linear.linear {AgroReg} | R Documentation |

This function performs linear linear regression analysis.

```
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"
)
```

`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 - |

`ylab` |
Variable response name (Accepts the |

`xlab` |
treatments name (Accepts the |

`theme` |
ggplot2 theme ( |

`point` |
defines whether you want to plot all points ("all") or only the mean ("mean") |

`width.bar` |
Bar width |

`legend.position` |
legend position ( |

`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 |

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)`

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.

Model imported from the SiZer package

Gabriel Danilo Shimizu

Leandro Simoes Azeredo Goncalves

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.

quadratic.plateau, linear.plateau

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

[Package *AgroReg* version 1.2.9 Index]