setwd ("C:/Work Files/Dissertations/Gina Drury" )
Attaching package: 'mice'
The following object is masked from 'package:stats':
filter
The following objects are masked from 'package:base':
cbind, rbind
This is lavaan 0.6-19
lavaan is FREE software! Please report any bugs.
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.2 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.4
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks mice::filter(), stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library (knitr) library (kableExtra)
Attaching package: 'kableExtra'
The following object is masked from 'package:dplyr':
group_rows
<- read.csv ("cleaned_data_final2.csv" ).1 <- cleaned_data_final2 %>% mutate (across (where (is.factor), ordered)).1 _oboe_only <- - (cleaned_data_final2.1 $ oboe_group== 2 )
<- names (cleaned_data_final2.1 )[sapply (cleaned_data_final2.1 , is.factor)]head (cat_vars)library (forcats)# Collapse rare levels in all categorical variables .1 [cat_vars] <- lapply (cleaned_data_final2.1 [cat_vars], function (x) {fct_lump_min (x, min = 5 ) # Change threshold based on your data
#categorical_vars <- c("final_nnns_class", "diabpprg", "gestdiab", "hyp", # "prenatal_collapsed", "hepc", "hiv", "syph", "chlmyd", # "any_subuse_y_n", "collapsed_mentheal", "sex") #cleaned_data_final2.1[categorical_vars] <- lapply(cleaned_data_final2##.1[categorical_vars], as.factor)
Model Block 2:
<- ' Mat_Health =~ diabpprg + gestdiab + hyp + prenatal_collapsed + hepc + hiv + syph + chlmyd + any_subuse_y_n MatMen_health =~ collapsed_mentheal + acescore + Dep_Score + Anx_Score + Anger_Score + Supp_Score + Meaning_Score Infant =~ imhbirthwt + imh_birthlt + imh_birthhcr + gawks + sex SocioDem =~ MatAge + mmins_combined + mrace_combined + pcedlevel + MomOnlyFSIQ + any_subuse_y_n + hseincom hepc ~~ any_subuse_y_n hepc ~~ mrace_combined any_subuse_y_n ~~ mrace_combined Mat_Health~~MatMen_health Mat_Health ~ a*SocioDem MatMen_health ~ b*SocioDem Infant ~ c*Mat_Health + d*MatMen_health + e*SocioDem final_nnns_class ~ f*Infant ' <- sem (SEM_model, estimator= "WLSMV" , data= cleaned_data_final2.1 , std.lv= TRUE , missing = "pairwise" , mimic = "Mplus" )
Warning: lavaan->lav_options_est_dwls():
estimator "DWLS" is not recommended for continuous data. Did you forget to
set the ordered= argument?
Warning: lavaan->lav_data_full():
some observed variances are (at least) a factor 1000 times larger than
others; use varTable(fit) to investigate
Warning: lavaan->lav_samplestats_from_data():
number of observations (291) too small to compute Gamma
Warning: lavaan->lav_model_vcov():
The variance-covariance matrix of the estimated parameters (vcov) does not
appear to be positive definite! The smallest eigenvalue (= -6.635754e-06)
is smaller than zero. This may be a symptom that the model is not
identified.
summary (SEM_model_fit, fit.measures = TRUE , standardized= TRUE )
lavaan 0.6-19 ended normally after 228 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 94
Number of observations 291
Number of missing patterns 27
Model Test User Model:
Standard Scaled
Test Statistic 486.753 452.007
Degrees of freedom 340 340
P-value (Chi-square) 0.000 0.000
Scaling correction factor 1.881
Shift parameter 193.170
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 2225.862 1129.246
Degrees of freedom 378 378
P-value 0.000 0.000
Scaling correction factor 2.460
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.921 0.851
Tucker-Lewis Index (TLI) 0.912 0.834
Robust Comparative Fit Index (CFI) 0.886
Robust Tucker-Lewis Index (TLI) 0.873
Root Mean Square Error of Approximation:
RMSEA 0.039 0.034
90 Percent confidence interval - lower 0.031 0.025
90 Percent confidence interval - upper 0.046 0.042
P-value H_0: RMSEA <= 0.050 0.995 1.000
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.046
90 Percent confidence interval - lower 0.034
90 Percent confidence interval - upper 0.057
P-value H_0: Robust RMSEA <= 0.050 0.703
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.060 0.060
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Mat_Health =~
diabpprg -0.003 0.003 -0.873 0.383 -0.003 -0.027
gestdiab -0.003 0.011 -0.293 0.770 -0.003 -0.015
hyp -0.078 0.034 -2.318 0.020 -0.081 -0.193
prenatl_cllpsd -0.060 0.029 -2.081 0.037 -0.062 -0.167
hepc 0.066 0.063 1.039 0.299 0.068 0.073
hiv 0.020 0.016 1.258 0.208 0.021 0.098
syph -0.018 0.015 -1.202 0.229 -0.019 -0.082
chlmyd -0.048 0.032 -1.483 0.138 -0.050 -0.124
any_subuse_y_n -0.067 0.032 -2.096 0.036 -0.070 -0.155
MatMen_health =~
collapsd_mnthl 0.305 0.051 5.941 0.000 0.309 0.329
acescore -0.930 0.150 -6.183 0.000 -0.942 -0.340
Dep_Score -7.969 0.426 -18.701 0.000 -8.069 -0.901
Anx_Score -8.515 0.454 -18.768 0.000 -8.622 -0.808
Anger_Score -6.770 0.564 -12.015 0.000 -6.855 -0.702
Supp_Score 5.281 0.476 11.090 0.000 5.347 0.647
Meaning_Score 5.316 0.530 10.021 0.000 5.382 0.590
Infant =~
imhbirthwt 0.333 1.355 0.246 0.806 0.431 0.950
imh_birthlt 1.330 5.404 0.246 0.806 1.718 0.727
imh_birthhcr 0.763 3.103 0.246 0.806 0.985 0.692
gawks 0.280 1.138 0.246 0.805 0.362 0.361
sex 0.067 0.270 0.247 0.805 0.086 0.173
SocioDem =~
MatAge 1.009 0.370 2.726 0.006 1.009 0.198
mmins_combined -0.159 0.038 -4.143 0.000 -0.159 -0.323
mrace_combined 0.354 0.079 4.506 0.000 0.354 0.303
pcedlevel 0.890 0.116 7.690 0.000 0.890 0.575
MomOnlyFSIQ 8.743 1.124 7.775 0.000 8.743 0.626
any_subuse_y_n -0.174 0.038 -4.555 0.000 -0.174 -0.382
hseincom -6.537 4.955 -1.319 0.187 -6.537 -0.056
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Mat_Health ~
SocioDem (a) -0.291 0.227 -1.281 0.200 -0.280 -0.280
MatMen_health ~
SocioDem (b) 0.159 0.074 2.150 0.032 0.157 0.157
Infant ~
Mat_Health (c) 3.421 81.232 0.042 0.966 2.758 2.758
MatMn_hlth (d) -3.245 80.498 -0.040 0.968 -2.543 -2.543
SocioDem (e) 2.016 38.811 0.052 0.959 1.560 1.560
final_nnns_class ~
Infant (f) -0.004 0.050 -0.089 0.929 -0.006 -0.006
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.hepc ~~
.any_subuse_y_n 0.141 0.021 6.800 0.000 0.141 0.358
.mrace_combined 0.386 0.050 7.738 0.000 0.386 0.370
.any_subuse_y_n ~~
.mrace_combined 0.170 0.033 5.088 0.000 0.170 0.362
.Mat_Health ~~
.MatMen_health 0.983 0.338 2.909 0.004 0.983 0.983
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.diabpprg 1.011 0.006 170.092 0.000 1.011 9.884
.gestdiab 1.057 0.013 78.931 0.000 1.057 4.546
.hyp 1.230 0.024 50.396 0.000 1.230 2.918
.prenatl_cllpsd 1.165 0.021 54.550 0.000 1.165 3.131
.hepc 1.732 0.055 31.448 0.000 1.732 1.847
.hiv 1.024 0.012 82.905 0.000 1.024 4.868
.syph 1.034 0.014 75.810 0.000 1.034 4.452
.chlmyd 1.100 0.023 46.963 0.000 1.100 2.758
.any_subuse_y_n 1.711 0.027 64.203 0.000 1.711 3.770
.collapsd_mnthl 2.014 0.055 36.503 0.000 2.014 2.144
.acescore 2.758 0.160 17.267 0.000 2.758 0.996
.Dep_Score 47.667 0.491 97.043 0.000 47.667 5.323
.Anx_Score 52.164 0.586 88.958 0.000 52.164 4.889
.Anger_Score 51.645 0.536 96.264 0.000 51.645 5.290
.Supp_Score 56.181 0.455 123.360 0.000 56.181 6.793
.Meaning_Score 59.531 0.501 118.714 0.000 59.531 6.524
.imhbirthwt 3.261 0.027 122.529 0.000 3.261 7.195
.imh_birthlt 50.265 0.138 363.413 0.000 50.265 21.267
.imh_birthhcr 34.200 0.083 410.346 0.000 34.200 24.013
.gawks 38.729 0.059 657.764 0.000 38.729 38.625
.sex 1.564 0.029 53.597 0.000 1.564 3.147
.MatAge 29.753 0.299 99.470 0.000 29.753 5.841
.mmins_combined 3.835 0.029 132.851 0.000 3.835 7.801
.mrace_combined 3.423 0.069 49.815 0.000 3.423 2.925
.pcedlevel 4.054 0.089 45.476 0.000 4.054 2.619
.MomOnlyFSIQ 94.385 0.778 121.311 0.000 94.385 6.758
.hseincom 20.643 6.465 3.193 0.001 20.643 0.177
.finl_nnns_clss 2.137 0.060 35.522 0.000 2.137 2.086
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.diabpprg 0.010 0.006 1.798 0.072 0.010 0.999
.gestdiab 0.054 0.012 4.558 0.000 0.054 1.000
.hyp 0.171 0.013 12.743 0.000 0.171 0.963
.prenatl_cllpsd 0.135 0.014 9.563 0.000 0.135 0.972
.hepc 0.875 0.032 27.296 0.000 0.875 0.995
.hiv 0.044 0.023 1.914 0.056 0.044 0.990
.syph 0.054 0.024 2.261 0.024 0.054 0.993
.chlmyd 0.157 0.039 4.048 0.000 0.157 0.984
.any_subuse_y_n 0.178 0.015 12.123 0.000 0.178 0.863
.collapsd_mnthl 0.787 0.035 22.590 0.000 0.787 0.892
.acescore 6.775 0.547 12.391 0.000 6.775 0.884
.Dep_Score 15.091 2.881 5.238 0.000 15.091 0.188
.Anx_Score 39.517 4.593 8.603 0.000 39.517 0.347
.Anger_Score 48.306 6.230 7.754 0.000 48.306 0.507
.Supp_Score 39.809 4.344 9.164 0.000 39.809 0.582
.Meaning_Score 54.289 5.385 10.081 0.000 54.289 0.652
.imhbirthwt 0.020 0.013 1.503 0.133 0.020 0.097
.imh_birthlt 2.634 0.418 6.305 0.000 2.634 0.472
.imh_birthhcr 1.057 0.127 8.333 0.000 1.057 0.521
.gawks 0.874 0.075 11.666 0.000 0.874 0.869
.sex 0.239 0.006 38.462 0.000 0.239 0.970
.MatAge 24.928 1.950 12.782 0.000 24.928 0.961
.mmins_combined 0.216 0.059 3.697 0.000 0.216 0.896
.mrace_combined 1.244 0.115 10.810 0.000 1.244 0.908
.pcedlevel 1.603 0.190 8.424 0.000 1.603 0.669
.MomOnlyFSIQ 118.626 17.094 6.939 0.000 118.626 0.608
.hseincom 13633.693 4744.947 2.873 0.004 13633.693 0.997
.finl_nnns_clss 1.050 0.060 17.393 0.000 1.050 1.000
.Mat_Health 1.000 0.922 0.922
.MatMen_health 1.000 0.975 0.975
.Infant 1.000 0.599 0.599
SocioDem 1.000 1.000 1.000
The model in Block 2 also yields an adequate fit to the data. [\(\chi^2(340) = 486.75, p<.001\) ; CFI = .92, TLI = .91; RMSEA = 0.039; SRMR = 0.06].
