Estimación de una variable:
Según lo que previamente conoce el investigador se toma una medida que puede ser similar a la tomada con el instrumento.
\(Altura^2 = Vmas^2- Vmenos^2\)
Siempre y cuando las vistas menos estén en negativo.
Considerando que las parcelas miden 0.04 ha y que son cuatro bosques
# Cargando librerias
library(tidyverse)
library(ggpubr)
library(dplyr)
# Leyendo base de datos
datos <- read.csv2("Datos_taller1.csv")
Se calcula el DAP
datos <- datos %>%
mutate(DAP_cm=CAP_cm/pi)
Histogramas por bosque
# Bosque 1
bosque1 <- datos %>%
subset(datos$Bosque == "A")
# Bosque 2
bosque2 <- datos %>%
subset(datos$Bosque == "B")
# Bosque 3
bosque3 <- datos %>%
subset(datos$Bosque == "C")
# Bosque 4
bosque4 <- datos %>%
subset(datos$Bosque == "D")
# Se saca el rango de los datos del DAP para cada bosque
range(bosque1$DAP_cm)
## [1] 12.6369 39.8524
range(bosque2$DAP_cm)
## [1] 10.00130 74.42085
range(bosque3$DAP_cm)
## [1] 12.35042 54.43099
range(bosque4$DAP_cm)
## [1] 10.00130 73.43409
# En la función `breaks()` va el valor mínimo, máximo y cada cuanto quiero el intervalo
par(mfrow = c(2,2))
hist1 <- hist(bosque1$DAP_cm, breaks = seq(10,40,10))
hist2 <- hist(bosque2$DAP_cm, breaks = seq (10,80,10))
hist3 <- hist(bosque3$DAP_cm, breaks = seq(10,60,10))
hist4 <- hist(bosque4$DAP_cm, breaks = seq(10,80,10))
El pimer bosque tiene individuos de 10 a 40 cm, siendo los de 40 cm en muy poca cantidad por lo cúal es posible que es un bosque muy joven y probablemente una plantación por que todos los individuos están creciendo al mismo tiempo.
En cuanto al bosque 2 y 4 se pude observr que presenta mucha similitud en cuanto a la distribución de sus individuos, los cuales varían mucho en sus DAP, se podría suponer que son bosques con más edad, los cuales presentan más estratos. El bosque 3 también presenta esta variación pero con la diferencia que tiene individuos más pequeños y en menor cantidad, se puede suponer que es un bosque con menos edad que el 2 y 4.
# Se debe de sacar la relación primero para saber cuantas parcelas de 0,04 ha me caben en una parcela
1/0.04 # En una Ha caben 25 parcelas de 0,04 Ha cada una
## [1] 25
# Creando la variable `plot` para que las parcelas se relacionen con cada tipo de bosque, el segundo argumento es como quiero que lo separe, para este caso es con "_"
datos$plot <- paste0(datos$Bosque, "_", datos$Parcela)
# Individuos por parcela
ind_parcela <- table(datos$Parcela)
ind_parcela
##
## P_1 P_10 P_11 P_12 P_2 P_3 P_4 P_5 P_6 P_7 P_8 P_9
## 125 84 105 104 104 110 101 97 110 105 84 112
# Primero se deben de sacar los individuos por parcela para despues saber cuantos hay por Ha
ind_Ha <- ind_parcela*25
ind_Ha
##
## P_1 P_10 P_11 P_12 P_2 P_3 P_4 P_5 P_6 P_7 P_8 P_9
## 3125 2100 2625 2600 2600 2750 2525 2425 2750 2625 2100 2800
# Individuos por Ha por bosque
ind_Ha_bosq <- as.vector(table(datos$plot)*25)
ind_Ha_bosq
## [1] 675 575 500 500 625 625 575 700 650 625 450 675 925 525
## [15] 675 550 775 825 775 450 650 800 725 650 575 525 450 350
## [29] 400 575 500 550 625 425 375 625 950 475 1000 1200 800 725
## [43] 675 725 825 775 550 850
# Primero se debe hallar el área basal
datos <- datos %>%
mutate(AB_cm = (pi/4)*DAP_cm^2)
# Como el diámetro cuadrático medio se da en metros cuadrados,y el área basal se calculó en cm cuadrados, se debe hacer la conversión.
datos$AB_m <- datos$AB_cm/10000
# El área basal se debe de hallar por Ha
AB_ha <- tapply(datos$AB_m,datos$plot, sum)*25 # el tapply aplica una operación (párametro 3) a cada celda (párametro 1)y las agrupa según el parametro que queramos (párametro 2)
AB_ha
## A_P_1 A_P_10 A_P_11 A_P_12 A_P_2 A_P_3 A_P_4
## 29.728592 26.754813 20.511636 20.796126 27.407635 28.067211 27.388328
## A_P_5 A_P_6 A_P_7 A_P_8 A_P_9 B_P_1 B_P_10
## 28.882202 30.788526 30.398616 20.449341 26.898882 22.850305 35.605977
## B_P_11 B_P_12 B_P_2 B_P_3 B_P_4 B_P_5 B_P_6
## 22.965457 29.735260 32.634759 35.218447 34.619111 21.367617 24.964340
## B_P_7 B_P_8 B_P_9 C_P_1 C_P_10 C_P_11 C_P_12
## 34.885157 33.177871 32.814089 25.099530 19.159572 15.237403 11.518746
## C_P_2 C_P_3 C_P_4 C_P_5 C_P_6 C_P_7 C_P_8
## 18.519664 20.099435 24.450107 30.164663 23.078095 20.808078 16.485006
## C_P_9 D_P_1 D_P_10 D_P_11 D_P_12 D_P_2 D_P_3
## 34.799570 41.359433 8.031261 28.291876 28.713869 27.568512 37.955623
## D_P_4 D_P_5 D_P_6 D_P_7 D_P_8 D_P_9
## 14.337702 19.754518 41.644999 24.853875 23.823555 19.951483
library(knitr)
# Se aplica la fórmula de el diámetro cuadrático medio
dqm <- sqrt((40000/pi)*AB_ha/ind_Ha_bosq)
dqm
## A_P_1 A_P_10 A_P_11 A_P_12 A_P_2 A_P_3 A_P_4 A_P_5
## 23.68047 24.34009 22.85442 23.01237 23.62930 23.91193 24.62657 22.92035
## A_P_6 A_P_7 A_P_8 A_P_9 B_P_1 B_P_10 B_P_11 B_P_12
## 24.55799 24.88525 24.05406 22.52528 17.73496 29.38576 20.81329 26.23672
## B_P_2 B_P_3 B_P_4 B_P_5 B_P_6 B_P_7 B_P_8 B_P_9
## 23.15498 23.31381 23.84856 24.58821 22.11355 23.56299 24.13850 25.35295
## C_P_1 C_P_10 C_P_11 C_P_12 C_P_2 C_P_3 C_P_4 C_P_5
## 23.57512 21.55601 20.76368 20.47027 24.27960 21.09663 24.95229 26.42548
## C_P_6 C_P_7 C_P_8 C_P_9 D_P_1 D_P_10 D_P_11 D_P_12
## 21.68278 24.96759 23.65832 26.62576 23.54402 14.67236 18.97955 17.45462
## D_P_2 D_P_3 D_P_4 D_P_5 D_P_6 D_P_7 D_P_8 D_P_9
## 20.94675 25.81809 16.44534 18.62599 25.35184 20.20697 23.48427 17.28754
resultados <- data.frame(ind_Ha_bosq, AB_ha, dqm) %>%
kable()
resultados
ind_Ha_bosq | AB_ha | dqm | |
---|---|---|---|
A_P_1 | 675 | 29.728592 | 23.68047 |
A_P_10 | 575 | 26.754813 | 24.34009 |
A_P_11 | 500 | 20.511636 | 22.85442 |
A_P_12 | 500 | 20.796126 | 23.01237 |
A_P_2 | 625 | 27.407635 | 23.62930 |
A_P_3 | 625 | 28.067211 | 23.91193 |
A_P_4 | 575 | 27.388328 | 24.62657 |
A_P_5 | 700 | 28.882202 | 22.92035 |
A_P_6 | 650 | 30.788526 | 24.55799 |
A_P_7 | 625 | 30.398616 | 24.88525 |
A_P_8 | 450 | 20.449341 | 24.05406 |
A_P_9 | 675 | 26.898882 | 22.52528 |
B_P_1 | 925 | 22.850305 | 17.73496 |
B_P_10 | 525 | 35.605977 | 29.38576 |
B_P_11 | 675 | 22.965457 | 20.81329 |
B_P_12 | 550 | 29.735260 | 26.23672 |
B_P_2 | 775 | 32.634759 | 23.15498 |
B_P_3 | 825 | 35.218447 | 23.31381 |
B_P_4 | 775 | 34.619111 | 23.84856 |
B_P_5 | 450 | 21.367617 | 24.58821 |
B_P_6 | 650 | 24.964340 | 22.11355 |
B_P_7 | 800 | 34.885157 | 23.56299 |
B_P_8 | 725 | 33.177871 | 24.13850 |
B_P_9 | 650 | 32.814089 | 25.35295 |
C_P_1 | 575 | 25.099530 | 23.57512 |
C_P_10 | 525 | 19.159572 | 21.55601 |
C_P_11 | 450 | 15.237403 | 20.76368 |
C_P_12 | 350 | 11.518746 | 20.47027 |
C_P_2 | 400 | 18.519664 | 24.27960 |
C_P_3 | 575 | 20.099435 | 21.09663 |
C_P_4 | 500 | 24.450107 | 24.95229 |
C_P_5 | 550 | 30.164663 | 26.42548 |
C_P_6 | 625 | 23.078095 | 21.68278 |
C_P_7 | 425 | 20.808078 | 24.96759 |
C_P_8 | 375 | 16.485006 | 23.65832 |
C_P_9 | 625 | 34.799570 | 26.62576 |
D_P_1 | 950 | 41.359433 | 23.54402 |
D_P_10 | 475 | 8.031261 | 14.67236 |
D_P_11 | 1000 | 28.291876 | 18.97955 |
D_P_12 | 1200 | 28.713869 | 17.45462 |
D_P_2 | 800 | 27.568512 | 20.94675 |
D_P_3 | 725 | 37.955623 | 25.81809 |
D_P_4 | 675 | 14.337702 | 16.44534 |
D_P_5 | 725 | 19.754518 | 18.62599 |
D_P_6 | 825 | 41.644999 | 25.35184 |
D_P_7 | 775 | 24.853875 | 20.20697 |
D_P_8 | 550 | 23.823555 | 23.48427 |
D_P_9 | 850 | 19.951483 | 17.28754 |
ind_bos_mean <- apply(table(datos$Bosque, datos$Parcela), 1, mean)*25
ind_bos_mean
## A B C D
## 597.9167 693.7500 497.9167 795.8333
ind_bos_sd <- apply(table(datos$Bosque, datos$Parcela), 1, sd)*25
ind_bos_sd
## A B C D
## 79.38566 137.80957 96.21138 195.64385
AB_bosq_mean <- apply(tapply(datos$AB_m, list(datos$Bosque, datos$Parcela), sum), 1, mean)*25
AB_bosq_mean
## A B C D
## 26.50599 30.06987 21.61832 26.35723
AB_bosq_sd <- apply(tapply(datos$AB_m, list(datos$Bosque, datos$Parcela), sum), 1, sd)*25
AB_bosq_sd
## A B C D
## 3.801760 5.471500 6.443694 10.347102
#ANOVA para comparar tipos de bosque
anova <- table(datos$Bosque, datos$Parcela)
anova
##
## P_1 P_10 P_11 P_12 P_2 P_3 P_4 P_5 P_6 P_7 P_8 P_9
## A 27 23 20 20 25 25 23 28 26 25 18 27
## B 37 21 27 22 31 33 31 18 26 32 29 26
## C 23 21 18 14 16 23 20 22 25 17 15 25
## D 38 19 40 48 32 29 27 29 33 31 22 34
# se realizan dos vectores para correr el anova
valor <- c(anova[1,], anova[2,], anova[3,],anova[4,] )
agrupacion <- c(rep("A", 12), rep("B",12), rep("C",12), rep("D",12))
a <- aov(valor~agrupacion)
a
## Call:
## aov(formula = valor ~ agrupacion)
##
## Terms:
## agrupacion Residuals
## Sum of Squares 940.2292 1281.7500
## Deg. of Freedom 3 44
##
## Residual standard error: 5.397285
## Estimated effects may be unbalanced
summary(a)
## Df Sum Sq Mean Sq F value Pr(>F)
## agrupacion 3 940.2 313.41 10.76 2e-05 ***
## Residuals 44 1281.8 29.13
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(a)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = valor ~ agrupacion)
##
## $agrupacion
## diff lwr upr p adj
## B-A 3.833333 -2.049843 9.716510 0.3159589
## C-A -4.000000 -9.883177 1.883177 0.2799444
## D-A 7.916667 2.033490 13.799843 0.0044015
## C-B -7.833333 -13.716510 -1.950157 0.0049051
## D-B 4.083333 -1.799843 9.966510 0.2629525
## D-C 11.916667 6.033490 17.799843 0.0000144
boxplot(valor~agrupacion)
anova <- tapply(datos$AB_m,list(datos$Bosque, datos$Parcela),sum)
anova
## P_1 P_10 P_11 P_12 P_2 P_3 P_4
## A 1.1891437 1.0701925 0.8204655 0.8318450 1.0963054 1.1226884 1.0955331
## B 0.9140122 1.4242391 0.9186183 1.1894104 1.3053904 1.4087379 1.3847644
## C 1.0039812 0.7663829 0.6094961 0.4607498 0.7407865 0.8039774 0.9780043
## D 1.6543773 0.3212504 1.1316750 1.1485548 1.1027405 1.5182249 0.5735081
## P_5 P_6 P_7 P_8 P_9
## A 1.1552881 1.2315410 1.2159446 0.8179736 1.0759553
## B 0.8547047 0.9985736 1.3954063 1.3271148 1.3125635
## C 1.2065865 0.9231238 0.8323231 0.6594003 1.3919828
## D 0.7901807 1.6658000 0.9941550 0.9529422 0.7980593
# se realizan dos vectores para correr el anova
valor <- c(anova[1,], anova[2,], anova[3,],anova[4,] )
agrupacion <- c(rep("A", 12), rep("B",12), rep("C",12), rep("D",12))
a <- aov(valor~agrupacion)
summary(a)
## Df Sum Sq Mean Sq F value Pr(>F)
## agrupacion 3 0.693 0.23085 2.991 0.041 *
## Residuals 44 3.396 0.07719
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(a)
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = valor ~ agrupacion)
##
## $agrupacion
## diff lwr upr p adj
## B-A 0.14255494 -0.1602873 0.44539715 0.5947395
## C-A -0.19550680 -0.4983490 0.10733541 0.3239283
## D-A -0.00595067 -0.3087929 0.29689154 0.9999473
## C-B -0.33806174 -0.6409039 -0.03521953 0.0233147
## D-B -0.14850561 -0.4513478 0.15433660 0.5620580
## D-C 0.18955613 -0.1132861 0.49239833 0.3508137
boxplot(valor~agrupacion)
La prueba demuestra que existe diferencias estadísticamente significativas entre los bosques.Esto se puede concluir, porque según la literatura, los valores p menores de 0,05 en la prueba ANOVA indican significancia, en este test se obtuvo un resultado de 0,041.
