Regression output is different on different computers

user_15771707 0
Viewed 308

We are fitting a generalized linear model to a set of data using R on Fedora 33. We are 3 people that have the exact same data set, exact same OS (Fedora 33) , exact same R version and package version. The sessionInfo() output also looks the same. This is the current function call:

probitModel <- glm(Y ~ a + b + c +
                     d + e + f + g,
                   control=glm.control(maxit=100), 
                   start=startingval,
                   family = binomial(link = "probit"),
                   data = thisData)

Something to note is that a normal "lm" fit yields the exact same result when compared. Something about "glm" yields different results on different computers by a tiny amount. Finally, the results are consistent in the sense that we all get the same result with the same data in the same computer. The problem only appears when we compare results.

We have checked:

  • Same OS version.
  • Same R version.
  • Same package version.
  • Same seed.
  • All computers are 64 bit and the R install is 64 bit.
  • Same sessionInfo()
  • Same g++ version.

The data file can be found here. We just use load() to set up the data, called the function above and we get slightly different outputs when the output should be the same.

My result for sprintf("%.20f", probitModel$coefficients):

"-2.87521118137333120401" "-0.29134882504319664642" "0.11641651353472137753"  "-0.00811350544106945529" "0.00939647781971938295"  "0.00000000889056301580"  "-0.00000000003021053820" "0.04553200818707187070"

Result from the other computer:

"-2.87521124006409856122" "-0.29134881939721285482" "0.11641651896472486605"  "-0.00811350937056383621" "0.00939647799201156347" "0.00000000889056417264"  "-0.00000000003021022404" "0.04553201421062236243"

My trace output:

    Deviance = 67234.8 Iterations - 1
    Deviance = 67079.56 Iterations - 2
    Deviance = 67102.68 Iterations - 3
    Deviance = 67088.05 Iterations - 4
    Deviance = 67078.07 Iterations - 5
    Deviance = 67073.77 Iterations - 6
    Deviance = 67076.34 Iterations - 7
    Deviance = 67074.41 Iterations - 8
    Deviance = 67075.74 Iterations - 9
    Deviance = 67074.77 Iterations - 10
    Deviance = 67075.45 Iterations - 11
    Deviance = 67074.96 Iterations - 12
    Deviance = 67075.31 Iterations - 13
    Deviance = 67075.06 Iterations - 14
    Deviance = 67075.23 Iterations - 15
    Deviance = 67075.11 Iterations - 16
    Deviance = 67075.2 Iterations - 17
    Deviance = 67075.13 Iterations - 18
    Deviance = 67075.18 Iterations - 19
    Deviance = 67075.15 Iterations - 20
    Deviance = 67075.17 Iterations - 21
    Deviance = 67075.15 Iterations - 22
    Deviance = 67075.16 Iterations - 23
    Deviance = 67075.16 Iterations - 24
    Deviance = 67075.16 Iterations - 25
    Deviance = 67075.16 Iterations - 26
    Deviance = 67075.16 Iterations - 27
    Deviance = 67075.16 Iterations - 28
    Deviance = 67075.16 Iterations - 29
    Deviance = 67075.16 Iterations - 30
    Deviance = 67075.16 Iterations - 31
    Deviance = 67075.16 Iterations - 32

Other computer's trace output:

Deviance = 67234.8 Iterations - 1
Deviance = 67079.56 Iterations - 2
Deviance = 67102.68 Iterations - 3
Deviance = 67088.05 Iterations - 4
Deviance = 67078.07 Iterations - 5
Deviance = 67073.77 Iterations - 6
Deviance = 67076.34 Iterations - 7
Deviance = 67074.41 Iterations - 8
Deviance = 67075.74 Iterations - 9
Deviance = 67074.77 Iterations - 10
Deviance = 67075.45 Iterations - 11
Deviance = 67074.96 Iterations - 12
Deviance = 67075.31 Iterations - 13
Deviance = 67075.06 Iterations - 14
Deviance = 67075.23 Iterations - 15
Deviance = 67075.11 Iterations - 16
Deviance = 67075.2 Iterations - 17
Deviance = 67075.13 Iterations - 18
Deviance = 67075.18 Iterations - 19
Deviance = 67075.15 Iterations - 20
Deviance = 67075.17 Iterations - 21
Deviance = 67075.15 Iterations - 22
Deviance = 67075.16 Iterations - 23
Deviance = 67075.16 Iterations - 24
Deviance = 67075.16 Iterations - 25
Deviance = 67075.16 Iterations - 26
Deviance = 67075.16 Iterations - 27
Deviance = 67075.16 Iterations - 28
Deviance = 67075.16 Iterations - 29
Deviance = 67075.16 Iterations - 30
Deviance = 67075.16 Iterations - 31
Deviance = 67075.16 Iterations - 32

