Description Usage Arguments Details Value Examples

The function merges partR2 object based on a full model with interactions with a partR2 object based on a reduced model without interaction. The reduced model is used to infer main effect semi-partial R2s. This function essentially takes over the complete partR2 object for the full model and adds semi-partial R2s which have been calculated based on the reduced model and are not already present in the full model partR2 object (which can be main effects). The function also combines the bootstrap estimates, accessible with partR2_obj$R2_boot.

1 |

`R2_full` |
partR2 object for the full model, with the interaction (but not the main effects) in partvars. |

`...` |
other partR2 objects, which do not contain the interaction so that the semi-partial R2s for the main effects could be calculated. |

This function is experimental and should be used with caution. See vignette or paper on how to use it to obtain semi-partial R2s for main effects which are also involved in interactions.

Returns an object of class `partR2`

, which takes most components from the
full model except for semi-partial R2s.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
data(biomass)
library(lme4)
# scale data
biomass[] <- lapply(biomass, function(x) if (is.double(x)) scale(x) else x)
# Full model
mod_full <- lmer(Biomass ~ Year + Temperature * Precipitation + SpeciesDiversity + (1|Population),
data = biomass)
# Semi-partial R2 for interaction and all other predictors of interest
(R2_full <- partR2(mod_full, partvars = c("Temperature:Precipitation", "SpeciesDiversity", "Year"),
data = biomass))
# model without interaction to get main effect semi-partial R2s
mod_noIA <- lmer(Biomass ~ Year + Temperature + Precipitation + SpeciesDiversity + (1|Population),
data = biomass)
(R2_noIA <- partR2(mod_noIA, partvars = c("Temperature", "Precipitation"), data = biomass))
# combine both
(R2_comb <- mergeR2(R2_full, R2_noIA))
``` |

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