I want to perform a multiple group CFA with lavaan in R.
I have several categorical variables and some variables contains 11 categories. So these variables will have 10 thresholds. In the results below you can see thatthe 10th threshold is smaller than the 9th, i.e., it is not in the creasing order.
Several variables with 11 categories have the same problem.
Question:
Why are the thresholds distorted?
R-code:
model2<-'range = ~ NA*gvjbevn + gvhlthc + gvslvol + gvslvue + gvcldcr + gvpdlwk
goals = ~ NA*sbprvpv + sbeqsoc + sbcwkfm
range~~1*range
goals~~1*goals
gvhlthc ~~ gvslvol
gvcldcr ~~ gvpdlwk
'
cfa.model2<-cfa(model2, ordered=varcat, estimator="WLSMV",data=sub)
summary(cfa.model2,fit.measures=TRUE,standardized=TRUE, modindices=TRUE)
Label assignation of the thresholds was sorted alphabetically, aka c('t1','t10','t2','t3'....) but summary() sorts it ""properly"".
You can try to add additional factors to check if your scale corresponds to:
c('t1','t10','t11','t12',...,'t2','t3'....)
Not much you can do on your side, except understand which row is each of your factors.
Well, it seems like I cannot add a comment due to not having enough reputation, so I can only reply with an answer, although this is not a proper answer (it will definitely not solve your issue, though I hope it points in the right direction).
For your example to be reproducible, you should provide the community with the data to fit the model.
On the other side, I guess your problem must have to do with the nature of the category: it's possible that your 11th category does not mean "the most level of agreement" with the item, or that the response categories are not ordered from 1 to 11, or something similar. Given that the rest of the thresholds seem to accurately represent a continuous, monotonically increasing scale, and that this same problem precisely happens in the same category in different variables (at least the two that you are showing), there must be something with the response scale in those items.
In summary, it seems to be more of a problem of interpretation of the parameters of the model rather than a statistical issue.
Related
I have a glm model with two fixed effects, Treatment and Date, to estimate Temperature from data collected in a time series. Within Treatment there are three different categories: Fucus, Terrycloth or Control, and temperature is measured beneath those canopies. The model is created like so mod1 <- glm(Temp ~ Treatment * Date, data = aveTerry.df )
I am trying to tell if Terrycloth has a similar effect as Fucus canopy (i.e. replicates it).
I found the emmeans package and believe it could help me compare between these levels within treatment by using my model, and have used it as so to find the estimated marginal means terry.emmeans <- emmeans(modAllTerry, poly ~ Treatment | Date) and plotted the comparisons via plot(terry.emmeans.average, comparison = TRUE) +theme_bw()
Giving me this output linked here.
I am looking for some help understanding what this graphical output is, especially what exactly are the comparisons (which are shown by the red arrows). I somewhat understand the that blue boxes are the confidence intervals for the mean value of temperature for each treatment on one day (based on model), but am wondering how is the comparison made? And why do some days only have a one sided arrow?
As described in the documentation for plot.emmGrid, the comparison arrows are created in such a way that two arrows are disjoint if and only if their respective means are significantly different at the stated level.
The lowest mean in the set has only a right-pointing arrow because that mean will not be compared with anything smaller, obviating the need for a left-pointing arrow. For similar reasons, the highest mean has only a left-pointing arrow. These arrows do not define intervals; their only purpose is depicting comparisons.
In situations where the SEs of pairwise comparisons vary widely, it may not be possible to construct comparison arrows. If that happens, an error message is displayed.
Confidence intervals are available as well, but those CIs should not be used for comparing means.
More information and examples may be found via vignette("comparisons", "emmeans"). Also, details of how the arrows are actually constructed are given in vignette("xplanations", "emmeans")
I'm having a huge problem with a nested model I am trying to fit in R.
I have response time experiment with 2 conditions with 46 people each and 32 measures each. I would like measures to be nested within people and people nested within conditions, but I can't get it to work.
