# Wrapper Functions

In BayesRTMB, you can freely define models using the `rtmb_code` block, but we also provide wrapper functions to quickly execute common statistical analyses (like t-tests, regressions, and factor analysis).

These functions allow you to specify models with syntax similar to standard R functions such as `lm()` and mixed-model formula interfaces, while fully utilizing the powerful estimation features of BayesRTMB (MCMC, MAP, ADVI).

This page introduces the usage of the main wrapper functions.

# 1. rtmb_ttest (Bayesian t-test)

This function is for examining the difference in means between two groups. `rtmb_ttest` is designed to easily perform effect size (delta) estimation and Bayes factor calculation, in addition to standard t-tests.

First, let’s generate some sample data and analyze it.

    library(BayesRTMB)
    set.seed(123)

    # Data generation (2 groups)
    y1 <- rnorm(30, mean = 0, sd = 1)
    y2 <- rnorm(30, mean = 1, sd = 1)

    # Method passing vectors directly
    mdl_ttest <- rtmb_ttest(y1, y2, prior = prior_jzs())

    # Method using formula format (when you have a dataframe)
    # df <- data.frame(Y = c(y1, y2), group = rep(c("A", "B"), each = 30))
    # mdl_ttest <- rtmb_ttest(Y ~ group, data = df, prior = prior_jzs())

    # MCMC estimation
    fit_ttest <- mdl_ttest$sample()
    fit_ttest$summary()

    ## variable    mean    sd     map    q2.5   q97.5  ess_bulk  ess_tail  rhat
    ## lp        -88.05  1.23  -87.08  -91.16  -86.63      1543      2467  1.00
    ## delta       1.24  0.30    1.25    0.65    1.84      1830      1654  1.01
    ## mean        0.57  0.12    0.58    0.34    0.81      3058      2436  1.00
    ## sd          0.93  0.09    0.92    0.78    1.13      2371      2755  1.00
    ## diff        1.15  0.25    1.11    0.64    1.63      2309      1695  1.01
    ## mean0      -0.00  0.17   -0.01   -0.34    0.34      2645      2468  1.00
    ## mean1       1.14  0.17    1.14    0.80    1.49      2507      2364  1.00

### Checking the Generated Code

You can check what `rtmb_code` the wrapper function generated internally by using the `print_code()` method on the model object. This can be used as a reference if you want to extend the wrapper’s model into your own custom model.

    # Display the internally generated RTMB code
    mdl_ttest$print_code()

    ## === RTMB Model Code ===
    ##
    ## rtmb_code(
    ##   parameters = {
    ##     total_mean = Dim(1)
    ##     sd = Dim(1, lower = 0)
    ##     delta = Dim(1)
    ##   },
    ##   transform = {
    ##     diff <- delta * sd
    ##     mean0 <- total_mean + diff/2
    ##     mean1 <- total_mean - diff/2
    ##   },
    ##   model = {
    ##     Y1 ~ normal(mean0, sd)
    ##     Y2 ~ normal(mean1, sd)
    ##     delta ~ cauchy(0, r)
    ##     lp <- lp - log(sd)
    ##   }
    ## )

The `lp <- lp - log(sd)` term applies the Jeffreys scale prior \(p(\sigma) \propto 1/\sigma\) used by the JZS t-test.

### Calculating the Bayes Factor

By using the `bayes_factor()` method, you can compute a comparison (Bayes factor) against a null model where the effect size is \(\delta = 0\).

