MIA: Marginal Increment Analysis Plot

In ballengerj/FishyR: Functions to Facilitate Standard Analyses Conducted in Fisheries Science

Marginal Increment Analysis Plot

Description

MIA creates a marginal increment analysis plot.

Usage

MIA(
  Data,
  Statistics,
  x.label = "Month",
  pch.raw = 20,
  col.raw = "#0080ff",
  alpha.raw = 0.5,
  cex.raw = 0.8,
  pch.fit = 16,
  col.fit = "black",
  cex.fit = 1,
  col.text = "Black",
  cex.text = 1
)

Arguments

Data	Data frame containing, at a minimum, two columns. The first column should represnt the x-variable. The 2nd column should represent the y-variable. Other columns may be present.
Statistics	Data frame containing, at a minimum, four colummns. The first column should represent the x-variable. The 2nd column should represent the y-variable, typically the mean of y at a given x. The 3rd column should represent a measure of variability, typically the confidence interval of y given x. The 4th column should represent the number of samples of y at a given level of x. Other columns may be present in the data frame.
x.label	Text label for the x-axis of the plot
pch.raw	symbol to use for the raw x and y data found in Data
col.raw	color to use for the raw x and y data found in Data
alpha.raw	amount of transparency of the raw x and y data found in Data
cex.raw	symbol size to use for the raw x and y data found in Data
pch.fit	symbol to use for the summarized marginal increment data found in Statistics
col.fit	color to use for the summarized marginal increment data found in Statistics
cex.fit	symbol size to use for the summarized marginal increment data found in Statistics
col.text	color of text to use for depiciting the sample size at each x variable level
cex.text	text size to use for depiciting the sample size at each x variable level bias plot between Reader 2 and Reader 1 ages.

Value

A xyplot using the package lattice depicting the results of a marginal increment analysis.

See Also

MIA.Edge

Examples

# Example with Sheepshead Data
data(Sheepshead)
Data <- data.frame(Month = Sheepshead$Month,
                   Perc.Comp = Sheepshead$MI/Sheepshead$Prev.Inc)
Data$Perc.Comp <- with(Data, ifelse(Perc.Comp >1, 1, Perc.Comp))
Statistics <- with(Data, aggregate(Perc.Comp ~ Month, FUN = describe,
   digits = 4 ))
Statistics <- cbind(Statistics[-ncol(Statistics)],
   Statistics[[ncol(Statistics)]])
MIA(Data = Data, Statistics = Statistics[, c(1, 3, 6, 2)])

ballengerj/FishyR documentation built on June 17, 2022, 10:33 p.m.