parameterEstimates (SEM_model_fit, standardized= TRUE ) %>% filter (op == "=~" ) %>% select ('Latent Factor' = lhs, Indicator= rhs, B= est, SE= se, Z= z, 'p-value' = pvalue, Beta= std.all,CI_Lower= ci.lower, CI_Upper= ci.upper) %>% :: kable (digits = 3 , format= "html" , booktabs= TRUE , caption= "Total Sample Factor Loadings" )%>% kable_classic (full_width = F, html_font = "Cambria" )
Total Sample Factor Loadings
Mat_Health
diabpprg
-0.003
0.003
-0.873
0.383
-0.027
-0.009
0.003
Mat_Health
gestdiab
-0.003
0.011
-0.293
0.770
-0.015
-0.025
0.019
Mat_Health
hyp
-0.078
0.034
-2.318
0.020
-0.193
-0.144
-0.012
Mat_Health
prenatal_collapsed
-0.060
0.029
-2.081
0.037
-0.167
-0.116
-0.003
Mat_Health
hepc
0.066
0.063
1.039
0.299
0.073
-0.058
0.189
Mat_Health
hiv
0.020
0.016
1.258
0.208
0.098
-0.011
0.050
Mat_Health
syph
-0.018
0.015
-1.202
0.229
-0.082
-0.048
0.012
Mat_Health
chlmyd
-0.048
0.032
-1.483
0.138
-0.124
-0.111
0.015
Mat_Health
any_subuse_y_n
-0.067
0.032
-2.096
0.036
-0.155
-0.130
-0.004
MatMen_health
collapsed_mentheal
0.305
0.051
5.941
0.000
0.329
0.204
0.406
MatMen_health
acescore
-0.930
0.150
-6.183
0.000
-0.340
-1.225
-0.636
MatMen_health
Dep_Score
-7.969
0.426
-18.701
0.000
-0.901
-8.805
-7.134
MatMen_health
Anx_Score
-8.515
0.454
-18.768
0.000
-0.808
-9.404
-7.626
MatMen_health
Anger_Score
-6.770
0.564
-12.015
0.000
-0.702
-7.875
-5.666
MatMen_health
Supp_Score
5.281
0.476
11.090
0.000
0.647
4.348
6.215
MatMen_health
Meaning_Score
5.316
0.530
10.021
0.000
0.590
4.276
6.356
Infant
imhbirthwt
0.333
1.355
0.246
0.806
0.950
-2.322
2.989
Infant
imh_birthlt
1.330
5.404
0.246
0.806
0.727
-9.262
11.922
Infant
imh_birthhcr
0.763
3.103
0.246
0.806
0.692
-5.319
6.845
Infant
gawks
0.280
1.138
0.246
0.805
0.361
-1.951
2.512
Infant
sex
0.067
0.270
0.247
0.805
0.173
-0.463
0.596
SocioDem
MatAge
1.009
0.370
2.726
0.006
0.198
0.283
1.734
SocioDem
mmins_combined
-0.159
0.038
-4.143
0.000
-0.323
-0.234
-0.084
SocioDem
mrace_combined
0.354
0.079
4.506
0.000
0.303
0.200
0.508
SocioDem
pcedlevel
0.890
0.116
7.690
0.000
0.575
0.663
1.117
SocioDem
MomOnlyFSIQ
8.743
1.124
7.775
0.000
0.626
6.539
10.946
SocioDem
any_subuse_y_n
-0.174
0.038
-4.555
0.000
-0.382
-0.248
-0.099
SocioDem
hseincom
-6.537
4.955
-1.319
0.187
-0.056
-16.249
3.175
##Regression Table parameterEstimates (SEM_model_fit, standardized= TRUE ) %>% filter (op == "~" ) %>% select ('LV1' = lhs, 'LV2' = rhs, B= est, SE= se, Z= z, 'p-value' = pvalue, Beta= std.all,CI_Lower= ci.lower, CI_Upper= ci.upper) %>% :: kable (digits = 3 , format= "html" , booktabs= TRUE , caption= "Total Sample Regressions" )%>% kable_classic (full_width = F, html_font = "Cambria" )
Total Sample Regressions
Mat_Health
SocioDem
-0.291
0.227
-1.281
0.200
-0.280
-0.737
0.154
MatMen_health
SocioDem
0.159
0.074
2.150
0.032
0.157
0.014
0.303
Infant
Mat_Health
3.421
81.232
0.042
0.966
2.758
-155.792
162.633
Infant
MatMen_health
-3.245
80.498
-0.040
0.968
-2.543
-161.018
154.529
Infant
SocioDem
2.016
38.811
0.052
0.959
1.560
-74.053
78.084
final_nnns_class
Infant
-0.004
0.050
-0.089
0.929
-0.006
-0.103
0.094
Model Block 3
<- ' Mat_Health =~ diabpprg + gestdiab + hyp + prenatal_collapsed + hepc + hiv + syph + chlmyd + any_subuse_y_n MatMen_health =~ collapsed_mentheal + acescore + Dep_Score + Anx_Score + Anger_Score + Supp_Score + Meaning_Score Infant =~ imhbirthwt + imh_birthlt + imh_birthhcr + gawks + sex SocioDem =~ MatAge + mmins_combined + mrace_combined + pcedlevel + MomOnlyFSIQ + any_subuse_y_n + hseincom hepc ~~ any_subuse_y_n hepc ~~ mrace_combined any_subuse_y_n ~~ mrace_combined Mat_Health~~MatMen_health Mat_Health ~ a*SocioDem MatMen_health ~ b*SocioDem Infant ~ c*Mat_Health + d*MatMen_health + e*SocioDem n2attention ~ f*Infant n2regulation ~ g*Infant n2arousal ~ h*Infant n2tone ~ i*Infant n2nonoptref ~ j*Infant n2qmove ~ k*Infant n2stress ~ l*Infant ' <- sem (SEM_model_2, estimator= "WLSMV" , data= cleaned_data_final2.1 , std.lv= TRUE ,missing = "pairwise" , mimic = "Mplus" )
Warning: lavaan->lav_options_est_dwls():
estimator "DWLS" is not recommended for continuous data. Did you forget to
set the ordered= argument?
