annotate p value in italicized font in ggplot

label = substitute(italic(p) == a, list(a=0.371) )
ggplot() + annotate('text', label=as.character(as.expression(label)), x=10, y=10, parse=T)

Relationship between Pearson correlation coefficient and the r-squared from linear regression

Simply put, if you take the square root of the r-squared value, which can be easily obtained in Excel, the outcome equals to the Pearson correlation coefficient.

Here's my R code to test this:


x<-rnorm c="" font="" nbsp="">
y<-rnorm c="" font="">
mdl <- font="" lm="" x="" y=""># linear regression model
r = summary(mdl)$r.squared # the r-squared value from linear regression
rp = cor.test(x,y, method='pearson')$estimate # rp is the Pearson correlation coefficient
sqrt(r) == rp

China PM2.5 & PM10 historical data repository

http://pan.baidu.com/s/1dD8KdpB

More data will be added to this repository in the future.

R figure label expression with superscripts and greek letters

ylab=expression("UFP number concentration (# " ~ cm^{-3} ~ ")")

 ylab=expression(paste (PM[2.5], "mass concentration (", mu, "g " ~ m^{-3} ~ ")"))

+labs(y=ylab)

Annotating subscript in R plots

I need to annotate one of my figure like this:

(b) PM<sub>2.5


And this is a way to do it in R with ggplot2 package:

b <- b="" font="">
annotate("text", x=-3.5, y=0.12, label=deparse(b),parse=TRUE)

Not sure about the details behind all the parameters though. Thanks to BondedDust for answering this question.</sub>

Differentiate time-series data

Aim: to differentiate time-series data

and to check the effects of choosing different time interval

Contents

• Initialization
• differentiate symbolic equation
• differentiate discret time-series data
• discret data, use larger time interval
• overlay different simulations

Initialization

clear all; close all; clc;

differentiate symbolic equation

syms x
f=sin(x);
fd=diff(f);
ezplot(f,[0 2*pi])
hold on,
h=ezplot(fd,[0 2*pi]);
set(h,'Color','g')
ylim([-1 1])
legend('sin(x)', 'deriv of sin(x)')

differentiate discret time-series data

Suppose these are measured data

timeInterval=0.01;
t=0:timeInterval/pi:2*pi;
y=sin(t);
yd=diff(y)./(0.01/pi);
figure(2),plot(t,y,'b.',t(1:end-1),yd,'g.')
legend('sin(x)', 'deriv of sin(x)')

discret data, use larger time interval

Suppose these are measured data

timeIntervalL=0.2;
tL=0:timeIntervalL/pi:2*pi;
yL=sin(tL);
ydL=diff(yL)./(timeIntervalL/pi);
figure(3),plot(tL,yL,'b.',tL(1:end-1),ydL,'g.')
legend('sin(x)', 'deriv of sin(x)')

overlay different simulations

figure(4)
ezplot(fd),hold on
plot(t(1:end-1),yd,'bo');
plot(tL(1:end-1),ydL,'ro');
xlim([0 2*pi])

Published with MATLAB® R2013a

Data and Questions: regression with factor variables

I was taking the Data Analysis course by Jeff Leek on CourseEra.org. The video ' Regression with Factor Variables' was very useful to me. However, I am a Matlab guy and don't like R very much. So I did all the analysis in Matlab. The code is published here:

http://matlabnewbie.blogspot.com/2014/01/matlab-code-regression-with-factor.html

and here's the video.