Package 'flexFitR'

Extra Sum-of-Squares F-Test for modeler objects

Description

Perform an extra sum-of-squares F-test to compare two nested models of class modeler. This test assesses whether the additional parameters in the full model significantly improve the fit compared to the reduced model.

Usage

## S3 method for class 'modeler'
anova(object, full_model = NULL, ...)

Arguments

object	An object of class modeler representing the reduced model with fewer parameters.
full_model	An optional object of class modeler representing the full model with more parameters.
...	Additional parameters for future functionality.

Value

A tibble containing columns with the F-statistic and corresponding p-values, indicating whether the full model provides a significantly better fit than the reduced model.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
dt <- data.frame(X = 1:6, Y = c(12, 16, 44, 50, 95, 100))
mo_1 <- modeler(dt, X, Y, fn = "fn_lin", param = c(m = 10, b = -5))
plot(mo_1)
mo_2 <- modeler(dt, X, Y, fn = "fn_quad", param = c(a = 1, b = 10, c = 5))
plot(mo_2)
anova(mo_1, mo_2)

---

Augment a modeler object with influence diagnostics

Description

This function computes various influence diagnostics, including standardized residuals, studentized residuals, and Cook's distance, for an object of class modeler.

Usage

augment(x, id = NULL, metadata = TRUE, ...)

Arguments

x	An object of class modeler.
id	Optional unique identifier to filter by a specific group. Default is NULL.
metadata	Logical. If TRUE, metadata is included with the predictions. Default is FALSE
...	Additional parameters for future functionality.

Value

A tibble containing the following columns:

uid	Unique identifier for the group.
fn_name	Function name associated with the model.
x	Predictor variable values.
y	Observed response values.
.fitted	Fitted values from the model.
.resid	Raw residuals (observed - fitted).
.hat	Leverage values for each observation.
.cooksd	Cook's distance for each observation.
.std.resid	Standardized residuals.
.stud.resid	Studentized residuals.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_logistic",
    parameters = c(L = 100, k = 4, t0 = 40),
    subset = 2
  )
print(mod_1)
augment(mod_1)

---

Combine objects of class modeler

Description

Combine objects of class modeler. Use with caution, some functions might not work as expected.

Usage

## S3 method for class 'modeler'
c(...)

Arguments

...	Objects of class modeler, typically the result of calling modeler().

Value

A modeler object.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_logistic",
    parameters = c(L = 100, k = 4, t0 = 40),
    subset = 1:2
  )
mod_2 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 100),
    subset = 1:2
  )
mod <- c(mod_1, mod_2)
print(mod)
plot(mod, id = 1:2)

---

Coefficients for an object of class modeler

Description

Extract the estimated coefficients from an object of class modeler.

Usage

## S3 method for class 'modeler'
coef(object, id = NULL, metadata = FALSE, df = FALSE, ...)

Arguments

object	An object of class modeler, typically the result of calling the modeler() function.
id	An optional unique identifier to filter by a specific group. Default is NULL.
metadata	Logical. If TRUE, metadata is included along with the coefficients. Default is FALSE.
df	Logical. If TRUE, the degrees of freedom for the fitted model are returned alongside the coefficients. Default is FALSE.
...	Additional parameters for future functionality.

Value

A data.frame containing the model's estimated coefficients, standard errors, and optional metadata or degrees of freedom if specified.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = c(15, 2, 45)
  )
print(mod_1)
coef(mod_1, id = 2)

---

Confidence intervals for a modeler object

Description

Extract confidence intervals for the estimated parameters of an object of class modeler.

Usage

## S3 method for class 'modeler'
confint(object, parm = NULL, level = 0.95, id = NULL, ...)

Arguments

object	An object of class modeler, typically the result of calling the modeler() function.
parm	A character vector specifying which parameters should have confidence intervals calculated. If NULL, confidence intervals for all parameters are returned. Default is NULL.
level	A numeric value indicating the confidence level for the intervals. Default is 0.95, corresponding to a 95% confidence interval.
id	An optional unique identifier to filter by a specific group. Default is NULL.
...	Additional parameters for future functionality.

Value

A tibble containing the lower and upper confidence limits for each specified parameter.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = c(15, 35, 45)
  )
print(mod_1)
confint(mod_1)

---

Drone-derived data from a potato breeding trial

Description

Canopy and Green Leaf Index for a potato trial arranged in a p-rep design.

Usage

dt_potato

Format

A tibble with 1372 rows and 8 variables:

Trial

chr trial name

Plot

dbl denoting the unique plot id

Row

dbl denoting the row coordinate

Range

dbl denoting range coordinate

gid

chr denoting the genotype id

DAP

dbl denoting Days after planting

Canopy

dbl Canopy UAV-Derived

GLI

dbl Green Leaf Index UAV-Derived

Source

UW - Potato Breeding Program

---

Explore data

Description

Explores data from a data frame in wide format.

Usage

explorer(data, x, y, id, metadata)

Arguments

data	A data.frame containing the input data for analysis.
x	The name of the column in data that contains x points.
y	The names of the columns in data that contain the variables to be analyzed.
id	The names of the columns in data that contains a grouping variable.
metadata	The names of the columns in data to keep across the analysis.

