Load packages

pacman::p_load(tidyverse, psych, mirt, arsenal, haven)

Import ds

ds <- read_sav("C:/Users/luisf/Downloads/Elie1.sav")
backup <- ds

Clean ds

ds <- janitor::clean_names(ds)

Remove spss attributes

ds[] <- lapply(ds, function(x) { attributes(x) <- NULL; x })

DS

ds %>% names

 [1] "cod"           "sexo"          "tb"            "est_afet"      "data"         
 [6] "cgib_pm_e"     "cgib_pd_e"     "cgib_pg_e"     "young1e"       "young2e"      
[11] "young3e"       "young4e"       "young5e"       "young6e"       "young7e"      
[16] "young8e"       "young9e"       "young10e"      "young11e"      "youngtote"    
[21] "data_d"        "cgib_pm_d"     "cgib_pd_d"     "cgib_pg_d"     "young1d"      
[26] "young2d"       "young3d"       "young4d"       "young5d"       "young6d"      
[31] "young7d"       "young8d"       "young9d"       "young10d"      "young11d"     
[36] "young_total_d" "data_m"        "cgib_pm_m"     "cgib_pd_m"     "cgib_pg_m"    
[41] "young1m"       "young2m"       "young3m"       "young4m"       "young5m"      
[46] "young6m"       "young7m"       "young8m"       "young9m"       "young10m"     
[51] "young11m"      "young_total_m"

DS for all Items

ds_young <- ds %>% select(young1m:young11m)

Check items’ distribution

ds_young %>% mutate_all(., as.factor) %>% DataExplorer::plot_bar()

Check items’ distribution

ds_young %>% 
  pivot_longer(everything()) %>% 
  count(value) %>% 
  mutate(percent = (100 * n / sum(n)) %>% round(digits = 1))

EFA models

Euthimic

ds %>% select(young1e: young11e) %>% 
  fa.parallel()

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.

Parallel analysis suggests that the number of factors =  1  and the number of components =  1

mod_1e <- ds %>% select(young1e: young11e) %>% fa(., nfactors = 1)
as.data.frame(unclass(mod_1e$loadings)) %>% mutate_if(is.numeric, round,2) %>%  
  DT::datatable(., options = list(
          lengthMenu = list(c(5, 15, -1), c('5', '15', 'All')),
          pageLength = 15
        ))

Maniac (3)

ds %>% select(young1m: young11m) %>% 
  fa.parallel()

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefullyThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  3  and the number of components =  2

mod_1m <- ds %>% select(young1m: young11m) %>% fa(., nfactors = 1)
as.data.frame(unclass(mod_1m$loadings)) %>% mutate_if(is.numeric, round,2) %>% 
  DT::datatable(., options = list(
    lengthMenu = list(c(5, 15, -1), c('5', '15', 'All')),
    pageLength = 15
  ))

Depression (3)

ds %>% select(young1d: young11d) %>% 
  fa.parallel()

The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zeroThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.An ultra-Heywood case was detected.  Examine the results carefully

Parallel analysis suggests that the number of factors =  1  and the number of components =  1

mod_1d <- ds %>% select(young1d: young11d) %>% fa(., nfactors = 1)
as.data.frame(unclass(mod_1d$loadings)) %>% mutate_if(is.numeric, round,2) %>% 
    DT::datatable(., options = list(
          lengthMenu = list(c(5, 15, -1), c('5', '15', 'All')),
          pageLength = 15
        ))

IRT models

irt_1d <- mirt(ds_young, 1, itemtype = "graded")

"young5m" re-mapped to ensure all categories have a distance of 1
"young6m" re-mapped to ensure all categories have a distance of 1
"young8m" re-mapped to ensure all categories have a distance of 1
"young9m" re-mapped to ensure all categories have a distance of 1


