Hi all,
I have the following data called "data1". After fitting the ancova model
with different slopes and intercepts for each region, I calculated the
regression coefficients and the corresponding standard error. The standard
error (for intercept or for slope) are all the same for different regi
Hi, I am analyzing the relationship between animal density and several
environmental factors using GAM in mgcv. I used Poisson distribution and
quasi-Poisson model separately because there is overdispersion. In my model,
there is a categorical predictor variable "Year". However, when I compared
do appear per request in the summary output.
Terry T.
On 06/28/2014 05:00 AM, r-help-requ...@r-project.org wrote:
> Message: 9
> Date: Fri, 27 Jun 2014 12:39:29 -0700
> To:"r-help@r-project.org"
> Subject: [R] standard error of survfit.coxph()
> Message-ID:
> <140
elp-requ...@r-project.org wrote:
> Message: 9
> Date: Fri, 27 Jun 2014 12:39:29 -0700
> To:"r-help@r-project.org"
> Subject: [R] standard error of survfit.coxph()
> Message-ID:
> <1403897969.91269.yahoomail...@web122906.mail.ne1.yahoo.com>
> Content-Typ
14 05:00 AM, r-help-requ...@r-project.org wrote:
> Message: 9
> Date: Fri, 27 Jun 2014 12:39:29 -0700
> To:"r-help@r-project.org"
> Subject: [R] standard error of survfit.coxph()
> Message-ID:
> <1403897969.91269.yahoomail...@web122906.mail.ne1.yahoo.com>
> Co
elp-requ...@r-project.org wrote:
> Message: 9
> Date: Fri, 27 Jun 2014 12:39:29 -0700
> To:"r-help@r-project.org"
> Subject: [R] standard error of survfit.coxph()
> Message-ID:
> <1403897969.91269.yahoomail...@web122906.mail.ne1.yahoo.com>
> Content
ear per request in the summary output.
Terry T.
On 06/28/2014 05:00 AM, r-help-requ...@r-project.org wrote:
Message: 9
Date: Fri, 27 Jun 2014 12:39:29 -0700
From: array chip
To:"r-help@r-project.org"
Subject: [R] standard error of survfit.coxph()
Message-ID:
<1403897969.
Hi, can anyone help me to understand the standard errors printed in the output
of survfit.coxph()?
time<-sample(1:15,100,replace=T)
status<-as.numeric(runif(100,0,1)<0.2)
x<-rnorm(100,10,2)
fit<-coxph(Surv(time,status)~x)
### method 1
survfit(fit, newdata=data.frame(time=time,status=status
Hi,
?cor.test
Regards,
Pascal
2013/4/19 Gundala Viswanath
> Is there a native way to produce SE of correlation in R's cor() functions
> and p-value from T-test?
>
> As explained in this web
> http://www.sjsu.edu/faculty/gerstman/StatPrimer/correlation.pdf
> (page 14.6)
>
> The standard erro
Is there a native way to produce SE of correlation in R's cor() functions
and p-value from T-test?
As explained in this web
http://www.sjsu.edu/faculty/gerstman/StatPrimer/correlation.pdf
(page 14.6)
The standard error is sqrt((1-r^2)/(n-2)), where n- is the number of sample.
- G.V.
[[
I fitted a mixture denstiy of two gaussians two my data. I now want to
calculated the standard errors of the estimates via the boot.se command of
the mixtools package. My question is now, if the output is correct? It
seems a bit odd to me, so is this correct what I am doing and can I rely on
the va
Abu Naser hotmail.com> writes:
> I have been trying to fit my data (only right censored)
> with gumbel distribution using fitdistrplus. I am
> getting very high standard error. I have been wondering why.
> The followings are the outputs:
>
> fit1=fitdistcens(dr0, "gumbel", start=list(a=99, b=0.
Dear all,
I have been trying to fit my data (only right censored) with gumbel
distribution using fitdistrplus. I am getting very high standard error. I have
been wondering why.
The followings are the outputs:
fit1=fitdistcens(dr0, "gumbel", start=list(a=99, b=0.6), optim.method=
"L-B
On Tue, Nov 13, 2012 at 11:59 AM, Rolf Turner wrote:
>
> My apologies for returning to this issue after such a considerable
> length of time ... but I wanted to check the result in Cramer's book,
> and only yesterday managed to get myself organised to go the
> library and check it out.
