Package 'seedreg' reference manual

Title:	Regression Analysis for Seed Germination as a Function of Temperature
Description:	Regression analysis using common models in seed temperature studies, such as the Gaussian model (Martins, JF, Barroso, AAM, & Alves, PLCA (2017) <doi:10.1590/s0100-83582017350100039>), quadratic (Nunes, AL, Sossmeier, S, Gotz, AP, & Bispo, NB (2018) <doi: 10.17265/2161-6264/2018.06.002>) and others with potential for use, such as those implemented in the 'drc' package (Ritz, C, Baty, F, Streibig, JC, & Gerhard, D (2015). <doi:10.1371/journal.pone.0146021>), in the estimation of the ideal and cardinal temperature for the occurrence of plant seed germination. The functions return graphs with the equations automatically.
Authors:	Gabriel Danilo Shimizu [aut, cre] , Hugo Roldi Guariz [aut, ctb] , Leandro Simoes Azeredo Goncalves [aut, ctb]
Maintainer:	Gabriel Danilo Shimizu <[email protected]>
License:	GPL (>= 2)
Version:	1.0.3
Built:	2025-02-28 05:19:03 UTC
Source:	https://github.com/cran/seedreg

Param: Area below the curve

Description

Calculates the area under the germination or emergence curve. A parameter that can replace the traditional emergence or germination speed index.

Usage

aac(dados, trat, nrep, time)
aac(dados, trat, nrep, time)

Arguments

`dados`	data.frame containing the responses of the evaluations in separate columns side by side and without the columns with the identification of the factors
`trat`	vector of treatments with n repetitions
`nrep`	Number of repetitions
`time`	vector containing time

Value

Returns a vector with the index

Examples

data("substrate")
aac(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)
data("substrate")
aac(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)

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")
data("aristolochia")

Format

data.frame containing data set

trat: numeric vector with factor 1
germ: Numeric vector with germination percentage
vel: numerical vector with germination speed

Author(s)

Hugo Roldi Guariz

Examples

data(aristolochia)
data(aristolochia)

Analysis: Logistic regression Brain-Cousens hormesis models

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 and adapted for temperature analysis in seed germination

Usage

BC_model(
  trat,
  resp,
  npar = "BC.4",
  error = "SE",
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  legend.position = "top",
  cardinal = 0,
  r2 = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)
BC_model(
  trat,
  resp,
  npar = "BC.4",
  error = "SE",
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  legend.position = "top",
  cardinal = 0,
  r2 = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)

Arguments

`trat`	Numerical or complex vector with treatments
`resp`	Numerical vector containing the response of the experiment.
`npar`	Number of model parameters (default is BC.4)
`error`	Error bar (It can be SE - default, SD or FALSE)
`ylab`	Variable response name (Accepts the expression() function)
`xlab`	Treatments name (Accepts the expression() function)
`theme`	ggplot2 theme (default is theme_bw())
`legend.position`	Legend position (default is c(0.3,0.8))
`cardinal`	Defines the value of y considered extreme (default considers 0 germination)
`r2`	Coefficient of determination of the mean or all values (default is all)
`width.bar`	bar width
`scale`	Sets x scale (default is none, can be "log")
`textsize`	Font size
`pointsize`	shape size
`linesize`	line size
`pointshape`	format point (default is 21)
`font.family`	Font family (default is sans)

Details

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

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

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

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

used by van Ewijk and Hoekstra (1993).

Value

Coefficients: Coefficients and their p values
Optimum temperature: Optimum temperature (equivalent to the maximum point)
Optimum temperature response: Response at the optimal temperature (equivalent to the maximum point)
Minimal temperature: Temperature that has the lowest response
Minimal temperature response: Lowest predicted response
Predicted maximum basal value: Lower basal limit temperature based on the value set by the user (default is 0)
Predicted minimum basal value: Upper basal limit temperature based on the value set by the user (default is 0)
AIC: Akaike information criterion
BIC: Bayesian Inference Criterion
r-squared: Determination coefficient
RMSE: Root mean square error
grafico: Graph in ggplot2 with equation

Note

if the maximum predicted value is equal to the maximum x, the curve does not have a maximum point within the studied range. If the minimum value is less than the lowest point studied, disregard the value.

