goffda (version 0.0.7)

aemet_temp: AEMET daily temperatures during 1974--2013

Description

Series of daily temperatures of 73 Spanish weather stations during the 40-year period 1974--2013.

Usage

aemet_temp

Format

A list with the following entries:

temp

an fdata with 2892 temperature (in Celsius degrees) curves, discretized on 365 equispaced grid points (days) on \([0.5, 364.5]\). Each curve corresponds to the yearly records of a weather station.

df

a dataframe with metadata for each curve:

• ind: identifier of the weather station.

• name: name of the weather station.

• year: year of the observation.

Details

For consistency with the fda.usc-package's aemet dataset, the names and identifiers of the 73 weather stations are the same as in that dataset. Only a minor fix has been applied to the "A CORU<U+00D1>A/ALVEDRO" station, whose identifier was the same as the "A CORU<U+00D1>A" station, "1387". The former was set to "1387A".

Information about the province, altitude, longitude, and latitude of each weather station can be retrieved in df from the fda.usc-package's aemet dataset.

The dataset is a curated version of a larger database of 115 stations. It excludes stations with inconsistent records or that were relocated, closed, or opened during the 40-year period. There are 9 stations with missing years. The total of missing years is 28.

In leap years, the daily-average temperature is computed as the average of February 28th and 29th.

References

Febrero-Bande, M. and Oviedo de la Fuente, M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4):1--28. http://www.jstatsoft.org/v51/i04/

Examples

# NOT RUN {
## Data splitting

# Load raw data
data("aemet_temp")

# Partition the dataset in the first and last 20 years
with(aemet_temp, {
  ind_pred <- which((1974 <= df$year) & (df$year <= 1993))
  ind_resp <- which((1994 <= df$year) & (df$year <= 2013))
  aemet_temp_pred <<- list("df" = df[ind_pred, ], "temp" = temp[ind_pred])
  aemet_temp_resp <<- list("df" = df[ind_resp, ], "temp" = temp[ind_resp])
})

# Average the temperature on each period
mean_aemet <- function(x) {
  m <- tapply(X = 1:nrow(x$temp$data), INDEX = x$df$ind,
              FUN = function(i) colMeans(x$temp$data[i, , drop = FALSE],
                                         na.rm = TRUE))
 x$temp$data <- do.call(rbind, m)
 return(x$temp)
}

# Build predictor and response fdatas
aemet_temp_pred <- mean_aemet(aemet_temp_pred)
aemet_temp_resp <- mean_aemet(aemet_temp_resp)

# Plot
old_par <- par(mfrow = c(1, 2))
plot(aemet_temp_pred)
plot(aemet_temp_resp)
par(old_par)

# Average daily temperatures
day_avg_pred <- func_mean(aemet_temp_pred)
day_avg_resp <- func_mean(aemet_temp_resp)

# Average yearly temperatures
avg_year_pred <- rowMeans(aemet_temp_pred$data)
avg_year_resp <- rowMeans(aemet_temp_resp$data)
# }
# NOT RUN {
## Test the linear model with functional response and predictor

(comp_flmfr <- flm_test(X = aemet_temp_pred, Y = aemet_temp_resp,
                        est_method = "fpcr_l1s"))
beta0 <- diag(rep(1, length(aemet_temp_pred$argvals)))
(simp_flmfr <- flm_test(X = aemet_temp_pred, Y = aemet_temp_resp,
                        beta0 = beta0, est_method = "fpcr_l1s"))

# Visualize estimation
filled.contour(x = aemet_temp_pred$argvals, y = aemet_temp_resp$argvals,
               z = comp_flmfr$fit_flm$Beta_hat,
               color.palette = viridisLite::viridis, nlevels = 20)

## Test the linear model with scalar response and functional predictor

(comp_flmsr <- flm_test(X = aemet_temp_pred, Y = avg_year_resp,
                        est_method = "fpcr_l1s"))
(simp_flmsr <- flm_test(X = aemet_temp_pred, Y = avg_year_resp,
                        beta0 = 1 / 365, est_method = "fpcr_l1s"))

# Visualize estimation
plot(aemet_temp_pred$argvals, comp_flmsr$fit_flm$Beta_hat, type = "l",
     ylim = c(0, 30 / 365))
abline(h = 1 / 365, col = 2)

## Test the linear model with functional response and scalar predictor

(comp_frsp <- flm_test(X = avg_year_pred, Y = aemet_temp_resp))
(simp_frsp <- flm_test(X = avg_year_pred, Y = aemet_temp_resp, beta0 = 1))

## Test the linear model with scalar response and predictor

(comp_srsp <- flm_test(X = avg_year_pred, Y = avg_year_resp))
(simp_srsp <- flm_test(X = avg_year_pred, Y = avg_year_resp, beta0 = 1))
# }