This function uses saemix::saemix() as a backend for fitting nonlinear mixed effects models created from mmkin row objects using the Stochastic Approximation Expectation Maximisation algorithm (SAEM).

saem(object, control, ...)

# S3 method for mmkin
saem(
  object,
  control = list(displayProgress = FALSE, print = FALSE, save = FALSE, save.graphs =
    FALSE),
  cores = 1,
  verbose = FALSE,
  suppressPlot = TRUE,
  quiet = FALSE,
  ...
)

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

saemix_model(object, cores = 1, verbose = FALSE, ...)

saemix_data(object, verbose = FALSE, ...)

Arguments

object

An mmkin row object containing several fits of the same mkinmod model to different datasets

control

Passed to saemix::saemix

...

Further parameters passed to saemix::saemixModel.

cores

The number of cores to be used for multicore processing using parallel::mclapply(). Using more than 1 core is experimental and may lead to excessive forking, apparently depending on the BLAS version used.

verbose

Should we print information about created objects of type saemix::SaemixModel and saemix::SaemixData?

suppressPlot

Should we suppress any plotting that is done by the saemix function?

quiet

Should we suppress the messages saemix prints at the beginning and the end of the optimisation process?

x

An saem.mmkin object to print

digits

Number of digits to use for printing

Value

An S3 object of class 'saem.mmkin', containing the fitted saemix::SaemixObject as a list component named 'so'. The object also inherits from 'mixed.mmkin'.

An saemix::SaemixModel object.

An saemix::SaemixData object.

Details

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 using mkinfit.

Starting values for the fixed effects (population mean parameters, argument psi0 of saemix::saemixModel() are the mean values of the parameters found using mmkin.

