communalities

General communalities

Correlation

basecom<-elsi %>% select(less_education, 
         hearing_loss, 
         high_cholesterol, 
         depression, 
         physical_inactivity, 
         smoking, 
         diabetes,
         hypertension, 
         obesity, 
         exc_alcohol, 
         social_isolation, 
         air_pollution, 
         vision_loss)
a<-tetrachoric(basecom)     

library(knitr)

rho_matrix <- a$rho


#loop for estimate the p values of the correlation

n <- nrow(basecom) # sample size
result <- data.frame() #output dataframe

#p-value loop
for (i in 1:ncol(rho_matrix)) {
  for (j in 1:i) {
    if (i != j) {
      rho <- rho_matrix[i, j]
      z <- rho * sqrt(n - 2) / sqrt(1 - rho^2) #z value for the correlation
      p_value <- 2 * (1 - pnorm(abs(z))) #p from z
      result <- rbind(result, data.frame(
        Var1 = colnames(rho_matrix)[i],
        Var2 = colnames(rho_matrix)[j],
        Rho = rho,
        P_Value = p_value,
        Significance = case_when(
          p_value < 0.01 ~ "***",
          p_value < 0.05 ~ "**",
          TRUE ~ "ns"
        )
      ))
    }
  }
}

result %>%
  arrange(desc(abs(Rho)))%>% #arrange by bigger correlation (absolute value)
  kable("html", digits = 4, caption = " Tetrachoric correlation") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Tetrachoric correlation

Var1	Var2	Rho	P_Value	Significance
vision_loss	hearing_loss	0.4879	0.0000	***
exc_alcohol	smoking	0.3724	0.0000	***
air_pollution	less_education	-0.3533	0.0000	***
diabetes	high_cholesterol	0.3482	0.0000	***
hypertension	diabetes	0.3256	0.0000	***
hypertension	high_cholesterol	0.2894	0.0000	***
obesity	hypertension	0.2490	0.0000	***
obesity	smoking	-0.2233	0.0000	***
hearing_loss	less_education	0.2168	0.0000	***
vision_loss	less_education	0.2139	0.0000	***
social_isolation	hearing_loss	0.1897	0.0000	***
air_pollution	social_isolation	-0.1812	0.0000	***
exc_alcohol	less_education	-0.1800	0.0000	***
air_pollution	exc_alcohol	0.1770	0.0000	***
physical_inactivity	less_education	0.1719	0.0000	***
vision_loss	air_pollution	-0.1687	0.0000	***
air_pollution	hearing_loss	-0.1674	0.0000	***
obesity	diabetes	0.1627	0.0000	***
hypertension	less_education	0.1624	0.0000	***
air_pollution	physical_inactivity	0.1575	0.0000	***
vision_loss	social_isolation	0.1552	0.0000	***
depression	high_cholesterol	0.1550	0.0000	***
vision_loss	physical_inactivity	0.1485	0.0000	***
social_isolation	exc_alcohol	0.1474	0.0000	***
physical_inactivity	hearing_loss	0.1467	0.0000	***
air_pollution	diabetes	0.1377	0.0000	***
social_isolation	depression	-0.1233	0.0000	***
diabetes	depression	0.1222	0.0000	***
social_isolation	less_education	0.1207	0.0000	***
obesity	high_cholesterol	0.1199	0.0000	***
vision_loss	diabetes	0.1173	0.0000	***
exc_alcohol	depression	-0.1153	0.0000	***
diabetes	physical_inactivity	0.1100	0.0000	***
exc_alcohol	diabetes	-0.1039	0.0000	***
hypertension	smoking	-0.1020	0.0000	***
depression	hearing_loss	0.0984	0.0000	***
obesity	depression	0.0976	0.0000	***
hypertension	hearing_loss	0.0901	0.0000	***
smoking	high_cholesterol	-0.0899	0.0000	***
vision_loss	high_cholesterol	0.0892	0.0000	***
air_pollution	high_cholesterol	0.0891	0.0000	***
air_pollution	depression	0.0883	0.0000	***
exc_alcohol	hearing_loss	-0.0876	0.0000	***
air_pollution	obesity	0.0830	0.0000	***
vision_loss	depression	0.0804	0.0000	***
vision_loss	hypertension	0.0790	0.0000	***
physical_inactivity	depression	0.0764	0.0000	***
hypertension	depression	0.0763	0.0000	***
exc_alcohol	physical_inactivity	-0.0756	0.0000	***
vision_loss	exc_alcohol	-0.0743	0.0000	***
hypertension	physical_inactivity	0.0718	0.0000	***
exc_alcohol	high_cholesterol	-0.0717	0.0000	***
high_cholesterol	hearing_loss	0.0703	0.0000	***
diabetes	less_education	0.0702	0.0000	***
obesity	hearing_loss	-0.0700	0.0000	***
diabetes	hearing_loss	0.0672	0.0000	***
exc_alcohol	obesity	-0.0648	0.0000	***
smoking	less_education	0.0593	0.0000	***
obesity	less_education	-0.0569	0.0000	***
social_isolation	smoking	0.0567	0.0000	***
exc_alcohol	hypertension	-0.0561	0.0000	***
smoking	hearing_loss	-0.0546	0.0000	***
social_isolation	physical_inactivity	0.0537	0.0000	***
social_isolation	hypertension	0.0522	0.0000	***
vision_loss	obesity	-0.0437	0.0000	***
diabetes	smoking	-0.0415	0.0000	***
vision_loss	smoking	0.0389	0.0001	***
obesity	physical_inactivity	0.0322	0.0013	***
social_isolation	obesity	-0.0306	0.0023	***
depression	less_education	0.0296	0.0031	***
smoking	physical_inactivity	0.0287	0.0041	***
air_pollution	hypertension	0.0178	0.0760	ns
air_pollution	smoking	-0.0140	0.1632	ns
physical_inactivity	high_cholesterol	0.0075	0.4561	ns
social_isolation	high_cholesterol	-0.0051	0.6136	ns
high_cholesterol	less_education	-0.0040	0.6900	ns
social_isolation	diabetes	-0.0038	0.7066	ns
smoking	depression	0.0021	0.8352	ns

corplot

corrplot(a$rho, method = "shade", diag = FALSE, order = 'AOE', addrect = 4)

communalities

b<-as.data.frame(a$rho)
# cuantos componentes principales tienen eigenvalues>1?
eigenvalues <-eigen(b)$values
eigenvalues

##  [1] 2.1754554 1.8764926 1.4013326 1.1725945 1.0192945 0.9985141 0.8764012
##  [8] 0.7463694 0.6548234 0.6195857 0.5251874 0.4829863 0.4509629

