Type:	Package
Title:	Regression Analysis Linear and Nonlinear for Agriculture
Version:	1.2.11
Date:	2025-06-22
Maintainer:	Gabriel Danilo Shimizu <gabrield.shimizu@gmail.com>
Description:	Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). <doi:10.2134/agronj2012.0506>). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://fisher.uel.br/AgroReg_shiny/, https://fisher.uel.br/AgroReg_shiny.pt/
Imports:	drc, ggplot2, boot, minpack.lm, dplyr, rcompanion, broom, egg, purrr
Depends:	R (≥ 3.6)
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-07-01 20:39:11 UTC; Administrador
Author:	Gabriel Danilo Shimizu [aut, cre], Leandro Simoes Azeredo Goncalves [aut, ctb]
Repository:	CRAN
Date/Publication:	2025-07-01 20:50:02 UTC

AgroReg: Regression Analysis Linear and Nonlinear for Agriculture

Description

Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). doi:10.2134/agronj2012.0506). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically.

Author(s)

Maintainer: Gabriel Danilo Shimizu gabrield.shimizu@gmail.com (ORCID)

Authors:

• Leandro Simoes Azeredo Goncalves (ORCID) [contributor]

See Also

Useful links:

• https://fisher.uel.br/AgroReg_shiny/

• https://fisher.uel.br/AgroReg_shiny.pt/

Analysis: Avhad and Marchetti

Description

This function performs Avhad and Marchetti regression analysis.

Usage

AM(
  trat,
  resp,
  initial = list(alpha, k, n),
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The Avhad e Marchetti model is defined by:

y = \alpha \times e^{kx^n}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Avhad, M. R., & Marchetti, J. M. (2016). Mathematical modelling of the drying kinetics of Hass avocado seeds. Industrial Crops and Products, 91, 76-87.

Examples

library(AgroReg)
data("granada")
attach(granada)
AM(time,100-WL,initial=list(alpha = 610.9129, k=-1.1810, n=0.1289 ))

Analysis: Brain-Cousens

Description

The 'BC.4' and 'BC.5' logistical models provide Brain-Cousens' modified logistical models to describe u-shaped hormesis. This model was extracted from the 'drc' package.

Usage

BC(
  trat,
  resp,
  npar = "BC.4",
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  r2 = "all",
  ic = FALSE,
  fill.ic = "gray70",
  alpha.ic = 0.5,
  error = "SE",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The model function for the Brain-Cousens model (Brain and Cousens, 1989) is

y = c + \frac{d-c+fx}{1+\exp(b(\log(x)-\log(e)))}

and it is a five-parameter model, obtained by extending the four-parameter log-logistic model (LL.4 to take into account inverse u-shaped hormesis effects. Fixing the lower limit at 0 yields the four-parameter model

y = 0 + \frac{d-0+fx}{1+\exp(b(\log(x)-\log(e)))}

used by van Ewijk and Hoekstra (1993).

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Model imported from the drc package (Ritz et al., 2016)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Ritz, C.; Strebig, J.C. and Ritz, M.C. Package ‘drc’. Creative Commons: Mountain View, CA, USA, 2016.

See Also

LL, CD,GP

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
BC(trat,resp)

Analysis: Cedergreen-Ritz-Streibig

Description

The 'CRS.4' and 'CRS.5' logistical models provide Brain-Cousens modified logistical models to describe u-shaped hormesis. This model was extracted from the 'drc' package.

Usage

CD(
  trat,
  resp,
  npar = "CRS.4",
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  ic = FALSE,
  fill.ic = "gray70",
  alpha.ic = 0.5,
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The four-parameter model is given by the expression:

y = 0 + \frac{d-0+f \exp(-1/x)}{1+\exp(b(\log(x)-\log(e)))}

while the five-parameter is:

y = c + \frac{d-c+f \exp(-1/x)}{1+\exp(b(\log(x)-\log(e)))}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Model imported from the drc package (Ritz et al., 2016)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Ritz, C.; Strebig, J.C.; Ritz, M.C. Package 'drc'. Creative Commons: Mountain View, CA, USA, 2016.

See Also

LL, BC, GP

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
CD(trat,resp)

Analysis: Gompertz

Description

The logistical models provide Gompertz modified logistical models. This model was extracted from the 'drc' package.

Usage

GP(
  trat,
  resp,
  npar = "g2",
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  r2 = "all",
  ic = FALSE,
  fill.ic = "gray70",
  alpha.ic = 0.5,
  error = "SE",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The two-parameter Gompertz model is given by the function:

y = exp^{-exp^{b(x-e)}}

The three-parameter Gompertz model is given by the function:

y = d \times exp^{-exp^{b(x-e)}}

The four-parameter Gompertz model is given by the function:

y = c + (d-c)(exp^{-exp^{b(x-e)}})

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Model imported from the drc package (Ritz et al., 2016)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley and Sons (p. 330).

Ritz, C.; Strebig, J.C. and Ritz, M.C. Package ‘drc’. Creative Commons: Mountain View, CA, USA, 2016.