See also

Examples

# \dontrun{ ds <- lapply(experimental_data_for_UBA_2019[6:10], function(x) subset(x$data[c("name", "time", "value")])) names(ds) <- paste("Dataset", 6:10) f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds, cores = 1, state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE) f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
#> Running main SAEM algorithm #> [1] "Mon Nov 30 15:53:02 2020" #> .... #> Minimisation finished #> [1] "Mon Nov 30 15:53:04 2020"
f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE) f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
#> Running main SAEM algorithm #> [1] "Mon Nov 30 15:53:05 2020" #> .... #> Minimisation finished #> [1] "Mon Nov 30 15:53:07 2020"
f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
#> Running main SAEM algorithm #> [1] "Mon Nov 30 15:53:07 2020" #> .... #> Minimisation finished #> [1] "Mon Nov 30 15:53:09 2020"
f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
#> Running main SAEM algorithm #> [1] "Mon Nov 30 15:53:10 2020" #> .... #> Minimisation finished #> [1] "Mon Nov 30 15:53:13 2020"
# The returned saem.mmkin object contains an SaemixObject, therefore we can use # functions from saemix library(saemix)
#> Package saemix, version 3.1.9000 #> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so))
#> Likelihoods computed by importance sampling
#> AIC BIC #> 1 624.2484 622.2956 #> 2 467.7096 464.9757 #> 3 495.4373 491.9222
plot(f_saem_fomc$so, plot.type = "convergence")
#> Plotting convergence plots
plot(f_saem_fomc$so, plot.type = "individual.fit")
#> Plotting individual fits
plot(f_saem_fomc$so, plot.type = "npde")
#> Simulating data using nsim = 1000 simulated datasets #> Computing WRES and npde . #> Plotting npde
#> --------------------------------------------- #> Distribution of npde: #> mean= -0.01528 (SE= 0.098 ) #> variance= 0.862 (SE= 0.13 ) #> skewness= 0.5016 #> kurtosis= 1.18 #> --------------------------------------------- #> #> Statistical tests #> Wilcoxon signed rank test : 0.679 #> Fisher variance test : 0.36 #> SW test of normality : 0.0855 . #> Global adjusted p-value : 0.257 #> --- #> Signif. codes: '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 #> ---------------------------------------------
plot(f_saem_fomc$so, plot.type = "vpc")
#> Performing simulations under the model. #> Plotting VPC #> Method used for VPC: binning by quantiles on X , dividing into the following intervals #> Interval Centered.On #> 1 (-1,3] 1.3 #> 2 (3,8] 7.4 #> 3 (8,14] 13.2 #> 4 (14,21] 20.5 #> 5 (21,37.7] 29.5 #> 6 (37.7,60] 50.4 #> 7 (60,90] 76.6 #> 8 (90,120] 109.0 #> 9 (120,180] 156.0
f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
#> Running main SAEM algorithm #> [1] "Mon Nov 30 15:53:15 2020" #> .... #> Minimisation finished #> [1] "Mon Nov 30 15:53:20 2020"
compare.saemix(list(f_saem_fomc$so, f_saem_fomc_tc$so))
#> Likelihoods computed by importance sampling
#> AIC BIC #> 1 467.7096 464.9757 #> 2 469.5208 466.3963
sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), A1 = mkinsub("SFO"))
#> Temporary DLL for differentials generated and loaded
fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"), A1 = mkinsub("SFO"))
#> Temporary DLL for differentials generated and loaded
dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"), A1 = mkinsub("SFO"))
#> Temporary DLL for differentials generated and loaded
# The following fit uses analytical solutions for SFO-SFO and DFOP-SFO, # and compiled ODEs for FOMC that are much slower f_mmkin <- mmkin(list( "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo), ds, quiet = TRUE) # These take about five seconds each on this system, as we use # analytical solutions written for saemix. When using the analytical # solutions written for mkin this took around four minutes f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
#> Running main SAEM algorithm #> [1] "Mon Nov 30 15:53:23 2020" #> .... #> Minimisation finished #> [1] "Mon Nov 30 15:53:28 2020"
f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
#> Running main SAEM algorithm #> [1] "Mon Nov 30 15:53:29 2020" #> .... #> Minimisation finished #> [1] "Mon Nov 30 15:53:38 2020"
# We can use print, plot and summary methods to check the results print(f_saem_dfop_sfo)
#> Kinetic nonlinear mixed-effects model fit by SAEM #> 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 #> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #> exp(-k2 * time))) * parent - k_A1 * A1 #> #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #> #> Likelihood computed by importance sampling #> AIC BIC logLik #> 841.6 836.5 -407.8 #> #> Fitted parameters: #> estimate lower upper #> parent_0 93.76647 91.15312 96.3798 #> log_k_A1 -6.13235 -8.45788 -3.8068 #> f_parent_qlogis -0.97364 -1.36940 -0.5779 #> log_k1 -2.53176 -3.80372 -1.2598 #> log_k2 -3.58667 -5.29524 -1.8781 #> g_qlogis 0.01238 -1.07968 1.1044 #> Var.parent_0 7.61106 -3.34955 18.5717 #> Var.log_k_A1 4.64679 -2.73133 12.0249 #> Var.f_parent_qlogis 0.19693 -0.05498 0.4488 #> Var.log_k1 2.01717 -0.51980 4.5542 #> Var.log_k2 3.63412 -0.92964 8.1979 #> Var.g_qlogis 0.20045 -0.97425 1.3751 #> a.1 1.88335 1.66636 2.1004 #> SD.parent_0 2.75881 0.77234 4.7453 #> SD.log_k_A1 2.15564 0.44429 3.8670 #> SD.f_parent_qlogis 0.44377 0.15994 0.7276 #> SD.log_k1 1.42027 0.52714 2.3134 #> SD.log_k2 1.90634 0.70934 3.1033 #> SD.g_qlogis 0.44771 -0.86417 1.7596
plot(f_saem_dfop_sfo)
summary(f_saem_dfop_sfo, data = TRUE)
#> saemix version used for fitting: 3.1.9000 #> mkin version used for pre-fitting: 0.9.50.4 #> R version used for fitting: 4.0.3 #> Date of fit: Mon Nov 30 15:53:38 2020 #> Date of summary: Mon Nov 30 15:53:39 2020 #> #> Equations: #> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * #> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) #> * parent #> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #> exp(-k2 * time))) * parent - k_A1 * A1 #> #> Data: #> 170 observations of 2 variable(s) grouped in 5 datasets #> #> Model predictions using solution type analytical #> #> Fitted in 9.963 s using 300, 100 iterations #> #> Variance model: Constant variance #> #> Mean of starting values for individual parameters: #> parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 #> 93.8101519 -9.7647455 -0.9711148 -1.8799371 -4.2708142 #> g_qlogis #> 0.1356441 #> #> Fixed degradation parameter values: #> None #> #> Results: #> #> Likelihood computed by importance sampling #> AIC BIC logLik #> 841.6 836.5 -407.8 #> #> Optimised, transformed parameters with symmetric confidence intervals: #> est. lower upper #> parent_0 93.76647 91.153 96.3798 #> log_k_A1 -6.13235 -8.458 -3.8068 #> f_parent_qlogis -0.97364 -1.369 -0.5779 #> log_k1 -2.53176 -3.804 -1.2598 #> log_k2 -3.58667 -5.295 -1.8781 #> g_qlogis 0.01238 -1.080 1.1044 #> #> Correlation: #> prnt_0 lg__A1 f_prn_ log_k1 log_k2 #> log_k_A1 -0.013 #> f_parent_qlogis -0.025 0.050 #> log_k1 0.030 0.000 -0.005 #> log_k2 0.010 0.005 -0.003 0.032 #> g_qlogis -0.063 -0.015 0.010 -0.167 -0.177 #> #> Random effects: #> est. lower upper #> SD.parent_0 2.7588 0.7723 4.7453 #> SD.log_k_A1 2.1556 0.4443 3.8670 #> SD.f_parent_qlogis 0.4438 0.1599 0.7276 #> SD.log_k1 1.4203 0.5271 2.3134 #> SD.log_k2 1.9063 0.7093 3.1033 #> SD.g_qlogis 0.4477 -0.8642 1.7596 #> #> Variance model: #> est. lower upper #> a.1 1.883 1.666 2.1 #> #> Backtransformed parameters with asymmetric confidence intervals: #> est. lower upper #> parent_0 93.766473 9.115e+01 96.37983 #> k_A1 0.002171 2.122e-04 0.02222 #> f_parent_to_A1 0.274156 2.027e-01 0.35942 #> k1 0.079519 2.229e-02 0.28371 #> k2 0.027691 5.015e-03 0.15288 #> g 0.503095 2.536e-01 0.75109 #> #> Resulting formation fractions: #> ff #> parent_A1 0.2742 #> parent_sink 0.7258 #> #> Estimated disappearance times: #> DT50 DT90 DT50back DT50_k1 DT50_k2 #> parent 14.11 59.53 17.92 8.717 25.03 #> A1 319.21 1060.38 NA NA NA #> #> Data: #> ds name time observed predicted residual std standardized #> Dataset 6 parent 0 97.2 95.79523 -1.40477 1.883 -0.745888 #> Dataset 6 parent 0 96.4 95.79523 -0.60477 1.883 -0.321114 #> Dataset 6 parent 3 71.1 71.32042 0.22042 1.883 0.117035 #> Dataset 6 parent 3 69.2 71.32042 2.12042 1.883 1.125873 #> Dataset 6 parent 6 58.1 56.45256 -1.64744 1.883 -0.874739 #> Dataset 6 parent 6 56.6 56.45256 -0.14744 1.883 -0.078288 #> Dataset 6 parent 10 44.4 44.48523 0.08523 1.883 0.045256 #> Dataset 6 parent 10 43.4 44.48523 1.08523 1.883 0.576224 #> Dataset 6 parent 20 33.3 29.75774 -3.54226 1.883 -1.880826 #> Dataset 6 parent 20 29.2 29.75774 0.55774 1.883 0.296141 #> Dataset 6 parent 34 17.6 19.35710 1.75710 1.883 0.932966 #> Dataset 6 parent 34 18.0 19.35710 1.35710 1.883 0.720578 #> Dataset 6 parent 55 10.5 10.48443 -0.01557 1.883 -0.008266 #> Dataset 6 parent 55 9.3 10.48443 1.18443 1.883 0.628895 #> Dataset 6 parent 90 4.5 3.78622 -0.71378 1.883 -0.378995 #> Dataset 6 parent 90 4.7 3.78622 -0.91378 1.883 -0.485188 #> Dataset 6 parent 112 3.0 1.99608 -1.00392 1.883 -0.533048 #> Dataset 6 parent 112 3.4 1.99608 -1.40392 1.883 -0.745435 #> Dataset 6 parent 132 2.3 1.11539 -1.18461 1.883 -0.628990 #> Dataset 6 parent 132 2.7 1.11539 -1.58461 1.883 -0.841377 #> Dataset 6 A1 3 4.3 4.66132 0.36132 1.883 0.191849 #> Dataset 6 A1 3 4.6 4.66132 0.06132 1.883 0.032559 #> Dataset 6 A1 6 7.0 7.41087 0.41087 1.883 0.218157 #> Dataset 6 A1 6 7.2 7.41087 0.21087 1.883 0.111964 #> Dataset 6 A1 10 8.2 9.50878 1.30878 1.883 0.694921 #> Dataset 6 A1 10 8.0 9.50878 1.50878 1.883 0.801114 #> Dataset 6 A1 20 11.0 11.69902 0.69902 1.883 0.371157 #> Dataset 6 A1 20 13.7 11.69902 -2.00098 1.883 -1.062455 #> Dataset 6 A1 34 11.5 12.67784 1.17784 1.883 0.625396 #> Dataset 6 A1 34 12.7 12.67784 -0.02216 1.883 -0.011765 #> Dataset 6 A1 55 14.9 12.78556 -2.11444 1.883 -1.122701 #> Dataset 6 A1 55 14.5 12.78556 -1.71444 1.883 -0.910314 #> Dataset 6 A1 90 12.1 11.52954 -0.57046 1.883 -0.302898 #> Dataset 6 A1 90 12.3 11.52954 -0.77046 1.883 -0.409092 #> Dataset 6 A1 112 9.9 10.43825 0.53825 1.883 0.285793 #> Dataset 6 A1 112 10.2 10.43825 0.23825 1.883 0.126503 #> Dataset 6 A1 132 8.8 9.42830 0.62830 1.883 0.333609 #> Dataset 6 A1 132 7.8 9.42830 1.62830 1.883 0.864577 #> Dataset 7 parent 0 93.6 90.91477 -2.68523 1.883 -1.425772 #> Dataset 7 parent 0 92.3 90.91477 -1.38523 1.883 -0.735514 #> Dataset 7 parent 3 87.0 84.76874 -2.23126 1.883 -1.184726 #> Dataset 7 parent 3 82.2 84.76874 2.56874 1.883 1.363919 #> Dataset 7 parent 7 74.0 77.62735 3.62735 1.883 1.926003 #> Dataset 7 parent 7 73.9 77.62735 3.72735 1.883 1.979100 #> Dataset 7 parent 14 64.2 67.52266 3.32266 1.883 1.764224 #> Dataset 7 parent 14 69.5 67.52266 -1.97734 1.883 -1.049904 #> Dataset 7 parent 30 54.0 52.41949 -1.58051 1.883 -0.839202 #> Dataset 7 parent 30 54.6 52.41949 -2.18051 1.883 -1.157783 #> Dataset 7 parent 60 41.1 39.36582 -1.73418 1.883 -0.920794 #> Dataset 7 parent 60 38.4 39.36582 0.96582 1.883 0.512818 #> Dataset 7 parent 90 32.5 33.75388 1.25388 1.883 0.665771 #> Dataset 7 parent 90 35.5 33.75388 -1.74612 1.883 -0.927132 #> Dataset 7 parent 120 28.1 30.41716 2.31716 1.883 1.230335 #> Dataset 7 parent 120 29.0 30.41716 1.41716 1.883 0.752464 #> Dataset 7 parent 180 26.5 25.66046 -0.83954 1.883 -0.445767 #> Dataset 7 parent 180 27.6 25.66046 -1.93954 1.883 -1.029832 #> Dataset 7 A1 3 3.9 2.69355 -1.20645 1.883 -0.640585 #> Dataset 7 A1 3 3.1 2.69355 -0.40645 1.883 -0.215811 #> Dataset 7 A1 7 6.9 5.81807 -1.08193 1.883 -0.574470 #> Dataset 7 A1 7 6.6 5.81807 -0.78193 1.883 -0.415180 #> Dataset 7 A1 14 10.4 10.22529 -0.17471 1.883 -0.092767 #> Dataset 7 A1 14 8.3 10.22529 1.92529 1.883 1.022265 #> Dataset 7 A1 30 14.4 16.75484 2.35484 1.883 1.250345 #> Dataset 7 A1 30 13.7 16.75484 3.05484 1.883 1.622022 #> Dataset 7 A1 60 22.1 22.22540 0.12540 1.883 0.066583 #> Dataset 7 A1 60 22.3 22.22540 -0.07460 1.883 -0.039610 #> Dataset 7 A1 90 27.5 24.38799 -3.11201 1.883 -1.652376 #> Dataset 7 A1 90 25.4 24.38799 -1.01201 1.883 -0.537344 #> Dataset 7 A1 120 28.0 25.53294 -2.46706 1.883 -1.309927 #> Dataset 7 A1 120 26.6 25.53294 -1.06706 1.883 -0.566572 #> Dataset 7 A1 180 25.8 26.94943 1.14943 1.883 0.610309 #> Dataset 7 A1 180 25.3 26.94943 1.64943 1.883 0.875793 #> Dataset 8 parent 0 91.9 91.53246 -0.36754 1.883 -0.195151 #> Dataset 8 parent 0 90.8 91.53246 0.73246 1.883 0.388914 #> Dataset 8 parent 1 64.9 67.73197 2.83197 1.883 1.503686 #> Dataset 8 parent 1 66.2 67.73197 1.53197 1.883 0.813428 #> Dataset 8 parent 3 43.5 41.58448 -1.91552 1.883 -1.017081 #> Dataset 8 parent 3 44.1 