summary
method for class “mpr
”
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object |
an object of class “ |
overall |
logical. If |
... |
further arguments passed to or from other methods. |
The function print.summary.lm
produces a typical table of coefficients, standard errors and
p-values along with “significance stars”. In addition, a table of overall p-values are shown.
Multi-Parameter Regression (MPR) models are defined by allowing mutliple distributional parameters to depend on covariates. The regression components are:
g1(λ) = x' β
g2(γ) = z' α
g3(ρ) = w' τ
and the table of coefficients displayed by print.summary.lm
follows this ordering.
Furthermore, the names of the coefficients in the table are proceeded by “.b
” for
β coefficients, “.a
” for α coefficients and “.t
” for
τ coefficients to avoid ambiguity.
Let us assume that a covariate c, say, appears in both the λ and γ regression components. The standard table of coefficients provides p-values corresponding to the following null hypotheses:
H0: β_c = 0
H0: α_c = 0
where β_c and α_c are the regression coefficients of c (one for each of the two components in which c appears). However, in the context of MPR models, it may be of interest to test the hypothesis that the overall effect of c is zero, i.e., that its β and α effects are jointly zero:
H0: β_c = α_c = 0
Thus, if overall=TRUE
, print.summary.lm
displays a table of such “overall p-values”.
The function summary.mpr
returns a list
containing the following components:
call |
the matched call from the |
model |
a |
coefmat |
a typical coefficient matrix whose columns are the estimated regression coefficients, standard errors and p-values. |
overallpmat |
a matrix containing the overall p-values as described above in “Details”. |
Kevin Burke.
mpr
, predict.mpr
.
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Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
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