fixed and random effects, in the format required by the start argument of nlme for the case of a single grouping variable ds?

A groupedData object

See also

nlme.mmkin

Examples

sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
m_SFO <- mkinmod(parent = mkinsub("SFO"))
d_SFO_1 <- mkinpredict(m_SFO,
  c(k_parent_sink = 0.1),
  c(parent = 98), sampling_times)
d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
d_SFO_2 <- mkinpredict(m_SFO,
  c(k_parent_sink = 0.05),
  c(parent = 102), sampling_times)
d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
d_SFO_3 <- mkinpredict(m_SFO,
  c(k_parent_sink = 0.02),
  c(parent = 103), sampling_times)
d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")

d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
ds <- c(d1 = d1, d2 = d2, d3 = d3)

f <- mmkin("SFO", ds, cores = 1, quiet = TRUE)
mean_dp <- mean_degparms(f)
grouped_data <- nlme_data(f)
nlme_f <- nlme_function(f)
# These assignments are necessary for these objects to be
# visible to nlme and augPred when evaluation is done by
# pkgdown to generated the html docs.
assign("nlme_f", nlme_f, globalenv())
assign("grouped_data", grouped_data, globalenv())

library(nlme)
m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
  data = grouped_data,
  fixed = parent_0 + log_k_parent_sink ~ 1,
  random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
  start = mean_dp)
summary(m_nlme)
#> Nonlinear mixed-effects model fit by maximum likelihood
#>   Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) 
#>  Data: grouped_data 
#>        AIC      BIC    logLik
#>   298.2781 307.7372 -144.1391
#> 
#> Random effects:
#>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
#>  Level: ds
#>  Structure: Diagonal
#>          parent_0 log_k_parent_sink Residual
#> StdDev: 0.9374733         0.7098105  3.83543
#> 
#> Fixed effects: parent_0 + log_k_parent_sink ~ 1 
#>                       Value Std.Error DF  t-value p-value
#> parent_0          101.76838 1.1445444 45 88.91606       0
#> log_k_parent_sink  -3.05444 0.4195622 45 -7.28008       0
#>  Correlation: 
#>                   prnt_0
#> log_k_parent_sink 0.034 
#> 
#> Standardized Within-Group Residuals:
#>        Min         Q1        Med         Q3        Max 
#> -2.6169360 -0.2185329  0.0574070  0.5720937  3.0459868 
#> 
#> Number of Observations: 49
#> Number of Groups: 3 
plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
# augPred does not seem to work on fits with more than one state
# variable