This function fits a high-dimensional model using hexagonal bins and provides options to customize the modeling process, including the choice of bin centroids or bin means, removal of low-density hexagons, and averaging of high-dimensional data.
Usage
fit_highd_model(
training_data,
emb_df,
bin1 = 4,
r2,
q = 0.1,
is_bin_centroid = TRUE,
is_rm_lwd_hex = FALSE,
benchmark_to_rm_lwd_hex = NULL,
col_start_highd = "x"
)Arguments
- training_data
A tibble that contains the training high-dimensional data.
- emb_df
A tibble that contains embedding with a unique identifier.
- bin1
Number of bins along the x axis.
- r2
The ratio of the ranges of the original embedding components.
- q
The buffer amount as proportion of data range.
- is_bin_centroid
Logical, indicating whether to use bin centroids (default is TRUE).
- is_rm_lwd_hex
Logical, indicating whether to remove low-density hexagons (default is FALSE).
- benchmark_to_rm_lwd_hex
The benchmark value to remove low-density hexagons.
- col_start_highd
The text prefix for columns in the high-dimensional data.
Value
A list containing the data frame with high-dimensional coordinates
for 2D bin centroids (df_bin) and the data frame containing
information about hexagonal bin centroids (df_bin_centroids) in 2D.
Examples
scurve_umap_scaled_obj <- s_curve_obj$nldr_scaled_obj
lim1 <- scurve_umap_scaled_obj$lim1
lim2 <- scurve_umap_scaled_obj$lim2
r2 <- diff(lim2)/diff(lim1)
fit_highd_model(training_data = s_curve_noise_training,
emb_df = s_curve_noise_umap_scaled, bin1 = 4, r2 = r2,
col_start_highd = "x")
#> Error in if (r2 > check_factor) { a1 <- (1 + 2 * q)/(bin1 - 1)} else { a1 <- (2 * (r2 + q * (1 + r2)))/(sqrt(3) * (bin2 - 1))}: argument is of length zero