![]() ![]() ![]() If you have negative numbers, you will need to convert those numbers to a positive value before calculating the geometric mean. Ignore zeros or missing data in your calculations.Ĭonvert zeros to a very small number (often called "below the detection limit") that is less than the next smallest number in the data set. So what should you do if you have data that do not meet this requirement? If you have values that equal zero, you have a few options:Īdjust your scale so that you add 1 to every number in the data set, and then subtract 1 from the resulting geometric mean. If any argument is zero, then the geometric mean is zero.įor strict positive values the geometric mean is computed as exp(MeanCI(log(x))).Ĭonsiderations (Roenfeldt 2018) "The calculation of the geometric mean requires that all values are non-zero and positive. Hence if any argument is negative, the result will be NA. The geometric mean and geometric standard deviation are restricted to positive inputs (because otherwise the answer can have an imaginary component). 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. Logical, indicating whether NA values should be stripped before the computation proceeds. "left" would be analogue to a hypothesis of "greater" in a t.test. Default is NA.Ī character string specifying the side of the confidence interval, must be one of "two.sided" (default), "left" or "right". The value should be any subset of the values "classic", "boot".Ĭonfidence level of the interval. An object which is not a vector is coerced (if possible) by as.vector.Ī vector of character strings representing the type of intervals required. Sides = c("two.sided","left","right"), na.rm = FALSE. Usage Gmean(x, method = c("classic", "boot"), conf.level = NA, CochranArmitageTest: Cochran-Armitage Test for TrendĬalculates the geometric mean, its confidence interval and the geometric standard deviation of a vector x.Coalesce: Return the First Element Not Being NA.Clockwise: Calculates Begin and End Angle From a List of Given Angles in.CCC: Concordance Correlation Coefficient.CartToPol: Transform Cartesian to Polar/Spherical Coordinates and Vice.BubbleLegend: Add a Legend to a Bubble Plot.BrierScore: Brier Score for Assessing Prediction Accuracy.BreuschGodfreyTest: Breusch-Godfrey Test.BreslowDayTest: Breslow-Day Test for Homogeneity of the Odds Ratios.BoxCoxLambda: Automatic Selection of Box Cox Transformation Parameter.BootCI: Simple Bootstrap Confidence Intervals.BinomRatioCI: Confidence Intervals for the Ratio of Binomial Proportions.BinomDiffCI: Confidence Interval for a Difference of Binomials.BinomCIn: Sample Size for a Given Width of a Binomial Confidence.BinomCI: Confidence Intervals for Binomial Proportions.BhapkarTest: Bhapkar Marginal Homogeneity Test. ![]() Between: Operators To Check, If a Value Lies Within Or Outside a Given.BaseConversions: Converts Numbers From Binmode, Octmode or Hexmode to Decimal.BarText: Place Value Labels on a Barplot.BartelsRankTest: Bartels Rank Test of Randomness.BarnardTest: Barnard's Unconditional Test.axTicks.POSIXct: Compute Axis Tickmark Locations (For POSIXct Axis).AxisBreak: Place a Break Mark on an Axis.Atkinson: Atkinson Index - A Measure of Inequality.Asp: Get Aspect Ratio of the Current Plot.AscToChar: Convert ASCII Codes to Characters and Vice Versa.AndersonDarlingTest: Anderson-Darling Test of Goodness-of-Fit.AllIdentical: Test Multiple Objects for Exact Equality. ![]()
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