| Title: | Easy Nonlinear Model |
|---|---|
| Description: | Fit and plot some nonlinear models. |
| Authors: | Emmanuel Arnhold |
| Maintainer: | Emmanuel Arnhold <[email protected]> |
| License: | GPL-2 |
| Version: | 5.0 |
| Built: | 2024-12-03 06:40:04 UTC |
| Source: | CRAN |
The package fit and plot some nonlinear models
| Package: | easynls |
| Type: | Package |
| Version: | 5.0 |
| Date: | 2017-11-14 |
| License: | GPL-2 |
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
time=c(2,6,9,24,48,72,96) deg=c(20,33,46,55,66,72,76) data5=data.frame(time,deg) nlsfit(data5, model=12)time=c(2,6,9,24,48,72,96) deg=c(20,33,46,55,66,72,76) data5=data.frame(time,deg) nlsfit(data5, model=12)
The function fit some nonlinear models
nlsfit(data, model = 1, start = c(a = 1, b = 1, c = 1, d = 1, e = 1))nlsfit(data, model = 1, start = c(a = 1, b = 1, c = 1, d = 1, e = 1))
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 |
start |
start iterations values of model |
Returns coefficients of the models, t test for coefficients, R squared, adjusted R squared, AIC, BIC and the maximum (or minimum) values of y and critical point of x
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
nls, nls2
# data represent 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 nlsfit(data1, model=1) # quadratic nlsfit(data1, model=2) # linear plateau nlsfit(data1, model=3) # quadratic plateau nlsfit(data1, model=4) # two linear nlsfit(data1, model=5, start=c(250,6,2,50)) # exponential nlsfit(data1, model=6, start=c(250,0.05)) # logistic nlsfit(data1, model=7, start=c(600,4,0.05)) # van bertalanffy nlsfit(data1, model=8, start=c(600,2,0.05)) # brody nlsfit(data1, model=9, start=c(600,4,0.05)) # gompertz nlsfit(data1, model=10, start=c(600,4,0.05)) # describe the 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 nlsfit(data2, model=5, start=c(25,6,10,20)) # using segmented regression to estimate a plateau # the requirement for the methionine will be estimated measurements of gain 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 nlsfit(data3, model=1) # quadratic nlsfit(data3, model=2) # linear plateau nlsfit(data3, model=3) # quadratic plateau nlsfit(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) nlsfit(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) nlsfit(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) nlsfit(data6, model=13, start=c(19,4,0.025,0.004,5))# data represent 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 nlsfit(data1, model=1) # quadratic nlsfit(data1, model=2) # linear plateau nlsfit(data1, model=3) # quadratic plateau nlsfit(data1, model=4) # two linear nlsfit(data1, model=5, start=c(250,6,2,50)) # exponential nlsfit(data1, model=6, start=c(250,0.05)) # logistic nlsfit(data1, model=7, start=c(600,4,0.05)) # van bertalanffy nlsfit(data1, model=8, start=c(600,2,0.05)) # brody nlsfit(data1, model=9, start=c(600,4,0.05)) # gompertz nlsfit(data1, model=10, start=c(600,4,0.05)) # describe the 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 nlsfit(data2, model=5, start=c(25,6,10,20)) # using segmented regression to estimate a plateau # the requirement for the methionine will be estimated measurements of gain 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 nlsfit(data3, model=1) # quadratic nlsfit(data3, model=2) # linear plateau nlsfit(data3, model=3) # quadratic plateau nlsfit(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) nlsfit(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) nlsfit(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) nlsfit(data6, model=13, start=c(19,4,0.025,0.004,5))
The function plot some nonlinear models
nlsplot(data, model = 1, start = c(a = 1, b = 1, c = 1, d = 1, e = 1), xlab = "Explanatory Variable" , ylab = "Response Variable", position = 1)nlsplot(data, model = 1, start = c(a = 1, b = 1, c = 1, d = 1, e = 1), xlab = "Explanatory Variable" , ylab = "Response Variable", position = 1)
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 |
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 |
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
nlsfit, nls,nls2
# 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 nlsplot(data1, model=1) # quadratic nlsplot(data1, model=2) # linear plateau nlsplot(data1, model=3) # quadratic plateau nlsplot(data1, model=4) # two linear nlsplot(data1, model=5, start=c(250,6,2,50)) # exponential nlsplot(data1, model=6, start=c(250,0.05)) # logistic nlsplot(data1, model=7, start=c(600,4,0.05)) # van bertalanffy nlsplot(data1, model=8, start=c(600,2,0.05)) # brody nlsplot(data1, model=9, start=c(600,4,0.05)) # gompertz nlsplot(data1, model=10, start=c(600,4,0.05)) # 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 nlsplot(data2, model=5, start=c(25,6,10,20)) # using segmented regression to estimate a plateau # requirement for the methionine will be estimated measurements gain 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 nlsplot(data3, model=1) # quadratic nlsplot(data3, model=2) # linear plateau nlsplot(data3, model=3) # quadratic plateau nlsplot(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) nlsplot(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) nlsplot(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) nlsplot(data6, model=13, start=c(19,4,0.025,0.004,5))# 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 nlsplot(data1, model=1) # quadratic nlsplot(data1, model=2) # linear plateau nlsplot(data1, model=3) # quadratic plateau nlsplot(data1, model=4) # two linear nlsplot(data1, model=5, start=c(250,6,2,50)) # exponential nlsplot(data1, model=6, start=c(250,0.05)) # logistic nlsplot(data1, model=7, start=c(600,4,0.05)) # van bertalanffy nlsplot(data1, model=8, start=c(600,2,0.05)) # brody nlsplot(data1, model=9, start=c(600,4,0.05)) # gompertz nlsplot(data1, model=10, start=c(600,4,0.05)) # 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 nlsplot(data2, model=5, start=c(25,6,10,20)) # using segmented regression to estimate a plateau # requirement for the methionine will be estimated measurements gain 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 nlsplot(data3, model=1) # quadratic nlsplot(data3, model=2) # linear plateau nlsplot(data3, model=3) # quadratic plateau nlsplot(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) nlsplot(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) nlsplot(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) nlsplot(data6, model=13, start=c(19,4,0.025,0.004,5))