# Adds or replaces the error intervals stored in a prediction component of an `alldiffs.object`. Given an `alldiffs.object`, adds or replaces `error.intervals` for its `prediction` component. If the `backtransforms` component is present, the `transform.power`, `offset` and `scale` will be retrieved from the `backtransforms` attributes, ignoring the values for the function's arguments, and the backtransformed `error.intervals` will also be calculated. ```r ## S3 method for class 'alldiffs' redoErrorIntervals(alldiffs.obj, error.intervals = "Confidence", alpha = 0.05, avsed.tolerance = 0.25, accuracy.threshold = NA, LSDtype = NULL, LSDsupplied = NULL, LSDby = NULL, LSDstatistic = "mean", LSDaccuracy = "maxAbsDeviation", retain.zeroLSDs = FALSE, zero.tolerance = .Machine$double.eps ^ 0.5, ...) ``` ## Arguments - `alldiffs.obj`: An `alldiffs.object`. - `error.intervals`: A `character` string indicating the type of error interval, if any, to calculate in order to indicate uncertainty in the predicted values. Possible values are `"none"`, `"StandardError"`, `"Confidence"` and `"halfLeastSignificant"`. The default is for confidence limits to be used. The `"halfLeastSignificant"` option results in half the Least Significant Difference (LSD) being added and subtracted to the predictions, the LSD being calculated using the square root of the mean of the variances of all or a subset of pairwise differences between the predictions. If the LSD is zero, as can happen when predictions are constrained to be equal, then the limits of the error intervals are set to `NA`. If `LSDtype` is set to `overall`, the `avsed.tolerance` is not `NA` and the range of the SEDs divided by the average of the SEDs exceeds `avsed.tolerance` then the `error.intervals` calculations and the plotting will revert to confidence intervals. - `alpha`: A `numeric` giving the significance level for LSDs or one minus the confidence level for confidence intervals. It is stored as an attribute to the `alldiffs.object`. - `avsed.tolerance`: A `numeric` giving the value of the SED range, the range of the SEDs divided by the square root of the mean of the variances of all or a subset of the pairwise differences, that is considered reasonable in calculating `error.intervals`. To have it ignored, set it to `NA`. It should be a value between 0 and 1. The following rules apply: 1. If `avsed.tolerance` is `NA` then mean LSDs of the type specified by `LSDtype` are calculated and used in `error.intervals` and plots. 2. Irrespective of the setting of `LSDtype`, if `avsed.tolerance` is not exceeded then the mean LSDs are used in `error.intervals` and plots. 3. If `LSDtype` is set to `overall`, `avsed.tolerance` is not `NA`, and `avsed.tolerance` is exceeded then `error.intervals` and plotting revert to confidence intervals. 4. If `LSDtype` is set to `factor.combinations` and `avsed.tolerance` is not exceeded for any factor combination then the half LSDs are used in `error.intervals` and plots; otherwise, `error.intervals` and plotting revert to confidence intervals. 5. If `LSDtype` is set to `per.prediction` and `avsed.tolerance` is not exceeded for any prediction then the half LSDs are used in `error.intervals` and plots; otherwise, `error.intervals` and plotting revert to confidence intervals. - `accuracy.threshold`: A `numeric` specifying the value of the LSD accuracy measure, which measure is specified by `LSDaccuracy`, as a threshold value in determining whether the `hallfLeastSignificant` `error.interval` for a predicted value is a reasonable approximation; this will be the case if the LSDs across all pairwise comparisons for which the interval's LSD was computed, as specified by `LSDtype` and `LSDby`, are similar enough to the interval's LSD, as measured by `LSDaccuracy`. If it is `NA`, it will be ignored. If it is not `NA`, a column of `logicals` named `LSDwarning` will be added to the `predictions` component of the `alldiffs.object`. The value of `LSDwarning` for a `predicted.value` will be `TRUE` if the value of the `LSDaccuracy` measure computed from the LSDs for differences between this `predicted.value` and the other `predicted.values` as compared to its `assignedLSD` exceeds the value of `accuracy.threshold`. Otherwise, the value of `LSDwarning` for a `predicted.value` will be `FALSE`. - `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. If `LSDtype` is `NULL` (the default), the `LSDtype` attribute of the `alldiffs.obj` will be used; it is also `NULL`, then the `LSDtype` will be set to `overall`. 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`. - `retain.zeroLSDs`: A `logical` indicating whether to retain or omit LSDs that are zero when calculating the summaries of LSDs. - `zero.tolerance`: A `numeric` specifying the value such that if an LSD is less than it, the LSD will be considered to be zero. - ``...``: further arguments passed to `recalcLSD.alldiffs`. ## Returns An `alldiffs.object` with components `predictions`, `vcov`, `differences`, `p.differences` `sed`, `LSD` and, if present in `alldiffs.obj`, `backtransforms`. If `error.intervals` is not `"none"`, then the `predictions` component and, if present, the `backtransforms` component will contain columns for the lower and upper values of the limits for the interval. The names of these columns will consist of three parts separated by full stops: 1) the first part will be `lower` or `upper`; 2) the second part will be one of `Confidence`, `StandardError` or `halfLeastSignificant`; 3) the third component will be `limits`. The name of the `response`, the `term`, the `classify` and `tdf`, as well as the degrees of freedom of the standard error, will be set as attributes to the object. Also, if `error.intervals` is `"halfLeastSignificant"`, then those of `LSDtype`, `LSDby` and `LSDstatistic` that are not `NULL` will be added as attributes of the object and of the `predictions frame`; additionally, `LSDvalues` will be added as attribute of the `predictions frame`, `LSDvalues` being the LSD values used in calculating the `error.intervals`. ## Author(s) Chris Brien ## See Also `recalcLSD.alldiffs`, `exploreLSDs.alldiffs`, `pickLSDstatistics.alldiffs`, `predictPresent.asreml`, `plotPredictions.data.frame`, `allDifferences.data.frame`, `as.alldiffs`, `print.alldiffs`, `sort.alldiffs`, `subset.alldiffs`, `as.Date`, `predict.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 <- redoErrorIntervals.alldiffs(TS.diffs, error.intervals = "halfLeastSignificant") } ```

redoErrorIntervals.alldiffs function