[英]R programming- Minimize a function(RMSE) with constrained variables
I want to minimize a function (rmse) in R just like solver does in excel.我想最小化 R 中的函数 (rmse),就像求解器在 excel 中所做的一样。 Using the constrained vaiables (i) and conditioning it for
使用受约束的变量 (i) 并将其调节为
**i >= 0 && i<=2**
ac = c(85,95,79,88,90,99,111,99,100,110)
ff = c(100,110,105,95,115,105,110,120,105,110)
ff1 = ff[2:5] ;ac1 = ac[2:5]
i=1.1 #Assume-Constraint variable
revff = ff1*i
dev1 = abs(ac1-revff)
rmse_function = function(ac1,ff1,i) sqrt(sum(abs(ac1-ff1*i)^2))
I want to minimize the function rmse by changing the variable i.我想通过更改变量 i 来最小化函数 rmse。
Write your function so its the first argument you want to minimise over, use optimise
:编写您的函数,使其成为您想要最小化的第一个参数,使用
optimise
:
> rmse_function = function(i,ac1,ff1)sqrt(sum(abs(ac1-ff1*i)^2))
> optimise(rmse_function,c(0,2),ac1=ac1, ff1=ff1)
$minimum
[1] 0.8254548
$objective
[1] 13.87804
Hence the minimum is at i=0.825
and the function has a value of 13.87
there.因此最小值在
i=0.825
,函数在那里的值为13.87
。
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