See Also

LL, CD, BC

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
GP(trat,resp, npar="g3")

Analysis: Log-logistic

Description

Logistic models with three (LL.3), four (LL.4) or five (LL.5) continuous data parameters. This model was extracted from the drc package.

Usage

LL(
  trat,
  resp,
  npar = "LL.3",
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  ic = FALSE,
  fill.ic = "gray70",
  alpha.ic = 0.5,
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The three-parameter log-logistic function with lower limit 0 is

y = 0 + \frac{d}{1+\exp(b(\log(x)-\log(e)))}

The four-parameter log-logistic function is given by the expression

y = c + \frac{d-c}{1+\exp(b(\log(x)-\log(e)))}

The function is symmetric about the inflection point (e).

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Model imported from the drc package (Ritz et al., 2016)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Ritz, C.; Strebig, J.C.; Ritz, M.C. Package ‘drc’. Creative Commons: Mountain View, CA, USA, 2016.

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
LL(trat,resp)

Analysis: Linear, quadratic, quadratic inverse, cubic and quartic

Description

Linear, quadratic, quadratic inverse, cubic and quartic regression.

Usage

LM(
  trat,
  resp,
  degree = NA,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  error = "SE",
  ic = FALSE,
  fill.ic = "gray70",
  alpha.ic = 0.5,
  point = "all",
  r2 = "all",
  theme = theme_classic(),
  legend.position = "top",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The linear model is defined by:

y = \beta_0 + \beta_1\cdot x

The quadratic model is defined by:

y = \beta_0 + \beta_1\cdot x + \beta_2\cdot x^2

The quadratic inverse model is defined by:

y = \beta_0 + \beta_1\cdot x + \beta_2\cdot x^{0.5}

The cubic model is defined by:

y = \beta_0 + \beta_1\cdot x + \beta_2\cdot x^2 + \beta_3\cdot x^3

The quartic model is defined by:

y = \beta_0 + \beta_1\cdot x + \beta_2\cdot x^2 + \beta_3\cdot x^3+ \beta_4\cdot x^4

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
LM(trat,resp, degree = 3)

Analysis: Cubic without beta2

Description

Degree 3 polynomial model without the beta 2 coefficient.

Usage

LM13(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  error = "SE",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

Degree 3 polynomial model without the beta 2 coefficient is defined by:

y = \beta_0 + \beta_1\cdot x + \beta_3\cdot x^{3}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(AgroReg)
data("granada")
attach(granada)
LM13(time, WL)

Analysis: Cubic inverse without beta2

Description

Degree 3 polynomial inverse model without the beta 2 coefficient.

Usage

LM13i(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  error = "SE",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

Inverse degree 3 polynomial model without the beta 2 coefficient is defined by:

y = \beta_0 + \beta_1\cdot x + \beta_3\cdot x^{1/3}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(AgroReg)
data("granada")
attach(granada)
LM13i(time, WL)

Analysis: Cubic without beta1

Description

Degree 3 polynomial model without the beta 1 coefficient.

Usage

LM23(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  error = "SE",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

Degree 3 polynomial model without the beta 2 coefficient is defined by:

y = \beta_0 + \beta_2\cdot x^2 + \beta_3\cdot x^{3}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(AgroReg)
data("granada")
attach(granada)
LM23(time, WL)

Analysis: Cubic inverse without beta1

Description

Degree 3 polynomial inverse model without the beta 1 coefficient.

Usage

LM23i(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  error = "SE",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

Inverse degree 3 polynomial model without the beta 1 coefficient is defined by:

y = \beta_0 + \beta_2\cdot x^{1/2} + \beta_3\cdot x^{1/3}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(AgroReg)
data("granada")
attach(granada)
LM23i(time, WL)

Analysis: Cubic without beta1, with inverse beta3

Description

Degree 3 polynomial model without the beta 1 coefficient, with inverse beta3.

Usage

LM2i3(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  error = "SE",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

Inverse degree 3 polynomial model without the beta 2 coefficient is defined by:

y = \beta_0 + \beta_1\cdot x^2 + \beta_3\cdot x^{1/3}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(AgroReg)
data("granada")
attach(granada)
LM2i3(time, WL)

Analysis: Linear, quadratic, quadratic inverse, cubic and quartic without intercept

Description

Linear, quadratic, quadratic inverse, cubic and quartic regression.

Usage

LM_i(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  error = "SE",
  ic = FALSE,
  fill.ic = "gray70",
  alpha.ic = 0.5,
  xlab = "Independent",
  degree = NA,
  theme = theme_classic(),
  legend.position = "top",
  point = "all",
  r2 = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The linear model is defined by:

y = \beta_1\cdot x

The quadratic model is defined by:

y = \beta_1\cdot x + \beta_2\cdot x^2

The quadratic inverse model is defined by:

y = \beta_1\cdot x + \beta_2\cdot x^{0.5}

The cubic model is defined by:

y = \beta_1\cdot x + \beta_2\cdot x^2 + \beta_3\cdot x^3

The quartic model is defined by:

y = \beta_1\cdot x + \beta_2\cdot x^2 + \beta_3\cdot x^3+ \beta_4\cdot x^4

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
LM_i(trat,resp, degree = 3)

Analysis: Logarithmic

Description

This function performs logarithmic regression analysis.

