Use emtrends to get pairwise comparison of slopes from a linear model.

Source: R/posthoc_Trends_Pairwise.R

This function is a wrapper based on emmeans, and needs a ordinary linear model produced by simple_model or a mixed effects model produced by mixed_model or mixed_model_slopes (or generated directly with lm, lme4 or lmerTest calls). At least one of the factors should be a numeric covariate whose slopes you wish to find. It also needs to know the fixed factor(s), which should match those in the model and data table.

posthoc_Trends_Pairwise(
  Model,
  Fixed_Factor,
  Trend_Factor,
  P_Adj = "sidak",
  ...
)

Arguments

Model

a model object fit using simple_model or mixed_model (or lm or lmer).

Fixed_Factor

one or more categorical variables, provided as a vector (see Examples), whose levels you wish to compare pairwise. Names of Fixed_Factor should match Fixed_Factor used to fit the model. When more than one factor is provided e.g. Fixed_factor = c("A", "B"), this function passes this on as specs = A:B (note the colon between the two Fixed_Factor) to emmeans to produce pairwise comparisons.

Trend_Factor

a quantitative variable that interacts with a factor and whose slope (trend) is to be compared

P_Adj

method for correcting P values for multiple comparisons. Default is "sidak", can be changed to "bonferroni". See Interaction analysis in emmeans in the manual for emmeans.

...

additional arguments for emmeans such as lmer.df or others. See help for sophisticated models in emmeans.

Value

returns an "emm_list" object containing slopes and their contrasts calculated through emtrends.

Details

Checkout the Interactions with covariates section in the emmeans vignette for more details. One of the independent variables should be a quantitative (e.g. time points) variable whose slope (trend) you want to find at levels of the other factor.

Examples

#create an lm model 
#Time2 is numeric (time points)
m1 <- simple_model(data = data_2w_Tdeath, 
Y_value = "PI", Fixed_Factor = c("Genotype", "Time2"))
posthoc_Trends_Pairwise(Model = m1, 
Fixed_Factor = "Genotype", 
Trend_Factor = "Time2")
#> $emtrends
#>  Genotype Time2.trend     SE df lower.CL upper.CL
#>  WT            0.0678 0.0172 20   0.0320   0.1036
#>  KO            0.0163 0.0172 20  -0.0194   0.0521
#> 
#> Results are averaged over the levels of: Time2 
#> Confidence level used: 0.95 
#> 
#> $contrasts
#>  contrast estimate     SE df t.ratio p.value
#>  WT - KO    0.0515 0.0243 20   2.121  0.0466
#> 
#> Results are averaged over the levels of: Time2 
#>