linear.linear {AgroReg} R Documentation

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"
)


Arguments

 trat Numeric vector with dependent variable. resp Numeric vector with independent variable. 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()) point defines whether you want to plot all points ("all") or only the mean ("mean") width.bar Bar width legend.position legend position (default is "top") textsize Font size pointsize shape size linesize line size linetype line type pointshape format point (default is 21) fillshape Fill shape colorline Color lines round round equation xname.formula Name of x in the equation yname.formula Name of y in the equation comment Add text after equation fontfamily Font family

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.

library(AgroReg)