monthly_response: monthly_response

Description

Function calculates all possible values of a selected statistical metric between one or more response variables and monthly sequences of environmental data. Calculations are based on moving window which slides through monthly environmental data. All calculated metrics are stored in a matrix. The location of stored calculated metric in the matrix is indicating a window width (row names) and a location in a matrix of monthly sequences of environmental data (column names).

Usage

monthly_response(
  response,
  env_data,
  method = "cor",
  metric = "r.squared",
  cor_method = "pearson",
  previous_year = FALSE,
  neurons = 1,
  lower_limit = 1,
  upper_limit = 12,
  fixed_width = 0,
  brnn_smooth = TRUE,
  remove_insignificant = TRUE,
  alpha = 0.05,
  row_names_subset = FALSE,
  reference_window = "start",
  aggregate_function = "mean",
  temporal_stability_check = "sequential",
  k = 2,
  k_running_window = 30,
  cross_validation_type = "blocked",
  subset_years = NULL,
  ylimits = NULL,
  seed = NULL,
  tidy_env_data = FALSE,
  boot = FALSE,
  boot_n = 1000,
  boot_ci_type = "norm",
  boot_conf_int = 0.95,
  month_interval = ifelse(c(previous_year == TRUE, previous_year == TRUE), c(-1, 12),
    c(1, 12)),
  dc_method = NULL,
  cor_na_use = "everything"
)

Value

a list with 17 elements:

$calculations - a matrix with calculated metrics
$method - the character string of a method
$metric - the character string indicating the metric used for calculations
$analysed_period - the character string specifying the analysed period based on the information from row names. If there are no row names, this argument is given as NA
$optimized_return - data frame with two columns, response variable and aggregated (averaged) monthly data that return the optimal results. This data.frame could be directly used to calibrate a model for climate reconstruction
$optimized_return_all - a data frame with aggregated monthly data, that returned the optimal result for the entire env_data (and not only subset of analysed years)
$transfer_function - a ggplot object: scatter plot of optimized return and a transfer line of the selected method
$temporal_stability - a data frame with calculations of selected metric for different temporal subsets
$cross_validation - a data frame with cross validation results
$plot_heatmap - ggplot2 object: a heatmap of calculated metrics
$plot_extreme - ggplot2 object: line or bar plot of a row with the highest value in a matrix of calculated metrics
$type - the character string describing type of analysis: daily or monthly
$reference_window - character string, which reference window was used for calculations
$boot_lower - matrix with lower limit of confidence intervals of bootstrap calculations
$boot_upper - matrix with upper limit of confidence intervals of bootstrap calculations
$aggregated_climate - matrix with all aggregated climate series

Arguments

response: a data frame with tree-ring proxy variables as columns and (optional) years as row names. Row.names should be matched with those from a env_data data frame. If not, set row_names_subset = TRUE.
env_data: a data frame of monthly sequences of environmental data as columns and years as row names. Each row represents a year and each column represents a day of a year (or month). Row.names should be matched with those from a response data frame. If not, set row_names_subset = TRUE. Alternatively, env_data could be a tidy data with three columns, i.e. Year, DOY (Month) and third column representing values of mean temperatures, sum of precipitation etc. If tidy data is passed to the function, set the argument tidy_env_data to TRUE.
method: a character string specifying which method to use. Current possibilities are "cor" (default), "lm" and "brnn".
metric: a character string specifying which metric to use. Current possibilities are "r.squared" and "adj.r.squared". If method = "cor", metric is not relevant.
cor_method: a character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman".
previous_year: if set to TRUE, env_data and response variables will be rearranged in a way, that also previous year will be used for calculations of selected statistical metric.
neurons: positive integer that indicates the number of neurons used for brnn method
lower_limit: lower limit of window width (i.e. number of consecutive months to be used for calculations)
upper_limit: upper limit of window width (i.e. number of consecutive months to be used for calculations)
fixed_width: fixed width used for calculations (i.e. number of consecutive months to be used for calculations)
brnn_smooth: if set to TRUE, a smoothing algorithm is applied that removes unrealistic calculations which are a result of neural net failure.
remove_insignificant: if set to TRUE, removes all correlations bellow the significant threshold level, based on a selected alpha. For "lm" and "brnn" method, squared threshold is used, which corresponds to R squared statistics.
alpha: significance level used to remove insignificant calculations.
row_names_subset: if set to TRUE, row.names are used to subset env_data and response data frames. Only years from both data frames are kept.
reference_window: character string, the reference_window argument describes, how each calculation is referred. There are two different options: 'start' (default) and 'end'. If the reference_window argument is set to 'start', then each calculation is related to the starting month of window. If the reference_window argument is set to 'end', then each calculation is related to the ending day of window calculation.
aggregate_function: character string specifying how the monthly data should be aggregated. The default is 'mean', the two other options are 'median' and 'sum'
temporal_stability_check: character string, specifying, how temporal stability between the optimal selection and response variable(s) will be analysed. Current possibilities are "sequential", "progressive" and "running_window". Sequential check will split data into k splits and calculate selected metric for each split. Progressive check will split data into k splits, calculate metric for the first split and then progressively add 1 split at a time and calculate selected metric. For running window, select the length of running window with the k_running_window argument.
k: integer, number of breaks (splits) for temporal stability and cross validation analysis.
k_running_window: the length of running window for temporal stability check. Applicable only if temporal_stability argument is set to running window.
cross_validation_type: character string, specifying, how to perform cross validation between the optimal selection and response variables. If the argument is set to "blocked", years will not be shuffled. If the argument is set to "randomized", years will be shuffled.
subset_years: a subset of years to be analyzed. Should be given in the form of subset_years = c(1980, 2005)
ylimits: limit of the y axes for plot_extreme. It should be given in the form of: ylimits = c(0,1)
seed: optional seed argument for reproducible results
tidy_env_data: if set to TRUE, env_data should be inserted as a data frame with three columns: "Year", "Month", "Precipitation/Temperature/etc."
boot: logical, if TRUE, bootstrap procedure will be used to calculate estimates correlation coefficients, R squared or adjusted R squared metrices
boot_n: The number of bootstrap replicates
boot_ci_type: A character string representing the type of bootstrap intervals required. The value should be any subset of the values c("norm","basic", "stud", "perc", "bca").
boot_conf_int: A scalar or vector containing the confidence level(s) of the required interval(s)
month_interval: a vector of two values: lower and upper time interval of months that will be used to calculate statistical metrics. Negative values indicate previous growing season months. This argument overwrites the calculation limits defined by lower_limit and upper_limit arguments.
dc_method: a character string to determine the method to detrend climate data. Possible values are "none" (default) and "SLD" which refers to Simple Linear Detrending
cor_na_use: an optional character string giving a method for computing covariances in the presence of missing values for correlation coefficients. This must be (an abbreviation of) one of the strings "everything" (default), "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs". See also the documentation for the base cor() function.

