toDataFrame: Convert an Array into a Data Frame

In memisc: Tools for Management of Survey Data and the Presentation of Analysis Results

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

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

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 May 2, 2019, 5:45 p.m.