En el ejemplo los instructores se centran en el efecto de sus diferentes programas de educación nutricional, que son los tratamientos; no están preocupados por el efecto de una ciudad específica u otra per se, pero quieren tener en cuenta las diferencias debidas a las diferentes ciudades.
Instructor: bloque fijo Town: factor aleatorio
df<-read.csv("https://raw.githubusercontent.com/PedroGonzalezBeermann2020/DExperimental2021/main/Problema1.csv")
df## Instructor Town Sodium
## 1 BrendonSmall Squiggleville 1200
## 2 BrendonSmall Squiggleville 1400
## 3 BrendonSmall Squiggleville 1350
## 4 BrendonSmall Metalocalypse 950
## 5 BrendonSmall Squiggleville 1400
## 6 BrendonSmall Squiggleville 1150
## 7 BrendonSmall Squiggleville 1300
## 8 BrendonSmall Metalocalypse 1325
## 9 BrendonSmall Metalocalypse 1425
## 10 BrendonSmall Squiggleville 1500
## 11 BrendonSmall Squiggleville 1250
## 12 BrendonSmall Metalocalypse 1150
## 13 BrendonSmall Metalocalypse 950
## 14 BrendonSmall Squiggleville 1150
## 15 BrendonSmall Metalocalypse 1600
## 16 BrendonSmall Metalocalypse 1300
## 17 BrendonSmall Metalocalypse 1050
## 18 BrendonSmall Metalocalypse 1300
## 19 BrendonSmall Squiggleville 1700
## 20 BrendonSmall Squiggleville 1300
## 21 CoachMcGuirk Squiggleville 1100
## 22 CoachMcGuirk Squiggleville 1200
## 23 CoachMcGuirk Squiggleville 1250
## 24 CoachMcGuirk Metalocalypse 1050
## 25 CoachMcGuirk Metalocalypse 1200
## 26 CoachMcGuirk Metalocalypse 1250
## 27 CoachMcGuirk Squiggleville 1350
## 28 CoachMcGuirk Squiggleville 1350
## 29 CoachMcGuirk Squiggleville 1325
## 30 CoachMcGuirk Squiggleville 1525
## 31 CoachMcGuirk Squiggleville 1225
## 32 CoachMcGuirk Squiggleville 1125
## 33 CoachMcGuirk Metalocalypse 1000
## 34 CoachMcGuirk Metalocalypse 1125
## 35 CoachMcGuirk Squiggleville 1400
## 36 CoachMcGuirk Metalocalypse 1200
## 37 CoachMcGuirk Squiggleville 1150
## 38 CoachMcGuirk Squiggleville 1400
## 39 CoachMcGuirk Squiggleville 1500
## 40 CoachMcGuirk Squiggleville 1200
## 41 MelissaRobins Metalocalypse 900
## 42 MelissaRobins Metalocalypse 1100
## 43 MelissaRobins Metalocalypse 1150
## 44 MelissaRobins Metalocalypse 950
## 45 MelissaRobins Metalocalypse 1100
## 46 MelissaRobins Metalocalypse 1150
## 47 MelissaRobins Squiggleville 1250
## 48 MelissaRobins Squiggleville 1250
## 49 MelissaRobins Squiggleville 1225
## 50 MelissaRobins Squiggleville 1325
## 51 MelissaRobins Metalocalypse 1125
## 52 MelissaRobins Metalocalypse 1025
## 53 MelissaRobins Metalocalypse 950
## 54 MelissaRobins Metalocalypse 925
## 55 MelissaRobins Squiggleville 1200
## 56 MelissaRobins Metalocalypse 1100
## 57 MelissaRobins Metalocalypse 950
## 58 MelissaRobins Metalocalypse 1300
## 59 MelissaRobins Squiggleville 1400
## 60 MelissaRobins Metalocalypse 1100df$Instructor<-factor(df$Instructor)
df$Town<-factor(df$Town)
df$Sodium<-as.numeric(df$Sodium)
df## Instructor Town Sodium
## 1 BrendonSmall Squiggleville 1200
## 2 BrendonSmall Squiggleville 1400
## 3 BrendonSmall Squiggleville 1350
## 4 BrendonSmall Metalocalypse 950
## 5 BrendonSmall Squiggleville 1400
## 6 BrendonSmall Squiggleville 1150
## 7 BrendonSmall Squiggleville 1300
## 8 BrendonSmall Metalocalypse 1325
## 9 BrendonSmall Metalocalypse 1425
## 10 BrendonSmall Squiggleville 1500
## 11 BrendonSmall Squiggleville 1250
## 12 BrendonSmall Metalocalypse 1150
## 13 BrendonSmall Metalocalypse 950
## 14 BrendonSmall Squiggleville 1150
## 15 BrendonSmall Metalocalypse 1600
## 16 BrendonSmall Metalocalypse 1300
## 17 BrendonSmall Metalocalypse 1050
## 18 BrendonSmall Metalocalypse 1300
## 19 BrendonSmall Squiggleville 1700
## 20 BrendonSmall Squiggleville 1300
## 21 CoachMcGuirk Squiggleville 1100
## 22 CoachMcGuirk Squiggleville 1200
## 23 CoachMcGuirk Squiggleville 1250
## 24 CoachMcGuirk Metalocalypse 1050