Iteration: 1, Log-Lik: -1506.248, Max-Change: 0.69950
Iteration: 2, Log-Lik: -1476.506, Max-Change: 0.39171
Iteration: 3, Log-Lik: -1468.973, Max-Change: 0.15796
Iteration: 4, Log-Lik: -1467.487, Max-Change: 0.11639
Iteration: 5, Log-Lik: -1467.039, Max-Change: 0.07047
Iteration: 6, Log-Lik: -1466.891, Max-Change: 0.04741
Iteration: 7, Log-Lik: -1466.815, Max-Change: 0.02686
Iteration: 8, Log-Lik: -1466.796, Max-Change: 0.01870
Iteration: 9, Log-Lik: -1466.788, Max-Change: 0.01629
Iteration: 10, Log-Lik: -1466.781, Max-Change: 0.00483
Iteration: 11, Log-Lik: -1466.779, Max-Change: 0.00710
Iteration: 12, Log-Lik: -1466.778, Max-Change: 0.00396
Iteration: 13, Log-Lik: -1466.778, Max-Change: 0.00402
Iteration: 14, Log-Lik: -1466.777, Max-Change: 0.00214
Iteration: 15, Log-Lik: -1466.777, Max-Change: 0.00027
Iteration: 16, Log-Lik: -1466.777, Max-Change: 0.00024
Iteration: 17, Log-Lik: -1466.777, Max-Change: 0.00137
Iteration: 18, Log-Lik: -1466.777, Max-Change: 0.00045
Iteration: 19, Log-Lik: -1466.777, Max-Change: 0.00016
Iteration: 20, Log-Lik: -1466.777, Max-Change: 0.00011
Iteration: 21, Log-Lik: -1466.777, Max-Change: 0.00044
Iteration: 22, Log-Lik: -1466.777, Max-Change: 0.00052
Iteration: 23, Log-Lik: -1466.777, Max-Change: 0.00032
Iteration: 24, Log-Lik: -1466.777, Max-Change: 0.00021
Iteration: 25, Log-Lik: -1466.777, Max-Change: 0.00041
Iteration: 26, Log-Lik: -1466.777, Max-Change: 0.00039
Iteration: 27, Log-Lik: -1466.777, Max-Change: 0.00025
Iteration: 28, Log-Lik: -1466.777, Max-Change: 0.00008

M2(irt_1d)

plot(irt_1d, type = "trace")

Item info

Theta <- seq(-4,4, by = .1)
nitems <- ncol(ds_young)
for(i in 1:nitems){
  info <- iteminfo(extract.item(irt_1d, i), Theta)
    plot(Theta, info, type = "l", main = paste("Information for ", 
                names(ds_young)[[i]], sep = ""), ylab = "Information", xlab = "Theta")
    lines(Theta, info, col = "steelblue")
}

Curva com 3 itens

mod_elie2 <- mirt(ds_2 , 1, "graded")


