Advanced Statistical Analysis - Science topic

Matyáš Strašík

That makes sense. Are you comparing the cross-lagged panel (auto)regression (path) coefficients to zero-order correlations? This could be part of the issue (explain the "discrepancy"/low autoregressive stability coefficient). Regression coefficients are not equal to zero-order (bivariate) correlations. The regression coefficients take the correlation with other independent variables into account. This may explain why the autoregressive "stability" coefficients in your model look very different from the zero-order correlations. It is impossible to know without looking at your data and model in more detail.

The model fit does not look completely horrible at first sight but the chi-square test is significant and the RMSEA value is a bit high. I would take a look at model residuals and/or modification indices to find out where the model may be misspecified.

Ma'Mon Abu Hammad Could you advise me on some documentation about this test?

Abolfazl Ghoodjani can you be more specific with your answer by the way the writing above are output of (chat gpt and lambda ) . my question is why this models can get sum of this number right even when they apply all the correct steps

The significance level of a test is a predetermined threshold that is used to determine if the evidence from the sample provides enough support to reject the null hypothesis. It is typically denoted by the symbol α. The significance level is relevant in several ways: Type I error rate, Confidence level, Decision-makingm, Sample size determination

In summary, the significance level guides the researcher in interpreting the test results and making informed decisions regarding the null and alternative hypotheses.

Discriminant function technique involves the development of selection criteria on a combination of various characters and aids the breeder in indirect selection for genetic improvement in yield. In plant breeding, the selection index refers to a linear combination of characters associated with yield.

R in Power BI ?

It should not contain complex R syntaxes though.

https://learn.microsoft.com/en-us/power-bi/create-reports/desktop-r-visuals

Hi Carlos Jimenez-Gallardo ,

precisely: two sides of the same coin.

Hello Pavel Makovsky. In that case, modify the code I posted earlier. With your dataset open and active:

REGRESSION

/STATISTICS COEFF OUTS CI(95) R ANOVA

/DEPENDENT YourDV

/METHOD=ENTER your explanatory variables

/SAVE PRED(yhat1).

* Now use another set of coefficients to generate yhat2.

COMPUTE yhat2 = use the other regression coefficients here but with the variables in your dataset.

REGRESSION

/STATISTICS R

/DEPENDENT YourDV

/METHOD=ENTER yhat1.

REGRESSION

/STATISTICS R

/DEPENDENT YourDV

/METHOD=ENTER yhat2.

To specify the significance of the marginal effects in binary logistic regression analysis, you can interpret the results by examining the p-values, B (coefficient estimates), and Exp(B) (exponentiated coefficient estimates) values. The p-value indicates the statistical significance of each predictor variable's effect on the outcome variable. A low p-value (typically less than 0.05) suggests a significant effect. The B values represent the estimated change in the log-odds of the outcome for a one-unit change in the predictor, with positive values indicating a positive association and negative values indicating a negative association. Exp(B) provides the odds ratio, which quantifies the change in odds for a one-unit increase in the predictor. An Exp(B) greater than 1 indicates an increased odds of the outcome, while a value less than 1 implies a decreased odds. By considering the significance of the marginal effects, you can determine the direction, magnitude, and statistical significance of the predictor variables' impacts on the binary outcome variable in your logistic regression analysis.

First, to change the reference level You can specify the order of items in categorical array

categorical(A,[1, 2, 3],{'red', 'green', 'blue'}) or

categorical(A,[3, 2, 1],{'blue', 'green', 'red'})

Second, You can specify the correct hypothesis matrix for coefTest function for comparison between every pair of categories.

Yes, it is possible to estimate the pre-post correlation coefficient in a meta-analysis using various methods, such as imputing a value or using a range of plausible values. Here are a few options:

Once you have estimated the pre-post correlation coefficient, you can enter it into the appropriate field in the CMA software and proceed with the analysis. It is important to carefully consider the implications of the chosen correlation coefficient and to conduct sensitivity analyses to test the robustness of the results.

