I Ejercicio Tamaño de la muestra para estimar la
proporcion de articulos defectuosos por turno de fabricación.
(Razón de estratificación) turno de fabricación: mañana,tarde y
noche.
# Se inicia con establecer una función que tenga en cuenta una serie de parámetros.Estos son:
#vector de los estratos Ni
#vector de defectusos pi
# error del muestro (e)
#tiempo de operación en cada estrato ti
f_sam_prop = function(Ni,pi,ti,e,z=1.96 ){
N= sum(Ni) #tamano de la poblacion
L=length(Ni)#Calculo el largo del vector, este me indica cuantos estratos tengo
qi =1-pi #q = 1-p proporcion de NO defectusos
ai=Ni/N #pesos por estratos
D=(e/z)**2 #Estimación del errror
numer = sum(Ni**2*pi*qi/ai)
denom=(N**2*D) + sum(Ni*pi*qi)
n=ceiling(numer/denom) #Aqui se obtiene el tamaño de la muestra.
#Asignacion proporcional para decir cuantas muestras se va a cada estrato.
ni =round(ai*n) # tamano de muestra en cada estrato
#cada cuantos minutos se va a hacer el muestreo
ki=floor(ti/ni) # tiempo de muestreo sistemático por estrato es decir cada cuanto (unidad de tiempo:minutos) debo muestrear en cada estrato
return(list(ni,ki))
}Partiendo del ejercio realizado en clase tenemos tres turnos: mañana,tarde y noche.
#Evaluacion de la función con los datos de los tres turnos
f_sam_prop(Ni=c(6000,5000,4000),
pi=c(0.04,0.05,0.08),
ti=c(480,480,480), #tiempo de operacion en minutos
e=0.05)## [[1]]
## [1] 31 26 21
##
## [[2]]
## [1] 15 18 22# Para dos estratos (2 turnos: Diurno y nocturno)
#Diurno (mañana + tarde)
#Ni = 6000+5000 = 11000
#pi =
#ti=480+480=960
#Nocturno
#Ni = 4000
#pi =0.08
#ti=480
f_sam_prop(Ni = c(11000,4000),
pi = c(0.065,0.08),
ti=c(960,480),
e = 0.05)## [[1]]
## [1] 73 26
##
## [[2]]
## [1] 13 18#Cuando se tiene tres turno podemos evaluar la probabilidad de que el articulo defectuoso provenga de cada turno (mañana,tarde y noche)
a=2/5*0.03 +1/3*0.04+4/15*0.06
#probabilidad de provenir del turno de la manana
Pt_manana=(2/5*0.03)/a
#turno de la tarde
Pt_tarde=(1/3*0.04)/a
#turno de la noche
Pt_noche=(4/15*0.06)/a#Ahora se evalua la probabiilidad de que el articulo provenga del turno Diurno o del nocturno
Pt_diurno =((11/5*0.035)/((11/5*0.035)+(4/15*0.06)))
Pt_diurno## [1] 0.827957Pt_nocturno= ((4/15*0.06)/((11/5*0.035)+(4/15*0.06)))
Pt_nocturno## [1] 0.172043# De acuerdo a los resultados podemos concluir que la probabilidad es mayor de que el articulo defectuoso provenga del turno diurno Muestreo espacial II Ejercicio:
El este ejemplo se emplea el muestreo implementado en un cultivo de palma y se emplea la libreria de hipercubo latino
# se establece la rejilla de coordenadas
palmas = expand.grid(x = seq(0, 112, 7),
y = seq(0, 144, 9))
set.seed(123)
