monthglm | R Documentation |
Fit a generalized linear model with a categorical variable of month.
monthglm( formula, data, family = gaussian(), refmonth = 1, monthvar = "month", offsetmonth = FALSE, offsetpop = NULL )
formula |
regression model formula, e.g., |
data |
a data frame. |
family |
a description of the error distribution and link function to
be used in the model (default= |
refmonth |
reference month, must be between 1 and 12 (default=1 for January). |
monthvar |
name of the month variable which is either an integer (1 to 12) or a character or factor (‘Jan’ to ‘Dec’ or ‘January’ to ‘December’) (default='month'). |
offsetmonth |
include an offset to account for the uneven number of
days in the month (TRUE/FALSE). Should be used for monthly counts (with
|
offsetpop |
include an offset for the population (optional), this should be a variable in the data frame. Do not log-transform the offset as the log-transform is applied by the function. |
Month is fitted as a categorical variable as part of a generalized linear
model. Other independent variables can be added to the right-hand side of
formula
.
This model is useful for examining non-sinusoidal seasonal patterns. For
sinusoidal seasonal patterns see cosinor
.
The data frame should contain the integer months and the year as a 4 digit number. These are used to calculate the number of days in each month accounting for leap years.
call |
the original call to the monthglm function. |
fit |
GLM model. |
fitted |
fitted values. |
residuals |
residuals. |
out |
details on the monthly estimates. |
Adrian Barnett a.barnett@qut.edu.au
Barnett, A.G., Dobson, A.J. (2010) Analysing Seasonal Health Data. Springer.
summary.monthglm
, plot.monthglm
data(CVD) mmodel = monthglm(formula=cvd~1 ,data=CVD, family=poisson(), offsetpop=expression(pop/100000), offsetmonth=TRUE) summary(mmodel)
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