I have the following function.
Let F(.) is the cumulative distribution function of the gamma
distribution with shape = 1
and rate =1
. The denominator is the survival function S(X) = 1 - F(X)
. The g(x)
is the mean residual life function.
I wrote the following function in r.
x = 5
denominator = 1 -pgamma(x, 1, 1)
numerator = function(t) (1 - pgamma(t, 1, 1))
intnum = integrate(numerator , x, Inf)
frac = intnum$value/denominator
frac
How can I find the maximum of the function g(x)
for all possible values of X >= 0
? Am I able to do this in r
? Thank you very much for your help.
Before start, I defined the function you made
surviveFunction<-function(x){
denominator = 1 -pgamma(x, 1, 1)
numerator = function(t) (1 - pgamma(t, 1, 1))
# I used sapply to get even vector x
intnum = sapply(x,function(x){integrate(numerator , x, Inf)$value})
frac = intnum/denominator
return(frac)
}
Then let's fit our function to function called 'curve' it will draw the plot with continuous data.
The result is shown below:
df = curve(surviveFunction, from=0, to=45)
plot(df, type='l')
And adjust the xlim
to find the maximum value
df = curve(surviveFunction, from=0, to=45,xlim = c(30,40))
plot(df, type='l')
And now we can guess the global maximum is located in near 35
I suggest two options to find the global maximum.
First using the df
data to find maximum:
> max(df$y,na.rm = TRUE)
1.054248 #maximum value
> df$x[which(df$y==(max(df$y,na.rm = TRUE)))]
35.55 #maximum value of x
Second using the optimize
:
> optimize(surviveFunction, interval=c(34, 36), maximum=TRUE)
$maximum
[1] 35.48536
$objective
[1] 1.085282
But the optimize
function finds the not the global maximum value i think.
If you see below
optimize(surviveFunction, interval=c(0, 36), maximum=TRUE)
$maximum
[1] 11.11381
$objective
[1] 0.9999887
Above result is not the global maximum I guess it is local maximum.
So, I suggest you using first solution.