Details

This function helps to explore the dataset before being analyzed with modeler().

Value

An object of class explorer, which is a list containing the following elements:

summ_vars

A data.frame containing summary statistics for each trait at each x point, including minimum, mean, median, maximum, standard deviation, coefficient of variation, number of non-missing values, percentage of missing values, and percentage of negative values.

summ_metadata

A data.frame summarizing the metadata.

locals_min_max

A data.frame containing the local minima and maxima of the mean y values over x.

dt_long

A data.frame in long format, with columns for uid, metadata, var, x, and y

metadata

A character vector with the names of the variables to keep across.

Examples

library(flexFitR)
data(dt_potato)
results <- dt_potato |>
  explorer(
    x = DAP,
    y = c(Canopy, GLI),
    id = Plot,
    metadata = c(gid, Row, Range)
  )
names(results)
head(results$summ_vars)
plot(results, label_size = 4, signif = TRUE, n_row = 2)
# New data format
head(results$dt_long)

---

Extract fitted values from a modeler object

Description

Extract fitted values from a modeler object

Usage

## S3 method for class 'modeler'
fitted(object, ...)

Arguments

object	An object of class 'modeler'
...	Additional parameters for future functionality.

Value

A numeric vector of fitted values.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = c(15, 2, 45)
  )
fitted(mod_1)

---

Exponential exponential function 1

Description

Computes a value based on an exponential growth curve and exponential decay model for time.

Usage

fn_exp1_exp(t, t1, t2, alpha, beta)

Arguments

t	Numeric. The time value.
t1	Numeric. The lower threshold time. Assumed to be known.
t2	Numeric. The upper threshold time.
alpha	Numeric. The parameter for the first exponential term. Must be greater than 0.
beta	Numeric. The parameter for the second exponential term. Must be less than 0.

Details

$f(t; t_1, t_2, \alpha, \beta) = \begin{cases} 0 & \text{if } t < t_1 \\ e^{\alpha \cdot (t - t_1)} - 1 & \text{if } t_1 \leq t \leq t_2 \\ \left(e^{\alpha \cdot (t_2 - t_1)} - 1\right) \cdot e^{\beta \cdot (t - t_2)} & \text{if } t > t_2 \end{cases}$

Value

A numeric value based on the double exponential model. If t is less than t1, the function returns 0. If t is between t1 and t2 (inclusive), the function returns exp(alpha * (t - t1)) - 1. If t is greater than t2, the function returns (exp(alpha * (t2 - t1)) - 1) * exp(beta * (t - t2)).

Examples

library(flexFitR)
plot_fn(
  fn = "fn_exp1_exp",
  params = c(t1 = 35, t2 = 55, alpha = 1 / 20, beta = -1 / 30),
  interval = c(0, 108),
  n_points = 2000,
  auc_label_size = 3,
  y_auc_label = 0.2
)

---

Exponential linear function 1

Description

Computes a value based on an exponential growth curve and linear decay model for time.

Usage

fn_exp1_lin(t, t1, t2, alpha, beta)

Arguments

t	Numeric. The time value.
t1	Numeric. The lower threshold time. Assumed to be known.
t2	Numeric. The upper threshold time.
alpha	Numeric. The parameter for the exponential term. Must be greater than 0.
beta	Numeric. The parameter for the linear term. Must be less than 0.

Details

$f(t; t_1, t_2, \alpha, \beta) = \begin{cases} 0 & \text{if } t < t_1 \\ e^{\alpha \cdot (t - t_1)} - 1 & \text{if } t_1 \leq t \leq t_2 \\ \beta \cdot (t - t_2) + \left(e^{\alpha \cdot (t_2 - t_1)} - 1\right) & \text{if } t > t_2 \end{cases}$

Value

A numeric value based on the exponential linear model. If t is less than t1, the function returns 0. If t is between t1 and t2 (inclusive), the function returns exp(alpha * (t - t1)) - 1. If t is greater than t2, the function returns beta * (t - t2) + (exp(alpha * (t2 - t1)) - 1).

Examples

library(flexFitR)
plot_fn(
  fn = "fn_exp1_lin",
  params = c(t1 = 35, t2 = 55, alpha = 1 / 20, beta = -1 / 40),
  interval = c(0, 108),
  n_points = 2000,
  auc_label_size = 3
)

---

Exponential exponential Function 2

Description

Computes a value based on an exponential growth curve and exponential decay model for time.

Usage

fn_exp2_exp(t, t1, t2, alpha, beta)

Arguments

t	Numeric. The time value.
t1	Numeric. The lower threshold time. Assumed to be known.
t2	Numeric. The upper threshold time.
alpha	Numeric. The parameter for the first exponential term. Must be greater than 0.
beta	Numeric. The parameter for the second exponential term. Must be less than 0.

