computeItems: Prediction of number of cells in colonies

In RclusTool: Graphical Toolbox for Clustering and Classification of Data Frames

Description

Apply a specific predictive model for counting of number of cells in colonies for each cluster.

Usage

computeItems(data.sample, method, cluster, modelFile)

Arguments

data.sample	list containing features, profiles and clustering results.
method	character vector specifying the name of the clustering result to use.
cluster	character vector specifying the name of the cluster to consider for the application of the specific model.
modelFile	character vector specifying the path and the name of the RData model file.

Details

computeItems applies a specific predictive model for counting of number of cells in colonies for each cluster

Value

data.sample list containing features, profiles and clustering results with the number of cells for each particle.

See Also

itemsModel, countItems

Examples

 
dat <- rbind(matrix(rnorm(100, mean = 2, sd = 0.3), ncol = 2), 
             matrix(rnorm(100, mean = 4, sd = 0.3), ncol = 2), 
             matrix(rnorm(100, mean = 6, sd = 0.3), ncol = 2))

colnames(dat) <- c("x","y")
tf <- tempfile()
write.table(dat, tf, sep=",", dec=".")
x <- importSample(file.features=tf, dir.save=tempdir())

x <- computeUnSupervised(x, K=3, method.name="K-means")
x <- computeItems(x, method="K-means", cluster="Cluster 1", modelFile)

RclusTool documentation built on Feb. 4, 2020, 5:08 p.m.