`R/posthoc_Trends_vsRef.R`

`posthoc_Trends_vsRef.Rd`

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_vsRef(
Model,
Fixed_Factor,
Trend_Factor,
Ref_Level = 1,
P_Adj = "sidak",
...
)
```

- 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

- Ref_Level
the level within that factor to be considered the reference or control to compare other levels to (to be provided as a number - by default R orders levels alphabetically); default

`Ref_Level = 1`

.- 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.

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

.

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.

```
#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_vsRef(Model = m1,
Fixed_Factor = "Genotype",
Trend_Factor = "Time2",
Ref_Level = 2)
#> $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
#>
```