globTempAnom

The <code>globTempAnom</code> data frame includes the global surface 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 of the annual (average) measurement 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 of the annual (average) measurement with respect to the 20th century average (1901-2000).</dd>

 
</dl>

globTempAnom: Global temperature anomalies 1850-2023

Description

Usage

Arguments

Format

Details

References

Examples