sandbox-01
Joshua Rosenberg
12/27/2017
Sandbox - 03
Background
This is a sandbox for exploring the data, in preparation for adding to the results section.
This document is organized around two sets of model specifications:
- Fit using the R package
mclust - Fit using the commerical software
MPlus
Both are interfaced to through the tidyLPA package.
Models that are specified
mclust
For models fit using mclust, eight model specifications are tested, where all five refers to the variables for cognitive (learning), behavioral (hard working), and affective (enjoy), and challenge and competence, where three refers to just the three engagement measures. Note that the prior is to regularize the parameter estimates to be more less likely to be extreme (and therefore to be less likely to lead to convergence problems, but possibly less meaningful / interpretable).
- All five variables w/ no prior
- All five variables standardized w/ prior
MPlus
- All five variables
- All five variables standardized
library(tidyverse)
library(lmerTest)
library(lme4)
library(corrr)
library(jmRtools)
library(tidyLPA)
library(sjPlot)esm <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-esm.csv")
pre_survey_data_processed <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-pre-survey.csv")
post_survey_data_partially_processed <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-post-survey.csv")
video <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-video.csv")
pqa <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-pqa.csv")
attendance <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-attendance.csv")
class_data <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-class-video.csv")
demographics <- read_csv("/Volumes/SCHMIDTLAB/PSE/data/STEM-IE/STEM-IE-demographics.csv")
pm <- read_csv("/Volumes/SCHMIDTLAB/PSE/Data/STEM-IE/STEM-IE-program-match.csv")# save.image("~/desktop/sandbox-01.Rdata")
load("~/desktop/sandbox-01.Rdata")attendance <- rename(attendance, participant_ID = ParticipantID)
attendance <- mutate(attendance, prop_attend = DaysAttended / DaysScheduled,
participant_ID = as.integer(participant_ID))
attendance <- select(attendance, participant_ID, prop_attend)
demographics <- filter(demographics, participant_ID!= 7187)
demographics <- left_join(demographics, attendance)
esm$overall_engagement <- jmRtools::composite_mean_maker(esm, hard_working, concentrating, enjoy, interest)df <- left_join(esm, pre_survey_data_processed, by = "participant_ID") # df & post-survey
df <- left_join(df, video, by = c("program_ID", "response_date", "sociedad_class", "signal_number")) # df & video
df <- left_join(df, demographics, by = c("participant_ID", "program_ID")) # df and demographicspqa <- mutate(pqa,
active = active_part_1 + active_part_2,
ho_thinking = ho_thinking_1 + ho_thinking_2 + ho_thinking_3,
belonging = belonging_1 + belonging_2,
agency = agency_1 + agency_2 + agency_3 + agency_4,
youth_development_overall = active_part_1 + active_part_2 + ho_thinking_1 + ho_thinking_2 + ho_thinking_3 + belonging_1 + belonging_2 + agency_1 + agency_2 + agency_3 + agency_4,
making_observations = stem_sb_8,
data_modeling = stem_sb_2 + stem_sb_3 + stem_sb_9,
interpreting_communicating = stem_sb_6,
generating_data = stem_sb_4,
asking_questions = stem_sb_1,
stem_sb = stem_sb_1 + stem_sb_2 + stem_sb_3 + stem_sb_4 + stem_sb_5 + stem_sb_6 + stem_sb_7 + stem_sb_8 + stem_sb_9)
# pqa <- rename(pqa, sixth_math_sociedad = sixth_math)
# pqa <- rename(pqa, seventh_math_sociedad = seventh_math)
# pqa <- rename(pqa, eighth_math_sociedad = eighth_math)
# pqa <- rename(pqa, dance_sociedad = dance)
# pqa <- rename(pqa, robotics_sociedad = robotics)
pqa$sociedad_class <- ifelse(pqa$eighth_math == 1, "8th Math",
ifelse(pqa$seventh_math == 1, "7th Math",
ifelse(pqa$sixth_math == 1, "6th Math",
ifelse(pqa$robotics == 1, "Robotics",
ifelse(pqa$dance == 1, "Dance", NA)))))
