Intelligence and general psychopathology in Vietnam Experience Study (VES): a closer look
Init
options(digits = 3)
library(pacman)
p_load(kirkegaard, rms, ggeffects, haven, readxl, mirt, future, doFuture, tidymodels, osfr, glmnet)
theme_set(theme_bw())
#parallel
registerDoFuture()
plan(multisession)
#redefine describe
describe = function(...) {
y = psych::describe(...)
class(y) = "data.frame"
y
}Data
#Main file
d = read_rds("data/VES_dataset.rds")
#Nerve file
ncv = read_sav("data/nerve conduction_workfile_3.sav")
#child file
children = read_excel("data/Emil_DYSCHILD.xlsx")
children_labels = names(children) %>% str_match("\\((.+)\\)") %>% .[, 2] %>% str_replace("\\[ ", "")
names(children) = names(children) %>% str_replace("\\(.+\\)", "") %>% str_trim()
for (i in seq_along(children)) label(children[[i]]) = children_labels[i]
children_vars = df_var_table(children)
#assume religion is constant, then subset
children2 = children %>% filter(!duplicated(AOP_ID)) %>% select(AOP_ID, MOTHEDUC)
#variable list
vars_d = read_csv("data/VES_dataset_variables.csv")## Parsed with column specification:
## cols(
## number = col_double(),
## code = col_character(),
## name = col_character()
## )vars_ncv = df_var_table(ncv)
#join
d$AOP_ID %<>% as.character()
ncv$AOP_ID %<>% as.character()
children2$AOP_ID %<>% as.character()
d = full_join(d, ncv %>% select(AOP_ID, !!(ncv %>% names() %>% setdiff(names(d)))), by = "AOP_ID") %>%
left_join(children2, by = "AOP_ID")
#MMPI items
MMPI_items_questions = read_excel("MMPI-1 items.xlsx")[-3]## New names:
## * `` -> ...3Recode
#modify variables
d %<>% mutate(
#rename tests for clarity
VE_time1 = VE,
AR_time1 = AR,
VE_time2 = VESS,
AR_time2 = ARSS
)
#MMPI items
mmpi_items = d %>% select(MM010001:MM010566) %>% names()
mmpi_scales = d %>% select(MM010568:MM010581) %>% names()
#recode NA
for (i in mmpi_items) d[[i]] = d[[i]] %>% mapvalues(from = 0:2, to = c(NA, 1, 0), warn_missing = F)
#rename scales
for (v in mmpi_scales) {
i_name = vars_d %>% filter(code == v) %>% pull(name) %>% str_match("(MMPI_[A-Z]+)_") %>% .[, 2]
d[[i_name]] = d[[v]]
}
mmpi_scales = d %>% select(MMPI_L:MMPI_ES) %>% names()Basics
#sample sizes
d %>% nrow()## [1] 4462d$race %>% table2()#age
d %>%
select(age) %>%
describe()g factor
#g tests
g_tests_early = c("VE_time1", "AR_time1", "PA", "GIT", "AFQT")
g_tests_later = c("VE_time2", "AR_time2", "WAIS_BD", "WAIS_GI", "WRAT", "PASAT", "WLGT", "copy_direct", "copy_immediate", "copy_delayed", "CVLT", "WCST", "GPT_left", "GPT_right")
g_tests = c(g_tests_early, g_tests_later)
#test correlations
wtd.cors(d[g_tests])## VE_time1 AR_time1 PA GIT AFQT VE_time2 AR_time2 WAIS_BD
## VE_time1 1.000 0.699 0.516 0.659 0.714 0.824 0.642 0.437
## AR_time1 0.699 1.000 0.576 0.589 0.737 0.658 0.785 0.502
## PA 0.516 0.576 1.000 0.467 0.728 0.484 0.545 0.634
## GIT 0.659 0.589 0.467 1.000 0.645 0.620 0.548 0.418
## AFQT 0.714 0.737 0.728 0.645 1.000 0.670 0.688 0.629
## VE_time2 0.824 0.658 0.484 0.620 0.670 1.000 0.691 0.453
## AR_time2 0.642 0.785 0.545 0.548 0.688 0.691 1.000 0.532
## WAIS_BD 0.437 0.502 0.634 0.418 0.629 0.453 0.532 1.000
## WAIS_GI 0.725 0.635 0.482 0.582 0.626 0.719 0.622 0.453
## WRAT 0.746 0.589 0.412 0.517 0.578 0.766 0.585 0.382
## PASAT 0.408 0.521 0.371 0.365 0.432 0.440 0.562 0.388
## WLGT 0.443 0.370 0.289 0.310 0.360 0.463 0.365 0.281
## copy_direct 0.290 0.333 0.380 0.254 0.372 0.325 0.384 0.398
## copy_immediate 0.303 0.335 0.456 0.311 0.452 0.316 0.387 0.490
## copy_delayed 0.301 0.337 0.452 0.308 0.451 0.315 0.385 0.492
## CVLT 0.317 0.331 0.264 0.250 0.312 0.333 0.356 0.269
## WCST 0.327 0.360 0.331 0.282 0.368 0.361 0.396 0.356
## GPT_left 0.208 0.212 0.263 0.192 0.269 0.226 0.236 0.307
## GPT_right 0.204 0.201 0.261 0.174 0.253 0.220 0.240 0.301
## WAIS_GI WRAT PASAT WLGT copy_direct copy_immediate
## VE_time1 0.725 0.746 0.408 0.443 0.290 0.303
## AR_time1 0.635 0.589 0.521 0.370 0.333 0.335
## PA 0.482 0.412 0.371 0.289 0.380 0.456
## GIT 0.582 0.517 0.365 0.310 0.254 0.311
## AFQT 0.626 0.578 0.432 0.360 0.372 0.452
## VE_time2 0.719 0.766 0.440 0.463 0.325 0.316
## AR_time2 0.622 0.585 0.562 0.365 0.384 0.387
## WAIS_BD 0.453 0.382 0.388 0.281 0.398 0.490
## WAIS_GI 1.000 0.652 0.366 0.414 0.278 0.343
## WRAT 0.652 1.000 0.417 0.504 0.269 0.269
## PASAT 0.366 0.417 1.000 0.357 0.247 0.282
## WLGT 0.414 0.504 0.357 1.000 0.176 0.213
## copy_direct 0.278 0.269 0.247 0.176 1.000 0.474
## copy_immediate 0.343 0.269 0.282 0.213 0.474 1.000
## copy_delayed 0.345 0.271 0.281 0.216 0.482 0.915
## CVLT 0.329 0.309 0.289 0.278 0.205 0.318
## WCST 0.330 0.292 0.285 0.209 0.291 0.268
## GPT_left 0.186 0.204 0.216 0.157 0.223 0.227
## GPT_right 0.173 0.196 0.226 0.168 0.223 0.194
## copy_delayed CVLT WCST GPT_left GPT_right
## VE_time1 0.301 0.317 0.327 0.208 0.204
## AR_time1 0.337 0.331 0.360 0.212 0.201
## PA 0.452 0.264 0.331 0.263 0.261
## GIT 0.308 0.250 0.282 0.192 0.174
## AFQT 0.451 0.312 0.368 0.269 0.253
## VE_time2 0.315 0.333 0.361 0.226 0.220
## AR_time2 0.385 0.356 0.396 0.236 0.240
## WAIS_BD 0.492 0.269 0.356 0.307 0.301
## WAIS_GI 0.345 0.329 0.330 0.186 0.173
## WRAT 0.271 0.309 0.292 0.204 0.196
## PASAT 0.281 0.289 0.285 0.216 0.226
## WLGT 0.216 0.278 0.209 0.157 0.168
## copy_direct 0.482 0.205 0.291 0.223 0.223
## copy_immediate 0.915 0.318 0.268 0.227 0.194
## copy_delayed 1.000 0.327 0.268 0.227 0.199
## CVLT 0.327 1.000 0.192 0.115 0.117
## WCST 0.268 0.192 1.000 0.196 0.186
## GPT_left 0.227 0.115 0.196 1.000 0.634
## GPT_right 0.199 0.117 0.186 0.634 1.000#all tests g
fa_g = fa(d[g_tests])
fa_g## Factor Analysis using method = minres
## Call: fa(r = d[g_tests])
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## VE_time1 0.82 0.67 0.33 1
## AR_time1 0.81 0.66 0.34 1
## PA 0.70 0.50 0.50 1
## GIT 0.69 0.47 0.53 1
## AFQT 0.85 0.73 0.27 1
## VE_time2 0.82 0.67 0.33 1
## AR_time2 0.82 0.67 0.33 1
## WAIS_BD 0.67 0.45 0.55 1
## WAIS_GI 0.76 0.58 0.42 1
## WRAT 0.73 0.53 0.47 1
## PASAT 0.57 0.32 0.68 1
## WLGT 0.49 0.24 0.76 1
## copy_direct 0.47 0.22 0.78 1
## copy_immediate 0.55 0.30 0.70 1
## copy_delayed 0.55 0.30 0.70 1
## CVLT 0.42 0.18 0.82 1
## WCST 0.46 0.21 0.79 1
## GPT_left 0.34 0.12 0.88 1
## GPT_right 0.33 0.11 0.89 1
##
## MR1
## SS loadings 7.93
## Proportion Var 0.42
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 171 and the objective function was 12.5 with Chi Square of 55829
## The degrees of freedom for the model are 152 and the objective function was 3.86
##
## The root mean square of the residuals (RMSR) is 0.09
## The df corrected root mean square of the residuals is 0.1
##
## The harmonic number of observations is 4426 with the empirical chi square 12980 with prob < 0
## The total number of observations was 4462 with Likelihood Chi Square = 17178 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.656
## RMSEA index = 0.158 and the 90 % confidence intervals are 0.156 0.16
## BIC = 15901
## Fit based upon off diagonal values = 0.95
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.97
## Multiple R square of scores with factors 0.95
## Minimum correlation of possible factor scores 0.89fa_g$loadings[, 1] %>% describe() %>% as.matrix()## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 19 0.624 0.172 0.673 0.628 0.212 0.331 0.853 0.522 -0.233 -1.44
## se
## X1 0.0394#earlier tests only
fa_g_time1 = fa(d[g_tests_early])
fa_g_time1## Factor Analysis using method = minres
## Call: fa(r = d[g_tests_early])
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## VE_time1 0.82 0.67 0.33 1
## AR_time1 0.82 0.68 0.32 1
## PA 0.70 0.49 0.51 1
## GIT 0.73 0.53 0.47 1
## AFQT 0.92 0.84 0.16 1
##
## MR1
## SS loadings 3.20
## Proportion Var 0.64
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 10 and the objective function was 3.1 with Chi Square of 13826
## The degrees of freedom for the model are 5 and the objective function was 0.15
##
## The root mean square of the residuals (RMSR) is 0.04
## The df corrected root mean square of the residuals is 0.06
##
## The harmonic number of observations is 4377 with the empirical chi square 169 with prob < 9.5e-35
## The total number of observations was 4462 with Likelihood Chi Square = 690 with prob < 8.8e-147
##
## Tucker Lewis Index of factoring reliability = 0.901
## RMSEA index = 0.175 and the 90 % confidence intervals are 0.164 0.186
## BIC = 648
## Fit based upon off diagonal values = 1
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.96
## Multiple R square of scores with factors 0.92
## Minimum correlation of possible factor scores 0.85fa_g_time1$loadings[, 1] %>% describe() %>% as.matrix()## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 5 0.797 0.0867 0.816 0.797 0.135 0.701 0.918 0.217 0.181 -1.84
## se
## X1 0.0388#later tests only
fa_g_time2 = fa(d[g_tests_later])
fa_g_time2## Factor Analysis using method = minres
## Call: fa(r = d[g_tests_later])
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## VE_time2 0.78 0.61 0.39 1
## AR_time2 0.79 0.62 0.38 1
## WAIS_BD 0.67 0.45 0.55 1
## WAIS_GI 0.72 0.52 0.48 1
## WRAT 0.71 0.50 0.50 1
## PASAT 0.58 0.33 0.67 1
## WLGT 0.50 0.25 0.75 1
## copy_direct 0.51 0.26 0.74 1
## copy_immediate 0.61 0.37 0.63 1
## copy_delayed 0.61 0.37 0.63 1
## CVLT 0.45 0.20 0.80 1
## WCST 0.47 0.22 0.78 1
## GPT_left 0.37 0.14 0.86 1
## GPT_right 0.36 0.13 0.87 1
##
## MR1
## SS loadings 5.00
## Proportion Var 0.36
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 91 and the objective function was 7.16 with Chi Square of 31892
## The degrees of freedom for the model are 77 and the objective function was 2.8
##
## The root mean square of the residuals (RMSR) is 0.11
## The df corrected root mean square of the residuals is 0.12
##
## The harmonic number of observations is 4457 with the empirical chi square 9373 with prob < 0
## The total number of observations was 4462 with Likelihood Chi Square = 12463 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.54
## RMSEA index = 0.19 and the 90 % confidence intervals are 0.187 0.193
## BIC = 11816
## Fit based upon off diagonal values = 0.92
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.95
## Multiple R square of scores with factors 0.90
## Minimum correlation of possible factor scores 0.80fa_g_time2$loadings[, 1] %>% describe() %>% as.matrix()## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 14 0.582 0.141 0.592 0.583 0.173 0.364 0.791 0.426 -0.0247 -1.42
## se
## X1 0.0378#save scores, standardize to white
d$g = fa_g$scores[, 1] %>% standardize(focal_group = d$race == "White")
d$g_time1 = fa_g_time1$scores[, 1] %>% standardize(focal_group = d$race == "White")
d$g_time2 = fa_g_time2$scores[, 1] %>% standardize(focal_group = d$race == "White")
#alpha
d[g_tests] %>% psych::alpha()##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.85 0.92 0.94 0.39 12 0.0023 42 9.8 0.34
##
## lower alpha upper 95% confidence boundaries
## 0.85 0.85 0.86
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## VE_time1 0.83 0.92 0.94 0.38 11 0.0027 0.026 0.34
## AR_time1 0.83 0.92 0.94 0.38 11 0.0027 0.027 0.33
## PA 0.84 0.92 0.94 0.38 11 0.0025 0.029 0.33
## GIT 0.84 0.92 0.94 0.39 11 0.0025 0.029 0.34
## AFQT 0.85 0.92 0.94 0.38 11 0.0023 0.026 0.33
## VE_time2 0.83 0.92 0.94 0.38 11 0.0027 0.026 0.33
## AR_time2 0.83 0.92 0.94 0.38 11 0.0028 0.027 0.33
## WAIS_BD 0.85 0.92 0.94 0.39 11 0.0023 0.030 0.33
## WAIS_GI 0.85 0.92 0.94 0.38 11 0.0024 0.028 0.33
## WRAT 0.84 0.92 0.94 0.38 11 0.0025 0.028 0.34
## PASAT 0.88 0.92 0.94 0.39 12 0.0017 0.030 0.34
## WLGT 0.85 0.92 0.94 0.40 12 0.0024 0.030 0.36
## copy_direct 0.85 0.92 0.94 0.40 12 0.0023 0.030 0.36
## copy_immediate 0.85 0.92 0.94 0.39 12 0.0024 0.029 0.36
## copy_delayed 0.85 0.92 0.94 0.39 12 0.0024 0.029 0.36
## CVLT 0.85 0.92 0.95 0.40 12 0.0023 0.029 0.37
## WCST 0.86 0.92 0.95 0.40 12 0.0023 0.030 0.36
## GPT_left 0.85 0.93 0.94 0.41 12 0.0023 0.027 0.37
## GPT_right 0.85 0.93 0.94 0.41 12 0.0023 0.027 0.37
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## VE_time1 4384 0.81 0.79 0.80 0.76 107.16 22.26
## AR_time1 4385 0.82 0.79 0.79 0.78 104.43 22.01
## PA 4386 0.69 0.72 0.71 0.62 104.32 22.64
## GIT 4376 0.70 0.69 0.67 0.64 102.06 18.43
## AFQT 4441 0.79 0.83 0.84 0.80 0.14 0.81
## VE_time2 4462 0.80 0.80 0.80 0.77 116.52 23.04
## AR_time2 4462 0.82 0.81 0.81 0.79 104.56 24.40
## WAIS_BD 4462 0.62 0.71 0.69 0.62 10.52 2.64
## WAIS_GI 4462 0.71 0.75 0.74 0.72 10.07 2.80
## WRAT 4460 0.72 0.73 0.72 0.71 61.17 14.73
## PASAT 4450 0.72 0.60 0.57 0.56 108.84 50.72
## WLGT 4462 0.51 0.53 0.49 0.49 35.12 10.92
## copy_direct 4462 0.43 0.53 0.49 0.43 32.73 3.31
## copy_immediate 4462 0.49 0.61 0.62 0.47 20.11 6.75
## copy_delayed 4462 0.49 0.61 0.62 0.47 20.27 6.33
## CVLT 4462 0.41 0.48 0.43 0.41 11.06 2.33
## WCST 4462 0.43 0.51 0.46 0.43 0.79 0.17
## GPT_left 4448 0.39 0.43 0.39 0.33 -77.38 13.77
## GPT_right 4450 0.38 0.42 0.38 0.33 -73.66 11.82#drop 1 at a time
for (i in seq_along(g_tests)) {
#drop i and FA
i_fa = fa(d[g_tests[-i]])
d[[str_glue("g_drop_{i}")]] = i_fa$scores[, 1] %>% as.vector()
}
#correlations
map2_dbl(g_tests, seq_along(g_tests), function(v, i) {
wtd.cors(d[[v]], d[[str_glue("g_drop_{i}")]])
}) %>% set_names(g_tests) %>% sort()## GPT_right GPT_left CVLT WCST copy_direct
## 0.308 0.316 0.406 0.437 0.452
## WLGT copy_delayed copy_immediate PASAT WAIS_BD
## 0.475 0.505 0.507 0.545 0.643
## GIT PA WRAT WAIS_GI AR_time2
## 0.677 0.684 0.710 0.741 0.792
## AR_time1 VE_time2 VE_time1 AFQT
## 0.793 0.795 0.795 0.828MMPI factor analysis
#scales
#missing data?
d[mmpi_scales] %>% miss_amount()## cases with missing data vars with missing data cells with missing data
## 0 0 0#factpr amaæuze
MMPI_scales_1p = fa(d[mmpi_scales])
MMPI_scales_1p## Factor Analysis using method = minres
## Call: fa(r = d[mmpi_scales])
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## MMPI_L -0.17 0.028 0.97 1
## MMPI_F 0.80 0.634 0.37 1
## MMPI_K -0.35 0.124 0.88 1
## MMPI_HS 0.65 0.421 0.58 1
## MMPI_D 0.74 0.555 0.45 1
## MMPI_HY 0.53 0.281 0.72 1
## MMPI_PD 0.62 0.384 0.62 1
## MMPI_MF 0.30 0.088 0.91 1
## MMPI_PA 0.69 0.478 0.52 1
## MMPI_PT 0.88 0.776 0.22 1
## MMPI_SC 0.91 0.823 0.18 1
## MMPI_MA 0.31 0.095 0.91 1
## MMPI_SI 0.46 0.207 0.79 1
## MMPI_ES -0.77 0.585 0.41 1
##
## MR1
## SS loadings 5.48
## Proportion Var 0.39
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 91 and the objective function was 9.45 with Chi Square of 42112
## The degrees of freedom for the model are 77 and the objective function was 3.76
##
## The root mean square of the residuals (RMSR) is 0.14
## The df corrected root mean square of the residuals is 0.15
##
## The harmonic number of observations is 4462 with the empirical chi square 15122 with prob < 0
## The total number of observations was 4462 with Likelihood Chi Square = 16764 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.531
## RMSEA index = 0.22 and the 90 % confidence intervals are 0.218 0.223
## BIC = 16117
## Fit based upon off diagonal values = 0.89
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 0.97
## Multiple R square of scores with factors 0.95
## Minimum correlation of possible factor scores 0.89MMPI_scales_1p$loadings[, 1] %>% describe()#save the scores
d$P_scales = MMPI_scales_1p$scores[, 1] %>% standardize(focal_group = d$race == "White")
#method variance
MMPI_scales_1p_methods = fa_all_methods(d[mmpi_scales])## 1 out of 40 - regression_minres## Saving results from regression_minres## 2 out of 40 - Thurstone_minres## Saving results from Thurstone_minres## 3 out of 40 - tenBerge_minres## Saving results from tenBerge_minres## 4 out of 40 - Anderson_minres## Skipping Anderson_minres due to extraction error## 5 out of 40 - Bartlett_minres## Saving results from Bartlett_minres## 6 out of 40 - regression_ols## Saving results from regression_ols## 7 out of 40 - Thurstone_ols## Saving results from Thurstone_ols## 8 out of 40 - tenBerge_ols## Saving results from tenBerge_ols## 9 out of 40 - Anderson_ols## Skipping Anderson_ols due to extraction error## 10 out of 40 - Bartlett_ols## Saving results from Bartlett_ols## 11 out of 40 - regression_wls## Saving results from regression_wls## 12 out of 40 - Thurstone_wls## Saving results from Thurstone_wls## 13 out of 40 - tenBerge_wls## Saving results from tenBerge_wls## 14 out of 40 - Anderson_wls## Skipping Anderson_wls due to extraction error## 15 out of 40 - Bartlett_wls## Saving results from Bartlett_wls## 16 out of 40 - regression_gls## Saving results from regression_gls## 17 out of 40 - Thurstone_gls## Saving results from Thurstone_gls## 18 out of 40 - tenBerge_gls## Saving results from tenBerge_gls## 19 out of 40 - Anderson_gls## Skipping Anderson_gls due to extraction error## 20 out of 40 - Bartlett_gls## Saving results from Bartlett_gls## 21 out of 40 - regression_pa## Saving results from regression_pa## 22 out of 40 - Thurstone_pa## Saving results from Thurstone_pa## 23 out of 40 - tenBerge_pa## Saving results from tenBerge_pa## 24 out of 40 - Anderson_pa## Skipping Anderson_pa due to extraction error## 25 out of 40 - Bartlett_pa## Saving results from Bartlett_pa## 26 out of 40 - regression_ml## Saving results from regression_ml## 27 out of 40 - Thurstone_ml## Saving results from Thurstone_ml## 28 out of 40 - tenBerge_ml## Saving results from tenBerge_ml## 29 out of 40 - Anderson_ml## Skipping Anderson_ml due to extraction error## 30 out of 40 - Bartlett_ml## Saving results from Bartlett_ml## 31 out of 40 - regression_minchi## Saving results from regression_minchi## 32 out of 40 - Thurstone_minchi## Saving results from Thurstone_minchi## 33 out of 40 - tenBerge_minchi## Saving results from tenBerge_minchi## 34 out of 40 - Anderson_minchi## Skipping Anderson_minchi due to extraction error## 35 out of 40 - Bartlett_minchi## Saving results from Bartlett_minchi## 36 out of 40 - regression_minrank## Saving results from regression_minrank## 37 out of 40 - Thurstone_minrank## Saving results from Thurstone_minrank## 38 out of 40 - tenBerge_minrank## Saving results from tenBerge_minrank## 39 out of 40 - Anderson_minrank## Skipping Anderson_minrank due to extraction error## 40 out of 40 - Bartlett_minrank## Saving results from Bartlett_minrankGG_heatmap(MMPI_scales_1p_methods$loadings)
GG_heatmap(MMPI_scales_1p_methods$scores)
#reliability
MMPI_scales_1p_methods$scores %>% psych::alpha()## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was done## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was done##
## Reliability analysis
## Call: psych::alpha(x = .)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 1 1 1 1 10448 2.5e-06 -1.3e-17 0.99 1
##
## lower alpha upper 95% confidence boundaries
## 1 1 1
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r
## regression_minres 1 1 1 1 10013 2.6e-06 1.0e-05
## Thurstone_minres 1 1 1 1 10013 2.6e-06 1.0e-05
## tenBerge_minres 1 1 1 1 10013 2.6e-06 1.0e-05
## Bartlett_minres 1 1 1 1 9937 2.6e-06 1.0e-05
## regression_ols 1 1 1 1 10013 2.6e-06 1.0e-05
## Thurstone_ols 1 1 1 1 10013 2.6e-06 1.0e-05
## tenBerge_ols 1 1 1 1 10013 2.6e-06 1.0e-05
## Bartlett_ols 1 1 1 1 9937 2.6e-06 1.0e-05
## regression_wls 1 1 1 1 9949 2.6e-06 1.0e-05
## Thurstone_wls 1 1 1 1 9949 2.6e-06 1.0e-05
## tenBerge_wls 1 1 1 1 9949 2.6e-06 1.0e-05
## Bartlett_wls 1 1 1 1 9953 2.6e-06 1.0e-05
## regression_gls 1 1 1 1 9984 2.6e-06 1.0e-05
## Thurstone_gls 1 1 1 1 9984 2.6e-06 1.0e-05
## tenBerge_gls 1 1 1 1 9984 2.6e-06 1.0e-05
## Bartlett_gls 1 1 1 1 9955 2.6e-06 1.0e-05
## regression_pa 1 1 1 1 10013 2.6e-06 1.0e-05
## Thurstone_pa 1 1 1 1 10013 2.6e-06 1.0e-05
## tenBerge_pa 1 1 1 1 10013 2.6e-06 1.0e-05
## Bartlett_pa 1 1 1 1 9937 2.6e-06 1.0e-05
## regression_ml 1 1 1 1 10061 2.6e-06 1.0e-05
## Thurstone_ml 1 1 1 1 10061 2.6e-06 1.0e-05
## tenBerge_ml 1 1 1 1 10061 2.6e-06 1.0e-05
## Bartlett_ml 1 1 1 1 10061 2.6e-06 1.0e-05
## regression_minchi 1 1 1 1 10020 2.6e-06 1.0e-05
## Thurstone_minchi 1 1 1 1 10020 2.6e-06 1.0e-05
## tenBerge_minchi 1 1 1 1 10020 2.6e-06 1.0e-05
## Bartlett_minchi 1 1 1 1 10035 2.6e-06 1.0e-05
## regression_minrank 1 1 1 1 11508 2.3e-06 8.3e-06
## Thurstone_minrank 1 1 1 1 11508 2.3e-06 8.3e-06
## tenBerge_minrank 1 1 1 1 11508 2.3e-06 8.3e-06
## Bartlett_minrank 1 1 1 1 9948 2.6e-06 1.0e-05
## med.r
## regression_minres 1
## Thurstone_minres 1
## tenBerge_minres 1
## Bartlett_minres 1
## regression_ols 1
## Thurstone_ols 1
## tenBerge_ols 1
## Bartlett_ols 1
## regression_wls 1
## Thurstone_wls 1
## tenBerge_wls 1
## Bartlett_wls 1
## regression_gls 1
## Thurstone_gls 1
## tenBerge_gls 1
## Bartlett_gls 1
## regression_pa 1
## Thurstone_pa 1
## tenBerge_pa 1
## Bartlett_pa 1
## regression_ml 1
## Thurstone_ml 1
## tenBerge_ml 1
## Bartlett_ml 1
## regression_minchi 1
## Thurstone_minchi 1
## tenBerge_minchi 1
## Bartlett_minchi 1
## regression_minrank 1
## Thurstone_minrank 1
## tenBerge_minrank 1
## Bartlett_minrank 1
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## regression_minres 4462 1.00 1.00 1.00 1.00 -2.8e-17 0.97
## Thurstone_minres 4462 1.00 1.00 1.00 1.00 -2.8e-17 0.97
## tenBerge_minres 4462 1.00 1.00 1.00 1.00 -1.5e-17 0.97
## Bartlett_minres 4462 1.00 1.00 1.00 1.00 3.0e-18 1.03
## regression_ols 4462 1.00 1.00 1.00 1.00 -2.2e-17 0.97
## Thurstone_ols 4462 1.00 1.00 1.00 1.00 -2.2e-17 0.97
## tenBerge_ols 4462 1.00 1.00 1.00 1.00 -3.2e-17 0.97
## Bartlett_ols 4462 1.00 1.00 1.00 1.00 5.1e-18 1.03
## regression_wls 4462 1.00 1.00 1.00 1.00 -2.9e-17 0.97
## Thurstone_wls 4462 1.00 1.00 1.00 1.00 -2.9e-17 0.97
## tenBerge_wls 4462 1.00 1.00 1.00 1.00 -2.1e-17 0.97
## Bartlett_wls 4462 1.00 1.00 1.00 1.00 1.3e-17 1.03
## regression_gls 4462 1.00 1.00 1.00 1.00 -2.3e-17 0.97
## Thurstone_gls 4462 1.00 1.00 1.00 1.00 -2.3e-17 0.97
## tenBerge_gls 4462 1.00 1.00 1.00 1.00 -1.9e-17 0.97
## Bartlett_gls 4462 1.00 1.00 1.00 1.00 -3.4e-18 1.03
## regression_pa 4462 1.00 1.00 1.00 1.00 -7.9e-18 0.97
## Thurstone_pa 4462 1.00 1.00 1.00 1.00 -7.9e-18 0.97
## tenBerge_pa 4462 1.00 1.00 1.00 1.00 -2.4e-17 0.97
## Bartlett_pa 4462 1.00 1.00 1.00 1.00 5.3e-18 1.03
## regression_ml 4462 1.00 1.00 1.00 1.00 -1.4e-17 0.97
## Thurstone_ml 4462 1.00 1.00 1.00 1.00 -1.4e-17 0.97
## tenBerge_ml 4462 1.00 1.00 1.00 1.00 -1.1e-17 0.97
## Bartlett_ml 4462 1.00 1.00 1.00 1.00 -1.9e-17 1.03
## regression_minchi 4462 1.00 1.00 1.00 1.00 -1.2e-17 0.97
## Thurstone_minchi 4462 1.00 1.00 1.00 1.00 -1.2e-17 0.97
## tenBerge_minchi 4462 1.00 1.00 1.00 1.00 -6.6e-18 0.97
## Bartlett_minchi 4462 1.00 1.00 1.00 1.00 -1.5e-17 1.03
## regression_minrank 4462 0.99 0.99 0.99 0.99 -2.1e-17 0.99
## Thurstone_minrank 4462 0.99 0.99 0.99 0.99 -2.1e-17 0.99
## tenBerge_minrank 4462 0.99 0.99 0.99 0.99 -1.9e-17 0.99
## Bartlett_minrank 4462 1.00 1.00 1.00 1.00 6.7e-18 1.02#items
MMPI_item_data = d %>% select(!!mmpi_items)
#missing data
MMPI_item_data %>% miss_amount()## cases with missing data vars with missing data cells with missing data
## 0.28000 0.97000 0.00221#score with missing data
d$P_mean_missing = MMPI_item_data %>% rowMeans(na.rm = T) %>% standardize(focal_group = d$race == "White")
#impute 0's
MMPI_item_data[is.na(MMPI_item_data)] = 0
#sum scores
d$P_sum = MMPI_item_data %>% rowSums(na.rm = T) %>% standardize(focal_group = d$race == "White")
#not much diference?
wtd.cors(d$P_mean_missing, d$P_sum)## [,1]
## [1,] 0.999#IRT
#simple P factor model
MMPI_1p = mirt(d %>% select(mmpi_items), model = 1)## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(mmpi_items)` instead of `mmpi_items` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.##
Iteration: 1, Log-Lik: -1239903.432, Max-Change: 5.93667
Iteration: 2, Log-Lik: -1170324.093, Max-Change: 3.78581
Iteration: 3, Log-Lik: -1160186.248, Max-Change: 0.77909
Iteration: 4, Log-Lik: -1158315.083, Max-Change: 0.36661
Iteration: 5, Log-Lik: -1158117.394, Max-Change: 0.13068
Iteration: 6, Log-Lik: -1158069.305, Max-Change: 0.04140
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Iteration: 10, Log-Lik: -1158015.937, Max-Change: 0.02134
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Iteration: 12, Log-Lik: -1157995.477, Max-Change: 0.02059
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Iteration: 267, Log-Lik: -1157824.691, Max-Change: 0.00010MMPI_1p@Fit$`F`## F1
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## MM010002 -0.41032
## MM010003 -0.43878
## MM010004 0.03625
## MM010005 0.22528
## MM010006 0.15826
## MM010007 -0.34060
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## MM010032 0.68170
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## MM010124 0.53313
## MM010125 0.55993
## MM010126 0.00738
## MM010127 0.11294
## MM010128 -0.06708
## MM010129 0.63551
## MM010130 -0.31939
## MM010131 -0.26796
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## MM010133 -0.20332
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## MM010135 0.36414
## MM010136 0.65416
## MM010137 -0.57140
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## MM010139 0.76949
## MM010140 0.01595
## MM010141 0.34118
## MM010142 0.69360
## MM010143 0.15002
## MM010144 0.29804
## MM010145 0.58911
## MM010146 0.61388
## MM010147 0.63661
## MM010148 0.46462
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## MM010152 -0.52678
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## MM010157 0.77428
## MM010158 0.48905
## MM010159 0.60485
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## MM010162 0.33661
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## MM010164 -0.27763
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## MM010166 0.24025
## MM010167 0.00679
## MM010168 0.79573
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## MM010172 0.48607
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## MM010180 0.44881
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## MM010562 0.19478
## MM010563 -0.14282
## MM010564 0.41294
## MM010565 0.49914
## MM010566 0.01455#score
MMPI_1p_scores = fscores(MMPI_1p)
d$P = MMPI_1p_scores[, 1] %>% standardize(focal_group = d$race == "White")
d$P_IRT = d$P
#do they correlate well?
