Description Usage Arguments Value Author(s) References See Also Examples
Predicts the classes of new observations based on a Prediction Analysis of Categorical Data.
1 2 3 |
object |
an object of class |
newdata |
a numeric matrix consisting of the integers between 1 and n.cat, where
n.cat is the number of levels each of the variables in |
theta |
a strictly positive numeric value specifying the value of the shrinkage parameter
of the Prediction Analysis that should be used in the class prediction.
If |
add.nvar |
should the number of variables used in the class prediction be added to the output? |
type |
either |
... |
Ignored. |
If add.nvar = FALSE
, the predicted classes or the class probabilities (depending on type
).
Otherwise, a list consisting of
pred |
a vector or matrix containing the predicted classes or the class probabilities, respectively. |
n.var |
the number of variables used in the prediction. |
Holger Schwender, holger.schwender@udo.edu
Schwender, H.\ (2007). Statistical Analysis of Genotype and Gene Expression Data. Dissertation, Department of Statistics, University of Dortmund.
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 27 28 29 30 31 32 33 34 | ## Not run:
# Generate a data set consisting of 2000 rows (variables) and 50 columns.
# Assume that the first 25 observations belong to class 1, and the other
# 50 observations to class 2.
mat <- matrix(sample(3, 100000, TRUE), 2000)
rownames(mat) <- paste("SNP", 1:2000, sep = "")
cl <- rep(1:2, e = 25)
# Apply PAM for categorical data to this matrix, and compute the
# misclassification rate on the training set, i.e. on mat.
pam.out <- pamCat(mat, cl)
pam.out
# Now generate a new data set consisting of 20 observations,
# and predict the classes of these observations using the
# value of theta that has led to the smallest misclassification
# rate in pam.out.
mat2 <- matrix(sample(3, 40000, TRUE), 2000)
rownames(mat2) <- paste("SNP", 1:2000, sep = "")
predict(pam.out, mat2)
# Another theta, say theta = 4, can also be specified.
predict(pam.out, mat2, theta = 4)
# The class probabilities for each observation can be obtained by
predict(pam.out, mat2, theta = 4, type = "prob")
## End(Not run)
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