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"
)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".
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)
#> -----------------------------------------------------------------
#> 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 Mann-Whitney (non-parametric)
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29),
nonparam = TRUE)
#> [statease] nonparam = TRUE -> Running Mann-Whitney U Test
#> Warning: Sample size is small (n < 10). Interpret results with caution.
#>
#> -- statease Mann-Whitney U Test Report --------------------------
#> Variable : Variable
#> Group 1 : n = 5 | Median = 34.00
#> Group 2 : n = 5 | Median = 29.00
#> -----------------------------------------------------------------
#> W statistic : 14.000
#> p-value : 0.8413
#> 95% CI : [-26.000, 38.000]
#> Effect size : 0.063 (negligible)
#> -----------------------------------------------------------------
#> Interpretation:
#> The result is not statistically significant (p = 0.8413 > alpha 0.05).
#> Group 1 had a higher median (34.00 vs 29.00).
#> Effect size is negligible (r = 0.063).
#> -----------------------------------------------------------------
#>
# Auto correlation
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29),
var1_name = "Scores", var2_name = "Hours")
#> [statease] Two numeric vectors with variable names -> Running Correlation
#> Warning: Sample size is small (n < 10). Interpret correlation with caution.
#>
#> -- statease Correlation Report -----------------------------------
#> Method : Pearson Product-Moment Correlation
#> Variables : Scores & Hours
#> N : 5 | Missing: 0
#> -----------------------------------------------------------------
#> r : 0.9626
#> p-value : 0.0086
#> 95% CI : [0.5332, 0.9976]
#> Strength : very large
#> Direction : positive (as one variable increases, the other tends to increase)
#> -----------------------------------------------------------------
#> Interpretation:
#> The correlation is statistically significant (p = 0.0086 < alpha 0.05).
#> The relationship between Scores and Hours is
#> very large and positive (as one variable increases, the other tends to increase) in direction.
#> -----------------------------------------------------------------
#>
# Auto One-Way 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)
#> -----------------------------------------------------------------
#> 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).
#> -----------------------------------------------------------------
#>
# Auto Kruskal-Wallis (non-parametric)
analyze(formula = score ~ group, data = df, nonparam = TRUE)
#> [statease] 3+ groups + nonparam = TRUE -> Running Kruskal-Wallis Test
#> Warning: One or more groups have very small sample sizes (n < 5).
#>
#> -- statease Kruskal-Wallis Test Report --------------------------
#> Outcome : score
#> Group : group (3 levels)
#> N : 12
#> -----------------------------------------------------------------
#> Group Medians:
#> A : Median = 34.00 (n = 4)
#> B : Median = 49.50 (n = 4)
#> C : Median = 56.00 (n = 4)
#> -----------------------------------------------------------------
#> H statistic : 0.762
#> df : 2
#> p-value : 0.6831
#> Eta squared : 0.0000 (negligible effect)
#> -----------------------------------------------------------------
#> Interpretation:
#> The result is not statistically significant (p = 0.6831 > alpha 0.05).
#> Effect size is negligible (eta^2 = 0.0000).
#>
#> Post-hoc tests not run (overall result not significant).
#> -----------------------------------------------------------------
#>
# Auto Two-Way ANOVA
df2 <- data.frame(
score = c(23,45,12,67,34,89,56,43,78,90,11,34),
method = rep(c("Online","Traditional"), each = 6),
gender = rep(c("Male","Female"), times = 6)
)
analyze(formula = score ~ method * gender, data = df2)
#> [statease] Two grouping variables detected -> Running Two-Way ANOVA
#> Warning: Sample size is small (n < 20). Interpret results with caution.
#>
#> statease Two-Way ANOVA Report
#> Outcome : score
#> Factor 1 : method
#> Factor 2 : gender
#> N : 12
#>
#> Means by method:
#> Online : 45.00
#> Traditional : 52.00
#>
#> Means by gender:
#> Female : 61.33
#> Male : 35.67
#>
#> Interaction Means:
#> Female Male
#> Online 67.00 23.00
#> Traditional 55.67 48.33
#>
#> ANOVA Results:
#> method : F = 0.220 df = 1,8 not significant (p = 0.6517) eta^2 = 0.0173 (small)
#> gender : F = 2.955 df = 1,8 not significant (p = 0.1240) eta^2 = 0.2330 (large)
#> Interaction : F = 1.507 df = 1,8 not significant (p = 0.2544) eta^2 = 0.1189 (moderate)
#>
#> Interpretation:
#> Main effect of method is not significant (p = 0.6517).
#> Main effect of gender is not significant (p = 0.1240).
#> Interaction (method x gender) is not significant (p = 0.2544).
#> -----------------------------------------------------------------
#>
# Auto Regression
df3 <- data.frame(
exam_score = c(23,45,12,67,34,89,56,43,78,90),
study_hours = c(2,5,1,7,3,9,6,4,8,10)
)
analyze(formula = exam_score ~ study_hours, data = df3)
#> [statease] Numeric predictor detected -> Running Simple Linear Regression
#>
#> -- statease Simple Linear Regression Report ---------------------
#> Outcome : exam_score
#> Predictor : study_hours
#> N : 10
#> -----------------------------------------------------------------
#> Model Equation:
#> exam_score = 4.800 + 8.891 * study_hours
#> -----------------------------------------------------------------
#> Coefficients:
#> Intercept : 4.800
#> Slope : 8.891 (SE = 0.336)
#> t-statistic : 26.442
#> p-value : 0.0000
#> 95% CI : [8.116, 9.666]
#> -----------------------------------------------------------------
#> Model Fit:
#> R-squared : 0.9887
#> Adj R-squared: 0.9873
#> F-statistic : 699.184 (df = 1, 8) p = 0.0000
#> -----------------------------------------------------------------
#> Interpretation:
#> The predictor study_hours is statistically significant (p = 0.0000 < alpha 0.05).
