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 |
|---|---|
| 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 |
| ... | 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(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0195#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.00985#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.011library(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#> Nonlinear mixed-effects model fit by maximum likelihood #> Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k1, log_k2, g_ilr) #> Data: "Not shown" #> Log-likelihood: -238.5635 #> Fixed: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1) #> parent_0 log_k1 log_k2 g_ilr #> 94.17015133 -1.80015306 -4.14738870 0.02290935 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k1 log_k2 g_ilr Residual #> StdDev: 2.488249 0.8447273 1.32965 0.3289311 2.321364 #> #> Number of Observations: 90 #> Number of Groups: 5#> $distimes #> DT50 DT90 DT50back DT50_k1 DT50_k2 #> parent 10.79857 100.7937 30.34193 4.193938 43.85443 #># \dontrun{ f_nlme_2 <- nlme(f["SFO", ], start = c(parent_0 = 100, log_k_parent = 0.1)) update(f_nlme_2, random = parent_0 ~ 1)#> Nonlinear mixed-effects model fit by maximum likelihood #> Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent) #> Data: "Not shown" #> Log-likelihood: -404.3729 #> Fixed: list(parent_0 ~ 1, log_k_parent ~ 1) #> parent_0 log_k_parent #> 75.933480 -3.555983 #> #> Random effects: #> Formula: parent_0 ~ 1 | ds #> parent_0 Residual #> StdDev: 0.002416792 21.63027 #> #> Number of Observations: 90 #> Number of Groups: 5ds_2 <- lapply(experimental_data_for_UBA_2019[6:10], function(x) x$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 4f_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-05f_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#> 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#> 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#> $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 #>#> $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 162.30536 539.1667 NA NA NA #>if (length(findFunction("varConstProp")) > 0) { # tc error model for nlme available # Attempts to fit metabolite kinetics with the tc error model #f_2_tc <- 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, # error_model = "tc") #f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ], control = list(maxIter = 100)) #f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ]) #f_nlme_dfop_sfo_tc <- update(f_nlme_dfop_sfo, weights = varConstProp(), # control = list(sigma = 1, msMaxIter = 100, pnlsMaxIter = 15)) # Fitting metabolite kinetics with nlme.mmkin and the two-component # error model currently does not work, at least not with these data. 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) }#> Nonlinear mixed-effects model fit by maximum likelihood #> Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k1, log_k2, g_ilr) #> Data: "Not shown" #> Log-likelihood: -238.4298 #> Fixed: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1) #> parent_0 log_k1 log_k2 g_ilr #> 94.04774463 -1.82339924 -4.16715509 0.04020161 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_ilr ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k1 log_k2 g_ilr Residual #> StdDev: 2.473883 0.8499901 1.337187 0.3294411 1 #> #> Variance function: #> Structure: Constant plus proportion of variance covariate #> Formula: ~fitted(.) #> Parameter estimates: #> const prop #> 2.23222625 0.01262414 #> Number of Observations: 90 #> Number of Groups: 5f_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)#> Nonlinear mixed-effects model fit by maximum likelihood #> Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent_sink, log_k_parent_A1, log_k_A1_sink) #> Data: "Not shown" #> Log-likelihood: -472.976 #> Fixed: 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 #> 87.975536 -3.669816 -4.164127 -4.645073 #> #> 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: 3.992214 1.77702 1.054733 0.4821383 6.482585 #> #> Variance function: #> Structure: Different standard deviations per stratum #> Formula: ~1 | name #> Parameter estimates: #> parent A1 #> 1.0000000 0.2050003 #> Number of Observations: 170 #> Number of Groups: 5# 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)) # }
