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I am attempting to run two similar generalized linear mixed models in R. Both models have the same input variables for predictors, covariates and random factors, however, response variables differ.
In the first model, the response variable is richness, family is poisson.
global.model <- glmer(ex.richness ~ forestloss562*forestloss17*roaddenssec*nearestroadprim +
elevation + soilPC1 + soilPC2 +
(1|block/fragment),
data = mydata,
family = "poisson")
In the second model, the response variable is biomass weight and family = gaussian (model not shown here).
Predictors have the following units: forestloss562 = %,
forestloss17 = %,
roaddenssec = (km/km2) and
nearestroadprim = (m).
The subsequent steps should be to standardize predictors,
stdz.model <- standardize(global.model, standardize.y = FALSE)
automated model selection with subsets of the supplied ‘global’ model
model.set <- dredge(stdz.model)
and finding the top 2AIC models
top.models <- get.models(model.set, subset = delta < 2)
Packages required: lme4 (model), arm (standardize), MuMIn (submodel generating)
I do have the following issue:
For the RICHNESS model I get the following error message:
Error: (maxstephalfit) PIRLS step-halvings failed to reduce deviance in pwrssUpdate
In addition: Warning messages:
1: Some predictor variables are on very different scales: consider rescaling
2: In pwrssUpdate(pp, resp, tolPwrss, GQmat, compDev, fac, verbose) :
Cholmod warning 'not positive definite' at file:../Cholesky/t_cholmod_rowfac.c, line 431
3: In pwrssUpdate(pp, resp, tolPwrss, GQmat, compDev, fac, verbose) :
Cholmod warning 'not positive definite' at file:../Cholesky/t_cholmod_rowfac.c, line 431
For the BIOMASS model I get this error message:
Warning in lme4::lmer(formula = ex.drywght ~ forestloss562 * forestloss17 * :
passing control as list is deprecated: please use lmerControl() instead
Error in (function (optimizer = "bobyqa", restart_edge = TRUE, boundary.tol = 1e-05, :
unused arguments (tolPwrss = 1e-07, compDev = TRUE, checkControl = list(check.nobs.vs.rankZ = "ignore", check.nobs.vs.nlev = "stop", check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", check.nobs.vs.nRE = "stop", check.rankX = "message+drop.cols", check.scaleX = "warning", check.formula.LHS = "stop"), checkConv = list(check.conv.grad = list(action = "warning", tol = 0.001, relTol = NULL), check.conv.singular = list(action = "ignore", tol = 1e-04), check.conv.hess = list(action = "warning", tol = 1e-06)))
In addition: Warning messages:
1: In lmer(ex.drywght ~ forestloss562 * forestloss17 * roaddenssec * :
calling lmer with 'family' is deprecated; please use glmer() instead
2: In lme4::glmer(formula = ex.drywght ~ forestloss562 * forestloss17 * :
calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is deprecated; please call lmer() directly
Any advice on how to possibly fix these problems would be very much appreciated.