##5!

eigenVectors<-eigen(b)$vectors


# I use the paper criteria for caculation:
#"Communality was calculated as the sum of the square of all factor loadings"

comunalities<-as.data.frame(eigenVectors)
comunalities$RF<-names(b)

comunalities$comunality<-comunalities$V1^2+comunalities$V2^2+comunalities$V3^2+
  comunalities$V4^2+comunalities$V5^2

# limpio todo y nos vamos
comunalities<-comunalities %>% select(RF, comunality)


comunalities %>%
  kable("html", digits = 4, caption = "Comunalities") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Comunalities

RF	comunality
less_education	0.2656
hearing_loss	0.3030
high_cholesterol	0.3216
depression	0.4144
physical_inactivity	0.4988
smoking	0.5242
diabetes	0.2937
hypertension	0.3358
obesity	0.3172
exc_alcohol	0.4750
social_isolation	0.4647
air_pollution	0.4969
vision_loss	0.2893

##by sex ##males

Correlation

basecom<-elsi %>% filter(man==1) %>% select(less_education, 
         hearing_loss, 
         high_cholesterol, 
         depression, 
         physical_inactivity, 
         smoking, 
         diabetes,
         hypertension, 
         obesity, 
         exc_alcohol, 
         social_isolation, 
         air_pollution, 
         vision_loss)
a<-tetrachoric(basecom)     

rho_matrix <- a$rho
n <- nrow(basecom) # sample size
result <- data.frame() #output dataframe

#p-value loop
for (i in 1:ncol(rho_matrix)) {
  for (j in 1:i) {
    if (i != j) {
      rho <- rho_matrix[i, j]
      z <- rho * sqrt(n - 2) / sqrt(1 - rho^2) #z value for the correlation
      p_value <- 2 * (1 - pnorm(abs(z))) #p from z
      result <- rbind(result, data.frame(
        Var1 = colnames(rho_matrix)[i],
        Var2 = colnames(rho_matrix)[j],
        Rho = rho,
        P_Value = p_value,
        Significance = case_when(
          p_value < 0.01 ~ "***",
          p_value < 0.05 ~ "**",
          TRUE ~ "ns"
        )
      ))
    }
  }
}

result %>%
  arrange(desc(abs(Rho)))%>% #arrange by bigger correlation (absolute value)
  kable("html", digits = 4, caption = " Tetrachoric correlation") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Tetrachoric correlation

Var1	Var2	Rho	P_Value	Significance
vision_loss	hearing_loss	0.4942	0.0000	***
air_pollution	less_education	-0.4627	0.0000	***
diabetes	high_cholesterol	0.4003	0.0000	***
exc_alcohol	smoking	0.3366	0.0000	***
hypertension	high_cholesterol	0.2976	0.0000	***
hypertension	diabetes	0.2690	0.0000	***
vision_loss	less_education	0.2597	0.0000	***
obesity	smoking	-0.2495	0.0000	***
air_pollution	exc_alcohol	0.2473	0.0000	***
obesity	hypertension	0.2335	0.0000	***
vision_loss	air_pollution	-0.2305	0.0000	***
hearing_loss	less_education	0.2082	0.0000	***
exc_alcohol	less_education	-0.2051	0.0000	***
air_pollution	physical_inactivity	0.1803	0.0000	***
air_pollution	hearing_loss	-0.1698	0.0000	***
air_pollution	social_isolation	-0.1666	0.0000	***
social_isolation	hearing_loss	0.1646	0.0000	***
obesity	diabetes	0.1593	0.0000	***
social_isolation	exc_alcohol	0.1572	0.0000	***
obesity	high_cholesterol	0.1508	0.0000	***
physical_inactivity	hearing_loss	0.1466	0.0000	***
air_pollution	diabetes	0.1371	0.0000	***
obesity	less_education	-0.1359	0.0000	***
physical_inactivity	less_education	0.1344	0.0000	***
vision_loss	social_isolation	0.1313	0.0000	***
vision_loss	physical_inactivity	0.1306	0.0000	***
physical_inactivity	depression	0.1118	0.0000	***
depression	high_cholesterol	0.1065	0.0000	***
air_pollution	high_cholesterol	0.1062	0.0000	***
exc_alcohol	hearing_loss	-0.1025	0.0000	***
smoking	less_education	0.1024	0.0000	***
social_isolation	diabetes	-0.1016	0.0000	***
hypertension	smoking	-0.0990	0.0000	***
social_isolation	smoking	0.0953	0.0000	***
vision_loss	hypertension	0.0865	0.0000	***
hypertension	hearing_loss	0.0857	0.0000	***
high_cholesterol	hearing_loss	0.0852	0.0000	***
social_isolation	hypertension	0.0823	0.0000	***
air_pollution	obesity	0.0800	0.0000	***
social_isolation	less_education	0.0778	0.0000	***
smoking	high_cholesterol	-0.0753	0.0000	***
depression	hearing_loss	0.0749	0.0000	***
hypertension	less_education	0.0744	0.0000	***
smoking	physical_inactivity	0.0705	0.0000	***
exc_alcohol	physical_inactivity	-0.0676	0.0000	***
vision_loss	exc_alcohol	-0.0665	0.0000	***
hypertension	physical_inactivity	0.0663	0.0000	***
exc_alcohol	depression	-0.0658	0.0000	***
vision_loss	smoking	0.0656	0.0000	***
vision_loss	high_cholesterol	0.0625	0.0001	***
exc_alcohol	diabetes	-0.0608	0.0001	***
vision_loss	diabetes	0.0561	0.0004	***
high_cholesterol	less_education	-0.0559	0.0004	***
diabetes	hearing_loss	0.0555	0.0004	***
diabetes	depression	0.0539	0.0006	***
vision_loss	obesity	-0.0521	0.0009	***
obesity	depression	0.0508	0.0012	***
air_pollution	hypertension	0.0505	0.0013	***
diabetes	physical_inactivity	0.0483	0.0021	***
air_pollution	depression	0.0481	0.0022	***
diabetes	smoking	-0.0420	0.0075	***
smoking	depression	-0.0405	0.0099	***
obesity	hearing_loss	-0.0398	0.0114	**
air_pollution	smoking	-0.0394	0.0122	**
obesity	physical_inactivity	0.0309	0.0490	**
hypertension	depression	0.0308	0.0498	**
social_isolation	physical_inactivity	0.0257	0.1021	ns
exc_alcohol	high_cholesterol	0.0249	0.1137	ns
depression	less_education	-0.0231	0.1419	ns
physical_inactivity	high_cholesterol	-0.0173	0.2722	ns
smoking	hearing_loss	-0.0152	0.3332	ns
social_isolation	depression	0.0125	0.4264	ns
exc_alcohol	obesity	-0.0114	0.4676	ns
exc_alcohol	hypertension	-0.0113	0.4711	ns
social_isolation	high_cholesterol	-0.0101	0.5194	ns
social_isolation	obesity	0.0100	0.5244	ns
vision_loss	depression	0.0050	0.7484	ns
diabetes	less_education	-0.0048	0.7598	ns

communalities

b<-as.data.frame(a$rho)
# cuantos componentes principales tienen eigenvalues>1?
eigenvalues <-eigen(b)$values
eigenvalues

