WISEsummmary — WISEsummary • nmmr

Within-subject Error Summary

WISEsummary(
  data,
  dependentvars,
  betweenvars = NULL,
  withinvars = NULL,
  idvar = NULL,
  CI_width = 0.95,
  na.rm = FALSE
)

Arguments

data	A data frame
dependentvars	Character vector giving the dependent variable
betweenvars	Character vector giving the between subject variables
withinvars	Character vector giving the within subject variables
idvar	Character vector giving the name of the column holding subject identifiers
CI_width	Numeric vector giving the confidence level for computing the confidence interval boundaries. Must be between 0 and 1, non-inclusive.
na.rm	a logical value indicating whether NA values should be removed from the Dependent Variables.

Value

A data frame with summary statistics

References

WISESummary, Morey (2008)

Examples


WISEsummary(sub02, y, withinvars = c(contrast, orientation), idvar = voxel)
#> # A tibble: 16 x 8
#>    contrast orientation y_mean y_recentered_mean  y_sem   y_n y_CI_lower
#>    <fct>          <dbl>  <dbl>             <dbl>  <dbl> <int>      <dbl>
#>  1 low           -3.14    2.05              2.05 0.0574  3366       1.94
#>  2 low           -2.36    1.66              1.66 0.0542  3366       1.55
#>  3 low           -1.57    1.62              1.62 0.0532  3366       1.52
#>  4 low           -0.785   1.31              1.31 0.0534  3366       1.20
#>  5 low            0       1.87              1.87 0.0540  3366       1.77
#>  6 low            0.785   1.58              1.58 0.0598  3366       1.47
#>  7 low            1.57    1.26              1.26 0.0562  3366       1.15
#>  8 low            2.36    2.26              2.26 0.0580  3366       2.15
#>  9 high          -3.14    2.14              2.14 0.0527  3366       2.03
#> 10 high          -2.36    2.46              2.46 0.0506  3366       2.36
#> 11 high          -1.57    1.92              1.92 0.0523  3366       1.82
#> 12 high          -0.785   1.92              1.92 0.0566  3366       1.81
#> 13 high           0       1.79              1.79 0.0582  3366       1.68
#> 14 high           0.785   1.76              1.76 0.0595  3366       1.65
#> 15 high           1.57    2.24              2.24 0.0559  3366       2.13
#> 16 high           2.36    2.31              2.31 0.0568  3366       2.20
#> # … with 1 more variable: y_CI_upper <dbl>