Details

$f(t; t_1, t_2, \alpha, \beta) = \begin{cases} 0 & \text{if } t < t_1 \\ e^{\alpha \cdot (t - t_1)^2} - 1 & \text{if } t_1 \leq t \leq t_2 \\ \left(e^{\alpha \cdot (t_2 - t_1)^2} - 1\right) \cdot e^{\beta \cdot (t - t_2)} & \text{if } t > t_2 \end{cases}$

Value

A numeric value based on the double exponential model. If t is less than t1, the function returns 0. If t is between t1 and t2 (inclusive), the function returns exp(alpha * (t - t1)^2) - 1. If t is greater than t2, the function returns (exp(alpha * (t2 - t1)^2) - 1) * exp(beta * (t - t2)).

Examples

library(flexFitR)
plot_fn(
  fn = "fn_exp2_exp",
  params = c(t1 = 35, t2 = 55, alpha = 1 / 600, beta = -1 / 30),
  interval = c(0, 108),
  n_points = 2000,
  auc_label_size = 3,
  y_auc_label = 0.15
)

---

Exponential linear function 2

Description

Computes a value based on an exponential growth curve and linear decay model for time.

Usage

fn_exp2_lin(t, t1, t2, alpha, beta)

Arguments

t	Numeric. The time value.
t1	Numeric. The lower threshold time. Assumed to be known.
t2	Numeric. The upper threshold time.
alpha	Numeric. The parameter for the exponential term. Must be greater than 0.
beta	Numeric. The parameter for the linear term. Must be less than 0.

Details

$f(t; t_1, t_2, \alpha, \beta) = \begin{cases} 0 & \text{if } t < t_1 \\ e^{\alpha \cdot (t - t_1)^2} - 1 & \text{if } t_1 \leq t \leq t_2 \\ \beta \cdot (t - t_2) + \left(e^{\alpha \cdot (t_2 - t_1)^2} - 1\right) & \text{if } t > t_2 \end{cases}$

Value

A numeric value based on the exponential linear model. If t is less than t1, the function returns 0. If t is between t1 and t2 (inclusive), the function returns exp(alpha * (t - t1)^2) - 1. If t is greater than t2, the function returns beta * (t - t2) + (exp(alpha * (t2 - t1)^2) - 1).

Examples

library(flexFitR)
plot_fn(
  fn = "fn_exp2_lin",
  params = c(t1 = 35, t2 = 55, alpha = 1 / 600, beta = -1 / 80),
  interval = c(0, 108),
  n_points = 2000,
  auc_label_size = 3
)

---

Linear function

Description

Computes a value based on a linear function.

Usage

fn_lin(t, m, b)

Arguments

t	Numeric value.
m	Numeric value for the slope coefficient.
b	Numeric value for the intercept coefficient.

Details

$f(t; m, b) = m \cdot t + b$

Value

A numeric value based on the linear function.

Examples

library(flexFitR)
plot_fn(
  fn = "fn_lin",
  params = c(m = 2, b = 10),
  interval = c(0, 108),
  n_points = 2000
)

---

Linear plateau linear function

Description

Linear plateau linear function

Usage

fn_lin_pl_lin(t, t1, t2, t3, k, beta)

Arguments

t	Numeric. The time value.
t1	Numeric. The lower threshold time. Default is 45.
t2	Numeric. The upper threshold time before plateau. Default is 80.
t3	Numeric. The lower threshold time after plateau. Default is 45.
k	Numeric. The maximum value of the function. Default is 0.9.
beta	Numeric. Slope of the linear decay.

Details

$f(t; t_1, t_2, t_3, k, \beta) = \begin{cases} 0 & \text{if } t < t_1 \\ \dfrac{k}{t_2 - t_1} \cdot (t - t_1) & \text{if } t_1 \leq t \leq t_2 \\ k & \text{if } t_2 \leq t \leq t_3 \\ k + \beta \cdot (t - t_3) & \text{if } t > t_3 \end{cases}$

Value

A numeric value based on the linear plateau linear model.

Examples

library(flexFitR)
plot_fn(
  fn = "fn_lin_pl_lin",
  params = c(t1 = 38.7, t2 = 62, t3 = 90, k = 0.32, beta = -0.01),
  interval = c(0, 108),
  n_points = 2000,
  auc_label_size = 3
)

---

Linear plateau linear with constrains

Description

Linear plateau linear with constrains

Usage

fn_lin_pl_lin2(t, t1, t2, dt, k, beta)

Arguments

t	Numeric. The time value.
t1	Numeric. The lower threshold time.
t2	Numeric. The upper threshold time before plateau.
dt	Numeric. dt = t3 - t2.
k	Numeric. The maximum value of the function.
beta	Numeric. Slope of the linear decay.

Details

$f(t; t_1, t_2, dt, k, \beta) = \begin{cases} 0 & \text{if } t < t_1 \\ \dfrac{k}{t_2 - t_1} \cdot (t - t_1) & \text{if } t_1 \leq t \leq t_2 \\ k & \text{if } t_2 \leq t \leq (t_2 + dt) \\ k + \beta \cdot (t - (t_2 + dt)) & \text{if } t > (t_2 + dt) \end{cases}$

Value

A numeric value based on the linear plateau linear model.

