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
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)
(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