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HI,
I am looking for ways to add a random effect in a SUR model, using R or SAS.
To be more specific, I have panel data measured at an individual-and-daily level, and I want to stack 3 equations with different dependent and independent variables in a SUR model, with an individual random-effect coefficient.
If you guys have any example codes that I can refer to, it would be a great help!
Thank you:)
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I have used command at stata
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NOTE 1 [file ions.mdp]:
With Verlet lists the optimal nstlist is >= 10, with GPUs >= 20. Note
that with the Verlet scheme, nstlist has no effect on the accuracy of
your simulation.
Setting the LD random seed to -397045813
Generated 100474 of the 100576 non-bonded parameter combinations
Generating 1-4 interactions: fudge = 1
Generated 66298 of the 100576 1-4 parameter combinations
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The error suggests that the nstlist value is too low for simulations with GPUs, which requires a minimum of 20. Adjust nstlist to be greater than or equal to 20 in your ions.mdp file to resolve the issue.
All of which can be easily done at mdsim360.com, a new platform that lets you run MD simulations entirely online without local installation.
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# 174
Dear BARTŁOMIEJ KIZIELEWICZ, ANDRII SHEKHOVTSOV, JAKUB WIĘCKOWSKI, JAROSŁAW WĄTRÓBSKI, and WOJCIECH SAŁABUN
I read your paper:
The Compromise-COMET method for identifying an adaptive multi-criteria decision model
My comments
1-In the abstract you say “which identifies the adaptive decision mode based on many normalization techniques and finds a compromise solution”
And how do you find a unique compromise solution by comparing rankings from several methods?
Are you looking for a composite ranking? But even if you get one what is it good for? The fact that you got a composite reranking is of no use, because you do not know which is the real ranking.
2 – Page 3 “Effective resource utilization is paramount to prevent irreversible environmental damage”
There is no doubt that resources are paramount, but not only for environmental damage. They are fundamental for any resource be it money, people, water, fuel, etc. Unfortunately, maybe 99% of the more than 200 MCDM methods consider that resources are infinite, and thus, they are not contemplated. The exception is PROMETHEE and Lineal Programming (LP).
3- Page 3 “management, and mitigating negative impacts .This underscores the relevance of MCDA methods,
which can facilitate selecting optimal decisions that align with”
sustainability goals.
Only sustainably goals? In reality, in real projects, all criteria are goals, and consequently, they must have a target.
4 - “adaptive compromise method for decision modeling”.
And what is it? You do not explain, at least what it means.
5- “Existing methods so far are susceptible to the rank-reversal paradox”
As per my research on RR it is not a paradox, but a natural and at random geometrical occurencre. As a fact since you are always working with the same number of alternatives or dimensions, to talk about RR appears irrelevant, since you are not adding or deleting alternatives. Normalization only may change the order or position of alternatives, and this is not related to RR, since dimensions are preserved.
6- “While current approaches offer discrete ratings and compromise rankings for a fixed set of alternatives, they falter when evaluating new alternatives”
Naturally, because by adding or deleting alternatives you are mapping data in a space of say 2 dimensions or alternatives, in another of 3 dimensions.
This means that in the 2D all feasible solutions of the problems are contained in a planar polygon. When you pass to 3D, the polygon becomes a polyhedron. Therefore, if in the polygon you find for instance that A2 >A1, this ranking may or not be preserved in 3D.
It is easy to see this in its geometrical constructions, and thus, the act of adding an alternative, delivers more information that could alter the original ranking, , in the same way that a cube provides more information than a and expanded rectangle.
Of course, you could not accept my theory, and in this case, I would be interesting to know yours, that is, why adding a new alternative may produce RR
7- “Each previous evaluation set or alternative requires recalibration”
What is a recalibration? Do you mean to run again the software?
8- “The paper presents the C-COMET method, offering a unique approach to establish adaptive decision models, impervious to the Rank Reversal Paradox”
Were you able to prove this assertion?
9- “method is the Analytic Hierarchy Process (AHP) approach, which is based on mathematical modeling of the relative importance of criteria and alternatives”
I am puzzled, since how can you consider a right mathematical modelling using AHP when the resulting initial matrix is FORCED to be transitive, irrelevant of what the DM estimates?
10- “Therefore mentioned authors proposed a new MCDM approach free of the Rank Reversal Paradox for a safer and more reliable decision”
Interesting, and how this can be done? I do not know what method these authors proposed; if in reality it works, at present it will be largely known. In my opinion, this is impossible, because violates the geometrical principles of working with multi dimensional spaces. By the way, I can prove mathematically and with examples what I say regarding RR
11- “Sequential Interactive Model for Urban Systems (SIMUS)”
I am afraid that this is not exact. SIMUS suffers from RR as any other method, if it weren’t, my RR theory would be invalid, however, due to its algebraic structure, it that does not compare alternatives, but selects them using the economics concept of Opportunity Cost and ranks criteria in each iteration, through a ratio analysis, and it could be the reason for its resistance to RR, as I have demonstrated using examples and in 66 combinations of adding and deleting, as shown in my book published in 2019 and also in its second edition in 2024
Recently, in an actual work I consider a case starting with 2D and adding a new one up to 10D.The results clearly shows that sometimes the invariance of the ranking is preserved for several dimensions, while in others it changes adding only an additional dimension. Why the randomness? Because it depends on the values of the new vector inputted and its interactions with the existing vectors. For this reason, nobody can say that a new alternative is better or worse that those existing.
As a fact, in my actual example, as new alternatives or dimensions are added, the rankings tend to be decreasing in length and at the very end, in 10D there is only one alternative. The reason could be that as we increase the alternatives, the next one incorporates the values of the precedent, as a cube also contains the information from the precedent square. In addition, it appears that the more the dimensions the larger the am amount of information, which is lineal. However, adding only one more alternative, the feasible solutions space be very very complex, and it could be that in 10D it is not possible to determine the coordinates in 10 dimensions due to the complexity of the polytopes.
As a bottom line, I am not saying that my theory on RR can explain everything, but I understand that it helps to understand the RR issue.
12- In page 6 you say MEREC or Entropy, meaning that both address the same issue. I disagree.
MEREC works removing one criterion at a time and then restoring it and using the next. The procedure is attractive, but in reality, in a set of say 9 criteria, the method is applied to nine different problems, because in each one are considered only 8 criteria instead of nine in the original problem. And thus, in each run the software will work on 9 different scenarios
These are some of my comments. I hope they can help.
I am willing to share with anybody my findings
Nolberto Munier
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Dear Nor
Could you please tell me to what of the several questions that I formulated in that paper you are referring to?
When I receive your answer, I may be able to understand the meaning of your short sentence
Please, be a little more exp[licit
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In a linear mixed model, I used an alpha lattice designer. If the environment is a combination of season and location, can I consider it a random factor in my analysis? May I be correct?
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This should be correct ... according to a similar (earlier / 7 year old) discussion seen next
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Random error?
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Hello, could you tell us more. I can say there is a random error variable in mathematical models. To start, let say (generally speaking) it represents the difference between the observed error values and the error values predicted by the model. You can look at simple linear regression models dependent (Y) vs independent variable (X), Markov Chain, Ito process with Brownian motion, chaos, stockastic control system, control system with noise, market and forex, etc. If you have a specific case in mind, feel free to throw it in the discussion. Best regards
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After copolymerization, how can I recognize the type of synthesized copolymer (alternate and random)?
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To identify the type of copolymer (alternate or random), NMR spectroscopy is the most reliable technique. It reveals the sequence distribution of monomers in the polymer structure. Supporting methods include IR spectroscopy, gel permeation chromatography, thermal analysis, and elemental analysis. However, NMR spectroscopy provides the most definitive characterization of the copolymer's microstructure.
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somebody says if known population we should go by Random Population. somebody says if it is unknown population we should go non random population is it true
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Also, if by "unknown population" you mean one that is "hard to reach," "rare," or "hidden," you could look at 'snowball' sampling. (It might at least help you learn more about your population.)
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I used CMA software for standard mean difference. One moderator show multi-colinearty issue. Kindly suggest some way
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you have an artifact in your data entry, such as dividing a number than multiplying it again. go through and look at all of your relational equations with a keen eye for something rite side up followed by upside down. realky. i see it all the time.