Examples

library(flexFitR)
plot_fn(
  fn = "fn_lin_pl_lin2",
  params = c(t1 = 38.7, t2 = 62, dt = 28, k = 0.32, beta = -0.01),
  interval = c(0, 108),
  n_points = 2000,
  auc_label_size = 3
)

---

Linear plateau function

Description

Computes a value based on a linear growth curve reaching a plateau for time.

Usage

fn_linear_sat(t, t1 = 45, t2 = 80, k = 0.9)

Arguments

t	Numeric. The time value.
t1	Numeric. The lower threshold time. Default is 45.
t2	Numeric. The upper threshold time. Default is 80.
k	Numeric. The maximum value of the function. Default is 0.9. Assumed to be known.

Details

$f(t; t_1, t_2, k) = \begin{cases} 0 & \text{if } t < t_1 \\ \dfrac{k}{t_2 - t_1} \cdot (t - t_1) & \text{if } t_1 \leq t \leq t_2 \\ k & \text{if } t > t_2 \end{cases}$

Value

A numeric value based on the threshold model. If t is less than t1, the function returns 0. If t is between t1 and t2 (inclusive), the function returns a value between 0 and k in a linear trend. If t is greater than t2, the function returns k.

Examples

library(flexFitR)
plot_fn(
  fn = "fn_linear_sat",
  params = c(t1 = 34.9, t2 = 61.8, k = 100),
  interval = c(0, 108),
  n_points = 2000,
  auc_label_size = 3
)

---

Logistic function

Description

Computes a value based on a logistic function.

Usage

fn_logistic(t, L, k, t0)

Arguments

t	Numeric value.
L	Numeric value.
k	Numeric value.
t0	Numeric value.

Details

$f(t; L, k, t0) = \frac{L}{1 + e^{-k(t - t_0)}}$

Value

A numeric value based on the logistic function.

Examples

library(flexFitR)
plot_fn(
  fn = "fn_logistic",
  params = c(L = 100, k = 0.199, t0 = 47.7),
  interval = c(0, 108),
  n_points = 2000
)

---

Quadratic function

Description

Computes a value based on a quadratic function..

Usage

fn_quad(t, a, b, c)

Arguments

t	Numeric value.
a	Numeric value for coefficient a.
b	Numeric value for coefficient b.
c	Numeric value for coefficient c.

Details

$f(t; a, b, c) = a \cdot t^2 + b \cdot t + c$

Value

A numeric value based on the linear function.

Examples

library(flexFitR)
plot_fn(fn = "fn_quad", params = c(a = 1, b = 10, c = 5))

---

Akaike's An Information Criterion for an object of class modeler

Description

Generic function calculating Akaike's ‘An Information Criterion’ for fitted model object of class modeler.

Usage

## S3 method for class 'modeler'
AIC(object, ..., k = 2)

## S3 method for class 'modeler'
BIC(object, ...)

Arguments

object	An object inheriting from class modeler resulting of executing the function modeler()
...	Further parameters. For future improvements.
k	Numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.

Value

A tibble with columns giving the corresponding AIC and BIC.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
dt <- data.frame(X = 1:6, Y = c(12, 16, 44, 50, 95, 100))
mo_1 <- modeler(dt, X, Y, fn = "fn_lin", param = c(m = 10, b = -5))
mo_2 <- modeler(dt, X, Y, fn = "fn_quad", param = c(a = 1, b = 10, c = 5))
AIC(mo_1)
AIC(mo_2)
BIC(mo_1)
BIC(mo_2)

---

Print available functions in flexFitR

Description

Print available functions in flexFitR

Usage

list_funs()

Value

A vector with available functions

Examples

library(flexFitR)
list_funs()

---

Print available methods in flexFitR

Description

Print available methods in flexFitR

Usage

list_methods(bounds = FALSE, check_package = FALSE)

Arguments

bounds	If TRUE, returns methods for box (or bounds) constraints. FALSE by default.
check_package	If TRUE, ensures solvers are installed. FALSE by default.

Value

A vector with available methods

Examples

library(flexFitR)
list_methods()

---

Extract Log-Likelihood for an object of class modeler

Description

logLik for an object of class modeler

Usage

## S3 method for class 'modeler'
logLik(object, ...)

Arguments

object	An object inheriting from class modeler resulting of executing the function modeler()
...	Further parameters. For future improvements.

Value

A tibble with the Log-Likelihood for the fitted models.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
dt <- data.frame(X = 1:6, Y = c(12, 16, 44, 50, 95, 100))
mo_1 <- modeler(dt, X, Y, fn = "fn_lin", param = c(m = 10, b = -5))
plot(mo_1)
logLik(mo_1)

---

Metrics for an object of class modeler

Description

Computes various performance metrics for a modeler object. The function calculates Sum of Squared Errors (SSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R-squared).

Usage

metrics(x, by_grp = TRUE)

Arguments

x	An object of class 'modeler' containing the necessary data to compute the metrics.
by_grp	Return the metrics by id? TRUE by default.

