Runs checks for appropriate arguments before running emuFit()
Source: R/emuFit_check.R
emuFit_check.RdRuns checks for appropriate arguments before running emuFit()
Usage
emuFit_check(
Y,
X = NULL,
formula = NULL,
data = NULL,
assay_name = NULL,
cluster = NULL,
B_null_list = NULL,
test_kj = NULL,
match_row_names = TRUE,
verbose = FALSE,
remove_zero_comparison_pvals = 0.01,
unobserved_taxon_error = TRUE,
constraint_fn,
constraint_grad_fn,
constraint_param,
run_score_tests = TRUE
)Arguments
- Y
an n x J matrix or dataframe of nonnegative observations, or a phyloseq object containing an otu table and sample data.
- X
an n x p matrix or dataframe of covariates (optional)
- formula
a one-sided formula specifying the form of the mean model to be fit
- data
an n x p data frame containing variables given in
formula- assay_name
a string containing the desired assay name within a
TreeSummarizedExperimentobject. This is only required if Y is aTreeSummarizedExperimentobject, otherwise this argument does nothing and can be ignored.- cluster
a vector giving cluster membership for each row of Y to be used in computing GEE test statistics. Default is NULL, in which case rows of Y are treated as independent.
- B_null_list
list of starting values of coefficient matrix (p x J) for null estimation. This should either be a list with the same length as
test_kj. If you only want to provide starting values for some tests, include the other elements of the list asNULL.- test_kj
a data frame whose rows give coordinates (in category j and covariate k) of elements of B to construct hypothesis tests for. If
test_kjis not provided, all elements of B save the intercept row will be tested.- match_row_names
logical: Make sure rows on covariate data and response data correspond to the same sample by comparing row names and subsetting/reordering if necessary.
- verbose
provide updates as model is being fitted? Defaults to FALSE. If user sets verbose = TRUE, then key messages about algorithm progress will be displayed. If user sets verbose = "development", then key messages and technical messages about convergence will be displayed. Most users who want status updates should set verbose = TRUE.
- remove_zero_comparison_pvals
Should score p-values be replaced with NA for zero-comparison parameters? These parameters occur for categorical covariates with three or more levels, and represent parameters that compare a covariate level to the reference level for a category in which the comparison level and reference level both have 0 counts in all samples. These parameters can have misleadingly small p-values and are not thought to have scientifically interesting signals. We recommend removing them before analyzing data further. If TRUE, all zero-comparison parameter p-values will be set to NA. If FALSE no zero-comparison parameter p-values will be set to NA. If a value between 0 and 1, all zero-comparison p-values below the value will be set to NA. Default is
0.01.- unobserved_taxon_error
logical: should an error be thrown if Y includes taxa that have 0 counts for all samples? Default is TRUE.
- constraint_fn
function g defining a constraint on rows of B; g(B_k) = 0 for rows k = 1, ..., p of B. Default function is a smoothed median (minimizer of pseudohuber loss). If a number is provided a single category constraint will be used with the provided category as a reference category. This argument can either be a single constraint function to be used for all rows of B, or a list of length p of constraints to be used for each row of B.
- constraint_grad_fn
derivative of constraint_fn with respect to its arguments (i.e., elements of a row of B). If
constraint_fnis a list of constraint functions, then this argument must also be a list.- constraint_param
If pseudohuber centering is used (this is the default), parameter controlling relative weighting of elements closer and further from center. (Limit as
constraint_paramapproaches infinity is the mean; as this parameter approaches zero, the minimizer of the pseudo-Huber loss approaches the median.)- run_score_tests
logical: perform robust score testing?