I am trying to do a logistic regression with robust clustered error using miceadds::glm.cluster:
model1 <- miceadds::glm.cluster(data=df_clean3, formula=recall ~ log(Population)+NoComplaintsReported+NoCrashesFiresReported+NoInjuriesReported+NoFatalityIncidentsReported+NoOtherFailuresReported+YearOpen+label, cluster="label", family="binomial")
I want to report a ready-to-publish regression table in Microsoft Word. I have tried the below methods, but each are not the "professionally prepared" version that I am looking for.
Can someone help me with this?
1-tab_df (from the sjPlot library): It stops with the error message that:
Error in UseMethod("family") :
no applicable method for 'family' applied to an object of class "NULL"
2- Stargazer: the output table does not look neat.
3- summ (from jtools library): the output table does not look neat.
4- apa.reg.table (from apaTables library): it stops with this error message:
Error in 2:last_model_number_predictors : argument of length 0
Related
I am trying to build a simple multibody plant system in Drake using the basic DrakeVisualizer. However, for my use case, I also want to be able to automatically track the derivatives through the physics simulation, so am using the AutoDiffXd version of system:
timestep = 1e-3
builder = DiagramBuilder_[AutoDiffXd]()
plant = MultibodyPlant(timestep)
scene_graph = SceneGraph_[AutoDiffXd]()
brick_file = FindResourceOrThrow("drake/examples/manipulation_station/models/061_foam_brick.sdf")
parser = Parser(plant)
brick = parser.AddModelFromFile(brick_file, model_name="brick")
plant.Finalize()
plant_ad = plant.ToAutoDiffXd()
plant_ad.RegisterAsSourceForSceneGraph(scene_graph)
scene_graph.AddRenderer("renderer", MakeRenderEngineVtk(RenderEngineVtkParams()))
DrakeVisualizer.AddToBuilder(builder, scene_graph)
builder.AddSystem(plant_ad)
builder.AddSystem(scene_graph)
builder.Connect(plant_ad.get_geometry_poses_output_port(), scene_graph.get_source_pose_port(plant_ad.get_source_id()))
builder.Connect(scene_graph.get_query_output_port(), plant_ad.get_geometry_query_input_port())
diagram = builder.Build()
context = diagram.CreateDefaultContext()
simulator = Simulator_[AutoDiffXd](diagram, context)
simulator.AdvanceTo(2.0)
However, when I run this, I get the following error:
File "/home/craig/Repos/drake-exps/autoDiffExperiment.py", line 102, in auto_phys
DrakeVisualizer.AddToBuilder(builder, scene_graph)
TypeError: AddToBuilder(): incompatible function arguments. The following argument types are supported:
1. (builder: pydrake.systems.framework.DiagramBuilder_[float], scene_graph: drake::geometry::SceneGraph<double>, lcm: pydrake.lcm.DrakeLcmInterface = None, params: pydrake.geometry.DrakeVisualizerParams = <pydrake.geometry.DrakeVisualizerParams object at 0x7ff6274e14b0>) -> pydrake.geometry.DrakeVisualizer
2. (builder: pydrake.systems.framework.DiagramBuilder_[float], query_object_port: pydrake.systems.framework.OutputPort_[float], lcm: pydrake.lcm.DrakeLcmInterface = None, params: pydrake.geometry.DrakeVisualizerParams = <pydrake.geometry.DrakeVisualizerParams object at 0x7ff627736730>) -> pydrake.geometry.DrakeVisualizer
Invoked with: <pydrake.systems.framework.DiagramBuilder_[AutoDiffXd] object at 0x7ff65654f8f0>, <pydrake.geometry.SceneGraph_[AutoDiffXd] object at 0x7ff656562130>
From this error, it appears the DrakeVisualizer class only accepts systems which use float scalars exlusively. So I am stuck --- either I can go back to floats (but lose the autodiff differentiable simulation functionality I was after in the first place), or continue to use autodiffxd systems (but be completely unable to visualize what is going on in my simulation).
Is there a way to get both that I am missing?
Sorry for the pain and inconvenience. Your description and assessment are all spot on. Most of the visualization mechanisms are float only and, in its current state, attempts to visualizing an AutoDiff diagram will fail.
You have a couple of options (neither of which is appealing):
Go with one of the outcomes you've described above (no vis or no derivatives).
Put in a Drake feature request to be able to attach a visualizer to an AutoDiff diagram.
I can come up with some hacky workarounds (that aren't immediately clear would even work). So, if you're desperate for derivatives and visualization, they could be explored. But, ultimately, the feature request and a formal Drake solution would be the best long-term resolution.
=====================================
Big update. As of #14569, the DrakeVisualizer class is now templated on the scalar type (item 2 in the list above). That has two implications:
You can build an AutoDiffXd-valued diagram with a visualizer in it (as in your example), or
You can create a double-valued diagram and scalar convert it (i.e., diagram.ToAutoDiffXd() into an AutoDiffXd-valued diagram.
We are receiving fix messages from WebICE exchange in a text file and our application is reading and parsing them line by line using QuickFixJ. We noticed that in some messages the repeating group fields are not being parsed and upon validating with data dictionary getting error.
quickfix.FieldException: Out of order repeating group members, field=326
For example in the sample file data-test.csv the first 2 rows parsed successfully but third one fails with the above error message.
