AUCmat
2023-02-26
Last updated: 2023-03-11
Checks: 6 1
Knit directory: paperscripts/
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AUC overall
source("code/utils.R")
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, union── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.1 ✔ purrr 0.3.5
✔ tibble 3.1.8 ✔ stringr 1.4.1
✔ tidyr 1.2.1 ✔ forcats 0.5.2
✔ readr 2.1.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
Loading required package: ggpubr
Attaching package: 'survminer'
The following object is masked from 'package:survival':
myeloma
Attaching package: 'reshape'
The following objects are masked from 'package:tidyr':
expand, smiths
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rename
Attaching package: 'gridExtra'
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combine
Attaching package: 'survMisc'
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autoplot
Attaching package: 'rsq'
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rsq
Type 'citation("pROC")' for a citation.
Attaching package: 'pROC'
The following object is masked from 'package:survMisc':
ci
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cov, smooth, var
Attaching package: 'data.table'
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melt
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transpose
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between, first, last
Attaching package: 'plotly'
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rename
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last_plot
The following object is masked from 'package:stats':
filter
The following object is masked from 'package:graphics':
layoutlibrary(ggplot2)
df=readRDS("output/amit_df.rds")
ages=c(40,45,50,55,60,65,70,75)
df$prscat <- cut(df$prs.r, breaks=c(0, 0.20,0.80,1), labels=c("low", "intermediate","high"))
set.seed(456)
aucmat=matrix(NA,nrow=length(ages)-1,ncol=3)
semat=matrix(NA,nrow=length(ages)-1,ncol=3)
for(i in 1:(length(ages)-1))
{
par(mfrow=c(1,2))
#which(df$phenos.enrollment>ages[i]&df$phenos.enrollment<ages[i+1])
d=df[which(df$phenos.enrollment>ages[i]&df$phenos.enrollment<ages[i+1]),]
indices=sample(nrow(d))
d_train=d[indices[1:(length(indices)/2)],]
d_test=d[indices[(length(indices)/2+1):length(indices)],]
model_glm = glm(phenos.has_CAD ~ prscat, data = d_train, family = "binomial")
test_prob = predict(model_glm, newdata = d_test, type = "response")
test_roc = roc(d_test$phenos.has_CAD~ test_prob, plot = F,main=paste0("PRS,age",ages[i],"to",ages[i+1]))
aucmat[i,1]=test_roc$auc
semat[i,1]=sqrt(var(test_roc))
model_glm = glm(phenos.has_CAD ~ ascvdcat_all, data = d_train, family = "binomial")
test_prob = predict(model_glm, newdata = d_test, type = "response")
test_roc = roc(d_test$phenos.has_CAD~ test_prob, plot = F,main=paste0("PCE,age",ages[i],"to",ages[i+1]))
aucmat[i,2]=test_roc$auc
semat[i,2]=sqrt(var(test_roc))
model_glm = glm(phenos.has_CAD ~ ascvdcat_all+prscat, data = d_train, family = "binomial")
test_prob = predict(model_glm, newdata = d_test, type = "response")
test_roc = roc(d_test$phenos.has_CAD~ test_prob, plot = F,main=paste0("PCE+PRS,age",ages[i],"to",ages[i+1]))
aucmat[i,3]=test_roc$auc
semat[i,3]=sqrt(var(test_roc))
}Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
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Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
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Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < cases
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Setting direction: controls < cases
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Setting direction: controls < cases
Setting levels: control = 0, case = 1
Setting direction: controls < casesrownames(aucmat)=c("40-45","45-50","50-55","55-60","60-65","65-70","70-75")