    # Compare with the model where effect size delta = 0
    bf <- fit_ttest$bayes_factor(fixed = list(delta = 0))
    bf

    ## Bayes Factor (BF12) : 4777.563
    ## Log Bayes Factor    : 8.4717 (Approx. Error = 0.0039)
    ## Interpretation      : Decisive evidence for Model 1

# 2. rtmb_lm (Linear Regression Analysis)

You can perform linear regression with a format identical to standard `lm()`. Here, we use the `debate` data included in the package (simulated data regarding debate sat).

    data(debate)

    # Model predicting sat using talk and skill
    mdl_lm <- rtmb_lm(sat ~ talk + skill, data = debate)

    # Quick check via MAP estimation
    fit_lm <- mdl_lm$optimize()
    fit_lm$summary()

    ## Call:
    ## MAP Estimation via RTMB
    ##
    ## Negative Log-Posterior: 416.94
    ## Approx. Log Marginal Likelihood (Laplace): -425.12
    ##
    ## Point Estimates and 95% Wald CI:
    ##    variable  Estimate  Std. Error  Lower 95%  Upper 95%
    ## Intercept     2.14693     0.20761    1.74001    2.55384
    ## b[talk]       0.28612     0.05434    0.17961    0.39264
    ## b[skill]      0.20106     0.06604    0.07162    0.33050
    ## sigma         0.92284     0.03725    0.85265    0.99880
    ## Intercept_c   3.43324     0.05333    3.32871    3.53777

### Recommended Settings for Weakly Informative Priors

When calculating Bayes factors (using `Bridge Sampling` or the `bayes_factor` method), the choice of priors strongly influences the results. BayesRTMB recommends automatically configuring appropriate Weakly Informative Priors by setting `use_weak_info = TRUE` and specifying the theoretical minimum and maximum values of the objective variable in `y_range`.

    # Create a model using weakly informative priors (assuming sat ranges from 1 to 5)
    mdl_lm_weak <- rtmb_lm(sat ~ talk + skill,
      data = debate,
      use_weak_info = TRUE,
      y_range = c(1, 5)
    )
    mdl_lm_weak$print_code()

    ## === RTMB Model Code ===
    ##
    ## rtmb_code(
    ##   setup = {
    ##     N <- length(Y)
    ##     K <- ncol(X)
    ##     half_d_y <- diff(y_range)/2
    ##     base_scale <- half_d_y * weak_info_prior$sd_ratio
    ##     alpha_prior_sd <- half_d_y
    ##     mid_y <- mean(y_range)
    ##     sigma_rate <- 1/base_scale
    ##     tau_rate <- 1/base_scale
    ##     X_mean <- apply(X, 2, mean)
    ##     X_sd <- apply(X, 2, sd)
    ##     beta_prior_sd <- weak_info_prior$max_beta * base_scale/X_sd
    ##     X_c <- X - rep(1, N) %*% t(X_mean)
    ##   },
    ##   parameters = {
    ##     Intercept_c <- Dim(1)
    ##     b <- Dim(K)
    ##     sigma <- Dim(1, lower = 0)
    ##   },
    ##   transform = {
    ##     Intercept <- Intercept_c - sum(X_mean * b)
    ##   },
    ##   model = {
    ##     # Transform
    ##     eta <- as.vector(Intercept_c + X_c %*% b)
    ##     # Likelihood (Data)
    ##     Y ~ normal(eta, sigma)
    ##     # Priors
    ##     sigma ~ exponential(sigma_rate)
    ##     Intercept_c ~ normal(mid_y, alpha_prior_sd)
    ##     b ~ normal(0, beta_prior_sd)
    ##   }
    ## )

# 3. rtmb_glm (Generalized Linear Models)

By specifying the `family` argument, you can perform logistic regression, Poisson regression, etc. As an example, we will execute a logistic regression predicting the experimental cond (cond: 0 or 1).

