Basic function for running mixed models for the multiverse analysis
mixed_mf(
cs1,
cs2,
data,
subj,
group = NULL,
phase = "acquisition",
dv = "scr",
exclusion = "full data",
cut_off = "full data"
)The column name(s) of the conditioned responses for the first conditioned stimulus
The column name(s) of the conditioned responses for the second conditioned stimulus
A data frame containing all the relevant columns for the analyses
The name of the column including the participant numbers. Unique numbers are expected
the name of the group, if included, default to NULL
The conditioned phase that the analyses refer to. Accepted values are acquisition, acq, extinction, or ext
name of the measured conditioned response. Default to "SCR"
Name of the data reduction procedure used. Default to full data
cut off Name of the cut_off applied. Default to full data
A data frame with the results.
The data frame returned is the standard one returned in all function in the package. Specifically we have:
A tibble with the following column names:
x: the name of the independent variable (e.g., cs). There, you can see the term of the model that is returned. So, not the full model is returned but only this particular term.
y: the name of the dependent variable as this defined in the dv argument
exclusion: see exclusion argument
model: the model that was run (e.g., mixed_model)
controls: ignore this column for this test
method: the model that was run
p.value: the p-value for each factor
effect.size: irrelevant here
effect.size.ma: irrelevant here
effect.size.ma.lci: irrelevant here
effect.size.ma.hci: irrelevant here
statistic: the t-value for each factor
conf.low: the lower confidence interval for the estimate
conf.high: the higher confidence interval for the estimate
data_used: a list with the data used for the specific test
The function assumes that you include more than 1 trial per CS. The function returns an error if that is not the function.
The function performs by default two dependent variable standardizations, the one per subject and the other one without taking subject into account.
In case time is included, the function computes the intercept -- i.e., the 0 point -- on the middle of the time sequence.
The following models are run and compared: a) Intercept only model, b) Intercept plus CS model, and c) Intercept plus CS x Time interaction.
Separate models are run with `Subject` as random factor, as well as `Subject and Time` as random factors.
The model is fit by maximizing the log-likelihood (i.e., "ML" term in nlme::lme).
The model comparison is done using `BIC`.
cs1 <- paste0("CSP", 1:2)
cs2 <- paste0("CSM", 1:2)
subj <- "id"
# mixed models without groups
mixed_mf(cs1 = cs1, cs2 = cs2, subj = subj, data = example_data)
#> # A tibble: 6 × 16
#> x y exclusion model controls method p.value effect.size
#> <chr> <chr> <chr> <chr> <lgl> <chr> <dbl> <lgl>
#> 1 cscs2 scr full data mixed_model NA mixed_mo… 0.0477 NA
#> 2 cscs2:time2 scr full data mixed_model NA mixed_mo… 0.240 NA
#> 3 cscs2 scr full data mixed_model NA mixed_mo… 0.157 NA
#> 4 cscs2:time2 scr full data mixed_model NA mixed_mo… 0.0306 NA
#> 5 cscs2 scr full data mixed_model NA mixed_mo… 0.0477 NA
#> 6 cscs2:time2 scr full data mixed_model NA mixed_mo… 0.240 NA
#> # … with 8 more variables: efffect.size.ma <lgl>, effect.size.ma.lci <lgl>,
#> # effect.size.ma.hci <lgl>, estimate <dbl>, statistic <dbl>, conf.low <lgl>,
#> # conf.high <lgl>, data_used <list>
# mixed models with groups
mixed_mf(cs1 = cs1, cs2 = cs2, subj = subj, group = "group", data = example_data)
#> # A tibble: 6 × 16
#> x y exclusion model controls method p.value effect.size
#> <chr> <chr> <chr> <chr> <lgl> <chr> <dbl> <lgl>
#> 1 cscs2 scr full data mixed_model NA mixed_mo… 0.0477 NA
#> 2 cscs2:time2 scr full data mixed_model NA mixed_mo… 0.240 NA
#> 3 cscs2 scr full data mixed_model NA mixed_mo… 0.157 NA
#> 4 cscs2:time2 scr full data mixed_model NA mixed_mo… 0.0306 NA
#> 5 cscs2 scr full data mixed_model NA mixed_mo… 0.0477 NA
#> 6 cscs2:time2 scr full data mixed_model NA mixed_mo… 0.240 NA
#> # … with 8 more variables: efffect.size.ma <lgl>, effect.size.ma.lci <lgl>,
#> # effect.size.ma.hci <lgl>, estimate <dbl>, statistic <dbl>, conf.low <lgl>,
#> # conf.high <lgl>, data_used <list>