CORRELACION DE LLUVIA CON ÍNDICES CLIMÁTICOS

Cargando los datos de ejemplo

knitr::opts_chunk$set(echo = TRUE)
setwd("G:/UAAAN/MATERIAS/2019/REGRESION APLICADA/DATOS")
library(corrplot)

## corrplot 0.84 loaded

library(ecospat)

## Loading required package: ade4

## Loading required package: ape

## Loading required package: gbm

## Loaded gbm 2.1.5

## Loading required package: sp

library(ggplot2)
library(ggpubr)

## Loading required package: magrittr

## 
## Attaching package: 'ggpubr'

## The following object is masked from 'package:ape':
## 
##     rotate

library(Hmisc)

## Loading required package: lattice

## Loading required package: survival

## Loading required package: Formula

## 
## Attaching package: 'Hmisc'

## The following object is masked from 'package:ape':
## 
##     zoom

## The following objects are masked from 'package:base':
## 
##     format.pval, units

mis_datos<-read.csv("Pinus_halepensis.csv", header = T)  #entre comillas es el archivo a abrir
head(mis_datos)

##   ID    DB     H    DC   VT     BH     BF      BT   PBH   PBF
## 1  1 55.40 10.60  8.78 0.82 201.30 518.43  719.73 27.97 72.03
## 2  2 54.30 11.10  8.00 0.86 166.56 498.91  665.47 25.03 74.97
## 3  3 30.20  7.20  5.50 0.14  72.19  82.52  154.71 46.66 53.34
## 4  4 57.50  9.57  8.50 0.60 191.30 348.40  539.70 35.45 64.55
## 5  5 39.30  8.60  8.90 0.34  70.60 212.33  282.93 24.95 75.05
## 6  6 75.45 12.99 12.30 1.34 487.67 864.70 1352.37 36.06 63.94

attach(mis_datos) #este es muy importante

Calculo de las estadísticas básicas

library("pastecs")

## 
## Attaching package: 'pastecs'

## The following object is masked from 'package:magrittr':
## 
##     extract

round(stat.desc(mis_datos [2:9]), digits = 2) 

##                   DB      H     DC    VT      BH       BF       BT     PBH
## nbr.val        40.00  40.00  40.00 40.00   40.00    40.00    40.00   40.00
## nbr.null        0.00   0.00   0.00  0.00    0.00     0.00     0.00    0.00
## nbr.na          0.00   0.00   0.00  0.00    0.00     0.00     0.00    0.00
## min            26.90   7.12   4.48  0.13   23.41    78.65   102.06   22.77
## max            75.45  13.86  12.30  1.34  487.67   864.70  1352.37   46.90
## range          48.55   6.74   7.82  1.21  464.26   786.05  1250.31   24.13
## sum          1874.50 393.23 303.21 23.71 7033.63 13961.87 20995.50 1371.55
## median         46.45   9.49   7.28  0.55  178.81   299.61   524.47   34.77
## mean           46.86   9.83   7.58  0.59  175.84   349.05   524.89   34.29
## SE.mean         1.70   0.30   0.28  0.05   13.27    28.95    40.83    1.04
## CI.mean.0.95    3.44   0.60   0.56  0.10   26.83    58.55    82.60    2.09
## var           115.65   3.49   3.07  0.10 7040.39 33516.31 66697.67   42.90
## std.dev        10.75   1.87   1.75  0.31   83.91   183.07   258.26    6.55
## coef.var        0.23   0.19   0.23  0.53    0.48     0.52     0.49    0.19

Una figura entre diámetro basal y altura

plot(DB, H, xlab = "Diámetro basal (cm)", ylab = "Altura (m)",  
     lty=1:5, pch=1, col=3)  

En esta figura puede observar que existe una correlacion importate entre ambas variables

Prueba de normalidad

shapiro.test(mis_datos$DB) #

## 
##  Shapiro-Wilk normality test
## 
## data:  mis_datos$DB
## W = 0.97914, p-value = 0.6577

Inspección de normalidad con q-q plots

ggqqplot(mis_datos$DB, ylab = "DB", color = 3)

Inspección de normalidad con box plots

boxplot(mis_datos$DB, col = 3, plot = TRUE) 

Inspección de normalidad con un histograma

hist(mis_datos$DB, col = 3) #

Calculo de correlación de Pearson a la matriz de datos q-q plots

cor_2 <- rcorr(as.matrix(mis_datos))

## Warning in sqrt(1 - h * h): Se han producido NaNs

cor_2

##        ID    DB     H    DC    VT    BH    BF    BT   PBH   PBF
## ID   1.00 -0.36 -0.21 -0.38 -0.29 -0.25 -0.32 -0.31  0.04 -0.04
## DB  -0.36  1.00  0.59  0.71  0.88  0.86  0.84  0.88 -0.09  0.09
## H   -0.21  0.59  1.00  0.56  0.74  0.53  0.68  0.65 -0.36  0.36
## DC  -0.38  0.71  0.56  1.00  0.76  0.75  0.80  0.81 -0.20  0.20
## VT  -0.29  0.88  0.74  0.76  1.00  0.82  0.95  0.94 -0.35  0.35
## BH  -0.25  0.86  0.53  0.75  0.82  1.00  0.85  0.93  0.08 -0.08
## BF  -0.32  0.84  0.68  0.80  0.95  0.85  1.00  0.99 -0.39  0.39
## BT  -0.31  0.88  0.65  0.81  0.94  0.93  0.99  1.00 -0.25  0.25
## PBH  0.04 -0.09 -0.36 -0.20 -0.35  0.08 -0.39 -0.25  1.00 -1.00
## PBF -0.04  0.09  0.36  0.20  0.35 -0.08  0.39  0.25 -1.00  1.00
## 
## n= 40 
## 
## 
## P
##     ID     DB     H      DC     VT     BH     BF     BT     PBH    PBF   
## ID         0.0233 0.1900 0.0169 0.0685 0.1221 0.0464 0.0554 0.8263 0.8263
## DB  0.0233        0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.5809 0.5809
## H   0.1900 0.0000        0.0002 0.0000 0.0004 0.0000 0.0000 0.0234 0.0234
## DC  0.0169 0.0000 0.0002        0.0000 0.0000 0.0000 0.0000 0.2081 0.2081
## VT  0.0685 0.0000 0.0000 0.0000        0.0000 0.0000 0.0000 0.0274 0.0274
## BH  0.1221 0.0000 0.0004 0.0000 0.0000        0.0000 0.0000 0.6031 0.6031
## BF  0.0464 0.0000 0.0000 0.0000 0.0000 0.0000        0.0000 0.0122 0.0122
## BT  0.0554 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000        0.1186 0.1186
## PBH 0.8263 0.5809 0.0234 0.2081 0.0274 0.6031 0.0122 0.1186              
## PBF 0.8263 0.5809 0.0234 0.2081 0.0274 0.6031 0.0122 0.1186

Tarea

De la siguiente liga: http://clicom-mex.cicese.mx/, descarge los datos de a) Precipitacion, Temperatura máxima y temperatura mínima de la estacion climatológica de Saltillo. Arreglelos para que con los datos del MEI https://www.esrl.noaa.gov/psd/enso/mei/, realice correlacion mensual, no sin antes probar la normalidad de los datos para que decida que tipo de correlacion debera realizar

---

Conteste

Que tipo de correlacion es la adecuada (Pearson, Spearman, Kendall)
Que variable climática responde mejor al MEI
Cuanto explica el fenomeno del ENSO la variable climática.

alt text