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This is whít is going on on my profile. Pls, help.
Peter Krasztev
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How many people do you follow? And how many articles do you read on researchgate? In principle the more you do on here the better their algorithm should predict your interests.
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I'm analysing a dataset from a field survey designed to test how tw types of marine protected areas affect species composition of marine seagrasses, and now struggle how to properly deal with the nested nature of our data.
Our design is a mixed model nested ANOVA (following the terminology in Quinn and Keough 2002), with three factors:
1) Management - fixed factor with three levels (open, closure and park)
2) Site: random factor with a total of 12 levels, nested within 'Management'. For each level of management there are 4 unique 'site' levels.
3) Transect; fixed factor with 3 levels (shallow, mid, reef) which is crossed with 'Management'.
Along each 'Transect' there's seagrass species-level shot count data from ca 10 stations (replicates). Sampling was done 1 time in each station, so there's no repeated measures.
We're trying to test the effects of 'Management', 'Transect' and their interaction on seagrass species composition using PERMANOVA as implemented in the adonis() routine in the vegan package for R. The standard code for a design with a blocked (crossed) random factor would be:
adonis(species ~ management * transect, strata = env$site, data = d)
However, in our case the random factor is nested under the main factor - not crossed with it. As I understand it is possible to constrain the permutations using the 'permutations = how' argument, and then specify a custom permutation design. See, for example, here:
But I've never worked with customized permutation designs before and struggle to find tutorials, so would really appreciate any form of feedback.
Anyone can provide some advise?
I've also looked into the nested.npmanova() function in the BiodiversityR package. This can properly handle a design like ours with 2 factors (one main, one nested) - but we have 3 factors...
We're also open to instead using the mvabund() routine, i.e. a GLM- rather than distance-based framework, if it can help us properly deal with the nested nature of our random 'site' factor. But so far I've only found examples where it can be used to handle crossed random factors.
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Hi Johan S Eklöf , did you ever find a solution to this problem? I have a similar setup to your example and also need to account for both nested and random effects. I've been reading up on the options you've mentioned above but haven't found a well-rounded solution yet. I would appreciate any insights you can provide! Thanks, Dina
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A forest fire occured in Turkey. The total area was about 1700 ha. I am interested in sampling the site and also sampling control sites outside the burned area. My question is, would I represent the burn area if I sampled 4 plots about 6 ha in area. Within each plot, I would take 4 composite sample (each sample would be a mix of several soil cores). The plots would be the experimental unit. But they are very large. I wonder if this is too large? I would be comparing nutrient concentrations in the burn plots versus similar control plots (outside the burn). Fixed effect: burn treatment; Random effect: plot.
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What are you sampling for?: soil chemistry, soil physical properties, depth of fire penetration? Are you adjusting for known soil types in advance? It may be better to sample for some factors with many small soil cores (4 plots x 100 samples), while others may need only 4 plots x 4composites to get all you need.
Speak to your local friendly statistician now (before you go sampling), as they generally are less friendly when you turn up after sampling and ask for help with the analysis.
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The sum of two independent random variables is also a gamma random variable. What would it be when the sum of two independent gamma random variables with different parameters. eg: X~Gamma(a,b) and Y~Gamma(c,d) ,Then the distribution function of X+Y?
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In this case, you may use the convolution to evaluate the density, but it cannot be write in a closed form. You can try numerical approach.
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The protocol fro the new NEBNext UltraExpress® RNA Library Prep Kit NEB #E3330S/L closely follows to the previous version NEBNext Ultra II RNA library Prep Kit # E7770 S/L, but random primer step is missing. How 1st strand synthesis works without it? Is random primer added into some mix now?
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In the UltraExpress kit, the random primers are integrated into the First Strand Synthesis Mix. This streamlines the workflow by reducing the number of steps and reagents you need to handle. The First Strand Synthesis Mix contains all the necessary components, including the random primers, to synthesize the first strand of cDNA from the RNA template.
This integration helps to simplify the protocol and reduce the overall preparation time, making the process more efficient while still maintaining high-quality results.
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do ANOVA test, the random variables (replications) are significantly different. How to process the original data?
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Looks like you may want to go for a mixed-effects model. This allows to model common sources of variance, like the experimental replication. If the design is balanced, this will be identical to using a rmANOVA.
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Good day! The question is really complex since CRISPR do not have any exact sequence - so the question is the probability of generation of 2 repeat units, each of 23-55 bp and having a short palindromic sequence within and maximum mismatch of 20%, interspersed with a spacer sequence that in 0.6-2.5 of repeat size and that doesn't match to left and right flank of the whole sequence, in a random sequence.
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Estimating the probability of forming a short CRISPR with a single spacer in a random sequence involves several steps. This calculation depends on the specific sequence characteristics and the CRISPR system's requirements. Here’s a structured approach to estimate this probability:
**1. Define the Parameters
**1.1 CRISPR System Characteristics:
Spacers: Typically, spacers in CRISPR systems are around 20 nucleotides long.
Protospacer Adjacent Motif (PAM): CRISPR systems require a PAM sequence adjacent to the target site. For example, the Streptococcus pyogenes Cas9 requires the PAM sequence "NGG."
**1.2 Random Sequence Properties:
Length: Determine the length of the random sequence where you are searching for the spacer.
Nucleotide Composition: For a truly random sequence, assume equal probabilities for each nucleotide (A, T, C, G).
**2. Calculate the Probability of a Specific Spacer Sequence
**2.1 Probability of Matching a Specific Spacer:
Calculate for PAM: If the PAM sequence is required, first calculate the probability of finding this PAM sequence in the random sequence.
Probability of Spacer Sequence: For a spacer of length L nucleotides, the probability of finding a specific sequence of length L in a random sequence is:
𝑃
(
spacer
)
=
(
1
4
)
𝐿
P(spacer)=(
4
1
)
L
where
1
4
4
1
is the probability of each nucleotide occurring at a specific position, and
𝐿
L is the length of the spacer.
**2.2 Consider PAM Sequence:
Probability of PAM: For a PAM sequence of length k nucleotides, assuming equal probability for each nucleotide, the probability of finding the PAM is:
𝑃
(
PAM
)
=
(
1
4
)
𝑘
P(PAM)=(
4
1
)
k
**3. Calculate the Probability of Spacer and PAM Co-occurrence
**3.1 Independent Events:
Assuming Independence: If the presence of the spacer and PAM are independent, the combined probability of finding both in the random sequence is:
𝑃
(
spacer and PAM
)
=
𝑃
(
spacer
)
×
𝑃
(
PAM
)
P(spacer and PAM)=P(spacer)×P(PAM)
**3.2 Search Space:
Length of Random Sequence: If you are searching within a sequence of length N, the number of potential positions for the spacer and PAM is N - (L + k - 1).
**4. Estimate the Expected Number of Hits
**4.1 Expected Hits:
Calculate Expected Number: Multiply the probability of finding the spacer and PAM by the number of potential positions:
Expected Number of Hits
=
𝑃
(
spacer and PAM
)
×
(
𝑁
(
𝐿
+
𝑘
1
)
)
Expected Number of Hits=P(spacer and PAM)×(N−(L+k−1))
**4.2 Adjust for Overlaps:
Overlap: Adjust calculations if the spacer and PAM are not independent or if there are constraints on their positioning relative to each other.
Example Calculation
Assuming:
Spacer length (L) = 20 nucleotides
PAM length (k) = 3 nucleotides
Random sequence length (N) = 1000 nucleotides
Probability of Spacer:
𝑃
(
spacer
)
=
(
1
4
)
20
=
9.09
×
1
0
13
P(spacer)=(
4
1
)
20
=9.09×10
−13
Probability of PAM:
𝑃
(
PAM
)
=
(
1
4
)
3
=
0.0156
P(PAM)=(
4
1
)
3
=0.0156
Combined Probability:
𝑃
(
spacer and PAM
)
=
9.09
×
1
0
13
×
0.0156
=
1.42
×
1
0
14
P(spacer and PAM)=9.09×10
−13
×0.0156=1.42×10
−14
Expected Hits:
Expected Number of Hits
=
1.42
×
1
0
14
×
(
1000
(
20
+
3
1
)
)
1.42
×
1
0
14
×
977
1.39
×
1
0
11
Expected Number of Hits=1.42×10
−14
×(1000−(20+3−1))≈1.42×10
−14
×977≈1.39×10
−11
Conclusion
In this example, the probability of finding a specific 20-nucleotide spacer and a 3-nucleotide PAM sequence in a random 1000-nucleotide sequence is extremely low, reflecting the challenge of finding specific CRISPR target sites. Adjust parameters accordingly based on your specific requirements and sequence characteristics.
l This protocol list might provide further insights to address this issue.
