R Notebook

library(tidyverse)
library(here)
library(performance)
library(lme4)

Loading required package: Matrix

Attaching package: ‘Matrix’

The following objects are masked from ‘package:tidyr’:

    expand, pack, unpack

Registered S3 methods overwritten by 'lme4':
  method                          from
  cooks.distance.influence.merMod car 
  influence.merMod                car 
  dfbeta.influence.merMod         car 
  dfbetas.influence.merMod        car 

library(glmmTMB)
library(see)
library(ggrepel)
library(qqplotr)

LuisModel <- 
  "dis_ped =~ i1 + i2 +i3
   prac_ped =~ i4 + i5 + i6
   gest_aula =~ i7 + i8 + i9
   segu_acad =~ i10 + i11 + i12 + i13 + i14
   prom_estu =~ i15 + i16 + i17 + i18 + i19
   rend_aca =~ i20
   gest_aca =~ dis_ped + prac_ped + gest_aula + segu_acad
   rend_aca =~ gest_aca + prom_estu
   "

Adquiriendo los datos

luis_raw <- read_csv(here("SEM lavaan/data_sem", "luis_lm.csv"))

── Column specification ────────────────────────────────────────────────────────────────────────────────────
cols(
  .default = col_double(),
  genero = col_character()
)
ℹ Use `spec()` for the full column specifications.

Organizando los datos

luis_LM <- 
  luis_raw %>% 
  rowwise() %>% 
  mutate(dis_ped = mean(c(i1, i2, i3)),
         prac_ped = mean(c(i4, i5, i6)),
         gest_aula = mean(c(i7, i8, i9)),
         segu_acad = mean(c(i10, i11, i12, i13, i14)),
         com_ev_pro = mean(c(i15, i16, i17, i18, i19)),
         rend_aca = i20) %>% 
  select(gest_aca, dis_ped, prac_ped, gest_aula, segu_acad, genero)

Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(gest_aca)` instead of `gest_aca` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
Error: Must subset columns with a valid subscript vector.
x Subscript has the wrong type `formula`.
ℹ It must be numeric or character.
Run `rlang::last_error()` to see where the error occurred.

Análisis descriptivo

Análisis inferencial

Modelo de regresión lineal múltiple

Construimos el modelo

luis_model_LM <- 
  lm(gest_aca ~ dis_ped + prac_ped + gest_aula + segu_acad + prom_estu + genero + institucion, 
     data = luis_LM)

Error in model.frame.default(formula = gest_aca ~ dis_ped + prac_ped +  : 
  object is not a matrix

Revisamos el modelo

summary(luis_model_LM)

Call:
lm(formula = rend_aca ~ dis_ped + prac_ped + gest_aula + segu_acad + 
    prom_estu + genero + institucion, data = luis_LM)

Residuals:
    Min      1Q  Median      3Q     Max 
-41.398  -9.768  -0.276   8.836  51.207 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -5.73287    7.14839  -0.802   0.4230    
dis_ped      4.83234    1.05952   4.561 6.44e-06 ***
prac_ped     6.28219    1.10727   5.674 2.39e-08 ***
gest_aula    4.94248    1.02666   4.814 1.97e-06 ***
segu_acad    2.40412    1.14881   2.093   0.0369 *  
prom_estu    1.40404    0.93088   1.508   0.1321    
generom      0.28721    1.43543   0.200   0.8415    
institucion  0.03078    0.17262   0.178   0.8586    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.61 on 492 degrees of freedom
Multiple R-squared:  0.2896,    Adjusted R-squared:  0.2795 
F-statistic: 28.66 on 7 and 492 DF,  p-value: < 2.2e-16

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