#> The slope is positive - as study_hours increases by 1 unit, exam_score increases by 8.891 units.
#> R-squared = 0.9887: study_hours explains 98.9% of the
#> variance in exam_score (large effect).
#> -----------------------------------------------------------------
#>
# Auto Multiple Regression
df4 <- data.frame(
exam_score = c(23,45,12,67,34,89,56,43,78,90),
study_hours = c(2,5,1,7,3,9,6,4,8,10),
attendance = c(60,80,50,90,70,95,85,75,88,92)
)
analyze(formula = exam_score ~ study_hours + attendance, data = df4)
#> [statease] Multiple numeric predictors -> Running Multiple Linear Regression
#> Warning: Sample size is small (n < 20). Interpret results with caution.
#>
#> -- statease Multiple Linear Regression Report -------------------
#> Outcome : exam_score
#> Predictors : study_hours, attendance
#> N : 10
#> -----------------------------------------------------------------
#> Model Equation:
#> exam_score = -2.664 + 8.240*study_hours + 0.141*attendance
#> -----------------------------------------------------------------
#> Overall Model Fit:
#> R-squared : 0.9893 (large effect)
#> Adj R-squared: 0.9862
#> F-statistic : 322.885 (df = 2, 7) p = 0.0000
#> The overall model is statistically significant (p = 0.0000 < alpha 0.05).
#> -----------------------------------------------------------------
#> Individual Predictors:
#>
#> study_hours
#> Coefficient : 8.240 (SE = 1.106)
#> t-statistic : 7.452
#> p-value : 0.0001 [significant]
#> 95% CI : [5.626, 10.855]
#> Direction : positive (b = 8.240)
#>
#> attendance
#> Coefficient : 0.141 (SE = 0.227)
#> t-statistic : 0.620
#> p-value : 0.5549 [not significant]
#> 95% CI : [-0.396, 0.677]
#> Direction : positive (b = 0.141)
#> -----------------------------------------------------------------
#> Interpretation:
#> The model explains 98.9% of the variance in exam_score
#> (R-squared = 0.9893, large effect).
#> Adjusted R-squared = 0.9862 accounting for
#> the number of predictors in the model.
#>
#> Significant predictors: study_hours
#> Non-significant predictors: attendance
#> -----------------------------------------------------------------
#>
# Auto MANOVA
df5 <- data.frame(
math = c(23,45,12,67,34,89,56,43,78,90,11,34),
english = c(34,56,23,78,45,90,67,54,89,95,22,45),
group = rep(c("A","B","C"), each = 4)
)
analyze(formula = cbind(math, english) ~ group, data = df5)
#> [statease] Multiple outcomes detected -> Running MANOVA
#> Warning: Sample size is small (n < 20). Interpret results with caution.
#>
#> -- statease MANOVA Report ----------------------------------------
#> Outcomes : math, english
#> Group : group (3 levels)
#> N : 12
#> -----------------------------------------------------------------
#> Group Means:
#>
#> math:
#> A : 36.75
#> B : 55.50
#> C : 53.25
#>
#> english:
#> A : 47.75
#> B : 64.00
#> C : 62.75
#> -----------------------------------------------------------------
#> Multivariate Test Results:
#> Pillai's Trace : 0.1372
#> Wilks' Lambda : 0.8645
#> F-statistic : 0.331 (df = 4, 18)
#> p-value : 0.8532
#> Effect size : small (Pillai = 0.1372)
#> -----------------------------------------------------------------
#> Interpretation:
#> The overall MANOVA result is not statistically significant (p = 0.8532 > alpha 0.05).
#> Pillai's Trace = 0.1372 indicates a small effect.
#> -----------------------------------------------------------------
#> Follow-Up Univariate ANOVAs:
#>
#> math
#> F = 0.494 (df = 2, 9) p = 0.6260 [not significant]
#>
#> english
#> F = 0.444 (df = 2, 9) p = 0.6550 [not significant]
#>
#> Note: Follow-up ANOVAs identify which outcomes
#> differ significantly across groups.
#> -----------------------------------------------------------------
#>
# Chi-square
analyze(
x = c("Yes","No","Yes","Yes","No"),
y = c("Male","Female","Male","Female","Male")
)
#> [statease] Two categorical vectors detected -> Running Chi-Square Test
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#>
#> -- statease Chi-Square Test Report ------------------------------
#> N : 5
#> -----------------------------------------------------------------
#> Contingency Table (Observed):
#> y
#> x Female Male
#> No 1 1
#> Yes 1 2
#>
#> Expected Frequencies:
#> y
#> x Female Male
#> No 0.8 1.2
#> Yes 1.2 1.8
#>
#> -----------------------------------------------------------------
#> Chi-square : 0.000
#> df : 1
#> p-value : 1.0000
#> Cramer's V : 0.000 (negligible effect)
#> -----------------------------------------------------------------
#> Interpretation:
#> The result is not statistically significant (p = 1.0000 > alpha 0.05).
#> There is no significant association between the two variables.
#> Effect size is negligible (V = 0.000).
#>
#> WARNING: Some expected frequencies are less than 5. Interpret with caution.
#> -----------------------------------------------------------------
#>