version 1.2

DESCRIPTION

@@ -1,9 +1,9 @@
 Package: catmap
-Version: 1.1
+Version: 1.2
 Date: 2007-06-09
 Title: Case-control And Tdt Meta-Analysis Package
 Author: Kristin K. Nicodemus <knicodem@jhsph.edu> 
 Maintainer: Kristin K. Nicodemus <knicodem@jhsph.edu> 
 Description: catmap is an R package that conducts fixed-effects (inverse variance) and random-effects (DerSimonian and Laird, 1986) meta-analyses of case-control or family-based (TDT) genetic data; in addition, it performs meta-analyses combining these two types of study designs.  The fixed-effects model was first described by Kazeem and Farrell (2005); the random-effects model is described in Nicodemus (submitted).}
 License: GPL (version 2 or newer) 
-Packaged: Tue Jun 12 07:37:23 2007; Owner
+Packaged: Wed Jun 20 13:12:11 2007; Owner

R/catmap.R

@@ -46,10 +46,18 @@ seLogOR<-ccse
 comvarlogOR<-ccvar
 }
 if(nlevels(a1$lev)==2){
-weight<-c(tdtweight, ccweight)
-logOR<-c(tdtlogOR, cclogOR)
-seLogOR<-c(tdtse, ccse)
-comvarlogOR<-c(tdtvar, ccvar)
+studyorder<-cbind(1:nrow(a1), a1$study)
+studyorder1<-studyorder[order(studyorder[,2]),]
+weight1<-c(tdtweight, ccweight)
+logOR1<-c(tdtlogOR, cclogOR)
+seLogOR1<-c(tdtse, ccse)
+comvarlogOR1<-c(tdtvar, ccvar)
+studyorder2<-cbind(studyorder1, weight1, logOR1, seLogOR1, comvarlogOR1)
+studyorder3<-studyorder2[order(studyorder2[,1]),]
+weight<-studyorder3[,3]
+logOR<-studyorder3[,4]
+seLogOR<-studyorder3[,5]
+comvarlogOR<-studyorder3[,6]
 }
 
 combinedLogOR<-((sum(weight*logOR))/sum(weight))
@@ -134,7 +142,12 @@ cat(results, sep="\n")
 sink()
 }
 
-return(comvarlogOR, combinedLogOR, combinedOR, combinedSeLogOR, weight, logOR, combinedVarLogOR, combinedChisq, combinedValue, combinedPvalue, lbci, ubci, combinedCI, SeLogOR, lbci.fe, ubci.fe, het.df, chisqHet, combinedHetValue, heterogeneityPvalue, tau2, weight.dsl, logOR.dsl, OR.dsl, seLogOR.dsl, varLogOR.dsl, lbci.dsl, ubci.dsl, ci.dsl, chisq.dsl, value.dsl, pvalue.dsl, studyname, a1, quantilenorm, ci, dataset)
+if(tau2 <=0){
+return(comvarlogOR, combinedLogOR, combinedOR, combinedSeLogOR, weight, logOR, combinedVarLogOR, combinedChisq, combinedValue, combinedPvalue, lbci, ubci, combinedCI, SeLogOR, lbci.fe, ubci.fe, het.df, chisqHet, combinedHetValue, heterogeneityPvalue, tau2, studyname, a1, quantilenorm, ci, dataset) 
+}
+if(tau2 > 0){
+return(comvarlogOR, combinedLogOR, combinedOR, combinedSeLogOR, weight, logOR, combinedVarLogOR, combinedChisq, combinedValue, combinedPvalue, lbci, ubci, combinedCI, SeLogOR, lbci.fe, ubci.fe, het.df, chisqHet, combinedHetValue, heterogeneityPvalue, tau2, studyname, a1, quantilenorm, ci, dataset, weight.dsl, logOR.dsl, OR.dsl, seLogOR.dsl, varLogOR.dsl, lbci.dsl, ubci.dsl, ci.dsl, chisq.dsl, value.dsl, pvalue.dsl)
+}
 }
 
 #sensitivity analysis
@@ -206,12 +219,16 @@ sink()
 
