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Asymptotic test for conditional independence

Source: R/cit_asymp.R
cit_asymp.Rd

Test the conditional independence of Y and X given Z.

Usage

cit_asymp(Y, X, Z = NULL, space_y = FALSE, number_y = 10, design = NULL)

Arguments

Y

a numeric vector of length n to test for conditional independence with X adjusted on Z

X

a data frame of size n x p of numeric or factor vector(s) containing the variable(s) to be tested for conditional independence against X adjusted on Z.

Z

a data frame of size n x q of numeric or factor vector(s) containing the covariate(s) to condition the independence test upon.

space_y

a logical flag indicating whether the y thresholds are spaced. When space_y is TRUE, a regular sequence between the minimum and the maximum of the observations is used. Default is FALSE.

number_y

an integer value indicating the number of y thresholds (and therefore the number of regressions) to perform the test. Only used if space_y is TRUE. Default is 10.

design

an optional (and technical) list of design quantities, as returned by the internal .cit_design(X, Z, n). This is used by cit_multi(), to loop-call over many genes while building the model matrix, and computing its cross-product and its inverse only once. Default is NULL, in which case they are computed from X and Z. Users should not be using this argument

Value

A data frame with the following elements:

  • raw_pval contains the raw p-values for a given gene.

  • test_statistic contains the test statistic for a given gene.

Details

The space_y / number_y grid controls both the resolution of the statistic and its computational cost. See cit_multi for details on this trade-off.

References

Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2021). Distribution-free complex hypothesis testing for single-cell RNA-seq differential expression analysis, bioRxiv 445165. doi:10.1101/2021.05.21.445165 .

See also

cit_perm, cit_multi, ccdf

Examples


set.seed(123)
X <- as.factor(rbinom(n = 100, size = 1, prob = 0.5))
Y <- ((X == 1) * rnorm(n = 100, 0, 1)) + ((X == 0) * rnorm(n = 100, 0.5, 1))
res_asymp <- cit_asymp(Y, data.frame(X = X))


Z <- as.factor(rbinom(n = 100, size = 1, prob = 0.5))
X <- as.numeric(Z) - 1  + rnorm(n = 100, sd = 1)
r <- 500
Y <- replicate(r, as.numeric(Z) - 1)
YY <- (Y == 1) * rnorm(n = 100 * r, 0, 1) + (Y == 0) * rnorm(n = 100 * r, 0.5, 1)
pvals_sim <- sapply(seq_len(r), function(i) {
  cit_asymp(YY[, i], data.frame(X = X), data.frame(Z = Z))$raw_pval
})
hist(pvals_sim) # well calibrated p-values are uniform under the null

quantile(pvals_sim)
#>           0%          25%          50%          75%         100% 
#> 0.0002470792 0.2409864033 0.4958293345 0.7513432132 0.9999476695