Iteration: 1, Log-Lik: -419.715, Max-Change: 0.73766
Iteration: 2, Log-Lik: -416.027, Max-Change: 0.25178
Iteration: 3, Log-Lik: -414.582, Max-Change: 0.19273
Iteration: 4, Log-Lik: -413.765, Max-Change: 0.08312
Iteration: 5, Log-Lik: -413.696, Max-Change: 0.06303
Iteration: 6, Log-Lik: -413.661, Max-Change: 0.05092
Iteration: 7, Log-Lik: -413.613, Max-Change: 0.02411
Iteration: 8, Log-Lik: -413.609, Max-Change: 0.01573
Iteration: 9, Log-Lik: -413.607, Max-Change: 0.00890
Iteration: 10, Log-Lik: -413.605, Max-Change: 0.02795
Iteration: 11, Log-Lik: -413.604, Max-Change: 0.00904
Iteration: 12, Log-Lik: -413.603, Max-Change: 0.00797
Iteration: 13, Log-Lik: -413.602, Max-Change: 0.00332
Iteration: 14, Log-Lik: -413.602, Max-Change: 0.00293
Iteration: 15, Log-Lik: -413.602, Max-Change: 0.01231
Iteration: 16, Log-Lik: -413.601, Max-Change: 0.00378
Iteration: 17, Log-Lik: -413.601, Max-Change: 0.00126
Iteration: 18, Log-Lik: -413.601, Max-Change: 0.00920
Iteration: 19, Log-Lik: -413.601, Max-Change: 0.00207
Iteration: 20, Log-Lik: -413.601, Max-Change: 0.00171
Iteration: 21, Log-Lik: -413.601, Max-Change: 0.00168
Iteration: 22, Log-Lik: -413.601, Max-Change: 0.00064
Iteration: 23, Log-Lik: -413.601, Max-Change: 0.00026
Iteration: 24, Log-Lik: -413.601, Max-Change: 0.00097
Iteration: 25, Log-Lik: -413.601, Max-Change: 0.00478
Iteration: 26, Log-Lik: -413.601, Max-Change: 0.00032
Iteration: 27, Log-Lik: -413.601, Max-Change: 0.00067
Iteration: 28, Log-Lik: -413.601, Max-Change: 0.00017
Iteration: 29, Log-Lik: -413.601, Max-Change: 0.00066
Iteration: 30, Log-Lik: -413.601, Max-Change: 0.00018
Iteration: 31, Log-Lik: -413.601, Max-Change: 0.00013
Iteration: 32, Log-Lik: -413.601, Max-Change: 0.00067
Iteration: 33, Log-Lik: -413.601, Max-Change: 0.00019
Iteration: 34, Log-Lik: -413.601, Max-Change: 0.00013
Iteration: 35, Log-Lik: -413.601, Max-Change: 0.00065
Iteration: 36, Log-Lik: -413.601, Max-Change: 0.00027
Iteration: 37, Log-Lik: -413.601, Max-Change: 0.00012
Iteration: 38, Log-Lik: -413.601, Max-Change: 0.00062
Iteration: 39, Log-Lik: -413.601, Max-Change: 0.00028
Iteration: 40, Log-Lik: -413.601, Max-Change: 0.00012
Iteration: 41, Log-Lik: -413.601, Max-Change: 0.00058
Iteration: 42, Log-Lik: -413.601, Max-Change: 0.00028
Iteration: 43, Log-Lik: -413.601, Max-Change: 0.00011
Iteration: 44, Log-Lik: -413.601, Max-Change: 0.00054
Iteration: 45, Log-Lik: -413.601, Max-Change: 0.00027
Iteration: 46, Log-Lik: -413.601, Max-Change: 0.00010
Iteration: 47, Log-Lik: -413.601, Max-Change: 0.00050
Iteration: 48, Log-Lik: -413.601, Max-Change: 0.00025
Iteration: 49, Log-Lik: -413.601, Max-Change: 0.00010

M2(mod_elie2)

Error: M2() statistic cannot be calculated due to too few degrees of freedom

Plots

p1 <-itemplot(object = mod_elie2, item = 1, type = "info")
p2 <-itemplot(object = mod_elie2, item = 2, type = "info")
p3 <-itemplot(object = mod_elie2, item = 3, type = "info")

x3 <- p3$panel.args[[1]]$x
y3 <- p3$panel.args[[1]]$y
u3 <- p3$panel.args.common$upper
l3 <- p3$panel.args.common$lower


plot(x3,y3, type ="l")
lines(x3,u3)

lines(x3,l3)

NA
NA

plt <- itemplot(mod_elie2, 1, type = 'info') #store the object
plt2 <- itemplot(mod_elie2, 2, type = 'info') #store the object
plt3 <- itemplot(mod_elie2, 3, type = 'info') #store the object