    # Logistic regression (family = "bernoulli")
    mdl_glm <- rtmb_glm(cond ~ sat + skill,
      data = debate,
      family = "bernoulli"
    )

    fit_glm <- mdl_glm$sample()
    fit_glm$summary()

           variable     mean    sd      map     q2.5    q97.5  ess_bulk  ess_tail  rhat
    ## lp               -213.72  1.24  -212.80  -216.90  -212.31      1567      2449  1.00
    ## Intercept          -1.35  0.50    -1.32    -2.31    -0.38      3740      2794  1.00
    ## b[sat]     0.40  0.13     0.38     0.16     0.64      3127      2838  1.00
    ## b[skill]           -0.01  0.15     0.01    -0.30     0.28      3445      2925  1.00
    ## Intercept_c        -0.00  0.12    -0.03    -0.23     0.23      4129      2544  1.00

### Available Distributions (`family`)

In `rtmb_glm` and `rtmb_glmer`, the following distributions can be specified. Standard link functions are configured internally for each distribution.

`family` | Link Function | Description
--- | --- | ---
`gaussian` | identity | Normal distribution. For standard regression analysis.
`lognormal` | log | Log-normal distribution. For data that takes positive values.
`student_t` | identity | t-distribution. Useful when you want to suppress the impact of outliers.
`gamma` | log | Gamma distribution. For positive, skewed data.
`bernoulli` | logit | Bernoulli distribution. For binary 0/1 data.
`binomial` | logit | Binomial distribution. For data representing counts of success/trials.
`poisson` | log | Poisson distribution. For count data.
`neg_binomial` | log | Negative binomial distribution. For overdispersed count data.
`ordered` | logit | Ordered logistic regression. For ordinal categorical data.

# 4. rtmb_glmer (Generalized Linear Mixed Models)

You can handle models including random effects (varying effects) using the `(1 | group)` notation, similar to the `lme4` package. Let’s build a model taking into account that individual sat varies depending on the group they belong to.

    # Random intercept model
    mdl_glmer <- rtmb_glmer(sat ~ talk + (1 | group),
      data = debate
    )

    # When including random effects, optimize() with Laplace approximation is fast
    opt_glmer <- mdl_glmer$optimize(laplace = TRUE)
    opt_glmer$summary()

    ## Call:
    ## MAP Estimation via RTMB
    ##
    ## Negative Log-Posterior: 404.65
    ## Approx. Log Marginal Likelihood (Laplace): -412.59
    ## Note: Random effects are stored in $random_effects
    ##
    ## Point Estimates and 95% Wald CI:
    ##    variable  Estimate  Std. Error  Lower 95%  Upper 95%
    ## Intercept     2.60450     0.17902    2.25363    2.95537
    ## b[talk]       0.27441     0.05477    0.16706    0.38176
    ## sigma         0.77389     0.03867    0.70170    0.85351
    ## sd[Int]       0.51833     0.06587    0.40406    0.66492
    ## Intercept_c   3.43322     0.06846    3.29904    3.56740

## Regularization

In `rtmb_lm`, `rtmb_glm`, and `rtmb_glmer`, you can use regularization. Regularization is a method that shrinks the coefficients of excessive parameters towards 0, allowing for the construction of more parsimonious models with high predictive power.

In the `BayesRTMB` package, you can select the horseshoe prior (`rhs`) or the spike-and-slab prior (`ssp`) using the `penalty` option. `rhs` is relatively easier to estimate, but coefficients with small effects do not shrink exactly to 0. `ssp` is somewhat heavier to estimate, but it can shrink coefficients completely to 0.

When using regularization, you must also use weakly informative priors, which means you need to input the range of the objective variable. Since our data uses a 5-point scale, we input `c(1, 5)`. MAP estimation often struggles with regularized models, so it’s better to use MCMC.

    mdl_glmer <-
      rtmb_glmer(
        sat ~ talk + perf + skill + cond + (1 | group),
        data = debate,
        penalty = "ssp",
        y_range = c(1, 5)
      )

    mcmc_glmer <- mdl_glmer$sample(parallel = TRUE)
    mcmc_glmer

    mcmc_glmer$draws("b[cond]") |> plot_dens()

Shrunk Posterior Distribution

# 5. Post-Estimation Analysis (Interaction & Visualization)

After fitting a regression model (LM, GLM, GLMER), you can use `conditional_effects()` and `simple_effects()` to analyze and visualize the results. These methods are available for MCMC fits and can also be used with MAP or classic fits when the fitted object contains the standard errors or sampling-based uncertainty needed for the requested summary.

