turn on suggestions

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

Showing results for

Find a Community

- Home
- /
- Analytics
- /
- Stat Procs
- /
- Questions about the Polynomial Distribution Lag Mo...

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
- Highlight
- Email to a Friend
- Report Inappropriate Content

05-09-2011 05:05 PM

Hi!

I am using the PDL model to analyze the correlation between 8-days insect abundances and weather conditions. I also want to adjust for seasonality and first order autoregressive term in the model. Here is my models.

DATA SF_MODEL; SET PDL.SF_ALL_F;

sea=sin(2*3.14*(image/46));

PROC PDLREG data=SF_MODEL;

MODEL tarsalis=tavg(4,3) rh(4,3) prec(4,3) sea / nlag=1 partial;

run;

My questions is:

1. Is it right to use "sea" to adjust the seasonality in the model? (46 is the total observation numbers of each year).

2. Does the statement "nlag=1" in the model option indicate that adjustment of the first order autoregressive term?

Thanks a lot!

I am using the PDL model to analyze the correlation between 8-days insect abundances and weather conditions. I also want to adjust for seasonality and first order autoregressive term in the model. Here is my models.

DATA SF_MODEL; SET PDL.SF_ALL_F;

sea=sin(2*3.14*(image/46));

PROC PDLREG data=SF_MODEL;

MODEL tarsalis=tavg(4,3) rh(4,3) prec(4,3) sea / nlag=1 partial;

run;

My questions is:

1. Is it right to use "sea" to adjust the seasonality in the model? (46 is the total observation numbers of each year).

2. Does the statement "nlag=1" in the model option indicate that adjustment of the first order autoregressive term?

Thanks a lot!

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

05-10-2011 08:58 AM

You mentioned 8 days, but your model is for 4 lags (time window of 5: current and previous 4). Is that what you want. You specified a cubic polynomial for the parameter constraints, which is just one degree less than the maximum (i.e., with no constraints). I think you are not gaining the advantages of PDL regression. With only 46 observations, I think you have an over-parameterized model. I have used PDL regression a lot, and you need a lot of data points to reliably estimate the parameters and determine the time length and polynomial order.

Your nlag option is fine for a 1-st order AR residual term.

Your seasonality term is probably OK, but it doesn't give you much flexibility. You might have to look into using a smoothing spline, but this can't be done within PDLREG. You would have to do this in another procedure, store the residuals, and then use PDLREG on the residuals. I suggest you check out the article by Schwartz (Epidemiology 11: 320-326 [2000]). Also check out Madden and Paul (Phytopathology 100: 1015-1029 [2010]).

Your nlag option is fine for a 1-st order AR residual term.

Your seasonality term is probably OK, but it doesn't give you much flexibility. You might have to look into using a smoothing spline, but this can't be done within PDLREG. You would have to do this in another procedure, store the residuals, and then use PDLREG on the residuals. I suggest you check out the article by Schwartz (Epidemiology 11: 320-326 [2000]). Also check out Madden and Paul (Phytopathology 100: 1015-1029 [2010]).

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

05-10-2011 11:41 AM

Thanks a lot!!

I will modify the number of lag terms in the model and also check whether I need the cubic polynomial term.

I just wanna make sure that the AR(1) and seasonality adjustment are appropriate.

Actually, I have data for 4 years so the OBS will be 184. (I understand this is a small sample size).

I am interested in the smooth spline method to adjust the seasonality. Could you tell more about that? Thanks!

I will modify the number of lag terms in the model and also check whether I need the cubic polynomial term.

I just wanna make sure that the AR(1) and seasonality adjustment are appropriate.

Actually, I have data for 4 years so the OBS will be 184. (I understand this is a small sample size).

I am interested in the smooth spline method to adjust the seasonality. Could you tell more about that? Thanks!

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

05-16-2011 08:45 AM

The references I mentioned deal with the subject.