Suponiendo que las alturas de los individuos del bosque no se conocen, se pasa a mirar el DAP, ya que este está relacionado con las alturas. Uno de los criterios para escoger parcelas para realizar mediciones de altura es que estas tengan un amplio rango en los DAP ya que esto me permite realizar una gráfica adecuada para la representación de la relacion entre estas dos variables. Se procede a carcular el valor máximo y mínimo de DAP por cada parcel de cada bosque para así hallar un rango, luego las cinco mejores parcelas se organizan de mayor a menor, siendo la primera la de mayor rango
min_bosq_par <- tapply(datos$DAP_cm,list(datos$Bosque, datos$Parcela),min)
min_bosq_par
## P_1 P_10 P_11 P_12 P_2 P_3 P_4 P_5
## A 12.95521 16.32930 15.34254 14.70592 16.36113 15.43803 16.87042 14.80141
## B 10.34507 10.12225 10.69521 10.31324 10.05859 10.02676 10.18592 10.00130
## C 12.70056 12.35042 13.75099 14.06930 13.30535 12.47775 15.59718 14.57859
## D 10.00130 10.18592 10.02676 10.12225 10.00130 10.59972 10.09042 10.15409
## P_6 P_7 P_8 P_9
## A 14.41944 14.13296 14.13296 12.63690
## B 10.02676 10.00130 10.15409 10.18592
## C 13.75099 14.32394 14.38761 14.83324
## D 10.15409 10.31324 10.21775 10.12225
max_bosq_par <- tapply(datos$DAP_cm,list(datos$Bosque, datos$Parcela),max)
max_bosq_par
## P_1 P_10 P_11 P_12 P_2 P_3 P_4 P_5
## A 30.08028 39.85240 37.14676 29.76197 37.11493 36.57381 33.93183 36.89212
## B 40.96648 74.42085 46.98254 57.80508 63.98029 58.12339 65.57184 49.33803
## C 47.74648 32.46761 35.01409 26.73803 42.65352 27.53381 34.21831 43.00367
## D 73.43409 22.12254 42.81268 35.55521 57.16846 70.53747 43.29014 64.93522
## P_6 P_7 P_8 P_9
## A 30.20761 39.31127 28.96620 32.81775
## B 56.14986 46.25043 51.40705 72.09719
## C 33.23155 47.42817 36.60564 54.43099
## D 57.51860 64.80789 67.16339 53.79437
sort(max_bosq_par[1,]- min_bosq_par[1,], decreasing = TRUE)[1:5]
## P_7 P_10 P_5 P_11 P_3
## 25.17831 23.52310 22.09071 21.80423 21.13578
sort(max_bosq_par[2,]- min_bosq_par[2,], decreasing = TRUE)[1:5]
## P_10 P_9 P_4 P_2 P_3
## 64.29860 61.91127 55.38592 53.92169 48.09662
sort(max_bosq_par[3,]- min_bosq_par[3,], decreasing = TRUE)[1:5]
## P_9 P_1 P_7 P_2 P_5
## 39.59775 35.04592 33.10423 29.34817 28.42507
sort(max_bosq_par[4,]- min_bosq_par[4,], decreasing = TRUE)[1:5]
## P_1 P_3 P_8 P_5 P_7
## 63.43279 59.93775 56.94564 54.78113 54.49465
A continuación se muestra las parcelas elegidas según el criterio anterior
Bosque A | P_7 | P_10 | P_5 | P_11 | P_3 |
---|---|---|---|---|---|
25.17831 | 23.52310 | 22.09071 | 21.80423 | 21.13578 |
Bosque B | P_10 | P_9 | P_4 | P_2 | P_3 |
---|---|---|---|---|---|
64.29860 | 61.91127 | 55.38592 | 53.92169 | 48.09662 |
Bosque C | P_9 | P_1 | P_7 | P_2 | P_5 |
---|---|---|---|---|---|
39.59775 | 35.04592 | 33.10423 | 29.34817 | 28.42507 |
Bosque D | P_1 | P_3 | P_8 | P_5 | P_7 |
---|---|---|---|---|---|
63.43279 | 59.93775 | 56.94564 | 54.78113 | 54.49465 |
mod1 <- lm(Ht~DAP_cm, datos)
summary(mod1)
##
## Call:
## lm(formula = Ht ~ DAP_cm, data = datos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7873 -0.3332 0.1699 0.4914 0.5707
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.808989 0.042341 231.7 <2e-16 ***
## DAP_cm 0.465573 0.001866 249.5 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6222 on 1239 degrees of freedom
## Multiple R-squared: 0.9805, Adjusted R-squared: 0.9805
## F-statistic: 6.225e+04 on 1 and 1239 DF, p-value: < 2.2e-16
{par(mfrow=c(2,2))
plot(mod1)}
anova(mod1)
## Analysis of Variance Table
##
## Response: Ht
## Df Sum Sq Mean Sq F value Pr(>F)
## DAP_cm 1 24099.9 24099.9 62252 < 2.2e-16 ***
## Residuals 1239 479.7 0.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residuales1 <- residuals(mod1)
predichos1 <- predict(mod1)
plot(Residuales1~predichos1)
ggplot(datos, aes(predichos1, Residuales1))+
geom_point()+
geom_smooth(method = "lm")
shapiro.test(Residuales1)
##
## Shapiro-Wilk normality test
##
## data: Residuales1
## W = 0.79301, p-value < 2.2e-16
# Error absoluto
Error1 <- abs(((predichos1-datos$Ht)/datos$Ht)*100)
Error1
## 1 2 3 4 5
## 2.539357e+00 2.065793e+00 2.500776e+00 2.244201e+00 2.529567e+00
## 6 7 8 9 10
## 1.996401e+00 1.501614e+00 2.082458e+00 2.426733e+00 1.075139e+00
## 11 12 13 14 15
## 2.544402e+00 2.148060e+00 2.660929e+00 2.370921e+00 2.555750e+00
## 16 17 18 19 20
## 2.390449e+00 2.155243e+00 2.553909e+00 2.467058e+00 2.513079e+00
## 21 22 23 24 25
## 2.403299e+00 2.555750e+00 8.804327e-01 2.455131e+00 2.556687e+00
## 26 27 28 29 30
## 2.521602e+00 2.500884e+00 2.019650e+00 2.401747e+00 2.518941e+00
## 31 32 33 34 35
## 2.547365e+00 4.781126e-01 2.495248e+00 2.474891e+00 2.528186e+00
## 36 37 38 39 40
## 2.245761e+00 1.902199e+00 2.534526e+00 2.199945e+00 2.266164e+00
## 41 42 43 44 45
## 1.075349e+00 2.515510e+00 2.536345e+00 2.388713e+00 1.656251e+00
## 46 47 48 49 50
## 2.135548e+00 2.544402e+00 1.579051e+00 2.521602e+00 2.561304e+00
## 51 52 53 54 55
## 5.554846e-01 1.630038e+00 2.556687e+00 1.738722e+00 2.224503e+00
## 56 57 58 59 60
## 1.973032e+00 2.324759e+00 2.545515e+00 2.065793e+00 2.508116e+00
## 61 62 63 64 65
## 2.468341e+00 2.543671e+00 2.041495e+00 2.551078e+00 2.543671e+00
## 66 67 68 69 70
## 2.497086e+00 2.541159e+00 2.152889e-01 2.201446e+00 2.442748e+00
## 71 72 73 74 75
## 9.643740e-01 2.365796e+00 2.524473e+00 2.064476e+00 7.149384e-01
## 76 77 78 79 80
## 1.020273e+00 2.490611e+00 1.360854e+00 2.378426e+00 2.533245e+00
## 81 82 83 84 85
## 2.541832e+00 7.674344e-01 2.518108e+00 2.406417e+00 2.526879e+00
## 86 87 88 89 90
## 2.551078e+00 1.705414e+00 2.526327e+00 2.497442e+00 2.460052e+00
## 91 92 93 94 95
## 2.551078e+00 2.201446e+00 2.552073e+00 1.553696e+00 2.563166e+00
## 96 97 98 99 100
## 2.468341e+00 2.385258e+00 2.549219e+00 2.386983e+00 2.535091e+00
## 101 102 103 104 105
## 2.440965e+00 2.539998e+00 2.513654e+00 2.548415e+00 1.579051e+00
## 106 107 108 109 110
## 2.176184e+00 2.524473e+00 7.108254e-01 2.439187e+00 2.383538e+00
## 111 112 113 114 115
## 1.344944e+00 2.488914e+00 2.242646e+00 2.317679e+00 2.364098e+00
## 116 117 118 119 120
## 2.519239e+00 2.535768e+00 2.383538e+00 2.520151e+00 2.176184e+00
## 121 122 123 124 125
## 6.445194e-01 7.244888e-01 6.530308e-01 2.492312e+00 2.329735e+00
## 126 127 128 129 130
## 2.264581e+00 1.047480e+00 2.156684e+00 2.518108e+00 2.531921e+00
## 131 132 133 134 135
## 2.326413e+00 2.520151e+00 2.080292e+00 1.900029e+00 2.222128e+00
## 136 137 138 139 140
## 2.297692e+00 2.542966e+00 2.448127e+00 1.680647e+00 2.455131e+00
## 141 142 143 144 145
## 2.532712e+00 2.544777e+00 2.148060e+00 2.493416e+00 2.284799e+00
## 146 147 148 149 150
## 2.555750e+00 2.081374e+00 2.560453e+00 1.053728e+00 2.538169e+00
## 151 152 153 154 155
## 2.539357e+00 2.149234e+00 2.513079e+00 7.888814e-01 2.557596e+00
## 156 157 158 159 160
## 7.108254e-01 1.973032e+00 2.523716e+00 2.375398e+00 2.329735e+00
## 161 162 163 164 165
## 2.519853e+00 1.604912e+00 2.544777e+00 2.526879e+00 2.223659e+00
## 166 167 168 169 170
## 1.657865e+00 1.902199e+00 2.527735e+00 2.548163e+00 4.000343e-02
## 171 172 173 174 175
## 2.427272e+00 2.222128e+00 1.804132e+00 2.530420e+00 2.537560e+00
## 176 177 178 179 180
## 1.353790e+00 2.527300e+00 2.538800e+00 8.038091e-01 2.266164e+00
## 181 182 183 184 185
## 2.320519e+00 2.555750e+00 2.344881e+00 2.495248e+00 2.110089e+00
## 186 187 188 189 190
## 1.353790e+00 2.551078e+00 2.152889e-01 2.245662e+00 2.542966e+00
## 191 192 193 194 195
## 2.530420e+00 8.244252e-01 2.488914e+00 2.521931e+00 2.537560e+00
## 196 197 198 199 200
## 2.329735e+00 2.272227e+00 1.576952e+00 2.715932e-01 6.824252e-01
## 201 202 203 204 205
## 2.531921e+00 2.548415e+00 2.557596e+00 2.528647e+00 2.064476e+00
## 206 207 208 209 210
## 2.451872e+00 2.177650e+00 2.504450e+00 2.523357e+00 1.186270e+00
## 211 212 213 214 215
## 1.995180e+00 1.184177e+00 2.513079e+00 2.511803e+00 2.246977e+00
## 216 217 218 219 220
## 9.370776e-01 2.515510e+00 2.329735e+00 2.044069e+00 1.047480e+00
## 221 222 223 224 225
## 2.498928e+00 3.036014e+00 2.309765e+00 2.548415e+00 2.546280e+00
## 226 227 228 229 230
## 2.376910e+00 2.044069e+00 9.643740e-01 2.492312e+00 1.853344e+00
## 231 232 233 234 235
## 1.878304e-01 2.506280e+00 2.533245e+00 2.390449e+00 2.517107e+00
## 236 237 238 239 240
## 2.537560e+00 2.220603e+00 1.679891e+00 2.531403e+00 2.156684e+00
## 241 242 243 244 245
## 1.246326e+00 2.530903e+00 4.490404e-01 2.543671e+00 1.923694e+00
## 246 247 248 249 250
## 2.544777e+00 2.467058e+00 2.490611e+00 2.421970e+00 2.555750e+00
## 251 252 253 254 255
## 1.424143e+00 2.484006e+00 1.755428e+00 2.437951e+00 5.740806e-01
## 256 257 258 259 260
## 2.465240e+00 6.815444e-01 6.098423e-02 2.565032e+00 2.486936e-01
## 261 262 263 264 265
## 2.565032e+00 2.548415e+00 1.102481e+00 2.546280e+00 1.102646e+00
## 266 267 268 269 270
## 2.520151e+00 1.238176e+00 8.804327e-01 2.283196e+00 2.531403e+00
## 271 272 273 274 275
## 2.470011e+00 7.956436e-01 2.394042e+00 2.110089e+00 2.538169e+00
## 276 277 278 279 280
## 2.509957e+00 1.450106e+00 2.199945e+00 2.511323e+00 1.755428e+00
## 281 282 283 284 285
## 2.446329e+00 2.482172e+00 7.602017e-01 2.128413e+00 2.497086e+00
## 286 287 288 289 290
## 2.375398e+00 2.520151e+00 3.106983e-01 2.500776e+00 2.998063e+00
## 291 292 293 294 295
## 6.361176e+00 6.988815e+00 6.719837e+00 5.250671e+00 1.219609e+00
## 296 297 298 299 300
## 1.397408e+00 6.845083e+00 7.108254e-01 2.421970e+00 2.230036e+00
## 301 302 303 304 305
## 3.218509e+00 4.936051e-01 2.122526e+00 1.884505e+00 2.497086e+00
## 306 307 308 309 310
## 5.083064e-01 2.465012e+00 5.958098e-01 4.395454e+00 3.729978e-01
## 311 312 313 314 315
## 1.153012e+00 3.489937e+00 2.477009e+00 5.753110e+00 1.947238e+00
## 316 317 318 319 320
## 1.528494e+00 1.344944e+00 2.715932e-01 3.334219e+00 2.544402e+00
## 321 322 323 324 325
## 6.098423e-02 6.219920e-02 1.460259e+00 9.104919e-02 3.179637e-02
## 326 327 328 329 330
## 1.657865e+00 1.770019e+00 2.199945e+00 2.796593e-01 3.756475e+00
## 331 332 333 334 335
## 2.388713e+00 6.604063e-01 1.969854e+00 4.718410e+00 2.104254e+00
## 336 337 338 339 340
## 1.906358e+00 9.267339e-02 1.119836e+00 5.579203e-02 2.482172e+00
## 341 342 343 344 345
## 2.385258e+00 3.529126e+00 1.576512e+00 5.207131e+00 1.597070e+00
## 346 347 348 349 350
## 1.947238e+00 7.667120e+00 1.852330e+00 8.305415e+00 3.529126e+00
## 351 352 353 354 355
## 6.940765e+00 7.956436e-01 1.426269e+00 