baseModel
2023-05-08
library(omicade4)## Loading required package: ade4library(mogsa)
library(RSpectra)
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(glmnet)## Loading required package: Matrix## Loaded glmnet 4.1-7library(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:mogsa':
##
## combine## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(cowplot)##
## Attaching package: 'cowplot'## The following object is masked from 'package:lubridate':
##
## stamplibrary(ggplot2)setwd("/Users/rnili/Desktop/repo/gitLab/cmi-pb-multiomics-main/results/main/cmi_pb_datasets/processed/harmonized")
# Read in metadata
meta.2020<-read.table('clinical_metadata.2020.tsv',sep='\t',header=TRUE,stringsAsFactors=TRUE,row.names=1)
meta.2021<-read.table('clinical_metadata.2021.tsv',sep='\t',header=TRUE,stringsAsFactors=TRUE,row.names=1)files from imputed data is already normalised so we don’t need to normalise them
# imputed_dir is path to local drive where data is saved
setwd("/Users/rnili/Desktop/repo/gitLab/cmi-pb-multiomics-main/results/main/cmi_pb_datasets/processed/imputed")
# Import imputed datasets
rnaseq_baseline_mat_imputed_20 <- read.csv('rnaseq_baseline_mat_imputed_20_051022.csv',row.names=1)
cytof_baseline_mat_imputed_20 <- read.csv('cytof_baseline_mat_imputed_20_051022.csv',row.names=1)
olink_baseline_mat_imputed_20 <- read.csv('olink_baseline_mat_imputed_20_051022.csv',row.names=1)
abtiters_baseline_mat_imputed_20 <- read.csv('abtiters_baseline_mat_imputed_20_051022.csv',row.names=1)
rnaseq_baseline_mat_imputed_21 <- read.csv('rnaseq_baseline_mat_imputed_21_051022.csv',row.names=1)
cytof_baseline_mat_imputed_21 <- read.csv('cytof_baseline_mat_imputed_21_051022.csv',row.names=1)
olink_baseline_mat_imputed_21 <- read.csv('olink_baseline_mat_imputed_21_051022.csv',row.names=1)
abtiters_baseline_mat_imputed_21 <- read.csv('abtiters_baseline_mat_imputed_21_051022.csv',row.names=1)tasks_seq<-c('ENSG00000277632','ENSG00000136244','ENSG00000100906','ENSG00000229807')
names(rnaseq_baseline_mat_imputed_20[tasks_seq])## [1] "ENSG00000277632" "ENSG00000136244" "ENSG00000100906" "ENSG00000229807"distPlot <- function(col, df){
p <-
ggplot(df) +
aes_string(col)
if(is.numeric(df[[col]])) {
p <- p + geom_density()
} else {
p <- p + geom_bar()
}
}
distPlots <- lapply(tasks_seq, distPlot, df=rnaseq_baseline_mat_imputed_20)## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.plot_grid(plotlist = distPlots)
# Get age at boost
library(lubridate)
meta.2020$date_of_boost<-parse_date_time(meta.2020$date_of_boost,"ymd")
meta.2020$year_of_birth<-parse_date_time(meta.2020$year_of_birth,"ymd")
meta.2020$age_at_boost<- as.numeric(round(difftime(meta.2020$date_of_boost,
meta.2020$year_of_birth,units="weeks")/52,2))
meta.2021$date_of_boost<-parse_date_time(meta.2021$date_of_boost,"ymd")
meta.2021$year_of_birth<-parse_date_time(meta.2021$year_of_birth,"ymd")
meta.2021$age_at_boost<- as.numeric(round(difftime(meta.2021$date_of_boost,
meta.2021$year_of_birth,units="weeks")/52,2))
meta <- rbind(meta.2020[c("age_at_boost", "infancy_vac", "biological_sex")], meta.2021[c("age_at_boost", "infancy_vac", "biological_sex")])
meta$infancy_vac <- as.numeric(meta$infancy_vac)
meta$biological_sex <- as.numeric(meta$biological_sex)
colnames(meta)## [1] "age_at_boost" "infancy_vac" "biological_sex"All data from abtiter been log2 transformed. Why? to make them normally distributed since our glm uses gaussian as the distribution kernel?
