Sapply memisc 0.99.26.3

A Dimension Preserving Variant of “sapply” and “lapply”

Description

Sapply 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.

Usage

Sapply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)
Lapply(X, 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 TRUE and if X is character, use X as 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)
  (Intercept) (Intercept)
0.4921214 0.5337493 -0.5355182 -0.7039581 -0.9569618 -2.7697438 1.5441974
  0.7537718
(Intercept) (Intercept) (Intercept) (Intercept)
 -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] [,6]
Estimate 4.921214e-01 5.337493e-01 -5.355182e-01 -7.039581e-01 -9.569618e-01
  -2.769744e+00
Std. Error 7.174966e-02 8.754301e-02 1.149408e-01 1.040702e-01 1.615992e-01
  2.197807e-01
z value 6.858868e+00 6.096995e+00 -4.659080e+00 -6.764263e+00 -5.921822e+00
  -1.260231e+01
Pr(>|z|) 6.940823e-12 1.080812e-09 3.176259e-06 1.339898e-11 3.183932e-09
  2.050557e-36
[,7] [,8] [,9] [,10] [,11] [,12]
Estimate 1.544197e+00 0.75377180 -6.604399e-01 -6.219709e-01 -1.157149e+00
  -2.580848e+00
Std. Error 2.527203e-01 0.42874646 8.664894e-02 1.083141e-01 1.182487e-01
  2.117103e-01
z value 6.110303e+00 1.75808285 -7.622019e+00 -5.742289e+00 -9.785721e+00
  -1.219047e+01
Pr(>|z|) 9.944221e-10 0.07873341 2.497388e-14 9.340538e-09 1.296674e-22
  3.493965e-34

Sapply(berktest1,function(x)drop(coef(summary(x))))

, , Gender = Male

            Dept
A B C D E F
Estimate 4.921214e-01 5.337493e-01 -5.355182e-01 -7.039581e-01 -9.569618e-01
  -2.769744e+00
Std. Error 7.174966e-02 8.754301e-02 1.149408e-01 1.040702e-01 1.615992e-01
  2.197807e-01
z value 6.858868e+00 6.096995e+00 -4.659080e+00 -6.764263e+00 -5.921822e+00
  -1.260231e+01
Pr(>|z|) 6.940823e-12 1.080812e-09 3.176259e-06 1.339898e-11 3.183932e-09
  2.050557e-36

, , Gender = Female

            Dept
A B C D E F
Estimate 1.544197e+00 0.75377180 -6.604399e-01 -6.219709e-01 -1.157149e+00
  -2.580848e+00
Std. Error 2.527203e-01 0.42874646 8.664894e-02 1.083141e-01 1.182487e-01
  2.117103e-01
z value 6.110303e+00 1.75808285 -7.622019e+00 -5.742289e+00 -9.785721e+00
  -1.219047e+01
Pr(>|z|) 9.944221e-10 0.07873341 2.497388e-14 9.340538e-09 1.296674e-22
  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 F
[1,] 4.921214e-01 5.337493e-01 -5.355182e-01 -7.039581e-01 -9.569618e-01
  -2.769744e+00
[2,] 1.052076e+00 2.200225e-01 -1.249216e-01 8.198719e-02 -2.001870e-01
  1.888958e-01
[3,] 7.174966e-02 8.754301e-02 1.149408e-01 1.040702e-01 1.615992e-01
  2.197807e-01
[4,] 2.627081e-01 4.375926e-01 1.439424e-01 1.502084e-01 2.002426e-01
  3.051635e-01
[5,] 6.858868e+00 6.096994e+00 -4.659080e+00 -6.764263e+00 -5.921822e+00
  -1.260231e+01
[6,] 4.004734e+00 5.028022e-01 -8.678583e-01 5.458231e-01 -9.997227e-01
  6.189987e-01
[7,] 6.940825e-12 1.080813e-09 3.176259e-06 1.339898e-11 3.183932e-09
  2.050557e-36
[8,] 6.208742e-05 6.151033e-01 3.854719e-01 5.851875e-01 3.174447e-01
  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