Easily compute moderation analyses, with effect sizes, and format in publication-ready format.
Usage
nice_mod(
data,
response,
predictor,
moderator,
moderator2 = NULL,
covariates = NULL,
b.label = "b",
standardize = TRUE,
mod.id = TRUE,
ci.alternative = "two.sided",
...
)Arguments
- data
The data frame
- response
The dependent variable.
- predictor
The independent variable.
- moderator
The moderating variable.
- moderator2
The second moderating variable, if applicable.
- covariates
The desired covariates in the model.
- b.label
What to rename the default "b" column (e.g., to capital B if using standardized data for it to be converted to the Greek beta symbol in the
nice_table()function). Now attempts to automatically detect whether the variables were standardized, and if so, setsb.label = "B"automatically. Factor variables or dummy variables (only two numeric values) are ignored when checking for standardization. This argument is now deprecated, please use argumentstandardizedirectly instead.- standardize
Logical, whether to standardize the data before fitting the model. If
TRUE, automatically setsb.label = "B". Defaults toTRUE.- mod.id
Logical. Whether to display the model number, when there is more than one model.
- ci.alternative
Alternative for the confidence interval of the sr2. It can be either "two.sided (the default in this package), "greater", or "less".
- ...
Further arguments to be passed to the
lm()function for the models.
Value
A formatted dataframe of the specified lm model, with DV, IV, degrees of freedom, regression coefficient, t-value, p-value, and the effect size, the semi-partial correlation squared, and its confidence interval.
Details
The effect size, sr2 (semi-partial correlation squared, also
known as delta R2), is computed through effectsize::r2_semipartial.
Please read the documentation for that function, especially regarding
the interpretation of the confidence interval. In rempsyc, instead
of using the default one-sided alternative ("greater"), we use the
two-sided alternative.
To interpret the sr2, use effectsize::interpret_r2_semipartial().
For the easystats equivalent, use report::report() on the lm()
model object.
See also
Checking simple slopes after testing for moderation:
nice_slopes, nice_lm,
nice_lm_slopes. Tutorial:
https://rempsyc.remi-theriault.com/articles/moderation
Examples
# Make the basic table
nice_mod(
data = mtcars,
response = "mpg",
predictor = "gear",
moderator = "wt"
)
#> Dependent Variable Predictor df B t p
#> 1 mpg gear 28 -0.08718042 -0.7982999 4.314156e-01
#> 2 mpg wt 28 -0.94959988 -8.6037724 2.383144e-09
#> 3 mpg gear:wt 28 -0.23559962 -2.1551077 3.989970e-02
#> sr2 CI_lower CI_upper
#> 1 0.004805465 0.0000000000 0.02702141
#> 2 0.558188818 0.3142326391 0.80214500
#> 3 0.035022025 0.0003502202 0.09723370
# Multiple dependent variables at once
nice_mod(
data = mtcars,
response = c("mpg", "disp", "hp"),
predictor = "gear",
moderator = "wt"
)
#> Model Number Dependent Variable Predictor df B t
#> 1 1 mpg gear 28 -0.08718042 -0.7982999
#> 2 1 mpg wt 28 -0.94959988 -8.6037724