# Get the y data
dataY = read.table('/Users/rnili/Desktop/repo/gitLab/cmi-pb-multiomics-main/results/yData_task_matrix.common_names.mfi_raw.tsv',sep='\t',header=TRUE,stringsAsFactors=TRUE,row.names=1)
dataY <- dataY[c("IgG.PT.day14", "ENSG00000277632.day3", "Monocytes.day1")]
colnames(dataY) <- c("IgG.PT.day14", "CCL3.day3", "Monocytes.day1")
# dataY$IgG.PT.day14 <- log2(dataY[,'IgG.PT.day14']+1)
# dataY$CCL3.day3 <- log2(dataY[,'CCL3.day3']+1)
dataY$subj_id <- rownames(dataY)
colnames(dataY)## [1] "IgG.PT.day14" "CCL3.day3" "Monocytes.day1" "subj_id"distPlot <- function(col, df){
p <-
ggplot(df) +
aes_string(col)
if(is.numeric(df[[col]])) {
p <- p + geom_density()
} else {
p <- p + geom_bar()
}
}
typeof(dataY)## [1] "list"#
distPlots <- lapply(c("IgG.PT.day14", "CCL3.day3", "Monocytes.day1"), distPlot, df=dataY)
plot_grid(plotlist = distPlots)## Warning: Removed 4 rows containing non-finite values (`stat_density()`).## Warning: Removed 22 rows containing non-finite values (`stat_density()`).## Warning: Removed 41 rows containing non-finite values (`stat_density()`).
colnames(rnaseq_baseline_mat_imputed_20)[which(names(rnaseq_baseline_mat_imputed_20) == "ENSG00000277632")] <- "CCL3"
colnames(rnaseq_baseline_mat_imputed_21)[which(names(rnaseq_baseline_mat_imputed_21) == "ENSG00000277632")] <- "CCL3"
rnaDf <- rbind(rnaseq_baseline_mat_imputed_20["CCL3"], rnaseq_baseline_mat_imputed_21["CCL3"])
abtiterDf <- rbind(abtiters_baseline_mat_imputed_20["IgG.PT"], abtiters_baseline_mat_imputed_21["IgG.PT"])
cytofDf <- rbind(cytof_baseline_mat_imputed_20["Monocytes"], cytof_baseline_mat_imputed_21["Monocytes"])
dataDf1 <- merge(rnaDf, abtiterDf, by='row.names', all=T)
colnames(dataDf1)[1] <- "subj_id"
dataDf2 <- merge(cytofDf, meta, by='row.names', all=T)
colnames(dataDf2)[1] <- "subj_id"
dataX <- merge(dataDf1, dataDf2, by='subj_id', all=T)
dataDf <- merge(dataX, dataY, by='subj_id', all=T)
rownames(dataDf) <- dataDf$subj_id
dataDf## subj_id CCL3 IgG.PT Monocytes age_at_boost infancy_vac
## 1 1 5.333531 2.24397113 7.5260062 30.80 2
## 10 10 4.101398 0.04154169 13.4642547 34.68 2
## 11 11 4.542939 1.77698271 10.7905171 30.76 2
## 12 12 NA NA NA 34.68 2
## 13 13 3.399718 1.36806068 5.0304685 19.63 1
## 14 14 NA NA NA 23.70 2
## 15 15 4.835874 0.38100554 15.6373679 27.71 2
## 16 16 NA NA NA 29.66 2
## 17 17 4.512669 3.45553366 14.4574172 36.82 2
## 18 18 4.083384 1.68734290 0.9568001 19.73 1
## 19 19 4.459300 2.16297720 13.5202756 22.81 2
## 2 2 NA NA NA 51.25 2
## 20 20 3.963474 2.55022450 21.3991621 35.78 2
## 21 21 3.823138 2.77448207 15.2266453 33.77 2
## 22 22 4.462314 2.16393744 11.9355890 31.77 2
## 23 23 4.421223 0.40194862 11.7041207 25.82 2
## 24 24 3.106516 0.26992483 11.5201600 24.79 2
## 25 25 4.042732 0.74967184 13.4709985 28.80 2
## 26 26 4.735685 0.72685734 5.5320667 33.85 2
## 27 27 3.782618 0.77080873 13.8965682 19.80 1
## 28 28 NA NA NA 34.85 2
## 29 29 4.447249 0.99688351 16.0947648 19.80 1
## 3 3 4.599972 1.06789053 13.0245725 33.89 2
## 30 30 NA NA NA 28.84 2
## 31 31 4.693487 0.16545091 4.8790233 27.83 2
## 32 32 5.779549 1.40368966 13.5492381 19.88 1
## 33 33 5.311794 