Description Usage Arguments Value Author(s) Examples
Estimate hypothesis test of lower- and higher-order non-linear relationships against an assumed target relationship.
1 2 3 4 5 6 7 8 9 10 | NKnotsTest(
form,
var,
data,
targetdf = 1,
degree = 3,
min.knots = 1,
max.knots = 10,
adjust = "none"
)
|
form |
A formula detailing the model for which smoothing is to be evaluated. |
var |
A character string identifying the variable for which smoothing is to be evaluated. |
data |
Data frame providing values of all variables in |
targetdf |
The assumed degrees of freedom against which the tests will be conducted. |
degree |
Degree of polynomial in B-spline basis functions. |
min.knots |
Minimum number of internal B-spline knots to be evaluated. |
max.knots |
Maximum number of internal B-spline knots to be evaluated. |
adjust |
Method by which p-values will be adjusted (see
|
A matrix with the following columns:
F |
F statistics of test of candidate models against target model |
DF1 |
Numerator DF from F-test |
DF2 |
Denominator DF from F-test |
p(F) |
p-value from the F-test |
Clarke |
Test statistic from the Clarke test |
Pr(Better) |
The Clarke statistic divided by the number of observations |
p(Clarke) |
p-value from the Clarke test. (T) means that the significant p-value is in favor of the Target model and (C) means the significant p-value is in favor of the candidate (alternative) model. |
Delta_AIC |
AIC(candidate model) - AIC(target model) |
Delta_AICc |
AICc(candidate model) - AICc(target model) |
Delta_BIC |
BIC(candidate model) - BIC(target model) |
Dave Armstrong
1 2 |
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