Documento1
Hector Cabezas Klinger
2024-12-14
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.4.2## Warning: package 'dplyr' was built under R version 4.4.2## Warning: package 'forcats' was built under R version 4.4.2## Warning: package 'lubridate' was built under R version 4.4.2## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(MASS)##
## Adjuntando el paquete: 'MASS'## The following object is masked from 'package:dplyr':
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## selectlibrary(UsingR)## Warning: package 'UsingR' was built under R version 4.4.2## Cargando paquete requerido: HistData## Warning: package 'HistData' was built under R version 4.4.2## Cargando paquete requerido: Hmisc## Warning: package 'Hmisc' was built under R version 4.4.2##
## Adjuntando el paquete: 'Hmisc'## The following objects are masked from 'package:dplyr':
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## src, summarize## The following objects are masked from 'package:base':
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## format.pval, unitsbrillo <- data.frame(brightness)library(readxl)Se trata de un conjunto de datos de 966 registros
dim(brillo)## [1] 966 1- Gráfico de frecuencia y densidad
ggplot(data = brillo)+
aes(x=brillo$brightness, y= ..density..)+
geom_histogram()+
geom_density()+
labs(title = "Brightness vs Frecuency of Stars", x="Brightness", y="Frecuency"
)## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: Use of `brillo$brightness` is discouraged.
## ℹ Use `brightness` instead.
## Use of `brillo$brightness` is discouraged.
## ℹ Use `brightness` instead.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(brillo, aes(x=brillo$brightness))+
geom_histogram(aes(y=..density..), fill="blue")+
geom_density(alpha=.2, fill="red")## Warning: Use of `brillo$brightness` is discouraged.
## ℹ Use `brightness` instead.
## Use of `brillo$brightness` is discouraged.
## ℹ Use `brightness` instead.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
- Diarama de cajas (Boxplot)
boxplot(brillo$brightness, col = palette(rainbow(8)))
- Hay valores atípicos, unos muy pequeños y otros muy grandes, como se indica en las pruebas siguientes. Q1 - 1.5RI, valor mínimo esperado Q3 + 1.5RI, valor máximo esperado 7.7:9.7 RI≈ 2 Intervalo Aceptable: (5.7:11) Fuera de esos valores los consideramos externos al rango intercuartílico(*atípicos)
library(outliers)Menor valor outlier es 2.07, según la prueba de outlier:
grubbs.test(brillo$brightness)##
## Grubbs test for one outlier
##
## data: brillo$brightness
## G = 4.90464, U = 0.97505, p-value = 0.0003884
## alternative hypothesis: lowest value 2.07 is an outlierMayor valor outlier es 12.43 según la prueba de outlier:
grubbs.test(brillo$brightness, opposite = TRUE)##
## Grubbs test for one outlier
##
## data: brillo$brightness
## G = 3.10011, U = 0.99003, p-value = 0.9126
## alternative hypothesis: highest value 12.43 is an outlierlibrary(EnvStats)## Warning: package 'EnvStats' was built under R version 4.4.2##
## Adjuntando el paquete: 'EnvStats'## The following object is masked from 'package:Hmisc':
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## stripChart## The following object is masked from 'package:MASS':
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## boxcox## The following objects are masked from 'package:stats':
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## predict, predict.lmPrueba de rosner para outliers:
