Why glm make an input error on this function - r

I'm trying to run a glm in R but it results me with an error I can't figure it out how to solve:
> GLM.3 <- glm(log(Total_Pass + 1) ~ Total_Pass + Total_Buzz + dm_plant + dm_cdeagua + dm_cultivo + dm_humed + dm_bnativ + dm_snaspe + Cultivos + BosqNat + Plantac + Pastizal + Matorral + Humedal + C_agua + Sup_imper + Tie_desnud + hielo + alt_media + pend_media + Temp_media + PP_media + CA _100 + PLAND _100 + PD _100 + ED _100 + AREA_MN _100 + ENN_MN_100 + CA _210 + PLAND _210 + PD _210 + ED _210 + AREA_MN _210 + ENN_MN_210 + CA _600 + PLAND _600 + PD _600 + ED _600 + AREA_MN _600 + ENN_MN_600 + SHDI + SIDI + MSIDI + SHEI + SIEI + MSIEI, family=gaussian(identity), data=bats_araucania_500)
Error: unexpected input in "Total_Pass + Total_Buzz + dm_plant + dm_cdeagua + dm_cultivo + dm_humed + dm_bnativ + dm_snaspe + Cultivos + BosqNat + Plantac + Pastizal + Matorral + Humedal + C_agua + Sup_imper + Tie_desnud"
Any help is useful


R can not handle column names with space: CA _210. Try to wrap these columns between two ` (backticks) or rename your columns without spaces.
FYI : If you are using all columns as predictors, you can write your code this way: glm(log(y+1) ~ . , nextargs...)

Related

IPTW with multiple imputations: ordinal regression analysis?

I am currently dealing with multiple imputations in the context of IPTW analyses. Specifically, I have proceeded as follows: for the multiple imputations I have used the mice package, then performed the weighted analyses with the survey package and then combined the results of the outcome models using the mitools package to form the final estimates.
So far, so good. For my two binary outcome variables (dead/alive & functionally dependent [mrs_0to2==no]/independent[mrs_0to2==yes]) this works fine using binary logistic regression (via svyglm() function); however, for my last ordinal outcome variable I cannot find a corresponding function in the package that would allow me to do an ordinal weighted regression with imputed data.
I figured if anyone knows, it would be here...
The code I used is:
#Load data
library(tidyverse)
library(MASS)
currentDataset <- read_csv("Data/df.csv")
#Impute missing variables
library(ipw)
library(mice)
library(mitools)
formulas <- make.formulas(currentDataset)
formulas$age =age ~ time + mrs + nihss + srr + drr + hypert + dm + afib + cbf + mism + ica + m1 + m2 + rtpa + glc + mt
formulas$time =time ~ age + mrs + nihss + srr + drr + hypert + dm + afib + cbf + mism + ica + m1 + m2 + rtpa + glc + mt
formulas$mrs =mrs ~ age + time + nihss + srr + drr + hypert + dm + afib + cbf + mism + ica + m1 + m2 + rtpa + glc + mt
formulas$nihss =nihss ~ age + time + mrs + srr + drr + hypert + dm + afib + cbf + mism + ica + m1 + m2 + rtpa + glc + mt
formulas$srr =srr ~ age + time + mrs + nihss + drr + hypert + dm + afib + cbf + mism + ica + m1 + m2 + rtpa + glc + mt
formulas$drr =drr ~ age + time + mrs + nihss + srr + hypert + dm + afib + cbf + mism + ica + m1 + m2 + rtpa + glc + mt
formulas$hypert =hypert ~ age + time + mrs + nihss + srr + drr + dm + afib + cbf + mism + ica + m1 + m2 + rtpa + glc + mt
formulas$dm =dm ~ age + time + mrs + nihss + srr + drr + hypert + afib + cbf + mism + ica + m1 + m2 + rtpa + glc + mt
formulas$afib =afib ~ age + time + mrs + nihss + srr + drr + hypert + dm + cbf + mism + ica + m1 + m2 + rtpa + glc + mt
formulas$cbf =cbf ~ age + time + mrs + nihss + srr + drr + hypert + dm + afib + mism + ica + m1 + m2 + rtpa + glc + mt
formulas$mism =mism ~ age + time + mrs + nihss + srr + drr + hypert + dm + afib + cbf + ica + m1 + m2 + rtpa + glc + mt
formulas$ica =ica ~ age + time + mrs + nihss + srr + drr + hypert + dm + afib + cbf + mism + m1 + m2 + rtpa + glc + mt
formulas$m1 =m1 ~ age + time + mrs + nihss + srr + drr + hypert + dm + afib + cbf + mism + ica + m2 + rtpa + glc + mt
formulas$m2 =m2 ~ age + time + mrs + nihss + srr + drr + hypert + dm + afib + cbf + mism + ica + m1 + rtpa + glc + mt
formulas$rtpa =rtpa ~ age + time + mrs + nihss + srr + drr + hypert + dm + afib + cbf + mism + ica + m1 + m2 + glc + mt
formulas$glc =glc ~ age + time + mrs + nihss + srr + drr + hypert + dm + afib + cbf + mism + ica + m1 + m2 + rtpa + mt
formulas$evt =mt ~ age + time + mrs + nihss + srr + drr + hypert + dm + afib + cbf + mism + ica + m1 + m2 + rtpa + glc
meth <- make.method(currentDataset)
meth["perf_type"] <- ""
meth["a1"] <- ""
meth["mrs_ord"] <- ""
meth["mrs_0to2"] <- ""
meth["death"] <- ""
meth["group1"] <- ""
imputedDataset <- parlmice(currentDataset,
method = meth,
formulas = formulas,
m = 5,
n.core = 1,
n.imp.core = 5)
plot(imputedDataset)
currentDataset <- complete(imputedDataset, action = "long", include = TRUE)
#Calculate IPTW
split_imp <- currentDataset$.imp
mi_dataList <- split(currentDataset, split_imp)
for(i in 2:length(mi_dataList)) {
weight <- ipwpoint(exposure = evt, family = "binomial", link = "logit",
numerator =~ age+time+mrs+nihss+srr+drr+hypert+dm+afib+cbf+mism+ica+m1+m2+rtpa+glc,
denominator =~ age+time+mrs+nihss+srr+drr+hypert+dm+afib+cbf+mism+ica+m1+m2+rtpa+glc,
trunc = 0.01, data = as.data.frame(mi_dataList[[i]]))
mi_dataList[[i]]$.ipw0 = weight$weights.trunc
mi_dataList[[i]]$.final_weight <- mi_dataList[[i]]$.ipw0
}
mi_dataList[[1]]$.ipw0 <- NA
mi_dataList[[1]]$.final_weight <- NA
currentDataset <- unsplit(mi_dataList, split_imp)
I have now been able to estimate a dichotomous outcome parameter as follows (e.g. death):
#### Weighted analysis: death
library(mice)
library(mitools)
library(survey)
mi_dataList <- currentDataset[currentDataset$.imp != 0,]
mi_dataList <- split(mi_dataList, mi_dataList$.imp)
mi_dataList <- imputationList(mi_dataList)
clus <- svydesign(id =~ 1, weights =~ .final_weight, data = mi_dataList)
res <- with(clus, svyglm(death ~ mt, family = binomial))
summary(MIcombine(res), alpha = 0.05,
logeffect = TRUE)
res1 <- res[[1]]
car::infIndexPlot(res1)
Where I get stuck is in the ordinal analysis. I have tried it with the polr function from MASS and the ordinal package...
library(ordinal)
mi_dataList <- currentDataset[currentDataset$.imp != 0,]
mi_dataList <- split(mi_dataList, mi_dataList$.imp)
mi_dataList <- imputationList(mi_dataList)
clus <- svydesign(id =~ 1, weights =~ .final_weight, data = mi_dataList)
res <- with(clus, polr(as.factor(mrs_ord) ~ mt))
summary(MIcombine(res), alpha = 0.05,
logeffect = TRUE)
res1 <- res[[1]]
car::infIndexPlot(res1)