41.58448 -2.51552 1.883 -1.335661 #> Dataset 8 parent 8 18.3 19.62286 1.32286 1.883 0.702395 #> Dataset 8 parent 8 18.1 19.62286 1.52286 1.883 0.808589 #> Dataset 8 parent 14 10.2 10.77819 0.57819 1.883 0.306999 #> Dataset 8 parent 14 10.8 10.77819 -0.02181 1.883 -0.011582 #> Dataset 8 parent 27 4.9 3.26977 -1.63023 1.883 -0.865599 #> Dataset 8 parent 27 3.3 3.26977 -0.03023 1.883 -0.016051 #> Dataset 8 parent 48 1.6 0.48024 -1.11976 1.883 -0.594557 #> Dataset 8 parent 48 1.5 0.48024 -1.01976 1.883 -0.541460 #> Dataset 8 parent 70 1.1 0.06438 -1.03562 1.883 -0.549881 #> Dataset 8 parent 70 0.9 0.06438 -0.83562 1.883 -0.443688 #> Dataset 8 A1 1 9.6 7.61539 -1.98461 1.883 -1.053761 #> Dataset 8 A1 1 7.7 7.61539 -0.08461 1.883 -0.044923 #> Dataset 8 A1 3 15.0 15.47954 0.47954 1.883 0.254622 #> Dataset 8 A1 3 15.1 15.47954 0.37954 1.883 0.201525 #> Dataset 8 A1 8 21.2 20.22616 -0.97384 1.883 -0.517076 #> Dataset 8 A1 8 21.1 20.22616 -0.87384 1.883 -0.463979 #> Dataset 8 A1 14 19.7 20.00067 0.30067 1.883 0.159645 #> Dataset 8 A1 14 18.9 20.00067 1.10067 1.883 0.584419 #> Dataset 8 A1 27 17.5 16.38142 -1.11858 1.883 -0.593929 #> Dataset 8 A1 27 15.9 16.38142 0.48142 1.883 0.255619 #> Dataset 8 A1 48 9.5 10.25357 0.75357 1.883 0.400123 #> Dataset 8 A1 48 9.8 10.25357 0.45357 1.883 0.240833 #> Dataset 8 A1 70 6.2 5.95728 -0.24272 1.883 -0.128878 #> Dataset 8 A1 70 6.1 5.95728 -0.14272 1.883 -0.075781 #> Dataset 9 parent 0 99.8 97.47274 -2.32726 1.883 -1.235697 #> Dataset 9 parent 0 98.3 97.47274 -0.82726 1.883 -0.439246 #> Dataset 9 parent 1 77.1 79.72257 2.62257 1.883 1.392500 #> Dataset 9 parent 1 77.2 79.72257 2.52257 1.883 1.339404 #> Dataset 9 parent 3 59.0 56.26497 -2.73503 1.883 -1.452212 #> Dataset 9 parent 3 58.1 56.26497 -1.83503 1.883 -0.974342 #> Dataset 9 parent 8 27.4 31.66985 4.26985 1.883 2.267151 #> Dataset 9 parent 8 29.2 31.66985 2.46985 1.883 1.311410 #> Dataset 9 parent 14 19.1 22.39789 3.29789 1.883 1.751071 #> Dataset 9 parent 14 29.6 22.39789 -7.20211 1.883 -3.824090 #> Dataset 9 parent 27 10.1 14.21758 4.11758 1.883 2.186301 #> Dataset 9 parent 27 18.2 14.21758 -3.98242 1.883 -2.114537 #> Dataset 9 parent 48 4.5 7.27921 2.77921 1.883 1.475671 #> Dataset 9 parent 48 9.1 7.27921 -1.82079 1.883 -0.966780 #> Dataset 9 parent 70 2.3 3.61470 1.31470 1.883 0.698065 #> Dataset 9 parent 70 2.9 3.61470 0.71470 1.883 0.379485 #> Dataset 9 parent 91 2.0 1.85303 -0.14697 1.883 -0.078038 #> Dataset 9 parent 91 1.8 1.85303 0.05303 1.883 0.028155 #> Dataset 9 parent 120 2.0 0.73645 -1.26355 1.883 -0.670906 #> Dataset 9 parent 120 2.2 0.73645 -1.46355 1.883 -0.777099 #> Dataset 9 A1 1 4.2 3.87843 -0.32157 1.883 -0.170743 #> Dataset 9 A1 1 3.9 3.87843 -0.02157 1.883 -0.011453 #> Dataset 9 A1 3 7.4 8.90535 1.50535 1.883 0.799291 #> Dataset 9 A1 3 7.9 8.90535 1.00535 1.883 0.533807 #> Dataset 9 A1 8 14.5 13.75172 -0.74828 1.883 -0.397312 #> Dataset 9 A1 8 13.7 13.75172 0.05172 1.883 0.027462 #> Dataset 9 A1 14 14.2 14.97541 0.77541 1.883 0.411715 #> Dataset 9 A1 14 12.2 14.97541 2.77541 1.883 1.473650 #> Dataset 9 A1 27 13.7 14.94728 1.24728 1.883 0.662266 #> Dataset 9 A1 27 