0.63824158 7.6518328 26.87 2
## 34 34 NA NA NA 33.93 2
## 35 35 5.434762 1.19302502 13.5420630 25.86 2
## 36 36 3.016496 1.04080942 11.2437986 19.88 1
## 37 37 NA NA NA 18.91 1
## 38 38 3.017922 0.77149215 13.7721006 19.88 1
## 39 39 NA NA NA 31.92 2
## 4 4 4.094574 1.60723380 5.4908380 28.76 2
## 40 40 NA NA NA 22.89 2
## 41 41 NA NA NA 31.96 2
## 42 42 5.230280 1.11654738 10.6095832 19.92 1
## 43 43 3.871351 1.00310977 13.6337391 18.91 1
## 44 44 4.518409 0.92865661 23.1032762 18.91 1
## 45 45 NA NA NA 19.98 1
## 46 46 NA NA NA 18.91 1
## 47 47 6.900831 2.65845646 10.1292397 20.98 1
## 48 48 6.731536 0.04154169 14.9597507 19.11 1
## 49 49 NA NA NA 20.11 1
## 5 5 4.916572 2.26243525 13.1258248 25.75 2
## 50 50 8.091139 0.50769248 9.3613078 19.98 1
## 51 51 NA NA NA 19.98 1
## 52 52 7.288949 1.97201547 28.5490352 19.07 1
## 53 53 9.867189 4.17711376 12.8294284 19.07 1
## 54 54 NA NA NA 20.11 1
## 55 55 NA NA NA 20.11 1
## 56 56 NA NA NA 20.15 1
## 57 57 NA NA NA 21.15 1
## 58 58 NA NA NA 20.15 1
## 59 59 NA NA NA 20.15 1
## 6 6 4.575554 0.27896948 22.7930283 28.87 2
## 60 60 NA NA NA 20.15 1
## 61 61 12.042340 1.00000000 12.8005992 32.38 2
## 62 62 6.937333 1.07602222 13.5692650 25.99 2
## 63 63 6.691953 1.79570532 15.3000000 23.98 2
## 64 64 6.760926 2.04705167 12.9000000 25.99 2
## 65 65 7.605079 0.80140188 15.8000000 29.02 2
## 66 66 5.042425 1.32512343 15.8000000 43.07 2
## 67 67 5.889230 0.70618673 34.6000000 47.24 2
## 68 68 5.853946 1.05459788 13.5000000 47.24 2
## 69 69 5.528321 1.07297696 13.1000000 29.17 2
## 7 7 NA NA NA 35.97 2
## 70 70 5.815166 0.47027882 32.3000000 21.15 1
## 71 71 5.987707 2.90659418 12.2000000 21.15 1
## 72 72 5.880881 2.08840338 19.9000000 28.25 2
## 73 73 4.529196 1.88327381 15.7000000 24.23 2
## 74 74 3.871745 0.52016941 36.3000000 24.23 2
## 75 75 5.678804 0.17736981 14.4559889 21.22 1
## 76 76 4.663800 3.53643892 18.0000000 21.22 1
## 77 77 8.217396 0.69081102 28.8000000 31.32 2
## 78 78 7.151839 0.99519368 29.0000000 26.30 2
## 79 79 6.969300 2.32256773 41.5000000 32.32 2
## 8 8 NA NA NA 34.27 2
## 80 80 7.178107 0.46841860 21.9000000 27.30 2
## 81 81 10.726589 1.81601157 34.9000000 26.30 2
## 82 82 9.413611 0.76723104 22.5000000 21.28 1
## 83 83 8.588209 1.26356779 32.4000000 20.34 1
## 84 84 7.593122 0.28053529 18.9000000 22.34 1
## 85 85 4.937862 0.33276350 19.7000000 19.39 1
## 86 86 5.362224 0.30403242 23.5000000 21.40 1
## 87 87 5.295650 1.07997538 22.9000000 19.39 1
## 88 88 5.567850 1.04544297 17.6000000 19.39 1
## 89 89 5.130601 0.06807137 15.7000000 22.49 1
## 9 9 4.238481 0.28783862 5.7229578 20.63 1
## 90 90 4.837136 0.33985853 20.6000000 20.49 1
## 91 91 4.140370 0.92749667 25.4000000 21.49 1
## 92 92 4.623047 0.23829881 40.6000000 19.54 1
## 93 93 4.616769 1.57747907 16.3000000 23.56 1
## 94 94 4.785446 2.65824315 26.2000000 20.55 1
## 95 95 4.878235 2.29792327 17.3000000 21.55 1
## 96 96 4.356355 0.76421696 34.2000000 19.54 1
## biological_sex IgG.PT.day14 CCL3.day3 Monocytes.day1
## 1 1 199.517666 369 NA
## 10 1 3.821589 179 NA
## 11 1 414.513947 144 7.257095
## 12 2 96.874274 NA NA
## 13 2 58.061932 911 NA
## 14 2 269.001265 NA NA
## 