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nathanb_1993
‎11-20-2017
  • 7 Posts
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Re: Logistic regression residuals

by Calcite | Level 5 in Statistical Procedures
‎11-09-2017 11:52 AM
‎11-09-2017 11:52 AM
Hi Reeza, Agreed, though due to this being a logistic regression, rather than simple liner, can it be computed in a manner as simple as this? I actually think my statistical knowledge is getting all a bit confused! ... View more

Logistic regression residuals

by Calcite | Level 5 in Statistical Procedures
‎11-09-2017 10:52 AM
‎11-09-2017 10:52 AM
Hi,   I have a dataset which has been created with both predicted and actual values from a logistic regression model (the only other information in the dataset are the original inputs into the model).    The dataset was already created so no PROC LOGISTIC / GLM has been used.   I know all of the individual parameters in the model and their estimates, however I need to do some analysis on the residuals which are not included in the dataset.   Probably a longshot, but does anyone know how I could compute the residuals from these logistic regression model estimates and actuals?   Thanks!   Nathan ... View more

Re: Enterprise Miner Surivival Modelling Help - Time Since Observation...

by Calcite | Level 5 in SAS Data Science
‎07-26-2017 04:50 AM
‎07-26-2017 04:50 AM
Hi, Very helpful video! I actually think we used it to expand our data which I've had a look over and it all seems to be OK. Just still having the issue of the _t_ = 0 month which is being included in the regression as a favourable month as there are no events, the post prior to this may help a little in explaining my confusion. Is there such a way that this can be avoided, i.e. avoid my hazard starting at 0? I realise this is probably quite confusing, but hopefully you can see where I'm coming from? Thanks ... View more

Re: Enterprise Miner Surivival Modelling Help - Time Since Observation...

by Calcite | Level 5 in SAS Data Science
‎07-25-2017 12:50 PM
‎07-25-2017 12:50 PM
Hi Wendy,   Thanks for this, I'll have a watch of the videos when I'm back in the office tomrorow morning.   As a little extra context (and you may be able to highlight what I've done incorrect with _t_), here is my current hazard function which I have output, my problem is with that initial spike at 1. When the node does the regression, it's treating 0 as a very favourable month (since no events occur here as it's my observation month)     Thank you ... View more

Re: Enterprise Miner Surivival Modelling Help - Time Since Observation...

by Calcite | Level 5 in SAS Data Science
‎07-25-2017 11:43 AM
‎07-25-2017 11:43 AM
So for Enterprise Miner we believe it's required we set the data up in the this structure, learnt from SAS documentation and guidance available, we need multiple records per ID for the change of the _t_ variable which is time since observation.   No worries though, I'll keep this in mind and see if anything is happening behind the scenes. Thanks for the input. ... View more

Re: Enterprise Miner Surivival Modelling Help - Time Since Observation...

by Calcite | Level 5 in SAS Data Science
‎07-25-2017 11:35 AM
‎07-25-2017 11:35 AM
Hi Reeza,   Below is an imagine which shows 3 (simple) examples of observations in my dataset. Hopefully this will help in understanding my data setup, obviously my actual dataset is considerably larger than this! But the image shows the two outcomes in my datset, either 'EVENT' = 1 if the event eventually happened in which case the END_DATE is populated, or event = 0 at the end of the ID, so END_DATE is not populated. Each ID is independent. Don't worry about the extra Variables, just in there as an example.   As mentioned previously, there are no instances where we have start_date = end_date since _t_ = 0 has no events as this is an initial observation month. ... View more

Enterprise Miner Surivival Modelling Help - Time Since Observation Con...

by Calcite | Level 5 in SAS Data Science
‎07-25-2017 10:15 AM
‎07-25-2017 10:15 AM
Hi,   I was hoping someone may be able to help me with some confusion around the survival node in Enterprise Miner, paticularly with the way that my data is structured.   Some quick background, I'm attempting to model the time to defaulting on a mortgage, a simple 1 or 0 binary flag. For every observation I have the _T_ variable which is my time since observation and starts from 0 and goes up to all the observations for following months, with an end date recorded if the event mentioned above happens.   Within my data _t_ = 0 is the initial obs month and no accounts here have hit the event described above. As a result of this, I've noticed that there are a couple of issues with the way that cubic splines are being calculated, mainly as the hazard function has a sharp spike at _t_ = 1, as this is actually where my highest event rate occurs.   Upon noticing that the sharp spike is being used as part of the spline fitting, I realise this is most likely not what I want to be happening. I am correct to include _t_ = 0 with no events in my dataset when using the surivival node?     Please let me know if some more information is needed.   Thanks! Nathan ... View more
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