Error Bars

[pthane]

@jvcasillas

Here's an example graph below. This is a PDF version that I had saved, but I need to update it with the data from one more participant. It comes out now without the error bars that you see in the version below. The code is below the PDF. Also, I think it would be best if the error bars reflected the SD for each item, because the participants have a good deal of variability.

Subjunctive by Matrix Item.pdf

Subjunctive_Aggregate %>% ggplot(aes(x = MainVerb, y = Average, color = ExpGroup, shape = Task)) + geom_hline(yintercept = 0.8, color = 'white', size = 2) + stat_summary(fun = mean, geom = 'pointrange', size = 1, position = position_dodge(width = 0.5)) + scale_color_brewer(palette = "Set1", name = "") + ylim(c(0,1)) + labs(x = 'Matrix verb', y = 'Proportion of subjunctive responses', caption = '', title = 'Average Use of Subjunctive by Matrix Item') + theme(plot.title = element_text(hjust = 0.5))

[jvcasillas]

Clearly this code didn't give you a measure of spread before. You supplied "mean" to the argument "fun". If you want a measure of spread you need to use the argument "fun.data" and give it something like "mean_sdl" or "mean_se".

[pthane]

OK, but just for my own edification then, why did the graph that I attached give me error bars if the code was incorrect? I obviously believe you, but don't understand why this (and other) graphs had error bars and now suddenly don't, but I haven't changed the code. I will look into the mean_sdl for fun.data in stat_summary. Thanks!

[jvcasillas]

You probably changed the code. Check your commit history.

[pthane]

Hi @jvcasillas,

No dice. I have not been able to get the spread to come through. When I run the code, all I get is the pointrange for the first function, but no SD. I've asked Kyle to run it and I have not been successful in looking at my history and finding an error. Is there anything you can suggest? Here is my code:

Subjunctive_Aggregate %>% ggplot(aes(x = MainVerb, y = Average, color = ExpGroup, shape = Task)) + geom_hline(yintercept = 0.8, color = 'white', size = 2) + stat_summary(fun = mean, geom = "pointrange", size = 1, position = position_dodge(width = 0.5)) + stat_summary(fun.data = mean_sdl, geom = "line", position = position_dodge(width=0.2)) + scale_color_brewer(palette = "Set1", name = "") + scale_y_continuous(breaks = seq (0, 1, 0.2), limits = c(0, 1)) + labs(x = "Matrix verb", y = "Proportion of subjunctive responses", caption = "", title = "Average Use of Subjunctive by Matrix Item") + theme(plot.title = element_text(hjust = 0.5))

Thanks, PT

[jvcasillas]

Subjunctive_Aggregate %>%
  ggplot(aes(x = MainVerb, y = Average, color = ExpGroup, shape = Task)) +
    geom_hline(yintercept = 0.8, color = 'white', size = 2) +
    stat_summary(fun.data = mean_sdl, geom = "pointrange", position = position_dodge(width=0.2)) +
    scale_color_brewer(palette = "Set1", name = "") +
    scale_y_continuous(breaks = seq (0, 1, 0.2), limits = c(0, 1)) +
    labs(x = "Matrix verb", y = "Proportion of subjunctive responses", caption = "",
      title = "Average Use of Subjunctive by Matrix Item") +
      theme(plot.title = element_text(hjust = 0.5))

[pthane]

Hola Joseph,

Tampoco me da las barras.

[jvcasillas]

Then you aren't giving it data that can produce a measure of spread. Type dput(Subjunctive_Aggregate) and paste the results here.

[jvcasillas]

If you are sure your data is repeated measures (you would certainly know better than I do) then triple check for warnings when you send the code (restart the r session first).

[pthane]

Then you aren't giving it data that can produce a measure of spread. Type dput(Subjunctive_Aggregate) and paste the results here.

