From d25974f643ee46b7cd5ccd8331dd7bb0b14ab27a Mon Sep 17 00:00:00 2001
From: Johannes Ranke Generate an anova object. The method to calculate the BIC is that from
+the saemix package. As in other prominent anova methods, models are sorted An saem.mmkin object further such objects Method for likelihood calculation: "is" (importance sampling),
+"lin" (linear approximation), or "gq" (Gaussian quadrature). Passed
+to saemix::logLik.SaemixObject Should a likelihood ratio test be performed? If TRUE,
+the alternative models are tested against the first model. Should
+only be done for nested models. Optional character vector of model names an "anova" data frame; the traditional (S3) result of anova()Short demo of the multistart method
Johannes Ranke
- Last change 19 September 2022 (rebuilt 2022-09-28)
+ Last change 19 September 2022 (rebuilt 2022-10-26)
Source: vignettes/web_only/multistart.rmd
multistart.rmd
Arguments
+ Value
+
+
+
summary(<saem.mmkin>)
print(<summary.saem.mmkin>)
Summary method for class "saem.mmkin"
Anova method for saem.mmkin objects
logLik method for saem.mmkin objects
Confidence intervals for parameters in saem.mmkin objects
Perform a hierarchical model fit with multiple starting values
Normalisation factors for aerobic soil degradation according to FOCUS guidance
Set non-detects and unquantified values in residue series without replicates
max_twa_parent()
max_twa_sfo()
max_twa_fomc()
max_twa_dfop()
max_twa_hs()
Produces a histogram of log-likelihoods, and an overlayed kernel density -estimate. In addition, the likelihood of the original fit is shown as -a red vertical line.
+Produces a histogram of log-likelihoods. In addition, the likelihood of the +original fit is shown as a red vertical line.
logLik method for saem.mmkin objects
+The fitted saem.mmkin object
Passed to saemix::logLik.SaemixObject
Passed to saemix::logLik.SaemixObject
A list of saem.mmkin objects, with class attributes 'multistart.saem.mmkin' and 'multistart'.
+ + +The object with the highest likelihood
+ + +The index of the object with the highest likelihood
Produces a boxplot with all parameters from the multiple runs, divided by -using their medians as in the paper by Duchesne et al. (2021).
+Produces a boxplot with all parameters from the multiple runs, scaled +either by the parameters of the run with the highest likelihood, +or by their medians as proposed in the paper by Duchesne et al. (2021).
parhist(object, lpos = "bottomleft", main = "", ...)
parhist(
+ object,
+ llmin = -Inf,
+ scale = c("best", "median"),
+ lpos = "bottomleft",
+ main = "",
+ ...
+)
The multistart object
The minimum likelihood of objects to be shown
By default, scale parameters using the best available fit. +If 'median', parameters are scaled using the median parameters from all fits.
Positioning of the legend.
Will be passed to saemix::SaemixModel()
. Per
+default, uncorrelated random effects are specified for all degradation
+parameters.
A data frame with covariate data for use in +'covariate_models', with dataset names as row names.
A list containing linear model formulas with one explanatory +variable, i.e. of the type 'parameter ~ covariate'. Covariates must be available +in the 'covariates' data frame.
Character vector of degradation parameters for +which there should be no variability over the groups. Only used +if the covariance model is not explicitly specified.
Convenience option to increase the number of iterations
R/set_nd_nq.R
+ set_nd_nq.Rd
This function automates replacing unquantified values in residue time and +depth series. For time series, the function performs part of the residue +processing proposed in the FOCUS kinetics guidance for parent compounds +and metabolites. For two-dimensional residue series over time and depth, +it automates the proposal of Boesten et al (2015).
+set_nd_nq(res_raw, lod, loq = NA, time_zero_presence = FALSE)
+
+set_nd_nq_focus(
+ res_raw,
+ lod,
+ loq = NA,
+ set_first_sample_nd = TRUE,
+ first_sample_nd_value = 0,
+ ignore_below_loq_after_first_nd = TRUE
+)
Character vector of a residue time series, or matrix of +residue values with rows representing depth profiles for a specific sampling +time, and columns representing time series of residues at the same depth. +Values below the limit of detection (lod) have to be coded as "nd", values +between the limit of detection and the limit of quantification, if any, have +to be coded as "nq". Samples not analysed have to be coded as "na". All +values that are not "na", "nd" or "nq" have to be coercible to numeric
Limit of detection (numeric)
Limit of quantification(numeric). Must be specified if the FOCUS rule to +stop after the first non-detection is to be applied
Do we assume that residues occur at time zero? +This only affects samples from the first sampling time that have been +reported as "nd" (not detected).
