cotram-methods {cotram} | R Documentation |
Methods for objects inheriting from class cotram
## S3 method for class 'cotram' predict(object, newdata = model.frame(object), type = c("lp", "trafo", "distribution", "survivor", "density", "logdensity", "cumhazard", "quantile"), smooth = FALSE, q = NULL, K = 20, prob = 1:(10-1)/10, ...) ## S3 method for class 'cotram' plot(x, newdata, type = c("distribution", "survivor","density", "logdensity", "cumhazard", "quantile", "trafo"), confidence = c("none", "band"), level = 0.95, smooth = FALSE, q = NULL, K = 20, cheat = K, prob = 1:(10-1)/10, col = "black", fill = "lightgrey", lty = 1, lwd = 1, add = FALSE, ...) ## S3 method for class 'cotram' as.mlt(object) ## S3 method for class 'cotram' logLik(object, parm = coef(as.mlt(object), fixed = FALSE), newdata, ...)
object, x |
a fitted linear count transformation model inheriting
from class |
newdata |
an optional data frame of observations. |
parm |
model parameters. |
type |
type of prediction, current options include
linear predictors ( |
confidence |
whether to plot a confidence band (see |
level |
the confidence level. |
smooth |
logical; if |
q |
quantiles at which to evaluate the model. |
prob |
probabilities for the evaluation of the quantile function |
K |
number of grid points the function is evaluated at
(for |
cheat |
number of grid points the function is evaluated at when
using the quantile obtained for |
col |
color for the lines to plot. |
fill |
color for the confidence band. |
lty |
line type for the lines to plot. |
lwd |
line width. |
add |
logical; indicating if a new plot shall be generated (the default). |
... |
additional arguments to the underlying methods for |
predict
and plot
can be used to inspect the model on
different scales.
predict.cotram
, confband.cotram
,
tram-methods
, mlt-methods
, plot.ctm
data("birds", package = "TH.data") ### fit count transformation model with cloglog link m_birds <- cotram(SG5 ~ AOT + AFS + GST + DBH + DWC + LOG, data = birds, method = "cloglog") logLik(m_birds) ### classical likelihood inference summary(m_birds) ### coefficients of the linear predictor (discrete hazard ratios) exp(-coef(m_birds)) ### compute predicted median along with 10% and 90% quantile for the first ### three observations nd <- birds[1:3,] predict(m_birds, newdata = nd, type = "quantile", prob = c(.1, .5, .9), smooth = TRUE) ### plot the predicted distribution for these observations plot(m_birds, newdata = nd, type = "distribution", col = c("skyblue", "grey", "seagreen"))