##  [1] 2.1002875 1.9398842 1.4519769 1.1807915 1.0790750 0.9488925 0.9386915
##  [8] 0.7262624 0.6696871 0.5766141 0.5136771 0.4871799 0.3869802

##5!

eigenVectors<-eigen(b)$vectors


# I use the paper criteria for caculation:
#"Communality was calculated as the sum of the square of all factor loadings"

comunalities<-as.data.frame(eigenVectors)
comunalities$RF<-names(b)

comunalities$comunality<-comunalities$V1^2+comunalities$V2^2+comunalities$V3^2+
  comunalities$V4^2+comunalities$V5^2

# limpio todo y nos vamos
comunalities<-comunalities %>% select(RF, comunality)


comunalities %>%
  kable("html", digits = 4, caption = "Comunalities") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Comunalities

RF	comunality
less_education	0.3103
hearing_loss	0.2935
high_cholesterol	0.3528
depression	0.2418
physical_inactivity	0.5072
smoking	0.4858
diabetes	0.4234
hypertension	0.2779
obesity	0.3833
exc_alcohol	0.4861
social_isolation	0.5414
air_pollution	0.4215
vision_loss	0.2750

##females

Correlation

basecom<-elsi %>% filter(man==0) %>% select(less_education, 
         hearing_loss, 
         high_cholesterol, 
         depression, 
         physical_inactivity, 
         smoking, 
         diabetes,
         hypertension, 
         obesity, 
         exc_alcohol, 
         social_isolation, 
         air_pollution, 
         vision_loss)
a<-tetrachoric(basecom)     

## For i = 11 j = 10  A cell entry of 0 was replaced with correct =  0.5.  Check your data!

rho_matrix <- a$rho
n <- nrow(basecom) # sample size
result <- data.frame() #output dataframe

#p-value loop
for (i in 1:ncol(rho_matrix)) {
  for (j in 1:i) {
    if (i != j) {
      rho <- rho_matrix[i, j]
      z <- rho * sqrt(n - 2) / sqrt(1 - rho^2) #z value for the correlation
      p_value <- 2 * (1 - pnorm(abs(z))) #p from z
      result <- rbind(result, data.frame(
        Var1 = colnames(rho_matrix)[i],
        Var2 = colnames(rho_matrix)[j],
        Rho = rho,
        P_Value = p_value,
        Significance = case_when(
          p_value < 0.01 ~ "***",
          p_value < 0.05 ~ "**",
          TRUE ~ "ns"
        )
      ))
    }
  }
}

result %>%
  arrange(desc(abs(Rho)))%>% #arrange by bigger correlation (absolute value)
  kable("html", digits = 4, caption = " Tetrachoric correlation") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Tetrachoric correlation

Var1	Var2	Rho	P_Value	Significance
vision_loss	hearing_loss	0.4842	0.0000	***
exc_alcohol	smoking	0.4056	0.0000	***
hypertension	diabetes	0.3582	0.0000	***
diabetes	high_cholesterol	0.3135	0.0000	***
air_pollution	less_education	-0.2802	0.0000	***
hypertension	high_cholesterol	0.2711	0.0000	***
obesity	hypertension	0.2425	0.0000	***
hypertension	less_education	0.2251	0.0000	***
hearing_loss	less_education	0.2227	0.0000	***
social_isolation	hearing_loss	0.2096	0.0000	***
physical_inactivity	less_education	0.2001	0.0000	***
social_isolation	depression	-0.2000	0.0000	***
air_pollution	social_isolation	-0.1915	0.0000	***
exc_alcohol	diabetes	-0.1878	0.0000	***
exc_alcohol	less_education	-0.1871	0.0000	***
vision_loss	social_isolation	0.1839	0.0000	***
vision_loss	less_education	0.1828	0.0000	***
obesity	smoking	-0.1744	0.0000	***
social_isolation	less_education	0.1716	0.0000	***
air_pollution	hearing_loss	-0.1637	0.0000	***
exc_alcohol	hearing_loss	-0.1637	0.0000	***
vision_loss	physical_inactivity	0.1620	0.0000	***
vision_loss	diabetes	0.1546	0.0000	***
obesity	diabetes	0.1527	0.0000	***
physical_inactivity	hearing_loss	0.1515	0.0000	***
diabetes	physical_inactivity	0.1472	0.0000	***
air_pollution	physical_inactivity	0.1363	0.0000	***
air_pollution	diabetes	0.1348	0.0000	***
diabetes	depression	0.1314	0.0000	***
depression	high_cholesterol	0.1277	0.0000	***
vision_loss	exc_alcohol	-0.1273	0.0000	***
depression	hearing_loss	0.1251	0.0000	***
vision_loss	air_pollution	-0.1231	0.0000	***
diabetes	less_education	0.1180	0.0000	***
vision_loss	depression	0.1114	0.0000	***
smoking	hearing_loss	-0.1076	0.0000	***
vision_loss	high_cholesterol	0.1045	0.0000	***
social_isolation	physical_inactivity	0.1006	0.0000	***
hypertension	hearing_loss	0.1006	0.0000	***
air_pollution	depression	0.0960	0.0000	***
hypertension	smoking	-0.0843	0.0000	***
social_isolation	diabetes	0.0831	0.0000	***
diabetes	hearing_loss	0.0789	0.0000	***
exc_alcohol	hypertension	-0.0779	0.0000	***
exc_alcohol	high_cholesterol	-0.0777	0.0000	***
obesity	hearing_loss	-0.0765	0.0000	***
air_pollution	obesity	0.0761	0.0000	***
high_cholesterol	hearing_loss	0.0755	0.0000	***
vision_loss	hypertension	0.0737	0.0000	***
social_isolation	exc_alcohol	0.0733	0.0000	***
air_pollution	high_cholesterol	0.0720	0.0000	***
smoking	depression	0.0704	0.0000	***
hypertension	depression	0.0701	0.0000	***
obesity	high_cholesterol	0.0687	0.0000	***
hypertension	physical_inactivity	0.0684	0.0000	***
obesity	depression	0.0651	0.0000	***
smoking	high_cholesterol	-0.0624	0.0000	***
depression	less_education	0.0545	0.0000	***
air_pollution	exc_alcohol	0.0538	0.0000	***
exc_alcohol	depression	0.0520	0.0001	***
physical_inactivity	depression	0.0482	0.0002	***
vision_loss	obesity	-0.0403	0.0020	***
social_isolation	smoking	-0.0365	0.0051	***
exc_alcohol	obesity	0.0364	0.0051	***
social_isolation	hypertension	0.0355	0.0064	***
social_isolation	obesity	-0.0347	0.0077	***
exc_alcohol	physical_inactivity	-0.0297	0.0224	**
social_isolation	high_cholesterol	0.0266	0.0412	**
diabetes	smoking	-0.0263	0.0436	**
high_cholesterol	less_education	0.0259	0.0468	**
air_pollution	smoking	0.0228	0.0797	ns
obesity	physical_inactivity	0.0188	0.1479	ns
smoking	less_education	0.0186	0.1529	ns
vision_loss	smoking	0.0151	0.2475	ns
air_pollution	hypertension	-0.0136	0.2972	ns
obesity	less_education	-0.0102	0.4319	ns
physical_inactivity	high_cholesterol	0.0062	0.6346	ns
smoking	physical_inactivity	0.0028	0.8308	ns

