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I am still a beginnner in R. For a project I am trying to fit a gam model on a simple dataset with a timeset and year. I am doing it in R and I keep getting an error message that claims an argument is unused, even though I specify it in the code.
It concerns a dataset which includes a categorical variable of "Year", with only two levels. 2020 and 2022. I want to investigate if there is a peak in the hourly rate of visitors ("H1") in a nature reserve. For each observation period the average time was taken, which is the predictor variable used here ("T"). I want to use a Gam model for this, and have the smoothing applied differently for the two years.
The following is the line of code that I tried to use
`gam1 <- gam(H1~Year+s(T,by=Year),data = d)`
When I try to run this code, I get the following error message
`Error in s(T, by = Year) : unused argument (by = Year)`
I also tried simply getting rid of the "by" argument
`gam1 <- gam(H1~Year+s(T,Year),data = d)`
This allows me to run the code, but when trying to summon the output using summary(gam1), I get
Error in [<-(tmp, snames, 2, value = round(nldf, 1)) : subscript out of bounds
Since I feel like both errors are probably related to the same thing that I'm doing wrong, I decided to combine the question.
Did you load the {mgcv} package or the {gam} package? The latter doesn't have factor by smooths and as such the first error message is what I would expect if you did library("gam") and then tried to fit the model you showed.
To fit the model you showed, you should restart R and try in a clean session:
library("mgcv")
# load you data
# fit model
gam1 <- gam(H1 ~ Year + s(T, by = Year), data = d)
It could well be that you have both {gam} and {mgcv} loaded, in which case whichever you loaded last will be earlier on the function search path. As both packages have functions gam() and s(), R might just be finding the wrong versions (masking), so you might also try
gam1 <- mgcv::gam(H1 ~ Year + mgcv::s(T, by = Year), data = d)
But you would be better off only loading {mgcv} if you wan factor by smooths.
#Gavin Simpson
I did have both loaded, and I tried just using mgcv as you suggested. However, then I get the following error.
Error in names(dat) <- object$term :
'names' attribute [1] must be the same length as the vector [0]
I am assuming this is simply because it's not actually trying to use the "gam" function, but rather it attempts to name something gam1. So I would assume I actually need the package of 'gam' before I could do this.
The second line of code also doesn't work. I get the following error
Error in model.frame.default(formula = H1 ~ Year + mgcv::s(T, by = Year), :
invalid type (list) for variable 'mgcv::s(T, by = Year)'
This happens no matter the order I download the two packages in. And if I don't download 'gam', I get the error as described above.
I am trying to visualize changes in gene expression as categorical variables (up, down, no change) over various timepoints.
I have a dataframe describing differential expression data that looks like this:
data = {'gene':['Svm3G0018840','Svm5G0011050','Svm9G0059770'],
'01h': ['nc','up','down'], '04h': ['up', 'down', 'nc'],'08h':['nc','down','up']}
df=pd.DataFrame.from_dict(data)
df=df.set_index('gene')
I can use this df to create the parallel plot using the following code:
fig = px.parallel_categories(herbdf, dimensions=['01h', '04h', '08h','24h','48h'],
labels={'01h':'', '04h':'', '08h':'','24h':'','48h':''})
fig.show()
However, the categories (up, down, nc) are not always in the same order for every time point which makes the figure very difficult to read. I can change this in the interactive figure in a notebook, but I only have the option to output the corrected figure as a low quality png. I need the image in an svg format, which means I need to use the line:
fig.write_image("/figs/herb_de_pp.svg")
But when I add this line in the code block to save the figure I have no control of the order the categorical boxes end up in:
I have tried to add fig.update_ lines to solve this problem, such as:
fig.update_layout(xaxis={'categoryorder':'total descending'})
but this doesn't seem to change the output at all.
I could be missing something simple- any help would be much appreciated!
Parallel coordinates diagrams don't have xaxis/yaxis properties, you need to update traces in order to change the dimensions order:
dimensions = ['01h', '04h', '08h','24h','48h']
...
fig.update_traces(dimensions=[{"categoryorder": "category descending"} for _ in dimensions])
not great answer here, but something that I think will work in a pinch...
It looks like the order of the categories of each figure/column come from the order that they are in the original dataset. That is, in your first column, nc is the first unique item, then down is the second unique item, up is third.
So, if you can rearrange/sort your data so that the data shows up in the order you want it displayed, that should work.
Have your first row be nc | nc | nc | nc | nc, second row down | down | down | down | down, and third row up | up | up | up | up (assuming you actually have records like that). That should do it, but isn't very elegant...