### Visualization with `conditional_effects()`

The `conditional_effects()` function is used to visualize the predicted values of a model. It is particularly powerful for understanding interaction effects.

    # Linear regression with interaction between talk and cond
    mdl_int <- rtmb_lm(sat ~ talk * perf, data = debate)
    fit_int <- mdl_int$sample()

    # Visualize the interaction effect
    # For continuous moderators, it automatically shows Mean +/- 1SD
    ce <- conditional_effects(fit_int, effect = "talk:perf")
    plot(ce)

### Simple Effects Analysis with `simple_effects()`

While `conditional_effects()` provides a visual overview, `simple_effects()` allows you to statistically examine the effect of a focal variable at specific levels of a moderator.

- Simple Slopes: When the focal variable is continuous.
- Pairwise Contrasts: When the focal variable is categorical.

    # Calculate simple slopes of 'talk' for each level of 'cond'
    se <- simple_effects(fit_int, effect = "talk:perf")
    print(se)

    ## --- Simple Effects Analysis ---
    ##    moderator perf          term estimate  lower upper
    ##  perf       2.930 Slope of talk    0.038 -0.118 0.191
    ##  perf       4.690 Slope of talk    0.266  0.161 0.369
    ##  perf       6.450 Slope of talk    0.494  0.354 0.631

By default, for continuous moderators, it evaluates the effect at the Mean and Mean +/- 1SD. You can change this behavior by specifying the `sd_multiplier` argument.

# 6. rtmb_corr (Correlation Matrix Estimation)

This function is for estimating the correlation relationships between variables. Here, we will introduce both a 2-variable case and a full correlation matrix case using a portion of the Big Five personality data.

### 2-Variable Correlation and Bayes Factor

We estimate the correlation between two variables (e.g., extraversion items BF1 and BF6), and calculate a Bayes factor testing the null hypothesis of zero correlation (\(r = 0\)).

    data(BigFive)

    # Correlation between BF1 and BF6
    mdl_corr2 <- rtmb_corr(BigFive[, c("BF1", "BF6")])
    mcmc_corr2 <- mdl_corr2$sample()
    mcmc_corr2$summary()

    ##  variable     mean    sd      map     q2.5    q97.5  ess_bulk  ess_tail  rhat
    ## lp         -493.27  1.55  -492.22  -497.08  -491.16      1799      2658  1.00
    ## corr[rho]    -0.60  0.05    -0.60    -0.69    -0.50      2365      2539  1.00
    ## mean[BF1]     3.56  0.08     3.56     3.39     3.72      3031      2914  1.00
    ## mean[BF6]     2.71  0.09     2.73     2.54     2.89      2978      2872  1.00
    ## sd[BF1]       1.10  0.06     1.07     0.99     1.22      3151      3313  1.00
    ## sd[BF6]       1.15  0.06     1.16     1.04     1.28      3072      2477  1.00

    # Compare against the zero-correlation model (fixed = list(corr = 0))
    bf_corr <- mcmc_corr2$bayes_factor(fixed = list(corr = 0))
    bf_corr

    Bayes Factor (BF12) : 6.839475e+15
    Log Bayes Factor    : 36.4615 (Approx. Error = 0.0043)
    Interpretation      : Decisive evidence for Model 1

### Correlation Matrix Estimation

If you specify multiple variables at once, the entire correlation matrix is estimated.

