| regplot {easyreg} | R Documentation |
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) |
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.
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))