Helper functions to create nlme models from mmkin row objects

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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.

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nlme_function(object)
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-nlme_data(object)

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Arguments

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

Value

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A function that can be used with nlme

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A groupedData object

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
-#>   266.6428 275.8935 -128.3214
-#> 
-#> Random effects:
-#>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
-#>  Level: ds
-#>  Structure: Diagonal
-#>             parent_0 log_k_parent_sink Residual
-#> StdDev: 0.0004253489         0.7058039 3.065183
-#> 
-#> Fixed effects:  parent_0 + log_k_parent_sink ~ 1 
-#>                       Value Std.Error DF   t-value p-value
-#> parent_0          101.18323 0.7900461 43 128.07256       0
-#> log_k_parent_sink  -3.08708 0.4171755 43  -7.39995       0
-#>  Correlation: 
-#>                   prnt_0
-#> log_k_parent_sink 0.031 
-#> 
-#> Standardized Within-Group Residuals:
-#>         Min          Q1         Med          Q3         Max 
-#> -2.38427071 -0.52059848  0.03593021  0.39987268  2.73188969 
-#> 
-#> Number of Observations: 47
-#> 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)
-
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Helper functions to create nlme models from mmkin row objects

Arguments

Value

See also

Examples