nls 奇异梯度矩阵 - 在积分的上限拟合参数
nls singular gradient matrix - fit parameters in integral's upper limits
我正在尝试 nls
拟合一个有点复杂的表达式,该表达式包含两个积分,其中两个拟合参数处于上限。
我收到错误消息
"Error in nlsModel(formula, mf, start, wts) : singular gradient
matrix at initial parameter estimates".
我已经在以前的答案中搜索过,但没有帮助。参数初始化似乎没问题,我尝试更改参数但 none 有效。如果我的函数只有一个积分,一切都很好,但是当添加第二个积分项时,就会出现错误。我不认为该函数参数化过度,因为我已经用更多参数执行了其他拟合并且它们起作用了。下面我写了一个包含一些数据的列表。
最小示例如下:
integrand <- function(X) {
return(X^4/(2*sinh(X/2))^2)
}
fitting = function(T1, T2, N, D, x){
int1 = integrate(integrand, lower=0, upper = T1)$value
int2 = integrate(integrand, lower=0, upper = T2)$value
return(N*(D/x)^2*(exp(D/x)/(1+exp(D/x))^2
)+(448.956*(x/T1)^3*int1)+(299.304*(x/T2)^3*int2))
}
fit = nls(y ~ fitting(T1, T2, N, D, x),
start=list(T1=400,T2=200,N=0.01,D=2))
------>作为参考,有效的配合如下:
integrand <- function(X) {
return(X^4/(2*sinh(X/2))^2)
}
fitting = function(T1, N, D, x){
int = integrate(integrand, lower=0, upper = T1)$value
return(N*(D/x)^2*(exp(D/x)/(1+exp(D/x))^2 )+(748.26)*(x/T1)^3*int)
}
fit = nls(y ~ fitting(T1 , N, D, x), start=list(T1=400,N=0.01,D=2))
-------->数据说明问题:
dat<- read.table(text="x y
0.38813 0.0198
0.79465 0.02206
1.40744 0.01676
1.81532 0.01538
2.23105 0.01513
2.64864 0.01547
3.05933 0.01706
3.47302 0.01852
3.88791 0.02074
4.26301 0.0256
4.67607 0.03028
5.08172 0.03507
5.48327 0.04283
5.88947 0.05017
6.2988 0.05953
6.7022 0.07185
7.10933 0.08598
7.51924 0.0998
7.92674 0.12022
8.3354 0.1423
8.7384 0.16382
9.14656 0.19114
9.55062 0.22218
9.95591 0.25542", header=TRUE)
我不知道发生了什么。我需要对三个组成部分执行此拟合,但即使是两个我也有这个问题。非常感谢你的帮助。谢谢。
您可以尝试其他一些优化器:
fitting1 <- function(par, x, y) {
sum((fitting(par[1], par[2], par[3], par[4], x) - y)^2)
}
library(optimx)
res <- optimx(c(400, 200, 0.01, 2),
fitting1,
x = DF$x, y = DF$y,
control = list(all.methods = TRUE))
print(res)
# p1 p2 p3 p4 value fevals gevals niter convcode kkt1 kkt2 xtimes
#BFGS 409.7992 288.6416 -0.7594461 39.00871 1.947484e-03 101 100 NA 1 NA NA 0.22
#CG 401.1281 210.9087 -0.9026459 20.80900 3.892929e-01 215 101 NA 1 NA NA 0.25
#Nelder-Mead 414.6402 446.5080 -1.1298606 -227.81280 2.064842e-03 89 NA NA 0 NA NA 0.02
#L-BFGS-B 412.4477 333.1338 -0.3650530 37.74779 1.581643e-03 34 34 NA 0 NA NA 0.06
#nlm 411.8639 333.4776 -0.3652356 37.74855 1.581644e-03 NA NA 45 0 NA NA 0.04
#nlminb 411.9678 333.4449 -0.3650271 37.74753 1.581643e-03 50 268 48 0 NA NA 0.07
