Biostat Lab Section PROC REG

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Biostat Lab Section PROC REG

• Yu-Fen Li

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PROC REG
proc reg datafitness model OxygenAge
Weight RunTime RunPulse RestPulse MaxPulse
/ selectionforward model
OxygenAge Weight RunTime RunPulse RestPulse
MaxPulse / selectionbackward
run
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PROC REG model selections
The nine methods of model selection implemented
in PROC REG are specified with the SELECTION
option in the MODEL statement
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Model-selection Methods in PROC REG

• The nine methods of model selection implemented
  in PROC REG are
• NONE
• FORWARD (forward selection)
• This method starts with no variables in the
  model and adds variables one by one to the model.
  At each step, the variable added is the one that
  maximizes the fit of the model. You can also
  specify groups of variables to treat as a unit
  during the selection process. An option enables
  you to specify the criterion for inclusion.

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Model-selection Methods in PROC REG

• BACKWARD (backward elimination.)
• This method starts with a full model and
  eliminates variables one by one from the model.
  At each step, the variable with the smallest
  contribution to the model is deleted. You can
  also specify groups of variables to treat as a
  unit during the selection process. An option
  enables you to specify the criterion for
  exclusion.

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Model-selection Methods in PROC REG

• STEPWISE
• MAXR
• MINR
• RSQUARE
• CP
• ADJRSQ

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Forward Selection in PROC REG

• The forward-selection technique begins with no
  variables in the model.
• For each of the independent variables, the
  FORWARD method calculates F statistics that
  reflect the variable's contribution to the model
  if it is included.
• The p-values for these F statistics are compared
  to the SLENTRY value that is specified in the
  MODEL statement (or to 0.50 if the SLENTRY
  option is omitted).
• If no F statistic has a significance level
  greater than the SLENTRY value, the FORWARD
  selection stops.

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Forward Selection in PROC REG

• Otherwise, the FORWARD method adds the variable
  that has the largest F statistic to the model.
• The FORWARD method then calculates F statistics
  again for the variables still remaining outside
  the model, and the evaluation process is
  repeated.
• Thus, variables are added one by one to the model
  until no remaining variable produces a
  significant F statistic.
• Once a variable is in the model, it stays.

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Backward Elimination in PROC REG

• The backward elimination technique begins by
  calculating F statistics for a model, including
  all of the independent variables.
• Then the variables are deleted from the model one
  by one until all the variables remaining in the
  model produce F statistics significant at the
  SLSTAY level specified in the MODEL statement
  (or at the 0.10 level if the SLSTAY option is
  omitted).
• At each step, the variable showing the smallest
  contribution to the model is deleted.

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Stepwisein PROC REG

• The stepwise method is a modification of the
  forward-selection technique and differs in that
  variables already in the model do not necessarily
  stay there.
• As in the forward-selection method, variables are
  added one by one to the model, and the F
  statistic for a variable to be added must be
  significant at the SLENTRY level.
• After a variable is added, however, the stepwise
  method looks at all the variables already
  included in the model and deletes any variable
  that does not produce an F statistic significant
  at the SLSTAY level.

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Stepwisein PROC REG

• Only after this check is made and the necessary
  deletions accomplished can another variable be
  added to the model.
• The stepwise process ends when none of the
  variables outside the model has an F statistic
  significant at the SLENTRY level and every
  variable in the model is significant at the
  SLSTAY level, or when the variable to be added
  to the model is the one just deleted from it.

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Regression in PROC GLM

• Unlike PROC REG, PROC GLM allows polynomial terms
  or interaction terms in the MODEL statement.
• e.g. xx, x1x2, etc
• MODEL option SOLUTION
• produces a solution to the normal equations
  (parameter estimates). PROC GLM displays a
  solution by default when your model involves no
  classification variables, so you need this option
  only if you want to see the solution for models
  with classification effects.

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Example

• data mileage
• input mph mpg _at__at_
• datalines
• 20 15.4
• 30 20.2
• 40 25.7
• 50 26.2 50 26.6 50 27.4
• 55 .
• 60 24.8

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Example

• proc glm
• model mpgmph mphmph / p clm
• output outpp pmpgpred rresid
• axis1 minornone major(number5)
• axis2 minornone major(number8)
• symbol1 cblack inone vplus
• symbol2 cblack ispline vnone
• proc gplot datapp
• plot mpgmph1 mpgpredmph2 /
• overlay haxisaxis1 vaxisaxis2
• run

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Assignment

• Read in lowbwt.xls
• Find a good model for HEADCIRC
• Model section (forward and backward) play with
  SLSTAY SLENTRY in PROC REG
• Check interaction between MOMAGE and TOX