similar to: Interval for rnorm command?

I've been trying to generate a series of random normal distributions using the rnorm function. After I generated the first set: set1<-rnorm(150, 505.5, 15.2) I checked the mean and standard deviation of set1. I was surprised to find the mean at 502.37 (rounded) and the standard deviation at 13.3 (again, rounded). While I know that it is unrealistic to expect the distribution of the

Hello, I use the command rnorm, and I feed these results into a lmer command. Since I am using the rnorm command I expect to get different results for each iteration, yet for each iteration I am getting the same answer. If someone understands why I am getting the same answer every time with a random number generator, I would appreciate help in understanding why this is happening. Is the lmer

Dear R-help listers, Under the helps from the ggplot2 list, I have set up method of drawing a graph with multiple density plots arranged one by one in one page. Now, I want to add 95% confidential interval as vertical lines to x axe in density plot. I have found the library(hdrcde) can do this work, but I do not know how to handle functions of this library when I used ggplot2 to draw the graph.

Sorry if this has been discussed here before, but anyway: The are differences between the confidence intervals of the t.test as done by R and Splus: *** R R> x<-rnorm(1000)+1 R> t.text(x) Error: couldn't find function "t.text" R> t.test(x) One Sample t-test data: x t = 31.4062, df = 999, p-value = 0 alternative hypothesis: true mean is not equal to 0 95

Hi there fellow R-users, Can anyone tell me how to build a prediction interval for a gaussian log-link model for the reponse variable?? I can find the standard error of the predictions but I cant seem to find the prediction interval. Is there a way I can calculate the prediction interval from the standard errors?? Here's the example: logX<-rnorm(100)

https://bugzilla.netfilter.org/show_bug.cgi?id=1091 Bug ID: 1091 Summary: named set with flag interval: delete multiple items in one command bug Product: nftables Version: unspecified Hardware: x86_64 OS: Gentoo Status: NEW Severity: major Priority: P5 Component:

Hi! I was trying to implement a probit model on a dichotomous outcome variable and found that the predictions were outside the (0,1) interval that one should get. I later tried it with some simulated data with a similar result. Here is a toy program I wrote and I cant figure why I should be getting such odd predictions. x1<-rnorm(1000) x2<-rnorm(1000) x3<-rnorm(1000)

Based on some recent r-help discussions, I have been trying out plotting confidence intervals using predict and matplot. Matplot appeared to not be plotting the linear regression when using the default column names generated by read.table (V1, V2, etc). On further inspection, the error seemed to be with predict and vector names (V1, V2) rather than with matplot. I was using some textbook

Dear R-help listers, I want to add 95% confidential interval as vertical lines to x axe in density plot. I have found the library(hdrcde) can do this work, but I do not know how to handle functions of this library when I used ggplot2 to draw the graph. Thank you in advance. The data and codes followed: # dummy data factor<-rep(c("Alice","Jone","Mike"),each=100)

Hello, I have some question on chron I currently doing this t1 <- chron(,"11:30:00") t2 <- chron(,"11:45:00") tt <- seq(t1,t2,by=times("00:00:01")) tt has 901 values (15 minutes * 60 secs) and then x1 <- rnorm(1:901) x2 <- rnorm(1:901) x3 <- rnorm(1:901) df <- data.frame(tt, x1, x2, x3) I would like to write a function such that I can

Dear R-helpers, I am trying to construct a confidence interval on a prediction of a gam fit. I have the Wood (2006) book, and section 5.2.7 seems relevant but I am not able to apply that to this, different, problem. Any help is appreciated! Basically I have a function Y = f(X) for two different treatments A and B. I am interested in the treatment ratios : Y(treatment = B) / Y(treatment = A) as

Good Morning: I posted an initial question a few days ago and I received some good advice from two R experts. I have re-examined the Davison-Hinkley text paying close attention to the examples of the boot() and boot.ci() in that text and the single example of a similar process in the MASS book (not the MASS package manual as I initially misunderstood). I think I understand how the stratified

Dear list, I'm currently analyzing some count data using a hurdle model. I've used the rcspline.eval function in the Hmisc-library to contruct the spline terms for the regression model, and what I want in the end is the ability to compute coefficients and confidence intervals for different changes in the smooth function as well as plotting the smooth function along with the

I am new to R and Im some trouble with the following question... Generate 100 standard normal N(0,1) samples of size 100, X1(k),...,X100(k) where k=1,...,100 (The k is and indicie in brackets) Calculate the sample mean for each sample. For each sample mean Xbark the 0.95-confidence interval for the mean mew=0 is given by... Ik= ( Xbark plus or minus 1.96/10) Find the number of intervals such

Hello, florian wrote > > Hy, > > I want to select an interval of a time series (containing intraday data) > by the data and a certain time frame (e.g. 9 AM to 10 AM). However, I do > not know how to specify the time during the day: > > #load data > library(RTAQ) > data("sample_tdata") > data=sample_tdata > return=makeReturns(data$PRICE) > >

I am using the quantreg package to build a quantile regression model and wish to generate confidence intervals for the fitted values. After fitting the model, I have tried running predict() and predict.rq(), but in each case I obtain a vector of the fitted values only. For example: library(quantreg) y<-rnorm(50,10,2) x<-seq(1,50,1)

> > Hi All: > > I am using R to calculate exact 95% confidence interval using Clopper > Pearson method. I am using the following code but it seems to get into a > loop and not get out of it, it goes on forever although I am looping it > only 10 times across 63 sites with 10 observations per site. I was hoping > to get some help. > > Thanks > Anamika >

Hi, I'm Sorry for any cross-posting. I've reviewed the archives and could not find an exact answer to my question below. I'm trying to generate very large sparse matrices (< 1% non-zero entries per row). I have a sparse matrix function below which works well until the row/col count exceeds 10,000. This is being run on a machine with 32G memory: sparse_matrix <-

First: R core: (Thank You!)^HUGE_VAL Now, down to business: In a loop or in repeated command lines: system.time(lm(rnorm(1000)~rnorm(1000))) ( or lm(rnorm(1000)~rnorm(1000))$coef ) fails after several iterations with the Windows message 'This program has performed an illegal operation and will be shut down. If the problem persists, please contact the vendor'. clicking on DETAILS

>>>>> Victor Tian <tianxu03 at gmail.com> >>>>> on Thu, 3 Aug 2017 09:49:57 -0400 writes: > To whom it may concern, > I happened to run the following R code just to check the layout of the > output, but found that the code doesn't work the way I thought it should > work. yes, your expectations were wrong. >>