rosnerTest(brillo$brightness, k=2)##
## Results of Outlier Test
## -------------------------
##
## Test Method: Rosner's Test for Outliers
##
## Hypothesized Distribution: Normal
##
## Data: brillo$brightness
##
## Sample Size: 966
##
## Test Statistics: R.1 = 4.904643
## R.2 = 4.805341
##
## Test Statistic Parameter: k = 2
##
## Alternative Hypothesis: Up to 2 observations are not
## from the same Distribution.
##
## Type I Error: 5%
##
## Number of Outliers Detected: 2
##
## i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
## 1 0 8.417743 1.294231 2.07 676 4.904643 4.031437 TRUE
## 2 1 8.424321 1.278644 2.28 744 4.805341 4.031181 TRUElibrary(EnvStats)Prueba de rosner para outliers:
rosnerTest(brillo$brightness, k=2)##
## Results of Outlier Test
## -------------------------
##
## Test Method: Rosner's Test for Outliers
##
## Hypothesized Distribution: Normal
##
## Data: brillo$brightness
##
## Sample Size: 966
##
## Test Statistics: R.1 = 4.904643
## R.2 = 4.805341
##
## Test Statistic Parameter: k = 2
##
## Alternative Hypothesis: Up to 2 observations are not
## from the same Distribution.
##
## Type I Error: 5%
##
## Number of Outliers Detected: 2
##
## i Mean.i SD.i Value Obs.Num R.i+1 lambda.i+1 Outlier
## 1 0 8.417743 1.294231 2.07 676 4.904643 4.031437 TRUE
## 2 1 8.424321 1.278644 2.28 744 4.805341 4.031181 TRUERespuesta: TRUE. Por lo cual se confirma el resultado de las pruebas anteriores.
- Valores No atípicos.
library(rstatix)##
## Adjuntando el paquete: 'rstatix'## The following object is masked from 'package:MASS':
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## select## The following object is masked from 'package:stats':
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## filterTabla de valores outliers y valores extremos
VA <- identify_outliers(brillo)Extremos: Las posisciones 25 y 28, es decir los valores: 2.07 y 2.28 El resto son outliers que se muestran en la tabla.
VA## brightness is.outlier is.extreme
## 1 12.31 TRUE FALSE
## 2 11.71 TRUE FALSE
## 3 5.53 TRUE FALSE
## 4 11.28 TRUE FALSE
## 5 4.78 TRUE FALSE
## 6 5.13 TRUE FALSE
## 7 4.37 TRUE FALSE
## 8 5.04 TRUE FALSE
## 9 12.43 TRUE FALSE
## 10 12.04 TRUE FALSE
## 11 4.55 TRUE FALSE
## 12 11.55 TRUE FALSE
## 13 12.14 TRUE FALSE
## 14 11.63 TRUE FALSE
## 15 4.99 TRUE FALSE
## 16 11.67 TRUE FALSE
## 17 4.61 TRUE FALSE
## 18 11.99 TRUE FALSE
## 19 12.04 TRUE FALSE
## 20 5.55 TRUE FALSE
## 21 12.17 TRUE FALSE
## 22 11.55 TRUE FALSE
## 23 11.79 TRUE FALSE
## 24 12.19 TRUE FALSE
## 25 2.07 TRUE TRUE
## 26 11.65 TRUE FALSE
## 27 11.73 TRUE FALSE
## 28 2.28 TRUE TRUE
## 29 5.42 TRUE FALSE
## 30 3.88 TRUE FALSE
## 31 5.54 TRUE FALSE
## 32 5.29 TRUE FALSE
## 33 5.01 TRUE FALSE
## 34 11.55 TRUE FALSE
## 35 4.89 TRUE FALSE
## 36 11.80 TRUE FALSE
## 37 5.41 TRUE FALSE
## 38 5.24 TRUE FALSEbrillo## brightness
## 1 9.10
## 2 9.27
## 3 6.61
## 4 8.06
## 5 8.55
## 6 12.31
## 7 9.64
## 8 9.05
## 9 8.59
## 10 8.59
## 11 7.34
## 12 8.43
## 13 8.80
## 14 7.25
## 15 8.60
## 16 8.15
## 17 11.71
## 18 11.03
## 19 6.53
## 20 8.51
## 21 7.55
## 22 8.69
## 23 7.57
## 24 9.05
## 25 6.28
## 26 9.13
## 27 9.32
## 