# Informacion auxiliar (produccion anterior)
p_racimo_u = rnorm(289, 17, 1.8) # peso promedio racimo de la última cosecha
p_racimo_p = rnorm(289, 17, 1.8) # peso promedio racimo de la penúltima cosecha
CaMg_h17 = runif(289, 1.8, 2.0) # Relacion Ca/Mg hoja 17
CaMg_s = runif(289, 1.2, 1.4) # Relacion Ca/Mg suelo
hibrid = rep(c('h1','h2'), c(144, 145))
df_datosPalma = data.frame(palmas,
p_racimo_u, p_racimo_p,
CaMg_h17, CaMg_s, hibrid)
head(df_datosPalma)## x y p_racimo_u p_racimo_p CaMg_h17 CaMg_s hibrid
## 1 0 0 15.99114 17.30252 1.813162 1.350780 h1
## 2 7 0 16.58568 19.10309 1.970874 1.212619 h1
## 3 14 0 19.80567 18.89753 1.902082 1.288496 h1
## 4 21 0 17.12692 19.06147 1.842000 1.300134 h1
## 5 28 0 17.23272 15.96056 1.875169 1.254049 h1
## 6 35 0 20.08712 20.60447 1.841473 1.319760 h1df_15palmas<-df_datosPalmalibrary(clhs)
library(ggplot2)
#Se estable el numero sugerido de palmas a evaluar
289*0.05 #Este resultado indica que se deben evaluar 15 palmas## [1] 14.45res <- clhs(df_datosPalma, size = 15, progress = FALSE, simple = TRUE)## Warning: NAs introduced by coerciondf_datosPalma[res, 'muestreo'] = 'si'
df_datosPalma$muestreo[is.na(df_datosPalma$muestreo)]='no'
length(df_datosPalma$muestreo[df_datosPalma$muestreo == "si"])## [1] 15ggplot(df_datosPalma)+aes(x,y,fill=muestreo)+geom_tile(color='white')+geom_point(data=df_datosPalma,aes(shape = factor(hibrid)), size = 1.8)+labs(shape="Hibrido", fill="Muestreo")
#Estimaciones sobre los datos:
mean(df_datosPalma$p_racimo_u) #media del peso promedio racimo de la última cosecha## [1] 17.02191mean(df_datosPalma$p_racimo_p)#peso promedio racimo de la penúltima cosecha ## [1] 17.09966#Ahora de los datos muestreados
mean(df_datosPalma$p_racimo_u[df_datosPalma$muestreo=='si'])## [1] 16.68358mean(df_datosPalma$p_racimo_u[df_datosPalma$muestreo=='no'])## [1] 17.04043Ahora se tomara un tamaño de muestra de 15
set.seed(123)
df2 <- clhs(df_15palmas, size = 15, progress = FALSE, simple = TRUE)## Warning: NAs introduced by coerciondf_15palmas[df2,'muestreo']= 'si'
df_15palmas$muestreo[is.na(df_15palmas$muestreo)]='no'
data.frame(df_15palmas)## x y p_racimo_u p_racimo_p CaMg_h17 CaMg_s hibrid muestreo
## 1 0 0 15.99114 17.30252 1.813162 1.350780 h1 no
## 2 7 0 16.58568 19.10309 1.970874 1.212619 h1 no
## 3 14 0 19.80567 18.89753 1.902082 1.288496 h1 no
## 4 21 0 17.12692 19.06147 1.842000 1.300134 h1 no
## 5 28 0 17.23272 15.96056 1.875169 1.254049 h1 no
## 6 35 0 20.08712 20.60447 1.841473 1.319760 h1 no
## 7 42 0 17.82965 17.12006 1.891848 1.347168 h1 no
## 8 49 0 14.72289 20.36033 1.848300 1.342716 h1 no
## 9 56 0 15.76366 14.56838 1.823738 1.223299 h1 si
## 10 63 0 16.19781 17.03777 1.814494 1.315024 h1 no
## 11 70 0 19.20335 19.24985 1.938198 1.359626 h1 no
## 12 77 0 