(cors = d %>% select(g, P_IRT, P_sum, P_scales) %>% wtd.cors())## g P_IRT P_sum P_scales
## g 1.000 -0.353 -0.306 -0.246
## P_IRT -0.353 1.000 0.823 0.705
## P_sum -0.306 0.823 1.000 0.518
## P_scales -0.246 0.705 0.518 1.000d %>% select(g, P_IRT, P_sum, P_scales) %>% GGally::ggpairs(mapping = aes(alpha = .1))## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2## Warning: Removed 142 rows containing non-finite values (stat_density).## Warning in (function (data, mapping, alignPercent = 0.6, method = "pearson", :
## Removed 142 rows containing missing values## Warning in (function (data, mapping, alignPercent = 0.6, method = "pearson", :
## Removed 142 rows containing missing values## Warning in (function (data, mapping, alignPercent = 0.6, method = "pearson", :
## Removed 142 rows containing missing values## Warning: Removed 142 rows containing missing values (geom_point).
## Warning: Removed 142 rows containing missing values (geom_point).
## Warning: Removed 142 rows containing missing values (geom_point).
GG_save("figs/correlation_pairs.png")## Warning: Removed 142 rows containing non-finite values (stat_density).## Warning in (function (data, mapping, alignPercent = 0.6, method = "pearson", :
## Removed 142 rows containing missing values## Warning in (function (data, mapping, alignPercent = 0.6, method = "pearson", :
## Removed 142 rows containing missing values## Warning in (function (data, mapping, alignPercent = 0.6, method = "pearson", :
## Removed 142 rows containing missing values## Warning: Removed 142 rows containing missing values (geom_point).
## Warning: Removed 142 rows containing missing values (geom_point).
## Warning: Removed 142 rows containing missing values (geom_point).Race gaps
#descriptives
(descs = d %>% select(g, P, P_sum, P_scales) %>% describeBy(d$race, mat = F))##
## Descriptive statistics by group
## group: White
## vars n mean sd median trimmed mad min max range skew kurtosis
## g 1 3555 0 1 0.05 0.03 1.05 -3.17 2.37 5.54 -0.28 -0.43
## P 2 3654 0 1 0.00 0.01 0.99 -3.59 3.53 7.12 -0.07 0.02
## P_sum 3 3654 0 1 -0.13 -0.07 0.95 -2.83 6.66 9.49 0.79 1.33
## P_scales 4 3654 0 1 -0.27 -0.14 0.70 -1.82 5.05 6.87 1.54 2.93
## se
## g 0.02
## P 0.02
## P_sum 0.02
## P_scales 0.02
## ------------------------------------------------------------
## group: Black
## vars n mean sd median trimmed mad min max range skew
## g 1 502 -1.27 0.86 -1.32 -1.30 0.83 -3.78 1.67 5.45 0.39
## P 2 525 0.37 0.91 0.36 0.38 0.90 -2.13 2.79 4.92 -0.09
## P_sum 3 525 0.55 1.19 0.48 0.52 1.14 -4.38 4.11 8.49 0.12
## P_scales 4 525 0.24 1.14 -0.04 0.09 0.88 -1.61 4.68 6.29 1.33
## kurtosis se
## g 0.07 0.04
## P -0.09 0.04
## P_sum 0.33 0.05
## P_scales 1.68 0.05
## ------------------------------------------------------------
## group: Hispanic
## vars n mean sd median trimmed mad min max range skew kurtosis
## g 1 181 -0.78 0.89 -0.81 -0.79 0.93 -2.84 1.47 4.30 0.16 -0.50
## P 2 200 0.37 1.06 0.29 0.35 1.12 -2.30 3.55 5.84 0.18 -0.41
## P_sum 3 200 0.37 1.33 0.15 0.30 1.22 -4.20 4.73 8.92 0.42 0.58
## P_scales 4 200 0.46 1.34 0.09 0.29 1.06 -1.67 4.91 6.58 1.10 0.70
## se
## g 0.07
## P 0.07
## P_sum 0.09
## P_scales 0.09
## ------------------------------------------------------------
## group: Asian
## vars n mean sd median trimmed mad min max range skew kurtosis
## g 1 34 -0.20 1.16 -0.12 -0.13 1.24 -2.74 1.79 4.53 -0.42 -0.75
## P 2 34 0.35 1.15 0.32 0.37 1.14 -2.25 2.72 4.97 -0.10 -0.52
## P_sum 3 34 0.69 1.50 0.28 0.60 1.26 -1.86 3.78 5.63 0.53 -0.64
## P_scales 4 34 0.30 1.36 -0.19 0.12 0.98 -1.27 3.97 5.24 1.09 0.22
## se
## g 0.20
## P 0.20
## P_sum 0.26
## P_scales 0.23
## ------------------------------------------------------------
## group: Native
## vars n mean sd median trimmed mad min max range skew kurtosis
## g 1 48 -0.41 1.05 -0.49 -0.45 1.09 -2.45 2.01 4.46 0.35 -0.39
## P 2 49 0.58 1.06 0.54 0.59 0.95 -2.36 3.08 5.44 -0.16 0.14
## P_sum 3 49 0.71 1.35 0.38 0.62 1.26 -1.70 4.86 6.56 0.73 0.46
## P_scales 4 49 0.49 1.37 0.06 0.32 0.75 -1.45 4.60 6.05 1.29 0.99
## se
## g 0.15
## P 0.15
## P_sum 0.19
## P_scales 0.20#long format
descs2 = d %>% select(g, P, P_sum, P_scales) %>% describeBy(d$race, mat = T) %>%
arrange(group1) %>%
filter(group1 %in% c("White", "Black", "Hispanic")) %>%
rownames_to_column("trait")
#make a table of observed and expected gaps, white, hispanic, black
tibble(
group = descs2$group1 %>% unique(),
g = descs2 %>% filter(str_detect(trait, "^g")) %>% pull(mean),
P_scales = descs2 %>% filter(str_detect(trait, "scales")) %>% pull(mean),
P_scales_expected = g * cors[1, 4],
P_sum = descs2 %>% filter(str_detect(trait, "sum")) %>% pull(mean),
P_sum_expected = g * cors[1, 3],
P_IRT = descs2 %>% filter(str_detect(trait, "P\\d")) %>% pull(mean),
P_IRT_expected = g * cors[1, 2],
)#regression approach to same question
d_bhw = d %>% filter(race %in% c("White", "Black", "Hispanic"))
list(
scales = ols(P_scales ~ g + race, data = d_bhw),
sum = ols(P_sum ~ g + race, data = d_bhw),
IRT = ols(P_IRT ~ g + race, data = d_bhw),
scales_I = ols(P_scales ~ g * race, data = d_bhw),
sum_I = ols(P_sum ~ g * race, data = d_bhw),
IRT_I = ols(P_IRT ~ g * race, data = d_bhw)
) %>% summarize_models()Figures
#race distributions
ggplot(d, aes(P, fill = race, color = race)) +
geom_density(alpha = .2) +
ggtitle("P factor (from MMPI items)",
subtitle = "Distribution by race")
GG_save("figs/P_density.png")
#numerics
describeBy(d %>% select(g, P_scales, P_sum, P), d$race)##
## Descriptive statistics by group
## group: White
## vars n mean sd median trimmed mad min max range skew kurtosis
## g 1 3555 0 1 0.05 0.03 1.05 -3.17 2.37 5.54 -0.28 -0.43
## P_scales 2 3654 0 1 -0.27 -0.14 0.70 -1.82 5.05 6.87 1.54 2.93
## P_sum 3 3654 0 1 -0.13 -0.07 0.95 -2.83 6.66 9.49 0.79 1.33
## P 4 3654 0 1 0.00 0.01 0.99 -3.59 3.53 7.12 -0.07 0.02
## se
## g 0.02
## P_scales 0.02
## P_sum 0.02
## P 0.02
## ------------------------------------------------------------
## group: Black
## vars n mean sd median trimmed mad min max range skew
## g 1 502 -1.27 0.86 -1.32 -1.30 0.83 -3.78 1.67 5.45 0.39
## P_scales 2 525 0.24 1.14 -0.04 0.09 0.88 -1.61 4.68 6.29 1.33
## P_sum 3 525 0.55 1.19 0.48 0.52 1.14 -4.38 4.11 8.49 0.12
## P 4 525 0.37 0.91 0.36 0.38 0.90 -2.13 2.79 4.92 -0.09
## kurtosis se
## g 0.07 0.04
## P_scales 1.68 0.05
## P_sum 0.33 0.05
## P -0.09 0.04
## ------------------------------------------------------------
## group: Hispanic
## vars n mean sd median trimmed mad min max range skew kurtosis
## g 1 181 -0.78 0.89 -0.81 -0.79 0.93 -2.84 1.47 4.30 0.16 -0.50
## P_scales 2 200 0.46 1.34 0.09 0.29 1.06 -1.67 4.91 6.58 1.10 0.70
## P_sum 3 200 0.37 1.33 0.15 0.30 1.22 -4.20 4.73 8.92 0.42 0.58
## P 4 200 0.37 1.06 0.29 0.35 1.12 -2.30 3.55 5.84 0.18 -0.41
## se
## g 0.07
## P_scales 0.09
## P_sum 0.09
## P 0.07
## ------------------------------------------------------------
## group: Asian
## vars n mean sd median trimmed mad min max range skew kurtosis
## g 1 34 -0.20 1.16 -0.12 -0.13 1.24 -2.74 1.79 4.53 -0.42 -0.75
## P_scales 2 34 0.30 1.36 -0.19 0.12 0.98 -1.27 3.97 5.24 1.09 0.22
## P_sum 3 34 0.69 1.50 0.28 0.60 1.26 -1.86 3.78 5.63 0.53 -0.64
## P 4 34 0.35 1.15 0.32 0.37 1.14 -2.25 2.72 4.97 -0.10 -0.52
## se
## g 0.20
## P_scales 0.23
## P_sum 0.26
## P 0.20
## ------------------------------------------------------------
## group: Native
## vars n mean sd median trimmed mad min max range skew kurtosis
## g 1 48 -0.41 1.05 -0.49 -0.45 1.09 -2.45 2.01 4.46 0.35 -0.39
## P_scales 2 49 0.49 1.37 0.06 0.32 0.75 -1.45 4.60 6.05 1.29 0.99
## P_sum 3 49 0.71 1.35 0.38 0.62 1.26 -1.70 4.86 6.56 0.73 0.46
## P 4 49 0.58 1.06 0.54 0.59 0.95 -2.36 3.08 5.44 -0.16 0.14
## se
## g 0.15
## P_scales 0.20
## P_sum 0.19
## P 0.15#g link
#all
GG_scatter(d, "g", "P") +
geom_smooth() +
ggtitle("Relationship between g factor and P factor (from MMPI items)")## `geom_smooth()` using formula 'y ~ x'## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
GG_save("figs/g_P_all.png")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#whites
GG_scatter(d %>% filter(race == "White"), "g", "P") +
geom_smooth() +
ggtitle("Relationship between g factor and P factor (from MMPI items)",
"White subset")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
GG_save("figs/g_P_whites.png")## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#Jensen pattern?
#scales
fa_Jensens_method(MMPI_scales_1p, df = d, criterion = "g", reverse_factor = F) +
xlab("P loading") + ylab("Correlation with g")## Using Pearson correlations for the criterion-indicators relationships.## `geom_smooth()` using formula 'y ~ x'
GG_save("figs/MMPI_Jensen_scales.png")## `geom_smooth()` using formula 'y ~ x'#without reversing
fa_Jensens_method(MMPI_scales_1p, df = d, criterion = "g", reverse_factor = F, loading_reversing = F) +
xlab("P loading") + ylab("Correlation with g")## Using Pearson correlations for the criterion-indicators relationships.
## `geom_smooth()` using formula 'y ~ x'
#items
sink("/dev/null") #silence newlines
MMPI_Jensen = tibble(
item = mmpi_items,
prevalence = map_dbl(d[mmpi_items], ~wtd_mean(.)),
P_loading = MMPI_1p@Fit$`F` %>% as.vector(),
Pearson_r = map_dbl(mmpi_items, function(item) {
wtd.cors(d[c("g", item)])[1, 2]
}),
latent_r = map_dbl(mmpi_items, function(item) {
psych::mixedCor(d[c("g", item)])$rho[1, 2]
}),
IQ_gap = map_dbl(mmpi_items, function(item) {
desc = psych::describeBy(d$g, group = d[[item]], mat = T, fast = T)
(desc$mean[2] - desc$mean[1]) * 15
})
)sink()
#cors
MMPI_Jensen[-1] %>% wtd.cors()## prevalence P_loading Pearson_r latent_r IQ_gap
## prevalence 1.000 -0.766 0.558 0.653 0.698
## P_loading -0.766 1.000 -0.708 -0.749 -0.756
## Pearson_r 0.558 -0.708 1.000 0.978 0.938
## latent_r 0.653 -0.749 0.978 1.000 0.990
## IQ_gap 0.698 -0.756 0.938 0.990 1.000#reverse negative loadings
MMPI_Jensen_r = MMPI_Jensen
for (r in seq_along_rows(MMPI_Jensen_r)) {
if (MMPI_Jensen_r$P_loading[r] < 0) {
MMPI_Jensen_r$P_loading[r] = MMPI_Jensen_r$P_loading[r] * -1
MMPI_Jensen_r$Pearson_r[r] = MMPI_Jensen_r$Pearson_r[r] * -1
MMPI_Jensen_r$latent_r[r] = MMPI_Jensen_r$latent_r[r] * -1
MMPI_Jensen_r$IQ_gap[r] = MMPI_Jensen_r$IQ_gap[r] * -1
}
}
#cors
MMPI_Jensen_r[-1] %>% wtd.cors()## prevalence P_loading Pearson_r latent_r IQ_gap
## prevalence 1.000 -0.425 0.192 0.283 0.333
## P_loading -0.425 1.000 -0.442 -0.513 -0.539
## Pearson_r 0.192 -0.442 1.000 0.963 0.898
## latent_r 0.283 -0.513 0.963 1.000 0.983
## IQ_gap 0.333 -0.539 0.898 0.983 1.000#plot
GG_scatter(MMPI_Jensen_r, "P_loading", "IQ_gap") +
ylab("IQ gap")## `geom_smooth()` using formula 'y ~ x'
GG_save("figs/MMPI_Jensen.png")## `geom_smooth()` using formula 'y ~ x'#write item metadata
cbind(
MMPI_items_questions,
MMPI_Jensen %>% select(-Pearson_r, -latent_r)
) %>% write_clipboard()## Question
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## Item
## 1 I like mechanics magazines.
## 2 I have a good appetite.
## 3 I wake up fresh and rested most mornings.
## 4 I think I would like the work of a librarian.
## 5 I am easily awakened by noise.
## 6 I like to read newspaper articles on crime.
## 7 My hands and feet are usually warm enough.
## 8 My daily life is full of things that keep me interested.
## 9 I am about as able to work as I ever was.
## 10 There seems to be a lump in my throat much of the time.
## 11 A person should try to understand his dreams and be guided by or take warning from them.
## 12 I enjoy detective or mystery stories.
## 13 I work under a great deal of tension.
## 14 I have diarrhea once a month or more.
## 15 Once in a while I think of things too bad to talk about.
## 16 I am sure I get a raw deal from life.
## 17 My father was a good man.
## 18 I am very seldom troubled by constipation.
## 19 When I take a new job, I like to be tipped off on who should be gotten next to.
## 20 My sex life is satisfactory.
## 21 At times I have very much wanted to leave home.
## 22 At times I have fits of laughing and crying that I cannot control.
## 23 I am troubled by attacks of nausea and vomiting.
## 24 No one seems to understand me.
## 25 I would like to be a singer.
## 26 I feel that it is certainly best to keep my mouth shut when I'm in trouble.
## 27 Evil spirits possess me at times.
## 28 When someone does me a wrong I feel I should pay him back if I can, just for the principle of the thing.
## 29 I am bothered by acid stomach several times a week.
## 30 At times I feel like swearing.
## 31 I have nightmares every few nights.
## 32 I find it hard to keep my mind on a task or job.
## 33 I have had very peculiar and strange experiences.
## 34 I have a cough most of the time.
## 35 If people had not had it in for me I would have been much more successful.
## 36 I seldom worry about my health.
## 37 I have never been in trouble because of my sex behavior.
## 38 During one period when I was a youngster I engaged in petty thievery.
## 39 At times I feel like smashing things.
## 40 Most any time I would rather sit and daydream than do anything else.
## 41 I have had periods of days, weeks, or months when I couldn't take care of things because I couldn't “get going.”
## 42 My family does not like the work I have chosen (or the work I intend to choose for my life work).
## 43 My sleep is fitful and disturbed.
## 44 Much of the time my head seems to hurt all over.
## 45 I do not always tell the truth.
## 46 My judgment is better than it ever was.
## 47 Once a week or oftener I feel suddenly hot all over, without apparent cause.
## 48 When I am with people I am bothered by hearing very queer things.
## 49 It would be better if almost all laws were thrown away.
## 50 My soul sometimes leaves my body.
## 51 I am in just as good physical health as most of my friends.
## 52 I prefer to pass by school friends, or people I know but have not seen for a long time, unless they speak to me first.
## 53 A minister can cure disease by praying and putting his hand on your head.
## 54 I am liked by most people who know me.
## 55 I am almost never bothered by pains over the heart or in my chest.
## 56 As a youngster I was suspended from school one or more times for cutting up.
## 57 I am a good mixer.
## 58 Everything is turning out just like the prophets of the Bible said it would.
## 59 I have often had to take orders from someone who did not know as much as I did.
## 60 I do not read every editorial in the newspaper every day.
## 61 I have not lived the right kind of life.
## 62 Parts of my body often have feelings like burning, tingling, crawling, or like “going to sleep.”
## 63 I have had no difficulty in starting or holding my bowel movement.
## 64 I sometimes keep on at a thing until others lose their patience with me.
## 65 I loved my father.
## 66 I see things or animals or people around me that others do not see.
## 67 I wish I could be as happy as others seem to be.
## 68 I hardly ever feel pain in the back of the neck.
## 69 I am very strongly attracted by members of my own sex.
## 70 I used to like drop-the-handkerchief.
## 71 I think a great many people exaggerate their misfortunes in order to gain the sympathy and help of others.
## 72 I am troubled by discomfort in the pit of my stomach every few days or oftener.
## 73 I am an important person.
## 74 I have often wished I were a girl (Or if you are a girl) I have never been sorry that I am a girl.
## 75 I get angry sometimes.
## 76 Most of the time I feel blue.
## 77 I enjoy reading love stories.
## 78 I like poetry.
## 79 My feelings are not easily hurt.
## 80 I sometimes tease animals.
## 81 I think I would like the kind of work a forest ranger does.
## 82 I am easily downed in an argument.
## 83 Any man who is able and willing to work hard has a good chance of succeeding.
## 84 These days I find it hard not to give up hope of amounting to something.
## 85 Sometimes I am strongly attracted by the personal articles of others such as shoes, gloves, etc., so that I want to handle or steal them though I have no use for them.
## 86 I am certainly lacking in self-confidence.
## 87 I would like to be a florist.
## 88 I usually feel that life is worth while.
## 89 It takes a lot of argument to convince most people of the truth.
## 90 Once in a while I put off until tomorrow what I ought to do today.
## 91 I do not mind being made fun of.
## 92 I would like to be a nurse.
## 93 I think most people would lie to get ahead.
## 94 I do many things which I regret afterwards (I regret things more or more often than others seem to).
## 95 I go to church almost every week.
## 96 I have very few quarrels with members of my family.
## 97 At times I have a strong urge to do something harmful or shocking.
## 98 I believe in the second coming of Christ.
## 99 I like to go to parties and other affairs where there is lots of loud fun.
## 100 I have met problems so full of possibilities that I have been unable to make up my mind about them.
## 101 I believe women ought to have as much sexual freedom as men.
## 102 My hardest battles are with myself.
## 103 I have little or no trouble with my muscles twitching or jumping.
## 104 I don't seem to care what happens to me.
## 105 Sometimes when I am not feeling well I am cross.
## 106 Much of the time I feel as if I have done something wrong or evil.
## 107 I am happy most of the time.
## 108 There seems to be a fullness in my head or nose most of the time.
## 109 Some people are so bossy that I feel like doing the opposite of what they request, even though I know they are right.
## 110 Someone has it in for me.
## 111 I have never done anything dangerous for the thrill of it.
## 112 I frequently find it necessary to stand up for what I think is right.
## 113 I believe in law enforcement.
## 114 Often I feel as if there were a tight band about my head.
## 115 I believe in a life hereafter.
## 116 I enjoy a race or game better when I bet on it.
## 117 Most people are honest chiefly through fear of being caught.
## 118 In school I was sometimes sent to the principal for cutting up.
## 119 My speech is the same as always (not faster or slower, or slurring; no hoarseness).
## 120 My table manners are not quite as good at home as when I am out in company.
## 121 I believe I am being plotted against.
## 122 I seem to be about as capable and smart as most others around me.
## 123 I believe I am being followed.
## 124 Most people will use somewhat unfair means to gain profit or an advantage rather than to lose it.
## 125 I have a great deal of stomach trouble.
## 126 I like dramatics.
## 127 I know who is responsible for most of my troubles.
## 128 The sight of blood neither frightens me nor makes me sick.
## 129 Often I can't understand why I have been so cross and grouchy.
## 130 I have never vomited blood or coughed up blood.
## 131 I do not worry about catching diseases.
## 132 I like collecting flowers or growing house plants.
## 133 I have never indulged in any unusual sex practices.
## 134 At times my thoughts have raced ahead faster than I could speak them.
## 135 If I could get into a movie without paying and be sure I was not seen I would probably do it.
## 136 I commonly wonder what hidden reason another person may have for doing something nice for me.
## 137 I believe that my home life is as pleasant as that of most people I know.
## 138 Criticism or scolding hurts me terribly.
## 139 Sometimes I feel as if I must injure either myself or someone else.
## 140 I like to cook.
## 141 My conduct is largely controlled by the customs of those about me.
## 142 I certainly feel useless at times.
## 143 When I was a child, I belonged to a crowd or gang that tried to stick together through thick and thin.
## 144 I would like to be a soldier.
## 145 At times I feel like picking a fist fight with someone.
## 146 I have the wanderlust and am never happy unless I am roaming or traveling about.
## 147 I have often lost out on things because I couldn't make up my mind soon enough.
## 148 It makes me impatient to have people ask my advice or otherwise interrupt me when I am working on something important.
## 149 I used to keep a diary.
## 150 I would rather win than lose in a game.
## 151 Someone has been trying to poison me.
## 152 Most nights I go to sleep without thoughts or ideas bothering me.
## 153 During the past few years I have been well most of the time.
## 154 I have never had a fit or convulsion.
## 155 I am neither gaining nor losing weight.
## 156 I have had periods in which I carried on activities without knowing later what I had been doing.
## 157 I feel that I have often been punished without cause.
## 158 I cry easily.
## 159 I cannot understand what I read as well as I used to.
## 160 I have never felt better in my life than I do now.
## 161 The top of my head sometimes feels tender.
## 162 I resent having anyone take me in so cleverly that I have had to admit that it was one on me.
## 163 I do not tire quickly.
## 164 I like to study and read about things that I am working at.
## 165 I like to know some important people because it makes me feel important.
## 166 I am afraid when I look down from a high place.
## 167 It wouldn't make me nervous if any members of my family got into trouble with the law.
## 168 There is something wrong with my mind.
## 169 Iam not afraid to handle money.
## 170 What others think of me does not bother me.
## 171 It makes me uncomfortable to put on a stunt at a party even when others are doing the same sort of things.
## 172 I frequently have to fight against showing that I am bashful.
## 173 I liked school.
## 174 I have never had a fainting spell.
## 175 I seldom or never have dizzy spells.
## 176 I do not have a great fear of snakes.
## 177 My mother was a good woman.
## 178 My memory seems to be all right.
## 179 I am worried about sex matters.
## 180 I find it hard to make talk when I meet new people.
## 181 When I get bored I like to stir up some excitement.
## 182 I am afraid of losing my mind.
## 183 I am against giving money to beggars.
## 184 I commonly hear voices without knowing where they come from.
## 185 My hearing is apparently as good as that of most people.
## 186 I frequently notice my hand shakes when I try to do something.
## 187 My hands have not become clumsy or awkward.
## 188 I can read a long while without tiring my eyes.
## 189 I feel weak all over much of the time.
## 190 I have very few headaches.
## 191 Sometimes, when embarrassed, I break out in a sweat which annoys me greatly.
## 192 I have had no difficulty in keeping my balance in walking.
## 193 I do not have spells of hay fever or asthma.
## 194 I have had attacks in which I could not control my movements or speech but in which I knew what was going on around me.
## 195 I do not like everyone I know.
## 196 I like to visit places where I have never been before.
## 197 Someone has been trying to rob me.
## 198 I daydream very little.
## 199 Children should be taught all the main facts of sex.
## 200 There are persons who are trying to steal my thoughts and ideas.
## 201 I wish I were not so shy.
## 202 I believe I am a condemned person.
## 203 If I were a reporter I would very much like to report news of the theater.
## 204 I would like to be a journalist.
## 205 At times it has been impossible for me to keep from stealing or shoplifting something.
## 206 I am very religious (more than most people).
## 207 I enjoy many different kinds of play and recreation.
## 208 I like to flirt.
## 209 I believe my sins are unpardonable.
## 210 Everything tastes the same.
## 211 I can sleep during the day but not at night.
## 212 My people treat me more like a child than a grown-up.
## 213 In walking I am very careful to step over sidewalk cracks.
## 214 I have never had any breaking out on my skin that has worried me.
## 215 I have used alcohol excessively.
## 216 There is very little love and companionship in my family as compared to other homes.
## 217 I frequently find myself worrying about something.
## 218 It does not bother me particularly to see animals suffer.
## 219 I think I would like the work of a building contractor.
## 220 I loved my mother.
## 221 I like science.
## 222 It is not hard for me to ask help from my friends even though I cannot return the favor.
## 223 I very much like hunting.
## 224 My parents have often objected to the kind of people I went around with.
## 225 I gossip a little at times.
## 226 Some of my family have habits that bother and annoy me very much.
## 227 I have been told that I walk during sleep.
## 228 At times I feel that I can make up my mind with unusually great ease.
## 229 I should like to belong to several clubs or lodges.
## 230 I hardly ever notice my heart pounding and I am seldom short of breath.
## 231 I like to talk about sex.
## 232 I have been inspired to a program of life based on duty which I have since carefully followed.
## 233 I have at times stood in the way of people who were trying to do something, not because it amounted to much but because of the principle of the thing.
## 234 I get mad easily and then get over it soon.
## 235 I have been quite independent and free from family rule.
## 236 I brood a great deal.
## 237 My relatives are nearly all in sympathy with me.
## 238 I have periods of such great restlessness that I cannot sit long in a chair.
## 239 I have been disappointed in love.
## 240 I never worry about my looks.
## 241 I dream frequently about things that are best kept to myself.
## 242 I believe I am no more nervous than most others.
## 243 I have few or no pains.
## 244 My way of doing things is apt to be misunderstood by others.
## 245 My parents and family find more fault with me than they should.
## 246 My neck spots with red often.
## 247 I have reason for feeling jealous of one or more members of my family.
## 248 Sometimes without any reason or even when things are going wrong I feel excitedly happy, “on top of the world.”
## 249 I believe there is a Devil and a Hell in afterlife.
## 250 I don't blame anyone for trying to grab everything he can get in this world.
## 251 I have had blank spells in which my activities were interrupted and I did not know what was going on around me.
## 252 No one cares much what happens to you.
## 253 I can be friendly with people who do things which I consider wrong.
## 254 I like to be with a crowd who play jokes on one another.
## 255 Sometimes at elections I vote for men about whom I know very little.
## 256 The only interesting part of newspapers is the “funnies.”
## 257 I usually expect to succeed in things I do.
## 258 I believe there is a God.
## 259 I have difficulty in starting to do things.
## 260 I was a slow learner in school.
## 261 If I were an artist I would like to draw flowers.
## 262 It does not bother me that I am not better looking.
## 263 I sweat very easily even on cool days.
## 264 I am entirely self-confident.
## 265 It is safer to trust nobody.
## 266 Once a week or oftener I become very excited.
## 267 When in a group of people I have trouble thinking of the right things to talk about.
## 268 Something exciting will almost always pull me out of it when I am feeling low.
## 269 I can easily make other people afraid of me, and sometimes do for the fun of it.
## 270 When I leave home I do not worry about whether the door is locked and the windows closed.
## 271 Ido not blame a person for taking advantage of someone who lays himself open to it.
## 272 At times I am full of energy.
## 273 I have numbness in one or more regions of my skin.
## 274 My eyesight is as good as it has been for years.
## 275 Someone has control over my mind.
## 276 I enjoy children.
## 277 At times I have been so entertained by the cleverness of a crook that I have hoped he would get by with it.
## 278 I have often felt that strangers were looking at me critically.
## 279 I drink an unusually large amount of water every day.
## 280 Most people make friends because friends are likely to be useful to them.
## 281 I do not often notice my ears ringing or buzzing.
## 282 Once in a while I feel hate toward members of my family whom I usually love.
## 283 If I were a reporter I would very much like to report sporting news.
## 284 I am sure I am being talked about.
## 285 Once in a while I laugh at a dirty joke.
## 286 I am never happier than when alone.
## 287 I have very few fears compared to my friends.
## 288 I am troubled by attacks of nausea and vomiting.
## 289 I am always disgusted with the law when a criminal is freed through the arguments of a smart lawyer.
## 290 I work under a great deal of tension.
## 291 At one or more times in my life I felt that someone was making me do things by hypnotizing me.
## 292 I am likely not to speak to people until they speak to me.
## 293 Someone has been trying to influence my mind.
## 294 I have never been in trouble with the law.
## 295 I liked “Alice in Wonderland” by Lewis Carroll.
## 296 I have periods in which I feel unusually cheerful without any special reason.
## 297 I wish I were not bothered by thoughts about sex.
## 298 If several people find themselves in trouble, the best thing for them to do is to agree upon a story and stick to it.