Upon investigation I found , in first 2 rows tag 326 comes after tag 9133 but in the third row it comes before that and hence fails in validation. If I adjust data dictionary as per the third one it succeeds but ofcourse the first one starts failing.
This is happening only for few messages for most of the other fix messages are getting validated and parsed quite fine. This is part of the migration project from existing C# application using QuickFix/N to our scala application using QuickFix/J. And its been working fine at the source end (with QuickFIx/N). Is there any difference in both the libraries QuickFIx/J and QuickFIx/N in terms of dealing with group fields ?
To help recreate the issue , I have shared the data file having 3 fix messages as explained above.
Data file : data-test.csv
Data dictionary : ICE-FIX42.xml
Here is the test code snippet
val dd: DataDictionary = new DataDictionary("ICE-FIX42.xml")
val mfile = new File("data-test.csv")
for (line <- Source.fromFile(mfile).getLines) {
val message = new quickfix.Message(line,dd)
dd.setCheckUnorderedGroupFields(true)
dd.validate(message)
val noOfunderlyings= message.getInt(711)
println("Number of Underlyings "+noOfunderlyings)
for(i <- 1 to noOfunderlyings ) {
val FixGroup: Group = message.getGroup(i, 711)
println("UnderlyingSecurityID : " + FixGroup.getString(311))
}
}
Request to fellow SO users , If you can help me with this.
Many Thanks
You should use setCheckUnorderedGroupFields(false) to disable the validation of the ordering in repeating groups. However, this is only a workaround.
I would suggest to approach your counterparty about this because especially in repeating groups the field order is required to follow the message definition, i.e. the order in the data dictionary.
FIX TagValue encoding spec
Field sequence within a repeating group
...
Fields within repeating groups must be specified in the order that the fields are specified in the message definition.
I've had great success using the gtsummary::tbl_regression function to display regression model results. I can't see how to use tbl_regression with pooled regression models from imputed data sets, however, and I'd really like to.
I don't have a reproducible example handy, I just wanted to see if anyone else has found a way to work with, say, mids objects created by the mice package in tbl_regression.
In the current development version of gtsummary, it's possible to summarize models estimated on imputed data from the mice package. Here's an example
# install dev version of gtsummary
remotes::install_github("ddsjoberg/gtsummary")
library(gtsummary)
packageVersion("gtsummary")
#> [1] ‘1.3.5.9012’
# impute the data
df_imputed <- mice::mice(trial, m = 2)
# build the model
imputed_model <- with(df_imputed, lm(age ~ marker + grade))
# present beautiful table with gtsummary
tbl_regression(imputed_model)
#> pool_and_tidy_mice: Tidying mice model with
#> `mice::pool(x) %>% mice::tidy(exponentiate = FALSE, conf.int = TRUE, conf.level = 0.95)`
Created on 2020-12-16 by the reprex package (v0.3.0)
It's important to note that you pass the mice model object to tbl_regression() BEFORE you pool the results. The tbl_regression() function needs access to the individual models in order to correctly identify the reference row and variable labels (among other things). Internally, the tidying function used on the mice model will first pool the results, then tidy the results. The code used for this process is printed to the console for transparency (as seen in the example above).
I am using ROCR library and the prediction function for creating ROC curves. I am doing like this (copied from Stack Overflow)
p_Lr <- predict(Model_Lr,newdata=Tst,type="response")
pr_Lr <- prediction(p_Lr, Tst$Survived)
prf_Lr <- performance(pr_Lr, measure = "tpr", x.measure = "fpr")
This works - in the beginning. Suddenly after programming and running various code (I am unfortunately not able to say precisely which code) the line
pr_Lr <- prediction(p_Lr, Tst$Survived)
doesn't work any more and gives following error msg:
Error in nn$covariate : $ operator is invalid for atomic vectors using rocr library prediction
Then if I detach and add the ROCR library like this
detach(package:ROCR)
library(ROCR)
it works again! Anybody have any idea why and what to do?
Using the sos findFn function, it appears that two other packages have a function called prediction: bootPLS and frailtypack. Loading any of these packages after ROCR would mask ROCR's prediction function and prevent performance from working.
By re-attaching ROCR you put its prediction function back in front of the search path.
An alternative solution would be to use ROCR's prediction function explicitly:
p_Lr <- predict(Model_Lr,newdata=Tst,type="response")
pr_Lr <- ROCR::prediction(p_Lr, Tst$Survived)
prf_Lr <- ROCR::performance(pr_Lr, measure = "tpr", x.measure = "fpr")
I'm trying to analyse TMS-EEG data from Nexstim with FieldTrip Toolbox. I want to make a trial matrix from my raw .nxe data. But how I know which triggers to assign for cfg.trialdef.eventvalue, when cfg is the output variable. I'm trying to mimic the same kind of code as you can find from the tutorial: http://www.fieldtriptoolbox.org/tutorial/tms-eeg
I came up with a solution to the problem. With a command event = ft_read_event('filename.nxe') I got a struct with fields: type, value, sample, duration and offset and this is all I need.