Tarea_7_am20071

library(readr)
library(kableExtra)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following object is masked from 'package:kableExtra':
## 
##     group_rows

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(corrplot)

## corrplot 0.92 loaded

library(Hmisc)

## 
## Attaching package: 'Hmisc'

## The following objects are masked from 'package:dplyr':
## 
##     src, summarize

## The following objects are masked from 'package:base':
## 
##     format.pval, units

library(knitr)
library(factoextra)

## Loading required package: ggplot2

## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa

Ejercicio: Una empresa especializada en el diseño de automóviles de turismo desea estudiar cuáles son los deseos del público que compra automóviles. Para ello diseña una encuesta con 10 preguntas donde se le pide a cada uno de los 20 encuestados que valore de 1 a 5 si una característica es o no muy importante. Los encuestados deberán contestar con un 5 si la característica es muy importante, un4 si es importante, un 3 si tiene regular importancia, un 2 si es poco importante y un 1 si no es nada importante. Las 10 características (V1 a V10) a valorar son: precio, financiación, consumo, combustible, seguridad, confort, capacidad, prestaciones, modernidad y aerodinámica.

load("C:/Users/meybelin alemán/Downloads/6-2.RData")

matriz_x <- X6_2
matriz_x %>% 
  head() %>% 
  kable(caption = "Matriz de informacion",
        align = "c") %>% 
  row_spec(0,background = "#E0B9AA" ) %>%
  kable_material(html_font = "sans-serif")

Matriz de informacion
V1	V2	V3	V4	V5	V6	V7	V8	V9	V10
4	1	4	3	3	2	4	4	4	4
5	5	4	4	3	3	4	1	1	3
2	1	3	1	4	2	1	5	4	5
1	1	1	1	4	4	2	5	5	4
1	1	2	1	5	5	4	3	3	2
5	5	5	5	3	3	4	2	2	1

Calcula la matriz de varianza covarianza para la batería de indicadores:

De forma “manual”

centrado <- function(X){
  X - mean(X)
}
Xcentrado <- apply(X = matriz_x, MARGIN = 2, centrado)
n_observaciones <- nrow(matriz_x)
matriz_v <- t(Xcentrado) %*% Xcentrado/(n_observaciones - 1)
matriz_v %>% 
   kable(caption ="Calculo de V(X) forma manual",
        align ="c",
        digits =2) %>%
  row_spec(0,background = "#E0D7AA" ) %>%
  kable_material(html_font = "sans-serif") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Calculo de V(X) forma manual
	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10
V1	1.80	1.92	1.32	1.73	-0.62	-0.31	0.36	-1.21	-1.27	-0.90
V2	1.92	2.67	1.42	2.14	-0.66	-0.14	0.52	-1.78	-1.81	-1.54
V3	1.32	1.42	1.42	1.53	-0.53	-0.32	0.29	-0.92	-1.11	-0.87
V4	1.73	2.14	1.53	2.48	-0.80	-0.48	0.35	-1.61	-1.83	-1.39
V5	-0.62	-0.66	-0.53	-0.80	0.85	0.80	0.21	0.37	0.46	0.15
V6	-0.31	-0.14	-0.32	-0.48	0.80	1.38	0.63	0.22	0.09	-0.37
V7	0.36	0.52	0.29	0.35	0.21	0.63	1.61	-0.53	-0.34	-0.71
V8	-1.21	-1.78	-0.92	-1.61	0.37	0.22	-0.53	1.92	1.81	1.37
V9	-1.27	-1.81	-1.11	-1.83	0.46	0.09	-0.34	1.81	2.17	1.56
V10	-0.90	-1.54	-0.87	-1.39	0.15	-0.37	-0.71	1.37	1.56	1.82

Usando el comando cov de R base

cov(matriz_x) %>% 
  kable(caption ="Calculo de V(X) a travez de R base",
        align ="c",
        digits =2) %>%
  row_spec(0,background = "#C1F" ) %>%
  kable_material(html_font = "sans-serif") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Calculo de V(X) a travez de R base
	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10
V1	1.80	1.92	1.32	1.73	-0.62	-0.31	0.36	-1.21	-1.27	-0.90
V2	1.92	2.67	1.42	2.14	-0.66	-0.14	0.52	-1.78	-1.81	-1.54
V3	1.32	1.42	1.42	1.53	-0.53	-0.32	0.29	-0.92	-1.11	-0.87
V4	1.73	2.14	1.53	2.48	-0.80	-0.48	0.35	-1.61	-1.83	-1.39
V5	-0.62	-0.66	-0.53	-0.80	0.85	0.80	0.21	0.37	0.46	0.15
V6	-0.31	-0.14	-0.32	-0.48	0.80	1.38	0.63	0.22	0.09	-0.37
V7	0.36	0.52	0.29	0.35	0.21	0.63	1.61	-0.53	-0.34	-0.71
V8	-1.21	-1.78	-0.92	-1.61	0.37	0.22	-0.53	1.92	1.81	1.37
V9	-1.27	-1.81	-1.11	-1.83	0.46	0.09	-0.34	1.81	2.17	1.56
V10	-0.90	-1.54	-0.87	-1.39	0.15	-0.37	-0.71	1.37	1.56	1.82

