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Re: Can I specify a predictor as both fixed and random with PROC MIXED

fbabinec — Wed, 29 Mar 2017 18:27:15 GMT

Yes, thanks. I have a second question: what happens if you use a class variable as both fixed and random?

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Can I specify a predictor as both fixed and random with PROC MIXED

fbabinec — Tue, 28 Mar 2017 16:10:02 GMT

Re: proc ttest question

fbabinec — Tue, 20 May 2014 12:57:01 GMT

My answer was focused on the apparent lack of precision of PROC TTEST and I have overlooked the serial nature of the data. I have used the Excel data analysis toolpack (Tools-> Data Analysis Toolpack in older versions, Data -> Data Analysis Toolpack in Excel 2007/2010) for simplicity. I am not sure if time series analysis solve your problem due to the paired nature of your data. I think repeated measures models with PROC GLM or PROC MIXED help you better. Or use advanced features of PROC TTEST (http://www2.sas.com/proceedings/sugi31/208-31.pdf). But your first analysis is just right.

Below is a simple example of repeated measures analysis with PROC MIXED, but statements are approximate:

*** create a new dataset;

data newtest; set forttest;

state="aggr"; rate=aggr_roll_on_rate; output;

state="end"; rate=roll_on_rate;

drop aggr_roll_on_rate roll_on_rate;

*** perform the analysis;

proc mixed;

class loan_price_cat y9c date state;

model rate=loan_price_cat y9c | state;

lsmeans loan_price_cat y9c * state / slice=loan_price_cat y9c;

repeated date / type=cs sub=loan_price_cat y9c;

*** and other models (AR, UN, etc);

run;

Re: proc ttest question

fbabinec — Thu, 15 May 2014 17:31:35 GMT

The result is correct. Do not look complete values but the differences after the third decimal values. Remember the properties of the variance of a variable, it remain invariante when adding or subtracting a constant. Analyze original value minus 4.74 and results must be equal. (excuse my poor english).

This is the analysis with Excel

Prueba t para medias de dos muestras emparejadas

	Aggr_Roll_on_Rate	Roll_On_Rate
Media	4.746407657	4.746419083
Varianza	0.000149777	0.000149767
Observaciones	23	23
Coeficiente de correlación de Pearson	0.999999868
Diferencia hipotética de las medias	0
Grados de libertad	22
Estadístico t	-8.702547638
P(T<=t) una cola	7.0983E-09
Valor crítico de t (una cola)	1.717144335
P(T<=t) dos colas	1.41966E-08
Valor crítico de t (dos colas)	2.073873058