Master Analysis Function - Auto-detects and runs the right test
Usage
analyze(
x = NULL,
y = NULL,
data = NULL,
formula = NULL,
mu = 0,
paired = FALSE,
nonparam = FALSE,
conf.level = 0.95,
var_name = "Variable",
var1_name = "Variable 1",
var2_name = "Variable 2",
method = "pearson",
test_type = NULL,
effect_size = NULL,
power = 0.8,
n_groups = 2,
n_predictors = 1,
check = FALSE,
context = NULL
)Arguments
- x
A numeric vector (required always)
- y
A numeric vector, factor, or character group variable (optional)
- data
A data frame (required if using a formula)
- formula
A formula of the form outcome ~ predictor or outcome ~ group1 * group2 or cbind(y1, y2) ~ group (optional)
- mu
Hypothesised mean for one-sample t-test. Default 0.
- paired
Logical. TRUE for paired t-test. Default FALSE.
- nonparam
Logical. TRUE to use non-parametric tests. Default FALSE.
- conf.level
Confidence level. Default 0.95.
- var_name
Optional label for the report.
- var1_name
Optional name for first variable in correlation.
- var2_name
Optional name for second variable in correlation.
- method
Correlation method: "pearson", "spearman", or "kendall". Default "pearson".
- test_type
For power analysis: one of "ttest.one", "ttest.two", "ttest.paired", "anova", "correlation", "chisq", "regression".
- effect_size
For power analysis: the expected effect size.
- power
For power analysis: desired power level. Default 0.80.
- n_groups
For power analysis ANOVA: number of groups. Default 2.
- n_predictors
For power analysis regression: number of predictors. Default 1.
- check
Logical. TRUE to run assumption checks before analysis. Default FALSE.
- context
Optional description of the study design or sampling method, echoed back in the report. Default NULL.
Examples
# Descriptive only
analyze(x = c(23, 45, 12, 67, 34))
#> [statease] Single numeric vector -> Running Descriptive Statistics
#> Warning: Sample size is small (n < 10). Interpret descriptive statistics with caution.
#>
#> -- statease Descriptive Report ----------------------------------
#> Variable : Variable
#> N : 5 | Missing: 0
#> -----------------------------------------------------------------
#> Mean : 36.20
#> Median : 34.00
#> Std Dev : 21.16
#> Min : 12.00 | Max: 67.00
#> Q1 : 23.00 | Q3: 45.00
#> IQR : 22.00
#> -----------------------------------------------------------------
#> Interpretation:
#> The distribution is approximately symmetric.
#> Spread shows high variability (CV = 58.5%).
#> Shapiro-Wilk test suggests normality is reasonable (W = 0.979, p = 0.9276).
#> -----------------------------------------------------------------
#>
# Auto t-test
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29))
#> [statease] Two numeric vectors detected -> Running T-Test
#> Warning: Sample size in x is small (n < 10). Interpret results with caution.
#> Warning: Sample size in y is small (n < 10). Interpret results with caution.
#>
#> -- statease T-Test Report ----------------------------------------
#> Test : Independent Samples T-Test
#> Variable : Variable
#> Groups : Group 1: n = 5 | Group 2: n = 5
#> -----------------------------------------------------------------
#> t-statistic : 0.396
#> df : 6.6
#> p-value : 0.7043
#> 95% CI : [-22.146, 30.946]
#> Cohen's d : 0.251 (small effect)
#> -----------------------------------------------------------------
#> Assumption Checks:
#> Normality (x) : PASSED (Shapiro-Wilk p = 0.928)
#> Normality (y) : PASSED (Shapiro-Wilk p = 0.637)
#> Equal variances : PASSED (F-test p = 0.367)
#>
#> NOTE: Assumption checks are diagnostic tools and may be
#> influenced by sample size and other characteristics of the
#> data. Passing a check does not prove that an assumption is
#> satisfied, and a warning does not automatically invalidate
#> the analysis. Interpret these results alongside your
#> knowledge of the data.
#> -----------------------------------------------------------------
#> Interpretation:
#> The result is not statistically significant (p = 0.704 > alpha 0.05).
#> Group 1 had a higher mean (36.20 vs 31.80).
#> Effect size is small (d = 0.251).
#> 95% CI: true difference lies between -22.146 and 30.946.
#> -----------------------------------------------------------------
#>
# Auto ANOVA
df <- data.frame(
score = c(23,45,12,67,34,89,56,43,78,90,11,34),
group = rep(c("A","B","C"), each = 4)
)
analyze(formula = score ~ group, data = df)
#> [statease] 3+ groups detected -> Running One-Way ANOVA
#> Warning: One or more groups have small sample sizes (n < 10). Interpret with caution.