## 25 CoachMcGuirk Metalocalypse 1200
## 26 CoachMcGuirk Metalocalypse 1250
## 27 CoachMcGuirk Squiggleville 1350
## 28 CoachMcGuirk Squiggleville 1350
## 29 CoachMcGuirk Squiggleville 1325
## 30 CoachMcGuirk Squiggleville 1525
## 31 CoachMcGuirk Squiggleville 1225
## 32 CoachMcGuirk Squiggleville 1125
## 33 CoachMcGuirk Metalocalypse 1000
## 34 CoachMcGuirk Metalocalypse 1125
## 35 CoachMcGuirk Squiggleville 1400
## 36 CoachMcGuirk Metalocalypse 1200
## 37 CoachMcGuirk Squiggleville 1150
## 38 CoachMcGuirk Squiggleville 1400
## 39 CoachMcGuirk Squiggleville 1500
## 40 CoachMcGuirk Squiggleville 1200
## 41 MelissaRobins Metalocalypse 900
## 42 MelissaRobins Metalocalypse 1100
## 43 MelissaRobins Metalocalypse 1150
## 44 MelissaRobins Metalocalypse 950
## 45 MelissaRobins Metalocalypse 1100
## 46 MelissaRobins Metalocalypse 1150
## 47 MelissaRobins Squiggleville 1250
## 48 MelissaRobins Squiggleville 1250
## 49 MelissaRobins Squiggleville 1225
## 50 MelissaRobins Squiggleville 1325
## 51 MelissaRobins Metalocalypse 1125
## 52 MelissaRobins Metalocalypse 1025
## 53 MelissaRobins Metalocalypse 950
## 54 MelissaRobins Metalocalypse 925
## 55 MelissaRobins Squiggleville 1200
## 56 MelissaRobins Metalocalypse 1100
## 57 MelissaRobins Metalocalypse 950
## 58 MelissaRobins Metalocalypse 1300
## 59 MelissaRobins Squiggleville 1400
## 60 MelissaRobins Metalocalypse 1100modelo<-lm(Sodium~Instructor*Town,data = df)
anova<-aov(modelo)
summary(anova)## Df Sum Sq Mean Sq F value Pr(>F)
## Instructor 2 290146 145073 6.920 0.002110 **
## Town 1 329551 329551 15.721 0.000218 ***
## Instructor:Town 2 26269 13135 0.627 0.538263
## Residuals 54 1131992 20963
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1library(lme4)## Loading required package: Matrixmodelo2<-lmer(Sodium~(1|Instructor)+(1|Town),data=df)
summary(modelo2)## Linear mixed model fit by REML ['lmerMod']
## Formula: Sodium ~ (1 | Instructor) + (1 | Town)
## Data: df
##
## REML criterion at convergence: 763.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.64670 -0.72097 0.03089 0.45329 2.87607
##
## Random effects:
## Groups Name Variance Std.Dev.
## Instructor (Intercept) 2954 54.35
## Town (Intercept) 12500 111.81
## Residual 20655 143.72
## Number of obs: 60, groups: Instructor, 3; Town, 2
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 1216.61 87.06 13.97PorcentajeInstructor<-(2954/(2954+12500+20655))*100
PorcentajeTown<-(12500/(2954+12500+20655))*100
PorcentajeInstructor## [1] 8.180786PorcentajeTown## [1] 34.61741Se miden la mano izquierda y la derecha de varios individuos, las medidas se emparejan dentro de cada individuo. Es decir, queremos hacer coincidir estadísticamente la mano izquierda del individuo A con la mano derecha del individuo A, ya que suponemos que alguien con una mano izquierda grande tendrá una mano derecha grande. Por tanto, la variable Individuo se incluirá en el modelo como variable aleatoria. Se podría pensar que cada individuo tiene un bloque que incluye una medida para la mano izquierda y una medida para la mano derecha.
df<-read.csv("https://raw.githubusercontent.com/PedroGonzalezBeermann2020/DExperimental2021/main/Problema2.csv")
df## IndividuO Mano Largo
## 1 A Left 17.5
## 2 B Left 18.4
## 3 C Left 16.2
## 4 D Left 14.5
## 5 E Left 13.5
## 6 F Left 18.9
## 7 G Left 19.5
## 8 H Left 21.1
## 9 I Left 17.8
## 10 J Left 16.8
## 11 K Left 18.4
## 12 L Left 17.3
## 13 M Left 18.9
## 14 N Left 16.4
## 15 O Left 17.5
## 16 P Left 15.0
## 17 A Right 17.6
## 18 B Right 18.5
## 19 C Right 15.9
## 20 D Right 14.9
## 21 E Right 13.7
## 22 F Right 18.9
## 23 G Right 19.5
## 24 H Right 21.5
## 25 I Right 18.5
## 26 J Right 17.1
## 27 K Right 18.9
## 28 L Right 17.5
## 29 M Right 19.5
## 30 N Right 16.5
## 31 O Right 17.4
## 32 P Right 15.6