Package 'epifitter' reference manual

Title:	Analysis and Simulation of Plant Disease Progress Curves
Description:	Analysis and visualization of plant disease progress curve data. Functions for fitting two-parameter population dynamics models (exponential, monomolecular, logistic and Gompertz) to proportion data for single or multiple epidemics using either linear or no-linear regression. Statistical and visual outputs are provided to aid in model selection. Synthetic curves can be simulated for any of the models given the parameters. See Laurence V. Madden, Gareth Hughes, and Frank van den Bosch (2007) <doi:10.1094/9780890545058> for further information on the methods.
Authors:	Kaique dos S. Alves [aut, cre] , Emerson M. Del Ponte [aut]
Maintainer:	Kaique dos S. Alves <[email protected]>
License:	MIT + file LICENSE
Version:	0.3.0
Built:	2024-09-17 04:12:58 UTC
Source:	https://github.com/alvesks/epifitter

Area under disease progress curve

Description

Calculates the area under disease progress curves.

Usage

AUDPC(time, y, y_proportion = TRUE, type = "absolute")
AUDPC(time, y, y_proportion = TRUE, type = "absolute")

Arguments

`time`	A vector object of time.
`y`	A vector object of disease intensity.
`y_proportion`	Logical. If disease intensity is proportion (TRUE) or percentage(FALSE).
`type`	Set if is absolute or relative AUDPC. type = "absolute" is default.

Author(s)

Kaique dos S. Alves

References

Madden, L. V., Hughes, G., and van den Bosch, F. 2007. The Study of Plant Disease Epidemics. American Phytopathological Society, St. Paul, MN.

Examples

epi =  sim_logistic(N = 30, y0 = 0.01,dt = 5, r = 0.3, alpha = 0.5, n = 1)
AUDPC(time = epi$time, y = epi$y, y_proportion = TRUE)

epi =  sim_logistic(N = 30, y0 = 0.01,dt = 5, r = 0.3, alpha = 0.5, n = 1)
AUDPC(time = epi$time, y = epi$y, y_proportion = TRUE)

Area under disease progress stairs

Description

Calculates the area under disease progress stairs.

Usage

AUDPS(time, y, y_proportion = TRUE, type = "absolute")
AUDPS(time, y, y_proportion = TRUE, type = "absolute")

Arguments

`time`	A vector object of time.
`y`	A vector object of disease intensity.
`y_proportion`	Logical. If disease intensity is proportion (TRUE) or percentage(FALSE)
`type`	Set if is absolute or relative AUDPC. type = "absolute" is default.

Author(s)

Kaique dos S. Alves

References

Simko, I., and Piepho, H.-P. 2012. The area under the disease progress stairs: Calculation, advantage, and application. Phytopathology 102:381- 389.

Examples

epi =  sim_logistic(N = 30, y0 = 0.01,dt = 5, r = 0.3, alpha = 0.5, n = 1)
AUDPS(time = epi$time, y = epi$y, y_proportion = TRUE)

epi =  sim_logistic(N = 30, y0 = 0.01,dt = 5, r = 0.3, alpha = 0.5, n = 1)
AUDPS(time = epi$time, y = epi$y, y_proportion = TRUE)

Function for Exponential model

Description

Base function for the Exponential model. This function is used in the Exponential model simulation function sim_exponential()

Usage

expo_fun(t, y, par)
expo_fun(t, y, par)

Arguments

`t`	Vector of time
`y`	Vector of disease intensity
`par`	List of parameters

Fits epidemic models using data linearization

Description

Fits epidemic models (Exponential, Monomolecular, Logistic and Gompertz) to data using data linearization

Usage

fit_lin(time,y)
fit_lin(time,y)

Arguments

`time`	Numeric vector which refers to the time steps in the epidemics
`y`	Numeric vector which refers to the disease intensity

Author(s)

Kaique dos S. Alves

Examples

set.seed(1)
epi1 <- sim_logistic(N = 30,
                     y0 = 0.01,
                     dt = 5,
                     r = 0.3,
                     alpha = 0.2,
                     n = 4)
data = data.frame(time =  epi1[,2], y = epi1[,4])
fit_lin( time = data$time, y =  data$y)

set.seed(1)
epi1 <- sim_logistic(N = 30,
                     y0 = 0.01,
                     dt = 5,
                     r = 0.3,
                     alpha = 0.2,
                     n = 4)
data = data.frame(time =  epi1[,2], y = epi1[,4])
fit_lin( time = data$time, y =  data$y)

