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POError in glmer: NA/NaN/Inf in foreign function call (arg 1)
 

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  1. POError in glmer: NA/NaN/Inf in foreign function call (arg 1)
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    <p>I'm trying to fit the model</p> <pre><code>glmer(trans.dummy ~ pop + year + (year | munname), data=pool, family=binomial(link = "logit"), REML=T, verbose=T) </code></pre> <p>but I keep receiving the following error:</p> <pre><code>Error in glm.fit(fr$X, fr$Y, weights = wts, offset = offset, family = family, : NA/NaN/Inf in foreign function call (arg 1) </code></pre> <p>I omitted NAs, changed the model specification, transformed pop to log(pop) but nothing solved the problem. I think the problem is on variable 'pop' given that is the only one that causes that problem. When I run</p> <pre><code>mod6 &lt;- glmer(trans.dummy ~ constituency.coa + I(governat.part) + I(district2) + gdp.cap + year + ifdm + year + (year | munname) + (year | uf), data=pool, family=binomial(link = "logit"), REML=T, verbose=T) </code></pre> <p>I don't have any problem.</p> <p>Any idea about what is happening?</p> <p>EDIT:</p> <p>This is the structure of my data:</p> <pre><code>'data.frame': 41484 obs. of 50 variables: $ munname : Factor w/ 5392 levels "ACACRELANDIA",..: 1 2 3 4 5 6 7 8 9 10 ... $ uf : Factor w/ 26 levels "AC","AL","AM",..: 1 1 1 1 1 1 1 1 1 1 ... $ year : int 2003 2003 2003 2003 2003 2003 2003 2003 2003 2003 ... $ party : Factor w/ 30 levels "DEM","PAN","PC do B",..: 21 25 25 8 21 10 10 25 10 10 ... $ candidate : Factor w/ 12600 levels "AARAO CRUZ MENDES",..: 11452 8535 6389 5910 8095 1995 6087 3996 4313 8417 ... $ may.coalit : Factor w/ 10442 levels "DEM","DEM / PC do B / PRB / PRP / PTN / PSL",..: 7987 10118 10119 4023 8004 10442 4023 10114 4023 3945 ... $ governat.part : Factor w/ 9 levels "PDT","PFL","PMDB",..: 9 9 9 9 9 9 9 9 9 9 ... $ transfers : num 920 100920 183698 200920 919 ... $ pop : num 8695 3667 17944 6641 6096 ... $ gdp : num 68507 17492 101626 47936 37903 ... $ bf : num 703 330 1339 335 455 ... $ ifdm : num 0.422 0.442 0.404 0.511 0.396 ... $ elec.num : int 45 13 13 15 45 11 11 13 11 11 ... $ may.votes : int 2394 1345 3864 1415 1434 9422 2313 5423 1078 3348 ... $ may.round : int 1 1 1 1 1 1 1 1 1 1 ... $ may.perc.votes : num 0.59 0.586 0.441 0.544 0.615 ... $ pt.position : Factor w/ 5 levels "0.No PT","1.PT mayor winner",..: 4 2 2 3 4 5 5 2 3 5 ... $ numb.cand : int 2 2 3 3 2 4 3 2 2 2 ... $ runup : Factor w/ 3 levels "1.PT","2.Coalition",..: 3 3 2 1 3 2 2 3 1 2 ... $ voters : num 6422 3158 12234 3721 3682 ... $ nation.turnout : num 4282 2150 7989 2624 2138 ... $ lula.vote : num 2202 1197 4579 1431 1138 ... $ lula.perc.vote : num 0.514 0.557 0.573 0.545 0.532 ... $ reelec : Factor w/ 2 levels "Able to Run",..: 1 1 1 2 2 1 1 1 1 1 ... $ constituency.pt : num 9.37e-05 9.41e-05 3.83e-04 4.92e-05 5.38e-05 ... $ constituency.coa: num 4.10e-05 1.64e-05 6.87e-05 2.88e-05 1.34e-05 ... $ constituency.opp: num 4.81e-05 1.16e-05 2.47e-05 2.41e-05 3.55e-05 ... $ transfers.cap : num 0.106 27.521 10.237 30.254 0.151 ... $ gdp.cap : num 7.88 4.77 5.66 7.22 6.22 ... $ bf.cap : num 0.0808 0.0899 0.0746 0.0504 0.0747 ... $ lula.power : num 1.32e-64 7.16e-65 2.74e-64 8.56e-65 6.80e-65 ... $ mun.vote : int 1 1 1 1 1 0 1 1 1 0 ... $ mun.vote2 : Factor w/ 2 levels "PSDB","PT": 2 2 2 2 2 1 2 2 2 1 ... $ mun.vote3 : Factor w/ 2 levels "0.PT","1.PSDB": 1 1 1 1 1 2 1 1 1 2 ... $ core : Factor w/ 3 levels "Lula Core Municipality",..: 3 3 3 3 3 3 3 1 3 3 ... $ mayor.party.r : int 0 2 2 1 0 0 0 2 0 0 ... $ mayor.party.r2 : Factor w/ 3 levels "0.PT","1.Coalition",..: 3 1 1 2 3 3 3 1 3 3 ... $ pt.dummy : int 0 1 1 0 0 0 0 1 0 0 ... $ trans.dummy : int 1 1 1 1 1 1 1 0 1 1 ... $ year2 : Factor w/ 4 levels "2003-2004","2005-2006",..: 1 1 1 1 1 1 1 1 1 1 ... $ district : Factor w/ 6 levels "PSDB/Coal","PSDB/Opo",..: 5 6 6 4 5 2 5 6 5 2 ... $ term : int 0 0 0 0 0 0 0 0 0 0 ... $ time : int 1 1 1 1 1 1 1 1 1 1 ... $ pt.pt : int 0 1 1 0 0 0 0 1 0 0 ... $ pt.coa : int 0 0 0 1 0 0 0 0 0 0 ... $ pt.opp : int 1 0 0 0 1 0 1 0 1 0 ... $ psdb.pt : int 0 0 0 0 0 0 0 0 0 0 ... $ psdb.coa : int 0 0 0 0 0 0 0 0 0 0 ... $ psdb.opp : int 0 0 0 0 0 1 0 0 0 1 ... $ district2 : Factor w/ 5 levels "Coal/Coal","Opp/Opp",..: 2 4 4 5 2 3 3 4 5 3 ... - attr(*, "na.action")=Class 'omit' Named int [1:1316] 59 235 242 409 437 555 588 594 645 673 ... .. ..- attr(*, "names")= chr [1:1316] "59" "235" "242" "409" ... </code></pre>
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    1. USuser1172558
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    1. COThis question will be easier to solve if you show us your data. What does `pool$pop` look like? Does it contain `NaN` or `Inf` values?
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      1. USRichie Cotton
    2. COR has an active mixed models mail list: https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models if you don't get the exact answer you need, try there.
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      1. UStim riffe
    3. CO@tim Thanks! I will post my question in the list too.
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      1. USuser1172558
 

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