Should the first sample be set to "first_sample_nd_value" +in case it is a non-detection?
Value to be used for the first sample if it is a non-detection
Should we ignore values below the LOQ after the first +non-detection that occurs after the quantified values?
A numeric vector, if a vector was supplied, or a numeric matrix otherwise
+set_nd_nq_focus()
: Set non-detects in residue time series according to FOCUS rules
Boesten, J. J. T. I., van der Linden, A. M. A., Beltman, W. H. +J. and Pol, J. W. (2015). Leaching of plant protection products and their +transformation products; Proposals for improving the assessment of leaching +to groundwater in the Netherlands — Version 2. Alterra report 2630, Alterra +Wageningen UR (University & Research centre)
+FOCUS (2014) Generic Guidance for Estimating Persistence and Degradation +Kinetics from Environmental Fate Studies on Pesticides in EU Registration, Version 1.1, +18 December 2014, p. 251
+# FOCUS (2014) p. 75/76 and 131/132
+parent_1 <- c(.12, .09, .05, .03, "nd", "nd", "nd", "nd", "nd", "nd")
+set_nd_nq(parent_1, 0.02)
+#> [1] 0.12 0.09 0.05 0.03 0.01 NA NA NA NA NA
+parent_2 <- c(.12, .09, .05, .03, "nd", "nd", .03, "nd", "nd", "nd")
+set_nd_nq(parent_2, 0.02)
+#> [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.03 0.01 NA NA
+set_nd_nq_focus(parent_2, 0.02, loq = 0.05)
+#> [1] 0.12 0.09 0.05 0.03 0.01 NA NA NA NA NA
+parent_3 <- c(.12, .09, .05, .03, "nd", "nd", .06, "nd", "nd", "nd")
+set_nd_nq(parent_3, 0.02)
+#> [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.06 0.01 NA NA
+set_nd_nq_focus(parent_3, 0.02, loq = 0.05)
+#> [1] 0.12 0.09 0.05 0.03 0.01 0.01 0.06 0.01 NA NA
+metabolite <- c("nd", "nd", "nd", 0.03, 0.06, 0.10, 0.11, 0.10, 0.09, 0.05, 0.03, "nd", "nd")
+set_nd_nq(metabolite, 0.02)
+#> [1] NA NA 0.01 0.03 0.06 0.10 0.11 0.10 0.09 0.05 0.03 0.01 NA
+set_nd_nq_focus(metabolite, 0.02, 0.05)
+#> [1] 0.00 NA 0.01 0.03 0.06 0.10 0.11 0.10 0.09 0.05 0.03 0.01 NA
+#
+# Boesten et al. (2015), p. 57/58
+table_8 <- matrix(
+ c(10, 10, rep("nd", 4),
+ 10, 10, rep("nq", 2), rep("nd", 2),
+ 10, 10, 10, "nq", "nd", "nd",
+ "nq", 10, "nq", rep("nd", 3),
+ "nd", "nq", "nq", rep("nd", 3),
+ rep("nd", 6), rep("nd", 6)),
+ ncol = 6, byrow = TRUE)
+set_nd_nq(table_8, 0.5, 1.5, time_zero_presence = TRUE)
+#> [,1] [,2] [,3] [,4] [,5] [,6]
+#> [1,] 10.00 10.00 0.25 0.25 NA NA
+#> [2,] 10.00 10.00 1.00 1.00 0.25 NA
+#> [3,] 10.00 10.00 10.00 1.00 0.25 NA
+#> [4,] 1.00 10.00 1.00 0.25 NA NA
+#> [5,] 0.25 1.00 1.00 0.25 NA NA
+#> [6,] NA 0.25 0.25 NA NA NA
+#> [7,] NA NA NA NA NA NA
+table_10 <- matrix(
+ c(10, 10, rep("nd", 4),
+ 10, 10, rep("nd", 4),
+ 10, 10, 10, rep("nd", 3),
+ "nd", 10, rep("nd", 4),
+ rep("nd", 18)),
+ ncol = 6, byrow = TRUE)
+set_nd_nq(table_10, 0.5, time_zero_presence = TRUE)
+#> [,1] [,2] [,3] [,4] [,5] [,6]
+#> [1,] 10.00 10.00 0.25 NA NA NA
+#> [2,] 10.00 10.00 0.25 NA NA NA
+#> [3,] 10.00 10.00 10.00 0.25 NA NA
+#> [4,] 0.25 10.00 0.25 NA NA NA
+#> [5,] NA 0.25 NA NA NA NA
+#> [6,] NA NA NA NA NA NA
+#> [7,] NA NA NA NA NA NA
+