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 - |

`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 |

### 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)
```

*AgroReg*version 1.2.10 Index]