stringsAsFactors = TRUEfor upcoming R 4.0.0
Release date: 11 November 2019
Release date: 2 September 2019
factorComp()function to obtain adjusted pairwise comparisons of factor levels in a model
Release date: 15 July 2019
y ~ 1)
Release date: 30 April 2019
Release date: 01 February 2019
Release date: 15 November 2018
Release date: 02 October 2017
Release date: 25 August 2017
Release date: 23 August 2017
This isn't a hugely updated version, however fixing up a bunch of bugs
to make the Model Fitting module better (over on
Poly()function, which is just a
poly()function that supports
update()) to perform bootraps, rather than the long-winded data-bootstrapping call-modifying version that was buggy
Release date: 18 August 2017
Release date: 23 March 2017
Release date: 9 January 2017
Release date: 20 July 2015
use.inzightplotsto the plotting functions that allows users to enabled/disable the use of them as desired. Currently, the default is
FALSEas the latest version of
iNZightPlotsis incompatible with
Release date: 17 September 2014
grid.rect()is not transparent - now enforces these to be transparent
Release date: 4 April 2014
iNZightPlotslibrary if it is installed.
Release date: 27 March 2014
Residual summary plots from
plotlm6 can now make use of
iNZightPlots graphics rather than the defaults. It requires
the user to have
iNZightPlots installed to work, but reverts to
the old plots if it is not.
gridbased plots, quantile smoothers are used rather than loess smoothers. This greatly increases efficiency when large data sets are analysed.
Maximum sample size for drawing bootstraps implemented
(currently at 4000), as over this they don't provide much
information (this can be overridded by
showBootstraps = TRUE).
Release date: 18 January 2014
Support for generalised lienar models (GLMs) and
objects from the
Changes to the
iNZightSummary output include:
Output now hides output of counfounding
variables through the
exclude argument, and lists these at the
top of the output.
grid, and minimises margin whitespace and draws simulated histograms in a different color.
The bootstrap models functions have been re-written to
account for the
design option in survey GLMs, as well as the
case when the GLM binomial response is SUCCESS / N.TRIALS. This
caused errors in the
fit$model that was previously being used.
The bootstrap lines from
plotlm6 have been fixed so that
they now work for
(svy)glm objects. There is also an optional
cut-off if the sample size becomes too small (which can be
overridden by the
showBootstraps = TRUE|FALSE argument.
adjustedMeanshave be fixed so they work for GLMs.
First release of new package. Contains model fitting subset
used for the
qqplotArray plots to show how
residuals from a model compare to the residuals generated from
New margin of error calculation functions. Initially written
by Danny Chang. Used for comparison between levels of a
moecalc has a few standard methods that can be used:
summary. In addition, a
has been added which is a useful tabulation of multiple comparison
output. Note however with
multicomp that the p-values are
New summary output,
iNZightSummary. Includes several Changes
compared to the R-base model summary output. These include the
Now showing the factor itself in the output, not just rows for coefficients for levels of the factor.
When a factor is included in a model, the summary output will show the name of the factor and show the p-value for the factor (based on Type-III sums of squares). This p-value is not affected by further use of the factor (i.e. in an interaction). Sometimes this p-value cannot be calculated (i.e. when there are unobserved factor level combinations) and the p-value will be omitted.
The baseline level of a factor is now shown, with an estimate of 0.
All p-value output for levels of a factor is indented to the right by two characters to distinguish it from being a level.
The output for each factor level is now just the level name and not the name of the variable concatenated with the level name. The level name is also indented by two characters, again to distinguish it from the variable itself.
Removing F-statistic and associated p-value as it's mostly useless. It only shows us whether nothing is correlated with the response variable, i.e. whether we're completely wasting our time.
Added a new
plot.lm function. The main difference being that
it includes bootstrapped smoothers in its output as well as the
regular trend lines. Also includes plots based on the
Added partial residual plots. Most useful for determining whether the inclusion of a transformation of a variable is necessary. For example, adding a logged or polynomial explanatory variable to the model.
iNZightSummary. Accessed by calling the function with
loessfor smoothers instead of
lowess(newer and more robust).