similar to: An interesting chat with ChatGPT

Duncan - Indeed, this has now been well documented; I have called these constructions "Schr?dinger Facts", since they arise from a superposition of truths in the training data that collapse into an untruth when observed. https://sentientsyllabus.substack.com/p/chatgpts-achilles-heel Now, the curious question is: why can it program. Why is its production of R-syntax less vulnerable

**OFF TOPIC** but perhaps of interest to some on this list. I apologize in advance to those who may be offended. The byline: ******************************** "ChatGPT's odds of getting code questions correct are worse than a coin flip But its suggestions are so annoyingly plausible" ************************************* from here:

I am looking for the python-samba module. I would like to add/disable/modify AD accounts using python running on a linux machine. ChatGPT suggested the python-samba module. Sample code as follows: from samba.credentials import Credentials from samba.netcmd.user import create as create_user from samba.netcmd.user import setpassword as set_user_password ... I am running Debuan bullseye and I can

I recently had the chance to read a book explaining how to use ChatGPT with a certain programming language. (I'm not going to describe the book any more than that because I don't want to embarrass whoever wrote it.) They have appendix material showing three queries to ChatGPT and the answers. Paraphrased, the queries are "if I throw 2 (3, 4) fair dice, what is the probability I get

On 07/04/2023 20:20, David Bear via samba wrote: > I am looking for the python-samba module. I would like to > add/disable/modify AD accounts using python running on a linux machine. > ChatGPT suggested the python-samba module. Sample code as follows: > > from samba.credentials import Credentials > from samba.netcmd.user import create as create_user > from samba.netcmd.user

Thanks Rowland. I really appreciate your rapid responses and many years of work helping the python community. To answer your question, I could wrap samba tool -- but I thought it would be easier -- and more pythonic to keep the code pure python. As for what chatgpt knows... for the curious I'll paste the relevant responses below. Chat did suggest making calls to the samba-tool. I thought

This was actually the first thing that ChatGPT debugged for me. The issue was that I was able to click on the link when I displayed the raw SVG in the browser (you can use that to test whether the syntax is even correct), but not when the svg displays inside a html page with the <img ...> tag. ChatGPT correctly identified the issue and suggested a solution using <object ...> tags

I've tried various commands. ?plot, Teetor's book, "R Cookbook", and Mittal's book, "R Graphs Cookbook" without seeing how to write the command to create scatterplots from my data.frame. The structure is: > str(chemdata) 'data.frame': 14886 obs. of 4 variables: $ site : Factor w/ 148 levels "BC-0.5","BC-1",..: 104 145 126 115

Good Morning I want to perform a synthetic control method with R. For this purpose, I created the following code: # Re-load packages library(Synth) library(readxl) # Pfadeinstellung Excel-Blatt excel_file_path <- ("C:\\Users\\xxxxx\\Desktop\\DATA_INVESTMENTVOLUMEN_FOR_R_WITHOUT_NA.xlsx") # Load the Excel file INVESTMENTVOLUME <- read_excel(excel_file_path) #

Buenas, En R, como en la mayoría del software estadístico, no se utiliza ningún nivel de confianza sino que lo que se calcula es el p-valor asociado al contraste. De forma que cuanto más cerca de 0 esté el p-valor "menos credibilidad le damos a la hipótesis nula". Dicho mejor, debemos rechazar la hipótesis nula si el p-valor está por debajo de nuestro nivel de confianza. Por ejemplo,

Gracias Carlos. Yo también he visto el ejemplo que te pone chatGPT, pero la salida que te da no soy capaz de interpretarla. Os paso las ordenes y las respuestas de R de la propuesta de chatGPT Ejemplo.aov<- aov(P~TRAT+CORTE+REP) > summary (Ejemplo.aov) Df Sum Sq Mean Sq F value Pr(>F) TRAT 6 0.0028 0.00046 0.777 0.590 CORTE 2 0.5022 0.25110 424.542 <2e-16

ChatGPT-4: ------ Query: ---------------------------------- Thank you. The `value` in the code example is 2874. The plot shows a large number at the center and a smaller value computed as a delta relative to the `reference` of 4800. But the large value is given as 2870, and the smaller value is given as -1930 i.e. both values are rounded. Can I control the precision of these two numbers?

Thank you very much, Dirk. That nudge solved the problem, of course. I am embarrassed. I was so fixated on Ubuntu repositories that I neglected to check the Debian 'testing' world! Regarding the RPi: The RPi 4 uses the 'arm64' architecture, the full 64-bit one. I stopped using dedicated distros, such as Raspian, when Ubuntu went all-in on RPi support, which happened in their

Esta es la respuesta que te da ChatGPT-4: Entiendo tu pregunta y, aunque no hay una función nativa en R que te permita hacer exactamente lo que estás pidiendo, puedes lograr el mismo resultado utilizando una función. Una función te permitiría encapsular la lógica de la expresión que quieres reutilizar y luego llamar a esa función donde sea necesario. He aquí cómo podrías hacerlo: V1 <-

Muchas gracias, Manuel: Que bueno! No se me había ocurrido lo de GPT! Lo pruebo. Saludos. On Fri, 11 Aug 2023 18:15:18 +0200 Manuel Mendoza <mmendoza en fulbrightmail.org> wrote: > Esta es la respuesta que te da ChatGPT-4: > > Entiendo tu pregunta y, aunque no hay una función nativa en R que te > permita hacer exactamente lo que estás pidiendo, puedes lograr el mismo >

Hmmmm. Perhaps I'm using the wrong terminology. My logic is: (1) I am running Ubuntu focal on the cluster. (2) Ubuntu focal is built on Debian bullseye but (3) Debian bullseye is not yet the stable release; it is the 'testing' release; hence (4) I will pull the r-base-core package from the 'testing' version of Debian. And, in fact, I found r-base-core for 4.0.2 in the bullseye

No tuve tiempo de mirarlo, pero, ¿es coherente lo que dice? El vie, 11 ago 2023 a las 21:02, Griera-yandex (<griera en yandex.com>) escribió: > Muchas gracias, Manuel: > > Que bueno! No se me había ocurrido lo de GPT! > > Lo pruebo. > > Saludos. > > On Fri, 11 Aug 2023 18:15:18 +0200 > Manuel Mendoza <mmendoza en fulbrightmail.org> wrote: > > >

Dear R-SIG-Debian folks, I seem to be chasing my tail, despite having a simple goal: - Install R 4.0.2 - On Ubuntu 20.04 - For an ARM processor (not Intel/AMD). Can someone please suggest a Debian/Ubuntu repository of the required packages (e.g., r-base-core) built for ARM? I can't seem to find one. (I can find the r-base-core package for R 3.6.3, but not R 4.0.2, built for ARM.) If there

As a newer R practicioner, it seems I stump myself weekly (at least) with issues that have spinning my wheels.  Here is yet another... I'm trying to turn a list of numeric vectors (of uneual length) inot a dataframe.  Each vector held in the list represents a row, and there are some rows of unequal length.  I would like NAs as placeholders for "missing" data in the shorter vectors. 

I want to create individual data.frames for each of the 8 param factors in chemdata. The syntax I tried (based on Teetor's book, page 132) and R's response are: > ars <- subset(chemdata, select=c(site,sampdate,param,quant), subset=(param = "As")) Error in subset.data.frame(chemdata, select = c(site, sampdate, param, : 'subset' must evaluate to logical