Displaying 20 results from an estimated 1100 matches similar to: "jackknife-after-bootstrap"
2012 Sep 11
1
boot() with glm/gnm on a contingency table
Hi everyone!
In a package I'm developing, I have created a custom function to get
jackknife standard errors for the parameters of a gnm model (which is
essentially the same as a glm model for this issue). I'd like to add
support for bootstrap using package boot, but I couldn't find how to
proceed.
The problem is, my data is a table object. Thus, I don't have one
individual per
2010 Nov 03
3
Using sample() to sample one value from a single value?
Hi, consider this one as an FYI, or a seed for further discussion.
I am aware that many traps on sample() have been reported over the
years. I know that these are also documents in help("sample"). Still
I got bitten by this while writing
sample(units, size=length(units));
where 'units' is an index (positive integer) vector. It works in all
cases as expected (=I expect)
2010 Nov 25
2
delete-d jackknife
Hi dear all,
Can aynone help me about delete-d jackknife
usually normal jackknife code for my data is:
n <- nrow(data)
y <- data$y
z <- data$z
theta.hat <- mean(y) / mean(z)
print (theta.hat)
theta.jack <- numeric(n)
for (i in 1:n)
theta.jack[i] <- mean(y[-i]) / mean(z[-i])
bias <- (n - 1) * (mean(theta.jack) - theta.hat)
print(bias)
but how i can apply delete-d jackknife
2010 Nov 04
2
How to do bootstrap for the complex sample design?
Hello;
Our survey is structured as : To be investigated area is divided into 6 regions,
within each region, one urban community and one rural community are randomly selected,
then samples are randomly drawn from each selected uran and rural community.
The problems is that in urban/rural stratum, we only have one sample.
In this case, how to do bootstrap?
Any comments or hints are greatly
2005 Nov 29
2
permutation test for linear models with continuous covariates
Hi I was wondering if there is a permutation test available in R for linear
models with continuous dependent covariates. I want to do a test like the
one shown here.
bmi<-rnorm(100,25)
x<-c(rep(0,75),rep(1,25))
y<-rnorm(100)+bmi^(1/2)+rnorm(100,2)*x+bmi*x
H0<-lm(y~1+x+bmi)
H1<-lm(y~1+x+bmi+x*bmi)
anova(H0,H1)
summary(lm(y~1+x+bmi))
But I want to use permutation testing to
2005 Nov 18
3
Method for $
Dear R experts,
I have defined a class "myclass" and would like the slots to be extractable
not only by "@" but also by "$". I now try to write a method for "$" that
simply executes the request object at slotname, whenever someone calls
object$slotname for any object of class "myclass".
I don't manage to find out how I can provide this
2008 Dec 18
1
using jackknife in linear models
Hi R-experts,
I want to use the jackknife function from the bootstrap package onto a
linear model.
I can't figure out how to do that. The manual says the following:
# To jackknife functions of more complex data structures,
# write theta so that its argument x
# is the set of observation numbers
# and simply pass as data to jackknife the vector 1,2,..n.
# For example, to jackknife
#
2005 Mar 25
3
Stratified bootstrap question
Dear experts,
I am asking for help with a question regarding to stratified bootstrap.
My dataset is a longitudinal dataset (3 measurements per person at year
1, 4 and 7) composed of multiple clinic centers and multiple participants
within each clinic. It has missing values.
I want to do a bootstrap to find the standard errors and confidence
intervals for my variance components. My model is a
2006 Apr 11
4
Bootstrap and Jackknife Bias using Survey Package
Dear R users,
I?m student of Master in Statistic and Data analysis, in New University of Lisbon. And now i?m writting my dissertation in variance estimation.So i?m using Survey Package to compute the principal estimators and theirs variances.
My data is from Incoming and Expendire Survey. This is stratified Multi-stage Survey care out by National Statistic Institute of Mozambique. My domain of
2007 Mar 27
1
Jackknife estimates of predict.lda success rate
Dear all
I have used the lda and predict functions to classify a set of objects
of unknown origin. I would like to use a jackknife reclassification to
assess the degree to which the outcomes deviate from that expected by
chance. However, I can't find any function that allows me to do this.
