Publications by Mark Sanderson-cimino
Redlat prelim
## Warning in min(x, na.rm = TRUE): no non-missing arguments to min; returning Inf ## Warning in max(x, na.rm = TRUE): no non-missing arguments to max; returning ## -Inf ## Warning in StdDiff(variable = var, group = strataVar): Variable has only NA's ## in at least one stratum. na.rm turned off. ## Stratified by demo_pl...
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Reliability.v1
Load Libraries # detach("package:ltm", unload = TRUE) # detach("package:MASS", unload = TRUE) library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.3 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5....
123 sym R (74079 sym/60 pcs) 9 img
Reliability.v1
Load Libraries library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.2 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.0 ## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1 ## ✔ lubridate 1.9.2 ✔ ...
168 sym R (44047 sym/105 pcs) 9 img
LME results draft
#LME model list 1. model with covariates, random slope and intercept for each subject, using all data points. ## [1] 1479 1781 ## Warning in xtfrm.data.frame(x): cannot xtfrm data frames ## [1] "#-----------------#" ## [1] "avg_stp_z" ## Computing profile confidence intervals ... ## [1] "#-----------------#" ## Warning in xtfrm.data.frame(x): c...
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Prop. Scores
Table below shows output from Matchit Package The formula is: group ~ age + sex (as a factor) +PSPRS The “distance variable” is the propensity score. We had to drop ~40 controls because they did not have PSPRSs print("FULL SAMPLE") ## [1] "FULL SAMPLE" summary(m.out.nearest) ## ## Call: ## matchit(formula = fmlaMatching, data = df.peter.ma...
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Prop scores
## ## Call: ## matchit(formula = fmlaMatching, data = df.peter.matchit, method = "nearest", ## link = "logit") ## ## Summary of Balance for All Data: ## Means Treated Means Control Std. Mean Diff. ## distance 0.1967 0.1179 0.5550 ## Baseline_Age ...
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Hanna heatmap.v1
## ## Attaching package: 'psych' ## The following objects are masked from 'package:ggplot2': ## ## %+%, alpha ## [[1]] ## ## [[2]] ## n mean sd median min max skew se ## ptau 349 0.164 0.147 0.103 0.022 0.812 1.651 0.008 ## logptau 349 -0.934 0.360 -0.988 -1.660 -0.090 0.241 0.019 ...
14 sym Python (2664 sym/8 pcs) 3 img
ren prep data
pandoc.table(full.table, split.table = 50, style = "simple", caption = "Full data") ## ## ## vars n mean sd ## -------------- ------ ------ ------ ------ ## **cdrtot** 1 1326 0.65 0.99 ## **boxscore** 1 1326 3.92 4.33 ## **t1corr** 1 651 3.54 1.88 ## **t2co...
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Examiner follow-up.v2
Data based on ~1300 participants. Those with at least one TabCat score. Data was NOT transformed. Drop 7 participants from rundots (>2.5 SD from mean) Scatterplot Correlation Matrix ## [1] "Corelations for original, non-recoded data" ## [1] "Corelations for Recoded data" EF Single factor CFA. fscores_EF_factor<-as.data.frame(read.csv("Fscores_...
1231 sym Python (31345 sym/79 pcs) 68 img
Updated examiner issues
Data based on ~1300 participants. Those with at least one TabCat score. Data was NOT transformed One factor EFA fscores_efa_one<-read.csv("Fscores_EFA_one_MIRT.csv") #look at scatterplots to see if correlated, want no correlation. # shows correlation between factors in upper triangle. Want uncorelated. ggpairs( data=fscores_efa_one, m...
1093 sym Python (23463 sym/61 pcs) 53 img