Calcula la matriz de correlación para la batería de indicadores:

De forma “manual”

Zx<-scale(x = matriz_x,center = TRUE)
Zx %>% head()%>%
   kable(caption = "Matriz de variables Estandarizadas:",
         align = "c",
         digits = 2) %>%
  kable_material(html_font = "sans-serif")

Matriz de variables Estandarizadas:
V1	V2	V3	V4	V5	V6	V7	V8	V9	V10
0.22	-1.47	0.42	0.13	-0.76	-1.45	0.28	0.83	0.81	1.00
0.97	0.98	0.42	0.76	-0.76	-0.60	0.28	-1.33	-1.22	0.26
-1.27	-1.47	-0.42	-1.14	0.32	-1.45	-2.09	1.55	0.81	1.74
-2.01	-1.47	-2.10	-1.14	0.32	0.26	-1.30	1.55	1.49	1.00
-2.01	-1.47	-1.26	-1.14	1.41	1.11	0.28	0.11	0.14	-0.48
0.97	0.98	1.26	1.40	-0.76	-0.60	0.28	-0.61	-0.54	-1.22

n_observaciones<-nrow(matriz_x)
matrix_R<-t(Zx)%*%Zx/(n_observaciones-1)
matrix_R%>% kable(caption = "Cálculo de R(x) forma manual:" ,
                  align = "c",
                  digits = 2) %>%
  kable_material(html_font = "sans-serif") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Cálculo de R(x) forma manual:
	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10
V1	1.00	0.87	0.82	0.82	-0.50	-0.19	0.21	-0.65	-0.64	-0.50
V2	0.87	1.00	0.73	0.83	-0.44	-0.07	0.25	-0.78	-0.75	-0.70
V3	0.82	0.73	1.00	0.81	-0.48	-0.23	0.19	-0.56	-0.63	-0.54
V4	0.82	0.83	0.81	1.00	-0.55	-0.26	0.17	-0.74	-0.79	-0.65
V5	-0.50	-0.44	-0.48	-0.55	1.00	0.74	0.18	0.29	0.34	0.12
V6	-0.19	-0.07	-0.23	-0.26	0.74	1.00	0.42	0.13	0.05	-0.24
V7	0.21	0.25	0.19	0.17	0.18	0.42	1.00	-0.30	-0.18	-0.41
V8	-0.65	-0.78	-0.56	-0.74	0.29	0.13	-0.30	1.00	0.89	0.73
V9	-0.64	-0.75	-0.63	-0.79	0.34	0.05	-0.18	0.89	1.00	0.78
V10	-0.50	-0.70	-0.54	-0.65	0.12	-0.24	-0.41	0.73	0.78	1.00

Usando el comando cor de R base

cor(matriz_x) %>% 
  kable(caption ="Calculo de R(X) a travez de R base",
        align ="c",
        digits =2) %>%
  row_spec(0,background = "#C1F" ) %>%
  kable_material(html_font = "sans-serif") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Calculo de R(X) a travez de R base
	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10
V1	1.00	0.87	0.82	0.82	-0.50	-0.19	0.21	-0.65	-0.64	-0.50
V2	0.87	1.00	0.73	0.83	-0.44	-0.07	0.25	-0.78	-0.75	-0.70
V3	0.82	0.73	1.00	0.81	-0.48	-0.23	0.19	-0.56	-0.63	-0.54
V4	0.82	0.83	0.81	1.00	-0.55	-0.26	0.17	-0.74	-0.79	-0.65
V5	-0.50	-0.44	-0.48	-0.55	1.00	0.74	0.18	0.29	0.34	0.12
V6	-0.19	-0.07	-0.23	-0.26	0.74	1.00	0.42	0.13	0.05	-0.24
V7	0.21	0.25	0.19	0.17	0.18	0.42	1.00	-0.30	-0.18	-0.41
V8	-0.65	-0.78	-0.56	-0.74	0.29	0.13	-0.30	1.00	0.89	0.73
V9	-0.64	-0.75	-0.63	-0.79	0.34	0.05	-0.18	0.89	1.00	0.78
V10	-0.50	-0.70	-0.54	-0.65	0.12	-0.24	-0.41	0.73	0.78	1.00

Presenta la matriz de correlación de forma gráfica (las dos versiones propuestas en clase)

Usando el paquete PerformanceAnalytics

library(PerformanceAnalytics)
chart.Correlation(as.matrix(matriz_x),histogram = TRUE,pch=12)

Grafico

library(corrplot)
library(grDevices)
library(Hmisc)
Mat_R<-rcorr(as.matrix(matriz_x))
corrplot(Mat_R$r,
         p.mat = Mat_R$r,
         type="upper",
         tl.col="black",
         tl.srt = 20,
         pch.col = "blue",
         insig = "p-value",
         sig.level = -1,
         col = terrain.colors(100))

