# Example output dataframe from function randwin.

### Description

Output file from function `randwin`

using temperature and mass data.
Generated with `Mass`

and `MassClimate`

dataframes.

### Format

A data frame with 5 rows and 21 variables.

- deltaAICc
Difference between model AICc of fitted climate window and a null model containing no climate.

- WindowOpen
The start day of each tested climate window. Furthest from the biological record.

- WindowClose
The end day of each tested climate window. Closest to the biological record.

- ModelBeta
Beta estimate of the relationship between temperature and mass.

- Std.Error
Standard error term for linear model betas.

- ModelBetaQ
Quadratic beta estimate of the relationship between temperature and mass.

- ModelBetaC
Cubic beta estimate of the relationship between temperature and mass.

- ModelInt
Model intercept.

- Function
The function used to fit climate (e.g. linear ("lin"), quadratic ("quad"))

- Furthest
Furthest day back considered in slidingwin.

- Closest
Closest day back considered in slidingwin.

- Statistics
The aggregate statistic used to analyse climate (e.g. mean, max, slope).

- Type
Whether "fixed" or "variable" climate windows were tested.

- K
Number of folds used for k-fold cross validation.

- ModWeight
Model weight of each fitted climate window.

- sample.size
Sample size (i.e. number of years or sites) used for climate window analysis.

- Reference.day,Reference.month
If type is "absolute", the date from which the climate window was tested.

- Randomised
Whether the data was generated using

`slidingwin`

or`randwin`

.- Repeat
The number of randomisations carried out.

- WeightDist
Model spread of 95 percent confidence set of models.