nlme.mmkin {mkin}

Create an nlme model for an mmkin row object

Description

This functions sets 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.

Usage

## S3 method for class 'mmkin'
nlme(
  model,
  data = "auto",
  fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text =
    paste(el, 1, sep = "~")))),
  random = pdDiag(fixed),
  groups,
  start = mean_degparms(model, random = TRUE, test_log_parms = TRUE),
  correlation = NULL,
  weights = NULL,
  subset,
  method = c("ML", "REML"),
  na.action = na.fail,
  naPattern,
  control = list(),
  verbose = FALSE
)

## S3 method for class 'nlme.mmkin'
print(x, digits = max(3, getOption("digits") - 3), ...)

## S3 method for class 'nlme.mmkin'
update(object, ...)

Arguments

model

An mmkin row object.

data

Ignored, data are taken from the mmkin model

fixed

Ignored, all degradation parameters fitted in the mmkin model are used as fixed parameters

random

If not specified, no correlations between random effects are set up for the optimised degradation model parameters. This is achieved by using the nlme::pdDiag method.

groups

See the documentation of nlme

start

If not specified, mean values of the fitted degradation parameters taken from the mmkin object are used

correlation

See the documentation of nlme

weights

passed to nlme

subset

passed to nlme

method

passed to nlme

na.action

passed to nlme

naPattern

passed to nlme

control

passed to nlme

verbose

passed to nlme

x

An nlme.mmkin object to print

digits

Number of digits to use for printing

...

Update specifications passed to update.nlme

object

An nlme.mmkin object to update

Details

Note that the convergence of the nlme algorithms depends on the quality of the data. In degradation kinetics, we often only have few datasets (e.g. data for few soils) and complicated degradation models, which may make it impossible to obtain convergence with nlme.

Value

Upon success, a fitted 'nlme.mmkin' object, which is an nlme object with additional elements. It also inherits from 'mixed.mmkin'.

Note

As the object inherits from nlme::nlme, there is a wealth of methods that will automatically work on 'nlme.mmkin' objects, such as nlme::intervals(), nlme::anova.lme() and nlme::coef.lme().

See Also

nlme_function(), plot.mixed.mmkin, summary.nlme.mmkin

Examples

ds <- lapply(experimental_data_for_UBA_2019[6:10],
 function(x) subset(x$data[c("name", "time", "value")], name == "parent"))

## Not run: 
  f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)
  library(nlme)
  f_nlme_sfo <- nlme(f["SFO", ])
  f_nlme_dfop <- nlme(f["DFOP", ])
  anova(f_nlme_sfo, f_nlme_dfop)
  print(f_nlme_dfop)
  plot(f_nlme_dfop)
  endpoints(f_nlme_dfop)

  ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
   function(x) x$data[c("name", "time", "value")])
  m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
    A1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE)
  m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
    A1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE)
  m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
    A1 = mkinsub("SFO"), quiet = TRUE)

  f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo,
   "SFO-SFO-ff" = m_sfo_sfo_ff,
   "DFOP-SFO" = m_dfop_sfo),
    ds_2, quiet = TRUE)

  f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ])
  plot(f_nlme_sfo_sfo)

  # With formation fractions this does not coverge with defaults
  # f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])
  #plot(f_nlme_sfo_sfo_ff)

  # For the following, we need to increase pnlsMaxIter and the tolerance
  # to get convergence
  f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
    control = list(pnlsMaxIter = 120, tolerance = 5e-4))

  plot(f_nlme_dfop_sfo)

  anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo)

  endpoints(f_nlme_sfo_sfo)
  endpoints(f_nlme_dfop_sfo)

  if (length(findFunction("varConstProp")) > 0) { # tc error model for nlme available
    # Attempts to fit metabolite kinetics with the tc error model are possible,
    # but need tweeking of control values and sometimes do not converge

    f_tc <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, error_model = "tc")
    f_nlme_sfo_tc <- nlme(f_tc["SFO", ])
    f_nlme_dfop_tc <- nlme(f_tc["DFOP", ])
    AIC(f_nlme_sfo, f_nlme_sfo_tc, f_nlme_dfop, f_nlme_dfop_tc)
    print(f_nlme_dfop_tc)
  }

  f_2_obs <- update(f_2, error_model = "obs")
  f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ])
  print(f_nlme_sfo_sfo_obs)
  f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ],
    control = list(pnlsMaxIter = 120, tolerance = 5e-4))

  f_2_tc <- update(f_2, error_model = "tc")
  # f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # No convergence with 50 iterations
  # f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ],
  #  control = list(pnlsMaxIter = 120, tolerance = 5e-4)) # Error in X[, fmap[[nm]]] <- gradnm

  anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs)


## End(Not run)