Hi @technocrat,
I managed to get the dput(l.format) to run. My code was simplified (I am not familiar with tidyverse or ggplot really at all), as I couldn't suss how to modify code kindly provided by @dromano:
rm(list=ls()) # clear out old junk
library(haven)
library(tidyverse)
library(mirt)
library(ggplot2)
# Summon data
newdat <- read_sav("/Downloads/IRTsampledata.sav")
# Group order
group <- c(rep('Early', sum(newdat$age == "1")),
rep('Middle', sum(newdat$age == "2")),
rep('Late', sum(newdat$age == "3")))
group <- factor(group, levels = c("Early", 'Middle', "Late"))
# Remove age column
dat <- newdat[,c(2-21)]
mod <- mirt(dat, 1, verbose=FALSE)
# Extract all items
# Compute the probability trace lines
# Put into a list
traceline <- NULL
for(i in 1:length(dat)){
extr.2 <- extract.item(mod, i)
Theta <- matrix(seq(-4,4, by = .5))
traceline[[i]] <- probtrace(extr.2, Theta)
}
# rename list
names(traceline) <- paste('item',1:length(traceline))
# rbind traceline
traceline.df <- do.call(rbind, traceline)
# create item names length based on length of theta provided
item <- rep(names(traceline),each=length(Theta))
# put them all together into a dataframe
l.format <- cbind.data.frame(Theta, item, traceline.df)
dput(l.format)
The issue is, my original dataset (not linked above) has about 5000 participants, and even reducing Theta to .5 increments, this spits out a result that's extremely long.
> dput(l.format)
structure(list(Theta = c(-4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5,
0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, -4, -3.5, -3, -2.5, -2, -1.5,
-1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, -4, -3.5, -3, -2.5,
-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, -4, -3.5,
-3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5,
4, -4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2,
2.5, 3, 3.5, 4, -4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5,
1, 1.5, 2, 2.5, 3, 3.5, 4, -4, -3.5, -3, -2.5, -2, -1.5, -1,
-0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, -4, -3.5, -3, -2.5,
-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, -4, -3.5,
-3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5,
4, -4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2,
2.5, 3, 3.5, 4, -4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5,
1, 1.5, 2, 2.5, 3, 3.5, 4, -4, -3.5, -3, -2.5, -2, -1.5, -1,
-0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, -4, -3.5, -3, -2.5,
-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, -4, -3.5,
-3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5,
4, -4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2,
2.5, 3, 3.5, 4, -4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5,
1, 1.5, 2, 2.5, 3, 3.5, 4, -4, -3.5, -3, -2.5, -2, -1.5, -1,
-0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, -4, -3.5, -3, -2.5,
-2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, -4, -3.5,
-3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5,
4, -4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0, 0.5, 1, 1.5, 2,
2.5, 3, 3.5, 4), item = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 12L, 12L, 12L, 12L, 12L,
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 14L,
14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L, 14L,
14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L, 15L,
15L, 15L, 15L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L,
16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 17L,
17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L,
18L, 18L, 18L, 18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 20L, 20L,
20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L, 20L,
20L, 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, 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, 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, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L,
