- Survival data analysis at target time (TT)
- Survival analysis using a toxicokinetic-toxicodynamic (TKTD) model: GUTS
- Reproduction data analysis at target-time

The package `morse`

is devoted to the analysis of data from standard toxicity tests. It provides a simple workflow to explore/visualize a data set, and compute estimations of risk assessment indicators. This document illustrates a typical use of `morse`

on survival and reproduction data, which can be followed step-by-step to analyze new data sets.

The following example shows all the steps to perform survival analysis on standard toxicity test data and to produce estimated values of the \(LC_x\). We will use a data set of the library named `cadmium2`

, which contains both survival and reproduction data from a chronic laboratory toxicity test. In this experiment, snails were exposed to six concentrations of a metal contaminant (cadmium) during 56 days.

The data from a survival toxicity test should be gathered in a `data.frame`

with a specific layout. This is documented in the paragraph on `survData`

in the reference manual, and you can also inspect one of the data sets provided in the package (e.g., `cadmium2`

). First, we load the data set and use the function `survDataCheck()`

to check that it has the expected layout:

```
data(cadmium2)
survDataCheck(cadmium2)
```

`## No message`

The output `## No message`

just informs that the data set is well-formed.

`survData`

objectThe class `survData`

corresponds to survival data and is the basic layout used for the subsequent operations. Note that if the call to `survDataCheck()`

reports no error (i.e., `## No message`

), it is guaranteed that `survData`

will not fail.

```
dat <- survData(cadmium2)
head(dat)
```

```
## # A tibble: 6 x 6
## conc time Nsurv Nrepro replicate Ninit
## <dbl> <int> <int> <int> <dbl> <int>
## 1 0 0 5 0 1 5
## 2 0 3 5 262 1 5
## 3 0 7 5 343 1 5
## 4 0 10 5 459 1 5
## 5 0 14 5 328 1 5
## 6 0 17 5 742 1 5
```

The function `plot()`

can be used to plot the number of surviving individuals as a function of time for all concentrations and replicates.

`plot(dat, pool.replicate = FALSE)`

Two graphical styles are available, `"generic"`

for standard `R`

plots or `"ggplot"`

to call package `ggplot2`

(default). If argument `pool.replicate`

is `TRUE`

, datapoints at a given time-point and a given concentration are pooled and only the mean number of survivors is plotted. To observe the full data set, we set this option to `FALSE`

.

By fixing the concentration at a (tested) value, we can visualize one subplot in particular:

```
plot(dat, concentration = 124, addlegend = TRUE,
pool.replicate = FALSE, style ="generic")
```

We can also plot the survival rate, at a given time-point, as a function of concentration, with binomial confidence intervals around the data. This is achieved by using function `plotDoseResponse()`

and by fixing the option `target.time`

(default is the end of the experiment).

`plotDoseResponse(dat, target.time = 21, addlegend = TRUE)`

Function `summary()`

provides some descriptive statistics on the experimental design.

`summary(dat)`

```
##
## Number of replicates per time and concentration:
## time
## conc 0 3 7 10 14 17 21 24 28 31 35 38 42 45 49 52 56
## 0 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## 53 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## 78 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## 124 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## 232 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
## 284 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6
##
## Number of survivors (sum of replicates) per time and concentration:
## 0 3 7 10 14 17 21 24 28 31 35 38 42 45 49 52 56
## 0 30 30 30 30 29 29 29 29 29 28 28 28 28 28 28 28 28
## 53 30 30 29 29 29 29 29 29 29 29 28 28 28 28 28 28 28
## 78 30 30 30 30 30 30 29 29 29 29 29 29 29 29 29 27 27
## 124 30 30 30 30 30 29 28 28 27 26 25 23 21 18 11 11 9
## 232 30 30 30 22 18 18 17 14 13 12 8 4 3 1 0 0 0
## 284 30 30 15 7 4 4 4 2 2 1 1 1 1 1 1 0 0
```

Now we are ready to fit a probabilistic model to the survival data, in order to describe the relationship between the concentration in chemical compound and survival rate at the target time. Our model assumes this latter is a log-logistic function of the former, from which the package delivers estimates of the parameters. Once we have estimated the parameters, we can then calculate the \(LC_x\) values for any \(x\). All this work is performed by the `survFitTT()`

function, which requires a `survData`

object as input and the levels of \(LC_x\) we want:

```
fit <- survFitTT(dat,
target.time = 21,
lcx = c(10, 20, 30, 40, 50))
```

