similar to: Asking, are simple effects different from 0

Dear R People: I have an AOV model that I get confidence intervals from via > confint(chick1.aov1) 2.5 % 97.5 % trtA 1.472085 1.607915 trtB 1.512085 1.647915 trtC 1.328751 1.464582 > I am using R2HTML to produce HTML output. However, the HTML code itself just has rounded values, i.e., 1.5 and 1.6. Has anyone run across this, please? Any suggestions would be much appreciated.

Hello all, Would there be any interest for adding as.data.frame() methods for model objects? Of course there is packages (e.g. broom), but I think providing methods would be more discoverable (and the patch would be small). It is really useful for exporting model results or for plotting. e.g.: as.data.frame.lm <- function(x) { # could get other arguments, e.g. exp = TRUE/FALSE to

Hi! I'm failing to understand the value of the intercept value in a multiple linear regression with categorical values. Taking the "warpbreaks" data set as an example, when I do: > lm(breaks ~ wool, data=warpbreaks) Call: lm(formula = breaks ~ wool, data = warpbreaks) Coefficients: (Intercept) woolB 31.037 -5.778 I'm able to understand that the value of

Following Duncan Murdoch's off-list comments (thanks again!), here is a more complete/flexible version: as.data.frame.lm <- function(x, ..., level = 0.95, exp = FALSE) { cf <- x |> summary() |> stats::coef() ci <- stats::confint(x, level = level) if (exp) { cf[, "Estimate"] <- exp(cf[, "Estimate"]) ci <- exp(ci) } df <-

Hello all, I have a question concerning how to get the P-value for a explanatory variables based on GLM. I'll run multiple regressions with GLM, and I'll need the P-value for the same explanatory variable from these multiple GLM results. I check the help and there are quite a few Value options but I just can not find anyone about the p-value. Could anyone help me with that?

>>>>> SOEIRO Thomas via R-devel >>>>> on Fri, 17 Jan 2025 14:19:31 +0000 writes: > Following Duncan Murdoch's off-list comments (thanks again!), here is a more complete/flexible version: > > as.data.frame.lm <- function(x, ..., level = 0.95, exp = FALSE) { > cf <- x |> summary() |> stats::coef() > ci <- stats::confint(x,

I'm still relatively new to R but was wondering if anyone could help me force R to compute the f-statistic etc using the the nested term rather than the residual. In my particular case we were nesting a treatment effect by a replicated tank which was not non-significant enough (p>0.25)to be dropped from the statistical model. Here's my code:

Hello, I am performing a BACI analysis with ANOVA using the following glm: fit1<-glm(log(Cucs_m+1)~(BA*Otter)+BA+Otter+ID+Primary, data=b1) The summary(aov(fit1)) shows significance in the interaction; however, now I would like to determine what combinations of BA and Otter are significantly different (each factor has two levels). ID and PRIMARY substrates are categorical and included in

I have three columns of data, Xc, Trt and fish. This was a repeated measures design with 6 measurements taken from each of 5 fish. Xc is the actual measurement, Trt is the treatment, and fish is the fish number. Data can be seen below (hopefully it is in the column format). I would like to look for differences between treatments in a repeated measures format. I used the following code

Hello, My name is Rodrigo, I am using R program and I have a trouble. I am trying to do a dendrogram with genetics information. Let me explain... The Similarity Matrix was already did, and with this matrix I want to construct a dendrogram. So, the distance is done. I need to transform this matrix (that I have) in a dendrogram, I woud be very grateful if someone could help me. PS: I am sending

I wonder if someone could explain the behavior of the anova() and lm() functions in the following situation: I have a standard 3x2 factorial design, factorA has 3 levels, factorB has 2 levels, they are fully crossed. I have a dependent variable DV. Of course I can do the following to get the usual anova table: > anova(lm(DV~factorA+factorB+factorA:factorB)) Analysis of Variance Table

Dear R-help-list, I have a problem in which the explanatory variables are categorical, the response variable is a proportion, and experiment contains technical replicates (pseudoreplicates) as well as biological replicated. I am new to both generalized linear models and mixed- effects models and would greatly appreciate the advice of experienced analysts in this matter. I analyzed the

Hello, I am working on my thesis and can't really figure out how to produce a reasonable graph from the output from my glm., I could just give the R-output in my results and then discuss them, but it would be more interesting if I could visualise what is going on. My research is how bees react to different fieldmargins, for this I have 4 different types of field margin (A,B,C & D) and

Quick question about the usage of glht. I'm working with a data set from an experiment where the response is bounded at 0 whose variance increases with the mean, and is continuous. A Gamma error distribution with a log link seemed like the logical choice, and so I've modeled it as such. However, when I use glht to look for differences between groups, I get significant

Dear R users, I have a linear model of the kind outcome ~ treatment + covariate where 'treatment' is a factor with three levels ("0", "1", and "2"), and the covariate is continuous. Treatments "1" and "2" both have regression coefficients significantly different from 0 when using treatment contrasts with treatment "0" as the

A non-text attachment was scrubbed... Name: not available Type: text Size: 3126 bytes Desc: not available Url : https://stat.ethz.ch/pipermail/r-help/attachments/19991130/5cb00c0f/attachment.pl

Hi, I'm using Asterisk 13.x and have defined a pjsip TCP IPv6 transport: [transport-tcp-ipv6] type=transport protocol=tcp bind=[2001:1234:5678:abcd::2]:5060 I also have an IPv4 version of that: [transport-tcp-ipv4] type=transport protocol=tcp bind=10.75.22.8:5060 I've then configured an endpoint to use it: [outgoing] type = endpoint context = default dtmf_mode = none disallow = all

Hi, > I recently converted my employer's firewall from pure iptabes to > firewalld and looked for something similar, more along the lines of > webmin, etc. funny, my webmin installation on a banana-pi has webmin 1.831, which has support for firewalld. I am not sure, but I believe I got it directly from www.webmin.com. best regards --- Michael Schumacher

Dear All, I am working on a large matrix of dimension 20000x700 say 'mat'. I have calculated pearson correlation for the rows of the matrix and their p-values using rcorr function in library Hmisc. Now I wish to filter out those pairs who's PCC value is above 0.8 cut off and p-value is less than 0.05. >library(Hmisc) >mat_cor=rcorr(t(mat),type="pearson")

Full_Name: Joaquin Diaz-Saiz Version: 1.3.0 OS: Windows Submission from: (NULL) (129.7.120.106) The lm() function applied to a particular data set produces a negative value for the adjusted R-square. See the output below. ======================================================= R : Copyright 2001, The R Development Core Team Version 1.3.0 (2001-06-22) R is free software and comes with