Estimate model parameters for multiple disease progress curves

Description

Estimate model parameters for multiple disease progress curves

Usage

fit_multi(time_col,
             intensity_col,
             data,
             strata_cols ,
             starting_par = list(y0 = 0.01, r = 0.03, K =  0.8),
             maxiter=500,
             nlin = FALSE,
             estimate_K = FALSE)
fit_multi(time_col,
             intensity_col,
             data,
             strata_cols ,
             starting_par = list(y0 = 0.01, r = 0.03, K =  0.8),
             maxiter=500,
             nlin = FALSE,
             estimate_K = FALSE)

Arguments

`time_col`	Character name specifying the column for the time. eg: time_col = "days".
`intensity_col`	Character name specifying the column for the disease intensity.
`data`	`data.frame` object containing the variables for model fitting.
`strata_cols`	Character name or vector specifying the columns for stratification.
`starting_par`	Starting value for initial inoculun (y0) and apparent infection rate (r). Please informe in that especific order
`maxiter`	Maximum number of iterations. Only used if is `nlin = TRUE`
`nlin`	Logical. If `FALSE` estimates parameters using data linearization. If `nlin=TRUE`, estimates nonlinear approach. `fit_nlin`.
`estimate_K`	Logical. If `nlin=TRUE`, estimates maximum disease intensity. (default: `nlin=FALSE`) `fit_nlin2`.

Value

Returns a data.frame containing estimated parameters for individual strata levels.

Examples

set.seed(1)
# create stratified dataset
data_A1 = sim_gompertz(N = 30, y0 = 0.01,dt = 5, r = 0.3, alpha = 0.5, n = 4)
data_A1 = dplyr::mutate(data_A1,
                        fun = "A",
                        cultivar = "BR1")
set.seed(1)
data_B1 = sim_gompertz(N = 30, y0 = 0.01, dt = 5, r = 0.2, alpha = 0.5, n = 4)
data_B1 = dplyr::mutate(data_B1,
                        fun = "B",
                        cultivar = "BR1")
set.seed(1)
data_A2 = sim_gompertz(N = 30, y0 = 0.01,dt = 5, r = 0.1, alpha = 0.5, n = 4)
data_A2 = dplyr::mutate(data_A2,
                        fun = "A",
                        cultivar = "BR2")
set.seed(1)
data_B2 = sim_gompertz(N = 30, y0 = 0.01,dt = 5, r = 0.1, alpha = 0.5, n = 4)
data_B2 = dplyr::mutate(data_B2,
                        fun = "B",
                        cultivar = "BR2")

data = dplyr::bind_rows(data_A1, data_B1,data_A2, data_B2)

fit_multi(time_col = "time",
             intensity_col = "random_y",
             data = data,
             strata_col = c("fun","cultivar"),
             starting_par = list(y0 = 0.01, r = 0.03),
             maxiter = 1024,
             nlin = FALSE,
             estimate_K = FALSE)
set.seed(1)
# create stratified dataset
data_A1 = sim_gompertz(N = 30, y0 = 0.01,dt = 5, r = 0.3, alpha = 0.5, n = 4)
data_A1 = dplyr::mutate(data_A1,
                        fun = "A",
                        cultivar = "BR1")
set.seed(1)
data_B1 = sim_gompertz(N = 30, y0 = 0.01, dt = 5, r = 0.2, alpha = 0.5, n = 4)
data_B1 = dplyr::mutate(data_B1,
                        fun = "B",
                        cultivar = "BR1")
set.seed(1)
data_A2 = sim_gompertz(N = 30, y0 = 0.01,dt = 5, r = 0.1, alpha = 0.5, n = 4)
data_A2 = dplyr::mutate(data_A2,
                        fun = "A",
                        cultivar = "BR2")
set.seed(1)
data_B2 = sim_gompertz(N = 30, y0 = 0.01,dt = 5, r = 0.1, alpha = 0.5, n = 4)
data_B2 = dplyr::mutate(data_B2,
                        fun = "B",
                        cultivar = "BR2")

data = dplyr::bind_rows(data_A1, data_B1,data_A2, data_B2)

fit_multi(time_col = "time",
             intensity_col = "random_y",
             data = data,
             strata_col = c("fun","cultivar"),
             starting_par = list(y0 = 0.01, r = 0.03),
             maxiter = 1024,
             nlin = FALSE,
             estimate_K = FALSE)

Fits epidemic models using nonlinear aproach

Description

Fits epidemic models (Exponential, Monomolecular, Logistic and Gompertz) using nonlinear approach for estimate parameters.