The code I thought should make sense was:
nestedmodel <- lmer(responsetime ~ 1 + condition +
(1|condition:person) + (1|person:measure), data=dat)
However, all I get is an error:
Error in checkNlevels(reTrms$flist, n = n, control) :
number of levels of each grouping factor must be < number of observations
Unfortunately, I do not even know where to start looking what the problem is here.
Any ideas? Please, please, please? =)
Cheers!
This might be more appropriate on CrossValidated, but: lme4 is trying to tell you that one or more of your random effects is confounded with the residual variance. As you've described your data, I don't quite see why: you should have 2*46*32=2944 total observations, 2*46=92 combinations of condition and person, and 46*32=1472 combinations of measure and person.
If you do
lf <- lFormula(responsetime ~ 1 + condition +
(1|condition:person) + (1|person:measure), data=dat)
and then
lapply(lf$reTrms$Ztlist,dim)
to look at the transposed random-effect design matrices for each term, what do you get? You should (based on your description of your data) see that these matrices are 1472 by 2944 and 92 by 2944, respectively.
As #MrFlick says, a reproducible example would be nice. Other things you could show us are:
fit the model anyway, using lmerControl(check.nobs.vs.nRE="ignore") to ignore the test, and show us the results (especially the random effects variances and the statement of the numbers of groups)
show us the results of with(dat,table(table(interaction(condition,person))) to give information on the number of replicates per combination (and similarly for measure)
I'm using the following code to try to get at post-hoc comparisons for my cell means:
result.lme3<-lme(Response~Pressure*Treatment*Gender*Group, mydata, ~1|Subject/Pressure/Treatment)
aov.result<-aov(result.lme3, mydata)
TukeyHSD(aov.result, "Pressure:Treatment:Gender:Group")
This gives me a result, but most of the adjusted p-values are incredibly small - so I'm not convinced the result is correct.
Alternatively I'm trying this:
summary(glht(result.lme3,linfct=mcp(????="Tukey")
I don't know how to get the Pressure:Treatment:Gender:Group in the glht code.
Help is appreciated - even if it is just a link to a question I didn't find previously.
I have 504 observations, Pressure has 4 levels and is repeated in each subject, Treatment has 2 levels and is repeated in each subject, Group has 3 levels, and Gender is obvious.
Thanks
I solved a similar problem creating a interaction dummy variable using interaction() function which contains all combinations of the leves of your 4 variables.
I made many tests, the estimates shown for the various levels of this variable show the joint effect of the active levels plus the interaction effect.
For example if:
temperature ~ interaction(infection(y/n), acetaminophen(y/n))
(i put the possible leves in the parenthesis for clarity) the interaction var will have a level like "infection.y:acetaminophen.y" which show the effect on temperature of both infection, acetaminophen and the interaction of the two in comparison with the intercept (where both variables are n).
Instead if the model was:
temperature ~ infection(y/n) * acetaminophen(y/n)
to have the same coefficient for the case when both vars are y, you would have had to add the two simple effect plus the interaction effect. The result is the same but i prefer using interaction since is more clean and elegant.
The in glht you use:
summary(glht(model, linfct= mcp(interaction_var = 'Tukey'))
to achieve your post-hoc, where interaction_var <- interaction(infection, acetaminophen).
TO BE NOTED: i never tested this methodology with nested and mixed models so beware!
I am a complete novice when it comes to survival analysis. I am working on a project that requires I use the coxph function in the "survival" package, but I am running into trouble because I do not understand what is required by the formula object.
Most descriptions I can find about the function are as follows:
"a formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function. "
I know what needs to be on the left of the operator, the issue is what the function expects from the right-hand side.
Here is a link of what my data looks like (The actual data set is much larger, I'm only displaying the first 20 data points for brevity):
Short explanation of data:
-Row 1 is the header
-Each row after that is a separate patient
-The first column is the age of the patient at the time of the study
-columns 2 through 14 (headed by x2-x13), and 19 (x18) and 20 (x19) are covariates such as race, relationship status, medical conditions that take on either true (1) or false (0) values.
-columns 15 (x14) through 18 (x17) are covariates such as tumor size, which take on whole number values greater than 0.