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.

Examples

library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
BC_model(trat,germ)

#================================
# Germination speed
#================================
BC_model(trat, vel, ylab=expression("v"~(dias^-1)))
library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
BC_model(trat,germ)

#================================
# Germination speed
#================================
BC_model(trat, vel, ylab=expression("v"~(dias^-1)))

Analysis: Logistic regression Cedergreen-Ritz-Streibig model

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 and adapted for temperature analysis in seed germination

Usage

CD_model(
  trat,
  resp,
  npar = "CRS.4",
  error = "SE",
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  legend.position = "top",
  cardinal = 0,
  r2 = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)
CD_model(
  trat,
  resp,
  npar = "CRS.4",
  error = "SE",
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  legend.position = "top",
  cardinal = 0,
  r2 = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)

Arguments

`trat`	Numerical or complex vector with treatments
`resp`	Numerical vector containing the response of the experiment.
`npar`	Number of model parameters
`error`	Error bar (It can be SE - default, SD or FALSE)
`ylab`	Variable response name (Accepts the expression() function)
`xlab`	treatments name (Accepts the expression() function)
`theme`	ggplot2 theme (default is theme_classic())
`legend.position`	legend position (default is c(0.3,0.8))
`cardinal`	defines the value of y considered extreme (default considers 0 germination)
`r2`	coefficient of determination of the mean or all values (default is all)
`width.bar`	bar width
`scale`	Sets x scale (default is none, can be "log")
`textsize`	Font size
`pointsize`	shape size
`linesize`	line size
`pointshape`	format point (default is 21)
`font.family`	Font family (default is sans)

Details

The four-parameter model is given by the expression:

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

while the five-parameter is:

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

Value

Coefficients: Coefficients and their p values
Optimum temperature: Optimum temperature (equivalent to the maximum point)
Optimum temperature response: Response at the optimal temperature (equivalent to the maximum point)
Minimal temperature: Temperature that has the lowest response
Minimal temperature response: Lowest predicted response
Predicted maximum basal value: Lower basal limit temperature based on the value set by the user (default is 0)
Predicted minimum basal value: Upper basal limit temperature based on the value set by the user (default is 0)
AIC: Akaike information criterion
BIC: Bayesian Inference Criterion
r-squared: Determination coefficient
RMSE: Root mean square error
grafico: Graph in ggplot2 with equation

Note

If the maximum predicted value is equal to the maximum x, the curve does not have a maximum point within the studied range. If the minimum value is less than the lowest point studied, disregard the value.

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

Examples

library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
CD_model(trat,germ)

#================================
# Germination speed
#================================
CD_model(trat, vel, ylab=expression("v"~(dias^-1)))
library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
CD_model(trat,germ)

#================================
# Germination speed
#================================
CD_model(trat, vel, ylab=expression("v"~(dias^-1)))

Comparison: correlation between parameters

Description

Correlation between the logistical model and the traditional model

Usage

correl(seeds)
correl(seeds)

Arguments

seeds

Object returned in the seeds function

Value

Returns correlation graphs between parameters calculated by traditional methods and by logistic regression

Examples

data("substrate")
a=seeds(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)
correl(a)
data("substrate")
a=seeds(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)
correl(a)

Analysis: Logistic regression by treatment over time

Description

Performs the construction of a logistic regression graph by treatment over time

Usage

curve(
  dados,
  trat,
  nrep,
  time,
  n,
  model = LL.3(),
  ylab = "Emergence (%)",
  xlab = "Time (days)",
  legend.position = c(0.2, 0.8)
)
curve(
  dados,
  trat,
  nrep,
  time,
  n,
  model = LL.3(),
  ylab = "Emergence (%)",
  xlab = "Time (days)",
  legend.position = c(0.2, 0.8)
)

Arguments

`dados`	data.frame containing the responses of the evaluations in separate columns side by side and without the columns with the identification of the factors
`trat`	vector of treatments with n repetitions
`nrep`	Number of repetitions
`time`	vector containing time
`n`	total seeds per repetition
`model`	logistic model according to drc package
`ylab`	y-axis name
`xlab`	x-axis name
`legend.position`	Legend position

Value

Returns a logistic regression graph by treatment over time.