## 299 I think that I feel more intensely than most people do.
## 300 There never was a time in my life when I liked to play with dolls.
## 301 Life is a strain for me much of the time.
## 302 I have never been in trouble because of my sex behavior.
## 303 I am so touchy on some subjects that I can't talk about them.
## 304 In school I found it very hard to talk before the class.
## 305 Even when I am with people I feel lonely much of the time.
## 306 I get all the sympathy I should.
## 307 I refuse to play some games because I am not good at them.
## 308 At times I have very much wanted to leave home.
## 309 I seem to make friends about as quickly as others do.
## 310 My sex life is satisfactory.
## 311 During one period when I was a youngster I engaged in petty thievery.
## 312 I dislike having people about me.
## 313 The man who provides temptation by leaving valuable property unprotected is about as much to blame for its theft as the one who steals it.
## 314 Once in a while I think of things too bad to talk about.
## 315 I am sure I get a raw deal from life.
## 316 I think nearly anyone would tell a lie to keep out of trouble.
## 317 Iam more sensitive than most other people.
## 318 My daily life is full of things that keep me interested.
## 319 Most people inwardly dislike putting themselves out to help other people.
## 320 Many of my dreams are about sex matters.
## 321 I am easily embarrassed.
## 322 I worry over money and business.
## 323 I have had very peculiar and strange experiences.
## 324 I have never been in love with anyone.
## 325 The things that some of my family have done have frightened me.
## 326 At times I have fits of laughing and crying that I cannot control.
## 327 My mother or father often made me obey even when I thought that it was unreasonable.
## 328 I find it hard to keep my mind on a task or job.
## 329 I almost never dream.
## 330 I have never been paralyzed or had any unusual weakness of any of my muscles.
## 331 If people had not had it in for me I would have been much more successful.
## 332 Sometimes my voice leaves me or changes even though I have no cold.
## 333 No one seems to understand me.
## 334 Peculiar odors come to me at times.
## 335 I cannot keep my mind on one thing.
## 336 I easily become impatient with people.
## 337 I feel anxiety about something or someone almost all the time.
## 338 I have certainly had more than my share of things to worry about.
## 339 Most of the time I wish I were dead.
## 340 Sometimes I become so excited that I find it hard to get to sleep.
## 341 At times I hear so well it bothers me.
## 342 I forget right away what people say to me.
## 343 I usually have to stop and think before I act even in trifling matters.
## 344 Often I cross the street in order not to meet someone I see.
## 345 I often feel as if things were not real.
## 346 I have a habit of counting things that are not important such as bulbs on electric signs, and so forth.
## 347 I have no enemies who really wish to harm me.
## 348 I tend to be on my guard with people who are somewhat more friendly than I had expected.
## 349 I have strange and peculiar thoughts.
## 350 I hear strange things when I am alone.
## 351 I get anxious and upset when I have to make a short trip away from home.
## 352 I have been afraid of things or people that I knew could not hurt me.
## 353 I have no dread of going into a room by myself where other people have already gathered and are talking.
## 354 I am afraid of using a knife or anything very sharp or pointed.
## 355 Sometimes I enjoy hurting persons I love.
## 356 I have more trouble concentrating than others seem to have.
## 357 I have several times given up doing a thing because I thought too little of my ability.
## 358 Bad words, often terrible words, come into my mind and I cannot get rid of them.
## 359 Sometimes some unimportant thought will run through my mind and bother me for days.
## 360 Almost every day something happens to frighten me.
## 361 I am inclined to take things hard.
## 362 I am more sensitive than most other people.
## 363 At times I have enjoyed being hurt by someone I loved.
## 364 People say insulting and vulgar things about me.
## 365 I feel uneasy indoors.
## 366 Even when I am with people I feel lonely much of the time.
## 367 I am not afraid of fire.
## 368 I have sometimes stayed away from another person because I feared doing or saying something that I might regret afterwards.
## 369 Religion gives me no worry.
## 370 I hate to have to rush when working.
## 371 I am not unusually self-conscious.
## 372 I tend to be interested in several different hobbies rather than to stick to one of them for a long time.
## 373 I feel sure that there is only one true religion.
## 374 At periods my mind seems to work more slowly than usual.
## 375 When I am feeling very happy and active, someone who is blue or low will spoil it all.
## 376 Policemen are usually honest.
## 377 At parties I am more likely to sit by myself or with just one other person than to join in with the crowd.
## 378 I do not like to see women smoke.
## 379 I very seldom have spells of the blues.
## 380 When someone says silly or ignorant things about something I know about, I try to set him right.
## 381 I am often said to be hotheaded.
## 382 I wish I could get over worrying about things I have said that may have injured other people's feelings.
## 383 People often disappoint me.
## 384 I feel unable to tell anyone all about myself,
## 385 Lightning is one of my fears.
## 386 I like to keep people guessing what I'm going to do next,
## 387 The only miracles I know of are simply tricks that people play on one another.
## 388 I am afraid to be alone in the dark.
## 389 My plans have frequently seemed so full of difficulties that I have had to give them up.
## 390 I have often felt badly over being misunderstood when trying to keep someone from making a mistake.
## 391 I love to go to dances.
## 392 A windstorm terrifies me.
## 393 Horses that don't pull should be beaten or kicked.
## 394 I frequently ask people for advice.
## 395 The future is too uncertain for a person to make serious plans.
## 396 Often, even though everything is going fine for me, I feel that | don't care about anything.
## 397 I have sometimes felt that difficulties were piling up so high that I could not overcome them.
## 398 I often think, “I wish I were a child again.”
## 399 I am not easily angered.
## 400 If given the chance I could do some things that would be of great benefit to the world.
## 401 I have no fear of water.
## 402 I often must sleep over a matter before I decide what to do.
## 403 It is great to be living in these times when so much is going on.
## 404 People have often misunderstood my intentions when I was trying to put them right and be helpful.
## 405 I have no trouble swallowing.
## 406 I have often met people who were supposed to be experts who were no better than I.
## 407 I am usually calm and not easily upset.
## 408 I am apt to hide my feelings in some things, to the point that people may hurt me without their knowing about it.
## 409 At times I have worn myself out by undertaking too much.
## 410 I would certainly enjoy beating a crook at his own game.
## 411 It makes me feel like a failure when I hear of the success of someone I know well.
## 412 I do not dread seeing a doctor about a sickness or injury.
## 413 I deserve severe punishment for my sins.
## 414 I am apt to take disappointments so keenly that I can't put them out of my mind.
## 415 If given the chance I would make a good leader of people.
## 416 It bothers me to have someone watch me at work even though I know I can do it well.
## 417 I am often so annoyed when someone tries to get ahead of me in a line of people that I speak to him about it.
## 418 At times I think Iam no good at all.
## 419 I played hooky from school quite often as a youngster.
## 420 I have had some very unusual religious experiences.
## 421 One or more members of my family is very nervous.
## 422 I have felt embarrassed over the type of work that one or more members of my family have done.
## 423 I like or have liked fishing very much.
## 424 I feel hungry almost all the time.
## 425 I dream frequently.
## 426 I have at times had to be rough with people who were rude or annoying.
## 427 I am embarrassed by dirty stories.
## 428 I like to read newspaper editorials.
## 429 I like to attend lectures on serious subjects.
## 430 I am attracted by members of the opposite sex.
## 431 I worry quite a bit over possible misfortunes.
## 432 I have strong political opinions.
## 433 I used to have imaginary companions.
## 434 I would like to be an auto racer.
## 435 Usually I would prefer to work with women.
## 436 People generally demand more respect for their own rights than they are willing to allow for others.
## 437 It is all right to get around the law if you don't actually break it.
## 438 There are certain people whom I dislike so much that I am inwardly pleased when they are catching it for something they have done.
## 439 It makes me nervous to have to wait.
## 440 I try to remember good stories to pass them on to other people.
## 441 I like tall women.
## 442 I have had periods in which I lost sleep over worry.
## 443 I am apt to pass up something I want to do because others feel that I am not going about it in the right way.
## 444 J do not try to correct people who express an ignorant belief.
## 445 I was fond of excitement when I was young (or in childhood).
## 446 I enjoy gambling for small stakes.
## 447 I am often inclined to go out of my way to win a point with someone who has opposed me.
## 448 I am bothered by people outside, on streetcars, in stores, etc., watching me.
## 449 I enjoy social gatherings just to be with people.
## 450 I enjoy the excitement of a crowd.
## 451 My worries seem to disappear when I get into a crowd of lively friends.
## 452 I like to poke fun at people.
## 453 When I was a child I didn't care to be a member of a crowd or gang.
## 454 I could be happy living all alone in a cabin in the woods or mountains.
## 455 I am quite often not in on the gossip and talk of the group I belong to.
## 456 A person shouldn't be punished for breaking a law that he thinks is unreasonable.
## 457 I believe that a person should never taste an alcoholic drink.
## 458 The man who had most to do with me when I was a child (such as my father, stepfather, etc.) was very strict with me.
## 459 I have one or more bad habits which are so strong that it is no use in fighting against them.
## 460 I have used alcohol moderately (or not at all).
## 461 I find it hard to set aside a task that I have undertaken, even for a short time.
## 462 I have had no difficulty starting or holding my urine.
## 463 I used to like hopscotch.
## 464 I have never seen a vision.
## 465 I have several times had a change of heart about my life work.
## 466 Except by a doctor's orders I never take drugs or sleeping powders.
## 467 I often memorize numbers that are not important (such as automobile licenses, etc)
## 468 I am often sorry because I am so cross and grouchy.
## 469 I have often found people jealous of my good ideas, just because they had not thought of them first.
## 470 Sexual things disgust me.
## 471 In school my marks in deportment were quite regularly bad.
## 472 I am fascinated by fire.
## 473 Whenever possible I avoid being in a crowd.
## 474 I have to urinate no more often than others.
## 475 When I am cornered I tell that portion of the truth which is not likely to hurt me.
## 476 I am a special agent of God.
## 477 If I were in trouble with several friends who were equally to blame, I would rather take the whole blame than to give them away.
## 478 I have never been made especially nervous over trouble that any members of my family have gotten into.
## 479 I do not mind meeting strangers.
## 480 I am often afraid of the dark.
## 481 I can remember “playing sick” to get out of something.
## 482 While in trains, busses, etc., I often talk to strangers.
## 483 Christ performed miracles such as changing water into wine.
## 484 I have one or more faults which are sobig that it seems better to accept them and try to control them rather than to try to get rid of them.
## 485 When a man is with a woman he is usually thinking about things related to her sex.
## 486 I have never noticed any blood in my urine.
## 487 I feel like giving up quickly when things go wrong.
## 488 I pray several times every week.
## 489 I feel sympathetic towards people who tend to hang on to their griefs and troubles.
## 490 I read in the Bible several times a week.
## 491 I have no patience with people who believe there is only one true religion.
## 492 I dread the thought of an earthquake.
## 493 I prefer work which requires close attention, to work which allows me to be careless.
## 494 I am afraid of finding myself in a closet or small closed place.
## 495 I usually “lay my cards on the table” with people that I am trying to correct or improve.
## 496 I have never seen things doubled (that is, an object never looks like two objects to me without my being able to make it look like one object).
## 497 I enjoy stories of adventure.
## 498 It is always a good thing to be frank.
## 499 I must admit that I have at times been worried beyond reason over something that really did not matter.
## 500 I readily become one hundred per cent sold on a good idea.
## 501 I usually work things out for myself rather than get someone to show me how.
## 502 I like to let people know where I stand on things.
## 503 It is unusual for me to express strong approval or disapproval of the actions of others.
## 504 I do not try to cover up my poor opinion or pity of a person so that he won't know how I feel.
## 505 I have had periods when I felt so full of pep that sleep did not seem necessary for days at a time.
## 506 I am a high-strung person.
## 507 I have frequently worked under people who seem to have things arranged so that they get credit for good work but are able to pass off mistakes onto those under them.
## 508 I believe my sense of smell is as good as other people's.
## 509 I sometimes find it hard to stick up for my rights because I am so reserved,
## 510 Dirt frightens or disgusts me.
## 511 I have a daydream life about which I do not tell other people.
## 512 I dislike to take a bath.
## 513 I think Lincoln was greater than Washington.
## 514 I like mannish women.
## 515 In my home we have always had the ordinary necessities (such as enough food, clothing, etc.).
## 516 Some of my family have quick tempers.
## 517 I cannot do anything well.
## 518 I have often felt guilty because I have pretended to feel more sorry about something than I really was.
## 519 There is something wrong with my sex organs.
## 520 I strongly defend my own opinions as a rule.
## 521 In a group of people I would not be embarrassed to be called upon to start a discussion or give an opinion about something I know well.
## 522 I have no fear of spiders.
## 523 I practically never blush.
## 524 I am not afraid of picking up a disease or germs from door knobs.
## 525 I am made nervous by certain animals.
## 526 The future seems hopeless to me.
## 527 The members of my family and my close relatives get along quite well.
## 528 I blush no more often than others.
## 529 I would like to wear expensive clothes.
## 530 I am often afraid that I am going to blush.
## 531 People can pretty easily change me even though I thought that my mind was already made up on a subject.
## 532 I can stand as much pain as others can.
## 533 I am not bothered by a great deal of belching of gas from my stomach.
## 534 Several times I have been the last to give up trying to do a thing.
## 535 My mouth feels dry almost all the time.
## 536 It makes me angry to have people hurry me.
## 537 I would like to hunt lions in Africa.
## 538 I think I would like the work of a dressmaker.
## 539 I am not afraid of mice.
## 540 My face has never been paralyzed.
## 541 My skin seems to be unusually sensitive to touch.
## 542 I have never had any black, tarry-looking bowel movements.
## 543 Several times a week I feel as if something dreadful is about to happen.
## 544 I feel tired a good deal of the time.
## 545 Sometimes I have the same dream over and over.
## 546 I like to read about history.
## 547 I like parties and socials.
## 548 I never attend a sexy show if I can avoid it.
## 549 I shrink from facing a crisis or difficulty.
## 550 I like repairing a door latch.
## 551 Sometimes I am sure that other people can tell what I am thinking.
## 552 I like to read about science.
## 553 I am afraid of being alone in a wide-open place.
## 554 If I were an artist I would like to draw children.
## 555 I sometimes feel that I am about to go to pieces.
## 556 I am very careful about my manner of dress.
## 557 I would like to be a private secretary.
## 558 A large number of people are guilty of bad sexual conduct.
## 559 I have often been frightened in the middle of the night.
## 560 I am greatly bothered by forgetting where I put things.
## 561 I very much like horseback riding.
## 562 The one to whom I was most attached and whom I most admired as a child was a woman
## 563 I like adventure stories better than romantic stories.
## 564 I am apt to pass up something I want to do when others feel that it isn't worth doing.
## 565 I feel like jumping off when I am on a high place.
## 566 I like movie love scenes.
## Item Prevalence P loading IQ gap
## 1 MM010001 0.64 0.03 -1.66
## 2 MM010002 0.95 -0.41 4.54
## 3 MM010003 0.69 -0.44 0.89
## 4 MM010004 0.09 0.04 4.68
## 5 MM010005 0.58 0.23 -8.24
## 6 MM010006 0.46 0.16 0.25
## 7 MM010007 0.83 -0.34 3.97
## 8 MM010008 0.81 -0.57 3.76
## 9 MM010009 0.88 -0.52 6.83
## 10 MM010010 0.06 0.60 -8.29
## 11 MM010011 0.30 0.33 -10.03
## 12 MM010012 0.65 -0.05 0.60
## 13 MM010013 0.48 0.29 0.23
## 14 MM010014 0.21 0.37 -1.26
## 15 MM010015 0.43 0.55 -3.52
## 16 MM010016 0.09 0.74 -12.60
## 17 MM010017 0.92 -0.31 2.94
## 18 MM010018 0.84 -0.32 10.31
## 19 MM010019 0.15 0.29 -2.85
## 20 MM010020 0.85 -0.46 -2.12
## 21 MM010021 0.33 0.59 -2.74
## 22 MM010022 0.09 0.62 -6.57
## 23 MM010023 0.03 0.66 -11.56
## 24 MM010024 0.12 0.76 -6.62
## 25 MM010025 0.29 0.11 2.66
## 26 MM010026 0.50 0.32 -4.14
## 27 MM010027 0.04 0.66 -11.20
## 28 MM010028 0.32 0.45 -2.05
## 29 MM010029 0.21 0.42 -3.37
## 30 MM010030 0.82 0.26 7.18
## 31 MM010031 0.12 0.65 -8.35
## 32 MM010032 0.18 0.68 -5.11
## 33 MM010033 0.27 0.52 -5.86
## 34 MM010034 0.12 0.43 -3.49
## 35 MM010035 0.06 0.67 -11.18
## 36 MM010036 0.58 -0.28 4.54
## 37 MM010037 0.85 -0.26 2.45
## 38 MM010038 0.53 0.14 4.58
## 39 MM010039 0.45 0.53 0.53
## 40 MM010040 0.14 0.63 -7.49
## 41 MM010041 0.33 0.64 -2.78
## 42 MM010042 0.09 0.47 -5.21
## 43 MM010043 0.18 0.67 -6.54
## 44 MM010044 0.07 0.69 -10.61
## 45 MM010045 0.55 0.22 3.23
## 46 MM010046 0.75 -0.43 5.89
## 47 MM010047 0.10 0.66 -11.91
## 48 MM010048 0.10 0.55 -8.78
## 49 MM010049 0.05 0.53 -4.74
## 50 MM010050 0.04 0.64 -9.67
## 51 MM010051 0.84 -0.46 5.97
## 52 MM010052 0.24 0.50 -0.81
## 53 MM010053 0.08 0.15 -4.81
## 54 MM010054 0.95 -0.52 4.04
## 55 MM010055 0.77 -0.47 6.05
## 56 MM010056 0.23 0.26 -4.55
## 57 MM010057 0.65 -0.29 -1.84
## 58 MM010058 0.50 0.13 -7.26
## 59 MM010059 0.67 0.39 -5.99
## 60 MM010060 0.96 -0.09 4.07
## 61 MM010061 0.28 0.61 -9.35
## 62 MM010062 0.27 0.60 -7.80
## 63 MM010063 0.86 -0.37 2.21
## 64 MM010064 0.38 0.34 0.93
## 65 MM010065 0.92 -0.26 -0.37
## 66 MM010066 0.13 0.36 -6.86
## 67 MM010067 0.39 0.63 -5.99
## 68 MM010068 0.69 -0.42 3.91
## 69 MM010069 0.04 0.31 -8.86
## 70 MM010070 0.13 -0.01 1.65
## 71 MM010071 0.71 0.41 -5.25
## 72 MM010072 0.12 0.60 -5.84
## 73 MM010073 0.71 -0.26 1.78
## 74 MM010074 0.02 0.54 -0.42
## 75 MM010075 0.97 0.29 4.18
## 76 MM010076 0.13 0.82 -7.58
## 77 MM010077 0.15 0.02 -4.94
## 78 MM010078 0.36 -0.09 1.93
## 79 MM010079 0.53 -0.36 -2.28
## 80 MM010080 0.25 0.25 -0.96
## 81 MM010081 0.69 0.05 1.68
## 82 MM010082 0.18 0.46 -4.94
## 83 MM010083 0.94 -0.44 -0.90
## 84 MM010084 0.47 0.39 -10.80
## 85 MM010085 0.01 0.61 -12.21
## 86 MM010086 0.23 0.65 -5.03
## 87 MM010087 0.06 0.13 -0.39
## 88 MM010088 0.96 -0.71 5.34
## 89 MM010089 0.40 0.51 -9.62
## 90 MM010090 0.92 0.16 6.95
## 91 MM010091 0.41 -0.18 0.71
## 92 MM010092 0.04 0.20 1.10
## 93 MM010093 0.61 0.52 -6.85
## 94 MM010094 0.25 0.74 -9.12
## 95 MM010095 0.25 -0.15 2.45
## 96 MM010096 0.76 -0.40 0.67
## 97 MM010097 0.18 0.65 -1.31
## 98 MM010098 0.69 0.05 -6.50
## 99 MM010099 0.41 0.01 -0.35
## 100 MM010100 0.49 0.55 -5.55
## 101 MM010101 0.83 -0.19 4.73
## 102 MM010102 0.70 0.41 -0.39
## 103 MM010103 0.84 -0.55 8.15
## 104 MM010104 0.09 0.67 -4.34
## 105 MM010105 0.74 0.25 4.60
## 106 MM010106 0.10 0.79 -8.81
## 107 MM010107 0.87 -0.74 3.07
## 108 MM010108 0.15 0.54 -4.21
## 109 MM010109 0.31 0.50 -2.46
## 110 MM010110 0.07 0.70 -8.36
## 111 MM010111 0.29 -0.02 -9.32
## 112 MM010112 0.74 0.24 -4.67
## 113 MM010113 0.98 -0.52 3.66
## 114 MM010114 0.08 0.74 -11.35
## 115 MM010115 0.76 -0.04 -1.47
## 116 MM010116 0.32 0.25 -3.54
## 117 MM010117 0.51 0.41 -8.19
## 118 MM010118 0.41 0.25 -2.47
## 119 MM010119 0.77 -0.42 3.10
## 120 MM010120 0.65 0.21 2.14
## 121 MM010121 0.04 0.75 -9.13
## 122 MM010122 0.92 -0.48 10.89
## 123 MM010123 0.02 0.74 -13.49
## 124 MM010124 0.66 0.53 -7.27
## 125 MM010125 0.12 0.56 -7.22
## 126 MM010126 0.40 0.01 1.96
## 127 MM010127 0.66 0.11 2.95
## 128 MM010128 0.74 -0.07 0.36
## 129 MM010129 0.37 0.64 -6.66
## 130 MM010130 0.81 -0.32 5.24
## 131 MM010131 0.48 -0.27 5.23
## 132 MM010132 0.32 0.05 -0.84
## 133 MM010133 0.72 -0.20 -3.22
## 134 MM010134 0.77 0.39 3.48
## 135 MM010135 0.31 0.36 -3.58
## 136 MM010136 0.36 0.65 -9.60
## 137 MM010137 0.86 -0.57 1.51
## 138 MM010138 0.36 0.55 -4.04
## 139 MM010139 0.08 0.77 -7.09
## 140 MM010140 0.62 0.02 -0.69
## 141 MM010141 0.45 0.34 -2.73
## 142 MM010142 0.37 0.69 -4.22
## 143 MM010143 0.30 0.15 -2.50
## 144 MM010144 0.18 0.30 -6.16
## 145 MM010145 0.18 0.59 -2.03
## 146 MM010146 0.13 0.61 -6.65
## 147 MM010147 0.38 0.64 -9.01
## 148 MM010148 0.36 0.46 -2.83
## 149 MM010149 0.06 0.19 3.75
## 150 MM010150 0.89 -0.13 9.66
## 151 MM010151 0.01 0.54 -15.37
## 152 MM010152 0.68 -0.53 4.04
## 153 MM010153 0.91 -0.64 10.59
## 154 MM010154 0.93 -0.44 7.45
## 155 MM010155 0.66 -0.22 3.08
## 156 MM010156 0.13 0.63 -3.69
## 157 MM010157 0.13 0.77 -11.67
## 158 MM010158 0.17 0.49 -3.40
## 159 MM010159 0.23 0.60 -11.74
## 160 MM010160 0.42 -0.37 -0.47
## 161 MM010161 0.14 0.52 -5.78
## 162 MM010162 0.44 0.34 1.31
## 163 MM010163 0.70 -0.52 4.25
## 164 MM010164 0.81 -0.28 3.61
## 165 MM010165 0.37 0.21 2.50
## 166 MM010166 0.42 0.24 0.01
## 167 MM010167 0.24 0.01 -0.10
## 168 MM010168 0.10 0.80 -8.47
## 169 MM010169 0.94 -0.35 6.74
## 170 MM010170 0.46 -0.06 -7.29
## 171 MM010171 0.59 0.25 -2.91
## 172 MM010172 0.42 0.49 -1.68
## 173 MM010173 0.59 -0.32 6.27
## 174 MM010174 0.77 -0.25 0.92
## 175 MM010175 0.85 -0.57 10.53
## 176 MM010176 0.61 -0.24 6.21
## 177 MM010177 0.97 -0.35 4.36
## 178 MM010178 0.81 -0.60 5.80
## 179 MM010179 0.15 0.61 -1.18
## 180 MM010180 0.45 0.45 -2.99
## 181 MM010181 0.41 0.15 -0.85
## 182 MM010182 0.13 0.73 -8.67
## 183 MM010183 0.44 0.06 0.45
## 184 MM010184 0.04 0.75 -14.62
## 185 MM010185 0.79 -0.22 -0.23
## 186 MM010186 0.16 0.62 -8.93
## 187 MM010187 0.88 -0.45 