Usage

LOG(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The logarithmic model is defined by:

y = \beta_0 + \beta_1 ln(\cdot x)

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Examples

library(AgroReg)
resp=c(10,8,6.8,6,5,4.3,4.1,4.2,4.1)
trat=seq(1,9,1)
LOG(trat,resp)

Analysis: Logarithmic quadratic

Description

This function performs logarithmic quadratic regression analysis.

Usage

LOG2(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The logarithmic model is defined by:

y = \beta_0 + \beta_1 ln(\cdot x) + \beta_2 ln(\cdot x)^2

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Examples

library(AgroReg)
resp=c(10,8,6.8,6,5,4.3,4.1,4.2,4.1)
trat=seq(1,9,1)
LOG2(trat,resp)

Analysis: Michaelis-Menten

Description

This function performs regression analysis using the Michaelis-Menten model.

Usage

MM(
  trat,
  resp,
  npar = "mm2",
  sample.curve = 1000,
  error = "SE",
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  point = "all",
  width.bar = NA,
  r2 = "all",
  ic = FALSE,
  fill.ic = "gray70",
  alpha.ic = 0.5,
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The two-parameter Michaelis-Menten model is defined by:

y = \frac{Vm \times x}{k + x}

The three-parameter Michaelis-Menten model is defined by:

y = c + \frac{Vm \times x}{k + x}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Examples

data("granada")
attach(granada)
MM(time,WL)
MM(time,WL,npar="mm3")

Analysis: Graph for not significant trend

Description

Graph for non-significant trend. Can be used within the multicurve command

Usage

Nreg(
  trat,
  resp,
  ylab = "Dependent",
  xlab = "Independent",
  error = "SE",
  theme = theme_classic(),
  legend.position = "top",
  legend.text = "not~significant",
  legend.add.mean = TRUE,
  legend.add.mean.name = "hat(y)",
  width.bar = NA,
  point = "all",
  textsize = 12,
  add.line = FALSE,
  add.line.mean = FALSE,
  linesize = 0.8,
  linetype = 1,
  pointsize = 4.5,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Value

The function returns an exploratory graph of segments

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
Nreg(trat,resp)

Analysis: Page

Description

This function performs exponential page regression analysis.

Usage

PAGE(
  trat,
  resp,
  initial = NA,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The exponential model is defined by:

y = e^{-k \cdot x^n}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Examples

library(AgroReg)
data("granada")
attach(granada)
PAGE(time,100-WL)

Analysis: Steinhart-Hart

Description

The Steinhart-Hart model. The Steinhart-Hart equation is a model used to explain the behavior of a semiconductor at different temperatures, however, Zhai et al. (2020) used this model to relate plant density and grain yield.

Usage

SH(
  trat,
  resp,
  initial = NA,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  r2 = "all",
  error = "SE",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The model function for the Steinhart-Hart model is:

y = \frac{1}{A+B \times ln(x)+C \times ln(x)^3}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Zhai, L., Li, H., Song, S., Zhai, L., Ming, B., Li, S., ... & Zhang, L. (2021). Intra-specific competition affects the density tolerance and grain yield of maize hybrids. Agronomy Journal, 113(1), 224-23. doi:10.1002/agj2.20438

See Also

LL, CD,GP

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
SH(trat,resp)

Analysis: Von Bertalanffy

Description

The Von Bertalanffy model. It's a kind of growth curve for a time series and takes its name from its creator, Ludwig von Bertalanffy. It is a special case of the generalized logistic function. The growth curve (biology) is used to model the average length from age in animals.

Usage

VB(
  trat,
  resp,
  initial = NA,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  r2 = "all",
  error = "SE",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The model function for the von Bertalanffy model is:

y = L(1-exp(-k(t-t0)))

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(AgroReg)
x=seq(1,20)
y=c(0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.91,
    0.92, 0.94, 0.96, 0.98, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00)
VB(x,y)

Analysis: Vega-Galvez

Description

This function performs Vega-Galvez regression analysis.

Usage

VG(
  trat,
  resp,
  sample.curve = 1000,
  error = "SE",
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  r2 = "mean",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The Vega-Galvez model is defined by:

y = \beta_0 + \beta_1 (\sqrt{x})

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Sadeghi, E., Haghighi Asl, A., and Movagharnejad, K. (2019). Mathematical modelling of infrared-dried kiwifruit slices under natural and forced convection. Food science & nutrition, 7(11), 3589-3606.