11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L,
11L, 11L, 11L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L,
13L, 13L, 13L, 13L, 13L, 13L, 13L), .Label = c("item 1", "item 10",
"item 11", "item 12", "item 13", "item 14", "item 15", "item 16",
"item 17", "item 18", "item 19", "item 2", "item 20", "item 3",
"item 4", "item 5", "item 6", "item 7", "item 8", "item 9"), class = "factor"),
P.1 = c(0.200118186078579, 0.225988192158921, 0.254139947680198,
0.284509553999383, 0.316966042933678, 0.351307145843956,
0.38725939242117, 0.424483325424149, 0.462584077758321, 0.501126870500897,
0.539656275675558, 0.577717495874043, 0.614877581606692,
0.650744517630828, 0.68498245887565, 0.717321999591108, 0.747565076592345,
0.992394113388282, 0.9861653343889, 0.974964233887823, 0.955107111719014,
0.920780037723831, 0.863944027806002, 0.776240465804061,
0.654604058038137, 0.50869609935126, 0.361291436134043, 0.236075990577557,
0.144443239826544, 0.0844460014292873, 0.0479723949795176,
0.0267913472713227, 0.0148167295385528, 0.0081494499401713,
0.98686185928437, 0.976757299128715, 0.959202584552502, 0.92934821133154,
0.880371889343258, 0.804583734820772, 0.697290723256495,
0.563078638702859, 0.418947030133841, 0.287437756481625,
0.184128282806036, 0.112107949510031, 0.0659796769085196,
0.0380187709618151, 0.0216327143096666, 0.012219325518536,
0.00687335790600263, 0.985280119743177, 0.971515333978013,
0.945589700056473, 0.898532052415914, 0.818584040474725,
0.696893356008727, 0.539495502845531, 0.373805823279976,
0.233230339121201, 0.134191512259061, 0.0731939450471064,
0.0386843574376721, 0.0200926617533994, 0.0103400241957331,
0.00529556966353628, 0.00270536086212281, 0.00138033632425794,
0.990902781955384, 0.982217263658585, 0.965527778686266,
0.934223784000934, 0.878082325208501, 0.785048144975203,
0.649369200597367, 0.484306220609807, 0.322597742734359,
0.194517011153224, 0.109098199145086, 0.0584668746627857,
0.0305278448122109, 0.0157169168824018, 0.00803214361734172,
0.00408922330910721, 0.00207779937052088, 0.972466774977879,
0.951795789422295, 0.916931394083013, 0.860545425808341,
0.775266078808291, 0.658530134043098, 0.51879427247639, 0.376055239074164,
0.252020692544902, 0.158503865709653, 0.0952683478886088,
0.0555940943518759, 0.0318602467526807, 0.0180648763326287,
0.0101800408540637, 0.00571668917954526, 0.00320392150200122,
0.947023088408773, 0.900263810015068, 0.820074335735178,
0.697103590250787, 0.537488294237861, 0.36980046215564, 0.228573959669926,
0.130143676084671, 0.0702407499976581, 0.0367453863370856,
0.0188981333759135, 0.00963261549434591, 0.00488723565059934,
0.00247376475413852, 0.00125064390496743, 0.000631896180851466,
0.000319171935513007, 0.942781085520461, 0.914896985406794,
0.87522276759143, 0.8206780156898, 0.74912429215208, 0.660819139378065,
0.559700374223807, 0.453372265742173, 0.351134748453087,
0.260946366798789, 0.187238245528313, 0.130668976142415,
0.0893125832923706, 0.0601398781716314, 0.0400766917204054,
0.0265179103603669, 0.0174628972054626, 0.978337069688509,
0.956970891556338, 0.916333410177258, 0.843589884598081,
0.726478639084737, 0.566718177739015, 0.391769126457064,
0.240811171080442, 0.135100657067138, 0.0714286202644513,
0.0364984530031179, 0.0183130272685552, 0.0091029202136319,
0.00450357052146155, 0.00222287926977538, 0.00109590069726828,
0.000539980289600006, 0.976833063762373, 0.959102949182024,
0.928792768249571, 0.878856097464348, 0.801388127339267,
0.69175643317981, 0.555197721977714, 0.409760688859905, 0.278562365465707,
0.176789355513988, 0.106699898436205, 0.0622951549133836,
0.0356329644327633, 0.0201370760521239, 0.011300994664043,
0.00631715961480173, 0.00352340567786169, 0.942518165514344,
0.903771809809897, 0.843251532740829, 0.754987363837469,
0.638338395733248, 0.502731947931675, 0.366722388671217,