print(plt) #plot the object

print(plt2) #plot the object

print(plt3) #plot the object


#str(plt) #find the data
plt$panel.args

[[1]]
[[1]]$x
  [1] -6.00000000 -5.93969849 -5.87939698 -5.81909548 -5.75879397 -5.69849246 -5.63819095 -5.57788945
  [9] -5.51758794 -5.45728643 -5.39698492 -5.33668342 -5.27638191 -5.21608040 -5.15577889 -5.09547739
 [17] -5.03517588 -4.97487437 -4.91457286 -4.85427136 -4.79396985 -4.73366834 -4.67336683 -4.61306533
 [25] -4.55276382 -4.49246231 -4.43216080 -4.37185930 -4.31155779 -4.25125628 -4.19095477 -4.13065327
 [33] -4.07035176 -4.01005025 -3.94974874 -3.88944724 -3.82914573 -3.76884422 -3.70854271 -3.64824121
 [41] -3.58793970 -3.52763819 -3.46733668 -3.40703518 -3.34673367 -3.28643216 -3.22613065 -3.16582915
 [49] -3.10552764 -3.04522613 -2.98492462 -2.92462312 -2.86432161 -2.80402010 -2.74371859 -2.68341709
 [57] -2.62311558 -2.56281407 -2.50251256 -2.44221106 -2.38190955 -2.32160804 -2.26130653 -2.20100503
 [65] -2.14070352 -2.08040201 -2.02010050 -1.95979899 -1.89949749 -1.83919598 -1.77889447 -1.71859296
 [73] -1.65829146 -1.59798995 -1.53768844 -1.47738693 -1.41708543 -1.35678392 -1.29648241 -1.23618090
 [81] -1.17587940 -1.11557789 -1.05527638 -0.99497487 -0.93467337 -0.87437186 -0.81407035 -0.75376884
 [89] -0.69346734 -0.63316583 -0.57286432 -0.51256281 -0.45226131 -0.39195980 -0.33165829 -0.27135678
 [97] -0.21105528 -0.15075377 -0.09045226 -0.03015075  0.03015075  0.09045226  0.15075377  0.21105528
[105]  0.27135678  0.33165829  0.39195980  0.45226131  0.51256281  0.57286432  0.63316583  0.69346734
[113]  0.75376884  0.81407035  0.87437186  0.93467337  0.99497487  1.05527638  1.11557789  1.17587940
[121]  1.23618090  1.29648241  1.35678392  1.41708543  1.47738693  1.53768844  1.59798995  1.65829146
[129]  1.71859296  1.77889447  1.83919598  1.89949749  1.95979899  2.02010050  2.08040201  2.14070352
[137]  2.20100503  2.26130653  2.32160804  2.38190955  2.44221106  2.50251256  2.56281407  2.62311558
[145]  2.68341709  2.74371859  2.80402010  2.86432161  2.92462312  2.98492462  3.04522613  3.10552764
[153]  3.16582915  3.22613065  3.28643216  3.34673367  3.40703518  3.46733668  3.52763819  3.58793970
[161]  3.64824121  3.70854271  3.76884422  3.82914573  3.88944724  3.94974874  4.01005025  4.07035176
[169]  4.13065327  4.19095477  4.25125628  4.31155779  4.37185930  4.43216080  4.49246231  4.55276382
[177]  4.61306533  4.67336683  4.73366834  4.79396985  4.85427136  4.91457286  4.97487437  5.03517588
[185]  5.09547739  5.15577889  5.21608040  5.27638191  5.33668342  5.39698492  5.45728643  5.51758794
[193]  5.57788945  5.63819095  5.69849246  5.75879397  5.81909548  5.87939698  5.93969849  6.00000000