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
VG(trat,resp)

Utils: Adjust y and x scale

Description

Adjust y and x scale for chart or charts

Usage

adjust_scale(
  plots,
  scale.x = "default",
  limits.x = "default",
  scale.y = "default",
  limits.y = "default"
)

Arguments

Value

Returns the scaled graph

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
a=LM(trat,resp)
b=LL(trat,resp,npar = "LL.3")
a=plot_arrange(list(a,b),gray = TRUE)
adjust_scale(a,scale.y = seq(0,100,10),limits.y = c(0,100))

Utils: Adjust x scale

Description

Adjust x scale for chart or charts

Usage

adjust_scale_x(plots, scale = "default", limits = "default")

Arguments

Value

Returns the scaled graph

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
a=LM(trat,resp)
b=LL(trat,resp,npar = "LL.3")
a=plot_arrange(list(a,b),gray = TRUE)
adjust_scale_x(a,scale = seq(10,40,5),limits = c(10,40))

Utils: Adjust y scale

Description

Adjust y scale for chart or charts

Usage

adjust_scale_y(plots, scale = "default", limits = "default")

Arguments

Value

Returns the scaled graph

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
a=LM(trat,resp)
b=LL(trat,resp,npar = "LL.3")
a=plot_arrange(list(a,b),gray = TRUE)
adjust_scale_y(a,scale = seq(0,100,10),limits = c(0,100))

Dataset: Aristolochia

Description

The data come from an experiment conducted at the Seed Analysis Laboratory of the Agricultural Sciences Center of the State University of Londrina, in which five temperatures (15, 20, 25, 30 and 35C) were evaluated in the germination of Aristolochia elegans. The experiment was conducted in a completely randomized design with four replications of 25 seeds each.

Usage

data("aristolochia")

Format

data.frame containing data set

trat

Numeric vector with temperature

resp

Numeric vector with response

Author(s)

Hugo Roldi Guariz

Examples

data(aristolochia)

Analysis: Asymptotic, exponential or Logarithmic

Description

This function performs asymptotic regression analysis.

Usage

asymptotic(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The exponential model is defined by:

y = \alpha \times e^{-\beta \cdot x} + \theta

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley and Sons (p. 330).

Examples

library(AgroReg)
data("granada")
attach(granada)
asymptotic(time,100-WL)

Analysis: Asymptotic without intercept

Description

This function performs asymptotic regression analysis without intercept.

Usage

asymptotic_i(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  fontfamily = "sans",
  comment = NA,
  print.on = TRUE
)

Arguments

Details

The asymptotic model without intercept is defined by:

y = \alpha \times e^{-\beta \cdot x}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley and Sons (p. 330).

Siqueira, V. C., Resende, O., & Chaves, T. H. (2013). Mathematical modelling of the drying of jatropha fruit: an empirical comparison. Revista Ciencia Agronomica, 44, 278-285.

Examples

library(AgroReg)
data("granada")
attach(granada)
asymptotic_i(time,100-WL)

Analysis: Asymptotic or Exponential Negative without intercept

Description

This function performs asymptotic regression analysis without intercept.

Usage

asymptotic_ineg(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The asymptotic negative model without intercept is defined by:

y = \alpha \times e^{-\beta \cdot x}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Siqueira, V. C., Resende, O., & Chaves, T. H. (2013). Mathematical modelling of the drying of jatropha fruit: an empirical comparison. Revista Ciencia Agronomica, 44, 278-285.

Examples

library(AgroReg)
data("granada")
attach(granada)
asymptotic_ineg(time,100-WL)

Analysis: Asymptotic or Exponential Negative

Description

This function performs asymptotic regression analysis.

Usage

asymptotic_neg(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The asymptotic model is defined by:

y = -\alpha \times e^{-\beta \cdot x}+\theta

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Examples

library(AgroReg)
data("granada")
attach(granada)
asymptotic_neg(time,WL)

Analysis: Beta

Description

This function performs beta regression analysis.

Usage

beta_reg(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The beta model is defined by:

Y = d \times \{(\frac{X-X_b}{X_o-X_b})(\frac{X_c-X}{X_c-X_o})^{\frac{X_c-X_o}{X_o-X_b}}\}^b

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Model imported from the aomisc package (Andrea Onofri)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Onofri, A., 2020. The broken bridge between biologists and statisticians: a blog and R package. Statforbiology. http://www.statforbiology.com/tags/aomisc/

Examples

library(AgroReg)
X <- c(1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50)
Y <- c(0, 0, 0, 7.7, 12.3, 19.7, 22.4, 20.3, 6.6, 0, 0)
beta_reg(X,Y)

Analysis: Biexponential

Description

This function performs biexponential regression analysis.

Usage

biexponential(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The biexponential model is defined by:

y = A1 \times e^{-e^{lrc1 \cdot x}} + A2 \times e^{-e^{lrc2 \cdot x}}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

See Also

asymptotic_neg

Examples

library(AgroReg)
data("granada")
attach(granada)
biexponential(time,WL)

Change the colors of a graph from the plot_arrange function

Description

Change the colors of a graph from the plot_arrange function

Usage

coloredit_arrange(graphs, color = NA)

Arguments

Value

The function changes the colors of a graph coming from the plot_arrange function

Author(s)

Gabriel Danilo Shimizu

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
graph1=LM(trat,resp)
graph2=LL(trat,resp,npar = "LL.3")
graph=plot_arrange(list(graph1,graph2))
coloredit_arrange(graph,color=c("red","blue"))

Analysis: Comparative models

Description

This function allows the construction of a table and/or graph with the statistical parameters to choose the model from the analysis functions.