28 8.83
## 29 9.14
## 30 8.26
## 31 7.63
## 32 9.09
## 33 8.10
## 34 6.43
## 35 9.07
## 36 7.68
## 37 10.44
## 38 8.65
## 39 7.46
## 40 8.70
## 41 10.61
## 42 8.20
## 43 6.18
## 44 7.91
## 45 9.59
## 46 8.57
## 47 10.78
## 48 7.31
## 49 9.53
## 50 6.49
## 51 8.94
## 52 8.56
## 53 10.96
## 54 10.57
## 55 7.40
## 56 8.12
## 57 8.27
## 58 7.05
## 59 9.09
## 60 8.34
## 61 8.86
## 62 8.27
## 63 6.36
## 64 8.08
## 65 11.00
## 66 8.55
## 67 7.83
## 68 8.79
## 69 8.33
## 70 10.42
## 71 8.26
## 72 8.97
## 73 6.90
## 74 9.93
## 75 7.42
## 76 9.03
## 77 8.41
## 78 8.06
## 79 8.69
## 80 8.40
## 81 8.57
## 82 9.50
## 83 8.85
## 84 9.61
## 85 10.62
## 86 8.05
## 87 7.80
## 88 5.71
## 89 7.87
## 90 7.64
## 91 7.66
## 92 8.68
## 93 8.12
## 94 10.10
## 95 8.67
## 96 10.46
## 97 9.87
## 98 9.48
## 99 7.04
## 100 8.44
## 101 9.88
## 102 7.05
## 103 8.29
## 104 9.34
## 105 7.73
## 106 6.22
## 107 5.53
## 108 8.53
## 109 7.23
## 110 8.61
## 111 11.28
## 112 10.76
## 113 8.93
## 114 7.95
## 115 7.46
## 116 8.60
## 117 8.55
## 118 9.20
## 119 6.82
## 120 8.29
## 121 6.83
## 122 4.78
## 123 7.21
## 124 5.58
## 125 8.70
## 126 8.06
## 127 10.86
## 128 6.50
## 129 9.32
## 130 9.14
## 131 8.13
## 132 10.62
## 133 6.62
## 134 9.96
## 135 8.64
## 136 6.60
## 137 6.25
## 138 7.83
## 139 10.03
## 140 9.04
## 141 8.47
## 142 7.33
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## 144 10.35
## 145 5.13
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## 191 12.43
## 192 8.16
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## 200 10.32
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## 456 7.47
## 457 6.97
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## 463 12.04
## 464 8.78
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## 790 8.43
## 791 8.45
## 792 8.84
## 793 11.18
## 794 8.64
## 795 8.42
## 796 6.34
## 797 7.93
## 798 8.36
## 799 8.32
## 800 7.77
## 801 6.84
## 802 8.78
## 803 7.19
## 804 8.50
## 805 8.82
## 806 9.04
## 807 7.93
## 808 7.66
## 809 10.07
## 810 9.03
## 811 5.54
## 812 5.29
## 813 8.13
## 814 7.51
## 815 9.08
## 816 7.10
## 817 7.88
## 818 9.40
## 819 9.06
## 820 8.38
## 821 10.65
## 822 7.77
## 823 8.50
## 824 8.61
## 825 10.05
## 826 8.71
## 827 9.37
## 828 6.97
## 829 8.56
## 830 9.34
## 831 9.47
## 832 8.11
## 833 8.91
## 834 7.83
## 835 8.95
## 836 7.20
## 837 9.37
## 838 5.84
## 839 5.01
## 840 9.81
## 841 9.27
## 842 9.50
## 843 9.32
## 844 8.92
## 845 8.38
## 846 7.74
## 847 8.60
## 848 9.49
## 849 8.35
## 850 7.11
## 851 9.87
## 852 8.98
## 853 7.75
## 854 8.24
## 855 6.74
## 856 6.83
## 857 7.70
## 858 6.70
## 859 8.67
## 860 9.94
## 861 8.73
## 862 9.63
## 863 6.66
## 864 8.29
## 865 8.47
## 866 8.16
## 867 8.97
## 868 7.51
## 869 8.97
## 870 8.55
## 871 5.84
## 872 7.85
## 873 8.68
## 874 8.05
## 875 8.27
## 876 7.68
## 877 9.40
## 878 7.77
## 879 6.89
## 880 7.55
## 881 8.27
## 882 8.16
## 883 8.07
## 884 7.91
## 885 7.71
## 886 10.16
## 887 8.41
## 888 8.88
## 889 9.64
## 890 7.93
## 891 7.78
## 892 8.90
## 893 8.55
## 894 9.15
## 895 10.86
## 896 11.55
## 897 9.08
## 898 7.44
## 899 10.35
## 900 6.68
## 901 8.85
## 902 8.90
## 903 8.24
## 904 