    # Estimate the correlation matrix of 5 variables
    mdl_corr_mat <- rtmb_corr(BigFive[, 1:5])
    opt_corr_mat <- mdl_corr_mat$optimize()
    opt_corr_mat$summary()

    ## Call:
    ## MAP Estimation via RTMB
    ##
    ## Negative Log-Posterior: 1254.77
    ## Approx. Log Marginal Likelihood (Laplace): -1289.58
    ##
    ## Point Estimates and 95% Wald CI:
    ##      variable  Estimate  Std. Error  Lower 95%  Upper 95%
    ## corr[BF1,BF1]   1.00000     0.00000    1.00000    1.00000
    ## corr[BF2,BF1]   0.09243     0.07555   -0.05565    0.24050
    ## corr[BF3,BF1]   0.09382     0.07552   -0.05420    0.24184
    ## corr[BF4,BF1]   0.08807     0.07562   -0.06014    0.23629
    ## corr[BF5,BF1]  -0.02158     0.07619   -0.17090    0.12775
    ## corr[BF1,BF2]   0.09243     0.07555   -0.05565    0.24050
    ## corr[BF2,BF2]   1.00000     0.00000    1.00000    1.00000
    ## corr[BF3,BF2]  -0.03374     0.07610   -0.18289    0.11542
    ## corr[BF4,BF2]  -0.13588     0.07481   -0.28250    0.01074
    ## corr[BF5,BF2]  -0.08526     0.07567   -0.23357    0.06306

# 7. rtmb_fa (Exploratory Factor Analysis)

Estimates the common latent factors behind observed variables.

## Factor Rotation

Factor analysis can be slightly time-consuming with MCMC, making MAP estimation faster and easier to use. Factor rotation can be performed via the `rotate` option. The rotated factor loadings will have the rotation name appended, like `_promax`. Because the rotated factor loadings are placed in the `generate` block, MAP does not calculate standard errors for them by default. By specifying `se_sampling = TRUE`, you can compute their standard errors as well.

    # 3-factor model, specifying Promax rotation
    mdl_fa <- rtmb_fa(BigFive, nfactors = 5, rotate = "promax")

    opt_fa <- mdl_fa$optimize(se_sampling = TRUE)

    # Check the factor loadings (L), etc.
    opt_fa$summary()

    ## Call:
    ## MAP Estimation via RTMB
    ##
    ## Negative Log-Posterior: 4791.18
    ## Approx. Log Marginal Likelihood (Laplace): -5016.21
    ##
    ## Point Estimates and 95% Wald CI:
    ##         variable  Estimate  Std. Error  Lower 95%  Upper 95%
    ## L_promax[BF1,1]    0.86657     0.05905    0.72847    0.95902
    ## L_promax[BF2,1]    0.02202     0.05325   -0.07631    0.12786
    ## L_promax[BF3,1]   -0.09276     0.06886   -0.20404    0.06562
    ## L_promax[BF4,1]    0.04927     0.06391   -0.07333    0.17433
    ## L_promax[BF5,1]   -0.02101     0.04856   -0.11742    0.07748
    ## L_promax[BF6,1]   -0.80459     0.05957   -0.89651   -0.66314
    ## L_promax[BF7,1]   -0.01111     0.05307   -0.11843    0.08566
    ## L_promax[BF8,1]   -0.07429     0.05917   -0.16758    0.06246
    ## L_promax[BF9,1]   -0.08225     0.07363   -0.23533    0.05242
    ## L_promax[BF10,1]   0.03086     0.05210   -0.07256    0.13540

The `sort_loadings()` function is useful to sort and display the rotated factor loadings nicely.

    opt_fa$generate$L_promax |> sort_loadings()