Hello Ravisha,

If cases are randomly assigned to treatment condition, there's no reason that post-only design results should be considered uninformative.

Designs with pre-post measures can offer the added benefits of: (a) allowing for estimation of change (though unless scores are completely reliable, the change scores will be less reliable than either the pre- or post- score by itself); or (b) pre-scores can be used as a covariate, to adjust for randomly occurring differences across groups.

One noted threat to pre-post designs is that if the interval separating them is too short, the post-results, and therefore group comparisons, can be biased, especially with measures of affect.

Ultimately, the answer depends on what your target ES might be: If it is post-treatment differences across groups/conditions, then either design can contribute. You could estimate ES separately by study type to see whether inclusion of pre-test appears to account for differences.

If it is strictly pre-post change, then post-only designs can't contribute (again, though, note the caveats above).

Good luck with your work.

At the same time，Collect two variables data,As a sample,After collecting N samples over time,erform data regression analysis on them,The correlation coefficient will be obtained.

In attachment an R script with the BH post-hoc test based on Benjamini & Hochberg (1995). You could replace this with Bonferroni, but in my opinion this last method is too conservative.

Why would these variables have to be normal? As far as I understand our problem, a logistic model might do well. You can try it with my software "FittingKVdm", but if you can send me some dat, I can try it for you.

Very helpful paper with references:

Best,

Wadie

Yes, that would be correct.

As your outcome/ dependent measure is only at one time point you would not have to consider time in relation to the outcome, so not a longitudinal model (no variation over time to model).

That is not to say that time may or may not be important in your research question. If trends/ differences/ averages in the repeated measures of independent variables are important in relation to your outcome then you can find ways to incorporate these things into your modelling strategy (in the way that you choose to use your repeated independent measures - being guided by research questions).

You can use layout() to define a matrix of plots with different heights/widths. In your case, this will produce a layout similar to your picture:

m <- rbind(c(0,1,0), c(2:4))

layout(m, widths = c(1,1,1.5), heights = c(1,1))

par(oma = c(3,3,3,3), mar = c(0,0,0,0), las = 1, xaxs = "i", yaxs="i")

plot(NA, xlim = c(-1, 9), ylim = c(-1, 4), xaxt="n", yaxt="n")

axis(2, at = 0:4)

axis(3, at = 0:4 * 2)

plot(NA, xlim = c(0, 4), ylim = c(0, 3.5), xaxt="n", yaxt="n")

axis(1, at = 0:4)

axis(2)

plot(NA, xlim = c(-1, 9), ylim = c(0, 3.5), xaxt="n", yaxt="n")

plot(NA, xlim = c(0, 7), ylim = c(0, 3.5), xaxt="n", yaxt="n")

axis(1, at = 0:7)

axis(4)

Good morning, without the code it is difficilt to know where is the difference I do not use Python i work on R but maybe these difference is due to the stage of spitting dataset do you try to add thr same number in the count of generator of randomly for example seed(1234) (if my memory is good this function is also used in Python language. Were your results and metrics of evaluation totally different? In this case, mayve there is a reliability issue in your model. You should check your data preparation and features selection .

I was unsure how to pool SD from the SE without knowing N. A method I found used the "baseline SD" for each group.

It depends on the cost function and the model that you are using. Gradient descent will converge to the optimal value (or very close to it) of the training loss function, given a properly set learning rate, if the optimization problem is convex with respect to the parameters. That is the case for linear regression using the mean squared error loss, or logistic regression using cross entropy. For the case of neural networks with several layers and non-linearities none of these loss functions make the problem convex, therefore there is no guarantee that you will find the optimal value. The same would happen if you used logistic regression with the mean squared error instead of cross entropy.

An important thing to note is that when I talk about the optimal value, I mean the value that minimizes the loss in your training set. It is always possible to overfit, which means that you find the optimal parameters for your training set, but those parameters make inaccurate predictions on the test set.

