Helper functions to create nlme models from mmkin row objects

@@ -150,137 +89,128 @@ datasets. They are used internally by the nlme.mmkin() method." />

These functions facilitate setting 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. They are used internally by the nlme.mmkin() method.

+datasets. They are used internally by the nlme.mmkin() method.

nlme_function(object)
-
-mean_degparms(object, random = FALSE)
-
-nlme_data(object)

- -

Arguments

- - - - - - - - - - -

object	An mmkin row object containing several fits of the same model to different datasets
random	Should a list with fixed and random effects be returned?

nlme_function(object)
 
-    Value
-
-    A function that can be used with nlme
-If random is FALSE (default), a named vector containing mean values
-of the fitted degradation model parameters. If random is TRUE, a list with
-fixed and random effects, in the format required by the start argument of
-nlme for the case of a single grouping variable ds.
-A groupedData object
-    See also
-
-    nlme.mmkin
-
-    Examples
-    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 = 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 = 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 = 0.02),
-  c(parent = 103), sampling_times)
-d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
-
-d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
-d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
-d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
-ds <- c(d1 = d1, d2 = d2, d3 = d3)
+nlme_data(object)

-f <- mmkin("SFO", ds, cores = 1, quiet = TRUE) -mean_dp <- mean_degparms(f) -grouped_data <- nlme_data(f) -nlme_f <- nlme_function(f) -# These assignments are necessary for these objects to be -# visible to nlme and augPred when evaluation is done by -# pkgdown to generated the html docs. -assign("nlme_f", nlme_f, globalenv()) -assign("grouped_data", grouped_data, globalenv()) +

Arguments

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

Value

A function that can be used with nlme +A groupedData object

#> Nonlinear mixed-effects model fit by maximum likelihood -#> Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) -#> Data: grouped_data -#> AIC BIC logLik -#> 300.6824 310.2426 -145.3412 -#> -#> Random effects: -#> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) -#> Level: ds -#> Structure: Diagonal -#> parent_0 log_k_parent_sink Residual -#> StdDev: 1.697361 0.6801209 3.666073 -#> -#> Fixed effects: parent_0 + log_k_parent_sink ~ 1 -#> Value Std.Error DF t-value p-value -#> parent_0 100.99378 1.3890416 46 72.70753 0 -#> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0 -#> Correlation: -#> prnt_0 -#> log_k_parent_sink 0.027 -#> -#> Standardized Within-Group Residuals: -#> Min Q1 Med Q3 Max -#> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 -#> -#> Number of Observations: 50 -#> Number of Groups: 3

plot(augPred(m_nlme, level = 0:1), layout = c(3, 1)) -

# augPred does not work on fits with more than one state -# variable -# -# The procedure is greatly simplified by the nlme.mmkin function -f_nlme <- nlme(f) -plot(f_nlme) -

Examples

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 = 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 = 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 = 0.02),
+  c(parent = 103), sampling_times)
+d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
+
+d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
+d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
+d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
+ds <- c(d1 = d1, d2 = d2, d3 = d3)
+
+f <- mmkin("SFO", ds, cores = 1, quiet = TRUE)
+mean_dp <- mean_degparms(f)
+grouped_data <- nlme_data(f)
+nlme_f <- nlme_function(f)
+# These assignments are necessary for these objects to be
+# visible to nlme and augPred when evaluation is done by
+# pkgdown to generate the html docs.
+assign("nlme_f", nlme_f, globalenv())
+assign("grouped_data", grouped_data, globalenv())
+
+library(nlme)
+m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
+  data = grouped_data,
+  fixed = parent_0 + log_k_parent_sink ~ 1,
+  random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
+  start = mean_dp)
+summary(m_nlme)
+#> Nonlinear mixed-effects model fit by maximum likelihood
+#>   Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) 
+#>   Data: grouped_data 
+#>        AIC      BIC    logLik
+#>   300.6824 310.2426 -145.3412
+#> 
+#> Random effects:
+#>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
+#>  Level: ds
+#>  Structure: Diagonal
+#>         parent_0 log_k_parent_sink Residual
+#> StdDev: 1.697361         0.6801209 3.666073
+#> 
+#> Fixed effects:  parent_0 + log_k_parent_sink ~ 1 
+#>                       Value Std.Error DF  t-value p-value
+#> parent_0          100.99378 1.3890416 46 72.70753       0
+#> log_k_parent_sink  -3.07521 0.4018589 46 -7.65246       0
+#>  Correlation: 
+#>                   prnt_0
+#> log_k_parent_sink 0.027 
+#> 
+#> Standardized Within-Group Residuals:
+#>        Min         Q1        Med         Q3        Max 
+#> -1.9942823 -0.5622565  0.1791579  0.7165038  2.0704781 
+#> 
+#> Number of Observations: 50
+#> Number of Groups: 3 
+plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
+
+# augPred does not work on fits with more than one state
+# variable
+#
+# The procedure is greatly simplified by the nlme.mmkin function
+f_nlme <- nlme(f)
+plot(f_nlme)
+
+