pqa <- rename(pqa,
program_ID = SiteIDNumeric,
response_date = resp_date,
signal_number = signal)
pqa$program_ID <- as.character(pqa$program_ID)
df <- left_join(df, pqa, by = c("response_date", "program_ID", "signal_number", "sociedad_class"))df <- df %>%
mutate(youth_activity_three = case_when(
youth_activity_rc == "Creating Product" ~ "Creating Product",
youth_activity_rc == "Basic Skills Activity" ~ "Basic Skills Activity",
TRUE ~ "Other"
))
df$youth_activity_three <- fct_relevel(df$youth_activity_three,
"Other")# <!-- Cognitive engagement As you were signaled, were you learning anything or getting better at something? -->
# <!-- Behavioral engagement As you were signaled, how hard were you working? -->
# <!-- Affective engagement As you were signaled, did you enjoy what you are doing? -->
# <!-- Perceived challenge As you were signaled, how challenging was the main activity? -->
# <!-- Perceived competence As you were signaled, were you good at the main activity? -->
library(jmRtools)
df <- df %>%
mutate(dm_cog_eng = learning,
dm_beh_eng = hard_working,
dm_aff_eng = enjoy,
dm_challenge = challenge,
dm_competence = good_at) %>%
rename(ssb_predict = stem_sb_1,
ssb_model = stem_sb_2 ,
ssb_analyze = stem_sb_3,
ssb_measure = stem_sb_4,
ssb_tools = stem_sb_5,
ssb_precision = stem_sb_6,
ssb_vocabulary = stem_sb_7,
ssb_classification = stem_sb_8,
ssb_symbols = stem_sb_9) %>%
mutate(dm_ask = ssb_predict,
dm_obs = ssb_classification,
dm_gen = ifelse(ssb_measure == 1 | ssb_precision == 1, 1, 0),
dm_mod = ifelse(ssb_model == 1 | ssb_analyze == 1, 1, 0),
dm_com = ssb_symbols) %>%
mutate(ov_cog_eng = (important + future_goals) / 2,
ov_beh_eng = (hard_working + concentrating) / 2,
ov_aff_eng = (enjoy + interest) / 2)out <- df %>%
group_by(program_ID) %>%
select(contains("ssb")) %>%
summarize_all(sum, na.rm = T)
out1 <- pqa %>%
select(contains("stem"), -sum_stem_sb, -stem_sb) %>%
summarize_all(sum, na.rm = T) / 236
names(out1) <- df %>% select(contains("ssb")) %>% names()
pqa_out <- pqa %>%
group_by(program_ID) %>%
select(contains("stem"), -sum_stem_sb, -stem_sb) %>%
summarize_all(sum, na.rm = T)
names(pqa_out) <- names(out)1. Specific solutions
m <- create_profiles_mplus(df, dm_cog_eng, dm_beh_eng, dm_aff_eng, dm_challenge, dm_competence, n_profiles=6, model = 2, return_save_data = TRUE)
plot_profiles_mplus(m) + scale_fill_brewer("", palette="Set2", labels = c("Affective Engagement", "Behavioral Engagement", "Challenge", "Cognitive Engagement", "Competence")) + ylab("Z-score") 
cc <- df %>% select(dm_cog_eng:dm_competence) %>% complete.cases()
C <- m[[2]] %>% pull(C)
df_ss <- df[cc, ]
df_ss$profile <- C
d <- df_ss %>% select(contains("dm"), participant_ID, program_ID, beep_ID = beep_ID_new, profile)d %>% psych::describe()## vars n mean sd median trimmed mad min max range
## dm_cog_eng 1 2958 2.77 1.06 3 2.84 1.48 1 4 3
## dm_beh_eng 2 2958 2.86 1.04 3 2.95 1.48 1 4 3
## dm_aff_eng 3 2958 2.83 1.05 3 2.92 1.48 1 4 3
## dm_challenge 4 2958 2.27 1.12 2 2.21 1.48 1 4 3
## dm_competence 5 2958 3.00 0.95 3 3.11 1.48 1 4 3
## dm_ask 6 2787 0.42 0.49 0 0.40 0.00 0 1 1
## dm_obs 7 2787 0.28 0.45 0 0.23 0.00 0 1 1
## dm_gen 8 2787 0.44 0.50 0 0.43 0.00 0 1 1
## dm_mod 9 2787 0.37 0.48 0 0.34 0.00 0 1 1
## dm_com 10 2787 0.46 0.50 0 0.44 0.00 0 1 1
## participant_ID* 11 2958 103.28 60.07 108 103.81 78.58 1 203 202
## program_ID* 12 2958 6.01 2.75 6 6.08 2.97 1 10 9
## beep_ID* 13 2958 134.18 74.66 145 136.46 99.33 1 248 247
## profile 14 2958 3.51 1.40 4 3.52 1.48 1 6 5
## skew kurtosis se
## dm_cog_eng -0.35 -1.12 0.02
## dm_beh_eng -0.46 -1.00 0.02
## dm_aff_eng -0.43 -1.04 0.02
## dm_challenge 0.26 -1.31 0.02
## dm_competence -0.61 -0.63 0.02
## dm_ask 0.33 -1.89 0.01
## dm_obs 0.96 -1.08 0.01
## dm_gen 0.23 -1.95 0.01
## dm_mod 0.53 -1.72 0.01
## dm_com 0.18 -1.97 0.01
## participant_ID* -0.09 -1.29 1.10
## program_ID* -0.22 -1.09 0.05
## beep_ID* -0.22 -1.22 1.37
## profile -0.09 -0.74 0.03d <- d %>%
mutate(
