Description Usage Arguments Details Value Examples
Function to do a scatterplot of features against factor values.
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object 
a 
factor 
string with the factor name, or an integer with the index of the factor. 
view 
string with the view name, or an integer with the index of the view. Default is the first view. 
groups 
groups to plot. Default is "all". 
features 
if an integer (default), the total number of features to plot. If a character vector, a set of manuallydefined features. 
sign 
can be 'positive', 'negative' or 'all' (default) to show only positive, negative or all weights, respectively. 
color_by 
specifies groups or values (either discrete or continuous) used to color the dots (samples). This can be either:

legend 
logical indicating whether to add a legend 
alpha 
numeric indicating dot transparency (default is 1). 
shape_by 
specifies groups or values (only discrete) used to shape the dots (samples). This can be either:

stroke 
numeric indicating the stroke size (the black border around the dots, default is NULL, infered automatically). 
dot_size 
numeric indicating dot size (default is 5). 
text_size 
numeric indicating text size (default is 5). 
add_lm 
logical indicating whether to add a linear regression line for each plot 
lm_per_group 
logical indicating whether to add a linear regression line separately for each group 
imputed 
logical indicating whether to include imputed measurements 
One of the first steps for the annotation of factors is to visualise the weights using plot_weights
or plot_top_weights
.
However, one might also be interested in visualising the direct relationship between features and factors, rather than looking at "abstract" weights.
A similar function for doing heatmaps rather than scatterplots is plot_data_heatmap
.
A ggplot
object
1 2 3 4  # Using an existing trained model
file < system.file("extdata", "model.hdf5", package = "MOFA2")
model < load_model(file)
plot_data_scatter(model)

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