modelvar: Get model variable

In bhagwataditya/importomics: Unified statistal Modeling of Omics Data

Get model variable

Description

Get model variable

Usage

modelvar(
  featuredt,
  quantity,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

effectvar(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

tvar(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

pvar(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

fdrvar(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

abstractvar(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

modelvec(
  featuredt,
  quantity,
  fit = fits(featuredt)[1],
  coef = default_coefs(featuredt, fit = fit)[1],
  fvar = "feature_id"
)

effectvec(
  featuredt,
  fit = fits(featuredt)[1],
  coef = default_coefs(featuredt, fit = fit)[1],
  fvar = "feature_id"
)

tvec(
  featuredt,
  fit = fits(featuredt)[1],
  coef = default_coefs(featuredt, fit = fit)[1],
  fvar = "feature_id"
)

pvec(
  featuredt,
  fit = fits(featuredt)[1],
  coef = default_coefs(featuredt, fit = fit)[1],
  fvar = "feature_id"
)

fdrvec(
  featuredt,
  fit = fits(featuredt)[1],
  coef = default_coefs(featuredt, fit = fit)[1],
  fvar = "feature_id"
)

modeldt(
  featuredt,
  quantity,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

effectdt(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

tdt(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

pdt(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

modelmat(
  featuredt,
  quantity,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

effectmat(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

effectsizemat(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

tmat(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

pmat(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

fdrmat(
  featuredt,
  fit = fits(featuredt),
  coef = default_coefs(featuredt, fit = fit)
)

modelfeatures(
  featuredt,
  fit = fits(featuredt)[1],
  coef = default_coefs(featuredt, fit = fit)[1],
  fvar = "feature_id",
  significancevar = "p",
  significance = 0.05,
  effectdirection = "<>",
  effectsize = 0
)

upfeatures(
  featuredt,
  fit = fits(featuredt)[1],
  coef = default_coefs(featuredt, fit = fit)[1],
  fvar = "feature_id",
  significancevar = "p",
  significance = 0.05,
  effectsize = 0
)

downfeatures(
  featuredt,
  fit = fits(featuredt)[1],
  coef = default_coefs(featuredt, fit = fit)[1],
  fvar = "feature_id",
  significancevar = "p",
  significance = 0.05,
  effectsize = 0
)

Arguments

featuredt	data.table
quantity	'p', 'effect', 'fdr', 't', or 'se'
fit	string (vector)
coef	string (vector)
fvar	'feature_id' or other fvar for values (pvec) or names (upfeatures)
significancevar	'p' or 'fdr'
significance	p or fdr cutoff (fractional number)
effectdirection	'<>', '<' or '>'
effectsize	effectsize cutoff (positive number)

Value

string (tvar), matrix (tmat), numeric vector (tvec), character vector (tfeatures)

Examples

# Read
    file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
    object <- read_metabolon(file)
    object %<>% fit_limma()
    object %<>% fit_lm()
    featuredt <- fdt(object)
    featuredt %<>% add_adjusted_pvalues('fdr')
# modelvar
    modelvar(featuredt, 'p');                                          pvar(featuredt)
    modelvar(featuredt, 'effect');                                effectvar(featuredt)
    modelvar(featuredt, 'fdr');                                      fdrvar(featuredt)
# modelvec
    modelvec(featuredt, 'p'     )[1:3];                                 pvec(featuredt)[1:3]
    modelvec(featuredt, 'effect')[1:3];                            effectvec(featuredt)[1:3]
    modelvec(featuredt, 'fdr'   )[1:3];                               fdrvec(featuredt)[1:3]
# modelmatrix
    modelmat(featuredt, 'p'     )[1:3, 1:3];                            pmat(featuredt)[1:3, 1:3]
    modelmat(featuredt, 'effect')[1:3, 1:3];                       effectmat(featuredt)[1:3, 1:3]
    modelmat(featuredt, 'fdr'   )[1:3, 1:3];                          fdrmat(featuredt)[1:3, 1:3]
# modelfeatures
    modelfeatures(featuredt      )[1:3]
    modelfeatures(featuredt, effectdirection = '<' )[1:3];      downfeatures(featuredt)[1:3]
    modelfeatures(featuredt, effectdirection = '>' )[1:3];        upfeatures(featuredt)[1:3]

bhagwataditya/importomics documentation built on May 1, 2024, 2:01 a.m.