Usage

comparative_model(models, names_model = NA, plot = FALSE, round.label = 2)

Arguments

Value

Returns a table and/or graph with the statistical parameters for choosing the model.

Author(s)

Gabriel Danilo Shimizu

Examples

library(AgroReg)
data(granada)
attach(granada)
a=LM(time,WL)
b=LL(time,WL)
c=BC(time,WL)
d=weibull(time,WL)
comparative_model(models=list(a,b,c,d),names_model=c("LM","LL","BC","Weibull"))

models <- c("LM1", "LM4", "L3", "BC4","weibull3","mitscherlich", "linear.plateau", "VG")
r <- lapply(models, function(x) {
r <- with(granada, regression(time, WL, model = x))
})
comparative_model(r,plot = TRUE)

Graph: Plot correlation

Description

Correlation analysis function (Pearson or Spearman)

Usage

correlation(
  x,
  y,
  method = "pearson",
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  textsize = 12,
  pointsize = 5,
  pointshape = 21,
  linesize = 0.8,
  fill.ic = "gray70",
  alpha.ic = 0.5,
  ic = TRUE,
  title = NA,
  fontfamily = "sans"
)

Arguments

Value

The function returns a graph for correlation

Author(s)

Gabriel Danilo Shimizu, shimizu@uel.br

Leandro Simoes Azeredo Goncalves

Examples

data("aristolochia")
with(aristolochia, correlation(trat,resp))

Analysis: Extract models

Description

This function allows extracting the model (type="model") or residuals (type="resids"). The model class depends on the function and can be (lm, drm or nls). This function also allows you to perform graphical analysis of residuals (type="residplot"), graphical analysis of standardized residuals (type="stdresidplot"), graph of theoretical quantiles (type="qqplot").

Usage

extract.model(model, type = "model")

Arguments

Value

Returns an object of class drm, lm or nls (type="model"), or vector of residuals (type="resids"), or graph of the residuals (type="residplot", type="stdresidplot", type=" qqplot").

Examples

data("aristolochia")
attach(aristolochia)
a=linear.linear(trat,resp,point = "mean")
extract.model(a,type = "qqplot")

Analysis: Analogous to the Gaussian model/Bragg

Description

Analysis: Analogous to the Gaussian model/Bragg

Usage

gaussianreg(
  trat,
  resp,
  npar = "g3",
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  error = "SE",
  legend.position = "top",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The model analogous to the three-parameter Gaussian is:

y = d \times e^{-b((x-e)^2)}

The model analogous to the three-parameter Gaussian is:

y = d \times c+(d-c)*e^{-b((x-e)^2)}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
gaussianreg(trat,resp)

Dataset: Granada

Description

The data are part of an experiment that studied the drying kinetics of pomegranate peel over time under an air-circulation oven. Mass loss was assessed.

Usage

data("granada")

Format

data.frame containing data set

time

numeric vector with times

WL

Numeric vector with response

Author(s)

Gabriel Danilo Shimizu

Examples

data(granada)

Analysis: Hill

Description

This function performs regression analysis using the Hill model.

Usage

hill(
  trat,
  resp,
  sample.curve = 1000,
  error = "SE",
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  point = "all",
  width.bar = NA,
  r2 = "all",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The Hill model is defined by:

y = \frac{a \times x^c}{b+x^c}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Model imported from the aomisc package (Onofri, 2020)

Gabriel Danilo Shimizu

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Onofri A. (2020) The broken bridge between biologists and statisticians: a blog and R package, Statforbiology, IT, web: https://www.statforbiology.com

Examples

data("granada")
attach(granada)
hill(time,WL)

Analysis: Interval of confidence

Description

Interval of confidence in model regression

Usage

interval.confidence(model)

Arguments

Value

Return in the interval of confidence

Author(s)

Gabriel Danilo Shimizu

Examples

data("granada")
attach(granada)
a=LM(time, WL)
interval.confidence(a)

Analysis: Linear-Linear

Description

This function performs linear linear regression analysis.

Usage

linear.linear(
  trat,
  resp,
  middle = 1,
  CI = FALSE,
  bootstrap.samples = 1000,
  sig.level = 0.05,
  error = "SE",
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  point = "all",
  width.bar = NA,
  legend.position = "top",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The linear-linear model is defined by: First curve:

y = \beta_0 + \beta_1 \times x (x < breakpoint)

Second curve:

y = \beta_0 + \beta_1 \times breakpoint + w \times x (x > breakpoint)

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); breakpoint and the graph using ggplot2 with the equation automatically.

Author(s)

Model imported from the SiZer package

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Chiu, G. S., R. Lockhart, and R. Routledge. 2006. Bent-cable regression theory and applications. Journal of the American Statistical Association 101:542-553.

Toms, J. D., and M. L. Lesperance. 2003. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84:2034-2041.

See Also

quadratic.plateau, linear.plateau

Examples

library(AgroReg)
data("granada")
attach(granada)
linear.linear(time,WL)

Analysis: Linear-Plateau

Description

This function performs the linear-plateau regression analysis.