communalities

b<-as.data.frame(a$rho)
# cuantos componentes principales tienen eigenvalues>1?
eigenvalues <-eigen(b)$values
eigenvalues

##  [1] 2.2861943 1.7095427 1.3940665 1.1904649 1.0725138 1.0010667 0.9492799
##  [8] 0.7085198 0.6594236 0.6149411 0.5454142 0.4654434 0.4031292

##5!

eigenVectors<-eigen(b)$vectors


# I use the paper criteria for caculation:
#"Communality was calculated as the sum of the square of all factor loadings"

comunalities<-as.data.frame(eigenVectors)
comunalities$RF<-names(b)

comunalities$comunality<-comunalities$V1^2+comunalities$V2^2+comunalities$V3^2+
  comunalities$V4^2+comunalities$V5^2

# limpio todo y nos vamos
comunalities<-comunalities %>% select(RF, comunality)


comunalities %>%
  kable("html", digits = 4, caption = "Comunalities") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Comunalities

RF	comunality
less_education	0.2276
hearing_loss	0.3296
high_cholesterol	0.2181
depression	0.5075
physical_inactivity	0.5375
smoking	0.4965
diabetes	0.2871
hypertension	0.3709
obesity	0.2493
exc_alcohol	0.4828
social_isolation	0.4438
air_pollution	0.5370
vision_loss	0.3123

##by rich or poor ##poor

Correlation

basecom<-elsi %>% filter(poor_regions==1) %>% select(less_education, 
         hearing_loss, 
         high_cholesterol, 
         depression, 
         physical_inactivity, 
         smoking, 
         diabetes,
         hypertension, 
         obesity, 
         exc_alcohol, 
         social_isolation, 
         air_pollution, 
         vision_loss)
a<-tetrachoric(basecom)     

rho_matrix <- a$rho
n <- nrow(basecom) # sample size
result <- data.frame() #output dataframe

#p-value loop
for (i in 1:ncol(rho_matrix)) {
  for (j in 1:i) {
    if (i != j) {
      rho <- rho_matrix[i, j]
      z <- rho * sqrt(n - 2) / sqrt(1 - rho^2) #z value for the correlation
      p_value <- 2 * (1 - pnorm(abs(z))) #p from z
      result <- rbind(result, data.frame(
        Var1 = colnames(rho_matrix)[i],
        Var2 = colnames(rho_matrix)[j],
        Rho = rho,
        P_Value = p_value,
        Significance = case_when(
          p_value < 0.01 ~ "***",
          p_value < 0.05 ~ "**",
          TRUE ~ "ns"
        )
      ))
    }
  }
}

result %>%
  arrange(desc(abs(Rho)))%>% #arrange by bigger correlation (absolute value)
  kable("html", digits = 4, caption = " Tetrachoric correlation") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Tetrachoric correlation

Var1	Var2	Rho	P_Value	Significance
vision_loss	hearing_loss	0.4598	0.0000	***
diabetes	high_cholesterol	0.3421	0.0000	***
exc_alcohol	smoking	0.3301	0.0000	***
hypertension	high_cholesterol	0.2979	0.0000	***
air_pollution	less_education	-0.2684	0.0000	***
hypertension	diabetes	0.2658	0.0000	***
obesity	hypertension	0.2563	0.0000	***
hearing_loss	less_education	0.2290	0.0000	***
air_pollution	exc_alcohol	0.2081	0.0000	***
physical_inactivity	less_education	0.2016	0.0000	***
obesity	smoking	-0.1986	0.0000	***
diabetes	depression	0.1892	0.0000	***
exc_alcohol	less_education	-0.1852	0.0000	***
exc_alcohol	hearing_loss	-0.1843	0.0000	***
depression	high_cholesterol	0.1830	0.0000	***
physical_inactivity	hearing_loss	0.1793	0.0000	***
social_isolation	hearing_loss	0.1791	0.0000	***
air_pollution	depression	0.1766	0.0000	***
social_isolation	depression	-0.1751	0.0000	***
air_pollution	hearing_loss	-0.1725	0.0000	***
vision_loss	physical_inactivity	0.1724	0.0000	***
air_pollution	diabetes	0.1655	0.0000	***
vision_loss	less_education	0.1628	0.0000	***
obesity	less_education	-0.1610	0.0000	***
air_pollution	obesity	0.1542	0.0000	***
depression	hearing_loss	0.1530	0.0000	***
social_isolation	exc_alcohol	0.1491	0.0000	***
obesity	high_cholesterol	0.1491	0.0000	***
air_pollution	social_isolation	-0.1459	0.0000	***
obesity	diabetes	0.1452	0.0000	***
exc_alcohol	physical_inactivity	-0.1436	0.0000	***
air_pollution	physical_inactivity	0.1382	0.0000	***
vision_loss	social_isolation	0.1357	0.0000	***
vision_loss	depression	0.1302	0.0000	***
exc_alcohol	depression	-0.1297	0.0000	***
physical_inactivity	depression	0.1273	0.0000	***
hypertension	smoking	-0.1261	0.0000	***
smoking	high_cholesterol	-0.1252	0.0000	***
diabetes	physical_inactivity	0.1240	0.0000	***
exc_alcohol	hypertension	-0.1237	0.0000	***
exc_alcohol	diabetes	-0.1206	0.0000	***
social_isolation	obesity	-0.1172	0.0000	***
vision_loss	exc_alcohol	-0.1168	0.0000	***
smoking	less_education	0.1146	0.0000	***
social_isolation	less_education	0.1125	0.0000	***
hypertension	less_education	0.1091	0.0000	***
hypertension	physical_inactivity	0.1034	0.0000	***
depression	less_education	-0.0973	0.0000	***
high_cholesterol	less_education	-0.0932	0.0000	***
vision_loss	air_pollution	-0.0932	0.0000	***
vision_loss	diabetes	0.0911	0.0000	***
hypertension	hearing_loss	0.0865	0.0000	***
smoking	physical_inactivity	0.0851	0.0000	***
physical_inactivity	high_cholesterol	0.0837	0.0000	***
social_isolation	physical_inactivity	0.0792	0.0000	***
hypertension	depression	0.0789	0.0000	***
obesity	depression	0.0774	0.0000	***
obesity	hearing_loss	-0.0752	0.0000	***
vision_loss	hypertension	0.0715	0.0000	***
smoking	hearing_loss	-0.0700	0.0000	***
diabetes	smoking	-0.0655	0.0000	***
social_isolation	diabetes	-0.0584	0.0001	***
social_isolation	smoking	0.0574	0.0001	***
air_pollution	hypertension	0.0568	0.0001	***
exc_alcohol	high_cholesterol	-0.0557	0.0002	***
diabetes	hearing_loss	0.0550	0.0002	***
exc_alcohol	obesity	-0.0545	0.0002	***
air_pollution	high_cholesterol	0.0536	0.0003	***
vision_loss	obesity	-0.0475	0.0014	***
air_pollution	smoking	-0.0425	0.0043	***
vision_loss	high_cholesterol	0.0346	0.0204	**
social_isolation	hypertension	0.0260	0.0806	ns
smoking	depression	-0.0246	0.0994	ns
high_cholesterol	hearing_loss	0.0162	0.2785	ns
obesity	physical_inactivity	0.0148	0.3209	ns
social_isolation	high_cholesterol	-0.0141	0.3455	ns
vision_loss	smoking	-0.0119	0.4231	ns
diabetes	less_education	-0.0099	0.5051	ns