Examples

data("substrate")
curve(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      n=10,
      time = 1:16)
data("substrate")
curve(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      n=10,
      time = 1:16)

Param: Index for germination speed

Description

Calculates the emergence or germination speed index according to Maguire (1962)

Usage

iv(data, trat, nrep, time)
iv(data, trat, nrep, time)

Arguments

`data`	Data.frame containing the responses of the evaluations in separate columns side by side and without the columns with the identification of the factors
`trat`	Vector of treatments with n repetitions
`nrep`	Number of repetitions
`time`	Vector containing time

Value

Returns the vector with the index

References

Maguire JD (1962). Seed of germination - aid in selection and evaluation for seedling emergence and vigour. J Crop Sci 2:176-177.

Examples

data("substrate")
iv(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)
data("substrate")
iv(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)

Graph: line chart

Description

Returns a graph with the frequencies of germinated or emerged seeds

Usage

lineplot(
  dados,
  trat,
  nrep,
  time,
  ylab = "Emergence",
  xlab = "Time (days)",
  nt = NA,
  percentage = FALSE,
  legend.position = c(0.2, 0.8)
)
lineplot(
  dados,
  trat,
  nrep,
  time,
  ylab = "Emergence",
  xlab = "Time (days)",
  nt = NA,
  percentage = FALSE,
  legend.position = c(0.2, 0.8)
)

Arguments

`dados`	data.frame containing the responses of the evaluations in separate columns side by side and without the columns with the identification of the factors
`trat`	vector of treatments with n repetitions
`nrep`	Number of repetitions
`time`	vector containing time
`ylab`	y-axis name
`xlab`	x-axis name
`nt`	total seeds per repetition
`percentage`	y scale in percentage
`legend.position`	Legend position

Value

Returns a graph with the frequencies of germinated or emerged seeds.

Examples

data("substrate")
lineplot(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)
data("substrate")
lineplot(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)

Analysis: Logistic regression

Description

Logistic models with three (LL.3) or four (LL.4) continuous data parameters. This model was extracted from the drc package and adapted for temperature analysis in seed germination.

Usage

LL_model(
  trat,
  resp,
  npar = "LL.3",
  error = "SE",
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  legend.position = "top",
  cardinal = 0,
  r2 = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)
LL_model(
  trat,
  resp,
  npar = "LL.3",
  error = "SE",
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  legend.position = "top",
  cardinal = 0,
  r2 = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)

Arguments

`trat`	Numerical or complex vector with treatments
`resp`	Numerical vector containing the response of the experiment.
`npar`	Number of model parameters
`error`	Error bar (It can be SE - default, SD or FALSE)
`ylab`	Variable response name (Accepts the expression() function)
`xlab`	Treatments name (Accepts the expression() function)
`theme`	ggplot2 theme (default is theme_bw())
`legend.position`	Legend position (default is c(0.3,0.8))
`cardinal`	Defines the value of y considered extreme (default considers 0 germination)
`r2`	Coefficient of determination of the mean or all values (default is all)
`width.bar`	Bar width
`scale`	Sets x scale (default is none, can be "log")
`textsize`	Font size
`pointsize`	shape size
`linesize`	line size
`pointshape`	format point (default is 21)
`font.family`	Font family (default is sans)