Dear Dr. Gaško,

I'm glad to hear that. You are very welcome.

Best wishes.

Not sure I understand your question. If there is a single predictor and by regression you mean linear OLS regression, then the r is the same. Can you provide more details>

Using R code, the bootstrap method can estimate the error rate in linear discriminant analysis. First, the data must be split into a training set and a test set and then normalized. The lda() function can then be used to run the calculations twice, with CV=TRUE for the first run to get predictions of class membership derived from leave-one-out cross-validation. The second run should use CV=FALSE to get predictions of class membership based on the entire training set. The true error rate estimator BT2 of the restricted linear or quadratic discriminant analysis can be calculated using the dawai package in R. Finally, resampling methods such as bootstrapping can be used to estimate the test error rate.

I've only glanced quickly at those two resources, but are you sure they are addressing the same thing? Yates' (continuity) correction as typically described entails subtraction 0.5 from |O-E| before squaring in the usual equation for Pearson's Chi2. E.g.,

But adding 0.5 to each cell in a 2x2 table is generally done to avoid division by 0 (e.g., when computing an odds ratio), not to correct for continuity (AFAIK). This is what makes me wonder if your two resources are really addressing the same issues. But as I said, I only had time for a very quick glance at each. HTH.

In order for the benefit to prevail, I have verified a group of packages that do the add of The robust confidence ellipses of 97.5%

View them here by package and its function

1- ellipses () using the package 'ellipse'

## ellipses () using the package 'rrcov'

## ellipses () using the package 'cluster'

Try to publish on your own then you have complete control. Collaborators will steal your data and treat you badly :)

You can use standard procedures for the fixed effects estimates as they are akin to regression model estimates if the response is continuous. Things are more complicated of the response is categorical.

A scale reliability of .39 (and even .53!) is very low. Even if your main focus is not on the psychometric properties of your measures, you should still care about those properties. Inadequate reliability and validity can jeopardize your substantive results.

My recommendation would be to examine why you get such a low alpha value. Most importantly, you should first check whether each scale (item set) can be seen as unidimensional (measuring a single factor). This is usually done by running a confirmatory factor analysis (CFA) or item response theory analysis. Unidimensionality is a prerequisite for a meaningful interpretation of Cronbach's alpha (alpha is a composite reliability index for essentially tau-equivalent measures). CFA allows you to test the assumption of unidimensionality/essential tau equivalence and to examine the item loadings.

Also, you can take a look at the item intercorrelations. If some items have low correlations with others, this may indicate that they do not measure the same factor (and/or that they contain a lot of measurement error). Another reason for a low alpha value can be an insufficient number of items.

Dear Panagiotis N. Stoikos

If your data is not normally distributed, you should use non-parametric statistical tests such as Wilcoxon rank sum tests or Mann-Whitney U tests in order to compare the expression levels between the two groups.

Regarding the one tailed or two tailed. one tailed can specify the direction of the effect (positive or negative) but the two tailed one can be used for both direction at the same time.

Best...

In general, permanova is a test of the effect of two parallel variables on the organism. It is equivalent to two one-way ANOVAs. Whereas GLMS is equivalent to the combined effect of all factors, in GLMS you can derive the contribution of each variable to determine the magnitude of the contribution of each environmental factor. You can understand that permanova is the parallel effect of several factors, while GLMS is the combined effect. GLMS is simple and easy to operate in R language.

In the last few months, a colleague of mine has written a version of the PCO-program in R. The first impressions are good, but we need a few more months to test it and prepare a publication aboiut it.

