Df for 'random-intercept-only' model

Hi Rune,

many thanks for the bug fix!

Best,
Maarten
Hi Maarten,

This is fixed in the development version of lmerTest on GitHub - you
may install with

library("devtools") # install if you don't have it already.
install_github("runehaubo/lmerTest?)

Furture bug-reports are also welcome here.

For your example data I get:

Linear mixed model fit by REML t-tests use Satterthwaite
approximations to degrees of
  freedom [merModLmerTest]
Formula: value ~ 1 + (1 | participant)
   Data: d

REML criterion at convergence: 430.6

Scaled residuals:
     Min       1Q   Median       3Q      Max
-1.95994 -0.51445  0.09534  0.47149  2.28058

Random effects:
 Groups      Name        Variance Std.Dev.
 participant (Intercept) 4.714    2.171
 Residual                1.945    1.395
Number of obs: 100, groups:  participant, 47

Fixed effects:
            Estimate Std. Error      df t value Pr(>|t|)
(Intercept)  15.0660     0.3529 44.6400   42.69   <2e-16 ***
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1

Best regards
Rune (for the lmerTest developers)

On 20 January 2018 at 15:13, Maarten Jung
Dear list,

I'm trying to get an estimate for the degrees of freedom of the following
'random-intercept-only' model: lmer(value ~ 1 + (1|participant), data =
d).
Normally, I know that one can achieve this via the lmerTest package but in
this case it doesn't seem to work.

library(lmerTest)

d <- structure(list(value = c(8.25313070764297, 17.0453980104411,
14.7103370498893, 15.8219809684075, 18.0962761595002, 15.0493332821287,
17.5226332786703, 18.0503969664744, 13.9450638512715, 16.5332566133129,
13.1299408088999, 15.7163333693101, 17.5128147209307, 13.8564235534667,
11.3690970113334, 11.458250790531, 12.4039353096013, 16.9168322671145,
14.5580055004946, 17.5039029057499, 17.4265303119576, 10.3394971583312,
14.3327437896945, 10.1075784647337, 13.5324234318869, 12.9997105711724,
18.0080570272635, 15.2842579562302, 15.2356443644651, 15.813831547332,
14.8905281019859, 12.9039538908697, 17.3783863341884, 16.0160603506154,
18.4540620620647, 8.68750928534364, 14.462724834603, 16.8719660771132,
16.3784470438622, 16.228431742704, 17.2260499546157, 14.4592109398455,
12.9768791191779, 16.0745074082946, 14.4003729683924, 18.3820335802969,
13.8298449655042, 13.3898039860578, 11.2943669087644, 11.3711693435468,
18.1917812063674, 13.5455973955032, 17.0505866395927, 15.6054278307471,
12.1163489978806, 16.5776960925959, 15.5595289759737, 13.1383036025308,
16.7858376357516, 18.5934575849391, 12.6122831749272, 17.0565311301485,
13.2374564449912, 15.6139433555906, 14.6293717224923, 17.3818235894563,
15.7412428493826, 22.1877337982038, 18.3842887975238, 10.0701573038888,
10.8804812404375, 11.7148149325827, 14.4690669558257, 14.50516878681,
17.9920135587834, 19.6609636797189, 14.3073652841217, 16.4785695045202,
12.6811309533188, 14.6032532299404, 12.3152507852885, 18.1340692925595,
15.4724734122014, 17.406638598528, 12.2625532078185, 11.8729946785576,
15.9063789963309, 15.8208702113247, 12.203938368013, 14.894840829895,
15.5397556234126, 17.0641047610171, 18.3147667118571, 16.3914422509424,
14.5539887230189, 15.2870912573834, 17.0165733030807, 16.5537600282835,
12.1300522052001, 18.291737264292), participant = structure(c(15L,
3L, 6L, 6L, 11L, 57L, 43L, 10L, 16L, 49L, 54L, 11L, 22L, 24L, 41L, 48L,
35L, 46L, 59L, 23L, 55L, 41L, 39L, 15L, 60L, 52L, 5L,
48L, 33L, 50L, 2L, 53L, 11L, 47L, 19L, 15L, 60L, 22L, 31L, 21L, 11L, 59L,
24L, 50L, 20L, 5L, 14L, 3L, 39L, 18L, 9L, 33L, 46L,
49L, 38L, 19L, 33L, 46L, 45L, 22L, 14L, 10L, 4L, 33L, 44L, 43L, 47L, 13L,
22L, 42L, 35L, 38L, 40L, 33L, 10L, 2L, 55L, 40L, 48L,
57L, 41L, 16L, 2L, 8L, 48L, 47L, 30L, 7L, 48L, 6L, 44L, 9L, 22L, 9L, 56L,
40L, 6L, 59L, 3L, 26L), .Label = c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17",
"18", "19", "20", "21", "22", "23", "24", "25", "26",
"27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38",
"39", "40", "41", "42", "43", "44", "45", "46", "47", "48",
"49", "50", "51", "52", "53", "54", "55", "56", "57", "58", "59", "60"),
class = "factor")), .Names = c("value", "participant"), row.names = c(NA,
-100L), class = c("tbl_df", "tbl", "data.frame"))

summary(m1 <- lmer(value ~ 1 + (1|participant), data = d))

Running this code returns "summary from lme4 is returned. some
computational error has occurred in lmerTest".

Thanks a lot for any help!

Best,

Maarten

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Df for 'random-intercept-only' model

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