profile_1 = ifelse(profile == 1, 1, 0),
profile_2 = ifelse(profile == 2, 1, 0),
profile_3 = ifelse(profile == 3, 1, 0),
profile_4 = ifelse(profile == 4, 1, 0),
profile_5 = ifelse(profile == 5, 1, 0),
profile_6 = ifelse(profile == 6, 1, 0),
)m1 <- glmer(profile_1 ~ 1 +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m1)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## profile_1 ~ 1 + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 1522.3 1546.2 -757.1 1514.3 2954
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5736 -0.2586 -0.1349 -0.1167 4.7246
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 0.1908 0.4368
## participant_ID (Intercept) 3.1202 1.7664
## program_ID (Intercept) 0.0000 0.0000
## Number of obs: 2958, groups:
## beep_ID, 248; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.4136 0.2146 -15.91 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(m1)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_1 ~ 1 + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.028900
## ICC (participant_ID): 0.472696
## ICC (program_ID): 0.000000m2 <- glmer(profile_2 ~ 1 +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m2)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## profile_2 ~ 1 + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 2386.9 2410.8 -1189.4 2378.9 2954
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5352 -0.3864 -0.2402 -0.1620 4.0061
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 2.561e-01 5.060e-01
## participant_ID (Intercept) 2.369e+00 1.539e+00
## program_ID (Intercept) 2.330e-10 1.527e-05
## Number of obs: 2958, groups:
## beep_ID, 248; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.2629 0.1521 -14.88 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(m2)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_2 ~ 1 + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.043293
## ICC (participant_ID): 0.400490
## ICC (program_ID): 0.000000m3 <- glmer(profile_3 ~ 1 +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m3)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## profile_3 ~ 1 + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 2343.7 2367.6 -1167.8 2335.7 2954
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8161 -0.3957 -0.2389 -0.1668 4.3318
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 0.08281 0.2878
## participant_ID (Intercept) 2.48074 1.5750
## program_ID (Intercept) 0.01829 0.1352
## Number of obs: 2958, groups:
## beep_ID, 248; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.2279 0.1578 -14.12 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(m3)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_3 ~ 1 + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.014104
## ICC (participant_ID): 0.422490
## ICC (program_ID): 0.003115m4 <- glmer(profile_4 ~ 1 +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m4)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## profile_4 ~ 1 + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 2957.3 2981.3 -1474.7 2949.3 2954
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5065 -0.4958 -0.2452 0.4891 4.5159
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 0.3947 0.6282
## participant_ID (Intercept) 3.4287 1.8517
## program_ID (Intercept) 0.5966 0.7724
## Number of obs: 2958, groups:
## beep_ID, 248; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.1087 0.3009 -3.684 0.000229 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(m4)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_4 ~ 1 + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.051192
## ICC (participant_ID): 0.444715
## ICC (program_ID): 0.077386m5 <- glmer(profile_5 ~ 1 +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m5)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## profile_5 ~ 1 + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 2187.7 2211.7 -1089.9 2179.7 2954
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1068 -0.3843 -0.2825 -0.2075 4.0392
