statsMS {pedometrics} | R Documentation |
This function returns several statistics measuring the performance of a
series of linear models built using the function buildMS
, with an
option to rank the models based on one of the returned performance
statistics.
statsMS(model, design.info, arrange.by, digits)
model |
A list of linear models returned by |
design.info |
Extra information about the linear models in the series. |
arrange.by |
Character string defining if the table with the
performance statistics of the linear models should be arranged, and which
column should be used. Available options are |
digits |
Integer or vector with six integers indicating the number of decimal places to be used to round the performance statistics. If a vector is passed to the function, the number of decimal places should be in the following order:
|
This function was devised to deal with a list of linear models generated by
the function buildMS
. The main objective is to compare several linear
models using several performance statistics. Such statistics can then be
used to rank the linear models and identify, for example, the best
performing model, given the selected performance statistics.
An important feature of statsMS
is that it uses the information about
the initial number of candidate predictor variables offered to the build the
model to calculate penalized or adjusted measures of model performance. Such
information is recorded as an attribute of the final model selected by
buildMS
. This feature was included in statsMS
because
data-driven variable selection results biased linear models (too optimistic),
and the effective number of degrees of freedom is close to the number of
candidate predictor variables initially offered to the model (Harrell, 2001).
A data frame with several performance statistics:
Identification of the model.
Number of candidate predictor variables initially offered to the model.
Number of degrees of freedom of the final selected model.
Akaike's Information Criterion (AIC). Obtained using
extractAIC
.
Root-mean squared error, calculated based on the number of candidate predictor variables initially offered to the model.
Normalized Root-mean squared error, calculated as the ratio between the RMSE and the standard deviation of the observed values of the dependent variable.
Multiple coefficient of determination.
Adjusted multiple coefficient of determination.
Adjusted multiple coefficient of determination. Calculations are done based on the number of candidate predictor variables initially offered to the model.
Include other performance statistics such as: PRESS, BIC, Mallow's Cp, max(VIF);
Add option to select which performance statistics should be returned.
Alessandro Samuel-Rosa alessandrosamuelrosa@gmail.com
Harrell, F. E. (2001) Regression modelling strategies: with applications to linear models, logistic regression, and survival analysis. First edition. New York: Springer.
Venables, W. N. and Ripley, B. D. (2002) Modern applied statistics with S. Fourth edition. New York: Springer.
## Not run: # based on the second example of function stepAIC require(MASS) cpus1 <- cpus for(v in names(cpus)[2:7]) cpus1[[v]] <- cut(cpus[[v]], unique(quantile(cpus[[v]])), include.lowest = TRUE) cpus0 <- cpus1[, 2:8] # excludes names, authors' predictions cpus.samp <- sample(1:209, 100) cpus.form <- list(formula(log10(perf) ~ syct + mmin + mmax + cach + chmin + chmax + perf), formula(log10(perf) ~ syct + mmin + cach + chmin + chmax), formula(log10(perf) ~ mmax + cach + chmin + chmax + perf)) data <- cpus1[cpus.samp,2:8] cpus.ms <- buildMS(cpus.form, data, vif = TRUE, aic = TRUE) cpus.des <- data.frame(a = c(0, 1, 0), b = c(1, 0, 1), c = c(1, 1, 0)) stats <- statsMS(cpus.ms, design.info = cpus.des, arrange.by = "aic") ## End(Not run)