communalities

b<-as.data.frame(a$rho)
# cuantos componentes principales tienen eigenvalues>1?
eigenvalues <-eigen(b)$values
eigenvalues

##  [1] 2.1715620 1.9959325 1.3460947 1.1796671 1.0491090 0.9469046 0.8201708
##  [8] 0.7196142 0.6700958 0.6265794 0.5440637 0.5106334 0.4195729

##5!

eigenVectors<-eigen(b)$vectors


# I use the paper criteria for caculation:
#"Communality was calculated as the sum of the square of all factor loadings"

comunalities<-as.data.frame(eigenVectors)
comunalities$RF<-names(b)

comunalities$comunality<-comunalities$V1^2+comunalities$V2^2+comunalities$V3^2+
  comunalities$V4^2+comunalities$V5^2

# limpio todo y nos vamos
comunalities<-comunalities %>% select(RF, comunality)


comunalities %>%
  kable("html", digits = 4, caption = "Comunalities") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Comunalities

RF	comunality
less_education	0.4795
hearing_loss	0.4011
high_cholesterol	0.2656
depression	0.4112
physical_inactivity	0.3679
smoking	0.4682
diabetes	0.2475
hypertension	0.3779
obesity	0.2165
exc_alcohol	0.5000
social_isolation	0.5130
air_pollution	0.3518
vision_loss	0.3999

##rich

Correlation

basecom<-elsi %>% filter(poor_regions==0) %>% select(less_education, 
         hearing_loss, 
         high_cholesterol, 
         depression, 
         physical_inactivity, 
         smoking, 
         diabetes,
         hypertension, 
         obesity, 
         exc_alcohol, 
         social_isolation, 
         air_pollution, 
         vision_loss)
a<-tetrachoric(basecom)     

rho_matrix <- a$rho
n <- nrow(basecom) # sample size
result <- data.frame() #output dataframe

#p-value loop
for (i in 1:ncol(rho_matrix)) {
  for (j in 1:i) {
    if (i != j) {
      rho <- rho_matrix[i, j]
      z <- rho * sqrt(n - 2) / sqrt(1 - rho^2) #z value for the correlation
      p_value <- 2 * (1 - pnorm(abs(z))) #p from z
      result <- rbind(result, data.frame(
        Var1 = colnames(rho_matrix)[i],
        Var2 = colnames(rho_matrix)[j],
        Rho = rho,
        P_Value = p_value,
        Significance = case_when(
          p_value < 0.01 ~ "***",
          p_value < 0.05 ~ "**",
          TRUE ~ "ns"
        )
      ))
    }
  }
}

result %>%
  arrange(desc(abs(Rho)))%>% #arrange by bigger correlation (absolute value)
  kable("html", digits = 4, caption = " Tetrachoric correlation") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Tetrachoric correlation