Examples

Run this code

# \donttest{

# The examples below are enclosed within donttest{} to minimize the execution
# time during R package checks.

# Load the dendroTools R package
library(dendroTools)

# Load data used for examples
data(data_MVA)
data(data_TRW)
data(data_TRW_1)
data(example_proxies_individual)
data(example_proxies_1)
data(LJ_monthly_temperatures)
data(LJ_monthly_precipitation)

# 1 Example with tidy precipitation data
example_tidy_data <- monthly_response(response = data_MVA,
    lower_limit = 1, upper = 24,
    env_data = LJ_monthly_precipitation, fixed_width = 0,
    method = "cor", row_names_subset = TRUE,
    remove_insignificant = TRUE, previous_year = FALSE,
    reference_window = "end",
    alpha = 0.05, aggregate_function = 'sum', boot = FALSE,
    tidy_env_data = TRUE, boot_n = 100, month_interval = c(-5, 10))

# summary(example_tidy_data)
# plot(example_tidy_data, type = 1)
# plot(example_tidy_data, type = 2)

# 2 Example with split data for early and late
example_MVA_early <- monthly_response(response = data_MVA,
    env_data = LJ_monthly_temperatures,
    method = "cor", row_names_subset = TRUE, previous_year = TRUE,
    remove_insignificant = TRUE, alpha = 0.05,
    subset_years = c(1940, 1980), aggregate_function = 'mean')

example_MVA_late <- monthly_response(response = data_MVA,
    env_data = LJ_monthly_temperatures,
    method = "cor", row_names_subset = TRUE, alpha = 0.05,
    previous_year = TRUE, remove_insignificant = TRUE,
    subset_years = c(1981, 2010), aggregate_function = 'mean')

# summary(example_MVA_late)
# plot(example_MVA_early, type = 1)
# plot(example_MVA_late, type = 1)
# plot(example_MVA_early, type = 2)
# plot(example_MVA_late, type = 2)

# 3 Example negative correlations
example_neg_cor <- monthly_response(response = data_TRW_1, alpha = 0.05,
   env_data = LJ_monthly_temperatures,
   method = "cor", row_names_subset = TRUE,
   remove_insignificant = TRUE, boot = FALSE)

# summary(example_neg_cor)
# plot(example_neg_cor, type = 1)
# plot(example_neg_cor, type = 2)
# example_neg_cor$temporal_stability

# 4 Example of multiproxy analysis
# summary(example_proxies_1)
# cor(example_proxies_1)

example_multiproxy <- monthly_response(response = example_proxies_1,
   env_data = LJ_monthly_temperatures,
   method = "lm", metric = "adj.r.squared",
   row_names_subset = TRUE, previous_year = FALSE,
   remove_insignificant = TRUE, alpha = 0.05)

# summary(example_multiproxy)
# plot(example_multiproxy, type = 1)

# 5 Example to test the temporal stability
example_MVA_ts <- monthly_response(response = data_MVA,
   env_data = LJ_monthly_temperatures,
   method = "lm", metric = "adj.r.squared", row_names_subset = TRUE,
   remove_insignificant = TRUE, alpha = 0.05,
   temporal_stability_check = "running_window", k_running_window = 10)

# summary(example_MVA_ts)
# example_MVA_ts$temporal_stability

# }

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