7.62
## 188 MM010188 0.49 -0.34 8.53
## 189 MM010189 0.08 0.72 -6.34
## 190 MM010190 0.78 -0.41 3.47
## 191 MM010191 0.32 0.43 -1.98
## 192 MM010192 0.91 -0.53 8.78
## 193 MM010193 0.80 -0.09 -2.30
## 194 MM010194 0.06 0.61 -12.67
## 195 MM010195 0.85 0.17 6.81
## 196 MM010196 0.95 -0.25 3.19
## 197 MM010197 0.03 0.64 -8.55
## 198 MM010198 0.56 -0.34 -4.40
## 199 MM010199 0.77 -0.07 6.89
## 200 MM010200 0.04 0.64 -7.52
## 201 MM010201 0.52 0.47 -2.39
## 202 MM010202 0.04 0.75 -12.28
## 203 MM010203 0.13 0.09 -2.53
## 204 MM010204 0.20 -0.01 5.02
## 205 MM010205 0.04 0.55 -3.90
## 206 MM010206 0.17 0.08 -0.62
## 207 MM010207 0.83 -0.30 1.51
## 208 MM010208 0.62 0.09 1.86
## 209 MM010209 0.05 0.59 -12.12
## 210 MM010210 0.01 0.60 -4.20
## 211 MM010211 0.05 0.65 -10.14
## 212 MM010212 0.04 0.71 -9.49
## 213 MM010213 0.09 0.43 -13.31
## 214 MM010214 0.49 -0.29 -1.80
## 215 MM010215 0.44 0.27 2.17
## 216 MM010216 0.11 0.56 -3.50
## 217 MM010217 0.51 0.72 -3.92
## 218 MM010218 0.07 0.16 -3.79
## 219 MM010219 0.57 -0.02 1.45
## 220 MM010220 0.96 -0.23 1.63
## 221 MM010221 0.78 -0.19 8.57
## 222 MM010222 0.41 -0.10 -1.18
## 223 MM010223 0.44 0.11 -7.01
## 224 MM010224 0.24 0.52 -7.89
## 225 MM010225 0.79 0.14 4.85
## 226 MM010226 0.46 0.42 0.27
## 227 MM010227 0.06 0.31 -3.40
## 228 MM010228 0.80 -0.26 0.85
## 229 MM010229 0.24 0.18 -4.57
## 230 MM010230 0.71 -0.52 5.37
## 231 MM010231 0.54 0.15 -2.60
## 232 MM010232 0.26 0.10 -4.22
## 233 MM010233 0.26 0.24 2.60
## 234 MM010234 0.46 0.42 -4.63
## 235 MM010235 0.72 0.00 1.17
## 236 MM010236 0.15 0.73 -3.36
## 237 MM010237 0.34 -0.02 6.20
## 238 MM010238 0.36 0.64 -5.18
## 239 MM010239 0.50 0.36 1.82
## 240 MM010240 0.21 -0.15 -5.77
## 241 MM010241 0.33 0.65 -8.18
## 242 MM010242 0.76 -0.50 4.77
## 243 MM010243 0.76 -0.56 5.12
## 244 MM010244 0.39 0.63 -6.95
## 245 MM010245 0.12 0.61 -6.11
## 246 MM010246 0.03 0.42 -5.81
## 247 MM010247 0.06 0.56 -4.72
## 248 MM010248 0.39 0.18 -3.92
## 249 MM010249 0.63 0.10 -7.33
## 250 MM010250 0.57 0.22 -5.83
## 251 MM010251 0.12 0.69 -7.24
## 252 MM010252 0.15 0.61 -4.64
## 253 MM010253 0.69 -0.07 3.51
## 254 MM010254 0.29 0.04 3.16
## 255 MM010255 0.52 0.00 7.44
## 256 MM010256 0.06 0.40 -9.03
## 257 MM010257 0.94 -0.45 5.61
## 258 MM010258 0.92 0.03 -9.31
## 259 MM010259 0.33 0.57 -1.45
## 260 MM010260 0.38 0.38 -14.85
## 261 MM010261 0.26 0.02 -0.15
## 262 MM010262 0.78 -0.38 -1.00
## 263 MM010263 0.35 0.32 -4.48
## 264 MM010264 0.44 -0.24 -4.97
## 265 MM010265 0.27 0.58 -5.14
## 266 MM010266 0.31 0.38 -4.90
## 267 MM010267 0.41 0.58 -4.88
## 268 MM010268 0.70 -0.03 -2.09
## 269 MM010269 0.08 0.56 -5.12
## 270 MM010270 0.34 -0.17 4.53
## 271 MM010271 0.16 0.31 -6.83
## 272 MM010272 0.90 -0.18 3.35
## 273 MM010273 0.15 0.45 -5.01
## 274 MM010274 0.64 -0.17 0.80
## 275 MM010275 0.02 0.51 -10.18
## 276 MM010276 0.92 -0.30 -3.57
## 277 MM010277 0.38 0.27 4.38
## 278 MM010278 0.30 0.73 -5.36
## 279 MM010279 0.12 0.41 -10.13
## 280 MM010280 0.29 0.54 -10.65
## 281 MM010281 0.73 -0.41 7.33
## 282 MM010282 0.26 0.53 -0.06
## 283 MM010283 0.58 -0.10 -1.15
## 284 MM010284 0.27 0.66 -7.43
## 285 MM010285 0.96 0.04 5.65
## 286 MM010286 0.10 0.59 -7.99
## 287 MM010287 0.55 -0.08 -1.99
## 288 MM010288 0.03 0.59 -9.94
## 289 MM010289 0.78 0.17 -1.26
## 290 MM010290 0.47 0.31 0.42
## 291 MM010291 0.02 0.72 -10.54
## 292 MM010292 0.40 0.43 -0.71
## 293 MM010293 0.05 0.65 -6.37
## 294 MM010294 0.46 -0.22 1.68
## 295 MM010295 0.53 -0.17 5.95
## 296 MM010296 0.63 0.19 -3.37
## 297 MM010297 0.18 0.53 -3.53
## 298 MM010298 0.29 0.40 -7.67
## 299 MM010299 0.40 0.44 0.33
## 300 MM010300 0.72 0.01 -1.38
## 301 MM010301 0.19 0.77 -4.95
## 302 MM010302 0.88 -0.28 -0.14
## 303 MM010303 0.25 0.68 -6.87
## 304 MM010304 0.66 0.36 -4.25
## 305 MM010305 0.22 0.78 -3.33
## 306 MM010306 0.87 -0.44 8.37
## 307 MM010307 0.44 0.43 -2.88
## 308 MM010308 0.35 0.61 -2.98
## 309 MM010309 0.78 -0.42 -0.93
## 310 MM010310 0.85 -0.46 -2.29
## 311 MM010311 0.53 0.16 3.95
## 312 MM010312 0.11 0.54 -3.81
## 313 MM010313 0.46 0.32 -9.67
## 314 MM010314 0.37 0.62 -4.43
## 315 MM010315 0.06 0.81 -13.81
## 316 MM010316 0.59 0.46 -6.12
## 317 MM010317 0.40 0.47 -0.82
## 318 MM010318 0.78 -0.59 4.17
## 319 MM010319 0.47 0.51 -9.76
## 320 MM010320 0.23 0.33 2.32
## 321 MM010321 0.40 0.51 -2.71
## 322 MM010322 0.67 0.45 -0.39
## 323 MM010323 0.27 0.53 -5.62
## 324 MM010324 0.02 0.51 -6.03
## 325 MM010325 0.22 0.40 -5.12
## 326 MM010326 0.07 0.65 -6.45
## 327 MM010327 0.64 0.27 -2.79
## 328 MM010328 0.16 0.73 -4.66
## 329 MM010329 0.33 -0.12 0.60
## 330 MM010330 0.83 -0.35 4.54
## 331 MM010331 0.05 0.74 -10.97
## 332 MM010332 0.14 0.53 -6.99
## 333 MM010333 0.10 0.81 -6.50
## 334 MM010334 0.15 0.56 -8.23
## 335 MM010335 0.16 0.74 -7.74
## 336 MM010336 0.38 0.63 -1.68
## 337 MM010337 0.15 0.73 -2.02
## 338 MM010338 0.42 0.65 -11.72
## 339 MM010339 0.04 0.82 -10.69
## 340 MM010340 0.52 0.34 -0.51
## 341 MM010341 0.09 0.53 -7.64
## 342 MM010342 0.20 0.60 -8.08
## 343 MM010343 0.36 0.55 -9.80
## 344 MM010344 0.10 0.65 -8.68
## 345 MM010345 0.11 0.81 -10.84
## 346 MM010346 0.16 0.42 -1.72
## 347 MM010347 0.85 -0.43 5.67
## 348 MM010348 0.54 0.49 -4.13
## 349 MM010349 0.17 0.77 -8.54
## 350 MM010350 0.06 0.77 -13.86
## 351 MM010351 0.10 0.67 -9.47
## 352 MM010352 0.14 0.56 -4.53
## 353 MM010353 0.65 -0.42 2.25
## 354 MM010354 0.03 0.51 -9.57
## 355 MM010355 0.06 0.60 -3.87
## 356 MM010356 0.27 0.70 -9.41
## 357 MM010357 0.27 0.66 -6.36
## 358 MM010358 0.12 0.75 -10.81
## 359 MM010359 0.25 0.70 -6.94
## 360 MM010360 0.03 0.80 -10.51
## 361 MM010361 0.34 0.65 -4.06
## 362 MM010362 0.41 0.49 -0.85
## 363 MM010363 0.03 0.67 -12.51
## 364 MM010364 0.11 0.66 -12.85
## 365 MM010365 0.10 0.60 -12.18
## 366 MM010366 0.18 0.78 -2.94
## 367 MM010367 0.64 -0.17 6.94
## 368 MM010368 0.44 0.61 -4.62
## 369 MM010369 0.76 -0.25 3.67
## 370 MM010370 0.75 0.32 -7.00
## 371 MM010371 0.63 -0.36 3.05
## 372 MM010372 0.65 0.10 0.29
## 373 MM010373 0.37 0.18 -9.18
## 374 MM010374 0.64 0.57 -1.59
## 375 MM010375 0.19 0.67 -9.23
## 376 MM010376 0.80 -0.44 8.75
## 377 MM010377 0.46 0.45 -0.88
## 378 MM010378 0.51 0.00 1.89
## 379 MM010379 0.71 -0.65 3.96
## 380 MM010380 0.62 0.10 1.10
## 381 MM010381 0.26 0.59 -7.43
## 382 MM010382 0.44 0.57 -6.87
## 383 MM010383 0.35 0.66 -4.19
## 384 MM010384 0.42 0.61 -2.87
## 385 MM010385 0.25 0.31 -8.51
## 386 MM010386 0.29 0.47 -3.40
## 387 MM010387 0.21 0.26 -4.16
## 388 MM010388 0.06 0.51 -3.16
## 389 MM010389 0.23 0.77 -8.55
## 390 MM010390 0.45 0.58 -8.45
## 391 MM010391 0.41 0.00 -5.92
## 392 MM010392 0.11 0.38 -13.34
## 393 MM010393 0.03 0.36 -3.49
## 394 MM010394 0.51 0.14 -1.69
## 395 MM010395 0.20 0.63 -12.70
## 396 MM010396 0.21 0.76 -4.23
## 397 MM010397 0.40 0.68 -3.10
## 398 MM010398 0.23 0.59 -5.10
## 399 MM010399 0.66 -0.50 2.51
## 400 MM010400 0.56 0.15 -2.77
## 401 MM010401 0.64 -0.23 4.13
## 402 MM010402 0.35 0.33 -0.58
## 403 MM010403 0.89 -0.56 5.10
## 404 MM010404 0.45 0.68 -10.17
## 405 MM010405 0.94 -0.39 4.98
## 406 MM010406 0.69 0.43 -5.24
## 407 MM010407 0.81 -0.59 3.85
## 408 MM010408 0.65 0.53 -2.06
## 409 MM010409 0.62 0.32 -1.58
## 410 MM010410 0.59 0.30 0.49
## 411 MM010411 0.19 0.64 -4.20
## 412 MM010412 0.71 -0.32 2.37
## 413 MM010413 0.14 0.33 -8.96
## 414 MM010414 0.27 0.66 -4.82
## 415 MM010415 0.64 -0.10 1.50
## 416 MM010416 0.44 0.56 -6.02
## 417 MM010417 0.36 0.25 -0.37
## 418 MM010418 0.23 0.74 -4.73
## 419 MM010419 0.23 0.40 -7.31
## 420 MM010420 0.10 0.38 -6.02
## 421 MM010421 0.29 0.41 -3.03
## 422 MM010422 0.07 0.50 -3.86
## 423 MM010423 0.73 0.02 -3.02
## 424 MM010424 0.09 0.53 -5.15
## 425 MM010425 0.43 0.25 -0.91
## 426 MM010426 0.57 0.33 -3.04
## 427 MM010427 0.11 0.19 -2.67
## 428 MM010428 0.48 -0.10 2.98
## 429 MM010429 0.41 -0.05 2.05
## 430 MM010430 0.91 -0.15 11.09
## 431 MM010431 0.32 0.71 -5.68
## 432 MM010432 0.40 0.03 3.67
## 433 MM010433 0.08 0.40 -2.89
## 434 MM010434 0.38 0.11 1.93
## 435 MM010435 0.27 0.27 -3.55
## 436 MM010436 0.76 0.52 -4.28
## 437 MM010437 0.52 0.33 -4.81
## 438 MM010438 0.50 0.46 2.06
## 439 MM010439 0.50 0.57 -4.85
## 440 MM010440 0.81 0.02 -1.31
## 441 MM010441 0.75 -0.01 2.55
## 442 MM010442 0.63 0.50 -1.02
## 443 MM010443 0.24 0.64 -8.98
## 444 MM010444 0.46 0.08 -5.09
## 445 MM010445 0.89 0.03 -1.55
## 446 MM010446 0.57 0.09 1.21
## 447 MM010447 0.40 0.37 -0.99
## 448 MM010448 0.14 0.67 -6.31
## 449 MM010449 0.60 -0.25 1.80
## 450 MM010450 0.50 -0.15 0.81
## 451 MM010451 0.65 -0.05 0.61
## 452 MM010452 0.27 0.11 4.03
## 453 MM010453 0.58 -0.01 -1.19
## 454 MM010454 0.39 0.29 -1.19
## 455 MM010455 0.53 0.28 -5.54
## 456 MM010456 0.16 0.37 -8.32
## 457 MM010457 0.10 0.22 -9.82
## 458 MM010458 0.42 0.22 -3.70
## 459 MM010459 0.17 0.56 -5.99
## 460 MM010460 0.80 -0.29 4.29
## 461 MM010461 0.40 0.18 -4.89
## 462 MM010462 0.89 -0.36 3.25
## 463 MM010463 0.42 -0.07 1.02
## 464 MM010464 0.82 -0.34 7.21
## 465 MM010465 0.60 0.47 -4.34
## 466 MM010466 0.71 -0.22 -0.39
## 467 MM010467 0.23 0.36 -3.36
## 468 MM010468 0.44 0.59 -4.07
## 469 MM010469 0.21 0.55 -10.08
## 470 MM010470 0.03 0.36 -13.86
## 471 MM010471 0.18 0.43 -10.86
## 472 MM010472 0.11 0.35 3.95
## 473 MM010473 0.41 0.47 -4.35
## 474 MM010474 0.75 -0.08 -1.05
## 475 MM010475 0.48 0.36 0.13
## 476 MM010476 0.09 0.28 -8.25
## 477 MM010477 0.49 0.11 1.31
## 478 MM010478 0.70 -0.33 6.82
## 479 MM010479 0.83 -0.47 2.27
## 480 MM010480 0.07 0.46 -6.02
## 481 MM010481 0.61 0.30 -1.15
## 482 MM010482 0.53 -0.08 -3.32
## 483 MM010483 0.77 0.00 -3.24
## 484 MM010484 0.33 0.54 -5.38
## 485 MM010485 0.41 0.44 -5.65
## 486 MM010486 0.91 -0.24 2.25
## 487 MM010487 0.19 0.72 -9.29
## 488 MM010488 0.36 0.04 -2.27
## 489 MM010489 0.51 0.15 -4.79
## 490 MM010490 0.13 -0.04 -1.39
## 491 MM010491 0.27 0.25 1.09
## 492 MM010492 0.45 0.27 -10.39
## 493 MM010493 0.59 -0.10 7.82
## 494 MM010494 0.12 0.51 -5.35
## 495 MM010495 0.81 -0.01 0.41
## 496 MM010496 0.83 -0.38 7.46
## 497 MM010497 0.96 -0.26 10.50
## 498 MM010498 0.65 0.13 -8.96
## 499 MM010499 0.59 0.56 -5.44
## 500 MM010500 0.42 0.18 -6.77
## 501 MM010501 0.73 0.04 2.35
## 502 MM010502 0.79 -0.01 -2.80
## 503 MM010503 0.55 0.07 -2.04
## 504 MM010504 0.40 0.12 -8.70
## 505 MM010505 0.17 0.48 -8.22
## 506 MM010506 0.30 0.45 -6.01
## 507 MM010507 0.49 0.56 -8.51
## 508 MM010508 0.91 -0.24 -1.02
## 509 MM010509 0.32 0.51 -2.29
## 510 MM010510 0.08 0.47 -14.34
## 511 MM010511 0.18 0.62 -7.70
## 512 MM010512 0.06 0.18 1.79
## 513 MM010513 0.44 0.10 -1.27
## 514 MM010514 0.09 0.29 -9.51
## 515 MM010515 0.91 -0.40 8.86
## 516 MM010516 0.65 0.39 -4.91
## 517 MM010517 0.03 0.62 -10.67
## 518 MM010518 0.22 0.65 -9.70
## 519 MM010519 0.04 0.55 -6.00
## 520 MM010520 0.70 0.04 -0.14
## 521 MM010521 0.66 -0.33 4.78
## 522 MM010522 0.62 -0.20 0.88
## 523 MM010523 0.46 -0.21 3.00
## 524 MM010524 0.81 -0.29 9.60
## 525 MM010525 0.47 0.30 -3.14
## 526 MM010526 0.06 0.76 -9.88
## 527 MM010527 0.82 -0.43 0.56
## 528 MM010528 0.80 -0.27 2.77
## 529 MM010529 0.54 0.14 0.04
## 530 MM010530 0.15 0.56 -7.38
## 531 MM010531 0.21 0.47 -6.96
## 532 MM010532 0.87 -0.24 7.75
## 533 MM010533 0.77 -0.37 4.79
## 534 MM010534 0.64 0.10 0.12
## 535 MM010535 0.09 0.59 -8.83
## 536 MM010536 0.60 0.54 -5.59
## 537 MM010537 0.25 0.12 -0.44
## 538 MM010538 0.03 0.31 -7.71
## 539 MM010539 0.89 -0.24 5.23
## 540 MM010540 0.96 -0.24 9.19
## 541 MM010541 0.17 0.40 -8.43
## 542 MM010542 0.80 -0.33 3.14
## 543 MM010543 0.08 0.83 -10.87
## 544 MM010544 0.24 0.66 -3.58
## 545 MM010545 0.26 0.47 -3.42
## 546 MM010546 0.70 -0.12 4.82
## 547 MM010547 0.62 -0.25 1.31
## 548 MM010548 0.21 0.08 -6.48
## 549 MM010549 0.16 0.59 -4.49
## 550 MM010550 0.59 -0.07 3.29
## 551 MM010551 0.30 0.42 -4.02
## 552 MM010552 0.70 -0.19 8.63
## 553 MM010553 0.04 0.63 -11.54
## 554 MM010554 0.49 -0.02 -0.65
## 555 MM010555 0.24 0.79 -6.18
## 556 MM010556 0.59 0.04 -5.05
## 557 MM010557 0.03 0.32 -3.79
## 558 MM010558 0.38 0.41 -9.56
## 559 MM010559 0.12 0.73 -12.27
## 560 MM010560 0.34 0.64 -6.26
## 561 MM010561 0.52 0.02 -4.17
## 562 MM010562 0.50 0.19 -5.92
## 563 MM010563 0.91 -0.14 11.06
## 564 MM010564 0.33 0.41 -4.80
## 565 MM010565 0.06 0.50 -0.87
## 566 MM010566 0.64 0.01 -2.16ML
Elastic Net
#data subset
MMPI_subset = cbind(
g = d$g,
MMPI_item_data
)
#which rows are we keeping
MMPI_subset_kept = miss_by_case(MMPI_subset) == 0
MMPI_subset %<>% miss_filter()
#make a recipe
ves_recipe <-
recipe(g ~ ., data = MMPI_subset)
#make a model
#use glmnet
ves_model <-
linear_reg( #tune both, enet
penalty = tune(),
mixture = tune()
) %>%
set_engine("glmnet")
#resampling method
set.seed(1)
ves_folds <- vfold_cv(MMPI_subset, v = 10)
#make workflow
ves_wf <-
workflow() %>%
add_model(ves_model) %>%
add_recipe(ves_recipe)
#fit
ves_fit_enet <- cache_object({
ves_wf %>%
tune_grid(
resamples = ves_folds,
grid = 100,
control = control_grid(
save_pred = TRUE
)
)
}, filename = "cache/ves_fit_enet.rds", renew = F)## Cache found, reading object from disk#metrics
collect_metrics(ves_fit_enet) %>%
filter(.metric == "rsq") %>%
arrange(.metric)collect_metrics(ves_fit_enet) %>%
ggplot(aes(penalty, mean)) +
geom_line() +
facet_wrap(".metric")
#plot data as it is
collect_metrics(ves_fit_enet) %>% filter(.metric == "rsq") %>%
ggplot(aes(penalty, mixture)) +
geom_point(aes(color = mean)) +
scale_x_log10()
#table with data
collect_metrics(ves_fit_enet) %>% filter(.metric == "rsq") %>%
mutate(
log10_penality = log10(penalty)
) %>%
arrange(mean)#smoothing plot with leoss
enet_par_model = loess(mean ~ penalty + mixture, data = collect_metrics(ves_fit_enet) %>% filter(.metric == "rsq"))
#plot it raster style
loess_data = expand_grid(
penalty = 10^-(seq(1, 7, length.out = 10)),
mixture = seq(0, 1, length.out = 10)
)
loess_data$r2 = predict(enet_par_model,
newdata = loess_data)
loess_data %>%
ggplot(aes(log10(penalty), mixture, fill = r2)) +
geom_raster(interpolate = T) +
scale_fill_gradient(high = "green", low = "red") +
coord_cartesian(expand = F)
GG_save("figs/enet_hyperparameters_r2.png")
#plot out of sample predictions
ves_fit_enet %>%
collect_predictions(parameters = select_best(ves_fit_enet, metric = "rsq")) %>%
ggplot(aes(.pred, g)) +
geom_point() +
geom_smooth() +
xlab("Prediction (out of sample)") +
ylab("g")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ggsave("figs/ves_scatter_net.png")## Saving 7 x 5 in image## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#numerical
ves_fit_enet %>%
collect_predictions(parameters = select_best(ves_fit_enet, metric = "rsq")) %$%
cor.test(.pred, g)##
## Pearson's product-moment correlation
##
## data: .pred and g
## t = 100, df = 4318, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.825 0.843
## sample estimates:
## cor
## 0.835#final model fit
ves_fit_enet_final = ves_wf %>%
finalize_workflow(ves_fit_enet %>% select_best(metric = "rsq")) %>%
fit(data = MMPI_subset)
#predict in full data
d$predict_enet_final = predict(ves_fit_enet_final, new_data = MMPI_item_data) %>%
unlist()
#how do they relate to outcomes?
d %>%
select(g, predict_enet_final, education, income, unemployment_3yrs, height, BMI, age) %>%
wtd.cors()## g predict_enet_final education income
## g 1.0000 0.8653 0.5517 0.3965
## predict_enet_final 0.8653 1.0000 0.5282 0.4002
## education 0.5517 0.5282 1.0000 0.3486
## income 0.3965 0.4002 0.3486 1.0000
## unemployment_3yrs -0.2175 -0.2286 -0.1521 -0.4842
## height 0.1311 0.1349 0.0908 0.0917
## BMI -0.0543 -0.0516 -0.0205 0.0197
## age 0.0816 0.0858 0.1633 0.1790
## unemployment_3yrs height BMI age
## g -0.2175 0.13112 -0.05427 0.0816
## predict_enet_final -0.2286 0.13493 -0.05158 0.0858
## education -0.1521 0.09083 -0.02050 0.1633
## income -0.4842 0.09170 0.01973 0.1790
## unemployment_3yrs 1.0000 -0.02779 -0.02026 -0.1070
## height -0.0278 1.00000 -0.00544 0.0140
## BMI -0.0203 -0.00544 1.00000 0.0582
## age -0.1070 0.01403 0.05815 1.0000d %>%
select(g, predict_enet_final, education, income, unemployment_3yrs, height, BMI, age) %>%
wtd.cors() %>%
.[-c(1:2), 1:2] %>%
{
cor.test(.[,1], .[, 2])
}##
## Pearson's product-moment correlation
##
## data: .[, 1] and .[, 2]
## t = 52, df = 4, p-value = 8e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.993 1.000
## sample estimates:
## cor
## 0.999#residual g not predicted
d$g_resid = ols(g ~ predict_enet_final, data = d) %>% resid()
#what does residual correlate with
d %>% select(
g_resid, education, income, unemployment_3yrs, height, BMI, age
) %>% wtd.cors()## g_resid education income unemployment_3yrs height BMI
## g_resid 1.0000 0.1802 0.0982 -0.0437 0.03640 -0.01857
## education 0.1802 1.0000 0.3486 -0.1521 0.09083 -0.02050
## income 0.0982 0.3486 1.0000 -0.4842 0.09170 0.01973
## unemployment_3yrs -0.0437 -0.1521 -0.4842 1.0000 -0.02779 -0.02026
## height 0.0364 0.0908 0.0917 -0.0278 1.00000 -0.00544
## BMI -0.0186 -0.0205 0.0197 -0.0203 -0.00544 1.00000
## age 0.0116 0.1633 0.1790 -0.1070 0.01403 0.05815
## age
## g_resid 0.0116
## education 0.1633
## income 0.1790
## unemployment_3yrs -0.1070
## height 0.0140
## BMI 0.0582
## age 1.0000#correlations to g tests
tibble(
test = fa_g$loadings[, 1] %>% names(),
g_loading = fa_g$loadings[, 1],
r_predicted_g = d %>%
select(g_tests, predict_enet_final) %>%
wtd.cors() %>%
.[g_tests, 20]
) %>%
GG_scatter("g_loading", "r_predicted_g", case_names = "test", repel_names = T)## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(g_tests)` instead of `g_tests` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.## `geom_smooth()` using formula 'y ~ x'
GG_save("figs/Jensen_predicted_g.png")## `geom_smooth()` using formula 'y ~ x'#race gaps
d %>%
select(g, predict_enet_final, P_IRT) %>%
map_df(~standardize(., focal_group = d$race == "White")) %>%
describeBy(group = d$race, mat = T) %>%
select(-item, -vars, -min, -max, -range, -se, -trimmed)#accuracy by group
list(
ols(g ~ predict_enet_final, data = d %>% filter(race %in% c("White", "Black", "Hispanic"))),
ols(g ~ predict_enet_final + race, data = d %>% filter(race %in% c("White", "Black", "Hispanic"))),
ols(g ~ predict_enet_final * race, data = d %>% filter(race %in% c("White", "Black", "Hispanic")))
) %>%
summarize_models()#correlations by group
d %>% plyr::ddply("race", function(x) {
tibble(
r = wtd.cors(x$g, x$predict_enet_final)
)
})#visual
ols(g ~ predict_enet_final * race, data = d %>% filter(race %in% c("White", "Black", "Hispanic"))) %>%
ggpredict(terms = c("predict_enet_final", "race")) %>%
plot() +
ggtitle(NULL) +
xlab("Predicted value of g")
GG_save("figs/predictive_bias.png")
#plot by group
d %>%
filter(race %in% c("White", "Black", "Hispanic")) %>%
ggplot(aes(predict_enet_final, g, color = race)) +
geom_point(alpha = .2) +
geom_smooth() +
xlab("Predicted value of g")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 141 rows containing non-finite values (stat_smooth).## Warning: Removed 141 rows containing missing values (geom_point).
GG_save("figs/predictive_bias2.png")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'## Warning: Removed 141 rows containing non-finite values (stat_smooth).
## Warning: Removed 141 rows containing missing values (geom_point).Lasso
Can we make it sparser?
#make a model
#use glmnet
ves_model <-
linear_reg(
penalty = tune(),
mixture = 1 # lasso
) %>%
set_engine("glmnet")
#make workflow
ves_wf <-
workflow() %>%
add_model(ves_model) %>%
add_recipe(ves_recipe)
#fit
set.seed(1)
ves_fit_lasso <- cache_object({
ves_wf %>%
tune_grid(
resamples = ves_folds,
grid = 100,
control = control_grid(
save_pred = TRUE
)
)
}, "cache/ves_fit_lasso.rds", renew = F)## Cache found, reading object from disk#metrics
collect_metrics(ves_fit_lasso) %>%
filter(.metric == "rsq") %>%
arrange(.metric)collect_metrics(ves_fit_lasso) %>%
ggplot(aes(penalty, mean)) +
geom_line() +
facet_wrap(".metric")## Warning: Removed 3 row(s) containing missing values (geom_path).
#plot out of sample predictions
ves_fit_lasso %>%
collect_predictions(parameters = select_best(ves_fit_lasso, metric = "rsq")) %>%
ggplot(aes(.pred, g)) +
geom_point() +
geom_smooth() +
xlab("Prediction (out of sample)") +
ylab("g")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ggsave("figs/ves_scatter_lasso.png")## Saving 7 x 5 in image## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#numerical
ves_fit_lasso %>%
collect_predictions(parameters = select_best(ves_fit_lasso, metric = "rsq")) %$%
cor.test(.pred, g)##
## Pearson's product-moment correlation
##
## data: .pred and g
## t = 99, df = 4318, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.825 0.843
## sample estimates:
## cor
## 0.834#final model fit
ves_fit_lasso_final = ves_wf %>%
finalize_workflow(parameters = ves_fit_lasso %>% select_best(metric = "rsq")) %>%
fit(data = MMPI_subset)
#get coefs at the right penalty
ves_fit_lasso_final %>%
extract_model() %>%
tidy() %>%
filter(lambda == unlist(select_best(ves_fit_lasso, metric = "rsq")))#except it isn't there, so we use the closest one?
ves_fit_lasso_final %>%
extract_model() %>%
tidy() %>%
pull(lambda) %>%
unique() %>% {
#which value is closest?
best = .[which.min(abs(. - unlist(select_best(ves_fit_lasso, metric = "rsq"))))]
#get results for that one
ves_fit_lasso_final$fit$fit$fit %>%
tidy() %>%
filter(lambda == best)
}50 random items
What’s the best 50 items to use?
#data subset, 50 first items
set.seed(1)
MMPI_subset_50 = cbind(
g = d$g,
MMPI_item_data
)[c(1, sample(2:ncol(MMPI_item_data), size = 50))]
#which rows are we keeping
MMPI_subset_50_kept = miss_by_case(MMPI_subset_50) == 0
MMPI_subset_50 %<>% miss_filter()
#make a recipe
ves_recipe <-
recipe(g ~ ., data = MMPI_subset_50)
#make a model
#use glmnet
ves_model <-
linear_reg( #tune penalty only
penalty = tune(),
mixture = 0
) %>%
set_engine("glmnet")
#resampling method
set.seed(1)
ves_folds <- vfold_cv(MMPI_subset_50, v = 10)
#make workflow
ves_wf <-
workflow() %>%
add_model(ves_model) %>%
add_recipe(ves_recipe)
#fit
ves_fit_enet_50 <- cache_object({
ves_wf %>%
tune_grid(
resamples = ves_folds,
grid = 100,
control = control_grid(
save_pred = TRUE
)
)
}, filename = "cache/ves_fit_enet_50.rds", renew = F)## Cache found, reading object from disk#metrics
collect_metrics(ves_fit_enet_50) %>%
filter(.metric == "rsq") %>%
arrange(-mean)collect_metrics(ves_fit_enet_50) %>%
ggplot(aes(penalty, mean)) +
geom_line() +
facet_wrap(".metric", scales = "free_y")
GG_save("figs/enet_hyperparameters_r2_50.png")glmnet directly
These are based on the deviance metric, but that’s different from what tidymodels does.
set.seed(1)
glmnet_fit = cv.glmnet(
x = MMPI_subset[-1] %>% as.matrix(),
y = MMPI_subset$g,
nfolds = 10
)
#results
glmnet_fit##
## Call: cv.glmnet(x = MMPI_subset[-1] %>% as.matrix(), y = MMPI_subset$g, nfolds = 10)
##
## Measure: Mean-Squared Error
##
## Lambda Measure SE Nonzero
## min 0.00666 0.348 0.00618 363
## 1se 0.01277 0.353 0.00602 258glmnet_fit %>% plot()
#validation results data frame
glmnet_fit_df = tibble(
r2 = (1 - glmnet_fit$cvm) / var(MMPI_subset$g),
r = r2 %>% sqrt(),
nonzero = glmnet_fit$nzero
)## Warning in sqrt(.): NaNs produced#r2 plot
glmnet_fit_df%>%
ggplot(aes(nonzero, r2)) +
geom_line() +
scale_x_continuous(breaks = seq(0, 600, by = 25)) +
scale_y_continuous(breaks = seq(-1, 1, by = .05))
#r
glmnet_fit_df%>%
ggplot(aes(nonzero, r)) +
geom_line() +
scale_x_continuous(breaks = seq(0, 600, by = 25)) +
scale_y_continuous(breaks = seq(0, 1, by = .05))## Warning: Removed 5 row(s) containing missing values (geom_path).
glmnet manual CV
We do it manually to get the same metric and with proper CV.