15 2 100.489455 277 10.585489
## 16 1 86.675397 NA NA
## 17 1 168.900882 295 16.401488
## 18 1 182.906088 99 NA
## 19 2 170.183735 133 NA
## 2 1 NA NA NA
## 20 1 133.269594 480 26.605583
## 21 2 35.368851 238 34.812168
## 22 1 89.989653 82 NA
## 23 1 132.219593 133 NA
## 24 1 23.631765 150 NA
## 25 1 23.099432 188 NA
## 26 1 45.748957 148 16.108508
## 27 1 43.959688 192 NA
## 28 2 116.478279 NA NA
## 29 2 75.090769 284 25.083209
## 3 1 129.197956 236 NA
## 30 1 62.699730 NA NA
## 31 1 33.003075 476 8.545243
## 32 2 272.717237 653 NA
## 33 2 13.370949 749 17.703064
## 34 1 172.314446 NA NA
## 35 2 77.340488 218 NA
## 36 1 41.400275 191 17.446750
## 37 1 NA NA NA
## 38 1 121.163359 62 NA
## 39 1 71.881127 NA NA
## 4 2 144.885339 133 7.211965
## 40 1 145.035713 NA NA
## 41 2 198.590973 NA NA
## 42 1 162.631291 504 NA
## 43 1 26.508813 318 NA
## 44 1 293.880891 147 35.241054
## 45 1 38.499070 NA 13.545087
## 46 1 201.239119 NA 30.018793
## 47 1 280.892693 9238 8.663056
## 48 1 0.536000 3997 18.252658
## 49 1 139.999552 NA 10.347126
## 5 2 97.743258 187 NA
## 50 1 52.882062 8629 NA
## 51 2 73.573545 NA NA
## 52 2 109.997984 10181 23.512649
## 53 1 219.279332 6593 NA
## 54 1 172.363131 NA NA
## 55 1 120.652266 NA 15.334381
## 56 1 202.521120 NA NA
## 57 1 83.003313 NA NA
## 58 1 223.309447 NA NA
## 59 1 352.958671 NA NA
## 6 1 167.496355 216 41.380502
## 60 2 21.183724 NA NA
## 61 1 304.000000 420 NA
## 62 1 1548.451277 1532 NA
## 63 1 1916.701277 615 18.700000
## 64 2 558.451277 644 13.800000
## 65 2 1837.750000 2353 20.400000
## 66 1 2059.500000 989 13.900000
## 67 1 2128.450062 453 31.100000
## 68 2 1565.447441 324 15.500000
## 69 1 1150.200062 792 18.900000
## 7 1 92.687631 NA NA
## 70 2 201.201277 360 46.100000
## 71 1 916.951277 942 18.200000
## 72 1 1736.250000 302 27.500000
## 73 1 1349.697441 420 23.400000
## 74 1 385.697441 207 50.000000
## 75 1 136.250000 242 NA
## 76 1 1247.000000 205 22.300000
## 77 2 506.500000 1928 31.000000
## 78 1 831.500000 312 35.700000
## 79 2 1019.250000 2348 52.600000
## 8 1 NA NA NA
## 80 1 248.500000 3592 26.400000
## 81 2 516.750000 2426 35.600000
## 82 1 NA 8124 20.500000
## 83 1 436.450062 130 27.700000
## 84 1 474.951277 595 25.100000
## 85 1 168.750000 356 18.700000
## 86 1 615.200062 571 25.200000
## 87 2 NA 1068 22.500000
## 88 2 NA 4288 20.200000
## 89 1 133.947441 664 16.600000
## 9 2 17.409695 334 NA
## 90 1 221.950062 439 21.600000
## 91 2 487.447441 175 40.400000
## 92 1 377.750000 275 33.000000
## 93 1 645.250000 291 15.000000
## 94 2 993.750000 140 39.500000
## 95 1 1078.697441 210 23.500000
## 96 2 386.000000 177 35.000000###Distination plot
distPlot <- function(col, df){
p <-
ggplot(df) +
aes_string(col)
if(is.numeric(df[[col]])) {
p <- p + geom_density()
} else {
p <- p + geom_bar()
}
}
names(dataDf)[2:length(dataDf)]## [1] "CCL3" "IgG.PT" "Monocytes" "age_at_boost"
## [5] "infancy_vac" "biological_sex" "IgG.PT.day14" "CCL3.day3"
## [9] "Monocytes.day1"distPlots <- lapply(names(dataDf)[2:length(dataDf)], distPlot, df=dataDf)
plot_grid(plotlist = distPlots)## Warning: Removed 24 rows containing non-finite values (`stat_density()`).
