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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