#we train on the folds we made above, and predict out of sample to evaluate
#premake penalities
glmnet_lambdas = 10^(seq(-.5, -4, length.out = 100))
#fit
glmnet_preds = cache_object({
set.seed(1)
glmnet_preds = tibble()
for (i_fold in seq_along_rows(ves_folds)) {
message(str_glue("{i_fold} of {nrow(ves_folds)}"))
#get the train and test
i_train = MMPI_subset[ves_folds$splits[[i_fold]]$in_id, ]
i_test = MMPI_subset[setdiff(1:nrow(MMPI_subset), ves_folds$splits[[i_fold]]$in_id), ]
#fit
i_fit = cv.glmnet(
x = i_train[-1] %>% as.matrix(),
y = i_train$g,
nfolds = 10,
lambda = glmnet_lambdas
)
#predict into test
i_predict = tibble(
g = i_test$g
) %>%
bind_cols(
predict(i_fit, s = glmnet_lambdas, newx = i_test[, -1] %>% as.matrix()) %>% as.data.frame() %>% set_colnames("predict"+(seq_along(glmnet_lambdas)))
)
#save
glmnet_preds = bind_rows(
glmnet_preds,
i_predict
)
}
glmnet_preds
}, filename = "cache/glmnet_manual_cv.rds", compress = "xz", renew = F)## Cache found, reading object from disk#numerical
glmnet_preds %>% wtd.cors()## g predict1 predict2 predict3 predict4 predict5 predict6 predict7
## g 1.000 0.496 0.532 0.556 0.577 0.603 0.627 0.648
## predict1 0.496 1.000 0.992 0.976 0.957 0.937 0.914 0.892
## predict2 0.532 0.992 1.000 0.995 0.985 0.970 0.951 0.932
## predict3 0.556 0.976 0.995 1.000 0.996 0.986 0.971 0.955
## predict4 0.577 0.957 0.985 0.996 1.000 0.996 0.986 0.972
## predict5 0.603 0.937 0.970 0.986 0.996 1.000 0.997 0.988
## predict6 0.627 0.914 0.951 0.971 0.986 0.997 1.000 0.997
## predict7 0.648 0.892 0.932 0.955 0.972 0.988 0.997 1.000
## predict8 0.667 0.871 0.913 0.938 0.958 0.978 0.991 0.998
## predict9 0.685 0.851 0.894 0.920 0.942 0.965 0.982 0.992
## predict10 0.700 0.832 0.876 0.904 0.927 0.952 0.971 0.984
## predict11 0.712 0.815 0.860 0.888 0.912 0.939 0.960 0.976
## predict12 0.724 0.798 0.844 0.873 0.898 0.926 0.949 0.966
## predict13 0.735 0.784 0.829 0.859 0.884 0.914 0.938 0.957
## predict14 0.744 0.770 0.816 0.845 0.871 0.902 0.927 0.947
## predict15 0.752 0.757 0.803 0.833 0.860 0.891 0.917 0.938
## predict16 0.760 0.745 0.792 0.822 0.848 0.880 0.907 0.929
## predict17 0.766 0.735 0.781 0.811 0.838 0.870 0.898 0.920
## predict18 0.773 0.725 0.770 0.801 0.828 0.861 0.888 0.911
## predict19 0.779 0.715 0.761 0.791 0.818 0.852 0.880 0.903
## predict20 0.784 0.706 0.752 0.782 0.810 0.843 0.871 0.895
## predict21 0.789 0.698 0.744 0.774 0.801 0.835 0.864 0.887
## predict22 0.794 0.691 0.736 0.766 0.794 0.827 0.856 0.880
## predict23 0.798 0.683 0.729 0.759 0.786 0.820 0.849 0.873
## predict24 0.801 0.677 0.722 0.752 0.779 0.813 0.842 0.867
## predict25 0.805 0.670 0.715 0.745 0.773 0.807 0.836 0.861
## predict26 0.808 0.665 0.709 0.739 0.767 0.801 0.830 0.855
## predict27 0.810 0.659 0.704 0.734 0.761 0.795 0.825 0.850
## predict28 0.813 0.654 0.699 0.729 0.756 0.790 0.819 0.844
## predict29 0.815 0.649 0.693 0.723 0.751 0.785 0.814 0.839
## predict30 0.818 0.644 0.689 0.719 0.746 0.780 0.809 0.834
## predict31 0.820 0.640 0.684 0.714 0.741 0.775 0.805 0.830
## predict32 0.822 0.636 0.680 0.710 0.737 0.771 0.800 0.825
## predict33 0.824 0.632 0.676 0.706 0.733 0.767 0.796 0.821
## predict34 0.825 0.629 0.672 0.702 0.729 0.763 0.792 0.817
## predict35 0.827 0.625 0.669 0.698 0.725 0.759 0.788 0.814
## predict36 0.828 0.622 0.665 0.695 0.722 0.755 0.785 0.810
## predict37 0.829 0.619 0.662 0.692 0.719 0.752 0.781 0.807
## predict38 0.830 0.616 0.659 0.689 0.715 0.749 0.778 0.803
## predict39 0.831 0.613 0.656 0.686 0.712 0.746 0.775 0.800
## predict40 0.832 0.611 0.654 0.683 0.709 0.743 0.772 0.797
## predict41 0.833 0.608 0.651 0.680 0.707 0.740 0.769 0.794
## predict42 0.833 0.606 0.648 0.678 0.704 0.737 0.766 0.791
## predict43 0.834 0.603 0.646 0.675 0.702 0.734 0.764 0.789
## predict44 0.834 0.601 0.644 0.673 0.699 0.732 0.761 0.786
## predict45 0.834 0.599 0.642 0.671 0.697 0.730 0.759 0.784
## predict46 0.834 0.598 0.640 0.669 0.695 0.728 0.757 0.782
## predict47 0.834 0.596 0.638 0.667 0.693 0.726 0.755 0.779
## predict48 0.834 0.594 0.636 0.665 0.691 0.724 0.752 0.777
## predict49 0.834 0.592 0.635 0.663 0.689 0.722 0.750 0.775
## predict50 0.834 0.591 0.633 0.662 0.687 0.720 0.749 0.773
## predict51 0.834 0.589 0.631 0.660 0.686 0.718 0.747 0.771
## predict52 0.834 0.588 0.630 0.658 0.684 0.716 0.745 0.770
## predict53 0.834 0.587 0.628 0.657 0.683 0.715 0.743 0.768
## predict54 0.833 0.585 0.627 0.656 0.681 0.713 0.742 0.766
## predict55 0.833 0.584 0.626 0.654 0.680 0.712 0.740 0.765
## predict56 0.833 0.583 0.625 0.653 0.679 0.711 0.739 0.763
## predict57 0.833 0.582 0.623 0.652 0.677 0.709 0.738 0.762
## predict58 0.832 0.581 0.622 0.651 0.676 0.708 0.736 0.761
## predict59 0.832 0.580 0.621 0.650 0.675 0.707 0.735 0.760
## predict60 0.832 0.579 0.620 0.649 0.674 0.706 0.734 0.759
## predict61 0.831 0.578 0.620 0.648 0.673 0.705 0.733 0.757
## predict62 0.831 0.577 0.619 0.647 0.672 0.704 0.732 0.756
## predict63 0.831 0.577 0.618 0.646 0.671 0.703 0.731 0.755
## predict64 0.830 0.576 0.617 0.645 0.671 0.702 0.730 0.755
## predict65 0.830 0.575 0.616 0.645 0.670 0.701 0.730 0.754
## predict66 0.830 0.575 0.616 0.644 0.669 0.701 0.729 0.753
## predict67 0.829 0.574 0.615 0.643 0.668 0.700 0.728 0.752
## predict68 0.829 0.574 0.615 0.643 0.668 0.699 0.727 0.751
## predict69 0.829 0.573 0.614 0.642 0.667 0.699 0.727 0.751
## predict70 0.828 0.573 0.614 0.642 0.667 0.698 0.726 0.750
## predict71 0.828 0.572 0.613 0.641 0.666 0.698 0.726 0.750
## predict72 0.828 0.572 0.613 0.641 0.666 0.697 0.725 0.749
## predict73 0.828 0.571 0.612 0.640 0.665 0.697 0.725 0.749
## predict74 0.827 0.571 0.612 0.640 0.665 0.696 0.724 0.748
## predict75 0.827 0.571 0.612 0.639 0.664 0.696 0.724 0.748
## predict76 0.827 0.570 0.611 0.639 0.664 0.695 0.723 0.747
## predict77 0.827 0.570 0.611 0.639 0.664 0.695 0.723 0.747
## predict78 0.826 0.570 0.611 0.638 0.663 0.695 0.723 0.747
## predict79 0.826 0.570 0.610 0.638 0.663 0.694 0.722 0.746
## predict80 0.826 0.569 0.610 0.638 0.663 0.694 0.722 0.746
## predict81 0.826 0.569 0.610 0.638 0.663 0.694 0.722 0.746
## predict82 0.826 0.569 0.610 0.637 0.662 0.694 0.722 0.746
## predict83 0.826 0.569 0.609 0.637 0.662 0.693 0.721 0.745
## predict84 0.826 0.569 0.609 0.637 0.662 0.693 0.721 0.745
## predict85 0.825 0.568 0.609 0.637 0.662 0.693 0.721 0.745
## predict86 0.825 0.568 0.609 0.637 0.662 0.693 0.721 0.745
## predict87 0.825 0.568 0.609 0.637 0.661 0.693 0.721 0.745
## predict88 0.825 0.568 0.609 0.636 0.661 0.693 0.720 0.744
## predict89 0.825 0.568 0.609 0.636 0.661 0.692 0.720 0.744
## predict90 0.825 0.568 0.608 0.636 0.661 0.692 0.720 0.744
## predict91 0.825 0.568 0.608 0.636 0.661 0.692 0.720 0.744
## predict92 0.825 0.568 0.608 0.636 0.661 0.692 0.720 0.744
## predict93 0.825 0.567 0.608 0.636 0.661 0.692 0.720 0.744
## predict94 0.825 0.567 0.608 0.636 0.661 0.692 0.720 0.744
## predict95 0.825 0.567 0.608 0.636 0.661 0.692 0.720 0.744
## predict96 0.825 0.567 0.608 0.636 0.661 0.692 0.720 0.743
## predict97 0.825 0.567 0.608 0.636 0.660 0.692 0.719 0.743
## predict98 0.825 0.567 0.608 0.635 0.660 0.692 0.719 0.743
## predict99 0.824 0.567 0.608 0.635 0.660 0.691 0.719 0.743
## predict100 0.824 0.567 0.608 0.635 0.660 0.691 0.719 0.743
## predict8 predict9 predict10 predict11 predict12 predict13 predict14
## g 0.667 0.685 0.700 0.712 0.724 0.735 0.744
## predict1 0.871 0.851 0.832 0.815 0.798 0.784 0.770
## predict2 0.913 0.894 0.876 0.860 0.844 0.829 0.816
## predict3 0.938 0.920 0.904 0.888 0.873 0.859 0.845
## predict4 0.958 0.942 0.927 0.912 0.898 0.884 0.871
## predict5 0.978 0.965 0.952 0.939 0.926 0.914 0.902
## predict6 0.991 0.982 0.971 0.960 0.949 0.938 0.927
## predict7 0.998 0.992 0.984 0.976 0.966 0.957 0.947
## predict8 1.000 0.998 0.993 0.987 0.980 0.972 0.963
## predict9 0.998 1.000 0.998 0.995 0.990 0.983 0.976
## predict10 0.993 0.998 1.000 0.999 0.996 0.991 0.986
## predict11 0.987 0.995 0.999 1.000 0.999 0.996 0.992
## predict12 0.980 0.990 0.996 0.999 1.000 0.999 0.997
## predict13 0.972 0.983 0.991 0.996 0.999 1.000 0.999
## predict14 0.963 0.976 0.986 0.992 0.997 0.999 1.000
## predict15 0.955 0.969 0.980 0.987 0.993 0.997 0.999
## predict16 0.947 0.962 0.974 0.982 0.989 0.994 0.998
## predict17 0.939 0.955 0.967 0.977 0.985 0.991 0.995
## predict18 0.931 0.948 0.961 0.971 0.980 0.987 0.992
## predict19 0.923 0.941 0.954 0.965 0.975 0.982 0.988
## predict20 0.916 0.934 0.948 0.959 0.969 0.978 0.984
## predict21 0.909 0.927 0.942 0.954 0.964 0.973 0.980
## predict22 0.902 0.921 0.936 0.948 0.959 0.968 0.976
## predict23 0.895 0.915 0.930 0.943 0.954 0.964 0.972
## predict24 0.889 0.909 0.924 0.937 0.949 0.959 0.967
## predict25 0.883 0.903 0.919 0.932 0.944 0.954 0.963
## predict26 0.878 0.897 0.913 0.927 0.939 0.950 0.959
## predict27 0.872 0.892 0.909 0.922 0.934 0.946 0.955
## predict28 0.867 0.887 0.904 0.918 0.930 0.941 0.951
## predict29 0.862 0.883 0.899 0.913 0.926 0.937 0.947
## predict30 0.857 0.878 0.895 0.909 0.921 0.933 0.943
## predict31 0.853 0.873 0.890 0.904 0.917 0.929 0.939
## predict32 0.849 0.869 0.886 0.900 0.913 0.926 0.936
## predict33 0.845 0.865 0.882 0.896 0.910 0.922 0.932
## predict34 0.841 0.861 0.878 0.893 0.906 0.918 0.929
## predict35 0.837 0.858 0.875 0.889 0.902 0.915 0.925
## predict36 0.833 0.854 0.871 0.886 0.899 0.912 0.922
## predict37 0.830 0.851 0.868 0.882 0.896 0.908 0.919
## predict38 0.827 0.848 0.865 0.879 0.893 0.905 0.916
## predict39 0.823 0.844 0.861 0.876 0.889 0.902 0.913
## predict40 0.820 0.841 0.858 0.873 0.886 0.899 0.910
## predict41 0.817 0.838 0.855 0.870 0.883 0.896 0.907
## predict42 0.814 0.835 0.853 0.867 0.881 0.894 0.904
## predict43 0.812 0.833 0.850 0.865 0.878 0.891 0.902
## predict44 0.809 0.830 0.847 0.862 0.876 0.889 0.899
## predict45 0.807 0.828 0.845 0.860 0.873 0.886 0.897
## predict46 0.805 0.826 0.843 0.857 0.871 0.884 0.895
## predict47 0.802 0.823 0.840 0.855 0.869 0.882 0.892
## predict48 0.800 0.821 0.838 0.853 0.866 0.879 0.890
## predict49 0.798 0.819 0.836 0.851 0.864 0.877 0.888
## predict50 0.796 0.817 0.834 0.849 0.862 0.875 0.886
## predict51 0.794 0.815 0.832 0.847 0.860 0.873 0.884
## predict52 0.792 0.813 0.830 0.845 0.858 0.872 0.882
## predict53 0.791 0.812 0.829 0.843 0.857 0.870 0.881
## predict54 0.789 0.810 0.827 0.841 0.855 0.868 0.879
## predict55 0.788 0.808 0.825 0.840 0.853 0.867 0.877
## predict56 0.786 0.807 0.824 0.838 0.852 0.865 0.876
## predict57 0.785 0.805 0.822 0.837 0.851 0.864 0.875
## predict58 0.784 0.804 0.821 0.836 0.849 0.862 0.873
## predict59 0.782 0.803 0.820 0.834 0.848 0.861 0.872
## predict60 0.781 0.802 0.819 0.833 0.847 0.860 0.871
## predict61 0.780 0.801 0.818 0.832 0.846 0.859 0.870
## predict62 0.779 0.800 0.816 0.831 0.844 0.858 0.868
## predict63 0.778 0.799 0.815 0.830 0.843 0.856 0.867
## predict64 0.777 0.798 0.815 0.829 0.842 0.856 0.866
## predict65 0.776 0.797 0.814 0.828 0.842 0.855 0.866
## predict66 0.775 0.796 0.813 0.827 0.841 0.854 0.865
## predict67 0.775 0.795 0.812 0.826 0.840 0.853 0.864
## predict68 0.774 0.794 0.811 0.826 0.839 0.852 0.863
## predict69 0.773 0.794 0.811 0.825 0.839 0.852 0.863
## predict70 0.773 0.793 0.810 0.824 0.838 0.851 0.862
## predict71 0.772 0.793 0.809 0.824 0.837 0.850 0.861
## predict72 0.772 0.792 0.809 0.823 0.837 0.850 0.861
## predict73 0.771 0.792 0.808 0.823 0.836 0.849 0.860
## predict74 0.771 0.791 0.808 0.822 0.836 0.849 0.860
## predict75 0.770 0.791 0.807 0.822 0.835 0.848 0.859
## predict76 0.770 0.790 0.807 0.821 0.835 0.848 0.859
## predict77 0.769 0.790 0.807 0.821 0.834 0.848 0.858
## predict78 0.769 0.789 0.806 0.821 0.834 0.847 0.858
## predict79 0.769 0.789 0.806 0.820 0.834 0.847 0.858
## predict80 0.768 0.789 0.806 0.820 0.833 0.847 0.857
## predict81 0.768 0.789 0.805 0.820 0.833 0.846 0.857
## predict82 0.768 0.788 0.805 0.819 0.833 0.846 0.857
## predict83 0.768 0.788 0.805 0.819 0.833 0.846 0.857
## predict84 0.767 0.788 0.805 0.819 0.832 0.845 0.856
## predict85 0.767 0.788 0.804 0.819 0.832 0.845 0.856
## predict86 0.767 0.787 0.804 0.819 0.832 0.845 0.856
## predict87 0.767 0.787 0.804 0.818 0.832 0.845 0.856
## predict88 0.767 0.787 0.804 0.818 0.832 0.845 0.856
## predict89 0.767 0.787 0.804 0.818 0.831 0.845 0.855
## predict90 0.766 0.787 0.803 0.818 0.831 0.844 0.855
## predict91 0.766 0.787 0.803 0.818 0.831 0.844 0.855
## predict92 0.766 0.786 0.803 0.818 0.831 0.844 0.855
## predict93 0.766 0.786 0.803 0.817 0.831 0.844 0.855
## predict94 0.766 0.786 0.803 0.817 0.831 0.844 0.855
## predict95 0.766 0.786 0.803 0.817 0.831 0.844 0.855
## predict96 0.766 0.786 0.803 0.817 0.831 0.844 0.855
## predict97 0.766 0.786 0.803 0.817 0.831 0.844 0.854
## predict98 0.766 0.786 0.803 0.817 0.830 0.844 0.854
## predict99 0.765 0.786 0.803 0.817 0.830 0.843 0.854
## predict100 0.765 0.786 0.803 0.817 0.830 0.843 0.854
## predict15 predict16 predict17 predict18 predict19 predict20
## g 0.752 0.760 0.766 0.773 0.779 0.784
## predict1 0.757 0.745 0.735 0.725 0.715 0.706
## predict2 0.803 0.792 0.781 0.770 0.761 0.752
## predict3 0.833 0.822 0.811 0.801 0.791 0.782
## predict4 0.860 0.848 0.838 0.828 0.818 0.810
## predict5 0.891 0.880 0.870 0.861 0.852 0.843
## predict6 0.917 0.907 0.898 0.888 0.880 0.871
## predict7 0.938 0.929 0.920 0.911 0.903 0.895
## predict8 0.955 0.947 0.939 0.931 0.923 0.916
## predict9 0.969 0.962 0.955 0.948 0.941 0.934
## predict10 0.980 0.974 0.967 0.961 0.954 0.948
## predict11 0.987 0.982 0.977 0.971 0.965 0.959
## predict12 0.993 0.989 0.985 0.980 0.975 0.969
## predict13 0.997 0.994 0.991 0.987 0.982 0.978
## predict14 0.999 0.998 0.995 0.992 0.988 0.984
## predict15 1.000 0.999 0.998 0.996 0.993 0.989
## predict16 0.999 1.000 0.999 0.998 0.996 0.993
## predict17 0.998 0.999 1.000 1.000 0.998 0.996
## predict18 0.996 0.998 1.000 1.000 1.000 0.998
## predict19 0.993 0.996 0.998 1.000 1.000 1.000
## predict20 0.989 0.993 0.996 0.998 1.000 1.000
## predict21 0.986 0.990 0.994 0.997 0.999 1.000
## predict22 0.982 0.987 0.991 0.995 0.997 0.999
## predict23 0.978 0.983 0.988 0.992 0.995 0.997
## predict24 0.974 0.980 0.985 0.989 0.993 0.995
## predict25 0.970 0.976 0.982 0.986 0.990 0.993
## predict26 0.966 0.973 0.978 0.984 0.988 0.991
## predict27 0.962 0.969 0.975 0.981 0.985 0.989
## predict28 0.959 0.966 0.972 0.978 0.982 0.986
## predict29 0.955 0.962 0.969 0.974 0.980 0.984
## predict30 0.951 0.959 0.965 0.971 0.977 0.981
## predict31 0.948 0.955 0.962 0.968 0.974 0.978
## predict32 0.944 0.952 0.959 0.965 0.971 0.976
## predict33 0.941 0.948 0.955 0.962 0.968 0.973
## predict34 0.937 0.945 0.952 0.959 0.965 0.970
## predict35 0.934 0.942 0.949 0.956 0.962 0.968
## predict36 0.931 0.939 0.946 0.953 0.960 0.965
## predict37 0.928 0.936 0.943 0.951 0.957 0.963
## predict38 0.925 0.933 0.941 0.948 0.954 0.960
## predict39 0.922 0.930 0.938 0.945 0.952 0.957
## predict40 0.919 0.927 0.935 0.942 0.949 0.955
## predict41 0.916 0.925 0.932 0.940 0.946 0.952
## predict42 0.914 0.922 0.930 0.937 0.944 0.950
## predict43 0.911 0.919 0.927 0.935 0.942 0.948
## predict44 0.909 0.917 0.925 0.932 0.939 0.945
## predict45 0.906 0.915 0.923 0.930 0.937 0.943
## predict46 0.904 0.912 0.920 0.928 0.935 0.941
## predict47 0.902 0.910 0.918 0.926 0.933 0.939
## predict48 0.900 0.908 0.916 0.924 0.931 0.937
## predict49 0.897 0.906 0.914 0.922 0.929 0.935
## predict50 0.895 0.904 0.912 0.920 0.927 0.933
## predict51 0.894 0.902 0.910 0.918 0.925 0.931
## predict52 0.892 0.900 0.908 0.916 0.923 0.930
## predict53 0.890 0.899 0.907 0.915 0.922 0.928
## predict54 0.888 0.897 0.905 0.913 0.920 0.926
## predict55 0.887 0.895 0.904 0.911 0.919 0.925
## predict56 0.885 0.894 0.902 0.910 0.917 0.923
## predict57 0.884 0.893 0.901 0.909 0.916 0.922
## predict58 0.883 0.891 0.899 0.907 0.914 0.921
## predict59 0.881 0.890 0.898 0.906 0.913 0.920
## predict60 0.880 0.889 0.897 0.905 0.912 0.918
## predict61 0.879 0.888 0.896 0.904 0.911 0.917
## predict62 0.878 0.886 0.895 0.903 0.910 0.916
## predict63 0.877 0.885 0.894 0.901 0.909 0.915
## predict64 0.876 0.885 0.893 0.901 0.908 0.914
## predict65 0.875 0.884 0.892 0.900 0.907 0.913
## predict66 0.874 0.883 0.891 0.899 0.906 0.913
## predict67 0.873 0.882 0.890 0.898 0.905 0.912
## predict68 0.873 0.881 0.889 0.897 0.905 0.911
## predict69 0.872 0.881 0.889 0.897 0.904 0.910
## predict70 0.871 0.880 0.888 0.896 0.903 0.910
## predict71 0.871 0.879 0.888 0.895 0.903 0.909
## predict72 0.870 0.879 0.887 0.895 0.902 0.909
## predict73 0.870 0.878 0.886 0.894 0.902 0.908
## predict74 0.869 0.878 0.886 0.894 0.901 0.908
## predict75 0.869 0.877 0.886 0.893 0.901 0.907
## predict76 0.868 0.877 0.885 0.893 0.900 0.907
## predict77 0.868 0.876 0.885 0.893 0.900 0.906
## predict78 0.867 0.876 0.884 0.892 0.899 0.906
## predict79 0.867 0.876 0.884 0.892 0.899 0.906
## predict80 0.867 0.875 0.884 0.892 0.899 0.905
## predict81 0.866 0.875 0.883 0.891 0.899 0.905
## predict82 0.866 0.875 0.883 0.891 0.898 0.905
## predict83 0.866 0.875 0.883 0.891 0.898 0.904
## predict84 0.866 0.874 0.883 0.890 0.898 0.904
## predict85 0.865 0.874 0.882 0.890 0.898 0.904
## predict86 0.865 0.874 0.882 0.890 0.897 0.904
## predict87 0.865 0.874 0.882 0.890 0.897 0.904
## predict88 0.865 0.874 0.882 0.890 0.897 0.903
## predict89 0.865 0.873 0.882 0.890 0.897 0.903
## predict90 0.865 0.873 0.882 0.889 0.897 0.903
## predict91 0.864 0.873 0.881 0.889 0.897 0.903
## predict92 0.864 0.873 0.881 0.889 0.896 0.903
## predict93 0.864 0.873 0.881 0.889 0.896 0.903
## predict94 0.864 0.873 0.881 0.889 0.896 0.903
## predict95 0.864 0.873 0.881 0.889 0.896 0.903
## predict96 0.864 0.873 0.881 0.889 0.896 0.902
## predict97 0.864 0.873 0.881 0.889 0.896 0.902
## predict98 0.864 0.872 0.881 0.889 0.896 0.902
## predict99 0.864 0.872 0.881 0.888 0.896 0.902
## predict100 0.864 0.872 0.881 0.888 0.896 0.902
## predict21 predict22 predict23 predict24 predict25 predict26
## g 0.789 0.794 0.798 0.801 0.805 0.808
## predict1 0.698 0.691 0.683 0.677 0.670 0.665
## predict2 0.744 0.736 0.729 0.722 0.715 0.709
## predict3 0.774 0.766 0.759 0.752 0.745 0.739
## predict4 0.801 0.794 0.786 0.779 0.773 0.767
## predict5 0.835 0.827 0.820 0.813 0.807 0.801
## predict6 0.864 0.856 0.849 0.842 0.836 0.830
## predict7 0.887 0.880 0.873 0.867 0.861 0.855
## predict8 0.909 0.902 0.895 0.889 0.883 0.878
## predict9 0.927 0.921 0.915 0.909 0.903 0.897
## predict10 0.942 0.936 0.930 0.924 0.919 0.913
## predict11 0.954 0.948 0.943 0.937 0.932 0.927
## predict12 0.964 0.959 0.954 0.949 0.944 0.939
## predict13 0.973 0.968 0.964 0.959 0.954 0.950
## predict14 0.980 0.976 0.972 0.967 0.963 0.959
## predict15 0.986 0.982 0.978 0.974 0.970 0.966
## predict16 0.990 0.987 0.983 0.980 0.976 0.973
## predict17 0.994 0.991 0.988 0.985 0.982 0.978
## predict18 0.997 0.995 0.992 0.989 0.986 0.984
## predict19 0.999 0.997 0.995 0.993 0.990 0.988
## predict20 1.000 0.999 0.997 0.995 0.993 0.991
## predict21 1.000 1.000 0.999 0.998 0.996 0.994
## predict22 1.000 1.000 1.000 0.999 0.998 0.996
## predict23 0.999 1.000 1.000 1.000 0.999 0.998
## predict24 0.998 0.999 1.000 1.000 1.000 0.999
## predict25 0.996 0.998 0.999 1.000 1.000 1.000
## predict26 0.994 0.996 0.998 0.999 1.000 1.000
## predict27 0.992 0.995 0.997 0.998 0.999 1.000
## predict28 0.990 0.993 0.995 0.997 0.998 0.999
## predict29 0.988 0.991 0.993 0.996 0.997 0.998
## predict30 0.985 0.989 0.991 0.994 0.996 0.997
## predict31 0.983 0.986 0.989 0.992 0.994 0.996
## predict32 0.980 0.984 0.987 0.990 0.992 0.994
## predict33 0.978 0.982 0.985 0.988 0.991 0.993
## predict34 0.975 0.979 0.983 0.986 0.989 0.991
## predict35 0.973 0.977 0.981 0.984 0.987 0.989
## predict36 0.970 0.975 0.978 0.982 0.985 0.987
## predict37 0.968 0.972 0.976 0.980 0.983 0.986
## predict38 0.965 0.970 0.974 0.978 0.981 0.984
## predict39 0.963 0.967 0.972 0.975 0.979 0.982
## predict40 0.960 0.965 0.969 0.973 0.977 0.980
## predict41 0.958 0.963 0.967 0.971 0.975 0.978
## predict42 0.955 0.960 0.965 0.969 0.973 0.976
## predict43 0.953 0.958 0.963 0.967 0.971 0.974
## predict44 0.951 0.956 0.961 0.965 0.969 0.972
## predict45 0.949 0.954 0.959 0.963 0.967 0.970
## predict46 0.947 0.952 0.957 0.961 0.965 0.968
## predict47 0.945 0.950 0.955 0.959 0.963 0.966
## predict48 0.943 0.948 0.953 0.957 0.961 0.965
## predict49 0.941 0.946 0.951 0.955 0.959 0.963
## predict50 0.939 0.944 0.949 0.954 0.958 0.961
## predict51 0.937 0.943 0.948 0.952 0.956 0.960
## predict52 0.936 0.941 0.946 0.950 0.954 0.958
## predict53 0.934 0.939 0.944 0.949 0.953 0.957
## predict54 0.932 0.938 0.943 0.947 0.951 0.955
## predict55 0.931 0.936 0.941 0.946 0.950 0.954
## predict56 0.929 0.935 0.940 0.944 0.949 0.952
## predict57 0.928 0.934 0.939 0.943 0.947 0.951
## predict58 0.927 0.932 0.937 0.942 0.946 0.950
## predict59 0.926 0.931 0.936 0.941 0.945 0.949
## predict60 0.924 0.930 0.935 0.940 0.944 0.948
## predict61 0.923 0.929 0.934 0.939 0.943 0.947
## predict62 0.922 0.928 0.933 0.938 0.942 0.946
## predict63 0.921 0.927 0.932 0.937 0.941 0.945
## predict64 0.920 0.926 0.931 0.936 0.940 0.944
## predict65 0.919 0.925 0.930 0.935 0.939 0.943
## predict66 0.919 0.924 0.929 0.934 0.938 0.942
## predict67 0.918 0.923 0.929 0.933 0.937 0.941
## predict68 0.917 0.923 0.928 0.933 0.937 0.941
## predict69 0.916 0.922 0.927 0.932 0.936 0.940
## predict70 0.916 0.921 0.927 0.931 0.936 0.940
## predict71 0.915 0.921 0.926 0.931 0.935 0.939
## predict72 0.915 0.920 0.925 0.930 0.934 0.938
## predict73 0.914 0.920 0.925 0.930 0.934 0.938
## predict74 0.914 0.919 0.925 0.929 0.933 0.938
## predict75 0.913 0.919 0.924 0.929 0.933 0.937
## predict76 0.913 0.918 0.924 0.928 0.933 0.937
## predict77 0.912 0.918 0.923 0.928 0.932 0.936
## predict78 0.912 0.918 0.923 0.928 0.932 0.936
## predict79 0.912 0.917 0.923 0.927 0.932 0.936
## predict80 0.911 0.917 0.922 0.927 0.931 0.935
## predict81 0.911 0.917 0.922 0.927 0.931 0.935
## predict82 0.911 0.917 0.922 0.926 0.931 0.935
## predict83 0.911 0.916 0.921 0.926 0.931 0.935
## predict84 0.910 0.916 0.921 0.926 0.930 0.934
## predict85 0.910 0.916 0.921 0.926 0.930 0.934
## predict86 0.910 0.916 0.921 0.926 0.930 0.934
## predict87 0.910 0.915 0.921 0.925 0.930 0.934
## predict88 0.910 0.915 0.920 0.925 0.930 0.934
## predict89 0.909 0.915 0.920 0.925 0.929 0.933
## predict90 0.909 0.915 0.920 0.925 0.929 0.933
## predict91 0.909 0.915 0.920 0.925 0.929 0.933
## predict92 0.909 0.915 0.920 0.925 0.929 0.933
## predict93 0.909 0.915 0.920 0.925 0.929 0.933
## predict94 0.909 0.914 0.920 0.924 0.929 0.933
## predict95 0.909 0.914 0.920 0.924 0.929 0.933
## predict96 0.909 0.914 0.920 0.924 0.929 0.933
## predict97 0.909 0.914 0.919 0.924 0.928 0.933
## predict98 0.908 0.914 0.919 0.924 0.928 0.932
## predict99 0.908 0.914 0.919 0.924 0.928 0.932
## predict100 0.908 0.914 0.919 0.924 0.928 0.932
## predict27 predict28 predict29 predict30 predict31 predict32
## g 0.810 0.813 