rtrim (version 2.1.1)

index: Extract time-indices from TRIM output.

Description

Indices are obtained by dividing the modelled or imputed time totals by a reference value. Most commonly, the time totals for the first time point are used as reference. As a result, the index value for this first time point will be 1.0, with a standard error of 0.0 by definition. Alternatively, a range of time points can be used as reference. In this case, the mean time totals for this range will be used as reference, and the standard errors will be larger than 0.0.

Usage

index(
  x,
  which = c("imputed", "fitted", "both"),
  covars = FALSE,
  base = 1,
  level = NULL
)

Arguments

x

an object of class trim

which

[character] Selector to distinguish between time indices based on the imputed data (default), the fitted model, or both.

covars

[logical] Switch to compute indices for covariate categories as well.

base

[integer|numeric] One or more base time point, used as as reference for the index. If just a single number is given, the time total of the correspondong time point will be uses as reference. If a range of numbers is given, the average of the corresponding time totals will be used as reference. The base time points can be given in the interval 1...J, or, if the time points are proper years, say year1...yearn, the base year can be given. So, if the data range 2000...2016, base=2 and base=2001 are equivalent.

level

[numeric] the confidence interval required. Must be in the range 0 to 1. A value of 0.95 results in 95% confidence intervals. The default value of NULL results in no confidence interval to be computed.

Value

A data frame containing indices and their uncertainty expressed as standard error. Depending on the chosen output, columns fitted and se_fit, and/or imputed and se_imp are present. If covars is TRUE, additional indices are computed for the individual covariate categories. In this case additional columns covariate and category are present. The overall indices are marked as covariate `Overall' and category 0.

See Also

Other analyses: coef.trim(), confint.trim(), gof(), now_what(), overall(), overdispersion(), plot.trim.index(), plot.trim.overall(), results(), serial_correlation(), summary.trim(), totals(), trim(), vcov.trim(), wald()

Examples

# NOT RUN {
data(skylark)
z <- trim(count ~ site + time, data=skylark, model=2)
index(z)
# mimic classic TRIM:
index(z, "both")
# Extract standard errors for the imputed data
SE <- index(z,"imputed")$se_mod
# Include covariates
skylark$Habitat <- factor(skylark$Habitat) # hack
z <- trim(count ~ site + time + Habitat, data=skylark, model=2)
ind <- index(z, covars=TRUE)
plot(ind)
# Use alternative base year
index(z, base=3)
# Use average of first 5 years as reference for indexing
index(z, base=1:5)
# }