Description Usage Arguments Details Value Note References See Also Examples
Linear discriminant analysis.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  lda(x, ...)
## S3 method for class 'formula'
lda(formula, data, ..., subset, na.action)
## Default S3 method:
lda(x, grouping, prior = proportions, tol = 1.0e4,
method, CV = FALSE, nu, ...)
## S3 method for class 'data.frame'
lda(x, ...)
## S3 method for class 'matrix'
lda(x, grouping, ..., subset, na.action)

formula 
A formula of the form 
data 
An optional data frame, list or environment from which variables
specified in 
x 
(required if no formula is given as the principal argument.) a matrix or data frame or Matrix containing the explanatory variables. 
grouping 
(required if no formula principal argument is given.) a factor specifying the class for each observation. 
prior 
the prior probabilities of class membership. If unspecified, the class proportions for the training set are used. If present, the probabilities should be specified in the order of the factor levels. 
tol 
A tolerance to decide if a matrix is singular; it will reject variables
and linear combinations of unitvariance variables whose variance is
less than 
subset 
An index vector specifying the cases to be used in the training sample. (NOTE: If given, this argument must be named.) 
na.action 
A function to specify the action to be taken if 
method 

CV 
If true, returns results (classes and posterior probabilities) for leaveoneout crossvalidation. Note that if the prior is estimated, the proportions in the whole dataset are used. 
nu 
degrees of freedom for 
... 
arguments passed to or from other methods. 
The function
tries hard to detect if the withinclass covariance matrix is
singular. If any variable has withingroup variance less than
tol^2
it will stop and report the variable as constant. This
could result from poor scaling of the problem, but is more
likely to result from constant variables.
Specifying the prior
will affect the classification unless
overridden in predict.lda
. Unlike in most statistical packages, it
will also affect the rotation of the linear discriminants within their
space, as a weighted betweengroups covariance matrix is used. Thus
the first few linear discriminants emphasize the differences between
groups with the weights given by the prior, which may differ from
their prevalence in the dataset.
If one or more groups is missing in the supplied data, they are dropped with a warning, but the classifications produced are with respect to the original set of levels.
If CV = TRUE
the return value is a list with components
class
, the MAP classification (a factor), and posterior
,
posterior probabilities for the classes.
Otherwise it is an object of class "lda"
containing the
following components:
prior 
the prior probabilities used. 
means 
the group means. 
scaling 
a matrix which transforms observations to discriminant functions, normalized so that within groups covariance matrix is spherical. 
svd 
the singular values, which give the ratio of the between and withingroup standard deviations on the linear discriminant variables. Their squares are the canonical Fstatistics. 
N 
The number of observations used. 
call 
The (matched) function call. 
This function may be called giving either a formula and
optional data frame, or a matrix and grouping factor as the first
two arguments. All other arguments are optional, but subset=
and
na.action=
, if required, must be fully named.
If a formula is given as the principal argument the object may be
modified using update()
in the usual way.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge University Press.
1 2 3 4 5 6 7 8 9 10 11 12 13  Iris < data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
Sp = rep(c("s","c","v"), rep(50,3)))
train < sample(1:150, 75)
table(Iris$Sp[train])
## your answer may differ
## c s v
## 22 23 30
z < lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train)
predict(z, Iris[train, ])$class
## [1] s s s s s s s s s s s s s s s s s s s s s s s s s s s c c c
## [31] c c c c c c c v c c c c v c c c c c c c c c c c c v v v v v
## [61] v v v v v v v v v v v v v v v
(z1 < update(z, . ~ .  Petal.W.))

c s v
27 28 20
[1] s s s s s s s s s s s s s s s s s s s s s s c c c c c c c c c c c v c c c c
[39] c c c c c c c v v v v v v v v v v c v v v v v v v v v c v v v v v v v v v
Levels: c s v
Call:
lda(Sp ~ Sepal.L. + Sepal.W. + Petal.L., data = Iris, prior = c(1,
1, 1)/3, subset = train)
Prior probabilities of groups:
c s v
0.3333333 0.3333333 0.3333333
Group means:
Sepal.L. Sepal.W. Petal.L.
c 5.877778 2.677778 4.211111
s 4.989286 3.432143 1.439286
v 6.630000 3.055000 5.600000
Coefficients of linear discriminants:
LD1 LD2
Sepal.L. 1.5591190 0.6513826
Sepal.W. 0.5347692 3.2471421
Petal.L. 3.4734648 0.6497855
Proportion of trace:
LD1 LD2
0.9816 0.0184
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