tvctomat | R Documentation |
tvctovmat
creates an object of class, tvcov
, from a list of
matrices with time-varying or intra-individual covariates for each
individual or one matrix or dataframe of such covariate values. It can
also combine two such objects or add interactions among covariates.
Such objects can be printed. Methods are available for extracting the
covariates and their names: covariates
and
names
. The method,
transform
, can transform variables in place or
by adding new variables to the object.
tvctomat(tvcov, names=NULL, units=NULL, interaction=NULL, ccov=NULL, oldtvcov=NULL, dataframe=TRUE, description=NULL)
tvcov |
Either (1) if unbalanced, a list of matrices or
dataframes with time-varying or intra-individual covariate values for
each individual (one column per variable), (2) if balanced, one matrix
or dataframe of such covariate values (when there is only one such
covariate) with dimensions: number of individuals by number of
observations/individual, or (3) an object of class, |
names |
The names of the time-varying or intra-individual
covariates in |
units |
Optional character vector specifying units of measurements of covariates. |
interaction |
A pair of index numbers or names of variables in
|
ccov |
Time-constant or inter-individual covariates for which an
interaction is to be introduced with time-varying or intra-individual
covariates in |
oldtvcov |
An object of class, |
dataframe |
If TRUE and factor variables are present, the covariates are stored as a dataframe; if FALSE, they are expanded to indicator variables. If no factor variables are present, covariates are always stored as a matrix. |
description |
An optional named list of character vectors with names of some or all covariates containing their descriptions. |
Returns an object of class, tvcov
, containing a matrix or
dataframe for the covariates (z$tvcov
) with one row per
response per individual and a vector giving the number of observations
per individual (z$nobs
).
J.K. Lindsey
DataMethods
, covariates
,
description
, formula
,
gettvc
, lvna
,
names
, restovec
,
rmna
, tcctomat
,
transform
, units
z <- matrix(rpois(20,5),ncol=5) print(tvc <- tvctomat(z, units="days")) covariates(tvc) names(tvc) v <- data.frame(matrix(rep(c("a","b","c","d","e"),4),ncol=5),stringsAsFactors=TRUE) print(tvc2 <- tvctomat(v, oldtvc=tvc, units=NA)) covariates(tvc2) print(tvc3 <- tvctomat(v, oldtvc=tvc, dataframe=FALSE, units=NA)) covariates(tvc3) print(tvc4 <- tvctomat(tvc2, interaction=c("z","v"))) covariates(tvc4) x1 <- 1:4 x2 <- gl(4,1) xx <- tcctomat(data.frame(x1,x2), dataframe=FALSE) tvctomat(tvc3, interaction="z", ccov=xx) tvctomat(tvc3, interaction="z", ccov=xx, names="x1") tvctomat(tvc3, interaction="z", ccov=xx, names=c("x22","x23","x24")) xx <- tcctomat(data.frame(x1,x2), dataframe=TRUE) tvctomat(tvc2, interaction="z", ccov=xx) tvctomat(tvc2, interaction="z", ccov=xx, names="x1") tvctomat(tvc2, interaction="z", ccov=xx, names="x2")
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