nls() : “Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates ”Modifying a curve to prevent singular gradient matrix at initial parameter estimatesHow to fix 'singular gradient matrix at initial parameter estimates' errorError: singular gradient matrix at initial parameter estimateswrapnls: Error: singular gradient matrix at initial parameter estimatesnls singular gradient matrix - fit parameters in integral's upper limitsFit 'nls': singular gradient matrix at initial parameter estimatesR and NLS: singular gradient matrix at initial parameterHow to solve singular gradient matrix error?Using R to fit: y ~ exp(-(x-x0)^a)R: nls() error. “singular gradient matrix at initial parameter estimates”
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nls() : “Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates ”
Modifying a curve to prevent singular gradient matrix at initial parameter estimatesHow to fix 'singular gradient matrix at initial parameter estimates' errorError: singular gradient matrix at initial parameter estimateswrapnls: Error: singular gradient matrix at initial parameter estimatesnls singular gradient matrix - fit parameters in integral's upper limitsFit 'nls': singular gradient matrix at initial parameter estimatesR and NLS: singular gradient matrix at initial parameterHow to solve singular gradient matrix error?Using R to fit: y ~ exp(-(x-x0)^a)R: nls() error. “singular gradient matrix at initial parameter estimates”
I'm trying to use nls(), but I keep getting the error
Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates
and I'm not sure where the problem is.
Code below:
TI <- c(0.5, 2, 5, 10, 30)
prices <- cbind(zi, TI)
prices = as.data.frame(prices)
lnz_i <- function(TI, Alpha, Beta, Sigma) -TI*(Alpha*(1 - exp(-Beta*TI)) / (Beta) - (Sigma^2/2)*(1 - exp(-Beta*TI)) / (Beta)^2) - 0.02*(1 - exp(-Beta*TI)) / (Beta)
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3, Sigma = 0.06), data = prices)
Any help is greatly appreciated.
r optimization nls
asked Mar 21 at 21:10
Chris AguilarChris Aguilar
84
add a comment |
I'm trying to use nls(), but I keep getting the error
Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates
and I'm not sure where the problem is.
Code below:
TI <- c(0.5, 2, 5, 10, 30)
prices <- cbind(zi, TI)
prices = as.data.frame(prices)
lnz_i <- function(TI, Alpha, Beta, Sigma) -TI*(Alpha*(1 - exp(-Beta*TI)) / (Beta) - (Sigma^2/2)*(1 - exp(-Beta*TI)) / (Beta)^2) - 0.02*(1 - exp(-Beta*TI)) / (Beta)
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3, Sigma = 0.06), data = prices)
Any help is greatly appreciated.
r optimization nls
asked Mar 21 at 21:10
Chris AguilarChris Aguilar
84
ziis missing.
– G. Grothendieck
Mar 22 at 2:21
add a comment |
I'm trying to use nls(), but I keep getting the error
Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates
and I'm not sure where the problem is.
Code below:
TI <- c(0.5, 2, 5, 10, 30)
prices <- cbind(zi, TI)
prices = as.data.frame(prices)
lnz_i <- function(TI, Alpha, Beta, Sigma) -TI*(Alpha*(1 - exp(-Beta*TI)) / (Beta) - (Sigma^2/2)*(1 - exp(-Beta*TI)) / (Beta)^2) - 0.02*(1 - exp(-Beta*TI)) / (Beta)
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3, Sigma = 0.06), data = prices)
Any help is greatly appreciated.
r optimization nls
asked Mar 21 at 21:10
Chris AguilarChris Aguilar
84
I'm trying to use nls(), but I keep getting the error
Error in nlsModel(formula, mf, start, wts) : singular gradient matrix at initial parameter estimates
and I'm not sure where the problem is.