0.815 0.818 0.820 0.822
## predict1 0.659 0.654 0.649 0.644 0.640 0.636
## predict2 0.704 0.699 0.693 0.689 0.684 0.680
## predict3 0.734 0.729 0.723 0.719 0.714 0.710
## predict4 0.761 0.756 0.751 0.746 0.741 0.737
## predict5 0.795 0.790 0.785 0.780 0.775 0.771
## predict6 0.825 0.819 0.814 0.809 0.805 0.800
## predict7 0.850 0.844 0.839 0.834 0.830 0.825
## predict8 0.872 0.867 0.862 0.857 0.853 0.849
## predict9 0.892 0.887 0.883 0.878 0.873 0.869
## predict10 0.909 0.904 0.899 0.895 0.890 0.886
## predict11 0.922 0.918 0.913 0.909 0.904 0.900
## predict12 0.934 0.930 0.926 0.921 0.917 0.913
## predict13 0.946 0.941 0.937 0.933 0.929 0.926
## predict14 0.955 0.951 0.947 0.943 0.939 0.936
## predict15 0.962 0.959 0.955 0.951 0.948 0.944
## predict16 0.969 0.966 0.962 0.959 0.955 0.952
## predict17 0.975 0.972 0.969 0.965 0.962 0.959
## predict18 0.981 0.978 0.974 0.971 0.968 0.965
## predict19 0.985 0.982 0.980 0.977 0.974 0.971
## predict20 0.989 0.986 0.984 0.981 0.978 0.976
## predict21 0.992 0.990 0.988 0.985 0.983 0.980
## predict22 0.995 0.993 0.991 0.989 0.986 0.984
## predict23 0.997 0.995 0.993 0.991 0.989 0.987
## predict24 0.998 0.997 0.996 0.994 0.992 0.990
## predict25 0.999 0.998 0.997 0.996 0.994 0.992
## predict26 1.000 0.999 0.998 0.997 0.996 0.994
## predict27 1.000 1.000 0.999 0.998 0.997 0.996
## predict28 1.000 1.000 1.000 0.999 0.999 0.997
## predict29 0.999 1.000 1.000 1.000 0.999 0.999
## predict30 0.998 0.999 1.000 1.000 1.000 0.999
## predict31 0.997 0.999 0.999 1.000 1.000 1.000
## predict32 0.996 0.997 0.999 0.999 1.000 1.000
## predict33 0.995 0.996 0.998 0.999 0.999 1.000
## predict34 0.993 0.995 0.997 0.998 0.999 0.999
## predict35 0.992 0.994 0.995 0.997 0.998 0.999
## predict36 0.990 0.992 0.994 0.996 0.997 0.998
## predict37 0.988 0.991 0.993 0.994 0.996 0.997
## predict38 0.986 0.989 0.991 0.993 0.995 0.996
## predict39 0.984 0.987 0.990 0.992 0.994 0.995
## predict40 0.983 0.985 0.988 0.990 0.992 0.994
## predict41 0.981 0.984 0.986 0.989 0.991 0.993
## predict42 0.979 0.982 0.985 0.987 0.989 0.991
## predict43 0.977 0.980 0.983 0.986 0.988 0.990
## predict44 0.975 0.978 0.981 0.984 0.986 0.988
## predict45 0.973 0.977 0.980 0.982 0.985 0.987
## predict46 0.972 0.975 0.978 0.981 0.983 0.986
## predict47 0.970 0.973 0.976 0.979 0.982 0.984
## predict48 0.968 0.971 0.975 0.978 0.980 0.983
## predict49 0.966 0.970 0.973 0.976 0.979 0.981
## predict50 0.965 0.968 0.972 0.975 0.977 0.980
## predict51 0.963 0.967 0.970 0.973 0.976 0.979
## predict52 0.962 0.965 0.969 0.972 0.975 0.977
## predict53 0.960 0.964 0.967 0.970 0.973 0.976
## predict54 0.959 0.962 0.966 0.969 0.972 0.975
## predict55 0.957 0.961 0.965 0.968 0.971 0.974
## predict56 0.956 0.960 0.963 0.967 0.970 0.972
## predict57 0.955 0.959 0.962 0.966 0.969 0.971
## predict58 0.954 0.957 0.961 0.964 0.967 0.970
## predict59 0.953 0.956 0.960 0.963 0.966 0.969
## predict60 0.951 0.955 0.959 0.962 0.965 0.968
## predict61 0.950 0.954 0.958 0.961 0.964 0.967
## predict62 0.949 0.953 0.957 0.960 0.964 0.966
## predict63 0.949 0.952 0.956 0.960 0.963 0.966
## predict64 0.948 0.952 0.955 0.959 0.962 0.965
## predict65 0.947 0.951 0.954 0.958 0.961 0.964
## predict66 0.946 0.950 0.954 0.957 0.960 0.963
## predict67 0.945 0.949 0.953 0.957 0.960 0.963
## predict68 0.945 0.949 0.952 0.956 0.959 0.962
## predict69 0.944 0.948 0.952 0.955 0.959 0.962
## predict70 0.943 0.947 0.951 0.955 0.958 0.961
## predict71 0.943 0.947 0.951 0.954 0.957 0.960
## predict72 0.942 0.946 0.950 0.954 0.957 0.960
## predict73 0.942 0.946 0.950 0.953 0.957 0.960
## predict74 0.941 0.945 0.949 0.953 0.956 0.959
## predict75 0.941 0.945 0.949 0.952 0.956 0.959
## predict76 0.941 0.945 0.948 0.952 0.955 0.958
## predict77 0.940 0.944 0.948 0.952 0.955 0.958
## predict78 0.940 0.944 0.948 0.951 0.955 0.958
## predict79 0.940 0.944 0.947 0.951 0.954 0.957
## predict80 0.939 0.943 0.947 0.951 0.954 0.957
## predict81 0.939 0.943 0.947 0.950 0.954 0.957
## predict82 0.939 0.943 0.947 0.950 0.954 0.957
## predict83 0.939 0.942 0.946 0.950 0.953 0.956
## predict84 0.938 0.942 0.946 0.950 0.953 0.956
## predict85 0.938 0.942 0.946 0.950 0.953 0.956
## predict86 0.938 0.942 0.946 0.949 0.953 0.956
## predict87 0.938 0.942 0.946 0.949 0.953 0.956
## predict88 0.938 0.942 0.945 0.949 0.952 0.956
## predict89 0.937 0.941 0.945 0.949 0.952 0.955
## predict90 0.937 0.941 0.945 0.949 0.952 0.955
## predict91 0.937 0.941 0.945 0.949 0.952 0.955
## predict92 0.937 0.941 0.945 0.949 0.952 0.955
## predict93 0.937 0.941 0.945 0.948 0.952 0.955
## predict94 0.937 0.941 0.945 0.948 0.952 0.955
## predict95 0.937 0.941 0.945 0.948 0.952 0.955
## predict96 0.937 0.941 0.944 0.948 0.952 0.955
## predict97 0.937 0.941 0.944 0.948 0.951 0.955
## predict98 0.936 0.940 0.944 0.948 0.951 0.954
## predict99 0.936 0.940 0.944 0.948 0.951 0.954
## predict100 0.936 0.940 0.944 0.948 0.951 0.954
## predict33 predict34 predict35 predict36 predict37 predict38
## g 0.824 0.825 0.827 0.828 0.829 0.830
## predict1 0.632 0.629 0.625 0.622 0.619 0.616
## predict2 0.676 0.672 0.669 0.665 0.662 0.659
## predict3 0.706 0.702 0.698 0.695 0.692 0.689
## predict4 0.733 0.729 0.725 0.722 0.719 0.715
## predict5 0.767 0.763 0.759 0.755 0.752 0.749
## predict6 0.796 0.792 0.788 0.785 0.781 0.778
## predict7 0.821 0.817 0.814 0.810 0.807 0.803
## predict8 0.845 0.841 0.837 0.833 0.830 0.827
## predict9 0.865 0.861 0.858 0.854 0.851 0.848
## predict10 0.882 0.878 0.875 0.871 0.868 0.865
## predict11 0.896 0.893 0.889 0.886 0.882 0.879
## predict12 0.910 0.906 0.902 0.899 0.896 0.893
## predict13 0.922 0.918 0.915 0.912 0.908 0.905
## predict14 0.932 0.929 0.925 0.922 0.919 0.916
## predict15 0.941 0.937 0.934 0.931 0.928 0.925
## predict16 0.948 0.945 0.942 0.939 0.936 0.933
## predict17 0.955 0.952 0.949 0.946 0.943 0.941
## predict18 0.962 0.959 0.956 0.953 0.951 0.948
## predict19 0.968 0.965 0.962 0.960 0.957 0.954
## predict20 0.973 0.970 0.968 0.965 0.963 0.960
## predict21 0.978 0.975 0.973 0.970 0.968 0.965
## predict22 0.982 0.979 0.977 0.975 0.972 0.970
## predict23 0.985 0.983 0.981 0.978 0.976 0.974
## predict24 0.988 0.986 0.984 0.982 0.980 0.978
## predict25 0.991 0.989 0.987 0.985 0.983 0.981
## predict26 0.993 0.991 0.989 0.987 0.986 0.984
## predict27 0.995 0.993 0.992 0.990 0.988 0.986
## predict28 0.996 0.995 0.994 0.992 0.991 0.989
## predict29 0.998 0.997 0.995 0.994 0.993 0.991
## predict30 0.999 0.998 0.997 0.996 0.994 0.993
## predict31 0.999 0.999 0.998 0.997 0.996 0.995
## predict32 1.000 0.999 0.999 0.998 0.997 0.996
## predict33 1.000 1.000 1.000 0.999 0.998 0.997
## predict34 1.000 1.000 1.000 1.000 0.999 0.998
## predict35 1.000 1.000 1.000 1.000 1.000 0.999
## predict36 0.999 1.000 1.000 1.000 1.000 1.000
## predict37 0.998 0.999 1.000 1.000 1.000 1.000
## predict38 0.997 0.998 0.999 1.000 1.000 1.000
## predict39 0.996 0.997 0.998 0.999 1.000 1.000
## predict40 0.995 0.997 0.998 0.998 0.999 1.000
## predict41 0.994 0.995 0.997 0.998 0.999 0.999
## predict42 0.993 0.994 0.996 0.997 0.998 0.999
## predict43 0.992 0.993 0.995 0.996 0.997 0.998
## predict44 0.990 0.992 0.994 0.995 0.996 0.997
## predict45 0.989 0.991 0.992 0.994 0.995 0.996
## predict46 0.988 0.990 0.991 0.993 0.994 0.996
## predict47 0.986 0.988 0.990 0.992 0.993 0.995
## predict48 0.985 0.987 0.989 0.991 0.992 0.994
## predict49 0.984 0.986 0.988 0.989 0.991 0.993
## predict50 0.982 0.984 0.986 0.988 0.990 0.992
## predict51 0.981 0.983 0.985 0.987 0.989 0.991
## predict52 0.980 0.982 0.984 0.986 0.988 0.990
## predict53 0.978 0.981 0.983 0.985 0.987 0.989
## predict54 0.977 0.980 0.982 0.984 0.986 0.988
## predict55 0.976 0.979 0.981 0.983 0.985 0.987
## predict56 0.975 0.977 0.980 0.982 0.984 0.986
## predict57 0.974 0.976 0.979 0.981 0.983 0.985
## predict58 0.973 0.975 0.978 0.980 0.982 0.984
## predict59 0.972 0.974 0.977 0.979 0.981 0.983
## predict60 0.971 0.974 0.976 0.978 0.980 0.983
## predict61 0.970 0.973 0.975 0.977 0.980 0.982
## predict62 0.969 0.972 0.974 0.977 0.979 0.981
## predict63 0.968 0.971 0.973 0.976 0.978 0.980
## predict64 0.968 0.970 0.973 0.975 0.977 0.980
## predict65 0.967 0.970 0.972 0.974 0.977 0.979
## predict66 0.966 0.969 0.971 0.974 0.976 0.978
## predict67 0.966 0.968 0.971 0.973 0.976 0.978
## predict68 0.965 0.968 0.970 0.973 0.975 0.977
## predict69 0.964 0.967 0.970 0.972 0.974 0.977
## predict70 0.964 0.967 0.969 0.972 0.974 0.976
## predict71 0.963 0.966 0.969 0.971 0.974 0.976
## predict72 0.963 0.966 0.968 0.971 0.973 0.975
## predict73 0.962 0.965 0.968 0.970 0.973 0.975
## predict74 0.962 0.965 0.967 0.970 0.972 0.975
## predict75 0.962 0.964 0.967 0.969 0.972 0.974
## predict76 0.961 0.964 0.967 0.969 0.972 0.974
## predict77 0.961 0.964 0.966 0.969 0.971 0.974
## predict78 0.961 0.963 0.966 0.969 0.971 0.973
## predict79 0.960 0.963 0.966 0.968 0.971 0.973
## predict80 0.960 0.963 0.965 0.968 0.970 0.973
## predict81 0.960 0.963 0.965 0.968 0.970 0.973
## predict82 0.960 0.962 0.965 0.968 0.970 0.972
## predict83 0.959 0.962 0.965 0.967 0.970 0.972
## predict84 0.959 0.962 0.965 0.967 0.970 0.972
## predict85 0.959 0.962 0.964 0.967 0.969 0.972
## predict86 0.959 0.962 0.964 0.967 0.969 0.972
## predict87 0.959 0.961 0.964 0.967 0.969 0.972
## predict88 0.958 0.961 0.964 0.966 0.969 0.971
## predict89 0.958 0.961 0.964 0.966 0.969 0.971
## predict90 0.958 0.961 0.964 0.966 0.969 0.971
## predict91 0.958 0.961 0.963 0.966 0.969 0.971
## predict92 0.958 0.961 0.963 0.966 0.969 0.971
## predict93 0.958 0.961 0.963 0.966 0.968 0.971
## predict94 0.958 0.961 0.963 0.966 0.968 0.971
## predict95 0.958 0.960 0.963 0.966 0.968 0.971
## predict96 0.958 0.960 0.963 0.966 0.968 0.971
## predict97 0.957 0.960 0.963 0.966 0.968 0.971
## predict98 0.957 0.960 0.963 0.965 0.968 0.970
## predict99 0.957 0.960 0.963 0.965 0.968 0.970
## predict100 0.957 0.960 0.963 0.965 0.968 0.970
## predict39 predict40 predict41 predict42 predict43 predict44
## g 0.831 0.832 0.833 0.833 0.834 0.834
## predict1 0.613 0.611 0.608 0.606 0.603 0.601
## predict2 0.656 0.654 0.651 0.648 0.646 0.644
## predict3 0.686 0.683 0.680 0.678 0.675 0.673
## predict4 0.712 0.709 0.707 0.704 0.702 0.699
## predict5 0.746 0.743 0.740 0.737 0.734 0.732
## predict6 0.775 0.772 0.769 0.766 0.764 0.761
## predict7 0.800 0.797 0.794 0.791 0.789 0.786
## predict8 0.823 0.820 0.817 0.814 0.812 0.809
## predict9 0.844 0.841 0.838 0.835 0.833 0.830
## predict10 0.861 0.858 0.855 0.853 0.850 0.847
## predict11 0.876 0.873 0.870 0.867 0.865 0.862
## predict12 0.889 0.886 0.883 0.881 0.878 0.876
## predict13 0.902 0.899 0.896 0.894 0.891 0.889
## predict14 0.913 0.910 0.907 0.904 0.902 0.899
## predict15 0.922 0.919 0.916 0.914 0.911 0.909
## predict16 0.930 0.927 0.925 0.922 0.919 0.917
## predict17 0.938 0.935 0.932 0.930 0.927 0.925
## predict18 0.945 0.942 0.940 0.937 0.935 0.932
## predict19 0.952 0.949 0.946 0.944 0.942 0.939
## predict20 0.957 0.955 0.952 0.950 0.948 0.945
## predict21 0.963 0.960 0.958 0.955 0.953 0.951
## predict22 0.967 0.965 0.963 0.960 0.958 0.956
## predict23 0.972 0.969 0.967 0.965 0.963 0.961
## predict24 0.975 0.973 0.971 0.969 0.967 0.965
## predict25 0.979 0.977 0.975 0.973 0.971 0.969
## predict26 0.982 0.980 0.978 0.976 0.974 0.972
## predict27 0.984 0.983 0.981 0.979 0.977 0.975
## predict28 0.987 0.985 0.984 0.982 0.980 0.978
## predict29 0.990 0.988 0.986 0.985 0.983 0.981
## predict30 0.992 0.990 0.989 0.987 0.986 0.984
## predict31 0.994 0.992 0.991 0.989 0.988 0.986
## predict32 0.995 0.994 0.993 0.991 0.990 0.988
## predict33 0.996 0.995 0.994 0.993 0.992 0.990
## predict34 0.997 0.997 0.995 0.994 0.993 0.992
## predict35 0.998 0.998 0.997 0.996 0.995 0.994
## predict36 0.999 0.998 0.998 0.997 0.996 0.995
## predict37 1.000 0.999 0.999 0.998 0.997 0.996
## predict38 1.000 1.000 0.999 0.999 0.998 0.997
## predict39 1.000 1.000 1.000 0.999 0.999 0.998
## predict40 1.000 1.000 1.000 1.000 0.999 0.999
## predict41 1.000 1.000 1.000 1.000 1.000 0.999
## predict42 0.999 1.000 1.000 1.000 1.000 1.000
## predict43 0.999 0.999 1.000 1.000 1.000 1.000
## predict44 0.998 0.999 0.999 1.000 1.000 1.000
## predict45 0.997 0.998 0.999 0.999 1.000 1.000
## predict46 0.997 0.998 0.998 0.999 0.999 1.000
## predict47 0.996 0.997 0.998 0.998 0.999 0.999
## predict48 0.995 0.996 0.997 0.998 0.999 0.999
## predict49 0.994 0.995 0.996 0.997 0.998 0.999
## predict50 0.993 0.994 0.996 0.997 0.997 0.998
## predict51 0.992 0.994 0.995 0.996 0.997 0.998
## predict52 0.991 0.993 0.994 0.995 0.996 0.997
## predict53 0.990 0.992 0.993 0.994 0.995 0.996
## predict54 0.990 0.991 0.992 0.994 0.995 0.996
## predict55 0.989 0.990 0.992 0.993 0.994 0.995
## predict56 0.988 0.989 0.991 0.992 0.993 0.995
## predict57 0.987 0.989 0.990 0.992 0.993 0.994
## predict58 0.986 0.988 0.989 0.991 0.992 0.993
## predict59 0.985 0.987 0.989 0.990 0.991 0.993
## predict60 0.984 0.986 0.988 0.989 0.991 0.992
## predict61 0.984 0.986 0.987 0.989 0.990 0.991
## predict62 0.983 0.985 0.987 0.988 0.990 0.991
## predict63 0.982 0.984 0.986 0.988 0.989 0.990
## predict64 0.982 0.984 0.985 0.987 0.988 0.990
## predict65 0.981 0.983 0.985 0.986 0.988 0.989
## predict66 0.980 0.982 0.984 0.986 0.987 0.989
## predict67 0.980 0.982 0.984 0.985 0.987 0.988
## predict68 0.979 0.981 0.983 0.985 0.987 0.988
## predict69 0.979 0.981 0.983 0.985 0.986 0.988
## predict70 0.978 0.980 0.982 0.984 0.986 0.987
## predict71 0.978 0.980 0.982 0.984 0.985 0.987
## predict72 0.978 0.980 0.982 0.983 0.985 0.987
## predict73 0.977 0.979 0.981 0.983 0.985 0.986
## predict74 0.977 0.979 0.981 0.983 0.984 0.986
## predict75 0.977 0.979 0.981 0.982 0.984 0.986
## predict76 0.976 0.978 0.980 0.982 0.984 0.985
## predict77 0.976 0.978 0.980 0.982 0.984 0.985
## predict78 0.976 0.978 0.980 0.982 0.983 0.985
## predict79 0.975 0.977 0.979 0.981 0.983 0.985
## predict80 0.975 0.977 0.979 0.981 0.983 0.984
## predict81 0.975 0.977 0.979 0.981 0.983 0.984
## predict82 0.975 0.977 0.979 0.981 0.982 0.984
## predict83 0.975 0.977 0.979 0.981 0.982 0.984
## predict84 0.974 0.976 0.978 0.980 0.982 0.984
## predict85 0.974 0.976 0.978 0.980 0.982 0.984
## predict86 0.974 0.976 0.978 0.980 0.982 0.983
## predict87 0.974 0.976 0.978 0.980 0.982 0.983
## predict88 0.974 0.976 0.978 0.980 0.982 0.983
## predict89 0.974 0.976 0.978 0.980 0.981 0.983
## predict90 0.973 0.976 0.978 0.980 0.981 0.983
## predict91 0.973 0.976 0.978 0.979 0.981 0.983
## predict92 0.973 0.975 0.977 0.979 0.981 0.983
## predict93 0.973 0.975 0.977 0.979 0.981 0.983
## predict94 0.973 0.975 0.977 0.979 0.981 0.983
## predict95 0.973 0.975 0.977 0.979 0.981 0.983
## predict96 0.973 0.975 0.977 0.979 0.981 0.983
## predict97 0.973 0.975 0.977 0.979 0.981 0.982
## predict98 0.973 0.975 0.977 0.979 0.981 0.982
## predict99 0.973 0.975 0.977 0.979 0.981 0.982
## predict100 0.973 0.975 0.977 0.979 0.981 0.982
## predict45 predict46 predict47 predict48 predict49 predict50
## g 0.834 0.834 0.834 0.834 0.834 0.834
## predict1 0.599 0.598 0.596 0.594 0.592 0.591
## predict2 0.642 0.640 0.638 0.636 0.635 0.633
## predict3 0.671 0.669 0.667 0.665 0.663 0.662
## predict4 0.697 0.695 0.693 0.691 0.689 0.687
## predict5 0.730 0.728 0.726 0.724 0.722 0.720
## predict6 0.759 0.757 0.755 0.752 0.750 0.749
## predict7 0.784 0.782 0.779 0.777 0.775 0.773
## predict8 0.807 0.805 0.802 0.800 0.798 0.796
## predict9 0.828 0.826 0.823 0.821 0.819 0.817
## predict10 0.845 0.843 0.840 0.838 0.836 0.834
## predict11 0.860 0.857 0.855 0.853 0.851 0.849
## predict12 0.873 0.871 0.869 0.866 0.864 0.862
## predict13 0.886 0.884 0.882 0.879 0.877 0.875
## predict14 0.897 0.895 0.892 0.890 0.888 0.886
## predict15 0.906 0.904 0.902 0.900 0.897 0.895
## predict16 0.915 0.912 0.910 0.908 0.906 0.904
## predict17 0.923 0.920 0.918 0.916 0.914 0.912
## predict18 0.930 0.928 0.926 0.924 0.922 0.920
## predict19 0.937 0.935 0.933 0.931 0.929 0.927
## predict20 0.943 0.941 0.939 0.937 0.935 0.933
## predict21 0.949 0.947 0.945 0.943 0.941 0.939
## predict22 0.954 0.952 0.950 0.948 0.946 0.944
## predict23 0.959 0.957 0.955 0.953 0.951 0.949
## predict24 0.963 0.961 0.959 0.957 0.955 0.954
## predict25 0.967 0.965 0.963 0.961 0.959 0.958
## predict26 0.970 0.968 0.966 0.965 0.963 0.961
## predict27 0.973 0.972 0.970 0.968 0.966 0.965
## predict28 0.977 0.975 0.973 0.971 0.970 0.968
## predict29 0.980 0.978 0.976 0.975 0.973 0.972
## predict30 0.982 0.981 0.979 0.978 0.976 0.975
## predict31 0.985 0.983 0.982 0.980 0.979 0.977
## predict32 0.987 0.986 0.984 0.983 0.981 0.980
## predict33 0.989 0.988 0.986 0.985 0.984 0.982
## predict34 0.991 0.990 0.988 0.987 0.986 0.984
## predict35 0.992 0.991 0.990 0.989 0.988 0.986
## predict36 0.994 0.993 0.992 0.991 0.989 0.988
## predict37 0.995 0.994 0.993 0.992 0.991 0.990
## predict38 0.996 0.996 0.995 0.994 0.993 0.992
## predict39 0.997 0.997 0.996 0.995 0.994 0.993
## predict40 0.998 0.998 0.997 0.996 0.995 0.994
## predict41 0.999 0.998 0.998 0.997 0.996 0.996
## predict42 0.999 0.999 0.998 0.998 0.997 0.997
## predict43 1.000 0.999 0.999 0.999 0.998 0.997
## predict44 1.000 1.000 0.999 0.999 0.999 0.998
## predict45 1.000 1.000 1.000 0.999 0.999 0.999
## predict46 1.000 1.000 1.000 1.000 1.000 0.999
## predict47 1.000 1.000 1.000 1.000 1.000 1.000
## predict48 0.999 1.000 1.000 1.000 1.000 1.000
## predict49 0.999 1.000 1.000 1.000 1.000 1.000
## predict50 0.999 0.999 1.000 1.000 1.000 1.000
## predict51 0.998 0.999 0.999 1.000 1.000 1.000
## predict52 0.998 0.998 0.999 0.999 1.000 1.000
## predict53 0.997 0.998 0.999 0.999 0.999 1.000
## predict54 0.997 0.997 0.998 0.999 0.999 0.999
## predict55 0.996 0.997 0.998 0.998 0.999 0.999
## predict56 0.996 0.996 0.997 0.998 0.998 0.999
## predict57 0.995 0.996 0.997 0.997 0.998 0.999
## predict58 0.994 0.995 0.996 0.997 0.998 0.998
## predict59 0.994 0.995 0.996 0.997 0.997 0.998
## predict60 0.993 0.994 0.995 0.996 0.997 0.998
## predict61 0.993 0.994 0.995 0.996 0.996 0.997
## predict62 0.992 0.993 0.994 0.995 0.996 0.997
## predict63 0.992 0.993 0.994 0.995 0.996 0.996
## predict64 0.991 0.992 0.993 0.994 0.995 0.996
## predict65 0.991 0.992 0.993 0.994 0.995 0.996
## predict66 0.990 0.991 0.993 0.994 0.995 0.995
## predict67 0.990 0.991 0.992 0.993 0.994 0.995
## predict68 0.989 0.991 0.992 0.993 0.994 0.995
## predict69 0.989 0.990 0.991 0.993 0.994 0.995
## predict70 0.989 0.990 0.991 0.992 0.993 0.994
## predict71 0.988 0.990 0.991 0.992 0.993 0.994
## predict72 0.988 0.989 0.991 0.992 0.993 0.994
## predict73 0.988 0.989 0.990 0.991 0.992 0.993
## predict74 0.987 0.989 0.990 0.991 0.992 0.993
## predict75 0.987 0.988 0.990 0.991 0.992 0.993
## predict76 0.987 0.988 0.990 0.991 0.992 0.993
## predict77 0.987 0.988 0.989 0.991 0.992 0.993
## predict78 0.986 0.988 0.989 0.990 0.991 0.992
## predict79 0.986 0.988 0.989 0.990 0.991 0.992
## predict80 0.986 0.987 0.989 0.990 0.991 0.992
## predict81 0.986 0.987 0.989 0.990 0.991 0.992
## predict82 0.986 0.987 0.988 0.990 0.991 0.992
## predict83 0.985 0.987 0.988 0.989 0.991 0.992
## predict84 0.985 0.987 0.988 0.989 0.991 0.992
## predict85 0.985 0.987 0.988 0.989 0.990 0.991
## predict86 0.985 0.986 0.988 0.989 0.990 0.991
## predict87 0.985 0.986 0.988 0.989 0.990 0.991
## predict88 0.985 0.986 0.988 0.989 0.990 0.991
## predict89 0.985 0.986 0.988 0.989 0.990 0.991
## predict90 0.985 0.986 0.987 0.989 0.990 0.991
## predict91 0.984 0.986 0.987 0.989 0.990 0.991
## predict92 0.984 0.986 0.987 0.989 0.990 0.991
## predict93 0.984 0.986 0.987 0.988 0.990 0.991
## predict94 0.984 0.986 0.987 0.988 0.990 0.991
## predict95 0.984 0.986 0.987 0.988 0.990 0.991
## predict96 0.984 0.986 0.987 0.988 0.990 0.991
## predict97 0.984 0.986 0.987 0.988 0.989 0.991
## predict98 0.984 0.985 0.987 0.988 0.989 0.991
## predict99 0.984 0.985 0.987 0.988 0.989 0.991
## predict100 0.984 0.985 0.987 0.988 0.989 0.990
## predict51 predict52 predict53 predict54 predict55 predict56
## g 0.834 0.834 0.834 0.833 0.833 0.833
## predict1 0.589 0.588 0.587 0.585 0.584 0.583
## predict2 0.631 0.630 0.628 0.627 0.626 0.625
## predict3 0.660 0.658 0.657 0.656 0.654 0.653
## predict4 0.686 0.684 0.683 0.681 0.680 0.679
## predict5 0.718 0.716 0.715 0.713 0.712 0.711
## predict6 0.747 0.745 0.743 0.742 0.740 0.739
## predict7 0.771 0.770 0.768 0.766 0.765 0.763
## predict8 0.794 0.792 0.791 0.789 0.788 0.786
## predict9 0.815 0.813 0.812 0.810 0.808 0.807
## predict10 0.832 0.830 0.829 0.827 0.825 0.824
## predict11 0.847 0.845 0.843 0.841 0.840 0.838
## predict12 0.860 0.858 0.857 0.855 0.853 0.852
## predict13 0.873 0.872 0.870 0.868 0.867 0.865
## predict14 0.884 0.882 0.881 0.879 0.877 0.876
## predict15 0.894 0.892 0.890 0.888 0.887 0.885
## predict16 0.902 0.900 0.899 0.897 0.895 0.894
## predict17 0.910 0.908 0.907 0.905 0.904 0.902
## predict18 0.918 0.916 0.915 0.913 0.911 0.910
## predict19 0.925 0.923 0.922 0.920 0.919 0.917
## predict20 0.931 0.930 0.928 0.926 0.925 0.923
## predict21 0.937 0.936 0.934 0.932 0.931 0.929
## predict22 0.943 0.941 0.939 0.938 0.936 0.935
## predict23 0.948 0.946 0.944 0.943 0.941 0.940
## predict24 0.952 0.950 0.949 0.947 0.946 0.944
## predict25 0.956 0.954 0.953 0.951 0.950 0.949
## predict26 0.960 0.958 0.957 0.955 0.954 0.952
## predict27 0.963 0.962 0.960 0.959 0.957 0.956
## predict28 0.967 0.965 0.964 0.962 0.961 0.960
## predict29 0.970 0.969 0.967 0.966 0.965 0.963
## predict30 0.973 0.972 0.970 0.969 0.968 0.967
## predict31 0.976 0.975 0.973 0.972 0.971 0.970
## predict32 0.979 0.977 0.976 0.975 0.974 0.972
## predict33 0.981 0.980 0.978 0.977 0.976 0.975
## predict34 0.983 0.982 0.981 0.980 0.979 0.977
## predict35 0.985 0.984 0.983 0.982 0.981 0.980
## predict36 0.987 0.986 0.985 0.984 0.983 0.982
## predict37 0.989 0.988 0.987 0.986 0.985 0.984
## predict38 0.991 0.990 0.989 0.988 0.987 0.986
## predict39 0.992 0.991 0.990 0.990 0.989 0.988
## predict40 0.994 0.993 0.992 0.991 0.990 0.989
## predict41 0.995 0.994 0.993 0.992 0.992 0.991
## predict42 0.996 0.995 0.994 0.994 0.993 0.992
## predict43 0.997 0.996 0.995 0.995 0.994 0.993
## predict44 0.998 0.997 0.996 0.996 0.995 0.995
## predict45 0.998 0.998 0.997 0.997 0.996 0.996
## predict46 0.999 0.998 0.998 0.997 0.997 0.996
## predict47 0.999 0.999 0.999 0.998 0.998 0.997
## predict48 1.000 0.999 0.999 0.999 0.998 0.998
## predict49 1.000 1.000 0.999 0.999 0.999 0.998
## predict50 1.000 1.000 1.000 0.999 0.999 0.999
## predict51 1.000 1.000 1.000 1.000 0.999 0.999
## predict52 1.000 1.000 1.000 1.000 1.000 1.000
## predict53 1.000 1.000 1.000 1.000 1.000 1.000
## predict54 1.000 1.000 1.000 1.000 1.000 1.000
## predict55 0.999 1.000 1.000 1.000 1.000 1.000
## predict56 0.999 1.000 1.000 1.000 1.000 1.000
## predict57 0.999 0.999 1.000 1.000 1.000 1.000
## predict58 0.999 0.999 0.999 1.000 1.000 1.000
## predict59 0.998 0.999 0.999 0.999 1.000 1.000
## predict60 0.998 0.999 0.999 0.999 1.000 