0.249078048311586, 0.159659051187708, 0.0981457986989067,
0.0586774468051837, 0.0344741842187408, 0.0200417230600476,
0.0115788878604891, 0.00666527109032256, 0.00382871974993815,
0.0021966551384861, 0.858187467105633, 0.809745425504801,
0.749584433611696, 0.677965442240404, 0.596879024089633,
0.510128124737807, 0.422762883781347, 0.339975954944538,
0.26593199060594, 0.203053058807698, 0.151963864086213, 0.111923707485524,
0.0814206096552608, 0.0586813577759898, 0.0420023676156409,
0.0299133936354724, 0.0212267311316623, 0.986827694571107,
0.977026476052853, 0.960226325529088, 0.931995632535611,
0.886104005823307, 0.81537796196457, 0.714865517202995, 0.587326929598819,
0.446880800822928, 0.314429985479169, 0.20657478823579, 0.128767061323777,
0.0774069334574859, 0.0454633528802845, 0.026325806950544,
0.0151165282645528, 0.00863771326094653, 0.971584409310144,
0.951238054711472, 0.917559311664789, 0.863946432633449,
0.783687470117243, 0.673951061455007, 0.541140273784748,
0.402215559240309, 0.27739543284525, 0.179668634062062, 0.111078658490976,
0.0665492329907409, 0.0390859373107165, 0.0226807177338116,
0.0130674884981338, 0.00749757110985894, 0.00429146514333445,
0.952899309935298, 0.916500042554516, 0.856215985898919,
0.763633571936217, 0.636729965511677, 0.487426050816731,
0.340332687949929, 0.218689862516552, 0.131835852519265,
0.0761159459867391, 0.042785248010569, 0.0236758295470757,
0.0129855741526033, 0.00708720923399686, 0.0038575546520605,
0.00209655209409376, 0.00113854154799264, 0.964290660880858,
0.933260595463436, 0.878659555680114, 0.789465357146336,
0.660071789056128, 0.501380636290737, 0.34241084581034, 0.212376319297615,
0.122522974777377, 0.0674306534533987, 0.036091625271217,
0.0190206350073361, 0.00994078688064937, 0.00517251575883571,
0.0026852254462435, 0.00139231614273927, 0.000721479442767681,
0.842986190985014, 0.771988173765107, 0.681033767622363,
0.573826748473445, 0.459200038504703, 0.348734021825543,
0.252437598779706, 0.175563577957583, 0.118392259972764,
0.0780753610897645, 0.0506983115709472, 0.0325817336586905,
0.0207971225084589, 0.0132166997298689, 0.00837566065552242,
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0.956004879831574, 0.939917308817298, 0.918448859426968,
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0.685331928524834, 0.610584727158459, 0.530252143542345,
0.448320809515051, 0.369101848206011, 0.296361233035036,
0.232669215521375, 0.179179786864631, 0.13581084581522, 0.955736328082368,
0.930199134644131, 0.891599736327571, 0.835431476442915,
0.758056885431659, 0.659145882130793, 0.544116182819849,
0.424179152173541, 0.312553966145213, 0.219125181970129,
0.14762677706195, 0.0965724278517864, 0.0618923476084406,
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0.00948296397353121, 0.970055685867997, 0.950362302439108,
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0.579930027229248, 0.449315890990069, 0.325335962298331,
0.22178786762937, 0.144155134242827, 0.0905349671092808,
0.0555645618313907, 0.0336028816537736, 0.0201363985558285,
0.0119996491695337, 0.00712689684608081), P.2 = c(0.602706112823896,
0.600148688061986, 0.593077749995742, 0.581647029286554,
0.566105773930048, 0.54679364175724, 0.524132495810648, 0.498614499459964,
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0.254746330145761, 0.164918814921628, 0.0998953814035062,
0.0579605570892359, 0.011479306069186, 0.0202815632778454,
0.0355169823385429, 0.0612526090097298, 0.102951415776027,
0.165994640918141, 0.251311699672175, 0.348626541907465,
0.433493445776889, 0.476268628058338, 0.459774673502395,
0.390787327643129, 0.295445858275372, 0.202480219254412,
0.128864165775766, 0.0779579132298328, 0.0456653317407004,
0.0129097809981067, 0.024938465402251, 0.0474744394650291,
0.0879463641734367, 0.155220344187712, 0.252961263275981,
0.366623842118618, 0.457219416748613, 0.48154170151461, 0.426615788330094,
