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.Rhistory (Joel Berman's conflicted copy 2020-06-18)
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.Rhistory (Joel Berman's conflicted copy 2020-06-18)
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View(RoysDataPrcp)
View(RoysDataPrcp)
View(RoysDataTemp)
View(weather)
source('~/Dropbox/E-value Team/COVID19/CountryRegressions/roysweather.R')
View(RoysData)
summary <- data.frame(summary(p1.fit)$coefficients)
View(summary)
(summary(p1.fit)$coefficients)
(summary(as.charaacter(p1.fit)$coefficients))
(summary(as.character(p1.fit)$coefficients))
as.character(summary(p2.fit)$coefficients)
add_row(math,as.character(summary(p2.fit)$coefficients))
math <- data.frame(summary(p1.fit)$coefficients)
add_row(math,as.character(summary(p2.fit)$coefficients))
as.character(summary(p2.fit)$coefficients)
as.character(summary(p1.fit)$coefficients)
(summary(as.character(p1.fit)$coefficients))
(summary(p1.fit)$coefficients)
summary(p1.fit)$coefficients)
math <- data.frame(summary(p1.fit)$coefficients)
math
add_row(math,as.character(summary(p2.fit)$coefficients))
add_row(math,summary(p2.fit)$coefficients)
summary(p2.fit)$coefficients)
summary(p2.fit)$coefficients
as.number(summary(p2.fit)$coefficients)
as.number(summary(p2.fit)$coefficients)
as.numeric(summary(p2.fit)$coefficients)
add_row(math,as.numeric(summary(p2.fit)$coefficients))
summary(p2.fit)$coefficients
summary(p2.fit)$coefficients[1]
summary(p2.fit)$coefficients[2]
add_row(math,summary(p2.fit)$coefficients[1])
add_row(math,as.data.frame(summary(p2.fit)$coefficients))
math
colnames(math)
colnames(as.data.frame(summary(p2.fit)$coefficients))
tibble(summary(p1.fit)$coefficients)
tibble(summary(p2.fit)$coefficients)
math <- tibble(summary(p2.fit)$coefficients)
math
math <- add_row(,math,tibble(summary(p2.fit)$coefficients))
math <- add_row(math,tibble(summary(p2.fit)$coefficients))
math
math <- tibble(summary(p1.fit)$coefficients)
math <- add_row(math,tibble(summary(p2.fit)$coefficients))
math <- data.frame(summary(p1.fit)$coefficients)
math <- data.frame(summary(p1.fit)$coefficients)
math <- merge(math,data.frame(summary(p2.fit)$coefficients))
math <- merge(math,data.frame(summary(p2.fit)$coefficients))
View(math)
math1 <- data.frame(summary(p1.fit)$coefficients)
math2 <- data.frame(summary(p2.fit)$coefficients)
math3 <- data.frame(summary(p3.fit)$coefficients)
View(math1)
View(math2)
View(math3)
merge(math1,math2)
math <- rbind(math1,math2)
math <- rbinf(math,math3)
math <- rbind(math1,math2)
math <- rbind(math,math3)
math
math1 <- cbind(data.frame(summary(p1.fit)$coefficients,data.frame(summary(p1.fit)$r.squared)))
mTH1
math1
math1 <- cbind(data.frame(summary(p1.fit)$coefficients,data.frame(summary(p1.fit)$r.squared)))
math2 <- cbind(data.frame(summary(p2.fit)$coefficients,data.frame(summary(p2.fit)$r.squared)))
math3 <- cbind(data.frame(summary(p3.fit)$coefficients,data.frame(summary(p3.fit)$r.squared)))
math <- rbind(math1,math2)
math <- NULL
math <- rbind(math1,math2)
rm(math)
math <- rbind(math1,math2)
math1
math2
rbind(math1,math2)
rbind(math1,math3)
rbind(math1,math3)
rownames(math1)
colnames(math1)
colnames(math2)
colnames(math3)
colnames(math4)
colnames(math1) <- cn
cn <- c("BCoef", "Std Error","t-Statistic", "p-Value", "R^2" )