Details

Sum of Squared Errors (SSE):

$SSE = \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$

Mean Absolute Error (MAE):

$MAE = \frac{1}{n} \sum_{i=1}^{n} |y_i - \hat{y}_i|$

Mean Squared Error (MSE):

$MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$

Root Mean Squared Error (RMSE):

$RMSE = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2}$

Coefficient of Determination (R-squared):

$R^2 = 1 - \frac{\sum_{i=1}^{n} (y_i - \hat{y}_i)^2}{\sum_{i=1}^{n} (y_i - \bar{y})^2}$

Value

A data frame containing the calculated metrics grouped by uid, metadata, and variables.

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = c(1:2)
  )
plot(mod_1, id = c(1:2))
print(mod_1)
metrics(mod_1)

---

Modeler: Non-linear regression for curve fitting

Description

A versatile function for performing non-linear least squares optimization on grouped data. It supports customizable optimization methods, flexible initial/fixed parameters, and parallel processing.

Usage

modeler(
  data,
  x,
  y,
  grp,
  keep,
  fn = "fn_linear_sat",
  parameters = NULL,
  lower = -Inf,
  upper = Inf,
  fixed_params = NULL,
  method = c("subplex", "pracmanm", "anms"),
  subset = NULL,
  options = modeler.options(),
  control = list()
)

Arguments

data	A data.frame containing the input data for analysis.
x	The name of the column in data representing the independent variable (x points).
y	The name of the column in data containing the dependent variable to analyze (response variable).
grp	Column(s) in data used as grouping variable(s). Defaults to NULL. (Optional)
keep	Names of columns to retain in the output. Defaults to NULL. (Optional)
fn	A string. The name of the function used for curve fitting. Example: "fn_lin". Defaults to "fn_linear_sat".
parameters	A numeric vector, named list, or data.frame providing initial values for parameters: Numeric vector Named vector specifying initial values (e.g., c(k = 0.5, t1 = 30)). Data frame Requires a uid column with group IDs and parameter values for each group. List Named list where parameter values can be numeric or expressions (e.g., list(k = "max(y)", t1 = 40)). Defaults to NULL.
lower	A numeric vector specifying lower bounds for parameters. Defaults to -Inf for all parameters.
upper	A numeric vector specifying upper bounds for parameters. Defaults to Inf for all parameters.
fixed_params	A list or data.frame for fixing specific parameters: List Named list where parameter values can be numeric or expressions (e.g., list(k = "max(y)", t1 = 40)). Data frame Requires a uid column for group IDs and fixed parameter values. Defaults to NULL.
method	A character vector specifying optimization methods. Check available methods using list_methods() and their dependencies using optimx::checkallsolvers(). Defaults to c("subplex", "pracmanm", "anms").
subset	A vector (optional) containing levels of grp to filter the data for analysis. Defaults to NULL (all groups are included).
options	A list of additional options. See modeler.options() progress Logical. If TRUE a progress bar is displayed. Default is FALSE. Try this before running the function: progressr::handlers("progress", "beepr"). parallel Logical. If TRUE the model fit is performed in parallel. Default is FALSE. workers The number of parallel processes to use. parallel::detectCores() trace If TRUE , convergence monitoring of the current fit is reported in the console. FALSE by default. return_method Logical. If TRUE, includes the optimization method used in the result. Default is FALSE.
control	A list of control parameters to be passed to the optimization function. For example: list(maxit = 500).

Value

An object of class modeler, which is a list containing the following elements:

param

Data frame containing optimized parameters and related information.

dt

Data frame with input data, fitted values, and residuals.

metrics

Metrics and summary of the models.

execution

Total execution time for the analysis.

response

Name of the response variable analyzed.

keep

Metadata retained based on the keep argument.

fun

Name of the curve-fitting function used.

parallel

List containing parallel execution details (if applicable).

fit

List of fitted models for each group.

Examples

library(flexFitR)
data(dt_potato)
explorer <- explorer(dt_potato, x = DAP, y = c(Canopy, GLI), id = Plot)
# Example 1
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = GLI,
    grp = Plot,
    fn = "fn_lin_pl_lin",
    parameters = c(t1 = 38.7, t2 = 62, t3 = 90, k = 0.32, beta = -0.01),
    subset = 195
  )
plot(mod_1, id = 195)
print(mod_1)
# Example 2
mod_2 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = 195
  )
plot(mod_2, id = 195)
print(mod_2)

---

Compare performance of different models

Description

Computes indices of model performance for different models at once and hence allows comparison of indices across models.

Usage

performance(..., metrics = "all", metadata = FALSE, digits = 2)

Arguments

...	Multiple model objects (only of class 'modeler').
metrics	Can be "all" or a character vector of metrics to be computed (one or more of "logLik", "AIC", "AICc", "BIC", "Sigma", "SSE", "MAE", "MSE", "RMSE", "R2"). "all" by default.
metadata	Logical. If TRUE, metadata is included with the performance metrics. Default is FALSE.
digits	An integer. The number of decimal places to round the output. Default is 2.