6.74
## 905 10.75
## 906 8.44
## 907 7.69
## 908 4.89
## 909 11.80
## 910 8.88
## 911 7.70
## 912 8.60
## 913 8.44
## 914 9.50
## 915 9.03
## 916 7.15
## 917 7.95
## 918 8.23
## 919 9.81
## 920 8.48
## 921 9.33
## 922 8.97
## 923 8.08
## 924 7.47
## 925 8.34
## 926 7.75
## 927 8.34
## 928 5.41
## 929 7.56
## 930 6.93
## 931 10.03
## 932 8.69
## 933 9.04
## 934 8.32
## 935 7.85
## 936 7.21
## 937 8.98
## 938 7.09
## 939 8.85
## 940 9.21
## 941 8.61
## 942 7.91
## 943 7.47
## 944 8.65
## 945 8.53
## 946 9.92
## 947 8.09
## 948 5.24
## 949 7.06
## 950 8.45
## 951 8.73
## 952 7.45
## 953 9.02
## 954 7.51
## 955 7.32
## 956 8.17
## 957 9.45
## 958 9.72
## 959 9.34
## 960 8.75
## 961 9.32
## 962 7.91
## 963 7.49
## 964 6.53
## 965 6.18
## 966 8.69Identificados los outliers
#brightness.without <- brillo[-C(6,17,107,111,122,145,154,183,191,263,300,307,320,353,355,390,441,454,463,475,522,548,560,569,676,730,736,744,759,763,811,812,839,896,908,909,928,948),]#brillo%<%identify_outliers(brillo$brightness)- UScereal
UScereal## mfr calories protein fat
## 100% Bran N 212.12121 12.1212121 3.0303030
## All-Bran K 212.12121 12.1212121 3.0303030
## All-Bran with Extra Fiber K 100.00000 8.0000000 0.0000000
## Apple Cinnamon Cheerios G 146.66667 2.6666667 2.6666667
## Apple Jacks K 110.00000 2.0000000 0.0000000
## Basic 4 G 173.33333 4.0000000 2.6666667
## Bran Chex R 134.32836 2.9850746 1.4925373
## Bran Flakes P 134.32836 4.4776119 0.0000000
## Cap'n'Crunch Q 160.00000 1.3333333 2.6666667
## Cheerios G 88.00000 4.8000000 1.6000000
## Cinnamon Toast Crunch G 160.00000 1.3333333 4.0000000
## Clusters G 220.00000 6.0000000 4.0000000
## Cocoa Puffs G 110.00000 1.0000000 1.0000000
## Corn Chex R 110.00000 2.0000000 0.0000000
## Corn Flakes K 100.00000 2.0000000 0.0000000
## Corn Pops K 110.00000 1.0000000 0.0000000
## Count Chocula G 110.00000 1.0000000 1.0000000
## Cracklin' Oat Bran K 220.00000 6.0000000 6.0000000
## Crispix K 110.00000 2.0000000 0.0000000
## Crispy Wheat & Raisins G 133.33333 2.6666667 1.3333333
## Double Chex R 133.33333 2.6666667 0.0000000
## Froot Loops K 110.00000 2.0000000 1.0000000
## Frosted Flakes K 146.66667 1.3333333 0.0000000
## Frosted Mini-Wheats K 125.00000 3.7500000 0.0000000
## Fruit & Fibre: Dates Walnuts and Oats P 179.10448 4.4776119 2.9850746
## Fruitful Bran K 179.10448 4.4776119 0.0000000
## Fruity Pebbles P 146.66667 1.3333333 1.3333333
## Golden Crisp P 113.63636 2.2727273 0.0000000
## Golden Grahams G 146.66667 1.3333333 1.3333333
## Grape Nuts Flakes P 113.63636 3.4090909 1.1363636
## Grape-Nuts P 440.00000 12.0000000 0.0000000
## Great Grains Pecan P 363.63636 9.0909091 9.0909091
## Honey Graham Ohs Q 120.00000 1.0000000 2.0000000
## Honey Nut Cheerios G 146.66667 4.0000000 1.3333333
## Honey-comb P 82.70677 0.7518797 0.0000000
## Just Right Fruit & Nut K 186.66667 4.0000000 1.3333333
## Kix G 73.33333 1.3333333 0.6666667
## Life Q 149.25373 5.9701493 2.9850746
## Lucky Charms G 110.00000 2.0000000 1.0000000
## Mueslix Crispy Blend K 238.80597 4.4776119 2.9850746
## Multi-Grain Cheerios G 100.00000 2.0000000 1.0000000
## Nut&Honey Crunch K 179.10448 