A Dimension Preserving Variant of "sapply" and "lapply"
Sapply.RdSapply is equivalent to sapply, except
that it preserves the dimension and dimension names of the
argument X. It also preserves the dimension of
results of the function FUN.
It is intended for application to results e.g.
of a call to by. Lapply is an analog
to lapply insofar as it does not try to simplify
the resulting list of results of FUN.
Arguments
- X
a vector or list appropriate to a call to
sapply.- FUN
a function.
- ...
optional arguments to
FUN.- simplify
a logical value; should the result be simplified to a vector or matrix if possible?
- USE.NAMES
logical; if
TRUEand ifXis character, useXas names for the result unless it had names already.
Value
If FUN returns a scalar, then the result has the same dimension
as X, otherwise the dimension of the result is enhanced relative
to X.
Examples
berkeley <- Aggregate(Table(Admit,Freq)~.,data=UCBAdmissions)
berktest1 <- By(~Dept+Gender,
glm(cbind(Admitted,Rejected)~1,family="binomial"),
data=berkeley)
berktest2 <- By(~Dept,
glm(cbind(Admitted,Rejected)~Gender,family="binomial"),
data=berkeley)
sapply(berktest1,coef)
#> (Intercept) (Intercept) (Intercept) (Intercept) (Intercept) (Intercept)
#> 0.4921214 0.5337493 -0.5355182 -0.7039581 -0.9569618 -2.7697438
#> (Intercept) (Intercept) (Intercept) (Intercept) (Intercept) (Intercept)
#> 1.5441974 0.7537718 -0.6604399 -0.6219709 -1.1571488 -2.5808479
Sapply(berktest1,coef)
#> Gender
#> Dept Male Female
#> A 0.4921214 1.5441974
#> B 0.5337493 0.7537718
#> C -0.5355182 -0.6604399
#> D -0.7039581 -0.6219709
#> E -0.9569618 -1.1571488
#> F -2.7697438 -2.5808479
sapply(berktest1,function(x)drop(coef(summary(x))))
#> [,1] [,2] [,3] [,4] [,5]
#> Estimate 4.921214e-01 5.337493e-01 -5.355182e-01 -7.039581e-01 -9.569618e-01
#> Std. Error 7.174966e-02 8.754301e-02 1.149408e-01 1.040702e-01 1.615992e-01
#> z value 6.858868e+00 6.096995e+00 -4.659080e+00 -6.764263e+00 -5.921822e+00
#> Pr(>|z|) 6.940823e-12 1.080812e-09 3.176259e-06 1.339898e-11 3.183932e-09
#> [,6] [,7] [,8] [,9] [,10]
#> Estimate -2.769744e+00 1.544197e+00 0.75377180 -6.604399e-01 -6.219709e-01
#> Std. Error 2.197807e-01 2.527203e-01 0.42874646 8.664894e-02 1.083141e-01
#> z value -1.260231e+01 6.110303e+00 1.75808285 -7.622019e+00 -5.742289e+00
#> Pr(>|z|) 2.050557e-36 9.944221e-10 0.07873341 2.497388e-14 9.340538e-09
#> [,11] [,12]
#> Estimate -1.157149e+00 -2.580848e+00
#> Std. Error 1.182487e-01 2.117103e-01
#> z value -9.785721e+00 -1.219047e+01
#> Pr(>|z|) 1.296674e-22 3.493965e-34
Sapply(berktest1,function(x)drop(coef(summary(x))))
#> , , Gender = Male
#>
#> Dept
#> A B C D
#> Estimate 4.921214e-01 5.337493e-01 -5.355182e-01 -7.039581e-01
#> Std. Error 7.174966e-02 8.754301e-02 1.149408e-01 1.040702e-01
#> z value 6.858868e+00 6.096995e+00 -4.659080e+00 -6.764263e+00
#> Pr(>|z|) 6.940823e-12 1.080812e-09 3.176259e-06 1.339898e-11
#> Dept
#> E F
#> Estimate -9.569618e-01 -2.769744e+00
