# bl: Analysis of broken line regression In easyreg: Easy Regression

## Description

The function performs analysis of broken line regression

## Usage

 ```1 2 3``` ```bl(data, model=1, alpha=0.05, xlab = "Explanatory Variable", ylab = "Response Variable", position = 1, digits = 6, mean = TRUE, sd=FALSE, legend = TRUE, lty=2, col="dark blue", pch=20, xlim="default.x",ylim="default.y", ...) ```

## Arguments

 `data` data is a data.frame The first column contain the treatments (explanatory variable) and the second column the response variable `model` model for analysis: 1=two linear; 2=linear plateau (LRP); 3= model 1 with blocks random; 4 = model 2 with blocks random `alpha` significant level for cofidence intervals (parameters estimated) `xlab` name of explanatory variable `ylab` name of response variable `position` position of equation in the graph top=1 bottomright=2 bottom=3 bottomleft=4 left=5 topleft=6 (default) topright=7 right=8 center=9 `digits` number of digits (default=6) `mean` mean=TRUE (plot mean of data) mean=FALSE (plot all data) `sd` sd=FALSE (plot without standard deviation) sd=TRUE (plot with standard deviation) `legend` legend=TRUE (plot legend) legend=FALSE (not plot legend) `lty` line type `col` line color `pch` point type `xlim` limits for x `ylim` limits for y `...` others graphical parameters (see par)

## Value

Returns coefficients of the models, t test for coefficients, knot (break point), R squared, adjusted R squared, AIC, BIC, residuals and shapiro-wilk test for residuals.

## Author(s)

Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br>

## References

KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34``` ```# the growth of Zagorje turkeys (Kaps and Lamberson, 2009) weight=c(44,66,100,150,265,370,455,605) age=c(1,7,14,21,28,35,42,49) data2=data.frame(age,weight) # two linear regplot(data2, model=5, start=c(25,6,10,20)) bl(data2, digits=2) #linear and quadratic plateau x=c(0,1,2,3,4,5,6) y=c(1,2,3,6.1,5.9,6,6.1) data=data.frame(x,y) bl(data,model=2, lty=1, col=1, digits=2, position=8) # effect os blocks x=c(1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8) y=c(4,12,9,20,16,25,21,31,28,42,33,46,33,46,34,44) blocks=rep(c(1,2),8) dat=data.frame(x,blocks,y) bl(dat, 3) bl(dat,4, sd=TRUE) bl(dat,4, mean=FALSE) ```