Given the above solution, this is the line needed to sort the dataframe and produce the figure with ordered categories:
sorteddf = df.sort_values(by=['01h','04h','08h'], axis=0, ascending=False)
I'm looking to set up keybindings to increase/decrease gaps and margins in Qtile similar to what the following does in i3-gaps:
bindsym $mod+equal gaps inner current plus 5
bindsym $mod+minus gaps inner current minus 5
bindsym $mod+Shift+equal gaps outer current plus 5
bindsym $mod+Shift+minus gaps outer current minus 5
I can somewhat get the equivalent of outer-gaps to work with the following code:
def increase_gap(qtile):
qtile.screens[0].top.size = screens[0].top.size+5
qtile.screens[0].right.size = screens[0].top.size+5
qtile.screens[0].left.size = screens[0].top.size+5
#margs = screens[0].bottom.margin
screens[0].bottom.margin[0] = screens[0].bottom.margin[0]+5
screens[0].bottom.size=20
qtile.screens[0].cmd_resize()
def decrease_gap(qtile):
qtile.screens[0].top.size = max(screens[0].top.size-5, 0)
qtile.screens[0].right.size = max(screens[0].top.size-5, 0)
qtile.screens[0].left.size = max(screens[0].top.size-5, 0)
#margs = screens[0].bottom.margin
screens[0].bottom.margin[0] = max(screens[0].bottom.margin[0]-5, 0)
screens[0].bottom.size=20
qtile.screens[0].cmd_resize()
Key([mod, "shift"], "equal", lazy.function(increase_gap), desc="Increase gap"),
Key([mod, "shift"], "minus", lazy.function(decrease_gap), desc="Decrease gap"),
I'm not sure this is the right way to do things, though. I'm not sure if this is supposed to be manipulated this way. I'm not really sure that the cmd_resize() function is what I should be using, however from trial and error it's what I've found to work. I'm not sure why the screen[0].bottom.size=20 (20 is the size of my bottom bar. I know I shouldn't hardcode, but I'm trying to produce a proof of concept before I clean the code) is needed, but the bar starts floating if I don't have that there. Finally, increasing and decreasing the gap gets close, but not quite to the original configuration. The gaps look slightly different than the original. So, I'm not sure this is the right way to accomplish this, and I could use the advice.
Secondly, though that gets close to accomplishing what I want on the outer gaps, I have not been able to make any headway to getting the inner gaps to work. I initially tried changing the margin parameter of a layout, and when that didn't work I tried simply initializing a new layout and replacing the old one as posted below, but neither approach worked.
def column_increase_margin(qtile):
current_margin = current_margin + 5
layouts[0] = layout.Columns(border_focus_stack='#d75f5f', margin=current_margin, border_width=0)
screens[0].bottom.size=20
qtile.screens[0].cmd_resize()
I've tried going through the code here, but it's a big project and I'm struggling to make heads or tails from it.
Any advice would be appreciated.
If I understand what you want, you can add the following to the Layout class in libqtile/layout/base.py:
def cmd_increase_margin(self):
self.margin += 10
self.group.layout_all()
def cmd_decrease_margin(self):
new_margin = self.margin - 10
if new_margin < 0:
new_margin = 0
self.margin = new_margin
self.group.layout_all()
You can then add some keys in your config.py to increase and decrease margins. e.g.
KeyChord([mod], "m", [
Key([], "Up", lazy.layout.increase_margin()),
Key([], "Down", lazy.layout.decrease_margin())
],
mode="Margins"
),
I'm new to qtile, so maybe there is something wrong with the above approach, but it seems to work.
I was wondering today about how finding a specific value on a plot and drawing the right line that goes with. I used to do that on an old chart library, and I was wondering that perhaps this functionnality exist but I don't know how to find it.
The result should look like this: https://miro.medium.com/max/1070/1*Ckhi9soE9Lx2lIf9tPVLMQ.png
To provide some context, I'm doing a PCA over my data, and I would like to point out some thresholds at 97.5, 99 and 99.5% of explained cumuled variance.
Have a great day!
EDIT:
See Answer
As solved by ImportanceOfBeingErnest, here is the code:
whole_pca = PCA().fit(np.array(inputs['Scale'].tolist()))
cumul = np.cumsum(np.round(whole_pca.explained_variance_ratio_, decimals=3)*100)
over_95 = np.argmax(cumul>95)
over_99 = np.argmax(cumul>99)
over_995 = np.argmax(cumul>99.5)
plt.plot(cumul)
plt.plot([0,over_95,over_95], [95,95,0])
plt.plot([0,over_99,over_99], [99,99,0])
plt.plot([0,over_995,over_995], [99.5,99.5,0])
plt.xlim(left=0)
plt.ylim(bottom=80)
plt.ylabel('% Variance Explained')
plt.xlabel('# of Features')
plt.title('PCA Analysis')
Result in:
Thank you!
I am working on an assignment where I need to draw a line between points on a map. I built it up using if statements, but that limits how many times I can do something so want to use a loop.
The code I have so far is below, I can add the cities and draw a line, but it draws a line to itself rather than from a start point to the next point (and continue on)
Can anybody please help at all?
def level1():
cityXvalue = [45,95,182,207,256,312,328,350,374,400]
cityYvalue = [310,147,84,201,337,375,434,348,335,265]
# Display the map image
map = makePicture(getMediaPath("map.png"))
show(map)
number = 0
# Ask user to enter numbers of the cities they wish to visit
cities = requestInteger("Enter the number of the city you would like to visit")
# Draw a line between previously entered point and current one
while cities > number:
city = requestInteger("Please choose a city number")
addLine(map,cityXvalue[city],cityYvalue[city], cityXvalue[city], cityYvalue[city])
repaint(map)