Deprecated 1.x Functions in BUCSS

The dot-named functions from BUCSS 1.x are deprecated as of BUCSS 2.0.0 and are no longer actively developed. They are retained so that scripts written for BUCSS 1.x continue to run. Each one translates its arguments to the 2.x convention and calls the corresponding ss_buc_* function, returning that function's bucss_plan object.

For backward compatibility the returned object still supports the 1.x positional extraction: result[[1]] is the necessary sample size and result[[2]] is the bias and uncertainty adjusted noncentrality parameter, the same two values the 1.x functions returned as an unnamed list (this is provided by a [[ method for bucss_plan). Only those two subscripts are kept: code that treats the 1.x result as a list in other ways (unlist(result), result[[1L]], or printing it through rbind()) now sees the data frame. The richer named view (result$value, tidy(result), glance(result)) is also available.

Calling one of these functions issues a deprecation warning that names its replacement, once per function per session; the ss_buc_* functions themselves never issue this deprecation warning. New code should call the ss_buc_* functions directly.

Usage

ss.power.it(
  t.observed,
  n,
  N,
  alpha.prior = 0.05,
  alpha.planned = 0.05,
  assurance = 0.8,
  power = 0.8,
  step = 0.001
)

ss.power.dt(
  t.observed,
  N,
  alpha.prior = 0.05,
  alpha.planned = 0.05,
  assurance = 0.8,
  power = 0.8,
  step = 0.001
)

ss.power.ba(
  F.observed,
  N,
  levels.A,
  levels.B = NULL,
  effect = c("factor.A", "factor.B", "interaction"),
  alpha.prior = 0.05,
  alpha.planned = 0.05,
  assurance = 0.8,
  power = 0.8,
  step = 0.001
)

ss.power.ba.general(
  F.observed,
  N,
  cells,
  df.numerator,
  df.denominator,
  alpha.prior = 0.05,
  alpha.planned = 0.05,
  assurance = 0.8,
  power = 0.8,
  step = 0.001
)

ss.power.wa(
  F.observed,
  N,
  levels.A,
  levels.B = NULL,
  effect = c("factor.A", "factor.B", "interaction"),
  alpha.prior = 0.05,
  alpha.planned = 0.05,
  assurance = 0.8,
  power = 0.8,
  step = 0.001
)

ss.power.wa.general(
  F.observed,
  N,
  df.numerator,
  alpha.prior = 0.05,
  alpha.planned = 0.05,
  assurance = 0.8,
  power = 0.8,
  step = 0.001
)

ss.power.spa(
  F.observed,
  N,
  levels.between,
  levels.within,
  effect = c("between", "within", "interaction"),
  alpha.prior = 0.05,
  alpha.planned = 0.05,
  assurance = 0.8,
  power = 0.8,
  step = 0.001
)

ss.power.spa.general(
  F.observed,
  N,
  df.numerator,
  num.groups,
  effect = c("between.only", "within.only", "between.within"),
  df.num.within,
  alpha.prior = 0.05,
  alpha.planned = 0.05,
  assurance = 0.8,
  power = 0.8,
  step = 0.001
)

ss.power.reg1(
  t.observed,
  N,
  p,
  alpha.prior = 0.05,
  alpha.planned = 0.05,
  assurance = 0.8,
  power = 0.8,
  step = 0.001
)

ss.power.reg.all(
  F.observed,
  N,
  p,
  alpha.prior = 0.05,
  alpha.planned = 0.05,
  assurance = 0.8,
  power = 0.8,
  step = 0.001
)

ss.power.reg.joint(
  F.observed,
  N,
  p,
  p.joint,
  alpha.prior = 0.05,
  alpha.planned = 0.05,
  assurance = 0.8,
  power = 0.8,
  step = 0.001
)

