I circled back to lasso because I have 48 variables and that is simply to much in my humble opinion for my data (the whole data set has about 3200 cases, but in many cases I slice it to as few as 48). I am not using k fold validation because it does not work well with too few cases. Nor am I building a training data set for the same reason.
I always get the following error.
WARNING: The adaptive weights for the LASSO method are not uniquely determined because the full least squares model is singular.
I am not sure how to fix this problem.
I always get the following error.
WARNING: The adaptive weights for the LASSO method are not uniquely determined because the full least squares model is singular.
I am not sure how to fix this problem.
Code:
proc glmselect data= work.setest;
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")
;
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
/ selection=lasso(adaptive choose=sbc stop=none);
run;