The returned value is an object of class `survFitTT`

providing the estimated parameters as a posterior^{1} distribution, which quantifies the uncertainty on their true value. For the parameters of the models, as well as for the \(LC_x\) values, we report the median (as the point estimated value) and the 2.5 % and 97.5 % quantiles of the posterior (as a measure of uncertainty, a.k.a. credible intervals). They can be obtained by using the `summary()`

method:

`summary(fit)`

```
## Summary:
##
## The loglogisticbinom_3 model with a binomial stochastic part was used !
##
## Priors on parameters (quantiles):
##
## 50% 2.5% 97.5%
## b 1.000e+00 1.259e-02 7.943e+01
## d 5.000e-01 2.500e-02 9.750e-01
## e 1.227e+02 5.390e+01 2.793e+02
##
## Posteriors of the parameters (quantiles):
##
## 50% 2.5% 97.5%
## b 8.598e+00 3.986e+00 1.616e+01
## d 9.568e-01 9.102e-01 9.860e-01
## e 2.367e+02 2.105e+02 2.535e+02
##
## Posteriors of the LCx (quantiles):
##
## 50% 2.5% 97.5%
## LC10 1.833e+02 1.266e+02 2.138e+02
## LC20 2.012e+02 1.543e+02 2.259e+02
## LC30 2.143e+02 1.749e+02 2.349e+02
## LC40 2.257e+02 1.934e+02 2.435e+02
## LC50 2.367e+02 2.105e+02 2.535e+02
```

If the inference went well, it is expected that the difference between quantiles in the posterior will be reduced compared to the prior, meaning that the data were helpful to reduce the uncertainty on the true value of the parameters. This simple check can be performed using the summary function.

The fit can also be plotted:

`plot(fit, log.scale = TRUE, adddata = TRUE, addlegend = TRUE)`

This representation shows the estimated relationship between concentration of chemical compound and survival rate (orange curve). It is computed by choosing for each parameter the median value of its posterior. To assess the uncertainty on this estimation, we compute many such curves by sampling the parameters in the posterior distribution. This gives rise to the grey band, showing for any given concentration an interval (called credible interval) containing the survival rate 95% of the time in the posterior distribution. The experimental data points are represented in black and correspond to the observed survival rate when pooling all replicates. The black error bars correspond to a 95% confidence interval, which is another, more straightforward way to bound the most probable value of the survival rate for a tested concentration. In favorable situations, we expect that the credible interval around the estimated curve and the confidence interval around the experimental data largely overlap.

A similar plot is obtained with the style `"generic"`

:

`plot(fit, log.scale = TRUE, style = "generic", adddata = TRUE, addlegend = TRUE)`

Note that `survFitTT()`

will warn you if the estimated \(LC_{x}\) lie outside the range of tested concentrations, as in the following example:

```
data("cadmium1")
doubtful_fit <- survFitTT(survData(cadmium1),
target.time = 21,
lcx = c(10, 20, 30, 40, 50))
```

```
## Warning: The LC50 estimation (model parameter e) lies outside the range of
## tested concentrations and may be unreliable as the prior distribution on
## this parameter is defined from this range !
```

```
plot(doubtful_fit, log.scale = TRUE, style = "ggplot", adddata = TRUE,
addlegend = TRUE)
```

In this example, the experimental design does not include sufficiently high concentrations, and we are missing measurements that would have a major influence on the final estimation. For this reason this result should be considered unreliable.

The fit can be further validated using so-called posterior predictive checks: the idea is to plot the observed values against the corresponding estimated predictions, along with their 95% credible interval. If the fit is correct, we expect to see 95% of the data inside the intervals.

`ppc(fit)`

In this plot, each black dot represents an observation made at a given concentration, and the corresponding number of survivors at target time is given by the value on the *x-axis*. Using the concentration and the fitted model, we can produce the corresponding prediction of the expected number of survivors at that concentration. This prediction is given by the *y-axis*. Ideally observations and predictions should coincide, so we’d expect to see the black dots on the points of coordinate \(Y = X\). Our model provides a tolerable variation around the predited mean value as an interval where we expect 95% of the dots to be in average. The intervals are represented in green if they overlap with the line \(Y=X\), and in red otherwise.