17.64766 15.71256 1.857392 1.246365 h1 no
## 13 84 0 17.72139 15.64516 1.829882 1.344971 h1 no
## 14 91 0 17.19923 15.31063 1.994486 1.367605 h1 no
## 15 98 0 15.99949 15.10548 1.882103 1.223768 h1 no
## 16 105 0 20.21644 16.21311 1.880363 1.391790 h1 no
## 17 112 0 17.89613 17.59612 1.929711 1.349553 h1 no
## 18 0 9 13.46009 13.37442 1.983261 1.362178 h1 no
## 19 7 9 18.26244 17.38156 1.843332 1.237984 h1 no
## 20 14 9 16.14898 19.22602 1.909600 1.205194 h1 no
## 21 21 9 15.07792 20.66763 1.944010 1.372756 h1 no
## 22 28 9 16.60765 19.34212 1.879449 1.383836 h1 no
## 23 35 9 15.15319 18.36219 1.818800 1.227526 h1 no
## 24 42 9 15.68800 13.89189 1.926979 1.295530 h1 no
## 25 49 9 15.87493 15.91729 1.800496 1.292592 h1 no
## 26 56 9 13.96395 16.36632 1.842105 1.340036 h1 no
## 27 63 9 18.50802 18.26634 1.935805 1.213115 h1 no
## 28 70 9 17.27607 16.80979 1.973238 1.346608 h1 no
## 29 77 9 14.95135 14.73443 1.959939 1.279186 h1 no
## 30 84 9 19.25687 20.03198 1.826999 1.292298 h1 no
## 31 91 9 17.76764 18.64050 1.877500 1.345060 h1 no
## 32 98 9 16.46887 17.42737 1.957694 1.294001 h1 no
## 33 105 9 18.61123 19.19260 1.938583 1.399905 h1 no
## 34 112 9 18.58064 14.59021 1.918855 1.369187 h1 no
## 35 0 18 18.47885 18.18948 1.824772 1.365641 h1 no
## 36 7 18 18.23955 16.05876 1.911016 1.352329 h1 no
## 37 14 18 17.99705 18.23074 1.889882 1.222421 h1 no
## 38 21 18 16.88856 16.89052 1.953233 1.367795 h1 no
## 39 28 18 16.44927 18.13933 1.805220 1.362770 h1 no
## 40 35 18 16.31515 19.40393 1.839034 1.398282 h1 si
## 41 42 18 15.74953 17.01312 1.976247 1.209663 h1 no
## 42 49 18 16.62575 18.83161 1.821960 1.349358 h1 no
## 43 56 18 14.72229 14.86082 1.993710 1.283863 h1 no
## 44 63 18 20.90412 15.70111 1.877040 1.316429 h1 no
## 45 70 18 19.17433 19.73459 1.971718 1.366147 h1 no
## 46 77 18 14.97840 17.67930 1.977477 1.202706 h1 no
## 47 84 18 16.27481 13.30600 1.897818 1.202304 h1 no
## 48 91 18 16.16002 14.54473 1.943618 1.399289 h1 no
## 49 98 18 18.40394 16.63859 1.897341 1.353138 h1 no
## 50 105 18 16.84994 18.55840 1.997742 1.262933 h1 no
## 51 112 18 17.45597 16.81661 1.812950 1.362402 h1 no
## 52 0 27 16.94862 18.12354 1.831533 1.398244 h1 no
## 53 7 27 16.92283 18.72621 1.957070 1.240152 h1 no
## 54 14 27 19.46348 20.00790 1.908438 1.329207 h1 no
## 55 21 27 16.59361 17.10083 1.883309 1.287510 h1 no
## 56 28 27 19.72965 16.90643 1.999777 1.387606 h1 no
## 57 35 27 14.21224 13.84417 1.851135 1.397601 h1 no
## 58 42 27 18.05230 17.17879 1.901575 1.291264 h1 no
## 59 49 27 17.22294 15.97067 1.815794 1.246123 h1 no
## 60 56 27 17.38869 15.24678 1.962734 1.339098 h1 no
## 61 63 27 17.68335 16.67617 1.876436 1.311326 h1 no
## 62 70 27 16.09582 18.82690 1.960437 1.316942 h1 