My options() output:

$askpass
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<environment: 0x5557fac219c8>

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[1] "/usr/bin/nano"

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[1] "native.enc"

$example.ask
[1] "default"

$expressions
[1] 5000

$ggvis.renderer
[1] "svg"

$help_type
[1] "html"

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[1] 2

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[1] TRUE

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[1] FALSE

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[1] TRUE

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[1] FALSE

$locatorBell
[1] TRUE

$mailer
[1] "mailto"

$matprod
[1] "default"

$max.print
[1] 1000

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[1] FALSE

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[1] 50

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[1] "letter"

$PCRE_limit_recursion
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[1] FALSE

$PCRE_use_JIT
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function () 
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function (id) 
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$terminal.manager$terminalKill
function (id) 
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function (id, text) 
{
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function (command, workingDir = NULL, env = character(), show = TRUE) 
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$useFancyQuotes
[1] TRUE

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[1] FALSE

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function (url, height = NULL) 
{
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<environment: 0x5557fac219c8>

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$warning.length
[1] 1000

$width
[1] 121

Second computer's options() output:

$add.smooth
[1] TRUE

$askpass
function (prompt) 
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<environment: 0x55ca5c0b0978>

$asksecret
function (name, title = name, prompt = paste(name, ":", sep = "")) 
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    if (requireNamespace("png", quietly = TRUE) && reticulate::py_module_available("matplotlib")) {
        matplotlib <- reticulate::import("matplotlib", convert = TRUE)
        backend <- matplotlib$get_backend()
        if (!identical(tolower(backend), "agg")) {
            sys <- reticulate::import("sys", convert = TRUE)
            if ("matplotlib.backends" %in% names(sys$modules)) 
                matplotlib$pyplot$switch_backend("agg")
            else matplotlib$use("agg", warn = FALSE, force = TRUE)
        }
        plt <- matplotlib$pyplot
        .rs.setVar("reticulate.matplotlib.show", plt$show)
        plt$show <- .rs.reticulate.matplotlib.showHook
    }
}
<environment: 0x55ca5c0b0978>

$reticulate.repl.teardown
function () 
{
    builtins <- reticulate::import_builtins(convert = FALSE)
    builtins$help <- .rs.getVar("reticulate.help")
    show <- .rs.getVar("reticulate.matplotlib.show")
    if (!is.null(show)) {
        matplotlib <- reticulate::import("matplotlib", convert = TRUE)
        plt <- matplotlib$pyplot
        plt$show <- show
    }
}
<environment: 0x55ca5c0b0978>

$rl_word_breaks
[1] " \t\n\"\\'`><=%;,|&{()}"

$rstudio.notebook.executing
[1] FALSE

$scipen
[1] 0

$shiny.launch.browser
function (url) 
{
    invisible(.Call("rs_shinyviewer", url, getwd(), "window", 
        NULL, PACKAGE = "(embedding)"))
}
<environment: 0x55ca5c0b0978>
attr(,"shinyViewerType")
[1] "window"


$str$digits.d
[1] 3

$str$vec.len
[1] 4

$str$list.len
[1] 99

$str$deparse.lines
NULL

$str$drop.deparse.attr
[1] TRUE

$str$formatNum
function (x, ...) 
format(x, trim = TRUE, drop0trailing = TRUE, ...)
<environment: 0x55ca5ba6a360>


$str.dendrogram.last
[1] "`"

$stringsAsFactors
[1] FALSE


$terminal.manager$terminalVisible
function () 
{
    .Call("rs_terminalVisible")
}
<environment: 0x55ca5c0b0978>

$terminal.manager$terminalBusy
function (id) 
{
    if (is.null(id) || !is.character(id)) 
        stop("'id' must be a character vector")
    .Call("rs_terminalBusy", id)
}
<environment: 0x55ca5c0b0978>

$terminal.manager$terminalRunning
function (id) 
{
    if (is.null(id) || !is.character(id)) 
        stop("'id' must be a character vector")
    .Call("rs_terminalRunning", id)
}
<environment: 0x55ca5c0b0978>

$terminal.manager$terminalKill
function (id) 
{
    if (is.null(id) || !is.character(id)) 
        stop("'id' must be a character vector")
    .Call("rs_terminalKill", id)
    invisible(NULL)
}
<environment: 0x55ca5c0b0978>

$terminal.manager$terminalSend
function (id, text) 
{
    if (!is.character(text)) 
        stop("'text' should be a character vector", call. = FALSE)
    if (is.null(id) || !is.character(id) || length(id) != 1) 
        stop("'id' must be a character vector of length one")
    .Call("rs_terminalSend", id, text)
    invisible(NULL)
}
<environment: 0x55ca5c0b0978>

$timeout
[1] 60

$ts.eps
[1] 1e-05
glm numeric regression statistics r
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