Sapply: A Dimension Preserving Variant of "sapply" and "lapply"
In memisc: Tools for Management of Survey Data and the Presentation of Analysis Results
Description
Usage
Arguments
Value
Examples
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
1
2
3
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
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
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)
Sapply(berktest1,coef)
sapply(berktest1,function(x)drop(coef(summary(x))))
Sapply(berktest1,function(x)drop(coef(summary(x))))
sapply(berktest2,coef)
Sapply(berktest2,coef)
sapply(berktest2,function(x)coef(summary(x)))
Sapply(berktest2,function(x)coef(summary(x)))
Example output
Loading required package: lattice
Loading required package: MASS
Attaching package: 'memisc'
The following objects are masked from 'package:stats':
contr.sum, contr.treatment, contrasts
The following object is masked from 'package:base':
as.array
(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
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
[,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
, , 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
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
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
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
, , 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
memisc documentation built on May 2, 2019, 5:45 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Examples
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
1 2 3 |
Arguments
X |
a vector or list appropriate to a call to |
FUN |
a function. |
... |
optional arguments to |
simplify |
a logical value; should the result be simplified to a vector or matrix if possible? |
USE.NAMES |
logical; if |
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | 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)
Sapply(berktest1,coef)
sapply(berktest1,function(x)drop(coef(summary(x))))
Sapply(berktest1,function(x)drop(coef(summary(x))))
sapply(berktest2,coef)
Sapply(berktest2,coef)
sapply(berktest2,function(x)coef(summary(x)))
Sapply(berktest2,function(x)coef(summary(x)))
|
Example output
Loading required package: lattice
Loading required package: MASS
Attaching package: 'memisc'
The following objects are masked from 'package:stats':
contr.sum, contr.treatment, contrasts
The following object is masked from 'package:base':
as.array
(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
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
[,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
, , 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
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
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
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
, , 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
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