similar to: Bootstrapping

Folks I have modified an existing function to calculate 'ec/ld/lc' 50 values and their associated Fieller's confidence limits. It is based on EC50.calc (writtien by John Bailer) - but also borrows from the dose.p (MASS) function. My goal was to make the original EC50.calc function flexible with respect to 1) probability at which to calculate the expected dose, and 2) the link

hello, the following questions will without doubt reveal some fundamental ignorance, but hopefully you can still help me out. I'd like to bootstrap a coefficient gained on the basis of the coefficients in a logistic regression model (the mean differences in the predicted probabilities between two groups, where each predict() operation uses as the newdata-argument a dataframe of equal size as

HI everyone, I try to get some bootstrap CIs for coefficients obtained by quantile regression. I have influencial values and thus switched to quantreg.. The data is clustered and within clusters the variance of my DV = 0.. Is this sensible for the below data? And what about the warnings? Thanks in advance for any guidance, Kay > dput(d) structure(list(Porenfläche = c(4990L, 7002L, 7558L,

Could someone help me on how to correctly try to correct this error message arning : BCa Intervals used Extreme Quantiles Some BCa intervals may be unstable Warning message: Extreme Order Statistics used as Endpoints in: norm.inter(t, adj.alpha) Regards IF [[alternative HTML version deleted]]

Hi List, Being new to R, I am trying to apply boot.stepAIC() for Model selection by bootstrapping the stepAIC() procedure. I had gone through the discussion in various thread on the variable selection methods. Understood the pros and cons of various method, also going through the regression modelling strategies in rms. I want to read Final model or Formula or list of variables automatically

A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645362 I am estimating a system of seemingly unrelated regressions (SUR) in R. Each of the equations has one unique regressor and one common regressor. I am using `gmm::sysGmm` and am experimenting with different weighting matrices. I get the same results (point estimates, standard errors and

A copy of this question can be found on Cross Validated: https://stats.stackexchange.com/questions/645610 I am estimating a system of seemingly unrelated regressions (SUR) with `gmm::sysGmm` in R. Each of the equations has one unique regressor and one common regressor. The common regressor is a dummy variable indicating the last observation (n-1 zeros followed by 1). I impose a restriction that

Hi R-helpers, My real data is a panel (unbalanced and with gaps in years) of thousands of firms, by year and industry, and with financial information (variables X, Y, Z, for example), the number of firms by year and industry is not always equal, the number of years by industry is not always equal. #reproducible example firm1<-sort(rep(1:10,5),decreasing=F) year1<-rep(2000:2004,10)

Dear experienced R-users, I am having some probably trivial trouble estimating the confidence interval for the difference of two group means, with groups been of unequal sample size. I am using the "Bootstrap" package and the function "bcanon"(bcanon(x, nboot, theta, ...,alpha=c(0.025, 0.05, 0.1, 0.16, 0.84, 0.9, 0.95, 0.975)) for Nonparametric BCa confidence limits. The

Hi. Thank you very much in advance for your help. I have generated data from two simple linear models and used k-means clustering (stats4) to identify two clusters in the generated data. Next, I would like to do simple linear regression for each separate cluster. I can do this if I first use the cluster labels to define two separate data frames with the subset function. However, I would

Generally speaking, this sort of detailed statistical question about a speccial package in R does not get a reply on this general R programming help list. Instead, I suggest you either email the maintainer (found by ?maintainer) or ask a question on a relevant R task view, such as https://cran.r-project.org/web/views/Econometrics.html . (or any other that you judge to be more appropriate).

Dear Terry, It's not surprising that different modeling functions behave differently in this respect because there's no articulated standard. Please see my response to Martin for my take on the singular.ok argument. For a highly sophisticated user like you, singular.ok=TRUE isn't problematic -- you're not going to fail to notice an NA in the coefficient vector -- but I've

I am working with some survival data with missing values. I am using the mice package to do multiple imputation. I have found code in this thread which handles pooling of the MI results: https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html Now I would like to plot a survival curve using the pooled results. Here is a reproducible example: require(survival) require(mice) set.seed(2) dt

Pleased to announce that the next version (2.3.0) of our package ?concurve? is out on CRAN (https://cran.r-project.org/package=concurve). In addition to plotting confidence (consonance) curves, it can now plot two functions next to one another to see the amount of overlap. It can also plot likelihood and deviance functions along with consonance densities and distributions. And it can utilize

Pleased to announce that the next version (2.3.0) of our package ?concurve? is out on CRAN (https://cran.r-project.org/package=concurve). In addition to plotting confidence (consonance) curves, it can now plot two functions next to one another to see the amount of overlap. It can also plot likelihood and deviance functions along with consonance densities and distributions. And it can utilize

Hello, I have a question regarding bootstrap confidence intervals. Suppose we have a data set consisting of single measurements, and that the measurements are independent but the distribution is unknown. If we want a confidence interval for the population mean, when should a bootstrap confidence interval be preferred over the elementary t interval? I was hoping the answer would be

--- silk/NSQ.c | 18 ++------------- silk/NSQ.h | 27 ++++++++++++++++++++++ silk/arm/NSQ_neon.c | 66 +++++++++++++++++++++++++++++++++++++++++++++++++++++ silk/arm/NSQ_neon.h | 10 ++++++++ 4 files changed, 105 insertions(+), 16 deletions(-) diff --git a/silk/NSQ.c b/silk/NSQ.c index d8513dc..ec81f3b 100644 --- a/silk/NSQ.c +++ b/silk/NSQ.c @@ -205,7 +205,7 @@ void

--- silk/NSQ.c | 18 ++------------- silk/NSQ.h | 27 ++++++++++++++++++++++ silk/arm/NSQ_neon.c | 66 +++++++++++++++++++++++++++++++++++++++++++++++++++++ silk/arm/NSQ_neon.h | 10 ++++++++ 4 files changed, 105 insertions(+), 16 deletions(-) diff --git a/silk/NSQ.c b/silk/NSQ.c index d8513dc..ec81f3b 100644 --- a/silk/NSQ.c +++ b/silk/NSQ.c @@ -205,7 +205,7 @@ void

I'm trying to use a self-written function with the summaryBy function (doBy package). I have lots of data from Monte Carlo experiments comparing different estimators across different (combinations of) parameter values, similar to the following form: colnames(mydata) <- c("X", "b0", "b1", # parameter combination, corresponding (true) parameter values

Hi all: I have two curve models: model1<-nls(result ~ exp(b0 + b1*(time)), start = list(b0 = 0, b1 = 5),trace=TRUE,data=data1) model2<-nls(result ~ exp(b0 + b1*(time)), start = list(b0 = 0, b1 = 5),trace=TRUE,data=data2) I wanna compare the two models to find out whether the difference between them is significant or not. How can I do then? Many thanks! My best [[alternative