    ##        V1    V2    V3    V4    V5
    ## V1   .867 -.117 -.179  .089  .057
    ## V6  -.805  .000  .048  .200 -.065
    ## V11  .584  .087  .126  .169 -.197
    ## V16  .516  .087  .276 -.009  .040
    ## V2   .022 -.806 -.076 -.013  .043
    ## V7  -.011 -.800 -.175 -.054  .002
    ## V12  .065 -.797 -.045 -.083 -.088
    ## V17  .007 -.763  .070  .009 -.012
    ## V8  -.074  .172  .830 -.044 -.061
    ## V3  -.093  .044  .533  .101  .060
    ## V18 -.036 -.196  .512 -.156 -.066
    ## V13 -.005 -.078  .488 -.121  .154
    ## V4   .049  .118 -.080  .607  .058
    ## V14 -.096 -.037 -.031  .586 -.022
    ## V19  .094  .054  .001  .506 -.085
    ## V9  -.082  .064 -.059  .463  .019
    ## V5  -.021 -.132  .021  .206 -.795
    ## V20 -.022 -.126  .161  .321 -.762
    ## V15  .000 -.138  .075  .228  .730
    ## V10  .031 -.120  .047  .317  .712

## Factor Scores

Factor scores can also be output by using the `score = TRUE` option. These scores are additionally pushed to the `generate` output.

    mdl_fa <- rtmb_fa(BigFive, nfactors = 5, rotate = "promax", score = TRUE)

    opt_fa <- mdl_fa$optimize()

    opt_fa$generate$score |> head()

    ##             [,1]        [,2]        [,3]       [,4]       [,5]
    ## [1,]  0.49722956 -1.18644757 -1.29011492 -0.1941201 -0.4712234
    ## [2,] -1.21954058 -0.46063346  1.17665101 -0.8095066  1.0632201
    ## [3,] -0.05617309  0.09471994 -1.47004442  0.5897946 -0.1808377
    ## [4,]  0.48324361 -1.40302694  0.04676041 -0.8297736 -0.4993311
    ## [5,] -0.23744265  1.18767815  0.51330882 -0.3034767 -1.6688545
    ## [6,]  0.20824541 -0.38166057 -1.12921376  0.8736080 -0.5483280

## Regularized Factor Analysis

It also supports regularized factor analysis using a Spike-and-Slab Prior (SSP) for estimating sparse factor loadings.

Regularization shrinks the effects of unnecessary parameters toward zero, facilitating the construction of more parsimonious and predictive models. When applied to factor analysis, it can remove rotational indeterminacy, yielding a unique solution.

Since regularized factor analysis can sometimes be difficult to estimate via MAP, it is usually better to use MCMC or ADVI. Here, we present the results from ADVI (Automatic Differentiation Variational Inference).

    mdl_fa <- rtmb_fa(BigFive, nfactors = 5, rotate = "ssp")

    vb_fa <- mdl_fa$variational(iter = 5000, parallel = TRUE)
    vb_fa

    ## variable      mean     sd       map      q2.5     q97.5
    ## lp        -5362.15  33.02  -5353.45  -5449.35  -5322.61
    ## L[BF1,1]     -0.02   0.07     -0.00     -0.22      0.04
    ## L[BF2,1]     -0.82   0.04     -0.83     -0.88     -0.74
    ## L[BF3,1]      0.00   0.04     -0.00     -0.05      0.05
    ## L[BF4,1]      0.02   0.07      0.00     -0.07      0.25
    ## L[BF5,1]     -0.02   0.06     -0.00     -0.19      0.05
    ## L[BF6,1]      0.00   0.03      0.00     -0.02      0.05
    ## L[BF7,1]     -0.82   0.04     -0.82     -0.88     -0.73
    ## L[BF8,1]      0.01   0.05      0.00     -0.05      0.11
    ## L[BF9,1]      0.01   0.04      0.00     -0.03      0.08

The output summary of MCMC or ADVI allows access via `EAP` or `MAP`. With regularization, the `MAP` summary makes the output easier to read as the shrunk parameters will rigorously collapse to 0.

    vb_fa$MAP("L") |> sort_loadings()

