- response
a data frame with tree-ring proxy variable and (optional)
years as row names. Row.names should be matched with those from env_data_primary
and env_data_control data frame. If not, set the row_names_subset argument to
TRUE.
- env_data_primary
primary 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. Row.names should be matched with
those from the response data frame. If not, set the argument row_names_subset
to TRUE. Alternatively, env_data_primary could be a tidy data with three columns,
i.e. Year, 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_primary to TRUE.
- env_data_control
a data frame of monthly sequences of environmental data as
columns and years as row names. This data is used as control for calculations of
partial correlation coefficients. Each row represents a year and each column
represents a day of a year. Row.names should be matched with those from the
response data frame. If not, set the row_names_subset argument to TRUE.
Alternatively, env_data_control could be a tidy data with three columns,
i.e. Year, 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_control to TRUE.
- previous_year
if set to TRUE, env_data_primary, env_data_control and
response variables will be rearranged in a way, that also previous year will
be used for calculations of selected statistical metric.
- pcor_method
a character string indicating which partial correlation
coefficient is to be computed. One of "pearson" (default), "kendall", or
"spearman", can be abbreviated.
- remove_insignificant
if set to TRUE, removes all correlations bellow
the significant threshold level, based on a selected alpha.
- 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)
- alpha
significance level used to remove insignificant calculations.
- row_names_subset
if set to TRUE, row.names are used to subset
env_data_primary, env_data_control and response data frames. Only years from
all three 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_env_data_primary
character string specifying how the
monthly data from env_data_primary should be aggregated. The default is 'mean',
the two other options are 'median' and 'sum'
- aggregate_function_env_data_control
character string specifying how the
monthly data from env_data_control 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
- k_running_window
the length of running window for temporal stability check.
Applicable only if temporal_stability argument is set to running window.
- 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_primary
if set to TRUE, env_data_primary should be inserted as a
data frame with three columns: "Year", "Month", "Precipitation/Temperature/etc."
- tidy_env_data_control
if set to TRUE, env_data_control 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
partial correlation coefficients
- 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
- pcor_na_use
an optional character string giving a method for computing
covariances in the presence of missing values for partial correlation
coefficients. This must be (an abbreviation of) one of the strings "all.obs",
"everything", "complete.obs", "na.or.complete", or "pairwise.complete.obs"
(default). See also the documentation for the base partial.r in psych R package