optim - setting a common contraint for multiple parameters
1
I a looking to set a common constraint to a subset of my parameters:
c2 <- 2
nFeatures = 6
W2 <- rep(1,nFeatures)
w2 <- W2/sum(W2)
fguess2 <- 0.3
startParms2 <- c(c2,fguess2,w2)
names(startParms2) <- c("c2", "fguess",
"w21","w22","w23","w24","w25","w26")
xout2 <- optim(par=startParms2, fn=rmsd,
data1=mydata2, method = "L-BFGS-B",
lower = rep(0,8),
upper = c(Inf,1,1,1,1,1,1,1))
print(xout2)
Currently, all 6 parameters belonging to the vector w2 are constrained to a individual maximum of 1.
What I would like to do is to constrain the sum of these 6 parameters to 1 (while the remaining 2 parameters keep their current constraints).
r optimization constraints
edited Nov 24 '18 at 18:18
Ben Bolker
133k11223311
asked Nov 24 '18 at 16:54
Moritz TruningerMoritz Truninger
91
add a comment |
1
I a looking to set a common constraint to a subset of my parameters:
c2 <- 2
nFeatures = 6
W2 <- rep(1,nFeatures)
w2 <- W2/sum(W2)
fguess2 <- 0.3
startParms2 <- c(c2,fguess2,w2)
names(startParms2) <- c("c2", "fguess",
"w21","w22","w23","w24","w25","w26")
xout2 <- optim(par=startParms2, fn=rmsd,
data1=mydata2, method = "L-BFGS-B",
lower = rep(0,8),
upper = c(Inf,1,1,1,1,1,1,1))
print(xout2)
Currently, all 6 parameters belonging to the vector w2 are constrained to a individual maximum of 1.
What I would like to do is to constrain the sum of these 6 parameters to 1 (while the remaining 2 parameters keep their current constraints).
r optimization constraints
edited Nov 24 '18 at 18:18
Ben Bolker
133k11223311
asked Nov 24 '18 at 16:54
Moritz TruningerMoritz Truninger
91
You can encode the constrain into the objective function. Then you will have to optimize only 5 parameters... - However, without a MCVE it is difficult to say more stackoverflow.com/help/mcve
– Florian
Nov 24 '18 at 18:15
in principle you can useconstrOptim(), but I suspect you really are trying to fit a composition parameter (all parameters bound in 0<p<1 and sum(p)==1), in which case you will be better off working with a softmax/Aitchison transformation
– Ben Bolker
Nov 26 '18 at 22:44
add a comment |
1
1
1
I a looking to set a common constraint to a subset of my parameters:
c2 <- 2
nFeatures = 6
W2 <- rep(1,nFeatures)
w2 <- W2/sum(W2)
fguess2 <- 0.3
startParms2 <- c(c2,fguess2,w2)
names(startParms2) <- c("c2", "fguess",
"w21","w22","w23","w24","w25","w26")
xout2 <- optim(par=startParms2, fn=rmsd,
data1=mydata2, method = "L-BFGS-B",
lower = rep(0,8),
upper = c(Inf,1,1,1,1,1,1,1))
print(xout2)
Currently, all 6 parameters belonging to the vector w2 are constrained to a individual maximum of 1.
What I would like to do is to constrain the sum of these 6 parameters to 1 (while the remaining 2 parameters keep their current constraints).
r optimization constraints
edited Nov 24 '18 at 18:18
Ben Bolker
133k11223311
asked Nov 24 '18 at 16:54
Moritz TruningerMoritz Truninger
91
I a looking to set a common constraint to a subset of my parameters:
c2 <- 2
nFeatures = 6
W2 <- rep(1,nFeatures)
w2 <- W2/sum(W2)
fguess2 <- 0.3
startParms2 <- c(c2,fguess2,w2)
names(startParms2) <- c("c2", "fguess",
"w21","w22","w23","w24","w25","w26")
xout2 <- optim(par=startParms2, fn=rmsd,
data1=mydata2, method = "L-BFGS-B",
lower = rep(0,8),
upper = c(Inf,1,1,1,1,1,1,1))
print(xout2)
Currently, all 6 parameters belonging to the vector w2 are constrained to a individual maximum of 1.
What I would like to do is to constrain the sum of these 6 parameters to 1 (while the remaining 2 parameters keep their current constraints).
r optimization constraints
r optimization constraints
edited Nov 24 '18 at 18:18
Ben Bolker
133k11223311
asked Nov 24 '18 at 16:54
Moritz TruningerMoritz Truninger
91
edited Nov 24 '18 at 18:18
Ben Bolker
133k11223311
asked Nov 24 '18 at 16:54
Moritz TruningerMoritz Truninger
91
edited Nov 24 '18 at 18:18
Ben Bolker
133k11223311
edited Nov 24 '18 at 18:18
Ben Bolker
133k11223311
edited Nov 24 '18 at 18:18
Ben Bolker
133k11223311
133k11223311
asked Nov 24 '18 at 16:54
Moritz TruningerMoritz Truninger
91
asked Nov 24 '18 at 16:54
Moritz TruningerMoritz Truninger
91
asked Nov 24 '18 at 16:54
Moritz TruningerMoritz Truninger
91
91
You can encode the constrain into the objective function. Then you will have to optimize only 5 parameters... - However, without a MCVE it is difficult to say more stackoverflow.com/help/mcve
– Florian
Nov 24 '18 at 18:15
in principle you can useconstrOptim(), but I suspect you really are trying to fit a composition parameter (all parameters bound in 0<p<1 and sum(p)==1), in which case you will be better off working with a softmax/Aitchison transformation
– Ben Bolker
Nov 26 '18 at 22:44
add a comment |
You can encode the constrain into the objective function. Then you will have to optimize only 5 parameters... - However, without a MCVE it is difficult to say more stackoverflow.com/help/mcve
– Florian
Nov 24 '18 at 18:15
in principle you can useconstrOptim(), but I suspect you really are trying to fit a composition parameter (all parameters bound in 0<p<1 and sum(p)==1), in which case you will be better off working with a softmax/Aitchison transformation
– Ben Bolker
Nov 26 '18 at 22:44
You can encode the constrain into the objective function. Then you will have to optimize only 5 parameters... - However, without a MCVE it is difficult to say more stackoverflow.com/help/mcve
– Florian
Nov 24 '18 at 18:15
You can encode the constrain into the objective function. Then you will have to optimize only 5 parameters... - However, without a MCVE it is difficult to say more stackoverflow.com/help/mcve
– Florian
Nov 24 '18 at 18:15
in principle you can use
constrOptim(), but I suspect you really are trying to fit a composition parameter (all parameters bound in 0<p<1 and sum(p)==1), in which case you will be better off working with a softmax/Aitchison transformation– Ben Bolker
Nov 26 '18 at 22:44
in principle you can use
constrOptim(), but I suspect you really are trying to fit a composition parameter (all parameters bound in 0<p<1 and sum(p)==1), in which case you will be better off working with a softmax/Aitchison transformation– Ben Bolker
Nov 26 '18 at 22:44
add a comment |
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You can encode the constrain into the objective function. Then you will have to optimize only 5 parameters... - However, without a MCVE it is difficult to say more stackoverflow.com/help/mcve
– Florian
Nov 24 '18 at 18:15
in principle you can use
constrOptim(), but I suspect you really are trying to fit a composition parameter (all parameters bound in 0<p<1 and sum(p)==1), in which case you will be better off working with a softmax/Aitchison transformation– Ben Bolker
Nov 26 '18 at 22:44