Var1	Var2	Rho	P_Value	Significance
air_pollution	less_education	-0.5197	0.0000	***
vision_loss	hearing_loss	0.4932	0.0000	***
exc_alcohol	smoking	0.4104	0.0000	***
hypertension	diabetes	0.3766	0.0000	***
diabetes	high_cholesterol	0.3527	0.0000	***
hypertension	high_cholesterol	0.2816	0.0000	***
vision_loss	less_education	0.2462	0.0000	***
obesity	hypertension	0.2451	0.0000	***
obesity	smoking	-0.2447	0.0000	***
social_isolation	obesity	0.2155	0.0000	***
hypertension	less_education	0.2018	0.0000	***
hearing_loss	less_education	0.1980	0.0000	***
vision_loss	air_pollution	-0.1895	0.0000	***
air_pollution	high_cholesterol	0.1822	0.0000	***
obesity	diabetes	0.1820	0.0000	***
exc_alcohol	less_education	-0.1798	0.0000	***
air_pollution	exc_alcohol	0.1696	0.0000	***
vision_loss	physical_inactivity	0.1589	0.0000	***
physical_inactivity	less_education	0.1555	0.0000	***
depression	high_cholesterol	0.1486	0.0000	***
physical_inactivity	hearing_loss	0.1394	0.0000	***
air_pollution	physical_inactivity	0.1390	0.0000	***
vision_loss	diabetes	0.1360	0.0000	***
diabetes	less_education	0.1356	0.0000	***
social_isolation	hearing_loss	0.1346	0.0000	***
vision_loss	high_cholesterol	0.1273	0.0000	***
air_pollution	diabetes	0.1215	0.0000	***
social_isolation	exc_alcohol	0.1190	0.0000	***
depression	less_education	0.1169	0.0000	***
social_isolation	less_education	0.1154	0.0000	***
high_cholesterol	hearing_loss	0.1139	0.0000	***
social_isolation	hypertension	0.1114	0.0000	***
vision_loss	social_isolation	0.1088	0.0000	***
diabetes	physical_inactivity	0.1007	0.0000	***
social_isolation	diabetes	0.0997	0.0000	***
exc_alcohol	depression	-0.0987	0.0000	***
depression	hearing_loss	0.0962	0.0000	***
obesity	high_cholesterol	0.0954	0.0000	***
obesity	depression	0.0951	0.0000	***
exc_alcohol	high_cholesterol	-0.0909	0.0000	***
air_pollution	social_isolation	-0.0905	0.0000	***
vision_loss	depression	0.0902	0.0000	***
exc_alcohol	diabetes	-0.0895	0.0000	***
hypertension	hearing_loss	0.0895	0.0000	***
air_pollution	hearing_loss	-0.0859	0.0000	***
diabetes	depression	0.0855	0.0000	***
hypertension	smoking	-0.0821	0.0000	***
vision_loss	hypertension	0.0808	0.0000	***
hypertension	depression	0.0806	0.0000	***
vision_loss	smoking	0.0783	0.0000	***
diabetes	hearing_loss	0.0740	0.0000	***
exc_alcohol	obesity	-0.0722	0.0000	***
high_cholesterol	less_education	0.0682	0.0000	***
social_isolation	physical_inactivity	0.0612	0.0000	***
smoking	high_cholesterol	-0.0604	0.0000	***
air_pollution	depression	-0.0601	0.0000	***
air_pollution	obesity	-0.0595	0.0000	***
physical_inactivity	high_cholesterol	-0.0585	0.0000	***
social_isolation	smoking	0.0500	0.0002	***
vision_loss	exc_alcohol	-0.0470	0.0005	***
obesity	hearing_loss	-0.0460	0.0007	***
hypertension	physical_inactivity	0.0458	0.0007	***
smoking	hearing_loss	-0.0442	0.0011	***
obesity	physical_inactivity	0.0412	0.0023	***
obesity	less_education	0.0376	0.0055	***
air_pollution	smoking	0.0369	0.0064	***
air_pollution	hypertension	-0.0245	0.0701	ns
physical_inactivity	depression	0.0235	0.0821	ns
vision_loss	obesity	-0.0230	0.0899	ns
diabetes	smoking	-0.0215	0.1131	ns
smoking	physical_inactivity	-0.0213	0.1164	ns
smoking	depression	0.0210	0.1205	ns
smoking	less_education	0.0159	0.2412	ns
exc_alcohol	physical_inactivity	0.0114	0.3985	ns
exc_alcohol	hypertension	0.0081	0.5479	ns
social_isolation	high_cholesterol	-0.0078	0.5642	ns
social_isolation	depression	0.0046	0.7337	ns
exc_alcohol	hearing_loss	-0.0022	0.8739	ns

communalities

b<-as.data.frame(a$rho)
# cuantos componentes principales tienen eigenvalues>1?
eigenvalues <-eigen(b)$values
eigenvalues

##  [1] 2.2949579 1.6842555 1.5405150 1.2418292 1.1304905 0.9568796 0.9461203
##  [8] 0.7250216 0.6326415 0.5968507 0.4932391 0.4322442 0.3249548

##5!

eigenVectors<-eigen(b)$vectors


# I use the paper criteria for caculation:
#"Communality was calculated as the sum of the square of all factor loadings"

comunalities<-as.data.frame(eigenVectors)
comunalities$RF<-names(b)

comunalities$comunality<-comunalities$V1^2+comunalities$V2^2+comunalities$V3^2+
  comunalities$V4^2+comunalities$V5^2

# limpio todo y nos vamos
comunalities<-comunalities %>% select(RF, comunality)


comunalities %>%
  kable("html", digits = 4, caption = "Comunalities") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Comunalities

RF	comunality
less_education	0.3455
hearing_loss	0.3700
high_cholesterol	0.4023
depression	0.1446
physical_inactivity	0.4293
smoking	0.5168
diabetes	0.2959
hypertension	0.2949
obesity	0.4249
exc_alcohol	0.4627
social_isolation	0.4300
air_pollution	0.5217
vision_loss	0.3614

##by race ##white

Correlation

basecom<-elsi %>% filter(race_2==1) %>% select(less_education, 
         hearing_loss, 
         high_cholesterol, 
         depression, 
         physical_inactivity, 
         smoking, 
         diabetes,
         hypertension, 
         obesity, 
         exc_alcohol, 
         social_isolation, 
         air_pollution, 
         vision_loss)
a<-tetrachoric(basecom)     

rho_matrix <- a$rho
n <- nrow(basecom) # sample size
result <- data.frame() #output dataframe

#p-value loop
for (i in 1:ncol(rho_matrix)) {
  for (j in 1:i) {
    if (i != j) {
      rho <- rho_matrix[i, j]
      z <- rho * sqrt(n - 2) / sqrt(1 - rho^2) #z value for the correlation
      p_value <- 2 * (1 - pnorm(abs(z))) #p from z
      result <- rbind(result, data.frame(
        Var1 = colnames(rho_matrix)[i],
        Var2 = colnames(rho_matrix)[j],
        Rho = rho,
        P_Value = p_value,
        Significance = case_when(
          p_value < 0.01 ~ "***",
          p_value < 0.05 ~ "**",
          TRUE ~ "ns"
        )
      ))
    }
  }
}

result %>%
  arrange(desc(abs(Rho)))%>% #arrange by bigger correlation (absolute value)
  kable("html", digits = 4, caption = " Tetrachoric correlation") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Tetrachoric correlation