-The second to last column "sur" is the number of months survived, and "index" is whether or not that is a right-censored time (1 for true, 0 for false).
Given this data I need to plot a Cox Proportional hazard curve, but I end up with an incorrect plot because the right hand side of the formula object is wrong.
Here is my code, "temp4" is the name I gave to the data table:
library("survival")
temp4 <- read.table("~/data.txt", header=TRUE)
seerCox <- coxph(Surv(sur, index)~ temp4$x1 + temp4$x2 + temp4$x3 + temp4$x4 + temp4$x5 + temp4$x6 + temp4$x7 + temp4$x8 + temp4$x9 + temp4$x10 + temp4$x11 + temp4$x12 + temp4$x13 + temp4$x14 + temp4$x15 + temp4$x16 + temp4$x17 + temp4$x18 + temp4$x19, data=temp4, singular.ok=TRUE)
plot(survfit(seerCox), main= "Cox Estimate", mark.time=FALSE, ylab="Probability", xlab="Survival Time in Months", col=c("blue", "red", "green"))
I should also note that I have tried replacing the right hand side that you're seeing with the number 1, a period, leaving it blank. These methods produce a kaplan-meier curve.
The following is the console output:
Each new line is an example of the error produced depending on how I filter the data. (ie if I only include patients with ages greater than 85, etc.)
If someone could explain how it works, it would be greatly appreciated.
PS- I have searched for over a week to my solution, and I am asking for help here as a last resort.
You should not be using the prefix temp$ if you are also using a data argument. The whole purpose of supplying a data argument is to allow dropping those in the formula.
seerCox <- coxph( Surv(sur, index) ~ . , data=temp4, singular.ok=TRUE)
The above would use all of the x-variables in your temp data.frame. This will use just the first 3:
seerCox <- coxph( Surv(sur, index) ~ x1+x2+x3 , data=temp4)
Exactly what the warnings signify depends on the data (as you have in one sense already exemplified by producing different sorts of collinearity with different subsets.) If you have collinear columns, then you get singularities in the inversion of the model matrix and the software will attempt to drop aliased columns with a warning. This is really telling you that you do not have enough data to build the large models you are attempting. Exploring that possibility with table calls is often informative.
Bottom line: This is not a problem with your formula construction, so much as it is a problem of not understanding the limitations of the chosen method with the dataset you have assembled. You need to be more careful about defining your goals. What is the highest priority in this research? Do you really need every variable? Is it possible to aggregate some of these anonymous variables into clinically meaningful categories such as diagnostic categories or comorbities?
I have data of various companies' financial information organized by company ticker. I'd like to regress one of the columns' values against the others while keeping the company constant. Is there an easy way to write this out in lm() notation?
I've tried using:
reg <- lmList(lead2.dDA ~ paudit1 + abs.d.GINDEX + logcapx + logmkvalt +
logmkvalt2|pp, data=reg.df)
where pp is a vector of company names, but this returns coefficients as though I regressed all the data at once (and did not separate by company name).
A convenient and apparently little-known syntax for estimating separate regression coefficients by group in lm() involves using the nesting operator, /. In this case it would look like:
reg <- lm(lead2.dDA ~ 0 + pp/(paudit1 + abs.d.GINDEX + logcapx +
logmkvalt + logmkvalt2), data=reg.df)
Make sure that pp is a factor and not a numeric. Also notice that the overall intercept must be suppressed for this to work; in the new formulation, we have a different "intercept" for each group.
A couple comments:
Although the regression coefficients obtained this way will match those given by lmList(), it should be noted that with lm() we estimate only a single residual variance across all the groups, whereas lmList() would estimate separate residual variances for each group.
Like I mentioned in my earlier comment, the lmList() syntax that you gave looks like it should have worked. Since you say it didn't, this leads me to expect that really the problem is something else (although it's hard to tell what without a reproducible example), and so it seems likely that the solution I posted will fail for you as well, for the same unknown reasons. If you want more detailed guidance, please provide more information; help us help you.