    ##        V1    V2    V3    V4    V5
    ## V7  -.823  .000 -.001  .001  .010
    ## V2  -.820  .001  .000  .000  .001
    ## V12 -.801 -.001  .000  .001  .000
    ## V17 -.738  .000  .000  .000  .001
    ## V1  -.001 -.795 -.001  .000  .001
    ## V6   .000  .773  .001 -.045  .002
    ## V11  .004 -.610  .096 -.099 -.021
    ## V16 -.001 -.554 -.001  .000 -.212
    ## V15 -.001 -.001 -.822 -.001 -.001
    ## V10  .000 -.001 -.809  .000  .000
    ## V5  -.004 -.002  .711 -.339 -.001
    ## V20  .000 -.001  .668 -.448 -.007
    ## V4   .002 -.001  .000 -.637  .002
    ## V19  .001  .000 -.001 -.569  .000
    ## V14 -.001 -.001 -.001 -.540  .002
    ## V9   .000  .002  .001 -.429  .002
    ## V8   .001 -.001  .000  .001 -.854
    ## V3  -.001 -.001 -.001  .000 -.487
    ## V13 -.027 -.001 -.003  .000 -.428
    ## V18 -.220 -.001  .001  .001 -.425

# 8. rtmb_irt (Item Response Theory)

`rtmb_irt()` constructs Item Response Theory (IRT) models suitable for analyzing test data or questionnaire surveys. Currently, the following models are supported:

- 1PL / 2PL / 3PL Models: Analysis of binary data (correct/incorrect).
- Graded Response Model (GRM): Analysis of polytomous data (e.g., Likert scales).

With `prior_normal()`, the default prior for a freely estimated discrimination parameter is `a ~ exponential(1 / 2)`, which has prior mean 2. Use `prior_normal(a_rate = ...)` to change its rate.

Because IRT models are computationally demanding, it is highly effective to utilize the fast approximation via `variational()` or marginalize the ability parameters using `optimize(laplace = TRUE)` when handling large datasets.

### Analysis Example (Graded Response Model)

Using the `BigFive` personality data (polytomous), we will fit a Graded Response Model (GRM). Here, we analyze four items related to “Agreeableness”.

    data(BigFive)
    # Select items 2, 7, 12, 17 (Agreeableness)
    dat_irt <- BigFive[, c("BF2", "BF7", "BF12", "BF17")]

    # Fit a 2PL Graded Response Model (Ordered Logistic)
    mdl_irt <- rtmb_irt(dat_irt, model = "2PL", type = "ordered")

    # Fast estimation using MAP with Laplace approximation for latent traits
    opt_irt <- mdl_irt$optimize(se_sampling = TRUE)
    opt_irt

    ## Call:
    ## MAP Estimation via RTMB
    ##
    ## Negative Log-Posterior: 855.97
    ## Approx. Log Marginal Likelihood (Laplace): -862.01
    ## Note: Random effects are stored in $random_effects
    ##
    ## Point Estimates and 95% Wald CI:
    ##           variable  Estimate  Std. Error  Lower 95%  Upper 95%
    ## a[BF2]               2.59656     0.39595    1.90122    3.46560
    ## a[BF7]               2.54272     0.35182    1.95367    3.32212
    ## a[BF12]              2.53327     0.39717    1.93398    3.53325
    ## a[BF17]              1.92822     0.27020    1.46972    2.50115
    ## b[BF2,Threshold1]   -5.52251     0.72815   -6.91884   -4.06858
    ## b[BF7,Threshold1]   -5.34943     0.64569   -6.63549   -4.19742
    ## b[BF12,Threshold1]  -5.29023     0.65049   -6.54949   -4.01624
    ## b[BF17,Threshold1]  -4.31129     0.50114   -5.32272   -3.36251
    ## b[BF2,Threshold2]   -2.68286     0.42440   -3.41160   -1.76330
    ## b[BF7,Threshold2]   -2.72371     0.39514   -3.49773   -1.93475

### Visualizing Item Response Curves

You can visualize the category response curves (CRCs) for each item using the `item_curve()` function. This helps in understanding how the probability of choosing each category changes with the latent trait (\(\theta\)).

