[,powerClass-method {ltable} | R Documentation |
[
Method for function [
with
signature(x = "powerClass")
## S4 method for signature 'powerClass' x[i, j, drop]
x |
the name of powerClass object. |
i |
the name of the slot of the object |
j |
picks up j-th element of the list in slot with name &i. |
drop |
not used |
Method provides access to slots of powerClass object. Its structure delivered in powerClass-class index. Access to particular vectors of lists supplied with $ operator. For example, log-linear reg.coefficients estimates of smallest size data accessible by obj["estim", 1]$betas, errors can be obtained by analogue: obj["estim", 1]$errors. Power values extraction slightly differs: obj["power11", 1]&power extracts power values vector for 1st effect given 11th (largest) sample size. By analogue we get vector of z-scores for second effect given smallest sample size by obj["power1", 2]&z. See powerClass-class index.
signature(x = "powerClass", i = "character", j = "integer", drop = "logical")
Method for function [
for object of S4 class powerClass.
require(ltable) data(tdata, package="ltable") pres<-PowerPoisson(Counts~smoker +contraceptive +tromb + contraceptive*tromb, scale_max=1.5, effect="contraceptive*tromb", data=tdata) # get call pres["cal"] # get effect contrasts names pres["effectsname"] # get Jacobian reciprocal condition number in smallest sample pres["estim",1]$`Jacobian reciprocal condition number` # get chisq/dof in smallest sample pres["estim",1]$`chisq/dof` # get reason for stopping iterations pres["estim",1]$`reason for stopping` # get initial sum of squared differences between observed and expected counts pres["estim",1]$`initial |f(x)|` # get final sum of squared differences between observed and expected counts pres["estim",1]$`final |f(x)|` # get iteration number of GSL non-linear LS fitting pres["estim",1]$`number of iterations`