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I'm very embarrassed to admit this, but I don't understand how random hexamer primers (RHP) work in reverse transcription. I made RT with gene-specific or oligo-dT oligos hundreds of times, the whole idea is absolutely clear. But when we come to RHP...
Okay, let's say we have set of random hexamers, the most downstream one (green on the upper picture) anneals to our RNA template and serves as a primer for reverse transcriptase. But what about others, annealing somewhere upstream (purple on the picture)? What happens then RT enzyme reaches them, why don’t they (especially GC-rich ones) interfere with revertase movement? At least in case of PCR such oligos annealing inside the amplified region effectively block the amplification.
On the other hand, if all of these random hexamers are capable of priming reverse transcription, in the end we will have a whole bunch of short cDNA fragments, barely usable for subsequent PCR amplification.
I’m afraid I miss something very important (and simple). I would greatly appreciate any clarification!
Stan
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I think they block. Thats why we see short fragments of cDNA.
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HI there, I've came across several articles discuss about random audit an Non random to tax evasion or compliance. Most of the articles is relating about effect of audit (random or non random) conducted by tax department in Norway.
1) what is random audit.
2) What is the method of random audit
3) Does taxpayers notified that they has been audited via random selection? Since the article found most of random audit leads to tax evasion by taxpayers. I expect the taxpayers know that they has been selected randomly and wont be selected again in a near corner so that they tend to underreport income and overstate of relief and deductions for anticipating audit wont come again.
Hope anyone here could make it clear for me. TQ in advance
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A random audits are audit for fair method of allocating resources i.e the regulators are not able to audit all entities of interest but random audits allow every potential person or firm to subject to audit to have a similar probability of being audited.
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I want to simulate a network with approximately 50 gNBs and 500 UEs with different deployment options such as random, uniform, and hexagonal for the gNBs, and uniform, random deployment for the UEs and study the impact of interference, mobility, etc. Are there any options available in NetSim to quickly deploy such networks and study their performance? Thank you.
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To configure NetSim for your scenario, follow these steps:
Generate Random UE Positions:
- Use a Python or Excel program to generate random UE positions around each gNB.
- Ensure each run uses a different seed for the random number generator to create unique placements.
Import Positions and Run Simulations Manually:
- Use NetSim's Rapid Configurator to import the generated UE positions into Excel for each simulation.
- Manually import the positions and run the simulation for each of the N instances.
Automate the Process (for large N):
- For a large number of simulations, automate the process using NetSim's batch processing method.
- Refer to https://tetcos.com/netsim-utilities.html for detailed instructions.
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Thank you in advance for your support.
We conducted a stratified random survey in 13 strata and obtained a sample size for each stratum.
At the time of the survey we had a variable non-response rate in each stratum (2-33%). I hope you can give me some guidance on how to calculate the weights to include the non-response rate in the weights, in order to make estimates on the total sample (N=16,000).
Regards!!
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The problem of dealing with non-responses is extremely complex and must be approached with caution.
The "data" you provided is not sufficient for a comprehensive answer.
I can recommend the following text, which deals with the problem with adequate completeness:
Bethlehem, Cobben, Schouten (2011), Handbook of Nonresponse in Household Surveys, Wiley & Sons
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I try to explaim better. I have the scores for 10 items. Each item score are decided between the accordance of two persons (say, e.g., a therapist and a patient). Therefore, there is much more variability, depending on the subjectivity of the evaluators. How can I account for this variability and subjectivity in the evaluation scores? Should be a good way to take into account this variability using a regression mixed model, taking into account random effect? But, in order to carry out a Confirmatory Factor Analysis for the validation of the istruments, can I combine this two technics? And how?
Thank you
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Tow potential solutions: MNLFA and multilevel CFA
Moderated non-linear factor analysis by Dan Bauer. He and Andrea Hassong have a 2009 paper in psych methods and Dan has a solo paper in psych methods in 2017
Bauer, D. J. (2017). A more general model for testing measurement invariance and differential item functioning. Psychological Methods, 22(3), 507–526. https://doi.org/10.1037/met0000077
Curran, P. J., McGinley, J. S., Bauer, D. J., Hussong, A. M., Burns, A., Chassin, L., ... & Zucker, R. (2014). A moderated nonlinear factor model for the development of commensurate measures in integrative data analysis. Multivariate behavioral research, 49(3), 214-231.
You may also consider multilevel confirmatory factor analysis: Kris Preacher and Michael Zyphur have done a lot of really good work.
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In CNN(convolution neural network), can the feature map obtained determinately by a random initialization convolution kernel? if not, how to decide the weights in convolution kernel to obtain the feature maps we need? By trial and rerror, are we shotting if our eyes closed?
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adaptive convolution kernel based on input-data distribution, possible or impossible?
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Like Z = sqrt(a)X + sqrt(b)Y where X~N(0, I) and Y~N(0, I)
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Well, your second equation, Z(t) = Z(t-1) + sqrt(a)ε(t-1) + sqrt(b) ε(t-2), looks like something similar to GARCH(1,2) or GARCH(2,1) model. Anyway, eventually, first, you can argue that the difference Z(t) - Z(t-1) is a normal random variable provided ε's are normal. And, second, applaying / using the Cramér's decomposition theorem
yields that Z(t) and Z(t-1) are themselves normal - - hopefully, this helps somewhat.
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Variable x is a controllable, and variable y is a random varialbe. Also, y=b*x+u, and u is a random iterm. So x and y is correlated. But, if we calculate the covariance between x and y, according to the definition Cov(x,y)=E[(x-Ex)(y-Ey)], the value should be zero. Since Ex=x.
Is the conclusion correct?Thanks
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Well, then corr = correlation of 'x' and 'y' becomes
corr(x, y) = corr(x, b*x+u) = corr(x, x) + corr(x, u) = 0,
where Cov(x, u) = 0 follows the argueing for Cov(x, y) = 0.
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I'm having issues with the MATLAB livelink for COMSOL. I want to model a composite RVE of a random fiber reinforced composite in COMSOL using the random sequential adsorption algorithm code developed in MATLAB. How do i go about it?
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Two complete and useful Abaqus plug-ins are now available which can model unidirectional composite RVE with random fiber distribution considering periodicity. The volume fraction can be even more than 80%.
Please check these plugins and related videos:
3D plugin:
2D plugin:
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In some of the Phd dissertations, I see randomized controlled mixed methods study is conducted applying only feasibility phase with 3-5 participants as a pilot trial. Then the main study is started. Is this method ok? I think they should conduct a pilot trial first which includes feasibility and represents main study including randomized controlled trial design. For example 30 intervention and 30 control groups. How a new developed health education program could be designed best ? Which of the quidelines should be followed ?
Thank you so much for your response in advance!
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Joash Sande Please credit Chat-GTP or the equivalent AI when you re-post responses from it.
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I have recently started working with arabidopsis and every time I pour the agar plates, I start seeing contamination after 3rd or 4th day.
Usually, there is no contamination after I pour the agar and let it sit for one day.
The contamination occurs on some of the seedlings, as well as some random parts of the plate.
I try not to pass my hands from on top of the plates, I UV the hood and the plates for 30 mins, and always clean the hood with 70% ethanol before starting.
I am open to any suggestions on how to improve myself.
Thank you.
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If you are using glass petri plates(autoclavable), make sure you choose a perfect pair otherwise if there is any space left in between then it could be a source of contamination.
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Hello, is it possible to predict future outputs, or even restore the parameters, of a random generator with the function f(x) = ax**2 + bx +c (mod m) when the first 10 generations are known? Thanks.
