Fits a logistic regression model (binomial GLM) of maturity stage as a
function of length, age, or another continuous predictor. Bootstrap
confidence intervals are computed via rsample.
Arguments
- mat
Binary maturity variable (0 = immature, 1 = mature)
- x
Continuous predictor variable (e.g. length, age)
- grouping_var
Optional categorical grouping variable (e.g. sex). Will be converted to a factor.
- data
A data frame containing the above variables
- times
Number of bootstrap replicates (default 1000)
Value
A nested tibble of class "maturity" with one row per group,
containing list columns data, coefs, preds, and mods,
each named by the grouping variable level.
Examples
library(ggplot2)
data(spottail)
lm50 <- maturity(maturity_stage, length, sex, data = spottail, times = 100)
#> The categorical variable sex has 2 levels.
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `.fit = map(data, ~maturity_mod(.x))`.
#> ℹ In group 2: `grouping_var = "f"`.
#> Caused by warning:
#> ! glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(lm50)
#> # A tibble: 3 × 10
#> a b L50 L50_lower L50_upper L95 L95_lower L95_upper n N
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 -55.4 0.0594 934. 924. 943. 983. 965 998. 430 341
#> 2 -124. 0.131 951. 940. 968. 973. 942 994. 118 97
#> 3 -51.1 0.0550 929. 921. 938. 983. 960. 998. 312 244
plot(lm50)
#> $all
#>
#> $f
#>
#> $m
#>