#>
#> -- statease ANOVA Report -----------------------------------------
#> Outcome : score
#> Group : group (3 levels)
#> -----------------------------------------------------------------
#> Group Means:
#> A : Mean = 36.75 (n = 4)
#> B : Mean = 55.50 (n = 4)
#> C : Mean = 53.25 (n = 4)
#> -----------------------------------------------------------------
#> F-statistic : 0.494
#> df : 2, 9
#> p-value : 0.6260
#> Eta squared : 0.0988 (moderate effect)
#> -----------------------------------------------------------------
#> Assumption Checks:
#> Normality (Group: A) : PASSED (Shapiro-Wilk p = 0.798)
#> Normality (Group: B) : PASSED (Shapiro-Wilk p = 0.522)
#> Normality (Group: C) : PASSED (Shapiro-Wilk p = 0.546)
#> Equal variances : PASSED (Bartlett's p = 0.715)
#>
#> NOTE: Assumption checks are diagnostic tools and may be
#> influenced by sample size and other characteristics of the
#> data. Passing a check does not prove that an assumption is
#> satisfied, and a warning does not automatically invalidate
#> the analysis. Interpret these results alongside your
#> knowledge of the data.
#> -----------------------------------------------------------------
#> Interpretation:
#> The overall ANOVA result is not statistically significant (p = 0.6260 > alpha 0.05).
#> Group differences explain 9.9% of variance
#> (eta^2 = 0.0988, moderate effect).
#>
#> Post-hoc tests not run (overall result not significant).
#> -----------------------------------------------------------------
#>
# Power analysis
analyze(test_type = "ttest.two", effect_size = 0.5)
#> [statease] Power analysis requested -> Running Power Analysis
#>
#> -- statease Power Analysis Report
#> Test : Independent Samples T-Test
#> Mode : Calculate required sample size
#> -----------------------------------------------------------------
#> Effect size : 0.500 (medium)
#> Alpha : 0.05
#> Desired power: 0.80 (80%)
#> Required n : 64
#> Total N : 128 (2 groups x 64)
#> -----------------------------------------------------------------
#> Interpretation:
#> To detect a medium effect (effect size = 0.50) with 80% power at alpha = 0.05, you need at least 64 participants per group (128 total for 2 groups).
#>
#> NOTE: Power analysis results are estimates based on assumptions about effect size, alpha, and power. Actual results may differ depending on the true effect size in the population.
#> NOTE: Effect sizes should ideally be based on previous research, pilot studies, or theoretically justified values, not chosen arbitrarily to reduce required sample size.
#> NOTE: A power of 0.80 is a conventional minimum. In high stakes research such as clinical trials, a higher power of 0.90 or 0.95 is often recommended.
#> NOTE: Power analysis assumes that the chosen statistical test and its assumptions are appropriate for the data.
#> -----------------------------------------------------------------
#>
# Check assumptions
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29),
check = TRUE)
#> [statease] check = TRUE -> Checking assumptions for ttest
#>
#> -- statease Assumption Check Report -------------------------------
#> Test : ttest
#> ---------------------------------------------------------------------
#>
#> [PASSED] Normality (x)
#> Shapiro-Wilk test: statistic = 0.979, p = 0.9276. Normality assumption appears satisfied.
#>
#> [WARNING] Sample size guidance (x)
#> n = 5. Small sample size. There is no formal assumption of sample size adequacy, but results should be interpreted with caution.
#>
#> [PASSED] Normality (y)
#> Shapiro-Wilk test: statistic = 0.936, p = 0.6369. Normality assumption appears satisfied.
#>
#> [WARNING] Sample size guidance (y)
#> n = 5. Small sample size. There is no formal assumption of sample size adequacy, but results should be interpreted with caution.
#>
#> [PASSED] Homogeneity of variance
#> Levene's Test: p = 0.4080. Variances appear approximately equal.
#>
#> ---------------------------------------------------------------------
#> NOTE: Assumption checks are based on statistical tests and
#> heuristics. They provide guidance but should not be
#> interpreted as definitive proof that assumptions are met
#> or violated.
#>
#> NOTE: Failure to reject an assumption test does not prove
#> that the assumption has been satisfied.
#>
#> NOTE: Visual inspection of residual plots is always
#> recommended alongside formal assumption tests.
#> ---------------------------------------------------------------------
#>