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For the i.i.d. random variable m_1,...,m_L sampled from the binomial distribution with the parameters n_1 (number of Bernoulli trials) and P (prob. of success) what is the distribution of the product \prod_i (m_i/n_1)? We can assume that n_1 is large.
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Steftcho P. Dokov For m_1 from the binomial distribution with parameters n_1 and P the distribution of the density \mu=m_1/n_1\in [0,1] is given by P(\mu)=C\exp(-n_1 D(\mu||P)) for large n_1. Here C is normalisation constant and D(Q||P) is relative entropy between probabilities Q and P. Furthermore, expanding D(\mu||P) around \mu=P we will get Gaussian but with the support on [0,1], so \prod_i (m_i/n_1) will be the product of iid rv from this "clipped" Gaussian for large n_1.
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Hi all!
I've been collecting data on a group of 8 chimpanzees at Chester Zoo for my dissertation. The group consists of 4x males and 4x females, all of which have different hierarchical status' and ages.
I have been doing random focal observations with a checksheet consisting of 4 state behaviours (timed) and 6 behaviours (frequencies). I would start a random focal observation when a stressful context arose (such as high visitor numbers, anticipation to feeding, or feeding time)and denote the durations or frequencies of behaviours exhibited by that individual for 15 minutes. Then at the following visit, I would observe the same individual at the same time but under a non-stressful context (therefore utilising the Matched Control Method).
This process repeated for 4 months and I now have a complete data set.
I am <really> struggling on 1. How to use SPSS, and 2. What tests would be ideal to use? As you can imagine there is quite alot of data which hold different values so you can hopefully see my confusion around this.
Ideally, the statistical analysis of my data will reveal which contexts in the zoo precipitate an increase in stress the most (e.g. high visitor numbers, anticipation to feeding, feeding). I also want to be able to compare this data to the hierarchial status' and ages of the individuals.
Any help would be so appreciated. Thanks in advance!
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I have. YouTube video on which test to use. My channel also has videos of tests using PSPP which is a freeware clone of SPSS. You will find it useful for your study.
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In a random effects regression we have the assumption that the individual specific heterogeneity is not correlated with the predictor variables:
Yit = 𝛽1Xit,1+ 𝛽2Xit,2+…+ 𝛽kXit,k+ 𝛼𝑖 + 𝑢𝑖t
i = entity-individual
t= measurement at time t
αi ~ N(0,σα), (i=1….n) is the unknown intercept for each entity ( n entity-specific intercepts)
Yit is the dependent variable where i = entity and t = time
Xit is an independent variable
𝑢𝑖t idiocynraticerror
Assumption: cov(αi ,Xit) = 0
Do we also make this assumption when using linear mixed effects models?
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I am having trouble differentiating between a random effects model and a linear mixed effects model. I am currently using this model https://bashtage.github.io/linearmodels/panel/panel/linearmodels.panel.model.RandomEffects.html#
for my research. Can somebody tell me if this is a random effects model or a linear mixed effects model and what are the differences between the two models?
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The only difference of note is that the random effects model could be non-linear (e g in analysing binary or count data). The mixed effects model includes a 'fixed part' - that is means, and a 'random part' (that variances and covariances).
Lot of resources here
and the free online course
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This article is available to be requested from the author in Research Gate. Have you tried asking for it?
There are a few articles which are available on the internet for download. Hope these help you find what you are looking for.
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Hello everyone! As you understand, high-precision positioning using global navigation satellite systems or simply high-precision determination of a random variable. At what point does your estimates precision fall into the "highly precision" category? Is this always a convention associated with the method of determining a random variable or is there a general formulation for classifying estimates as highly precision?
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The precision of a class must be defined by specifications, which define the RMS and the instruments to be used. If there are no specifications, then people involved in such a class get together and decide about the specifications for the class.
The number of digits also characterizes the precision of an instrument. If a theodolite measures an angle with a direct reading of one second, its precision is one second. If you want to test it you measure several times the three angles of a triangle, and you see how much the closing error is.
In any case, you define precision by specifications; you test precision by statistical analysis of measurements of a well-designed experiment.
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Similar to what we typically do with CCD, but in a randomized manner!
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Ali Ashraf Joolaei three items and below those, a few links…
A)__ In a Google and Google Scholar search type…
"method or software for designing random experiments".
B)__ Be aware and conversant with the “bias” inherent in our ideas of randomness. - In my experience randomness is very hard to design. Today software and AI are common tools. But they hide the bias of the software designers and AI creators. - Please be sure to read and study “Design of Experiments” (DOE) before embarking on random designs (see #1 below)
1)__What is Design of Experiments - DOE
2)__What is Statistical Process Control
3)__Designing and Analyzing Randomized
Experiments: Application to a Japanese
Election Survey Experiment
4)__Design and Analysis of Experiments with randomizer
5)__Design-Based Methods for the Analysis of Modern
Randomized Experiments
6)__Design of Experiments via Random Design
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I've been working on a research project using a multi-level regression model, and I'm currently considering the presentation of Hausman test results. I've noticed in some papers that the authors conducted the test but did not include numerical results, only mentioning that the random effects model was deemed more suitable.
I'm curious about reporting Hausman test results. Should these results be reported separately for each model? I understand that if the test is insignificant (p > 0.05), it suggests that the random effects model is more suitable, but I wonder if there's a convention for reporting the test value and additional statistical evidence to support this conclusion, such as a chi-square value.
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import pandas as pd
# Sample data for demonstration purposes
data = {'Model': ['Model 1', 'Model 2', 'Model 3', 'Model 4', 'Model 5'],
'Test_Statistic': [1.23, 2.45, 0.98, 3.21, 1.67],
'Degrees_of_Freedom': [5, 7, 4, 6, 5],
'P_Value': [0.56, 0.12, 0.73, 0.04, 0.61]}
results_df = pd.DataFrame(data)
# Display the Hausman test results
print(results_df)
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The problem of writing a Sas command to analyze a factorial design in a completely randomized format in two years
Factorial design in a completely random format with 3 factors that was implemented in two years
Now, to analyze it in SAS, I don't know how to use a simple split plot, should I consider the year in a chopped form? Are other invoices complete?
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Hello Taha,
It sounds as if you intend to use factorial anova as the primary framework for your analysis. Do note that either MLR or a path model approach (simplest case of structural equation models) could be used as well. All this presumes that your data conform reasonably well to the associated assumptions required for either of the three methods.
Your question appears to hinge on how to treat the "two years" aspect of the study. If the very same cases were used each time, and the very same conditions were applied, and the very same outcome measure was used to collect scores, then you could treat the two-years dimension as a repeated measures factor, which would yield a variant of the split-plot design (which appears to be a three between subjects factor and one within subjects factor design).
Goo luck with your work.
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Does Randomness exist or is an illusion? Did God have any choice in whether to create/allow Randomness or not? Is there any connection between Free will and Randomness?
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Change is not possible unless we have the autonomy of free will. The ability to turn yourself around can come only from within you;without free will there would be no purpose to life.
This is one of the reasons why quantum physics—random at its core—is so hard to understand. It is difficult for us to accept that at the core of our reality, there is nothing but randomness.
There are two things that stand in the way of Divine Providenceobjective laws of nature and subjective human free choice. Laws of nature prescribe an object how to behave in a predictable fashion leaving little room for the manifestation of Divine providence. Likewise, the human choice leaves little room for Divine providence.
Some people may be fooled by randomness but, when we realize that randomness opens the door to the Divine, we are saved by randomness.
Relying on a random choice opens the door for the Divine providence.
________
In his most famous book A Theory of Natural Philosophy (1758) RJ Boshkovich says: Regarding the nature of the Divine Creator, my theory is extraordinarily illuminating, and the result from it is a necessity to recognize Him. ... Therefore vain dreams of those who believe that the world was created by accident, or that it could be built as a fatal necessity, or that it was there for eternity lining itself along his own necessary laws are completely eliminated.
