easyreg (version 4.0)

regplot: Plot data and equation

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)

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

# NOT RUN {
# 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))


# }