Displaying 20 results from an estimated 40000 matches similar to: "standard error for median"
2012 Jul 19
3
median comparison tests
Hi,
A client has a consumption measure on each of four products. The sample
size is 75. The consumption distributions are highly skewed for each
product. He would like a pairwise comparison test of the products, much
like Tukey's HSD but using medians rather than means. Is there such a
median comparison test in R?
Thanks,
Walt
________________________
Walter R. Paczkowski, Ph.D.
2011 Feb 28
1
Robust variance estimation with rq (failure of the bootstrap?)
I am fitting quantile regression models using data collected from a
sample of 124 patients. When modeling cross-sectional associations, I
have noticed that nonparametric bootstrap estimates of the variances
of parameter estimates are much greater in magnitude than the
empirical Huber estimates derived using summary.rq's "nid" option.
The outcome variable is severely skewed, and I am
2009 Dec 23
2
Mean, median and other moments
Hi!
Suppose I have a dataset as follows
pd = c(10,7,10,11,7,11,7,6,8,3,12,7,7,10,10)
I wish to calculate the mean, standard deviation, median, skewness and kurtosis i.e. regular standard statistical measures.
average = mean(pd)
stdev = sd(pd)
median = median(pd)
skew = skewness(pd)
kurt = kurtosis(pd)
Q. No (1)
How do I get these at a stretch using some R package? I came across
2005 May 10
1
Using function 'boot on a bi-polar sample'
Hello
I'm not sure I'm using boot correctly:
I have a list of values for a variable in BUCKET[, j]
I want to use function 'boot' to estimate a confidence interval on the mean of the non-zero data. The data can be bi-polar or skewed.
Is this the correct use of boot to establish a mean and standard deviation or median and percentiles?
x<-BUCKET[, j]
2011 Jan 30
3
medians in Wilcoxon disagree with median function
I am sure I am opening myself up to looking stupid, but I have two samples
with medians of 613.5 and 189 (difference in location of 424 compared to
the difference suggested from the wilcoxon of 291.5)
> wilcox.test(pipwtCount,pipwdCount, conf.int=TRUE, na.rm=TRUE)
Wilcoxon rank sum test
data: pipwtCount and pipwdCount
W = 822, p-value = 0.01227
alternative hypothesis: true location
2009 Mar 17
1
Need a little help setting the upper median using "layout"...
The code I'm using is shown below.
I would like to have a larger median at the top of the plot so that I can show the entirity of "title_text".
Several times I tried messing with "par(mar", but that seemed to make matters worse.
By any chance can anyone provide any insight as to the best way to increase the top/upper/northern margin, so the entirity of the title is
2005 Jul 19
1
R: stats
for the stats gurus
does anyone know if there exists a general formula relating the median
of a continuous distribution to its moments. the distribution could be
skewed or symmetric and is definitely not normal.
the reason for asking is since the median of the particular distribution
that i am interested in is difficult (probably impossible) to obtain.
the median depends depends on an incomplete
2012 Feb 04
2
How to Compare the median to the mean?
Okay, so I have a homework projecr for R, and we had to input the following
link as some sort of data:
nb10 <- read.table("http://www.adjoint-functors.net/su/web/314/R/NB10").
Afterwards, we have to use
fivenum(nb10) to find max, min, quantiles, and sd, but I'm okay with this.
The next question is where I'm stuck. The question is as follows;
Compare the median (use the
2002 Aug 20
1
Running median
I have a Date x Stock (223 x 520) matrix of "trading volume". I can calculate
a 5-day (past) average in about 1 second using:
R> apply(vol, 1, filter, filter=c(0, rep(1/5,5)), sides=1)
I would like to do the same with a 5-day median, e.g.:
R> mymed <- function(x, n=5) {
R> r <- rep(NA, length(x))
R> for (i in (n+1):length(x)) r[i] <- median(x[i-(1:n)])
R>
2005 Feb 19
2
Warnings by functions mean(), median()
Hello,
following functions doesnt work correct with my data: median(), geo.mean().
My datafiles contain more than 10.000 lines and six columns from a
flow-cytometer-measurment. I need the arithmetic and geometric mean and
median. For the calculation of the geometric mean i wrote following
function:
fix(geo.mean)
function(x)
{
n<-length(x)
2010 Apr 02
2
Cross-validation for parameter selection (glm/logit)
If my aim is to select a good subset of parameters for my final logit
model built using glm(). What is the best way to cross-validate the
results so that they are reliable?
Let's say that I have a large dataset of 1000's of observations. I
split this data into two groups, one that I use for training and
another for validation. First I use the training set to build a model,
and the the
2005 Mar 31
2
how to simulate a time series
Dear useRs,
I want to simulate a time series (stationary; the distribution of
values is skewed to the right; quite a few ARMA absolute standardized
residuals above 2 - about 8% of them). Is this the right way to do it?
#--------------------------------
load("rdtb") #the time series
> summary(rdtb)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-1.11800 -0.65010 -0.09091
2011 Jan 17
1
median by geometric mean -- are we missing what's important?
Folks:
I know this may be overreaching, but are we missing what's important?
WHY do the zeros occur? Are they values less then a known or unknown
LOD? -- and/or is there positive mass on zero? In either case, using
logs to calculate a geometric mean may not make sense. Paraphrasing
Greg Snow, what is the scientific question? What is the model?
Cheers,
Bert
On Mon, Jan 17, 2011 at 9:13 AM,
2005 May 27
1
logistic regression
Hi
I am working on corpora of automatically recognized utterances, looking
for features that predict error in the hypothesis the recognizer is
proposing.
I am using the glm functions to do logistic regression. I do this type
of thing:
* logistic.model = glm(formula = similarity ~., family = binomial,
data = data)
and end up with a model:
> summary(logistic.model)
Call:
2007 Aug 30
2
Q: Mean, median and confidence intervals with functions "summary" & "boxplot.stats"
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2006 Mar 22
3
ordering boxplots according to median
Dear R-users,
Does anyone knows how I can order my serie of boxplots from lowest to
highest median (which is much better for visualization purposes).
thanks in advance,
willem
[[alternative HTML version deleted]]
2008 Sep 18
3
Oja median
Hi,
Can we get the code for calculating Oja median for multivariate data
Thanks and Regards
Rahul Agarwal
Analyst
Equities Quantitative Research
UBS_ISC, Hyderabad
On Net: 19 533 6363
[[alternative HTML version deleted]]
2019 Jan 22
2
Objectsize function visiting every element for alt-rep strings
On Mon, 21 Jan 2019, Martin Maechler wrote:
>>>>>> Travers Ching
>>>>>> on Tue, 15 Jan 2019 12:50:45 -0800 writes:
>
> > I have a toy alt-rep string package that generates
> > randomly seeded strings. example: library(altstringisode)
> > x <- altrandomStrings(1e8) head(x) [1]
> >
2007 Feb 28
3
Packages in R for least median squares regression and computing outliers (thompson tau technique etc.)
Hi
I am looking for suitable packages in R that do
regression analyses using least median squares method
(or better). Additionally, I am also looking for
packages that implement algorithms/methods for
detecting outliers that can be discarded before doing
the regression analyses.
Although some websites refer to "lms" method under
package "lps" in R, I am unable to find such a
2012 Oct 30
6
standard error for quantile
Dear all
I have a question about quantiles standard error, partly practical
partly theoretical. I know that
x<-rlnorm(100000, log(200), log(2))
quantile(x, c(.10,.5,.99))
computes quantiles but I would like to know if there is any function to
find standard error (or any dispersion measure) of these estimated
values.
And here is a theoretical one. I feel that when I compute median from
given