Create a mixed effects model from an mmkin row object
mixed(object, ...) # S3 method for mmkin mixed(object, method = c("none"), ...) # S3 method for mixed.mmkin print(x, digits = max(3, getOption("digits") - 3), ...)
Arguments
| object | An mmkin row object |
|---|---|
| ... | Currently not used |
| method | The method to be used |
| x | A mixed.mmkin object to print |
| digits | Number of digits to use for printing. |
Examples
sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) n_biphasic <- 8 err_1 = list(const = 1, prop = 0.07) DFOP_SFO <- mkinmod( parent = mkinsub("DFOP", "m1"), m1 = mkinsub("SFO"), quiet = TRUE) set.seed(123456) log_sd <- 0.3 syn_biphasic_parms <- as.matrix(data.frame( k1 = rlnorm(n_biphasic, log(0.05), log_sd), k2 = rlnorm(n_biphasic, log(0.01), log_sd), g = plogis(rnorm(n_biphasic, 0, log_sd)), f_parent_to_m1 = plogis(rnorm(n_biphasic, 0, log_sd)), k_m1 = rlnorm(n_biphasic, log(0.002), log_sd))) ds_biphasic_mean <- lapply(1:n_biphasic, function(i) { mkinpredict(DFOP_SFO, syn_biphasic_parms[i, ], c(parent = 100, m1 = 0), sampling_times) } ) set.seed(123456L) ds_biphasic <- lapply(ds_biphasic_mean, function(ds) { add_err(ds, sdfunc = function(value) sqrt(err_1$const^2 + value^2 * err_1$prop^2), n = 1, secondary = "m1")[[1]] }) # \dontrun{ f_mmkin <- mmkin(list("DFOP-SFO" = DFOP_SFO), ds_biphasic, error_model = "tc", quiet = TRUE) f_mixed <- mixed(f_mmkin) print(f_mixed)#> Kinetic model fitted by nonlinear regression to each dataset #> 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_m1/dt = + f_parent_to_m1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) #> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * #> exp(-k2 * time))) * parent - k_m1 * m1 #> #> Data: #> 271 observations of 2 variable(s) grouped in 8 datasets #> #> <mmkin> object #> Status of individual fits: #> #> dataset #> model 1 2 3 4 5 6 7 8 #> DFOP-SFO OK OK OK OK OK OK OK OK #> #> OK: No warnings #> #> Mean fitted parameters: #> parent_0 log_k_m1 f_parent_qlogis log_k1 log_k2 #> 100.674757 -8.761916 -0.004347 -3.348812 -3.986853 #> g_qlogis #> -0.087392# }
