Re: [R] which() vs. just logical selection in df

> > "The trouble with having an open mind is that people keep coming along and > sticking things into it." > -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > > > On Wed, Oct 14, 2020 at 3:23 PM 1/k^c wrote: > >> Is which() in

Re: [R] which() vs. just logical selection in df

hich() still seems superfluous to me, and I'd like to know whether it's considered best practice to keep it. What is R inoking when which() isn't called explicitly? Is R invoking which() eventually anyway? Cheers! Keith > Message: 2 > Date: Mon, 12 Oct 2020 13:01:36 -0600 >

[R] which() vs. just logical selection in df

Hi R-helpers, Does anyone know why adding which() makes the select call more efficient than just using logical selection in a dataframe? Doesn't which() technically add another conversion/function call on top of the logical selection? Here is a reproducible example with a slight difference in timi

Re: [R] gamlss() vs glm() standard errors via summary() vs vcov()

need to multiply with exp(coef()) here??? > > -pd > >> On 4 Jul 2018, at 11:08 , 1/k^c wrote: >> >> Hi R-helpers, >> >> I was working with some count data using gamlss() and glm(), and >> noticed that the standard errors from the two functions corresp

[R] gamlss() vs glm() standard errors via summary() vs vcov()

Hi R-helpers, I was working with some count data using gamlss() and glm(), and noticed that the standard errors from the two functions correspond when extracting from either the model summary for both functions, or using vcov for both functions, but the standard errors between those methods do not

[R] model selection for nested factorial design

Stats beginner here. I have a dataset composed of observations taken from 16 separate experimental panels, each nested into one of 4 conditions (Treatment A Level 1, Treatment A Level 2, Treatment B Level 1, Treatment B Level 2; see photo: http://imgur.com/ZbzFPNq). There are 100 observations of t