toDataFrame: Convert an Array into a Data Frame

Description Usage Arguments Value Examples

Description

to.data.frame converts an array into a data frame, in such a way that a chosen dimensional extent forms variables in the data frame. The elements of the array must be either atomic, data frames with matching variables, or coercable into such data frames.

Usage

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to.data.frame(X,as.vars=1,name="Freq")

Arguments

X

an array.

as.vars

a numeric value; indicates the dimensional extend which defines the variables. Takes effect only if X is an atomic array. If as.vars equals zero, a new variable is created that contains the values of the array, that is, to.data.frame acts on the array X like as.data.frame(as.table(X))

name

a character string; the name of the variable created if X is an atomic array and as.vars equals zero.

Value

A data frame.

Examples

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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)
Stest1 <- Lapply(berktest2,function(x)predict(x,,se.fit=TRUE)[c("fit","se.fit")])
Stest2 <- Sapply(berktest2,function(x)coef(summary(x)))
Stest2.1 <- Lapply(berktest1,function(x)predict(x,,se.fit=TRUE)[c("fit","se.fit")])
to.data.frame(Stest1)
to.data.frame(Stest2,as.vars=2)
to.data.frame(Stest2.1)

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

   Dept        fit     se.fit
1     A  0.4921214 0.07174966
2     A  1.5441974 0.25272027
3     B  0.5337493 0.08754301
4     B  0.7537718 0.42874646
5     C -0.5355182 0.11494077
6     C -0.6604399 0.08664894
7     D -0.7039581 0.10407019
8     D -0.6219709 0.10831411
9     E -0.9569618 0.16159920
10    E -1.1571488 0.11824880
11    F -2.7697438 0.21978068
12    F -2.5808479 0.21171027
           Var1 Dept    Estimate Std. Error     z value     Pr(>|z|)
1   (Intercept)    A  0.49212143 0.07174966   6.8588682 6.940825e-12
2  GenderFemale    A  1.05207596 0.26270810   4.0047336 6.208742e-05
3   (Intercept)    B  0.53374926 0.08754301   6.0969945 1.080813e-09
4  GenderFemale    B  0.22002254 0.43759263   0.5028022 6.151033e-01
5   (Intercept)    C -0.53551824 0.11494077  -4.6590799 3.176259e-06
6  GenderFemale    C -0.12492163 0.14394242  -0.8678583 3.854719e-01
7   (Intercept)    D -0.70395810 0.10407019  -6.7642627 1.339898e-11
8  GenderFemale    D  0.08198719 0.15020836   0.5458231 5.851875e-01
9   (Intercept)    E -0.95696177 0.16159920  -5.9218225 3.183932e-09
10 GenderFemale    E -0.20018702 0.20024255  -0.9997227 3.174447e-01
11  (Intercept)    F -2.76974377 0.21978068 -12.6023077 2.050557e-36
12 GenderFemale    F  0.18889583 0.30516354   0.6189987 5.359172e-01
   Dept Gender        fit     se.fit
1     A   Male  0.4921214 0.07174966
2     B   Male  0.5337493 0.08754301
3     C   Male -0.5355182 0.11494077
4     D   Male -0.7039581 0.10407019
5     E   Male -0.9569618 0.16159920
6     F   Male -2.7697438 0.21978068
7     A Female  1.5441974 0.25272027
8     B Female  0.7537718 0.42874646
9     C Female -0.6604399 0.08664894
10    D Female -0.6219709 0.10831411
11    E Female -1.1571488 0.11824870
12    F Female -2.5808479 0.21171027

memisc documentation built on Dec. 23, 2017, 5:09 p.m.

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