Usage

linear.plateau(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The linear-plateau model is defined by: First curve:

y = \beta_0 + \beta_1 \times x (x < breakpoint)

Second curve:

y = \beta_0 + \beta_1 \times breakpoint (x > breakpoint)

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); breakpoint and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Chiu, G. S., R. Lockhart, and R. Routledge. 2006. Bent-cable regression theory and applications. Journal of the American Statistical Association 101:542-553.

Toms, J. D., and M. L. Lesperance. 2003. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84:2034-2041.

See Also

quadratic.plateau, linear.linear

Examples

library(AgroReg)
data("granada")
attach(granada)
linear.plateau(time,WL)

Analysis: loess regression (degree 0, 1 or 2)

Description

Fit a polynomial surface determined by one or more numerical predictors, using local fitting.

Usage

loessreg(
  trat,
  resp,
  degree = 2,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Value

The function returns a list containing the loess regression and graph using ggplot2.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

See Also

loess

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
loessreg(trat,resp)

Analysis: Logistic

Description

Logistic models with three (L.3), four (L.4) or five (L.5) continuous data parameters. This model was extracted from the drc package.

Usage

logistic(
  trat,
  resp,
  npar = "L.3",
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  ic = FALSE,
  fill.ic = "gray70",
  alpha.ic = 0.5,
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The three-parameter logistic function with lower limit 0 is

y = 0 + \frac{d}{1+\exp(b(x-e))}

The four-parameter logistic function is given by the expression

y = c + \frac{d-c}{1+\exp(b(x-e))}

The five-parameter logistic function is given by the expression

y = c + \frac{d-c}{1+\exp(b(x-e))^f}

The function is symmetric about the inflection point (e).

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Model imported from the drc package (Ritz et al., 2016)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Ritz, C.; Strebig, J.C.; Ritz, M.C. Package ‘drc’. Creative Commons: Mountain View, CA, USA, 2016.

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
logistic(trat,resp)

Analysis: Lorentz

Description

Analysis: Lorentz

Usage

lorentz(
  trat,
  resp,
  npar = "lo3",
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  error = "SE",
  legend.position = "top",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The model to the three-parameter Lorentz is:

y = frac{d}{1+b(x-e)^2}

The model to the three-parameter Lorentz is:

y = c+frac{d-c}{1+b(x-e)^2}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Model imported from the aomisc package (Onofri, 2020)

Gabriel Danilo Shimizu

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Onofri A. (2020) The broken bridge between biologists and statisticians: a blog and R package, Statforbiology, IT, web: https://www.statforbiology.com

Examples

library(AgroReg)
data("granada")
attach(granada)
x=time[length(time):1]
lorentz(x,WL)

Analysis: Midilli

Description

This function performs Midilli regression analysis.

Usage

midilli(
  trat,
  resp,
  initial = NA,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The exponential model is defined by:

y = \alpha \times e^{-\beta \cdot x^n} + \theta \cdot x

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Examples

library(AgroReg)
data("granada")
attach(granada)
midilli(time,100-WL)

Analysis: Modified Midilli

Description

This function performs modified Midilli regression analysis.

Usage

midillim(
  trat,
  resp,
  initial = NA,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The exponential model is defined by:

y = \alpha \times e^{-\beta \cdot x} + \theta \cdot x

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Examples

library(AgroReg)
data("granada")
attach(granada)
midillim(time,100-WL)

Analysis: Mitscherlich

Description

This function performs Mitscherlich regression analysis.

Usage

mitscherlich(
  trat,
  resp,
  initial = NA,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The Mitscherlich model is defined by:

y = A \times (1-10^{-eb-ex})

where "y" is the yield obtained when "b" units of a nutrient are in the soil and "x" units of it are added as fertilizer, "A" is the maximum yield, and "e" is the proportionality factor, has recently received increasing interest.

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(AgroReg)
data("granada")
attach(granada)
mitscherlich(time,WL)

Analysis: Newton

Description

This function performs exponential regression analysis. This model was used by Newton.

Usage

newton(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The exponential model is defined by:

y = e^{-\beta \cdot x}\cdot x

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Siqueira, V. C., Resende, O., and Chaves, T. H. (2013). Mathematical modelling of the drying of jatropha fruit: an empirical comparison. Revista Ciencia Agronomica, 44, 278-285.

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
newton(trat,resp+0.001)

Analysis: Peleg

Description

This function performs Peleg regression analysis.

Usage

peleg(
  trat,
  resp,
  initial = NA,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The Peleg model is defined by:

y = \frac{(1-x)}{a+bx}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Examples

library(AgroReg)
data("granada")
attach(granada)
peleg(time,WL)

Analysis: Plateau-Linear

Description

This function performs the plateau-linear regression analysis.

Usage

plateau.linear(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  xname.formula = "x",
  yname.formula = "y",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The plateau-linear model is defined by: First curve:

y = \beta_0 + \beta_1 \times breakpoint (x < breakpoint)

Second curve:

y = \beta_0 + \beta_1 \times x (x > breakpoint)

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); breakpoint and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Chiu, G. S., R. Lockhart, and R. Routledge. 2006. Bent-cable regression theory and applications. Journal of the American Statistical Association 101:542-553.