Code below:
TI <- c(0.5, 2, 5, 10, 30)
prices <- cbind(zi, TI)
prices = as.data.frame(prices)
lnz_i <- function(TI, Alpha, Beta, Sigma) -TI*(Alpha*(1 - exp(-Beta*TI)) / (Beta) - (Sigma^2/2)*(1 - exp(-Beta*TI)) / (Beta)^2) - 0.02*(1 - exp(-Beta*TI)) / (Beta)
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3, Sigma = 0.06), data = prices)
Any help is greatly appreciated.
r optimization nls
r optimization nls
asked Mar 21 at 21:10
Chris AguilarChris Aguilar
84
asked Mar 21 at 21:10
Chris AguilarChris Aguilar
84
asked Mar 21 at 21:10
Chris AguilarChris Aguilar
84
asked Mar 21 at 21:10
Chris AguilarChris Aguilar
84
asked Mar 21 at 21:10
Chris AguilarChris Aguilar
84
84
ziis missing.
– G. Grothendieck
Mar 22 at 2:21
add a comment |
ziis missing.
– G. Grothendieck
Mar 22 at 2:21
zi is missing.– G. Grothendieck
Mar 22 at 2:21
zi is missing.– G. Grothendieck
Mar 22 at 2:21
add a comment |
1 Answer
1
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oldest
votes
You have inter-correlation between the coefficients Alpha and Sigma. A simple solution is to hold one of them constant. Maybe it would be better to reformulate the equation and substitute Alpha or Sigma.
set.seed(1)
lnz_i <- function(TI, Alpha, Beta, Sigma) -TI*(Alpha*(1 - exp(-Beta*TI)) / (Beta) - (Sigma^2/2)*(1 - exp(-Beta*TI)) / (Beta)^2) - 0.02*(1 - exp(-Beta*TI)) / (Beta)
TI <- c(0.5, 2, 5, 10, 30)
prices <- data.frame(TI, zi=lnz_i(TI, 0.02, 0.3, 0.06)*runif(length(TI), .9, 1.1))
#Hold Alpha Fixed
Alpha <- 0.02
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.06), data = prices)
Alpha <- 0.04
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.06), data = prices)
Alpha <- 0.1
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.2), data = prices)
#Estimate for Beta is all the time 0.401 and residuals are at 0.003768,
#only Sigma is changing when Alpha is changed
#Hold Sigma Fixed
Sigma <- 0.06
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3), data = prices)
Sigma <- 0.03
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3), data = prices)
answered Mar 26 at 8:52
user10488504user10488504
1114
add a comment |
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You have inter-correlation between the coefficients Alpha and Sigma. A simple solution is to hold one of them constant. Maybe it would be better to reformulate the equation and substitute Alpha or Sigma.
set.seed(1)
lnz_i <- function(TI, Alpha, Beta, Sigma) -TI*(Alpha*(1 - exp(-Beta*TI)) / (Beta) - (Sigma^2/2)*(1 - exp(-Beta*TI)) / (Beta)^2) - 0.02*(1 - exp(-Beta*TI)) / (Beta)
TI <- c(0.5, 2, 5, 10, 30)
prices <- data.frame(TI, zi=lnz_i(TI, 0.02, 0.3, 0.06)*runif(length(TI), .9, 1.1))
#Hold Alpha Fixed
Alpha <- 0.02
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.06), data = prices)
Alpha <- 0.04
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.06), data = prices)
Alpha <- 0.1
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.2), data = prices)
#Estimate for Beta is all the time 0.401 and residuals are at 0.003768,
#only Sigma is changing when Alpha is changed
#Hold Sigma Fixed
Sigma <- 0.06
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3), data = prices)
Sigma <- 0.03
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3), data = prices)
answered Mar 26 at 8:52
user10488504user10488504
1114
add a comment |
You have inter-correlation between the coefficients Alpha and Sigma. A simple solution is to hold one of them constant. Maybe it would be better to reformulate the equation and substitute Alpha or Sigma.
