Planning to run the following code and get a simple univariate table
lang2 <- glm(
langr52 ~ r9racehisp,
data = test,
family = binomial(link = "logit")
)
summary(lang2)
lang_tbl_1 <-
tbl_regression(
lang2
)
Then get the following error
There were 18 warnings (use warnings() to see them)"
appreciate guidance
Related
We periodically runs jobs and we need to save the output into a PDS and then parse the output to extract parts of it to save into another member. It needs to be done by issuing a REXX command using the percent sign and the REXX member name as an SDSF command line. I've attempted to code a REXX to do this, but it is getting an error when trying to invoke an ISPF service, saying the ISPF environment has not been established. But, this is SDSF running under ISPF.
My code has this in it (copied from several sources and modified):
parse arg PSDSFPARMS "(" PUSERPARMS
parse var PSDSFPARMS PCURRPNL PPRIMPNL PROWTOKEN PPRIMCMD .
PRIMCMD=x2c(PPRIMCMD)
RC = isfquery()
if RC <> 0 then
do
Say "** SDSF environment does not exist, exec ending."
exit 20
end
RC = isfcalls("ON")
Address SDSF "ISFGET" PPRIMPNL "TOKEN('"PROWTOKEN"')" ,
" (" VERBOSE ")"
LRC = RC
if LRC > 0 then
call msgrtn "ISFGET"
if LRC <> 0 then
Exit 20
JOBNAME = value(JNAME.1)
JOBNBR = value(JOBID.1)
SMPDSN = "SMPE.*.OUTPUT.LISTINGS"
LISTC. = ''
SMPODSNS. = ''
SMPODSNS.0 = 0
$ = outtrap('LISTC.')
MSGVAL = msg('ON')
address TSO "LISTC LVL('"SMPDSN"') ALL"
MSGVAL = msg(MSGVAL)
$ = outtrap('OFF')
do LISTCi = 1 to LISTC.0
if word(LISTC.LISTCi,1) = 'NONVSAM' then
do
parse var LISTC.LISTCi . . DSN
SMPODSNS.0 = SMPODSNS.0 + 1
i = SMPODSNS.0
SMPODSNS.i = DSN
end
IX = pos('ENTRY',LISTC.LISTCi)
if IX <> 0 then
do
IX = pos('NOT FOUND',LISTC.LISTCi,IX + 8)
if IX <> 0 then
do
address ISPEXEC "SETMSG MSG(IPLL403E)"
EXITRC = 16
leave
end
end
end
LISTC. = ''
if EXITRC = 16 then
exit 0
address ISPEXEC "TBCREATE SMPDSNS NOWRITE" ,
"NAMES(TSEL TSMPDSN)"
I execute this code by typing %SMPSAVE next to the spool output line on the "H" SDSF panel and it runs fine until it gets to this point in the REXX:
114 *-* address ISPEXEC "TBCREATE SMPDSNS NOWRITE" ,
"NAMES(TSEL TSMPDSN)"
>>> "TBCREATE SMPDSNS NOWRITE NAMES(TSEL TSMPDSN)"
ISPS118S SERVICE NOT INVOKED. A VALID ISPF ENVIRONMENT DOES NOT EXIST.
+++ RC(20) +++
Does anyone know why it says I don't have a valid ISPF environment and how I can get around this?
I've done quite a bit in the past with REXX, including writing REXX code to handle line commands, but this is the first time I've tried to use ISPEXEC commands within this code.
Thank you,
Alan
I'm still not very familiar with numba and my problem is that I have the piece of code bellow that I use for randomize the edges of graphs.
This code is simply used to swap some edges in a connectivity matrix given the number of desired swaps and a seed for the random number generator.
My problem is that when I try to use it with numba to speed it up I did not menage to run it. The error it returns is also pasted bellow.
#nb.jit(nopython=True)
def _randomize_adjacency_wei(A, n_swaps, seed):
np.random.seed(seed)
# Number of nodes
n_nodes = A.shape[0]
# Copy the adj. matrix
Arnd = A.copy()
# Choose edges that will be swaped
edges = np.random.choice(n_nodes, size=(4, n_swaps), replace=True).T
#itr = range(n_swaps)
#for it in tqdm(itr) if verbose else itr:
it = 0
for it in range(n_swaps):
i,j,k,l = edges[it,:]
if len(np.unique([i,j,k,l]))<4:
continue
else:
# Old values of weigths
w_ij,w_il,w_kj,w_kl=Arnd[i,j],Arnd[i,l],Arnd[k,j],Arnd[k,l]
# Swaping edges
Arnd[i,j]=Arnd[j,i]=w_il
Arnd[k,l]=Arnd[l,k]=w_kj
Arnd[i,l]=Arnd[l,i]=w_ij
Arnd[k,j]=Arnd[j,k]=w_kl
return Arnd
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
No implementation of function Function(<function unique at 0x7f1a1c03b0d0>) found for signature:
>>> unique(list(int64)<iv=None>)
There are 2 candidate implementations:
- Of which 2 did not match due to:
Overload in function 'np_unique': File: numba/np/arrayobj.py: Line 1915.
