[R] Fitting a line on trellis plot
Frederic Ntirenganya
ntfredo at gmail.com
Thu Mar 26 08:51:04 CET 2015
Thanks for the help.
Frederic Ntirenganya
Maseno University,
African Maths Initiative,
Kenya.
Mobile:(+254)718492836
Email: fredo at aims.ac.za
https://sites.google.com/a/aims.ac.za/fredo/
On Wed, Mar 25, 2015 at 4:48 PM, S Ellison <S.Ellison at lgcgroup.com> wrote:
>
> > Call:
> > lm(formula = curr_data[[tmin_col]] ~ curr_data[[year_col]] -
> > 1 | curr_data[[month_col]])
>
>
> First, this is not a sensible formula for lm; lm() does not use '|' to
> denote grouping. It would be a valid formula for xyplot, in which |
> specifies grouping variables. In lm(), '|' is simply being treated as 'or',
> which is why one of your coefficients is for ' curr_data[[year_col]] |
> curr_data[[month_col]]TRUE'
>
> Second, you should not normally need things like curr_data[[month_col]],
> either in lm or xyplot. If curr_data is a data frame, things like
>
> lm(tmin ~ year, data=curr_data)
>
> should work.
>
> Third, 'nested' models in lm use the nesting operator '/', not '|'. So if
> you want 12 lines with separate intercept and gradient from an lm, you need
> (with month as a factor and the default intercept suppressed)
> lm(tmin~month+year/month -1, data=curr_data) #-1 suppresses the intercept
> and provides a zero-based intercept for each month
>
> This gives you 12 'month' intercepts and one gradient per month. If you
> wanted a common intercept you'd do
> lm(tmin~ year/month, data=curr_data)
> But beware; the coefficients in both cases cannot be interpreted as a
> simple gradient and intercept for each month: if I recall correctly, the
> gradients for month2 and on are modelled as an additive increment on the
> first month gradient. Use predict() if you want an easy way to predict a
> value for a given (possibly fractional) time of year and 'month'.
> [Incidentally I don’t immediately see why that is a sensible thing to do -
> this fits a monthly summary against a numeric year. But I'm going to assume
> you know what you want there.]
>
> Finally, though, this model will not help you much with lattice as there's
> no _simple_ way of putting those lines on different panels in a lattice
> plot. If you just want a line on each of 12 panels, that's much easier. You
> can use the panel() function with panel.lmline to put it there. For
> example, if you want to plot a line over the data, use
>
> xyplot(tmin~year|month, curr_data,
> panel=function(x, y, ...) {
> panel.xyplot(x, y, ...)
> panel.lmline(x, y, ...)
> }
> )
>
>
> S Ellison
>
>
>
>
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