2.9850746 1.4925373
## Nutri-Grain Almond-Raisin K 208.95522 4.4776119 2.9850746
## Oatmeal Raisin Crisp G 260.00000 6.0000000 4.0000000
## Post Nat. Raisin Bran P 179.10448 4.4776119 1.4925373
## Product 19 K 100.00000 3.0000000 0.0000000
## Puffed Rice Q 50.00000 1.0000000 0.0000000
## Quaker Oat Squares Q 200.00000 8.0000000 2.0000000
## Raisin Bran K 160.00000 4.0000000 1.3333333
## Raisin Nut Bran G 200.00000 6.0000000 4.0000000
## Raisin Squares K 180.00000 4.0000000 0.0000000
## Rice Chex R 97.34513 0.8849558 0.0000000
## Rice Krispies K 110.00000 2.0000000 0.0000000
## Shredded Wheat 'n'Bran N 134.32836 4.4776119 0.0000000
## Shredded Wheat spoon size N 134.32836 4.4776119 0.0000000
## Smacks K 146.66667 2.6666667 1.3333333
## Special K K 110.00000 6.0000000 0.0000000
## Total Corn Flakes G 110.00000 2.0000000 1.0000000
## Total Raisin Bran G 140.00000 3.0000000 1.0000000
## Total Whole Grain G 100.00000 3.0000000 1.0000000
## Triples G 146.66667 2.6666667 1.3333333
## Trix G 110.00000 1.0000000 1.0000000
## Wheat Chex R 149.25373 4.4776119 1.4925373
## Wheaties G 100.00000 3.0000000 1.0000000
## Wheaties Honey Gold G 146.66667 2.6666667 1.3333333
## sodium fibre carbo sugars
## 100% Bran 393.93939 30.303030 15.15152 18.181818
## All-Bran 787.87879 27.272727 21.21212 15.151515
## All-Bran with Extra Fiber 280.00000 28.000000 16.00000 0.000000
## Apple Cinnamon Cheerios 240.00000 2.000000 14.00000 13.333333
## Apple Jacks 125.00000 1.000000 11.00000 14.000000
## Basic 4 280.00000 2.666667 24.00000 10.666667
## Bran Chex 298.50746 5.970149 22.38806 8.955224
## Bran Flakes 313.43284 7.462687 19.40299 7.462687
## Cap'n'Crunch 293.33333 0.000000 16.00000 16.000000
## Cheerios 232.00000 1.600000 13.60000 0.800000
## Cinnamon Toast Crunch 280.00000 0.000000 17.33333 12.000000
## Clusters 280.00000 4.000000 26.00000 14.000000
## Cocoa Puffs 180.00000 0.000000 12.00000 13.000000
## Corn Chex 280.00000 0.000000 22.00000 3.000000
## Corn Flakes 290.00000 1.000000 21.00000 2.000000
## Corn Pops 90.00000 1.000000 13.00000 12.000000
## Count Chocula 180.00000 0.000000 12.00000 13.000000
## Cracklin' Oat Bran 280.00000 8.000000 20.00000 14.000000
## Crispix 220.00000 1.000000 21.00000 3.000000
## Crispy Wheat & Raisins 186.66667 2.666667 14.66667 13.333333
## Double Chex 253.33333 1.333333 24.00000 6.666667
## Froot Loops 125.00000 1.000000 11.00000 13.000000
## Frosted Flakes 266.66667 1.333333 18.66667 14.666667
## Frosted Mini-Wheats 0.00000 3.750000 17.50000 8.750000
## Fruit & Fibre: Dates Walnuts and Oats 238.80597 7.462687 17.91045 14.925373
## Fruitful Bran 358.20896 7.462687 20.89552 17.910448
## Fruity Pebbles 180.00000 0.000000 17.33333 16.000000
## Golden Crisp 51.13636 0.000000 12.50000 17.045455
## Golden Grahams 373.33333 0.000000 20.00000 12.000000
## Grape Nuts Flakes 159.09091 3.409091 17.04545 5.681818
## Grape-Nuts 680.00000 12.000000 68.00000 12.000000
## Great Grains Pecan 227.27273 9.090909 39.39394 12.121212
## Honey Graham Ohs 220.00000 1.000000 12.00000 11.000000
## Honey Nut Cheerios 333.33333 2.000000 15.33333 13.333333
## Honey-comb 135.33835 0.000000 10.52632 