Paquetes

abro mi df

summary(df)

       n                         campamento sexo         C                P           O          
 Min.   :1   Futura Esperanza         :2    F: 9   Min.   :0.0000   Min.   :0   Min.   :0.00000  
 1st Qu.:1   Laderas de Angelm\303\263:3    M:10   1st Qu.:0.0000   1st Qu.:0   1st Qu.:0.00000  
 Median :1   Mediaguas                :7           Median :0.0000   Median :0   Median :0.00000  
 Mean   :1   Nuevo Amanecer           :2           Mean   :0.2222   Mean   :0   Mean   :0.05556  
 3rd Qu.:1   Pelluhuin                :5           3rd Qu.:0.0000   3rd Qu.:0   3rd Qu.:0.00000  
 Max.   :1                                         Max.   :1.0000   Max.   :0   Max.   :1.00000  
                                                   NA's   :1        NA's   :1   NA's   :1        
      COPD              c               e                o               ceod       tto    
 Min.   :0.0000   Min.   :0.000   Min.   :0.0000   Min.   :0.0000   Min.   :0.000   NO:10  
 1st Qu.:0.0000   1st Qu.:0.500   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:1.000   SI: 9  
 Median :0.0000   Median :1.000   Median :0.0000   Median :0.0000   Median :3.000          
 Mean   :0.2778   Mean   :2.053   Mean   :0.4737   Mean   :0.7895   Mean   :3.316          
 3rd Qu.:0.7500   3rd Qu.:4.000   3rd Qu.:0.5000   3rd Qu.:1.0000   3rd Qu.:5.500          
 Max.   :1.0000   Max.   :6.000   Max.   :4.0000   Max.   :6.0000   Max.   :9.000          
 NA's   :1

NA

existe diferencia de ceod entre campamentos????

diferenciacampamentos <- aov(df$ceod~df$campamento)
diferenciacampamentos
summary(diferenciacampamentos)

              Df Sum Sq Mean Sq F value Pr(>F)  
df$campamento  4  87.28  21.820   4.572 0.0143 *
Residuals     14  66.82   4.773                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(diferenciacampamentos)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = df$ceod ~ df$campamento)

$`df$campamento`
                                                 diff        lwr       upr     p adj
Laderas de Angelm\303\263-Futura Esperanza -0.6666667 -6.8810860  5.547753 0.9969750
Mediaguas-Futura Esperanza                 -1.8571429 -7.3153299  3.601044 0.8232728
Nuevo Amanecer-Futura Esperanza             3.5000000 -3.3075553 10.307555 0.5195993
Pelluhuin -Futura Esperanza                 2.8000000 -2.8956094  8.495609 0.5601798
Mediaguas-Laderas de Angelm\303\263        -1.1904762 -5.8881356  3.507183 0.9294325
Nuevo Amanecer-Laderas de Angelm\303\263    4.1666667 -2.0477526 10.381086 0.2774677
Pelluhuin -Laderas de Angelm\303\263        3.4666667 -1.5048688  8.438202 0.2452723
Nuevo Amanecer-Mediaguas                    5.3571429 -0.1010442 10.815330 0.0554990
Pelluhuin -Mediaguas                        4.6571429  0.6710466  8.643239 0.0189980
Pelluhuin -Nuevo Amanecer                  -0.7000000 -6.3956094  4.995609 0.9948946

existe diferencia para el componente c del ceod entre campamentos????

diferenciacampamentosparacomp_c <- aov(df$c~df$campamento)
diferenciacampamentosparacomp_c 
summary(diferenciacampamentosparacomp_c)

              Df Sum Sq Mean Sq F value Pr(>F)  
df$campamento  4  44.89  11.223   4.613 0.0138 *
Residuals     14  34.06   2.433                 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(diferenciacampamentosparacomp_c)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = df$c ~ df$campamento)

$`df$campamento`
                                                 diff        lwr      upr     p adj
Laderas de Angelm\303\263-Futura Esperanza  2.0000000 -2.4364853 6.436485 0.6347339
Mediaguas-Futura Esperanza                  0.8571429 -3.0394668 4.753753 0.9564190
Nuevo Amanecer-Futura Esperanza             5.0000000  0.1400739 9.859926 0.0424578
Pelluhuin -Futura Esperanza                 3.4000000 -0.6661059 7.466106 0.1226837
Mediaguas-Laderas de Angelm\303\263        -1.1428571 -4.4965248 2.210810 0.8224818
Nuevo Amanecer-Laderas de Angelm\303\263    3.0000000 -1.4364853 7.436485 0.2703450
Pelluhuin -Laderas de Angelm\303\263        1.4000000 -2.1491882 4.949188 0.7354932
Nuevo Amanecer-Mediaguas                    4.1428571  0.2462475 8.039467 0.0348850
Pelluhuin -Mediaguas                        2.5428571 -0.3028242 5.388538 0.0902524
Pelluhuin -Nuevo Amanecer                  -1.6000000 -5.6661059 2.466106 0.7371468

analizar entre campamenos por acceso a tto

chisq.test(df$tto,df$campamento)