Var1	Var2	Rho	P_Value	Significance
vision_loss	hearing_loss	0.5198	0.0000	***
air_pollution	less_education	-0.4895	0.0000	***
diabetes	high_cholesterol	0.3425	0.0000	***
hypertension	diabetes	0.3362	0.0000	***
air_pollution	exc_alcohol	0.3309	0.0000	***
exc_alcohol	smoking	0.3282	0.0000	***
hypertension	high_cholesterol	0.2493	0.0000	***
obesity	hypertension	0.2492	0.0000	***
vision_loss	less_education	0.2346	0.0000	***
social_isolation	hearing_loss	0.2270	0.0000	***
obesity	smoking	-0.2269	0.0000	***
hearing_loss	less_education	0.2180	0.0000	***
social_isolation	exc_alcohol	0.2081	0.0000	***
air_pollution	social_isolation	-0.1962	0.0000	***
air_pollution	physical_inactivity	0.1938	0.0000	***
vision_loss	air_pollution	-0.1830	0.0000	***
exc_alcohol	less_education	-0.1745	0.0000	***
hypertension	less_education	0.1733	0.0000	***
physical_inactivity	hearing_loss	0.1629	0.0000	***
air_pollution	high_cholesterol	0.1588	0.0000	***
physical_inactivity	less_education	0.1551	0.0000	***
depression	high_cholesterol	0.1429	0.0000	***
air_pollution	hearing_loss	-0.1424	0.0000	***
air_pollution	diabetes	0.1421	0.0000	***
exc_alcohol	depression	-0.1398	0.0000	***
obesity	diabetes	0.1393	0.0000	***
diabetes	depression	0.1340	0.0000	***
vision_loss	high_cholesterol	0.1338	0.0000	***
vision_loss	diabetes	0.1302	0.0000	***
obesity	depression	0.1272	0.0000	***
vision_loss	hypertension	0.1255	0.0000	***
high_cholesterol	hearing_loss	0.1250	0.0000	***
diabetes	physical_inactivity	0.1231	0.0000	***
depression	hearing_loss	0.1211	0.0000	***
vision_loss	physical_inactivity	0.1151	0.0000	***
hypertension	depression	0.1110	0.0000	***
hypertension	hearing_loss	0.1101	0.0000	***
exc_alcohol	physical_inactivity	-0.0947	0.0000	***
depression	less_education	0.0906	0.0000	***
obesity	high_cholesterol	0.0902	0.0000	***
diabetes	less_education	0.0855	0.0000	***
hypertension	physical_inactivity	0.0844	0.0000	***
vision_loss	depression	0.0841	0.0000	***
hypertension	smoking	-0.0820	0.0000	***
obesity	hearing_loss	-0.0797	0.0000	***
vision_loss	exc_alcohol	-0.0792	0.0000	***
exc_alcohol	hearing_loss	-0.0782	0.0000	***
smoking	high_cholesterol	-0.0723	0.0000	***
social_isolation	diabetes	-0.0686	0.0000	***
social_isolation	less_education	0.0671	0.0000	***
exc_alcohol	high_cholesterol	-0.0671	0.0000	***
social_isolation	obesity	0.0666	0.0000	***
vision_loss	obesity	-0.0610	0.0000	***
diabetes	hearing_loss	0.0609	0.0000	***
smoking	depression	-0.0558	0.0002	***
exc_alcohol	diabetes	-0.0530	0.0003	***
social_isolation	smoking	-0.0522	0.0004	***
diabetes	smoking	-0.0509	0.0006	***
social_isolation	depression	-0.0508	0.0006	***
physical_inactivity	depression	0.0475	0.0013	***
smoking	hearing_loss	-0.0448	0.0024	***
obesity	physical_inactivity	0.0442	0.0027	***
vision_loss	smoking	0.0430	0.0036	***
vision_loss	social_isolation	0.0421	0.0043	***
high_cholesterol	less_education	0.0351	0.0174	**
exc_alcohol	hypertension	-0.0250	0.0903	ns
air_pollution	smoking	0.0250	0.0904	ns
air_pollution	hypertension	-0.0239	0.1055	ns
social_isolation	hypertension	0.0235	0.1117	ns
social_isolation	physical_inactivity	0.0214	0.1471	ns
physical_inactivity	high_cholesterol	-0.0178	0.2279	ns
smoking	physical_inactivity	0.0164	0.2655	ns
exc_alcohol	obesity	0.0152	0.3036	ns
air_pollution	obesity	0.0071	0.6327	ns
smoking	less_education	0.0069	0.6424	ns
obesity	less_education	0.0035	0.8146	ns
air_pollution	depression	0.0026	0.8617	ns
social_isolation	high_cholesterol	-0.0018	0.9049	ns

communalities

b<-as.data.frame(a$rho)
# cuantos componentes principales tienen eigenvalues>1?
eigenvalues <-eigen(b)$values
eigenvalues

##  [1] 2.2711787 1.7884856 1.4596122 1.2350918 1.0721664 1.0286244 0.9150664
##  [8] 0.7642354 0.6315420 0.6075716 0.5422593 0.4245568 0.2596095

##5!

eigenVectors<-eigen(b)$vectors


# I use the paper criteria for caculation:
#"Communality was calculated as the sum of the square of all factor loadings"

comunalities<-as.data.frame(eigenVectors)
comunalities$RF<-names(b)

comunalities$comunality<-comunalities$V1^2+comunalities$V2^2+comunalities$V3^2+
  comunalities$V4^2+comunalities$V5^2

# limpio todo y nos vamos
comunalities<-comunalities %>% select(RF, comunality)


comunalities %>%
  kable("html", digits = 4, caption = "Comunalities") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Comunalities

RF	comunality
less_education	0.3079
hearing_loss	0.3970
high_cholesterol	0.2610
depression	0.1103
physical_inactivity	0.4196
smoking	0.5209
diabetes	0.3209
hypertension	0.3195
obesity	0.4103
exc_alcohol	0.5203
social_isolation	0.5734
air_pollution	0.5180
vision_loss	0.3209

##black

Correlation

basecom<-elsi %>% filter(race_2==2) %>% select(less_education, 
         hearing_loss, 
         high_cholesterol, 
         depression, 
         physical_inactivity, 
         smoking, 
         diabetes,
         hypertension, 
         obesity, 
         exc_alcohol, 
         social_isolation, 
         air_pollution, 
         vision_loss)
a<-tetrachoric(basecom)     

## For i = 10 j = 4  A cell entry of 0 was replaced with correct =  0.5.  Check your data!

## For i = 11 j = 1  A cell entry of 0 was replaced with correct =  0.5.  Check your data!

## For i = 11 j = 4  A cell entry of 0 was replaced with correct =  0.5.  Check your data!

## For i = 13 j = 11  A cell entry of 0 was replaced with correct =  0.5.  Check your data!

rho_matrix <- a$rho
n <- nrow(basecom) # sample size
result <- data.frame() #output dataframe