1.000
## predict61 0.998 0.998 0.999 0.999 0.999 1.000
## predict62 0.997 0.998 0.998 0.999 0.999 0.999
## predict63 0.997 0.998 0.998 0.999 0.999 0.999
## predict64 0.997 0.997 0.998 0.998 0.999 0.999
## predict65 0.996 0.997 0.998 0.998 0.999 0.999
## predict66 0.996 0.997 0.997 0.998 0.998 0.999
## predict67 0.996 0.997 0.997 0.998 0.998 0.998
## predict68 0.996 0.996 0.997 0.997 0.998 0.998
## predict69 0.995 0.996 0.997 0.997 0.998 0.998
## predict70 0.995 0.996 0.996 0.997 0.998 0.998
## predict71 0.995 0.996 0.996 0.997 0.997 0.998
## predict72 0.995 0.995 0.996 0.997 0.997 0.998
## predict73 0.994 0.995 0.996 0.996 0.997 0.997
## predict74 0.994 0.995 0.996 0.996 0.997 0.997
## predict75 0.994 0.995 0.995 0.996 0.997 0.997
## predict76 0.994 0.995 0.995 0.996 0.997 0.997
## predict77 0.994 0.994 0.995 0.996 0.996 0.997
## predict78 0.993 0.994 0.995 0.996 0.996 0.997
## predict79 0.993 0.994 0.995 0.996 0.996 0.997
## predict80 0.993 0.994 0.995 0.995 0.996 0.997
## predict81 0.993 0.994 0.995 0.995 0.996 0.996
## predict82 0.993 0.994 0.994 0.995 0.996 0.996
## predict83 0.993 0.994 0.994 0.995 0.996 0.996
## predict84 0.993 0.993 0.994 0.995 0.996 0.996
## predict85 0.992 0.993 0.994 0.995 0.995 0.996
## predict86 0.992 0.993 0.994 0.995 0.995 0.996
## predict87 0.992 0.993 0.994 0.995 0.995 0.996
## predict88 0.992 0.993 0.994 0.995 0.995 0.996
## predict89 0.992 0.993 0.994 0.995 0.995 0.996
## predict90 0.992 0.993 0.994 0.994 0.995 0.996
## predict91 0.992 0.993 0.994 0.994 0.995 0.996
## predict92 0.992 0.993 0.994 0.994 0.995 0.996
## predict93 0.992 0.993 0.994 0.994 0.995 0.996
## predict94 0.992 0.993 0.994 0.994 0.995 0.996
## predict95 0.992 0.993 0.993 0.994 0.995 0.995
## predict96 0.992 0.993 0.993 0.994 0.995 0.995
## predict97 0.992 0.993 0.993 0.994 0.995 0.995
## predict98 0.992 0.993 0.993 0.994 0.995 0.995
## predict99 0.992 0.992 0.993 0.994 0.995 0.995
## predict100 0.992 0.992 0.993 0.994 0.995 0.995
## predict57 predict58 predict59 predict60 predict61 predict62
## g 0.833 0.832 0.832 0.832 0.831 0.831
## predict1 0.582 0.581 0.580 0.579 0.578 0.577
## predict2 0.623 0.622 0.621 0.620 0.620 0.619
## predict3 0.652 0.651 0.650 0.649 0.648 0.647
## predict4 0.677 0.676 0.675 0.674 0.673 0.672
## predict5 0.709 0.708 0.707 0.706 0.705 0.704
## predict6 0.738 0.736 0.735 0.734 0.733 0.732
## predict7 0.762 0.761 0.760 0.759 0.757 0.756
## predict8 0.785 0.784 0.782 0.781 0.780 0.779
## predict9 0.805 0.804 0.803 0.802 0.801 0.800
## predict10 0.822 0.821 0.820 0.819 0.818 0.816
## predict11 0.837 0.836 0.834 0.833 0.832 0.831
## predict12 0.851 0.849 0.848 0.847 0.846 0.844
## predict13 0.864 0.862 0.861 0.860 0.859 0.858
## predict14 0.875 0.873 0.872 0.871 0.870 0.868
## predict15 0.884 0.883 0.881 0.880 0.879 0.878
## predict16 0.893 0.891 0.890 0.889 0.888 0.886
## predict17 0.901 0.899 0.898 0.897 0.896 0.895
## predict18 0.909 0.907 0.906 0.905 0.904 0.903
## predict19 0.916 0.914 0.913 0.912 0.911 0.910
## predict20 0.922 0.921 0.920 0.918 0.917 0.916
## predict21 0.928 0.927 0.926 0.924 0.923 0.922
## predict22 0.934 0.932 0.931 0.930 0.929 0.928
## predict23 0.939 0.937 0.936 0.935 0.934 0.933
## predict24 0.943 0.942 0.941 0.940 0.939 0.938
## predict25 0.947 0.946 0.945 0.944 0.943 0.942
## predict26 0.951 0.950 0.949 0.948 0.947 0.946
## predict27 0.955 0.954 0.953 0.951 0.950 0.949
## predict28 0.959 0.957 0.956 0.955 0.954 0.953
## predict29 0.962 0.961 0.960 0.959 0.958 0.957
## predict30 0.966 0.964 0.963 0.962 0.961 0.960
## predict31 0.969 0.967 0.966 0.965 0.964 0.964
## predict32 0.971 0.970 0.969 0.968 0.967 0.966
## predict33 0.974 0.973 0.972 0.971 0.970 0.969
## predict34 0.976 0.975 0.974 0.974 0.973 0.972
## predict35 0.979 0.978 0.977 0.976 0.975 0.974
## predict36 0.981 0.980 0.979 0.978 0.977 0.977
## predict37 0.983 0.982 0.981 0.980 0.980 0.979
## predict38 0.985 0.984 0.983 0.983 0.982 0.981
## predict39 0.987 0.986 0.985 0.984 0.984 0.983
## predict40 0.989 0.988 0.987 0.986 0.986 0.985
## predict41 0.990 0.989 0.989 0.988 0.987 0.987
## predict42 0.992 0.991 0.990 0.989 0.989 0.988
## predict43 0.993 0.992 0.991 0.991 0.990 0.990
## predict44 0.994 0.993 0.993 0.992 0.991 0.991
## predict45 0.995 0.994 0.994 0.993 0.993 0.992
## predict46 0.996 0.995 0.995 0.994 0.994 0.993
## predict47 0.997 0.996 0.996 0.995 0.995 0.994
## predict48 0.997 0.997 0.997 0.996 0.996 0.995
## predict49 0.998 0.998 0.997 0.997 0.996 0.996
## predict50 0.999 0.998 0.998 0.998 0.997 0.997
## predict51 0.999 0.999 0.998 0.998 0.998 0.997
## predict52 0.999 0.999 0.999 0.999 0.998 0.998
## predict53 1.000 0.999 0.999 0.999 0.999 0.998
## predict54 1.000 1.000 0.999 0.999 0.999 0.999
## predict55 1.000 1.000 1.000 1.000 0.999 0.999
## predict56 1.000 1.000 1.000 1.000 1.000 0.999
## predict57 1.000 1.000 1.000 1.000 1.000 1.000
## predict58 1.000 1.000 1.000 1.000 1.000 1.000
## predict59 1.000 1.000 1.000 1.000 1.000 1.000
## predict60 1.000 1.000 1.000 1.000 1.000 1.000
## predict61 1.000 1.000 1.000 1.000 1.000 1.000
## predict62 1.000 1.000 1.000 1.000 1.000 1.000
## predict63 0.999 1.000 1.000 1.000 1.000 1.000
## predict64 0.999 1.000 1.000 1.000 1.000 1.000
## predict65 0.999 0.999 1.000 1.000 1.000 1.000
## predict66 0.999 0.999 0.999 1.000 1.000 1.000
## predict67 0.999 0.999 0.999 1.000 1.000 1.000
## predict68 0.999 0.999 0.999 0.999 1.000 1.000
## predict69 0.998 0.999 0.999 0.999 0.999 1.000
## predict70 0.998 0.999 0.999 0.999 0.999 1.000
## predict71 0.998 0.999 0.999 0.999 0.999 0.999
## predict72 0.998 0.998 0.999 0.999 0.999 0.999
## predict73 0.998 0.998 0.999 0.999 0.999 0.999
## predict74 0.998 0.998 0.998 0.999 0.999 0.999
## predict75 0.998 0.998 0.998 0.999 0.999 0.999
## predict76 0.997 0.998 0.998 0.999 0.999 0.999
## predict77 0.997 0.998 0.998 0.998 0.999 0.999
## predict78 0.997 0.998 0.998 0.998 0.999 0.999
## predict79 0.997 0.998 0.998 0.998 0.999 0.999
## predict80 0.997 0.997 0.998 0.998 0.998 0.999
## predict81 0.997 0.997 0.998 0.998 0.998 0.999
## predict82 0.997 0.997 0.998 0.998 0.998 0.999
## predict83 0.997 0.997 0.998 0.998 0.998 0.999
## predict84 0.997 0.997 0.998 0.998 0.998 0.998
## predict85 0.997 0.997 0.997 0.998 0.998 0.998
## predict86 0.997 0.997 0.997 0.998 0.998 0.998
## predict87 0.996 0.997 0.997 0.998 0.998 0.998
## predict88 0.996 0.997 0.997 0.998 0.998 0.998
## predict89 0.996 0.997 0.997 0.998 0.998 0.998
## predict90 0.996 0.997 0.997 0.998 0.998 0.998
## predict91 0.996 0.997 0.997 0.998 0.998 0.998
## predict92 0.996 0.997 0.997 0.997 0.998 0.998
## predict93 0.996 0.997 0.997 0.997 0.998 0.998
## predict94 0.996 0.997 0.997 0.997 0.998 0.998
## predict95 0.996 0.997 0.997 0.997 0.998 0.998
## predict96 0.996 0.997 0.997 0.997 0.998 0.998
## predict97 0.996 0.996 0.997 0.997 0.998 0.998
## predict98 0.996 0.996 0.997 0.997 0.998 0.998
## predict99 0.996 0.996 0.997 0.997 0.998 0.998
## predict100 0.996 0.996 0.997 0.997 0.998 0.998
## predict63 predict64 predict65 predict66 predict67 predict68
## g 0.831 0.830 0.830 0.830 0.829 0.829
## predict1 0.577 0.576 0.575 0.575 0.574 0.574
## predict2 0.618 0.617 0.616 0.616 0.615 0.615
## predict3 0.646 0.645 0.645 0.644 0.643 0.643
## predict4 0.671 0.671 0.670 0.669 0.668 0.668
## predict5 0.703 0.702 0.701 0.701 0.700 0.699
## predict6 0.731 0.730 0.730 0.729 0.728 0.727
## predict7 0.755 0.755 0.754 0.753 0.752 0.751
## predict8 0.778 0.777 0.776 0.775 0.775 0.774
## predict9 0.799 0.798 0.797 0.796 0.795 0.794
## predict10 0.815 0.815 0.814 0.813 0.812 0.811
## predict11 0.830 0.829 0.828 0.827 0.826 0.826
## predict12 0.843 0.842 0.842 0.841 0.840 0.839
## predict13 0.856 0.856 0.855 0.854 0.853 0.852
## predict14 0.867 0.866 0.866 0.865 0.864 0.863
## predict15 0.877 0.876 0.875 0.874 0.873 0.873
## predict16 0.885 0.885 0.884 0.883 0.882 0.881
## predict17 0.894 0.893 0.892 0.891 0.890 0.889
## predict18 0.901 0.901 0.900 0.899 0.898 0.897
## predict19 0.909 0.908 0.907 0.906 0.905 0.905
## predict20 0.915 0.914 0.913 0.913 0.912 0.911
## predict21 0.921 0.920 0.919 0.919 0.918 0.917
## predict22 0.927 0.926 0.925 0.924 0.923 0.923
## predict23 0.932 0.931 0.930 0.929 0.929 0.928
## predict24 0.937 0.936 0.935 0.934 0.933 0.933
## predict25 0.941 0.940 0.939 0.938 0.937 0.937
## predict26 0.945 0.944 0.943 0.942 0.941 0.941
## predict27 0.949 0.948 0.947 0.946 0.945 0.945
## predict28 0.952 0.952 0.951 0.950 0.949 0.949
## predict29 0.956 0.955 0.954 0.954 0.953 0.952
## predict30 0.960 0.959 0.958 0.957 0.957 0.956
## predict31 0.963 0.962 0.961 0.960 0.960 0.959
## predict32 0.966 0.965 0.964 0.963 0.963 0.962
## predict33 0.968 0.968 0.967 0.966 0.966 0.965
## predict34 0.971 0.970 0.970 0.969 0.968 0.968
## predict35 0.973 0.973 0.972 0.971 0.971 0.970
## predict36 0.976 0.975 0.974 0.974 0.973 0.973
## predict37 0.978 0.977 0.977 0.976 0.976 0.975
## predict38 0.980 0.980 0.979 0.978 0.978 0.977
## predict39 0.982 0.982 0.981 0.980 0.980 0.979
## predict40 0.984 0.984 0.983 0.982 0.982 0.981
## predict41 0.986 0.985 0.985 0.984 0.984 0.983
## predict42 0.988 0.987 0.986 0.986 0.985 0.985
## predict43 0.989 0.988 0.988 0.987 0.987 0.987
## predict44 0.990 0.990 0.989 0.989 0.988 0.988
## predict45 0.992 0.991 0.991 0.990 0.990 0.989
## predict46 0.993 0.992 0.992 0.991 0.991 0.991
## predict47 0.994 0.993 0.993 0.993 0.992 0.992
## predict48 0.995 0.994 0.994 0.994 0.993 0.993
## predict49 0.996 0.995 0.995 0.995 0.994 0.994
## predict50 0.996 0.996 0.996 0.995 0.995 0.995
## predict51 0.997 0.997 0.996 0.996 0.996 0.996
## predict52 0.998 0.997 0.997 0.997 0.997 0.996
## predict53 0.998 0.998 0.998 0.997 0.997 0.997
## predict54 0.999 0.998 0.998 0.998 0.998 0.997
## predict55 0.999 0.999 0.999 0.998 0.998 0.998
## predict56 0.999 0.999 0.999 0.999 0.998 0.998
## predict57 0.999 0.999 0.999 0.999 0.999 0.999
## predict58 1.000 1.000 0.999 0.999 0.999 0.999
## predict59 1.000 1.000 1.000 0.999 0.999 0.999
## predict60 1.000 1.000 1.000 1.000 1.000 0.999
## predict61 1.000 1.000 1.000 1.000 1.000 1.000
## predict62 1.000 1.000 1.000 1.000 1.000 1.000
## predict63 1.000 1.000 1.000 1.000 1.000 1.000
## predict64 1.000 1.000 1.000 1.000 1.000 1.000
## predict65 1.000 1.000 1.000 1.000 1.000 1.000
## predict66 1.000 1.000 1.000 1.000 1.000 1.000
## predict67 1.000 1.000 1.000 1.000 1.000 1.000
## predict68 1.000 1.000 1.000 1.000 1.000 1.000
## predict69 1.000 1.000 1.000 1.000 1.000 1.000
## predict70 1.000 1.000 1.000 1.000 1.000 1.000
## predict71 1.000 1.000 1.000 1.000 1.000 1.000
## predict72 1.000 1.000 1.000 1.000 1.000 1.000
## predict73 0.999 1.000 1.000 1.000 1.000 1.000
## predict74 0.999 1.000 1.000 1.000 1.000 1.000
## predict75 0.999 0.999 1.000 1.000 1.000 1.000
## predict76 0.999 0.999 1.000 1.000 1.000 1.000
## predict77 0.999 0.999 0.999 1.000 1.000 1.000
## predict78 0.999 0.999 0.999 1.000 1.000 1.000
## predict79 0.999 0.999 0.999 0.999 1.000 1.000
## predict80 0.999 0.999 0.999 0.999 1.000 1.000
## predict81 0.999 0.999 0.999 0.999 0.999 1.000
## predict82 0.999 0.999 0.999 0.999 0.999 1.000
## predict83 0.999 0.999 0.999 0.999 0.999 1.000
## predict84 0.999 0.999 0.999 0.999 0.999 0.999
## predict85 0.999 0.999 0.999 0.999 0.999 0.999
## predict86 0.999 0.999 0.999 0.999 0.999 0.999
## predict87 0.999 0.999 0.999 0.999 0.999 0.999
## predict88 0.999 0.999 0.999 0.999 0.999 0.999
## predict89 0.998 0.999 0.999 0.999 0.999 0.999
## predict90 0.998 0.999 0.999 0.999 0.999 0.999
## predict91 0.998 0.999 0.999 0.999 0.999 0.999
## predict92 0.998 0.999 0.999 0.999 0.999 0.999
## predict93 0.998 0.999 0.999 0.999 0.999 0.999
## predict94 0.998 0.999 0.999 0.999 0.999 0.999
## predict95 0.998 0.999 0.999 0.999 0.999 0.999
## predict96 0.998 0.999 0.999 0.999 0.999 0.999
## predict97 0.998 0.998 0.999 0.999 0.999 0.999
## predict98 0.998 0.998 0.999 0.999 0.999 0.999
## predict99 0.998 0.998 0.999 0.999 0.999 0.999
## predict100 0.998 0.998 0.999 0.999 0.999 0.999
## predict69 predict70 predict71 predict72 predict73 predict74
## g 0.829 0.828 0.828 0.828 0.828 0.827
## predict1 0.573 0.573 0.572 0.572 0.571 0.571
## predict2 0.614 0.614 0.613 0.613 0.612 0.612
## predict3 0.642 0.642 0.641 0.641 0.640 0.640
## predict4 0.667 0.667 0.666 0.666 0.665 0.665
## predict5 0.699 0.698 0.698 0.697 0.697 0.696
## predict6 0.727 0.726 0.726 0.725 0.725 0.724
## predict7 0.751 0.750 0.750 0.749 0.749 0.748
## predict8 0.773 0.773 0.772 0.772 0.771 0.771
## predict9 0.794 0.793 0.793 0.792 0.792 0.791
## predict10 0.811 0.810 0.809 0.809 0.808 0.808
## predict11 0.825 0.824 0.824 0.823 0.823 0.822
## predict12 0.839 0.838 0.837 0.837 0.836 0.836
## predict13 0.852 0.851 0.850 0.850 0.849 0.849
## predict14 0.863 0.862 0.861 0.861 0.860 0.860
## predict15 0.872 0.871 0.871 0.870 0.870 0.869
## predict16 0.881 0.880 0.879 0.879 0.878 0.878
## predict17 0.889 0.888 0.888 0.887 0.886 0.886
## predict18 0.897 0.896 0.895 0.895 0.894 0.894
## predict19 0.904 0.903 0.903 0.902 0.902 0.901
## predict20 0.910 0.910 0.909 0.909 0.908 0.908
## predict21 0.916 0.916 0.915 0.915 0.914 0.914
## predict22 0.922 0.921 0.921 0.920 0.920 0.919
## predict23 0.927 0.927 0.926 0.925 0.925 0.925
## predict24 0.932 0.931 0.931 0.930 0.930 0.929
## predict25 0.936 0.936 0.935 0.934 0.934 0.933
## predict26 0.940 0.940 0.939 0.938 0.938 0.938
## predict27 0.944 0.943 0.943 0.942 0.942 0.941
## predict28 0.948 0.947 0.947 0.946 0.946 0.945
## predict29 0.952 0.951 0.951 0.950 0.950 0.949
## predict30 0.955 0.955 0.954 0.954 0.953 0.953
## predict31 0.959 0.958 0.957 0.957 0.957 0.956
## predict32 0.962 0.961 0.960 0.960 0.960 0.959
## predict33 0.964 0.964 0.963 0.963 0.962 0.962
## predict34 0.967 0.967 0.966 0.966 0.965 0.965
## predict35 0.970 0.969 0.969 0.968 0.968 0.967
## predict36 0.972 0.972 0.971 0.971 0.970 0.970
## predict37 0.974 0.974 0.974 0.973 0.973 0.972
## predict38 0.977 0.976 0.976 0.975 0.975 0.975
## predict39 0.979 0.978 0.978 0.978 0.977 0.977
## predict40 0.981 0.980 0.980 0.980 0.979 0.979
## predict41 0.983 0.982 0.982 0.982 0.981 0.981
## predict42 0.985 0.984 0.984 0.983 0.983 0.983
## predict43 0.986 0.986 0.985 0.985 0.985 0.984
## predict44 0.988 0.987 0.987 0.987 0.986 0.986
## predict45 0.989 0.989 0.988 0.988 0.988 0.987
## predict46 0.990 0.990 0.990 0.989 0.989 0.989
## predict47 0.991 0.991 0.991 0.991 0.990 0.990
## predict48 0.993 0.992 0.992 0.992 0.991 0.991
## predict49 0.994 0.993 0.993 0.993 0.992 0.992
## predict50 0.995 0.994 0.994 0.994 0.993 0.993
## predict51 0.995 0.995 0.995 0.995 0.994 0.994
## predict52 0.996 0.996 0.996 0.995 0.995 0.995
## predict53 0.997 0.996 0.996 0.996 0.996 0.996
## predict54 0.997 0.997 0.997 0.997 0.996 0.996
## predict55 0.998 0.998 0.997 0.997 0.997 0.997
## predict56 0.998 0.998 0.998 0.998 0.997 0.997
## predict57 0.998 0.998 0.998 0.998 0.998 0.998
## predict58 0.999 0.999 0.999 0.998 0.998 0.998
## predict59 0.999 0.999 0.999 0.999 0.999 0.998
## predict60 0.999 0.999 0.999 0.999 0.999 0.999
## predict61 0.999 0.999 0.999 0.999 0.999 0.999
## predict62 1.000 1.000 0.999 0.999 0.999 0.999
## predict63 1.000 1.000 1.000 1.000 0.999 0.999
## predict64 1.000 1.000 1.000 1.000 1.000 1.000
## predict65 1.000 1.000 1.000 1.000 1.000 1.000
## predict66 1.000 1.000 1.000 1.000 1.000 1.000
## predict67 1.000 1.000 1.000 1.000 1.000 1.000
## predict68 1.000 1.000 1.000 1.000 1.000 1.000
## predict69 1.000 1.000 1.000 1.000 1.000 1.000
## predict70 1.000 1.000 1.000 1.000 1.000 1.000
## predict71 1.000 1.000 1.000 1.000 1.000 1.000
## predict72 1.000 1.000 1.000 1.000 1.000 1.000
## predict73 1.000 1.000 1.000 1.000 1.000 1.000
## predict74 1.000 1.000 1.000 1.000 1.000 1.000
## predict75 1.000 1.000 1.000 1.000 1.000 1.000
## predict76 1.000 1.000 1.000 1.000 1.000 1.000
## predict77 1.000 1.000 1.000 1.000 1.000 1.000
## predict78 1.000 1.000 1.000 1.000 1.000 1.000
## predict79 1.000 1.000 1.000 1.000 1.000 1.000
## predict80 1.000 1.000 1.000 1.000 1.000 1.000
## predict81 1.000 1.000 1.000 1.000 1.000 1.000
## predict82 1.000 1.000 1.000 1.000 1.000 1.000
## predict83 1.000 1.000 1.000 1.000 1.000 1.000
## predict84 1.000 1.000 1.000 1.000 1.000 1.000
## predict85 1.000 1.000 1.000 1.000 1.000 1.000
## predict86 1.000 1.000 1.000 1.000 1.000 1.000
## predict87 0.999 1.000 1.000 1.000 1.000 1.000
## predict88 0.999 1.000 1.000 1.000 1.000 1.000
## predict89 0.999 1.000 1.000 1.000 1.000 1.000
## predict90 0.999 1.000 1.000 1.000 1.000 1.000
## predict91 0.999 1.000 1.000 1.000 1.000 1.000
## predict92 0.999 0.999 1.000 1.000 1.000 1.000
## predict93 0.999 0.999 1.000 1.000 1.000 1.000
## predict94 0.999 0.999 1.000 1.000 1.000 1.000
## predict95 0.999 0.999 1.000 1.000 1.000 1.000
## predict96 0.999 0.999 1.000 1.000 1.000 1.000
## predict97 0.999 0.999 0.999 1.000 1.000 1.000
## predict98 0.999 0.999 0.999 1.000 1.000 1.000
## predict99 0.999 0.999 0.999 1.000 1.000 1.000
## predict100 0.999 0.999 0.999 1.000 1.000 1.000
## predict75 predict76 predict77 predict78 predict79 predict80
## g 0.827 0.827 0.827 0.826 0.826 0.826
## predict1 0.571 0.570 0.570 0.570 0.570 0.569
## predict2 0.612 0.611 0.611 0.611 0.610 0.610
## predict3 0.639 0.639 0.639 0.638 0.638 0.638
## predict4 0.664 0.664 0.664 0.663 0.663 0.663
## predict5 0.696 0.695 0.695 0.695 0.694 0.694
## predict6 0.724 0.723 0.723 0.723 0.722 0.722
## predict7 0.748 0.747 0.747 0.747 0.746 0.746
## predict8 0.770 0.770 0.769 0.769 0.769 0.768
## predict9 0.791 0.790 0.790 0.789 0.789 0.789
## predict10 0.807 0.807 0.807 0.806 0.806 0.806
## predict11 0.822 0.821 0.821 0.821 0.820 0.820
## predict12 0.835 0.835 0.834 0.834 0.834 0.833
## predict13 0.848 0.848 0.848 0.847 0.847 0.847
## predict14 0.859 0.859 0.858 0.858 0.858 0.857
## predict15 0.869 0.868 0.868 0.867 0.867 0.867
## predict16 0.877 0.877 0.876 0.876 0.876 0.875
## predict17 0.886 0.885 0.885 0.884 0.884 0.884
## predict18 0.893 0.893 0.893 0.892 0.892 0.892
## predict19 0.901 0.900 0.900 0.899 0.899 0.899
## predict20 0.907 0.907 0.906 0.906 0.906 0.905
## predict21 0.913 0.913 0.912 0.912 0.912 0.911
## predict22 0.919 0.918 0.918 0.918 0.917 0.917
## predict23 0.924 0.924 0.923 0.923 0.923 0.922
## predict24 0.929 0.928 0.928 0.928 0.927 0.927
## predict25 0.933 0.933 0.932 0.932 0.932 0.931
## predict26 0.937 0.937 0.936 0.936 0.936 0.935
## predict27 0.941 0.941 0.940 0.940 0.940 0.939
## predict28 0.945 0.945 0.944 0.944 0.944 0.943
## predict29 0.949 0.948 0.948 0.948 0.947 0.947
## predict30 0.952 0.952 0.952 0.951 0.951 0.951
## predict31 0.956 0.955 0.955 0.955 0.954 0.954
## predict32 0.959 0.958 0.958 0.958 0.957 0.957
## predict33 0.962 0.961 0.961 0.961 0.960 0.960
## predict34 0.964 0.964 0.964 0.963 0.963 0.963
## predict35 0.967 0.967 0.966 0.966 0.966 0.965
## predict36 0.969 0.969 0.969 0.969 0.968 0.968
## predict37 0.972 0.972 0.971 0.971 0.971 0.970
## predict38 0.974 0.974 0.974 0.973 0.973 0.973
## predict39 0.977 0.976 0.976 0.976 0.975 0.975
## predict40 0.979 0.978 0.978 0.978 0.977 0.977
## predict41 0.981 0.980 0.980 0.980 0.979 0.979
## predict42 0.982 0.982 0.982 0.982 0.981 0.981
## predict43 0.984 0.984 0.984 0.983 0.983 0.983
## predict44 0.986 0.985 0.985 0.985 0.985 0.984
## predict45 0.987 0.987 0.987 0.986 0.986 0.986
## predict46 0.988 0.988 0.988 0.988 0.988 0.987
## predict47 0.990 0.990 0.989 0.989 0.989 0.989
## predict48 0.991 0.991 0.991 0.990 0.990 0.990
## predict49 0.992 0.992 0.992 0.991 0.991 0.991
## predict50 0.993 0.993 0.993 0.992 0.992 0.992
## predict51 0.994 0.994 0.994 0.993 0.993 0.993
## predict52 0.995 0.995 0.994 0.994 0.994 0.994
## predict53 0.995 0.995 0.995 0.995 0.995 0.995
## predict54 0.996 0.996 0.996 0.996 0.996 0.995
## predict55 0.997 0.997 0.996 0.996 0.996 0.996
## predict56 0.997 0.997 0.997 0.997 0.997 0.997
## predict57 0.998 0.997 0.997 0.997 0.997 0.997
## predict58 0.998 0.998 0.998 0.998 0.998 0.997
## predict59 0.998 0.998 0.998 0.998 0.998 0.998
## predict60 0.999 0.999 0.998 0.998 0.998 0.998
## predict61 0.999 0.999 0.999 0.999 0.999 0.998
## predict62 0.999 0.999 0.999 0.999 0.999 0.999
## predict63 0.999 0.999 0.999 0.999 0.999 0.999
## predict64 0.999 0.999 0.999 0.999 0.999 0.999
## predict65 1.000 1.000 0.999 0.999 0.999 0.999
## predict66 1.000 1.000 1.000 1.000 0.999 0.999
## predict67 1.000 1.000 1.000 1.000 1.000 1.000
## predict68 1.000 1.000 1.000 1.000 1.000 1.000
## predict69 1.000 1.000 1.000 1.000 1.000 1.000
## predict70 1.000 1.000 1.000 1.000 1.000 1.000
## predict71 1.000 1.000 1.000 1.000 1.000 1.000
## predict72 1.000 1.000 1.000 1.000 1.000 1.000
## predict73 1.000 1.000 1.000 1.000 1.000 1.000
## predict74 1.000 1.000 1.000 1.000 1.000 1.000
## predict75 1.000 1.000 1.000 1.000 1.000 1.000
## predict76 1.000 1.000 1.000 1.000 1.000 1.000
## predict77 1.000 1.000 1.000 1.000 1.000 1.000
## predict78 1.000 1.000 1.000 1.000 1.000 1.000
## predict79 1.000 1.000 1.000 1.000 1.000 1.000
## predict80 1.000 1.000 1.000 1.000 1.000 1.000
## predict81 1.000 1.000 1.000 1.000 1.000 1.000
## predict82 1.000 1.000 1.000 1.000 1.000 1.000
## predict83 1.000 1.000 1.000 1.000 1.000 1.000
## predict84 1.000 1.000 1.000 1.000 1.000 1.000
## predict85 1.000 1.000 1.000 1.000 1.000 1.000
## predict86 1.000 1.000 1.000 1.000 1.000 1.000
## predict87 1.000 1.000 1.000 1.000 1.000 1.000
## predict88 1.000 1.000 1.000 1.000 1.000 1.000
## predict89 1.000 1.000 1.000 1.000 1.000 1.000
## predict90 1.000 1.000 1.000 1.000 1.000 1.000
## predict91 1.000 1.000 1.000 1.000 1.000 1.000
## predict92 1.000 1.000 1.000 1.000 1.000 1.000
## predict93 1.000 1.000 1.000 1.000 1.000 1.000
## predict94 1.000 1.000 1.000 1.000 1.000 1.000
## predict95 1.000 1.000 1.000 1.000 1.000 1.000
## predict96 1.000 1.000 1.000 1.000 1.000 1.000
## predict97 1.000 1.000 1.000 1.000 1.000 1.000
## predict98 1.000 1.000 1.000 1.000 1.000 1.000
## predict99 1.000 1.000 1.000 1.000 1.000 1.000
## predict100 1.000 1.000 1.000 1.000 1.000 1.000
## predict81 predict82 predict83 predict84 predict85 predict86
## g 0.826 0.826 0.826 0.826 0.825 0.825
## predict1 0.569 0.569 0.569 0.569 0.568 0.568
## predict2 0.610 0.610 0.609 0.609 0.609 0.609
## predict3 0.638 0.637 0.637 0.637 0.637 0.637
## predict4 0.663 0.662 0.662 0.662 0.662 0.662
## predict5 0.694 0.694 0.693 0.693 0.693 0.693
## predict6 0.722 0.722 0.721 0.721 0.721 0.721
## predict7 0.746 0.746 0.745 0.745 0.745 0.745
## predict8 0.768 0.768 0.768 0.767 0.767 0.767
## predict9 0.789 0.788 0.788 0.788 0.788 0.787
## predict10 0.805 0.805 0.805 0.805 0.804 0.804
## predict11 0.820 0.819 0.819 0.819 0.819 0.819
## predict12 0.833 0.833 0.833 0.832 0.832 0.832
## predict13 0.846 0.846 0.846 0.845 0.845 0.845
## predict14 0.857 0.857 0.857 0.856 0.856 0.856
## predict15 0.866 0.866 0.866 0.866 0.865 0.865
## predict16 0.875 0.875 0.875 0.874 0.874 0.874
## predict17 0.883 0.883 0.883 0.883 0.882 0.882
## predict18 0.891 0.891 0.891 0.890 0.890 0.890