Arguments

t.observed, F.observed: The observed t or F statistic from the prior study (the 1.x spelling of t_observed / F_observed).
n: Per-group sample size(s) of the prior study, for ss.power.it.
N: Total sample size of the prior study (number of pairs for ss.power.dt).
alpha.prior, alpha.planned: The prior-study and planned-study Type I error rates (the 1.x spelling of alpha_prior / alpha_planned). alpha.prior also accepts "No Publication Filter", which means 1.
assurance: The desired assurance.
power: The desired statistical power of the planned study, which must exceed alpha.planned.
step: Ignored. Accepted only so that 1.x calls that set it do not error.
levels.A, levels.B: Number of levels of the first and (optional) second factor, for ss.power.ba and ss.power.wa.
effect: The effect to plan for, in the 1.x dotted spelling (translated internally to the 2.x form).
cells: Number of cells (groups) in the between-subjects design, for ss.power.ba.general.
df.numerator, df.denominator: Numerator and denominator degrees of freedom for the effect of interest in the prior study, for the .general planners.
levels.between, levels.within: Number of levels of the between- and within-subjects factors, for ss.power.spa.
num.groups: Number of between-subjects groups, for ss.power.spa.general.
df.num.within: Numerator degrees of freedom of the within-subjects component, for ss.power.spa.general with effect = "between.within".
p, p.joint: Number of predictors, and the number tested jointly, for the regression planners.

Value

A bucss_plan object, as returned by the corresponding ss_buc_* function.

Details

The replacements are:

Deprecated	Replacement
`ss.power.it`	`ss_buc_independent_t` (alias `ss_buc_smd`)
`ss.power.dt`	`ss_buc_paired_t` (alias `ss_buc_smd_paired`)
`ss.power.ba`	`ss_buc_one_way_anova` (when `levels.B` is `NULL`) or `ss_buc_factorial_anova`
`ss.power.ba.general`	`ss_buc_factorial_anova_general`
`ss.power.wa`	`ss_buc_rm_anova`
`ss.power.wa.general`	`ss_buc_rm_anova_general`
`ss.power.spa`	`ss_buc_mixed_anova`
`ss.power.spa.general`	`ss_buc_mixed_anova_general`
`ss.power.reg1`	`ss_buc_reg_coef`
`ss.power.reg.all`	`ss_buc_R2`
`ss.power.reg.joint`	`ss_buc_reg_joint`

Because BUCSS 2.0.0 finds the noncentrality parameter by direct root finding rather than the fixed 0 to 100 grid used in 1.x, the values these wrappers return can differ slightly from 1.x and are no longer capped when the implied parameter exceeds 100. The step argument is accepted for call compatibility and ignored.

Some 1.x behaviors deliberately changed, so a 1.x call can now error where it used to return a number. ss.power.ba.general rejects a df.denominator greater than N - cells (1.x accepted such a value and ignored the argument entirely, including in its own documented example). As in 1.x, an assurance or power of 1 or more is read as a percentage, so exactly 1 means 1 percent; after that reading, an assurance or power of 0, or of 100 (which means 1), is rejected rather than returning an artifact of the internal search. A power at or below alpha.planned after that reading is not allowable and stops too, so power = 1 (which means .01) stops unless alpha.planned is below .01; 1.x returned the smallest sample size it considers for such a target, with no notice. An alpha.prior of 1 is computed as exactly 1, where 1.x computed with .999 in its place: most such plans are unchanged, and the rest are slightly smaller. ss.power.ba and ss.power.wa refuse effect = "factor.B" or "interaction" when levels.B is not given (1.x refused only the interaction, and silently returned the Factor A plan for "factor.B"). ss.power.ba.general and ss.power.spa.general reject numerator degrees of freedom no design of their kind can have (df.numerator above cells - 1; for the split plot, above num.groups - 1 for the between-subjects effect, or not a multiple of df.num.within within that bound for the interaction). Non-scalar, non-finite, and fractional inputs are rejected with a message naming the argument. Most errors raised through these wrappers come from the ss_buc_* planners and name their arguments (t_observed for t.observed, and so on); an unrecognized effect is rejected by the wrapper itself. The regression wrappers keep the 1.x fixed-predictor computation (random_predictors = FALSE); call ss_buc_reg_coef, ss_buc_R2, or ss_buc_reg_joint directly for predictors sampled along with the outcome. See NEWS for the full accounting.

Usage

Arguments

Value

Details

See also