The first step is to input the count data. On the app, we can select to either load the count data from a file (supported formats are `.csv`

, `.dat`

, and `.txt`

) or to input the data manually. Once the table is generated and filled, the “Calculate parameters” button will calculate the number of cells (\(N\)), number of aberrations (\(X\)), as well as mean (\(\bar{y}\)), variance (\(\hat{\sigma}^{2}\)), dispersion index (\(\hat{\sigma}^{2}/\bar{y}\)), and \(u\)-value.

This step is accomplished in R by calling the `calculate_aberr_table()`

function:

```
<- system.file("extdata", "count-data-barquinero-1995.csv",
count_data package = "biodosetools") %>%
::read.csv() %>%
utilscalculate_aberr_table(type = "count")
```

```
count_data#> # A tibble: 11 × 13
#> D N X C0 C1 C2 C3 C4 C5 mean var DI
#> <dbl> <int> <dbl> <int> <int> <int> <int> <int> <int> <dbl> <dbl> <dbl>
#> 1 0 5000 8 4992 8 0 0 0 0 0.0016 0.00160 0.999
#> 2 0.1 5002 14 4988 14 0 0 0 0 0.00280 0.00279 0.997
#> 3 0.25 2008 22 1987 20 1 0 0 0 0.0110 0.0118 1.08
#> 4 0.5 2002 55 1947 55 0 0 0 0 0.0275 0.0267 0.973
#> 5 0.75 1832 100 1736 92 4 0 0 0 0.0546 0.0560 1.03
#> 6 1 1168 109 1064 99 5 0 0 0 0.0933 0.0933 0.999
#> 7 1.5 562 100 474 76 12 0 0 0 0.178 0.189 1.06
#> 8 2 333 103 251 63 17 2 0 0 0.309 0.353 1.14
#> 9 3 193 108 104 72 15 2 0 0 0.560 0.466 0.834
#> 10 4 103 103 35 41 21 4 2 0 1 0.882 0.882
#> 11 5 59 107 11 19 11 9 6 3 1.81 2.09 1.15
#> # … with 1 more variable: u <dbl>
```

Because irradiation conditions during calibration may influence future dose estimates, and for a better traceability, the user can input the conditions under which the samples used to construct the curve were irradiated. This option is only available in the Shiny app, so that these can be saved into the generated reports.

To perform the fitting the user needs select the appropriate fitting options to click the “Calculate fitting” button on the “Data input” box.

The fitting results and summary statistics are shown in the “Results” tabbed box, and the dose-effect curve is displayed in the “Curve plot” box.

The “Export results” box shows two buttons: (a) “Save fitting data”, and (b) “Download report”. The “Save fitting data” will generate an `.rds`

file that contains all information about the count data, irradiation conditions, and options selected when performing the fitting. This file can be then loaded in the dose estimation module to load the dose-effect curve coefficients.

Similarly, the “Download report” will generate a `.pdf`

or a `.docx`

report containing all inputs and fitting results.

To perform the fitting in R we call the `fit()`

function:

```
<- fit(
fit_results count_data = count_data,
model_formula = "lin-quad",
model_family = "automatic",
fit_link = "identity",
aberr_module = "dicentrics"
)
```

The `fit_results`

object is a list that contains all necessary information about the count data as well as options selected when performing the fitting. This is a vital step to ensure traceability and reproducibility. Below we can see its elements:

```
names(fit_results)
#> [1] "fit_raw_data" "fit_formula_raw" "fit_formula_tex"
#> [4] "fit_coeffs" "fit_cor_mat" "fit_var_cov_mat"
#> [7] "fit_dispersion" "fit_model_statistics" "fit_algorithm"
#> [10] "fit_model_summary"
```

In particular, we can see how `fit_coeffs`

matches the results obtained in the UI:

```
$fit_coeffs
fit_results#> estimate std.error statistic p.value
#> coeff_C 0.001280319 0.0004714055 2.715961 6.608367e-03
#> coeff_alpha 0.021038724 0.0051576170 4.079156 4.519949e-05
#> coeff_beta 0.063032534 0.0040073856 15.729091 9.557291e-56
```

To visualise the dose-effect curve, we call the `plot_fit_dose_curve()`

function:

```
plot_fit_dose_curve(
fit_results,aberr_name = "Dicentrics"
)
```