Warning: lavaan->lav_data_full():
some observed variances are (at least) a factor 1000 times larger than
others; use varTable(fit) to investigate
Warning: lavaan->lav_samplestats_from_data():
number of observations (291) too small to compute Gamma
Warning: lavaan->lav_model_vcov():
The variance-covariance matrix of the estimated parameters (vcov) does not
appear to be positive definite! The smallest eigenvalue (= -1.095361e-05)
is smaller than zero. This may be a symptom that the model is not
identified.
summary (SEM_model_fit_2, fit.measures = TRUE , standardized= TRUE )
lavaan 0.6-19 ended normally after 166 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 133
Number of observations 291
Number of missing patterns 37
Model Test User Model:
Standard Scaled
Test Statistic 819.855 680.221
Degrees of freedom 496 496
P-value (Chi-square) 0.000 0.000
Scaling correction factor 2.169
Shift parameter 302.265
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 2837.560 1408.357
Degrees of freedom 561 561
P-value 0.000 0.000
Scaling correction factor 2.687
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.858 0.783
Tucker-Lewis Index (TLI) 0.839 0.754
Robust Comparative Fit Index (CFI) 0.824
Robust Tucker-Lewis Index (TLI) 0.801
Root Mean Square Error of Approximation:
RMSEA 0.047 0.036
90 Percent confidence interval - lower 0.042 0.029
90 Percent confidence interval - upper 0.053 0.042
P-value H_0: RMSEA <= 0.050 0.763 1.000
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.053
90 Percent confidence interval - lower 0.043
90 Percent confidence interval - upper 0.062
P-value H_0: Robust RMSEA <= 0.050 0.320
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.065 0.065
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Mat_Health =~
diabpprg -0.003 0.003 -0.881 0.378 -0.003 -0.028
gestdiab -0.003 0.011 -0.281 0.778 -0.003 -0.014
hyp -0.079 0.034 -2.344 0.019 -0.083 -0.196
prenatl_cllpsd -0.060 0.029 -2.094 0.036 -0.063 -0.169
hepc 0.067 0.064 1.046 0.296 0.070 0.074
hiv 0.020 0.016 1.267 0.205 0.021 0.099
syph -0.018 0.015 -1.190 0.234 -0.019 -0.082
chlmyd -0.048 0.032 -1.475 0.140 -0.050 -0.124
any_subuse_y_n -0.067 0.032 -2.088 0.037 -0.070 -0.155
MatMen_health =~
collapsd_mnthl 0.306 0.052 5.927 0.000 0.310 0.329
acescore -0.925 0.151 -6.112 0.000 -0.937 -0.339
Dep_Score -7.966 0.427 -18.676 0.000 -8.068 -0.901
Anx_Score -8.479 0.460 -18.446 0.000 -8.587 -0.805
Anger_Score -6.787 0.563 -12.052 0.000 -6.874 -0.704
Supp_Score 5.325 0.477 11.157 0.000 5.393 0.652
Meaning_Score 5.294 0.535 9.889 0.000 5.362 0.588
Infant =~
imhbirthwt 0.349 0.333 1.046 0.296 0.416 0.917
imh_birthlt 1.437 1.379 1.042 0.298 1.713 0.725
imh_birthhcr 0.796 0.768 1.036 0.300 0.949 0.667
gawks 0.283 0.273 1.038 0.299 0.338 0.337
sex 0.071 0.071 0.995 0.320 0.084 0.169
SocioDem =~
MatAge 0.940 0.375 2.509 0.012 0.940 0.185
mmins_combined -0.154 0.038 -4.031 0.000 -0.154 -0.314
mrace_combined 0.381 0.080 4.742 0.000 0.381 0.326
pcedlevel 0.851 0.115 7.408 0.000 0.851 0.550
MomOnlyFSIQ 8.774 1.122 7.818 0.000 8.774 0.628
any_subuse_y_n -0.180 0.039 -4.653 0.000 -0.180 -0.397
hseincom -6.867 5.085 -1.350 0.177 -6.867 -0.059
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Mat_Health ~
SocioDem (a) -0.297 0.228 -1.306 0.192 -0.285 -0.285
MatMen_health ~
SocioDem (b) 0.160 0.075 2.144 0.032 0.158 0.158
Infant ~
Mat_Health (c) 1.642 21.383 0.077 0.939 1.436 1.436
MatMn_hlth (d) -1.444 21.170 -0.068 0.946 -1.226 -1.226
SocioDem (e) 1.219 10.375 0.117 0.906 1.022 1.022
n2attention ~
Infant (f) -0.006 0.066 -0.088 0.930 -0.007 -0.005
n2regulation ~
Infant (g) 0.005 0.057 0.094 0.925 0.006 0.006
n2arousal ~
Infant (h) 0.082 0.128 0.642 0.521 0.098 0.062
n2tone ~
Infant (i) -0.055 0.062 -0.886 0.376 -0.065 -0.118
n2nonoptref ~
Infant (j) -0.208 0.200 -1.040 0.298 -0.248 -0.220
n2qmove ~
Infant (k) 0.106 0.121 0.880 0.379 0.127 0.106
n2stress ~
Infant (l) -0.042 0.051 -0.826 0.409 -0.050 -0.071
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.hepc ~~
.any_subuse_y_n 0.141 0.021 6.787 0.000 0.141 0.359
.mrace_combined 0.387 0.050 7.721 0.000 0.387 0.374
.any_subuse_y_n ~~
.mrace_combined 0.177 0.033 5.313 0.000 0.177 0.381
.Mat_Health ~~
.MatMen_health 0.972 0.330 2.946 0.003 0.972 0.972
.n2attention ~~
.n2regulation 0.297 0.084 3.523 0.000 0.297 0.215
.n2arousal -0.519 0.110 -4.738 0.000 -0.519 -0.255
.n2tone 0.010 0.040 0.246 0.806 0.010 0.014
.n2nonoptref 0.123 0.077 1.612 0.107 0.123 0.088
.n2qmove 0.186 0.077 2.407 0.016 0.186 0.122
.n2stress 0.058 0.044 1.306 0.192 0.058 0.064
.n2regulation ~~
.n2arousal -0.479 0.095 -5.037 0.000 -0.479 -0.279
.n2tone -0.019 0.034 -0.554 0.579 -0.019 -0.032
.n2nonoptref -0.038 0.080 -0.477 0.633 -0.038 -0.032
.n2qmove 0.136 0.075 1.806 0.071 0.136 0.106
.n2stress -0.077 0.047 -1.653 0.098 -0.077 -0.102
.n2arousal ~~
.n2tone 0.322 0.063 5.117 0.000 0.322 0.369
.n2nonoptref 0.030 0.105 0.287 0.774 0.030 0.017
.n2qmove -0.378 0.131 -2.878 0.004 -0.378 -0.200
.n2stress 0.429 0.074 5.786 0.000 0.429 0.386
.n2tone ~~
.n2nonoptref -0.044 0.048 -0.899 0.369 -0.044 -0.072
.n2qmove -0.088 0.055 -1.584 0.113 -0.088 -0.134
.n2stress 0.155 0.037 4.232 0.000 0.155 0.403
.n2nonoptref ~~
.n2qmove -0.281 0.085 -3.330 0.001 -0.281 -0.215
.n2stress 0.178 0.048 3.755 0.000 0.178 0.232
.n2qmove ~~
.n2stress -0.383 0.062 -6.133 0.000 -0.383 -0.462
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.diabpprg 1.011 0.006 170.092 0.000 1.011 9.884
.gestdiab 1.057 0.013 78.931 0.000 1.057 4.546
.hyp 1.230 0.024 50.396 0.000 1.230 2.918
.prenatl_cllpsd 1.165 0.021 54.550 0.000 1.165 3.131
.hepc 1.732 0.055 31.448 0.000 1.732 1.847
.hiv 1.024 0.012 82.905 0.000 1.024 4.868
.syph 1.034 0.014 75.810 0.000 1.034 4.452
.chlmyd 1.100 0.023 46.963 0.000 1.100 2.758
.any_subuse_y_n 1.711 0.027 64.203 0.000 1.711 3.770
.collapsd_mnthl 2.014 0.055 36.503 0.000 2.014 2.144
.acescore 2.758 0.160 17.267 0.000 2.758 0.996
.Dep_Score 47.667 0.491 97.043 0.000 47.667 5.323
.Anx_Score 52.164 0.586 88.958 0.000 52.164 4.889
.Anger_Score 51.645 0.536 96.264 0.000 51.645 5.290
.Supp_Score 56.181 0.455 123.360 0.000 56.181 6.793
.Meaning_Score 59.531 0.501 118.714 0.000 59.531 6.524
.imhbirthwt 3.261 0.027 122.529 0.000 3.261 7.195
.imh_birthlt 50.265 0.138 363.413 0.000 50.265 21.267
.imh_birthhcr 34.200 0.083 410.346 0.000 34.200 24.013
.gawks 38.729 0.059 657.764 0.000 38.729 38.625
.sex 1.564 0.029 53.597 0.000 1.564 3.147