1.253030e+00 1.909952e+00
## 356 357 358 359 360
## 2.518941e+00 7.108254e-01 5.047213e+00 5.947769e-01 2.633974e-01
## 361 362 363 364 365
## 2.367499e+00 4.322465e-01 2.798928e-01 2.500776e+00 1.053728e+00
## 366 367 368 369 370
## 2.527300e+00 2.544402e+00 5.966315e-01 6.892854e+00 5.648587e-01
## 371 372 373 374 375
## 4.071313e-01 1.246376e+00 7.717073e+00 2.474891e+00 3.106983e-01
## 376 377 378 379 380
## 6.221972e+00 1.219609e+00 7.924997e-01 6.988815e+00 1.450106e+00
## 381 382 383 384 385
## 3.836255e+00 2.879617e-02 2.552943e+00 2.071197e+00 1.475929e+00
## 386 387 388 389 390
## 2.735166e+00 3.716745e+00 2.370885e+00 2.153807e+00 1.808258e-01
## 391 392 393 394 395
## 5.740806e-01 5.753110e+00 1.475929e+00 1.265298e+00 6.259068e-01
## 396 397 398 399 400
## 7.602017e-01 2.329735e+00 4.592141e+00 8.804327e-01 3.923790e+00
## 401 402 403 404 405
## 5.015413e-01 1.153012e+00 7.674344e-01 7.389467e+00 2.809790e+00
## 406 407 408 409 410
## 5.015413e-01 2.178011e-01 2.123326e-01 4.961243e+00 7.163963e+00
## 411 412 413 414 415
## 1.735255e+00 2.194108e+00 2.155243e+00 8.872987e+00 2.423732e+00
## 416 417 418 419 420
## 2.283196e+00 1.020273e+00 6.530308e-01 4.355942e-01 4.953773e-01
## 421 422 423 424 425
## 4.347421e-01 4.355942e-01 1.994049e+00 2.735166e+00 7.789262e+00
## 426 427 428 429 430
## 3.177295e+00 1.426269e+00 2.302169e+00 1.211207e+00 2.922458e+00
## 431 432 433 434 435
## 2.660929e+00 5.456132e+00 6.604063e-01 2.559448e+00 6.361176e+00
## 436 437 438 439 440
## 4.000343e-02 7.391618e-01 3.411871e+00 3.106983e-01 1.980451e+00
## 441 442 443 444 445
## 1.460259e+00 4.385262e-01 1.631486e+00 5.958098e-01 2.448127e+00
## 446 447 448 449 450
## 1.019971e+00 2.477009e+00 5.520485e+00 3.608234e+00 1.324803e+00
## 451 452 453 454 455
## 5.648587e-01 6.314788e+00 6.988815e+00 1.973032e+00 1.604271e+00
## 456 457 458 459 460
## 7.717073e+00 1.902199e+00 1.884505e+00 3.026759e-01 4.699961e-01
## 461 462 463 464 465
## 4.503304e-01 2.557596e+00 1.424143e+00 7.789262e+00 1.170978e+00
## 466 467 468 469 470
## 2.294915e+00 1.497236e+00 2.223218e+00 4.148801e+00 1.926577e+00
## 471 472 473 474 475
## 5.753110e+00 1.705414e+00 4.490404e-01 2.403299e+00 3.411871e+00
## 476 477 478 479 480
## 1.700579e+00 6.815444e-01 6.625282e+00 1.578450e+00 4.312788e+00
## 481 482 483 484 485
## 2.370921e+00 1.501614e+00 5.798042e+00 1.970788e+00 4.170387e-01
## 486 487 488 489 490
## 1.902199e+00 2.437951e+00 1.047736e+00 1.265443e+00 2.157503e+00
## 491 492 493 494 495
## 2.152889e-01 1.808258e-01 2.152773e+00 3.084915e-02 9.551707e-01
## 496 497 498 499 500
## 7.888814e-01 9.392752e-02 3.887633e+00 8.898650e-01 5.958098e-01
## 501 502 503 504 505
## 2.796593e-01 1.825994e-01 7.438679e+00 2.222128e+00 2.846176e+00
## 506 507 508 509 510
## 6.824252e-01 2.525909e+00 8.898650e-01 1.320120e+00 1.129883e+00
## 511 512 513 514 515
## 1.516962e+00 1.754559e+00 2.421970e+00 2.491589e+00 8.809712e-01
## 516 517 518 519 520
## 6.988815e+00 4.676204e+00 2.122526e+00 1.886155e+00 5.204671e-01
## 521 522 523 524 525
## 5.004168e+00 2.129563e+00 4.042588e-01 5.015413e-01 1.020796e+00
## 526 527 528 529 530
## 5.500298e+00 6.098423e-02 3.716745e+00 1.704620e+00 4.849434e+00
## 531 532 533 534 535
## 6.098423e-02 7.389467e+00 1.770019e+00 2.158271e+00 1.124767e+01
## 536 537 538 539 540
## 1.494334e+00 5.331621e-01 3.614232e-01 2.321944e+00 1.804132e+00
## 541 542 543 544 545
## 4.271624e+00 2.453499e+00 3.320833e-01 2.697999e+00 4.676204e+00
## 546 547 548 549 550
## 8.248761e-01 1.665989e+00 8.804327e-01 2.708960e+00 2.326413e+00
## 551 552 553 554 555
## 1.020796e+00 1.423785e+00 2.385258e+00 3.663537e+00 1.379019e+00
## 556 557 558 559 560
## 1.206781e+01 2.459815e+00 2.875006e+00 3.036014e+00 1.324803e+00
## 561 562 563 564 565
## 6.815444e-01 1.928335e+00 1.053728e+00 7.488035e+00 2.497086e+00
## 566 567 568 569 570
## 2.483852e+00 2.550292e+00 7.888814e-01 1.324803e+00 1.527973e-01
## 571 572 573 574 575
## 1.392363e+00 1.358541e+00 2.518108e+00 1.909952e+00 1.878304e-01
## 576 577 578 579 580
## 1.735255e+00 2.477009e+00 1.874836e+00 8.248761e-01 5.500298e+00
## 581 582 583 584 585
## 2.268180e+00 2.513603e+00 1.291618e+00 6.314788e+00 2.400690e+00
## 586 587 588 589 590
## 2.560453e+00 2.044069e+00 1.528494e+00 2.419971e-01 2.122526e+00
## 591 592 593 594 595
## 2.369388e+00 4.676204e+00 2.218112e+00 2.623954e+00 2.219082e+00
## 596 597 598 599 600
## 1.320120e+00 4.312788e+00 3.450852e+00 2.440509e+00 1.730884e+00
## 601 602 603 604 605
## 5.798042e+00 1.022087e+00 1.102481e+00 2.998063e+00 1.778708e+00
## 606 607 608 609 610
## 2.482172e+00 6.100694e+00 8.573311e-01 5.589005e+00 7.115479e+00
## 611 612 613 614 615
## 5.207131e+00 9.125375e-01 3.074065e+00 3.074065e+00 6.672491e+00
## 616 617 618 619 620
## 3.177295e+00 1.597070e+00 2.158271e+00 1.042768e-01 4.071313e-01
## 621 622 623 624 625
## 2.090041e+00 5.947769e-01 2.385258e+00 2.458017e+00 2.960211e+00
## 626 627 628 629 630
## 1.397408e+00 2.654421e+00 2.509957e+00 2.549219e+00 7.963272e-01
## 631 632 633 634 635
## 2.174903e+00 2.364098e+00 4.042588e-01 2.347863e+00 1.845267e-01
## 636 637 638 639 640
## 2.302169e+00 2.542966e+00 2.365796e+00 2.531921e+00 2.442748e+00
## 641 642 643 644 645
## 3.011211e-01 1.438974e+00 2.248295e+00 2.194108e+00 3.011211e-01
## 646 647 648 649 650
## 1.808258e-01 1.012267e+00 2.269526e+00 1.902199e+00 1.371247e+00
## 651 652 653 654 655
## 2.328072e+00 2.488142e+00 1.264970e+00 2.440965e+00 2.546331e+00
## 656 657 658 659 660
## 1.878100e+00 1.341933e+00 2.370885e+00 2.326413e+00 1.680647e+00
## 661 662 663 664 665
## 2.480344e+00 2.423565e+00 2.177650e+00 1.778938e+00 1.654313e+00
## 666 667 668 669 670
## 7.956436e-01 1.923694e+00 2.548163e+00 2.245761e+00 2.484006e+00
## 671 672 673 674 675
## 2.529099e+00 1.186270e+00 2.364098e+00 2.533981e+00 2.437951e+00
## 676 677 678 679 680
## 2.556687e+00 3.295547e+00 2.555750e+00 4.699961e-01 1.494334e+00
## 681 682 683 684 685
## 2.242646e+00 6.815444e-01 2.400198e+00 2.321944e+00 2.531403e+00
## 686 687 688 689 690
## 1.397408e+00 1.265443e+00 2.148060e+00 2.067114e+00 2.431516e+00
## 691 692 693 694 695
## 2.326413e+00 2.489768e+00 2.516367e+00 1.020273e+00 1.019971e+00
## 696 697 698 699 700
## 1.799736e+00 2.111469e+00 6.219920e-02 1.799736e+00 2.493416e+00
## 701 702 703 704 705
## 2.563166e+00 2.527735e+00 2.156684e+00 2.347806e+00 1.656788e+00
## 706 707 708 709 710
## 8.898650e-01 2.390449e+00 2.245761e+00 2.542966e+00 2.245662e+00
## 711 712 713 714 715
## 2.105369e+00 1.112235e-01 2.245761e+00 2.404570e+00 1.018981e-02
## 716 717 718 719 720
## 2.516606e+00 1.119725e+00 1.047736e+00 2.796593e-01 2.421324e+00
## 721 722 723 724 725
## 2.325422e+00 2.328072e+00 2.442748e+00 1.804132e+00 1.700579e+00
## 726 727 728 729 730
## 3.906959e-01 1.102646e+00 2.243041e+00 2.426733e+00 1.211207e+00
## 731 732 733 734 735
## 2.444536e+00 1.730884e+00 6.219920e-02 2.177650e+00 1.516594e+00
## 736 737 738 739 740
## 7.244888e-01 1.086742e+00 1.528205e+00 1.842703e+00 1.900029e+00
## 741 742 743 744 745
## 2.525505e+00 5.371955e-01 1.947238e+00 2.041495e+00 2.176184e+00
## 746 747 748 749 750
## 2.544402e+00 2.266164e+00 2.161254e+00 2.526879e+00 6.530308e-01
## 751 752 753 754 755
## 1.322704e+00 2.324759e+00 1.318499e+00 2.535768e+00 2.283196e+00
## 756 757 758 759 760
## 1.102481e+00 1.449707e+00 2.060735e+00 2.540992e+00 2.324759e+00
## 761 762 763 764 765
## 8.804327e-01 2.466674e+00 1.596486e+00 1.153012e+00 2.326413e+00
## 766 767 768 769 770
## 6.284805e-01 6.968499e-01 2.467058e+00 1.211221e+00 1.842703e+00
## 771 772 773 774 775
## 1.527162e+00 2.525909e+00 1.086742e+00 1.729221e+00 7.956436e-01
## 776 777 778 779 780
## 1.971845e+00 1.718276e+00 2.563166e+00 4.355942e-01 1.655749e+00
## 781 782 783 784 785
## 2.320519e+00 2.532712e+00 2.152889e-01 2.541832e+00 2.328072e+00
## 786 787 788 789 790
## 1.578450e+00 2.134134e+00 2.493416e+00 2.269526e+00 2.197106e+00
## 791 792 793 794 795
## 4.467301e+00 2.088689e+00 2.525909e+00 4.927071e+00 1.928335e+00
## 796 797 798 799 800
## 2.516367e+00 2.319097e+00 1.808258e-01 1.527162e+00 1.869815e-01
## 801 802 803 804 805
## 1.886155e+00 1.157060e+00 2.019650e+00 1.579051e+00 2.112852e+00
## 806 807 808 809 810
## 6.924090e-01 1.730051e+00 2.041495e+00 3.450852e+00 2.557302e+00
## 811 812 813 814 815
## 1.676454e+00 2.198449e+00 1.424143e+00 2.533796e+00 7.674344e-01
## 816 817 818 819 820
## 2.292151e+00 1.509697e-01 2.509957e+00 1.973032e+00 2.201446e+00
## 821 822 823 824 825
## 2.458017e+00 2.058626e+00 5.648587e-01 1.527973e-01 2.546280e+00
## 826 827 828 829 830
## 9.370776e-01 6.824252e-01 1.654313e+00 2.293531e+00 1.096250e+00
## 831 832 833 834 835
## 1.869815e-01 1.700579e+00 1.075349e+00 2.151590e+00 2.155243e+00
## 836 837 838 839 840
## 2.406316e+00 2.446329e+00 2.178011e-01 9.924011e-01 2.532712e+00
## 841 842 843 844 845
## 8.573311e-01 2.715932e-01 4.738952e-01 1.825994e-01 1.324803e+00
## 846 847 848 849 850
## 2.135548e+00 2.134134e+00 9.366349e-01 2.465012e+00 3.614232e-01
## 851 852 853 854 855
## 2.306489e+00 2.176184e+00 4.042588e-01 2.528647e+00 1.730051e+00
## 856 857 858 859 860
## 2.561304e+00 1.876241e+00 1.925932e+00 1.358541e+00 1.945157e+00
## 861 862 863 864 865
## 3.568420e+00 5.648587e-01 6.192300e+00 3.011211e-01 1.232192e-01
## 866 867 868 869 870
## 3.529126e+00 4.693861e-01 4.833181e+00 1.291763e+00 1.528205e+00
## 871 872 873 874 875
## 2.847248e+00 2.367499e+00 7.956436e-01 4.503304e-01 1.238118e+00
## 876 877 878 879 880
## 1.075349e+00 2.697999e+00 1.075139e+00 3.700368e+00 3.482170e-01
## 881 882 883 884 885
## 5.004168e+00 1.419193e+00 6.845083e+00 7.789262e+00 1.172649e+01
## 886 887 888 889 890
## 1.735255e+00 1.779844e+00 1.700579e+00 2.660929e+00 3.821092e-01
## 891 892 893 894 895
## 7.212589e+00 2.036007e+00 4.395454e+00 7.212589e+00 5.004168e+00
## 896 897 898 899 900
## 3.256977e+00 3.418111e-01 2.104254e+00 6.192300e+00 8.244252e-01
## 901 902 903 904 905
## 3.906959e-01 3.489937e+00 2.266056e+00 3.084915e-02 1.948389e+00
## 906 907 908 909 910
## 4.961243e+00 5.886795e+00 1.020273e+00 1.538369e-01 3.180143e+00
## 911 912 913 914 915
## 5.753110e+00 5.207131e+00 4.718410e+00 2.112852e+00 6.314788e+00
## 916 917 918 919 920
## 5.500298e+00 4.676204e+00 5.152659e+00 5.633546e+00 5.753110e+00
## 921 922 923 924 925
## 7.789262e+00 6.940765e+00 1.874836e+00 2.328072e+00 2.556687e+00
## 926 