8.270677
## Just Right Fruit & Nut 226.66667 2.666667 26.66667 12.000000
## Kix 173.33333 0.000000 14.00000 2.000000
## Life 223.88060 2.985075 17.91045 8.955224
## Lucky Charms 180.00000 0.000000 12.00000 12.000000
## Mueslix Crispy Blend 223.88060 4.477612 25.37313 19.402985
## Multi-Grain Cheerios 220.00000 2.000000 15.00000 6.000000
## Nut&Honey Crunch 283.58209 0.000000 22.38806 13.432836
## Nutri-Grain Almond-Raisin 328.35821 4.477612 31.34328 10.447761
## Oatmeal Raisin Crisp 340.00000 3.000000 27.00000 20.000000
## Post Nat. Raisin Bran 298.50746 8.955224 16.41791 20.895522
## Product 19 320.00000 1.000000 20.00000 3.000000
## Puffed Rice 0.00000 0.000000 13.00000 0.000000
## Quaker Oat Squares 270.00000 4.000000 28.00000 12.000000
## Raisin Bran 280.00000 6.666667 18.66667 16.000000
## Raisin Nut Bran 280.00000 5.000000 21.00000 16.000000
## Raisin Squares 0.00000 4.000000 30.00000 12.000000
## Rice Chex 212.38938 0.000000 20.35398 1.769912
## Rice Krispies 290.00000 0.000000 22.00000 3.000000
## Shredded Wheat 'n'Bran 0.00000 5.970149 28.35821 0.000000
## Shredded Wheat spoon size 0.00000 4.477612 29.85075 0.000000
## Smacks 93.33333 1.333333 12.00000 20.000000
## Special K 230.00000 1.000000 16.00000 3.000000
## Total Corn Flakes 200.00000 0.000000 21.00000 3.000000
## Total Raisin Bran 190.00000 4.000000 15.00000 14.000000
## Total Whole Grain 200.00000 3.000000 16.00000 3.000000
## Triples 333.33333 0.000000 28.00000 4.000000
## Trix 140.00000 0.000000 13.00000 12.000000
## Wheat Chex 343.28358 4.477612 25.37313 4.477612
## Wheaties 200.00000 3.000000 17.00000 3.000000
## Wheaties Honey Gold 266.66667 1.333333 21.33333 10.666667
## shelf potassium vitamins
## 100% Bran 3 848.48485 enriched
## All-Bran 3 969.69697 enriched
## All-Bran with Extra Fiber 3 660.00000 enriched
## Apple Cinnamon Cheerios 1 93.33333 enriched
## Apple Jacks 2 30.00000 enriched
## Basic 4 3 133.33333 enriched
## Bran Chex 1 186.56716 enriched
## Bran Flakes 3 283.58209 enriched
## Cap'n'Crunch 2 46.66667 enriched
## Cheerios 1 84.00000 enriched
## Cinnamon Toast Crunch 2 60.00000 enriched
## Clusters 3 210.00000 enriched
## Cocoa Puffs 2 55.00000 enriched
## Corn Chex 1 25.00000 enriched
## Corn Flakes 1 35.00000 enriched
## Corn Pops 2 20.00000 enriched
## Count Chocula 2 65.00000 enriched
## Cracklin' Oat Bran 3 320.00000 enriched
## Crispix 3 30.00000 enriched
## Crispy Wheat & Raisins 3 160.00000 enriched
## Double Chex 3 106.66667 enriched
## Froot Loops 2 30.00000 enriched
## Frosted Flakes 1 33.33333 enriched
## Frosted Mini-Wheats 2 125.00000 enriched
## Fruit & Fibre: Dates Walnuts and Oats 3 298.50746 enriched
## Fruitful Bran 3 283.58209 enriched
## Fruity Pebbles 2 33.33333 enriched
## Golden Crisp 1 45.45455 enriched
## Golden Grahams 2 60.00000 enriched
## Grape Nuts Flakes 3 96.59091 enriched
## Grape-Nuts 3 360.00000 enriched
## Great Grains Pecan 3 303.03030 enriched
## Honey Graham Ohs 2 45.00000 enriched
## Honey Nut Cheerios 1 120.00000 enriched
## Honey-comb 1 26.31579 enriched
## Just Right Fruit & Nut 3 126.66667 100%
## Kix 2 26.66667 enriched
## Life 2 141.79104 