test_pdiff is suitable for testing a hypothesis about a
difference in proportions between two conditions with a categorical outcome
variable. It can test hypotheses for both independent and paired designs.
Usage
test_pdiff(estimate, rope = c(0, 0), output_html = FALSE)Arguments
- estimate
An esci_estimate object generated by an estimate_pdiff_ function
- rope
A two-element vector defining the Region of Practical Equivalence (ROPE). Specify c(0, 0) to test a point null of exactly 0. Specify any two ascending values between -1 and 1 to test an interval null (e.g. c(-.25, .25) to test the hypothesis that the difference in proportion is between -.25 and .25).
- output_html
TRUE to return results in HTML; FALSE (default) to return standard output
Value
Returns a list with 1-2 data frames
point_null - always returned
test_type - 'Nil hypothesis test', meaning a test against H0 = 0
outcome_variable_name - Name of the outcome variable
effect - Label for the effect being tested
null_words - Express the null in words
confidence - Confidence level, integer (95 for 95%, etc.)
LL - Lower boundary of the confidence% CI for the effect
UL - Upper boundary of the confidence% CI for the effect
CI - Character representation of the CI for the effect
CI_compare - Text description of relation between CI and null
t - If applicable, t value for hypothesis test
df - If applicable, degrees of freedom for hypothesis test
p - If applicable, p value for hypothesis test
p_result - Text representation of p value obtained
null_decision - Text represention of the decision for the null
conclusion - Text representation of conclusion to draw
significant - TRUE/FALSE if significant at alpha = 1-CI
interval_null - returned only if an interval null is specified
test_type - 'Practical significance test', meaning a test against an interval null
outcome_variable_name -
effect - Name of the outcome variable
rope - Test representation of null interval
confidence - Confidence level, integer (95 for 95%, etc.)
CI - Character representation of the CI for the effect
rope_compare - Text description of relation between CI and null interval
p_result - Text representation of p value obtained
conclusion - Text representation of conclusion to draw
significant - TRUE/FALSE if significant at alpha = 1-CI
Details
This function can be passed an esci_estimate object generated by
estimate_pdiff_one(), estimate_pdiff_two(),
estimate_pdiff_paired(), or estimate_pdiff_ind_contrast().
It can test hypotheses about a specific value for the difference (a point null) or about a range of values (an interval null)
Examples
estimate <- estimate_pdiff_two(
comparison_cases = 10,
comparison_n = 20,
reference_cases = 78,
reference_n = 252,
grouping_variable_levels = c("Original", "Replication"),
conf_level = 0.95
)
# Test against null of exactly
test_pdiff(estimate)
#> $properties
#> $properties$effect_size_name
#> [1] "P"
#>
#> $properties$alpha
#> [1] 0.05
#>
#> $properties$interval_null
#> [1] FALSE
#>
#> $properties$rope
#> [1] 0 0
#>
#> $properties$rope_units
#> [1] "raw"
#>
#>
#> $point_null
#> test_type outcome_variable_name case_label effect
#> 1 Nil Hypothesis Test My outcome variable P_Affected Replication ‒ Original
#> null_words confidence LL UL CI
#> 1 0.00 95 -0.02757339 0.4055261 95% CI [-0.02757339, 0.4055261]
#> CI_compare t df p p_result null_decision
#> 1 The 95% CI contains H_0 1.710401 NA 0.08719178 p ≥ 0.05 Fail to reject H_0
#> conclusion significant
#> 1 At α = 0.05, 0.00 remains a plausible value of Π_diff FALSE
#>
# Test against null of (-0.1, 0.1)
test_pdiff(estimate, rope = c(-0.1, 0.1))
#> $properties
#> $properties$effect_size_name
#> [1] "P"
#>
#> $properties$alpha
#> [1] 0.05
#>
#> $properties$interval_null
#> [1] TRUE
#>
#> $properties$rope
#> [1] -0.1 0.1
#>
#> $properties$rope_units
#> [1] "raw"
#>
#>
#> $point_null
#> test_type outcome_variable_name case_label effect
#> 1 Nil Hypothesis Test My outcome variable P_Affected Replication ‒ Original
#> null_words confidence LL UL CI
#> 1 0.00 95 -0.02757339 0.4055261 95% CI [-0.02757339, 0.4055261]
#> CI_compare t df p p_result null_decision
#> 1 The 95% CI contains H_0 1.710401 NA 0.08719178 p ≥ 0.05 Fail to reject H_0
#> conclusion significant
#> 1 At α = 0.05, 0.00 remains a plausible value of Π_diff FALSE
#>
#> $interval_null
#> test_type outcome_variable_name case_label
#> 1 Practical significance test My outcome variable P_Affected
#> effect rope confidence
#> 1 Replication ‒ Original (-0.10, 0.10) 95
#> CI
#> 1 95% CI [-0.02757339, 0.4055261]\n90% CI [0.007242087, 0.3707107]
#> rope_compare p_result
#> 1 95% CI has values inside and outside H_0 p ≥ 0.05
#> conclusion significant
#> 1 At α = 0.05, not clear if Π_diff is substantive or negligible FALSE
#>