si
## 63 77 27 16.40023 13.41305 1.839585 1.286726 h1 no
## 64 84 27 15.16656 16.23090 1.989280 1.285235 h1 no
## 65 91 27 15.07078 17.20995 1.869134 1.319371 h1 no
## 66 98 27 17.54635 15.39223 1.904410 1.290417 h1 no
## 67 105 27 17.80678 17.60103 1.822338 1.391322 h1 no
## 68 112 27 17.09541 17.74057 1.977195 1.368886 h1 no
## 69 0 36 18.66008 16.94053 1.990851 1.244554 h1 no
## 70 7 36 20.69015 12.56138 1.808076 1.284034 h1 no
## 71 14 36 16.11614 21.62862 1.898723 1.283997 h1 no
## 72 21 36 12.84350 16.63046 1.845213 1.204158 h1 no
## 73 28 36 18.81033 18.17215 1.971760 1.276090 h1 no
## 74 35 36 15.72344 17.49278 1.906187 1.284470 h1 no
## 75 42 36 15.76158 18.84441 1.800928 1.238285 h1 no
## 76 49 36 18.84603 18.47179 1.855512 1.341066 h1 si
## 77 56 36 16.48741 16.62237 1.865041 1.202704 h1 no
## 78 63 36 14.80271 17.68070 1.917741 1.346241 h1 no
## 79 70 36 17.32635 15.29826 1.849937 1.286545 h1 no
## 80 77 36 16.75000 18.54246 1.808623 1.346248 h1 no
## 81 84 36 17.01038 16.17013 1.822136 1.202788 h1 no
## 82 91 36 17.69350 21.35019 1.940751 1.209063 h1 no
## 83 98 36 16.33281 14.02811 1.987804 1.283683 h1 no
## 84 105 36 18.15988 16.16482 1.862234 1.328768 h1 no
## 85 112 36 16.60312 18.48568 1.815699 1.243959 h1 no
## 86 0 45 17.59721 17.91824 1.864349 1.366689 h1 no
## 87 7 45 18.97431 15.93893 1.924981 1.338953 h1 no
## 88 14 45 17.78333 15.20579 1.888048 1.379825 h1 no
## 89 21 45 16.41332 17.26006 1.960269 1.282848 h1 no
## 90 28 45 19.06785 16.97425 1.855857 1.282201 h1 no
## 91 35 45 18.78831 13.77749 1.914143 1.250003 h1 no
## 92 42 45 17.98711 17.06219 1.808426 1.274753 h1 no
## 93 49 45 17.42972 17.34241 1.838143 1.246379 h1 no
## 94 56 45 15.86977 17.31451 1.945417 1.386321 h1 no
## 95 63 45 19.44917 15.10097 1.965338 1.295269 h1 no
## 96 70 45 15.91953 17.85704 1.902144 1.209533 h1 no
## 97 77 45 20.93720 19.48143 1.913545 1.354706 h1 no
## 98 84 45 19.75870 17.82123 1.800231 1.266223 h1 no
## 99 91 45 16.57574 14.95594 1.828756 1.310575 h1 no
## 100 98 45 15.15244 16.21584 1.973193 1.335266 h1 no
## 101 105 45 15.72127 17.62299 1.816512 1.255467 h1 no
## 102 112 45 17.46239 15.83532 1.848914 1.210571 h1 no
## 103 0 54 16.55595 13.11624 1.996309 1.285152 h1 no
## 104 7 54 16.37442 18.59165 1.915516 1.322024 h1 no
## 105 14 54 15.28709 15.50694 1.849745 1.395209 h1 no
## 106 21 54 16.91895 15.96759 1.922991 1.393960 h1 no
## 107 28 54 15.58717 19.70702 1.806351 1.217697 h1 no
## 108 35 54 13.99770 15.60654 1.829285 1.299676 h1 no
## 109 42 54 16.31559 18.52232 1.940615 1.279138 h1 no
## 110 49 54 18.65419 14.73077 1.813112 1.278066 h1 no
## 111 56 54 15.96438 16.36182 1.924361 1.263700 h1 no
## 112 63 54 18.09434 16.86760 1.987465 1.336515 h1 no
