loftest: Lack-of-fit test for models fitted to data with replicates

In mkin: Kinetic Evaluation of Chemical Degradation Data

Lack-of-fit test for models fitted to data with replicates

Description

This is a generic function with a method currently only defined for mkinfit objects. It fits an anova model to the data contained in the object and compares the likelihoods using the likelihood ratio test lrtest.default from the lmtest package.

Usage

loftest(object, ...)

## S3 method for class 'mkinfit'
loftest(object, ...)

Arguments

object	A model object with a defined loftest method
...	Not used

Details

The anova model is interpreted as the simplest form of an mkinfit model, assuming only a constant variance about the means, but not enforcing any structure of the means, so we have one model parameter for every mean of replicate samples.

See Also

lrtest

Examples

## Not run: 
test_data <- subset(synthetic_data_for_UBA_2014[[12]]$data, name == "parent")
sfo_fit <- mkinfit("SFO", test_data, quiet = TRUE)
plot_res(sfo_fit) # We see a clear pattern in the residuals
loftest(sfo_fit)  # We have a clear lack of fit
#
# We try a different model (the one that was used to generate the data)
dfop_fit <- mkinfit("DFOP", test_data, quiet = TRUE)
plot_res(dfop_fit) # We don't see systematic deviations, but heteroscedastic residuals
# therefore we should consider adapting the error model, although we have
loftest(dfop_fit) # no lack of fit
#
# This is the anova model used internally for the comparison
test_data_anova <- test_data
test_data_anova$time <- as.factor(test_data_anova$time)
anova_fit <- lm(value ~ time, data = test_data_anova)
summary(anova_fit)
logLik(anova_fit) # We get the same likelihood and degrees of freedom
#
test_data_2 <- synthetic_data_for_UBA_2014[[12]]$data
m_synth_SFO_lin <- mkinmod(parent = list(type = "SFO", to = "M1"),
  M1 = list(type = "SFO", to = "M2"),
  M2 = list(type = "SFO"), use_of_ff = "max")
sfo_lin_fit <- mkinfit(m_synth_SFO_lin, test_data_2, quiet = TRUE)
plot_res(sfo_lin_fit) # not a good model, we try parallel formation
loftest(sfo_lin_fit)
#
m_synth_SFO_par <- mkinmod(parent = list(type = "SFO", to = c("M1", "M2")),
  M1 = list(type = "SFO"),
  M2 = list(type = "SFO"), use_of_ff = "max")
sfo_par_fit <- mkinfit(m_synth_SFO_par, test_data_2, quiet = TRUE)
plot_res(sfo_par_fit) # much better for metabolites
loftest(sfo_par_fit)
#
m_synth_DFOP_par <- mkinmod(parent = list(type = "DFOP", to = c("M1", "M2")),
  M1 = list(type = "SFO"),
  M2 = list(type = "SFO"), use_of_ff = "max")
dfop_par_fit <- mkinfit(m_synth_DFOP_par, test_data_2, quiet = TRUE)
plot_res(dfop_par_fit) # No visual lack of fit
loftest(dfop_par_fit)  # no lack of fit found by the test
#
# The anova model used for comparison in the case of transformation products
test_data_anova_2 <- dfop_par_fit$data
test_data_anova_2$variable <- as.factor(test_data_anova_2$variable)
test_data_anova_2$time <- as.factor(test_data_anova_2$time)
anova_fit_2 <- lm(observed ~ time:variable - 1, data = test_data_anova_2)
summary(anova_fit_2)

## End(Not run)

mkin documentation built on Oct. 14, 2023, 5:08 p.m.