希尔方程曲线拟合 NLS
Hill Equation Curve Fitting NLS
我正在尝试计算以下数据的比率。我尝试了 Michaelis menten 方程,但是,Km 变为负值。我现在正在尝试拟合希尔方程,但收到错误消息。我认为我的起始值不太好。任何帮助将不胜感激。
谢谢,
克里纳
x<- c(0.0, 2.5, 5.0, 10.0, 25.0)
y <- c(4.91, 1.32, 1.18, 1.12, 1.09)
fo <- y~(Emax*(x^hill)/((EC50^hill)+(x^hill)))
st <- c(Emax=1.06, EC50=0.5, hill=1)
fit <- nls(fo, data = data.frame(x, y), start = st, trace = T)
Error in numericDeriv(form[[3L]], names(ind), env) :
Missing value or an infinity produced when evaluating the model
我能够使用 drc 库中的对数逻辑模型得到很好的拟合。但是,我无法找到该模型的参数定义。是不是类似于对数变换的hill模型?
library(drc)
fit.ll <- drm(y~x, data=data.frame(x,y), fct=LL.5(), type="continuous")
print(summary(fit.ll))
plot(fit.ll)
我将您发布的数据拟合到数百个已知的命名方程中,使用遗传算法进行初始参数估计,并发现非常适合简单的幂律方程,如下所示(另请参见附图):
y = (a + x)b + Offset
a = 3.6792869983309306E-01
b = -1.3439157691306818E+00
Offset = 1.0766655470363218E+00
Degrees of freedom (error): 2
Degrees of freedom (regression): 2
Chi-squared: 1.98157151386e-06
R-squared: 0.999999822702
R-squared adjusted: 0.999999645405
Model F-statistic: 5640229.45337
Model F-statistic p-value: 1.77297720061e-07
Model log-likelihood: 29.7579529506
AIC: -10.7031811802
BIC: -10.9375184328
Root Mean Squared Error (RMSE): 0.000629534989315
a = 3.6792869983309306E-01
std err: 2.36769E-06
t-stat: 2.39112E+02
p-stat: 1.74898E-05
95% confidence intervals: [3.61308E-01, 3.74549E-01]
b = -1.3439157691306818E+00
std err: 2.91468E-05
t-stat: -2.48929E+02
p-stat: 1.61375E-05
95% confidence intervals: [-1.36714E+00, -1.32069E+00]
Offset = 1.0766655470363218E+00
std err: 9.37265E-07
t-stat: 1.11211E+03
p-stat: 8.08538E-07
95% confidence intervals: [1.07250E+00, 1.08083E+00]
Coefficient Covariance Matrix
[ 2.38970842 -8.3732707 1.30483649]
[ -8.3732707 29.41789844 -4.52058247]
[ 1.30483649 -4.52058247 0.94598199]
我正在尝试计算以下数据的比率。我尝试了 Michaelis menten 方程,但是,Km 变为负值。我现在正在尝试拟合希尔方程,但收到错误消息。我认为我的起始值不太好。任何帮助将不胜感激。
谢谢, 克里纳
x<- c(0.0, 2.5, 5.0, 10.0, 25.0)
y <- c(4.91, 1.32, 1.18, 1.12, 1.09)
fo <- y~(Emax*(x^hill)/((EC50^hill)+(x^hill)))
st <- c(Emax=1.06, EC50=0.5, hill=1)
fit <- nls(fo, data = data.frame(x, y), start = st, trace = T)
Error in numericDeriv(form[[3L]], names(ind), env) :
Missing value or an infinity produced when evaluating the model
我能够使用 drc 库中的对数逻辑模型得到很好的拟合。但是,我无法找到该模型的参数定义。是不是类似于对数变换的hill模型?
library(drc)
fit.ll <- drm(y~x, data=data.frame(x,y), fct=LL.5(), type="continuous")
print(summary(fit.ll))
plot(fit.ll)
我将您发布的数据拟合到数百个已知的命名方程中,使用遗传算法进行初始参数估计,并发现非常适合简单的幂律方程,如下所示(另请参见附图):
y = (a + x)b + Offset
a = 3.6792869983309306E-01
b = -1.3439157691306818E+00
Offset = 1.0766655470363218E+00
Degrees of freedom (error): 2
Degrees of freedom (regression): 2
Chi-squared: 1.98157151386e-06
R-squared: 0.999999822702
R-squared adjusted: 0.999999645405
Model F-statistic: 5640229.45337
Model F-statistic p-value: 1.77297720061e-07
Model log-likelihood: 29.7579529506
AIC: -10.7031811802
BIC: -10.9375184328
Root Mean Squared Error (RMSE): 0.000629534989315
a = 3.6792869983309306E-01
std err: 2.36769E-06
t-stat: 2.39112E+02
p-stat: 1.74898E-05
95% confidence intervals: [3.61308E-01, 3.74549E-01]
b = -1.3439157691306818E+00
std err: 2.91468E-05
t-stat: -2.48929E+02
p-stat: 1.61375E-05
95% confidence intervals: [-1.36714E+00, -1.32069E+00]
Offset = 1.0766655470363218E+00
std err: 9.37265E-07
t-stat: 1.11211E+03
p-stat: 8.08538E-07
95% confidence intervals: [1.07250E+00, 1.08083E+00]
Coefficient Covariance Matrix
[ 2.38970842 -8.3732707 1.30483649]
[ -8.3732707 29.41789844 -4.52058247]
[ 1.30483649 -4.52058247 0.94598199]