[[1]]$y
  [1] 0.11303179 0.12116970 0.12977974 0.13887130 0.14845079 0.15852118 0.16908134 0.18012547
  [9] 0.19164242 0.20361510 0.21601982 0.22882570 0.24199419 0.25547858 0.26922374 0.28316591
 [17] 0.29723279 0.31134372 0.32541027 0.33933698 0.35302244 0.36636069 0.37924291 0.39155935
 [25] 0.40320159 0.41406490 0.42405077 0.43306951 0.44104277 0.44790591 0.45361015 0.45812440
 [33] 0.46143663 0.46355477 0.46450706 0.46434187 0.46312693 0.46094799 0.45790706 0.45412020
 [41] 0.44971491 0.44482740 0.43959958 0.43417610 0.42870135 0.42331660 0.41815736 0.41335089
 [49] 0.40901402 0.40525126 0.40215323 0.39979532 0.39823675 0.39751987 0.39766974 0.39869407
 [57] 0.40058332 0.40331119 0.40683533 0.41109834 0.41602908 0.42154423 0.42755014 0.43394496
 [65] 0.44062103 0.44746739 0.45437260 0.46122746 0.46792792 0.47437777 0.48049122 0.48619515
 [73] 0.49143096 0.49615599 0.50034427 0.50398670 0.50709059 0.50967849 0.51178644 0.51346164
 [81] 0.51475972 0.51574165 0.51647047 0.51700800 0.51741177 0.51773212 0.51800982 0.51827426
 [89] 0.51854224 0.51881749 0.51909092 0.51934161 0.51953841 0.51964215 0.51960830 0.51939001
 [97] 0.51894116 0.51821959 0.51719007 0.51582689 0.51411617 0.51205737 0.50966437 0.50696570
[105] 0.50400414 0.50083562 0.49752750 0.49415639 0.49080541 0.48756134 0.48451145 0.48174039
[113] 0.47932717 0.47734233 0.47584546 0.47488315 0.47448735 0.47467434 0.47544423 0.47678104
[121] 0.47865337 0.48101559 0.48380954 0.48696668 0.49041060 0.49405977 0.49783042 0.50163953
[129] 0.50540758 0.50906118 0.51253525 0.51577480 0.51873611 0.52138723 0.52370794 0.52568893
[137] 0.52733045 0.52864043 0.52963210 0.53032146 0.53072455 0.53085476 0.53072044 0.53032283
[145] 0.52965445 0.52869818 0.52742697 0.52580416 0.52378459 0.52131621 0.51834226 0.51480373
[153] 0.51064217 0.50580249 0.50023569 0.49390137 0.48676984 0.47882384 0.47005964 0.46048762
[161] 0.45013226 0.43903162 0.42723628 0.41480792 0.40181758 0.38834365 0.37446988 0.36028322
[169] 0.34587180 0.33132311 0.31672221 0.30215035 0.28768373 0.27339256 0.25934040 0.24558366
[177] 0.23217145 0.21914556 0.20654053 0.19438404 0.18269721 0.17149514 0.16078736 0.15057845
[185] 0.14086856 0.13165398 0.12292767 0.11467978 0.10689817 0.09956878 0.09267609 0.08620345
[193] 0.08013343 0.07444807 0.06912915 0.06415836 0.05951754 0.05518876 0.05115448 0.04739761

pltdata <- data.frame(lapply(plt$panel.args, function(x) do.call(cbind, x))[[1]])
pltdata$item <- rep("1", each = 50)

pltdata2 <- data.frame(lapply(plt2$panel.args, function(x) do.call(cbind, x))[[1]])
pltdata2$item <- rep("2", each = 50)


pltdata3 <- data.frame(lapply(plt3$panel.args, function(x) do.call(cbind, x))[[1]])
pltdata3$item <- rep("3", each = 50)

ggplot(pltdata, aes(x, y, colour=item)) + geom_line() + ggtitle('ggplot2 Tracelines') +
    xlab(expression(theta)) + ylab(expression(P(theta)))

ggplot(pltdata2, aes(x, y, colour=item)) + geom_line() + ggtitle('ggplot2 Tracelines') +
    xlab(expression(theta)) + ylab(expression(P(theta)))

ggplot(pltdata3, aes(x, y, colour=item)) + geom_line() + ggtitle('ggplot2 Tracelines') +
    xlab(expression(theta)) + ylab(expression(P(theta)))

ggplot(plot_combinado, aes(x, y, colour=item)) + geom_line(aes(linetype = item), size = 1) + ggtitle('') +
  xlab(expression(theta)) + ylab("item information") +
  theme_bw()

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

IRT Elie Landeira

Load packages

Import ds

Clean ds

Remove spss attributes

DS

DS for all Items

Check items’ distribution

Check items’ distribution

EFA models

Euthimic

Maniac (3)

Depression (3)

IRT models

Item info

Curva com 3 itens

Plots