>
> What b
My apologies for returning to this issue after such a considerable
length of time ... but I wanted to check the result in Cramer's book,
and only yesterday managed to get myself organised to go the
library and check it out.
What bothers me is what happens when f(Q.p) = 0. The formula
that you
[see in-line below]
On 31-Oct-2012 10:26:14 PIKAL Petr wrote:
> Hi Ted
>
>> -Original Message-
>> From: ted@deb [mailto:ted@deb] On Behalf Of Ted Harding
>> Sent: Tuesday, October 30, 2012 6:41 PM
>> To: r-help@r-project.org
>
>
>
>>
>> The general asymptotic result for the pth quanti
The rank test inversion option that you are trying to use won't
work with only one coefficient, and therefore with univariate
quantiles, if you use summary(rq(rnorm(50) ~ 1, tau = .9), cov = TRUE)
you will have better luck.
url:www.econ.uiuc.edu/~rogerRoger Koenker
emailrkoen.
t; Cc: r-help@r-project.org help
> Subject: Re: [R] standard error for quantile
>
> Petr,
>
> You can do:
>
> require(quantreg)
> summary(rq(x ~ 1, tau = c(.10,.50,.99))
>
>
> url:www.econ.uiuc.edu/~rogerRoger Koenker
> emailrkoen..
Hi Ted
> -Original Message-
> From: ted@deb [mailto:ted@deb] On Behalf Of Ted Harding
> Sent: Tuesday, October 30, 2012 6:41 PM
> To: r-help@r-project.org
>
> The general asymptotic result for the pth quantile (0 sample of size n is that it is asymptotically Normally distributed with
>
Hi Bert
> -Original Message-
> From: Bert Gunter [mailto:gunter.ber...@gene.com]
> Sent: Tuesday, October 30, 2012 3:37 PM
> To: PIKAL Petr
> Cc: r-help@r-project.org
> Subject: Re: [R] standard error for quantile
>
> Petr:
>
> 1. Not an R question.
Par
> -Original Message-
> From: Jim Lemon [mailto:j...@bitwrit.com.au]
> Sent: Wednesday, October 31, 2012 9:56 AM
> To: PIKAL Petr
> Cc: r-help@r-project.org
> Subject: Re: [R] standard error for quantile
>
> On 10/31/2012 12:46 AM, PIKAL Petr wrote:
> > Dear all
> >
&
On 10/31/2012 12:46 AM, PIKAL Petr wrote:
Dear all
I have a question about quantiles standard error, partly practical
partly theoretical. I know that
x<-rlnorm(10, 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 stan
On 30-Oct-2012 13:46:17 PIKAL Petr wrote:
> Dear all
>
> I have a question about quantiles standard error, partly practical
> partly theoretical. I know that
>
> x<-rlnorm(10, log(200), log(2))
> quantile(x, c(.10,.5,.99))
>
> computes quantiles but I would like to know if there is any funct
Petr,
You can do:
require(quantreg)
summary(rq(x ~ 1, tau = c(.10,.50,.99))
url:www.econ.uiuc.edu/~rogerRoger Koenker
emailrkoen...@uiuc.eduDepartment of Economics
vox: 217-333-4558University of Illinois
fax: 217-244-6678
Petr:
1. Not an R question.
2. You want the distribution of order statistics. Search on that. It's
basically binomial/beta.
-- Bert
On Tue, Oct 30, 2012 at 6:46 AM, PIKAL Petr wrote:
> Dear all
>
> I have a question about quantiles standard error, partly practical
> partly theoretical. I know
Dear all
I have a question about quantiles standard error, partly practical
partly theoretical. I know that
x<-rlnorm(10, 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 th
On May 28, 2012, at 5:20 AM, Christopher Kelvin wrote:
Dear all,
I want to determine the standard error or the mean squared error
for the parameter estimate for beta and eta base on the real data.
Any help on how to obtain these estimated errors.
library(survival)
d <- data.frame(ob=c(14
Dear all,
I want to determine the standard error or the mean squared error for the
parameter estimate for beta and eta base on the real data.