.MatAge 29.753 0.299 99.470 0.000 29.753 5.841
.mmins_combined 3.835 0.029 132.851 0.000 3.835 7.801
.mrace_combined 3.423 0.069 49.815 0.000 3.423 2.925
.pcedlevel 4.054 0.089 45.476 0.000 4.054 2.619
.MomOnlyFSIQ 94.385 0.778 121.311 0.000 94.385 6.758
.hseincom 20.643 6.465 3.193 0.001 20.643 0.177
.n2attention 4.627 0.071 65.274 0.000 4.627 3.622
.n2regulation 4.201 0.063 66.678 0.000 4.201 3.895
.n2arousal 4.640 0.093 49.658 0.000 4.640 2.911
.n2tone 4.940 0.033 152.002 0.000 4.940 8.926
.n2nonoptref 3.122 0.066 47.107 0.000 3.122 2.761
.n2qmove 6.697 0.070 95.993 0.000 6.697 5.608
.n2stress 1.637 0.041 39.871 0.000 1.637 2.337
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.diabpprg 0.010 0.006 1.798 0.072 0.010 0.999
.gestdiab 0.054 0.012 4.558 0.000 0.054 1.000
.hyp 0.171 0.013 12.706 0.000 0.171 0.961
.prenatl_cllpsd 0.135 0.014 9.557 0.000 0.135 0.971
.hepc 0.875 0.032 27.228 0.000 0.875 0.994
.hiv 0.044 0.023 1.915 0.056 0.044 0.990
.syph 0.054 0.024 2.261 0.024 0.054 0.993
.chlmyd 0.157 0.039 4.048 0.000 0.157 0.985
.any_subuse_y_n 0.176 0.015 11.711 0.000 0.176 0.854
.collapsd_mnthl 0.787 0.035 22.477 0.000 0.787 0.891
.acescore 6.785 0.548 12.383 0.000 6.785 0.885
.Dep_Score 15.114 2.949 5.125 0.000 15.114 0.188
.Anx_Score 40.115 4.706 8.524 0.000 40.115 0.352
.Anger_Score 48.053 6.229 7.715 0.000 48.053 0.504
.Supp_Score 39.320 4.347 9.046 0.000 39.320 0.575
.Meaning_Score 54.511 5.443 10.015 0.000 54.511 0.655
.imhbirthwt 0.033 0.014 2.419 0.016 0.033 0.159
.imh_birthlt 2.653 0.430 6.170 0.000 2.653 0.475
.imh_birthhcr 1.127 0.133 8.456 0.000 1.127 0.556
.gawks 0.891 0.077 11.639 0.000 0.891 0.886
.sex 0.240 0.006 38.349 0.000 0.240 0.971
.MatAge 25.062 1.955 12.818 0.000 25.062 0.966
.mmins_combined 0.218 0.058 3.732 0.000 0.218 0.901
.mrace_combined 1.224 0.114 10.693 0.000 1.224 0.894
.pcedlevel 1.670 0.182 9.162 0.000 1.670 0.697
.MomOnlyFSIQ 118.081 16.911 6.983 0.000 118.081 0.605
.hseincom 13629.398 4742.471 2.874 0.004 13629.398 0.997
.n2attention 1.632 0.133 12.292 0.000 1.632 1.000
.n2regulation 1.163 0.098 11.873 0.000 1.163 1.000
.n2arousal 2.531 0.165 15.333 0.000 2.531 0.996
.n2tone 0.302 0.042 7.139 0.000 0.302 0.986
.n2nonoptref 1.217 0.128 9.494 0.000 1.217 0.952
.n2qmove 1.410 0.127 11.087 0.000 1.410 0.989
.n2stress 0.488 0.056 8.747 0.000 0.488 0.995
.Mat_Health 1.000 0.919 0.919
.MatMen_health 1.000 0.975 0.975
.Infant 1.000 0.703 0.703
SocioDem 1.000 1.000 1.000
parameterEstimates (SEM_model_fit_2, standardized= TRUE ) %>% filter (op == "=~" ) %>% select ('Latent Factor' = lhs, Indicator= rhs, B= est, SE= se, Z= z, 'p-value' = pvalue, Beta= std.all,CI_Lower= ci.lower, CI_Upper= ci.upper) %>% :: kable (digits = 3 , format= "html" , booktabs= TRUE , caption= "Total Sample Factor Loadings" )%>% kable_classic (full_width = F, html_font = "Cambria" )
Total Sample Factor Loadings
Mat_Health
diabpprg
-0.003
0.003
-0.881
0.378
-0.028
-0.009
0.003
Mat_Health
gestdiab
-0.003
0.011
-0.281
0.778
-0.014
-0.026
0.019
Mat_Health
hyp
-0.079
0.034
-2.344
0.019
-0.196
-0.146
-0.013
Mat_Health
prenatal_collapsed
-0.060
0.029
-2.094
0.036
-0.169
-0.117
-0.004
Mat_Health
hepc
0.067
0.064
1.046
0.296
0.074
-0.058
0.192
Mat_Health
hiv
0.020
0.016
1.267
0.205
0.099
-0.011
0.051
Mat_Health
syph
-0.018
0.015
-1.190
0.234
-0.082
-0.048
0.012
Mat_Health
chlmyd
-0.048
0.032
-1.475
0.140
-0.124
-0.111
0.016
Mat_Health
any_subuse_y_n
-0.067
0.032
-2.088
0.037
-0.155
-0.130
-0.004
MatMen_health
collapsed_mentheal
0.306
0.052
5.927
0.000
0.329
0.205
0.407
MatMen_health
acescore
-0.925
0.151
-6.112
0.000
-0.339
-1.222
-0.629
MatMen_health
Dep_Score
-7.966
0.427
-18.676
0.000
-0.901
-8.802
-7.130
MatMen_health
Anx_Score
-8.479
0.460
-18.446
0.000
-0.805
-9.380
-7.578
MatMen_health
Anger_Score
-6.787
0.563
-12.052
0.000
-0.704
-7.891
-5.683
MatMen_health
Supp_Score
5.325
0.477
11.157
0.000
0.652
4.390
6.260
MatMen_health
Meaning_Score
5.294
0.535
9.889
0.000
0.588
4.245
6.344
Infant
imhbirthwt
0.349
0.333
1.046
0.296
0.917
-0.305
1.002
Infant
imh_birthlt
1.437
1.379
1.042
0.298
0.725
-1.266
4.139
Infant
imh_birthhcr
0.796
0.768
1.036
0.300
0.667
-0.710
2.302
Infant
gawks
0.283
0.273
1.038
0.299
0.337
-0.252
0.819
Infant
sex
0.071
0.071
0.995
0.320
0.169
-0.068
0.210
SocioDem
MatAge
0.940
0.375
2.509
0.012
0.185
0.206
1.674
SocioDem
mmins_combined
-0.154
0.038
-4.031
0.000
-0.314
-0.229
-0.079
SocioDem
mrace_combined
0.381
0.080
4.742
0.000
0.326
0.224
0.539
SocioDem
pcedlevel
0.851
0.115
7.408
0.000
0.550
0.626
1.077
SocioDem
MomOnlyFSIQ
8.774
1.122
7.818
0.000
0.628
6.574
10.973
SocioDem
any_subuse_y_n
-0.180
0.039
-4.653
0.000
-0.397
-0.256
-0.104
SocioDem
hseincom
-6.867
5.085
-1.350
0.177
-0.059
-16.835
3.100
##Regression Table parameterEstimates (SEM_model_fit_2, standardized= TRUE ) %>% filter (op == "~" ) %>% select ('LV1' = lhs, 'LV2' = rhs, B= est, SE= se, Z= z, 'p-value' = pvalue, Beta= std.all,CI_Lower= ci.lower, CI_Upper= ci.upper) %>% :: kable (digits = 3 , format= "html" , booktabs= TRUE , caption= "Total Sample Regressions" )%>% kable_classic (full_width = F, html_font = "Cambria" )
Total Sample Regressions
Mat_Health
SocioDem
-0.297
0.228
-1.306
0.192
-0.285
-0.743
0.149
MatMen_health
SocioDem
0.160
0.075
2.144
0.032
0.158
0.014
0.307
Infant
Mat_Health
1.642
21.383
0.077
0.939
1.436
-40.268
43.552
Infant
MatMen_health
-1.444
21.170
-0.068
0.946
-1.226
-42.937
40.049
Infant
SocioDem
1.219
10.375
0.117
0.906
1.022
-19.116
21.554
n2attention
Infant
-0.006
0.066
-0.088
0.930
-0.005
-0.135
0.123
n2regulation
Infant
0.005
0.057
0.094
0.925
0.006
-0.106
0.117
n2arousal
Infant
0.082
0.128
0.642
0.521
0.062
-0.169
0.334
n2tone
Infant
-0.055
0.062
-0.886
0.376
-0.118
-0.176
0.066
n2nonoptref
Infant
-0.208
0.200
-1.040
0.298
-0.220
-0.601
0.184
n2qmove
Infant
0.106
0.121
0.880
0.379
0.106
-0.130
0.343
n2stress
Infant
-0.042
0.051
-0.826
0.409
-0.071
-0.141
0.057
Sandbox Block:
This block is just for diagnostics and data management strategies post model:
str (cleaned_data_final2.1 $ final_nnns_class)
int [1:291] 3 1 1 2 1 2 1 2 1 1 ...
Measurement Model Tests:
<- ' SocioDem =~ MatAge + mmins_combined + pcedlevel + MomOnlyFSIQ + any_subuse_y_n + hseincom ' <- sem (M_model_2, estimator= "WLSMV" , data= cleaned_data_final2.1 , std.lv= TRUE , mimic = "Mplus" )
Warning: lavaan->lav_options_est_dwls():
estimator "DWLS" is not recommended for continuous data. Did you forget to
set the ordered= argument?