#spg 422.0394 300.5336 -0.5776862 38.48655 1.693119e-03 1197 NA 619 0 NA NA 1.06
#ucminf 412.7390 332.9228 -0.3652029 37.74829 1.581644e-03 45 45 NA 0 NA NA 0.05
#Rcgmin NA NA NA NA 8.988466e+307 NA NA NA 9999 NA NA 0.00
#Rvmmin NA NA NA NA 8.988466e+307 NA NA NA 9999 NA NA 0.00
#newuoa 396.3071 345.1165 -0.3650286 37.74754 1.581643e-03 3877 NA NA 0 NA NA 1.02
#bobyqa 410.0392 334.7074 -0.3650289 37.74753 1.581643e-03 7866 NA NA 0 NA NA 2.07
#nmkb 569.0139 346.0856 282.6526588 -335.32320 2.064859e-03 75 NA NA 0 NA NA 0.01
#hjkb 400.0000 200.0000 0.0100000 2.00000 3.200269e+00 1 NA 0 9999 NA NA 0.01
Levenberg-Marquardt 也收敛了,但是 nlsLM
尝试从结果创建 nls
模型对象时失败,因为梯度矩阵是奇异的:
library(minpack.lm)
fit <- nlsLM(y ~ fitting(T1, T2, N, D, x),
start=list(T1=412,T2=333,N=-0.36,D=38), data = DF, trace = TRUE)
#It. 0, RSS = 0.00165827, Par. = 412 333 -0.36 38
#It. 1, RSS = 0.00158186, Par. = 417.352 329.978 -0.3652 37.746
#It. 2, RSS = 0.00158164, Par. = 416.397 330.694 -0.365025 37.7475
#It. 3, RSS = 0.00158164, Par. = 416.618 330.568 -0.365027 37.7475
#It. 4, RSS = 0.00158164, Par. = 416.618 330.568 -0.365027 37.7475
#Error in nlsModel(formula, mf, start, wts) :
# singular gradient matrix at initial parameter estimates
我正在尝试 nls
拟合一个有点复杂的表达式,该表达式包含两个积分,其中两个拟合参数处于上限。
我收到错误消息
"Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates".
我已经在以前的答案中搜索过,但没有帮助。参数初始化似乎没问题,我尝试更改参数但 none 有效。如果我的函数只有一个积分,一切都很好,但是当添加第二个积分项时,就会出现错误。我不认为该函数参数化过度,因为我已经用更多参数执行了其他拟合并且它们起作用了。下面我写了一个包含一些数据的列表。
最小示例如下:
integrand <- function(X) {
return(X^4/(2*sinh(X/2))^2)
}
fitting = function(T1, T2, N, D, x){
int1 = integrate(integrand, lower=0, upper = T1)$value
int2 = integrate(integrand, lower=0, upper = T2)$value
return(N*(D/x)^2*(exp(D/x)/(1+exp(D/x))^2
)+(448.956*(x/T1)^3*int1)+(299.304*(x/T2)^3*int2))
}
fit = nls(y ~ fitting(T1, T2, N, D, x),
start=list(T1=400,T2=200,N=0.01,D=2))
------>作为参考,有效的配合如下:
integrand <- function(X) {
return(X^4/(2*sinh(X/2))^2)
}
fitting = function(T1, N, D, x){
int = integrate(integrand, lower=0, upper = T1)$value
return(N*(D/x)^2*(exp(D/x)/(1+exp(D/x))^2 )+(748.26)*(x/T1)^3*int)
}
fit = nls(y ~ fitting(T1 , N, D, x), start=list(T1=400,N=0.01,D=2))
-------->数据说明问题:
dat<- read.table(text="x y
0.38813 0.0198
0.79465 0.02206
1.40744 0.01676
1.81532 0.01538
2.23105 0.01513
2.64864 0.01547
3.05933 0.01706