## 113 70 54 14.08781 14.89643 1.817579 1.256923 h1 no
## 114 77 54 16.89999 15.85745 1.994561 1.278019 h1 no
## 115 84 54 17.93493 16.94809 1.805202 1.398034 h1 no
## 116 91 54 17.54208 18.20725 1.945046 1.358401 h1 no
## 117 98 54 17.19022 14.02902 1.890741 1.262481 h1 no
## 118 105 54 15.84673 16.37044 1.913236 1.363424 h1 si
## 119 112 54 15.47053 18.36153 1.974546 1.244182 h1 no
## 120 0 63 15.15657 16.03014 1.910913 1.300215 h1 no
## 121 7 63 17.21176 17.40913 1.947530 1.233394 h1 no
## 122 14 63 15.29455 17.88601 1.924626 1.223932 h1 no
## 123 21 63 16.11700 17.48210 1.862206 1.379522 h1 no
## 124 28 63 16.53903 18.17586 1.878439 1.218743 h1 si
## 125 35 63 20.31895 16.77912 1.840417 1.373012 h1 no
## 126 42 63 15.82649 16.25538 1.970556 1.255318 h1 no
## 127 49 63 17.42370 12.24233 1.921366 1.205457 h1 no
## 128 56 63 17.14033 16.83271 1.951220 1.357477 h1 no
## 129 63 63 15.26866 17.77451 1.912503 1.271732 h1 no
## 130 70 63 16.87165 17.96372 1.855180 1.205021 h1 no
## 131 77 63 19.60019 16.00050 1.947112 1.349219 h1 no
## 132 84 63 17.81271 20.20311 1.909856 1.205925 h1 no
## 133 91 63 17.07422 17.51556 1.869225 1.286773 h1 no
## 134 98 63 16.23951 17.22737 1.902993 1.251325 h1 no
## 135 105 63 13.30416 19.29008 1.963154 1.299603 h1 no
## 136 112 63 19.03641 15.70676 1.905255 1.330671 h1 no
## 137 0 72 14.37085 16.18939 1.840608 1.216731 h1 no
## 138 7 72 18.33191 21.31541 1.969622 1.236094 h1 no
## 139 14 72 20.43639 17.02003 1.874099 1.220893 h1 no
## 140 21 72 14.40099 19.94042 1.860665 1.362479 h1 no
## 141 28 72 18.26321 14.41069 1.954119 1.325500 h1 no
## 142 35 72 16.52804 16.65707 1.946690 1.326114 h1 no
## 143 42 72 14.17014 17.68116 1.967781 1.278178 h1 si
## 144 49 72 14.27360 17.54007 1.913810 1.286451 h1 no
## 145 56 72 14.11723 15.18985 1.805256 1.269727 h2 no
## 146 63 72 16.04437 17.03467 1.897225 1.358712 h2 no
## 147 70 72 14.36884 15.06064 1.908536 1.377387 h2 no
## 148 77 72 18.23825 18.28287 1.967332 1.330225 h2 no
## 149 84 72 20.78020 18.95260 1.945733 1.318573 h2 no
## 150 91 72 14.68335 12.99502 1.911023 1.265564 h2 no
## 151 98 72 18.41793 19.22425 1.814683 1.215138 h2 no
## 152 105 72 18.38428 14.76612 1.827300 1.318277 h2 no
## 153 112 72 17.59796 17.81858 1.936922 1.235667 h2 no
## 154 0 81 15.18492 18.18782 1.819924 1.348270 h2 no
## 155 7 81 16.78499 16.64020 1.840757 1.258775 h2 no
## 156 14 81 16.49529 15.83879 1.873196 1.327837 h2 no
## 157 21 81 18.01338 17.29758 1.969239 1.224964 h2 no
## 158 28 81 16.32961 17.78987 1.847375 1.251053 h2 no
## 159 35 81 18.75855 18.58995 1.940976 1.364115 h2 no
## 160 42 81 16.32575 13.30579 1.821522 1.360756 h2 no
## 161 49 81 18.89488 14.05452 1.955814 1.209167 h2 no
## 162 56 81 15.11148 