tidyMicro (version 1.47)

micro_heatmap: Create heatmaps of estiamted coefficients from negative binomial models

Description

A function to create heatmaps of estimated beta coeffients from each model fit by nb_mods

Usage

micro_heatmap(
  modsum,
  low_grad,
  high_grad,
  mid_grad,
  midpoint = 0,
  top_taxa = 10,
  low_lim,
  high_lim,
  mute_cols = T,
  alpha = 0.05,
  dot_size = 2,
  dot_shape = 8,
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  subtitle = NULL,
  xaxis = NULL,
  legend_title = NULL,
  caption = NULL
)

Arguments

modsum

The output from nb_mods

low_grad

The low gradient colors for the coefficient magnitude. Will be fed into scale_fill_gradient

high_grad

The high gradient colors for the coefficient magnitude. Will be fed into scale_fill_gradient

mid_grad

The medium gradient colors for the coefficient magnitude. Will be fed into scale_fill_gradient

midpoint

Midpoint for coefficient magnitude in legend

top_taxa

Only plot X taxa with the largest magnitude beta coefficients

low_lim

Lower limits of the fill gradient. Will default to the largest magnitude effect size

high_lim

Upper limits of the fill gradient. Will default to the largest magnitude effect size

mute_cols

Mute the colors of the fill gradients

alpha

Mark beta coefficient cells with p-values below this cutoff

dot_size

size of marker in cells

dot_shape

shape of marker in cells

main

Plot title

xlab

x-axis label

ylab

y-axis label

subtitle

Plot label

xaxis

Labels for the x-axis ticks

legend_title

Title of figure legend

caption

plot caption to be displayed at the bottom of plot

Value

Returns a ggplot that you can add geoms to if you'd like

Details

The output will give gray columns if there are missing values in the supplied continuous variable

Examples

# NOT RUN {
data(bpd_phy); data(bpd_cla); data(bpd_ord); data(bpd_fam); data(bpd_clin)

otu_tabs <- list(Phylum = bpd_phy, Class = bpd_cla,
Order = bpd_ord, Family = bpd_fam)
set <- tidy_micro(otu_tabs = otu_tabs, clinical = bpd_clin) %>%
filter(day == 7) ## Only including the first week

## Creating negative binomial models on filtered tidy_micro set
nb_fam <- set %>%
mutate(bpd1 = factor(bpd1)) %>% ## making bpd1 a factor
otu_filter(ra_cutoff = 0.1, exclude_taxa = c("Unclassified", "Bacteria")) %>%
nb_mods(table = "Family", bpd1)

nb_fam %>% micro_heatmap
# }