enriched
## Lucky Charms 2 55.00000 enriched
## Mueslix Crispy Blend 3 238.80597 enriched
## Multi-Grain Cheerios 1 90.00000 enriched
## Nut&Honey Crunch 2 59.70149 enriched
## Nutri-Grain Almond-Raisin 3 194.02985 enriched
## Oatmeal Raisin Crisp 3 240.00000 enriched
## Post Nat. Raisin Bran 3 388.05970 enriched
## Product 19 3 45.00000 100%
## Puffed Rice 3 15.00000 none
## Quaker Oat Squares 3 220.00000 enriched
## Raisin Bran 2 320.00000 enriched
## Raisin Nut Bran 3 280.00000 enriched
## Raisin Squares 3 220.00000 enriched
## Rice Chex 1 26.54867 enriched
## Rice Krispies 1 35.00000 enriched
## Shredded Wheat 'n'Bran 1 208.95522 none
## Shredded Wheat spoon size 1 179.10448 none
## Smacks 2 53.33333 enriched
## Special K 1 55.00000 enriched
## Total Corn Flakes 3 35.00000 100%
## Total Raisin Bran 3 230.00000 100%
## Total Whole Grain 3 110.00000 100%
## Triples 3 80.00000 enriched
## Trix 2 25.00000 enriched
## Wheat Chex 1 171.64179 enriched
## Wheaties 1 110.00000 enriched
## Wheaties Honey Gold 1 80.00000 enricheddim(UScereal)## [1] 65 11view(UScereal)class(UScereal)## [1] "data.frame"UScereal es un conjunto de datos en formato dataframe de 65 registros X 11 variables.
library(readxl)
UScerealMOD <- read_excel("C:/Users/admin/Desktop/UScerealMOD.xlsx")
View(UScerealMOD)UScerealMOD1 <- UScerealMOD[,-c(1)]UScerealMOD1 <- scale(UScerealMOD1)cor(UScerealMOD1)## mfr calories protein fat sodium
## mfr 1.00000000 0.07368375 -0.06138204 0.1394717 0.06506238
## calories 0.07368375 1.00000000 0.70601046 0.5901757 0.52865521
## protein -0.06138204 0.70601046 1.00000000 0.4112661 0.57272218
## fat 0.13947168 0.59017565 0.41126609 1.0000000 0.25956061
## sodium 0.06506238 0.52865521 0.57272218 0.2595606 1.00000000
## fibre -0.18892655 0.38821786 0.80963967 0.2260715 0.49548309
## carbo 0.02102933 0.78872268 0.54709029 0.1828522 0.42356172
## sugars 0.06457892 0.49529421 0.18484845 0.4156740 0.21124365
## shelf 0.02171155 0.42634004 0.39633111 0.3256975 0.23412749
## potassium -0.16331472 0.47659552 0.84175404 0.3232754 0.55664265
## vitamins -0.14642588 -0.19020808 -0.06820574 -0.2206739 -0.36388041
## fibre carbo sugars shelf potassium
## mfr -0.18892655 0.02102933 0.06457892 0.02171155 -0.16331472
## calories 0.38821786 0.78872268 0.49529421 0.42634004 0.47659552
## protein 0.80963967 0.54709029 0.18484845 0.39633111 0.84175404
## fat 0.22607148 0.18285220 0.41567397 0.32569752 0.32327538
## sodium 0.49548309 0.42356172 0.21124365 0.23412749 0.55664265
## fibre 1.00000000 0.20307489 0.14891577 0.35784289 0.96386621
## carbo 0.20307489 1.00000000 -0.04082599 0.26045989 0.24204848
## sugars 0.14891577 -0.04082599 1.00000000 0.29005112 0.27183347
## shelf 0.35784289 0.26045989 0.29005112 1.00000000 0.42625294
## potassium 0.96386621 0.24204848 0.27183347 0.42625294 1.00000000
## vitamins -0.05770782 0.08134011 -0.41600466 0.04355160 -0.07083895
## vitamins
## mfr -0.14642588
## calories -0.19020808
## protein -0.06820574
## fat -0.22067394
## sodium -0.36388041
## fibre -0.05770782
## carbo 0.08134011
## sugars -0.41600466
## shelf 0.04355160
## potassium -0.07083895
## vitamins 1.00000000correlac <- cor(UScerealMOD1)library(ggplot2)
library(corrplot)## corrplot 0.92 loadedcorrplot(correlac, method = "shade", shade.col = NA, tl.col = "black", tl.srt = 45)
