model test proc genmod

noetsi

No cake for spunky
#1
I expected proc genmod to run a f or t test of the overall model. But if it did I can't find it. This is as close as I can find to what I expected to see, but there is no p value.


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noetsi

No cake for spunky
#2
Ok I have a question. I know the theoretical answer, but this one baffles me in practice. One of my services has 43 eligible customers. Only one got the service. That customer had higher income than any other customer by a good margin. But in the regression, I can't find any mistakes, it shows that those who get the service on average earn about 45 hundred dollars less.

I honestly don't understand this. I know that descriptives don't tell you much about regression and the regression looks at the unique impact controlling for the other variables, but I can't think of a way that this result should occur.

And how do I deal with this. It seems clear theoretically and based on the descriptives those who get a college service in this subgroup should earn more money. But in fact the effect size is highly negative.

This is the code.

Code:
PROC GENMOD DATA=WORK.SORTTempTableSorted
        PLOTS(ONLY)=None
;
    CLASS 
"Age 25 to 44"n (ref ="0")
"Associate’s degree"n (ref ="0")
"Bachelor’s degree"n (ref ="0")
"Beyond a bachelor’s degree"n (ref ="0")
"High school diploma or equivalen"n (ref ="0")
/*"Individuals has a significant di"n (ref ="0")removed for SE analysis */
"Postsecondary education no degre"n (ref ="0")
"Race: Black"n (ref ="0")
"Race: More than one"n (ref ="0")
"Special education certicate/comp"n (ref ="0")
"Age 19 to 24"n (ref ="0")
"Age 45 to 54"n (ref ="0")
"Age 55 to 59"n (ref ="0")
"Age 60+"n (ref ="0")
'Age 16 to 18'n (ref ="0")
"Race: Asian"n (ref ="0")
"Race: Hawaiian/Pacific Islander"n (ref ="0")
"Race: White"n (ref ="0")
 "Foster care youth"n (ref ="0")
"Psychosocial and psychological d"n (ref ="0")
"Intellectual and learning disabi"n (ref ="0")
"Physical disability"n (ref ="0")
"Auditory and communicative disab"n (ref ="0")
Veteran (ref ="0")
"TANF recipient"n (ref ="0")
"Single parent"n (ref ="0")
"Received career services"n (ref ="0") 
"Received training services"n (ref ="0")
"Received other services"n (ref ="0")
"Received public support at appli"n (ref ="0")
"Employed at application"n (ref ="0")
"Homeless individual, runaway you"n (ref ="0")
"Low-income"n (ref ="0")
"Limited English-language profici"n (ref ="0")
"Migrant and seasonal farmworker"n (ref ="0")
"Long-term unemployed"n (ref ="0")
/* "Individuals is most significant"n (ref ="0")removed for SE analysis */
"Ethnicity-Hispanic Ethnicity"n (ref ="0")
"Ex-offender"n (ref ="0")
"Displaced homemaker"n (ref ="0")
Female (ref ="0")
 secol(ref ="0")
    ;
    MODEL Qtr2_Wage=    
"Age 25 to 44"n 
"Associate’s degree"n 
"Bachelor’s degree"n
"Beyond a bachelor’s degree"n
"High school diploma or equivalen"n 
/*"Individuals has a significant di"n */
"Postsecondary education no degre"n 
"Race: Black"n 
"Race: More than one"n
"Special education certicate/comp"n
"Age 19 to 24"n 
"Age 45 to 54"n 
"Age 55 to 59"n
"Age 60+"n
'Age 16 to 18'n 
"Race: Asian"n 
"Race: Hawaiian/Pacific Islander"n 
"Race: White"n
"Foster care youth"n
"Psychosocial and psychological d"n 
"Intellectual and learning disabi"n
"Physical disability"n 
"Auditory and communicative disab"n 
Veteran
"TANF recipient"n
"Single parent"n 
"Received career services"n
"Received training services"n 
"Received other services"n 
"Received public support at appli"n
"Employed at application"n
"Homeless individual, runaway you"n 
"Low-income"n 
"Limited English-language profici"n
"Migrant and seasonal farmworker"n 
"Long-term unemployed"n 
/*"Individuals is most significant"n */
"Ethnicity-Hispanic Ethnicity"n 
"Ex-offender"n 
"Displaced homemaker"n
Female 
"Construction Employment"n 
"Educational, or Health Care Rela"n 
"Financial Services Employment"n
"Information Services Employment"n
"Leisure, Hospitality, or Enterta"n
"Natural Resources Employment"n 
"Other Services Employment"n 
"Trade and Transportation Employm"n 
"Professional and Business Servic"n 
"Manufacturing Related Employment"n
"totalgovernment"n
 unemployment
 secol
 
        /
    ;

/*    OUTPUT OUT=WORK.PREDGLMPredictions(LABEL="Generalized Linear Models predictions and statistics for WORK.INCOME21")
        PREDICTED=predicted_Qtr2_Wage ; */
RUN; QUIT;
 

noetsi

No cake for spunky
#4
What I meant was that I don't think the existing model works. It has more predictors than cases. So I am trying lasso.