Details

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

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

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

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

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

Value

Coefficients: Coefficients and their p values
Optimum temperature: Optimum temperature (equivalent to the maximum point)
Optimum temperature response: Response at the optimal temperature (equivalent to the maximum point)
Minimal temperature: Temperature that has the lowest response
Minimal temperature response: Lowest predicted response
Predicted maximum basal value: Lower basal limit temperature based on the value set by the user (default is 0)
Predicted minimum basal value: Upper basal limit temperature based on the value set by the user (default is 0)
AIC: Akaike information criterion
BIC: Bayesian Inference Criterion
r-squared: Determination coefficient
RMSE: Root mean square error
grafico: Graph in ggplot2 with equation

Note

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

Examples

library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
LL_model(trat,germ)

#================================
# Germination speed
#================================
LL_model(trat, vel, ylab=expression("v"~(dias^-1)))
library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
LL_model(trat,germ)

#================================
# Germination speed
#================================
LL_model(trat, vel, ylab=expression("v"~(dias^-1)))

Analysis: Linear regression graph

Description

Linear regression analysis of an experiment with a quantitative factor or isolated effect of a quantitative factor

Usage

LM_model(
  trat,
  resp,
  ylab = "Germination (%)",
  error = "SE",
  xlab = expression("Temperature ("^"o" * "C)"),
  grau = NA,
  theme = theme_classic(),
  cardinal = 0,
  legend.position = "top",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)
LM_model(
  trat,
  resp,
  ylab = "Germination (%)",
  error = "SE",
  xlab = expression("Temperature ("^"o" * "C)"),
  grau = NA,
  theme = theme_classic(),
  cardinal = 0,
  legend.position = "top",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)

Arguments

`trat`	Numerical vector with treatments (Declare as numeric)
`resp`	Numerical vector containing the response of the experiment.
`ylab`	Dependent variable name (Accepts the expression() function)
`error`	Error bar (It can be SE - default, SD or FALSE)
`xlab`	Independent variable name (Accepts the expression() function)
`grau`	Degree of the polynomial (1,2 or 3)
`theme`	ggplot2 theme (default is theme_classic())
`cardinal`	Defines the value of y considered extreme (default considers 0 germination)
`legend.position`	Legend position (default is "top")
`width.bar`	Bar width
`scale`	Sets x scale (default is none, can be "log")
`textsize`	Font size
`pointsize`	shape size
`linesize`	line size
`pointshape`	format point (default is 21)
`font.family`	Font family (default is sans)

Value

Coefficients: Coefficients and their p values
Optimum temperature: Optimum temperature (equivalent to the maximum point)
Optimum temperature response: Response at the optimal temperature (equivalent to the maximum point)
Minimal temperature: Temperature that has the lowest response
Minimal temperature response: Lowest predicted response
Predicted maximum basal value: Lower basal limit temperature based on the value set by the user (default is 0)
Predicted minimum basal value: Upper basal limit temperature based on the value set by the user (default is 0)
AIC: Akaike information criterion
BIC: Bayesian Inference Criterion
VIF: Variance inflation factor (multicollinearity)
r-squared: Determination coefficient
RMSE: Root mean square error
grafico: Graph in ggplot2 with equation

Note

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
LM_model(trat,germ, grau=3)

#================================
# Germination speed
#================================
LM_model(trat, vel, grau=3,
ylab=expression("v"~(dias^-1)))
library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
LM_model(trat,germ, grau=3)

#================================
# Germination speed
#================================
LM_model(trat, vel, grau=3,
ylab=expression("v"~(dias^-1)))

Analysis: loess regression

Description

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

Usage

loess_model(
  trat,
  resp,
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  error = "SE",
  cardinal = 0,
  width.bar = NA,
  legend.position = "top",
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)
loess_model(
  trat,
  resp,
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  error = "SE",
  cardinal = 0,
  width.bar = NA,
  legend.position = "top",
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)

