Thanks.
Can someone point me to the procedure for switching from the Intel Math Library
back to the standard math library so that I can see if the problem is associated
with using MKL?
Thomas R. LaBone
PhD student
Department of Epidemiology and Biostatistics
Arnold School of Public Health
University of South Carolina
Columbia, South Carolina USA
________________________________
From: J C Nash <profjcnash at gmail.com>
Sent: Thursday, December 2, 2021 9:31 AM
To: Labone, Thomas <labone at email.sc.edu>; r-help at r-project.org
<r-help at r-project.org>
Subject: Re: [R] Problem with lm Giving Wrong Results
I get two similar graphs.
https://protect2.fireeye.com/v1/url?k=99a20372-c6393a53-99a24db3-862c53b6784d-c549bca0bcc4210d&q=1&e=8ce563a7-fac1-41d3-8699-13c4a9417e00&u=https%3A%2F%2Fweb.ncf.ca%2Fnashjc%2Fjfiles%2FRplot-Labone-4095.pdf
https://protect2.fireeye.com/v1/url?k=6d56bd36-32cd8417-6d56f3f7-862c53b6784d-85baef704a412b23&q=1&e=8ce563a7-fac1-41d3-8699-13c4a9417e00&u=https%3A%2F%2Fweb.ncf.ca%2Fnashjc%2Fjfiles%2FRplotLabone10K.pdf
Context:
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Linux Mint 20.2
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
locale:
[1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C LC_TIME=en_CA.UTF-8
LC_COLLATE=en_CA.UTF-8
[5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8 LC_PAPER=en_CA.UTF-8
LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] compiler_4.1.2 fastmap_1.1.0 htmltools_0.5.2 tools_4.1.2 yaml_2.2.1
rmarkdown_2.11 knitr_1.36
[8] xfun_0.28 digest_0.6.28 rlang_0.4.12 evaluate_0.14
>
Hope this helps,
JN
On 2021-12-02 5:50 a.m., Labone, Thomas wrote:>
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> # This works
> n <- 1000# OK <= 4095
> Z <- qnorm(ppoints(n))
>
> k <- sort(rlnorm(n,log(2131),log(1.61)) / rlnorm(n,log(355),log(1.61)))
>
> quantile(k,probs=c(0.025,0.5,0.975))
> summary(k)
>
> fit <- lm(log(k) ~ Z)
> summary(fit)
>
> gm <- exp(coef(fit)[1])
> gsd <- exp(coef(fit)[2])
> gm
> gsd
>
> plot(Z,k,log="y",xlim=c(-4,4),ylim=c(0.1,100))
> lines(Z,gm*gsd^Z,col="red")
>
>
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
> #this does not
> n <- 10000# fails >= 4096 = 2^12
> Z <- qnorm(ppoints(n))
>
> k <- sort(rlnorm(n,log(2131),log(1.61)) / rlnorm(n,log(355),log(1.61)))
>
> quantile(k,probs=c(0.025,0.5,0.975))
> summary(k)
>
> fit <- lm(log(k) ~ Z)
> summary(fit)
>
> gm <- exp(coef(fit)[1])
> gsd <- exp(coef(fit)[2])
> gm
> gsd
>
> plot(Z,k,log="y",xlim=c(-4,4),ylim=c(0.1,100))
> lines(Z,gm*gsd^Z,col="red")
>
>
>
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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