 #random-effects sensitivity
 
+if (re.sense==TRUE & catmapobject$tau2 <=0){
+cat("NOTICE: tau2 is less than or equal to 0;\n no random effects estimates will be calculated.\n")
+}
+
 if (re.sense==TRUE & catmapobject$tau2 > 0){
 srplot<-matrix(0,nrow(catmapobject$a1),3)
 for(r in 1:nrow(catmapobject$a1)){
 sr.weight<-catmapobject$weight[-r]
 sr.logOR<-catmapobject$logOR[-r]
-sr.comvarlogOR<- catmapobject$comvarlogOR[-r]
+sr.comvarlogOR<-catmapobject$comvarlogOR[-r]
 
 sr.combinedLogOR<-((sum(sr.weight*sr.logOR))/sum(sr.weight))
 sr.combinedOR<-exp(sr.combinedLogOR)
@@ -242,7 +259,9 @@ sr.heterogeneityPvalue<-(1-sr.combinedHetValue)
 
 sr.tau2<-((sr.chisqHet-sr.df)/(sum(sr.weight)-(sum(sr.weight^2)/(sum(sr.weight)))))
 
-if (sr.tau2 > 0){
+if (sr.tau2 <=0){
+sr.tau2<-0
+}
 srweight.dsl<-(1/(sr.comvarlogOR+sr.tau2))
 srlogOR.dsl<-((sum(srweight.dsl*sr.logOR))/(sum(srweight.dsl)))
 srOR.dsl<-exp(srlogOR.dsl)
@@ -254,7 +273,6 @@ srci.dsl<-c(srlbci.dsl, srubci.dsl)
 srchisq.dsl<-(((srlogOR.dsl-0)^2)/srvarLogOR.dsl)
 srvalue.dsl<-pchisq(srchisq.dsl, df=1)
 srpvalue.dsl<-(1-srvalue.dsl)
-}
 
 srstudy.removed<-paste("Study Removed =", catmapobject$studyname[r], sep=" ")
 srtable.header<-c("Q Statistic (Heterogeneity) Chi-Square", "Q Statistic (Heterogeneity) p-value", "DerSimonian & Laird Random-Effects OR", "DerSimonian & Laird Random-Effects Lower Bound CI", "DerSimonian & Laird Random-Effects Chi-Square", "DerSimonian & Laird Random-Effects p-value")
@@ -289,6 +307,10 @@ sink()
 ci100<- catmapobject$ci*100
 
 #create fixed-effects sensitivity plots
+if (fe.sense==FALSE & fe.senseplot==TRUE){
+cat("NOTICE: No fixed-efffects sensitivity analyses have been performed. \n Please set fe.sense==TRUE and try again.\n")
+}
+
 if (fe.senseplot==TRUE){
 if(catmapobject$dataset!=catmapdata){
 pdf(file=(paste(catmapobject$dataset, "fixed.effects.sensitivity.plot.pdf", sep=".")))
@@ -320,12 +342,18 @@ segments(sfplot[z,2],sfpstudy[z],sfplot[z,3],sfpstudy[z], bg="black", col="black
 sfpstudyname<-paste("Study Removed:", catmapobject$studyname[z], sep="\n")
 mtext(paste(sfpstudyname),side=2, at=sfpstudy[z], cex=0.80)
 }
-dev.off()
+#dev.off()
 }
 
 ci100<- catmapobject$ci*100
 
 #create random-effects sensitivity plots
+if (re.sense==FALSE & re.senseplot==TRUE){
+cat("NOTICE: No random-efffects sensitivity analyses have been performed. \n Please set re.sense==TRUE and try again.\n")
+}
+if(fe.senseplot==TRUE){
+dev.off()
+}
 if (re.senseplot==TRUE & catmapobject$tau2 > 0){
 if(catmapobject$dataset!=catmapdata){
 pdf(file=(paste(catmapobject$dataset, "random.effects.sensitivity.plot.pdf", sep=".")))
@@ -357,11 +385,11 @@ segments(srplot[y,2],srpstudy[y],srplot[y,3],srpstudy[y], bg="black", col="black
 srpstudyname<-paste("Study Removed:", catmapobject$studyname[y], sep="\n")
 mtext(paste(srpstudyname),side=2, at=srpstudy[y], cex=0.80)
 }
-#dev.off()
+graphics.off()
 }
 }
 
-catmap.cumulative<-function(catmapobject, fe.cumulative, fe.cumplot, re.cumulative, re.cumplot){
+catmap.cumulative<-function(catmapobject, fe.cumulative, re.cumulative, fe.cumplot, re.cumplot){
 
 #fixed-effect cumulative meta analyses
 ci100<- catmapobject$ci*100
@@ -430,6 +458,10 @@ sink()
 