Massimo Sivo very nice of you that you want to try to help your colleague, however, as was mentioned earlier, you should understand experimental design very well before you have sufficient information to construct an appropriate statistical model (that can than provide meaningful insights). To understand experimental design of a clinical trial it is not enough to understand some statistics, you should also understand some medicine. E.g. From what Shahnawaz Ahmad Wani described one can by no means understand what was actually measured, how, and why. It is super challenging to sample 5 patients with appropriate controls to make any kind of biologically meaningful inference about the population of patients. Tons of confounders and no power to take them into account. From how the problem was presented I can image such (confounder) data was not collected. Using such data and shoving them into e.g. mixed model to account for repeated measures (and dependence of some parameters) will most likely give meaningless results. Even if everything was done right but you did not account for a biologically meaningful confounder... it would still be meaningless. For example, Shahnawaz Ahmad Wani told us he is measuring some kind of biochemical parameter from the blood from female patients. Many parameters in the blood change with the part of the menstrual cycle, and it is not clear whether the authors analysed this. Without accounting for such a serious confounder the study makes no sense. Furthermore, how would you construct a model around the dependence of venous blood and "tissue blood". The variables are most definitely related, but we cannot imagine the nature of their dependence without having much more information. So, before asking for data, you should really ask for the explanation of the design to make sure all the confounders were ruled out the the level of design as this is the only scenario in which several lines of code or several clicks in some statistical software will help you in any way to make some kind of inference.

Please be mindful about this kind of stuff, uncertain and misleading conclusions can be very dangerous in medicine and it is healthier for the community to talk about them than to just generate some numbers from the data.

Best,

jan

I'm not sure I fully understand your question but when you have a column with a constant value (e.g., 1), this constant by definition cannot covary with another column/variable. A constant does not have any variance and therefore, the covariance/correlation with another variable will also be zero by definition.

* Create a toy dataset to illustrate.

NEW FILE.

DATASET CLOSE ALL.

DATA LIST LIST / A B C D E (5F1).

BEGIN DATA

1 0 0 0 0

0 1 0 0 0

0 0 1 0 0

0 0 0 1 0

0 0 0 0 1

1 1 0 0 0

0 1 1 0 0

0 0 1 1 0

0 0 0 1 1

1 0 2 0 0

END DATA.

IF A EQ 1 and MIN(B,C,D,E) EQ 0 AND MAX(B,C,D,E) EQ 0 severity = 1.

IF B EQ 1 and MIN(A,C,D,E) EQ 0 AND MAX(A,C,D,E) EQ 0 severity = 2.

IF C EQ 1 and MIN(B,A,D,E) EQ 0 AND MAX(B,A,D,E) EQ 0 severity = 3.

IF D EQ 1 and MIN(B,C,A,E) EQ 0 AND MAX(B,C,A,E) EQ 0 severity = 4.

IF E EQ 1 and MIN(B,C,D,A) EQ 0 AND MAX(B,C,D,A) EQ 0 severity = 5.

FORMATS severity (F1).

LIST.

* End of code.

Q. Is it possible for any of the variables A to E to be missing? If so, what do you want to do in that case?

Maybe the attached is what you mean. David Booth

Ange, I think the easiest way for you to find an answer to your question would be to google something such as "SPSS recode variables YouTube". You'll probably find several sites that demonstrate what you want to do.

All the best with your research.

What do you mean "hypothetical study?" Is this a homework question?

You use beta diversity (β), which is a measure of the difference in composition of species between locations :)

Sorry, Jianhing. But I think you have misunderstood something in the lecture. Frequentist statistics, which is an interpretation of probability to be assigned on the basis of many random experiments.

In this setting, on designs functions of the data (also called statistics) which estimate certain quantities from data. For example, the probability p of a coin to land heads is given from n independent trials with the same coin and just counting the fraction of heads. This is then an estimator for the parameter p.

Each estimator should have desirable properties, as unbiasedness, consistency, efficiency and low variance and so on. Not every estimator has these properties. But, in principle one can proof, whether a given estimator has these properties.

So, it is not a characteristics of frequentist statistics, but a property of an individual estimator based on frequentist statistics.

Thank you so much, Daniel Wright , Jochen Wilhelm , Richard Johansen ! Your answers were very helpful. I was able to do it through the string package.