Warning: lavaan->lav_data_full():
some observed variances are (at least) a factor 1000 times larger than
others; use varTable(fit) to investigate
summary (M_model_fit_2, fit.measures = TRUE , standardized= TRUE )
lavaan 0.6-19 ended normally after 44 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 18
Used Total
Number of observations 233 291
Model Test User Model:
Standard Scaled
Test Statistic 17.877 21.145
Degrees of freedom 9 9
P-value (Chi-square) 0.037 0.012
Scaling correction factor 0.925
Shift parameter 1.815
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 120.492 94.355
Degrees of freedom 15 15
P-value 0.000 0.000
Scaling correction factor 1.329
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.916 0.847
Tucker-Lewis Index (TLI) 0.860 0.745
Robust Comparative Fit Index (CFI) 0.894
Robust Tucker-Lewis Index (TLI) 0.823
Root Mean Square Error of Approximation:
RMSEA 0.065 0.076
90 Percent confidence interval - lower 0.016 0.034
90 Percent confidence interval - upper 0.109 0.119
P-value H_0: RMSEA <= 0.050 0.249 0.134
P-value H_0: RMSEA >= 0.080 0.326 0.486
Robust RMSEA 0.073
90 Percent confidence interval - lower 0.033
90 Percent confidence interval - upper 0.114
P-value H_0: Robust RMSEA <= 0.050 0.150
P-value H_0: Robust RMSEA >= 0.080 0.435
Standardized Root Mean Square Residual:
SRMR 0.058 0.058
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
SocioDem =~
MatAge 1.165 0.377 3.094 0.002 1.165 0.232
mmins_combined -0.237 0.041 -5.830 0.000 -0.237 -0.531
pcedlevel 1.117 0.123 9.081 0.000 1.117 0.726
MomOnlyFSIQ 7.280 1.149 6.336 0.000 7.280 0.524
any_subuse_y_n -0.127 0.037 -3.418 0.001 -0.127 -0.286
hseincom -7.398 4.122 -1.795 0.073 -7.398 -0.066
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.MatAge 29.918 0.329 90.801 0.000 29.918 5.961
.mmins_combined 3.845 0.029 130.920 0.000 3.845 8.595
.pcedlevel 4.017 0.101 39.742 0.000 4.017 2.609
.MomOnlyFSIQ 95.107 0.911 104.375 0.000 95.107 6.853
.any_subuse_y_n 1.730 0.029 59.186 0.000 1.730 3.886
.hseincom 19.120 7.385 2.589 0.010 19.120 0.170
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.MatAge 23.830 2.044 11.659 0.000 23.830 0.946
.mmins_combined 0.144 0.049 2.915 0.004 0.144 0.718
.pcedlevel 1.122 0.253 4.428 0.000 1.122 0.473
.MomOnlyFSIQ 139.639 15.873 8.797 0.000 139.639 0.725
.any_subuse_y_n 0.182 0.015 12.086 0.000 0.182 0.918
.hseincom 12597.856 5418.906 2.325 0.020 12597.856 0.996
SocioDem 1.000 1.000 1.000
Maternal Health LV is a mess when run as a single factor CFA
Maternal Mental Health is ok as a single factor CFA (CFI = .92, TLI = .89, robust RMSEA = .05, SRMR = .043 ) ~ using scaled chi-sq because of the data issues
infant sex has to be left out of the lv I think - it works ok with and without but maybe leave out and use as a control or run as a multigroup
SocioDem has modest fit but could consider running separate
SEM Mixed Version
<- ' MatMen_health =~ collapsed_mentheal + acescore + Dep_Score + Anx_Score + Anger_Score + Supp_Score + Meaning_Score Infant =~ imhbirthwt + imh_birthlt + imh_birthhcr + gawks + sex SocioDem =~ MatAge + mmins_combined + mrace_combined + pcedlevel + MomOnlyFSIQ + any_subuse_y_n + hseincom #Regressions diabpprg ~ SocioDem gestdiab ~ SocioDem hyp ~ SocioDem prenatal_collapsed ~ SocioDem hepc ~ SocioDem hiv ~ SocioDem syph ~ SocioDem chlmyd ~ SocioDem any_subuse_y_n ~ SocioDem MatMen_health ~ SocioDem Infant ~ diabpprg + gestdiab + hyp + prenatal_collapsed + hepc + hiv + syph + chlmyd + any_subuse_y_n + MatMen_health + SocioDem final_nnns_class ~ Infant ' <- sem (SEM_model_3, estimator= "WLSMV" , data= cleaned_data_final2.1 , std.lv= TRUE , missing = "pairwise" , mimic = "Mplus" )
Warning: lavaan->lav_options_est_dwls():
estimator "DWLS" is not recommended for continuous data. Did you forget to
set the ordered= argument?
Warning: lavaan->lav_data_full():
some observed variances are (at least) a factor 1000 times larger than
others; use varTable(fit) to investigate
Warning: lavaan->lav_samplestats_from_data():
number of observations (291) too small to compute Gamma
Warning: lavaan->lav_model_vcov():
Could not compute standard errors! The information matrix could not be
inverted. This may be a symptom that the model is not identified.
Warning: lavaan->lav_test_satorra_bentler():
could not invert information matrix needed for robust test statistic
summary (SEM_model_fit_3, fit.measures = TRUE , standardized= TRUE )
lavaan 0.6-19 ended normally after 143 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 97
Number of observations 291
Number of missing patterns 27
Model Test User Model:
Standard Scaled
Test Statistic 669.249 NA
Degrees of freedom 337 337
P-value (Chi-square) 0.000 NA
Scaling correction factor NA
Shift parameter NA
Model Test Baseline Model:
Test statistic 2225.862 1129.246
Degrees of freedom 378 378
P-value 0.000 0.000
Scaling correction factor 2.460
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.820 NA
Tucker-Lewis Index (TLI) 0.798 NA
Robust Comparative Fit Index (CFI) NA
Robust Tucker-Lewis Index (TLI) NA
Root Mean Square Error of Approximation:
RMSEA 0.058 NA
90 Percent confidence interval - lower 0.052 NA
90 Percent confidence interval - upper 0.065 NA
P-value H_0: RMSEA <= 0.050 0.018 NA
P-value H_0: RMSEA >= 0.080 0.000 NA
Robust RMSEA NA
90 Percent confidence interval - lower NA
90 Percent confidence interval - upper NA
P-value H_0: Robust RMSEA <= 0.050 NA
P-value H_0: Robust RMSEA >= 0.080 NA
Standardized Root Mean Square Residual:
SRMR 0.069 0.069
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
MatMen_health =~
collapsd_mnthl 0.292 NA 0.302 0.321
acescore -0.908 NA -0.940 -0.339
Dep_Score -7.920 NA -8.192 -0.915
Anx_Score -8.311 NA -8.597 -0.806
Anger_Score -6.587 NA -6.813 -0.698
Supp_Score 5.245 NA 5.425 0.656
Meaning_Score 5.110 NA 5.285 0.579
Infant =~
imhbirthwt 0.383 NA 0.431 0.950
imh_birthlt 1.495 NA 1.682 0.712
imh_birthhcr 0.883 NA 0.993 0.697
gawks 0.337 NA 0.380 0.379
sex 0.075 NA 0.084 0.169
SocioDem =~
MatAge 0.876 NA 0.876 0.172
mmins_combined -0.182 NA -0.182 -0.370
mrace_combined 0.250 NA 0.250 0.213
pcedlevel 0.860 NA 0.860 0.555
MomOnlyFSIQ 7.275 NA 7.275 0.521
any_subuse_y_n 0.000 NA 0.000 0.000
hseincom -3.596 NA -3.596 -0.031
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
diabpprg ~
SocioDem -0.001 NA -0.001 -0.009
gestdiab ~
SocioDem 0.029 NA 0.029 0.124
hyp ~
SocioDem -0.013 NA -0.013 -0.030
prenatal_collapsed ~
SocioDem -0.071 NA -0.071 -0.190
hepc ~
SocioDem -0.063 NA -0.063 -0.067
hiv ~
SocioDem -0.012 NA -0.012 -0.056
syph ~
SocioDem -0.000 NA -0.000 -0.002
chlmyd ~
SocioDem -0.040 NA -0.040 -0.102
any_subuse_y_n ~
SocioDem -0.188 NA -0.188 -0.414
MatMen_health ~
SocioDem 0.264 NA 0.256 0.256
Infant ~
diabpprg -1.039 NA -0.924 -0.094
gestdiab -0.141 NA -0.125 -0.029
hyp -0.324 NA -0.288 -0.121
prenatl_cllpsd 0.281 NA 0.250 0.093
hepc 0.053 NA 0.047 0.044
hiv -0.129 NA -0.115 -0.024
syph 0.046 NA 0.041 0.010
chlmyd 0.283 NA 0.252 0.100
any_subuse_y_n -0.115 NA -0.102 -0.046
MatMen_health 0.056 NA 0.051 0.051
SocioDem 0.446 NA 0.397 0.397
final_nnns_class ~
Infant -0.014 NA -0.016 -0.015
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.collapsd_mnthl 2.014 NA 2.014 2.144
.acescore 2.758 NA 2.758 0.996
.Dep_Score 47.667 NA 47.667 5.323
.Anx_Score 52.164 NA 52.164 4.889
.Anger_Score 51.645 NA 51.645 5.290
.Supp_Score 56.181 NA 56.181 6.793
.Meaning_Score 59.531 NA 59.531 6.524
.imhbirthwt 3.701 NA 3.701 8.164
.imh_birthlt 51.981 NA 51.981 21.992
.imh_birthhcr 35.212 NA 35.212 24.724
.gawks 39.116 NA 39.116 39.011
.sex 1.649 NA 1.649 3.320
.MatAge 29.753 NA 29.753 5.841
.mmins_combined 3.835 NA 3.835 7.801
.mrace_combined 3.423 NA 3.423 2.925
.pcedlevel 4.054 NA 4.054 2.619
.MomOnlyFSIQ 94.385 NA 94.385 6.758
.any_subuse_y_n 1.711 NA 1.711 3.770
.hseincom 20.643 NA 20.643 0.177
.diabpprg 1.011 NA 1.011 9.884
.gestdiab 1.057 NA 1.057 4.546
.hyp 1.230 NA 1.230 2.918
.prenatl_cllpsd 1.165 NA 1.165 3.131
.hepc 1.732 NA 1.732 1.847
.hiv 1.024 NA 1.024 4.868
.syph 1.034 NA 1.034 4.452
.chlmyd 1.100 NA 1.100 2.758
.finl_nnns_clss 2.121 NA 2.121 2.070
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.collapsd_mnthl 0.791 NA 0.791 0.897
.acescore 6.780 NA 6.780 0.885
.Dep_Score 13.099 NA 13.099 0.163
.Anx_Score 39.947 NA 39.947 0.351
.Anger_Score 48.881 NA 48.881 0.513
.Supp_Score 38.969 NA 38.969 0.570
.Meaning_Score 55.328 NA 55.328 0.665
.imhbirthwt 0.020 NA 0.020 0.098
.imh_birthlt 2.757 NA 2.757 0.494
.imh_birthhcr 1.042 NA 1.042 0.514
.gawks 0.861 NA 0.861 0.857
.sex 0.240 NA 0.240 0.971
.MatAge 25.178 NA 25.178 0.970
.mmins_combined 0.208 NA 0.208 0.863
.mrace_combined 1.307 NA 1.307 0.955
.pcedlevel 1.656 NA 1.656 0.691
.MomOnlyFSIQ 142.128 NA 142.128 0.729
.any_subuse_y_n 0.171 NA 0.171 0.828
.hseincom 13663.523 NA 13663.523 0.999
.diabpprg 0.010 NA 0.010 1.000
.gestdiab 0.053 NA 0.053 0.985
.hyp 0.177 NA 0.177 0.999
.prenatl_cllpsd 0.134 NA 0.134 0.964
.hepc 0.876 NA 0.876 0.996
.hiv 0.044 NA 0.044 0.997
.syph 0.054 NA 0.054 1.000
.chlmyd 0.157 NA 0.157 0.990
.finl_nnns_clss 1.050 NA 1.050 1.000
.MatMen_health 1.000 0.935 0.935
.Infant 1.000 0.790 0.790
SocioDem 1.000 1.000 1.000
sociodemographic model
<- ' Infant =~ imhbirthwt + imh_birthlt + imh_birthhcr + gawks + sex SocioDem =~ MatAge + mmins_combined + mrace_combined + pcedlevel + MomOnlyFSIQ + any_subuse_y_n + hseincom #Regressions Infant ~ SocioDem final_nnns_class ~ Infant ' <- sem (SEM_model_4, estimator= "WLSMV" , data= cleaned_data_final2.1 , std.lv= TRUE , missing = "pairwise" , mimic = "Mplus" )
Warning: lavaan->lav_options_est_dwls():
estimator "DWLS" is not recommended for continuous data. Did you forget to
set the ordered= argument?