Any help on how to obtain these estimated errors.
library(survival)
d <- data.frame(ob=c(149971, 70808, 133518, 145658, 175701, 50960, 126606,
82329),
Hello,
I have tried obtaining the value of standard error from the code below but i
get different values when i compare it with the
standard error obtained from the hessian matrix. Can somebody help me out?
Thank you
n=100;rr=1000
p1=1.2;b=1.5
sq11=sq21=0
for (i in 1:rr){
t<-rweibull(n,shape=p1,
Hello,
I have tried obtaining the value of standard error from the code below but i
get different values when i compare it with the
standard error obtained from the hessian matrix. Can somebody help me out?
Thank you
n=100;rr=1000
p1=1.2;b=1.5
sq11=sq21=0
for (i in 1:rr){
t<-rweibull(n,shape=p1,
Dear R-help,
I am using R 2.14.1 on Windows 7 with the 'gfcure' package (cure rate model).
I have included the treatment variable in the cure part of the model as shown
below:
Ø ref_treat <-
gfcure(Surv(rem.Remtime,rem.Rcens)~1,~1+strata(drpa)+factor(treat(delcure)),data=delcure,dist="loglogi
To: array chip
Cc: "r-help@r-project.org"
Sent: Thursday, February 9, 2012 2:59 PM
Subject: Re: [R] standard error for lda()
On Feb 9, 2012, at 4:45 PM, array chip wrote:
> Hi, didn't hear any response yet. want to give it another try..
appreciate any suggestions.
>
My
osterior probabilities, the predicted class, etc.
My question is whether it's possible to produce standard errors for these
posterior probabilities?
Thanks again.
John
From: David Winsemius
Cc: "r-help@r-project.org"
Sent: Thursday, February
nner that seems rather cavalier , admittedly this
being a very particular reaction from this non-expert audience of one.
John
To: "r-help@r-project.org"
Sent: Wednesday, February 8, 2012 12:11 PM
Subject: [R] standard error for lda()
Hi, I am wo
Hi, didn't hear any response yet. want to give it another try.. appreciate any
suggestions.
John
To: "r-help@r-project.org"
Sent: Wednesday, February 8, 2012 12:11 PM
Subject: [R] standard error for lda()
Hi, I am wondering if it is pos
Hi, I am wondering if it is possible to get an estimate of standard error of
the predicted posterior probability from LDA using lda() from MASS? Logistic
regression using glm() would generate a standard error for predicted
probability with se.fit=T argument in predict(), so would it make sense t
plot.ci=TRUE,ci.l=means,ci.u=means+halfSE)
Mikhail
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
On
> Behalf Of Uwe Ligges
> Sent: Friday, August 12, 2011 12:04 PM
> To: Christopher Crooks
> Cc: r-help@r-project.or
On 12.08.2011 18:43, Christopher Crooks wrote:
Hi,
I know there have been numerous posts about this but I am unable to find one,
or at least carry out one, that gives me the plot I want.
I have managed to add the error bars to the plot, but they end up not aligned
with the centre of the bars
Hi,
I know there have been numerous posts about this but I am unable to find one,
or at least carry out one, that gives me the plot I want.
I have managed to add the error bars to the plot, but they end up not aligned
with the centre of the bars themselves.
Here is my script:
means<-c(0.135
On Jul 23, 2011, at 1:41 AM, Ehsan Karim wrote:
Dear List,
Must be a silly question, but I was wondering whether there is a
direct way of calculating "standard error of a HR or exp(coef)" from
coxph objects
x <- coxph(Surv(time, status) ~ age + inst, lung)> xcoef
exp(coef) se(coe
Dear List,
Must be a silly question, but I was wondering whether there is a direct way of
calculating "standard error of a HR or exp(coef)" from coxph objects
x <- coxph(Surv(time, status) ~ age + inst, lung)> xcoef exp(coef)
se(coef) zpage 0.0190 1.02 0.00925 2.06 0.04i
Hi
I'm using the lattice function to plot catch data for two types of catch
between two communities over time using the xyplot. I've used
*"smooth"*when specifying
* type=c( )* . Here is a sample of my code below:
> xyplot(ns+ rc~wk|location, type=c("p", "smooth"), xlab="Week",
+ main="Native sh
Dear Sir/Madam,
I have used ca.jo in urca package to identify the cointegration and cajorls to
estimate the vecm. Althought both return the coefficients for long run
relationship (or ect1 in cajorls), I am unable to find the standard error and t
statistics.
