analyze: Master Analysis Function - Auto-detects and runs the right...

In statease: Simplified Statistical Analysis with Plain-English Interpretation

Master Analysis Function - Auto-detects and runs the right test

Description

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".

Value

A printed analysis report from the appropriate test

Examples

# Descriptive only
analyze(x = c(23, 45, 12, 67, 34))

# Auto t-test
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29))

# Auto Mann-Whitney (non-parametric)
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29),
        nonparam = TRUE)

# Auto correlation
analyze(x = c(23,45,12,67,34), y = c(19,38,22,51,29),
        var1_name = "Scores", var2_name = "Hours")

# 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)

# Auto Kruskal-Wallis (non-parametric)
analyze(formula = score ~ group, data = df, nonparam = TRUE)

# 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)

# 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)

# 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)

# 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)

# Chi-square
analyze(
  x = c("Yes","No","Yes","Yes","No"),
  y = c("Male","Female","Male","Female","Male")
)

statease documentation built on June 7, 2026, 5:06 p.m.