13.2 14.94728 1.74728 1.883 0.927750 #> Dataset 9 A1 48 13.6 13.66078 0.06078 1.883 0.032272 #> Dataset 9 A1 48 15.4 13.66078 -1.73922 1.883 -0.923470 #> Dataset 9 A1 70 10.4 11.84899 1.44899 1.883 0.769365 #> Dataset 9 A1 70 11.6 11.84899 0.24899 1.883 0.132204 #> Dataset 9 A1 91 10.0 10.09177 0.09177 1.883 0.048727 #> Dataset 9 A1 91 9.5 10.09177 0.59177 1.883 0.314211 #> Dataset 9 A1 120 9.1 7.91379 -1.18621 1.883 -0.629841 #> Dataset 9 A1 120 9.0 7.91379 -1.08621 1.883 -0.576745 #> Dataset 10 parent 0 96.1 93.65257 -2.44743 1.883 -1.299505 #> Dataset 10 parent 0 94.3 93.65257 -0.64743 1.883 -0.343763 #> Dataset 10 parent 8 73.9 77.85906 3.95906 1.883 2.102132 #> Dataset 10 parent 8 73.9 77.85906 3.95906 1.883 2.102132 #> Dataset 10 parent 14 69.4 70.17143 0.77143 1.883 0.409606 #> Dataset 10 parent 14 73.1 70.17143 -2.92857 1.883 -1.554974 #> Dataset 10 parent 21 65.6 63.99188 -1.60812 1.883 -0.853862 #> Dataset 10 parent 21 65.3 63.99188 -1.30812 1.883 -0.694572 #> Dataset 10 parent 41 55.9 54.64292 -1.25708 1.883 -0.667467 #> Dataset 10 parent 41 54.4 54.64292 0.24292 1.883 0.128985 #> Dataset 10 parent 63 47.0 49.61303 2.61303 1.883 1.387433 #> Dataset 10 parent 63 49.3 49.61303 0.31303 1.883 0.166207 #> Dataset 10 parent 91 44.7 45.17807 0.47807 1.883 0.253839 #> Dataset 10 parent 91 46.7 45.17807 -1.52193 1.883 -0.808096 #> Dataset 10 parent 120 42.1 41.27970 -0.82030 1.883 -0.435552 #> Dataset 10 parent 120 41.3 41.27970 -0.02030 1.883 -0.010778 #> Dataset 10 A1 8 3.3 3.99294 0.69294 1.883 0.367929 #> Dataset 10 A1 8 3.4 3.99294 0.59294 1.883 0.314832 #> Dataset 10 A1 14 3.9 5.92756 2.02756 1.883 1.076570 #> Dataset 10 A1 14 2.9 5.92756 3.02756 1.883 1.607538 #> Dataset 10 A1 21 6.4 7.47313 1.07313 1.883 0.569799 #> Dataset 10 A1 21 7.2 7.47313 0.27313 1.883 0.145025 #> Dataset 10 A1 41 9.1 9.76819 0.66819 1.883 0.354786 #> Dataset 10 A1 41 8.5 9.76819 1.26819 1.883 0.673367 #> Dataset 10 A1 63 11.7 10.94733 -0.75267 1.883 -0.399643 #> Dataset 10 A1 63 12.0 10.94733 -1.05267 1.883 -0.558933 #> Dataset 10 A1 91 13.3 11.93773 -1.36227 1.883 -0.723321 #> Dataset 10 A1 91 13.2 11.93773 -1.26227 1.883 -0.670224 #> Dataset 10 A1 120 14.3 12.77666 -1.52334 1.883 -0.808847 #> Dataset 10 A1 120 12.1 12.77666 0.67666 1.883 0.359282
# Using a single core, the following takes about 6 minutes as we do not have an # analytical solution. Using 10 cores it is slower instead of faster f_saem_fomc <- saem(f_mmkin["FOMC-SFO", ], cores = 1)
#> Running main SAEM algorithm #> [1] "Mon Nov 30 15:53:39 2020" #> DLSODA- At current T (=R1), MXSTEP (=I1) steps #> taken on this call before reaching TOUT #> In above message, I1 = 5000 #> #> In above message, R1 = 0.00156238 #> #> DLSODA- At T (=R1) and step size H (=R2), the #> corrector convergence failed repeatedly #> or with ABS(H) = HMIN #> In above message, R1 = 0, R2 = 1.1373e-10 #> #> DLSODA- At current T (=R1), MXSTEP (=I1) steps #> taken on this call before reaching TOUT #> In above message, I1 = 5000 #> #> In above message, R1 = 2.24752e-06 #> #> .... #> Minimisation finished #> [1] "Mon Nov 30 16:00:45 2020"
plot(f_saem_fomc)
# }