Nothing
echo_boot <- (count_pearsoncorr>0) echo_pformat <- (count_pformat>0) eval_seed <- (refs3 || refs10 || refs11)
```{whites, eval=FALSE, echo = refs3}
pearsoncorr <- function(data,i){ d <- data[i, ] return(cor(d, use="complete.obs", method="pearson")[1,2]) }
```{whites, eval=FALSE, echo = echo_pformat} # Function for the p-value format: pformat<-function(p){ if (p<0.001) return("<0.001") else return (round(p,3)) }
options(na.action=na.omit) # Analysis for each pair for (i in 1:ncol(combs)){ j = combs[1,i] k = combs[2,i] # Define relevant datasets for a pair datapair <- data.frame(df_cont[,j],df_cont[,k]) datapaircomplete <- datapair[complete.cases(datapair),] # Unique values unique1 <- length(unique(datapaircomplete[,1])) unique2 <- length(unique(datapaircomplete[,2])) # Consider pair only sample size >= 7 and uniques >=5 if (min(unique1,unique2) >= 5 && nrow(datapaircomplete)>=7){ a <- which(colnames(df_code)==colnames(df_cont)[j]) b <- which(colnames(df_code)==colnames(df_cont)[k]) cat("`j = `",a) cat("\\newline ") cat("`k =`",b) cat("\\newline ") cat("\\# `Columns:`") cat("\\newline ") cat("`cat(colnames(df)[j],\"and\",colnames(df)[k],fill=TRUE)`") cat("\\newline ") tryCatch({ # Define check function for Pearson check <- linearity(df_cont[,j],df_cont[,k]) }, error=function(e){cat("")}) tryCatch({ # Define check function for Spearman & Kendall checkm <- linearity(rank(df_cont[,j]),rank(df_cont[,k])) }, error=function(e){cat("")}) tryCatch({ pcorrelation <- cor(df_cont[,j],df_cont[,k], use="complete.obs", method="pearson") }, error=function(e){cat("")}) tryCatch({ spcorrelation <- cor(df_cont[,j],df_cont[,k], use="complete.obs", method="spearman") }, error=function(e){cat("")}) tryCatch({ ken <- cor(df_cont[,j],df_cont[,k], use="complete.obs", method="kendall") }, error=function(e){cat("")}) tryCatch({ if (check==TRUE && exists("pcorrelation")) { cat("`pcorrelation <- cor(df[,j],df[,k], use=\"complete.obs\", method=\"pearson\")`") cat("\\newline ") cat("`round(pcorrelation,4)` # `Pearson correlation coefficient`") cat("\\newline ") cat("`round(pcorrelation**2*100,1)` # `Variance explained by one variable (percent)`") cat("\\newline ") cat("\\newline ") } }, error=function(e){cat("")}) tryCatch({ if (check==TRUE && normality(df_cont[,j],df_cont[,k])==TRUE) { cat("`fisherci <- cor.test(df[,j],df[,k], method=\"pearson\")`") cat("\\newline ") cat("`c(round(fisherci$conf.int[1],4),round(fisherci$conf.int[2],4))` # `confidence interval`") cat("\\newline ") cat("`pformat(fisherci$p.value)` # `p-value`") cat("\\newline ") cat("\\newline ") } }, error=function(e){cat("")}) tryCatch({ if (check==TRUE && normality(df_cont[,j],df_cont[,k])==FALSE && nrow(datapaircomplete) < 800) { set.seed(seed) bootdata <- boot(datapaircomplete, statistic=pearsoncorr, R=1000) if (length(unique(round(bootdata$t,2)))>1){ cat("`temp <- data.frame(df[,j],df[,k])`") cat("\\newline ") cat("`temp2 <- temp[complete.cases(temp),]`") cat("\\newline ") cat("`set.seed(`",seed,"`)`") cat("\\newline ") cat("`bootdata <- boot(temp2, statistic=pearsoncorr, R=1000)`") cat("\\newline ") cat("`bootresult <- boot.ci(bootdata, type=\"bca\")`") cat("\\newline ") cat("`c(round(bootresult$bca[4],4),round(bootresult$bca[5],4))` # `bootstrap conf. interval`") cat("\\newline ") cat("\\newline ") }} }, error=function(e){cat("")}) tryCatch({ if ((check==TRUE && normality(df_cont[,j],df_cont[,k])==FALSE && exists("spearman")) || (check==FALSE && checkm==TRUE && exists("spearman"))) { cat("`spcorrelation <- cor(df[,j],df[,k], use=\"complete.obs\", method=\"spearman\")`") cat("\\newline ") cat("`round(spcorrelation,4)` # `Spearman correlation coefficient`") cat("\\newline ") cat("\\newline ") } }, error=function(e){cat("")}) tryCatch({ if ((check==TRUE && normality(df_cont[,j],df_cont[,k])==FALSE && exists("spearman")) || (check==FALSE && checkm==TRUE && exists("spearman"))) { spearmanci <- SpearmanRho(df_cont[,j],df_cont[,k], use="complete.obs", conf.level=0.95) spearmanci2 <- cor.test(df_cont[,j],df_cont[,k], method="spearman") cat("`spearmanci <- SpearmanRho(df[,j],df[,k], use=\"complete.obs\", conf.level=0.95)`") cat("\\newline ") if (exists("spearmanci") && !is.nan(spearmanci[2]) && !is.nan(spearmanci[3])) { cat("`c(round(spearmanci[2],4),round(spearmanci[3],4))` # `confidence interval`") cat("\\newline ") } cat("`spearmanci2 <- suppressWarnings(cor.test(df[,j],df[,k], method=\"spearman\"))`") cat("\\newline ") cat("`pformat(spearmanci2$p.value)` # `p-value`") cat("\\newline ") cat("\\newline ") } }, error=function(e){cat("")}) tryCatch({ if ((check==TRUE && normality(df_cont[,j],df_cont[,k])==FALSE && exists("ken")) || (check==FALSE && checkm==TRUE && exists("ken"))) { cat("`ken <- cor(df[,j],df[,k], use=\"complete.obs\", method=\"kendall\")`") cat("\\newline ") cat("`round(ken,4)` # `Kendall's tau correlation coefficient`") cat("\\newline ") cat("\\newline ") } }, error=function(e){cat("")}) tryCatch({ if ((check==TRUE && normality(df_cont[,j],df_cont[,k])==FALSE) || (check==FALSE && checkm==TRUE)) { kentest <- cor.test(df_cont[,j],df_cont[,k], method="kendall") cat("`kentest <- suppressWarnings(cor.test(df[,j],df[,k], method=\"kendall\"))`") cat("\\newline ") cat("`pformat(kentest$p.value)` # `p-value`") cat("\\newline ") cat("\\newline ") } }, error=function(e){cat("")}) tryCatch({ if (check==FALSE && checkm==FALSE){ tryCatch({ if (nrow(datapaircomplete) <= 100){ set.seed(seed) mic <- testforDEP(df_cont[,j],df_cont[,k], test="MIC", rm.na=TRUE, p.opt="MC", num.MC = 5000, set.seed = TRUE) cat("\\newline ") cat("`set.seed(`",seed,"`)`") cat("\\newline ") cat("`mic <- suppressWarnings(testforDEP(df[,j],df[,k], test=\"MIC\", set.seed = TRUE, rm.na=TRUE, num.MC = 5000, p.opt=\"MC\"))`") cat("\\newline ") cat("`micval <- minerva::mine(df[,j],df[,k], na.rm=TRUE)$MIC`") cat("\\newline ") cat("`round(micval,3)` # `Maximal Information Coefficient (MIC)`") cat("\\newline ") cat("`round(micval*100,2)` # `Variance explained by one variable (percent)`") cat("\\newline ") cat("`pformat(mic@p_value)` # `p-value MIC`") cat("\\newline ") cat("\\newline ") } else if (nrow(datapaircomplete) < 5000){ mic <- testforDEP(df_cont[,j],df_cont[,k], test="MIC", rm.na=TRUE, p.opt="table") cat("\\newline ") cat("`mic <- testforDEP(df[,j],df[,k], test=\"MIC\", rm.na=TRUE, p.opt=\"table\")`") cat("\\newline ") cat("`micval <- minerva::mine(df[,j],df[,k], na.rm=TRUE)$MIC`") cat("\\newline ") cat("`round(micval,3)` # `Maximal Information Coefficient (MIC)`") cat("\\newline ") cat("`round(micval*100,2)` # `Variance explained by one variable (percent)`") cat("\\newline ") cat("`pformat(mic@p_value)` # `p-value MIC`") cat("\\newline ") cat("\\newline ") } }, error=function(e){cat("")} ) tryCatch({ set.seed(seed) dist <- dcor.test(datapaircomplete[,1],datapaircomplete[,2], R=100) cat("`temp <- data.frame(df[,j],df[,k])`") cat("\\newline ") cat("`temp2 <- temp[complete.cases(temp),]`") cat("\\newline ") cat("`set.seed(`",seed,"`)`") cat("\\newline ") cat("`dist <- dcor.test(temp2[,1],temp2[,2], R=100)`") cat("\\newline ") cat("`round(dist$statistic,3)` # `Distance correlation coefficient`") cat("\\newline ") cat("`pformat(dist$p.value)` # `p-value`") cat("\\newline ") cat("\\newline ") }, error=function(e){cat("")}) } }, error=function(e){cat("")}) cat("`scatterplotMatrix(~df[,j]+df[,k], smooth=list(smoother=loessLine, spread=FALSE, lty.smooth=1, lwd.smooth=1.5, col.smooth=\"#396e9f\"), var.labels=colnames(df)[c(j,k)],`") cat("\\newline ") cat("`main=\"Enhanced Scatterplots\", col = \"`#`2fa42d\")`") cat("\\newline ") cat("\\newline ") rm(list=c("check", "checkm","pcorrelation", "spcorrelation", "ken", "dist", "mic", "fisherci", "bootresult", "spearmanci", "spearmanci2", "kentest")) }}
cat("\n# References", fill=TRUE)
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