#> 3 1 mpg gear:wt 28 -0.23559962 -2.1551077
#> 4 2 disp gear 28 -0.07488985 -0.6967831
#> 5 2 disp wt 28 0.83273987 7.6662883
#> 6 2 disp gear:wt 28 -0.08758665 -0.8140664
#> 7 3 hp gear 28 0.42308208 2.6537930
#> 8 3 hp wt 28 0.92572761 5.7454866
#> 9 3 hp gear:wt 28 0.15308972 0.9592587
#> p sr2 CI_lower CI_upper
#> 1 4.314156e-01 0.004805465 0.0000000000 0.02702141
#> 2 2.383144e-09 0.558188818 0.3142326391 0.80214500
#> 3 3.989970e-02 0.035022025 0.0003502202 0.09723370
#> 4 4.916834e-01 0.003546038 0.0000000000 0.02230154
#> 5 2.373171e-08 0.429258143 0.1916386492 0.66687764
#> 6 4.224765e-01 0.004840251 0.0000000000 0.02679265
#> 7 1.297126e-02 0.113174016 0.0011317402 0.26928944
#> 8 3.637782e-06 0.530476631 0.2928664478 0.76808681
#> 9 3.456390e-01 0.014787139 0.0000000000 0.07139000
# Add covariates
nice_mod(
data = mtcars,
response = "mpg",
predictor = "gear",
moderator = "wt",
covariates = c("am", "vs")
)
#> Dependent Variable Predictor df B t p
#> 1 mpg gear 26 -0.1106937 -0.8787865 3.875666e-01
#> 2 mpg wt 26 -0.6977072 -5.0686874 2.803684e-05
#> 3 mpg am 26 0.1306864 0.8569286 3.993141e-01
#> 4 mpg vs 26 0.3192472 3.2441426 3.228614e-03
#> 5 mpg gear:wt 26 -0.2511143 -2.5615471 1.656773e-02
#> sr2 CI_lower CI_upper
#> 1 0.004459982 0.0000000000 0.02257663
#> 2 0.148373834 0.0198201790 0.27692749
#> 3 0.004240876 0.0000000000 0.02189819
#> 4 0.060780767 0.0006078077 0.13504910
#> 5 0.037894048 0.0003789405 0.09436130
# Three-way interaction
x <- nice_mod(
data = mtcars,
response = "mpg",
predictor = "gear",
moderator = "wt",
moderator2 = "am"
)
x
#> Dependent Variable Predictor df B t p sr2
#> 1 mpg gear 24 0.3013096 1.791291 0.085867454 0.015880099
#> 2 mpg wt 24 -1.9824141 -3.296782 0.003035645 0.053790002
#> 3 mpg am 24 -0.3381137 -1.431537 0.165170887 0.010142070
#> 4 mpg gear:wt 24 -0.9906361 -1.567980 0.129977525 0.012167517
#> 5 mpg gear:am 24 -0.2934245 -1.649909 0.111990122 0.013472278
#> 6 mpg wt:am 24 0.2636573 0.499489 0.621983258 0.001234733
#> 7 mpg gear:wt:am 24 1.0037369 1.854357 0.076016767 0.017017980
#> CI_lower CI_upper
#> 1 0.0000000 0.047441438
#> 2 0.0005379 0.117448925
#> 3 0.0000000 0.034953502
#> 4 0.0000000 0.039504439
#> 5 0.0000000 0.042345492
#> 6 0.0000000 0.009659682
#> 7 0.0000000 0.049794162
# Get interpretations
cbind(x, Interpretation = effectsize::interpret_r2_semipartial(x$sr2))
#> Dependent Variable Predictor df B t p sr2
#> 1 mpg gear 24 0.3013096 1.791291 0.085867454 0.015880099
#> 2 mpg wt 24 -1.9824141 -3.296782 0.003035645 0.053790002
#> 3 mpg am 24 -0.3381137 -1.431537 0.165170887 0.010142070
#> 4 mpg gear:wt 24 -0.9906361 -1.567980 0.129977525 0.012167517
#> 5 mpg gear:am 24 -0.2934245 -1.649909 0.111990122 0.013472278
#> 6 mpg wt:am 24 0.2636573 0.499489 0.621983258 0.001234733
#> 7 mpg gear:wt:am 24 1.0037369 1.854357 0.076016767 0.017017980
#> CI_lower CI_upper Interpretation
#> 1 0.0000000 0.047441438 small
#> 2 0.0005379 0.117448925 small
#> 3 0.0000000 0.034953502 small
#> 4 0.0000000 0.039504439 small
#> 5 0.0000000 0.042345492 small
#> 6 0.0000000 0.009659682 very small
#> 7 0.0000000 0.049794162 small