clustrd
Methods for Joint Dimension Reduction and Clustering
v1.4.0
·
Jul 16, 2022
·
GPL-3
Description
A class of methods that combine dimension reduction and clustering of continuous, categorical or mixed-type data (Markos, Iodice D'Enza and van de Velden 2019; <DOI:10.18637/jss.v091.i10>). For continuous data, the package contains implementations of factorial K-means (Vichi and Kiers 2001; <DOI:10.1016/S0167-9473(00)00064-5>) and reduced K-means (De Soete and Carroll 1994; <DOI:10.1007/978-3-642-51175-2_24>); both methods that combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means (Hwang, Dillon and Takane 2006; <DOI:10.1007/s11336-004-1173-x>), i-FCB (Iodice D'Enza and Palumbo 2013, <DOI:10.1007/s00180-012-0329-x>) and Cluster Correspondence Analysis (van de Velden, Iodice D'Enza and Palumbo 2017; <DOI:10.1007/s11336-016-9514-0>), which combine multiple correspondence analysis with K-means. For mixed-type data, it provides mixed Reduced K-means and mixed Factorial K-means (van de Velden, Iodice D'Enza and Markos 2019; <DOI:10.1002/wics.1456>), which combine PCA for mixed-type data with K-means.
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CRAN incoming feasibility
Maintainer: ‘Angelos Markos <amarkos@gmail.com>’
No Authors@R field in DESCRIPTION.
Please add one, modifying
Authors@R: c(person(given = "Angelos",
family = "Markos",
role = c("aut", "cre"),
email = "amarkos@gmail.com"),
person(given = c("Alfonso", "Iodice"),
family = "D'Enza",
role = "aut"),
person(given = "Michel",
family = "van de Velden",
role = "aut"))
as necessary.
NOTE
r-devel-linux-x86_64-debian-clang
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-devel-linux-x86_64-debian-gcc
CRAN incoming feasibility
Maintainer: ‘Angelos Markos <amarkos@gmail.com>’
No Authors@R field in DESCRIPTION.
Please add one, modifying
Authors@R: c(person(given = "Angelos",
family = "Markos",
role = c("aut", "cre"),
email = "amarkos@gmail.com"),
person(given = c("Alfonso", "Iodice"),
family = "D'Enza",
role = "aut"),
person(given = "Michel",
family = "van de Velden",
role = "aut"))
as necessary.
NOTE
r-devel-linux-x86_64-debian-gcc
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-devel-linux-x86_64-fedora-clang
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-devel-linux-x86_64-fedora-gcc
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-devel-macos-arm64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-devel-windows-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-oldrel-macos-arm64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-oldrel-macos-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-oldrel-windows-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-patched-linux-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-release-linux-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-release-macos-arm64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-release-macos-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
NOTE
r-release-windows-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:40: Lost braces; missing escapes or markup?
40 | \emph{Step 2. Mapping:} Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}, where C^{XS_i} is the partition of the original data, X, predicted from clustering bootstrap sample S_i (same for T_i and C^{XT_i}).
| ^
checkRd: (-1) global_bootclus.Rd:53: Lost braces; missing escapes or markup?
53 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) global_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:37: Lost braces; missing escapes or markup?
37 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size n from the data and use the original data as evaluation set E_i = X. Apply a joint dimension reduction and clustering method to S_i and T_i and obtain C^{S_i} and C^{T_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:39: Lost braces; missing escapes or markup?
39 | \emph{Step 2. Mapping}: Assign each observation x_i to the closest centers of C^{S_i} and C^{T_i} using Euclidean distance, resulting in partitions C^{XS_i} and C^{XT_i}.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:41: Lost braces; missing escapes or markup?
41 | \emph{Step 3. Evaluation}: Obtain the maximum Jaccard agreement between each original cluster C_k and each one of the two bootstrap clusters, C_^k'{XS_i} and C_^k'{XT_i} as measure of agreement and stability, and take the average of each pair.
| ^
checkRd: (-1) local_bootclus.Rd:54: Lost braces; missing escapes or markup?
54 | \item{clust1}{Partitions, C^{XS_i} of the original data, X, predicted from clustering bootstrap sample S_i (see Details)}
| ^
checkRd: (-1) local_bootclus.Rd:55: Lost braces; missing escapes or markup?
55 | \item{clust2}{Partitions, C^{XT_i} of the original data, X, predicted from clustering bootstrap sample T_i (see Details)}
| ^
Check History
NOTE 0 OK · 14 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE
r-devel-linux-x86_64-debian-clang
CRAN incoming feasibility
Maintainer: ‘Angelos Markos <amarkos@gmail.com>’
No Authors@R field in DESCRIPTION.
Please add one, modifying
Authors@R: c(person(given = "Angelos",
family = "Markos",
role = c("aut", "cre"),
email = "amarkos@gmail.com"),
person(given = c("Alfonso", "Iodice"),
family = "D'Enza",
role = "aut"),
person(given = "Michel",
family = "van d
NOTE
r-devel-linux-x86_64-debian-gcc
CRAN incoming feasibility
Maintainer: ‘Angelos Markos <amarkos@gmail.com>’
No Authors@R field in DESCRIPTION.
Please add one, modifying
Authors@R: c(person(given = "Angelos",
family = "Markos",
role = c("aut", "cre"),
email = "amarkos@gmail.com"),
person(given = c("Alfonso", "Iodice"),
family = "D'Enza",
role = "aut"),
person(given = "Michel",
family = "van d
NOTE
r-devel-linux-x86_64-fedora-clang
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
|
NOTE
r-devel-linux-x86_64-fedora-gcc
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
|
NOTE
r-devel-macos-arm64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
|
NOTE
r-devel-windows-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
|
NOTE
r-patched-linux-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
|
NOTE
r-release-linux-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
|
NOTE
r-release-macos-arm64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
|
NOTE
r-release-macos-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
|
NOTE
r-release-windows-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
|
NOTE
r-oldrel-macos-arm64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
|
NOTE
r-oldrel-macos-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
|
NOTE
r-oldrel-windows-x86_64
Rd files
checkRd: (-1) global_bootclus.Rd:38: Lost braces; missing escapes or markup?
38 | \emph{Step 1. Resampling:} Draw bootstrap samples S_i and T_i of size \emph{n} from the data and use the original data, X, as evaluation set E_i = X. Apply the clustering method of choice to S_i and T_i and obtain C^{S_i} and C^{T_i}.
|
Reverse Dependencies (2)
Dependency Network
Version History
new
1.4.0
Mar 9, 2026