## Removed 24 rows containing non-finite values (`stat_density()`).
## Removed 24 rows containing non-finite values (`stat_density()`).## Warning: Removed 6 rows containing non-finite values (`stat_density()`).## Warning: Removed 24 rows containing non-finite values (`stat_density()`).## Warning: Removed 43 rows containing non-finite values (`stat_density()`).
Test model quality
options(warn=-1)
xCols = c("CCL3", "IgG.PT", "Monocytes", "age_at_boost", "infancy_vac", "biological_sex")
yCols = c("IgG.PT.day14", "CCL3.day3", "Monocytes.day1")
pred_cor <- data.frame(matrix(nrow=length(yCols), ncol=3))
rownames(pred_cor) <- yCols
colnames(pred_cor) <- c('pearson.cor.pred.true', 'spearman.cor.pred.true', 'ranked.spearman.cor.pred.true')
rownames(dataDf) <- attr(dataDf, "row.names")
print(typeof(row.names(dataDf)))## [1] "character"#
for (i in 1:length(yCols)){
all_preds <- c()
all_true <- c()
set.seed(1)
filteredY <- na.omit(dataDf[yCols[i]])
filteredX <- na.omit(dataDf[xCols])
row_int <- intersect(rownames(filteredY), rownames(filteredX))
for (j in 1:length(row_int)){
train <- row_int[-c(j)]
xData <- filteredX[train, xCols]
yData <- filteredY[train,]
a1= nrow(xData[train,])
a2= nrow(xData[train,]-1)
allidx = row_int
predidx = setdiff(allidx, train)
# create lasso model
cvfit_out <- cv.glmnet(x=as.matrix(xData), yData, family='gaussian',
alpha=1, nfolds=nrow(xData[train,]))
preds <- predict(cvfit_out, newx = as.matrix(data.frame(filteredX[predidx,])), s='lambda.min')
all_preds <- c(all_preds, preds)
all_true<- c(all_true, filteredY[predidx, yCols[i]])
}
b = data_frame(all_preds, rank(all_preds,na.last="keep",ties.method="min"), all_true, rank(all_true,na.last="keep",ties.method="min"))
pred_cor[yCols[i],'pearson.cor.pred.true'] <- cor(all_preds,all_true)
pred_cor[yCols[i],'spearman.cor.pred.true'] <- cor(all_preds,all_true, method="spearman")
pred_cor[yCols[i],'ranked.spearman.cor.pred.true'] <- cor(rank(all_preds,na.last="keep",ties.method="min"),rank(all_true,na.last="keep",ties.method="min"), method="spearman")
}
pred_cor## pearson.cor.pred.true spearman.cor.pred.true
## IgG.PT.day14 0.2878062 0.4298137
## CCL3.day3 0.4554237 0.6179585
## Monocytes.day1 0.8026556 0.8240516
## ranked.spearman.cor.pred.true
## IgG.PT.day14 0.4298137
## CCL3.day3 0.6179585
## Monocytes.day1 0.8240516typeof(pred_cor)## [1] "list"For each model, can assess which features contribute to non-zero coefficients
Consider only choosing models for follow-on analysis that show good correlation scores
size <- length(yCols)
all_models_coef<-vector(mode='list',length=size)
all_models_names<-vector(mode='list',length=size)
all_models<-vector(mode='list',length=size)
for (i in 1:length(yCols)){
set.seed(1)
filteredY <- na.omit(dataDf[yCols[i]])
filteredX <- na.omit(dataDf[xCols])
row_int <- intersect(rownames(filteredY), rownames(filteredX))