The steps for a TKTD data analysis are absolutely analogous to what we described for the analysis at target time. Here the goal is to estimate the relationship between chemical compound concentration, time and survival rate using the GUTS models. GUTS, for General Unified Threshold models of Survival, is a TKTD models generalising most of existing mechanistic models for survival description. For details about GUTS models, see the vignette theory under modelling approaches, and the included references.

Here is a typical session to analyse concentration-dependent time-course data using the so-called “Stochastic Death” (SD) model:

```
# (1) load data set
data(propiconazole)
# (2) check structure and integrity of the data set
survDataCheck(propiconazole)
```

`## No message`

```
# (3) create a `survData` object
dat <- survData(propiconazole)
# (4) represent the number of survivors as a function of time
plot(dat, pool.replicate = FALSE)
```

```
# (5) check information on the experimental design
summary(dat)
```

```
##
## Number of replicates per time and concentration:
## time
## conc 0 1 2 3 4
## 0 1 1 1 1 1
## 8.05 1 1 1 1 1
## 11.91 1 1 1 1 1
## 13.8 1 1 1 1 1
## 17.87 1 1 1 1 1
## 24.19 1 1 1 1 1
## 28.93 1 1 1 1 1
## 35.92 1 1 1 1 1
##
## Number of survivors (sum of replicates) per time and concentration:
## 0 1 2 3 4
## 0 20 19 19 19 19
## 8.05 20 20 20 20 19
## 11.91 20 19 19 19 17
## 13.8 20 19 19 18 16
## 17.87 21 21 20 16 16
## 24.19 20 17 6 2 1
## 28.93 20 11 4 0 0
## 35.92 20 11 1 0 0
```

To fit the *Stochastic Death* model, we have to specify the `model_type`

as `"SD"`

:

```
# (6) fit the TKTD model SD
fit_cstSD <- survFit(dat, quiet = TRUE, model_type = "SD")
```

Then, the `summary()`

function provides parameters estimates as medians and 95% credible intervals.

```
# (7) summary of parameters estimates
summary(fit_cstSD)
```

```
## Summary:
##
## Priors of the parameters (quantiles) (select with '$Qpriors'):
##
## parameters median Q2.5 Q97.5
## kd 4.157e-02 2.771e-04 6.236e+00
## hb 1.317e-02 2.708e-04 6.403e-01
## z 1.700e+01 8.171e+00 3.539e+01
## kk 5.727e-03 1.021e-05 3.212e+00
##
## Posteriors of the parameters (quantiles) (select with '$Qposteriors'):
##
## parameters median Q2.5 Q97.5
## kd 2.184e+00 1.581e+00 3.506e+00
## hb 2.668e-02 1.274e-02 4.893e-02
## z 1.689e+01 1.546e+01 1.866e+01
## kk 1.231e-01 7.867e-02 1.894e-01
```

```
# OR
fit_cstSD$estim.par
```

```
## parameters median Q2.5 Q97.5
## 1 kd 2.18384163 1.58096837 3.50550149
## 2 hb 0.02668378 0.01274180 0.04892699
## 3 z 16.88632674 15.45933339 18.66204545
## 4 kk 0.12313328 0.07867125 0.18941270
```

Once fitting is done, we can compute posteriors vs. priors distribution with the function `plot_prior_post()`

as follow:

`plot_prior_post(fit_cstSD)`

The `plot()`

function provides a representation of the fitting for each replicates

`plot(fit_cstSD)`

Original data can be removed by using the option `adddata = FALSE`

`plot(fit_cstSD, adddata = FALSE)`

A posterior predictive check is also possible using function `ppc()`

:

`ppc(fit_cstSD)`

The *Individual Tolerance* (IT) model is a variant of TKTD survival analysis. It can also be used with `morse`

as demonstrated hereafter. For the *IT* model, we have to specify the `model_type`

as `"IT"`

:

`fit_cstIT <- survFit(dat, quiet = TRUE, model_type = "IT")`

We can first get a summary of the estimated parameters:

