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
)

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

# S3 method for 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, 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

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

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

Examples

ds <- lapply(experimental_data_for_UBA_2019[6:10], function(x) subset(x$data[c("name", "time", "value")], name == "parent")) f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1) library(nlme) f_nlme_sfo <- nlme(f["SFO", ]) f_nlme_dfop <- nlme(f["DFOP", ]) AIC(f_nlme_sfo, f_nlme_dfop)
#> df AIC #> f_nlme_sfo 5 625.0539 #> f_nlme_dfop 9 495.1270
print(f_nlme_dfop)
#> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #> * parent #> #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #> #> Log-likelihood: -238.5635 #> #> Fixed effects: #> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> parent_0 log_k1 log_k2 g_qlogis #> 94.1702 -1.8002 -4.1474 0.0324 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k1 log_k2 g_qlogis Residual #> StdDev: 2.488 0.8447 1.33 0.4652 2.321 #>
plot(f_nlme_dfop)
endpoints(f_nlme_dfop)
#> $distimes #> DT50 DT90 DT50back DT50_k1 DT50_k2 #> parent 10.79857 100.7937 30.34192 4.193937 43.85442 #>
# \dontrun{ f_nlme_2 <- nlme(f["SFO", ], start = c(parent_0 = 100, log_k_parent = 0.1)) update(f_nlme_2, random = parent_0 ~ 1)
#> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: #> d_parent/dt = - k_parent * parent #> #> Data: #> observations of 0 variable(s) grouped in 0 datasets #> #> Log-likelihood: -404.3729 #> #> Fixed effects: #> list(parent_0 ~ 1, log_k_parent ~ 1) #> parent_0 log_k_parent #> 75.933 -3.556 #> #> Random effects: #> Formula: parent_0 ~ 1 | ds #> parent_0 Residual #> StdDev: 0.002417 21.63 #>
ds_2 <- lapply(experimental_data_for_UBA_2019[6:10], function(x) x$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 f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ]) plot(f_nlme_sfo_sfo_ff)
# For the following fit 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), verbose = TRUE)
#> #> **Iteration 1 #> LME step: Loglik: -404.9583, nlminb iterations: 1 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 ds6 #> -0.4114356 0.9798646 1.3524300 0.7293315 0.3354323 1.3647313 #> Beginning PNLS step: .. completed fit_nlme() step. #> PNLS step: RSS = 630.3633 #> fixed effects: 93.82269 -5.455993 -0.9601037 -1.862196 -4.199671 0.07824609 #> iterations: 120 #> Convergence crit. (must all become <= tolerance = 0.0005): #> fixed reStruct #> 0.7897284 0.5822782 #> #> **Iteration 2 #> LME step: Loglik: -407.7755, nlminb iterations: 11 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 ds6 #> -0.37122411 0.00305562 1.44336560 0.72467122 0.30160310 1.40762692 #> Beginning PNLS step: .. completed fit_nlme() step. #> PNLS step: RSS = 630.3637 #> fixed effects: 93.82269 -5.455992 -0.9601036 -1.862196 -4.199671 0.0782462 #> iterations: 120 #> Convergence crit. (must all become <= tolerance = 0.0005): #> fixed reStruct #> 1.375673e-06 9.758294e-06
plot(f_nlme_dfop_sfo)
anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo)
#> Model df AIC BIC logLik Test L.Ratio p-value #> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 #> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3274 <.0001
endpoints(f_nlme_sfo_sfo)
#> $ff #> parent_sink parent_A1 A1_sink #> 0.5912432 0.4087568 1.0000000 #> #> $distimes #> DT50 DT90 #> parent 19.13518 63.5657 #> A1 66.02155 219.3189 #>
endpoints(f_nlme_dfop_sfo)
#> $ff #> parent_A1 parent_sink #> 0.2768574 0.7231426 #> #> $distimes #> DT50 DT90 DT50back DT50_k1 DT50_k2 #> parent 11.07091 104.6320 31.49738 4.462384 46.20825 #> A1 162.30523 539.1663 NA NA NA #>
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) }
#> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #> * parent #> #> Data: #> 90 observations of 1 variable(s) grouped in 5 datasets #> #> Log-likelihood: -238.4298 #> #> Fixed effects: #> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> parent_0 log_k1 log_k2 g_qlogis #> 94.04775 -1.82340 -4.16715 0.05685 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k1 log_k2 g_qlogis Residual #> StdDev: 2.474 0.85 1.337 0.4659 1 #> #> Variance function: #> Structure: Constant plus proportion of variance covariate #> Formula: ~fitted(.) #> Parameter estimates: #> const prop #> 2.23224114 0.01262341
f_2_obs <- mmkin(list("SFO-SFO" = m_sfo_sfo, "DFOP-SFO" = m_dfop_sfo), ds_2, quiet = TRUE, error_model = "obs") f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ]) print(f_nlme_sfo_sfo_obs)
#> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: #> d_parent/dt = - k_parent_sink * parent - k_parent_A1 * parent #> d_A1/dt = + k_parent_A1 * parent - k_A1_sink * A1 #> #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #> #> Log-likelihood: -472.976 #> #> Fixed effects: #> list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1, log_k_A1_sink ~ 1) #> parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink #> 87.976 -3.670 -4.164 -4.645 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1, log_k_parent_A1 ~ 1, log_k_A1_sink ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k_parent_sink log_k_parent_A1 log_k_A1_sink Residual #> StdDev: 3.992 1.777 1.055 0.4821 6.483 #> #> Variance function: #> Structure: Different standard deviations per stratum #> Formula: ~1 | name #> Parameter estimates: #> parent A1 #> 1.0000000 0.2050003
# The same with DFOP-SFO does not converge, apparently the variances of # parent and A1 are too similar in this case, so that the model is # overparameterised #f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ], control = list(maxIter = 100)) # }