Toms, J. D., and M. L. Lesperance. 2003. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84:2034-2041.

See Also

quadratic.plateau, linear.linear

Examples

library(AgroReg)
data("granada")
attach(granada)
x=time[length(time):1]
plateau.linear(x,WL)

Analysis: Plateau-quadratic

Description

This function performs the plateau-quadratic regression analysis.

Usage

plateau.quadratic(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

plquadratic(x, a, breakpoint, b, c)

Arguments

Details

The Plateau-quadratic model is defined by:

First curve:

y = \beta_0 + \beta_1 \cdot breakpoint + \beta_2 \cdot breakpoint^2 (x < breakpoint)

Second curve:

y = \beta_0 + \beta_1 \cdot x + \beta_2 \cdot x^2 (x > breakpoint)

or

y = a + b(x+breakpoint) + c(x+breakpoint)^2 (x > breakpoint)

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Miguez, F. (2020). nlraa: nonlinear Regression for Agricultural Applications. R package version 0.65.

Chiu, G. S., R. Lockhart, and R. Routledge. 2006. Bent-cable regression theory and applications. Journal of the American Statistical Association 101:542-553.

Toms, J. D., and M. L. Lesperance. 2003. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84:2034-2041.

See Also

linear.linear, linear.plateau

Examples

library(AgroReg)
data("granada")
attach(granada)
x=time[length(time):1]
plateau.quadratic(x,WL)

Merge multiple curves into a single graph

Description

Merge multiple curves into a single graph

Usage

plot_arrange(
  plots,
  point = "mean",
  theme = theme_classic(),
  legend.title = NULL,
  legend.position = "top",
  trat = NA,
  gray = FALSE,
  ylab = "Dependent",
  xlab = "Independent",
  widthbar = 0,
  pointsize = 4.5,
  linesize = 0.8,
  textsize = 12,
  legendsize = 12,
  legendtitlesize = 12,
  fontfamily = "sans"
)

Arguments

Value

The function returns a graph joining the outputs of the functions LM_model, LL_model, BC_model, CD_model, loess_model, normal_model, piecewise_model and N_model

Author(s)

Gabriel Danilo Shimizu

Examples

library(AgroReg)
library(ggplot2)
data("aristolochia")
attach(aristolochia)
a=LM(trat,resp)
b=LL(trat,resp,npar = "LL.3")
plot_arrange(list(a,b))

models <- c("LM1", "LL3")
r <- lapply(models, function(x) {
r <- with(granada, regression(time, WL, model = x,print.on=FALSE))
})
plot_arrange(r,trat=models,ylab="WL (%)",xlab="Time (Minutes)")

models = c("asymptotic_neg", "biexponential", "LL4", "BC4", "CD5", "linear.linear",
           "linear.plateau", "quadratic.plateau", "mitscherlich", "MM2")
m = lapply(models, function(x) {
           m = with(granada, regression(time, WL, model = x,print.on=FALSE))})
           plot_arrange(m, trat = paste("(",models,")"))

Analysis: Potencial

Description

This function performs potencial regression analysis.

Usage

potential(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The exponential model is defined by:

y = \alpha \times trat^{\beta}

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Siqueira, V. C., Resende, O., & Chaves, T. H. (2013). Mathematical modelling of the drying of jatropha fruit: an empirical comparison. Revista Ciencia Agronomica, 44, 278-285.

Examples

library(AgroReg)
data("granada")
attach(granada)
potential(time,WL)

Analysis: Quadratic-plateau

Description

This function performs the quadratic-plateau regression analysis.

Usage

quadratic.plateau(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The quadratic-plateau model is defined by:

First curve:

y = \beta_0 + \beta_1 \cdot x + \beta_2 \cdot x^2 (x < breakpoint)

Second curve:

y = \beta_0 + \beta_1 \cdot breakpoint + \beta_2 \cdot breakpoint^2 (x > breakpoint)

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Chiu, G. S., R. Lockhart, and R. Routledge. 2006. Bent-cable regression theory and applications. Journal of the American Statistical Association 101:542-553.

Toms, J. D., and M. L. Lesperance. 2003. Piecewise regression: a tool for identifying ecological thresholds. Ecology 84:2034-2041.

See Also

linear.linear, linear.plateau

Examples

library(AgroReg)
data("granada")
attach(granada)
quadratic.plateau(time,WL)

Analysis: Regression linear or nonlinear

Description

This function is a simplification of all the analysis functions present in the package.