Please find a reproducible subset of my data below:
structure(list(plot.code = structure(c(1L, 4L, 3L, 2L, 6L, 5L,
9L, 8L, 7L, 10L, 11L, 13L, 12L, 16L, 15L, 14L, 17L, 18L, 20L,
19L, 23L, 22L, 21L, 25L, 24L, 27L, 26L, 29L, 28L, 32L, 31L, 30L,
33L, 34L, 36L, 35L, 39L, 38L, 37L, 40L, 41L, 43L, 42L, 45L, 44L,
47L, 46L, 50L, 49L, 48L, 52L, 51L, 54L, 53L, 55L, 56L, 58L, 57L,
61L, 60L, 59L, 63L, 62L, 65L, 64L, 68L, 67L, 66L, 69L, 72L, 71L,
70L, 73L, 76L, 75L, 74L, 77L, 79L, 78L, 80L, 83L, 82L, 81L, 84L,
85L, 87L, 86L, 89L, 88L, 91L, 90L, 94L, 93L, 92L, 96L, 95L, 98L,
97L, 99L, 101L, 100L, 103L, 102L, 106L, 105L, 104L, 108L, 107L,
111L, 110L, 109L, 113L, 112L, 115L, 114L, 117L, 116L, 119L, 118L,
122L, 121L, 120L, 123L, 125L, 124L, 127L, 126L, 130L, 129L, 128L,
131L, 133L, 132L, 135L, 134L, 137L, 136L, 139L, 138L, 142L, 141L,
140L, 144L, 143L, 146L, 145L, 147L, 149L, 148L, 150L, 153L, 152L,
151L, 154L, 155L, 157L, 156L, 159L, 158L), .Label = c("a100f177r",
"a100f562r", "a100f56r", "a100f5r", "a100m177r", "a100m17r",
"a100m562r", "a100m56r", "a100m5r", "a10f177r", "a10f56r", "a10m177r",
"a10m17r", "a10m562r", "a10m56r", "a10m5r", "a1f17r", "a1f56r",
"a1m177r", "a1m17r", "a1m562r", "a1m56r", "a1m5r", "b100f177r",
"b100f17r", "b100f562r", "b100f56r", "b100m177r", "b100m17r",
"b100m562r", "b100m56r", "b100m5r", "b10f177r", "b10f56r", "b10m177r",
"b10m1r", "b10m562r", "b10m56r", "b10m5r", "b1f1r", "b1f56r",
"b1m177r", "b1m1r", "b1m562r", "b1m56r", "c100f177r", "c100f17r",
"c100f562r", "c100f56r", "c100f5r", "c100m177r", "c100m17r",
"c100m562r", "c100m56r", "c10f177r", "c10f56r", "c10m177r", "c10m17r",
"c10m562r", "c10m56r", "c10m5r", "c1f56r", "c1f5r", "c1m177r",
"c1m1r", "c1m562r", "c1m56r", "c1m5r", "d100f177r", "d100f562r",
"d100f56r", "d100f5r", "d100m177r", "d100m562r", "d100m56r",
"d100m5r", "d10f177r", "d10f56r", "d10f5r", "d10m177r", "d10m562r",
"d10m56r", "d10m5r", "d1f1r", "d1f56r", "d1m177r", "d1m17r",
"d1m562r", "d1m56r", "e100f177r", "e100f1r", "e100f562r", "e100f56r",
"e100f5r", "e100m177r", "e100m1r", "e100m562r", "e100m56r", "e10f177r",
"e10f56r", "e10f5r", "e10m177r", "e10m1r", "e10m562r", "e10m56r",
"e10m5r", "e1f56r", "e1f5r", "e1m177r", "e1m17r", "e1m1r", "e1m562r",
"e1m56r", "f100f177r", "f100f17r", "f100f562r", "f100f56r", "f100m177r",
"f100m1r", "f100m562r", "f100m56r", "f100m5r", "f10f177r", "f10f56r",
"f10f5r", "f10m177r", "f10m17r", "f10m562r", "f10m56r", "f10m5r",
"f1f17r", "f1f56r", "f1f5r", "f1m177r", "f1m17r", "f1m562r",
"f1m56r", "lf100f177r", "lf100f17r", "lf100f562r", "lf100f56r",
"lf100f5r", "lf100m177r", "lf100m17r", "lf100m562r", "lf100m56r",