col <- colorRampPalette(c("#BB4444", "#EE9988", "#FFFFFF", "#77AADD", "#4477AA"))Matriz de correlaciones
corrplot(correlac, method = "shade", sade.col=NA, tl.col = "black", tl.srt = 45, col=col(200), addCoef.col = "black",addcolorlabel="no", order="AOE", type = "upper", diag = F, addshade = "all" )## Warning in text.default(pos.xlabel[, 1], pos.xlabel[, 2], newcolnames, srt =
## tl.srt, : "sade.col" es un parámetro gráfico inválido## Warning in text.default(pos.xlabel[, 1], pos.xlabel[, 2], newcolnames, srt =
## tl.srt, : "addcolorlabel" es un parámetro gráfico inválido## Warning in text.default(pos.ylabel[, 1], pos.ylabel[, 2], newrownames, col =
## tl.col, : "sade.col" es un parámetro gráfico inválido## Warning in text.default(pos.ylabel[, 1], pos.ylabel[, 2], newrownames, col =
## tl.col, : "addcolorlabel" es un parámetro gráfico inválido## Warning in title(title, ...): "sade.col" es un parámetro gráfico inválido## Warning in title(title, ...): "addcolorlabel" es un parámetro gráfico inválido
Conjunto de datos de cereales para desayunos. Contiene valores numéricos relacionados con la marca de cereal, su calidad y nutrición. Se analian esas relaciones. Relaciones: En gráfico las correlaciones directas se representan en color azul. Las inversas en rojo. 0.02 i. Manufactura y Shelf: la relación entre estas variables tiende a ser directa pero es baja ii. fat y vitamins: es inversamente proporcional. 0.22 iii. fat y Shelf: es directa; 0.33 iv. carbohydrates & sugars: inversa pero baja; 0.04 v. fibre & manufacturer: es inversa y baja; 0.19 vi. sodium & sugars;relación directa al 21%
2.9. Uso del paquete pwr. Análisis de correlaciones Ejemplo: Se quiere analizar el tamaño de la muestra para un ensayo clínico relacionado con un tratamiento que queremos disponer el prpoximo año. Con un nivel de significancia de 5%, partiendo de que el efecto mínimo es de 40%. Necesitamos determinar el tamaño muestral con un poder del 90%
library(pwr)muestra <- pwr.r.test(r=0.4,
sig.level = 0.05,
power = 0.90)La relación se representa en el gráfico de poder estadístico
muestra##
## approximate correlation power calculation (arctangh transformation)
##
## n = 60.70866
## r = 0.4
## sig.level = 0.05
## power = 0.9
## alternative = two.sidedplot(muestra)
De acuerdo con el resultado de la prueba se requiere al menos 61 participantes en el ensayo clinico.
Luego de las pruebas iniciales se requiere mejorar el efecto y se proyecta a 70%. Calculamos el tamaños de la muestra para para los parámetros anteriores.
muestra1 <- pwr.r.test(r=0.7,
sig.level = 0.05,
power = 0.90)
muestra1##
## approximate correlation power calculation (arctangh transformation)
##
## n = 16.32292
## r = 0.7
## sig.level = 0.05
## power = 0.9
## alternative = two.sidedAhora se requiere un tamaño de muetsra más pequeño, pasando de 61 participantes a 17.
2.10. Ejemplo de caso de uso en la tesis. Ver archivo adjunto de nombre: “AnalisisFactorialDepresionTOPICOS”