OLSengine: Assisted Simplicity Tutorial
1. Introduction
The philosophy of Assisted Simplicity aims to bridge the gap between complex computation and methodological rigor. OLSengine provides six estimation engines in a single, unified interface: OLS regression, ANOVA/t-tests, logistic regression, panel data, instrumental variables (2SLS), and difference-in-differences.
All methods are implemented in pure base R with integrated Methodological Customs that audit assumptions and guide researchers toward robust alternatives.
2. OLS Regression: Handling Heteroskedasticity
In social sciences, variance often increases with the scale of the
predictor. Let’s see how OLSengine detects and handles
this.
library(OLSengine)
# Simulate data with non-constant variance
set.seed(123)
n <- 200
x <- rnorm(n, 50, 10)
y <- 10 + 0.5 * x + rnorm(n, 0, x * 0.2) # Heteroskedasticity
df <- data.frame(y, x)
# Run the engine
model <- paper_engine(y ~ x, data = df, model = "ols")
model$messages## [1] "CRITICAL WARNING: Heteroskedasticity detected (Breusch-Pagan p < .05). Reported classic standard errors are biased. It is highly recommended to run the model with 'robust = TRUE' or 'robust = \"auto\"'."Correcting with Robust Standard Errors
Following the Aduana’s advice, we apply HC3 robust standard errors:
model_robust <- paper_engine(y ~ x, data = df, model = "ols", robust = TRUE)
model_robust$tables$Table2_OLS_Estimation## Predictor B SE t p CI_95_Low CI_95_High f2
## (Intercept) (Intercept) 9.70 4.28 2.27 0.02 1.26 18.14 NA
## x x 0.51 0.09 5.72 < .001 0.34 0.69 0.23
3. ANOVA/t-tests: Experimental Comparisons
In experimental research, we often compare groups.
OLSengine automatically checks normality and variance
homogeneity.
# Simulating 3 groups with non-normal distribution
set.seed(789)
group_data <- data.frame(
score = c(rgamma(30, 2, 0.5), rgamma(30, 5, 0.5), rgamma(30, 3, 0.5)),
group = rep(c("Control", "Treatment A", "Treatment B"), each = 30)
)
# Auto-pilot switches to non-parametric if normality fails
model_anova <- paper_engine(score ~ group, data = group_data,
model = "anova", non_parametric = "auto")
model_anova$tables$Table4_Mean_Differences## Test Metric Statistic df p Effect_Metric Effect_Size
## 1 Kruskal-Wallis Chi-square 42.93 2 < .001 Eta-squared (H) 0.47
4. Logistic Regression: Binary Outcomes
When the outcome is binary (e.g., Success/Failure), the engine calculates Odds Ratios, McFadden’s and Nagelkerke’s Pseudo R², and classification accuracy.
# Simulating binary data
set.seed(101)
n_logit <- 100
age <- rnorm(n_logit, 40, 10)
passed <- rbinom(n_logit, 1, plogis(-5 + 0.12 * age))
logit_df <- data.frame(passed, age)
# Run Logit engine
model_logit <- paper_engine(passed ~ age, data = logit_df, model = "logit")
model_logit$tables$Table2_Logit_Estimation## Predictor B OR SE z p OR_CI_95_Low OR_CI_95_High
## (Intercept) (Intercept) -5.97 0.00 1.35 -4.42 < .001 0.00 0.04
## age age 0.14 1.15 0.03 4.41 < .001 1.08 1.23## [1] "INFO: Classification Accuracy (Threshold 0.5) = 72.0%"
## [2] "INFO: McFadden Pseudo R² = 0.207 | Nagelkerke Pseudo R² = 0.332"
5. Panel Data: Fixed and Random Effects
Panel data tracks the same entities over time. The Hausman test automatically selects between Fixed Effects (FE) and Random Effects (RE).
# Simulate panel data: 50 workers observed over 3 years
set.seed(456)
n_entities <- 50
n_time <- 3
panel_df <- data.frame(
worker_id = rep(1:n_entities, each = n_time),
year = rep(2018:2020, times = n_entities),
wage = rnorm(n_entities * n_time, 50, 10) +
rep(rnorm(n_entities, 0, 5), each = n_time), # Entity fixed effect
experience = rep(5:7, times = n_entities) + rnorm(n_entities * n_time, 0, 1)
)
# Hausman test decides between FE and RE
model_panel <- paper_engine(wage ~ experience,
data = panel_df,
model = "panel",
entity_id = "worker_id",
time_id = "year",
method = "auto")
model_panel$tables[[1]]## Predictor B SE t p CI_95_Low CI_95_High
## experience experience 0.76 0.58 1.31 0.19 -0.39 1.9## [1] "Customs Auto-Pilot: Hausman test supports random effects (p = 0.810). Random Effects estimator selected for efficiency gains (Wooldridge, 2010)."
## [2] "INFO: Panel structure - N entities = 50, T periods = 3, Total obs = 150"
## [3] "INFO: R² (overall) = 0.000"
## [4] "INFO: Hausman test statistic = 0.06 (p = 0.810)"
6. Instrumental Variables: Addressing Endogeneity
When a predictor is correlated with the error term (endogeneity), Instrumental Variables (IV) provide consistent estimates using Two-Stage Least Squares (2SLS).