Realiza un análisis de componentes principales, y con base en los criterios vistos en clase:

¿Cuántas Componentes habría que retener?

library(kableExtra)
library(dplyr)
library(Hmisc)
Rx<-matriz_x %>% as.matrix() %>% rcorr()
Rx$r %>% kable(caption="Matriz R(X)",
        align = "c",
        digits = 2) %>% 
  kable_material(html_font = "sans-serif") %>% 
  kable_styling(bootstrap_options = c("striped", "hover"))

Matriz R(X)
	V1	V2	V3	V4	V5	V6	V7	V8	V9	V10
V1	1.00	0.87	0.82	0.82	-0.50	-0.19	0.21	-0.65	-0.64	-0.50
V2	0.87	1.00	0.73	0.83	-0.44	-0.07	0.25	-0.78	-0.75	-0.70
V3	0.82	0.73	1.00	0.81	-0.48	-0.23	0.19	-0.56	-0.63	-0.54
V4	0.82	0.83	0.81	1.00	-0.55	-0.26	0.17	-0.74	-0.79	-0.65
V5	-0.50	-0.44	-0.48	-0.55	1.00	0.74	0.18	0.29	0.34	0.12
V6	-0.19	-0.07	-0.23	-0.26	0.74	1.00	0.42	0.13	0.05	-0.24
V7	0.21	0.25	0.19	0.17	0.18	0.42	1.00	-0.30	-0.18	-0.41
V8	-0.65	-0.78	-0.56	-0.74	0.29	0.13	-0.30	1.00	0.89	0.73
V9	-0.64	-0.75	-0.63	-0.79	0.34	0.05	-0.18	0.89	1.00	0.78
V10	-0.50	-0.70	-0.54	-0.65	0.12	-0.24	-0.41	0.73	0.78	1.00

library(dplyr)
library(factoextra)
library(kableExtra)
library(stargazer)

## 
## Please cite as:

##  Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.

##  R package version 5.2.3. https://CRAN.R-project.org/package=stargazer

library(ggplot2)
options(scipen = 99999)
PC<-princomp(x = matriz_x,cor = TRUE,fix_sign = FALSE)
factoextra::get_eig(PC) %>% kable(caption="Resumen de PCA",
        align = "c",
        digits = 2) %>% 
  kable_material(html_font = "sans-serif") %>% 
  kable_styling(bootstrap_options = c("hover"))

Resumen de PCA
	eigenvalue	variance.percent	cumulative.variance.percent
Dim.1	5.70	57.01	57.01
Dim.2	2.07	20.69	77.70
Dim.3	0.72	7.20	84.91
Dim.4	0.55	5.48	90.39
Dim.5	0.32	3.16	93.54
Dim.6	0.27	2.71	96.25
Dim.7	0.15	1.46	97.72
Dim.8	0.13	1.28	99.00
Dim.9	0.07	0.68	99.68
Dim.10	0.03	0.32	100.00

Incluye las tablas y gráficos vistos en clase.

fviz_eig(PC,
         choice = "eigenvalue",
         barcolor = "red",
         barfill = "red",
         addlabels = TRUE, 
       )+labs(title = "Gráfico de Sedimentación",subtitle = "Usando princomp, con Autovalores")+
  xlab(label = "Componentes")+
  ylab(label = "Autovalores")+geom_hline(yintercept = 1)

fviz_eig(PC,
         choice = "variance",
         barcolor = "green",
         barfill = "green",
         addlabels = TRUE,
       )+labs(title = "Gráfico de Sedimentación",
              subtitle = "Usando princomp, con %Varianza Explicada")+
  xlab(label = "Componentes")+
  ylab(label = "%Varianza")

Tarea_7_am20071

MEYBELIN NALLELY ALEMAN MENJIVAR AM20071

2023-10-29

V1	V2	V3	V4	V5	V6	V7	V8	V9	V10
4	1	4	3	3	2	4	4	4	4
5	5	4	4	3	3	4	1	1	3
2	1	3	1	4	2	1	5	4	5
1	1	1	1	4	4	2	5	5	4
1	1	2	1	5	5	4	3	3	2
5	5	5	5	3	3	4	2	2	1

V1	V2	V3	V4	V5	V6	V7	V8	V9	V10
4	1	4	3	3	2	4	4	4	4
5	5	4	4	3	3	4	1	1	3
2	1	3	1	4	2	1	5	4	5
1	1	1	1	4	4	2	5	5	4
1	1	2	1	5	5	4	3	3	2
5	5	5	5	3	3	4	2	2	1

V1	V2	V3	V4	V5	V6	V7	V8	V9	V10
4	1	4	3	3	2	4	4	4	4
5	5	4	4	3	3	4	1	1	3
2	1	3	1	4	2	1	5	4	5
1	1	1	1	4	4	2	5	5	4
1	1	2	1	5	5	4	3	3	2
5	5	5	5	3	3	4	2	2	1