Description Usage Arguments Value Author(s) References See Also Examples
The function performs analysis of some linear and nonlinear models
1 2 |
data |
data is a data.frame The first column should contain the treatments (explanatory variable) and the remaining columns the response variables. |
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 values of the iteration process |
mixed |
FALSE/defalt for fixed model or TRUE for mixed model |
digits |
number of digits in results (default=6) |
alpha |
significant level of the confident intervals for parameters in the models |
Returns coefficients of the models, t test for coefficients, R squared, adjusted R squared, AIC, BIC, and residuals of the model
Emmanuel Arnhold <emmanuelarnhold@yahoo.com.br>
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, nls2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | # weights of an 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
er1(data1, model=1)
# quadratic
er1(data1, model=2)
# linear plateau
er1(data1, model=3)
# quadratic plateau
er1(data1, model=4)
# two linear
er1(data1, model=5, start=c(250,6,2,50))
# exponential
er1(data1, model=6, start=c(250,0.05))
# logistic
er1(data1, model=7, start=c(600,4,0.05))
# van bertalanffy
er1(data1, model=8, start=c(600,2,0.05))
# brody
er1(data1, model=9, start=c(600,4,0.05))
# gompertz
er1(data1, model=10, start=c(600,4,0.05))
# richards
er1(data1, model=16, start=c(600,2,0.05,1.4))
# allometric
er1(data1, model=14)
# cubic
er1(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
er1(data2, model=5, start=c(25,6,10,20))
# gain weight 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
er1(data3, model=1)
# quadratic
er1(data3, model=2)
# linear plateau
er1(data3, model=3)
# quadratic plateau
er1(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)
er1(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)
er1(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)
er1(data6, model=13, start=c(19,4,0.025,0.004,5))
# 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])
er1(halibut,model=17, start=c(a=t1,b=t2,c=0.15,d=-0.50))
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