# Adds or recalculates the `LSD.frame` that is a component of an `alldiffs.object`. Given an `alldiffs.object`, adds or recalculate its `LSD.frame`. N.B. No changes are made to the error.intervals - use‘redoErrorIntervals.alldiffs’ to modify both the error.intervals andthe ‘LSD.frame’. ```r ## S3 method for class 'alldiffs' recalcLSD(alldiffs.obj, LSDtype = "overall", LSDsupplied = NULL, LSDby = NULL, LSDstatistic = "mean", LSDaccuracy = "maxAbsDeviation", alpha = 0.05, ...) ``` ## Arguments - `alldiffs.obj`: An `alldiffs.object`. - `LSDtype`: A `character` string that can be `overall`, `factor.combinations`, `per.prediction` or `supplied`. It determines whether the values stored in a row of a `LSD.frame` are the values calculated (i) `overall` from the LSD values for all pairwise comparison2, (ii) the values calculated from the pairwise LSDs for the levels of each `factor.combination`, unless there is only one prediction for a level of the `factor.combination`, when a notional LSD is calculated, (iii) `per.prediction`, being based, for each prediction, on all pairwise differences involving that prediction, or (iv) as `supplied` values of the LSD, specified with the `LSDsupplied` argument; these supplied values are to be placed in the `assignedLSD` column of the `LSD.frame` stored in an `alldiffs.object` so that they can be used in LSD calculations. See `LSD.frame` for further information on the values in a row of this `data.frame` and how they are calculated. - `LSDsupplied`: A `data.frame` or a named `numeric` containing a set of `LSD` values that correspond to the observed combinations of the values of the `LSDby` variables in the `predictions.frame` or a single LSD value that is an overall LSD. If a `data.frame`, it may have (i) a column for the `LSDby` variable and a column of `LSD` values or (ii) a single column of `LSD` values with rownames being the combinations of the observed values of the `LSDby` variables. Any name can be used for the column of `LSD` values; `assignedLSD` is sensible, but not obligatory. Otherwise, a `numeric` containing the `LSD` values, each of which is named for the observed combination of the values of the `LSDby` variables to which it corresponds. (Applying the `function` `dae::fac.combine` to the `predictions` component is one way of forming the required combinations for the (row) names.) The values supplied will be incorporated into `assignedLSD` column of the `LSD.frame` stored as the `LSD` component of the `alldiffs.object`. - `LSDby`: A `character` (vector) of variables names, being the names of the `factors` or `numerics` in the `classify`; for each combination of their levels and values, there will be or is a row in the `LSD.frame` stored in the `LSD` component of the `alldiffs.object` when `LSDtype` is `factor.combinatons`. - `LSDstatistic`: A `character` nominating one or more of `minimum`, `q10`, `q25`, `mean`, `median`, `q75`, `q90` or `maximum` as the value(s) to be stored in the `assignedLSD` column in an `LSD.frame`; the values in the `assignedLSD` column are used in computing `halfLeastSignificant` `error.intervals`. Here `q10`, `q25`, `q75` and `q90` indicate the sample quantiles corresponding to probabilities of 0.1, 0.25, 0.75 and 0.9 for the group of LSDs from which a single LSD value is calculated. The function `quantile` is used to obtain them. The `mean` LSD is calculated as the square root of the mean of the squares of the LSDs for the group. The `median` is calculated using the `median` function. Multiple values are only produced for `LSDtype` set to `factor.combination`, in which case `LSDby` must not be `NULL` and the number of values must equal the number of observed combinations of the values of the variables specified by `LSDby`. If `LSDstatistic` is `NULL`, it is reset to `mean`. - `LSDaccuracy`: A `character` nominating one of `maxAbsDeviation`, `maxDeviation`, `q90Deviation` or `RootMeanSqDeviation` as the statistic to be calculated as a measure of the accuracy of `assignedLSD`. The option `q90Deviation` produces the sample quantile corresponding to a probability of 0.90. The deviations are the differences between the LSDs used in calculating the LSD statistics and each assigned LSD and the accuracy is expressed as a proportion of the assigned LSD value. The calculated values are stored in the column named `accuracyLSD` in an `LSD.frame`. - `alpha`: The significance level for an LSD to compare a pair of predictions. It is stored as an attribute to the `alldiffs.object`. - ``...``: further arguments passed to `allDifferences.data.frame`; attributes `transform.power`, `offset` and `scale` cannot be passed. ## Returns An `alldiffs.object` with components `predictions`, `vcov`, `differences`, `p.differences` `sed`, `LSD` and, if present in `alldiffs.obj`, `backtransforms`. ## Author(s) Chris Brien ## See Also `asremlPlus-package`, `as.alldiffs`, `sort.alldiffs`, `subset.alldiffs`, `print.alldiffs`, `renewClassify.alldiffs`, `exploreLSDs.alldiffs`, `pickLSDstatistics.alldiffs`, `redoErrorIntervals.alldiffs`, `plotPredictions.data.frame`, `predictPlus.asreml`, `predictPresent.asreml` ## Examples ```r data(WaterRunoff.dat) ##Use asreml to get predictions and associated statistics ## Not run: asreml.options(keep.order = TRUE) #required for asreml-R4 only current.asr <- asreml(fixed = pH ~ Benches + (Sources * (Type + Species)), random = ~ Benches:MainPlots, keep.order=TRUE, data= WaterRunoff.dat) current.asrt <- as.asrtests(current.asr, NULL, NULL) TS.diffs <- predictPlus(classify = "Sources:Type", asreml.obj = current.asr, wald.tab = current.asrt$wald.tab, present = c("Sources", "Type", "Species")) ## End(Not run) ## Use lmeTest and emmmeans to get predictions and associated statistics if (requireNamespace("lmerTest", quietly = TRUE) & requireNamespace("emmeans", quietly = TRUE)) { m1.lmer <- lmerTest::lmer(pH ~ Benches + (Sources * (Type + Species)) + (1|Benches:MainPlots), data=na.omit(WaterRunoff.dat)) TS.emm <- emmeans::emmeans(m1.lmer, specs = ~ Sources:Species) TS.preds <- summary(TS.emm) den.df <- min(TS.preds$df, na.rm = TRUE) ## Modify TS.preds to be compatible with a predictions.frame TS.preds <- as.predictions.frame(TS.preds, predictions = "emmean", se = "SE", interval.type = "CI", interval.names = c("lower.CL", "upper.CL")) ## Form an all.diffs object and check its validity TS.vcov <- vcov(TS.emm) TS.diffs <- allDifferences(predictions = TS.preds, classify = "Sources:Species", vcov = TS.vcov, tdf = den.df) validAlldiffs(TS.diffs) } ## Plot p-values for predictions obtained using asreml or lmerTest if (exists("TS.diffs")) { ##Recalculate the LSD values for predictions obtained using asreml or lmerTest TS.diffs <- recalcLSD.alldiffs(TS.diffs, LSDtype = "factor.combinations", LSDby = "Sources") } ```

recalcLSD.alldiffs function