927 928 929 930
## 6.625282e+00 4.071313e-01 1.464667e-03 2.014510e+00 1.825994e-01
## 931 932 933 934 935
## 1.075139e+00 2.429917e+00 8.359896e+00 6.361176e+00 6.454355e+00
## 936 937 938 939 940
## 6.192300e+00 2.266056e+00 3.568420e+00 8.244252e-01 5.633546e+00
## 941 942 943 944 945
## 1.020273e+00 1.066244e+01 9.224781e-01 1.020796e+00 4.108082e+00
## 946 947 948 949 950
## 6.604063e-01 2.286406e+00 2.198449e+00 1.735255e+00 3.411871e+00
## 951 952 953 954 955
## 1.562739e+00 1.285318e-01 8.860740e-02 5.892532e-01 5.163714e+00
## 956 957 958 959 960
## 4.189631e+00 5.544589e+00 2.051308e+00 6.845083e+00 2.879617e-02
## 961 962 963 964 965
## 2.517372e+00 5.843102e+00 3.372994e+00 9.924011e-01 1.324803e+00
## 966 967 968 969 970
## 5.163714e+00 3.489937e+00 3.180143e+00 1.210443e-01 7.617314e+00
## 971 972 973 974 975
## 5.250671e+00 2.559448e+00 2.122526e+00 5.500298e+00 5.500298e+00
## 976 977 978 979 980
## 7.617314e+00 2.772429e+00 1.874836e+00 3.529126e+00 6.845083e+00
## 981 982 983 984 985
## 3.489937e+00 2.513603e+00 1.228701e+00 1.344713e+00 2.199945e+00
## 986 987 988 989 990
## 5.250671e+00 5.798042e+00 3.836255e+00 4.108082e+00 6.284805e-01
## 991 992 993 994 995
## 6.988815e+00 4.718410e+00 2.266056e+00 3.180143e+00 4.189631e+00
## 996 997 998 999 1000
## 2.051308e+00 6.314788e+00 8.659563e+00 1.874836e+00 6.100472e+00
## 1001 1002 1003 1004 1005
## 2.884804e+00 7.261358e+00 2.798928e-01 4.634115e+00 7.212589e+00
## 1006 1007 1008 1009 1010
## 6.314788e+00 1.973032e+00 7.212589e+00 2.922458e+00 1.553696e+00
## 1011 1012 1013 1014 1015
## 1.264970e+00 5.294332e+00 4.108082e+00 7.438679e+00 1.805076e+00
## 1016 1017 1018 1019 1020
## 2.199945e+00 6.672491e+00 5.207131e+00 2.065793e+00 6.437087e-04
## 1021 1022 1023 1024 1025
## 2.064476e+00 1.821672e+00 9.636965e-01 2.466674e+00 2.015834e+00
## 1026 1027 1028 1029 1030
## 4.592141e+00 2.442748e+00 1.677016e+00 2.521602e+00 6.625282e+00
## 1031 1032 1033 1034 1035
## 2.809790e+00 2.055413e+00 2.112852e+00 3.084915e-02 2.922458e+00
## 1036 1037 1038 1039 1040
## 1.018981e-02 5.544589e+00 3.179637e-02 7.888814e-01 7.168763e-01
## 1041 1042 1043 1044 1045
## 2.548163e+00 6.021656e+00 1.665989e+00 2.341970e+00 7.438679e+00
## 1046 1047 1048 1049 1050
## 2.477009e+00 9.643740e-01 1.075349e+00 1.086742e+00 3.372994e+00
## 1051 1052 1053 1054 1055
## 1.874836e+00 4.385262e-01 5.888291e+00 4.592141e+00 6.719837e+00
## 1056 1057 1058 1059 1060
## 1.157134e+00 6.940765e+00 2.123326e-01 7.115479e+00 2.531403e+00
## 1061 1062 1063 1064 1065
## 2.123326e-01 3.614232e-01 3.074065e+00 3.074065e+00 5.843102e+00
## 1066 1067 1068 1069 1070
## 1.926577e+00 1.878342e+00 3.139931e-01 5.004168e+00 8.616600e+00
## 1071 1072 1073 1074 1075
## 9.551707e-01 2.178011e-01 1.874836e+00 5.083064e-01 8.804327e-01
## 1076 1077 1078 1079 1080
## 3.607818e+00 2.230036e+00 2.486936e-01 3.796311e+00 6.892854e+00
## 1081 1082 1083 1084 1085
## 2.957318e+00 2.478521e+00 5.456132e+00 7.261358e+00 5.798042e+00
## 1086 1087 1088 1089 1090
## 1.730884e+00 9.454899e+00 4.108082e+00 2.544402e+00 2.241096e+00
## 1091 1092 1093 1094 1095
## 2.152889e-01 3.997110e+00 3.796311e+00 2.288019e+00 2.697999e+00
## 1096 1097 1098 1099 1100
## 6.454355e+00 5.250671e+00 1.527162e+00 3.450852e+00 2.488914e+00
## 1101 1102 1103 1104 1105
## 7.488035e+00 6.192300e+00 2.067114e+00 7.212589e+00 6.454355e+00
## 1106 1107 1108 1109 1110
## 5.456132e+00 5.047213e+00 2.502629e+00 6.314788e+00 9.643740e-01
## 1111 1112 1113 1114 1115
## 5.633546e+00 6.578209e+00 1.825994e-01 3.180143e+00 2.557302e+00
## 1116 1117 1118 1119 1120
## 2.086872e+00 5.294332e+00 4.490404e-01 1.170942e+00 4.698636e+00
## 1121 1122 1123 1124 1125
## 6.940765e+00 2.288019e+00 2.735166e+00 6.845083e+00 7.602017e-01
## 1126 1127 1128 1129 1130
## 3.956733e+00 2.513603e+00 6.625282e+00 6.054754e+00 6.845083e+00
## 1131 1132 1133 1134 1135
## 7.389467e+00 4.833181e+00 6.146320e+00 6.940765e+00 7.389467e+00
## 1136 1137 1138 1139 1140
## 1.086742e+00 5.331621e-01 2.385258e+00 4.354065e+00 1.232192e-01
## 1141 1142 1143 1144 1145
## 1.845267e-01 3.756475e+00 3.334219e+00 5.047213e+00 4.354065e+00
## 1146 1147 1148 1149 1150
## 1.324803e+00 8.248761e-01 1.654313e+00 1.804132e+00 5.633546e+00
## 1151 1152 1153 1154 1155
## 3.836255e+00 4.230571e+00 1.318310e+00 4.108082e+00 5.843102e+00
## 1156 1157 1158 1159 1160
## 3.074065e+00 2.015834e+00 5.371955e-01 1.805076e+00 1.211207e+00
## 1161 1162 1163 1164 1165
## 6.284805e-01 5.371955e-01 2.152889e-01 5.544589e+00 1.058108e+00
## 1166 1167 1168 1169 1170
## 3.256977e+00 6.054754e+00 1.246326e+00 6.192300e+00 1.303534e+00
## 1171 1172 1173 1174 1175
## 1.020796e+00 2.431516e+00 6.625282e+00 3.489937e+00 2.526879e+00
## 1176 1177 1178 1179 1180
## 6.192300e+00 1.246326e+00 4.148801e+00 7.667120e+00 1.475929e+00
## 1181 1182 1183 1184 1185
## 4.918438e+00 1.839809e+00 2.338375e+00 5.047213e+00 3.489937e+00
## 1186 1187 1188 1189 1190
## 2.283196e+00 1.075139e+00 7.717073e+00 2.548163e+00 8.573311e-01
## 1191 1192 1193 1194 1195
## 1.945157e+00 2.273583e+00 3.106983e-01 2.847248e+00 2.065793e+00
## 1196 1197 1198 1199 1200
## 9.366349e-01 2.194108e+00 2.623954e+00 2.194108e+00 1.945157e+00
## 1201 1202 1203 1204 1205
## 1.020796e+00 2.486936e-01 5.331621e-01 4.918438e+00 1.426269e+00
## 1206 1207 1208 1209 1210
## 2.523716e+00 3.529126e+00 5.843102e+00 2.502629e+00 1.902199e+00
## 1211 1212 1213 1214 1215
## 7.212589e+00 1.909952e+00 1.948389e+00 4.189631e+00 1.562739e+00
## 1216 1217 1218 1219 1220
## 2.427272e+00 1.517330e+00 1.426269e+00 9.370776e-01 4.148801e+00
## 1221 1222 1223 1224 1225
## 6.530308e-01 6.146320e+00 4.718410e+00 6.539609e-01 9.366349e-01
## 1226 1227 1228 1229 1230
## 3.836255e+00 2.525115e+00 2.534526e+00 2.439187e+00 2.201446e+00
## 1231 1232 1233 1234 1235
## 5.047213e+00 2.051308e+00 9.672285e-01 2.552073e+00 6.054754e+00
## 1236 1237 1238 1239 1240
## 8.248761e-01 7.488035e+00 1.909952e+00 7.566462e-01 2.715932e-01
## 1241
## 2.051308e+00
mean(Error1)
## [1] 2.481516
sd(Error1)
## [1] 1.841579
sqrt(sum(Residuales1^2)/summary(mod1)$df[2])
## [1] 0.6222003
AIC
AIC(mod1)
## [1] 2348.112
mod2 <- lm(Ht~DAP_cm+I(DAP_cm^2),datos)
summary(mod2)
##
## Call:
## lm(formula = Ht ~ DAP_cm + I(DAP_cm^2), data = datos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.36854 -0.08481 0.04423 0.10650 1.28200
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.369e+00 2.001e-02 368.2 <2e-16 ***
## DAP_cm 6.683e-01 1.499e-03 445.8 <2e-16 ***
## I(DAP_cm^2) -3.379e-03 2.384e-05 -141.7 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.15 on 1238 degrees of freedom
## Multiple R-squared: 0.9989, Adjusted R-squared: 0.9989
## F-statistic: 5.458e+05 on 2 and 1238 DF, p-value: < 2.2e-16
{par(mfrow=c(2,2))
plot(mod2)}
anova(mod2)
## Analysis of Variance Table
##
## Response: Ht
## Df Sum Sq Mean Sq F value Pr(>F)
## DAP_cm 1 24099.9 24099.9 1071599 < 2.2e-16 ***
## I(DAP_cm^2) 1 451.8 451.8 20090 < 2.2e-16 ***
## Residuals 1238 27.8 0.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residuales2 <- residuals(mod2)
predichos2 <- predict(mod2)
plot(Residuales2~predichos2, main = "Residuales Vs Predichos Modelo Cuadrático")
ggplot(mod2, aes(predichos2, mod2$residuals)) +
geom_point() +
geom_smooth(method = "lm")
shapiro.test(Residuales2)
##
## Shapiro-Wilk normality test
##
## data: Residuales2
## W = 0.83838, p-value < 2.2e-16
# Error absoluto
Error2 <- abs(((predichos2-datos$Ht)/datos$Ht)*100)
Error2
## 1 2 3 4 5
## 0.1436636722 0.6488590894 0.4350540289 0.5974643564 0.2583926671
## 6 7 8 9 10
## 0.6668302689 0.6856725143 0.3824280920 0.0672326833 0.6897321560
## 11 12 13 14 15
## 0.3418819069 0.3285890016 0.1883777018 0.5546400404 0.2131275985
## 16 17 18 19 20
## 0.5385537300 0.6103195287 0.2048285471 0.4836617493 0.1040930327
## 21 22 23 24 25
## 0.0934358790 0.2131275985 0.6887739042 0.0299420205 0.3064536463
## 26 27 28 29 30
## 0.0799466612 0.0606692025 0.6604938533 0.0963231645 0.3022958773
## 31 32 33 34 35
## 0.2572221218 0.6410688530 0.3962861302 0.3918732542 0.3582591754
## 36 37 38 39 40
## 0.6157810679 0.6956989387 0.1946544040 0.6032426066 0.5950561820
## 41 42 43 44 45
## 0.6610124617 0.4009689898 0.2031454394 0.5227023489 0.6170564763
## 46 47 48 49 50
## 0.6537725602 0.3418819069 0.7016444889 0.0799466612 0.2389103113
## 51 52 53 54 55
## 1.0163842185 0.7044370927 0.3064536463 0.5860954725 0.2760430590
## 56 57 58 59 60
## 0.6735162668 0.5223301857 0.2478335921 0.6488590894 0.3524531678
## 61 62 63 64 65
## 0.0043802849 0.2385961875 0.6338238369 0.2764550604 0.2385961875
## 66 67 68 69 70
## 0.4090422955 0.1508065285 0.4713708554 0.6220719228 0.4526540489
## 71 72 73 74 75
## 0.6968726978 0.5063683859 0.3353962185 0.6284486157 0.9618107541
## 76 77 78 79 80
## 0.6638466830 0.0287818808 0.7466372448 0.1219554375 0.2783324295
## 81 82 83 84 85
## 0.2295092865 0.6690569652 0.0688568088 0.0872832884 0.0976052627
## 86 87 88 89 90
## 0.2764550604 0.6975851211 0.3467476441 0.0494996363 0.0180520128
## 91 92 93 94 95
## 0.2764550604 0.6220719228 0.1966760861 0.7131671029 0.2478017569
## 96 97 98 99 100
## 0.0043802849 0.4915369444 0.2667624017 0.5070303483 0.2885338301
## 101 102 103 104 105
## 0.4385547073 0.2205722708 0.3885949935 0.1808085518 0.7016444889
## 106 107 108 109 110
## 0.5877239174 0.3353962185 0.6440943681 0.4246268841 0.4762213581
## 111 112 113 114 115
## 0.7096442702 0.0239371148 0.5793385791 0.1879278257 0.4906383410
## 116 117 118 119 120
## 0.4262112389 0.1298031066 0.4762213581 0.1319840557 0.5877239174
## 121 122 123 124 125
## 0.9708365815 0.3638684231 0.6504882020 0.0337599856 0.5726007149
## 126 127 128 129 130
## 0.5771828461 0.7058371381 0.6296685807 0.0688568088 0.3817652613
## 131 132 133 134 135
## 0.5389031298 0.1319840557 0.3780003280 0.6512269030 0.6001923638
## 136 137 138 139 140
## 0.2086508325 0.1580918653 0.4959886231 0.7077877593 0.0299420205
## 141 142 143 144 145
## 0.1863108146 0.1655202605 0.3285890016 0.3836954889 0.5579078791
## 146 147 148 149 150
## 0.2131275985 0.3802677201 0.3272262774 0.2928130163 0.2117845256
## 151 152 153 154 155
## 0.1436636722 0.3300754158 0.1040930327 0.3417080158 0.2215738403
## 156 157 158 159 160
## 0.6440943681 0.6735162668 0.1467836173 0.1269265207 0.5726007149
## 161 162 163 164 165
## 0.0743338423 0.7154874435 0.1655202605 0.0976052627 0.6187644948
## 166 167 168 169 170
## 0.6361448379 