With argument(s): '(list(int64)<iv=None>)':
Rejected as the implementation raised a specific error:
TypingError: Failed in nopython mode pipeline (step: nopython frontend)
Unknown attribute 'ravel' of type list(int64)<iv=None>
File "../../../home/vinicius/anaconda3/lib/python3.8/site-packages/numba/np/arrayobj.py", line 1918:
def np_unique_impl(a):
b = np.sort(a.ravel())
^
During: typing of get attribute at /home/vinicius/anaconda3/lib/python3.8/site-packages/numba/np/arrayobj.py (1918)
File "../../../home/vinicius/anaconda3/lib/python3.8/site-packages/numba/np/arrayobj.py", line 1918:
def np_unique_impl(a):
b = np.sort(a.ravel())
^
raised from /home/vinicius/anaconda3/lib/python3.8/site-packages/numba/core/typeinfer.py:1071
During: resolving callee type: Function(<function unique at 0x7f1a1c03b0d0>)
During: typing of call at <ipython-input-165-90ffd30fe0e8> (19)
File "<ipython-input-165-90ffd30fe0e8>", line 19:
def _randomize_adjacency_wei(A, n_swaps, seed):
<source elided>
i,j,k,l = edges[it,:]
if len(np.unique([i,j,k,l]))<4:
^
Thanks in advance,
Vinicius
According to the comments, you are passing a list to np.unique() but this is not supported by Numba.
Modifying the code this way:
i, j, k, l = e = edges[it, :]
if len(np.unique(e)) < 4:
...
The following example doesn't produce any errors:
>>> A = np.random.randint(0, 5, (8,8))
>>> r = _randomize_adjacency_wei(A, 4, 33)
I want to transform my excel (bank return & the date) in a zoo object, with the data in the zoo object being numeric & date. I used the following data:
Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1455 obs. of 2 variables:
$ date : POSIXct, format: "1925-01-02" "1925-01-03" "1925-01-05" "1925-01-06" ...
$ Deutsche Bank: num 0.181 0.191 0.191 0.184 0.186 ...
I used the following code:
db.xts <- na.omit(as.data.frame(db.kurs))
db.xts2 <- db.xts %>% mutate(date = as.Date(date, format = "%d.%m.%Y")) %>% mutate(`Deutsche Bank` = as.numeric(`Deutsche Bank`))
db.xts3 <- as.xts(db.xts2, db.kurs$date)
db.zoo <- as.zoo(db.xts3)
db.zoo <- db.zoo[, colnames(db.zoo) != "date"]
which leaves me with the following:
‘zoo’ series from 1925-01-02 to 1929-12-31
Data: chr [1:1455] "0.1807194" "0.1911455" "0.1911455" "0.1841948" "0.1859325" "0.1841948" "0.1807194" "0.1789817" ...
Index: POSIXct[1:1455], format: "1925-01-02" "1925-01-03" "1925-01-05" "1925-01-06" "1925-01-07" "1925-01-08" "1925-01-09" "1925-01-10" ...
If I try to run it without the as.xts command R deletes all the dates and uses an iteger from 1 to 1455.
Does anybody have an idea how to solve it?
Thanks for the help,
Nick
I've written a python script to evaluate a physical quantity and, for some reason, python decided to raise an OverflowError for no justified reason. Here's the script
import numpy as np
from math import sqrt,cos, log, pi
from scipy import integrate as sciint
from scipy import optimize as sciopt
rt=np.inf
ymin=cos(np.radians(0.5))
def func(u,y, D, rt):
return (1.+u)**(-4)/u/sqrt(u*u-D**2*(1-y*y))
def lim_u(y, D, rt):
return [D*sqrt(1-y*y), rt]
def lim_y(D, rt):
return [ymin,1]
def Jfactor(D,rt,r0,rho0,tmax):
ymin=cos(np.radians(tmax))
Dprime=D/r0
rtprime=rt/r0
Msun2kpc5_GeVcm5 = 4463954.894661358
cst = 4*pi*rho0**2*r0*Msun2kpc5_GeVcm5
res = sciint.nquad(func, ranges=[lim_u, lim_y], args=(Dprime,rtprime),
opts=[{'epsabs':1.e-10,'epsrel':1.e-10,'limit':1000},
{'epsabs':1.e-10,'epsrel':1.e-10,'limit':1000}])
return cst*res[0]
def deltaJ(rho0,J,D,rt,r0,tmax):
#return J-Jfactor(D,rt,r0,1.,tmax)*10.**(rho0*2.)
return long(J-Jfactor(D,rt,r0,1.,tmax)*10.**(rho0*2.))
