# Fitting the kinetic respiration model In twKinresp: Fitting kinetic models to microbial respiration data

# twDev::genVigs()
rmarkdown::render("kinrespExperiment.Rmd","md_document")

library(knitr)
opts_chunkset(out.extra = 'style="display:block; margin: auto"' #, fig.align = "center" #, fig.width = 4.6, fig.height = 3.2 , fig.width = 6, fig.height = 3.75 #goldener Schnitt 1.6 , dev.args = list(pointsize = 10) , dev = c('png','pdf') ) knit_hooksset(spar = function(before, options, envir) {
if (before) {
par( las = 1 )                   #also y axis labels horizontal
par(mar = c(2.0,3.3,0,0) + 0.3 )  #margins
par(tck = 0.02 )                          #axe-tick length inside plots
par(mgp = c(1.1,0.2,0) )  #positioning of axis title, axis labels, axis
}
})
# if (!require(ggplot2) || !require(dplyr) || !require(purrr)) {
#   print("To generate this vignette, ggplo2, dplyr, and purrr are required.")
#   exit(0)
# }
#themeTw <- theme_bw(base_size = 10) +
#  theme(axis.title = element_text(size = 9))


## Experimental data

Here we take respiration time series from the dataset in Wutzler 2012 of one experiment consisting of incubation of three replicate soil samples.

library(twKinresp, quietly = TRUE)
rde <- subset(respWutzler10, suite == "Face" & experiment == 9 )
summary(rde)

plot( resp ~ time, data = rde, col = rde$replicate , xlab = "Time (hour)", ylab = "Respiration (gC/gCsoil/hour)")  First, we constrain each time series to the unlimited exponential growth phase. The package achieves this byfitting a three parameter exponential equation to successively shortened time series. A deviation from the exponential model introduces positive correlations in the model-data residuals. Hence, the longest time series is selected where there was no positive correlation in the model-data residuals between the mimimum and the last last point. This works quite well with data that displays a typical exponential growth phase but fails if there are deviations from exponential growth present, such as small oscillations in the respiration. Then the records have to be selected manually. res4 <- kinrespGrowthphaseExperiment(rde, weights = varPower(fixed = 0.5) ) rde.e <- getUnlimitedGrowthData(res4) plot( resp ~ time, data = rde.e, col = rde.e$replicate
, xlab = "Time (hour)", ylab = "Respiration (gC/gCsoil/hour)")


## Mixed linear models

We are interested of the microbial growth parameters for the soil, i.e. across the replicates.

There are several suboptimal approaches to aggregate across replicates.

• Fitting a single growth curve to all the data points
• Computing the mean respiration for each time and fitting a model to the single aggregated series
• Fitting a growth curve to each replicate, and then taking the average across the parameters

The last approach is wrong because the growth curve of the averaged parameters is not an average growth curve.

The two first approaches will provide a good average growth curve but provide wrong uncertainty bounds. The first approach neglects the groupings, i.e. dependency between the observations of one replicate and underestimates uncertainty. The second approach drops the advantage of having multiple replicates and can overestimate uncertainty.

Nonlinear mixed effects models provide an proper statistical approach that uses all of the records and accounts for the groupings ob observations of a replicate. This approach is implemented in this package.

## Fitting and inspecting the mixed effects kinetic respiration model

Several versions with random effects in different parameters are fitted, and the model with lowest AIC is selected.

res5Scen <- fitKinrespExperiment(
#,showFitErrorMsg = TRUE
rde.e, coefKinresp(res4,rde.e), weights = varPower(fixed = 0.5))
res5Scen$aics  In the example, the variant with the lowest AIC had a random effect in initial biomass,$x_0$, and initial proportion of active biomass,$r_0$, indicating that those properties differed across replicates. The variant with a random effect in additionally the maximum growth rate,$\mu_{max}$, could not be fitted to the data (AIC==NA). This indicates either no differences between the replicates or not enough replicates to resolve their differences. A plot of the fits to the individual replicates shows a good agreement between the model and the observations.  rde.e$fitted <- fitted(res5Scen$model) plot( resp ~ time, data = rde.e, col = rde.e$replicate
, xlab = "Time (hour)", ylab = "Respiration (gC/gCsoil/hour)")
tmp <- by( rde.e, rde.e$replicate, function(rder){ lines(fitted~time,data = rder, col = as.numeric(rder[1,replicate]))})  The selected model object is stored in entry model of the result. kFit = res5Scen$model
fixef(kFit)


## Transformed and original scale

The model parameters $x_0$ and $\mu_{max}$ are constrained to be strictly positive and their confidence interval should not include values below zero. Therefore, log-transformed versions, $x_{0l}$ and $\mu_{maxl}$ are fitted with the model. This corresponds to assuming that they are log-normally distributed. The values and distribution quantiles at original scale are simply found by taking the exponential of their values.

Similarly, model parameter $r_0$ is constrained in interval $(0,1)$. Hence, the model fits a logit-transformed version $r_{0l}$, and the values at original scale are found by applying the invlogit function.

To simplify the process, function coefKinresp and confintKinresp apply these conversions.

#exp(fixef(kFit)["x0l"])
#invlogit(fixef(kFit)["r0l"])
coefKinresp(fixef(kFit))


The uncertainty bounds of the population grwoth parameters at original scale are:

confintKinresp(confint(kFit))


The help page ?coefKinresp links to further functions accessing the fitted results at transformed and original scale.

Note, that the expected value of a lognormal distribution depends on both, the location, $\mu$, and the shape parameter, $\sigma$, and is larger than the exponential of $\mu$:

$$E(x) = e^{\mu + \sigma^2/2}$$

Function kinrespParDist computes this expectec value and other statistics. We recommend reporting the distribution parameters $(\mu,\sigma)$ along with the expected value and the confidence bounds.

pars <- kinrespParDist(kFit)
pars[,c("mean","cf025","cf975","mu","sigma")]


The skewness is vizalized by the following plot of the density distribution of initially active fraction $r_0$.

iPar = "r0"
xGrid <- seq( pars[iPar,"cf025"]*0.8, pars[iPar,"cf975"]*1.2, length.out = 80)
#fx <- dlnorm(xGrid, mean = pars[iPar,"mu"],sd = pars[iPar,"sigma"])
fx <- dlogitnorm(xGrid, mu = pars[iPar,"mu"],sigma = pars[iPar,"sigma"])
plot( fx ~ xGrid, type = "l", xlab = iPar, ylab = "density" )
cols = c("red","green","blue","gray","gray")
abline(v = pars[iPar,c("mle","median","mean","cf025","cf975")]
, col = cols)
legend("topright", c("density","mode","median","mean","95% bounds")
,col = c("black",cols), lty = "solid")


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twKinresp documentation built on May 2, 2019, 4:47 p.m.