`summary(fit_cstIT)`

```
## Summary:
##
## Priors of the parameters (quantiles) (select with '$Qpriors'):
##
## parameters median Q2.5 Q97.5
## kd 4.157e-02 2.771e-04 6.236e+00
## hb 1.317e-02 2.708e-04 6.403e-01
## alpha 1.700e+01 8.171e+00 3.539e+01
## beta 1.000e+00 1.259e-02 7.943e+01
##
## Posteriors of the parameters (quantiles) (select with '$Qposteriors'):
##
## parameters median Q2.5 Q97.5
## kd 7.186e-01 5.289e-01 9.413e-01
## hb 1.597e-02 3.682e-03 3.763e-02
## alpha 1.771e+01 1.505e+01 2.026e+01
## beta 6.685e+00 4.875e+00 9.103e+00
```

```
# OR
fit_cstIT$estim.par
```

```
## parameters median Q2.5 Q97.5
## 1 kd 0.7185555 0.528888116 0.94132495
## 2 hb 0.0159749 0.003681877 0.03763295
## 3 alpha 17.7068809 15.046087432 20.25596345
## 4 beta 6.6854653 4.875476050 9.10324854
```

And the plot of posteriors vs. priors distributions:

`plot_prior_post(fit_cstIT)`

`plot(fit_cstIT)`

`ppc(fit_cstIT)`

Here is a typical session fitting an SD or an IT model for a data set under time-variable exposure scenario.

```
# (1) load data set
data("propiconazole_pulse_exposure")
# (2) check structure and integrity of the data set
survDataCheck(propiconazole_pulse_exposure)
```

`## No message`

```
# (3) create a `survData` object
dat <- survData(propiconazole_pulse_exposure)
# (4) represent the number of survivor as a function of time
plot(dat)
```

```
# (5) check information on the experimental design
summary(dat)
```

```
##
## Occurence of 'replicate' for each 'time':
## time
## replicate 0 0.96 1 1.96 2 2.96 3 3.96 4 4.96 4.97 5 5.96 6 6.96 7 7.96
## varA 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## varB 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## varC 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## varControl 1 0 1 0 1 0 1 0 1 0 0 1 0 1 0 1 0
## time
## replicate 8 9 9.96 10
## varA 1 1 1 1
## varB 1 1 1 1
## varC 1 1 1 1
## varControl 1 1 0 1
##
## Number of survivors per 'time' and 'replicate':
## 0 0.96 1 1.96 2 2.96 3 3.96 4 4.96 4.97 5 5.96 6 6.96 7
## varA 70 NA 50 NA 49 NA 49 NA 45 NA NA 45 NA 45 NA 42
## varB 70 NA 57 NA 53 NA 52 NA 50 NA NA 46 NA 45 NA 44
## varC 70 NA 70 NA 69 NA 69 NA 68 NA NA 66 NA 65 NA 64
## varControl 60 NA 59 NA 58 NA 58 NA 57 NA NA 57 NA 56 NA 56
## 7.96 8 9 9.96 10
## varA NA 38 37 NA 36
## varB NA 40 38 NA 37
## varC NA 60 55 NA 54
## varControl NA 56 55 NA 54
##
## Concentrations per 'time' and 'replicate':
## 0 0.96 1 1.96 2 2.96 3 3.96 4 4.96 4.97 5
## varA 30.56 27.93 0.00 0.26 NA 0.21 27.69 26.49 0.00 0.18 0.18 NA
## varB 28.98 27.66 0.00 0.27 NA 0.26 0.26 0.26 0.26 0.25 0.25 NA
## varC 4.93 4.69 4.69 4.58 NA 4.58 4.58 4.54 4.54 4.58 4.71 NA
## varControl 0.00 NA 0.00 NA 0 NA 0.00 NA 0.00 NA NA 0
## 5.96 6 6.96 7 7.96 8 9 9.96 10
## varA 0.18 NA 0.14 0.14 0.18 0.18 0.00 0.00 0.00
## varB 0.03 NA 0.00 26.98 26.28 0.00 0.12 0.12 0.12
## varC 4.71 NA 4.60 4.60 4.59 4.59 4.46 4.51 4.51
## varControl NA 0 NA 0.00 NA 0.00 0.00 NA 0.00
```

```
# (6) fit the TKTD model SD
fit_varSD <- survFit(dat, quiet = TRUE, model_type = "SD")
```

```
## Warning: The estimation of the dominant rate constant (model parameter kd) lies
## outside the range used to define its prior distribution which indicates that this
## rate is very high and difficult to estimate from this experiment !
```

```
## Warning: The estimation of Non Effect Concentration threshold (NEC)
## (model parameter z) lies outside the range of tested concentration
## and may be unreliable as the prior distribution on this parameter is
## defined from this range !
```

```
# (7) summary of the fit object
summary(fit_varSD)
```

```
## Summary:
##
## Priors of the parameters (quantiles) (select with '$Qpriors'):
##
## parameters median Q2.5 Q97.5
## kd 2.683e-02 1.119e-04 6.434e+00
## hb 8.499e-03 1.094e-04 6.606e-01
## z 9.154e-01 3.212e-02 2.608e+01
## kk 5.080e-02 4.342e-06 5.942e+02
##
## Posteriors of the parameters (quantiles) (select with '$Qposteriors'):
##
## parameters median Q2.5 Q97.5
## kd 3.693e+00 1.567e+00 2.634e+01
## hb 2.320e-02 1.638e-02 3.110e-02
## z 2.097e+01 2.626e+00 3.181e+01
## kk 8.648e-02 5.431e-03 7.014e-01
```

