Description Usage Arguments Value Examples
Calculate indirect effects of X on Y through a mediator in two-condition within-subjects design
1 2 3 | tidy.bootci(x, estimate = FALSE)
mediate_ws(df, M1, M2, Y1, Y2, Reps = 5000, CONF = 0.95, BSType = "basic")
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df |
a tidy dataframe, each row one observation and with columns for that have the value for the mediating variable at time 1, the mediating variable at time 2, the value of the dependent variable (Y) at time 1, and the value of the dependent variable at time 2, respectively |
M1 |
the name of the column with the values of the mediating variable at time 1 |
M2 |
the name of the column with the values of the mediating variable at time 2 |
Y1 |
the name of the column with the values of the dependent variable at time 1 |
Y2 |
the name of the column with teh values of the dependent variable at time2 |
Reps |
the number of bootstrap samples |
CONF |
the confidence interval width. You can have multiple values here. |
BSType |
the bootstrap CI that is calculated |
A data frame, where each row is a different bootstrap calculation (if you did more than one). There is a column indicating the bootstrap method used, the confidence interval level, the lower bound of the confidence interval, the higher bound of the confidence interval, and the estimate
1 | mediate_ws(sample_data, M1, M2, Y1, Y2, Reps = 3000, CONF = c(.9, .95, .99), BSType = "bca")
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