Usage

fit_nlin(time,
  y,
  starting_par = list(y0 = 0.01, r = 0.03),
  maxiter = 50)
fit_nlin(time,
  y,
  starting_par = list(y0 = 0.01, r = 0.03),
  maxiter = 50)

Arguments

`time`	Numeric vector which refers to the time steps in the epidemics
`y`	Numeric vector which refers to the disease intensity
`starting_par`	Starting value for initial inoculun (y0) and apparent infection rate (r). Please informe in that especific order
`maxiter`	Maximun number of iterations

Author(s)

Kaique dos S. Alves

Examples

set.seed(1)
epi1 <- sim_logistic(N = 30,
                     y0 = 0.01,
                     dt = 5,
                     r = 0.3,
                     alpha = 0.5,
                     n = 4)
data = data.frame(time =  epi1[,2], y = epi1[,4])
fit_nlin(time = data$time, y =  data$y, starting_par = list(y0 = 0.001, r = 0.03), maxiter = 1024)

set.seed(1)
epi1 <- sim_logistic(N = 30,
                     y0 = 0.01,
                     dt = 5,
                     r = 0.3,
                     alpha = 0.5,
                     n = 4)
data = data.frame(time =  epi1[,2], y = epi1[,4])
fit_nlin(time = data$time, y =  data$y, starting_par = list(y0 = 0.001, r = 0.03), maxiter = 1024)

Fits epidemic models using nonlinear aproach. This function also estimates the maximum disease intensity parameter K

Description

Fits epidemic models (Exponential, Monomolecular, Logistic and Gompertz) using nonlinear approach for estimate parameters. This function also estimates the maximum disease intensity parameter K.

Usage

fit_nlin2(time,
  y,
  starting_par = list(y0 = 0.01, r = 0.03, K =  0.8),
  maxiter = 50)
fit_nlin2(time,
  y,
  starting_par = list(y0 = 0.01, r = 0.03, K =  0.8),
  maxiter = 50)

Arguments

`time`	Numeric vector which refers to the time steps in the epidemics.
`y`	Numeric vector which refers to the disease intensity.
`starting_par`	starting value for initial inoculun (y0) and apparent infection rate (r), and maximum disease intensity (K). Please informe in that especific order
`maxiter`	Maximun number of iterations.

Examples


set.seed(1)

epi1 <- sim_logistic(N = 30,
                     y0 = 0.01,
                     dt = 5,
                     r = 0.3,
                     alpha = 0.5,
                     n = 4)

data = data.frame(time =  epi1[,2], y = epi1[,4])
fit_nlin2(time = data$time,
          y =  data$y,
          starting_par = list(y0 = 0.01, r = 0.03, K = 1),
          maxiter = 1024)
set.seed(1)

epi1 <- sim_logistic(N = 30,
                     y0 = 0.01,
                     dt = 5,
                     r = 0.3,
                     alpha = 0.5,
                     n = 4)

data = data.frame(time =  epi1[,2], y = epi1[,4])
fit_nlin2(time = data$time,
          y =  data$y,
          starting_par = list(y0 = 0.01, r = 0.03, K = 1),
          maxiter = 1024)

Function for Gompertz model

Description

Base function for the Gompertz model. This function is used in the Gompertz model simulation function sim_gompertz()

Usage

gompi_fun(t, y, par)
gompi_fun(t, y, par)

Arguments

`t`	Vector of time
`y`	Vector of disease intensity
`par`	List of parameters

Function for logistic model

Description

Base function for the Logistic model. This function is used in the Logistic model simulation function sim_logistic()

Usage

logi_fun(t, y, par)
logi_fun(t, y, par)

Arguments

`t`	Vector of time
`y`	Vector of disease intensity
`par`	List of parameters

Function for Monomolecular model

Description

Base function for the Monomolecular model. This function is used in the Monomolecular model simulation function sim_monomolecular()

Usage

mono_fun(t, y, par)
mono_fun(t, y, par)

Arguments

`t`	Vector of time
`y`	Vector of disease intensity
`par`	List of parameters

Creates a plot panel for the fitted models

Description

Create a ggplot2-style plot with the fitted models curves and the epidemic data.