`plot_prior_post(fit_varSD)`

`plot(fit_varSD)`

`ppc(fit_varSD)`

```
# fit a TKTD model IT
fit_varIT <- survFit(dat, quiet = TRUE, model_type = "IT")
```

```
## Warning: The estimation of the dominant rate constant (model parameter kd) lies
## outside the range used to define its prior distribution which indicates that this
## rate is very high and difficult to estimate from this experiment !
```

```
## Warning: The estimation of log-logistic median (model parameter alpha)
## lies outside the range of tested concentration and may be unreliable as
## the prior distribution on this parameter is defined from this range !
```

```
# (7) summary of the fit object
summary(fit_varIT)
```

```
## Summary:
##
## Priors of the parameters (quantiles) (select with '$Qpriors'):
##
## parameters median Q2.5 Q97.5
## kd 2.683e-02 1.119e-04 6.434e+00
## hb 8.499e-03 1.094e-04 6.606e-01
## alpha 9.154e-01 3.212e-02 2.608e+01
## beta 1.000e+00 1.259e-02 7.943e+01
##
## Posteriors of the parameters (quantiles) (select with '$Qposteriors'):
##
## parameters median Q2.5 Q97.5
## kd 4.301e-01 5.587e-04 1.842e+01
## hb 2.430e-02 1.619e-02 3.327e-02
## alpha 1.765e+01 2.042e-01 3.943e+01
## beta 2.236e+00 4.916e-01 2.359e+01
```

`plot_prior_post(fit_varIT)`

`plot(fit_varIT)`

`ppc(fit_varIT)`

GUTS models can be used to simulate the survival of the organisms under any exposure pattern, using the calibration done with function `survFit()`

from observed data. The function for prediction is called `predict()`

and returns an object of class `survFitPredict`

.

```
# (1) upload or build a data frame with the exposure profile
# argument `replicate` is used to provide several profiles of exposure
data_4prediction <- data.frame(time = c(1:10, 1:10),
conc = c(c(0,0,40,0,0,0,40,0,0,0),
c(21,19,18,23,20,14,25,8,13,5)),
replicate = c(rep("pulse", 10), rep("random", 10)))
# (2) Use the fit on constant exposure propiconazole with model SD (see previously)
predict_PRZ_cstSD_4pred <- predict(object = fit_cstSD, data_predict = data_4prediction)
```

From an object `survFitPredict`

, results can ben plotted with function `plot()`

:

```
# (3) Plot the predicted survival rate under the new exposure profiles.
plot(predict_PRZ_cstSD_4pred)
```

`deSolve`

It appears that with some extreme data set, the fast way used to compute predictions return `NA`

data, due to numerical error (e.g. number greater or lower than \(10^{300}\) or \(10^{-300}\)).

When this issue happens, the function `predict()`

returns an error, with the message providing the way to use the robust implementation with ODE solver provided by `deSolve`

.

This way is implemented through the use of the function `predict_ode()`

. Robustness goes often with longer time to compute. Time to compute can be long, so we use by default MCMC chain size of 1000 independent iterations.

`predict_PRZ_cstSD_4pred_ode <- predict_ode(object = fit_cstSD, data_predict = data_4prediction)`

This new object `predict_PRZ_cstSD_4pred_ode`

is a `survFitPredict`

object and so it has exactly the same properties as an object returned by a `predict()`

function.

Note that since `predict_ode()`

can be very long to compute, the `mcmc_size`

is reduced to 1000 MCMC chains by default.

