Package 'AgroReg'

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))

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))

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))

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"
)

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 ))

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)

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"
)

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)

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
)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"))

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

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, [email protected]

Leandro Simoes Azeredo Goncalves

Examples

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

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")

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"
)

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)

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"
)

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")

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)

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"
)

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)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"
)

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)

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"
)

Arguments

Details

The logarithmic model is defined by:

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