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smatr (version 2.1)

shift.com: Test for no mean shift along a common (standardised) major axis

Description

Test if several groups of observations have no shift in location along major axis or standardised major axis lines with a common slope.

Usage

shift.com( y, x, groups, data = NULL, method = 'SMA', intercept = TRUE,
   V=array( 0, c( 2,2,length(unique(groups)) ) ), group.names=sort(unique(groups)) )

Arguments

y
The Y-variable for all observations (as a vector)
x
The X-variable for all observations (as a vector)
groups
Coding variable identifying which group each observation belongs to (as a factor or vector)
data
(optional) data frame containing the data
method
The line fitting method: [object Object],[object Object]
intercept
(logical) Whether or not the line includes an intercept. [object Object],[object Object]
V
The estimated variance matrices of measurement error, for each group. This is a 3-dimensional array with measurement error in Y in the first row and column, error in X in the second row and column, and groups running along the third dimension. Default is
group.names
(optional: rarely required). A vector containing the labels for `groups'. (Only actually useful for reducing computation time in simulation work).

Value

  • statThe Wald statistic testing for no shift along the common axis
  • pThe P-value of the test. This is calculated assuming that stat has a chi-square distribution with (g-1) df, if there are g groups
  • f.meanThe fitted axis means for each group

Details

Calculates a Wald statistic to test for no shift along several MA's or SMA's of common slope. This is done by testing for equal fitted axis means across groups.

Note that this test is only valid if it is reasonable to assume that the axes for the different groups all have the same slope.

The test assumes the following:

  1. each group of observations was independently sampled
  2. the axes fitted to all groups have a common slope
  3. y and x are linearly related within each group
  4. fitted axis scores independently follow a normal distribution with equal variance at all points along the line, within each group
Note that we do not need to assume equal variance across groups, unlike in tests comparing several linear regression lines.

The assumptions can be visually checked by plotting residuals against fitted axis scores, and by constructing a Q-Q plot of residuals against a normal distribution. An appropriate residual variable is y-bx, and for fitted axis scores y+bx (for SMA) or by+lx (for MA or `lamest'), where b represents the common slope estimate, and l the error variance ratio. If there is a distinct increasing or decreasing trend within any of the groups, this suggests that all groups do not share a common slope.

A plot of residual scores against fitted axis scores can also be used as a visual test for common elevation. If fitted axis scores systematically differ across groups then this is evidence of a shift along the common axis.

The common slope ($\hat{\beta}$) is estimated from a maximum of 100 iterations, convergence is reached when the change in $\hat{\beta}$ is $< 10^{-6}$.

References

Warton D. I., Wright I. J., Falster D. S. and Westoby M. (2006) A review of bivariate line-fitting methods for allometry. Biological Reviews (in press)

See Also

line.cis, elev.com, shift.com

Examples

Run this code
#load leaf longevity data
data(leaflife)

#Test for common SMA slope amongst species at low rainfall sites
#with different levels of soil nutrients
leaf.low.rain=leaflife[leaflife$rain=='low',]
slope.com(log10(longev), log10(lma), soilp, data=leaf.low.rain)

#Now test for no shift along the axes of common slope, for sites
#with different soil nutrient levels but low rainfall:
shift.com(log10(longev), log10(lma), soilp, data = leaf.low.rain)

#Now test for no shift along the axes of common slope, for sites
#with different soil nutrient levels but low rainfall:
shift.com(log10(longev), log10(lma), soilp, data = leaf.low.rain,
   method='MA')

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