colnames(math1) <- cn
colnames(math2) <- cn
colnames(math3) <- cn
math <- rbind(math1,math2)
math <- rbind(math1,math2)
math <- rbind(math,math3)
math
summary(results_coef_ci)
(results_coef_ci)
results_coef_ci$R
results_coef_ci$t0
results_coef_ci$call
results_coef_ci$bca
names(results_coef_ci$bca)
results_coef_ci$bca
results_coef_ci
results_coef_ci[,1]
results_coef_ci[2]
results_coef_ci[3]
results_coef_ci[4]
math5 <- data.frame(results_coef_ci[4])
math5
math6
math6 <- data.frame(results_rsq_ci[4])
math6
math6 <- rbind(math5,math6)
math6
math6[,c(1,4:5)]
text2 <- rbind(math5[,c(1,4:5)],math6[,c(1,4:5)])
text2
math5 <- data.frame(results_coef_ci[4])
math6 <- data.frame(results_rsq_ci[4])
text2 <- rbind(math5[,c(1,4:5)],math6[,c(1,4:5)])
text2
math1 <- cbind(data.frame(summary(p1.fit)$coefficients,data.frame(summary(p1.fit)$r.squared)))
math2 <- cbind(data.frame(summary(p2.fit)$coefficients,data.frame(summary(p2.fit)$r.squared)))
math3 <- cbind(data.frame(summary(p3.fit)$coefficients,data.frame(summary(p3.fit)$r.squared)))
cn1 <- c("BCoef", "Std Error","t-Statistic", "p-Value", "R^2" )
cn2 <- c("Confidence", "Lower", "Upper")
colnames(math1) <- cn1
colnames(math2) <- cn1
colnames(math3) <- cn1
math4 <- rbind(math1,math2)
text1 <- rbind(math3,math4)
math5 <- data.frame(results_coef_ci[4])
math6 <- data.frame(results_rsq_ci[4])
text2 <- rbind(math5[,c(1,4:5)],math6[,c(1,4:5)])
text1
text2
text2
cn2 <- c("Confidence", "Lower", "Upper")
colnames(text2) <- cn2
cn2
text2
results_coef_ci[4]
math4 <- rbind(math2,math1)
text1 <- rbind(math3,math4)
text1
math4 <- rbind(math1,math2)
text1 <- rbind(math4,math3)
text1
math1 <- cbind(data.frame(summary(p1.fit)$coefficients,data.frame(summary(p1.fit)$r.squared)))
math2 <- cbind(data.frame(summary(p2.fit)$coefficients,data.frame(summary(p2.fit)$r.squared)))
math3 <- cbind(data.frame(summary(p3.fit)$coefficients,data.frame(summary(p3.fit)$r.squared)))
cn1 <- c("BCoef", "Std Error","t-Statistic", "p-Value", "R^2" )
cn2 <- c("Confidence", "Lower", "Upper")
rn1 <- c("Log of Diff", "Diff of Logs", "Differences")
rn2 <- C("Bootstrapped ln(Diff) Coef", "Bootstrapped ln(Diff) R")
colnames(math1) <- cn1
colnames(math2) <- cn1
colnames(math3) <- cn1
math4 <- rbind(math1,math2)
tab1 <- rbind(math4,math3)
rn2 <- C("Bootstrapped ln(Diff) Coef", "Bootstrapped ln(Diff) R^2")
rn2 <- c("Bootstrapped ln(Diff) Coef", "Bootstrapped ln(Diff) R^2")
colnames(math1) <- cn1
colnames(math2) <- cn1
colnames(math3) <- cn1
math4 <- rbind(math1,math2)
tab1 <- rbind(math4,math3)
rownames(tab1) <- rn1
math5 <- data.frame(results_coef_ci[4])
math6 <- data.frame(results_rsq_ci[4])
text2 <- rbind(math5[,c(1,4:5)],math6[,c(1,4:5)])
colnames(text2) <- cn2
rownames(text2) <- rn2
text1
text2
p4 <- plotCI(results_coef_ci$t0,li = results_coef_ci$bca[4],ui = results_coef_ci$bca[5]) #confidence plot coef
p5 <- plotCI(results_rsq_ci$t0,li = results_rsq_ci$bca[4],ui = results_rsq_ci$bca[5]) #confidence plot rsq
source('~/EconoMedRx Dropbox/Joel Berman/E-value Team/COVID19/CountryRegressions/regressions1.R')
# NOT RUN {
# show comparison with boxplot
data(state)
plotmeans(state.area ~ state.region)
library(gplots)
install.packages("gplots")
library(gplots)
# NOT RUN {
# show comparison with boxplot
data(state)
plotmeans(state.area ~ state.region)