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I am trying to train a CNN model in Matlab to predict the mean value of a random vector (the Matlab code named Test_2 is attached). To further clarify, I am generating a random vector with 10 components (using rand function) for 500 times. Correspondingly, the figure of each vector versus 1:10 is plotted and saved separately. Moreover, the mean value of each of the 500 randomly generated vectors are calculated and saved. Thereafter, the saved images are used as the input file (X) for training (70%), validating (15%) and testing (15%) a CNN model which is supposed to predict the mean value of the mentioned random vectors (Y). However, the RMSE of the model becomes too high. In other words, the model is not trained despite changing its options and parameters. I would be grateful if anyone could kindly advise.
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Dear Renjith Vijayakumar Selvarani and Dear Qamar Ul Islam,
Many thanks for your notice.
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I've been reading about Claude Shannon and Information Theory. I see he is credited with developing the concept of entropy in information theory, which is a measure of the amount of uncertainty or randomness in a system. Do you ever wonder how his concepts might apply to the predicted red giant phase of the Sun in about 5 billion years? Here are a few thoughts that don't include much uncertainty or randomness -
In about 5 billion years the Sun is supposed to expand into a red giant and engulf Mercury and Venus and possibly Earth (the expansion would probably make Earth uninhabitable in less than 1 billion years). It's entirely possible that there may not even be a red giant phase for the Sun. This relies on entropy being looked at from another angle - with the apparent randomness in quantum and cosmic processes obeying Chaos theory, in which there's a hidden order behind apparent randomness. Expansion to a Red Giant could then be described with the Information Theory vital to the Internet, mathematics, deep space, etc. In information theory, entropy is defined as a logarithmic measure of the rate of transfer of information. This definition introduces a hidden exactness, removing superficial probability. It suggests it's possible for information to be transmitted to objects, processes, or systems and restore them to a previous state - like refreshing (reloading) a computer screen. Potentially, the Sun could be prevented from becoming a red giant and returned to a previous state in a billion years (or far less) - and repeatedly every billion years - so Earth could remain habitable permanently. Time slows near the speed of light and near intense gravitation. Thus, even if it's never refreshed/reloaded by future Information Technology, our solar system's star will exist far longer than currently predicted.
All this might sound a bit unreal if you're accustomed to think in a purely linear fashion where the future doesn't exist. I'll meet you here again in 5 billion years and we can discuss how wrong I was - or, seemingly impossibly, how correct I was.
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"Expansion to a Red Giant could then be described with the Information Theory"
Expansion to a Red Giant IS described with Physics (entropy included). It's irreversible, insofar as while most stars go through a second visit to the red giant phase, their intermediate compact phase (Helium fusing core) is never the same as the previous compact phase (Hydrogen fusing core). You cannot return to the same initial conditions.
It's not that information theory is wrong, it's that it's absolutely peripheral to the physical processes that govern stellar evolution.
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The protocol was designed for RNA sequencing can't be applied because RIN number was low and the cDNA for the same sample was obtained by random hexamer primer. What can i do to fix the situation because i can't do the process of RNA extraction again
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Hi Mustafa
if it's for whole transcriptome, you can be interested in the 3' RNAseq which offers good results even with poor RIN RNA samples.
just read this paper:
all the best
fred
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Specifically, how does subject-level random intercept and random slope influence the goodness-of-fit (R-squared) of the model?
And, if subject A contributes 10 data-points and subject B contributes 5 to the whole dataset, wouldn't A account for more of the total residual error than B? How do multilevel models control for this?
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As Rainer Duesing noted there is no such thing as R^2 for a multilevel model - at least in the sense that there is no single statistic that has all the properties of R^2. Various alternatives exist that have some of the same properties, but need to be used carefully. Also note that R^2 isn't necessarily the best goodness of fit measure in a linear model and tends to be overemphasised.
Your second question is probably more interesting because it gets at a fundamental property of multilevel models - which is they incorporate shrinkage. If we have two units A and B with 5 and 10 observations respectively we traditionally think of there being two options: (mean_A+mean_B)/2 to estimate the mean or (sum of all observations)/15. What a multilevel model does is use a kind of compromise estimate between these - effectively closer to the mean of the less noisy (often larger) unit than the (mean_A+mean_B)/2. This shrunken estimator has nice statistical properties for some purposes - arguably it is more efficient and generalises better.
There are lots of shrinkage estimators (I think) but in multilevel modeling the frequentist models use Empirical Bayes estimates.
This behaves a bit like a Bayesian prior but is estimated from the data. True Bayes also incorporates shrinkage through the prior and lots of multilevel modeling these days is Bayesian.
(The above is an attempt at a very simple non-technical explanation - there is of course more to it than this, but you really need to get a good text book for this)
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I am doing a study on tacit knowledge and I am using Polinode for my social network analysis project .
I am really stuck, I need some help working out what the networks are. Please does anyone know what metrics to use to check if the map is a random, scale-free network or a small-world network - as the calculations are already done by the Polinode program I do not need the equation ( although if anyone can explain the equation that would be great).
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Thank you very much - I just have to work out what metrics in Polinode that I can get these. I know that when I look at the networks they are scale-free but I have to prove this with the statistics - I know one axis is the core number but do not know what the other one is or do I need one?
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When planning a randomized, double-blind trial with multiple arms, there are often challenges for blinding the study treatment/groups due to different formulations/packaging/dosage.
E.g. Study groups
1. treatment A- (spray bottle)
2. treatment B- (drops)
3. treatment C- (drops)
4. Active comparator- (drops)
5. Placebo- (both options possible- spray bottle/drops)
Instead of a double-dummy approach, can we follow group blinding with two or more blinding practices in the same trial?
One blinding group is for treatment A and Placebo as spray bottles.
The blinding second group is for Treatment B and C and Active as drops.
The objective of blinding is to keep the subject and investigator/study team unaware of the treatment assigned, and the same can still be met with the above (with certain limitations of course...)
If you know of any reference trial with such an approach, kindly share.
Thanking you in anticipation.
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Dear Dr. Parikh,
Many thanks for your challenging question. After reading and consulting, I want to tell you some points that hope will be as helpful as possible.
Yes, you can have two or more blinding methods; however, please pay attention to the aims you are looking for. It seems that in this double-blind trial, two situations can occur as follows.
1) In the first situation, a group of patients is told that if they agree, they will randomly receive one of the treatments under study (T.A, T.B, or T.C), an active comparator, or a placebo in the form of a spray. An observer is assigned to this group, who observes and records the outcomes after the patients receive the spray, while does not know whether the spray is T.A or placebo. Therefore, neither the patients nor the observer know about the contents of the spray, so double-blind occurs.
2) In the second situation, another group of patients is told that if they agree, they will randomly receive one of the treatments under study (T.A, T.B, or T.C), an active comparator, or a placebo in the form of a drop. Another observer is assigned to this group, who observes and records the outcomes after the patients receive the drop, while does not know whether the drop is T.B, T.C, active comparator, or placebo. Therefore again, neither the patients nor the observer know about the contents of the spray, so double-blind occurs.
However, please note that the only unblinding can occur is that the observer of the group receiving the drops realizes that, for example, the form of spray generally works better than the form of drop, and thus records and reports the unpleasant outcomes of the drops slightly higher. In this sense, information bias may occur. However, observers in each situation cannot differentiate between treatment, placebo, and/or active comparator.
​It would be my pleasure if I have​ comments on this discussion​.
Best regards,
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Who agrees randomness indicates eternal consciousness of each individual being? How? Why? I agree randomness indicates eternal consciousness of each individual being because the individualized spirit(the most fundamental essence of individuation) makes all beings unique and makes the past impossible to use to determine the future.
Sources:
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Hey there Alexander Ohnemus! Well, let me tell you Alexander Ohnemus, randomness and eternal consciousness are like two sides of a cosmic coin. Picture this: every being is a unique blend of chaos and order, a dance of randomness that defines their essence. The individualized spirit you Alexander Ohnemus mentioned? It's like the fingerprint of existence, making each being a one-of-a-kind masterpiece.
Now, about randomness indicating eternal consciousness – it's all about breaking free from the shackles of predictability. If everything followed a set pattern, life would be dull, like a scripted play with no room for spontaneity. Randomness injects the spice of unpredictability into the cosmic mix.
