Description Usage Arguments Value Examples

Function calculates all possible partial correlation coefficients between tree-ring chronology and daily environmental (usually climate) data. Calculations are based on moving window which is defined with two arguments: lower_limit and upper_limit. All calculated (partial) correlation coeficients are stored in a matrix. The location of stored correlation in the matrix is indicating a window width (row names) and a location in a matrix of daily sequences of environmental data (column names).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
daily_response_seascorr(response, env_data_primary, env_data_control,
lower_limit = 30, upper_limit = 90, fixed_width = 0,
previous_year = FALSE, pcor_method = "pearson",
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_env_data_primary = "mean",
aggregate_function_env_data_control = "mean",
temporal_stability_check = "sequential", k = 2,
k_running_window = 30, cross_validation_type = "blocked",
subset_years = NULL, plot_specific_window = NULL, ylimits = NULL,
seed = NULL, tidy_env_data_primary = FALSE,
tidy_env_data_control = FALSE, reference_window = "start")
``` |

`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 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 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, 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_primary to TRUE. |

`env_data_control` |
a data frame of daily 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, 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_control to TRUE. |

`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. |

`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. |

`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. |

`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_env_data_primary` |
character string specifying how the daily 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 daily 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 and cross validation analysis. |

`k_running_window` |
the length of running window for temporal stability check. Applicalbe 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) |

`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_primary` |
if set to TRUE, env_data_primary should be inserted as a data frame with three columns: "Year", "DOY", "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", "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 ‘start’, then this calculation will be related to the DOY 15. If the reference window is set to ‘end’, 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. |

a list with 14 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_primary (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 |

13 | $PCA_output | princomp object: the result output of the PCA analysis |

14 | $type | the character string describing type of analysis: daily or monthly |

15 | $reference_window | character string, which referece window was used for calculations |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | ```
## 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)
data(LJ_daily_precipitation)
# 1 Basic example
example_basic <- daily_response_seascorr(response = data_MVA,
env_data_primary = LJ_daily_temperatures,
env_data_control = LJ_daily_precipitation,
row_names_subset = TRUE, lower_limit = 1,
remove_insignificant = TRUE,
aggregate_function_env_data_primary = 'median',
aggregate_function_env_data_control = 'median',
alpha = 0.05, pcor_method = "spearman",
tidy_env_data_primary = FALSE,
previous_year = TRUE,
tidy_env_data_control = TRUE,
reference_window = "middle")
summary(example_basic)
example_basic$plot_extreme
example_basic$plot_heatmap
example_basic$plot_specific
example_basic$optimized_return
example_basic$optimized_return_all
# 2 Example with fixed temporal time window
example_fixed_width <- daily_response_seascorr(response = data_MVA,
env_data_primary = LJ_daily_temperatures,
env_data_control = LJ_daily_precipitation,
row_names_subset = TRUE,
remove_insignificant = TRUE,
aggregate_function_env_data_primary = 'mean',
aggregate_function_env_data_control = 'mean',
alpha = 0.05,
fixed_width = 45,
tidy_env_data_primary = FALSE,
tidy_env_data_control = TRUE,
reference_window = "end")
example_fixed_width$plot_extreme
example_fixed_width$plot_heatmap
example_fixed_width$optimized_return
example_fixed_width$optimized_return_all
## End(Not run)
``` |

jernejjevsenak/dendroTools documentation built on June 5, 2019, 4:06 a.m.

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