regression.model.functions {kyotil} | R Documentation |
getFormattedSummary prints a table of regression coefficient estimates and standard errors.
getFormattedSummary(fits, type = 2, est.digits = 2, se.digits = 2, robust, random = FALSE, VE = FALSE, to.trim = FALSE, rows = NULL, coef.direct = FALSE, ...) getVarComponent(object, ...) getFixedEf(object, ...) risk.cal(risk, binary.outcome, weights = NULL, ngroups = NULL, cuts = NULL, main = "", add = FALSE, show.emp.risk = TRUE, lcol = 2, ylim = NULL, scale = c("logit", "risk")) interaction.table(fit, v1, v2, v1.type = "continuous", v2.type = "continuous", logistic.regression = TRUE) ## S3 method for class 'coxph' getFixedEf(object, exp=FALSE,robust=FALSE, ...) ## S3 method for class 'gam' getFixedEf(object, ...) ## S3 method for class 'gee' getFixedEf(object, exp = FALSE, ...) ## S3 method for class 'geese' getFixedEf(object, ...) ## S3 method for class 'tps' getFixedEf(object, exp=FALSE, robust=TRUE, ...) ## S3 method for class 'glm' getFixedEf(object, exp = FALSE, robust = TRUE, ret.robcov = FALSE, ...) ## S3 method for class 'inla' getFixedEf(object, ...) ## S3 method for class 'lm' getFixedEf(object, ...) ## S3 method for class 'lme' getFixedEf(object, ...) ## S3 method for class 'logistf' getFixedEf(object, exp = FALSE, ...) ## S3 method for class 'matrix' getFixedEf(object, ...) ## S3 method for class 'MIresult' getFixedEf(object, ...) ## S3 method for class 'hyperpar.inla' getVarComponent(object, transformation = NULL, ...) ## S3 method for class 'matrix' getVarComponent(object, ...) ## S3 method for class 'geese' coef(object, ...) ## S3 method for class 'tps' coef(object, ...) ## S3 method for class 'geese' predict(object, x, ...) ## S3 method for class 'tps' predict(object, newdata = NULL, type = c("link", "response"), ...) ## S3 method for class 'geese' residuals(object, y, x,...) ## S3 method for class 'geese' vcov(object, ...) ## S3 method for class 'tps' vcov(object, robust, ...) ## S3 method for class 'logistf' vcov(object, ...)
... |
|
object |
|
fit |
|
coef.direct |
|
robust |
Boolean, whether to return robust variance estimate |
exp |
|
cuts |
|
ret.robcov |
|
fits |
|
type |
|
est.digits |
|
se.digits |
|
random |
|
VE |
|
transformation |
|
weights |
|
v1 |
|
v2 |
|
v1.type |
|
v2.type |
|
logistic.regression |
|
newdata |
|
x |
|
y |
|
to.trim |
|
rows |
|
risk |
|
binary.outcome |
|
ngroups |
|
main |
|
add |
|
show.emp.risk |
|
lcol |
|
ylim |
|
scale |
getFormattedSummary: from a list of fits, say lmer, inla fits, return formatted summary controlled by "type". For a matrix, return Monte Carlo variance random=TRUE returns variance components type=1: est type=2: est (se) type=3: est (2.5 percent, 97.5 percent) type=4: est se
getFixedEf returns a matrix, first column coef, second column se,
getFixedEf.matrix used to get mean and sd from a jags or winbugs sample, getVarComponent.matrix and getFixedEf.matrix do the same thing. Each column of samples is a variable
interaction.table expects coef and vcov to work with fit.
## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## Page 9: Plant Weight Data. ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2, 10, 20, labels = c("Ctl","Trt")) weight <- c(ctl, trt) lm.D9 <- lm(weight ~ group) glm.D9 <- glm(weight ~ group) getFormattedSummary (list(lm.D9, glm.D9), robust=FALSE)