"lf10f177r", "lf10f56r", "lf10f5r", "lf10m177r", "lf10m562r",
"lf10m56r", "lf10m5r", "lf1f17r", "lf1f56r", "lf1m177r", "lf1m17r",
"lf1m562r", "lf1m56r"), class = "factor"), site.code = structure(1:159, .Label = c("a100f177",
"a100f5", "a100f56", "a100f562", "a100m17", "a100m177", "a100m5",
"a100m56", "a100m562", "a10f177", "a10f56", "a10m17", "a10m177",
"a10m5", "a10m56", "a10m562", "a1f17", "a1f56", "a1m17", "a1m177",
"a1m5", "a1m56", "a1m562", "b100f17", "b100f177", "b100f56",
"b100f562", "b100m17", "b100m177", "b100m5", "b100m56", "b100m562",
"b10f177", "b10f56", "b10m1", "b10m177", "b10m5", "b10m56", "b10m562",
"b1f1", "b1f56", "b1m1", "b1m177", "b1m56", "b1m562", "c100f17",
"c100f177", "c100f5", "c100f56", "c100f562", "c100m17", "c100m177",
"c100m56", "c100m562", "c10f177", "c10f56", "c10m17", "c10m177",
"c10m5", "c10m56", "c10m562", "c1f5", "c1f56", "c1m1", "c1m177",
"c1m5", "c1m56", "c1m562", "d100f177", "d100f5", "d100f56", "d100f562",
"d100m177", "d100m5", "d100m56", "d100m562", "d10f177", "d10f5",
"d10f56", "d10m177", "d10m5", "d10m56", "d10m562", "d1f1", "d1f56",
"d1m17", "d1m177", "d1m56", "d1m562", "e100f1", "e100f177", "e100f5",
"e100f56", "e100f562", "e100m1", "e100m177", "e100m56", "e100m562",
"e10f177", "e10f5", "e10f56", "e10m1", "e10m177", "e10m5", "e10m56",
"e10m562", "e1f5", "e1f56", "e1m1", "e1m17", "e1m177", "e1m56",
"e1m562", "f100f17", "f100f177", "f100f56", "f100f562", "f100m1",
"f100m177", "f100m5", "f100m56", "f100m562", "f10f177", "f10f5",
"f10f56", "f10m17", "f10m177", "f10m5", "f10m56", "f10m562",
"f1f17", "f1f5", "f1f56", "f1m17", "f1m177", "f1m56", "f1m562",
"lf100f17", "lf100f177", "lf100f5", "lf100f56", "lf100f562",
"lf100m17", "lf100m177", "lf100m56", "lf100m562", "lf10f177",
"lf10f5", "lf10f56", "lf10m177", "lf10m5", "lf10m56", "lf10m562",
"lf1f17", "lf1f56", "lf1m17", "lf1m177", "lf1m56", "lf1m562"), class = "factor"),
block = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L), .Label = c("a",
"b", "c", "d", "e", "f", "lf"), class = "factor"), fragment = structure(c(3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 4L, 4L, 4L, 4L, 4L, 4L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 12L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 10L, 10L, 10L,
10L, 10L, 10L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L, 21L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 19L, 19L, 19L, 19L, 19L,
19L), .Label = c("a1", "a10", "a100", "b1", "b10", "b100",
"c1", "c10", "c100", "d1", "d10", "d100", "e1", "e10", "e100",
"f1", "f10", "f100", "lf1", "lf10", "lf100"), class = "factor"),
elevation = c(391L, 336L, 351L, 394L, 335L, 304L, 333L, 309L,