Warning: lavaan->lav_data_full():
some observed variances are (at least) a factor 1000 times larger than
others; use varTable(fit) to investigate
Warning: lavaan->lav_model_vcov():
The variance-covariance matrix of the estimated parameters (vcov) does not
appear to be positive definite! The smallest eigenvalue (= -2.350048e-06)
is smaller than zero. This may be a symptom that the model is not
identified.
summary (SEM_model_fit_4, fit.measures = TRUE , standardized= TRUE )
lavaan 0.6-19 ended normally after 64 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 40
Number of observations 291
Number of missing patterns 7
Model Test User Model:
Standard Scaled
Test Statistic 101.769 112.186
Degrees of freedom 64 64
P-value (Chi-square) 0.002 0.000
Scaling correction factor 1.079
Shift parameter 17.836
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 663.986 464.573
Degrees of freedom 78 78
P-value 0.000 0.000
Scaling correction factor 1.516
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.936 0.875
Tucker-Lewis Index (TLI) 0.921 0.848
Robust Comparative Fit Index (CFI) 0.936
Robust Tucker-Lewis Index (TLI) 0.921
Root Mean Square Error of Approximation:
RMSEA 0.045 0.051
90 Percent confidence interval - lower 0.028 0.035
90 Percent confidence interval - upper 0.061 0.066
P-value H_0: RMSEA <= 0.050 0.674 0.441
P-value H_0: RMSEA >= 0.080 0.000 0.001
Robust RMSEA 0.053
90 Percent confidence interval - lower 0.036
90 Percent confidence interval - upper 0.069
P-value H_0: Robust RMSEA <= 0.050 0.366
P-value H_0: Robust RMSEA >= 0.080 0.002
Standardized Root Mean Square Residual:
SRMR 0.057 0.057
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Infant =~
imhbirthwt 0.390 0.026 14.873 0.000 0.423 0.933
imh_birthlt 1.538 0.133 11.539 0.000 1.670 0.706
imh_birthhcr 0.937 0.076 12.355 0.000 1.017 0.714
gawks 0.351 0.060 5.814 0.000 0.381 0.380
sex 0.080 0.028 2.864 0.004 0.087 0.175
SocioDem =~
MatAge 0.833 0.363 2.294 0.022 0.833 0.164
mmins_combined -0.165 0.036 -4.562 0.000 -0.165 -0.337
mrace_combined 0.272 0.073 3.745 0.000 0.272 0.233
pcedlevel 0.895 0.112 7.961 0.000 0.895 0.578
MomOnlyFSIQ 9.476 1.159 8.178 0.000 9.476 0.679
any_subuse_y_n -0.137 0.035 -3.945 0.000 -0.137 -0.303
hseincom -4.737 4.356 -1.088 0.277 -4.737 -0.041
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Infant ~
SocioDem 0.424 0.090 4.722 0.000 0.390 0.390
final_nnns_class ~
Infant 0.011 0.058 0.192 0.848 0.012 0.012
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.imhbirthwt 3.261 0.027 122.529 0.000 3.261 7.195
.imh_birthlt 50.265 0.138 363.413 0.000 50.265 21.267
.imh_birthhcr 34.200 0.083 410.346 0.000 34.200 24.013
.gawks 38.729 0.059 657.764 0.000 38.729 38.625
.sex 1.564 0.029 53.597 0.000 1.564 3.147
.MatAge 29.753 0.299 99.470 0.000 29.753 5.841
.mmins_combined 3.835 0.029 132.851 0.000 3.835 7.801
.mrace_combined 3.423 0.069 49.815 0.000 3.423 2.925
.pcedlevel 4.054 0.089 45.476 0.000 4.054 2.619
.MomOnlyFSIQ 94.385 0.778 121.311 0.000 94.385 6.758
.any_subuse_y_n 1.711 0.027 64.203 0.000 1.711 3.770
.hseincom 20.643 6.465 3.193 0.001 20.643 0.177
.finl_nnns_clss 2.137 0.060 35.522 0.000 2.137 2.086
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.imhbirthwt 0.026 0.011 2.368 0.018 0.026 0.129
.imh_birthlt 2.798 0.380 7.358 0.000 2.798 0.501
.imh_birthhcr 0.993 0.123 8.052 0.000 0.993 0.490
.gawks 0.860 0.073 11.719 0.000 0.860 0.856
.sex 0.239 0.006 38.472 0.000 0.239 0.969
.MatAge 25.251 1.935 13.049 0.000 25.251 0.973
.mmins_combined 0.214 0.058 3.716 0.000 0.214 0.887
.mrace_combined 1.295 0.118 10.934 0.000 1.295 0.946
.pcedlevel 1.594 0.184 8.671 0.000 1.594 0.666
.MomOnlyFSIQ 105.255 18.256 5.765 0.000 105.255 0.540
.any_subuse_y_n 0.187 0.014 13.862 0.000 0.187 0.908
.hseincom 13653.979 4758.063 2.870 0.004 13653.979 0.998
.finl_nnns_clss 1.050 0.060 17.405 0.000 1.050 1.000
.Infant 1.000 0.848 0.848
SocioDem 1.000 1.000 1.000
Sociodemographic model tables
parameterEstimates (SEM_model_fit_4, standardized= TRUE ) %>% filter (op == "=~" ) %>% select ('Latent Factor' = lhs, Indicator= rhs, B= est, SE= se, Z= z, 'p-value' = pvalue, Beta= std.all,CI_Lower= ci.lower, CI_Upper= ci.upper) %>% :: kable (digits = 3 , format= "html" , booktabs= TRUE , caption= "Total Sample Factor Loadings" )%>% kable_classic (full_width = F, html_font = "Cambria" )
Total Sample Factor Loadings
Infant
imhbirthwt
0.390
0.026
14.873
0.000
0.933
0.338
0.441
Infant
imh_birthlt
1.538
0.133
11.539
0.000
0.706
1.276
1.799
Infant
imh_birthhcr
0.937
0.076
12.355
0.000
0.714
0.788
1.085
Infant
gawks
0.351
0.060
5.814
0.000
0.380
0.232
0.469
Infant
sex
0.080
0.028
2.864
0.004
0.175
0.025
0.135
SocioDem
MatAge
0.833
0.363
2.294
0.022
0.164
0.121
1.545
SocioDem
mmins_combined
-0.165
0.036
-4.562
0.000
-0.337
-0.237
-0.094
SocioDem
mrace_combined
0.272
0.073
3.745
0.000
0.233
0.130
0.415
SocioDem
pcedlevel
0.895
0.112
7.961
0.000
0.578
0.674
1.115
SocioDem
MomOnlyFSIQ
9.476
1.159
8.178
0.000
0.679
7.205
11.748
SocioDem
any_subuse_y_n
-0.137
0.035
-3.945
0.000
-0.303
-0.206
-0.069
SocioDem
hseincom
-4.737
4.356
-1.088
0.277
-0.041
-13.274
3.800
##Regression Table parameterEstimates (SEM_model_fit_4, standardized= TRUE ) %>% filter (op == "~" ) %>% select ('LV1' = lhs, 'LV2' = rhs, B= est, SE= se, Z= z, 'p-value' = pvalue, Beta= std.all,CI_Lower= ci.lower, CI_Upper= ci.upper) %>% :: kable (digits = 3 , format= "html" , booktabs= TRUE , caption= "Total Sample Regressions" )%>% kable_classic (full_width = F, html_font = "Cambria" )
Total Sample Regressions
Infant
SocioDem
0.424
0.090
4.722
0.000
0.390
0.248
0.600
final_nnns_class
Infant
0.011
0.058
0.192
0.848
0.012
-0.102
0.124
Maternal Health Model
<- ' Infant =~ imhbirthwt + imh_birthlt + imh_birthhcr + gawks + sex #Regressions Infant ~ diabpprg + gestdiab + hyp + prenatal_collapsed + hepc + hiv + syph + chlmyd + any_subuse_y_n final_nnns_class ~ Infant ' <- sem (SEM_model_5, estimator= "WLSMV" , data= cleaned_data_final2.1 , std.lv= TRUE , missing = "pairwise" , mimic = "Mplus" )
Warning: lavaan->lav_options_est_dwls():
estimator "DWLS" is not recommended for continuous data. Did you forget to
set the ordered= argument?