What if the p-value of the HL test doesn't appear? it just appeared as this code ".". what is that mean? thank you

It is not very clear what you are trying to do.

However, the Student's t-test (parametric) assumes the comparison between two distributions, one of which is normally distributed, the other according to chi-squared.

The Mann-Whitney test (non-parametric) can be a valid alternative to the t-test, but the distributions must be independent.

If they are not, you can apply the Wilcoxon test, but with due caution, given the unclearness of your "problem".

Regards

Javier Ernesto Vilaso Cadre , tests do not corroborate that a distribution is normal. They may only fail to corroborate that a distribution is not normal, and that may simple be due to the sample size. Actually, such tests only tell you if your sample size is already large enough to see that the normal distribution model (an idealized model!) does not account for all features of a real distribution. So actually they don't give you any useful information (you may fail to see relevant discrepancies because the sample size is too small, or you may get blinede with "statistically significant" discrepancies that are irrelevant for your problem). The only sensible way is to understand the variable and have some theoretical justification of its distribution, and then to judge if the presumed discrepancies are relevent for your problem. One may then certainly have a look at the empirical distribution of the observed data: if it screams at you that your thoughts and arguments are very likely very wrong you may go back and refine or deepen your understanding of the data-gereative process you like to study.

Hi Mairtin Mac Siurtain, thanks so much for your reply!

This is great information. I'll take a look first at MANOVA and then at RBSCIs if indicated. Many thanks!

Best,

Matt

نعم يؤدي الى اختلاف النتائج لان كل اداة احصائية تستخدم وفق معايير خاصة لها لايمكن استخدام عدة وسائل احصائية للغرض نفسه David Morse

You should try this text.

Harrer, M., Cuijpers, P., Furukawa, T.A., & Ebert, D.D. (2021). Doing Meta-Analysis with R: A Hands-On Guide. Boca Raton, FL and London: Chapman & Hall/CRC Press. ISBN 978-0-367-61007-4.

I found the code linked to by Jeff Rothschild to be a little difficult to follow. I adapted the code to make it a little more straightforward. Below is some R code which calculates the confidence interval by this method and by bootstrap. RMSE is calculated from a vector of Actual values and a vector of Predicted values.

I have no first-hand experience to offer you either. But this systematic review article may give you some ideas.

I suggest using mosaic plots rather than (stacked) barplots to visualize your data.

The chi²- and p-values can be calculated simply via chi²-tests (one for each ABA conc) -- assuming the data are all independent (again, please note that seedlings on the same plate are not independent). If you have no possibility to account for this (using a hierarchical/multilevel/mixed model), you may ignore this in the analysis but then interpret the results more carefully (e.g., use a more stringent level of significance than usual).

A binomial model (including genotype and ABA conc as well as their interaction) would allow you to analyse the difference between genotypes in conjunction with ABA conc. However, due to the given experimental design (only three different conc values) this is cumbersome to interpret (because you cannot establish a meaningful functional relationship between cons and probability of germination).

Kindly follow the SPSS structure for determining the critical value here: It is pretty simple and intuitive

You should use the model that makes more sense, practically and/or theoretically. A high R² is not in indication for the "goodness" of the model. A higher R² can also mean that the model makes more wrong predictions with a higher precision.

Do not build your model based on observed data. Build your model based on understanding (theory) and the targeted purpose (simple prediction, exptrapolation (e.g. forecast), testing meaningful hypotheses etc.)

Removing a variable from the model changes the meaning of the intercept. The intercepts in the two models have different meanings. They are (very usually) not comparable. The hypothesis tests of the intercepts of the two models test very different hypotheses.

PS: a "non-significant" intercept term just means that the data are not sufficient to statistically distinguish the estimated value (the log odds given all X=0) from 0, what means that you cannot distinguish the probability of the event (given all X=0) from 0.5 (the data are compatible with probabilities larger and lower 0.5). This is rarely a sensible hypothesis to test.