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c("collector_double", "collector")), Gender = structure(list(), class = c("collector_character", "collector")), ExpGroup = structure(list(), class = c("collector_character", "collector")), HerGroup = structure(list(), class = c("collector_character", "collector")), Group_No = structure(list(), class = c("collector_double", "collector")), Age = structure(list(), class = c("collector_double", "collector")), AoA_SPA = structure(list(), class = c("collector_double", "collector")), AoA_ENG = structure(list(), class = c("collector_double", "collector")), LOR = structure(list(), class = c("collector_double", "collector")), SelfProf = structure(list(), class = c("collector_double", "collector")), SelfDom = structure(list(), class = c("collector_double", "collector")), DELE = structure(list(), class = c("collector_double", "collector")), FofA_Prod = structure(list(), class = c("collector_double", "collector")), FofA_Comp = structure(list(), class = c("collector_double", "collector")), Battery = structure(list(), class = c("collector_character", "collector")), Item = structure(list(), class = c("collector_double", "collector")), Seq_Item = structure(list(), class = c("collector_double", "collector")), Average = structure(list(), class = c("collector_double", "collector")), MainVerb = structure(list(), class = c("collector_character", "collector")), Token_Main_Lemma = structure(list(), class = c("collector_double", "collector")), Token_Main_Pret = structure(list(), class = c("collector_logical", "collector")), Token_Main_Imp = structure(list(), class = c("collector_logical", "collector")), Reg_Main = structure(list(), class = c("collector_character", "collector")), Syntax_Main = structure(list(), class = c("collector_character", "collector")), SubVerb = structure(list(), class = c("collector_character", "collector")), Token_Sub = structure(list(), class = c("collector_double", "collector")), Morpheme = structure(list(), class = c("collector_character", "collector")), Property = structure(list(), class = c("collector_character", "collector")), Task = structure(list(), class = c("collector_character", "collector")), Condition = structure(list(), class = c("collector_character", "collector")), Verb_Condition = structure(list(), class = c("collector_character", "collector")), FofA = structure(list(), class = c("collector_double", "collector")), FofA_Std = structure(list(), class = c("collector_double", "collector")), AoA_ENG_Std = structure(list(), class = c("collector_double", "collector")), DELE_Std = structure(list(), class = c("collector_double", "collector")), Token_Main_Lemma_Std = structure(list(), class = c("collector_double", "collector")), Token_Sub_Std = structure(list(), class = c("collector_double", "collector")), Token_Pret_Std = structure(list(), class = c("collector_logical", "collector")), LOR_Std = structure(list(), class = c("collector_double", "collector"))), default = structure(list(), class = c("collector_guess", "collector")), skip = 1L), class = "col_spec"), class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"))

[jvcasillas]

It looks like the dataframe is too long to get all of the output. Hmm.

[jvcasillas]

The function you want, mean_sdl() is from the Hmisc package. Try reinstalling it.

[pthane]

The function you want, mean_sdl() is from the Hmisc package. Try reinstalling it.

I have already reinstalled Hmisc.

[pthane]

My question is how I got error bars in the first place with the previous code, but I suppose the problem could be that I have the color aesthetic set to group, and my y value is "Average," which is the binary DV of suppliance of the subjunctive. I have x set to give me a score for each matrix verb. This should also yield a measure of spread, no? Or do I have to somehow specify average score by participant? If so, is this easy to do?

[jvcasillas]

It looks correct to me (now). Im not sure what the issue is, though Im sure its something silly that we are missing. Can you link which script it is and the line where the relevant code starts?

[pthane]

Hi Joseph,

Yes; the script (in Scripts folder, of course) is Subjunctive Descriptive Stats. The graph you sent me is on lines 47-55, but the I create the Subjunctive_Aggregate dataframe through the aggregate function on lines 22-42.

Thanks!

[jvcasillas]

Ok. It was a silly error. First, the code, as is, doesn't include the changes I suggested so it wouldn't have worked anyway.

In any case, the issue is that you limited the y-axis, which filters out anything outside the range specified.

Long story short, get rid of ylim.

[pthane]

Hi Joseph,

Thanks for doing this. The first thing: I think I hadn't uploaded the latest version, so that would do it (I had another graph below that as well that was redundant – I might have put your code in there). I still think the graph looks a bit strange. The question I have is why average reaches 1.5. It should be between 0 and 1, no? And it seems that the task aesthetic disappeared. If the average had a max. of 1 and a min. of 0, I'm not entirely sure why I'm seeing values of 1.5, and the SDs seem…huuuuge. Which I guess makes sense considering there is enormous variability within my populations (on purpose).

Thanks again for doing this; I think if I can resolve the axis issue/get your feedback on it, I should be good to go.

[jvcasillas]

I dont think you are getting any means above 1, as you mention that wouldn't make sense. Be default mean_sdl() plots 2 SDs so that isn't helping, but in any case you should probably plot the standard error. Switch to mean_se and it will probably look a lot better. If you need to limit the yaxis use coord_cartesian(ylim = c(0, 1)).

[pthane]

Sounds good. Thanks so much, Joseph!

PT

[pthane]

Hi Joseph,

Thanks for your help. I finally got what I wanted to work. The question is what exactly does the standard error tell me? My very incomplete knowledge still tells me that visualizing the SD would give me a better idea of how much variability there is within populations. I've made the SE work and that's fine for now, but I am not entirely sure what that tells me.

[jvcasillas]

The standard deviation gives you information about how the data points vary around the mean (not the population). The standard error describes how precise the mean of the sample is as an estimate of the true mean of the population. It's the SD divided by the square root of your sample size.

[pthane]

Thanks. I think we had covered that in class, but it’s hard for me to take what I hear and connect it to what I do sometimes.

[jvcasillas]

Yeah, same here. No worries.