 #random-effect cumulative meta analyses
 
+if(re.cumulative==TRUE & catmapobject$tau2 <= 0){
+cat("NOTICE: tau2 is less than or equal to 0;\n no random effects estimates will be calculated.\n")
+}
+
 if (re.cumulative==TRUE & catmapobject$tau2 > 0){
 crplot<-matrix(0,(nrow(catmapobject$a1)-1),3)
 for(u in 2:nrow(catmapobject$a1)){
@@ -467,7 +499,10 @@ cr.tau2c<-(cr.chisqHet-cr.df)/(sum(cr.weight)-(sum(cr.weight^2)/(sum(cr.weight))
 
 cr.tau2<-max(0,cr.tau2c)
 
-if (cr.tau2 >= 0){
+if (cr.tau2 <=0){
+cr.tau2<-0
+}
+
 crweight.dsl<-(1/(cr.comvarlogOR+cr.tau2))
 crlogOR.dsl<-((sum(crweight.dsl*cr.logOR))/(sum(crweight.dsl)))
 crOR.dsl<-exp(crlogOR.dsl)
@@ -479,7 +514,6 @@ crci.dsl<-c(crlbci.dsl, crubci.dsl)
 crchisq.dsl<-(((crlogOR.dsl-0)^2)/crvarLogOR.dsl)
 crvalue.dsl<-pchisq(crchisq.dsl, df=1)
 crpvalue.dsl<-(1-crvalue.dsl)
-}
 
 crstudy.added<-paste("Study Added =", catmapobject$studyname[u], sep=" ")
 crtable.header<-c("Q Statistic (Heterogeneity) Chi-Square", "Q Statistic (Heterogeneity) p-value", "DerSimonian & Laird Random-Effects OR", "DerSimonian & Laird Random-Effects Lower Bound CI", "DerSimonian & Laird Random-Effects Chi-Square", "DerSimonian & Laird Random-Effects p-value")
@@ -512,6 +546,11 @@ sink()
 }
 
 #create fixed-effects cumulative plots
+
+if(fe.cumulative==FALSE & fe.cumplot==TRUE){
+cat("NOTICE: No fixed-efffects cumulative analyses have been performed.\n Please set fe.cumulative==TRUE and try again.\n")
+}
+
 if (fe.cumplot==TRUE){
 if(catmapobject$dataset!=catmapdata){
 pdf(file=(paste(catmapobject$dataset, "fixed.effects.cumulative.plot.pdf", sep=".")))
@@ -547,6 +586,9 @@ dev.off()
 }
 
 #create random-effects cumulative plots
+if(re.cumulative==FALSE & re.cumplot==TRUE){
+cat("NOTICE: No random-efffects cumulative analyses have been performed. \n Please set re.cumulative==TRUE and try again.\n")
+}
 if (re.cumplot==TRUE & catmapobject$tau2 > 0){
 if(catmapobject$dataset!=catmapdata){
 pdf(file=(paste(catmapobject$dataset, "random.effects.cumulative.plot.pdf", sep=".")))
@@ -579,7 +621,7 @@ segments(crplot[w,2],crpstudy[w],crplot[w,3],crpstudy[w], bg="black", col="black
 crpstudyname<-paste("Study Added:", catmapobject$studyname[w], sep="\n")
 mtext(paste(crpstudyname),side=2, at=crpstudy[w], cex=0.80)
 }
-#dev.off()
+graphics.off()
 }
 }
 
@@ -630,15 +672,20 @@ dev.off()
 