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 2.532e-08 0.0001591
## participant_ID (Intercept) 1.198e+00 1.0943746
## program_ID (Intercept) 6.477e-02 0.2545091
## Number of obs: 2958, groups:
## beep_ID, 248; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.287565 0.000603 -3794 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## convergence code: 0
## Model failed to converge with max|grad| = 0.117255 (tol = 0.001, component 1)
## Model is nearly unidentifiable: very large eigenvalue
## - Rescale variables?sjstats::icc(m5)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_5 ~ 1 + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.000000
## ICC (participant_ID): 0.263088
## ICC (program_ID): 0.014229m6 <- glmer(profile_6 ~ 1 +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m6)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## profile_6 ~ 1 + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 1670.9 1694.9 -831.5 1662.9 2954
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.9902 -0.2918 -0.2155 -0.1719 4.6578
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 0.3107 0.5574
## participant_ID (Intercept) 1.1224 1.0595
## program_ID (Intercept) 0.0000 0.0000
## Number of obs: 2958, groups:
## beep_ID, 248; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.9115 0.1476 -19.72 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1sjstats::icc(m6)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_6 ~ 1 + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.065790
## ICC (participant_ID): 0.237654
## ICC (program_ID): 0.000000m4a <- glmer(profile_4 ~ 1 +
dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m4a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: profile_4 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
## (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 2792.5 2845.9 -1387.3 2774.5 2778
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7228 -0.5038 -0.2427 0.4676 4.3202
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 0.3227 0.5680
## participant_ID (Intercept) 3.5233 1.8771
## program_ID (Intercept) 0.7143 0.8451
## Number of obs: 2787, groups:
## beep_ID, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.14123 0.33344 -3.423 0.00062 ***
## dm_ask -0.15678 0.16134 -0.972 0.33121
## dm_obs -0.36204 0.16496 -2.195 0.02818 *
## dm_gen 0.33313 0.15337 2.172 0.02985 *
## dm_mod 0.16141 0.16510 0.978 0.32826
## dm_com 0.01383 0.15949 0.087 0.93092
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dm_ask dm_obs dm_gen dm_mod
## dm_ask -0.060
## dm_obs -0.026 -0.256
## dm_gen -0.112 -0.105 -0.168
## dm_mod -0.013 -0.232 -0.091 -0.175
## dm_com -0.094 -0.161 0.011 -0.142 -0.344sjstats::icc(m4a)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_4 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.041102
## ICC (participant_ID): 0.448826
## ICC (program_ID): 0.090986m1a <- glmer(profile_1 ~ 1 +
dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m1a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: profile_1 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
## (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 1453.1 1506.4 -717.5 1435.1 2778
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5698 -0.2590 -0.1406 -0.1171 4.5335
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 0.2073578 0.45537
