@@ -177,15 +185,15 @@ datasets.
sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
 m_SFO <- mkinmod(parent = mkinsub("SFO"))
 d_SFO_1 <- mkinpredict(m_SFO,
-  c(k_parent_sink = 0.1),
+  c(k_parent = 0.1),
   c(parent = 98), sampling_times)
 d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
 d_SFO_2 <- mkinpredict(m_SFO,
-  c(k_parent_sink = 0.05),
+  c(k_parent = 0.05),
   c(parent = 102), sampling_times)
 d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
 d_SFO_3 <- mkinpredict(m_SFO,
-  c(k_parent_sink = 0.02),
+  c(k_parent = 0.02),
   c(parent = 103), sampling_times)
 d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
 
@@ -214,42 +222,37 @@ datasets.
 #>   Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) 
 #>  Data: grouped_data 
 #>        AIC      BIC    logLik
-#>   298.2781 307.7372 -144.1391
+#>   252.7798 262.1358 -121.3899
 #> 
 #> Random effects:
 #>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
 #>  Level: ds
 #>  Structure: Diagonal
-#>          parent_0 log_k_parent_sink Residual
-#> StdDev: 0.9374733         0.7098105  3.83543
+#>             parent_0 log_k_parent_sink Residual
+#> StdDev: 0.0006768135         0.6800777 2.489397
 #> 
 #> Fixed effects: parent_0 + log_k_parent_sink ~ 1 
-#>                       Value Std.Error DF  t-value p-value
-#> parent_0          101.76838 1.1445444 45 88.91606       0
-#> log_k_parent_sink  -3.05444 0.4195622 45 -7.28008       0
+#>                       Value Std.Error DF   t-value p-value
+#> parent_0          101.74884 0.6456014 44 157.60321       0
+#> log_k_parent_sink  -3.05575 0.4015811 44  -7.60929       0
 #>  Correlation: 
 #>                   prnt_0
-#> log_k_parent_sink 0.034 
+#> log_k_parent_sink 0.026 
 #> 
 #> Standardized Within-Group Residuals:
 #>        Min         Q1        Med         Q3        Max 
-#> -2.6169360 -0.2185329  0.0574070  0.5720937  3.0459868 
+#> -2.1317488 -0.6878121  0.0828385  0.8592270  2.9529864 
 #> 
-#> Number of Observations: 49
-#> Number of Groups: 3 
plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
# augPred does not seem to work on fits with more than one state
+#> Number of Observations: 48
+#> Number of Groups: 3 
plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
# augPred does not seem to work on fits with more than one state
 # variable
Helper functions to create nlme models from mmkin row objects

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