0.6956989387 0.2486530274 0.3643258854 1.1296686144
## 171 172 173 174 175
## 0.4942142060 0.6001923638 0.6835760687 0.1100665998 0.1366627220
## 176 177 178 179 180
## 0.1959253002 0.1621587232 0.3094028915 0.9498940099 0.5950561820
## 181 182 183 184 185
## 0.1857370412 0.2131275985 0.1610023798 0.3962861302 0.6187148292
## 186 187 188 189 190
## 0.1959253002 0.2764550604 0.4713708554 0.2550199757 0.1580918653
## 191 192 193 194 195
## 0.1100665998 0.6635493174 0.0239371148 0.1393121850 0.1366627220
## 196 197 198 199 200
## 0.5726007149 0.2326012774 0.6692371056 0.5899560716 0.6316287496
## 201 202 203 204 205
## 0.3817652613 0.1808085518 0.2215738403 0.1037666697 0.6284486157
## 206 207 208 209 210
## 0.0372232949 0.6066175113 0.3291685400 0.0856958074 0.2495956436
## 211 212 213 214 215
## 0.6456039557 0.7132341364 0.1040930327 0.3763848190 0.2549407954
## 216 217 218 219 220
## 0.6544872975 0.4009689898 0.5726007149 0.6753883059 0.7058371381
## 221 222 223 224 225
## 0.4219646915 0.2880891304 0.5911898417 0.1808085518 0.3530243231
## 226 227 228 229 230
## 0.1245030312 0.6753883059 0.6968726978 0.0337599856 0.6893745877
## 231 232 233 234 235
## 1.1455749872 0.3407303171 0.2783324295 0.5385537300 0.2915781944
## 236 237 238 239 240
## 0.1366627220 0.5818124075 0.6834862764 0.2682855888 0.6296685807
## 241 242 243 244 245
## 0.7891246958 0.1781140696 0.6289696604 0.2385961875 0.6440379322
## 246 247 248 249 250
## 0.1655202605 0.4836617493 0.0287818808 0.4501902855 0.2131275985
## 251 252 253 254 255
## 0.7231228369 0.4584291207 0.7019462323 0.0419342273 0.9910038567
## 256 257 258 259 260
## 0.4694454081 0.6628517321 0.5410612176 0.2568428256 0.4957281692
## 261 262 263 264 265
## 0.2568428256 0.1808085518 0.7026886852 0.3530243231 0.6740544821
## 266 267 268 269 270
## 0.1319840557 0.7114303844 0.6887739042 0.5406597269 0.2682855888
## 271 272 273 274 275
## 0.0092637476 0.6815538732 0.1088889218 0.6187148292 0.2117845256
## 276 277 278 279 280
## 0.3643377734 0.7102409776 0.6032426066 0.0974727935 0.7019462323
## 281 282 283 284 285
## 0.4813702586 0.4447805660 0.3909457765 0.3500922525 0.4090422955
## 286 287 288 289 290
## 0.1269265207 0.1319840557 0.4729339595 0.4350540289 0.2782023360
## 291 292 293 294 295
## 1.5868340551 1.8309491857 1.7181767115 1.1259812244 0.2388301496
## 296 297 298 299 300
## 0.6831229409 1.8018848904 0.6440943681 0.4501902855 0.0228432922
## 301 302 303 304 305
## 0.3838077040 1.1641953469 0.0077007697 0.5029994698 0.4090422955
## 306 307 308 309 310
## 0.6208731515 0.0049880716 0.6257934624 0.7725785909 0.4501941310
## 311 312 313 314 315
## 0.2603767345 0.4527335316 0.1378317566 1.3454787635 0.6372027475
## 316 317 318 319 320
## 0.1837242267 0.7096442702 0.5899560716 0.4134629066 0.3418819069
## 321 322 323 324 325
## 0.5410612176 0.5291688659 0.2047525135 0.5528107955 0.5407675153
## 326 327 328 329 330
## 0.6361448379 0.1106484944 0.6032426066 0.4843242908 0.5687868370
## 331 332 333 334 335
## 0.5227023489 0.3860869495 0.4495196273 0.8993550585 0.3647946589
## 336 337 338 339 340
## 0.4933009445 0.5175831712 0.2711733649 1.1224335300 0.4447805660
## 341 342 343 344 345
## 0.4915369444 0.4625025234 0.6622239170 1.1163792013 0.1627619718
## 346 347 348 349 350
## 0.6372027475 2.1441076820 0.6666772293 0.7295181311 0.4625025234
## 351 352 353 354 355
## 1.8212827943 0.6815538732 0.2152912651 0.2280803102 0.0692623989
## 356 357 358 359 360
## 0.3022958773 0.6440943681 1.0267104204 0.6575545802 1.0603855699
## 361 362 363 364 365
## 0.5222781562 1.1871794765 1.0650451708 0.4350540289 0.2928130163
## 366 367 368 369 370
## 0.1621587232 0.3418819069 0.4083631765 1.8115946642 0.4195227979
## 371 372 373 374 375
## 0.4755339342 0.7986760786 2.1539104450 0.3918732542 0.4729339595
## 376 377 378 379 380
## 0.2468743296 0.2388301496 0.3799819190 1.8309491857 0.7102409776
## 381 382 383 384 385
## 0.5883013131 0.5640034965 0.2863007298 0.0133973480 0.6977586048
## 386 387 388 389 390
## 0.2084685762 0.5590000206 0.1334571388 0.5911664423 0.5881342110
## 391 392 393 394 395
## 0.9910038567 1.3454787635 0.6977586048 0.7841318544 0.9960937726
## 396 397 398 399 400
## 0.3909457765 0.5726007149 0.8705469428 0.6887739042 0.9342042059
## 401 402 403 404 405
## 0.4418847995 0.2603767345 0.6690569652 2.0293265344 0.2284857371
## 406 407 408 409 410
## 0.4418847995 0.5071455466 0.5661257194 1.0075693146 1.9249962641
## 411 412 413 414 415
## 0.1210375485 0.0126796547 0.6103195287 1.0521197938 0.4646905879
## 416 417 418 419 420
## 0.5406597269 0.6638466830 0.6504882020 0.4275090724 1.1830615788
## 421 422 423 424 425
## 1.0261103852 0.4275090724 0.4439652747 0.2084685762 2.1921875549
## 426 427 428 429 430
## 1.0848273092 0.2152912651 0.0431170773 0.6704493611 0.2583720000
## 431 432 433 434 435
## 0.1883777018 1.2257960280 0.3860869495 0.2301678757 1.5868340551
## 436 437 438 439 440
## 1.1296686144 0.6565704518 0.4331369956 0.4729339595 0.0486723292
## 441 442 443 444 445
## 0.2047525135 0.4643034726 0.1523057612 0.6257934624 0.4959886231
## 446 447 448 449 450
## 0.6929854670 0.1378317566 0.4955970794 1.0106294502 0.2470052215
## 451 452 453 454 455
## 0.4195227979 1.5771793083 1.8309491857 0.6735162668 0.6905101180
## 456 457 458 459 460
## 2.1539104450 0.6956989387 0.5029994698 0.5686468365 0.4530870775
## 461 462 463 464 465
## 0.5964746897 0.2215738403 0.7231228369 2.1921875549 0.8167420027
## 466 467 468 469 470
## 0.2104325393 0.6966138379 0.2759234796 0.7146468278 0.4750504057
## 471 472 473 474 475
## 1.3454787635 0.6975851211 0.6289696604 0.0934358790 0.4331369956
## 476 477 478 479 480
## 0.1314434866 0.6628517321 1.6988783683 0.6765899240 0.7533494443
## 481 482 483 484 485
## 0.5546400404 0.6856725143 1.3551049283 0.4530145013 1.0372701278
## 486 487 488 489 490
## 0.6956989387 0.0419342273 0.6767848118 0.8028542030 1.2012238385
## 491 492 493 494 495
## 0.4713708554 0.5881342110 0.3338744619 0.5293241592 0.3253876213
## 496 497 498 499 500
## 0.3417080158 1.1004218655 0.9636912915 0.3471801236 0.6257934624
## 501 502 503 504 505
## 0.4843242908 0.5542286063 2.0390968437 0.6001923638 1.1189719280
## 506 507 508 509 510
## 0.6316287496 0.2390660359 0.3471801236 0.2066278271 0.6871054543
## 511 512 513 514 515
## 0.6884171009 0.6783091693 0.4501902855 0.3712696690 0.6586953020
## 516 517 518 519 520
## 1.8309491857 0.8897730690 0.0077007697 0.5115091565 1.0258715293
## 521 522 523 524 525
## 1.0171503637 0.3515499065 0.4388447310 0.4418847995 0.3036559238
## 526 527 528 529 530
## 1.2354201459 0.5410612176 0.5590000206 0.6735797620 0.7024897756
## 531 532 533 534 535
## 0.5410612176 2.0293265344 0.1106484944 0.0024982781 2.5762404972
## 536 537 538 539 540
## 0.1942301470 0.4306966893 0.5927411794 0.1844628668 0.6835760687
## 541 542 543 544 545
## 0.7437042057 0.0336468612 0.5806881835 0.1984323173 0.8897730690
## 546 547 548 549 550
## 0.3690329880 0.1418662452 0.6887739042 1.1478100487 0.5389031298
## 551 552 553 554 555
## 0.3036559238 0.6966781290 0.4915369444 1.0139802381 1.2298457877
## 556 557 558 559 560
## 3.2316705771 0.4278250492 1.1237109952 0.2880891304 0.2470052215
## 561 562 563 564 565
## 0.6628517321 0.4828550435 0.2928130163 2.0488454002 0.4090422955
## 566 567 568 569 570
## 0.0101976029 0.1581044263 0.3417080158 0.2470052215 0.5423435911
## 571 572 573 574 575
## 0.2258463407 0.2364176796 0.0688568088 0.0692623989 1.1455749872
## 576 577 578 579 580
## 0.1210375485 0.1378317566 0.0795832758 0.3690329880 1.2354201459
## 581 582 583 584 585
## 0.2337871520 0.1479770934 0.6829135679 1.5771793083 1.1866585963
## 586 587 588 589 590
## 0.3272262774 0.6753883059 0.1837242267 0.5780348884 0.0077007697
## 591 592 593 594 595
## 0.1353889743 0.8897730690 0.2743065214 0.1783048009 0.5636237644
## 596 597 598 599 600
## 0.2066278271 0.7533494443 0.4429449964 0.1276684855 0.7116903206
## 601 602 603 604 605
## 1.3551049283 0.8854404782 0.7026886852 0.2782023360 1.2106794863
## 606 607 608 609 610
## 0.4447805660 0.2821651378 0.3580990380 1.2546048799 1.9152569347
## 611 612 613 614 615
## 1.1163792013 0.9087513737 0.2979569096 0.2979569096 1.7085383790
## 616 617 618 619 620
## 1.0848273092 0.1627619718 0.0024982781 1.1167208181 0.4755339342
## 621 622 623 624 625
## 0.6638985782 0.6575545802 0.4915369444 0.4142920329 0.2682966011
## 626 627 628 629 630
## 0.6831229409 1.1635738535 0.3643377734 0.2667624017 0.6509521671
## 631 632 633 634 635
## 0.3145316475 0.4906383410 0.4388447310 0.5384841700 0.4829042727
## 636 637 638 639 640
## 0.0431170773 0.1580918653 0.5063683859 0.3817652613 0.4526540489
## 641 642 643 644 645
## 0.6018892275 0.7357704341 0.2547461955 0.0126796547 0.6018892275
## 646 647 648 649 650
## 0.5881342110 1.2098350995 0.2335081269 0.6956989387 0.6961813282
## 651 652 653 654 655
## 0.5556597015 0.0385011464 0.6969654416 0.4385547073 0.2611153140
## 656 657 658 659 660
## 0.6468886427 0.7704352286 0.1334571388 0.5389031298 0.7077877593
## 661 662 663 664 665
## 0.4313014250 0.0733137435 0.6066175113 0.6692299521 0.6693938065
## 666 667 668 669 670
## 0.6815538732 0.6440379322 0.3643258854 0.6157810679 0.4584291207
## 671 672 673 674 675
## 0.1700635709 0.2495956436 0.4906383410 0.1230842575 0.0419342273
## 676 677 678 679 680
## 0.3064536463 0.4035969720 0.2131275985 0.4530870775 0.1942301470
## 681 682 683 684 685
## 0.5793385791 0.6628517321 0.0990850945 0.1844628668 0.2682855888
## 686 687 688 689 690
## 0.6831229409 0.8028542030 0.3285890016 0.6694716744 0.0571601798
## 691 692 693 694 695
## 0.5389031298 0.3590079716 0.0635150221 0.6638466830 0.6929854670
## 696 697 698 699 700
## 0.5451186142 0.6385908865 0.5291688659 0.5451186142 0.3836954889
## 701 702 703 704 705
## 0.2478017569 0.2486530274 0.6296685807 0.1576784631 0.6235122054
## 706 707 708 709 710
## 0.3471801236 0.5385537300 0.6157810679 0.1580918653 0.2550199757
## 711 712 713 714 715
## 0.3666597772 1.0903759714 0.6157810679 0.4776479770 1.1172375802
## 716 717 718 719 720
## 0.1177553927 1.2134828812 0.6767848118 0.4843242908 0.0671304587
## 721 722 723 724 725
## 0.5728190442 0.5556597015 0.4526540489 0.6835760687 0.1314434866
## 726 727 728 729 730
## 0.6048057840 0.6740544821 0.2548338168 0.0672326833 0.6704493611
## 731 732 733 734 735
## 0.4669256506 0.7116903206 0.5291688659 0.6066175113 0.6808356741
## 736 737 738 739 740
## 0.3638684231 0.2819854778 0.7250810761 