## participant_ID (Intercept) 3.0627722 1.75008
## program_ID (Intercept) 0.0008134 0.02852
## Number of obs: 2787, groups:
## beep_ID, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.4369 0.2447 -14.046 <2e-16 ***
## dm_ask 0.2114 0.2077 1.018 0.309
## dm_obs 0.2440 0.2063 1.183 0.237
## dm_gen -0.2277 0.1987 -1.146 0.252
## dm_mod -0.2992 0.2116 -1.414 0.157
## dm_com 0.1895 0.2039 0.929 0.353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dm_ask dm_obs dm_gen dm_mod
## dm_ask -0.150
## dm_obs -0.134 -0.253
## dm_gen -0.115 -0.114 -0.189
## dm_mod 0.008 -0.235 -0.079 -0.191
## dm_com -0.197 -0.178 0.062 -0.174 -0.297
## convergence code: 0
## Model failed to converge with max|grad| = 0.156466 (tol = 0.001, component 1)sjstats::icc(m1a)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_1 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.031606
## ICC (participant_ID): 0.466828
## ICC (program_ID): 0.000124m2a <- glmer(profile_2 ~ 1 +
dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m2a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: profile_2 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
## (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 2242.7 2296.1 -1112.3 2224.7 2778
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2737 -0.3867 -0.2370 -0.1608 4.0763
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 1.922e-01 0.4384243
## participant_ID (Intercept) 2.358e+00 1.5356592
## program_ID (Intercept) 8.858e-07 0.0009412
## Number of obs: 2787, groups:
## beep_ID, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.00606 0.16990 -11.807 <2e-16 ***
## dm_ask -0.10443 0.16445 -0.635 0.5254
## dm_obs -0.01302 0.16770 -0.078 0.9381
## dm_gen -0.28237 0.15838 -1.783 0.0746 .
## dm_mod -0.04334 0.16836 -0.257 0.7968
## dm_com -0.19503 0.16362 -1.192 0.2333
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dm_ask dm_obs dm_gen dm_mod
## dm_ask -0.122
## dm_obs -0.081 -0.265
## dm_gen -0.152 -0.085 -0.177
## dm_mod -0.029 -0.205 -0.079 -0.202
## dm_com -0.166 -0.192 0.052 -0.167 -0.312
## convergence code: 0
## Model failed to converge with max|grad| = 0.00440029 (tol = 0.001, component 1)sjstats::icc(m2a)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_2 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.032912
## ICC (participant_ID): 0.403787
## ICC (program_ID): 0.000000m3a <- glmer(profile_3 ~ 1 +
dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m3a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: profile_3 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
## (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 2216.6 2270.0 -1099.3 2198.6 2778
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6509 -0.4027 -0.2318 -0.1635 4.3869
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 0.07580 0.2753
## participant_ID (Intercept) 2.48712 1.5771
## program_ID (Intercept) 0.05465 0.2338
## Number of obs: 2787, groups:
## beep_ID, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.34196 0.19194 -12.202 <2e-16 ***
## dm_ask -0.10813 0.15618 -0.692 0.489
## dm_obs 0.07836 0.15514 0.505 0.613
## dm_gen 0.20437 0.14639 1.396 0.163
## dm_mod -0.11890 0.15641 -0.760 0.447
## dm_com 0.18233 0.15310 1.191 0.234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dm_ask dm_obs dm_gen dm_mod
## dm_ask -0.074
## dm_obs -0.093 -0.273
## dm_gen -0.172 -0.121 -0.172
## dm_mod -0.010 -0.232 -0.072 -0.183
## dm_com -0.191 -0.175 0.050 -0.161 -0.319
## convergence code: 0
## Model failed to converge with max|grad| = 0.044158 (tol = 0.001, component 1)sjstats::icc(m3a)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_3 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.012832
## ICC (participant_ID): 0.421014
## ICC (program_ID): 0.009251m4a <- glmer(profile_4 ~ 1 +
dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m4a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: profile_4 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
## (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 2792.5 2845.9 -1387.3 2774.5 2778
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7228 -0.5038 -0.2427 0.4676 4.3202
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 0.3227 0.5680