 #create random-effects forest plots 
 
+if(re.forest==TRUE & catmapobject$tau2 <= 0){
+cat("NOTICE: tau2 is less than or equal to 0;\n no random effects estimates will be calculated.\n")
+}
+
 if (re.forest==TRUE & catmapobject$tau2 > 0){
 if(catmapobject$dataset!=catmapdata){
 pdf(file=(paste(catmapobject$dataset, "random.effects.plot.pdf", sep=".")))
 }
 if(catmapobject$dataset==catmapdata){
 pdf(file=(paste("catmapdata", "random.effects.plot.pdf", sep=".")))
 }
-dslprop.weight<-(catmapobject$weight.dsl/(sum(catmapobject$weight.dsl))*100)
+dslprop.weight<-(catmapobject$weight.dsl/(sum(catmapobject$weight.dsl))*10)
 dslOR<-c(catmapobject$OR.dsl, exp(catmapobject$logOR))
+OR<-c(catmapobject$combinedOR, exp(catmapobject$logOR))
 rstudy<-1:(nrow(catmapobject$a1)+1)
 rlx<-max((min(catmapobject$lbci.fe)-0.25),0)
 rhx<-max(catmapobject$ubci.fe)+0.25
@@ -662,13 +709,13 @@ mtext("Pooled OR",side=2,at=1, cex=0.80)
 abline(v=1.0)
 limit<-nrow(catmapobject$a1)+1
 counts<-c(1:limit)
-for(i in 2:(nrow(catmapobject$a1)+1)){
-j<-i-1
-points(OR[i],counts[i], pch=22, cex=prop.weight[j], bg="black", col="black")
-segments(catmapobject$lbci.fe[j],counts[i], catmapobject$ubci.fe[j],counts[i], bg="black", col="black")
-mtext(paste(catmapobject$studyname[j]),side=2, at=counts[i], cex=0.80)
+for(e in 2:(nrow(catmapobject$a1)+1)){
+f<-e-1
+points(OR[e],counts[e], pch=22, cex=dslprop.weight[f], bg="black", col="black")
+segments(catmapobject$lbci.fe[f],counts[e], catmapobject$ubci.fe[f],counts[e], bg="black", col="black")
+mtext(paste(catmapobject$studyname[f]),side=2, at=counts[e], cex=0.80)
 }
-#dev.off()
+graphics.off()
 }
 }
 
@@ -689,7 +736,6 @@ minse<-min(fun.se)
 plot(OddsRatio, fun.se, ylim=rev(c(minse,maxse)),log="x", ylab="Standard Error Log OR")
 abline(v=1)
 abline(v=catmapobject$combinedOR, lty=2)
-#dev.off()
+graphics.off()
 }
 }
-

man/catmap.cumulative.Rd

@@ -5,15 +5,15 @@
 \description{ 
 \code{catmap.cumulative} conducts cumulative meta-analyses and creates plots of the ORs and confidence intervals using either fixed- or random-effects analyses.
 } 
-\usage{catmap.cumulative(catmapobject, fe.cumulative, fe.cumplot, 
-re.cumulative, re.cumplot) } 
+\usage{catmap.cumulative(catmapobject, fe.cumulative, re.cumulative, fe.cumplot, 
+re.cumplot) } 
 \arguments{
 	\item{catmapobject}{The catmap object created by a previous call to catmap}   
 	\item{fe.cumulative}{Logical.  Should a cumulative meta-analysis be performed using fixed-effects estimates?  catmap assumes the order in which the studies are listed is the chronological ordering.  Automatic output result file is saved with the default name of \bold{dataset.fixed.effects.cumulative
 .txt}, where dataset is the name of the file given as the first argument to catmap.  Note that repeated runs of the same input file will be appended to the default output file.}
+	\item{re.cumulative}{Logical.  Should a cumulative meta-analysis be performed using random-effects estimates?  catmap assumes the order in which the studies are listed is the chronological ordering.  Automatic output result file is saved with the default name of \bold{dataset.random.effects.cumulative .txt}, where dataset is the name of the file given as the first argument to catmap.  Note that repeated runs of the same input file will be appended to the default output file.  Also note that random-effects estimates are undefined for a single study; calculations begin with the first 2 studies and then adds studies to them in a cumulative fashion.  The OR and CI for the first study may be found using the fixed effects estimates.}
 	\item{fe.cumplot}{Logical.  Should a .pdf plot of the ORs and CIs from the cumulative meta-analysis using fixed-effects be output?  Can be TRUE only if fe.cumulative=TRUE.  Output plot file is saved with the default name of \bold{dataset.fixed.effects.cumulative
 .plot.pdf} where dataset is the name of the file given as the first argument to catmap.}
-	\item{re.cumulative}{Logical.  Should a cumulative meta-analysis be performed using random-effects estimates?  catmap assumes the order in which the studies are listed is the chronological ordering.  Automatic output result file is saved with the default name of \bold{dataset.random.effects.cumulative .txt}, where dataset is the name of the file given as the first argument to catmap.  Note that repeated runs of the same input file will be appended to the default output file.  Also note that random-effects estimates are undefined for a single study; calculations begin with the first 2 studies and then adds studies to them in a cumulative fashion.  The OR and CI for the first study may be found using the fixed effects estimates.}
 	\item{re.cumplot}{Logical.  Should a .pdf plot of the ORs and CIs from the cumulative meta-analysis using random-effects be output?  Can be TRUE only if re.cumulative= TRUE.  Output plot file is saved with the default name of \bold{dataset.random.effects
 .cumulative.plot.pdf} where dataset is the name of the file given as the first argument to catmap.}
 }