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Implements a monthly bootstrap

bootstrap_month.Rd

Months in which at least one variable exceeds the user-specified minimum proportion of non-missing values are sampled with replacement. February of leap years are treated as a 13th month.

Usage

bootstrap_month(data, boot_prop = 0.8)

Arguments

data

Data frame of raw data detrended if necessary. First column should be of the Date.

boot_prop

Numeric vector of length one specifying the minimum proportion of non-missing values of at least one of the variables for a month to be included in the bootstrap. Default is 0.8.

Value

Dataframe containing a bootstrap undertaken with replacement that accounts for monthly-scale seasonality.

Examples

#Let's assess the sampling variability in kendall's tau
#correlation coefficient between rainfall and OsWL at S-22.

#Data starts on first day of 1948
head(S22.Detrend.Declustered.df)
#>         Date Rainfall OsWL Groundwater
#> 1 1948-01-01     0.00   NA          NA
#> 2 1948-01-02     0.14   NA          NA
#> 3 1948-01-03     0.00   NA          NA
#> 4 1948-01-04     0.00   NA          NA
#> 5 1948-01-05     0.00   NA          NA
#> 6 1948-01-06     0.00   NA          NA

#Dataframe ends on 1948-02-03
tail(S22.Detrend.Declustered.df)
#>             Date Rainfall OsWL Groundwater
#> 25962 2019-01-29       NA   NA        2.69
#> 25963 2019-01-30       NA   NA        2.68
#> 25964 2019-01-31       NA   NA        2.65
#> 25965 2019-02-01       NA   NA        2.64
#> 25966 2019-02-02       NA   NA        2.62
#> 25967 2019-02-03       NA   NA        2.60

#Adding dates to complete final month of combined records
final.month = data.frame(seq(as.Date("2019-02-04"),as.Date("2019-02-28"),by="day"),NA,NA,NA)
colnames(final.month) = c("Date","Rainfall","OsWL","Groundwater")
S22.Detrend.Declustered.df = rbind(S22.Detrend.Declustered.df,final.month)

#Generate 100 monthly bootstrap samples of rainfall and OsWL
cor = rep(NA,100)
for(i in 1:100){
 boot_df = bootstrap_month(S22.Detrend.df[,c(1:3)], boot_prop=0.8)
 boot_df = na.omit(boot_df)
 cor[i] = cor(boot_df$Rainfall, boot_df$OsWL, method="kendall")
}

#Compare means of bootstrap samples with the mean of the observed data
hist(cor)
df = na.omit(S22.Detrend.df[,1:3])
abline(v=cor(df$Rainfall,df$OsWL, method="kendall"),col=2,lwd=2)