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This to the beginning:

strt<-Sys.time()

And this to the end:

print(Sys.time()-strt)

Example output:

print(Sys.time()-strt)
Time difference of 16.39691 secs

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By default, LaTex tables are very tight:

Adding this to the document preamble will add space between the rows:

And this command can be used to add space between rows manually:

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My minimal example:

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One picture says more than a thousand words. You have what is one the left, and you want what is on the right.

There are a few different ways to do this, but by far the cleanest and quickest way is to just select the rows and columns multiple times, by replicating row and column numbers (instead of actually replicating each element):

Note that by default, in rep(something, n) the n is times so equivalent to rep(something, times=n), but in this case we need to use each instead of times.

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ncks -d time,start_time,end_time in.nc out.nc

start_time and end_time are integers.

Add -F, if you want to use Fortran indexing (to start from 1).

No space between dimension name and start-end points!

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Install Pandoc: http://pandoc.org/

Open the Terminal, Command Prompt (search for cmd) or Windows Powershell, go to the folder and do:

pandoc -s report.md -o report.tex

And that’s it!

(Read this, if you want vector images.)

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I’ve recently started using ggplot2 in addition to lattice (see this post that I made a while ago, explaining how I got into using lattice in the first place). Hint: when using ggplot2, you’ll need to use of the reshape2 package (also written by the amazing Hadley Wickham) to get your data into a form that ggplot2 works best with. Another thing that you’ll want to think about when using ggplo2 is factor levels. This post will show how to (and also how not to) rearrange factor levels in R.

Let’s create a quick barplot with strings as x labels.

As df$a is an array of strings, R sets the factor levels alphabetically: my 1, my 10, my 11, my 2…which is not what we want, so let’s rearrange factor levels:

And finally, the wrong way to rearrange factor levels would be by using the levels() function:

So be careful – if your data is not as obvious as this example and you are a bit new to factors and levels, you might end up plotting wrong results (like on the last example, “my 2” and “my 3” were plotted with the values 10 and 11).

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Firstly, start off your table in http://www.tablesgenerator.com/.

Tables Generator will do a lot for you. Its most useful features are importing from .csv and merging cells. The Booktabs table style (alternative to default table style from the menu) looks a bit nicer and is “publication quality”. Note that publication quality tables should not contain vertical lines.

Screen shot of Tables Generator

Code#1

Code #1 is the code from Tables Generator with the addition of caption, label and Latex document begin-end (so it’s compilable). Continuing from that table, let’s centre the contents of columns 1-3 and the whole table in your document, by adding centering and changing the table specs from l’s to c’s: Code #2.

Code#2

Finally, if your cell contents are long and need wrapping:

Note that if your table is too wide for your document margins, then LaTex issues a warning, not an error. So you need check for warnings like “Overfull hbox (9.75735pt too wide) in paragraph at lines 55–63” in your compilation log. A quick solution to wide cells is like this (Code#4):

Code#4

But this solution does not include decent central alignment. Using m (so m{2cm} instead of p{2cm}) would do the vertical centering (e.g. look how the first row is alligned), but still not horizontal. So following this StackOverflow post, I started defining column types and widths using the array package. See Code#5.

Code#5

Next time I might write a post on how to add extra space between lines.

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This is a short note based on this.

Answer in short: Because different formulas are used to calculate the R-squared of a linear regression, depending on whether it has an intercept or not.

R2 for a linear model that has an intercept:

,

where y is the variable that the linear model is trying to predict (the response variable), y^ is the predicted value and y- is the mean value of the response variable.

And the R2 for a linear model that is forced through the origin:

,

basically the mean value of the response variable is removed from the equation, making the denominator bigger (and the result of the division smaller). The reason why the mean can not be used for this calculation is that it does not make sense any more – forcing the fit through zero kind of means adding an infinite number of (0,0) points into the dataset.

This means that the R-squared values of two different linear models (one with an intercept, one without) can not really be compared, because when the intercept is quite small compared to the residuals (basically the numerator) then the R2 “advantange” that the through-origin regression gets is relatively bigger than the decrease in residuals, when including the intercept.

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convert -rotate 270 -density 300 -compress lzw in.pdf out.pdf