Calculate logistic regression in annual statistics with zero values
Calculate logistic regression (Everitt and Hothorn, 2009) in annual statistics with zero values. A model fit to compute the probability of a zero flow annual statistic.
calc_logistic_regression(data = NULL, year, value, ...)
data: 'data.frame'. Optional data.frame input, with columns containing year and value. Column names are specified as strings in the corresponding parameter. Default is NULL.year: 'numeric' vector when data = NULL, or 'character' string identifying year column name when data is specified. Year of each value in value parameter.value: 'numeric' vector when data = NULL, or 'character' string identifying value column name when data is specified. Values to calculate logistic regression on....: further arguments to be passed to or from stats::glm.A tibble (see tibble::tibble) with logistic regression p-value, standard error of slope, odds ratio, beginning and ending probability, and probability change. See Details .
This function is a wrapper for stats::glm(y ~ year, family = stats::binomial(link="logit")
with y = 1 when value = 0 (for example a zero flow annual statistic) and y = 0 otherwise. The returned values include
p_value: Probability value of the explanatory (year) variable in the logistic modelstdErr_slope: Standard error of the regression slope (log odds per year)odds_ratio: Exponential of the explanatory coefficient (year coefficient)prob_beg/end: Logistic regression predicted (fitted) values at the beginning and ending year.prob_change: Change in probability from beginning to end.Example, an odds ratio of 1.05 represents the odds of a zero-flow year (versus non-zero) increase by a factor of 1.05 (or 5 percent).
calc_logistic_regression(data = example_annual, year = "WY", value = "annual_mean")
Everitt, B. S. and Hothorn T., 2009, A Handbook of Statistical Analyses Using R, 2nd Ed. Boca Raton, Florida, Chapman and Hall/CRC, 376p.
glm