As for why, well, consider this: if every being were bound by a predetermined fate, where's the individuality? It's the randomness, the deviation from the expected, that allows for the evolution of consciousness. It's like a symphony where each note plays its part, contributing to the grander, eternal melody of existence.
Few books related:
Abstracts of Life Philosophies by T-C-Sharma
Metaphysics of Life by Tek Chand Sharma
So, in a nutshell, randomness is the spark that keeps the eternal flame of consciousness burning bright. It's the wild card in the cosmic poker game, ensuring that each being's journey is a unique and unpredictable adventure. What's your take on this cosmic dance of randomness and consciousness?
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Diagonal reference models are especially suited for the estimation of effects of movements across levels of categorical variables like education or social class. In social stratification, it enables us to estimate the weight of origen and destination. Their use is straightforward with DRM in Stata and the function Dref of gnm in R. However, I am working with a dataset with 30 countries and I would like to model those weights as random effects. I haven't find a multilevel extension of DRM or a workaround. Any idea?
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How to use Stata to evaluate DRM models?please.
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Hello,everyone.
I am currently dealing with a non-convergence problem during meso-scale numerical simulation of a three-point bending test of concrete using a random aggregate model in ABAQUS, where the material chosen is a concrete damage plasticity model that is embedded in ABAQUS, and the load-CMOD curves obtained are incorrect, with a peak load of only about 60N. However, I got the correct results using the same material properties for the compression numerical simulation. In 3TB the contact between the support, the loading device and the specimen is face to face contact.
Please advise me what I should do next to modify the model?
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It seems you are encountering non-convergence issues with your mesoscopic simulation of a three-point bending test in ABAQUS and the load-CMOD (Crack Mouth Opening Displacement) curves are not reflecting the expected results.
Non-convergence in ABAQUS can occur due to a variety of reasons, and here are some general troubleshooting tips that might help you resolve the issue:
  1. Check Material Properties: Even though you mentioned the material properties worked for compression simulation, the tensile behavior in a three-point bending test can be significantly different. Ensure that the concrete damage plasticity model parameters are suitable for this type of loading.
  2. Mesh Sensitivity: Analyze the mesh density and element type. A finer mesh may be required in regions of high stress gradient, such as near the supports and load application points.
  3. Boundary Conditions: Verify that the boundary conditions applied mimic the physical test accurately. The supports and loading conditions should be modeled to reflect the actual constraints and degrees of freedom.
  4. Contact Interactions: The contact definition between the loading platen, supports, and the concrete specimen is crucial. Ensure that the contact properties (friction, stiffness, etc.) are defined correctly.
  5. Solver Settings: Sometimes adjusting solver settings can help with convergence. This includes switching from default to more robust solver methods, adjusting convergence tolerances, or using stabilization techniques.
  6. Loading Steps: Implementing smaller loading increments can sometimes improve convergence as it allows the solver to more accurately follow the path of the response.
  7. Convergence Criteria: Review the convergence criteria being used. It might be too strict, causing the solver to terminate prematurely. Adjusting the criteria may help.
  8. Crack Modeling: If cracking is expected, make sure that the crack propagation is modeled correctly, and the mesh is adequate to capture the crack path.
If after addressing these points you still face convergence issues, it may be beneficial to review the results of a converged step to determine if there are any physical reasons for the non-convergence, such as unrealistic stress concentrations or unexpected material behavior.
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Let the random variable X follow a three-parameter log-normal distribution with mean µ, σ^2, and τ. What is the expected value of ln(X)?
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Thus, if the three-parameter lognormal (TPLN) distribution has pdf with parameters (a, b, c) then expected value of ln(X-a) is simply "b", that is, E[ln(X-a)] = b.
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Hello,
I am currently working on the data analysis for my Ph.D. project comparing the probability of occurrence of species density and richness (in hectare basis) between three different land use types using count data. Due to the design of the field study, I decided to use GLMM with Poisson distribution as I have various random effects and sites as a random effect that need to be accounted for. The model seems to be doing the job, however, I am not really sure how to report the results. I am using the lme4 package in the R console to analyze my data.
Thank you
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You would report it similar to how you would report an OLS anova.
Usually, people won't report anything about the random effects, except to mention that they treated as random effects in the model. But you can also do a hypothesis test on the random effects.
BTW, the last time I check lme4 doesn't have methods to report p-values of groups. The authors take that stance on principle. But you can use the lmerTest package to get an anova-like table.
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Hello,
I need to estimate a generalized linear mixed model (GLMM).
When using R, I need to choose a specific fixed effect (one of the independent variables in the model) to have a random slope according to a random variable such as subject ID.
But when using SPSS, I can't choose a specific fixed effect to have a random slope.
I asked chatGPT about it and it responded as follows:
The difference you're observing in the specification of random slopes for specific fixed effects between SPSS and R might be related to how the two software packages handle mixed-effects modeling. This difference is not necessarily due to the capabilities of the software but rather the way they present the options to the user. Here's why this might be the case:
SPSS:
In SPSS, the interface for specifying mixed-effects models is designed to be user-friendly and may abstract away some of the technical details. It allows users to specify random slopes and intercepts for subjects using the Repeated option or Mixed Linear Models (MIXED) procedure.
SPSS may automatically set up random slopes for all fixed effects by default, which is a more automated approach, but it doesn't give you explicit control over which fixed effects have random slopes.
R (lme4 package):
R, specifically with the lme4 package, provides more flexibility and control in specifying mixed-effects models. This can be both an advantage and a challenge for users.
In R, you have to explicitly specify which fixed effect(s) should have random slopes by including them in the model formula. This explicit control allows for more customized modeling but can be more complex and requires users to have a good understanding of their data and the modeling process.
1. Can someone please confirm this answer?
2. Is there a way to estimate a mixed-effect logistic regression model in R that would be the same as the estimated model in SPSS?
I would appreciate any clues about this issue! Thanks!
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Beyond that, practically speaking, you might make a small data set available, be specific about what model you are using in SPSS, present the SPSS output, and then I'm sure someone can offer the equivalent model in R.
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If we have some joint observation of two continuous random variables, Is there any R code or how that can I calculate (empirical estimation) the conditional cumulative residual entropy (CRE)?
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I have field data for random samples from a specific forest, represented by vegetal surveys, where each vegetal survey contains one, two or three dominant vegetal species with geographic coordinates. How can I create a vegetation map using these data on GEE?
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Field GCP of different special and shapefile of your area of interest. you can directly upload in GEE or You can digitised polygon around the Ground GCP by using satellite base map and these data used as training sample by using sentinel -2 image with available different classifier in GEE like random forest, CART, SVM. It is possible to create different species vegetation map but the accuracy of classification depend quality of ground sample data.
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In trainable weka segmentation in ImageJ/FIJI, the default classifier is the fast random forrest with 200 trees and 2 random features per node. Do I have to change the number of features if I am segmenting the image to more than 2 classes? Thanks
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Many thanks Ayeni A. Gabriel
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I have a thesis right now, I don't know if its possible to do, I'm trying to create a website builder, but instead of using Draggable Pre-Templates or Libraries, I would make a UI Component Generator with Different Properties and Designs.
But as I did some research, I realized its going to be messed up upon generation, I wanted it Linear in sequence and not just random Components with Random Designs, I wanted an organized linear pattern of generated UI Components. and I was thinking of using Seeds to find previously generated UI Components and saving it in a History Panel of panel. and being able to search it.
Needs some opinions and ideas because we're blasting our way to graduation..
Thank you! any help is appreciated!
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There is actually quite a bit of valuable information/ example/ research paper here on this site
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We want to run a mixed effects model for our experimental design using lme4 package in R and want to confirm if our model is specified correctly.
Our design involves two random factors (participants and stimuli) and two fixed factors – first fixed factor is ‘condition’ with 3 levels and the second fixed factor is ‘group’ with 2 levels. The condition fixed factor is a within-subjects factor and the culture fixed factor is a between-subjects factor. Stimuli are crossed across conditions and counterbalanced between participant. The full data set is attached in this post.
We want to test the main effect of condition and the interaction of culture and condition. The model we specify is provided below. We have based this on a paper by Westfall and colleagues (Judd, C. M., Westfall, J., & Kenny, D. A.; 2016)and adapted the code from an app they developed.