set.seed(1)
lnz_i <- function(TI, Alpha, Beta, Sigma) -TI*(Alpha*(1 - exp(-Beta*TI)) / (Beta) - (Sigma^2/2)*(1 - exp(-Beta*TI)) / (Beta)^2) - 0.02*(1 - exp(-Beta*TI)) / (Beta)
TI <- c(0.5, 2, 5, 10, 30)
prices <- data.frame(TI, zi=lnz_i(TI, 0.02, 0.3, 0.06)*runif(length(TI), .9, 1.1))
#Hold Alpha Fixed
Alpha <- 0.02
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.06), data = prices)
Alpha <- 0.04
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.06), data = prices)
Alpha <- 0.1
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.2), data = prices)
#Estimate for Beta is all the time 0.401 and residuals are at 0.003768,
#only Sigma is changing when Alpha is changed
#Hold Sigma Fixed
Sigma <- 0.06
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3), data = prices)
Sigma <- 0.03
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3), data = prices)
answered Mar 26 at 8:52
user10488504user10488504
1114
add a comment |
You have inter-correlation between the coefficients Alpha and Sigma. A simple solution is to hold one of them constant. Maybe it would be better to reformulate the equation and substitute Alpha or Sigma.
set.seed(1)
lnz_i <- function(TI, Alpha, Beta, Sigma) -TI*(Alpha*(1 - exp(-Beta*TI)) / (Beta) - (Sigma^2/2)*(1 - exp(-Beta*TI)) / (Beta)^2) - 0.02*(1 - exp(-Beta*TI)) / (Beta)
TI <- c(0.5, 2, 5, 10, 30)
prices <- data.frame(TI, zi=lnz_i(TI, 0.02, 0.3, 0.06)*runif(length(TI), .9, 1.1))
#Hold Alpha Fixed
Alpha <- 0.02
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.06), data = prices)
Alpha <- 0.04
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.06), data = prices)
Alpha <- 0.1
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.2), data = prices)
#Estimate for Beta is all the time 0.401 and residuals are at 0.003768,
#only Sigma is changing when Alpha is changed
#Hold Sigma Fixed
Sigma <- 0.06
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3), data = prices)
Sigma <- 0.03
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3), data = prices)
answered Mar 26 at 8:52
user10488504user10488504
1114
You have inter-correlation between the coefficients Alpha and Sigma. A simple solution is to hold one of them constant. Maybe it would be better to reformulate the equation and substitute Alpha or Sigma.
set.seed(1)
lnz_i <- function(TI, Alpha, Beta, Sigma) -TI*(Alpha*(1 - exp(-Beta*TI)) / (Beta) - (Sigma^2/2)*(1 - exp(-Beta*TI)) / (Beta)^2) - 0.02*(1 - exp(-Beta*TI)) / (Beta)
TI <- c(0.5, 2, 5, 10, 30)
prices <- data.frame(TI, zi=lnz_i(TI, 0.02, 0.3, 0.06)*runif(length(TI), .9, 1.1))
#Hold Alpha Fixed
Alpha <- 0.02
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.06), data = prices)
Alpha <- 0.04
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.06), data = prices)
Alpha <- 0.1
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Beta=0.3, Sigma = 0.2), data = prices)
#Estimate for Beta is all the time 0.401 and residuals are at 0.003768,
#only Sigma is changing when Alpha is changed
#Hold Sigma Fixed
Sigma <- 0.06
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3), data = prices)
Sigma <- 0.03
nls(zi ~ lnz_i(TI, Alpha, Beta, Sigma), start = c(Alpha = 0.02, Beta = 0.3), data = prices)
answered Mar 26 at 8:52
user10488504user10488504
1114
answered Mar 26 at 8:52
user10488504user10488504
1114
answered Mar 26 at 8:52
user10488504user10488504
1114
answered Mar 26 at 8:52
user10488504user10488504
1114
1114
add a comment |
add a comment |
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ziis missing.– G. Grothendieck
Mar 22 at 2:21