glm objectsinzplot (from iNZightPlots)exponentiate.cis argument to iNZightSummary which replaces the CIs
with exponentiated versions if appropriate (FALSE by default)Release date: 21 April 2020
stringsAsFactors = TRUE for upcoming R 4.0.0exluded variablesRelease date: 11 November 2019
Release date: 2 September 2019
factorComp() function to obtain adjusted pairwise comparisons of
factor levels in a modelRelease date: 15 July 2019
y ~ 1)Release date: 30 April 2019
loess() callsRelease 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 iNZightModules).
Poly() function, which is just a poly() function that supports NAssubset argument to lm (via update()) to perform bootstraps, rather than the long-winded data-bootstrapping call-modifying version that was buggyRelease date: 18 August 2017
Release date: 23 March 2017
Release date: 9 January 2017
glm objectRelease date: 20 July 2015
use.inzightplots to the
plotting functions that allows users to enable/disable the use of
them as desired. Currently, the default is FALSE as the
latest version of iNZightPlots is incompatible with
iNZightRegression.Release date: 17 September 2014
grid.rect() is not
transparent; now enforces these to be transparentRelease date: 4 April 2014
iNZightPlots library
if it is installed.Release date: 27 March 2014
Residual summary plots from plotlm6 can now make use of
the 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.
grid based 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 overridden by showBootstraps = TRUE).
Release date: 18 January 2014
Support for generalized linear models (GLMs) and svyglm
objects from the survey package.
Changes to the iNZightSummary output include:
Output now hides output of confounding
variables through the exclude argument, and lists these at the
top of the output.
grid, and
minimizes margin whitespace and draws simulated histograms in a
different colour.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.
factorMeans and adjustedMeans have been enhanced to work with GLMs.survey package's svyglm.First release of new package. Contains model fitting subset
used for the iNZight package.
Added histogramArray and qqplotArray plots to show how
residuals from a model compare to the residuals generated from
that model.
New margin of error calculation functions. Initially written
by Danny Chang. Used for comparison between levels of a
factor. moecalc has a few standard methods that can be used:
print, plot, and summary. In addition, a multicomp method
has been added which is a useful tabulation of multiple comparison
output. Note however with multicomp that the p-values are
currently unadjusted.
New summary output, iNZightSummary. Includes several Changes
compared to the R-base model summary output. These include the
following:
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 s20x
package's normcheck function.
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 method="bootstrap".loess for smoothers instead of lowess (newer and more robust).