# show some color and mean labels
plotmeans(state.area ~ state.region,
mean.labels=TRUE, digits=-3,
col="red", connect=FALSE)
# more complicated example showing how to show an interaction
data(esoph)
par(las=2, # use perpendicular axis labels
mar=c(10.1,4.1,4.1,2.1), # create enough space for long x labels
mgp=c(8,1,0) # move x axis legend down to avoid overlap
)
plotmeans(ncases/ncontrols ~ interaction(agegp , alcgp, sep =" "),
connect=list(1:6,7:12,13:18,19:24),
barwidth=2,
col="dark green",
data=esoph,
xlab="Age Group and Alcohol Consumption",
ylab="# Cases / # Controls",
main=c("Fraction of Cases for by Age and Alcohol Consumption",
"Ile-et-Vilaine Esophageal Cancer Study")
)
library(gplots)
y<-runif(10)
err<-runif(10)
plotCI(1:10,y,err,main="Basic plotCI")
plotCI(1:5,y,err,main="Basic plotCI")
y<-runif(5)
err<-runif(5)
plotCI(1:5,y,err,main="Basic plotCI")
plotCI(1,y,err,main="Basic plotCI")
plotCI(1:1,y,err,main="Basic plotCI")
library(gplots)
y<-runif(2)
err<-runif(2)
plotCI(1:2,y,err,main="Basic plotCI")
plotCI(1:2,y,err,2*err,lwd=2,col="red",scol="blue",
main="Add colors to the points and error bars")
err.x<-runif(10)
err.y<-runif(10)
plotCI(1:10,y,err.y,pt.bg=par("bg"),pch=21,xlim=c(0,11),
main="plotCI with extra space on the x axis")
plotCI(1:10,y,err.y,pt.bg=par("bg"),pch=21,xlim=c(0,11),
main="plotCI with extra space on the x axis")
plotCI(1:10,y,err.x,pt.bg=par("bg"),pch=21,err="x",add=TRUE)
mtext("for adding horizontal error bars",3,0.5)
data(warpbreaks)
attach(warpbreaks)
wmeans<-by(breaks,tension,mean)
wsd<-by(breaks,tension,sd)
## note that barplot() returns the midpoints of the bars, which plotCI
## uses as x-coordinates
plotCI(barplot(wmeans,col="gray",ylim=c(0,max(wmeans+wsd))),wmeans,wsd,add=TRUE)
## using labels instead of points
labs<-sample(LETTERS,replace=TRUE,size=10)
plotCI(1:10,y,err,pch=NA,gap=0.02,main="plotCI with labels at points")
text(1:10,y,labs)
library(gplots)
y<-runif(10)
err<-runif(10)
plotCI(1:2,y,err,main="Basic plotCI")
plotCI(1:2,y,err,2*err,lwd=2,col="red",scol="blue",
main="Add colors to the points and error bars")
err.x<-runif(10)
err.y<-runif(10)
plotCI(1:10,y,err.y,pt.bg=par("bg"),pch=21,xlim=c(0,11),
main="plotCI with extra space on the x axis")
plotCI(1:10,y,err.x,pt.bg=par("bg"),pch=21,err="x",add=TRUE)
mtext("for adding horizontal error bars",3,0.5)
data(warpbreaks)
attach(warpbreaks)
wmeans<-by(breaks,tension,mean)
wsd<-by(breaks,tension,sd)
## note that barplot() returns the midpoints of the bars, which plotCI
## uses as x-coordinates
plotCI(barplot(wmeans,col="gray",ylim=c(0,max(wmeans+wsd))),wmeans,wsd,add=TRUE)
## using labels instead of points
labs<-sample(LETTERS,replace=TRUE,size=10)
plotCI(1:10,y,err,pch=NA,gap=0.02,main="plotCI with labels at points")
text(1:10,y,labs)
=
=
par(mfrow=c(2,1)) # to stack the charts on column
upperlimit = c(10,12,8,14)
lowerlimit = c(5,9,4,7)
mean = c(8,10,6,12)
df = data.frame(cbind(upperlimit,lowerlimit,mean))
plot(df$mean, ylim = c(0,30), xlim = c(1,4))
install.packages("plotrix")
require(plotrix)
plotCI(df$mean,y=NULL, uiw=df$upperlimit-df$mean, liw=df$mean-df$lowerlimit, err="y", pch=20, slty=3, scol = "black", add=TRUE)
upperlimit_2 = upperlimit*1.5
lowerlimit_2 = lowerlimit*0.8
mean_2 = upperlimit_2-lowerlimit_2
df_2 = data.frame(cbind(upperlimit_2,lowerlimit_2,mean_2))