See for instance, with the plot:

`plot(predict_PRZ_cstSD_4pred_ode)`

While the model has been estimated using the background mortality parameter `hb`

, it can be interesting to see the prediction without it. This is possible with the argument `hb_value`

. If `TRUE`

, the background mortality is taken into account, and if `FALSE`

, the background mortality is set to \(0\) in the prediction.

```
# Use the same data set profile to predict without 'hb'
predict_PRZ_cstSD_4pred_hbOUT <- predict(object = fit_cstSD, data_predict = data_4prediction, hb_value = FALSE)
# Plot the prediction:
plot(predict_PRZ_cstSD_4pred_hbOUT)
```

Following EFSA recommendations, the next functions compute qualitative and quantitative model performance criteria suitable for GUTS, and TKTD modelling in general: the percentage of observations within the 95% credible interval of the Posterior Prediction Check (PPC), the Normalised Root Mean Square Error (NRMSE) and the Survival-Probability Prediction Error (SPPE).

**PPC**

The PPC compares the predicted median numbers of survivors associated to their uncertainty limits with the observed numbers of survivors. This can be visualised by plotting the predicted versus the observed values and counting how frequently the confidence/credible limits intersect with the 1:1 prediction line [see previous plot]. Based on experience, PPC resulting in less than 50% of the observations within the uncertainty limits indicate poor model performance.

**Normalised Root Mean Square Error NRMSE**

NRMSE criterion is also based on the expectation that predicted and observed survival numbers matches the 1:1 line in a scatter plot. The criterion is based on the classical root-mean-square error (RMSE), used to aggregate the magnitudes of the errors in predictions for various time-points into a single measure of predictive power. In order to provide a criterion expressed as a percentage, it is suggested using a normalised RMSE by the mean of the observations.

\[ NRMSE = \frac{RMSE}{\overline{Y}} = \frac{1}{\overline{Y}} \sqrt{\frac{1}{n} \sum_{i=1}^{n} (Y_{obs,i} - Y_{pred,i})^2} \times 100 \]

**Survival Probability Prediction Error (SPPE)**

The SPPE indicator is negative (between 0 and -100%) for an underestimation of effects, and positive (between 0 and 100%) for an overestimation of effects. An SPPE value of 0% means an exact prediction of the observed survival probability at the end of the experiment.

\[ SPPE = \left( \frac{Y_{obs, t_{end}}}{Y_{init}} - \frac{Y_{pred, t_{end}}}{Y_{init}} \right) \times 100 = \frac{Y_{obs, t_{end}} - Y_{pred, t_{end}}}{Y_{init}} \times 100 \]

For *NRMSE* and *SPPE*, we need to compute the number of survivors. To do so, we use the function `predict_Nsurv()`

where two arguments are required: the first argument is a `survFit`

object, and the other is a data set with four columns (`time`

, `conc`

, `replicate`

and `Nsurv`

). Contrary to the function `predict()`

, here the column `Nsurv`

is necessary.

`predict_Nsurv_PRZ_SD_cstTOcst <- predict_Nsurv(fit_cstSD, propiconazole)`

`## Note that computing can be quite long (several minutes).`

`predict_Nsurv_PRZ_SD_varTOcst <- predict_Nsurv(fit_varSD, propiconazole)`

`## Note that computing can be quite long (several minutes).`

`predict_Nsurv_PRZ_SD_cstTOvar <- predict_Nsurv(fit_cstSD, propiconazole_pulse_exposure)`

`## Note that computing can be quite long (several minutes).`

`predict_Nsurv_PRZ_SD_varTOvar <- predict_Nsurv(fit_varSD, propiconazole_pulse_exposure)`

`## Note that computing can be quite long (several minutes).`

`predict_Nsurv_ode`

For the same reason that a `predict_ode`

function as been implemented to compute `predict`

function using the ODE solver of *deSolve*, a `predict_Nsurv_ode`

function as been implemented as equivalent to `predict_Nsurv`

. The time to compute is subtentially longer than the original function.

`predict_Nsurv_PRZ_SD_cstTOcst_ode <- predict_Nsurv_ode(fit_cstSD, propiconazole)`

`## Note that computing can be quite long (several minutes).`

When both function work well, their results are identical (or highly similar):

```
plot(predict_Nsurv_PRZ_SD_cstTOcst)
plot(predict_Nsurv_PRZ_SD_cstTOcst_ode)
```

`plot(predict_Nsurv_PRZ_SD_cstTOcst_ode)`