Optimization and model fitting Flashcards
optim(par, fn, method = c(“Nelder-Mead”, “BFGS”, “CG”, “L-BFGS-B”, “SANN”)
general-purpose optimization; par is initial values, fn is function to optimize (normally minimize)
nlm(f,p)
minimize function f using a Newton-type algorithm with starting values p
lm(formula)
it linear models; formula is typically of the form response termA + termB + …; use I(x*y) + I(xˆ2) for terms made of nonlinear components
glm(formula,family=)
it generalized linear models, specified by giv- ing a symbolic description of the linear predictor and a description of the error distribution; family is a description of the error distribution and link function to be used in the model; see ?family
nls(formula)
nonlinear least-squares estimates of the nonlinear model parameters
approx(x,y=)
linearly interpolate given data points; x can be an xy plot- ting structure
spline(x,y=)
cubic spline interpolation
loess(formula)
it a polynomial surface using local fitting
Many of the formula-based modeling functions have several common argu- ments: data= the data frame for the formula variables, subset= a subset of variables used in the fit, na.action= action for missing values: “na.fail”, “na.omit”, or a function. The following generics often apply to model fitting functions:
predict(fit,…)
predictions from fit based on input data
df.residual(fit)
returns the number of residual degrees of freedom
coef(fit)
returns the estimated coefficients (sometimes with their
standard-errors)
residuals(fit)
returns the residuals
deviance(fit)
returns the deviance
fitted(fit)
returns the fitted values
logLik(fit)
computes the logarithm of the likelihood and the number of
parameters
AIC(fit)
computes the Akaike information criterion or AIC
