Transcript Lecture 10 All Possible Regressions and Statistics for Comparing

All Possible Regressions and
Statistics for Comparing
Models
Animal Science 500
Lecture No. 12
October 12, 2010
IOWA STATE UNIVERSITY
Department of Animal Science
Example analysis
 The
RSQUARE Procedure RECALL
 The
RSQUARE procedure selects optimal
subsets of independent variables in a multiple
regression analysis
IOWA STATE UNIVERSITY
Department of Animal Science
Example analysis
PROC RSQUARE options;
MODEL dependents = independents / options;
(options can appear in either PROC RSQUARE or
any MODEL statement).
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SELECT = n specific maximum number of subset models
INCLUDE = I requests that the first I variables after the
equal sign be included in every regression
SIGMA = n specifies the true standard deviation of the
error term
ADJRSQ computes R2 adjusted for degrees of freedom
CP computes MALLOWS’ Cp statistic
IOWA STATE UNIVERSITY
Department of Animal Science
Example analysis
PROC RSQUARE options;
MODEL dependents = independents / options;
(options can appear in either PROC RSQUARE or
any MODEL statement).
PROC RSQUARE DATA=name OUTEST=EST
ADJRSQ MSE CP;
SELECT=n;
MODEL = variable list;
IOWA STATE UNIVERSITY
Department of Animal Science
Example analysis
PROC PRINT DATA=EST;
PROC PLOT;
PLOT _CP_*_P_ = ‘C’ _P_*_P_ = ‘P’ /
OVERLAY;
PLOT _MSE_*_P_ = ‘M’;
Run;
Quit
IOWA STATE UNIVERSITY
Department of Animal Science
PROC STEPWISE
The STEPWISE procedure provides five methods
for stepwise regression.
General form:
PROC STEPWISE;
MODEL dependents = independents / options;
Run;
Quit;
** Assumes that you have at least one dependent variable and 2 or more independent
variables. If only one independent variable exists then you are just doing a simple
regression of x on y or y on x.
IOWA STATE UNIVERSITY
Department of Animal Science
Types of Regression
 Uses
of PROC REG for standard problems:
1.
PROC REG;
model y = x;
/* simple linear regression */
2.
PROC REG;
model y = x;
weight w;
/* weighted linear regression */
3.
PROC REG;
/* multiple regression */
model y = x1 x2 x3;
IOWA STATE UNIVERSITY
Department of Animal Science
PROC REG
General form:
PROC REG;
MODEL dependents = independents / options;
Options available include:
NOINT – regression with no intercept
FORWARD
A forward selection analysis starts out with no predictors in the model.
Each predictor that that was chosen by the user is evaluated with respect to see how much the R 2 is increased
by adding it to the model.
The predictor that increases the R2 will be added if it meets the statistical conditions for entry
With SAS the statistical conditions is the significance level for the increase in the R2 produced by addition of the
predictor.
If no predictor meets the condition, the analysis stops.
If a predictor is added, then the second step involves re-evaluating all of the available predictors which have not
yet been entered into the model.
If any satisfy the statistical condition for entry, the predictor increasing the R2 the greatest is added.
This process is continued until no predictors remain that could enter.
IOWA STATE UNIVERSITY
Department of Animal Science
PROC REG
General form:
PROC REG;
MODEL dependents = independents / options;
Options available include:
BACKWARD
In a backwards elimination analysis we start out with all of the predictors in the model.
At each step we evaluate the predictors which are in the model and eliminate any that meet the criterion for
removal.
STEPWISE
Stepwise selection begins similar to forwards selection. However at each “step” variables that are in the model
are first evaluated for removal. Those meeting removal criteria are evaluated to see which would lower the R 2,
the least.
How does this work where a variable enters and then might leave later? If two predictors ultimately enter the
model, one may be removed because they are well correlated and removing one impacts the R 2 very little if at
all.
IOWA STATE UNIVERSITY
Department of Animal Science
PROC REG
General form:
PROC REG;
MODEL dependents = independents / options;
Options available include:
MAXR
The maximum R2 option does not settle on a single model. Instead, it tries to
find the "best" one-variable model, the "best" two-variable model, and so forth. ,
MAXR starts out by finding the single variable model producing the greatest R2
After finding the one variable MAXR then another variable is added until it finds
the variable that increases the R2 the most. It continues this process until it
stops where the addition of another variable is no better than the previous (i.e.
adding the 4th variable did not significantly improve the R2 compared to the 3
variable model for example.
The difference between the STEPWISE and MAXR options is that all switches
are evaluated before any switch is made in the MAXR method .
Using the STEPWISE option, the "worst" variable may be removed without
considering what adding the "best" remaining variable might accomplish.
IOWA STATE UNIVERSITY
Department of Animal Science
PROC REG
General form:
PROC REG;
MODEL dependents = independents / options;
Options available include:
MINR
The MINR option closely resembles the MAXR method. However, the switch
chosen with the MINR option is switch that produces the smallest increase in
R2. In a way approaching the “best” model in reverse compared to MAXR.
IOWA STATE UNIVERSITY
Department of Animal Science
PROC REG
General form:
PROC REG;
MODEL dependents = independents / options;
Options available include:
SLE=value
This option sets some criterion for entry into the model. This can be defined
by the user by meeting some level of change or Δ to the R2
SLS=value
This option sets some criterion for staying or remaining in the model. This can
be defined by the user by meeting some level of change or Δ to the R2
to stay in the model.
IOWA STATE UNIVERSITY
Department of Animal Science
PROC REG
 The
default statistical levels for each type of
regression analysis is different unless it is
changed by the user:
 The
defaults are:
BACKWARD = 0.10
FORWARD = 0.10
STEPWISE = 0.15
User can set it by using the SLSTAY option for
example / SLSTAY=.05.
IOWA STATE UNIVERSITY
Department of Animal Science
Significance Tests for the Regression Coefficients
1.
Finding the significance of the parameter estimates by using the F or t
test (will see in a couple of slides)
2.
R2 = R-Square is the proportion of variation in the dependent variable
(Y) that can be explained by the predictors (X variables) in the
regression model.
3.
Adjusted R2 Predictors could be added to the model which would
continue to improve the ability of the predictors to explain the dependent
variable. Some of the improvement in the R-Square would be simply
due to chance variation. The adjusted R-Square attempts to yield a
more honest value to estimate R-Square.
= 1-(1-R2) (n-1)/(n-p-1)
where
R2 = the unadjusted R2
n = the number of number of observations, and
p = the number of predictors
IOWA STATE UNIVERSITY
Department of Animal Science
Significance Tests for the Regression Coefficients

The Mallows’ Cp statistic

CP (Cp) = SSE / σ2 + 2p – n
where
SSE
=
error sums of squares
σ2
=
the estimate of pure error variance from
the SIGMA = option for from fitting the full
model
p
=
the number of parameters including
the intercept, and
n
=
the number of observations
IOWA STATE UNIVERSITY
Department of Animal Science
F and T tests for significance for overall model
F 
Model variance
error variance

R2 / p
(1  R 2 ) /( n  p  1)
where
p  number of parameters
n  sample size
t 

F
(n  2) * r 2
1 r2
IOWA STATE UNIVERSITY
Department of Animal Science