Fit a growth rate model to time series observations.

This function fits a growth rate model to time series observations and provides parameter estimates along with confidence intervals. For binomial data use family = "binomial" or family = "quasibinomial".

Usage

fit_growth_rate(
  cases,
  denominator = NULL,
  level = 0.95,
  family = c("quasipoisson", "poisson", "quasibinomial", "binomial")
)

Arguments

cases: An integer vector containing cases.
denominator: An integer vector containing population or binomial sample size. This is mandatory for binomial models
level: The confidence level for parameter estimates, a numeric value between 0 and 1.
family: A character string, family-generator, or family object specifying the distribution family for growth-rate modeling. Choose between 'poisson', 'quasipoisson', 'binomial', or 'quasibinomial'. Use 'poisson' or 'quasipoisson' for cases/incidences, and use 'binomial' or 'quasibinomial' for binomial data supplied as cases and samples.

Value

A list containing:

'fit': The fitted growth rate model.
'estimate': A numeric vector with parameter estimates, including the growth rate and its confidence interval.
'level': The confidence level used for estimating parameter confidence intervals.

Examples

# Fit a growth rate model to a time series of counts
# (e.g., population growth)
data <- c(100, 120, 150, 180, 220, 270)
fit_growth_rate(
  cases = data,
  level = 0.95,
  family = "poisson"
)
#> $fit
#> 
#> Call:  stats::glm(formula = stats::as.formula(paste(response, "~", paste(terms, 
#>     collapse = " + "))), family = fam_obj, data = growth_data)
#> 
#> Coefficients:
#> (Intercept)  growth_rate  
#>      4.4008       0.1992  
#> 
#> Degrees of Freedom: 5 Total (i.e. Null);  4 Residual
#> Null Deviance:	    116.2 
#> Residual Deviance: 0.04923 	AIC: 45.67
#> 
#> $estimate
#> growth_rate       2.5 %      97.5 % 
#>   0.1992211   0.1624836   0.2362807 
#> 
#> $level
#> [1] 0.95
#> 

# Fit a binomial growth rate model to successes out of trials
fit_growth_rate(
  cases = c(1, 2, 3, 4),
  denominator = c(10, 10, 10, 10),
  family = "binomial"
)
#> $fit
#> 
#> Call:  stats::glm(formula = stats::as.formula(paste(response, "~", paste(terms, 
#>     collapse = " + "))), family = fam_obj, data = growth_data)
#> 
#> Coefficients:
#> (Intercept)  growth_rate  
#>     -2.6137       0.5666  
#> 
#> Degrees of Freedom: 3 Total (i.e. Null);  2 Residual
#> Null Deviance:	    2.8 
#> Residual Deviance: 0.05243 	AIC: 13.75
#> 
#> $estimate
#> growth_rate       2.5 %      97.5 % 
#>   0.5666205  -0.1000528   1.3345240 
#> 
#> $level
#> [1] 0.95
#>