英文:
Fitting complex non-linear function with nls() in R gives singular gradient error
问题
我正在尝试使用R的nls函数拟合口服药物的药代动力学函数。该函数可以在这里看到：
dat <- data.frame(
Time = c(10, 15, 20, 30, 40, 60, 90, 120, 180, 210, 240, 300, 360),
C_PO = c(0,0.28,0.55,1.2,2,1.95,1.85,1.6,
0.86,0.78,0.6,0.21,0.18))
plot(dat$C_PO ~ dat$Time, data = dat, log = "y")
fit <- nls(C_PO~ka*100* (exp(-k*Time) - exp(-ka*Time))/(v*(ka- k)),
data = dat,
start = list(ka = 0.09, k = 0.01, v = 50))
#> Error in nls(C_PO ~ ka * 100 * (exp(-k * Time) - exp(-ka * Time))/(v * : singular gradient
该代码尝试根据浓度值C_PO随时间段Time进行非线性模型拟合。初始值是使用图形剥离方法计算的。然而，错误消息显示"singular gradient"。如何修复这个问题？
英文:
I am trying to fit oral pharmacokinetic function using R nls. The function can be seen here:
I tried the following:
dat <- data.frame(
Time = c(10, 15, 20, 30, 40, 60, 90, 120, 180, 210, 240, 300, 360),
C_PO = c(0,0.28,0.55,1.2,2,1.95,1.85,1.6,
0.86,0.78,0.6,0.21,0.18))
plot(dat$C_PO ~ dat$Time, data = dat, log = "y")
fit <- nls(C_PO~ka*100* (exp(-k*Time) - exp(-ka*Time))/(v*(ka- k)),
data = dat,
start = list(ka = 0.09, k = 0.01, v = 50))
#> Error in nls(C_PO ~ ka * 100 * (exp(-k * Time) - exp(-ka * Time))/(v * : singular gradient
The code is trying to fit a non-linear model according to the concentration values C_PO over a time period Time. The initial values were calculated using graph stripping methods. However, the error message reads "singular gradient." How can this be fixed?
答案1
得分: 1
你可以使用gslnls包来拟合非线性模型，如下所示：
library(gslnls)
dat <- data.frame(
Time = c(10, 15, 20, 30, 40, 60, 90, 120, 180, 210, 240, 300, 360),
C_PO = c(0, 0.28, 0.55, 1.2, 2, 1.95, 1.85, 1.6,
0.86, 0.78, 0.6, 0.21, 0.18))
plot(dat$C_PO ~ dat$Time, data = dat, log = "y")
fit <- gsl_nls(C_PO ~ ka * 100 * (exp(-k * Time) - exp(-ka * Time))/(v * (ka - k)),
data = dat,
start = c(ka = 0.09, k = 0.01, v = 50),
control = gsl_nls_control(maxiter = 500))
summary(fit)
#> Formula: C_PO ~ ka * 100 * (exp(-k * Time) - exp(-ka * Time))/(v * (ka -
#> k))
#>
#> Parameters:
#> Estimate Std. Error t value Pr(>|t|)
#> ka 0.0132 4.0027 0.003 0.997
#> k 0.0132 4.0025 0.003 0.997
#> v 21.0181 6372.7704 0.003 0.997
#>
#> Residual standard error: 0.3363 on 10 degrees of freedom
#>
#> Number of iterations to convergence: 18
#> Achieved convergence tolerance: 0
英文:
You can use gslnls package for fitting non-linear model like
library(gslnls)
dat <- data.frame(
Time = c(10, 15, 20, 30, 40, 60, 90, 120, 180, 210, 240, 300, 360),
C_PO = c(0,0.28,0.55,1.2,2,1.95,1.85,1.6,
0.86,0.78,0.6,0.21,0.18))
plot(dat$C_PO ~ dat$Time, data = dat, log = "y")
fit <- gsl_nls(C_PO ~ ka*100* (exp(-k*Time) - exp(-ka*Time))/(v*(ka- k)),
data = dat,
start = c(ka = 0.09, k = 0.01, v = 50),
control = gsl_nls_control(maxiter = 500))
summary(fit)
#> Formula: C_PO ~ ka * 100 * (exp(-k * Time) - exp(-ka * Time))/(v * (ka -
#> k))
#>
#> Parameters:
#> Estimate Std. Error t value Pr(>|t|)
#> ka 0.0132 4.0027 0.003 0.997
#> k 0.0132 4.0025 0.003 0.997
#> v 21.0181 6372.7704 0.003 0.997
#>
#> Residual standard error: 0.3363 on 10 degrees of freedom
#>
#> Number of iterations to convergence: 18
#> Achieved convergence tolerance: 0
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