README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

options(scipen = 999, digits = 5)
```

# criticalESvalue

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<!-- badges: end -->

The goal of criticalvalue is to ...
<!-- TODO aggiungi qualche riga qui -->

## Installation

You can install the development version of criticalESvalue like so:

``` r
# require(remotes)
remotes::install_github("psicostat/criticalESvalue")
```

## Examples

```{r}
# loading the package
library(criticalESvalue)
```

### T Test

```{r example}
# t-test (welch)

x <- rnorm(30, 0.5, 1)
y <- rnorm(30, 0, 1)

ttest <- t.test(x, y)
critical(ttest)

# t-test (standard)

ttest <- t.test(x, y, var.equal = TRUE)
critical(ttest)

# t-test (standard) with monodirectional hyp

ttest <- t.test(x, y, var.equal = TRUE, alternative = "less")
critical(ttest)

# within the t-test object saved from critical we have all the new values

ttest <- critical(ttest)
str(ttest)
```

We can check the results using:

```{r}
ttest <- t.test(x, y)
ttest <- critical(ttest)

t <- ttest$bc/ttest$stderr # critical numerator / standard error

# should be 0.05 (or alpha)
(1 - pt(ttest$bc/ttest$stderr, ttest$parameter)) * 2
```

### Correlation Test

```{r}
# cor.test
ctest <- cor.test(x, y)
critical(ctest)
```

### Linear Model

```{r}
# linear model (unstandardized)
z <- rnorm(30)
q <- rnorm(30)

dat <- data.frame(x, y, z, q)
fit <- lm(y ~ x + q + z, data = dat)
fit <- critical(fit)
fit
summary(fit)
```

### Meta-analysis

```{r}
library(metafor)
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
fit <- rma(yi, vi, mods=cbind(ablat, year), data=dat)
critical(fit)
```

## Example from summary statistics

```{r}
x <- rnorm(30, 0.5, 1)
y <- rnorm(30, 0, 1)

ttest <- t.test(x, y)

m1 <- mean(x)
m2 <- mean(y)
sd1 <- sd(x)
sd2 <- sd(y)
n1 <- n2 <- 30

critical_t2s(m1, m2, sd1 = sd1, sd2 = sd2, n1 = n1, n2 = n2)

critical_t2s(t = ttest$statistic, se = ttest$stderr, n1 = n1, n2 = n2)

critical_t2s(t = ttest$statistic, n1 = n1, n2 = n2)
```