Arguments

`trat`	Numerical or complex vector with treatments
`resp`	Numerical vector containing the response of the experiment.
`ylab`	Variable response name (Accepts the expression() function)
`xlab`	treatments name (Accepts the expression() function)
`theme`	ggplot2 theme (default is theme_bw())
`error`	Error bar (It can be SE - default, SD or FALSE)
`cardinal`	defines the value of y considered extreme (default considers 0 germination)
`width.bar`	bar width
`legend.position`	legend position (default is c(0.3,0.8))
`scale`	Sets x scale (default is none, can be "log")
`textsize`	Font size
`pointsize`	shape size
`linesize`	line size
`pointshape`	format point (default is 21)
`font.family`	Font family (default is sans)

Value

Optimum temperature: Optimum temperature (equivalent to the maximum point)
Optimum temperature response: Response at the optimal temperature (equivalent to the maximum point)
Minimal temperature: Temperature that has the lowest response
Minimal temperature response: Lowest predicted response
Predicted maximum basal value: Lower basal limit temperature based on the value set by the user (default is 0)
Predicted minimum basal value: Upper basal limit temperature based on the value set by the user (default is 0)
grafico: Graph in ggplot2 with equation

Note

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
loess_model(trat,germ)

#================================
# Germination speed
#================================
loess_model(trat, vel, ylab=expression("v"~(dias^-1)))
library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
loess_model(trat,germ)

#================================
# Germination speed
#================================
loess_model(trat, vel, ylab=expression("v"~(dias^-1)))

Graph: Merge multiple curves into a single graph

Description

Graph: Merge multiple curves into a single graph

Usage

multicurve(
  plots,
  theme = theme_classic(),
  legend.title = NULL,
  legend.position = "top",
  trat = NA,
  method = "shape_color",
  fill = "gray90",
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  width.bar = NA,
  pointsize = 4.5,
  linesize = 0.8,
  textsize = 12,
  font.family = "sans"
)
multicurve(
  plots,
  theme = theme_classic(),
  legend.title = NULL,
  legend.position = "top",
  trat = NA,
  method = "shape_color",
  fill = "gray90",
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  width.bar = NA,
  pointsize = 4.5,
  linesize = 0.8,
  textsize = 12,
  font.family = "sans"
)

Arguments

`plots`	list with objects of type LM_model, BC_model, CD_model, LL_model or normal_model
`theme`	ggplot2 theme (default is theme_classi())
`legend.title`	caption title
`legend.position`	legend position (default is c(0.3,0.8))
`trat`	name of the curves
`method`	marking method
`fill`	dot fill color in case gray=F
`ylab`	Variable response name (Accepts the expression() function)
`xlab`	treatments name (Accepts the expression() function)
`width.bar`	bar width
`pointsize`	shape size
`linesize`	line size
`textsize`	Font size
`font.family`	Font family (default is sans)

Details

The method argument defines the type of markup desired by the user. By default, method="shape_color" is used, which differentiates by color and dot shape. For gray scale, use method="shape_gray". To use only color, use method="color", in this case, the dot shape is 16 (filled circle). You can change the stitch pattern by setting the fill color in quotes followed by a space and the stitch number (eg "gray 21"). Still starting from this last method, if the user uses the change to point format without filling, such as 15, 16, 17 or 18, the function will ignore the first argument (ex. "gray 16"), however, of either way the user must define a color.

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(seedreg)
data("aristolochia")
attach(aristolochia)
a=LM_model(trat,germ)
b=LL_model(trat,germ,npar = "LL.3")
c=BC_model(trat,germ, npar = "BC.4")
d=CD_model(trat,germ, npar = "CRS.4")
multicurve(list(a,b,c,d))
library(seedreg)
data("aristolochia")
attach(aristolochia)
a=LM_model(trat,germ)
b=LL_model(trat,germ,npar = "LL.3")
c=BC_model(trat,germ, npar = "BC.4")
d=CD_model(trat,germ, npar = "CRS.4")
multicurve(list(a,b,c,d))

