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, ...)

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

Arguments

model

An mmkin row object.

data

Should the data be printed?

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

...

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

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("SFO", ds, quiet = TRUE, cores = 1) library(nlme) endpoints(f[[1]])
#> $distimes #> DT50 DT90 #> parent 11.96183 39.73634 #>
f_nlme <- nlme(f) print(f_nlme)
#> Nonlinear mixed-effects model fit by maximum likelihood #> Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent_sink) #> Data: "Not shown" #> Log-likelihood: -307.5269 #> Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> parent_0 log_k_parent_sink #> 85.541149 -3.229596 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k_parent_sink Residual #> StdDev: 1.30857 1.288591 6.304906 #> #> Number of Observations: 90 #> Number of Groups: 5
endpoints(f_nlme)
#> $distimes #> DT50 DT90 #> parent 17.51545 58.18505 #>
# \dontrun{ f_nlme_2 <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1)) update(f_nlme_2, random = parent_0 ~ 1)
#> Nonlinear mixed-effects model fit by maximum likelihood #> Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent_sink) #> Data: "Not shown" #> Log-likelihood: -404.3729 #> Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> parent_0 log_k_parent_sink #> 75.933480 -3.555983 #> #> Random effects: #> Formula: parent_0 ~ 1 | ds #> parent_0 Residual #> StdDev: 0.002416792 21.63027 #> #> Number of Observations: 90 #> Number of Groups: 5
# Test on some real data ds_2 <- lapply(experimental_data_for_UBA_2019[6:10], function(x) x$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_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), A1 = mkinsub("SFO"), 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, "FOMC-SFO" = m_fomc_sfo, "DFOP-SFO" = m_dfop_sfo), ds_2, quiet = TRUE) plot(f_2["SFO-SFO", 3:4]) # Separate fits for datasets 3 and 4
f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ]) # plot(f_nlme_sfo_sfo) # not feasible with pkgdown figures plot(f_nlme_sfo_sfo, 3:4) # Global mixed model: Fits for datasets 3 and 4
# With formation fractions f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ]) plot(f_nlme_sfo_sfo_ff, 3:4) # chi2 different due to different df attribution
# For more parameters, we need to increase pnlsMaxIter and the tolerance # to get convergence f_nlme_fomc_sfo <- nlme(f_2["FOMC-SFO", ], control = list(pnlsMaxIter = 100, tolerance = 1e-4), verbose = TRUE)
#> #> **Iteration 1 #> LME step: Loglik: -394.1603, nlminb iterations: 3 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 #> -0.2079793 0.8563830 1.7454105 1.0917354 1.2756825 #> Beginning PNLS step: .. completed fit_nlme() step. #> PNLS step: RSS = 643.8803 #> fixed effects: 94.17379 -5.473193 -0.6970236 -0.2025091 2.103883 #> iterations: 100 #> Convergence crit. (must all become <= tolerance = 0.0001): #> fixed reStruct #> 0.7960134 0.1447728 #> #> **Iteration 2 #> LME step: Loglik: -396.3824, nlminb iterations: 7 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 #> -1.712404e-01 -2.432655e-05 1.842120e+00 1.073975e+00 1.322925e+00 #> Beginning PNLS step: .. completed fit_nlme() step. #> PNLS step: RSS = 643.8035 #> fixed effects: 94.17385 -5.473487 -0.6970404 -0.2025137 2.103871 #> iterations: 100 #> Convergence crit. (must all become <= tolerance = 0.0001): #> fixed reStruct #> 5.382757e-05 1.236667e-03 #> #> **Iteration 3 #> LME step: Loglik: -396.3825, nlminb iterations: 7 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 #> -0.1712499044 -0.0001499831 1.8420971364 1.0739799123 1.3229167796 #> Beginning PNLS step: .. completed fit_nlme() step. #> PNLS step: RSS = 643.7948 #> fixed effects: 94.17386 -5.473521 -0.6970422 -0.2025144 2.10387 #> iterations: 100 #> Convergence crit. (must all become <= tolerance = 0.0001): #> fixed reStruct #> 6.072817e-06 1.400857e-04 #> #> **Iteration 4 #> LME step: Loglik: -396.3825, nlminb iterations: 7 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 #> -0.1712529502 -0.0001641277 1.8420957542 1.0739797181 1.3229173076 #> Beginning PNLS step: .. completed fit_nlme() step. #> PNLS step: RSS = 643.7936 #> fixed effects: 94.17386 -5.473526 -0.6970426 -0.2025146 2.103869 #> iterations: 100 #> Convergence crit. (must all become <= tolerance = 0.0001): #> fixed reStruct #> 1.027451e-06 2.275704e-05
f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ], control = list(pnlsMaxIter = 120, tolerance = 5e-4), verbose = TRUE)
#> #> **Iteration 1 #> LME step: Loglik: -404.9582, nlminb iterations: 1 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 ds6 #> -0.4114355 0.9798697 1.6990037 0.7293315 0.3354323 1.7113046 #> Beginning PNLS step: .. completed fit_nlme() step. #> PNLS step: RSS = 630.3644 #> fixed effects: 93.82269 -5.455991 -0.6788957 -1.862196 -4.199671 0.05532828 #> iterations: 120 #> Convergence crit. (must all become <= tolerance = 0.0005): #> fixed reStruct #> 0.7885368 0.5822683 #> #> **Iteration 2 #> LME step: Loglik: -407.7755, nlminb iterations: 11 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 ds6 #> -0.371224133 0.003056179 1.789939402 0.724671158 0.301602977 1.754200729 #> Beginning PNLS step: .. completed fit_nlme() step. #> PNLS step: RSS = 630.3633 #> fixed effects: 93.82269 -5.455992 -0.6788958 -1.862196 -4.199671 0.05532831 #> iterations: 120 #> Convergence crit. (must all become <= tolerance = 0.0005): #> fixed reStruct #> 4.789774e-07 2.200661e-05
plot(f_2["FOMC-SFO", 3:4])
plot(f_nlme_fomc_sfo, 3:4)
plot(f_2["DFOP-SFO", 3:4])
plot(f_nlme_dfop_sfo, 3:4)
anova(f_nlme_dfop_sfo, f_nlme_fomc_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 -408.9273 #> f_nlme_fomc_sfo 2 11 818.5149 853.0087 -398.2575 1 vs 2 21.33975 <.0001 #> f_nlme_sfo_sfo 3 9 1085.1821 1113.4043 -533.5910 2 vs 3 270.66716 <.0001
anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo) # if we ignore FOMC
#> Model df AIC BIC logLik Test L.Ratio p-value #> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9273 #> 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 DT50_k1 DT50_k2 #> parent 11.07091 104.6320 4.462384 46.20825 #> A1 162.30536 539.1667 NA NA #>
# }