242L, 358L, 412L, 443L, 518L, 430L, 454L, 585L, 495L, 499L,
513L, 517L, 510L, 529L, 580L, 501L, 486L, 484L, 514L, 468L,
436L, 498L, 490L, 456L, 335L, 320L, 338L, 365L, 309L, 344L,
384L, 402L, 393L, 381L, 435L, 401L, 512L, 348L, 392L, 364L,
363L, 448L, 342L, 323L, 342L, 250L, 372L, 356L, 362L, 325L,
355L, 356L, 264L, 352L, 345L, 346L, 312L, 329L, 326L, 252L,
294L, 317L, 301L, 329L, 335L, 304L, 316L, 331L, 354L, 422L,
383L, 404L, 399L, 415L, 487L, 495L, 481L, 485L, 528L, 496L,
619L, 286L, 358L, 266L, 331L, 420L, 300L, 260L, 304L, 164L,
353L, 374L, 393L, 361L, 385L, 385L, 379L, 249L, 430L, 454L,
427L, 429L, 443L, 443L, 468L, 295L, 321L, 311L, 299L, 291L,
254L, 290L, 280L, 207L, 506L, 610L, 579L, 596L, 680L, 606L,
601L, 751L, 469L, 462L, 473L, 465L, 397L, 452L, 280L, 410L,
395L, 409L, 400L, 451L, 407L, 437L, 414L, 479L, 385L, 430L,
439L, 458L, 422L, 421L, 444L, 475L, 483L, 477L, 493L, 490L,
550L), forestloss562 = c(28.64, 25.49, 27.4, 31.71, 24.02,
21.81, 25.69, 25.9, 15.74, 8.23, 5.92, 5.3, 5.67, 4.7, 4.76,
10.56, 14.8, 14.79, 14.81, 19.58, 15.69, 15.79, 21.31, 6.49,
7.4, 6.78, 5.34, 6.52, 6.89, 6.32, 6.15, 7.57, 7.38, 8.03,
8.42, 10.75, 8.28, 8.99, 15.7, 6.26, 6.48, 6.06, 5.32, 6.19,
3.44, 55.8, 54.35, 58.44, 56.17, 73.35, 56.77, 60.54, 56.77,
53.78, 30.22, 31.9, 33.21, 31.48, 33.66, 32.24, 30.11, 22.52,
22.62, 22.88, 26.76, 23.52, 23.74, 30.53, 46.35, 46.4, 46.4,
49.97, 43.82, 46.11, 45.35, 32.64, 29.5, 31.75, 31.15, 33.76,
31.99, 31.82, 32.92, 27.4, 28.48, 28.23, 25.8, 27.6, 15.16,
13.49, 12.96, 14.5, 13.14, 14.33, 13.42, 14.45, 13.61, 39.91,
10.87, 10.55, 12.06, 9.79, 10.91, 11.45, 11.18, 10.58, 26.2,
27.51, 26.37, 27.06, 24.25, 26.36, 30.6, 23.79, 21.9, 23.65,
18.8, 23.87, 23.64, 23.78, 24.24, 20.2, 11.56, 11.45, 11.66,
9.81, 5.3, 8.65, 7.45, 1.34, 2.43, 2.52, 2.32, 3.09, 7.45,
4.55, 14.74, 2.32, 0.61, 2.19, 1.77, 1.41, 2.48, 2.48, 2.48,
2.63, 5.43, 7.65, 7.07, 7.86, 7.94, 8.06, 6.7, 7.98, 7.61,
8.11, 23.07, 9.08, 7.07), forestloss17 = c(5, 22.47, 0, 6.86,
0, 0, 0, 7.47, 20.23, 5.62, 11.71, 0, 0, 0, 0, 7.68, 82.99,
85.47, 0, 73.87, 25.12, 10.9, 9.55, 4.93, 66.82, 24.52, 0,
32.59, 22.29, 10.02, 7.04, 21.29, 0, 22.87, 31.63, 60.09,
0, 3.53, 15.22, 55.47, 7.1, 73.56, 79.76, 0, 0, 47.4, 99.18,
85.65, 81.19, 99.19, 12, 54.04, 51.93, 78.64, 78.47, 86.33,
57.29, 39.19, 72.08, 81.08, 0, 53.77, 99.08, 61.74, 45.73,
14.28, 53.75, 85.35, 60.37, 64.08, 88.1, 76.12, 71.47, 62.49,
99.75, 18.85, 54.47, 75.25, 87.5, 68.01, 81.93, 76.82, 70.14,
70.41, 77.67, 8.47, 34.25, 29.08, 32.83, 79.8, 29.9, 63.2,
68.74, 53.62, 57.4, 31.07, 26.2, 29.29, 11.23, 51.33, 10.2,