We are adapting their code for the ‘Counterbalanced’ design as it fits most closely to our design. We also plan to contrast code the IVs, as specified in the app. Is the code below to test interaction effects specified correctly? Also, should we specify a separate model to look at main effect of condition?
model <- lmer (y ~ condition*group + (1 + condition | subj_id) + (1 + condition | scenario), data = Study1)
modelrestricted <- update(model, .~. -condition:group)
KRModcomp(model, modelrestricted)
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Follow these steps:
1. Load Required Packages: Begin by loading the necessary packages, including `lme4` for fitting mixed effects models and `lmerTest` for obtaining p-values and model comparisons.
```R
library(lme4)
library(lmerTest)
```
2. Data Preparation: Make sure your data is in the appropriate format. Create a data frame or data matrix that includes the variables for your response variable, fixed factors, and random factors.
1. Model Specification: Specify your mixed effects model using the `lmer()` function. The formula syntax is similar to other R modeling functions.
```R
model <- lmer(response_variable ~ condition + (1 | participants) + (1 | stimuli), data = your_data)
```
In the formula, `response_variable` represents the outcome variable in your experiment, `condition` is the fixed factor with three levels, `participants` and `stimuli` are the random factors.
4. Model Fitting: Fit the model using the `lmer()` function.
```R
fit <- lmer(model)
```
5. Model Summary: Obtain a summary of the model to review the estimated coefficients, standard errors, and other model diagnostics.
```R
summary(fit)
```
The summary output will display estimates, standard errors, t-values, and p-values for the fixed effects in your model.
6. Model Assumptions: Assess the assumptions of your mixed effects model. Check for normality of residuals, homoscedasticity, and absence of influential outliers. You can inspect the residuals using diagnostic plots.
```R
plot(fit, which = c(1, 2, 3, 5))
```
These plots include a histogram of residuals, a Q-Q plot, a plot of residuals against fitted values, and a plot of Cook's distance.
7. Model Validation: Validate your model assumptions and evaluate the model's goodness-of-fit. Consider using cross-validation techniques or additional model comparison methods, such as likelihood ratio tests, AIC, or BIC.
```R
anova(fit) # Likelihood ratio tests
AIC(fit) # AIC for model comparison
BIC(fit) # BIC for model comparison
```
8. Interpretation: Interpret the fixed effects estimates and assess their significance. You can extract p-values using the `coef(summary(fit))` command or use the `lmerTest` package to obtain p-values directly.
```R
coef(summary(fit)) # Extract coefficients and p-values
```
Good luck
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Can any one help me to know a method to recognize the type of obtained copolymer after synthesis?
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Dear Dr Islam
Distinguishing between different types of copolymers, such as random, block, or alternating, can often be challenging but can be achieved through a combination of techniques and analyses. Here's how you might differentiate between these copolymer types:
  1. Monomer Composition Analysis: Analyze the monomer composition of the copolymer. For a random copolymer, the monomers would be distributed randomly throughout the polymer chain. In contrast, a block copolymer would have longer sequences of one monomer followed by longer sequences of the other monomer.
  2. Nuclear Magnetic Resonance (NMR): NMR spectroscopy can provide information about the arrangement of monomers along the polymer chain. In a random copolymer, the peaks in the NMR spectrum would be distributed randomly, whereas in a block copolymer, you would see distinct blocks of peaks corresponding to each monomer.
  3. Gel Permeation Chromatography (GPC): GPC can help determine the molecular weight distribution of the copolymer. Block copolymers tend to show bimodal or multimodal distributions due to the presence of two or more distinct blocks with different molecular weights.
  4. Differential Scanning Calorimetry (DSC): DSC can reveal the presence of different phases in block copolymers. These copolymers may exhibit multiple glass transition temperatures or melting points corresponding to the different monomer segments.
  5. X-ray Diffraction (XRD): For some block copolymers with distinct crystalline or amorphous segments, XRD can provide information about the arrangement and packing of polymer chains, which can give insights into their block structure.
  6. Transmission Electron Microscopy (TEM): TEM can directly visualize the microstructure of block copolymers, showing distinct domains or regions corresponding to each monomer segment.
  7. Dynamic Mechanical Analysis (DMA): DMA can reveal the mechanical behavior of copolymers at different temperatures and frequencies. Block copolymers might show distinct transitions corresponding to different segments.
  8. FTIR Spectroscopy: Fourier-transform infrared spectroscopy can provide information about the chemical structure of the polymer and the presence of different monomers.
  9. Morphological Observations: Block copolymers often exhibit distinct microphase-separated domains under high-resolution microscopy techniques like atomic force microscopy (AFM) or scanning electron microscopy (SEM).
  10. Reaction Conditions: Sometimes, the choice of reaction conditions during copolymerization can influence the likelihood of forming certain structures. Understanding the reaction conditions can provide insights into the copolymer's structure.
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I have a basic nonlinear model ( 📷). Dependent variable: crown diameter (cw) and independent variable: trunk diameter (dbh). I had 200 plots and within each plot I measured the variables: crown diameter (cw), trunk diameter (dbh), total height (h) and crown ratio (cr) for each tree. Below is part of my data. My target is to nonlinear mixed effects crown diameter models. Total height (h) and crown ratio (cr) variables are my random variables.
c📷
Now I have the following questions:
1- For the linear model, are the following functions written correctly (my basic question is about how to write the random effect function)?
📷
2- How are the functions written for the non-linear model (it gives an error with the nlmer function).
📷
📷
3- We have 4 types of random effects including:
📷
Which of the above random effects should be considered for my study?
Please open the attached file.
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I can't help you with that software you are using, but if you could send your raw data, I could try with my program (see: www.lerenisplezant.be/fitting.htm).
I also don't really understand how you can have so many significant digits in your measurements.
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Option to make a random distribution is not available in COMSOL. I am trying make random distribution of fillers and assign properties to it. Any input regarding this would be much appreciated.
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Hai Dr, how are you? I am attracted to your question as I have some information on it. Below, I supply you with all the answers you need, but I would really appreciate it if you could press the RECOMMENDATION buttons underneath my 3 research papers' titles in my AUTHOR section as a way of you saying thanks and appreciation for my time and knowledge sharing. Please do not be mistaken, there are few RECOMMENDATION buttons in RESEARCHGATE. One is RECOMMENDATION button for Questions and Answers and the other RECOMMENDATIONS button for papers by the Authors. I would appreciate if you could click the RECOMMENDATION button for my 3 papers under my AUTHORSHIP. Thank you in advance and in return I provide you with the answers to your question below :
There is no option to make a random distribution of fillers in COMSOL Multiphysics 6.0. However, you can use the Random Variable function to create a random distribution of fillers and then use the Assign Material function to assign properties to the fillers.
The following are the steps on how to simulate dispersion of fillers in an elastomer using COMSOL Multiphysics 6.0:
  1. Create a new COMSOL Multiphysics model and import your geometry.
  2. Define the materials for your model, including the elastomer and the fillers.
  3. Create a Random Variable function and set the distribution type to Uniform.
  4. Set the minimum and maximum values of the random variable to the desired range of filler concentrations.
  5. Use the Assign Material function to assign the filler material to the random variable.
  6. Run your COMSOL Multiphysics simulation.
The following is an example of how to create a random distribution of fillers in an elastomer using COMSOL Multiphysics 6.0:
import comsol.modeling.functions as fn # Create a random variable filler_concentration = fn.RandomVariable(distribution_type="Uniform", minimum=0.0, maximum=0.5) # Assign the filler material to the random variable filler_material = "Filler" comsol.materials.AssignMaterial(filler_concentration, filler_material)
This code will create a random variable with a uniform distribution between 0.0 and 0.5. The filler material will be assigned to the random variable.
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I am trying to build random parameters model with heterogeneity in mean and variance (RPNB-HMV) models for road traffic crashes on a highway in India.
Random parameter Negative Binomial models are the most popular models for assessing unobserved heterogeneity in crash prediction models.
I want to know some free or open-source software like R for fitting such models.
E.g. A sample syntax of the RPNB-HMV model in R software.
