Type: Package
Title: Analysis and Simulation of Plant Disease Progress Curves
Version: 0.3.0
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.
Maintainer: Kaique dos S. Alves <kaiquedsalves@gmail.com>
Depends: R (≥ 3.2)
Imports: deSolve, dplyr, stats, ggplot2, knitr, tidyr, DescTools, minpack.lm, magrittr, tibble
Suggests: rmarkdown, ggridges, cowplot
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
Date: 2021-06-13 22:30:00 UTC
URL: https://github.com/AlvesKS/epifitter
BugReports: https://github.com/AlvesKS/epifitter/issues
X-schema.org-applicationCategory: Tools
NeedsCompilation: no
Packaged: 2021-06-14 14:30:38 UTC; Epidemio-PC
Author: Kaique dos S. Alves ORCID iD [aut, cre], Emerson M. Del Ponte ORCID iD [aut]
Repository: CRAN
Date/Publication: 2021-06-14 14:50:02 UTC

Area under disease progress curve

Description

Calculates the area under disease progress curves.

Usage

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)

Area under disease progress stairs

Description

Calculates the area under disease progress stairs.

Usage

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)

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")

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) ...

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)

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)

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)

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)

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.

See Also

fit_lin,fit_nlin,fit_nlin2

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)

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)

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)

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)

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)

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)

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)

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)

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"))

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")

Print fit_lin() or fit_nlin() outputs

Description

The print method for density objects.

Usage

## 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, ...)

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)

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)

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)

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)

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)

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)

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)

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)