Data Engineering and BI courses are free this week!
The function plot data and equation
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",...)data is a data.frame The first column contain the treatments (explanatory variable) and the remaining column the response variable
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 (iterations) values of model
names of variable x
names of variable y
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
number of digits (defalt=6)
mean=TRUE (plot mean of data) mean=FALSE (plot all data)
sd=FALSE (plot without standard deviation) sd=TRUE (plot with standard deviation)
legend=TRUE (plot legend) legend=FALSE (not plot legend)
line type
line color
point type
limits for x
limits for y
others graphical parameters (see par)
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.
nls,er1,er2,bl
# 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))
# }
Run the code above in your browser using DataLab