Analysis: Graph for not significant trend

Description

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

Usage

N_model(
  trat,
  resp,
  ylab = "Germination (%)",
  error = "SE",
  legend = "not~signifcant",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  width.bar = NA,
  legend.position = "top",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)
N_model(
  trat,
  resp,
  ylab = "Germination (%)",
  error = "SE",
  legend = "not~signifcant",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  width.bar = NA,
  legend.position = "top",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)

Arguments

`trat`	Numerical vector with treatments (Declare as numeric)
`resp`	Numerical vector containing the response of the experiment.
`ylab`	Dependent variable name (Accepts the expression() function)
`error`	Error bar (It can be SE - default, SD or FALSE)
`legend`	Add the legend
`xlab`	Independent variable name (Accepts the expression() function)
`theme`	ggplot2 theme (default is theme_classic())
`width.bar`	Bar width
`legend.position`	Legend position (default is "top")
`textsize`	Font size
`pointsize`	shape size
`linesize`	line size
`pointshape`	format point (default is 21)
`font.family`	Font family (default is sans)

Value

The function returns an exploratory graph of segments

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
N_model(trat,germ)

#================================
# Germination speed
#================================
N_model(trat, vel, ylab=expression("v"~(dias^-1)))
library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
N_model(trat,germ)

#================================
# Germination speed
#================================
N_model(trat, vel, ylab=expression("v"~(dias^-1)))

Analysis: Normal model

Description

Analysis: Normal model

Usage

normal_model(
  trat,
  resp,
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  error = "SE",
  legend.position = "top",
  cardinal = 0,
  r2 = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)
normal_model(
  trat,
  resp,
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  error = "SE",
  legend.position = "top",
  cardinal = 0,
  r2 = "all",
  width.bar = NA,
  scale = "none",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)

Arguments

`trat`	Numerical or complex vector with treatments
`resp`	Numerical vector containing the response of the experiment.
`ylab`	Variable response name (Accepts the expression() function)
`xlab`	treatments name (Accepts the expression() function)
`theme`	ggplot2 theme (default is theme_classic())
`error`	Error bar (It can be SE - default, SD or FALSE)
`legend.position`	legend position (default is c(0.3,0.8))
`cardinal`	defines the value of y considered extreme (default considers 0 germination)
`r2`	coefficient of determination of the mean or all values (default is all)
`width.bar`	bar width
`scale`	Sets x scale (default is none, can be "log")
`textsize`	Font size
`pointsize`	shape size
`linesize`	line size
`pointshape`	format point (default is 21)
`font.family`	Font family (default is sans)

Details

The model function for the normal model is:

$f(x) = a \epsilon^{-\frac{(x-b)^2)}{c^2}}$

Value

Coefficients: Coefficients and their p values
Optimum temperature: Optimum temperature (equivalent to the maximum point)
Optimum temperature response: Response at the optimal temperature (equivalent to the maximum point)
Minimal temperature: Temperature that has the lowest response
Minimal temperature response: Lowest predicted response
Predicted maximum basal value: Lower basal limit temperature based on the value set by the user (default is 0)
Predicted minimum basal value: Upper basal limit temperature based on the value set by the user (default is 0)
AIC: Akaike information criterion
BIC: Bayesian Inference Criterion
r-squared: Determination coefficient
RMSE: Root mean square error
grafico: Graph in ggplot2 with equation

Note

Author(s)

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

Examples

library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
normal_model(trat,germ)

#================================
# Germination speed
#================================
normal_model(trat, vel, ylab=expression("v"~(dias^-1)))
library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
normal_model(trat,germ)

#================================
# Germination speed
#================================
normal_model(trat, vel, ylab=expression("v"~(dias^-1)))

Analysis: Piecewise regression

Description

Fit a degree 1 spline with 1 knot point where the location of the knot point is unknown.