#> Std. Error 1.615992e-01 2.197807e-01
#> z value -5.921822e+00 -1.260231e+01
#> Pr(>|z|) 3.183932e-09 2.050557e-36
#>
#> , , Gender = Female
#>
#> Dept
#> A B C D E
#> Estimate 1.544197e+00 0.75377180 -6.604399e-01 -6.219709e-01 -1.157149e+00
#> Std. Error 2.527203e-01 0.42874646 8.664894e-02 1.083141e-01 1.182487e-01
#> z value 6.110303e+00 1.75808285 -7.622019e+00 -5.742289e+00 -9.785721e+00
#> Pr(>|z|) 9.944221e-10 0.07873341 2.497388e-14 9.340538e-09 1.296674e-22
#> Dept
#> F
#> Estimate -2.580848e+00
#> Std. Error 2.117103e-01
#> z value -1.219047e+01
#> Pr(>|z|) 3.493965e-34
#>
sapply(berktest2,coef)
#> A B C D E F
#> (Intercept) 0.4921214 0.5337493 -0.5355182 -0.70395810 -0.9569618 -2.7697438
#> GenderFemale 1.0520760 0.2200225 -0.1249216 0.08198719 -0.2001870 0.1888958
Sapply(berktest2,coef)
#> Dept
#> A B C D E F
#> (Intercept) 0.4921214 0.5337493 -0.5355182 -0.70395810 -0.9569618 -2.7697438
#> GenderFemale 1.0520760 0.2200225 -0.1249216 0.08198719 -0.2001870 0.1888958
sapply(berktest2,function(x)coef(summary(x)))
#> A B C D E
#> [1,] 4.921214e-01 5.337493e-01 -5.355182e-01 -7.039581e-01 -9.569618e-01
#> [2,] 1.052076e+00 2.200225e-01 -1.249216e-01 8.198719e-02 -2.001870e-01
#> [3,] 7.174966e-02 8.754301e-02 1.149408e-01 1.040702e-01 1.615992e-01
#> [4,] 2.627081e-01 4.375926e-01 1.439424e-01 1.502084e-01 2.002426e-01
#> [5,] 6.858868e+00 6.096994e+00 -4.659080e+00 -6.764263e+00 -5.921822e+00
#> [6,] 4.004734e+00 5.028022e-01 -8.678583e-01 5.458231e-01 -9.997227e-01
#> [7,] 6.940825e-12 1.080813e-09 3.176259e-06 1.339898e-11 3.183932e-09
#> [8,] 6.208742e-05 6.151033e-01 3.854719e-01 5.851875e-01 3.174447e-01
#> F
#> [1,] -2.769744e+00
#> [2,] 1.888958e-01
#> [3,] 2.197807e-01
#> [4,] 3.051635e-01
#> [5,] -1.260231e+01
#> [6,] 6.189987e-01
#> [7,] 2.050557e-36
#> [8,] 5.359172e-01
Sapply(berktest2,function(x)coef(summary(x)))
#> , , Dept = A
#>
#>
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.4921214 0.07174966 6.858868 6.940825e-12
#> GenderFemale 1.0520760 0.26270810 4.004734 6.208742e-05
#>
#> , , Dept = B
#>
#>
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) 0.5337493 0.08754301 6.0969945 1.080813e-09
#> GenderFemale 0.2200225 0.43759263 0.5028022 6.151033e-01
#>
#> , , Dept = C
#>
#>
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -0.5355182 0.1149408 -4.6590799 3.176259e-06
#> GenderFemale -0.1249216 0.1439424 -0.8678583 3.854719e-01
#>
#> , , Dept = D
#>
#>
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -0.70395810 0.1040702 -6.7642627 1.339898e-11
#> GenderFemale 0.08198719 0.1502084 0.5458231 5.851875e-01
#>
#> , , Dept = E
#>
#>
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -0.9569618 0.1615992 -5.9218225 3.183932e-09
#> GenderFemale -0.2001870 0.2002426 -0.9997227 3.174447e-01
#>
#> , , Dept = F
#>
#>
#> Estimate Std. Error z value Pr(>|z|)
#> (Intercept) -2.7697438 0.2197807 -12.6023077 2.050557e-36
#> GenderFemale 0.1888958 0.3051635 0.6189987 5.359172e-01
#>