View source: R/pca_cor_samplevar.R

h_pca_df_r2_matrix | R Documentation |

This function processes sample variables from `AnyHermesData`

and the
corresponding principal components matrix, and then generates the matrix of R2 values.

h_pca_df_r2_matrix(pca, df)

`pca` |
( |

`df` |
( |

Note that only the

`df`

columns which are`numeric`

,`character`

,`factor`

or`logical`

are included in the resulting matrix, because other variable types are not supported.In addition,

`df`

columns which are constant, all`NA`

, or`character`

or`factor`

columns with too many levels are also dropped before the analysis.

A matrix with R2 values for all combinations of sample variables and principal components.

`h_pca_var_rsquared()`

which is used internally to calculate the R2 for one
sample variable.

object <- hermes_data %>% add_quality_flags() %>% filter() %>% normalize() # Obtain the principal components. pca <- calc_pca(object)$x # Obtain the `colData` as a `data.frame`. df <- as.data.frame(colData(object)) # Correlate them. r2_all <- h_pca_df_r2_matrix(pca, df) str(r2_all) # We can see that only about half of the columns from `df` were # used for the correlations. ncol(r2_all) ncol(df)

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