View source: R/discdd.predict.R
discdd.predict  R Documentation 
Assigns several groups of individuals, one group after another, to the class of groups (among K
classes of groups) which achieves the minimum of the distances or divergences between the probability distribution associated to the group to assign and the K
probability distributions associated to the K
classes.
discdd.predict(xf, class.var, distance = c("l1", "l2", "chisqsym", "hellinger",
"jeffreys", "jensen", "lp"), crit = 1, misclass.ratio = FALSE, p)
xf 
object of class

class.var 
string (if

distance 
The distance or dissimilarity used to compute the distance matrix between the densities. It can be:

crit 
1 or 2. In order to select the densities associated to the classes. See Details. 
misclass.ratio 
logical (default 
p 
integer. Optional. When 
If xf
is an object of class "folderh"
containing the data:
The T
probability distributions f_t
corresponding to the T
groups of individuals are estimated by frequency distributions within each group.
To the class k
consisting of T_k
groups is associated the probability distribution g_k
.
The crit
argument selects the estimation method of the g_k
's.
crit=1
The probability distribution g_k
is estimated using the whole data of this class, that is the rows of x
corresponding to the T_k
groups of the class k
.
The estimation of the g_k
's uses the same method as the estimation of the f_t
's.
crit=2
The T_k
probability distributions f_t
are estimated using the corresponding data from xf
. Then they are averaged to obtain an estimation of the density g_k
, that is g_k = \frac{1}{T_k} \, \sum{f_t}
.
If xf
is a list of arrays (or list of tables):
The t^{th}
array is the joint frequency distribution of the t^{th}
group. The frequencies can be absolute or relative.
To the class k
consisting of T_k
groups is associated the probability distribution g_k
.
The crit
argument selects the estimation method of the g_k
's.
crit=1
g_k = \frac{1}{\sum n_t} \sum n_t f_t
,
where n_t
is the total of xf[[t]]
.
Notice that when xf[[t]]
contains relative frequencies, its total is 1.
That is equivalent to crit=2
.
crit=2
g_k = \frac{1}{T_k} \, \sum f_t
.
Returns an object of class discdd.predict
, that is a list including:
prediction 
data frame with 3 columns:

distances 
matrix with 
proximities 
matrix of the proximities (in percents). The proximity of a group 
confusion.mat 
the confusion matrix (if 
misclassed 
the misclassification ratio (if 
Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Gilles Hunault, Sabine DemotesMainard
Rudrauf, J.M., Boumaza, R. (2001). Contribution à l'étude de l'architecture médiévale: les caractéristiques des pierres à bossage des châteaux forts alsaciens, Centre de Recherches Archéologiques médiévales de Saverne, 5, 538.
data(castles.dated)
data(castles.nondated)
stones < rbind(castles.dated$stones, castles.nondated$stones)
periods < rbind(castles.dated$periods, castles.nondated$periods)
stones$height < cut(stones$height, breaks = c(19, 27, 40, 71), include.lowest = TRUE)
stones$width < cut(stones$width, breaks = c(24, 45, 62, 144), include.lowest = TRUE)
stones$edging < cut(stones$edging, breaks = c(0, 3, 4, 8), include.lowest = TRUE)
stones$boss < cut(stones$boss, breaks = c(0, 6, 9, 20), include.lowest = TRUE )
castlesfh < folderh(periods, "castle", stones)
# Default: dist="l1", crit=1
discdd.predict(castlesfh, "period")
# With the calculation of the confusion matrix and misclassification ratio
discdd.predict(castlesfh, "period", misclass.ratio = TRUE)
# Hellinger distance
discdd.predict(castlesfh, "period", distance = "hellinger")
# crit=2
discdd.predict(castlesfh, "period", crit = 2)
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