## predict19 0.899 0.898 0.898 0.898 0.898 0.897
## predict20 0.905 0.905 0.904 0.904 0.904 0.904
## predict21 0.911 0.911 0.911 0.910 0.910 0.910
## predict22 0.917 0.917 0.916 0.916 0.916 0.916
## predict23 0.922 0.922 0.921 0.921 0.921 0.921
## predict24 0.927 0.926 0.926 0.926 0.926 0.926
## predict25 0.931 0.931 0.931 0.930 0.930 0.930
## predict26 0.935 0.935 0.935 0.934 0.934 0.934
## predict27 0.939 0.939 0.939 0.938 0.938 0.938
## predict28 0.943 0.943 0.942 0.942 0.942 0.942
## predict29 0.947 0.947 0.946 0.946 0.946 0.946
## predict30 0.950 0.950 0.950 0.950 0.950 0.949
## predict31 0.954 0.954 0.953 0.953 0.953 0.953
## predict32 0.957 0.957 0.956 0.956 0.956 0.956
## predict33 0.960 0.960 0.959 0.959 0.959 0.959
## predict34 0.963 0.962 0.962 0.962 0.962 0.962
## predict35 0.965 0.965 0.965 0.965 0.964 0.964
## predict36 0.968 0.968 0.967 0.967 0.967 0.967
## predict37 0.970 0.970 0.970 0.970 0.969 0.969
## predict38 0.973 0.972 0.972 0.972 0.972 0.972
## predict39 0.975 0.975 0.975 0.974 0.974 0.974
## predict40 0.977 0.977 0.977 0.976 0.976 0.976
## predict41 0.979 0.979 0.979 0.978 0.978 0.978
## predict42 0.981 0.981 0.981 0.980 0.980 0.980
## predict43 0.983 0.982 0.982 0.982 0.982 0.982
## predict44 0.984 0.984 0.984 0.984 0.984 0.983
## predict45 0.986 0.986 0.985 0.985 0.985 0.985
## predict46 0.987 0.987 0.987 0.987 0.987 0.986
## predict47 0.989 0.988 0.988 0.988 0.988 0.988
## predict48 0.990 0.990 0.989 0.989 0.989 0.989
## predict49 0.991 0.991 0.991 0.991 0.990 0.990
## predict50 0.992 0.992 0.992 0.992 0.991 0.991
## predict51 0.993 0.993 0.993 0.993 0.992 0.992
## predict52 0.994 0.994 0.994 0.993 0.993 0.993
## predict53 0.995 0.994 0.994 0.994 0.994 0.994
## predict54 0.995 0.995 0.995 0.995 0.995 0.995
## predict55 0.996 0.996 0.996 0.996 0.995 0.995
## predict56 0.996 0.996 0.996 0.996 0.996 0.996
## predict57 0.997 0.997 0.997 0.997 0.997 0.997
## predict58 0.997 0.997 0.997 0.997 0.997 0.997
## predict59 0.998 0.998 0.998 0.998 0.997 0.997
## predict60 0.998 0.998 0.998 0.998 0.998 0.998
## predict61 0.998 0.998 0.998 0.998 0.998 0.998
## predict62 0.999 0.999 0.999 0.998 0.998 0.998
## predict63 0.999 0.999 0.999 0.999 0.999 0.999
## predict64 0.999 0.999 0.999 0.999 0.999 0.999
## predict65 0.999 0.999 0.999 0.999 0.999 0.999
## predict66 0.999 0.999 0.999 0.999 0.999 0.999
## predict67 0.999 0.999 0.999 0.999 0.999 0.999
## predict68 1.000 1.000 1.000 0.999 0.999 0.999
## predict69 1.000 1.000 1.000 1.000 1.000 1.000
## predict70 1.000 1.000 1.000 1.000 1.000 1.000
## predict71 1.000 1.000 1.000 1.000 1.000 1.000
## predict72 1.000 1.000 1.000 1.000 1.000 1.000
## predict73 1.000 1.000 1.000 1.000 1.000 1.000
## predict74 1.000 1.000 1.000 1.000 1.000 1.000
## predict75 1.000 1.000 1.000 1.000 1.000 1.000
## predict76 1.000 1.000 1.000 1.000 1.000 1.000
## predict77 1.000 1.000 1.000 1.000 1.000 1.000
## predict78 1.000 1.000 1.000 1.000 1.000 1.000
## predict79 1.000 1.000 1.000 1.000 1.000 1.000
## predict80 1.000 1.000 1.000 1.000 1.000 1.000
## predict81 1.000 1.000 1.000 1.000 1.000 1.000
## predict82 1.000 1.000 1.000 1.000 1.000 1.000
## predict83 1.000 1.000 1.000 1.000 1.000 1.000
## predict84 1.000 1.000 1.000 1.000 1.000 1.000
## predict85 1.000 1.000 1.000 1.000 1.000 1.000
## predict86 1.000 1.000 1.000 1.000 1.000 1.000
## predict87 1.000 1.000 1.000 1.000 1.000 1.000
## predict88 1.000 1.000 1.000 1.000 1.000 1.000
## predict89 1.000 1.000 1.000 1.000 1.000 1.000
## predict90 1.000 1.000 1.000 1.000 1.000 1.000
## predict91 1.000 1.000 1.000 1.000 1.000 1.000
## predict92 1.000 1.000 1.000 1.000 1.000 1.000
## predict93 1.000 1.000 1.000 1.000 1.000 1.000
## predict94 1.000 1.000 1.000 1.000 1.000 1.000
## predict95 1.000 1.000 1.000 1.000 1.000 1.000
## predict96 1.000 1.000 1.000 1.000 1.000 1.000
## predict97 1.000 1.000 1.000 1.000 1.000 1.000
## predict98 1.000 1.000 1.000 1.000 1.000 1.000
## predict99 1.000 1.000 1.000 1.000 1.000 1.000
## predict100 1.000 1.000 1.000 1.000 1.000 1.000
## predict87 predict88 predict89 predict90 predict91 predict92
## g 0.825 0.825 0.825 0.825 0.825 0.825
## predict1 0.568 0.568 0.568 0.568 0.568 0.568
## predict2 0.609 0.609 0.609 0.608 0.608 0.608
## predict3 0.637 0.636 0.636 0.636 0.636 0.636
## predict4 0.661 0.661 0.661 0.661 0.661 0.661
## predict5 0.693 0.693 0.692 0.692 0.692 0.692
## predict6 0.721 0.720 0.720 0.720 0.720 0.720
## predict7 0.745 0.744 0.744 0.744 0.744 0.744
## predict8 0.767 0.767 0.767 0.766 0.766 0.766
## predict9 0.787 0.787 0.787 0.787 0.787 0.786
## predict10 0.804 0.804 0.804 0.803 0.803 0.803
## predict11 0.818 0.818 0.818 0.818 0.818 0.818
## predict12 0.832 0.832 0.831 0.831 0.831 0.831
## predict13 0.845 0.845 0.845 0.844 0.844 0.844
## predict14 0.856 0.856 0.855 0.855 0.855 0.855
## predict15 0.865 0.865 0.865 0.865 0.864 0.864
## predict16 0.874 0.874 0.873 0.873 0.873 0.873
## predict17 0.882 0.882 0.882 0.882 0.881 0.881
## predict18 0.890 0.890 0.890 0.889 0.889 0.889
## predict19 0.897 0.897 0.897 0.897 0.897 0.896
## predict20 0.904 0.903 0.903 0.903 0.903 0.903
## predict21 0.910 0.910 0.909 0.909 0.909 0.909
## predict22 0.915 0.915 0.915 0.915 0.915 0.915
## predict23 0.921 0.920 0.920 0.920 0.920 0.920
## predict24 0.925 0.925 0.925 0.925 0.925 0.925
## predict25 0.930 0.930 0.929 0.929 0.929 0.929
## predict26 0.934 0.934 0.933 0.933 0.933 0.933
## predict27 0.938 0.938 0.937 0.937 0.937 0.937
## predict28 0.942 0.942 0.941 0.941 0.941 0.941
## predict29 0.946 0.945 0.945 0.945 0.945 0.945
## predict30 0.949 0.949 0.949 0.949 0.949 0.949
## predict31 0.953 0.952 0.952 0.952 0.952 0.952
## predict32 0.956 0.956 0.955 0.955 0.955 0.955
## predict33 0.959 0.958 0.958 0.958 0.958 0.958
## predict34 0.961 0.961 0.961 0.961 0.961 0.961
## predict35 0.964 0.964 0.964 0.964 0.963 0.963
## predict36 0.967 0.966 0.966 0.966 0.966 0.966
## predict37 0.969 0.969 0.969 0.969 0.969 0.969
## predict38 0.972 0.971 0.971 0.971 0.971 0.971
## predict39 0.974 0.974 0.974 0.973 0.973 0.973
## predict40 0.976 0.976 0.976 0.976 0.976 0.975
## predict41 0.978 0.978 0.978 0.978 0.978 0.977
## predict42 0.980 0.980 0.980 0.980 0.979 0.979
## predict43 0.982 0.982 0.981 0.981 0.981 0.981
## predict44 0.983 0.983 0.983 0.983 0.983 0.983
## predict45 0.985 0.985 0.985 0.985 0.984 0.984
## predict46 0.986 0.986 0.986 0.986 0.986 0.986
## predict47 0.988 0.988 0.988 0.987 0.987 0.987
## predict48 0.989 0.989 0.989 0.989 0.989 0.989
## predict49 0.990 0.990 0.990 0.990 0.990 0.990
## predict50 0.991 0.991 0.991 0.991 0.991 0.991
## predict51 0.992 0.992 0.992 0.992 0.992 0.992
## predict52 0.993 0.993 0.993 0.993 0.993 0.993
## predict53 0.994 0.994 0.994 0.994 0.994 0.994
## predict54 0.995 0.995 0.995 0.994 0.994 0.994
## predict55 0.995 0.995 0.995 0.995 0.995 0.995
## predict56 0.996 0.996 0.996 0.996 0.996 0.996
## predict57 0.996 0.996 0.996 0.996 0.996 0.996
## predict58 0.997 0.997 0.997 0.997 0.997 0.997
## predict59 0.997 0.997 0.997 0.997 0.997 0.997
## predict60 0.998 0.998 0.998 0.998 0.998 0.997
## predict61 0.998 0.998 0.998 0.998 0.998 0.998
## predict62 0.998 0.998 0.998 0.998 0.998 0.998
## predict63 0.999 0.999 0.998 0.998 0.998 0.998
## predict64 0.999 0.999 0.999 0.999 0.999 0.999
## predict65 0.999 0.999 0.999 0.999 0.999 0.999
## predict66 0.999 0.999 0.999 0.999 0.999 0.999
## predict67 0.999 0.999 0.999 0.999 0.999 0.999
## predict68 0.999 0.999 0.999 0.999 0.999 0.999
## predict69 0.999 0.999 0.999 0.999 0.999 0.999
## predict70 1.000 1.000 1.000 1.000 1.000 0.999
## predict71 1.000 1.000 1.000 1.000 1.000 1.000
## predict72 1.000 1.000 1.000 1.000 1.000 1.000
## predict73 1.000 1.000 1.000 1.000 1.000 1.000
## predict74 1.000 1.000 1.000 1.000 1.000 1.000
## predict75 1.000 1.000 1.000 1.000 1.000 1.000
## predict76 1.000 1.000 1.000 1.000 1.000 1.000
## predict77 1.000 1.000 1.000 1.000 1.000 1.000
## predict78 1.000 1.000 1.000 1.000 1.000 1.000
## predict79 1.000 1.000 1.000 1.000 1.000 1.000
## predict80 1.000 1.000 1.000 1.000 1.000 1.000
## predict81 1.000 1.000 1.000 1.000 1.000 1.000
## predict82 1.000 1.000 1.000 1.000 1.000 1.000
## predict83 1.000 1.000 1.000 1.000 1.000 1.000
## predict84 1.000 1.000 1.000 1.000 1.000 1.000
## predict85 1.000 1.000 1.000 1.000 1.000 1.000
## predict86 1.000 1.000 1.000 1.000 1.000 1.000
## predict87 1.000 1.000 1.000 1.000 1.000 1.000
## predict88 1.000 1.000 1.000 1.000 1.000 1.000
## predict89 1.000 1.000 1.000 1.000 1.000 1.000
## predict90 1.000 1.000 1.000 1.000 1.000 1.000
## predict91 1.000 1.000 1.000 1.000 1.000 1.000
## predict92 1.000 1.000 1.000 1.000 1.000 1.000
## predict93 1.000 1.000 1.000 1.000 1.000 1.000
## predict94 1.000 1.000 1.000 1.000 1.000 1.000
## predict95 1.000 1.000 1.000 1.000 1.000 1.000
## predict96 1.000 1.000 1.000 1.000 1.000 1.000
## predict97 1.000 1.000 1.000 1.000 1.000 1.000
## predict98 1.000 1.000 1.000 1.000 1.000 1.000
## predict99 1.000 1.000 1.000 1.000 1.000 1.000
## predict100 1.000 1.000 1.000 1.000 1.000 1.000
## predict93 predict94 predict95 predict96 predict97 predict98
## g 0.825 0.825 0.825 0.825 0.825 0.825
## predict1 0.567 0.567 0.567 0.567 0.567 0.567
## predict2 0.608 0.608 0.608 0.608 0.608 0.608
## predict3 0.636 0.636 0.636 0.636 0.636 0.635
## predict4 0.661 0.661 0.661 0.661 0.660 0.660
## predict5 0.692 0.692 0.692 0.692 0.692 0.692
## predict6 0.720 0.720 0.720 0.720 0.719 0.719
## predict7 0.744 0.744 0.744 0.743 0.743 0.743
## predict8 0.766 0.766 0.766 0.766 0.766 0.766
## predict9 0.786 0.786 0.786 0.786 0.786 0.786
## predict10 0.803 0.803 0.803 0.803 0.803 0.803
## predict11 0.817 0.817 0.817 0.817 0.817 0.817
## predict12 0.831 0.831 0.831 0.831 0.831 0.830
## predict13 0.844 0.844 0.844 0.844 0.844 0.844
## predict14 0.855 0.855 0.855 0.855 0.854 0.854
## predict15 0.864 0.864 0.864 0.864 0.864 0.864
## predict16 0.873 0.873 0.873 0.873 0.873 0.872
## predict17 0.881 0.881 0.881 0.881 0.881 0.881
## predict18 0.889 0.889 0.889 0.889 0.889 0.889
## predict19 0.896 0.896 0.896 0.896 0.896 0.896
## predict20 0.903 0.903 0.903 0.902 0.902 0.902
## predict21 0.909 0.909 0.909 0.909 0.909 0.908
## predict22 0.915 0.914 0.914 0.914 0.914 0.914
## predict23 0.920 0.920 0.920 0.920 0.919 0.919
## predict24 0.925 0.924 0.924 0.924 0.924 0.924
## predict25 0.929 0.929 0.929 0.929 0.928 0.928
## predict26 0.933 0.933 0.933 0.933 0.933 0.932
## predict27 0.937 0.937 0.937 0.937 0.937 0.936
## predict28 0.941 0.941 0.941 0.941 0.941 0.940
## predict29 0.945 0.945 0.945 0.944 0.944 0.944
## predict30 0.948 0.948 0.948 0.948 0.948 0.948
## predict31 0.952 0.952 0.952 0.952 0.951 0.951
## predict32 0.955 0.955 0.955 0.955 0.955 0.954
## predict33 0.958 0.958 0.958 0.958 0.957 0.957
## predict34 0.961 0.961 0.960 0.960 0.960 0.960
## predict35 0.963 0.963 0.963 0.963 0.963 0.963
## predict36 0.966 0.966 0.966 0.966 0.966 0.965
## predict37 0.968 0.968 0.968 0.968 0.968 0.968
## predict38 0.971 0.971 0.971 0.971 0.971 0.970
## predict39 0.973 0.973 0.973 0.973 0.973 0.973
## predict40 0.975 0.975 0.975 0.975 0.975 0.975
## predict41 0.977 0.977 0.977 0.977 0.977 0.977
## predict42 0.979 0.979 0.979 0.979 0.979 0.979
## predict43 0.981 0.981 0.981 0.981 0.981 0.981
## predict44 0.983 0.983 0.983 0.983 0.982 0.982
## predict45 0.984 0.984 0.984 0.984 0.984 0.984
## predict46 0.986 0.986 0.986 0.986 0.986 0.985
## predict47 0.987 0.987 0.987 0.987 0.987 0.987
## predict48 0.988 0.988 0.988 0.988 0.988 0.988
## predict49 0.990 0.990 0.990 0.990 0.989 0.989
## predict50 0.991 0.991 0.991 0.991 0.991 0.991
## predict51 0.992 0.992 0.992 0.992 0.992 0.992
## predict52 0.993 0.993 0.993 0.993 0.993 0.993
## predict53 0.994 0.994 0.993 0.993 0.993 0.993
## predict54 0.994 0.994 0.994 0.994 0.994 0.994
## predict55 0.995 0.995 0.995 0.995 0.995 0.995
## predict56 0.996 0.996 0.995 0.995 0.995 0.995
## predict57 0.996 0.996 0.996 0.996 0.996 0.996
## predict58 0.997 0.997 0.997 0.997 0.996 0.996
## predict59 0.997 0.997 0.997 0.997 0.997 0.997
## predict60 0.997 0.997 0.997 0.997 0.997 0.997
## predict61 0.998 0.998 0.998 0.998 0.998 0.998
## predict62 0.998 0.998 0.998 0.998 0.998 0.998
## predict63 0.998 0.998 0.998 0.998 0.998 0.998
## predict64 0.999 0.999 0.999 0.999 0.998 0.998
## predict65 0.999 0.999 0.999 0.999 0.999 0.999
## predict66 0.999 0.999 0.999 0.999 0.999 0.999
## predict67 0.999 0.999 0.999 0.999 0.999 0.999
## predict68 0.999 0.999 0.999 0.999 0.999 0.999
## predict69 0.999 0.999 0.999 0.999 0.999 0.999
## predict70 0.999 0.999 0.999 0.999 0.999 0.999
## predict71 1.000 1.000 1.000 1.000 0.999 0.999
## predict72 1.000 1.000 1.000 1.000 1.000 1.000
## predict73 1.000 1.000 1.000 1.000 1.000 1.000
## predict74 1.000 1.000 1.000 1.000 1.000 1.000
## predict75 1.000 1.000 1.000 1.000 1.000 1.000
## predict76 1.000 1.000 1.000 1.000 1.000 1.000
## predict77 1.000 1.000 1.000 1.000 1.000 1.000
## predict78 1.000 1.000 1.000 1.000 1.000 1.000
## predict79 1.000 1.000 1.000 1.000 1.000 1.000
## predict80 1.000 1.000 1.000 1.000 1.000 1.000
## predict81 1.000 1.000 1.000 1.000 1.000 1.000
## predict82 1.000 1.000 1.000 1.000 1.000 1.000
## predict83 1.000 1.000 1.000 1.000 1.000 1.000
## predict84 1.000 1.000 1.000 1.000 1.000 1.000
## predict85 1.000 1.000 1.000 1.000 1.000 1.000
## predict86 1.000 1.000 1.000 1.000 1.000 1.000
## predict87 1.000 1.000 1.000 1.000 1.000 1.000
## predict88 1.000 1.000 1.000 1.000 1.000 1.000
## predict89 1.000 1.000 1.000 1.000 1.000 1.000
## predict90 1.000 1.000 1.000 1.000 1.000 1.000
## predict91 1.000 1.000 1.000 1.000 1.000 1.000
## predict92 1.000 1.000 1.000 1.000 1.000 1.000
## predict93 1.000 1.000 1.000 1.000 1.000 1.000
## predict94 1.000 1.000 1.000 1.000 1.000 1.000
## predict95 1.000 1.000 1.000 1.000 1.000 1.000
## predict96 1.000 1.000 1.000 1.000 1.000 1.000
## predict97 1.000 1.000 1.000 1.000 1.000 1.000
## predict98 1.000 1.000 1.000 1.000 1.000 1.000
## predict99 1.000 1.000 1.000 1.000 1.000 1.000
## predict100 1.000 1.000 1.000 1.000 1.000 1.000
## predict99 predict100
## g 0.824 0.824
## predict1 0.567 0.567
## predict2 0.608 0.608
## predict3 0.635 0.635
## predict4 0.660 0.660
## predict5 0.691 0.691
## predict6 0.719 0.719
## predict7 0.743 0.743
## predict8 0.765 0.765
## predict9 0.786 0.786
## predict10 0.803 0.803
## predict11 0.817 0.817
## predict12 0.830 0.830
## predict13 0.843 0.843
## predict14 0.854 0.854
## predict15 0.864 0.864
## predict16 0.872 0.872
## predict17 0.881 0.881
## predict18 0.888 0.888
## predict19 0.896 0.896
## predict20 0.902 0.902
## predict21 0.908 0.908
## predict22 0.914 0.914
## predict23 0.919 0.919
## predict24 0.924 0.924
## predict25 0.928 0.928
## predict26 0.932 0.932
## predict27 0.936 0.936
## predict28 0.940 0.940
## predict29 0.944 0.944
## predict30 0.948 0.948
## predict31 0.951 0.951
## predict32 0.954 0.954
## predict33 0.957 0.957
## predict34 0.960 0.960
## predict35 0.963 0.963
## predict36 0.965 0.965
## predict37 0.968 0.968
## predict38 0.970 0.970
## predict39 0.973 0.973
## predict40 0.975 0.975
## predict41 0.977 0.977
## predict42 0.979 0.979
## predict43 0.981 0.981
## predict44 0.982 0.982
## predict45 0.984 0.984
## predict46 0.985 0.985
## predict47 0.987 0.987
## predict48 0.988 0.988
## predict49 0.989 0.989
## predict50 0.991 0.990
## predict51 0.992 0.992
## predict52 0.992 0.992
## predict53 0.993 0.993
## predict54 0.994 0.994
## predict55 0.995 0.995
## predict56 0.995 0.995
## predict57 0.996 0.996
## predict58 0.996 0.996
## predict59 0.997 0.997
## predict60 0.997 0.997
## predict61 0.998 0.998
## predict62 0.998 0.998
## predict63 0.998 0.998
## predict64 0.998 0.998
## predict65 0.999 0.999
## predict66 0.999 0.999
## predict67 0.999 0.999
## predict68 0.999 0.999
## predict69 0.999 0.999
## predict70 0.999 0.999
## predict71 0.999 0.999
## predict72 1.000 1.000
## predict73 1.000 1.000
## predict74 1.000 1.000
## predict75 1.000 1.000
## predict76 1.000 1.000
## predict77 1.000 1.000
## predict78 1.000 1.000
## predict79 1.000 1.000
## predict80 1.000 1.000
## predict81 1.000 1.000
## predict82 1.000 1.000
## predict83 1.000 1.000
## predict84 1.000 1.000
## predict85 1.000 1.000
## predict86 1.000 1.000
## predict87 1.000 1.000
## predict88 1.000 1.000
## predict89 1.000 1.000
## predict90 1.000 1.000
## predict91 1.000 1.000
## predict92 1.000 1.000
## predict93 1.000 1.000
## predict94 1.000 1.000
## predict95 1.000 1.000
## predict96 1.000 1.000
## predict97 1.000 1.000
## predict98 1.000 1.000
## predict99 1.000 1.000
## predict100 1.000 1.000#plots
glmnet_preds %>%
ggplot(aes(predict45, g)) +
geom_point() +
geom_smooth()## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
glmnet_preds %>%
ggplot(aes(predict14, g)) +
geom_point() +
geom_smooth()## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
#fit on entire sample
#with the same penalty values
set.seed(1)
glmnet_fit_same_penalty = cv.glmnet(
x = MMPI_subset[-1] %>% as.matrix(),
y = MMPI_subset$g,
nfolds = 10,
lambda = glmnet_lambdas
)
#derive data for plotting
glmnet_summary = tibble(
#penalty values
penalty = glmnet_lambdas,
#get the item counts in full fit
items = coef.glmnet(glmnet_fit_same_penalty, s = glmnet_lambdas) %>% as.matrix() %>% as.data.frame() %>% map_int(~sum(.!=0)),
#compute correlations on the OOS rows
r_cv = glmnet_preds[-1] %>% map_dbl(~cor(., glmnet_preds$g)),
r2_cv = r_cv^2
)
#plot
glmnet_summary %>%
ggplot(aes(items, r_cv)) +
geom_line() +
scale_x_continuous("Items in prediction model", breaks = seq(0, 600, by = 25)) +
scale_y_continuous("Correlation to test set (cross-validation)", breaks = seq(-1, 1, by = .05))
GG_save("figs/items_accuracy.png")Random Forest
#make a model
#use ranger
ves_model <-
rand_forest(
mode = "regression",
trees = 100,
mtry = tune()
) %>%
set_engine("ranger")
#make workflow
ves_wf <-
workflow() %>%
add_model(ves_model) %>%
add_recipe(ves_recipe)
#fit
ves_fit_rf <- cache_object({
ves_wf %>%
tune_grid(
resamples = ves_folds,
grid = 25,
control = control_grid(
save_pred = TRUE
)
)
}, filename = "cache/ves_fit_rf.rds")## Cache found, reading object from disk#metrics
collect_metrics(ves_fit_rf) %>%
filter(.metric == "rsq") %>%
arrange(.metric)collect_metrics(ves_fit_rf) %>%
ggplot(aes(mtry, mean)) +
geom_line() +
facet_wrap(".metric")
#plot out of sample predictions
ves_fit_rf %>%
collect_predictions(parameters = select_best(ves_fit_rf, metric = "rsq")) %>%
ggplot(aes(.pred, g)) +
geom_point() +
geom_smooth() +
xlab("Prediction (out of sample)") +
ylab("g")## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
ggsave("figs/ves_scatter_rf.png")## Saving 7 x 5 in image## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'#numerical
ves_fit_rf %>%
collect_predictions(parameters = select_best(ves_fit_rf, metric = "rsq")) %$%
cor.test(.pred, g)##
## Pearson's product-moment correlation
##
## data: .pred and g
## t = 81, df = 4318, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.766 0.790
## sample estimates:
## cor
## 0.778Meta
#save data
d %>% write_rds("data/data_out.rds")
d %>% writexl::write_xlsx("data/data_out.xlsx")
#versions
write_sessioninfo()## R version 4.0.1 (2020-06-06)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Linux Mint 19.3
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] glmnet_4.0-2 osfr_0.2.8 yardstick_0.0.6
## [4] workflows_0.1.1 tune_0.1.0 rsample_0.0.6
## [7] recipes_0.1.12 parsnip_0.1.1 infer_0.5.1
## [10] dials_0.0.6 scales_1.1.1 broom_0.5.6
## [13] tidymodels_0.1.0 doFuture_0.9.0 iterators_1.0.12
## [16] foreach_1.5.0 globals_0.12.5 future_1.17.0
## [19] mirt_1.32.1 readxl_1.3.1 haven_2.3.1
## [22] ggeffects_0.14.3 rms_5.1-4 SparseM_1.78
## [25] kirkegaard_2020-07-02 metafor_2.4-0 Matrix_1.2-18
## [28] psych_1.9.12.31 magrittr_1.5 assertthat_0.2.1
## [31] weights_1.0.1 mice_3.9.0 gdata_2.18.0
## [34] Hmisc_4.4-0 Formula_1.2-3 survival_3.1-12
## [37] lattice_0.20-41 forcats_0.5.0 stringr_1.4.0
## [40] dplyr_0.8.99.9003 purrr_0.3.4 readr_1.3.1
## [43] tidyr_1.1.0 tibble_3.0.1 ggplot2_3.3.2
## [46] tidyverse_1.3.0 pacman_0.5.1
##
## loaded via a namespace (and not attached):
## [1] SnowballC_0.7.0 GGally_1.5.0 Rcsdp_0.1.57.1
## [4] acepack_1.4.1 knitr_1.29 dygraphs_1.1.1.6
## [7] multcomp_1.4-13 data.table_1.12.8 rpart_4.1-15
## [10] inline_0.3.15 hardhat_0.1.2 generics_0.0.2
## [13] GPfit_1.0-8 callr_3.4.3 TH.data_1.0-10
## [16] polspline_1.1.19 tokenizers_0.2.1 xml2_1.3.2
## [19] lubridate_1.7.9 httpuv_1.5.2 StanHeaders_2.19.2
## [22] gower_0.2.1 xfun_0.15 hms_0.5.3
## [25] bayesplot_1.7.1 evaluate_0.14 promises_1.1.0
## [28] fansi_0.4.1 dbplyr_1.4.4 igraph_1.2.5
## [31] DBI_1.1.0 tmvnsim_1.0-2 htmlwidgets_1.5.1
## [34] reshape_0.8.8 ellipsis_0.3.1 crosstalk_1.1.0.1
## [37] backports_1.1.8 insight_0.8.4 permute_0.9-5
## [40] markdown_1.1 vctrs_0.3.1 quantreg_5.55
## [43] sjlabelled_1.1.4 withr_2.2.0 checkmate_2.0.0
## [46] vegan_2.5-6 xts_0.12-0 prettyunits_1.1.1
## [49] mnormt_2.0.1 cluster_2.1.0 crayon_1.3.4
## [52] crul_0.9.0 pkgconfig_2.0.3 labeling_0.3
## [55] nlme_3.1-147 nnet_7.3-14 rlang_0.4.6
## [58] lifecycle_0.2.0 miniUI_0.1.1.1 psychometric_2.2
## [61] colourpicker_1.0 MatrixModels_0.4-1 sandwich_2.5-1
## [64] httpcode_0.3.0 modelr_0.1.8 tidytext_0.2.4
## [67] cellranger_1.1.0 matrixStats_0.56.0 loo_2.2.0
## [70] boot_1.3-25 zoo_1.8-8 reprex_0.3.0
## [73] base64enc_0.1-3 ggridges_0.5.2 processx_3.4.2
## [76] png_0.1-7 pROC_1.16.2 blob_1.2.1
## [79] shape_1.4.4 jpeg_0.1-8.1 shinystan_2.5.0
## [82] memoise_1.1.0 plyr_1.8.6 threejs_0.3.3
## [85] compiler_4.0.1 rstantools_2.0.0 RColorBrewer_1.1-2
## [88] lme4_1.1-23 cli_2.0.2 DiceDesign_1.8-1
## [91] listenv_0.8.0 janeaustenr_0.1.5 ps_1.3.3
## [94] htmlTable_2.0.0 MASS_7.3-51.6 mgcv_1.8-31
## [97] tidyselect_1.1.0 stringi_1.4.6 yaml_2.2.1
## [100] ggrepel_0.8.2 latticeExtra_0.6-29 grid_4.0.1
## [103] tidypredict_0.4.5 tools_4.0.1 rstudioapi_0.11
## [106] foreign_0.8-79 GPArotation_2014.11-1 gridExtra_2.3
## [109] prodlim_2019.11.13 farver_2.0.3 digest_0.6.25
## [112] shiny_1.4.0.2 lava_1.6.7 Rcpp_1.0.4.6
## [115] writexl_1.3 later_1.0.0 httr_1.4.1
## [118] rsconnect_0.8.16 Deriv_4.0 colorspace_1.4-1
## [121] rvest_0.3.5 fs_1.4.2 splines_4.0.1
## [124] statmod_1.4.34 multilevel_2.6 shinythemes_1.1.2
## [127] xtable_1.8-4 rstanarm_2.19.3 jsonlite_1.7.0
## [130] nloptr_1.2.2.1 timeDate_3043.102 rstan_2.19.3
## [133] dcurver_0.9.1 ipred_0.9-9 R6_2.4.1
## [136] lhs_1.0.2 pillar_1.4.4 htmltools_0.5.0
## [139] mime_0.9 glue_1.4.1 fastmap_1.0.1
## [142] minqa_1.2.4 DT_0.13 class_7.3-17
## [145] codetools_0.2-16 pkgbuild_1.0.8 mvtnorm_1.1-0
## [148] furrr_0.1.0 curl_4.3 gtools_3.8.2
## [151] tidyposterior_0.0.2 shinyjs_1.1 rmarkdown_2.3
## [154] munsell_0.5.0 reshape2_1.4.4 gtable_0.3.0#upload files to OSF
if (F) {
osf_auth(read_lines("~/.config/osf_token"))
osf_proj = osf_retrieve_node("https://osf.io/dbn4k/")
osf_upload(osf_proj,
path = c("notebook.Rmd", "notebook.html", "figs", "data"),
conflicts = "overwrite")
}