0.5297808993 0.6512269030
## 741 742 743 744 745
## 0.1543989349 0.6330891677 0.6372027475 0.6338238369 0.5877239174
## 746 747 748 749 750
## 0.3418819069 0.5950561820 0.5996125124 0.0976052627 0.6504882020
## 751 752 753 754 755
## 0.7765925667 0.5223301857 0.7235150629 0.1298031066 0.5406597269
## 756 757 758 759 760
## 0.7026886852 0.6841085000 0.3997506501 1.1665520016 0.5223301857
## 761 762 763 764 765
## 0.6887739042 0.0003701466 0.6529543372 0.2603767345 0.5389031298
## 766 767 768 769 770
## 0.3972178764 0.9740326735 0.4836617493 0.6982599557 0.5297808993
## 771 772 773 774 775
## 0.6739795357 0.2390660359 0.2819854778 0.6640485305 0.6815538732
## 776 777 778 779 780
## 0.6520141419 0.5974591325 0.2478017569 0.4275090724 0.7183708266
## 781 782 783 784 785
## 0.1857370412 0.1863108146 0.4713708554 0.2295092865 0.5556597015
## 786 787 788 789 790
## 0.6765899240 0.6339627716 0.3836954889 0.2335081269 0.2948675970
## 791 792 793 794 795
## 0.7992947010 0.6435554692 0.2390660359 0.6937776671 0.4828550435
## 796 797 798 799 800
## 0.0635150221 0.1868918760 0.5881342110 0.6739795357 0.5185763752
## 801 802 803 804 805
## 0.5115091565 0.7001653489 0.6604938533 0.7016444889 0.6586664028
## 806 807 808 809 810
## 0.3749704476 0.6877599135 0.6338238369 0.4429449964 0.1697733654
## 811 812 813 814 815
## 0.6068578022 0.5846067170 0.7231228369 0.3937611784 0.6690569652
## 816 817 818 819 820
## 0.2117433819 0.5763473355 0.3643377734 0.6735162668 0.6220719228
## 821 822 823 824 825
## 0.4142920329 0.3949511187 0.4195227979 0.5423435911 0.3530243231
## 826 827 828 829 830
## 0.6544872975 0.6316287496 0.6693938065 0.2111465950 0.8520512825
## 831 832 833 834 835
## 0.5185763752 0.1314434866 0.6610124617 0.3327185567 0.6103195287
## 836 837 838 839 840
## 0.4927296097 0.4813702586 0.5071455466 0.6801432488 0.1863108146
## 841 842 843 844 845
## 0.3580990380 0.5899560716 0.4212850432 0.5542286063 0.2470052215
## 846 847 848 849 850
## 0.6537725602 0.6339627716 0.6841371294 0.0049880716 0.5927411794
## 851 852 853 854 855
## 0.5566290899 0.5877239174 0.4388447310 0.1037666697 0.6877599135
## 856 857 858 859 860
## 0.2389103113 0.6587724093 0.6879478553 0.2364176796 0.0589587282
## 861 862 863 864 865
## 0.4722518934 0.4195227979 1.4946426569 0.6018892275 0.5060104768
## 866 867 868 869 870
## 0.4625025234 1.0179812953 0.9787010837 0.7102360733 0.7250810761
## 871 872 873 874 875
## 0.2384664947 0.5222781562 0.6815538732 0.5964746897 0.6837031950
## 876 877 878 879 880
## 0.6610124617 0.1984323173 0.6897321560 0.9864149548 1.0520027323
## 881 882 883 884 885
## 1.0171503637 0.7350368562 1.8018848904 2.1921875549 2.9417453621
## 886 887 888 889 890
## 0.1210375485 0.6925757906 0.1314434866 0.1883777018 1.1617817519
## 891 892 893 894 895
## 1.9347138935 0.4088516259 0.7725785909 1.9347138935 1.0171503637
## 896 897 898 899 900
## 0.3937118792 0.4615572234 0.3647946589 1.4946426569 0.6635493174
## 901 902 903 904 905
## 0.6048057840 0.4527335316 0.0329891226 0.5293241592 0.6587746504
## 906 907 908 909 910
## 1.0075693146 0.3765634391 0.6638466830 0.4944508286 0.3738845218
## 911 912 913 914 915
## 1.3454787635 1.1163792013 0.8993550585 0.6586664028 1.5771793083
## 916 917 918 919 920
## 1.2354201459 0.8897730690 0.6298183755 1.2641653163 1.3454787635
## 921 922 923 924 925
## 2.1921875549 1.8212827943 0.0795832758 0.5556597015 0.3064536463
## 926 927 928 929 930
## 1.6988783683 0.4755339342 0.5523790739 0.4250061965 0.5542286063
## 931 932 933 934 935
## 0.6897321560 0.0606451289 0.7627735707 1.5868340551 1.6060788910
## 936 937 938 939 940
## 1.4946426569 0.0329891226 0.4722518934 0.6635493174 1.2641653163
## 941 942 943 944 945
## 0.6638466830 2.1927721699 0.3362762996 0.3036559238 0.7049207558
## 946 947 948 949 950
## 0.3860869495 0.5753428464 0.5846067170 0.1210375485 0.4331369956
## 951 952 953 954 955
## 0.1732348143 1.0802983463 1.1244347565 1.1740912816 1.1067562003
## 956 957 958 959 960
## 0.7243528148 1.2450231264 0.0281516095 1.8018848904 0.5640034965
## 961 962 963 964 965
## 0.4135075047 1.3647097963 0.4233096066 0.6801432488 0.2470052215
## 966 967 968 969 970
## 1.1067562003 0.4527335316 0.3738845218 0.5645728493 2.1342832140
## 971 972 973 974 975
## 1.1259812244 0.2301678757 0.0077007697 1.2354201459 1.2354201459
## 976 977 978 979 980
## 2.1342832140 0.2184864069 0.0795832758 0.4625025234 1.8018848904
## 981 982 983 984 985
## 0.4527335316 0.1479770934 1.2361220567 0.6827224070 0.6032426066
## 986 987 988 989 990
## 1.1259812244 1.3551049283 0.5883013131 0.7049207558 0.3972178764
## 991 992 993 994 995
## 1.8309491857 0.8993550585 0.0329891226 0.3738845218 0.7243528148
## 996 997 998 999 1000
## 0.0281516095 1.5771793083 0.9077798691 0.0795832758 1.4754487465
## 1001 1002 1003 1004 1005
## 0.2484286067 1.9444097270 1.0650451708 0.8801703352 1.9347138935
## 1006 1007 1008 1009 1010
## 1.5771793083 0.6735162668 1.9347138935 0.2583720000 0.7131671029
## 1011 1012 1013 1014 1015
## 0.6969654416 1.1355621816 0.7049207558 2.0390968437 0.7068483758
## 1016 1017 1018 1019 1020
## 0.6032426066 1.7085383790 1.1163792013 0.6488590894 0.5175996642
## 1021 1022 1023 1024 1025
## 0.6284486157 1.2009042576 0.8881263393 0.0003701466 0.0384032676
## 1026 1027 1028 1029 1030
## 0.8705469428 0.4526540489 0.6131493966 0.0799466612 1.6988783683
## 1031 1032 1033 1034 1035
## 0.2284857371 1.1980107186 0.6586664028 0.5293241592 0.2583720000
## 1036 1037 1038 1039 1040
## 1.1172375802 1.2450231264 0.5407675153 0.3417080158 1.1966984486
## 1041 1042 1043 1044 1045
## 0.3643258854 0.3414734522 0.1418662452 0.1638441750 2.0390968437
## 1046 1047 1048 1049 1050
## 0.1378317566 0.6968726978 0.6610124617 0.2819854778 0.4233096066
## 1051 1052 1053 1054 1055
## 0.0795832758 0.4643034726 1.3742932765 0.8705469428 1.7181767115
## 1056 1057 1058 1059 1060
## 0.6719450363 1.8212827943 0.5661257194 1.9152569347 0.2682855888
## 1061 1062 1063 1064 1065
## 0.5661257194 0.5927411794 0.2979569096 0.2979569096 1.3647097963
## 1066 1067 1068 1069 1070
## 0.4750504057 0.7038105911 1.0587340749 1.0171503637 0.8796165014
## 1071 1072 1073 1074 1075
## 0.3253876213 0.5071455466 0.0795832758 0.6208731515 0.6887739042
## 1076 1077 1078 1079 1080
## 0.4819815630 0.0228432922 0.4957281692 0.5785539610 1.8115946642
## 1081 1082 1083 1084 1085
## 1.1096812091 0.4179909726 1.2257960280 1.9444097270 1.3551049283
## 1086 1087 1088 1089 1090
## 0.7116903206 1.3856282201 0.7049207558 0.3418819069 0.5614028822
## 1091 1092 1093 1094 1095
## 0.4713708554 0.6270913980 0.5785539610 0.5929654621 0.1984323173
## 1096 1097 1098 1099 1100
## 1.6060788910 1.1259812244 0.6739795357 0.4429449964 0.0239371148
## 1101 1102 1103 1104 1105
## 2.0488454002 1.4946426569 0.6694716744 1.9347138935 1.6060788910
## 1106 1107 1108 1109 1110
## 1.2257960280 1.0267104204 0.4483110225 1.5771793083 0.6968726978
## 1111 1112 1113 1114 1115
## 1.2641653163 1.6891967734 0.5542286063 0.3738845218 0.1697733654
## 1116 1117 1118 1119 1120
## 0.0179174212 1.1355621816 0.6289696604 0.8267989319 0.7503168801
## 1121 1122 1123 1124 1125
## 1.8212827943 0.5929654621 0.2084685762 1.8018848904 0.3909457765
## 1126 1127 1128 1129 1130
## 0.6174239367 0.1479770934 1.6988783683 1.4658195611 1.8018848904
## 1131 1132 1133 1134 1135
## 2.0293265344 0.9787010837 1.4850564759 1.8212827943 2.0293265344
## 1136 1137 1138 1139 1140
## 0.2819854778 0.4306966893 0.4915369444 0.7629742672 0.5060104768
## 1141 1142 1143 1144 1145
## 0.4829042727 0.5687868370 0.4134629066 1.0267104204 0.7629742672
## 1146 1147 1148 1149 1150
## 0.2470052215 0.3690329880 0.6693938065 0.6835760687 1.2641653163
## 1151 1152 1153 1154 1155
## 0.5883013131 0.7340386349 0.6962748496 0.7049207558 1.3647097963
## 1156 1157 1158 1159 1160
## 0.2979569096 0.0384032676 0.6330891677 0.7068483758 0.6704493611
## 1161 1162 1163 1164 1165
## 0.3972178764 0.6330891677 0.4713708554 1.2450231264 1.2087197375
## 1166 1167 1168 1169 1170
## 0.3937118792 1.4658195611 0.7891246958 1.4946426569 0.7824941466
## 1171 1172 1173 1174 1175
## 0.3036559238 0.0571601798 1.6988783683 0.4527335316 0.0976052627
## 1176 1177 1178 1179 1180
## 1.4946426569 0.7891246958 0.7146468278 2.1441076820 0.6977586048
## 1181 1182 1183 1184 1185
## 0.9979673604 0.0899212938 0.0532270876 1.0267104204 0.4527335316
## 1186 1187 1188 1189 1190
## 0.5406597269 0.6897321560 2.1539104450 0.3643258854 0.3580990380
## 1191 1192 1193 1194 1195
## 0.0589587282 0.2319725688 0.4729339595 0.2384664947 0.6488590894
## 1196 1197 1198 1199 1200
## 0.6841371294 0.0126796547 0.1783048009 0.0126796547 0.0589587282
## 1201 1202 1203 1204 1205
## 0.3036559238 0.4957281692 0.4306966893 0.9979673604 0.2152912651
## 1206 1207 1208 1209 1210
## 0.1467836173 0.4625025234 1.3647097963 0.4483110225 0.6956989387
## 1211 1212 1213 1214 1215
## 1.9347138935 0.0692623989 0.6587746504 0.7243528148 0.1732348143
## 1216 1217 1218 1219 1220
## 0.4942142060 0.6959093482 0.2152912651 0.6544872975 0.7146468278
## 1221 1222 1223 1224 1225
## 0.6504882020 1.4850564759 0.8993550585 0.6191736315 0.6841371294
## 1226 1227 1228 1229 1230
## 0.5883013131 0.0915818254 0.1946544040 0.4246268841 0.6220719228
## 1231 1232 1233 1234 1235
## 1.0267104204 0.0281516095 0.8923014577 0.1966760861 1.4658195611
## 1236 1237 1238 1239 1240
## 0.3690329880 2.0488454002 0.0692623989 0.3527809282 0.5899560716
## 1241
## 0.0281516095
mean(Error2)
## [1] 0.6042886
sd(Error2)
## [1] 0.4595743
sqrt(sum(Residuales2^2)/summary(mod2)$df[2])
## [1] 0.1499655
AIC(mod2)
## [1] -1182.421
mod3 <- lm(log(Ht)~log(DAP_cm), datos)
mod3
##
## Call:
## lm(formula = log(Ht) ~ log(DAP_cm), data = datos)
##
## Coefficients:
## (Intercept) log(DAP_cm)
## 1.353 0.540
summary(mod3)
##
## Call:
## lm(formula = log(Ht) ~ log(DAP_cm), data = datos)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.669e-04 -1.324e-04 1.733e-05 1.412e-04 3.216e-04
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.353e+00 3.511e-05 38548 <2e-16 ***
## log(DAP_cm) 5.400e-01 1.183e-05 45665 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0001652 on 1239 degrees of freedom
## Multiple R-squared: 1, Adjusted R-squared: 1
## F-statistic: 2.085e+09 on 1 and 1239 DF, p-value: < 2.2e-16
{par(mfrow=c(2,2))
plot(mod3)}
En este modelo solo hallamos los residulaes ya que el procesos para hallar los predichos se hace con un Factor de correción (FC) que se hace posteriormente.
