The file "parameter_estimations.rda" is an R data object. In order to work with it, it is therefore necessary to have R installed on ones computer. See https://www.r-project.org for more information about R.

After entering an R environment, the information can be loaded by the command

	load("parameter_estimation.rda")
	
Of course, if the file is located in a different directory, the path to that directory needs to be specified.
Once the data is loaded, the relevant data is stored in the variable

	parameter.estimations
	
A first overview can be obtained by the command

	str(parameter.estimations)
	
It reveals that parameter.estimations is a 9D array. The dimensions signify the following:

1st dimension:
This dimesion specifies the model (QM, WM, or CM) that was used for analysis. The length of this dimension is 3.

2nd dimension:
This dimension specifies the algorithm that was used for analysis. "AI" stands for AIREML, "EM" stands for EMREML. The length of this dimension is 2.

3rd dimension:
This dimension specifies the true genetic covariance sigma_A_QW between queen and worker effects. The dimesion names "pos. cov.", "zero cov.", "weak neg. cov", and "strong neg. cov." represent the values of sigma_A_QW = 0.5, 0, -0.5, and -1, respectively. The length of this dimension is 4.

4th dimension:
This dimension specifies the true residual variance sigma_E^2. The dimesion names "small residual" and "large residual" represent the values of sigma_E^2 = 1 and 4, respectively. The length of this dimension is 2.

5th dimension:
This dimension specifies the percentages of queens that mated controlledly, from 0% to 100% in steps of 20%. The dimension names are "x perc. contr. mating", where x takes on the numbers 0, 20, ..., 100. The length of this dimension is 6.

6th dimension:
This dimension specifies the apiary sizes, which are 5, 10, 20, 50, and 500. The dimension names are "apiary size x", where x takes on the numbers 5, 10, 20, 50, and 500. The length of this dimension is 5.

7th dimension:
This dimension specifies the percentages of colonies with recorded performance data: 10%, 20%, 50%, 70%, 80%, 90%, and 100%. The dimension names are "x perc. data", where x takes on the numbers 10, 20, 50, 70, 80, 90, and 100. The length of this dimension is 7.

8th dimension:
This dimension specifies the repetition of the Monte Carlo simulation with specified settings. It ranges from "Repetition 1" to "Repetition 500". The length of this repetition is 500.

9th dimension:
The 44 entries in this dimension specify the data aspect of interest. The dimension names are fairly self-explicatory. True values have the prefix "true", estimated values have the prefix "est". (Co)variances are named "...sigma...", heritabilities are named "...h...", standard errors have the prefix "SE" and refer to the standard errors inherently predicted by AIREML. Note that the names for variances and heritabilities do not contain squares but are actually the squared numbers. In other words: The values under "true_sigma_E" are values for sigma_E^2 (i.e. 1 and 4) and NOT for the residual standard deviation (which would be 1 and 2).


So if one is interested in the estimate for sigma_AQ^2 that was received by 5th AIREML run with the colony model (CM) for the trait with zero covariance between effects and sigma_E^2=4 under complete mating control and data recording on apiaries of size 20, the required command would be

	parameter.estimations["CM", "AI", "zero cov.", "large residual", "100 perc. contr. mating", "apiary size 20", "100 perc. data", "Repetition 5", "est_sigma_AQ"]
	
and the result will be 

	0.62917