---
title: "Resultados"
---
## Analisis descriptivo
Resumen de las estadisticas descriptivas clave y visualizaciones de apoyo.
```{r grafico-ejemplo}
#| fig-cap: "Relacion X^2"
#| warning: false
if (requireNamespace("ggplot2", quietly = TRUE)) {
library(ggplot2)
ggplot(mtcars, aes(wt, mpg)) + geom_point()
}
```
## Modelos
Presentacion de los resultados de los modelos propuestos, con interpretacion y conclusiones.
```{r ejemplo-modelo-tabla}
#| echo: false
#| warning: false
#| tbl-cap: "Resumen comparativo de modelos"
#| tbl-label: tbl-modelos
if (requireNamespace("broom", quietly = TRUE) &&
requireNamespace("dplyr", quietly = TRUE) &&
requireNamespace("tibble", quietly = TRUE)) {
library(broom)
library(dplyr)
library(tibble)
modelo <- lm(mpg ~ wt + hp, data = mtcars)
tabla <- tidy(modelo, conf.int = TRUE) |>
mutate(
term = recode(
term,
"(Intercept)" = "Constante",
"wt" = "Capital social barrial",
"hp" = "Precariedad laboral"
),
`Coef.` = sprintf("%0.3f", estimate),
`Std. Err.` = sprintf("%0.3f", std.error),
`t-valor` = sprintf("%0.2f", statistic),
p_valor_num = p.value,
`p-valor` = if_else(p.value < 0.001, "<0.001", sprintf("%0.3f", p.value)),
`Sig.` = case_when(
p.value < 0.001 ~ "***",
p.value < 0.01 ~ "**",
p.value < 0.05 ~ "*",
p.value < 0.1 ~ ".",
TRUE ~ ""
),
`IC 95 %` = sprintf("[%0.3f, %0.3f]", conf.low, conf.high)
) |>
select(Predictor = term, `Coef.`, `Std. Err.`, `t-valor`, `p-valor`, `Sig.`, `IC 95 %`, p_valor_num)
gof <- glance(modelo)
tabla <- bind_rows(
tabla,
tibble(
Predictor = c("N", "R^2", "R^2 ajustado"),
`Coef.` = c(sprintf("%0.0f", gof$nobs), sprintf("%0.3f", gof$r.squared), sprintf("%0.3f", gof$adj.r.squared)),
`Std. Err.` = "",
`t-valor` = "",
`p-valor` = "",
`Sig.` = "",
`IC 95 %` = "",
p_valor_num = NA_real_
)
)
if (knitr::is_latex_output()) {
tabla_latex <- select(tabla, -p_valor_num)
knitr::kable(
tabla_latex,
format = "pipe",
escape = FALSE,
align = c("l", rep("c", ncol(tabla_latex) - 1))
)
} else if (requireNamespace("gt", quietly = TRUE)) {
tabla_display <- tabla |>
mutate(`p-valor` = gsub("<", "<", `p-valor`, fixed = TRUE)) |>
select(-p_valor_num)
gt::gt(tabla_display) |>
gt::tab_header(
title = gt::md("**Resultados del modelo de regresión**"),
subtitle = gt::md("Coeficientes estimados para mpg ~ wt + hp")
) |>
gt::cols_label(
Predictor = "Predictor",
`Coef.` = "Coef.",
`Std. Err.` = "Std. Err.",
`t-valor` = "t-valor",
`p-valor` = "p-valor",
`Sig.` = "Sig.",
`IC 95 %` = "IC 95 %"
) |>
gt::cols_align(align = "center", columns = c(`Coef.`, `Std. Err.`, `t-valor`, `p-valor`, `Sig.`, `IC 95 %`)) |>
gt::cols_align(align = "left", columns = Predictor) |>
gt::tab_options(
table.width = gt::pct(85),
heading.title.font.size = gt::px(16),
heading.subtitle.font.size = gt::px(11),
table.border.top.width = gt::px(1.5),
table.border.top.color = "#000000",
table.border.bottom.width = gt::px(1.5),
table.border.bottom.color = "#000000",
column_labels.border.top.width = gt::px(1.2),
column_labels.border.top.color = "#000000",
column_labels.border.bottom.width = gt::px(1.2),
column_labels.border.bottom.color = "#000000",
data_row.padding = gt::px(4),
table.font.size = 13
) |>
gt::opt_table_font(
font = list(
gt::google_font("Source Sans Pro"),
gt::default_fonts()
)
) |>
gt::tab_source_note(gt::md("Notas: *** p < 0.001, ** p < 0.01, * p < 0.05, . p < 0.10"))
} else {
tabla_ligera <- select(tabla, -p_valor_num)
knitr::kable(
tabla_ligera,
format = "html",
escape = FALSE,
align = c("l", rep("c", ncol(tabla_ligera) - 1)),
table.attr = "class='table table-sm table-striped align-middle w-100'"
)
}
}
```