Warning: lavaan->lav_model_vcov():
The variance-covariance matrix of the estimated parameters (vcov) does not
appear to be positive definite! The smallest eigenvalue (= -3.451017e-06)
is smaller than zero. This may be a symptom that the model is not
identified.
summary (SEM_model_fit_5, fit.measures = TRUE , standardized= TRUE )
lavaan 0.6-19 ended normally after 211 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 81
Number of observations 291
Number of missing patterns 7
Model Test User Model:
Standard Scaled
Test Statistic 57.277 76.047
Degrees of freedom 54 54
P-value (Chi-square) 0.355 0.026
Scaling correction factor 0.923
Shift parameter 13.987
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 431.877 311.036
Degrees of freedom 69 69
P-value 0.000 0.000
Scaling correction factor 1.499
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.991 0.909
Tucker-Lewis Index (TLI) 0.988 0.884
Robust Comparative Fit Index (CFI) 0.991
Robust Tucker-Lewis Index (TLI) 0.988
Root Mean Square Error of Approximation:
RMSEA 0.014 0.038
90 Percent confidence interval - lower 0.000 0.014
90 Percent confidence interval - upper 0.040 0.056
P-value H_0: RMSEA <= 0.050 0.993 0.856
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.036
90 Percent confidence interval - lower 0.013
90 Percent confidence interval - upper 0.054
P-value H_0: Robust RMSEA <= 0.050 0.896
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.038 0.038
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Infant =~
imhbirthwt 0.419 0.024 17.179 0.000 0.438 0.966
imh_birthlt 1.547 0.131 11.791 0.000 1.618 0.685
imh_birthhcr 0.924 0.074 12.554 0.000 0.966 0.678
gawks 0.405 0.062 6.548 0.000 0.423 0.422
sex 0.081 0.029 2.781 0.005 0.084 0.170
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Infant ~
diabpprg -0.861 0.259 -3.323 0.001 -0.823 -0.084
gestdiab 0.155 0.209 0.743 0.457 0.148 0.034
hyp -0.347 0.150 -2.304 0.021 -0.331 -0.140
prenatl_cllpsd 0.074 0.196 0.379 0.704 0.071 0.027
hepc 0.112 0.082 1.367 0.172 0.107 0.100
hiv -0.269 0.118 -2.280 0.023 -0.257 -0.054
syph -0.121 0.168 -0.721 0.471 -0.116 -0.027
chlmyd 0.213 0.133 1.599 0.110 0.204 0.081
any_subuse_y_n -0.548 0.165 -3.314 0.001 -0.524 -0.238
final_nnns_class ~
Infant 0.001 0.060 0.014 0.989 0.001 0.001
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
diabpprg ~~
gestdiab -0.001 0.000 -1.687 0.092 -0.001 -0.026
hyp 0.005 0.004 1.224 0.221 0.005 0.108
prenatl_cllpsd -0.002 0.001 -1.783 0.075 -0.002 -0.047
hepc -0.001 0.006 -0.112 0.911 -0.001 -0.007
hiv -0.000 0.000 -1.351 0.177 -0.000 -0.012
syph -0.000 0.000 -1.359 0.174 -0.000 -0.014
chlmyd -0.001 0.001 -1.635 0.102 -0.001 -0.025
any_subuse_y_n -0.000 0.003 -0.171 0.864 -0.000 -0.010
gestdiab ~~
hyp 0.009 0.007 1.282 0.200 0.009 0.087
prenatl_cllpsd 0.007 0.006 1.136 0.256 0.007 0.076
hepc -0.013 0.012 -1.057 0.291 -0.013 -0.058
hiv 0.009 0.007 1.299 0.194 0.009 0.191
syph 0.002 0.003 0.600 0.549 0.002 0.036
chlmyd -0.002 0.003 -0.566 0.571 -0.002 -0.021
any_subuse_y_n -0.005 0.007 -0.750 0.453 -0.005 -0.047
hyp ~~
prenatl_cllpsd 0.010 0.009 1.083 0.279 0.010 0.065
hepc -0.023 0.022 -1.052 0.293 -0.023 -0.059
hiv -0.006 0.003 -1.994 0.046 -0.006 -0.064
syph -0.000 0.006 -0.040 0.968 -0.000 -0.002
chlmyd 0.009 0.011 0.799 0.424 0.009 0.054
any_subuse_y_n -0.004 0.011 -0.369 0.712 -0.004 -0.022
prenatal_collapsed ~~
hepc 0.010 0.020 0.487 0.626 0.010 0.028
hiv -0.001 0.003 -0.165 0.869 -0.001 -0.007
syph 0.014 0.008 1.849 0.065 0.014 0.160
chlmyd 0.005 0.009 0.580 0.562 0.005 0.033
any_subuse_y_n 0.007 0.009 0.808 0.419 0.007 0.044
hepc ~~
hiv 0.017 0.014 1.210 0.226 0.017 0.085
syph 0.026 0.015 1.755 0.079 0.026 0.122
chlmyd -0.021 0.021 -1.045 0.296 -0.021 -0.057
any_subuse_y_n 0.140 0.021 6.710 0.000 0.140 0.328
hiv ~~
syph 0.003 0.003 0.797 0.426 0.003 0.054
chlmyd 0.001 0.003 0.316 0.752 0.001 0.012
any_subuse_y_n 0.007 0.004 1.948 0.051 0.007 0.073
syph ~~
chlmyd 0.021 0.014 1.490 0.136 0.021 0.223
any_subuse_y_n -0.000 0.006 -0.062 0.951 -0.000 -0.004
chlmyd ~~
any_subuse_y_n 0.005 0.010 0.455 0.649 0.005 0.026
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.imhbirthwt 4.081 0.219 18.632 0.000 4.081 9.002
.imh_birthlt 53.292 0.857 62.188 0.000 53.292 22.547
.imh_birthhcr 36.006 0.496 72.662 0.000 36.006 25.281
.gawks 39.520 0.239 165.664 0.000 39.520 39.414
.sex 1.721 0.078 22.095 0.000 1.721 3.465
.finl_nnns_clss 2.139 0.135 15.833 0.000 2.139 2.088
diabpprg 1.011 0.006 170.092 0.000 1.011 9.884
gestdiab 1.057 0.013 78.931 0.000 1.057 4.546
hyp 1.230 0.024 50.396 0.000 1.230 2.918
prenatl_cllpsd 1.165 0.021 54.550 0.000 1.165 3.131
hepc 1.732 0.055 31.448 0.000 1.732 1.847
hiv 1.024 0.012 82.905 0.000 1.024 4.868
syph 1.034 0.014 75.810 0.000 1.034 4.452
chlmyd 1.100 0.023 46.963 0.000 1.100 2.758
any_subuse_y_n 1.711 0.027 64.203 0.000 1.711 3.770
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.imhbirthwt 0.014 0.011 1.293 0.196 0.014 0.066
.imh_birthlt 2.968 0.392 7.572 0.000 2.968 0.531
.imh_birthhcr 1.096 0.113 9.675 0.000 1.096 0.540
.gawks 0.826 0.072 11.401 0.000 0.826 0.822
.sex 0.240 0.006 39.572 0.000 0.240 0.971
.finl_nnns_clss 1.050 0.060 17.392 0.000 1.050 1.000
.Infant 1.000 0.914 0.914
diabpprg 0.010 0.006 1.797 0.072 0.010 1.000
gestdiab 0.054 0.012 4.559 0.000 0.054 1.000
hyp 0.178 0.013 13.489 0.000 0.178 1.000
prenatl_cllpsd 0.139 0.014 9.706 0.000 0.139 1.000
hepc 0.880 0.031 28.225 0.000 0.880 1.000
hiv 0.044 0.023 1.890 0.059 0.044 1.000
syph 0.054 0.024 2.274 0.023 0.054 1.000
chlmyd 0.159 0.039 4.060 0.000 0.159 1.000
any_subuse_y_n 0.206 0.011 18.361 0.000 0.206 1.000
Maternal Health Tables
parameterEstimates (SEM_model_fit_5, standardized= TRUE ) %>% filter (op == "=~" ) %>% select ('Latent Factor' = lhs, Indicator= rhs, B= est, SE= se, Z= z, 'p-value' = pvalue, Beta= std.all,CI_Lower= ci.lower, CI_Upper= ci.upper) %>% :: kable (digits = 3 , format= "html" , booktabs= TRUE , caption= "Total Sample Factor Loadings" )%>% kable_classic (full_width = F, html_font = "Cambria" )
Total Sample Factor Loadings
Infant
imhbirthwt
0.419
0.024
17.179
0.000
0.966
0.371
0.467
Infant
imh_birthlt
1.547
0.131
11.791
0.000
0.685
1.290
1.805
Infant
imh_birthhcr
0.924
0.074
12.554
0.000
0.678
0.779
1.068
Infant
gawks
0.405
0.062
6.548
0.000
0.422
0.283
0.526
Infant
sex
0.081
0.029
2.781
0.005
0.170
0.024
0.137
##Regression Table parameterEstimates (SEM_model_fit_5, standardized= TRUE ) %>% filter (op == "~" ) %>% select ('LV1' = lhs, 'LV2' = rhs, B= est, SE= se, Z= z, 'p-value' = pvalue, Beta= std.all,CI_Lower= ci.lower, CI_Upper= ci.upper) %>% :: kable (digits = 3 , format= "html" , booktabs= TRUE , caption= "Total Sample Regressions" )%>% kable_classic (full_width = F, html_font = "Cambria" )
Total Sample Regressions
Infant
diabpprg
-0.861
0.259
-3.323
0.001
-0.084
-1.368
-0.353
Infant
gestdiab
0.155
0.209
0.743
0.457
0.034
-0.254
0.564
Infant
hyp
-0.347
0.150
-2.304
0.021
-0.140
-0.642
-0.052
Infant
prenatal_collapsed
0.074
0.196
0.379
0.704
0.027
-0.310
0.459
Infant
hepc
0.112
0.082
1.367
0.172
0.100
-0.048
0.272
Infant
hiv
-0.269
0.118
-2.280
0.023
-0.054
-0.500
-0.038
Infant
syph
-0.121
0.168
-0.721
0.471
-0.027
-0.451
0.208
Infant
chlmyd
0.213
0.133
1.599
0.110
0.081
-0.048
0.475
Infant
any_subuse_y_n
-0.548
0.165
-3.314
0.001
-0.238
-0.872
-0.224
final_nnns_class
Infant
0.001
0.060
0.014
0.989
0.001
-0.117
0.119
Maternal Mental Health Model
<- ' MatMen_health =~ collapsed_mentheal + acescore + Dep_Score + Anx_Score + Anger_Score + Supp_Score + Meaning_Score Infant =~ imhbirthwt + imh_birthlt + imh_birthhcr + gawks + sex #Regressions Infant ~ MatMen_health final_nnns_class ~ Infant ' <- sem (SEM_model_6, estimator= "WLSMV" , data= cleaned_data_final2.1 , std.lv= TRUE , missing = "pairwise" , mimic = "Mplus" )
Warning: lavaan->lav_options_est_dwls():
estimator "DWLS" is not recommended for continuous data. Did you forget to
set the ordered= argument?
