Replacing missing data by regressed values

[#1]

Hi

I have a time series for which the variable has missing values. I would like to replace the missing values by a linear model derived from the closest available values in time.

What would be the best way?

And if the script could do that to all the columns of my table (except the time), that would be even better

Thanks

Example

What I have

t	Output
0	10
1	.
2	.
4	30
10	.
20	10

What I want:

t	Output
0	10
1	15
2	20
4	30
10	22.5
20	10

[#2]

try this for starters

Imputation Addin

[#3]

Thanks Ron but I don’t see anything in this that does interpolation?

[#4]

here is a script that will do the task

Names Default To Here( 1 );
dt = Current Data Table();
For( rows = 1, rows <= N Rows( dt ), rows++,
       If( Rows == 1, counter = 0 );
       If( Is Missing( dt:output[rows] ) == 1,
              counter = counter + 1,
              If( counter > 0,
                     incr = (dt:output[Rows] - dt:output[Rows - counter - 1]) / (counter + 1);
                     For( i = Rows - counter, i <= Rows - 1, i++,
                           dt:output = dt:output[i - 1] + incr
                     );
                     counter = 0;
              )
       );
);

// Set all default referenced memory variables to this unique application
Names Default To Here( 1 );

// Set the variable dt to be a pointer to the current active data table
dt = Current Data Table();

// Loop through the data table from row 1 to the last row and use a
// variable names "rows" as the index
For( rows = 1, rows <= N Rows( dt ), rows++, 

	// If this is row 1 initialize a variable named counter to 1
	If( Rows == 1, counter = 0 );
       
    // If the current row's value for the column named "Output" in the 
	// data table referenced by "dt" is missing (blank, null) increase
	// the counter
	If( Is Missing( dt:output[rows] ) == 1, 
		counter++
	);
	
	// If row is found with a non missing value for the column called Output,
	// and the counter is > 0, which indicates previous rows with missing
	// values, then calculate the required values and place them in their
	// appropriate rows
	If( counter > 0 & Is Missing( dt:output[rows] ) == 0,
		// Calculate the incremental steps to use for the missing cells
		incr = (dt:output[Rows] - dt:output[Rows - counter - 1]) / (counter + 1);
		
		// Loop across the rows with missing values and fill in the data
		For( i = Rows - counter, i <= Rows - 1, i++,
			dt:output[i] = dt:output[i - 1] + incr
		);
		
		// reinitialize the counter
		counter = 0;
	);
);

[#5]

I had a similar problem some years ago and wrote a script to do imputation by "local regression".

Here's an updated version:

dt = Current Data Table();
n = N Rows(dt);
x = Column(1);// Column("t")
For(i = 2, i <= N Col(dt), i++,
    y = Column(i);
    For Each Row(
        If(1 < Row() < n & Is Missing(y[]),
         // find previous non-missing row
            Eval(Eval Expr(r1 = Max(dt << get rows where(!Is Missing(y[]) & Row() < Expr(Row())))));
          // find next non-missing row
            Eval(Eval Expr(r2 = Min(dt << get rows where(!Is Missing(y[]) & Row() > Expr(Row())))));
            y[] = y[r1] + (y[r2] - y[r1]) / (x[r2] - x[r1]) * (x[] - x[r1]);
        )
    )
   
);

[#6]

Spot on MS,

Cheers!

[#7]

txnelson,

could you update the script with comments? I am new to JSL and need help understanding how it applies to my problem. I have the same need as OP but only have one column that i need to fill in.  alternatively, I have used the following VBA code in the past in to fill in missing data in excel. I was wondering if there is something similar to the End(xlDown).value function in JSL. the offset is used to specify where the time point data is. Thanks!

Sub interpolatedata()
For Each cell In Range("k4:k29") 
If cell.Value = "" Then
y2 = cell.End(xlDown).Value
x2 = cell.End(xlDown).Offset(0, -8).Value
y1 = cell.End(xlUp).Value
x1 = cell.End(xlUp).Offset(0, -8).Value
x = cell.Offset(0, -8).Value
y = (y1) + (x - x1) * (y2 - y1) / (x2 - x1)
cell.Value = y
End If
Next

[#8]

Here is an updated/annotated version of my original code.  All you should have to do, is to change the column name from "Output" to whatever the name of the column you want to change values to.

// Set all default referenced memory variables to this unique application
Names Default To Here( 1 );

// Set the variable dt to be a pointer to the current active data table
dt = Current Data Table();

// Loop through the data table from row 1 to the last row and use a
// variable names "rows" as the index
For( rows = 1, rows <= N Rows( dt ), rows++, 

	// If this is row 1 initialize a variable named counter to 1
	If( Rows == 1, counter = 0 );
       
    // If the current row's value for the column named "Output" in the 
	// data table referenced by "dt" is missing (blank, null) increase
	// the counter
	If( Is Missing( dt:output[rows] ) == 1, 
		counter++
	);
	
	// If row is found with a non missing value for the column called Output,
	// and the counter is > 0, which indicates previous rows with missing
	// values, then calculate the required values and place them in their
	// appropriate rows
	If( counter > 0 & Is Missing( dt:output[rows] ) == 0,
		// Calculate the incremental steps to use for the missing cells
		incr = (dt:output[Rows] - dt:output[Rows - counter - 1]) / (counter + 1);
		
		// Loop across the rows with missing values and fill in the data
		For( i = Rows - counter, i <= Rows - 1, i++,
			dt:output[i] = dt:output[i - 1] + incr
		);
		
		// reinitialize the counter
		counter = 0;
	);
);

[#9]

thanks it worked!  you commented 

If this is row 1 initialize a variable named counter to 1

, did you mean "counter to 0"? 

[#10]

Good catch....it should be

If this is row 1 initialize a variable named counter to 0