80.92, 10.56, 0, 71.66, 0, 11.08, 48.36, 85.53, 83.63, 80.55,
73.88, 99.75, 13.8, 44.23, 29.7, 0, 48.75, 0, 17.98, 17.94,
39.56, 0, 0, 0, 0, 0, 0, 0, 0, 23.83, 9.42, 0, 0, 0, 0, 89.32,
0, 0, 29.38, 0, 0, 29.2, 0, 0, 0, 0, 25.13, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), roaddenssec = c(2.99, 2.99, 2.99, 2.99,
2.99, 2.99, 2.99, 2.99, 2.99, 2.99, 2.99, 2.99, 2.99, 2.99,
2.99, 2.99, 2.99, 2.99, 2.99, 2.99, 2.99, 2.99, 2.99, 2.61,
2.61, 2.61, 2.61, 2.61, 2.61, 2.61, 2.61, 2.61, 2.61, 2.61,
2.61, 2.61, 2.61, 2.61, 2.61, 2.61, 2.61, 2.61, 2.61, 2.61,
2.61, 3.92, 3.92, 3.92, 3.92, 3.92, 3.92, 3.92, 3.92, 3.92,
3.92, 3.92, 3.92, 3.92, 3.92, 3.92, 3.92, 3.92, 3.92, 3.92,
3.92, 3.92, 3.92, 3.92, 1.58, 1.58, 1.58, 1.58, 1.58, 1.58,
1.58, 1.58, 1.58, 1.58, 1.58, 1.58, 1.58, 1.58, 1.58, 1.58,
1.58, 1.58, 1.58, 1.58, 1.58, 1.49, 1.49, 1.49, 1.49, 1.49,
1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49,
1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.49, 1.12,
1.12, 1.12, 1.12, 1.12, 1.12, 1.12, 1.12, 1.12, 1.12, 1.12,
1.12, 1.12, 1.12, 1.12, 1.12, 1.12, 1.12, 1.12, 1.12, 1.12,
1.12, 1.12, 1.12, 1.16, 1.16, 1.16, 1.16, 1.16, 1.16, 1.16,
1.16, 1.16, 1.16, 1.16, 1.16, 1.16, 1.16, 1.16, 1.16, 1.16,
1.16, 1.16, 1.16, 1.16, 1.16), nearestroadprim = c(502L,
495L, 499L, 514L, 520L, 471L, 467L, 438L, 563L, 1297L, 1360L,
1428L, 1551L, 1450L, 1478L, 1603L, 552L, 533L, 549L, 577L,
505L, 535L, 636L, 2481L, 2623L, 2497L, 2942L, 2431L, 2321L,
2505L, 2436L, 2007L, 1812L, 1750L, 1793L, 1725L, 1741L, 1756L,
1713L, 2518L, 2491L, 2544L, 2637L, 2539L, 2850L, 304L, 438L,
278L, 336L, 539L, 268L, 119L, 237L, 27L, 576L, 550L, 534L,
555L, 572L, 566L, 653L, 1015L, 996L, 967L, 832L, 932L, 905L,
661L, 107L, 209L, 164L, 26L, 245L, 171L, 222L, 112L, 121L,
110L, 124L, 29L, 64L, 60L, 15L, 327L, 357L, 363L, 236L, 318L,
127L, 163L, 335L, 156L, 224L, 699L, 172L, 17L, 116L, 168L,
378L, 414L, 443L, 380L, 317L, 453L, 442L, 74L, 641L, 657L,
679L, 713L, 655L, 743L, 449L, 23L, 16L, 23L, 42L, 32L, 210L,
62L, 96L, 536L, 975L, 1127L, 1085L, 1182L, 1329L, 1175L,
1226L, 1758L, 553L, 557L, 586L, 528L, 367L, 491L, 55L, 3176L,
3009L, 3177L, 3129L, 2636L, 3205L, 3362L, 3252L, 3743L, 2523L,
2538L, 2525L, 2549L, 2494L, 2473L, 2551L, 3681L, 3678L, 3743L,
3866L, 3765L, 4184L), soilPC1 = c(1.05, 1.17, 1.06, 2.94,
1.58, 1.67, 0.91, 0.31, 0.45, 1.53, 0.76, 1.72, -0.82, 0.17,
0.31, 0.86, 1.7, 0.42, 1.75, 2.5, 0.84, -1.08, 1.14, 2.1,
2.31, -0.59, 0.65, 1.04, 2.09, 1.78, 2.31, 0.28, 1.42, 1.49,
1.14, 1.67, 1.56, 0.9, 1.33, 2.3, 1.09, 1.22, 1.67, 1.3,
0.1, -0.13, -0.21, 2.4, 1.39, -1.12, -0.37, 0.15, -0.08,
1.13, 0.82, 0.69, -0.49, -0.22, 0.27, 0.36, -0.44, 2.35,