Any suggestions of how to generate the jackknife reclassification to
assess classification accuracy?
2007 Feb 28
2
sort of OT: bootstrap tutorial
There is now a tutorial on bootstrapping and other resampling
methods at:
http://www.burns-stat.com/pages/Tutor/bootstrap_resampling.html
Corrections and other suggestions are welcome.
The project started because a novice asked me about bootstrapping.
My response was, "How dare you bug me while I'm playing with my
cats, just google for it." My correspondent was not very impressed
2012 Nov 14
2
Jackknife in Logistic Regression
Dear R friends
I´m interested into apply a Jackknife analysis to in order to quantify the
uncertainty of my coefficients estimated by the logistic regression. I´m
using a glm(family=’binomial’) because my independent variable is in 0 - 1
format.
My dataset has 76000 obs, and I´m using 7 independent variables plus an
offset. The idea involves to split the data in let’s say 5 random subsets
and
2005 Nov 08
1
Poisson/negbin followed by jackknife
Folks,
Thanks for the help with the hier.part analysis. All the problems
stemmed from an import problem which was solved with file.chose().
Now that I have the variables that I'd like to use I need to run some
GLM models. I think I have that part under control but I'd like to use
a jackknife approach to model validation (I was using a hold out sample
but this seems to have fallen out
2006 Oct 24
1
Variance Component/ICC Confidence Intervals via Bootstrap or Jackknife
I'm using the lme function in nmle to estimate the variance components
of a fully nested two-level model:
Y_ijk = mu + a_i + b_j(i) + e_k(j(i))
lme computes estimates of the variances for a, b, and e, call them v_a,
v_b, and v_e, and I can use the intervals function to get confidence
intervals. My understanding is that these intervals are probably not
that robust plus I need intervals on the
2003 Apr 16
2
Jackknife and rpart
Hi,
First, thanks to those who helped me see my gross misunderstanding of
randomForest. I worked through a baging tutorial and now understand the
"many tree" approach. However, it is not what I want to do! My bagged
errors are accpetable but I need to use the actual tree and need a single
tree application.
I am using rpart for a classification tree but am interested in a more
unbaised
2007 Jan 26
1
bootstrap bca confidence intervals for large number of statistics in one model; library("boot")
Sometimes one might like to obtain pointwise bootstrap bias-corrected,
accelerated (BCA) confidence intervals for a large number of statistics
computed from a single dataset. For instance, one might like to get
(so as to plot graphically) bootstrap confidence bands for the fitted
values in a regression model.
(Example: Chiu S et al., Early Acceleration of Head Circumference in
Children with
2004 Mar 02
0
Jackknife after bootstrap influence values in boot package?
Is there a routine in the boot package to get the jackknife-after-
bootstrap influence values? That is, the influence values of
a jackknife of the bootstrap estimates?
I can see how one would go about it from the jack.after.boot code, but that
routine only makes pretty pictures.
It wouldn't be hard to write, but I find it hard to believe this
isn't part of the package already.
Thanks
2004 Jan 19
2
January advanced R/Splus course in Boston?
Hello,
I learnt there's an advanced R/Splus course in Boston
this january. Anyone got the announcement? please
kindly forward it to me.
Best, Eugene
2007 Jan 22
1
Time-varying correlation calculation
Dear R useres,
I'm interested in getting a series of time-varying correlation, simply between two random variables.
Could you please introduce a package to do this task?
Thank you so much for any help.
Amir
---------------------------------
Don't pick lemons.
[[alternative HTML version deleted]]
2007 Mar 01
2
Row-wise two sample T-test on subsets of a matrix
Hello all,
I am trying to run a two sample t-test on a matrix which is a
196002*22 matrix. I want to run the t-test, row-wise, with the
first 11 columns being a part of the first group and columns
12-22 being a part of the second group.
I tried running something like (temp.matrix being my 196002*22
matrix)
t.test(temp.matrix[,1:11],temp.matrix[,12:22],paired=TRUE)
or somthing like