Round(t(sapply(d.pizza, MeanCI, na.An efficient and widely used method of analysis for multi-environment trial (MET) data in plant improvement programs involves a linear mixed model with a factor analytic (FA) model for the variety by environment effects. # Getting the MeanCI for more than 1 column MeanCI(x, trim=0.1, method='boot', type='bca', na.rm=TRUE) MeanCI(x, trim=0.1, method='boot', type='perc', na.rm=TRUE) MeanCI(x, trim=0.1, method='boot', type='stud', na.rm=TRUE) MeanCI(x, trim=0.1, method='boot', type='basic', na.rm=TRUE) MeanCI(x, trim=0.1, method='boot', type='norm', na.rm=TRUE) MeanCI(x, method='boot', type='norm', na.rm=TRUE) # the different types of bootstrap confints T.test, MeanDiffCI, MedianCI, VarCI, MeanCIn Examples x <- d.pizza$price

(2005) Introduction to robust estimation and hypothesis testing Elsevier Academic Press (2003) Modern robust data analysis methods: measures of central tendency Psychol Methods, 8(3):254-74 The confidence intervals for the trimmed means use winsorized variances as described in the references. If not defined those will be set to their defaults, being 'basic' for type, option 'boot.parallel' (and if that is not set, 'no') for parallel Supported arguments are type ( 'norm', 'basic', 'stud', 'perc', 'bca'), parallel and the number of bootstrap replicates R. Defaults to FALSE.įurther arguments are passed to the boot function. 'left' would be analogue to a hypothesis of 'greater' in a t.test.Ī logical value indicating whether NA values should be stripped before the computation proceeds. The value should be any subset of the values 'classic', 'boot'.Ī character string specifying the side of the confidence interval, must be one of 'two.sided' (default), 'left' or 'right'. Values of trim outside that range are taken as the nearest endpoint.Ī vector of character strings representing the type of intervals required. The fraction (0 to 0.5) of observations to be trimmed from each end of x before the mean is computed.

If left to NULL (default) the sample sd(x) will be calculated and used in combination with the t-distribution. If provided it's interpreted as sd of the population and the normal quantiles will be used for constructing the confidence intervals. Usage MeanCI(x, sd = NULL, trim = 0, method = c('classic', 'boot'),Ĭonf.level = 0.95, sides = c('two.sided', 'left', 'right'),Ī (non-empty) numeric vector of data values. Both, the classical way and bootstrap intervals are implemented for both, normal and trimmed means. Collection of several approaches to determine confidence intervals for the mean.

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