#p-value loop
for (i in 1:ncol(rho_matrix)) {
  for (j in 1:i) {
    if (i != j) {
      rho <- rho_matrix[i, j]
      z <- rho * sqrt(n - 2) / sqrt(1 - rho^2) #z value for the correlation
      p_value <- 2 * (1 - pnorm(abs(z))) #p from z
      result <- rbind(result, data.frame(
        Var1 = colnames(rho_matrix)[i],
        Var2 = colnames(rho_matrix)[j],
        Rho = rho,
        P_Value = p_value,
        Significance = case_when(
          p_value < 0.01 ~ "***",
          p_value < 0.05 ~ "**",
          TRUE ~ "ns"
        )
      ))
    }
  }
}

result %>%
  arrange(desc(abs(Rho)))%>% #arrange by bigger correlation (absolute value)
  kable("html", digits = 4, caption = " Tetrachoric correlation") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Tetrachoric correlation

Var1	Var2	Rho	P_Value	Significance
vision_loss	social_isolation	0.5086	0.0000	***
diabetes	high_cholesterol	0.4358	0.0000	***
vision_loss	hearing_loss	0.4270	0.0000	***
exc_alcohol	smoking	0.3907	0.0000	***
exc_alcohol	less_education	-0.3652	0.0000	***
hypertension	diabetes	0.3327	0.0000	***
exc_alcohol	depression	-0.3228	0.0000	***
air_pollution	physical_inactivity	0.3178	0.0000	***
air_pollution	less_education	-0.2918	0.0000	***
obesity	hypertension	0.2763	0.0000	***
social_isolation	less_education	0.2661	0.0000	***
hypertension	high_cholesterol	0.2599	0.0000	***
obesity	smoking	-0.2508	0.0000	***
depression	high_cholesterol	0.2440	0.0000	***
social_isolation	exc_alcohol	0.2354	0.0000	***
obesity	high_cholesterol	0.2249	0.0000	***
obesity	diabetes	0.2164	0.0000	***
social_isolation	hearing_loss	0.1864	0.0000	***
social_isolation	smoking	0.1763	0.0000	***
vision_loss	less_education	0.1709	0.0000	***
social_isolation	diabetes	-0.1656	0.0000	***
obesity	less_education	-0.1643	0.0000	***
hypertension	less_education	0.1612	0.0000	***
diabetes	physical_inactivity	0.1551	0.0000	***
vision_loss	high_cholesterol	0.1452	0.0000	***
air_pollution	diabetes	0.1447	0.0000	***
depression	hearing_loss	0.1421	0.0000	***
air_pollution	social_isolation	-0.1413	0.0000	***
air_pollution	high_cholesterol	0.1396	0.0000	***
social_isolation	obesity	-0.1314	0.0000	***
smoking	high_cholesterol	-0.1293	0.0000	***
diabetes	depression	0.1292	0.0000	***
social_isolation	depression	-0.1266	0.0000	***
physical_inactivity	less_education	0.1247	0.0000	***
exc_alcohol	hypertension	-0.1247	0.0000	***
hypertension	physical_inactivity	0.1190	0.0001	***
air_pollution	depression	0.1186	0.0001	***
hypertension	smoking	-0.1140	0.0002	***
diabetes	less_education	0.1138	0.0002	***
vision_loss	physical_inactivity	0.1066	0.0005	***
obesity	hearing_loss	-0.1031	0.0008	***
air_pollution	exc_alcohol	0.1027	0.0008	***
exc_alcohol	hearing_loss	-0.1009	0.0010	***
physical_inactivity	depression	0.0987	0.0013	***
air_pollution	smoking	-0.0971	0.0016	***
depression	less_education	0.0928	0.0025	***
physical_inactivity	hearing_loss	0.0906	0.0032	***
social_isolation	physical_inactivity	-0.0888	0.0038	***
vision_loss	diabetes	0.0877	0.0043	***
air_pollution	obesity	0.0874	0.0045	***
hearing_loss	less_education	0.0856	0.0054	***
physical_inactivity	high_cholesterol	0.0851	0.0057	***
smoking	hearing_loss	-0.0851	0.0057	***
hypertension	depression	0.0836	0.0065	***
vision_loss	air_pollution	-0.0770	0.0124	**
exc_alcohol	diabetes	-0.0757	0.0139	**
vision_loss	obesity	0.0747	0.0152	**
vision_loss	depression	0.0723	0.0188	**
smoking	depression	0.0706	0.0218	**
air_pollution	hearing_loss	-0.0655	0.0333	**
exc_alcohol	obesity	-0.0655	0.0335	**
vision_loss	hypertension	0.0592	0.0548	ns
exc_alcohol	high_cholesterol	-0.0586	0.0570	ns
obesity	physical_inactivity	0.0538	0.0809	ns
diabetes	smoking	-0.0533	0.0839	ns
hypertension	hearing_loss	0.0532	0.0843	ns
smoking	physical_inactivity	-0.0529	0.0860	ns
exc_alcohol	physical_inactivity	-0.0503	0.1029	ns
high_cholesterol	less_education	-0.0420	0.1733	ns
smoking	less_education	0.0400	0.1945	ns
social_isolation	high_cholesterol	-0.0384	0.2129	ns
air_pollution	hypertension	0.0357	0.2467	ns
high_cholesterol	hearing_loss	0.0258	0.4032	ns
vision_loss	smoking	-0.0249	0.4192	ns
obesity	depression	0.0217	0.4817	ns
diabetes	hearing_loss	0.0188	0.5422	ns
social_isolation	hypertension	0.0175	0.5709	ns
vision_loss	exc_alcohol	-0.0037	0.9053	ns

communalities

b<-as.data.frame(a$rho)
# cuantos componentes principales tienen eigenvalues>1?
eigenvalues <-eigen(b)$values
eigenvalues

##  [1] 2.3211050 1.9973514 1.5202020 1.2515780 1.1881592 1.0272203 0.7680002
##  [8] 0.7316772 0.6655096 0.5387383 0.3824100 0.3458471 0.2622019

##5!

eigenVectors<-eigen(b)$vectors


# I use the paper criteria for caculation:
#"Communality was calculated as the sum of the square of all factor loadings"

comunalities<-as.data.frame(eigenVectors)
comunalities$RF<-names(b)

comunalities$comunality<-comunalities$V1^2+comunalities$V2^2+comunalities$V3^2+
  comunalities$V4^2+comunalities$V5^2

# limpio todo y nos vamos
comunalities<-comunalities %>% select(RF, comunality)


comunalities %>%
  kable("html", digits = 4, caption = "Comunalities") %>%
  kable_styling(full_width = TRUE, bootstrap_options = c("striped", "hover", "condensed"))

Comunalities

RF	comunality
less_education	0.4244
hearing_loss	0.3628
high_cholesterol	0.2660
depression	0.3380
physical_inactivity	0.3105
smoking	0.5608
diabetes	0.3340
hypertension	0.2847
obesity	0.3675
exc_alcohol	0.4981
social_isolation	0.3857
air_pollution	0.4406
vision_loss	0.4268