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NaN values in timetable - how to calculate Nash–Sutcliffe model efficiency coefficient?

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I would like the calculate the Nash–Sutcliffe model efficiency coefficient between various variables in a dataset. However a number of rows do contain missing data so I would like to exclue them from the calculate too. Is there a function that will allow this in matlab?
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Jan
Jan am 24 Mär. 2021
So the actual problem is how to remove rows with missing data? Then please post an example, which shows, how "missing" data are represented.

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Star Strider
Star Strider am 24 Mär. 2021
If the ‘missing’ data are NaN entries, this works:
data = rand(10,3); % Create Array
data(randi(30,1,5)) = NaN; % Create Missing Data
rowidx = ~any(isnan(data),2); % Rows Without ‘NaN’ Entries
data_new = data(rowidx,:); % Matrix With No ‘NaN’ Values
If the missing entries are in a cell array with empty cells, this works:
data = num2cell(rand(10,3)); % Create Cell Array
data(randi(30,1,5)) = {[]}; % Create Missing Data
rowidx = ~any(cellfun(@isempty,data),2); % Rows Without Empty Cells
data_new = data(rowidx,:); % Matrix With No Empty Cells
.

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