Residuales3 <- residuals(mod3)
shapiro.test(Residuales3)
##
## Shapiro-Wilk normality test
##
## data: Residuales3
## W = 0.97111, p-value = 4.896e-15
Para encontrar los valores predichos, usamos un factor de correcion (FC)
FC <- exp(anova(mod3)$'Mean Sq'[2]/2)
FC
## [1] 1
predichos3 <- exp(predict(mod3))*FC
Error3 <- abs(((predichos3-datos$Ht)/datos$Ht)*100)
Error3
## 1 2 3 4 5
## 4.056369e-03 4.792180e-03 1.459643e-02 6.946395e-03 1.792116e-02
## 6 7 8 9 10
## 7.727279e-03 1.547355e-02 5.534930e-03 4.522517e-03 3.364564e-03
## 11 12 13 14 15
## 1.033412e-02 1.657517e-02 1.570569e-02 1.937037e-02 8.056198e-03
## 16 17 18 19 20
## 1.688351e-02 6.725376e-03 6.972388e-03 1.987145e-02 1.717809e-02
## 21 22 23 24 25
## 1.051921e-02 8.056198e-03 2.141901e-02 8.330715e-03 1.276478e-02
## 26 27 28 29 30
## 1.411707e-02 8.882090e-03 6.181970e-03 5.981170e-03 1.910458e-02
## 31 32 33 34 35
## 1.440347e-03 2.591403e-02 5.047034e-03 1.916516e-02 3.044857e-03
## 36 37 38 39 40
## 2.074582e-02 1.977447e-02 1.406578e-02 8.942959e-04 1.160245e-02
## 41 42 43 44 45
## 2.776059e-02 9.424168e-03 1.276623e-02 6.525099e-03 1.733850e-02
## 46 47 48 49 50
## 2.283210e-02 1.033412e-02 1.925815e-03 1.411707e-02 1.229670e-02
## 51 52 53 54 55
## 1.493688e-02 6.421265e-03 1.276478e-02 1.663986e-03 2.027584e-02
## 56 57 58 59 60
## 9.850627e-03 1.848483e-02 3.754622e-03 4.792180e-03 1.283494e-02
## 61 62 63 64 65
## 1.689704e-02 5.896479e-03 1.212029e-02 3.710848e-03 5.896479e-03
## 66 67 68 69 70
## 1.293931e-03 3.872805e-03 2.950198e-02 1.365318e-02 8.241197e-03
## 71 72 73 74 75
## 2.127361e-02 1.268919e-02 6.385673e-03 1.212147e-02 4.260007e-03
## 76 77 78 79 80
## 2.030629e-02 3.790309e-03 1.408689e-02 2.046267e-02 1.194822e-02
## 81 82 83 84 85
## 7.867252e-03 1.323484e-02 1.786519e-02 1.922980e-02 9.560482e-03
## 86 87 88 89 90
## 3.710848e-03 3.761733e-03 1.766532e-03 5.215344e-03 2.111399e-03
## 91 92 93 94 95
## 3.710848e-03 1.365318e-02 6.051308e-03 1.263375e-02 1.404416e-02
## 96 97 98 99 100
## 1.689704e-02 1.347967e-02 1.047690e-03 3.595322e-03 8.692576e-03
## 101 102 103 104 105
## 1.629158e-02 9.668263e-03 3.559891e-03 4.692345e-03 1.925815e-03
## 106 107 108 109 110
## 1.871604e-02 6.385673e-03 7.576184e-03 2.412311e-02 2.312983e-02
## 111 112 113 114 115
## 9.944695e-03 6.383936e-03 6.585463e-03 9.464498e-03 2.289529e-02
## 116 117 118 119 120
## 2.176125e-02 4.881333e-03 2.312983e-02 1.864238e-02 1.871604e-02
## 121 122 123 124 125
## 1.610947e-02 2.151303e-02 8.151865e-03 1.332523e-03 1.632421e-02
## 126 127 128 129 130
## 1.565914e-03 2.324046e-02 8.590084e-03 1.786519e-02 1.325046e-02
## 131 132 133 134 135
## 7.131216e-03 1.864238e-02 1.278058e-02 1.975081e-02 2.778456e-03
## 136 137 138 139 140
## 1.487671e-02 3.843425e-03 1.724076e-02 1.202093e-02 8.330715e-03
## 141 142 143 144 145
## 1.520075e-02 3.969420e-03 1.657517e-02 1.118332e-02 8.620522e-03
## 146 147 148 149 150
## 8.056198e-03 3.578713e-03 1.970172e-02 6.581881e-03 1.130082e-02
## 151 152 153 154 155
## 4.056369e-03 8.041186e-03 1.717809e-02 3.030083e-02 9.304003e-03
## 156 157 158 159 160
## 7.576184e-03 9.850627e-03 1.845085e-02 1.061114e-02 1.632421e-02
## 161 162 163 164 165
## 1.592081e-02 1.623527e-02 3.969420e-03 9.560482e-03 1.694945e-02
## 166 167 168 169 170
## 2.011198e-02 1.977447e-02 2.064124e-02 1.925449e-02 1.120868e-02
## 171 172 173 174 175
## 6.594218e-03 2.778456e-03 1.582382e-03 7.245952e-03 4.392935e-03
## 176 177 178 179 180
## 2.995308e-02 1.763078e-02 1.635895e-03 1.220493e-02 1.160245e-02
## 181 182 183 184 185
## 2.997024e-03 8.056198e-03 3.332678e-03 5.047034e-03 1.048800e-02
## 186 187 188 189 190
## 2.995308e-02 3.710848e-03 2.950198e-02 2.920476e-03 3.843425e-03
## 191 192 193 194 195
## 7.245952e-03 2.966816e-04 6.383936e-03 1.862458e-02 4.392935e-03
## 196 197 198 199 200
## 1.632421e-02 1.321766e-02 1.022576e-02 4.207147e-03 1.788320e-02
## 201 202 203 204 205
## 1.325046e-02 4.692345e-03 9.304003e-03 8.330157e-03 1.212147e-02
## 206 207 208 209 210
## 1.593018e-02 4.064673e-03 2.278211e-02 1.245504e-02 1.813897e-02
## 211 212 213 214 215
## 1.044201e-02 2.095985e-02 1.717809e-02 2.103669e-03 1.007928e-02
## 216 217 218 219 220
## 2.291029e-02 9.424168e-03 1.632421e-02 2.283447e-02 2.324046e-02
## 221 222 223 224 225
## 7.841204e-03 2.300453e-02 2.235111e-02 4.692345e-03 1.469929e-02
## 226 227 228 229 230
## 1.559605e-02 2.283447e-02 2.127361e-02 1.332523e-03 8.602671e-03
## 231 232 233 234 235
## 4.911023e-03 1.790577e-02 1.194822e-02 1.688351e-02 2.296963e-02
## 236 237 238 239 240
## 4.392935e-03 1.112169e-02 1.113976e-02 1.502395e-02 8.590084e-03
## 241 242 243 244 245
## 1.527981e-02 1.617240e-02 1.679162e-02 5.896479e-03 2.276877e-02
## 246 247 248 249 250
## 3.969420e-03 1.987145e-02 3.790309e-03 1.980041e-02 8.056198e-03
## 251 252 253 254 255
## 2.219251e-02 1.603313e-02 1.128732e-02 2.226682e-02 1.216537e-02
## 256 257 258 259 260
## 1.167841e-02 1.818901e-02 9.961463e-03 1.596078e-02 1.031522e-02
## 261 262 263 264 265
## 1.596078e-02 4.692345e-03 1.528448e-02 1.469929e-02 1.566239e-02
## 266 267 268 269 270
## 1.864238e-02 1.599276e-02 2.141901e-02 2.091422e-02 1.502395e-02
## 271 272 273 274 275
## 1.945243e-02 2.373792e-02 1.849997e-02 1.048800e-02 1.130082e-02
## 276 277 278 279 280
## 7.568092e-03 8.895245e-03 8.942959e-04 1.643400e-02 1.128732e-02
## 281 282 283 284 285
## 8.523196e-03 8.569303e-03 2.598017e-02 8.379086e-03 1.293931e-03
## 286 287 288 289 290
## 1.061114e-02 1.864238e-02 1.976894e-03 1.459643e-02 1.838909e-02
## 291 292 293 294 295
## 1.722536e-02 2.272244e-02 1.837853e-02 4.385156e-03 2.073855e-02
## 296 297 298 299 300
## 1.823664e-02 3.550260e-02 7.576184e-03 1.980041e-02 2.534673e-02
## 301 302 303 304 305
## 2.806272e-02 1.368723e-02 2.114449e-02 6.741360e-03 1.293931e-03
## 306 307 308 309 310
## 3.199406e-03 1.138147e-02 1.171842e-02 3.143806e-02 1.389515e-02
## 311 312 313 314 315
## 1.542269e-02 1.218905e-02 2.906474e-02 3.412890e-02 2.519471e-02
## 316 317 318 319 320
## 2.098149e-02 9.944695e-03 4.207147e-03 1.187959e-02 1.033412e-02
## 321 322 323 324 325
## 9.961463e-03 4.538836e-03 2.346970e-02 2.308617e-03 1.161040e-02
## 326 327 328 329 330
## 2.011198e-02 1.632997e-02 8.942959e-04 4.122762e-03 2.063898e-02
## 331 332 333 334 335
## 6.525099e-03 1.230902e-02 1.334075e-02 2.596682e-02 1.558492e-03
## 336 337 338 339 340
## 1.408861e-04 2.445753e-03 1.259053e-02 2.361078e-05 8.569303e-03
## 341 342 343 344 345
## 1.347967e-02 1.865991e-02 2.746860e-03 1.728562e-02 1.900233e-02
## 346 347 348 349 350
## 2.519471e-02 8.917871e-03 1.190384e-02 5.695452e-03 1.865991e-02
## 351 352 353 354 355
## 3.024007e-03 2.373792e-02 2.490243e-02 2.322060e-02 1.659508e-02
## 356 357 358 359 360
## 1.910458e-02 7.576184e-03 2.728103e-02 2.545800e-02 8.815808e-03
## 361 362 363 364 365
## 2.244178e-03 1.844918e-02 8.079084e-04 1.459643e-02 6.581881e-03
## 366 367 368 369 370
## 1.763078e-02 1.033412e-02 2.694650e-03 1.638357e-02 2.264615e-03
## 371 372 373 374 375
## 2.855336e-02 4.883163e-04 1.354085e-02 1.916516e-02 1.976894e-03
## 376 377 378 379 380
## 2.587678e-03 2.073855e-02 2.192631e-02 2.272244e-02 8.895245e-03
## 381 382 383 384 385
## 5.806787e-03 2.601200e-02 6.550492e-03 3.247146e-02 3.656419e-03
## 386 387 388 389 390
## 9.264624e-03 2.776989e-02 5.044437e-03 2.175983e-02 2.114357e-02
## 391 392 393 394 395
## 1.216537e-02 3.412890e-02 3.656419e-03 1.224819e-02 1.094236e-02
## 396 397 398 399 400
## 2.598017e-02 1.632421e-02 6.478841e-03 2.141901e-02 5.569536e-03
## 401 402 403 404 405
## 1.248374e-02 1.542269e-02 1.323484e-02 2.175618e-02 2.182586e-03
## 406 407 408 409 410
## 1.248374e-02 1.659984e-02 1.245314e-02 2.925240e-03 1.037396e-02
## 411 412 413 414 415
## 1.660063e-02 2.380043e-02 6.725376e-03 1.019892e-02 1.122784e-02
## 416 417 418 419 420
## 2.091422e-02 2.030629e-02 8.151865e-03 2.543424e-02 6.768817e-03
## 421 422 423 424 425
## 7.827922e-03 2.543424e-02 1.163512e-02 9.264624e-03 1.939583e-03
## 426 427 428 429 430
## 3.934463e-03 2.490243e-02 2.888125e-02 2.595447e-02 9.662983e-03
## 431 432 433 434 435
## 1.570569e-02 3.456006e-02 1.230902e-02 1.071707e-02 1.722536e-02
## 436 437 438 439 440
## 1.120868e-02 2.796653e-03 2.055431e-04 1.976894e-03 1.754999e-02
## 441 442 443 444 445
## 2.346970e-02 2.309896e-02 1.820593e-02 1.171842e-02 1.724076e-02
## 446 447 448 449 450
## 1.161543e-02 2.906474e-02 1.731159e-03 4.362513e-03 2.994445e-02
## 451 452 453 454 455
## 2.264615e-03 3.429980e-02 2.272244e-02 9.850627e-03 8.075990e-03
## 456 457 458 459 460
## 1.354085e-02 1.977447e-02 6.741360e-03 2.770597e-02 1.774218e-02
## 461 462 463 464 465
## 2.194809e-02 9.304003e-03 2.219251e-02 1.939583e-03 1.401541e-02
## 466 467 468 469 470
## 2.034371e-03 4.061256e-03 1.293668e-02 2.445681e-02 1.469792e-02
## 471 472 473 474 475
## 3.412890e-02 3.761733e-03 1.679162e-02 1.051921e-02 2.055431e-04
## 476 477 478 479 480
## 1.700414e-02 1.818901e-02 1.862141e-02 2.253238e-02 1.198095e-02
## 481 482 483 484 485
## 1.937037e-02 1.547355e-02 1.921888e-02 3.118272e-03 1.389480e-03
## 486 487 488 489 490
## 1.977447e-02 2.226682e-02 8.499162e-03 1.707904e-02 5.333547e-03
## 491 492 493 494 495
## 2.950198e-02 2.114357e-02 1.700101e-02 1.753106e-02 3.279806e-03
## 496 497 498 499 500
## 3.030083e-02 2.381870e-03 1.206420e-02 1.041077e-02 1.171842e-02
## 501 502 503 504 505
## 4.122762e-03 2.066489e-02 4.490067e-04 2.778456e-03 7.332313e-03
## 506 507 508 509 510
## 1.788320e-02 2.318557e-02 1.041077e-02 2.782872e-02 3.509321e-03
## 511 512 513 514 515
## 3.424483e-03 1.075950e-02 1.980041e-02 1.711654e-02 1.233426e-02
## 516 517 518 519 520
## 2.272244e-02 1.493355e-02 2.114449e-02 1.494182e-02 1.648466e-02
## 521 522 523 524 525
## 1.498876e-02 1.689418e-02 1.971243e-02 1.248374e-02 3.407010e-03
## 526 527 528 529 530
## 2.079468e-02 9.961463e-03 2.776989e-02 1.890700e-02 3.997456e-03
## 531 532 533 534 535
## 9.961463e-03 2.175618e-02 1.632997e-02 2.240004e-02 9.063019e-03
## 536 537 538 539 540
## 2.216242e-02 7.324314e-03 1.013192e-02 9.063841e-03 1.582382e-03
## 541 542 543 544 545
## 2.564290e-03 1.206729e-02 1.895680e-02 1.256469e-02 1.493355e-02
## 546 547 548 549 550
## 1.797961e-02 1.753956e-02 2.141901e-02 3.196317e-03 7.131216e-03
## 551 552 553 554 555
## 3.407010e-03 4.693791e-03 1.347967e-02 1.593504e-02 8.854851e-03
## 556 557 558 559 560
## 6.989557e-03 1.158903e-02 8.079919e-05 2.300453e-02 2.994445e-02
## 561 562 563 564 565
## 1.818901e-02 6.412342e-03 6.581881e-03 2.116571e-02 1.293931e-03
## 566 567 568 569 570
## 1.496959e-02 2.418576e-02 3.030083e-02 2.994445e-02 2.879697e-02
## 571 572 573 574 575
## 2.645972e-02 2.814069e-02 1.786519e-02 1.659508e-02 4.911023e-03
## 576 577 578 579 580
## 1.660063e-02 2.906474e-02 1.632469e-02 1.797961e-02 2.079468e-02
## 581 582 583 584 585
## 7.399838e-03 2.670194e-02 1.616178e-02 3.429980e-02 9.540948e-03
## 586 587 588 589 590
## 1.970172e-02 2.283447e-02 2.098149e-02 4.162273e-03 2.114449e-02
## 591 592 593 594 595
## 1.049365e-02 1.493355e-02 1.741883e-02 1.868877e-02 2.475320e-02
## 596 597 598 599 600
## 2.782872e-02 1.198095e-02 5.901016e-03 3.127526e-02 1.870148e-02
## 601 602 603 604 605
## 1.921888e-02 1.387869e-02 1.528448e-02 1.838909e-02 4.064851e-03
## 606 607 608 609 610
## 8.569303e-03 5.438018e-03 1.414084e-02 7.467261e-03 3.056841e-02
## 611 612 613 614 615
## 1.728562e-02 1.239845e-03 2.778988e-02 2.778988e-02 2.623415e-04
## 616 617 618 619 620
## 3.934463e-03 1.900233e-02 2.240004e-02 1.486838e-02 2.855336e-02
## 621 622 623 624 625
## 2.173158e-02 2.545800e-02 1.347967e-02 1.891339e-02 1.394232e-02
## 626 627 628 629 630
## 1.823664e-02 1.166234e-02 7.568092e-03 1.047690e-03 1.106033e-02
## 631 632 633 634 635
## 8.398114e-03 2.289529e-02 1.971243e-02 4.346923e-04 2.287149e-02
## 636 637 638 639 640
## 2.888125e-02 3.843425e-03 1.268919e-02 1.325046e-02 8.241197e-03
## 641 642 643 644 645
## 1.265457e-02 2.032712e-02 1.713548e-02 2.380043e-02 1.265457e-02
## 646 647 648 649 650
## 2.114357e-02 2.853422e-03 