Warning: lavaan->lav_model_vcov():
The variance-covariance matrix of the estimated parameters (vcov) does not
appear to be positive definite! The smallest eigenvalue (= -1.465163e-06)
is smaller than zero. This may be a symptom that the model is not
identified.
summary (SEM_model_fit_6, fit.measures = TRUE , standardized= TRUE )
lavaan 0.6-19 ended normally after 46 iterations
Estimator DWLS
Optimization method NLMINB
Number of model parameters 40
Number of observations 291
Number of missing patterns 6
Model Test User Model:
Standard Scaled
Test Statistic 62.508 83.798
Degrees of freedom 64 64
P-value (Chi-square) 0.529 0.049
Scaling correction factor 1.024
Shift parameter 22.740
simple second-order correction (WLSMV)
Model Test Baseline Model:
Test statistic 1278.477 705.814
Degrees of freedom 78 78
P-value 0.000 0.000
Scaling correction factor 1.912
User Model versus Baseline Model:
Comparative Fit Index (CFI) 1.000 0.968
Tucker-Lewis Index (TLI) 1.002 0.962
Robust Comparative Fit Index (CFI) 1.000
Robust Tucker-Lewis Index (TLI) 1.002
Root Mean Square Error of Approximation:
RMSEA 0.000 0.033
90 Percent confidence interval - lower 0.000 0.002
90 Percent confidence interval - upper 0.034 0.051
P-value H_0: RMSEA <= 0.050 0.999 0.943
P-value H_0: RMSEA >= 0.080 0.000 0.000
Robust RMSEA 0.033
90 Percent confidence interval - lower 0.002
90 Percent confidence interval - upper 0.051
P-value H_0: Robust RMSEA <= 0.050 0.936
P-value H_0: Robust RMSEA >= 0.080 0.000
Standardized Root Mean Square Residual:
SRMR 0.044 0.044
Parameter Estimates:
Standard errors Robust.sem
Information Expected
Information saturated (h1) model Unstructured
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
MatMen_health =~
collapsd_mnthl 0.303 0.051 5.908 0.000 0.303 0.322
acescore -0.912 0.154 -5.910 0.000 -0.912 -0.330
Dep_Score -8.099 0.419 -19.342 0.000 -8.099 -0.904
Anx_Score -8.571 0.441 -19.444 0.000 -8.571 -0.803
Anger_Score -6.904 0.578 -11.935 0.000 -6.904 -0.707
Supp_Score 5.386 0.478 11.277 0.000 5.386 0.651
Meaning_Score 5.452 0.519 10.495 0.000 5.452 0.597
Infant =~
imhbirthwt 0.440 0.026 16.645 0.000 0.445 0.981
imh_birthlt 1.704 0.139 12.261 0.000 1.723 0.729
imh_birthhcr 0.909 0.080 11.347 0.000 0.919 0.645
gawks 0.385 0.063 6.064 0.000 0.389 0.388
sex 0.085 0.030 2.807 0.005 0.086 0.172
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
Infant ~
MatMen_health 0.151 0.066 2.300 0.021 0.150 0.150
final_nnns_class ~
Infant -0.002 0.062 -0.034 0.973 -0.002 -0.002
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.collapsd_mnthl 2.014 0.055 36.503 0.000 2.014 2.144
.acescore 2.758 0.160 17.267 0.000 2.758 0.996
.Dep_Score 47.667 0.491 97.043 0.000 47.667 5.323
.Anx_Score 52.164 0.586 88.958 0.000 52.164 4.889
.Anger_Score 51.645 0.536 96.264 0.000 51.645 5.290
.Supp_Score 56.181 0.455 123.360 0.000 56.181 6.793
.Meaning_Score 59.531 0.501 118.714 0.000 59.531 6.524
.imhbirthwt 3.261 0.027 122.529 0.000 3.261 7.195
.imh_birthlt 50.265 0.138 363.413 0.000 50.265 21.267
.imh_birthhcr 34.200 0.083 410.346 0.000 34.200 24.013
.gawks 38.729 0.059 657.764 0.000 38.729 38.625
.sex 1.564 0.029 53.597 0.000 1.564 3.147
.finl_nnns_clss 2.137 0.060 35.522 0.000 2.137 2.086
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.collapsd_mnthl 0.791 0.034 23.069 0.000 0.791 0.896
.acescore 6.831 0.550 12.418 0.000 6.831 0.891
.Dep_Score 14.601 2.828 5.164 0.000 14.601 0.182
.Anx_Score 40.394 4.696 8.602 0.000 40.394 0.355
.Anger_Score 47.635 6.287 7.577 0.000 47.635 0.500
.Supp_Score 39.400 3.999 9.853 0.000 39.400 0.576
.Meaning_Score 53.537 5.239 10.218 0.000 53.537 0.643
.imhbirthwt 0.008 0.014 0.546 0.585 0.008 0.038
.imh_birthlt 2.618 0.412 6.355 0.000 2.618 0.469
.imh_birthhcr 1.184 0.117 10.126 0.000 1.184 0.584
.gawks 0.854 0.073 11.693 0.000 0.854 0.849
.sex 0.239 0.006 39.039 0.000 0.239 0.970
.finl_nnns_clss 1.050 0.060 17.392 0.000 1.050 1.000
MatMen_health 1.000 1.000 1.000
.Infant 1.000 0.978 0.978
Maternal Mental Health Tables
parameterEstimates (SEM_model_fit_6, standardized= TRUE ) %>% filter (op == "=~" ) %>% select ('Latent Factor' = lhs, Indicator= rhs, B= est, SE= se, Z= z, 'p-value' = pvalue, Beta= std.all,CI_Lower= ci.lower, CI_Upper= ci.upper) %>% :: kable (digits = 3 , format= "html" , booktabs= TRUE , caption= "Total Sample Factor Loadings" )%>% kable_classic (full_width = F, html_font = "Cambria" )
Total Sample Factor Loadings
MatMen_health
collapsed_mentheal
0.303
0.051
5.908
0.000
0.322
0.202
0.403
MatMen_health
acescore
-0.912
0.154
-5.910
0.000
-0.330
-1.215
-0.610
MatMen_health
Dep_Score
-8.099
0.419
-19.342
0.000
-0.904
-8.920
-7.279
MatMen_health
Anx_Score
-8.571
0.441
-19.444
0.000
-0.803
-9.434
-7.707
MatMen_health
Anger_Score
-6.904
0.578
-11.935
0.000
-0.707
-8.038
-5.770
MatMen_health
Supp_Score
5.386
0.478
11.277
0.000
0.651
4.450
6.322
MatMen_health
Meaning_Score
5.452
0.519
10.495
0.000
0.597
4.434
6.470
Infant
imhbirthwt
0.440
0.026
16.645
0.000
0.981
0.388
0.491
Infant
imh_birthlt
1.704
0.139
12.261
0.000
0.729
1.431
1.976
Infant
imh_birthhcr
0.909
0.080
11.347
0.000
0.645
0.752
1.066
Infant
gawks
0.385
0.063
6.064
0.000
0.388
0.260
0.509
Infant
sex
0.085
0.030
2.807
0.005
0.172
0.026
0.144
##Regression Table parameterEstimates (SEM_model_fit_6, standardized= TRUE ) %>% filter (op == "~" ) %>% select ('LV1' = lhs, 'LV2' = rhs, B= est, SE= se, Z= z, 'p-value' = pvalue, Beta= std.all,CI_Lower= ci.lower, CI_Upper= ci.upper) %>% :: kable (digits = 3 , format= "html" , booktabs= TRUE , caption= "Total Sample Regressions" )%>% kable_classic (full_width = F, html_font = "Cambria" )
Total Sample Regressions
Infant
MatMen_health
0.151
0.066
2.300
0.021
0.150
0.022
0.280
final_nnns_class
Infant
-0.002
0.062
-0.034
0.973
-0.002
-0.123
0.119