Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/expandCategorical.R

Expands the rows of a data frame by re-expressing observations of a
categorical variable specified by `catvar`

, such that the
column(s) corresponding to `catvar`

are replaced by a factor
specifying the possible categories for each observation and a vector
of 0/1 counts over these categories.

1 2 | ```
expandCategorical(data, catvar, sep = ".", countvar = "count",
idvar = "id", as.ordered = FALSE, group = TRUE)
``` |

`data` |
a data frame. |

`catvar` |
a character vector specifying factors in |

`sep` |
a character string used to separate the concatenated
values of |

`countvar` |
(optional) a character string to be used for the name of the new count variable. |

`idvar` |
(optional) a character string to be used for the name of the new factor identifying the original rows (cases). |

`as.ordered` |
logical - whether the new interaction factor should
be of class |

`group` |
logical: whether or not to group individuals with common values over all covariates. |

Each row of the data frame is replicated *c* times, where *c*
is the number of levels of the interaction of the factors specified by
`catvar`

. In the expanded data frame, the columns specified by
`catvar`

are replaced by a factor specifying the *r* possible
categories for each case, named by the concatenated values of
`catvar`

separated by `sep`

. The ordering of factor levels
will be preserved in the creation of the new factor, but this factor
will not be of class `"ordered"`

unless the argument
`as.ordered = TRUE`

. A variable with name `countvar`

is added
to the data frame which is equal to 1 for the observed category in each
case and 0 elsewhere. Finally a factor with name `idvar`

is added
to index the cases.

The expanded data frame as described in Details.

Re-expressing categorical data in this way allows a multinomial response to be modelled as a poisson response, see examples.

Heather Turner

Anderson, J. A. (1984) Regression and Ordered Categorical
Variables. *J. R. Statist. Soc. B*, **46(1)**, 1-30.

`gnm`

, `multinom`

,
`reshape`

, `mclgen`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
### Example from help(multinom, package = "nnet")
library(MASS)
example(birthwt)
library(nnet)
bwt.mu <- multinom(low ~ ., data = bwt)
## Equivalent using gnm - include unestimable main effects in model so
## that interactions with low0 automatically set to zero, else could use
## 'constrain' argument.
bwtLong <- expandCategorical(bwt, "low", group = FALSE)
bwt.po <- gnm(count ~ low*(. - id), eliminate = id, data = bwtLong, family =
"poisson")
summary(bwt.po) # same deviance; df reflect extra id parameters
### Example from ?backPain
set.seed(1)
summary(backPain)
backPainLong <- expandCategorical(backPain, "pain")
## Fit models described in Table 5 of Anderson (1984)
noRelationship <- gnm(count ~ pain, eliminate = id,
family = "poisson", data = backPainLong)
oneDimensional <- update(noRelationship,
~ . + Mult(pain, x1 + x2 + x3))
``` |

hturner/gnm documentation built on Sept. 3, 2017, 10:20 p.m.

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