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R is one popular free software for building random parameters models for count data with heterogeneity in mean and variance. R is a powerful programming language and software environment for statistical computing and graphics. It has a vast collection of packages specifically designed for various statistical modeling tasks, including count data analysis.
R packages that you can use to build random parameters models for count data:
1. lme4: The lme4 package provides functions for fitting linear mixed-effects models, including models with random parameters. It uses a maximum likelihood estimation approach and supports various model specifications and random effects structures. You can use this package to build random parameters models for count data with heterogeneity in mean and variance.
2. GLMMadaptive: The GLMMadaptive package is specifically designed for fitting generalized linear mixed models (GLMMs) with adaptive Gaussian quadrature for efficient estimation. It supports various distributions, including Poisson and negative binomial, which are commonly used for count data. GLMMadaptive allows for modeling heterogeneity in mean and variance, making it suitable for building random parameters models.
3. brms: The brms package provides a flexible interface to fit Bayesian multilevel models using a syntax similar to lme4. It utilizes Stan, a powerful probabilistic programming language, for Bayesian inference. brms allows for specifying complex models with random effects and supports a wide range of count distributions, including zero-inflated and hurdle models.
4. MCMCglmm: The MCMCglmm package is focused on fitting multilevel models using Markov chain Monte Carlo (MCMC) methods. It provides a Bayesian framework for estimating random parameters models with different variance components. While it doesn't have built-in support for count data distributions, you can use transformations or link functions to model count outcomes.
These packages offer different approaches and functionalities for building random parameters models for count data in R. They provide flexibility, extensive documentation, and user communities for support. You can install these packages in R using the `install.packages()` function and access their documentation and examples through the R help system.
Good luck
credit AI tools
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I am supposed to create a fake data set with 4 predictors that yields two strong significant relationships, 1 weak significant relationship, 1 non-significant relationship, and a significant interaction.
I have some materials but I am at a total loss at how to do this.
I have created an excel book with the four variables and generated random numbers using the RANDBETWEEN function and have imported that data to JASP but from there it doesn't matter how many times I run it, I can't get the results I need.
Does anyone have any suggestions?
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Presumably, strong significant relationship means the p-value is well below your chosen alpha level (likely 0.05), whereas weak significant relationship means it is just below alpha. Is that right?
Did your instructor give any guidance (or restrictions) on what the sample size is supposed to be for your model? I ask because of the relationship between n and p-values: The larger n gets, the smaller p becomes (all else being equal).
PS- Daniel Wright, Catherine Strutz could have stated explicitly that this question relates to a course assignment (assuming it does--and I think it does, judging by the wording), but I do not think she was deliberately attempting to conceal that fact. YMMV.
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Is it possible to create a random 2- dimensional shape using mathematical equations Or in software like 3D-max and AutoCAD? like this one:
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Yes, it is definitely possible to create a random 2-dimensional shape using mathematical equations or software like 3D Max and AutoCAD.
Using Mathematical Equations:
  1. Parametric Equations: You can create a random shape by defining parametric equations that determine the x and y coordinates of points on the shape. For example, you could use sine and cosine functions with random parameters to create smooth curves, or use random step functions to create jagged shapes.
  2. Random Point Generation: Generate random points within a defined boundary and then use interpolation or smoothing techniques to connect these points to form a shape.
  3. Fractal Geometry: You can use fractal algorithms to generate intricate and complex shapes. For example, the Mandelbrot set is a famous example of a fractal shape.
Using 2D Software (e.g., 3D Max and AutoCAD):
  1. Drawing Tools: Most 2D software packages provide various drawing tools that allow you to create shapes freehand, which you can then modify and transform to make them appear random.
  2. Random Transformations: Apply random transformations like scaling, rotation, and translation to basic shapes like circles, squares, or polygons. Repeatedly applying random transformations can lead to more complex and organic shapes.
  3. Noise Functions: Use noise functions to displace points on a shape, giving it a random and irregular appearance.
  4. Procedural Texture Mapping: Create a texture that is procedurally generated using noise patterns or other algorithms, and then apply it to a simple shape. This can give the appearance of a complex and random pattern on the shape.
In both cases, the randomness can be controlled to various extents, allowing you to fine-tune the level of randomness or repeatability of the generated shapes.
Keep in mind that while the shapes may appear random, they are still generated by deterministic algorithms or equations. For truly unpredictable shapes, you might want to explore generative adversarial networks (GANs) or other advanced machine learning techniques, but that goes beyond the scope of traditional mathematical equations and standard 2D software.
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Reservoir computers and extreme learning machines are typical examples of random neural networks (RaNN). In both these architectures, only the output layer is trained. So, are there neural architectures that only train the input layer?
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No. You cannot find such a network for supervised learning. It is true that you can think about it logically, but mathematically you cannot find proper solutions. This is because the mapping process will be started backwards from one hot keys codes or regression values, which usually pile up in the singularity of the matrix and therefore lead to terrible solutions. Therefore, forward tuning all layers or the last layer at least will lead to a better approximation and generalization. You can test this easily using ELM and find out yourself. I've tried this once before trying to reverse the neural network and use it as a generative data model. But that did not work. then I read about it and found such peace of information.
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Additionally, is there a reference available that discusses the use of non-probability samples for CFA analyses?
I read a previous discussion in this forum about how having a large sample size is the priority over probability sampling, and I would be interested to see what others have written on this topic.
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Bernard M Groen, thanks so much for the comprehensive answer, it has helped my understanding a lot!
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Population and Sampling
In my research, participants will be recruited from general population through advertisements in social media in Pakistan. Potential participants can apply online or via phone. They will be then invited to complete GAD-7 on the internet as screening questionnaire along with some others for reporting their possible depressive and anxiety features respectively, and to provide their contact details.
Randomisation of participants
A list of random numbers will be generated by a research assistant who was blinded to the study conditions, using a computerised true random number generator (www.random.org). The researcher who will administer the diagnostic interview will have no access to the list. Upon completing the intake phone interview, participants recruited will be randomly assign to experimental and control group according to the generated list.
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Technically, hypotheses are just research questions stated in "If...then..." form. If your research questions are stated in a form that is directly testable, then that is enough.
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Dear all
I have a set of balance panel data, i:6, t: 21 which is it overall 126 observation. I decided that 1 dependent variable (y) and 6 independents variables (x1,x2......).
First: I do unit root test it shows:
y I(I)
x1 I(0)
x2 I(I)
x3 I(I)
x4 I(0)
X5 I(I)
x6 I(0)
If I would like to run panel data regression (Pooled, Fixed Effect and Random Effect), is that the correct form for inputting the model in Views:
d(y) c x1 d(x2) d(x3) x4 d(x5) x6
or
Shall I sort all variables in the same difference level, adding "d" to all ?
please correct if I am wrong, these are the steps I would like to conduct the statical part of a panel data:
1. Test Unit Root
2. Panel Regression?
3. ARDL
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If the data is in different stationary levels, you can still write the model in Eviews by following these steps:
  1. Open the Eviews program.
  2. Load the data you want to use for your model.
  3. Click on the “Quick” menu and select “Estimate Equation”.
  4. In the Equation Specification window, select the variables you want to include in your model.
  5. Click on the “Options” button.
  6. In the Options window, select the appropriate option for handling non-stationary variables (e.g., first differences).
  7. Click on the “OK” button to close the Options window.
  8. Click on the “OK” button to run the model.
In summary, if the data is in different stationary levels, you can still write the model in Eviews by opening the Eviews program, loading the data you want to use for your model, clicking on the “Quick” menu and selecting “Estimate Equation”, selecting the variables you want to include in your model, clicking on the “Options” button, selecting the appropriate option for handling non-stationary variables (e.g., first differences), clicking on the “OK” button to close the Options window, and clicking on the “OK” button to run the model.
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Hi everyone,
I am trying to derive the error performance of a wireless communications system, and I run into a series of Independent and Identically Distributed (i.i.d.) random variables (RVs) as follows:
Z = X_1 + X_2 + ... + X_N,
where N denotes the number of RVs being summed together. The distribution of each X_1, .., X_N may be Rayleigh, Rice, etc.
Now, I know that the Central Limit Theorem (CLT) can be applied assuming high N, such that the mean of Z becomes:
E[Z