I spend some weeks to search arou
Hi,
predict(model_fit, se.fit=TRUE)
see ?predict.lm for details.
-Ista
On Tue, Sep 28, 2010 at 12:16 PM, Brima wrote:
>
> Hi all,
>
> This is very basic but for a starter nothing is. I have a simple linear
> regression I am using to predict some values and I need the standard error
> of the pr
Hi all,
This is very basic but for a starter nothing is. I have a simple linear
regression I am using to predict some values and I need the standard error
of the prediction (forecast). Whats the easiest/bestway of getting this
error?
Best regards
--
View this message in context:
http://r.78969
I think you can use the bootstrap to obtain the std error. My
attempt for your problem and data is below. I would be interested if
anyone can point out a problem with this approach.
Darin
y=rbinom(100,1,.4)
x1=rnorm(100, 3, 2)
x2=rbinom(100, 1, .7)
diff <- vector(mode="numeric", length=200)
for
Is there a way to estimate the standard error for the difference in
predicted probabilities obtained from a logistic regression model?
For example, this code gives the difference for the predicted
probability of when x2==1 vs. when x2==0, holding x1 constant at its
mean:
y=rbinom(100,1,.4)
hn Tukey
> -Oorspronkelijk bericht-
> Van: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] Namens Meissner, Tony (DFW)
> Verzonden: donderdag 16 september 2010 2:58
> Aan: r-help@r-project.org
> Onderwerp: [R] standard error of difference for m
Hi:
Why is phase nested within sites? Shouldn't sites and phase be crossed?
After all, there is one intervention (works) that purportedly affects the
river both upstream and downstream. Moreover, why is log(flow) being treated
as both a fixed and random effect?
Just curious,
Dennis
On Wed, Sep 1
I have a dataset relating the effects of engineering works on the level of
salinity in a river before and after the works. I have modelled this using
linear mixed effects models to determine if the significance and level of the
response to the works. I am wanting to calculate the se of differe
Does anyone have code for computing the standard error of the predicted
mean count from a zero-inflated Poisson regression model estimated by the
zeroinfl() function from the pscl package (and yes, we've checked with A.
Z. already)?
Thank you
Brian
Brian S. Cade, PhD
U. S. Geological Survey
Hi,
Just to extend the excellent suggestions, if you are interested in the
odds ratio, you can just use exp():
#Odds Ratio
exp(fit4$coefficients)
#Confidence interval around OR
exp(confint(fit4))
To give you an idea graphically of the log odds (or logit) look at:
p <- seq(0, 1, by = .001)
plot
Medicine
jsor...@grecc.umaryland.edu
-Original Message-
From: Bessy
To:
Sent: 7/27/2010 11:40:33 AM
Subject: [R] standard error of Binary logistic regression coefficient.
Dear all,
I am struggling with the calculation of standard error of the coefficient in
Binary logistic regression
Maryland School of Medicine
Division of Gerontology and Geriatric Medicine
jsor...@grecc.umaryland.edu
-Original Message-
From: Bessy
To:
Sent: 7/27/2010 11:40:33 AM
Subject: [R] standard error of Binary logistic regression coefficient.
Dear all,
I am struggling with the calculation of
Dear all,
I am struggling with the calculation of standard error of the coefficient in
Binary logistic regression analysis.
I built a binary logsitic regression model as follows and got confused since
the calculation of standard error of coefficients of X1, X2 and X3 are not
the same as the Line
> Can anyone tell me what is the difference between these two standard
> errors and how should I interpret the confidence intervals and std.err
> given these differences?
help(survfit.object) will give you the answer. The std in the object is
for the cumulative hazard, the printout uses a Taylor
I am using the coxph, survfit and summary.survfit functions to calculate an
estimate of predicted survival with confidence interval for future patients
based on the survival distribution of an existing cohort of subjects. I am
trying to understand the calculation and interpretation of the std.