19.57472 1.851021 1.375106 h2 no
## 163 63 81 14.73172 18.88393 1.909609 1.225602 h2 no
## 164 70 81 22.83387 17.78352 1.806780 1.321766 h2 no
## 165 77 81 16.24966 18.28732 1.961222 1.293913 h2 no
## 166 84 81 17.53681 18.65091 1.867551 1.311458 h2 no
## 167 91 81 18.14583 12.21034 1.983257 1.346079 h2 no
## 168 98 81 16.12919 18.99850 1.871421 1.293972 h2 no
## 169 105 81 17.93035 16.12702 1.995073 1.397722 h2 no
## 170 112 81 17.66414 17.41511 1.876876 1.396934 h2 no
## 171 0 90 16.61232 16.46872 1.897735 1.278657 h2 no
## 172 7 90 17.11753 18.56954 1.898155 1.321769 h2 no
## 173 14 90 16.93868 16.37275 1.802451 1.269097 h2 no
## 174 21 90 20.83121 17.93331 1.928295 1.287423 h2 no
## 175 28 90 15.66560 16.29677 1.902515 1.245826 h2 no
## 176 35 90 15.02721 15.03298 1.864070 1.201214 h2 no
## 177 42 90 17.06802 19.17802 1.916287 1.204775 h2 no
## 178 49 90 17.55887 18.33362 1.923469 1.211752 h2 no
## 179 56 90 17.78574 20.10367 1.887655 1.352915 h2 no
## 180 63 90 16.17494 17.11728 1.834074 1.293809 h2 no
## 181 70 90 15.08601 19.02500 1.943016 1.370166 h2 no
## 182 77 90 19.27373 20.55575 1.949557 1.316344 h2 no
## 183 84 90 16.37063 16.49333 1.968783 1.237378 h2 no
## 184 91 90 15.44208 14.61869 1.906952 1.321812 h2 no
## 185 98 90 16.57470 16.56917 1.860447 1.378668 h2 no
## 186 105 90 16.64508 16.61473 1.891304 1.358864 h2 no
## 187 112 90 18.99786 17.27302 1.876869 1.300819 h2 si
## 188 0 99 17.15253 20.08215 1.849809 1.284606 h2 no
## 189 7 99 18.35730 16.41294 1.930925 1.330876 h2 no
## 190 14 99 16.10127 17.67141 1.991065 1.377548 h2 no
## 191 21 99 17.38600 16.59017 1.858217 1.378403 h2 no
## 192 28 99 16.41557 17.03681 1.930038 1.235678 h2 no
## 193 35 99 17.17025 17.56530 1.907272 1.251500 h2 no
## 194 42 99 15.38835 19.39079 1.919490 1.222688 h2 no
## 195 49 99 14.64056 17.21837 1.804980 1.359432 h2 no
## 196 56 99 20.59498 18.28312 1.800968 1.221908 h2 no
## 197 63 99 18.08128 18.40195 1.826276 1.365814 h2 no
## 198 70 99 14.74771 18.64659 1.804963 1.354209 h2 no
## 199 77 99 15.89990 15.96609 1.818426 1.388738 h2 no
## 200 84 99 14.86614 19.92839 1.895112 1.293392 h2 si
## 201 91 99 20.95786 16.31428 1.839331 1.387971 h2 no
## 202 98 99 19.36234 16.80959 1.962935 1.357037 h2 no
## 203 105 99 16.52274 19.52729 1.848812 1.374922 h2 no
## 204 112 99 17.97775 19.32935 1.897762 1.278893 h2 no
## 205 0 108 16.25419 15.03801 1.929776 1.219940 h2 no
## 206 7 108 16.14276 15.42847 1.831743 1.255392 h2 no
## 207 14 108 15.58051 14.55546 1.967396 1.344444 h2 no
## 208 21 108 15.92969 17.32732 1.887319 1.271261 h2 no
## 209 28 108 19.97163 17.29671 1.846699 1.276542 h2 no
## 210 35 108 16.90275 17.65541 1.859771 1.206394 h2 no
## 211 42 108 17.21464 17.99388 1.831884 