Usage

plot_fit(object,
  point_size =1.2,
  line_size = 1,
  models = c("Exponential","Monomolecular", "Logistic", "Gompertz"))
plot_fit(object,
  point_size =1.2,
  line_size = 1,
  models = c("Exponential","Monomolecular", "Logistic", "Gompertz"))

Arguments

`object`	A `fit_lin` or a `fit_nlin` object
`point_size`	Point size
`line_size`	Line size
`models`	Select the models to be displayed in the panel

Details

It is possible to add more ggplot components by using the + syntax. See examples below.

Examples

epi1 <- sim_logistic(N = 30,
                     y0 = 0.01,
                     dt = 5,
                     r = 0.3,
                     alpha = 0.5,
                     n = 4)
data = data.frame(time =  epi1[,2], y = epi1[,4])
fitted = fit_lin( time = data$time, y =  data$y)
plot_fit(fitted)

# adding ggplot components
library(ggplot2)
plot_fit(fitted)+
  theme_minimal()+
  ylim(0,1)+
  labs(y = "Disease internsity", x = "Time")
epi1 <- sim_logistic(N = 30,
                     y0 = 0.01,
                     dt = 5,
                     r = 0.3,
                     alpha = 0.5,
                     n = 4)
data = data.frame(time =  epi1[,2], y = epi1[,4])
fitted = fit_lin( time = data$time, y =  data$y)
plot_fit(fitted)

# adding ggplot components
library(ggplot2)
plot_fit(fitted)+
  theme_minimal()+
  ylim(0,1)+
  labs(y = "Disease internsity", x = "Time")

Dataset powdery mildew disease progress curves

Description

Dataset containing experimental data of disease progress curves of powdery mildew under different irrigation systems and soil moisture levels in organic tomato.

Usage

data("PowderyMildew")data("PowderyMildew")

Format

A data frame with 240 observations on the following 2 variables.

irrigation_type: Irrigations Systems: MS = Micro Sprinkler
moisture: Levels of soils moisture
block: Experimental blocks
time: a numeric vector containing the time points
sev: a numeric vector containg disease severity data in proportinal scales

References

Lage, D. A. C., Marouelli, W. A., and Café-Filho, A. C. 2019. Management of powdery mildew and behaviour of late blight under different irrigation configurations in organic tomato. Crop Protection. 125:104886.

Examples

data(PowderyMildew)
## maybe str(PowderyMildew) ; plot(PowderyMildew) ...
data(PowderyMildew)
## maybe str(PowderyMildew) ; plot(PowderyMildew) ...

Print `fit_lin()` or `fit_nlin()` outputs

Description

The print method for density objects.

Usage

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

Arguments

`x`	output from `fit_lin()` or `fit_nlin()`
`...`	...

Print `fit_nlin2()` outputs

Description

The print method for density objects.

Usage

## S3 method for class 'fit_nlin2'
print(x, ...)
## S3 method for class 'fit_nlin2'
print(x, ...)

Arguments

`x`	output from `fit_nlin2()`
`...`	...

Simulate an epidemic using the Exponential model

Description

Simulate a stochastic epidemic curve using the Exponential model.

Usage

sim_exponential(N = 10,dt = 1, y0 = 0.01, r, n,  alpha = 0.2)
sim_exponential(N = 10,dt = 1, y0 = 0.01, r, n,  alpha = 0.2)

Arguments

`N`	Total time course of the epidemic
`dt`	Time step
`y0`	Initial inoculum or initial disease intensity
`r`	Infection rate
`n`	Number or replicates or sample size for each time step
`alpha`	Variation parameter. stands for the variation for the replicates for each time step. The standard deviation is calculated as sd = alpha * y * (1 - y), being y the disease intensity for each time step.

Value

`rep`	Replicates
`time`	Time after epidemic start
`y`	Disease intensity
`random_y`	Disease intensity after applying the random `alpha` error

Examples

sim_exponential(N = 30, y0 = 0.01,dt = 5, r = 0.1, alpha = 0.5, n = 4)
sim_exponential(N = 30, y0 = 0.01,dt = 5, r = 0.1, alpha = 0.5, n = 4)

Simulate an epidemic using the Gompertz model

Description

Simulate a stochastic epidemic curve using the Gompertz model.

Usage

sim_gompertz(N = 10,dt = 1, y0 = 0.01, r, K = 1, n,  alpha = 0.2)
sim_gompertz(N = 10,dt = 1, y0 = 0.01, r, K = 1, n,  alpha = 0.2)

Arguments

`N`	Total time course of the epidemic
`dt`	Time step
`y0`	Initial inoculum or initial disease intensity
`r`	Infection rate
`K`	Maximum asymptote
`n`	Number or replicates or sample size for each time step
`alpha`	Variation parameter. stands for the variation for the replicates for each time step. The standard deviation is calculated as sd = alpha * y * (1 - y), being y the disease intensity for each time step.

