Create an nlme model for an mmkin row object

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.

# S3 method for mmkin
nlme(
  model,
  data = sys.frame(sys.parent()),
  fixed,
  random = fixed,
  groups,
  start,
  correlation = NULL,
  weights = NULL,
  subset,
  method = c("ML", "REML"),
  na.action = na.fail,
  naPattern,
  control = list(),
  verbose = FALSE
)

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, all fixed effects are complemented with uncorrelated random effects
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

Value

Upon success, a fitted nlme.mmkin object, which is an nlme object with additional elements

See also

nlme_function

Examples

ds <- lapply(experimental_data_for_UBA_2019[6:10],
 function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
library(nlme)
f_nlme <- nlme(f)
nlme(f, random = parent_0 ~ 1)
#> Nonlinear mixed-effects model fit by maximum likelihood
#>   Model: value ~ deg_func(name, time, parent_0, log_k_parent_sink) 
#>   Data: structure(list(ds = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,  3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,  4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,  4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,  5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = c("ordered",  "factor")), name = c("parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent"), time = c(0, 0, 3, 3, 6, 6, 10, 10, 20, 20,  34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 0, 0, 3, 3, 7, 7,  14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 0, 0, 1,  1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 0, 0, 1, 1, 3,  3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 0,  0, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120), value = c(97.2,  96.4, 71.1, 69.2, 58.1, 56.6, 44.4, 43.4, 33.3, 29.2, 17.6, 18,  10.5, 9.3, 4.5, 4.7, 3, 3.4, 2.3, 2.7, 93.6, 92.3, 87, 82.2,  74, 73.9, 64.2, 69.5, 54, 54.6, 41.1, 38.4, 32.5, 35.5, 28.1,  29, 26.5, 27.6, 91.9, 90.8, 64.9, 66.2, 43.5, 44.1, 18.3, 18.1,  10.2, 10.8, 4.9, 3.3, 1.6, 1.5, 1.1, 0.9, 99.8, 98.3, 77.1, 77.2,  59, 58.1, 27.4, 29.2, 19.1, 29.6, 10.1, 18.2, 4.5, 9.1, 2.3,  2.9, 2, 1.8, 2, 2.2, 96.1, 94.3, 73.9, 73.9, 69.4, 73.1, 65.6,  65.3, 55.9, 54.4, 47, 49.3, 44.7, 46.7, 42.1, 41.3)), row.names = c(NA,  -90L), class = c("nfnGroupedData", "nfGroupedData", "groupedData",  "data.frame"), formula = value ~ time | ds, FUN = function (x)  max(x, na.rm = TRUE), order.groups = FALSE) 
#>   Log-likelihood: -394.4901
#>   Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) 
#>          parent_0 log_k_parent_sink 
#>         73.985522         -3.869079 
#> 
#> Random effects:
#>  Formula: parent_0 ~ 1 | ds
#>         parent_0 Residual
#> StdDev:  18.6134 18.22029
#> 
#> Number of Observations: 90
#> Number of Groups: 5 
f_nlme <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1))
update(f_nlme, random = parent_0 ~ 1)
#> Nonlinear mixed-effects model fit by maximum likelihood
#>   Model: value ~ deg_func(name, time, parent_0, log_k_parent_sink) 
#>   Data: structure(list(ds = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,  1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,  2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,  3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,  4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,  4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,  5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = c("ordered",  "factor")), name = c("parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent", "parent", "parent", "parent", "parent", "parent",  "parent", "parent"), time = c(0, 0, 3, 3, 6, 6, 10, 10, 20, 20,  34, 34, 55, 55, 90, 90, 112, 112, 132, 132, 0, 0, 3, 3, 7, 7,  14, 14, 30, 30, 60, 60, 90, 90, 120, 120, 180, 180, 0, 0, 1,  1, 3, 3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 0, 0, 1, 1, 3,  3, 8, 8, 14, 14, 27, 27, 48, 48, 70, 70, 91, 91, 120, 120, 0,  0, 8, 8, 14, 14, 21, 21, 41, 41, 63, 63, 91, 91, 120, 120), value = c(97.2,  96.4, 71.1, 69.2, 58.1, 56.6, 44.4, 43.4, 33.3, 29.2, 17.6, 18,  10.5, 9.3, 4.5, 4.7, 3, 3.4, 2.3, 2.7, 93.6, 92.3, 87, 82.2,  74, 73.9, 64.2, 69.5, 54, 54.6, 41.1, 38.4, 32.5, 35.5, 28.1,  29, 26.5, 27.6, 91.9, 90.8, 64.9, 66.2, 43.5, 44.1, 18.3, 18.1,  10.2, 10.8, 4.9, 3.3, 1.6, 1.5, 1.1, 0.9, 99.8, 98.3, 77.1, 77.2,  59, 58.1, 27.4, 29.2, 19.1, 29.6, 10.1, 18.2, 4.5, 9.1, 2.3,  2.9, 2, 1.8, 2, 2.2, 96.1, 94.3, 73.9, 73.9, 69.4, 73.1, 65.6,  65.3, 55.9, 54.4, 47, 49.3, 44.7, 46.7, 42.1, 41.3)), row.names = c(NA,  -90L), class = c("nfnGroupedData", "nfGroupedData", "groupedData",  "data.frame"), formula = value ~ time | ds, FUN = function (x)  max(x, na.rm = TRUE), order.groups = FALSE) 
#>   Log-likelihood: -404.3729
#>   Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) 
#>          parent_0 log_k_parent_sink 
#>         75.933480         -3.555983 
#> 
#> Random effects:
#>  Formula: parent_0 ~ 1 | ds
#>            parent_0 Residual
#> StdDev: 0.002416792 21.63027
#> 
#> Number of Observations: 90
#> Number of Groups: 5