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Multiple Comparisons for REML

Multiple Comparisons for REML is available via the Linear Mixed Models Further Output dialog, but only if the option Show multiple comparisons on menus has been enabled on the Tools | Options | Menus tab.

Multiple comparison tests are designed to take account of the fact that there may be many possible comparisons between pairs of treatment means in an analysis of variance (with n treatments there are n(n – 1)/2). So, some researchers feel that their significance levels should be adjusted to take account of all the tests that they might make – and this can be achieved by use of a multiple comparison test.

Conversely, it has been pointed out that multiple comparisons are unnecessary if you have only a small number of comparisons to make – either because there are few treatments, or because you should have identified beforehand the comparisons that you feel are likely to be of interest. Also, they are inappropriate if the treatments have any sort of structure. For example, the levels of a treatment factor may represent different amounts of a substance like a fertiliser or a drug. It would then be more sensible to assess the treatment effect over all its levels by fitting some sort of trend (see ANOVA Contrasts), and implausible to assume that only some of the amounts might have an effect. Alternatively, the treatments may have a factorial structure, and you should then be more interested in studying the main effects and interactions of the various factors. For further discussion of the issues see Nelder (1971), Maindonald and Cox (1984) and Perry (1986).

For a REML analysis, the estimated degrees of freedom apply to the term as a whole, and may vary over the contrasts within the term. Further, the standard errors of the contrasts may also vary within the term, and this can lead to inconsistent results, where there may be gaps in the columns of letters indicating non-significant differences. Thus, the results should from this analysis be taken with caution.

Term

Lists the treatment factors for the current analysis. Select the treatment factor that the multiple comparisons and/or simultaneous confidence intervals are to be performed on.

Method used for degrees of freedom

Specifies which denominator degrees of freedom (d.f.) to use for the tests.

Calculated d.f. using method The REML analysis calculates the degrees of freedom using the selected method: Automatic – allow Genstat to assess the model itself and decide automatically whether to do the computations and which method to use, Algebraic – d.f. are calculated using algebraic derivatives (which may involve large matrix calculations) or Numerical – d.f. are calculated using numerical derivatives (which require an extra evaluation of the mixed model equations for every variance parameter).
Given d.f. The degrees of freedom entered into the field are used.
Try calculating the d.f. and use given d.f. if this fails. Try calculating the d.f., as in the first option and use the given d.f. in the second option if this fails.

Test

This lets you select the type of multiple comparison tests to be performed. The following choices are available:

Fisher’s protected LSD
Fisher’s unprotected LSD
Bonferroni
Sidak

Please see the references below for a full technical description of the methods used when calculating these statistics.

Use studentized range test in LSD test

If the Fisher’s Protected Least Significant Difference or the Fisher’s Unprotected Least Significant Difference test are selected, the LSD test uses the Studentized Range statistic rather than Student’s t (for further information see Hsu, 1996, page 139).

Significance level

Specifies the experiment-wise significance level for the intervals. You must enter a number between 0 and 1 (Default 0.05).

Sort means

Lets you arrange the means in either Ascending or Descending order.

Display

Specifies which items of output are to be produced.

Comparisons The differences between the pair of means, upper and lower confidence limits for the differences, t-statistics and an indication of whether or not they are significant.
Description Description including information such as the experiment-wise and compartment-wise error rates.
Critical values Gives critical values for the t-statistic for situations where these do not vary amongst the comparisons (i.e. for the Scheffe, Bonferroni and Sidak methods, as well as the Fisher LSD methods, provided all the comparisons have the same number of residual degrees of freedom).
Pairwise probability plot The probabilities of differences between means displayed in a shade plot.
Means with letters The means, with identical letters (a, b etc.) alongside those that do not differ significantly.
Means with lines The means, with lines joining those that do not differ significantly.
Mean-mean scatter plot Produces a mean-mean scatter plot (see Hsu 1996, pages 151-153).

Store

Specifies which items of output are to be saved. After selecting the appropriate boxes, you need to type the names for the identifiers of the data structures into the corresponding In: fields. The results will be in the order specified in the Sort means option, unless the Unsorted option is selected.

Labels The labels for the predicted means.
Means The predicted mean values.
Letters Letters indicating which means are significantly different.
Significances A symmetric matrix indicating which means are significantly different (0 = not different, 1 = different).
Confidence interval widths A symmetric matrix containing the width of the confidence interval for the absolute differences between the (sorted) means.

Display in spreadsheet

The saved results will also be displayed within a new spreadsheet window.

Unsorted (original factor order)

The saved results will also be displayed in order of the original factors. Using this option allows multiple saved sets of results from different y-variates to be combined into a single spreadsheet later.

References

  • Bechhofer, R.E., Santner, T.J. and Goldsman, D.M. (1995). Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons. Wiley, New York.
  • Hsu, J.C. (1996). Multiple Comparisons Theory and Methods. Chapman & Hall, London. (https://books.google.com/books?id=8AK8PUbw3lsC).
  • Maindonald, J.H. and Cox, N.R. (1984). Use of statistical evidence in some recent issues of DSIR agricultural journals. New Zealand Journal of Agricultural Research 27, 597-610.
  • Nelder, J.A. (1976). Discussion on papers by Wynn, Bloomfield, O’Neill and Wetherall. Journal of the Royal Statistical Society, Series B 33, 244-246.
  • Perry, J.N. (1986). Multiple-comparison procedures: a dissenting view. J. Econ. Entomol. 79, 1149-1155.
  • Bonferroni correction. https://en.wikipedia.org/wiki/Bonferroni_correction
  • Šidák correction. https://en.wikipedia.org/wiki/%C5%A0id%C3%A1k_correction

See also

Updated on October 28, 2020

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