turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

- JMP User Community
- :
- Discussions
- :
- Replacing missing data by regressed values

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Jul 8, 2016 7:39 AM
(1133 views)

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 |

5 REPLIES

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Jul 8, 2016 9:08 AM
(1007 views)

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Jul 10, 2016 2:31 PM
(1007 views)

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

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Jul 10, 2016 5:16 PM
(1007 views)

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;**

** )**

** );**

**);**

Jim

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Jul 10, 2016 5:47 PM
(1007 views)

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**])**;

**)**

**)**

**)**;

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Jul 11, 2016 1:28 AM
(1007 views)