# Simulate IV data with endogeneity
set.seed(789)
n_iv <- 200
z <- rnorm(n_iv, 10, 3) # Instrument
u <- rnorm(n_iv, 0, 2) # Unobserved confounder
x <- 2 + 0.8 * z + u + rnorm(n_iv, sd = 1) # Endogenous predictor
y <- 5 + 1.5 * x + u + rnorm(n_iv, sd = 3) # Outcome
iv_df <- data.frame(y, x, z)
# 2SLS estimation
model_iv <- paper_engine(y ~ x,
data = iv_df,
model = "iv",
instruments = ~ z)
model_iv$tables$Table2_IV_2SLS## Predictor B SE t p CI_95_Low CI_95_High
## x x 1.4 0.1 13.7 < .001 1.2 1.6## [1] "INFO: First-stage F-statistic = 242.98 (> 10). Instruments pass relevance threshold (Stock & Yogo, 2005)."
## [2] "INFO: Exact identification (# instruments = # endogenous variables). Sargan test not applicable."
## [3] "INFO: Sample size N = 200, Number of instruments = 1"
## [4] "INFO: R² = 0.724"
7. Difference-in-Differences: Causal Policy Evaluation
DiD estimates treatment effects by comparing changes over time between treated and control groups.
# Simulate policy intervention data
set.seed(321)
n_per_group <- 100
did_df <- data.frame(
outcome = c(rnorm(n_per_group, 50, 10), rnorm(n_per_group, 53, 10), # Control: pre/post
rnorm(n_per_group, 50, 10), rnorm(n_per_group, 58, 10)), # Treated: pre/post
group = rep(c("Control", "Treated"), each = n_per_group * 2),
period = rep(c("Pre", "Post"), times = n_per_group * 2)
)
# DiD estimation
model_did <- paper_engine(outcome ~ 1,
data = did_df,
model = "did",
treatment_var = "group",
time_var = "period",
treatment_level = "Treated",
post_level = "Post")
model_did$tables$Table2_DiD_Estimation## Predictor B SE t p CI_95_Low CI_95_High
## (Intercept) (Intercept) 51.08 1.04 49.10 < .001 49.04 53.13
## Treated Treated 3.84 1.47 2.61 0.01 0.94 6.73
## Post Post 0.91 1.47 0.62 0.54 -1.99 3.80
## Treated:Post Treated:Post -0.26 2.08 -0.12 0.90 -4.35 3.83## Group Time Mean
## 1 Control Pre 51.08468
## 2 Control Post 51.99048
## 3 Treated Pre 54.92013
## 4 Treated Post 55.56740## [1] "WARNING (Parallel Trends): Significant pre-treatment difference detected (p = 0.008). The parallel trends assumption may be violated. Consider including covariates or using alternative identification strategies (Roth et al., 2023)."
## [2] "INFO: DiD Effect (Treated:Post interaction) = -0.259 (SE = 2.081, p 0.90)"
## [3] "INFO: Sample size N = 400"
## [4] "INFO: R² = 0.032"
8. Real Data Example: Academic Salaries
The package includes a real dataset of 397 U.S. college professors.
data(academic_salaries)
# Explore salary determinants
salary_model <- paper_engine(salary ~ rank + discipline + years_since_phd + sex,
data = academic_salaries,
model = "ols",
robust = "auto")
salary_model$tables$Table2_OLS_Estimation## Predictor B SE t p CI_95_Low
## (Intercept) (Intercept) 67884.32 2784.30 24.38 < .001 62410.24
## rankAssocProf rankAssocProf 13104.15 2243.19 5.84 < .001 8693.92
## rankProf rankProf 46032.55 3359.53 13.70 < .001 39427.55
## disciplineB disciplineB 13937.47 2372.64 5.87 < .001 9272.73
## years_since_phd years_since_phd 61.01 155.64 0.39 0.70 -244.99
## sexMale sexMale 4349.37 2423.53 1.79 0.07 -415.42
## CI_95_High f2
## (Intercept) 73358.40 NA
## rankAssocProf 17514.38 0.03
## rankProf 52637.55 0.30
## disciplineB 18602.21 0.09
## years_since_phd 367.02 0.00
## sexMale 9114.15 0.00## [1] "Customs Auto-Pilot: Heteroskedasticity detected (p < .05). Robust Standard Errors (HC3) were applied automatically (Hayes & Cai, 2007)."
## [2] "INFO (Multicollinearity): Maximum VIF = 3.07. Moderate collinearity detected but below critical threshold. Standard errors may be slightly inflated (O'Brien, 2007)."
## [3] "WARNING (Normality): p < .05 in Shapiro-Wilk. With N=397, OLS may be robust due to CLT (Lumley et al., 2002)."Conclusion
OLSengine simplifies the transition from raw data to
paper-ready results across six estimation methods. Every step is backed
by rigorous methodological audits, ensuring researchers can focus on
interpretation rather than implementation.
Key Features: - Zero external dependencies (pure base R) - Integrated diagnostics with actionable warnings - APA-formatted tables and publication-ready plots - Unified interface across OLS, ANOVA, Logit, Panel, IV, and DiD
- 1. Introduction
- 2. OLS Regression: Handling Heteroskedasticity
- 3. ANOVA/t-tests: Experimental Comparisons
- 4. Logistic Regression: Binary Outcomes
- 5. Panel Data: Fixed and Random Effects
- 6. Instrumental Variables: Addressing Endogeneity
- 7. Difference-in-Differences: Causal Policy Evaluation
- 8. Real Data Example: Academic Salaries
- Conclusion