1.304289 h2 no
## 212 49 108 17.43864 15.91659 1.917113 1.224216 h2 no
## 213 56 108 19.21846 15.21134 1.829756 1.377037 h2 no
## 214 63 108 16.07109 18.84821 1.835812 1.214929 h2 no
## 215 70 108 15.21349 18.35191 1.867844 1.356846 h2 no
## 216 77 108 20.01625 14.28350 1.836780 1.370826 h2 no
## 217 84 108 16.20591 16.82873 1.881108 1.207436 h2 no
## 218 91 108 15.69848 15.38729 1.922712 1.255029 h2 si
## 219 98 108 14.77471 13.27265 1.937134 1.328414 h2 no
## 220 105 108 14.68751 17.27022 1.964705 1.357016 h2 no
## 221 112 108 15.96685 16.85742 1.874796 1.280013 h2 no
## 222 0 117 18.11237 16.82474 1.990568 1.387060 h2 si
## 223 7 117 18.99773 17.38907 1.803766 1.278103 h2 no
## 224 14 117 18.27366 18.58844 1.922475 1.284792 h2 no
## 225 21 117 16.34542 17.37008 1.892715 1.257284 h2 no
## 226 28 117 17.10755 15.89042 1.870946 1.322350 h2 no
## 227 35 117 15.73173 15.67736 1.976485 1.311763 h2 no
## 228 42 117 15.70901 16.76275 1.815161 1.207993 h2 no
## 229 49 117 18.59237 17.55803 1.976658 1.349056 h2 no
## 230 56 117 15.17193 15.12858 1.837644 1.241297 h2 no
## 231 63 117 20.51953 16.66824 1.843013 1.331299 h2 no
## 232 70 117 16.83742 18.74108 1.973906 1.304483 h2 no
## 233 77 117 17.38617 16.80510 1.812168 1.326687 h2 si
## 234 84 117 15.67065 15.74284 1.846980 1.323513 h2 no
## 235 91 117 15.96610 16.50330 1.998603 1.256979 h2 no
## 236 98 117 14.62937 19.00637 1.935640 1.244761 h2 no
## 237 105 117 16.67073 17.99008 1.887097 1.307804 h2 no
## 238 112 117 17.75417 19.22602 1.875462 1.399274 h2 no
## 239 0 126 17.58375 17.25038 1.892237 1.265783 h2 no
## 240 7 126 15.59323 17.73850 1.960370 1.264667 h2 no
## 241 14 126 15.58048 15.99478 1.932593 1.207065 h2 si
## 242 21 126 16.09604 18.08967 1.893201 1.305122 h2 no
## 243 28 126 19.69291 16.08860 1.810652 1.319361 h2 no
## 244 35 126 14.95285 14.44298 1.847440 1.348417 h2 no
## 245 42 126 16.67771 17.23039 1.846648 1.390567 h2 no
## 246 49 126 20.42425 20.50253 1.846131 1.297924 h2 no
## 247 56 126 16.81825 18.44165 1.812345 1.216657 h2 no
## 248 63 126 14.55229 19.09746 1.899424 1.251986 h2 no
## 249 70 126 15.80342 17.64594 1.848825 1.374969 h2 no
## 250 77 126 17.87383 15.90460 1.951637 1.370412 h2 no
## 251 84 126 16.32391 16.63597 1.909461 1.368581 h2 no
## 252 91 126 15.98862 16.50815 1.989613 1.389381 h2 no
## 253 98 126 16.38095 16.15634 1.817234 1.330536 h2 no
## 254 105 126 17.16289 18.26750 1.989577 1.297631 h2 no
## 255 112 126 19.87732 14.84475 1.944480 1.237551 h2 no
## 256 0 135 16.84058 18.55946 1.984401 1.342607 h2 no
## 257 7 135 18.94544 18.55547 1.922768 1.217014 h2 no
## 258 14 135 18.13536 14.84248 1.832193 1.292240 h2 no
## 259 21 135 16.79545 18.15109 1.836588 1.387181 h2 no
## 260 28 135 