Chi-squared approximation may be incorrect

    Pearson's Chi-squared test

data:  df$tto and df$campamento
X-squared = 7.3105, df = 4, p-value = 0.1204

Ahora agrupo por sexo para ceod

Ahora agrupo por sexo y veo el componente caries del ceod

Visualizo mis datos en grafico componente ceod segun sexo

Visualizo mis datos en grafico componente c del ceod segun sexo

grafico boxplot, para campamentos y ceod

grafico boxplot con ggplot2, para campamentos y componte c de ceod

Existe diferencia entre sexo para ceod??

t.test(df$ceod~df$sexo)

    Welch Two Sample t-test

data:  df$ceod by df$sexo
t = -2.0412, df = 16.556, p-value = 0.0575
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -5.08926954  0.08926954
sample estimates:
mean in group F mean in group M 
            2.0             4.5 
There were 50 or more warnings (use warnings() to see the first 50)

Existe diferencia entre sexo y componente c del ceod

t.test(df$c~df$sexo)

    Welch Two Sample t-test

data:  df$c by df$sexo
t = -2.0732, df = 14.019, p-value = 0.05707
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.63935420  0.06157643
sample estimates:
mean in group F mean in group M 
       1.111111        2.900000

ahora hago analisis para COPD

diferenciacampamentoscopd <- aov(df$COPD~df$campamento)
diferenciacampamentoscopd
Call:
   aov(formula = df$COPD ~ df$campamento)

Terms:
                df$campamento Residuals
Sum of Squares       0.411111  3.200000
Deg. of Freedom             4        13

Residual standard error: 0.4961389
Estimated effects may be unbalanced
1 observation deleted due to missingness
summary(diferenciacampamentoscopd)
              Df Sum Sq Mean Sq F value Pr(>F)
df$campamento  4  0.411  0.1028   0.418  0.793
Residuals     13  3.200  0.2462               
1 observation deleted due to missingness

diferencia para el componente C del COPD por campamento

diferenciacampamentosC<- aov(df$C~df$campamento)
diferenciacampamentosC
Call:
   aov(formula = df$C ~ df$campamento)

Terms:
                df$campamento Residuals
Sum of Squares      0.5777778 2.5333333
Deg. of Freedom             4        13

Residual standard error: 0.4414429
Estimated effects may be unbalanced
1 observation deleted due to missingness
summary(diferenciacampamentosC)
              Df Sum Sq Mean Sq F value Pr(>F)
df$campamento  4 0.5778  0.1444   0.741  0.581
Residuals     13 2.5333  0.1949               
1 observation deleted due to missingness

agrupo por campamento para el COPD

df %>% 
  group_by(campamento) %>% 
  summarise(n=n(), Prom = mean(COPD), DE = sd(COPD), mediana=median(COPD)) %>% 
  ungroup()

Ahora agrupo por sexo para COPD

df %>% 
  group_by(sexo) %>% 
  summarise(n=n(), Prom = mean(COPD), DE = sd(COPD), mediana=median(COPD)) %>% 
  ungroup()

Ahora agrupo por sexo y veo el componente caries del COPD

df %>% 
  group_by(sexo) %>% 
  summarise(n=n(), Prom = mean(C), DE = sd(C), mediana=median(C)) %>% 
  ungroup()

Visualizo mis datos en grafico boxplot COPD segun sexo

boxplot(df$COPD~df$sexo)

Diferencia entre sexo y COPD ???

t.test(df$COPD~df$sexo)

    Welch Two Sample t-test

data:  df$COPD by df$sexo
t = 1.6036, df = 13.517, p-value = 0.1319
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.1140016  0.7806683
sample estimates:
mean in group F mean in group M 
      0.4444444       0.1111111

Agrupo por acceso a tto para COPD

df %>% 
  group_by(tto) %>% 
  summarise(n=n(), Prom = mean(COPD), DE = sd(COPD), mediana=median(COPD)) %>% 
  ungroup()

agrupo por acceso a tto para ceod

df %>% 
  group_by(tto) %>% 
  summarise(n=n(), Prom = mean(ceod), DE = sd(ceod), mediana=median(ceod)) %>% 
  ungroup()
t.test(df$ceod~df$tto)

    Welch Two Sample t-test

data:  df$ceod by df$tto
t = 1.2628, df = 16.929, p-value = 0.2238
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.111237  4.422348
sample estimates:
mean in group NO mean in group SI 
        4.100000         2.444444

agrupo por acceso a tto componente c del ceod

df %>% 
  group_by(tto) %>% 
  summarise(n=n(), Prom = mean(c), DE = sd(c), mediana=median(c)) %>% 
  ungroup()
df %>% 
  group_by(tto) %>% 
  summarise(n=n(), Prom = mean(e), DE = sd(e), mediana=median(e)) %>% 
  ungroup()
df %>% 
  group_by(tto) %>% 
  summarise(n=n(), Prom = mean(o), DE = sd(o), mediana=median(o)) %>% 
  ungroup()

t.test(df$ceod~df$tto)


    Welch Two Sample t-test

data:  df$ceod by df$tto
t = 1.2628, df = 16.929, p-value = 0.2238
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -1.111237  4.422348
sample estimates:
mean in group NO mean in group SI 
        4.100000         2.444444

Desviacion estandar para ceod

sd(df$ceod)

[1] 2.925988

Desviacion estandar para COPD

sd(df$COPD)

[1] NA
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