Logistic regression effect and fit statistics

Derivative at mean (equation 4.9), percent correct (4.12), Cox and Snell R squared (4.13), model chi square (4.14), Wald (4.15), z (4.16) and likelihood-ratio chi square (4.18). Mirrors ca4e9 to ca4e18.

Usage

morie_logistic_effects(
  ybar,
  b,
  se,
  n_correct,
  n_total,
  neg2ll_null,
  neg2ll_full,
  neg2ll_reduced = NA,
  n = NA
)

Arguments

ybar

Mean of the binary response.

b

Coefficient.

se

Its standard error.

n_correct, n_total

Prediction counts.

neg2ll_null, neg2ll_full, neg2ll_reduced

Reported -2LL values.

n

Sample size for Cox and Snell.

Value

List of the named statistics.

Examples

morie_logistic_effects(ybar = 0.3, b = 0.8, se = 0.25,
                       n_correct = 78, n_total = 100,
                       neg2ll_null = 240, neg2ll_full = 215, n = 100)
#> $dm
#> [1] 0.168
#> 
#> $pct_correct
#> [1] 78
#> 
#> $model_chi2
#> [1] 25
#> 
#> $wald
#> [1] 10.24
#> 
#> $z
#> [1] 3.2
#> 
#> $cox_snell_r2
#> [1] 0.2211992
#>