0.320980915301524, 0.210300704400087, 0.124424408727965,
0.0689155782321691, 0.0367220543818162, 0.0191550289856454,
0.0098792498068957, 0.00835596736826006, 0.0163220204021339,
0.0315957301887865, 0.0601195061867694, 0.110823475909668,
0.193307178253695, 0.308826386500103, 0.436473364271269,
0.532347150104668, 0.554778762739612, 0.49337877195226, 0.376093955967163,
0.249902060094271, 0.149295861920842, 0.0830529873528887,
0.0442704805389348, 0.0230438283193901, 0.0236748646769811,
0.0413233630838712, 0.0708266716444209, 0.117765781808482,
0.186589832388515, 0.275231140259249, 0.368602039780954,
0.438951788147668, 0.459204220750101, 0.420772193472095,
0.339668644559932, 0.245251514133173, 0.162047964403144,
0.100472839251937, 0.0597418789690884, 0.034615872836866,
0.0197517724117848, 0.0483970636563578, 0.0907067195335195,
0.162200451736265, 0.268392685875872, 0.39641580003789, 0.507268156071842,
0.554156698762562, 0.515133401652551, 0.408530713330424,
0.280107365348271, 0.17086018692488, 0.0961191064519198,
0.0514614722249427, 0.0267956621129705, 0.0137460758221331,
0.00699724564146986, 0.00354772100268974, 0.0493720069442373,
0.0731264820471121, 0.106537713474981, 0.151636098857228,
0.20905950032888, 0.276487505552981, 0.34731746406874, 0.410843326552085,
0.454791392519307, 0.469473279342923, 0.451467736401694,
0.404958435132423, 0.340099801005224, 0.26918290814728, 0.202565086454119,
0.146376580428154, 0.102555990528631, 0.0201361276910083,
0.0399335788844142, 0.0774006457795228, 0.143767824292489,
0.248179499857336, 0.383131251021608, 0.511397728787368,
0.580398160762635, 0.558329895593346, 0.455508071120609,
0.31772984834491, 0.194364641724851, 0.108304290490662, 0.0569776440893088,
0.0290289496262991, 0.0145422143872662, 0.0072226742259941,
0.0208206513309425, 0.0366864223556858, 0.063662067472804,
0.10765923327438, 0.174625161605502, 0.265926530233591, 0.371203419408059,
0.465252267919682, 0.517096451826053, 0.507321840567224,
0.439686580403951, 0.338880675660504, 0.235829077399259,
0.151528797812314, 0.0920516456932789, 0.0539297992289319,
0.0309057996247798, 0.0487375396077539, 0.0810610307038752,
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0.372104180761315, 0.43341944778333, 0.447266184740329, 0.408100934958334,
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0.0069358604784981, 0.0135215834106497, 0.0261956153375631,
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0.315888803918788, 0.453613521159844, 0.598824169426112,
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0.925078698792669, 0.955666662172651, 0.974115962658721,
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0.77083305310949, 0.827066898292977, 0.871796867356229, 0.906268403636466,
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0.925651108687649, 0.954680959238146, 0.972710148524751)), class = "data.frame", row.names = c(NA,
-340L))
But here it is! Hope it's useful and I've done it correctly.
The rest of the code basically is:
l.format$item<-as.factor(l.format$item)
aux<-l.format %>%
group_by(item) %>%
slice(which.min(abs(P.1-0.5)))
aux<-aux[order(aux$Theta),]
ord<-as.integer(aux$item)
l.format$item = factor(l.format$item,levels(l.format$item)[ord])
# plot chart
ggplot(l.format, aes(Theta, P.1, colour = item)) +
geom_line() +
ggtitle('Probability Tracelines') +
xlab(expression(theta)) +
ylab(expression(P(theta))) +
geom_hline(aes(yintercept = 0.5)) + theme_bw() +
theme(text = element_text(size=16),
axis.text.x=element_text(colour="black"),
axis.text.y=element_text(colour="black"),
legend.title=element_blank())
Which yielded the following plot:
Initial_ICC.pdf (7.1 KB)
Which is a good start, though I'm unsure why the curve is backwards. My next steps are comparing items by group for each item (on individual plots), formatting the lines themselves, and combining items to create TCCs.