3.47302 0.01852
3.88791 0.02074
4.26301 0.0256
4.67607 0.03028
5.08172 0.03507
5.48327 0.04283
5.88947 0.05017
6.2988 0.05953
6.7022 0.07185
7.10933 0.08598
7.51924 0.0998
7.92674 0.12022
8.3354 0.1423
8.7384 0.16382
9.14656 0.19114
9.55062 0.22218
9.95591 0.25542", header=TRUE)
我不知道发生了什么。我需要对三个组成部分执行此拟合,但即使是两个我也有这个问题。非常感谢你的帮助。谢谢。
您可以尝试其他一些优化器:
fitting1 <- function(par, x, y) {
sum((fitting(par[1], par[2], par[3], par[4], x) - y)^2)
}
library(optimx)
res <- optimx(c(400, 200, 0.01, 2),
fitting1,
x = DF$x, y = DF$y,
control = list(all.methods = TRUE))
print(res)
# p1 p2 p3 p4 value fevals gevals niter convcode kkt1 kkt2 xtimes
#BFGS 409.7992 288.6416 -0.7594461 39.00871 1.947484e-03 101 100 NA 1 NA NA 0.22
#CG 401.1281 210.9087 -0.9026459 20.80900 3.892929e-01 215 101 NA 1 NA NA 0.25
#Nelder-Mead 414.6402 446.5080 -1.1298606 -227.81280 2.064842e-03 89 NA NA 0 NA NA 0.02
#L-BFGS-B 412.4477 333.1338 -0.3650530 37.74779 1.581643e-03 34 34 NA 0 NA NA 0.06
#nlm 411.8639 333.4776 -0.3652356 37.74855 1.581644e-03 NA NA 45 0 NA NA 0.04
#nlminb 411.9678 333.4449 -0.3650271 37.74753 1.581643e-03 50 268 48 0 NA NA 0.07
#spg 422.0394 300.5336 -0.5776862 38.48655 1.693119e-03 1197 NA 619 0 NA NA 1.06
#ucminf 412.7390 332.9228 -0.3652029 37.74829 1.581644e-03 45 45 NA 0 NA NA 0.05
#Rcgmin NA NA NA NA 8.988466e+307 NA NA NA 9999 NA NA 0.00
#Rvmmin NA NA NA NA 8.988466e+307 NA NA NA 9999 NA NA 0.00
#newuoa 396.3071 345.1165 -0.3650286 37.74754 1.581643e-03 3877 NA NA 0 NA NA 1.02
#bobyqa 410.0392 334.7074 -0.3650289 37.74753 1.581643e-03 7866 NA NA 0 NA NA 2.07
#nmkb 569.0139 346.0856 282.6526588 -335.32320 2.064859e-03 75 NA NA 0 NA NA 0.01
#hjkb 400.0000 200.0000 0.0100000 2.00000 3.200269e+00 1 NA 0 9999 NA NA 0.01
Levenberg-Marquardt 也收敛了,但是 nlsLM
尝试从结果创建 nls
模型对象时失败,因为梯度矩阵是奇异的:
library(minpack.lm)
fit <- nlsLM(y ~ fitting(T1, T2, N, D, x),
start=list(T1=412,T2=333,N=-0.36,D=38), data = DF, trace = TRUE)
#It. 0, RSS = 0.00165827, Par. = 412 333 -0.36 38
#It. 1, RSS = 0.00158186, Par. = 417.352 329.978 -0.3652 37.746
#It. 2, RSS = 0.00158164, Par. = 416.397 330.694 -0.365025 37.7475
#It. 3, RSS = 0.00158164, Par. = 416.618 330.568 -0.365027 37.7475
#It. 4, RSS = 0.00158164, Par. = 416.618 330.568 -0.365027 37.7475
#Error in nlsModel(formula, mf, start, wts) :
# singular gradient matrix at initial parameter estimates