Create a mixed effects model from an mmkin row object

@@ -147,131 +86,126 @@

mixed(object, ...)
-
-# S3 method for mmkin
-mixed(object, method = c("none"), ...)
-
-# S3 method for mixed.mmkin
-print(x, digits = max(3, getOption("digits") - 3), ...)

mixed(object, ...)
 
-    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.
+# S3 method for mmkin
+mixed(object, method = c("none"), ...)
 
+# S3 method for mixed.mmkin
+print(x, digits = max(3, getOption("digits") - 3), ...)

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)
+    
+    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.
+
+    
+    Value
+    An object of class 'mixed.mmkin' which has the observed data in a
+single dataframe which is convenient for plotting
+    
 
-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 
plot(f_mixed)
-
# }
-

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 
+plot(f_mixed)
+
+# }
+