---
title: "parameterEstimates_boot"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{parameterEstimates_boot}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(semboottools)
library(lavaan)
```

## Function Syntax

```{r eval = FALSE}
parameterEstimates_boot(object,
                        level = .95,
                        standardized = FALSE,
                        boot_org_ratio = FALSE,
                        boot_ci_type = c("perc", "bc", "bca.simple"),
                        save_boot_est = TRUE,
                        boot_pvalue = TRUE,
                        boot_pvalue_min_size = 1000,
                        ...)
```


## Arguments

| Argument               | Description                                                                                                                                                                                                |
|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `object`               | A model fitted by `lavaan`.                                                                                                                                                                                 |
| `level`                | Confidence level for the confidence intervals. For example, `.95` gives 95% confidence intervals.                                                                                                          |
| `standardized`         | Whether to return standardized estimates. Same as in `lavaan::parameterEstimates()`. You can use `"std.all"`, `"std.lv"`, etc. For detailed standardized results with CIs, use `standardizedSolution_boot()` instead. |
| `boot_org_ratio`       | Whether to calculate how wide the bootstrap confidence interval is compared to the original confidence interval (from delta method). Useful to compare the two methods.                                     |
| `boot_ci_type`         | Method for forming bootstrap confidence intervals. `"perc"` gives percentile intervals; `"bc"` and `"bca.simple"` give bias-corrected intervals.                                                            |
| `save_boot_est`        | Whether to save the bootstrap estimates in the result. Saved in attributes `boot_est_ustd` (free parameters) and `boot_def` (user-defined parameters) if `TRUE`.                                            |
| `boot_pvalue`          | Whether to compute asymmetric *p*-values based on bootstrap results. Only available when percentile confidence intervals are used.                                                                          |
| `boot_pvalue_min_size` | Minimum number of valid bootstrap samples needed to compute asymmetric *p*-values. If fewer samples are available, *p*-values will not be computed and will be shown as `NA`.                                |
| `...`                  | Additional arguments passed to `lavaan::parameterEstimates()`.                                                                                                                                             |

## Example

### Data and Model

```{r}
# Set seed for reproducibility
set.seed(1234)

# Generate data
n <- 1000
x <- runif(n) - 0.5
m <- 0.20 * x + rnorm(n)
y <- 0.17 * m + rnorm(n)
dat <- data.frame(x, y, m)

# Specify mediation model in lavaan syntax
mod <- '
  m ~ a * x
  y ~ b * m + cp * x
  ab := a * b
  total := a * b + cp
'
```

### Basic usage: default settings
```{r}
# Ensure bootstrap estimates are stored
fit <- sem(mod, data = dat, fixed.x = FALSE)
fit <- store_boot(fit) 
est_boot <- parameterEstimates_boot(fit)
print(est_boot)
```

### parameterEstimates_boot(): Different Options

```{r eval = FALSE}

# Change confidence level to 99%
est_boot <- parameterEstimates_boot(fit, level = 0.99)
# Use bias-corrected (BC) bootstrap confidence intervals
est_boot <- parameterEstimates_boot(fit, boot_ci_type = "bc")
# Turn off asymmetric bootstrap p-values
est_boot <- parameterEstimates_boot(fit, boot_pvalue = FALSE)
# Do not save bootstrap estimates (for memory saving)
est_boot <- parameterEstimates_boot(fit, save_boot_est = FALSE)
# Compute and display bootstrap-to-original CI ratio
est_boot <- parameterEstimates_boot(fit, boot_org_ratio = TRUE)
# Combine options: BC CI, 99% level, no p-values
est_boot <- parameterEstimates_boot(fit,
                                    level = 0.99,
                                    boot_ci_type = "bc",
                                    boot_pvalue = FALSE)
```

### print(): Different Options

```{r eval = FALSE}
# Print with more decimal places (e.g., 5 digits)
print(est_boot, nd = 5)
# Print in lavaan-style text format (similar to summary())
print(est_boot, output = "text")
# Print as a clean data frame table
print(est_boot, output = "table")
# Drop specific columns (e.g., "Z") in lavaan.printer format
print(est_boot, drop_cols = "Z")
# Combine options: 5 decimal digits, text format
print(est_boot, nd = 5, output = "text")
```
