seqtree | R Documentation |
Facility for growing a regression tree for a state sequence object.
seqtree(formula, data = NULL, weighted = TRUE, min.size = 0.05,
max.depth = 5, R = 1000, pval = 0.01, weight.permutation = "replicate",
seqdist.args = list(method = "LCS", norm = "auto"), diss = NULL,
squared = FALSE, first = NULL, minSize, maxdepth, seqdist_arg)
formula |
a formula where the left hand side is a state sequence object (see |
weighted |
Logical. If |
data |
a data frame where variables in the formula will be searched |
min.size |
minimum number of cases in a node, in percentage if less than 1. |
max.depth |
maximum depth of the tree. |
R |
Number of permutations used to assess the significance of the split. |
pval |
Maximum p-value, in percent. |
weight.permutation |
Weights permutation method: "diss" (attach weights to the dissimilarity matrix), "replicate" (replicate case according to the |
seqdist.args |
list of arguments directly passed to |
diss |
An optional dissimilarity matrix. If not provided, a dissimilarity matrix is computed using |
squared |
Logical. If |
first |
Character. An optional variable name to force the first split. |
minSize |
Deprecated. Use |
maxdepth |
Deprecated. Use |
seqdist_arg |
Deprecated. Use |
The function provides a simplified interface for applying disstree
on state sequence objects.
The seqtree
objects can be "plotted" with seqtreedisplay
. A print method is also available which prints the medoid sequence for each terminal node.
A seqtree
object with same attributes as disstree
objects.
The leaf membership is in the first column of the fitted attribute. For example, the leaf memberships for a tree dt
are in dt$fitted[,1]
.
Matthias Studer (with Gilbert Ritschard for the help page)
Studer, M., G. Ritschard, A. Gabadinho and N. S. Müller (2011). Discrepancy analysis of state sequences, Sociological Methods and Research, Vol. 40(3), 471-510, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0049124111415372")}.
seqtreedisplay
, disstree
data(mvad)
## Defining a state sequence object
mvad.seq <- seqdef(mvad[, 17:86])
## Growing a seqtree from Hamming distances:
## Warning: The R=10 used here to save computation time is
## much too small and will generate strongly unstable results.
## We recommend to set R at least as R=1000.
## To comply with this small R value, we set pval = 0.1.
seqt <- seqtree(mvad.seq~ male + Grammar + funemp + gcse5eq + fmpr + livboth,
data=mvad, R=10, pval=0.1, seqdist.arg=list(method="HAM", norm="auto"))
print(seqt)
## Growing a seqtree from an existing distance matrix
mvad.dhd <- seqdist(mvad.seq, method="DHD")
seqt <- seqtree(mvad.seq~ male + Grammar + funemp + gcse5eq + fmpr + livboth,
data=mvad, R=10, pval=0.1, diss=mvad.dhd)
print(seqt)
### Following commands only work if GraphViz is properly installed
## Not run:
seqtreedisplay(seqt, type="d", border=NA)
seqtreedisplay(seqt, type="I", sortv=cmdscale(mvad.dhd, k=1))
## End(Not run)
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