Value

A data.frame with performance metrics for models in (...).

Examples

library(flexFitR)
data(dt_potato)
# Model 1
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 90),
    subset = 40
  )
print(mod_1)
# Model 2
mod_2 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_logistic",
    parameters = c(L = 100, k = 4, t0 = 50),
    subset = 40
  )
print(mod_2)
# Model 3
mod_3 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_lin",
    parameters = c(m = 20, b = 2),
    subset = 40
  )
print(mod_3)
performance(mod_1, mod_2, mod_3, metrics = c("AIC", "AICc", "BIC", "Sigma"))

---

Plot user-defined function

Description

This function plots a function over a specified interval and annotates the plot with the calculated Area Under the Curve (AUC) and parameter values. The aim of 'plot_fn' is to allow users to play with different starting values in their functions before fitting any models.

Usage

plot_fn(
  fn = "fn_linear_sat",
  params = c(t1 = 34.9, t2 = 61.8, k = 100),
  interval = c(0, 100),
  n_points = 1000,
  auc = FALSE,
  x_auc_label = NULL,
  y_auc_label = NULL,
  auc_label_size = 4,
  param_label_size = 4,
  base_size = 12,
  color = "red",
  label_color = "grey30"
)

Arguments

fn	A character string representing the name of the function to be plotted. Default is "fn_linear_sat".
params	A named numeric vector of parameters to be passed to the function. Default is c(t1 = 34.9, t2 = 61.8, k = 100).
interval	A numeric vector of length 2 specifying the interval over which the function is to be plotted. Default is c(0, 100).
n_points	An integer specifying the number of points to be used for plotting. Default is 1000.
auc	Print AUC in the plot? Default is FALSE.
x_auc_label	A numeric value specifying the x-coordinate for the AUC label. Default is NULL.
y_auc_label	A numeric value specifying the y-coordinate for the AUC label. Default is NULL.
auc_label_size	A numeric value specifying the size of the AUC label text. Default is 3.
param_label_size	A numeric value specifying the size of the parameter label text. Default is 3.
base_size	A numeric value specifying the base size for the plot's theme. Default is 12.
color	A character string specifying the color for the plot lines and area fill. Default is "red".
label_color	A character string specifying the color for the labels. Default is "grey30".

Value

A ggplot object representing the plot.

Examples

# Example usage
plot_fn(
  fn = "fn_linear_sat",
  params = c(t1 = 34.9, t2 = 61.8, k = 100),
  interval = c(0, 100),
  n_points = 1000
)
plot_fn(
  fn = "fn_lin_pl_lin",
  params <- c(t1 = 38.7, t2 = 62, t3 = 90, k = 0.32, beta = -0.01),
  interval = c(0, 100),
  n_points = 1000,
  base_size = 12
)

---

Plot an object of class explorer

Description

Creates various plots for an object of class explorer. Depending on the specified type, the function can generate plots that show correlations between variables over x, correlations between x values for each variable, or the evolution of variables over x.

Usage

## S3 method for class 'explorer'
plot(
  x,
  type = "var_by_x",
  label_size = 4,
  signif = FALSE,
  method = "pearson",
  filter_var = NULL,
  id = NULL,
  n_row = NULL,
  n_col = NULL,
  base_size = 13,
  return_gg = FALSE,
  add_avg = FALSE,
  ...
)

Arguments

x	An object inheriting from class explorer, resulting from executing the function explorer().
type	Character string or number specifying the type of plot to generate. Available options are: "var_by_x" or 1 Plots correlations between variables over x (default). "x_by_var" or 2 Plots correlations between x points for each variable (y). "evolution" or 3 Plot the evolution of the variables (y) over x. "xy" or 4 Scatterplot (x, y)
label_size	Numeric. Size of the labels in the plot. Default is 4. Only works with type 1 and 2.
signif	Logical. If TRUE, adds p-values to the correlation plot labels. Default is FALSE. Only works with type 1 and 2.
method	Character string specifying the method for correlation calculation. Available options are "pearson" (default), "spearman", and "kendall". Only works with type 1 and 2.
filter_var	Character vector specifying the variables to exclude from the plot.
id	Optional unique identifier to filter the evolution type of plot. Default is NULL. Only works with type 3.
n_row	Integer specifying the number of rows to use in facet_wrap(). Default is NULL. Only works with type 1 and 2.
n_col	Integer specifying the number of columns to use in facet_wrap(). Default is NULL. Only works with type 1 and 2.
base_size	Numeric. Base font size for the plot. Default is 13.
return_gg	Logical. If TRUE, returns the ggplot object instead of printing it. Default is FALSE.
add_avg	Logical. If TRUE, returns evolution plot with the average trend across groups. Default is FALSE.
...	Further graphical parameters for future improvements.

Value

A ggplot object and an invisible data.frame containing the correlation table when type is "var_by_x" or "x_by_var".

