globTempAnom

The <code>globTempAnom</code> data frame includes the global temperature anomalies from 1850 to 2023.

datasets

Fitting regression models where, in addition to possible linear terms, one or more covariates have segmented (i.e., broken-line
or piece-wise linear) or stepmented (i.e. piece-wise constant) effects.
Multiple breakpoints for the same variable are allowed. The estimation method is discussed in Muggeo (2003, <doi:10.1002/sim.1545>) and
illustrated in Muggeo (2008, <https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf>). An approach for hypothesis testing is presented
in Muggeo (2016, <doi:10.1080/00949655.2016.1149855>), and interval estimation for the breakpoint is discussed in Muggeo (2017, <doi:10.1111/anzs.12200>).
Segmented mixed models, i.e. random effects in the change point, are discussed in Muggeo (2014, <doi:10.1177/1471082X13504721>).
Estimation of piecewise-constant relationships and changepoints (mean-shift models) is
discussed in Fasola et al. (2018, <doi:10.1007/s00180-017-0740-4>).

Vito MR Muggeo

segmented

Regression Models with Break-Points / Change-Points Estimation
(with Possibly Random Effects)

globTempAnom function

The included variables are (clearly).<dl>
 <dt><code>Year</code></dt>
<dd>the calendar year.</dd> <dt><code>Anomaly</code></dt>
<dd>the temperature anomalies computed as differences with respect to the 20th century average (1901-2000)</dd> 
</dl>

Format

Global temperature anomalies 1850-2023 — globTempAnom

The included variables are (clearly).<dl>
 <dt><code>Year</code></dt>
<dd>the calendar year.</dd>

 <dt><code>Anomaly</code></dt>
<dd>the temperature anomalies computed as differences with respect to the 20th century average (1901-2000)</dd>

 
</dl>

globTempAnom: Global temperature anomalies 1850-2023

Description

Usage

Arguments

Format

References

Examples