plot(df$mean_2, ylim = c(0,30), xlim = c(1,4))
plotCI(df_2$mean_2,y=NULL, uiw=df_2$upperlimit_2-df_2$mean_2, liw=df_2$mean_2- df_2$lowerlimit_2, err="y", pch=20, slty=3, scol = "black", add=TRUE)
rm(upperlimit,lowerlimit,mean,df,upperlimit_2,lowerlimit_2,mean_2,df_2) #remove the objects stored from workspace
require(plotrix)
plotCI(df$mean,y=NULL, uiw=df$upperlimit-df$mean, liw=df$mean-df$lowerlimit, err="y", pch=20, slty=3, scol = "black", add=TRUE)
upperlimit_2 = upperlimit*1.5
lowerlimit_2 = lowerlimit*0.8
mean_2 = upperlimit_2-lowerlimit_2
df_2 = data.frame(cbind(upperlimit_2,lowerlimit_2,mean_2))
plot(df$mean_2, ylim = c(0,30), xlim = c(1,4))
plotCI(df_2$mean_2,y=NULL, uiw=df_2$upperlimit_2-df_2$mean_2, liw=df_2$mean_2- df_2$lowerlimit_2, err="y", pch=20, slty=3, scol = "black", add=TRUE)
par(mfrow=c(2,1)) # to stack the charts on column
upperlimit = c(10,12,8,14)
lowerlimit = c(5,9,4,7)
mean = c(8,10,6,12)
df = data.frame(cbind(upperlimit,lowerlimit,mean))
plot(df$mean, ylim = c(0,30), xlim = c(1,4))
require(plotrix)
plotCI(df$mean,y=NULL, uiw=df$upperlimit-df$mean, liw=df$mean-df$lowerlimit, err="y", pch=20, slty=3, scol = "black", add=TRUE)
plotCI(df$mean,y=NULL, uiw=df$upperlimit-df$mean, liw=df$mean-df$lowerlimit, err="y", pch=20, slty=3, scol = "black", add=TRUE)
upperlimit = c(10,12,8,14)
lowerlimit = c(5,9,4,7)
mean = c(8,10,6,12)
df = data.frame(cbind(upperlimit,lowerlimit,mean))
plot(df$mean, ylim = c(0,30), xlim = c(1,4))
require(plotrix)
plotCI(df$mean,y=NULL, uiw=df$upperlimit-df$mean, liw=df$mean-df$lowerlimit, err="y", pch=20, slty=3, scol = "black", add=TRUE)
upperlimit_2 = upperlimit*1.5
lowerlimit_2 = lowerlimit*0.8
mean_2 = upperlimit_2-lowerlimit_2
df_2 = data.frame(cbind(upperlimit_2,lowerlimit_2,mean_2))
plot(df$mean_2, ylim = c(0,30), xlim = c(1,4))
plotCI(df_2$mean_2,y=NULL, uiw=df_2$upperlimit_2-df_2$mean_2, liw=df_2$mean_2- df_2$lowerlimit_2, err="y", pch=20, slty=3, scol = "black", add=TRUE)
rm(upperlimit,lowerlimit,mean,df,upperlimit_2,lowerlimit_2,mean_2,df_2) #remove the objects stored from workspace
upperlimit = c(10,12,8,14)
lowerlimit = c(5,9,4,7)
mean = c(8,10,6,12)
df = data.frame(cbind(upperlimit,lowerlimit,mean))
plot(df$mean, ylim = c(0,30), xlim = c(1,4))
require(plotrix)
plotCI(df$mean,y=NULL, uiw=df$upperlimit-df$mean, liw=df$mean-df$lowerlimit, err="y", pch=20, slty=3, scol = "black", add=TRUE)
upperlimit_2 = upperlimit*1.5
lowerlimit_2 = lowerlimit*0.8
mean_2 = upperlimit_2-lowerlimit_2
df_2 = data.frame(cbind(upperlimit_2,lowerlimit_2,mean_2))
plot(df$mean_2, ylim = c(0,30), xlim = c(1,4))
plotCI(df_2$mean_2,y=NULL, uiw=df_2$upperlimit_2-df_2$mean_2, liw=df_2$mean_2- df_2$lowerlimit_2, err="y", pch=20, slty=3, scol = "black", add=TRUE)
df_2 = data.frame(cbind(upperlimit_2,lowerlimit_2,mean_2))
plot(df$mean_2, ylim = c(0,30), xlim = c(1,4))
plotCI(df_2$mean_2,y=NULL, uiw=df_2$upperlimit_2-df_2$mean_2, liw=df_2$mean_2- df_2$lowerlimit_2, err="y", pch=20, slty=3, scol = "black", add=TRUE)
ci.plot(lm.object,
xlim=range(data[, x.name]),
newdata,
conf.level=.95,
data=model.frame(lm.object),
newfit,
ylim,
pch=19,
lty=c(1,3,4,2),
lwd=2,
main.cex=1,
main=list(paste(100*conf.level,
"% confidence and prediction intervals for ",
substitute(lm.object), sep=""), cex=main.cex), ...
