Memisc | R Documentation |

This package collects an assortment of tools that are intended to make
work with `R`

easier for the author of this package
and are submitted to the public in the hope that they will be also be useful to others.

The tools in this package can be grouped into four major categories:

Data preparation and management

Data analysis

Presentation of analysis results

Programming

`memisc`

provides facilities to work with what users from other
packages like SPSS, SAS, or Stata know as ‘variable labels’, ‘value labels’
and ‘user-defined missing values’. In the context of this package these
aspects of the data are represented by the `"description"`

,
`"labels"`

, and `"missing.values"`

attributes of a
data vector.
These facilities are useful, for example, if you work with
survey data that contain coded items like vote intention that
may have the following structure:

Question: “If there was a parliamentary election next tuesday, which party would you vote for?”

1 | Conservative Party |

2 | Labour Party |

3 | Liberal Democrat Party |

4 | Scottish Nation Party |

5 | Plaid Cymru |

6 | Green Party |

7 | British National Party |

8 | Other party |

96 | Not allowed to vote |

97 | Would not vote |

98 | Would vote, do not know yet for which party |

99 | No answer |

A statistical package like SPSS allows to
attach labels like ‘Conservative Party’, ‘Labour Party’, etc.
to the codes 1,2,3, etc. and to mark
mark the codes 96, 97, 98, 99
as ‘missing’ and thus to exclude these variables from statistical
analyses. `memisc`

provides similar facilities.
Labels can be attached to codes by calls like `labels(x) <- something`

and expendanded by calls like `labels(x) <- labels(x) + something`

,
codes can be marked as ‘missing’ by
calls like `missing.values(x) <- something`

and
`missing.values(x) <- missing.values(x) + something`

.

`memisc`

defines a class called "data.set", which is similar to the class "data.frame".
The main difference is that it is especially geared toward containing survey item data.
Transformations of and within "data.set" objects retain the information about
value labels, missing values etc. Using `as.data.frame`

sets the data up for
*R*'s statistical functions, but doing this explicitely is seldom necessary.
See `data.set`

.

Survey data sets are often relative large and contain up to a few thousand variables.
For specific analyses one needs however only a relatively small subset of these variables.
Although modern computers have enough RAM to load such data sets completely into an R session,
this is not very efficient having to drop most of the variables after loading. Also, loading
such a large data set completely can be time-consuming, because R has to allocate space for
each of the many variables. Loading just the subset of variables really needed for an analysis
is more efficient and convenient - it tends to be much quicker. Thus this package provides
facilities to load such subsets of variables, without the need to load a complete data set.
Further, the loading of data from SPSS files is organized in such a way that all informations
about variable labels, value labels, and user-defined missing values are retained.
This is made possible by the definition of `importer`

objects, for which
a `subset`

method exists. `importer`

objects contain only
the information about the variables in the external data set but not the data.
The data itself is loaded into memory when the functions `subset`

or `as.data.set`

are used.

`memisc`

also contains facilities for recoding
survey items. Simple recodings, for example collapsing answer
categories, can be done using the function `recode`

. More
complex recodings, for example the construction of indices from
multiple items, and complex case distinctions, can be done
using the function `cases`

. This function may also
be useful for programming, in so far as it is a generalization of
`ifelse`

.

There is a function `codebook`

which produces a code book of an
external data set or an internal "data.set" object. A codebook contains in a
conveniently formatted way concise information about every variable in a data set,
such as which value labels and missing values are defined and some univariate statistics.

An extended example of all these facilities is contained in the vignette "anes48",
and in `demo(anes48)`

`genTable`

is a generalization of `xtabs`

:
Instead of counts, also descriptive statistics like means or variances
can be reported conditional on levels of factors. Also conditional
percentages of a factor can be obtained using this function.

In addition an `Aggregate`

function is provided, which has the same syntax as `genTable`

, but
gives a data frame of descriptive statistics instead of a `table`

object.

`By`

is a variant of the
standard function `by`

: Conditioning factors
are specified by a formula and are
obtained from the data frame the subsets of which are to be analysed.
Therefore there is no need to `attach`

the data frame
or to use the dollar operator.

Journals of the Political and Social Sciences usually require that estimates of regression models are presented in the following form:

================================================== Model 1 Model 2 Model 3 -------------------------------------------------- Coefficients (Intercept) 30.628*** 6.360*** 28.566*** (7.409) (1.252) (7.355) pop15 -0.471** -0.461** (0.147) (0.145) pop75 -1.934 -1.691 (1.041) (1.084) dpi 0.001 -0.000 (0.001) (0.001) ddpi 0.529* 0.410* (0.210) (0.196) -------------------------------------------------- Summaries R-squared 0.262 0.162 0.338 adj. R-squared 0.230 0.126 0.280 N 50 50 50 ==================================================

Such tables of coefficient estimates can be produced
by `mtable`

. To see some of the possibilities of
this function, use `example(mtable)`

.

Output produced by `mtable`

can be transformed into
LaTeX tables by an appropriate method of the generic function
`toLatex`

which is defined in the package
`utils`

. In addition, `memisc`

defines `toLatex`

methods
for matrices and `ftable`

objects. Note that
results produced by `genTable`

can be coerced into
`ftable`

objects. Also, a default method
for the `toLatex`

function is defined which coerces its
argument to a matrix and applies the matrix method of `toLatex`

.

Sometimes users want to contruct loops that run over variables rather than values.
For example, if one wants to set the missing values of a battery of items.
For this purpose, the package contains the function `foreach`

.
To set 8 and 9 as missing values for the items `knowledge1`

,
`knowledge2`

, `knowledge3`

, one can use

foreach(x=c(knowledge1,knowledge2,knowledge3), missing.values(x) <- 8:9)

`R`

already makes it possible to change the names of an object.
Substituting the `names`

or `dimnames`

can be done with some programming tricks. This package defines
the function `rename`

,
`dimrename`

, `colrename`

, and `rowrename`

that implement these tricks in a convenient way, so that programmers
(like the author of this package) need not reinvent the weel in
every instance of changing names of an object.

`lapply`

and `sapply`

If a function that is involved in a call to
`sapply`

returns a result an array or a matrix, the
dimensional information gets lost. Also, if a list object to which
`lapply`

or `sapply`

are applied
have a dimension attribute, the result looses this information.
The functions `Lapply`

and
`Sapply`

defined in this package preserve such
dimensional information.

The generic function `collect`

collects several objects of the
same mode into one object, using their names, `rownames`

,
`colnames`

and/or `dimnames`

. There are methods for
atomic vectors, arrays (including matrices), and data frames.
For example

a <- c(a=1,b=2) b <- c(a=10,c=30) collect(a,b)

leads to

x y a 1 10 b 2 NA c NA 30

The `memisc`

package includes a `reorder`

method for arrays and matrices. For example, the matrix
method by default reorders the rows of a matrix according the results
of a function.

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