## participant_ID (Intercept) 3.5233 1.8771
## program_ID (Intercept) 0.7143 0.8451
## Number of obs: 2787, groups:
## beep_ID, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.14123 0.33344 -3.423 0.00062 ***
## dm_ask -0.15678 0.16134 -0.972 0.33121
## dm_obs -0.36204 0.16496 -2.195 0.02818 *
## dm_gen 0.33313 0.15337 2.172 0.02985 *
## dm_mod 0.16141 0.16510 0.978 0.32826
## dm_com 0.01383 0.15949 0.087 0.93092
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dm_ask dm_obs dm_gen dm_mod
## dm_ask -0.060
## dm_obs -0.026 -0.256
## dm_gen -0.112 -0.105 -0.168
## dm_mod -0.013 -0.232 -0.091 -0.175
## dm_com -0.094 -0.161 0.011 -0.142 -0.344sjstats::icc(m4a)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_4 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.041102
## ICC (participant_ID): 0.448826
## ICC (program_ID): 0.090986m5a <- glmer(profile_5 ~ 1 +
dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m5a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: profile_5 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
## (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 2043.2 2096.6 -1012.6 2025.2 2778
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2554 -0.3758 -0.2753 -0.2045 4.2732
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 3.790e-08 0.0001947
## participant_ID (Intercept) 1.227e+00 1.1078815
## program_ID (Intercept) 6.467e-02 0.2543001
## Number of obs: 2787, groups:
## beep_ID, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.51236 0.16886 -14.878 <2e-16 ***
## dm_ask 0.18233 0.14858 1.227 0.220
## dm_obs 0.14153 0.14887 0.951 0.342
## dm_gen -0.07064 0.14435 -0.489 0.625
## dm_mod 0.04861 0.15098 0.322 0.747
## dm_com 0.19676 0.15000 1.312 0.190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dm_ask dm_obs dm_gen dm_mod
## dm_ask -0.144
## dm_obs -0.087 -0.266
## dm_gen -0.155 -0.094 -0.185
## dm_mod -0.027 -0.206 -0.096 -0.189
## dm_com -0.198 -0.176 0.054 -0.192 -0.323
## convergence code: 0
## Model failed to converge with max|grad| = 0.00223344 (tol = 0.001, component 1)sjstats::icc(m5a)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_5 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.000000
## ICC (participant_ID): 0.267878
## ICC (program_ID): 0.014114m6a <- glmer(profile_6 ~ 1 +
dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
(1 | participant_ID) +
(1 | beep_ID) +
(1 | program_ID),
family = binomial(link = "logit"),
data = d)
summary(m6a)## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: profile_6 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com +
## (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
## Data: d
##
## AIC BIC logLik deviance df.resid
## 1604.2 1657.6 -793.1 1586.2 2778
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -0.9962 -0.2931 -0.2168 -0.1716 4.5537
##
## Random effects:
## Groups Name Variance Std.Dev.
## beep_ID (Intercept) 0.3116 0.5582
## participant_ID (Intercept) 1.1523 1.0735
## program_ID (Intercept) 0.0000 0.0000
## Number of obs: 2787, groups:
## beep_ID, 236; participant_ID, 203; program_ID, 9
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.85922 0.18314 -15.612 <2e-16 ***
## dm_ask 0.12106 0.20565 0.589 0.5561
## dm_obs 0.08188 0.20702 0.396 0.6925
## dm_gen -0.11704 0.19314 -0.606 0.5445
## dm_mod 0.25695 0.21133 1.216 0.2240
## dm_com -0.36072 0.20336 -1.774 0.0761 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) dm_ask dm_obs dm_gen dm_mod
## dm_ask -0.176
## dm_obs -0.107 -0.288
## dm_gen -0.184 -0.088 -0.184
## dm_mod -0.089 -0.251 -0.045 -0.191
## dm_com -0.138 -0.168 -0.004 -0.152 -0.336sjstats::icc(m6a)## Generalized linear mixed model
## Family: binomial (logit)
## Formula: profile_6 ~ 1 + dm_ask + dm_obs + dm_gen + dm_mod + dm_com + (1 | participant_ID) + (1 | beep_ID) + (1 | program_ID)
##
## ICC (beep_ID): 0.065548
## ICC (participant_ID): 0.242399
## ICC (program_ID): 0.000000