similar to: small sample size confidence interval by bootstrap

Hello! I am trying to use lm to do a simple regression but on a batch of different files. Each file has different column names. I know the first column is the dependent variable and all the rest are explanatory variables. The column names are in the first line of the file. But it seems lm() requires I know the variable names in advance? Is there a way to dynamically read in variable names

Hello, I am struggling to produce an MDS plot using the randomForest package with a moderately large data set. My data set has one categorical response variables, 7 predictor variables and just under 19000 observations. That means my proximity matrix is approximately 133000 by 133000 which is quite large. To train a random forest on this large a dataset I have to use my institutions high

Package: logcheck-database Version: 1.2.33 Severity: normal I just installed 1.2.33, and it made my rules dirs setuid, not setgid... - Marc -- System Information: Debian Release: 3.1 APT prefers testing APT policy: (900, 'testing'), (300, 'unstable') Architecture: i386 (i686) Kernel: Linux 2.6.8-1-k7 Locale: LANG=en_CA, LC_CTYPE=en_CA (charmap=ISO-8859-1) Versions of

Hello all, could you please tell me how to create a 95 percent confidence interval using R, if I have the next data: > blue [1] 4 69 87 35 39 79 31 79 65 95 68 62 70 80 84 79 66 75 59 77 36 86 39 85 74 [26] 72 69 85 85 72 > red [1] 62 80 82 83 0 81 28 69 48 90 63 77 0 55 83 85 54 72 58 68 88 83 78 30 58 [26] 45 78 64 87 65 Build a confidence interval of 95 % for the difference of the

Package: logcheck Version: 1.2.32 Severity: wishlist /etc/cron.daily/sysklogd restarts syslogd at the end of the script. This causes a daily log message, currently missed by logcheck: Jan 14 06:55:22 pyloric syslogd 1.4.1#16: restart (remote reception). I'm currently using this regex in ignore.server.d/local-syslogd: ^\w{3} [ :0-9]{11} [._[:alnum:]-]+ syslogd 1\.4\.1#16: restart \(remote

Dear R users, I am new to R. I would like to calculate bootstrap confidence intervals using the BCa method for a parameter of interest. My situation is this: I already have a set of 1000 bootstrap replicates created from my original data set. I have already calculated the statistic of interest for each bootstrap replicate, and have also calculated the mean for this statistic across all the

http://bugs.freedesktop.org/show_bug.cgi?id=16269 Summary: NV47 DVI DPMS scary loop Product: xorg Version: unspecified Platform: Other OS/Version: All Status: NEW Severity: normal Priority: medium Component: Driver/nouveau AssignedTo: nouveau at lists.freedesktop.org ReportedBy: vallesroc

Package: logcheck-database Version: 1.2.32 Severity: normal Tags: patch The fix for 283331 exposed a bug in the dnsmasq rules. The rule was looking for DHCPINFO, but the actual message is DHCPINFORM. Prior to the 283331 fix, the old rule worked, because the "[()[:alnum:]]+" part of the rule matched the "RM" at the end of DHCPINFORM. -- System Information: Debian Release:

I have a dataset of 10000 records which I want to use to compare two prediction models. I split the records into test dataset (size = ntest) and training dataset (size = ntrain). Then I run the two models. Now I want to shuffle the data and rerun the models. I want many shuffles. I know that the following command sample ((1:10000), ntrain) can pick ntrain numbers from 1 to 10000. Then I just

http://bugs.freedesktop.org/show_bug.cgi?id=15233 Summary: geforce 7800gs and AGP 3.0 DBI function Product: xorg Version: unspecified Platform: Other OS/Version: All Status: NEW Severity: normal Priority: medium Component: Driver/nouveau AssignedTo: nouveau at lists.freedesktop.org

Hello, I am doing a simple linear regression analysis that includes few variables. I am using a bootstrap analysis to obtain the variation of my variables to replacement. I am trying to obtain the coefficients 95% confidence interval from the bootstrap procedure. Here is my script for the bootstrap: N = length (data_Pb[,1]) B = 10000 stor.r2 = rep(0,B) stor.r2 = rep(0,B) stor.inter =

In the logistic regression model, there is no residual log (pi/(1-pi)) = beta_0 + beta_1*X_1 + ..... But glm model will return residuals What is that? How to understand this? Can we put some residual in the logistic regression model by replacing pi with pi' (the estimated pi)? log (pi'/(1-pi')) = beta_0 + beta_1*X_1 + .....+ ei Thanks! [[alternative HTML version deleted]]

How can I performing Bootstrap Confidence Intervals for the estimates of nonparametric regression y=f(x) such as loess and spline smoothing Thanks in advance [[alternative HTML version deleted]]

Good morning, I am trying to find a S-Plus code which shows how to find a confidence interval using a bootstrapping on a corrected correlation value of a two bivariate variables. If you happen to know one, please shows me. I am greatly appreciated your help. Have a wonderful day, Minh -- View this message in context:

Dear R-Experts, Thanks to Prof. Bonnett, I have got an R script working to calculate confidence intervals around the semipartial correlation coefficients. Now, I would like to calculate BCa bootstrap CIs using the boot library and the boot.ci(results, type="all") function. How could I modify my R script (here below reproducible example) to get the BCa bootstrap CIs ? CIsemipartcorr

Hello, I am using R.12.2.0. I am trying to generate bootstrap confidence intervals using bootcov from the rms package. I am able to impute the missing data using aregImpute and to perform a linear regression on the imputed datasets using fit.mult.impute, but I am unable to use bootcov to generate the confidence intervals for the R-squared. Here is a small example that should duplicate the

Hi all. This is my first post in this forum. Finally I find a forum in the web about R, although is not in my language. Now I'm working with Bootstrap CI. I'd like to know how I can calculate a Bootstrap CI for any statistic, in particular, for Kurtosis Coeficient. I have done the following code lines: > library(boot) > x=rnorm(20) > kurtosis=function(x)

Hi All: I have to compute bootstrap confidence intervals, the statistic (incremental cost effectiveness ratio) is computed from two samples (intervention and control) of different sizes. All the bootstrap functions that I have seen use one dataset as argument. I may go ahead and get the desired number of bootstrap replications separately. I would appreciate if you could point me to a source of a

There appear to be reports in the literature that transform continuous independent variablea by the use of splines, e.g., assume the dependent variable is hot dogs eaten per week (HD) and the independent variable is waistline (WL), a normal linear regression model would be: nonconfusing_regression <- lm(HD ~ WL) One might use a spline, confusion_inducing_regression_with_spline <- lm(HD

d = c(0L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0L, 0L, 7375L, NA, NA, 17092L, 0L, 0L, 32390L, 2326L, 22672L, 13550L, 18285L) boot.out <-boot(d, mean, R=1000, sim="permutation") Error in mean.default(data, original, ...) : 'trim' must be numeric of length one I know that I am missing something but I can't figure it out. thanks stephen -- Let's not spend our