m=melt(aucmat,id.vars="Age")Warning in melt(aucmat, id.vars = "Age"): The melt generic in data.table has
been passed a matrix and will attempt to redirect to the relevant reshape2
method; please note that reshape2 is deprecated, and this redirection is now
deprecated as well. To continue using melt methods from reshape2 while both
libraries are attached, e.g. melt.list, you can prepend the namespace like
reshape2::melt(aucmat). In the next version, this warning will become an error.m3=melt(semat)Warning in melt(semat): The melt generic in data.table has been passed a matrix
and will attempt to redirect to the relevant reshape2 method; please note that
reshape2 is deprecated, and this redirection is now deprecated as well. To
continue using melt methods from reshape2 while both libraries are attached,
e.g. melt.list, you can prepend the namespace like reshape2::melt(semat). In the
next version, this warning will become an error.m[which(m$X2==1),2]="PRS"
m[which(m$X2==2),2]="PCE"
m[which(m$X2==3),2]="PRS+PCE"
names(m)=c("Age","Model","AUC")
m$Model=as.factor(m$Model)
levels(m$Model)[1]="Genomic"
levels(m$Model)[2]="Non Genomic"
levels(m$Model)[3]="Combined"
print(levels(m$Model))[1] "Genomic" "Non Genomic" "Combined" m$se=m3$value
gplot <- ggplot(m,aes(x = Age,y = AUC,color = Model,ymin=AUC+se,ymax=AUC-se))+geom_point()+geom_line(aes(group=Model,color =Model),size=3)+geom_pointrange()+ylim(0.5,1)+scale_color_manual(values=c("tomato","cornflowerblue","darkorchid4"))+theme_classic(base_size = 25)+theme(axis.text.x = element_text(angle = 90))Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.gplot
#ggsave(gplot,filename = "aucplot.pdf",dpi=300)
ggsave(gplot,filename = "Figs/Fig5/aucplot.tiff",dpi=300,width = 9,height = 7)
msave=m
msave$upper=m$AUC+m$se
msave$lower=m$AUC-m$se
write.table(msave,"output/AUC.csv",sep=",",quote=FALSE,row.names = F)
sessionInfo()R version 4.2.1 (2022-06-23)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] babynames_1.0.1 gapminder_0.3.0 carData_3.0-5 plotly_4.10.0
[5] DT_0.24 gt_0.8.0 RColorBrewer_1.1-3 data.table_1.14.2
[9] pROC_1.18.0 rsq_2.5 survMisc_0.5.6 gridExtra_2.3
[13] ggfortify_0.4.14 reshape_0.8.9 eulerr_6.1.1 survminer_0.4.9
[17] ggpubr_0.4.0 survival_3.4-0 forcats_0.5.2 stringr_1.4.1
[21] purrr_0.3.5 readr_2.1.2 tidyr_1.2.1 tibble_3.1.8
[25] ggplot2_3.4.1 tidyverse_1.3.2 dplyr_1.0.10
loaded via a namespace (and not attached):
[1] googledrive_2.0.0 minqa_1.2.4 colorspace_2.0-3
[4] ggsignif_0.6.4 ellipsis_0.3.2 rprojroot_2.0.3
[7] fs_1.5.2 rstudioapi_0.14 farver_2.1.1
[10] Deriv_4.1.3 fansi_1.0.3 lubridate_1.8.0
[13] xml2_1.3.3 splines_4.2.1 cachem_1.0.6
[16] knitr_1.40 jsonlite_1.8.2 workflowr_1.7.0
[19] nloptr_2.0.3 broom_1.0.1 km.ci_0.5-6
[22] dbplyr_2.2.1 compiler_4.2.1 httr_1.4.4
[25] backports_1.4.1 assertthat_0.2.1 Matrix_1.5-1
[28] fastmap_1.1.0 lazyeval_0.2.2 gargle_1.2.0
[31] cli_3.4.1 later_1.3.0 htmltools_0.5.3
[34] tools_4.2.1 gtable_0.3.1 glue_1.6.2
[37] reshape2_1.4.4 Rcpp_1.0.9 cellranger_1.1.0
[40] jquerylib_0.1.4 vctrs_0.5.2 nlme_3.1-159
[43] xfun_0.33 lme4_1.1-30 rvest_1.0.3
[46] lifecycle_1.0.3 rstatix_0.7.0 googlesheets4_1.0.1
[49] MASS_7.3-58.1 zoo_1.8-11 scales_1.2.1
[52] ragg_1.2.3 hms_1.1.2 promises_1.2.0.1
[55] yaml_2.3.5 KMsurv_0.1-5 sass_0.4.2
[58] stringi_1.7.8 highr_0.9 boot_1.3-28
[61] systemfonts_1.0.4 rlang_1.0.6 pkgconfig_2.0.3
[64] evaluate_0.17 lattice_0.20-45 labeling_0.4.2
[67] htmlwidgets_1.5.4 tidyselect_1.2.0 plyr_1.8.7
[70] magrittr_2.0.3 R6_2.5.1 generics_0.1.3
[73] DBI_1.1.3 pillar_1.8.1 haven_2.5.1
[76] withr_2.5.0 abind_1.4-5 modelr_0.1.9
[79] crayon_1.5.2 car_3.1-0 utf8_1.2.2
[82] tzdb_0.3.0 rmarkdown_2.17 grid_4.2.1
[85] readxl_1.4.1 git2r_0.30.1 reprex_2.0.2
[88] digest_0.6.29 xtable_1.8-4 httpuv_1.6.6
[91] textshaping_0.3.6 munsell_0.5.0 viridisLite_0.4.1
[94] bslib_0.4.0