Hi dear Mario

I'm trying to classify hard-wired human settlement preferences based on archaeological evidence in the Paleolithic era, this is an effort to update biophilia hypothesis.

But for doing hypothesis testing i need to quantify the data and the first step is to make a database from as many as possible Paleolithic geolocations then put them through GIS analysis and test my hypothesis based on the data acquired in SEM (which mostly is that what environmental attribute has how much impact on our decision to select a settlement in what period of paleohistory).

PS. I tested a small database but i was wondering if there is a geolocation database of archaeological sites that i coulden't find yet.

Bests

SAM

I think you have answered your question: "It should be noted that when I plot the data, very very few participants chose yes in response to the neutral and positive images. Almost all yes responses were given in response to the negative images."

This is what you'd expect even in a simple 2x2 design. If the probability of a yes response in the positive condition is very high and the probability very low in the negative condition then the OR could be high as its the ratio of a big probability to a very low one.

This isn't unnatural unless the raw probabilities don't reflect this pattern. (There might still be issues but not from what you described).

Thank you Noramira Nozmi for the question and professors for the useful answers.

Although the ideas by Thom Baguley and Christian Geiser to use MLM seem reasonable, I still have 2 (blocks of) questions.

1) What is the hypothesis in the fist place? What should be correlated? What are the 100 measurement points?

2) You say, you have "two" variables, each measured 100 times. But wouldnt this be 200 data points per subject, and with N=20, you should have 4000 data points and not 2000.

Mr a. D.

The ECT(-1)os always the lagged value of your dependent variable.

Regards

I am not specifically expert in the Mann-Kendall Trend test but it is related to classical non-parametric tests, like the Kendall correlation test that I know better. Be careful with XLSTAT (which works in ExceI, not in Word). Indeed, in the procedure I used a few years ago, I had many problems and had to contact the support. I think you should read more about the test and more generally on non-parametric tests. Asymptotic means when the number of observations n grows to infinity. Otherwise, these tests are based on tables of critical values depending on n. When n is too large, use the asymptotic distribution, often normal with a given mean and a given variance (depending on n, of course). For the continuity correction, it is because the test statistic takes discrete values whereas the asymptotic distribution is continuous. The same kind of correction appears with a binomial distribution. Look in your statistics course.

Hello Seyyed,

I guess you mean with beta the factor loading? Traditionally, these are denoted with lambda but probably, Stata treats these differently.

The fixation of the "marker variable" is needed a) to assign a metric to the latent variable--those of the marker, and to b) identify the equation system.

As far as I know it does not matter which variable you choose unless it is no valid indicator of the latent.

HTH

Holger

I believe Blaine Tomkins meant to describe the data as having a LONG format, not a wide format. Apart from that, I concur with his advice. SPSS can estimate that model. Look up the GENLINMIXED command:

A good resource is the book by Heck, Thomas & Tabata (if you can get your hands on it):

HTH.

Kabbilawsh Peruvazhuthi, Serial correlation & auto-correlation are same thing but seasonality is different.

With low expected cell counts there are various suggestions as to when Chi-sq is no longer trustworthy. I like the Np15 rule because it's simple and seems to work well. In this rule, N is the total number of observations, and p is the proportion in the smaller group. So if you have a table that contains 50 observations, and 10% of them fall into the smaller group (so P = 0·1). Then Np is (50 x 0·1) which is 5. The table fails the Np > 15 test – not enough data to do a Chi-squared test.

Just do logistic regression is what I had in mind. The DV might be antcancer activity (yes /no) same for antioxidant activity. Best wishes David Booth

Plotting observed vs predicted is not sensible here.

You don't have observed probabilities; you have observed events. You might use "Temp", "Time", and "Conc.Log10" as factors (with 4 levels) and define 128 different "groups" (all combinations of all levels of all factors) and use the proportion of observed events within each of these 128 groups. But you have only 171 observations in total. No chance to get any reasonable proportions (you would need some tens or hundreds of observation per groups for this to work reasonably well).