exploreit v1.0.3
as.dissimilarity() as_dissimilarity()
as.dissimilarity()
as_dissimilarity()
Convert a dist or matrix object into a Dissimilarity object
ca() autoplot(<ca>) chart(<ca>)
ca()
autoplot(<ca>)
chart(<ca>)
Correspondence Analysis (CA)
circle()
Draw circles in a plot
cluster() str(<Cluster>) labels(<Cluster>) nobs(<Cluster>) predict(<Cluster>) augment(<Cluster>) plot(<Cluster>) autoplot(<Cluster>) chart(<Cluster>)
cluster()
str(<Cluster>)
labels(<Cluster>)
nobs(<Cluster>)
predict(<Cluster>)
augment(<Cluster>)
plot(<Cluster>)
autoplot(<Cluster>)
chart(<Cluster>)
Hierarchical Clustering Analysis
dissimilarity() print(<Dissimilarity>) labels(<Dissimilarity>) nobs(<Dissimilarity>) autoplot(<Dissimilarity>) chart(<Dissimilarity>)
dissimilarity()
print(<Dissimilarity>)
labels(<Dissimilarity>)
nobs(<Dissimilarity>)
autoplot(<Dissimilarity>)
chart(<Dissimilarity>)
Calculate a dissimilarity matrix
exploreit-package
Exploratory Data Analysis for 'SciViews::R'
geom_dendroline()
Draw a line to cut a dendrogram
k_means() profile_k() augment(<kmeans>) predict(<k_means>) plot(<k_means>) autoplot(<k_means>) chart(<k_means>)
k_means()
profile_k()
augment(<kmeans>)
predict(<k_means>)
plot(<k_means>)
autoplot(<k_means>)
chart(<k_means>)
K-means clustering
mds() plot(<mds>) autoplot(<mds>) chart(<mds>) shepard() plot(<shepard>) autoplot(<shepard>) chart(<shepard>) augment(<mds>) glance(<mds>)
mds()
plot(<mds>)
autoplot(<mds>)
chart(<mds>)
shepard()
plot(<shepard>)
autoplot(<shepard>)
chart(<shepard>)
augment(<mds>)
glance(<mds>)
Multidimensional scaling or principal coordinates analysis
mfa() autoplot(<MFA>) chart(<MFA>)
mfa()
autoplot(<MFA>)
chart(<MFA>)
Multiple Factor Analysis (MFA)
pca() autoplot(<pcomp>) chart(<pcomp>) augment(<princomp>) tidy(<princomp>) as.prcomp()
pca()
autoplot(<pcomp>)
chart(<pcomp>)
augment(<princomp>)
tidy(<princomp>)
as.prcomp()
Principal Component Analysis (PCA)
scale(<data.frame>) scale(<tbl_df>) scale(<data.table>)
scale(<data.frame>)
scale(<tbl_df>)
scale(<data.table>)
Scale a data frame (data.frame, data.table or tibble's tbl_df)