)
install.packages("HH")
library(ci.plot)
library(ci.plot)
library(HH)
ci.plot(lm.object,
xlim=range(data[, x.name]),
newdata,
conf.level=.95,
data=model.frame(lm.object),
newfit,
ylim,
pch=19,
lty=c(1,3,4,2),
lwd=2,
main.cex=1,
main=list(paste(100*conf.level,
"% confidence and prediction intervals for ",
substitute(lm.object), sep=""), cex=main.cex), ...
)
ci.plot(lm.object,
xlim=range(data[, x.name]),
newdata,
conf.level=.95,
data=model.frame(lm.object),
newfit,
ylim,
pch=19,
lty=c(1,3,4,2),
lwd=2,
main.cex=1,
main=list(paste(100*conf.level,
"% confidence and prediction intervals for ",
substitute(lm.object), sep=""), cex=main.cex)
)
ci.plot(p1.fit,
xlim=range(data[, x.name]),
newdata,
conf.level=.95,
data=model.frame(lm.object),
newfit,
ylim,
pch=19,
lty=c(1,3,4,2),
lwd=2,
main.cex=1,
main=list(paste(100*conf.level,
"% confidence and prediction intervals for ",
substitute(lm.object), sep=""), cex=main.cex)
)
ci.plot(p1.fit,
xlim=range(data[, p1.name]),
newdata,
conf.level=.95,
data=model.frame(lm.object),
newfit,
ylim,
pch=19,
lty=c(1,3,4,2),
lwd=2,
main.cex=1,
main=list(paste(100*conf.level,
"% confidence and prediction intervals for ",
substitute(lm.object), sep=""), cex=main.cex)
)
ci.plot(p1.fit,
# xlim=range(data[, p1.name]),
newdata,
conf.level=.95,
data=model.frame(lm.object),
newfit,
ylim,
pch=19,
lty=c(1,3,4,2),
lwd=2,
main.cex=1,
main=list(paste(100*conf.level,
"% confidence and prediction intervals for ",
substitute(lm.object), sep=""), cex=main.cex)
)
ci.plot(p1.fit,
# xlim=range(data[, p1.name]),
# newdata,
conf.level=.95,
data=model.frame(lm.object),
newfit,
ylim,
pch=19,
lty=c(1,3,4,2),
lwd=2,
main.cex=1,
main=list(paste(100*conf.level,
"% confidence and prediction intervals for ",
substitute(lm.object), sep=""), cex=main.cex)
)
ci.plot(p1.fit,
# xlim=range(data[, p1.name]),
# newdata,
conf.level=.95,
data=model.frame(lm.object),
# newfit,
# ylim,
pch=19,
lty=c(1,3,4,2),
lwd=2,
main.cex=1,
main=list(paste(100*conf.level,
"% confidence and prediction intervals for ",
substitute(lm.object), sep=""), cex=main.cex)
)
ci.plot(p1.fit,
# xlim=range(data[, p1.name]),
# newdata,
data=model.frame(lm.object),
# newfit,
# ylim,
lty=c(1,3,4,2),
main.cex=1,
ci.plot(p1.fit,
ci.plot(p1.fit,
ci.plot(p1.fit)
ci.plot(p1.fit)
install.packages("poppr")
library(popr)
diversity_ci(
tab,
n = 1000,
n.boot = 1L,
ci = 95,
total = TRUE,
rarefy = FALSE,
n.rare = 10,
plot = TRUE,
raw = TRUE,
center = TRUE,
...
)
library(popr)
library(poppr)
diversity_ci(
tab,
n = 1000,
n.boot = 1L,
ci = 95,
total = TRUE,
rarefy = FALSE,
n.rare = 10,
plot = TRUE,
raw = TRUE,
center = TRUE,
...
)
diversity_ci(
tab,
n = 1000,
n.boot = 1L,
ci = 95,
total = TRUE,
rarefy = FALSE,
n.rare = 10,
plot = TRUE,
raw = TRUE,
center = TRUE
)
boxplot(1,2,3,4,5)
boxplot(1,2,3,4)