Usage

piecewise_model(
  trat,
  resp,
  middle = 1,
  CI = FALSE,
  bootstrap.samples = 1000,
  sig.level = 0.05,
  error = "SE",
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  cardinal = 0,
  width.bar = NA,
  legend.position = "top",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)
piecewise_model(
  trat,
  resp,
  middle = 1,
  CI = FALSE,
  bootstrap.samples = 1000,
  sig.level = 0.05,
  error = "SE",
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  theme = theme_classic(),
  cardinal = 0,
  width.bar = NA,
  legend.position = "top",
  textsize = 12,
  pointsize = 4.5,
  linesize = 0.8,
  pointshape = 21,
  font.family = "sans"
)

Arguments

`trat`	Numerical or complex vector with treatments
`resp`	Numerical vector containing the response of the experiment.
`middle`	A scalar in [0,1]. This represents the range that the change-point can occur in. 0 means the change-point must occur at the middle of the range of x-values. 1 means that the change-point can occur anywhere along the range of the x-values.
`CI`	Whether or not a bootstrap confidence interval should be calculated. Defaults to FALSE because the interval takes a non-trivial amount of time to calculate
`bootstrap.samples`	The number of bootstrap samples to take when calculating the CI.
`sig.level`	What significance level to use for the confidence intervals.
`error`	Error bar (It can be SE - default, SD or FALSE)
`ylab`	Variable response name (Accepts the expression() function)
`xlab`	treatments name (Accepts the expression() function)
`theme`	ggplot2 theme (default is theme_classic())
`cardinal`	defines the value of y considered extreme (default considers 0 germination)
`width.bar`	bar width
`legend.position`	legend position (default is c(0.3,0.8))
`textsize`	Font size
`pointsize`	shape size
`linesize`	line size
`pointshape`	format point (default is 21)
`font.family`	Font family (default is sans)

Value

Coefficients: Coefficients and their p values
Optimum temperature: Optimum temperature (equivalent to the maximum point)
Optimum temperature response: Response at the optimal temperature (equivalent to the maximum point)
Minimal temperature: Temperature that has the lowest response
Minimal temperature response: Lowest predicted response
Predicted maximum basal value: Lower basal limit temperature based on the value set by the user (default is 0)
Predicted minimum basal value: Upper basal limit temperature based on the value set by the user (default is 0)
AIC: Akaike information criterion
BIC: Bayesian Inference Criterion
r-squared: Determination coefficient
RMSE: Root mean square error
grafico: Graph in ggplot2 with equation

Note

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.

Examples

library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
piecewise_model(trat,germ)

#================================
# Germination speed
#================================
piecewise_model(trat, vel, ylab=expression("v"~(dias^-1)))
library(seedreg)
data("aristolochia")
attach(aristolochia)

#================================
# Germination
#================================
piecewise_model(trat,germ)

#================================
# Germination speed
#================================
piecewise_model(trat, vel, ylab=expression("v"~(dias^-1)))

Analysis: generalized linear models for factor qualitative

Description

Performs the deviance analysis for the generalized linear model using binomial or quasibinomial family. The function also returns multiple comparison test with tukey adjustment

Usage

quali_model(
  trat,
  resp,
  method = "glm",
  n = 50,
  family = "binomial",
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  reversed = TRUE,
  angle = 0,
  sup = NA,
  theme = theme_classic(),
  font.family = "sans",
  geom = "bar"
)
quali_model(
  trat,
  resp,
  method = "glm",
  n = 50,
  family = "binomial",
  ylab = "Germination (%)",
  xlab = expression("Temperature ("^"o" * "C)"),
  reversed = TRUE,
  angle = 0,
  sup = NA,
  theme = theme_classic(),
  font.family = "sans",
  geom = "bar"
)

