regplot: Plot data and equation

In easyreg: Easy Regression

Description

The function plot data and equation

Usage

regplot(data, model=1, start=c(a=1,b=1,c=1,d=1,e=1), xlab="Explanatory Variable", 
ylab="Response Variable", position=1, digits=6, mean=TRUE, sd=FALSE, 
legend = TRUE, lty=2, col="dark blue", pch=20,  xlim="defalt.x",ylim="defalt.y",...)

Arguments

data	data is a data.frame The first column contain the treatments (explanatory variable) and the remaining column the response variable
model	define the model 1 = "y~a+b*x" linear 2 = "y~a+b*x+c*x^2" quadratic 3 = "y ~ a + b * (x - c) * (x <= c)" linear plateau 4 = "y ~ (a + b * x + c * I(x^2)) * (x <= -0.5 * b/c) + (a + I(-b^2/(4 * c))) * (x > -0.5 * b/c)" quadratic plateau 5 = "ifelse(x>=d,(a-c*d)+(b+c)*x, a+b*x)" two linear 6 = "y~a*exp(b*x)" exponential 7 = "y~a*(1+b*(exp(-c*x)))^-1" logistic 8 = "y~a*(1-b*(exp(-c*x)))^3" van bertalanffy 9 = "y~a*(1-b*(exp(-c*x)))" brody 10 = "y~a*exp(-b*exp(-c*x)" gompertz 11 = "y~(a*x^b)*exp(-c*x)" lactation curve 12 = "y ~ a + b * (1 - exp(-c * x))" ruminal degradation curve 13 = "y~(a/(1+exp(2-4*c*(x-e))))+(b/(1+exp(2-4*d*(x-e))))" logistic bi-compartmental 14 = "y~a*(x^b)" exponential (allometric model) 15 = "y~a+b*x+c*x^2+d*x^3" cubic 16 = "y~a/(1+b*(exp(-c*x)))^d" richards 17 = "y~(a^d+ ((b^d)-(a^d) )*((1-exp(-c*(x-t1)))/ (1-exp(-c*(t2-t1)))))^(1/d)" schnute
start	start (iterations) values of model
xlab	names of variable x
ylab	names of variable y
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 (defalt=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)

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.

TERRANCE J. QUINN II and RICHARD B. DERISO. Quantitative Fish Dynamics, New York, Oxford, Oxford University Press, 1999.

See Also

nls,er1,er2,bl

Examples

# weights of Angus cow at ages from 8 to 108 months (Kaps and Lamberson, 2009)

weight=c(280,340,430,480,550,580,590,600,590,600)
age=c(8,12,24,36,48,60,72,84,96,108)

data1=data.frame(age, weight)

# linear
regplot(data1, model=1, digits=3, position=3, ylab="weight", xlab="age")

# quadratic
regplot(data1, model=2, digits=3, position=3, col=1, ylim=c(200,700))

# linear plateau
regplot(data1, model=3,ylab="weight", xlab="age", lty=5, col="dark green",
position=3, ylim=c(200,700), xlim=c(0,150), lwd=2)

# quadratic plateau
regplot(data1, model=4,ylab="weight", xlab="age")

# two linear
regplot(data1, model=5, start=c(250,6,2,50),digits=3, position=3 )

# exponential
regplot(data1, model=6, start=c(250,0.05))

# logistic
regplot(data1, model=7, start=c(600,4,0.05))

# van bertalanffy
regplot(data1, model=8, start=c(600,2,0.05))

# brody
regplot(data1, model=9, start=c(600,4,0.05))

# gompertz
regplot(data1, model=10, start=c(600,4,0.05))

# richards
regplot(data1, model=16, start=c(600,2,0.05,1.4))

# allometric
regplot(data1, model=14)

# cubic
regplot(data1, model=15)



# growth of Zagorje turkeys (Kaps and Lamberson, 2009)


weight=c(44,66,100,150,265,370,455,605,770)
age=c(1,7,14,21,28,35,42,49,56)

data2=data.frame(age,weight)

# two linear
regplot(data2, model=5, start=c(25,6,10,20))


# weight gain measurements of turkey poults (Kaps and Lamberson, 2009)

methionine=c(80,85,90,95,100,105,110,115,120)
gain=c(102,115,125,133,140,141,142,140,142)

data3=data.frame(methionine, gain)

# linear
regplot(data3, model=1)

# quadratic
regplot(data3, model=2)

# linear plateau
regplot(data3, model=3)

# quadratic plateau
regplot(data3, model=4)

# lactation curve
 milk=c(25,24,26,28,30,31,27,26,25,24,23,24,22,21,22,20,21,19,
18,17,18,18,16,17,15,16,14)
 days=c(15,15,15,75,75,75,135,135,135,195,195,195,255,255,255,315,
315,315,375,375,375,435,435,435,495,495,495)
    
data4=data.frame(days,milk)
	

regplot(data4, model=11, start=c(16,0.25,0.004))

# ruminal degradation 
time=c(2,6,9,24,48,72,96)
deg=c(20,33,46,55,66,72,76)

data5=data.frame(time,deg)

regplot(data5, model=12)

# logistic bi-compartmental (gas production)
time=c(0,12,24,36,48,60,72,84,96,108,120,144,168,192)
gas=c(0.002,3.8,8,14.5,16,16.5,17,17.4,17.9,18.1,18.8,19,19.2,19.3)
    
data6=data.frame(time,gas)

regplot(data6, model=13, start=c(19,4,0.025,0.004,5))

# multiple curves
time=c(0,12,24,48,64,72,96)
t1=c(36,48,59,72,85,86,87)
t2=c(14,25,36,49,59,65,72)
t3=c(55,78,86,87,86,87,88)

data=data.frame(time,t1,t2,t3)

regplot(data, model=12)
regplot(data, model=4)

# include standard deviation in graph
data(data1)

regplot(data1, sd=TRUE)

# Schnute model
#pacific halibut weight-age data of females (Terrance and Richard, 1999)
 
age=c(4,5,6,7,8,9,10,11,12,13,14,15,16,17,
18,19,20,21,22,23,24,28)
weight=c(1.7,2,3.9, 4.2,6.4,7.6,10.9,14.9,18.2,21.6,25.4,28.8,
30.9,	35.6,37.9,34.7,44.8,52.6,49.1,56.7,58.6,54.1)

halibut=data.frame(age,weight)


t1=min(halibut[,2])
t2=max(halibut[,2])

regplot(halibut,model=17,start=c(t1,t2,0.22,-0.63), ylim=c(0,100))

easyreg documentation built on Sept. 13, 2019, 5:04 p.m.