all.regs {hier.part} | R Documentation |
Calculates goodness of fit measures for regressions of a single dependent variable to all combinations of N independent variables
all.regs(y, xcan, family = "gaussian", gof = "RMSPE", print.vars = FALSE)
y |
a vector containing the dependent variables |
xcan |
a dataframe containing the n independent variables |
family |
family argument of glm
|
gof |
Goodness-of-fit measure. Currently "RMSPE", Root-mean-square 'prediction' error, "logLik", Log-Likelihood or "Rsqu", R-squared |
print.vars |
if FALSE, the function returns a vector of goodness-of-fit measures. If TRUE, a data frame is returned with first column listing variable combinations and the second column listing goodness-of-fit measures. |
This function calculates goodness of fit measures for the entire
hierarchy of models using all combinations of N dependent variables, and
returns them as an ordered list ready for input into the function
partition
.
gfs |
If print.vars is FALSE, a vector of goodness of fit measures for all combinations of independent varaiables in the hierarchy or, if print.vars is TRUE, a data frame listing all combinations of independent variables in the first column in ascending order, and the corresponding goodness of fit measure for the model using those variables |
Chris Walsh Chris.Walsh@sci.monash.edu.au.
#linear regression with four independent variables data(urban) env <- urban[,3:6] all.regs(urban$chl, env, fam = "gaussian", gof = "Rsqu", print.vars = TRUE) #logistic regression with four independent variables data(urban1) env1 <- urban1[,2:5] all.regs(urban1$pa, env1, fam = "binomial", gof = "logLik")