problems with params

[bea...@gmx.de]

Hello Phil,

i am stuck (again) with the setting of parameters before estimation of an IRT model. The mirt-function doesn't accept my parameter-table anymore (after update, i guess) and gives an error:

Error in UpdatePrepList(PrepList, pars, random = mixed.design$random, :
Graded model intercepts for item 1 in group 1 do not descend from highest to lowest. Please fix

However, i cannot see that the intercepts are ordered wrong?

Also, i encountered this error when using a random dataset as below in the example:

Error in UpdatePrepList(PrepList, pars, random = mixed.design$random, :
Critical internal parameter labels do not match those returned from pars = 'values'

could you have a look at this parameter-table? what could be wrong with it? Any hints would be appreciated!

Best wishes!

parameters_for_mirt =
structure(list(group = 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,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "all", class = "factor"),
item = c("item_1", "item_1", "item_1", "item_1", "item_1",
"item_2", "item_2", "item_2", "item_2", "item_2", "item_3",
"item_3", "item_3", "item_3", "item_3", "item_4", "item_4",
"item_4", "item_4", "item_4", "item_5", "item_5", "item_5",
"item_5", "item_5", "item_6", "item_6", "item_6", "item_6",
"item_6", "item_7", "item_7", "item_7", "item_7", "item_7",
"item_8", "item_8", "item_8", "item_8", "item_8", "item_9",
"item_9", "item_9", "item_9", "item_9", "item_10", "item_10",
"item_10", "item_10", "item_10", "item_11", "item_11", "item_11",
"item_11", "item_11", "item_12", "item_12", "item_12", "item_12",
"item_12", "item_13", "item_13", "item_13", "item_13", "item_13",
"item_14", "item_14", "item_14", "item_14", "item_14", "item_15",
"item_15", "item_15", "item_15", "item_15", "item_16", "item_16",
"item_16", "item_16", "item_16", "item_17", "item_17", "item_17",
"item_17", "item_17", "item_18", "item_18", "item_18", "item_18",
"item_18", "item_19", "item_19", "item_19", "item_19", "item_19",
"item_20", "item_20", "item_20", "item_20", "item_20", "item_21",
"item_21", "item_21", "item_21", "item_21", "item_22", "item_22",
"item_22", "item_22", "item_22", "item_23", "item_23", "item_23",
"item_23", "item_23", "item_24", "item_24", "item_24", "item_24",
"item_24", "item_25", "item_25", "item_25", "item_25", "item_25",
"item_26", "item_26", "item_26", "item_26", "item_26", "item_27",
"item_27", "item_27", "item_27", "item_27", "item_28", "item_28",
"item_28", "item_28", "item_28", "GROUP", "GROUP"), class = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 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), .Label = c("graded", "GroupPars"
), class = "factor"), name = structure(c(1L, 3L, 4L, 5L,
6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L,
6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L,
6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L,
6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L,
6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L,
6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L,
6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L,
6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L,
6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L, 6L, 1L, 3L, 4L, 5L,
6L, 7L, 2L), .Label = c("a1", "COV_11", "d1", "d2", "d3",
"d4", "MEAN_1"), class = "factor"), parnum = 1:142, value = c(4.27,
-1.4091, -3.843, -6.9174, -10.1199, 3.94, -0.9062, -3.3096,
-5.9888, -9.2196, 4.15, -1.2035, -3.486, -6.6815, -9.96,
2.8, -0.224, -1.96, -4.284, -6.888, 3.66, -0.8784, -3.3306,
-6.2586, -9.15, 2.34, -0.2808, -2.0592, -3.8844, -5.9904,
3.28, 1.8696, -1.0824, -4.3952, -7.544, 3.24, -1.2636, -3.1104,
-5.7024, -7.9056, 2.74, 0, -2.0276, -4.7402, -7.124, 3.97,
-0.5161, -2.8584, -6.2726, -8.8134, 2.57, 0.2827, -1.5934,
-4.0606, -6.4507, 3.1, 1.333, -1.054, -4.123, -6.665, 2.92,
-0.3796, -2.3944, -4.6136, -7.1832, 2.59, 0.3885, -1.4504,
-3.6519, -5.8275, 4.35, 0.8265, -2.3055, -5.916, -9.57, 2.62,
0.2358, -2.096, -4.6898, -7.205, 3.19, 1.0527, -1.0527, -3.9237,
-6.5714, 3.11, 0.0933, -2.0215, -4.8827, -7.464, 3.49, 2.1289,
-0.9772, -4.4323, -7.9572, 3.13, -2.6605, -4.4133, -6.5417,
-8.7014, 4.46, -2.1854, -4.46, -7.6266, -10.9716, 2.37, -0.3318,
-2.1804, -4.3371, -6.7782, 2.55, -0.306, -2.397, -4.947,
-7.7775, 2.84, -0.1988, -2.3572, -5.0268, -7.952, 2.38, 1.2614,
-0.9758, -3.4986, -6.0928, 3.19, -0.4147, -2.2649, -4.6255,
-7.1775, 2.02, 0.2424, -1.717, -3.8986, -5.8378, 2.69, 0.9953,
-0.9415, -3.4701, -5.9987, 0, 1), lbound = c(-Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf,
-Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, -Inf, 1e-04
), ubound = c(Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf,
Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf,
Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf,
Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf,
Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf,
Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf,
Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf,
Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf,
Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf,
Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf,
Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf,
Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf, Inf,
Inf), est = c(FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE
), prior.type = 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,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "none", class = "factor"),
prior_1 = c(NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN), prior_2 = c(NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN,
NaN, NaN)), .Names = c("group", "item", "class", "name",
"parnum", "value", "lbound", "ubound", "est", "prior.type", "prior_1",
"prior_2"), row.names = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L,
23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L,
49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L,
62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 71L, 72L, 73L, 74L,
75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L, 84L, 85L, 86L, 87L,
88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L,
101L, 102L, 103L, 104L, 105L, 106L, 107L, 108L, 109L, 110L, 111L,
112L, 113L, 114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L,
123L, 124L, 125L, 126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L,
134L, 135L, 136L, 137L, 138L, 139L, 140L, 142L, 141L), class = "data.frame")

