plotGradient cuts off original data

[stephanJG]

Hi,
I recently realized that the "plotGradient" function is cutting off original data. The problem is that the ylim is set to min and max of the predictions, not the original data.
Best
Jörg

plotGradient

ggplot

[ovaskain]

Thanks, I also noted this and fixed manually for my own purposes... We should fix that more properly for the package. Jari, if you have time, you could make it?

…

On 18.6.2020 10.53, stephanJG wrote: Hi, I recently realized that the "plotGradient" function is cutting off original data. The problem is that the ylim is set to min and max of the predictions, not the original data. Best Jörg *plotGradient* plotGr <https://user-images.githubusercontent.com/65352673/84993230-4c5b4400-b149-11ea-8a43-be4078760ad4.png> *ggplot* image <https://user-images.githubusercontent.com/65352673/84992007-95aa9400-b147-11ea-9279-43b028b67459.png> — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#48>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEIYMZTNMHGBJLT2CHPCVW3RXHBWNANCNFSM4OBKEROQ>.

[jarioksa]

@ovaskain : I'll do this. I haven't peeked at the code yet, but sounds pretty trivial a fix.

[jarioksa]

It seems that the max was correctly set with data, but the line of code for setting min was just forgotten. Should be fixed with commit 91874d6.

[stephanJG]

PS: as I have been ask for how to plot with ggplot here some code

#Percentiles used in calculation
p <- c(.025,.5,.975)#95%
p_names <- paste0(p*100)
p_funs <- map(p, ~partial(quantile, probs = .x, na.rm = TRUE)) %>%
set_names(nm = p_names)

#names
v_expV<-MODEL$covNames %>% .[-1]
df_CIs<-NULL

#loop over all exp vars
for (i in 1:length(v_expV)){
#gradients
grad<-constructGradient(
MODEL,
focalVariable = v_expV[i],
non.focalVariables = 2) #(2 = net effect)
#use gradient to make predictions
pred<-predict(
MODEL,Gradient = grad,expected = T) #(probit: expected = T)
#df with 1000 pred * 20 units of exp vars
predGrad<-pred %>%
#richness of all pred values (see line 53 in plotGradient function for CWM)
lapply(rowSums) %>%
#merge list elements to rows
rlist::list.rbind() %>% as_tibble()
#df with quantiles as cols
CIs<-predGrad %>%
pivot_longer(everything(),names_to = "grad",values_to = "pred") %>%
mutate(across(everything(),type.convert)) %>%
group_by(grad) %>%
summarise(across(everything(),p_funs)) %>%
select(-1) %>%
rename_with(~c("Qua_low","Qua_mid","Qua_high"))
#df with all exp vars
df_CIs<-CIs %>% add_column(covNames=v_expV[i],.before = 1) %>%
bind_rows(df_CIs)}