Examples

library(flexFitR)
data(dt_potato)
results <- explorer(dt_potato, x = DAP, y = c(Canopy, GLI), id = Plot)
table <- plot(results, label_size = 4, signif = TRUE, n_row = 2)
table
plot(results, type = "x_by_var", label_size = 4, signif = TRUE)

---

Plot an object of class modeler

Description

Creates several plots for an object of class modeler.

Usage

## S3 method for class 'modeler'
plot(
  x,
  id = NULL,
  type = 1,
  label_size = 4,
  base_size = 14,
  color = "red",
  color_points = "black",
  parm = NULL,
  n_points = 1000,
  title = NULL,
  add_ci = TRUE,
  color_ci = "blue",
  color_pi = "red",
  add_ribbon_ci = FALSE,
  add_ribbon_pi = FALSE,
  color_ribbon_ci = "blue",
  color_ribbon_pi = "red",
  ...
)

Arguments

x	An object of class modeler, typically the result of calling modeler().
id	An optional group ID to filter the data for plotting, useful for avoiding overcrowded plots.
type	Numeric value (1-6) to specify the type of plot to generate. Default is 1. type = 1 Plot of raw data with fitted curves. type = 2 Plot of coefficients with confidence intervals. type = 3 Plot of fitted curves, colored by group. type = 4 Plot of fitted curves with confidence intervals. type = 5 Plot of first derivative with confidence intervals. type = 6 Plot of second derivative with confidence intervals.
label_size	Numeric value for the size of labels. Default is 4.
base_size	Numeric value for the base font size in pts. Default is 14.
color	Character string specifying the color for the fitted line when type = 1. Default is "red".
color_points	Character string specifying the color for the raw data points when type = 1. Default is "black".
parm	Character vector specifying the parameters to plot for type = 2. If NULL, all parameters are included.
n_points	Numeric value specifying the number of points for interpolation along the x-axis. Default is 2000.
title	Optional character string to add a title to the plot.
add_ci	Logical value indicating whether to add confidence intervals for type = 4, 5, 6. Default is TRUE.
color_ci	Character string specifying the color of the confidence interval when type = 4, 5, 6. Default is "blue".
color_pi	Character string specifying the color of the prediction interval when type = 4. Default is "red".
add_ribbon_ci	Logical value indicating whether to add a ribbon for confidence intervals in type = 4, 5, 6. Default is FALSE.
add_ribbon_pi	Logical value indicating whether to add a ribbon for prediction intervals in type = 4. Default is FALSE.
color_ribbon_ci	Character string specifying the color of the ribbon (ci). Default is "blue".
color_ribbon_pi	Character string specifying the color of the ribbon (pi). Default is "red".
...	Additional graphical parameters for future extensions.

Value

A ggplot object representing the specified plot.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
# Example 1
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = c(1:3)
  )
print(mod_1)
plot(mod_1, id = 1:2)
plot(mod_1, id = 1:3, type = 2, label_size = 10)

---

Plot an object of class performance

Description

Creates plots for an object of class performance

Usage

## S3 method for class 'performance'
plot(
  x,
  id = NULL,
  type = 1,
  rescale = FALSE,
  linewidth = 1,
  base_size = 12,
  return_table = FALSE,
  ...
)

Arguments

x	An object of class performance, typically the result of calling performance().
id	An optional group ID to filter the data for plotting, useful for avoiding overcrowded plots. This argument is not used when type = 2.
type	Numeric value (1-3) to specify the type of plot to generate. Default is 1. type = 1 Radar plot by uid type = 2 Radar plot averaging type = 3 Bar plot by model-metric
rescale	Logical. If TRUE, metrics in type 3 plot are (0, 1) rescaled to improve interpretation. Higher values are better models. FALSE by default.
linewidth	Numeric value specifying size of line geoms.
base_size	Numeric value for the base font size in pts. Default is 12
return_table	Logical. If TRUE, table to generate the plot is returned. FALSE by default.
...	Additional graphical parameters for future extensions.

Value

A ggplot object representing the specified plot.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
# Model 1
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 90),
    subset = 40
  )
# Model 2
mod_2 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_logistic",
    parameters = c(L = 100, k = 4, t0 = 50),
    subset = 40
  )
# Model 3
mod_3 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_lin",
    parameters = c(m = 20, b = 2),
    subset = 40
  )
plot(performance(mod_1, mod_2, mod_3), type = 1)
plot(performance(mod_1, mod_2, mod_3, metrics = c("AICc", "BIC")), type = 3)

---

Predict an object of class modeler

Description

Generate model predictions from an object of class modeler. This function allows for flexible prediction types, including point predictions, area under the curve (AUC), first or second order derivatives, and functions of the parameters.