D=104.
rt=np.inf
tmax=0.5
J = 10.**18
for j,r0 in enumerate(np.logspace(-1.,np.log10(5),10)):
results = sciopt.minimize_scalar(deltaJ,bracket=(7.,9.),args=(J,D,rt,r0,tmax))
print r0,results.x,results.fun
Very quickly: I want scipy.optimize.minimize_scalar to minimize deltaJ but here's (part of) the error message I get
File "test2.py", line 26, in deltaJ
return long(J-Jfactor(D,rt,r0,1.,tmax)*10.**(rho0*2.))
OverflowError: (34, 'Numerical result out of range')
Now, if J = 10**18, Jfactor(D,rt,r0,1.,tmax) is ~ 5, I expect minimize_scalar to easily scan over rho0 to find ~ 8.5. Instead I get this error message.
As you can see, I've even bracketed the range and tried using long(), but nothing helped. Using instead another minimizing method, eg. bounded, gives again a weird result (function value of ~ -7e17)...
Does anybody have an idea to have this working? Thank you
I used to use the code below to calculate standardized coefficients of a lmer model. However, with the new version of lme the structure of the returned object has changed.
How to adapt the function stdCoef.lmer to make it work with the new lme4 version?
# Install old version of lme 4
install.packages("lme4.0", type="both",
repos=c("http://lme4.r-forge.r-project.org/repos",
getOption("repos")[["CRAN"]]))
# Load package
detach("package:lme4", unload=TRUE)
library(lme4.0)
# Define function to get standardized coefficients from an lmer
# See: https://github.com/jebyrnes/ext-meta/blob/master/r/lmerMetaPrep.R
stdCoef.lmer <- function(object) {
sdy <- sd(attr(object, "y"))
sdx <- apply(attr(object, "X"), 2, sd)
sc <- fixef(object)*sdx/sdy
#mimic se.ranef from pacakge "arm"
se.fixef <- function(obj) attr(summary(obj), "coefs")[,2]
se <- se.fixef(object)*sdx/sdy
return(list(stdcoef=sc, stdse=se))
}
# Run model
fm0 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
# Get standardized coefficients
stdCoef.lmer(fm0)
# Comparison model with prescaled variables
fm0.comparison <- lmer(scale(Reaction) ~ scale(Days) + (scale(Days) | Subject), sleepstudy)
The answer by #LeonardoBergamini works, but this one is more compact and understandable and only uses standard accessors — less likely to break in the future if/when the structure of the summary() output, or the internal structure of the fitted model, changes.
stdCoef.merMod <- function(object) {
sdy <- sd(getME(object,"y"))
sdx <- apply(getME(object,"X"), 2, sd)
sc <- fixef(object)*sdx/sdy
se.fixef <- coef(summary(object))[,"Std. Error"]
se <- se.fixef*sdx/sdy
return(data.frame(stdcoef=sc, stdse=se))
}
library("lme4")
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
fixef(fm1)
## (Intercept) Days
## 251.40510 10.46729
stdCoef.merMod(fm1)
## stdcoef stdse
## (Intercept) 0.0000000 0.00000000
## Days 0.5352302 0.07904178
(This does give the same results as stdCoef.lmer in
#LeonardoBergamini's answer ...)
You can get partially scaled coefficients — scaled by 2 times the SD of x, but not scaled by SD(y), and not centred — using broom.mixed::tidy + dotwhisker::by_2sd:
library(broom.mixed)
library(dotwhisker)
(fm1
|> tidy(effect="fixed")
|> by_2sd(data=sleepstudy)
|> dplyr::select(term, estimate, std.error)
)
this should work:
stdCoef.lmer <- function(object) {
sdy <- sd(attr(object, "resp")$y) # the y values are now in the 'y' slot
### of the resp attribute
sdx <- apply(attr(object, "pp")$X, 2, sd) # And the X matriz is in the 'X' slot of the pp attr
sc <- fixef(object)*sdx/sdy
#mimic se.ranef from pacakge "arm"
se.fixef <- function(obj) as.data.frame(summary(obj)[10])[,2] # last change - extracting
## the standard errors from the summary
se <- se.fixef(object)*sdx/sdy
return(data.frame(stdcoef=sc, stdse=se))
}