14.24078 21.37441 1.946037 1.369059 h2 no
## 261 35 135 16.06199 15.99701 1.917905 1.343237 h2 no
## 262 42 135 16.11823 18.52083 1.862496 1.224761 h2 no
## 263 49 135 17.08488 15.59204 1.954559 1.318965 h2 no
## 264 56 135 19.34036 18.99928 1.867260 1.397971 h2 no
## 265 63 135 21.12754 17.44968 1.971135 1.323082 h2 si
## 266 70 135 19.78565 19.97345 1.916106 1.254616 h2 no
## 267 77 135 16.76033 14.37385 1.883145 1.212741 h2 no
## 268 84 135 13.83825 16.90766 1.986515 1.214461 h2 no
## 269 91 135 16.30020 16.05153 1.892540 1.374702 h2 no
## 270 98 135 17.16057 16.64492 1.819869 1.272565 h2 si
## 271 105 135 18.52102 15.86676 1.893090 1.389124 h2 no
## 272 112 135 18.73255 15.49908 1.996817 1.311664 h2 no
## 273 0 144 18.23176 18.04170 1.985055 1.302532 h2 no
## 274 7 144 14.48851 15.04235 1.887080 1.389554 h2 no
## 275 14 144 18.52936 19.67126 1.973944 1.221960 h2 no
## 276 21 144 16.19620 14.86483 1.860071 1.363782 h2 no
## 277 28 144 17.31464 17.18194 1.959595 1.275468 h2 no
## 278 35 144 17.13419 17.95938 1.960102 1.311328 h2 no
## 279 42 144 17.77070 18.05612 1.877385 1.262774 h2 no
## 280 49 144 17.04441 16.45686 1.876318 1.216469 h2 no
## 281 56 144 13.99854 17.14310 1.929103 1.355988 h2 no
## 282 63 144 18.32569 18.73028 1.972333 1.238647 h2 no
## 283 70 144 17.69485 14.37836 1.931327 1.331117 h2 no
## 284 77 144 16.52183 15.59287 1.903265 1.287029 h2 no
## 285 84 144 17.21266 17.57672 1.829756 1.239430 h2 no
## 286 91 144 17.24127 16.19939 1.922553 1.398932 h2 no
## 287 98 144 17.39784 19.46601 1.808393 1.289349 h2 no
## 288 105 144 19.95352 18.21186 1.899672 1.206285 h2 no
## 289 112 144 16.60571 17.12990 1.947902 1.387403 h2 nolength(df_15palmas$muestreo[df_15palmas$muestreo == "si"])## [1] 15length(df2)## [1] 15library(ggplot2)
ggplot(df_15palmas)+ aes(x,y,fill=muestreo)+ geom_tile(color='white')+ geom_point(data=df_15palmas,aes(shape = factor(hibrid)), size = 1.8)+labs(shape="Hibrido", fill="Muestreo")
mean(df_15palmas$p_racimo_u)## [1] 17.02191mean(df_15palmas$p_racimo_p)## [1] 17.09966#datos muestreados
mean(df_15palmas$p_racimo_u[df_15palmas$muestreo=='si'])## [1] 16.83375mean(df_15palmas$p_racimo_p[df_15palmas$muestreo=='no'])## [1] 17.08757#datos inciales por hibrido
mean(df_15palmas$p_racimo_u[df_15palmas$hibrido =='h1'])## [1] NaNmean(df_15palmas$p_racimo_pe[df_15palmas$hibrido =='h2'])## Warning in mean.default(df_15palmas$p_racimo_pe[df_15palmas$hibrido == "h2"]):
## argument is not numeric or logical: returning NA## [1] NA#datos muestreados por hibrido
mean(df_15palmas$p_racimo_u[df_15palmas$muestreo=='si' & df_15palmas$hibrid=='h1'])## [1] 16.22522mean(df_15palmas$p_racimo_u[df_15palmas$muestreo=='si' & df_15palmas$hibrid=='h2'])## [1] 17.3662