Permutation test for binomial logistic regression

grouped_perm_binoglm is a nonparametric test for binomial logistic regression. It assesses the significance of coefficients by permutation, computing the null distribution of the z-statistic by randomly rearranging the binary outcome variable.

Usage

grouped_perm_binoglm(tbl, formla, var_to_perm, permNum = 1000, seed = 42)

Arguments

tbl

A data frame or tibble containing all model variables.

formla

A formula describing the logistic regression model; passed to stats::glm() with family = binomial.

var_to_perm

Character. Name of the binary outcome column to permute.

permNum

Integer. Number of permutations to generate. Default 1000.

seed

Integer. Random seed for reproducibility; passed to base::set.seed(). Default 42.

Value

A tibble with one row per model term and columns:

term

Name of the regression term.

estimate

Estimated log-odds coefficient.

statistic

Observed z-statistic.

p.value

Asymptotic two-sided p-value from the fitted model.

p.perm

Permutation p-value: proportion of permuted |statistics| >= |observed statistic|. A value of 0 means no permuted statistic was as extreme; report as p < 1/permNum.

Examples

counts <- c(10, 11, 8, 9, 6, 3, 5, 1)
gender <- c(0, 0, 0, 0, 0, 1, 1, 1)
TBL    <- data.frame(counts, gender)
grouped_perm_binoglm(TBL, gender ~ counts, "gender", permNum = 500, seed = 1)
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> # A tibble: 2 × 5
#>   term        estimate statistic p.value p.perm
#>   <chr>          <dbl>     <dbl>   <dbl>  <dbl>
#> 1 (Intercept)    246.   0.000723   0.999      1
#> 2 counts         -44.7 -0.000728   0.999      1