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.

Usage

fit_growth_rate(
  observations,
  level = 0.95,
  family = c("poisson", "quasipoisson")
)

Arguments

observations: A numeric vector containing the time series observations.
level: The confidence level for parameter estimates, a numeric value between 0 and 1.
family: A character string specifying the family for modeling. Choose between "poisson," or "quasipoisson".

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(
  observations = data,
  level = 0.95,
  family = "poisson"
)
#> $fit
#> 
#> Call:  stats::glm(formula = x ~ growth_rate, family = stats::poisson(link = "log"), 
#>     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
#>