# create lasso model
suppressWarnings(cvfit_out <- cv.glmnet(x=as.matrix(filteredX[row_int,]), as.matrix(filteredY[row_int,]), family='gaussian', alpha=1, nfolds=nrow(filteredX[row_int,])))
plot(cvfit_out)
all_models_coef[i]=list(coef(cvfit_out, s = 'lambda.min')[coef(cvfit_out, s = 'lambda.min')[,1]!= 0])
all_models_names[i]=list(rownames(coef(cvfit_out, s = 'lambda.min'))[coef(cvfit_out, s = 'lambda.min')[,1]!= 0])
}## <sparse>[ <logic> ]: .M.sub.i.logical() maybe inefficient
## <sparse>[ <logic> ]: .M.sub.i.logical() maybe inefficient
## <sparse>[ <logic> ]: .M.sub.i.logical() maybe inefficient
names(all_models_coef) <- yCols
names(all_models_names) <- yColsfor (i in 1:size){
all_models[[i]] = data.frame(cbind(all_models_names[[i]],all_models_coef[[i]]))
colnames(all_models[[i]])<-c("Variable","Coefficient")
all_models[[i]]$Coefficient<-as.numeric(all_models[[i]]$Coefficient)
all_models[[i]]$Coefficient=round(all_models[[i]]$Coefficient,3)
all_models[[i]]<-all_models[[i]] %>% arrange(desc(abs(Coefficient)))
}
names(all_models)<-yCols
all_models## $IgG.PT.day14
## Variable Coefficient
## 1 (Intercept) -672.811
## 2 IgG.PT 90.869
## 3 biological_sex -79.601
## 4 CCL3 33.668
## 5 age_at_boost 27.876
## 6 Monocytes 13.367
##
## $CCL3.day3
## Variable Coefficient
## 1 (Intercept) -713.729
## 2 infancy_vac -691.341
## 3 CCL3 534.168
## 4 IgG.PT 10.866
## 5 age_at_boost -0.127
##
## $Monocytes.day1
## Variable Coefficient
## 1 (Intercept) 7.845
## 2 infancy_vac 4.884
## 3 biological_sex 1.847
## 4 CCL3 -1.499
## 5 Monocytes 1.068
## 6 IgG.PT 0.509
## 7 age_at_boost -0.273# library(capture)
setwd("/Users/rnili/Desktop/repo/gitLab/cmi-pb-multiomics-main/rasteh/models")
a= append(list(pred_cor),all_models)
names(a)[1] <- 'pred_cor'
sink("allModels_predCor_base_notNormalised.txt")
print(a)## $pred_cor
## pearson.cor.pred.true spearman.cor.pred.true
## IgG.PT.day14 0.2878062 0.4298137
## CCL3.day3 0.4554237 0.6179585
## Monocytes.day1 0.8026556 0.8240516
## ranked.spearman.cor.pred.true
## IgG.PT.day14 0.4298137
## CCL3.day3 0.6179585
## Monocytes.day1 0.8240516
##
## $IgG.PT.day14
## Variable Coefficient
## 1 (Intercept) -672.811
## 2 IgG.PT 90.869
## 3 biological_sex -79.601
## 4 CCL3 33.668
## 5 age_at_boost 27.876
## 6 Monocytes 13.367
##
## $CCL3.day3
## Variable Coefficient
## 1 (Intercept) -713.729
## 2 infancy_vac -691.341
## 3 CCL3 534.168
## 4 IgG.PT 10.866
## 5 age_at_boost -0.127
##
## $Monocytes.day1
## Variable Coefficient
## 1 (Intercept) 7.845
## 2 infancy_vac 4.884
## 3 biological_sex 1.847
## 4 CCL3 -1.499
## 5 Monocytes 1.068
## 6 IgG.PT 0.509
## 7 age_at_boost -0.273sink()
# lapply(append(pred_cor,all_models), write, "allModels_predCor_base.txt", append=TRUE, ncolumns=1000)
# saveRDS(all_models, file="base_allModels.RData")
# load(file="base_allModels.RData")Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.