2.19, 1.59, 2.1, 0.67, 2.4, -0.31, -1.02, -1.69, -0.96, -0.87,
-1.96, -1.59, -1.22, -0.75, -1.17, -0.95, -1.32, 0.21, -0.66,
0.77, 0.79, 2.39, -0.49, 0.87, -0.1, -0.3, -2.61, -0.2, -0.98,
-1.31, -0.88, 0.76, 1.98, 0.03, -0.5, 0.93, 2.77, -0.53,
-2.66, 2.59, -2.05, -3.15, -1.17, -1.17, 1.08, -0.19, -0.24,
-1.48, -1.33, -0.89, -2.14, -0.79, 0.32, -1.74, -1.2, -0.22,
-0.84, 0.17, -1.26, -1.28, -1.42, -3.04, -1.86, -2.21, -3.15,
-1.45, -2.48, -2.63, -0.8, -1.86, -2.69, -1.84, -3.06, -2.46,
0.38, 0, -0.48, 0.84, -0.89, -0.32, 0.72, -0.6, -0.45, 0.3,
0.16, -0.83, 0.66, 0.54, 0.47, -0.25, -1.61, -1.33, -0.57,
-0.87, -2, -0.98, 0.95), soilPC2 = c(0.64, -0.1, -0.01, 0.97,
-0.39, 1.95, 0.06, 0.4, -0.88, -0.59, -0.4, -0.22, -1.3,
-0.26, -0.84, 0.22, -0.74, -1.93, 1.15, 1.53, -0.51, 0.2,
-0.6, 0.5, 1, 0.23, 0.32, -0.26, -1.23, 0.05, 0.69, 1.4,
-0.06, -0.84, -0.51, -1.07, 0.69, 0.17, -0.2, -1.77, -0.29,
-0.66, -0.16, 0.49, 0.64, 1.29, -0.94, 0.55, -0.34, -1.37,
0.73, -1.88, 0.41, 0.52, -0.43, 0.86, -0.28, -1.25, -1.03,
0.1, 0.33, 0.22, -0.47, 0.53, 1.4, -1.33, 2.06, 0.92, -0.56,
0.43, -2.44, -1.15, -0.04, -1.61, 0.25, 0.87, -0.27, 0.45,
-1.33, 0.94, -2.65, -2.86, -0.78, 0.15, 2.44, 0.47, 1.23,
0.12, -0.5, -0.76, 1.13, 2.5, 0.12, 1.89, 0.83, 0.21, 1.08,
0.91, -2.67, -1.63, -0.47, -1.67, -0.09, -0.92, -4.48, 0.4,
0.04, 0.2, 0.19, -0.68, 0.03, -1.28, 0.02, 0.36, -0.31, -0.3,
0.23, 0, 0.7, -1.48, -0.7, 0.63, -0.72, 1.69, 2.22, 1.78,
2.01, 1.81, 0.32, 1.72, -0.59, -0.56, -0.77, -0.73, -1.19,
-1.46, -0.79, -0.08, 0.52, -0.37, 0.34, 0.87, 0.13, 0.66,
1.03, -0.14, 1.34, 0.27, 1.23, 1.02, 0.35, 0.51, 0.57, 0.31,
0.02, 0.23, 0.55, 0.1, 0.5), ex.richness = c(0L, 1L, 1L,
1L, 1L, 0L, 0L, 0L, 1L, 2L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 2L,
1L, 1L, 1L, 0L, 0L, 1L, 2L, 2L, 0L, 1L, 1L, 2L, 1L, 1L, 0L,
1L, 1L, 1L, 1L, 0L, 2L, 3L, 0L, 2L, 1L, 1L, 1L, 0L, 2L, 2L,
1L, 1L, 0L, 0L, 0L, 0L, 2L, 8L, 1L, 0L, 8L, 2L, 0L, 0L, 1L,
0L, 3L, 0L, 3L, 3L, 1L, 1L, 5L, 1L, 0L, 0L, 2L, 1L, 1L, 1L,
1L, 0L, 1L, 3L, 2L, 7L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 1L, 2L, 0L, 2L, 6L, 0L, 0L, 5L, 0L, 1L, 2L,
4L, 1L, 0L, 4L, 2L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 4L, 0L,
0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L,
0L, 1L, 1L, 0L, 1L, 0L)), .Names = c("plot.code", "site.code",
"block", "fragment", "elevation", "forestloss562", "forestloss17",
"roaddenssec", "nearestroadprim", "soilPC1", "soilPC2", "ex.richness"
), class = "data.frame", row.names = c(NA, -159L))
asked 48 secs ago
Error messages when running glmer in R
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