Josh B wrote:
Hi all,
This should be a very simple question for you, whereas it is proving devilish
for me.
How do I output the STANDARD ERROR of the regression coefficient (i.e., the
standard error of b) from a simple linear regression?
The first 'See Also' in ?lm is for ?summary.lm, whic
Hi all,
This should be a very simple question for you, whereas it is proving devilish
for me.
How do I output the STANDARD ERROR of the regression coefficient (i.e., the
standard error of b) from a simple linear regression?
Consider this data, taken directly from ?lm:
ctl <- c(4.17,5.58,5.18,
Dear R users,
I want to draw standard error lines for the predicted regression line
estimated by logistic regression using lmer. I have two predictors: cafr and
its quadratic form I(cafr^2), where cafr is a variable centered around the
mean of original variable. Now, the estimated value from the f
Here are some options for confidence intervals.
#by hand
sample_r <- .5
n_sample <- 100
df <- n_sample-1
fisher_z <- 0.5*(log(1+sample_r)-log(1-sample_r))
se_z <- 1/sqrt(n_sample-3)
t_crit <- qt(.975,df ,lower.tail=TRUE)
z_lci <- fisher_z - (t_crit * se_z)
z_uci <- fisher_z + (t_crit * se_z)
(r_l
I want the standard error associated with a correlation. I can calculate
using cor & var, but am wondering if there are libraries that already
provide this function.
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
http
801.408.8111
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-
> project.org] On Behalf Of Lisa Wang
> Sent: Wednesday, July 29, 2009 12:05 PM
> To: r-help@r-project.org
> Subject: [R] Standard error of Median in MASS library
>
>
Dear All,
I wonder which function in MASS library calculates and output the
standard error of median.
Thank you in advance for your help
Lisa Wang
Biostatistics, Princess Margaret hospital,
toronto, On
__
R-help@r-project.org mailing list
https://st
Hello all,
I wonder which function in MASS library is calculating the standard
error of median?
thank you very much in advance,
Lisa
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide htt
Dear All,
I used an AR(1) model to explain the process of the stationary residual and
have used an 'ar' command in R. From the results, i tried to extract
the standard error and p-value for the estimated parameter, but unfortunately,
i never find any way to extract it from the output.
What
Ricardo Arias Brito wrote:
>
> Dear All,
>
> Is posible calculate Std. Error for glm as lm, using
> cov(hat beta) = phi * solve(t(X) %*% hat W %*% X)^-1
> on R? Who is hat W and phi output glm?
>
> y=rpois(20,4)
> fit.glm <- glm(y ~ x, family=poisson
> summary(fit.glm)
>
> Fitted to a model g
Dear All,
The std. error of the estimated coefficients
obtained by the summary.lm function can be calculated
as:
y=rnorm(20)
x=y+rnorm(20)
fit <- lm(y ~ x)
summary(fit)
sqrt( sum(fit$resid**2)/fit$df.resid *
solve(t(model.matrix(fit))%*%model.matrix(fit)) )
Is posible calculate Std. Error
Ralph Scherer [Sat, Mar 07, 2009 at 10:34:28PM CET]:
> Dear Prof. Ripley,
>
> thank you for your fast answer.
> But which book do you mean?
> I can't find an MASS book.
Try
library(MASS)
citation(package="MASS")
--
Johannes Hüsing There is something fascinating about science.
Ok, found it.
Thanks.
Am Samstag, den 07.03.2009, 22:34 +0100 schrieb Ralph Scherer:
> Dear Prof. Ripley,
>
> thank you for your fast answer.
> But which book do you mean?
> I can't find an MASS book.
> Do you mean the R book?
>
> Best wishes,
> Ralph
>
>
>
> Am Samstag, den 07.03.2009, 21:
Dear Prof. Ripley,
thank you for your fast answer.
But which book do you mean?
I can't find an MASS book.
Do you mean the R book?
