predPow: Statistical analysis of a model results compared to...
In cvq2: Calculate the predictive squared correlation coefficient

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/predPow.R

Determines the model calibration or its prediction power. The statistical analysis is done with the observed values and their related prediction only, as no data about the model used to calculate the prediction is available.

  calibPow(data, obs = "observed", pred = "predicted", 
  nu = 0, round = 4, extOut = FALSE, extOutFile = NULL)
  predPow(data, obs = "observed", pred = "predicted", 
  obs_mean = NULL, nu = 0, round = 4, extOut = FALSE, 
  extOutFile = NULL)

`data`	A data frame that contains at least two columns containing the observations and their predictions. The data frame can be extended e.g. by a column that specifies the individual mean of the observed values y_{mean}^{N-k,i}.
`obs`	The name of the column that contains the observations
`pred`	The name of the column that contains the predictions
`obs_mean`	The mean of the observations y_{mean}^{N-k,i}. Can be either a string that names the actual column or the column itself
`nu`	The degrees of freedom used for `rmse` calculation, DEFAULT: 0
`round`	The rounding value used in the output, DEFAULT: 4
`extOut`	Extended output, DEFAULT: FALSE If `extOutFile` is not specified, write to stdout()
`extOutFile`	Write extended output into file (implies `extOut = TRUE`), DEFAULT: NULL

data contains the observation and the its predictions calculated with model M.

`calibPow()`

Alias: calibrationPower()
The calibration power of model M is calculated with data.

`predPow()`

Alias: predictionPower()
The prediction power of model M is calculated with data.

Returns an object of class "q2". It contains information about the model calibration or its prediction performance.

Torsten Thalheim <torstenthalheim@gmx.de>

cvq2

  require(methods)
  require(stats)
  library(cvq2)
  
  data(cvq2.sample.C)
  result <- calibPow( cvq2.sample.C )
  result
  
  data(cvq2.sample.D)
  result <- predPow( cvq2.sample.D, obs_mean="observed_mean" )
  result