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Conditional Evaluation of an Expression

By.Rd

The function By evaluates an expression within subsets of a data frame, where the subsets are defined by a formula.

Usage

By(formula,expr,data=parent.frame())

Arguments

formula

an expression or (preferably) a formula containing the names of conditioning variables or factors.

expr

an expression that is evaluated for any unique combination of values of the variables contained in formula.

data

a data frame, an object that can be coerced into a data frame (for example, a table), or an environment, from which values for the variables in formula or expr are taken.

Value

A list of class "by", giving the results for each combination of values of variables in formula.

Examples


berkeley <- Aggregate(Table(Admit,Freq)~.,data=UCBAdmissions)
(Bres <- By(~Dept,glm(cbind(Admitted,Rejected)~Gender,family="binomial"),data=berkeley))
#> Dept: A
#> 
#> Call:  glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial")
#> 
#> Coefficients:
#>  (Intercept)  GenderFemale  
#>       0.4921        1.0521  
#> 
#> Degrees of Freedom: 1 Total (i.e. Null);  0 Residual
#> Null Deviance:	    19.05 
#> Residual Deviance: 5.551e-15 	AIC: 15.71
#> ------------------------------------------------------------ 
#> Dept: B
#> 
#> Call:  glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial")
#> 
#> Coefficients:
#>  (Intercept)  GenderFemale  
#>       0.5337        0.2200  
#> 
#> Degrees of Freedom: 1 Total (i.e. Null);  0 Residual
#> Null Deviance:	    0.2586 
#> Residual Deviance: 0 	AIC: 14.27
#> ------------------------------------------------------------ 
#> Dept: C
#> 
#> Call:  glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial")
#> 
#> Coefficients:
#>  (Intercept)  GenderFemale  
#>      -0.5355       -0.1249  
#> 
#> Degrees of Freedom: 1 Total (i.e. Null);  0 Residual
#> Null Deviance:	    0.751 
#> Residual Deviance: -4.929e-14 	AIC: 16.9
#> ------------------------------------------------------------ 
#> Dept: D
#> 
#> Call:  glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial")
#> 
#> Coefficients:
#>  (Intercept)  GenderFemale  
#>     -0.70396       0.08199  
#> 
#> Degrees of Freedom: 1 Total (i.e. Null);  0 Residual
#> Null Deviance:	    0.2979 
#> Residual Deviance: 1.883e-13 	AIC: 16.65
#> ------------------------------------------------------------ 
#> Dept: E
#> 
#> Call:  glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial")
#> 
#> Coefficients:
#>  (Intercept)  GenderFemale  
#>      -0.9570       -0.2002  
#> 
#> Degrees of Freedom: 1 Total (i.e. Null);  0 Residual
#> Null Deviance:	    0.9904 
#> Residual Deviance: 1.821e-14 	AIC: 15.6
#> ------------------------------------------------------------ 
#> Dept: F
#> 
#> Call:  glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial")
#> 
#> Coefficients:
#>  (Intercept)  GenderFemale  
#>      -2.7697        0.1889  
#> 
#> Degrees of Freedom: 1 Total (i.e. Null);  0 Residual
#> Null Deviance:	    0.3836 
#> Residual Deviance: -4.863e-14 	AIC: 13.83
# The results all have 'data' components
str(Bres[[1]]$data)
#> 'data.frame':	2 obs. of  4 variables:
#>  $ Gender  : Factor w/ 2 levels "Male","Female": 1 2
#>  $ Dept    : Factor w/ 6 levels "A","B","C","D",..: 1 1
#>  $ Admitted: num  512 89
#>  $ Rejected: num  313 19

attach(berkeley)
(Bres <- By(~Dept,glm(cbind(Admitted,Rejected)~Gender,family="binomial")))
#> Dept: A
#> 
#> Call:  glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial")
#> 
#> Coefficients:
#>  (Intercept)  GenderFemale  
#>       0.4921        1.0521  
#> 
#> Degrees of Freedom: 1 Total (i.e. Null);  0 Residual
#> Null Deviance:	    19.05 
#> Residual Deviance: 5.551e-15 	AIC: 15.71
#> ------------------------------------------------------------ 
#> Dept: B
#> 
#> Call:  glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial")
#> 
#> Coefficients:
#>  (Intercept)  GenderFemale  
#>       0.5337        0.2200  
#> 
#> Degrees of Freedom: 1 Total (i.e. Null);  0 Residual
#> Null Deviance:	    0.2586 
#> Residual Deviance: 0 	AIC: 14.27
#> ------------------------------------------------------------ 
#> Dept: C
#> 
#> Call:  glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial")
#> 
#> Coefficients:
#>  (Intercept)  GenderFemale  
#>      -0.5355       -0.1249  
#> 
#> Degrees of Freedom: 1 Total (i.e. Null);  0 Residual
#> Null Deviance:	    0.751 
#> Residual Deviance: -4.929e-14 	AIC: 16.9
#> ------------------------------------------------------------ 
#> Dept: D
#> 
#> Call:  glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial")
#> 
#> Coefficients:
#>  (Intercept)  GenderFemale  
#>     -0.70396       0.08199  
#> 
#> Degrees of Freedom: 1 Total (i.e. Null);  0 Residual
#> Null Deviance:	    0.2979 
#> Residual Deviance: 1.883e-13 	AIC: 16.65
#> ------------------------------------------------------------ 
#> Dept: E
#> 
#> Call:  glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial")
#> 
#> Coefficients:
#>  (Intercept)  GenderFemale  
#>      -0.9570       -0.2002  
#> 
#> Degrees of Freedom: 1 Total (i.e. Null);  0 Residual
#> Null Deviance:	    0.9904 
#> Residual Deviance: 1.821e-14 	AIC: 15.6
#> ------------------------------------------------------------ 
#> Dept: F
#> 
#> Call:  glm(formula = cbind(Admitted, Rejected) ~ Gender, family = "binomial")
#> 
#> Coefficients:
#>  (Intercept)  GenderFemale  
#>      -2.7697        0.1889  
#> 
#> Degrees of Freedom: 1 Total (i.e. Null);  0 Residual
#> Null Deviance:	    0.3836 
#> Residual Deviance: -4.863e-14 	AIC: 13.83
detach(berkeley)