Usage

## S3 method for class 'modeler'
predict(
  object,
  x = NULL,
  id = NULL,
  type = c("point", "auc", "fd", "sd"),
  se_interval = c("confidence", "prediction"),
  n_points = 1000,
  formula = NULL,
  metadata = FALSE,
  ...
)

Arguments

object	An object of class modeler, typically the result of calling the modeler() function.
x	A numeric value or vector specifying the points at which predictions are made. For type = "auc", x must be a vector of length 2 that specifies the interval over which to calculate the AUC.
id	Optional unique identifier to filter predictions by a specific group. Default is NULL.
type	A character string specifying the type of prediction. Default is "point". "point" Predicts the value of y for the given x. "auc" Calculates the area under the curve (AUC) for the fitted model over the interval specified by x. "fd" Returns the first derivative (rate of change) of the model at the given x value(s). "sd" Returns the second derivative of the model at the given x value(s).
se_interval	A character string specifying the type of interval for standard error calculation. Options are "confidence" (default) or "prediction". Only works with "point" estimation.
n_points	An integer specifying the number of points used to approximate the area under the curve (AUC) when type = "auc". Default is 1000.
formula	A formula specifying a function of the parameters to be estimated (e.g., ~ b * 500). Default is NULL.
metadata	Logical. If TRUE, metadata is included with the predictions. Default is FALSE.
...	Additional parameters for future functionality.

Value

A data.frame containing the predicted values, their associated standard errors, and optionally the metadata.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = c(15, 2, 45)
  )
print(mod_1)
# Point Prediction
predict(mod_1, x = 45, type = "point", id = 2)
# AUC Prediction
predict(mod_1, x = c(0, 108), type = "auc", id = 2)
# First Derivative
predict(mod_1, x = 45, type = "fd", id = 2)
# Second Derivative
predict(mod_1, x = 45, type = "sd", id = 2)
# Function of the parameters
predict(mod_1, formula = ~ t2 - t1, id = 2)

---

Print an object of class modeler

Description

Prints information about modeler function.

Usage

## S3 method for class 'modeler'
print(x, ...)

Arguments

x	An object fitted with the function modeler().
...	Options used by the tibble package to format the output. See 'tibble::print()' for more details.

Value

an object inheriting from class modeler.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = c(1:5)
  )
plot(mod_1, id = c(1:4))
print(mod_1)

---

Extract residuals from a modeler object

Description

Extract residuals from a modeler object

Usage

## S3 method for class 'modeler'
residuals(object, ...)

Arguments

object	An object of class 'modeler'
...	Additional parameters for future functionality.

Value

A numeric vector of residuals

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = c(15, 2, 45)
  )
residuals(mod_1)

---

Transform variables in a data frame

Description

This function performs transformations on specified columns of a data frame, including truncating maximum values, handling negative values, and adding a zero to the series. It allows for grouping and supports retaining metadata in the output.

Usage

series_mutate(
  data,
  x,
  y,
  grp,
  metadata,
  max_as_last = FALSE,
  check_negative = FALSE,
  add_zero = FALSE,
  interval = NULL
)

Arguments

data	A data.frame containing the input data for analysis.
x	The name of the column in data representing the independent variable (x points).
y	The name of the column(s) in data containing variables to transform.
grp	Column(s) in data used as grouping variable(s). Defaults to NULL (optional).
metadata	Names of columns to retain in the output. Defaults to NULL (optional).
max_as_last	Logical. If TRUE, appends the maximum value after reaching the maximum. Default is FALSE.
check_negative	Logical. If TRUE, converts negative values in the data to zero. Default is FALSE.
add_zero	Logical. If TRUE, adds a zero value to the series at the start. Default is FALSE.
interval	A numeric vector of length 2 (start and end) specifying the range to filter the data. Defaults to NULL.

Value

A transformed data.frame with the specified modifications applied.

Examples

data(dt_potato)
new_data <- series_mutate(
  data = dt_potato,
  x = DAP,
  y = GLI,
  grp = gid,
  max_as_last = TRUE,
  check_negative = TRUE
)

---

Subset an object of class modeler

Description

Subset an object of class modeler

Usage

## S3 method for class 'modeler'
subset(x, id = NULL, ...)

Arguments

x	An object of class modeler, typically the result of calling modeler().
id	Unique identifier to filter a modeler object by a specific group. Default is NULL.
...	Additional parameters for future functionality.

Value

A modeler object.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
mod <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_logistic",
    parameters = c(L = 100, k = 4, t0 = 40),
    subset = 1:2
  )
print(mod)
mod_new <- subset(mod, id = 2)
print(mod_new)

---

Variance-Covariance matrix for an object of class modeler

Description

Extract the variance-covariance matrix for the parameter estimates from an object of class modeler.

Usage

## S3 method for class 'modeler'
vcov(object, id = NULL, ...)

Arguments

object	An object of class modeler, typically the result of calling the modeler() function.
id	An optional unique identifier to filter by a specific group. Default is NULL.
...	Additional parameters for future functionality.

Value

A list of matrices, where each matrix represents the variance-covariance matrix of the estimated parameters for each group or fit.

Author(s)

Johan Aparicio [aut]

Examples

library(flexFitR)
data(dt_potato)
mod_1 <- dt_potato |>
  modeler(
    x = DAP,
    y = Canopy,
    grp = Plot,
    fn = "fn_linear_sat",
    parameters = c(t1 = 45, t2 = 80, k = 0.9),
    subset = c(15, 2, 45)
  )
print(mod_1)
vcov(mod_1)

---