4.232492e-04 1.977447e-02 4.528095e-03
## 651 652 653 654 655
## 4.471119e-03 1.324881e-02 4.821840e-04 1.629158e-02 2.139708e-03
## 656 657 658 659 660
## 2.640271e-02 1.230340e-02 5.044437e-03 7.131216e-03 1.202093e-02
## 661 662 663 664 665
## 1.319248e-03 1.329748e-02 4.064673e-03 1.704099e-02 2.600272e-02
## 666 667 668 669 670
## 2.373792e-02 2.276877e-02 1.925449e-02 2.074582e-02 1.603313e-02
## 671 672 673 674 675
## 1.698200e-02 1.813897e-02 2.289529e-02 5.520402e-03 2.226682e-02
## 676 677 678 679 680
## 1.276478e-02 1.744971e-02 8.056198e-03 1.774218e-02 2.216242e-02
## 681 682 683 684 685
## 6.585463e-03 1.818901e-02 1.322527e-03 9.063841e-03 1.502395e-02
## 686 687 688 689 690
## 1.823664e-02 1.707904e-02 1.657517e-02 2.200359e-02 7.715352e-03
## 691 692 693 694 695
## 7.131216e-03 2.284829e-02 1.994914e-02 2.030629e-02 1.161543e-02
## 696 697 698 699 700
## 1.681086e-02 5.615975e-03 4.538836e-03 1.681086e-02 1.118332e-02
## 701 702 703 704 705
## 1.404416e-02 2.064124e-02 8.590084e-03 8.076313e-03 4.789141e-03
## 706 707 708 709 710
## 1.041077e-02 1.688351e-02 2.074582e-02 3.843425e-03 2.920476e-03
## 711 712 713 714 715
## 7.304335e-03 6.052221e-03 2.074582e-02 1.298316e-02 5.890978e-03
## 716 717 718 719 720
## 1.821498e-02 3.028334e-03 8.499162e-03 4.122762e-03 1.954077e-02
## 721 722 723 724 725
## 1.500178e-02 4.471119e-03 8.241197e-03 1.582382e-03 1.700414e-02
## 726 727 728 729 730
## 1.231873e-03 1.566239e-02 1.170188e-02 4.522517e-03 2.595447e-02
## 731 732 733 734 735
## 2.980592e-05 1.870148e-02 4.538836e-03 4.064673e-03 1.681125e-02
## 736 737 738 739 740
## 2.151303e-02 9.643326e-03 2.405840e-02 2.436152e-04 1.975081e-02
## 741 742 743 744 745
## 1.811999e-02 6.282317e-03 2.519471e-02 1.212029e-02 1.871604e-02
## 746 747 748 749 750
## 1.033412e-02 1.160245e-02 1.347547e-02 9.560482e-03 8.151865e-03
## 751 752 753 754 755
## 1.011974e-02 1.848483e-02 2.519331e-02 4.881333e-03 2.091422e-02
## 756 757 758 759 760
## 1.528448e-02 1.744690e-02 1.235511e-02 3.527724e-03 1.848483e-02
## 761 762 763 764 765
## 2.141901e-02 1.420638e-02 2.938044e-03 1.542269e-02 7.131216e-03
## 766 767 768 769 770
## 7.552769e-03 5.728232e-03 1.987145e-02 3.452249e-03 2.436152e-04
## 771 772 773 774 775
## 2.656703e-02 2.318557e-02 9.643326e-03 2.601192e-02 2.373792e-02
## 776 777 778 779 780
## 8.748505e-03 3.506596e-03 1.404416e-02 2.543424e-02 2.095908e-02
## 781 782 783 784 785
## 2.997024e-03 1.520075e-02 2.950198e-02 7.867252e-03 4.471119e-03
## 786 787 788 789 790
## 2.253238e-02 6.838117e-03 1.118332e-02 4.232492e-04 4.951976e-04
## 791 792 793 794 795
## 9.971605e-03 4.931380e-03 2.318557e-02 6.545723e-03 6.412342e-03
## 796 797 798 799 800
## 1.994914e-02 3.179364e-03 2.114357e-02 2.656703e-02 2.297595e-02
## 801 802 803 804 805
## 1.494182e-02 8.698221e-03 6.181970e-03 1.925815e-03 2.201040e-02
## 806 807 808 809 810
## 1.696269e-02 3.828473e-03 1.212029e-02 5.901016e-03 1.957017e-02
## 811 812 813 814 815
## 1.826322e-02 1.516759e-02 2.219251e-02 1.864776e-02 1.323484e-02
## 816 817 818 819 820
## 1.123462e-02 1.324448e-02 7.568092e-03 9.850627e-03 1.365318e-02
## 821 822 823 824 825
## 1.891339e-02 6.277803e-03 2.264615e-03 2.879697e-02 1.469929e-02
## 826 827 828 829 830
## 2.291029e-02 1.788320e-02 2.600272e-02 4.547197e-03 6.414098e-04
## 831 832 833 834 835
## 2.297595e-02 1.700414e-02 2.776059e-02 8.747608e-03 6.725376e-03
## 836 837 838 839 840
## 3.647863e-03 8.523196e-03 1.659984e-02 4.834871e-05 1.520075e-02
## 841 842 843 844 845
## 1.414084e-02 4.207147e-03 2.245159e-02 2.066489e-02 2.994445e-02
## 846 847 848 849 850
## 2.283210e-02 6.838117e-03 1.001066e-02 1.138147e-02 1.013192e-02
## 851 852 853 854 855
## 2.303903e-03 1.871604e-02 1.971243e-02 8.330157e-03 3.828473e-03
## 856 857 858 859 860
## 1.229670e-02 1.613212e-02 1.586553e-02 2.814069e-02 1.700318e-02
## 861 862 863 864 865
## 2.531495e-02 2.264615e-03 2.066724e-02 1.265457e-02 9.342752e-03
## 866 867 868 869 870
## 1.865991e-02 8.476080e-03 3.191020e-02 1.233035e-02 2.405840e-02
## 871 872 873 874 875
## 1.601741e-03 2.244178e-03 2.373792e-02 2.194809e-02 1.324391e-02
## 876 877 878 879 880
## 2.776059e-02 1.256469e-02 3.364564e-03 8.186976e-04 1.657090e-03
## 881 882 883 884 885
## 1.498876e-02 5.278250e-03 3.550260e-02 1.939583e-03 5.936098e-03
## 886 887 888 889 890
## 1.660063e-02 4.529236e-03 1.700414e-02 1.570569e-02 3.648901e-03
## 891 892 893 894 895
## 1.011835e-02 9.644667e-03 3.143806e-02 1.011835e-02 1.498876e-02
## 896 897 898 899 900
## 2.284366e-02 7.983087e-03 1.558492e-03 2.066724e-02 2.966816e-04
## 901 902 903 904 905
## 1.231873e-03 1.218905e-02 2.703998e-02 1.753106e-02 6.474275e-03
## 906 907 908 909 910
## 2.925240e-03 5.154655e-03 2.030629e-02 1.615154e-02 3.310818e-02
## 911 912 913 914 915
## 3.412890e-02 1.728562e-02 2.596682e-02 2.201040e-02 3.429980e-02
## 916 917 918 919 920
## 2.079468e-02 1.493355e-02 9.555052e-03 2.196762e-02 3.412890e-02
## 921 922 923 924 925
## 1.939583e-03 3.024007e-03 1.632469e-02 4.471119e-03 1.276478e-02
## 926 927 928 929 930
## 1.862141e-02 2.855336e-02 1.876832e-02 3.828470e-03 2.066489e-02
## 931 932 933 934 935
## 3.364564e-03 3.760590e-03 8.200902e-03 1.722536e-02 1.773850e-02
## 936 937 938 939 940
## 2.066724e-02 2.703998e-02 2.531495e-02 2.966816e-04 2.196762e-02
## 941 942 943 944 945
## 2.030629e-02 1.112390e-02 6.790161e-03 3.407010e-03 3.305932e-02
## 946 947 948 949 950
## 1.230902e-02 3.930060e-03 1.516759e-02 1.660063e-02 2.055431e-04
## 951 952 953 954 955
## 1.992782e-02 1.450905e-02 3.354068e-03 1.239321e-02 2.995209e-02
## 956 957 958 959 960
## 1.565306e-02 6.786206e-03 1.906375e-02 3.550260e-02 2.601200e-02
## 961 962 963 964 965
## 1.549075e-02 4.056221e-03 6.131967e-03 4.834871e-05 2.994445e-02
## 966 967 968 969 970
## 2.995209e-02 1.218905e-02 3.310818e-02 5.426890e-03 3.106153e-02
## 971 972 973 974 975
## 4.385156e-03 1.071707e-02 2.114449e-02 2.079468e-02 2.079468e-02
## 976 977 978 979 980
## 3.106153e-02 5.804312e-03 1.632469e-02 1.865991e-02 3.550260e-02
## 981 982 983 984 985
## 1.218905e-02 2.670194e-02 1.710621e-02 1.781704e-02 8.942959e-04
## 986 987 988 989 990
## 4.385156e-03 1.921888e-02 5.806787e-03 3.305932e-02 7.552769e-03
## 991 992 993 994 995
## 2.272244e-02 2.596682e-02 2.703998e-02 3.310818e-02 1.565306e-02
## 996 997 998 999 1000
## 1.906375e-02 3.429980e-02 4.237097e-03 1.632469e-02 1.232787e-02
## 1001 1002 1003 1004 1005
## 5.549862e-03 3.091092e-02 8.079084e-04 4.118884e-03 1.011835e-02
## 1006 1007 1008 1009 1010
## 3.429980e-02 9.850627e-03 1.011835e-02 9.662983e-03 1.263375e-02
## 1011 1012 1013 1014 1015
## 4.821840e-04 8.751099e-03 3.305932e-02 4.490067e-04 1.985400e-02
## 1016 1017 1018 1019 1020
## 8.942959e-04 2.623415e-04 1.728562e-02 4.792180e-03 2.501682e-02
## 1021 1022 1023 1024 1025
## 1.212147e-02 1.187484e-02 8.534679e-03 1.420638e-02 1.823651e-02
## 1026 1027 1028 1029 1030
## 6.478841e-03 8.241197e-03 5.838424e-03 1.411707e-02 1.862141e-02
## 1031 1032 1033 1034 1035
## 2.182586e-03 3.711687e-03 2.201040e-02 1.753106e-02 9.662983e-03
## 1036 1037 1038 1039 1040
## 5.890978e-03 6.786206e-03 1.161040e-02 3.030083e-02 9.989987e-04
## 1041 1042 1043 1044 1045
## 1.925449e-02 1.562686e-02 1.753956e-02 1.518853e-02 4.490067e-04
## 1046 1047 1048 1049 1050
## 2.906474e-02 2.127361e-02 2.776059e-02 9.643326e-03 6.131967e-03
## 1051 1052 1053 1054 1055
## 1.632469e-02 2.309896e-02 1.136105e-02 6.478841e-03 1.837853e-02
## 1056 1057 1058 1059 1060
## 2.147113e-02 3.024007e-03 1.245314e-02 3.056841e-02 1.502395e-02
## 1061 1062 1063 1064 1065
## 1.245314e-02 1.013192e-02 2.778988e-02 2.778988e-02 4.056221e-03
## 1066 1067 1068 1069 1070
## 1.469792e-02 2.429622e-02 1.370657e-03 1.498876e-02 6.857383e-03
## 1071 1072 1073 1074 1075
## 3.279806e-03 1.659984e-02 1.632469e-02 3.199406e-03 2.141901e-02
## 1076 1077 1078 1079 1080
## 3.215555e-02 2.534673e-02 1.031522e-02 1.331841e-02 1.638357e-02
## 1081 1082 1083 1084 1085
## 2.492765e-03 5.718732e-03 3.456006e-02 3.091092e-02 1.921888e-02
## 1086 1087 1088 1089 1090
## 1.870148e-02 2.391048e-03 3.305932e-02 1.033412e-02 1.985195e-02
## 1091 1092 1093 1094 1095
## 2.950198e-02 2.617919e-02 1.331841e-02 1.673964e-02 1.256469e-02
## 1096 1097 1098 1099 1100
## 1.773850e-02 4.385156e-03 2.656703e-02 5.901016e-03 6.383936e-03
## 1101 1102 1103 1104 1105
## 2.116571e-02 2.066724e-02 2.200359e-02 1.011835e-02 1.773850e-02
## 1106 1107 1108 1109 1110
## 3.456006e-02 2.728103e-02 2.156125e-02 3.429980e-02 2.127361e-02
## 1111 1112 1113 1114 1115
## 2.196762e-02 3.670090e-02 2.066489e-02 3.310818e-02 1.957017e-02
## 1116 1117 1118 1119 1120
## 2.003274e-02 8.751099e-03 1.679162e-02 1.124746e-03 7.866322e-05
## 1121 1122 1123 1124 1125
## 3.024007e-03 1.673964e-02 9.264624e-03 3.550260e-02 2.598017e-02
## 1126 1127 1128 1129 1130
## 1.788887e-02 2.670194e-02 1.862141e-02 2.843045e-02 3.550260e-02
## 1131 1132 1133 1134 1135
## 2.175618e-02 3.191020e-02 4.037339e-03 3.024007e-03 2.175618e-02
## 1136 1137 1138 1139 1140
## 9.643326e-03 7.324314e-03 1.347967e-02 2.160502e-02 9.342752e-03
## 1141 1142 1143 1144 1145
## 2.287149e-02 2.063898e-02 1.187959e-02 2.728103e-02 2.160502e-02
## 1146 1147 1148 1149 1150
## 2.994445e-02 1.797961e-02 2.600272e-02 1.582382e-03 2.196762e-02
## 1151 1152 1153 1154 1155
## 5.806787e-03 6.646532e-03 3.133157e-03 3.305932e-02 4.056221e-03
## 1156 1157 1158 1159 1160
## 2.778988e-02 1.823651e-02 6.282317e-03 1.985400e-02 2.595447e-02
## 1161 1162 1163 1164 1165
## 7.552769e-03 6.282317e-03 2.950198e-02 6.786206e-03 5.816141e-03
## 1166 1167 1168 1169 1170
## 2.284366e-02 2.843045e-02 1.527981e-02 2.066724e-02 7.747078e-03
## 1171 1172 1173 1174 1175
## 3.407010e-03 7.715352e-03 1.862141e-02 1.218905e-02 9.560482e-03
## 1176 1177 1178 1179 1180
## 2.066724e-02 1.527981e-02 2.445681e-02 8.917871e-03 3.656419e-03
## 1181 1182 1183 1184 1185
## 8.911278e-03 1.619103e-02 3.087163e-02 2.728103e-02 1.218905e-02
## 1186 1187 1188 1189 1190
## 2.091422e-02 3.364564e-03 1.354085e-02 1.925449e-02 1.414084e-02
## 1191 1192 1193 1194 1195
## 1.700318e-02 1.988039e-02 1.976894e-03 1.601741e-03 4.792180e-03
## 1196 1197 1198 1199 1200
## 1.001066e-02 2.380043e-02 1.868877e-02 2.380043e-02 1.700318e-02
## 1201 1202 1203 1204 1205
## 3.407010e-03 1.031522e-02 7.324314e-03 8.911278e-03 2.490243e-02
## 1206 1207 1208 1209 1210
## 1.845085e-02 1.865991e-02 4.056221e-03 2.156125e-02 1.977447e-02
## 1211 1212 1213 1214 1215
## 1.011835e-02 1.659508e-02 6.474275e-03 1.565306e-02 1.992782e-02
## 1216 1217 1218 1219 1220
## 6.594218e-03 9.902029e-03 2.490243e-02 2.291029e-02 2.445681e-02
## 1221 1222 1223 1224 1225
## 8.151865e-03 4.037339e-03 2.596682e-02 2.812393e-02 1.001066e-02
## 1226 1227 1228 1229 1230
## 5.806787e-03 1.093581e-02 1.406578e-02 2.412311e-02 1.365318e-02
## 1231 1232 1233 1234 1235
## 2.728103e-02 1.906375e-02 1.071116e-03 6.051308e-03 2.843045e-02
## 1236 1237 1238 1239 1240
## 1.797961e-02 2.116571e-02 1.659508e-02 2.595933e-02 4.207147e-03
## 1241
## 1.906375e-02
mean(Error3)
## [1] 0.01410847
sd(Error3)
## [1] 0.008574073
sd(Error3)/mean(Error3)
## [1] 0.607725
Residuales3_1 <- predichos3-datos$Ht
sqrt(sum(Residuales3_1^2)/summary(mod3)$df[2])
## [1] 0.002964127
AIC(mod3)
## [1] -18088.22
El mejor modelo es el logarítmico por lo siguiente:
Se puede observar que el ajuste de los residuales en el modelo logarítmico se ajustan a la línea de tendencia, por lo tanto entre más muestras se tomen, los datos de van a ajustar más a dicha línea al contrario de los otros dos modelos.
El \(R^2\) en el modelo logarítmico da un valor de 1, lo que quiere decir que explica muy bien la distribución de los datos.
El AIC en el modelo logarítmico tambíen es el más pequeño en comparación con los otros dos modelos por lo cúal este modelo generaliza mejor a partir de los datos con los que se construyó.
datos$logDAP <- log(datos$DAP_cm)
ggplot(data = datos, aes(color = Bosque)) +
geom_point(aes(x = Ht, y = logDAP)) +
stat_smooth(method = "lm", aes(x = Ht, y = logDAP,color=Bosque))