`R/mixed_model_slopes.R`

`mixed_model_slopes.Rd`

There are four related functions for mixed effects analyses: `mixed_model`

, `mixed_anova`

, `mixed_model_slopes`

, and `mixed_anova_slopes`

.

```
mixed_model_slopes(
data,
Y_value,
Fixed_Factor,
Slopes_Factor,
Random_Factor,
...
)
```

- data
a data table object, e.g. data.frame or tibble.

- Y_value
name of column containing quantitative (dependent) variable, provided within "quotes".

- Fixed_Factor
name(s) of categorical fixed factors (independent variables) provided as a vector if more than one or within "quotes".

- Slopes_Factor
name of factor to allow varying slopes on.

- Random_Factor
name(s) of random factors to allow random intercepts; to be provided as a vector when more than one or within "quotes".

- ...
any additional arguments to pass on to

`lmer`

if required.

This function returns an S4 object of class "lmerModLmerTest".

This function uses `lmer`

to fit a linear mixed effect model and provides the model object, which could be used for post-hoc comparisons. The model object is converted to class `lmerModLmerTest`

object by `as_lmerModLmerTest`

.
It requires a data table, one dependent variable (Y_value), one or more independent variables (Fixed_Factor). Exactly one random factor (Random_Factor) and Slope_Factor should be provided.
This function is related to `mixed_anova_slopes`

.
Output of this function can be used with `posthoc_Pairwise`

, `posthoc_Levelwise`

and `posthoc_vsRef`

, or with `emmeans`

.

More than one fixed factors can be provided as a vector (e.g. c("A", "B")). A full model with interaction term is fitted with one term each for varying slopes and intercepts.
This means when `Y_value = Y, Fixed_factor = c("A", "B"), Slopes_Factor = "S", Random_factor = "R"`

are entered as arguments, these are passed on as `Y ~ A*B + (S|R)`

(which is equivalent to `Y ~ A + B + A:B + (S|R)`

).
In this experimental implementation, random slopes and intercepts are fitted (`(Slopes_Factor|Random_Factor)`

). Only one term each is allowed for `Slopes_Factor`

and `Random_Factor`

.

```
#two fixed factors as a vector,
#exactly one slope factor and random factor
mod <- mixed_model_slopes(data = data_2w_Tdeath,
Y_value = "PI",
Fixed_Factor = c("Genotype", "Time"),
Slopes_Factor = "Time",
Random_Factor = "Experiment")
#> boundary (singular) fit: see help('isSingular')
#get summary
summary(mod)
#> Linear mixed model fit by REML. t-tests use Satterthwaite's method [
#> lmerModLmerTest]
#> Formula: PI ~ Genotype * Time + (Time | Experiment)
#> Data: data_2w_Tdeath
#>
#> REML criterion at convergence: 132.9
#>
#> Scaled residuals:
#> Min 1Q Median 3Q Max
#> -1.74243 -0.50144 0.01573 0.55592 1.88600
#>
#> Random effects:
#> Groups Name Variance Std.Dev. Corr
#> Experiment (Intercept) 7.372 2.715
#> Timet300 1.231 1.110 1.00
#> Residual 24.327 4.932
#> Number of obs: 24, groups: Experiment, 6
#>
#> Fixed effects:
#> Estimate Std. Error df t value Pr(>|t|)
#> (Intercept) 23.841 2.299 12.624 10.373 1.53e-07 ***
#> GenotypeKO -16.918 2.848 15.000 -5.941 2.71e-05 ***
#> Timet300 13.563 2.883 14.912 4.704 0.000287 ***
#> GenotypeKO:Timet300 -10.293 4.027 15.000 -2.556 0.021937 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Correlation of Fixed Effects:
#> (Intr) GntyKO Tmt300
#> GenotypeKO -0.619
#> Timet300 -0.536 0.494
#> GntyKO:T300 0.438 -0.707 -0.698
#> optimizer (nloptwrap) convergence code: 0 (OK)
#> boundary (singular) fit: see help('isSingular')
#>
```