coef for lm.rrpp model fits
Computes ordinary or generalized least squares coefficients
over the permutations of an lm.rrpp model fit with predefined
random permutations.
For each coefficient vector, the Euclidean distance is calculated as an
estimate of
the amount of change in Y, the n x p matrix of dependent variables; larger
distances mean more change
in location in the data space associated with a one unit change in the model
design, for the parameter
described. Random coefficients are based on either RRPP or FRPP, as defined
by the
lm.rrpp model fit. If RRPP is used, all distributions of
coefficient vector distances are
based on appropriate null models as defined by SS type.
This function can be used to test the specific coefficients of an lm.rrpp fit. The test statistics are the distances (d), which are also standardized (Z-scores). The Z-scores might be easier to compare, as the expected values for random distances can vary among coefficient vectors (Adams and Collyer 2016).
## S3 method for class 'lm.rrpp' coef(object, test = FALSE, confidence = 0.95, ...)
object |
Object from |
test |
Logical argument that if TRUE, performs hypothesis tests (Null hypothesis is vector distance = 0) for the observed coefficients. If FALSE, only the observed coefficients are returned. |
confidence |
The desired confidence limit to print with a table of summary statistics, if test = TRUE. Because distances are directionless, confidence limits are one-tailed. |
... |
Other arguments (currently none) |
Michael Collyer
# See examples for lm.rrpp to see how anova.lm.rrpp works in conjunction # with other functions data(Pupfish) names(Pupfish) Pupfish$logSize <- log(Pupfish$CS) fit <- lm.rrpp(coords ~ logSize + Sex*Pop, SS.type = "I", data = Pupfish) coef(fit) coef(fit, test = TRUE, confidence = 0.99)
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