Usage

regression(
  trat,
  resp,
  model = "LM1",
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  point = "all",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  round = NA,
  fontfamily = "sans",
  error = "SE",
  width.bar = NA,
  xname.formula = "x",
  yname.formula = "y",
  print.on = TRUE
)

Arguments

Details

To change the regression model, change the "model" argument to:

1. N: Graph for not significant trend.

2. loess0: Loess non-parametric degree 0

3. loess1: Loess non-parametric degree 1

4. loess2: Loess non-parametric degree 2

5. LM0.5: Quadratic inverse

6. LM1: Linear regression.

7. LM2: Quadratic

8. LM3: Cubic

9. LM4: Quartic

10. LM0.5_i: Quadratic inverse without intercept.

11. LM1_i: Linear without intercept.

12. LM2_i: Quadratic regression without intercept.

13. LM3_i: Cubic without intercept.

14. LM4_i: Quartic without intercept.

15. LM13: Cubic without beta2

16. LM13i: Cubic inverse without beta2

17. LM23: Cubic without beta1

18. LM23i: Cubic inverse without beta2

19. LM2i3: Cubic without beta1, with inverse beta3

20. valcam: Valcam

21. L3: Three-parameter logistics.

22. L4: Four-parameter logistics.

23. L5: Five-parameter logistics.

24. LL3: Three-parameter log-logistics.

25. LL4: Four-parameter log-logistics.

26. LL5: Five-parameter log-logistics.

27. BC4: Brain-Cousens with four parameter.

28. BC5: Brain-Cousens with five parameter.

29. CD4: Cedergreen-Ritz-Streibig with four parameter.

30. CD5: Cedergreen-Ritz-Streibig with five parameter.

31. weibull3: Weibull with three parameter.

32. weibull4: Weibull with four parameter.

33. GP2: Gompertz with two parameter.

34. GP3: Gompertz with three parameter.

35. GP4: Gompertz with four parameter.

36. VB: Von Bertalanffy

37. lo3: Lorentz with three parameter

38. lo4: Lorentz with four parameter

39. beta: Beta

40. gaussian3: Analogous to the Gaussian model/Bragg with three parameters.

41. gaussian4: Analogous to the Gaussian model/Bragg with four parameters.

42. linear.linear: Linear-linear

43. linear.plateau: Linear-plateau

44. quadratic.plateau: Quadratic-plateau

45. plateau.linear: Plateau-linear

46. plateau.quadratic: Plateau-Quadratic

47. log: Logarithmic

48. log2: Logarithmic quadratic

49. thompson: Thompson

50. asymptotic: Exponential

51. asymptotic_neg: Exponential negative

52. asymptotic_i: Exponential without intercept.

53. asymptotic_ineg: Exponential negative without intercept.

54. biexponential: Biexponential

55. mitscherlich: Mitscherlich

56. yieldloss: Yield-loss

57. hill: Hill

58. MM2: Michaelis-Menten with two parameter.

59. MM3: Michaelis-Menten with three parameter.

60. SH: Steinhart-Hart

61. page: Page

62. newton: Newton

63. potential: Potential

64. midilli: Midilli

65. midillim: Modified Midilli

66. AM: Avhad and Marchetti

67. peleg: Peleg

68. VG: Vega-Galvez

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Examples

library(AgroReg)
data("aristolochia")
attach(aristolochia)
regression(trat, resp)

Analysis: Other statistical parameters

Description

This function calculates other statistical parameters such as Mean (Bias) Error, Relative Mean (Bias) Error, Mean Absolute Error, Relative Mean Absolute Error, Root Mean Square Error, Relative Root Mean Square Error, Modeling Efficiency, Standard deviation of differences, Coefficient of Residual Mass.

Usage

stat_param(models, names_model = NA, round = 3)

Arguments

Value

Returns a table with the statistical parameters for choosing the model.

Author(s)

Gabriel Danilo Shimizu

Examples

library(AgroReg)
data(granada)
attach(granada)
a=LM(time,WL)
b=LL(time,WL)
c=BC(time,WL)
d=weibull(time,WL)
stat_param(models=list(a,b,c,d))

Analysis: Thompson

Description

This function performs Thompson regression analysis.

Usage

thompson(
  trat,
  resp,
  sample.curve = 1000,
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  error = "SE",
  r2 = "all",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments

Details

The logarithmic model is defined by:

y = \beta_1 ln(\cdot x) + \beta_2 ln(\cdot x)^2

Value

The function returns a list containing the coefficients and their respective values of p; statistical parameters such as AIC, BIC, pseudo-R2, RMSE (root mean square error); largest and smallest estimated value and the graph using ggplot2 with the equation automatically.

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

References

Seber, G. A. F. and Wild, C. J (1989) Nonlinear Regression, New York: Wiley & Sons (p. 330).

Sadeghi, E., Haghighi Asl, A., & Movagharnejad, K. (2019). Mathematical modelling of infrared-dried kiwifruit slices under natural and forced convection. Food science & nutrition, 7(11), 3589-3606.

Examples

library(AgroReg)
resp=c(10,8,6.8,6,5,4.3,4.1,4.2,4.1)
trat=seq(1,9,1)
thompson(trat,resp)

Analysis: Valcam

Description

This function performs Valcam regression analysis.

Usage

valcam(
  trat,
  resp,
  sample.curve = 1000,
  error = "SE",
  ylab = "Dependent",
  xlab = "Independent",
  theme = theme_classic(),
  legend.position = "top",
  r2 = "mean",
  point = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  linetype = 1,
  pointshape = 21,
  fillshape = "gray",
  colorline = "black",
  round = NA,
  yname.formula = "y",
  xname.formula = "x",
  comment = NA,
  fontfamily = "sans",
  print.on = TRUE
)

Arguments