# R problem with regularize.values(x, y, ties, missing(ties)

## R problem with regularize.values(x, y, ties, missing(ties)

Contents

Problem Description:

I would like to ask a question

My code in R:

``````a=1.1
b=1.3

C=c(0,0.3,1.5,3.5,19.5)
v2=c(0.00, 0.56, 1.00, 1.00, 1.00)

fun <-
function( C, a, b ){
1 / (1 + (C / a)^b)
}

x_d=seq(0,10,by=0.1)
y_s <- fun(v2, a, b)
y_d <- splinefun(v2, y_s)(x_d)
y_d <- approx(v2, y_s, xout = x_d)\$x
which(y_d <= 0.5)
which(y_d <= 0.5)
x_d[which(y_d <= 0.5)]

``````

I’m getting a warning messages:

`````` y_d <- splinefun(v2, y_s)(x_d)
Warning message:
In regularize.values(x, y, ties, missing(ties)) :
collapsing to unique 'x' values
> y_d<- approx(v2, y_s, xout = x_d)\$x
Warning message:
In regularize.values(x, y, ties, missing(ties), na.rm = na.rm) :
collapsing to unique 'x' values
> which(y_d <= 0.5)
 11 12 13 14 15 16 17 18
> which(y_d <= 0.5)
 11
> x_d[which(y_d <= 0.5)]
 1
``````

I would like to improve my code so that I don’t get such a warning messages. It ruins the results for me. However, I wouldn’t want to remove the duplicate values of v2 and its results, I need them later.

I do not hide that it is important for me to find a way where I can use non distinct values for v2 and treat them as if they were unique and continue to use them in my code. Is there such a way in R? Thank you in advance for any help with this problem. I am quite new to R so please help me

## Solution – 1

First, there is a problem with your function `fun()`. It only has one argument (`variables`), but you feed it more than one object (`v2, a, b`). But if I fix it to the below, I can reproduce your error message:

``````fun <-
function( C, a, b ){
1 / (1 + (C / a)^b)
}
``````

If you simply want to avoid the warning messages, you can use `ties = "ordered"` or `ties = mean` with functions `splinefun()` and `approx()`. Please read here under "Details" if `ties = "ordered"`, `ties = mean`, or something else is best for your specific programming goal.

``````a=1.1
b=1.3
C=c(0,0.3,1.5,3.5,19.5)
v2=c(0.00, 0.56, 1.00, 1.00, 1.00)

fun <-
function( C, a, b ){
1 / (1 + (C / a)^b)
}

## no ties specified
x_d=seq(0,10,by=0.1)
y_s <- fun(v2, a, b)
y_d <- splinefun(v2, y_s)(x_d)
#> Warning in regularize.values(x, y, ties, missing(ties)): collapsing to unique
#> 'x' values
y_d <- approx(v2, y_s, xout = x_d)\$x
#> Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
#> collapsing to unique 'x' values
which(y_d <= 0.5)
#>  1 2 3 4 5 6
which(y_d <= 0.5)
#>  1
x_d[which(y_d <= 0.5)]
#>  0
rm(x_d, y_s, y_d)

## ties specified
x_d=seq(0,10,by=0.1)
y_s <- fun(v2, a, b)
y_d <- splinefun(v2, y_s, ties = "ordered")(x_d)
y_d <- approx(v2, y_s, xout = x_d, ties = "ordered")\$x
which(y_d <= 0.5)
#>  1 2 3 4 5 6
which(y_d <= 0.5)
#>  1
x_d[which(y_d <= 0.5)]
#>  0
``````
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