Displaying 5 results from an estimated 5 matches for "hosmerlem".
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homerlex
2011 May 08
1
Hosmer-Lemeshow 'goodness of fit'
...#################
# Compute the Hosmer-Lemeshow 'goodness-of-fit' test
cd.full_model = glm(formula = Collaterals ~ CHF + Age + CABG
+ relevel (as.factor (num.obst.vessels),"one")
+ Current.smoker + DM + HTN + ace.inhibitor
+ MI, family = binomial(link = "logit"))
hosmerlem = function(y, yhat, g=10) {
cutyhat = cut(yhat,
breaks = quantile(yhat, probs=seq(0,
1, 1/g)), include.lowest=TRUE)
obs = xtabs(cbind(1 - y, y) ~ cutyhat)
expect = xtabs(cbind(1 - yhat, yhat) ~ cutyhat)
chisq = sum((obs - expect)^2/expect)
P = 1 - pchisq(chisq, g - 2)
return...
2008 Sep 16
2
Hosmer- Lemeshow test
...elated data 'diseaseoutbreak' is available at the following URL
http://www.stat.sc.edu/~hitchcock/diseaseoutbreakdata.txt
The R code as mentioned therein is
####
# A function to do the Hosmer-Lemeshow test in R.
# R Function is due to Peter D. M. Macdonald, McMaster University.
#
hosmerlem <-
function (y, yhat, g = 10)
{
cutyhat <- cut(yhat, breaks = quantile(yhat, probs = seq(0,
1, 1/g)), include.lowest = T)
obs <- xtabs(cbind(1 - y, y) ~ cutyhat)
expect <- xtabs(cbind(1 - yhat, yhat) ~ cutyhat)
chisq <- sum((obs - expect)^2/expect)
P <...
2010 Jul 07
1
Different goodness of fit tests leads to contradictory conclusions
...= list(epsilon = 0.0001, maxit = 50, trace = F))
> # Option 1: Hosmer-Lemeshow test
> mod.fit <- glm(formula = no.NA$repcnd ~ no.NA$svl, family = binomial(link = logit), data = no.NA, na.action = na.exclude, control = list(epsilon = 0.0001, maxit = 50, trace = F))
> hosmerlem <- function (y, yhat, g = 10)
{
cutyhat <- cut(yhat, breaks = quantile(yhat, probs = seq(0, 1, 1/g)), include.lowest = T)
obs <- xtabs(cbind(1 - y, y) ~ cutyhat)
expect <- xtabs(cbind(1 - yhat, yhat) ~ cutyhat)
chisq <- sum((obs - expect)^2/expect)
P <- 1 - pchisq(chisq, g - 2)
c...
2010 Jul 09
1
Appropriate tests for logistic regression with a continuous predictor variable and Bernoulli response variable
...2 on 217 degrees of freedom
Residual deviance: 285.04 on 216 degrees of freedom
(8 observations deleted due to missingness)
AIC: 289.04
Number of Fisher Scoring iterations: 3
-------------------------------------------------------------------------------
> Hosmer-Lemeshow test
>
> hosmerlem <- function (y, yhat, g = 10)
+ {
+ cutyhat <- cut(yhat, breaks = quantile(yhat, probs = seq(0, 1, 1/g)), include.lowest = T)
+ obs <- xtabs(cbind(1 - y, y) ~ cutyhat)
+ expect <- xtabs(cbind(1 - yhat, yhat) ~ cutyhat)
+ chisq <- sum((obs - expect)^2/expect)
+ P <- 1 - pchisq(ch...
2008 Nov 07
1
two kind of Hosmer and Lemeshow’s test
...and Lemeshow?s. One of
them is calculated based on the fixed and pre-determined cut-off points of
the estimated probability of success. One of them is calculated based on
the percentiles of estimated probabilities.
In the previous post,i find that the Hosmer and Lemeshow?s test how to use
in R.
hosmerlem <-
function (y, yhat, g = 10)
{
cutyhat <- cut(x, breaks = quantile(yhat, probs = seq(0,
1, 1/g)), include.lowest = T)
obs <- xtabs(cbind(1 - y, y) ~ cutyhat)
expect <- xtabs(cbind(1 - yhat, yhat) ~ cutyhat)
chisq <- sum((obs - expect)^2/expect)
P <-...