Arguments

`trat`	Numerical or complex vector with treatments
`resp`	Numerical vector containing the response in percentage of the experiment.
`method`	method for analysis (analysis of variance - aov or analysis by generalized linear model - glm)
`n`	Number of seeds per repetition
`family`	a description of the error distribution and link function to be used in the model. For glm this can be a character string naming a family function, a family function or the result of a call to a family function.
`ylab`	Variable response name (Accepts the expression() function)
`xlab`	treatments name (Accepts the expression() function)
`reversed`	Letter order (default is FALSE)
`angle`	x-axis scale text rotation
`sup`	Number of units above the standard deviation or average bar on the graph
`theme`	ggplot2 theme (default is theme_bw())
`font.family`	Font family (default is sans)
`geom`	type of graph ("bar" or "point")

Value

The function returns analysis by glm (binomial or quasibinomial family), post-hoc and column graph

Examples

library(seedreg)
data("aristolochia")
attach(aristolochia)
quali_model(trat, germ, n=25, family="quasibinomial")
library(seedreg)
data("aristolochia")
attach(aristolochia)
quali_model(trat, germ, n=25, family="quasibinomial")

Param: Seeds

Description

Simplification of functions: acc, iv, tm and tml.

Usage

seeds(data, trat, nrep, time)
seeds(data, trat, nrep, time)

Arguments

`data`	Data.frame containing the responses of the evaluations in separate columns side by side and without the columns with the identification of the factors
`trat`	Vector of treatments with n repetitions
`nrep`	Number of repetitions
`time`	Vector containing time

Value

Returns a data.frame with the indices

Examples

data("substrate")
seeds(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)
data("substrate")
seeds(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)

dataset: substrate

Description

The data come from an experiment carried out at the Universidade Estadual de Londrina, in which four types of substrates were tested in the emergence of sour passion fruit seeds. The experiment was carried out in a completely randomized design with four replications of 10 seeds each.

Usage

data("substrate")
data("substrate")

Format

data.frame containing data set

Trat: Vector with factor 1
bloco: Vector with block
1,2,3...: Numerical vector with germination

Examples

data(substrate)
data(substrate)

Param: Average time

Description

Calculates the average germination/emergence time according to Silva and Nakagawa (1995)

Usage

tm(data, trat, nrep, time)
tm(data, trat, nrep, time)

Arguments

`data`	data.frame containing the responses of the evaluations in separate columns side by side and without the columns with the identification of the factors
`trat`	vector of treatments with n repetitions
`nrep`	Number of repetitions
`time`	vector containing time

Value

Returns the vector with the average time.

References

SILVA, J. B. C.; NAKAGAWA, J. Estudos de formulas para calculo de germinacao. Informativo ABRATES, Londrina, v. 5, n. 1, p. 62-73, 1995.

Examples

data("substrate")
tm(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)
data("substrate")
tm(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)

Param: Logistic average time

Description

Param: Logistic average time

Usage

tml(dados, trat, nrep, time)
tml(dados, trat, nrep, time)

Arguments

`dados`	Data.frame containing the responses of the evaluations in separate columns side by side and without the columns with the identification of the factors
`trat`	Vector of treatments with n repetitions
`nrep`	Number of repetitions
`time`	Vector containing time

Value

Returns the vector with the average time.

Examples

data("substrate")
tml(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)
data("substrate")
tml(substrate[,c(3:18)],
      trat = substrate$Trat,
      nrep = 4,
      time = 1:16)

Package 'seedreg'

Help Index

Param: Area below the curve

Description

Usage

Arguments

Value

Examples

dataset: aristolochia

Description

Usage

Format

Author(s)

Examples

Analysis: Logistic regression Brain-Cousens hormesis models

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

Examples

Analysis: Logistic regression Cedergreen-Ritz-Streibig model

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

Examples

Comparison: correlation between parameters

Description

Usage

Arguments

Value

Examples

Analysis: Logistic regression by treatment over time

Description

Usage

Arguments

Value

Examples

Param: Index for germination speed

Description

Usage

Arguments

Value

References

Examples

Graph: line chart

Description

Usage

Arguments

Value

Examples

Analysis: Logistic regression

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

Examples

Analysis: Linear regression graph

Description

Usage

Arguments

Value

Note

Author(s)

Examples

Analysis: loess regression

Description

Usage