Value

`rep`	Replicates
`time`	Time after epidemic start
`y`	Disease intensity
`random_y`	Disease intensity after applying the random `alpha` error

Examples

sim_gompertz(N = 30, y0 = 0.01,dt = 5, r = 0.3, K = 1, alpha = 0.5, n = 4)
sim_gompertz(N = 30, y0 = 0.01,dt = 5, r = 0.3, K = 1, alpha = 0.5, n = 4)

Simulate an epidemic using the logistic model

Description

Simulate a stochastic epidemic curve using the logistic model.

Usage

sim_logistic(N = 10,dt = 1, y0 = 0.01, r, K = 1, n,  alpha = 0.2)
sim_logistic(N = 10,dt = 1, y0 = 0.01, r, K = 1, n,  alpha = 0.2)

Arguments

`N`	Total time course of the epidemic
`dt`	Time step
`y0`	Initial inoculum or initial disease intensity
`r`	Infection rate
`K`	Maximum asymptote
`n`	Number or replicates or sample size for each time step
`alpha`	Variation parameter. stands for the variation for the replicates for each time step. The standard deviation is calculated as sd = alpha * y * (1 - y), being y the disease intensity for each time step.

Value

`rep`	Replicates
`time`	Time after epidemic start
`y`	Disease intensity
`random_y`	Disease intensity after applying the random `alpha` error

Examples

sim_logistic(N = 30, y0 = 0.01,dt = 5, r = 0.3, K = 1, alpha = 0.5, n = 4)
sim_logistic(N = 30, y0 = 0.01,dt = 5, r = 0.3, K = 1, alpha = 0.5, n = 4)

Simulate an epidemic using the Monomolecular model

Description

Simulate a stochastic epidemic curve using the Monomolecular model.

Usage

sim_monomolecular(N = 10,dt = 1, y0 = 0.01, r,K = 1, n,  alpha = 0.2)
sim_monomolecular(N = 10,dt = 1, y0 = 0.01, r,K = 1, n,  alpha = 0.2)

Arguments

`N`	Total time course of the epidemic
`dt`	Time step
`y0`	Initial inoculum or initial disease intensity
`r`	Infection rate
`K`	Maximum asymptote
`n`	Number or replicates or sample size for each time step
`alpha`	Variation parameter. stands for the variation for the replicates for each time step. The standard deviation is calculated as sd = alpha * y * (1 - y), being y the disease intensity for each time step.

Value

`rep`	Replicates
`time`	Time after epidemic start
`y`	Disease intensity
`random_y`	Disease intensity after applying the random `alpha` error

Examples

sim_monomolecular(N = 30, y0 = 0.01,dt = 5, r = 0.3, K = 1, alpha = 0.5, n = 4)
sim_monomolecular(N = 30, y0 = 0.01,dt = 5, r = 0.3, K = 1, alpha = 0.5, n = 4)

Package 'epifitter'

Help Index

Area under disease progress curve

Description

Usage

Arguments

Author(s)

References

Examples

Area under disease progress stairs

Description

Usage

Arguments

Author(s)

References

Examples

Function for Exponential model

Description

Usage

Arguments

Fits epidemic models using data linearization

Description

Usage

Arguments

Author(s)

Examples

Estimate model parameters for multiple disease progress curves

Description

Usage

Arguments

Value

See Also

Examples

Fits epidemic models using nonlinear aproach

Description

Usage

Arguments

Author(s)

Examples

Fits epidemic models using nonlinear aproach. This function also estimates the maximum disease intensity parameter K

Description

Usage

Arguments

Examples

Function for Gompertz model

Description

Usage

Arguments

Function for logistic model

Description

Usage

Arguments

Function for Monomolecular model

Description

Usage

Arguments

Creates a plot panel for the fitted models

Description

Usage

Arguments

Details

Examples

Dataset powdery mildew disease progress curves

Description

Usage

Format

References

Examples

Print fit_lin() or fit_nlin() outputs

Description

Usage

Arguments

Print fit_nlin2() outputs

Description

Usage

Arguments

Simulate an epidemic using the Exponential model

Description

Usage

Arguments

Print `fit_lin()` or `fit_nlin()` outputs

Print `fit_nlin2()` outputs