# R Error: (all(xi <= xj) && any(xi < xj)) { : missing value where TRUE/FALSE needed

I am working with the R programming language. I am trying to use the following library to optimize an arbitrary function I wrote: https://cran.r-project.org/web/packages/nsga2R/nsga2R.pdf

First I created some data for this example:

``````#load library
library(dplyr)
library(nsga2r)

#create data for this example
# create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)
``````

Then, I defined the function for optimization (7 inputs, 4 outputs):

``````#define function

funct_set <- function (x) {
x1 <- x; x2 <- x; x3 <- x ; x4 <- x; x5 <- x; x6 <- x; x <- x
f <- numeric(4)

#bin data according to random criteria
train_data <- train_data %>%
mutate(cat = ifelse(a1 <= x1 & b1 <= x3, "a",
ifelse(a1 <= x2 & b1 <= x4, "b", "c")))

train_data\$cat = as.factor(train_data\$cat)

#new splits
a_table = train_data %>%
filter(cat == "a") %>%
select(a1, b1, c1, cat)

b_table = train_data %>%
filter(cat == "b") %>%
select(a1, b1, c1, cat)

c_table = train_data %>%
filter(cat == "c") %>%
select(a1, b1, c1, cat)

#calculate  quantile ("quant") for each bin

table_a = data.frame(a_table%>% group_by(cat) %>%
mutate(quant = ifelse(c1 > x,1,0 )))

table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = ifelse(c1 > x,1,0 )))

table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = ifelse(c1 > x,1,0 )))

f = mean(table_a\$quant)
f = mean(table_b\$quant)
f = mean(table_c\$quant)

#group all tables

final_table = rbind(table_a, table_b, table_c)
# calculate the total mean : this is what needs to be optimized

f = mean(final_table\$quant)

return (f);
}
``````

Then, I ran the optimization code:

``````#optimization
results <- nsga2R(fn=funct_set, varNo=7, objDim=4, lowerBounds=c(80,80,80,80, 100, 200, 300), upperBounds=c(120,120,120,120,200,300,400),
popSize=50, tourSize=2, generations=50, cprob=0.9, XoverDistIdx=20, mprob=0.1,MuDistIdx=3)
``````

But this returns the following error:

``````Error in if (all(xi <= xj) && any(xi < xj)) { :
missing value where TRUE/FALSE needed
``````

Does anyone know if the error being produced is because of the way I have defined the function/data for this problem? Or is there another reason why this error is being produced?

Thanks

Hello @swaheera ,

• always use `set.seed` to that we can exactly reproduce your results
• please always include a reprex . Of course I know that you included all parts of your code but while copying you made a mistake: you mentioned `library(nsga2r)` instead of `library(nsga2R)`.
Not something that can't be solved, but this could have been avoid when you had used the `reprex` package to show us the code.

That said, I don't understand the purpose of your optimization so I can't help you there.
What strikes me is that you want to optimize (in some way?) four dependent numbers: the mean of a variable and then the mean of subsets thereof. Apparently this is not accepted here. By the way, I think you could have shown more of the context of the error message:

``````initializing the population
ranking the initial population
Error in if (all(xj <= xi) && any(xj < xi)) { :
missing value where TRUE/FALSE needed
``````

I think your code is not wrong because I can run it when I slightly change the function and the optimization call:

``````set.seed(2021)

#define function

funct_set <- function (x) {
....
return(f[3:4])
}

#optimization
results <- nsga2R(fn=funct_set, varNo=7, objDim=2,
lowerBounds=c(80,80,80,80, 100, 200, 300),
upperBounds=c(120,120,120,120,200,300,400),
popSize=50, tourSize=2, generations=50,
cprob=0.9, XoverDistIdx=20, mprob=0.1,MuDistIdx=3)

``````

I think this suggests that the code is not wrong but that this optimization is not fit for your purpose (?)

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