parameters_for_mirt
df = daten[1:250, grep("eddep", names(daten))]
names(df) = paste("item", 1:28, sep = "_")
mirt(df, model=1, pars=parameters_for_mirt)

[bea...@gmx.de]

sorry, i messed up the example :(

df = data.frame(matrix(sample(5,size=2800, replace=TRUE),100,28))

[Phil Chalmers]

Hi Felix,

I'm having difficulty reproducing the error on my end, but at any rate I think I know what the problem was. On the 1.0 version the graded model test was accidental done just before overwriting the starting values that are computed automatically, and can sometimes raise this 'out of order error'. It's fixed on the dev version. The second error is related to an internal labelling bug, which also should be fixed now as well. Sorry for the headaches!

Phil

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[bea...@gmx.de]

i installed the dev-version, works again :D thank you

[Eva de Schipper]

Dear Hans and Phil, 

My name is Eva de Schipper, and I'm currently doing a master's in methodology and statistics in the Netherlands. I stumbled upon this chain of messages because I'm stuck with the same error message as Hans was (Graded model intercepts for item 1 in group 1 do not descend from highest to lowest. Please fix). I have installed the developers version of Mirt, but it does not fix my problem. Do you happen to have any other insights for me? You seem to be the only ones who have run into the same problem (albeit 3 years ago...). 

Kind regards,

Eva

Op vrijdag 13 december 2013 17:55:51 UTC+1 schreef Dr. Hans Hansen:

[Phil Chalmers]

Hi Eva,

This generally shouldn't happen in real datasets any more. However, are you by any chance modifying the starting values? Or, perhaps, are you constraining some intercepts to be equal across groups while allowing others to be freely estimated? There is a very good chance that the intercepts can go out of order as well (wouldn't happen if they were all constrained).  

Phil

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