dendroTools (version 1.0.0)

daily_response: daily_response

Description

Function calculates all possible values of a selected statistical metric between one or more response variables and daily sequences of environmental data. Calculations are based on moving window which is defined with two arguments: window width and a location in a matrix of daily sequences of environmental data. Window width could be fixed (use fixed_width) or variable width (use lower_limit and upper_limit arguments). In this case, all window widths between lower and upper limit will be used. 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 daily sequences of environmental data (column names).

Usage

daily_response(response, env_data, method = "lm", metric = "r.squared",
  lower_limit = 30, upper_limit = 90, fixed_width = 0,
  previous_year = FALSE, neurons = 1, brnn_smooth = TRUE,
  remove_insignificant = TRUE, alpha = 0.05, row_names_subset = FALSE,
  PCA_transformation = FALSE, log_preprocess = TRUE,
  components_selection = "automatic", eigenvalues_threshold = 1,
  N_components = 2, aggregate_function = "mean",
  temporal_stability_check = "sequential", k = 2,
  cross_validation_type = "blocked", subset_years = NULL,
  plot_specific_window = NULL, ylimits = NULL, seed = NULL,
  tidy_env_data = FALSE, reference_window = "start")

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 daily 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. 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 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", "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.

lower_limit

lower limit of window width

upper_limit

upper limit of window width

fixed_width

fixed width used for calculation. If fixed_width is assigned a value, upper_limit and lower_limit will be ignored

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

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.

PCA_transformation

if set to TRUE, all variables in the response data frame will be transformed using PCA transformation.

log_preprocess

if set to TRUE, variables will be transformed with logarithmic transformation before used in PCA

components_selection

character string specifying how to select the Principal Components used as predictors. There are three options: "automatic", "manual" and "plot_selection". If argument is set to automatic, all scores with eigenvalues above 1 will be selected. This threshold could be changed by changing the eigenvalues_threshold argument. If parameter is set to "manual", user should set the number of components with N_components argument. If components selection is set to "plot_selection", Scree plot will be shown and a user must manually enter the number of components to be used as predictors.

eigenvalues_threshold

threshold for automatic selection of Principal Components

N_components

number of Principal Components used as predictors

aggregate_function

character string specifying how the daily 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 variables will be analysed. Current possibilities are "sequential" and "progressive". 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.

k

integer, number of breaks (splits) for temporal stability and cross validation analysis.

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)

plot_specific_window

integer representing window width to be displayed for plot_specific

ylimits

limit of the y axes for plot_extreme and plot_specific. 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", "DOY", "Precipitation/Temperature/etc."

reference_window

character string, the reference_window argument describes, how each calculation is referred. There are three different options: 'start' (default), 'end' and 'middle'. If the reference_window argument is set to 'start', then each calculation is related to the starting day of window. If the reference_window argument is set to 'middle', each calculation is related to the middle day of window calculation. If the reference_window argument is set to 'end', then each calculation is related to the ending day of window calculation. For example, if we consider correlations with window from DOY 15 to DOY 35. If reference window is set to <U+2018>start<U+2019>, then this calculation will be related to the DOY 15. If the reference window is set to <U+2018>end<U+2019>, then this calculation will be related to the DOY 35. If the reference_window is set to 'middle', then this calculation is related to DOY 25. The optimal selection, which describes the optimal consecutive days that returns the highest calculated metric and is obtained by the $plot_extreme output, is the same for all three reference windows.

Value

a list with 13 elements:

1	$calculations	a matrix with calculated metrics
2	$method	the character string of a method
3	$metric	the character string indicating the metric used for calculations
4	$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
5	$optimized_return	data frame with two columns, response variable and aggregated (averaged) daily data that return the optimal results. This data.frame could be directly used to calibrate a model for climate reconstruction
6	$optimized_return_all	a data frame with aggregated daily data, that returned the optimal result for the entire env_data (and not only subset of analysed years)
7	$transfer_function	a ggplot object: scatter plot of optimized return and a transfer line of the selected method
8	$temporal_stability	a data frame with calculations of selected metric for different temporal subsets
9	$cross_validation	a data frame with cross validation results
10	$plot_heatmap	ggplot2 object: a heatmap of calculated metrics
11	$plot_extreme	ggplot2 object: line plot of a row with the highest value in a matrix of calculated metrics
12	$plot_specific	ggplot2 object: line plot of a row with a selected window width in a matrix of calculated metrics

Examples

# NOT RUN {
# Load the dendroTools R package
library(dendroTools)

# Load data
data(data_MVA)
data(data_TRW)
data(data_TRW_1)
data(example_proxies_individual)
data(example_proxies_1)
data(LJ_daily_temperatures)

# 1 Example with fixed width
example_fixed_width <- daily_response(response = data_MVA, env_data = LJ_daily_temperatures,
                                     method = "cor", fixed_width = 0,
                                     row_names_subset = TRUE, remove_insignificant = TRUE,
                                     alpha = 0.05, aggregate_function = 'mean',
                                     reference_window = "end")
example_fixed_width$plot_extreme

# 2 Example for past and present
example_MVA_past <- daily_response(response = data_MVA, env_data = LJ_daily_temperatures,
method = "cor", lower_limit = 21, upper_limit = 180,
row_names_subset = TRUE, previous_year = TRUE,
remove_insignificant = TRUE, alpha = 0.05,
plot_specific_window = 60, subset_years = c(1940, 1980), aggregate_function = 'sum')

example_MVA_present <- daily_response(response = data_MVA, env_data = LJ_daily_temperatures,
                                      method = "cor", lower_limit = 21, upper_limit = 60,
                                      row_names_subset = TRUE, previous_year = TRUE,
                                      remove_insignificant = TRUE, alpha = 0.05,
                                      plot_specific_window = 60, subset_years = c(1981, 2010),
                                      aggregate_function = 'sum')

example_MVA_past$plot_heatmap
example_MVA_present$plot_heatmap
example_MVA_past$plot_specific
example_MVA_present$plot_specific

# 3 Example PCA
example_PCA <- daily_response(response = example_proxies_individual,
                              env_data = LJ_daily_temperatures, method = "lm",
                              lower_limit = 21, upper_limit = 180,
                              row_names_subset = TRUE, remove_insignificant = TRUE,
                              alpha = 0.01, PCA_transformation = TRUE,
                              components_selection = "manual", N_components = 2)

summary(example_PCA$PCA_output)
example_PCA$plot_heatmap

# 4 Example negative correlations
example_neg_cor <- daily_response(response = data_TRW_1, env_data = LJ_daily_temperatures,
                                  method = "cor", lower_limit = 21, upper_limit = 180,
                                  row_names_subset = TRUE, remove_insignificant = TRUE,
                                  alpha = 0.05)

example_neg_cor$plot_heatmap
example_neg_cor$plot_extreme
example_neg_cor$temporal_stability

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

example_multiproxy <- daily_response(response = example_proxies_1,
                                     env_data = LJ_daily_temperatures,
                                     method = "lm", metric = "adj.r.squared",
                                     lower_limit = 21, upper_limit = 180,
                                     row_names_subset = TRUE, previous_year = FALSE,
                                     remove_insignificant = TRUE, alpha = 0.05)

example_multiproxy$plot_heatmap
# }