How can I avoid error in mnrfit line 164?

I am getting an error message when I use the mnrfit function on a matrix X and Y like this:

X =

     1   NaN     2
   NaN     3   NaN
   NaN   NaN     4
     6   NaN   NaN
     5   NaN   NaN

Y =

    -1
     1
    -1
     1
    -1

 b=mnrfit(X,Y+2);
Error using mnrfit (line 164)
X and Y must contain at least one valid observation.

I cannot understand why I'm running into such an error...please advise...

Answer 1

[Wayne King]

The problem is that you have NaNs in every single row of your X matrix.

As the documentation states:

"mnrfit treats NaNs in X and Y as missing data, and removes the corresponding observations."

Every row in your X matrix is an observation on P predictor variables.

Since you have at least one NaN for every observation, all the observations are removed.

[Shiladitya Sen]

In that case, how should I perform logistic regression on a sparse dataset like this?

Answer 2

[Thomas]

I'm assuming mnrfit does not like 'NaN' values as its input.

works fine with

 x =
          0.14          0.66          0.76
          0.42          0.04          0.74
          0.92          0.85          0.39
          0.79          0.93          0.66
          0.96          0.68          0.17
 y =
         -1.00
          1.00
         -1.00
          1.00
         -1.00

 b=mnrfit(x,y+2)

 b =
         95.33         47.37
        -58.97        -31.76
         15.27          6.52
       -112.15        -62.24