    # Calculate item curves
    ic <- item_curve(opt_irt)

    # Plot curves (shows category probabilities for each item)
    plot(ic)

### Item and Test Information

Information functions help evaluate the precision of measurement across different levels of the latent trait.

    # Item Information Function: shows which items provide the most information
    ii <- item_info(opt_irt)
    plot(ii)

    # Test Information Function: total information of the test/scale
    ti <- test_info(opt_irt)
    plot(ti)

# 9. Missing Data Handling

BayesRTMB wrapper functions feature a standardized `missing` argument to handle missing values (`NA`) gracefully. You can choose the most appropriate method depending on your analysis type.

The default for most wrappers is `"listwise"`, but advanced multivariate wrappers support Full Information Maximum Likelihood (FIML) and Pairwise methods.

Wrapper Function | Supported `missing` methods | Default | Description
--- | --- | --- | ---
`rtmb_lm`, `rtmb_glm`, `rtmb_lmer`, `rtmb_glmer`, `rtmb_ttest` | `"listwise"` | `"listwise"` | Removes rows containing `NA` in any of the variables present in the model formula.
`rtmb_fa`, `rtmb_corr` | `"listwise"`, `"fiml"` | `"listwise"` | `"listwise"` calculates fast sufficient statistics on complete cases. `"fiml"` performs row-wise likelihood evaluation using partial observations.
`rtmb_corr` (extra method) | `"pairwise"` |  | Available when `method = "pearson"` or `"spearman"`. Produces identical results to base R’s `cor(..., use = "pairwise.complete.obs")`.
`rtmb_mdu` | `"listwise"`, `"fiml"` | `"listwise"` | `"fiml"` skips missing ratings in the internal likelihood loop, whereas `"listwise"` deletes rows containing any `NA`.
`rtmb_irt` | `"listwise"`, `"fiml"` | `"fiml"` | `"fiml"` natively handles missingness by omitting individual-item missing observations (long-format), while `"listwise"` drops students/respondents with any missing items.

### Example: FIML in Factor Analysis (`rtmb_fa`)

When your data contains missing responses, you can easily enable FIML to keep all respondents in the model and estimate factor loadings using all available data:

    data(BigFive)
    # Introduce some random missingness
    set.seed(42)
    BigFive_miss <- BigFive
    for (col in 1:ncol(BigFive_miss)) {
      BigFive_miss[sample(1:nrow(BigFive_miss), 10), col] <- NA
    }

    # Run Factor Analysis with FIML
    mdl_fa <- rtmb_fa(BigFive_miss, nfactors = 5, missing = "fiml")
    fit_fa <- mdl_fa$optimize()

### Example: Pairwise Deletion in Correlation (`rtmb_corr`)

If you want to match R’s default correlation tests using pairwise deletion, you can specify `missing = "pairwise"`:

    # Classic Pearson correlation with pairwise deletion
    mdl_corr <- rtmb_corr(BigFive_miss[, 1:5], method = "pearson", missing = "pairwise")
    fit_corr <- mdl_corr$classic()
    fit_corr$summary()

# Summary

By leveraging the wrapper functions in BayesRTMB, you gain the following benefits: - Perform sophisticated analyses using familiar syntax without writing complex model codes from scratch. - Seamlessly switch between MAP, MCMC, and VB estimations using the same model object. - Calculate Bayes factors smoothly.

For a more detailed guide to mixed models and GLMMs, see Hierarchical Models and GLMMs. If you require a more customized model, check out Writing Model Codes and try building your own using `rtmb_code()`. For the model-building pipeline behind the wrappers, see RTMB Internals and Inference Algorithms. For a compact reference to fit-object methods, model comparison, fixed parameters, and distributions, see Analysis Reference.