bl {easyreg}

Analysis of broken line regression

Description

The function performs analysis of broken line regression

Usage

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 <[e-mail]>

References

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

See Also

lm, ea1(easyanova package), er1

Examples

# 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)