Best wishes,
Ralph
Am Samstag, den 07.03.2009, 21:24 + schrieb Prof Brian Ripley:
> It is an example (both via asymptotic theory and the bootstrap) in
> chapt
It is an example (both via asymptotic theory and the bootstrap) in
chapter 5 of MASS (the book). The functions used are in the scripts
of the MASS package, but you will need to understand the theory being
used as described in the book.
On Sat, 7 Mar 2009, Ralph Scherer wrote:
Dear all,
is
Dear all,
is it possible to estimate a standard error for the median?
And is there a function in R for this?
I want to use it to describe a skewed distribution.
Thanks in advance,
Ralph
[[alternative HTML version deleted]]
__
R-help@r-project.
Hi everyone.
I am now estimating the parameters for a logit model, and trying to
get the estimates by laximizing the log_likelihood.
The nlm function works nicely for maximizing the -(log_likelihood) and
returns the parameter estimates that minimize the static, and the
gradients also, but d
Thanks for all of your help, David, I finally got it. Here's some generic
syntax in case it helps someone else down the road (using a 4-way ANOVA
with repeated measures on all factors):
# LOAD DATA
data <- read.table("PATH\\datafile.txt")
# RUN THE ANOVA
data.aov <- aov(y ~ factor(x1)*factor(
On Dec 13, 2008, at 2:15 PM, js.augus...@gmail.com wrote:
> Does not this give you what you need?
> model.tables(rawfixtimedata.aov,"means", se=TRUE)
I tried that, but get an error:
SEs for type 'means' are not yet implemented
I don't get that error. Using the example and this call
model.ta
> Does not this give you what you need?
> model.tables(rawfixtimedata.aov,"means", se=TRUE)
I tried that, but get an error:
SEs for type 'means' are not yet implemented
Maybe I'm not using the correct terminology to describe what I need to do.
Using the main effect of Marking as an example, I h
On Dec 13, 2008, at 11:37 AM, Jason Augustyn wrote:
Hi David, thanks for the quick response. I did look at the help
files for model.tables and se.contrast and neither seemed
appropriate. I probably wasn't clear enough in my original email, so
here's more information:
I'm analyzing data f
Hi David, thanks for the quick response. I did look at the help files for
model.tables and se.contrast and neither seemed appropriate. I probably
wasn't clear enough in my original email, so here's more information:
I'm analyzing data from a psychology experiment on how people allocate
visual atten
On Dec 12, 2008, at 10:59 PM, js.augus...@gmail.com wrote:
Hi all,
I'm quite new to R and have a very basic question regarding how one
gets
the standard error of the mean for factor levels under aov. I was
able to
get the factor level means using:
summary(print(model.tables(rawfixtimedat
Hi all,
I'm quite new to R and have a very basic question regarding how one gets
the standard error of the mean for factor levels under aov. I was able to
get the factor level means using:
summary(print(model.tables(rawfixtimedata.aov,"means"),digits=3)),
where rawfixtimedata.aov is my aov m
You can use the esticon function in the doBy package.
Regards
Søren
Fra: [EMAIL PROTECTED] på vegne af Mark Donoghoe
Sendt: ma 12-05-2008 09:11
Til: r-help@r-project.org
Emne: [R] Standard error of combination of parameter estimates
Hi,
Is there a simple
Hi,
Is there a simple command for computing the standard error of a
combination of parameter estimates in a GLM?
For example:
riskdata$age1 <- riskdata$age
riskdata$age2 <- ifelse(riskdata$age<67,0,riskdata$age-67)
model <- glm(death~age1+age2+ldl,
data=riskdata,family=binomial(lin
Type summary.lm and read the code.
Best,
Uwe Ligges
Nick Chorley wrote:
> Hi,
>
> This may be a stupid question, but how are the "std. error" values returned
> by lm() calculated? For example
>
>> summary(lm)
>
> Call:
> lm(formula = log10(moments[2, 1:10]) ~ log10(L_vals[1:10]))
>
> Residu
Hi,
This may be a stupid question, but how are the "std. error" values returned
by lm() calculated? For example
> summary(lm)
Call:
lm(formula = log10(moments[2, 1:10]) ~ log10(L_vals[1:10]))
Residuals:
Min 1Q Median 3QMax
-0.0052534 -0.0019473 0.0006785 0.0
85 matches
Mail list logo
