library(tidyverse)
library(here)
library(performance)
library(lme4)
library(glmmTMB)
library(see)
library(ggrepel)
library(qqplotr)
DeivisModel <- 
  "elem_lab_emo =~ i1 + i2 +i3
   efi_gest_doc =~ i4 + i5 + i6
   con_ped_CP =~ i7 + i8 + i9
   con_ped_CP =~ elem_lab_emo + efi_gest_doc 
   efi_gest_doc =~ elem_lab_emo
   "

Adquiriendo los datos

deivis_raw <- 
  read_csv(here("SEM lavaan/data_sem", 
                "deivis.csv"))


── Column specification ────────────────────────────────────────────────────────────────────────────────
cols(
  i1 = col_double(),
  i2 = col_double(),
  i3 = col_double(),
  i4 = col_double(),
  i5 = col_double(),
  i6 = col_double(),
  i7 = col_double(),
  i8 = col_double(),
  i9 = col_double(),
  genero = col_character()
)

Organizando los datos

Análisis descriptivo

Análisis inferencial

Modelo de regresión lineal múltiple

Construimos el modelo

deivis_model_LM <- 
  lm(con_ped_CP ~ efi_gest_doc + elem_lab_emo, 
     data = deivis_LM)

Revisamos el modelo

summary(deivis_model_LM)


Call:
lm(formula = con_ped_CP ~ efi_gest_doc + elem_lab_emo, data = deivis_LM)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.52078 -0.32478 -0.00998  0.32335  1.44343 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1.47760    0.06072  24.334  < 2e-16 ***
efi_gest_doc  0.10737    0.02673   4.017 6.19e-05 ***
elem_lab_emo  0.40343    0.02289  17.624  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.4697 on 1497 degrees of freedom
Multiple R-squared:  0.3675,    Adjusted R-squared:  0.3666 
F-statistic: 434.8 on 2 and 1497 DF,  p-value: < 2.2e-16

Que por cada unidad incrementada en asimilador el desempeño aumentará en un 2.40 unidades.

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