Problem with Lavaan not computing standard errors, the information matrix could not be converted

I am trying to run a CFA in R. The code looks like this:
item.model1 <- '
Reflective =~ IES_EFPR_3 + IES_EFPR_10 + IES_EFPR_16 + IES_EFPR_17 + IES_EFPR_23 + IES_EFPR_24 + IES_EFPR_25 + IES_EFPR_26 + IES_RHSC_11 + IES_RHSC_12 + IES_RHSC_13 + IES_RHSC_35 + IES_RHSC_36 + IES_RHSC_37 + IES_BFCC_31 + IES_BFCC_32 + IES_BFCC_33 + SREBQ_A + SREBQ_B + SREBQ_C + SREBQ_D + SREBQ_E
Reactive =~ BES_1 + BES_2 + BES_3 + BES_4 + BES_5 + BES_6 + BES_7 + BES_8 + BES_9 + BES_10 + BES_11 + BES_12 + BES_13 + BES_14 + BES_15 + BES_16 + PFS_1 + PFS_2 + PFS_3 + PFS_4 + PFS_5 + PFS_6 + PFS_7 + PFS_8 + PFS_9 + PFS_10 + PFS_11 + PFS_12 + PFS_13 + PFS_14 + PFS_15 + AEBQ_153 + AEBQ_155 + AEBQ_154 + AEBQ_156 + AEBQ_157 + AEBQ_146 + AEBQ_145 + AEBQ_144 + AEBQ_147 + AEBQ_148 + AEBQ_149 + AEBQ_150 + AEBQ_151 + AEBQ_152 + DEBQ_11 + DEBQ_12 + DEBQ_13 + DEBQ_14 + DEBQ_15 + DEBQ_16 + DEBQ_17 + DEBQ_18 + DEBQ_19 + DEBQ_20 + TFEQ_D_16 + TFEQ_D_25 + TFEQ_D_31 + TFEQ_D_1 + TFEQ_D_2 + TFEQ_D_7 + TFEQ_D_9 + TFEQ_D_11 + TFEQ_D_13 + TFEQ_D_15 + TFEQ_D_20 + TFEQ_D_27 + TFEQ_D_36 + TFEQ_D_45 + TFEQ_D_49 + TFEQ_D_51 + TFEQ_H_3 + TFEQ_H_5 + TFEQ_H_8 + TFEQ_H_12 + TFEQ_H_17 + TFEQ_H_19 + TFEQ_H_22 + TFEQ_H_24 + TFEQ_H_26 + TFEQ_H_29 + TFEQ_H_34 + TFEQ_H_39 + TFEQ_H_41 + TFEQ_H_47 + PNEES_1 + PNEES_2 + PNEES_4 + PNEES_6 + PNEES_7 + PNEES_8 + PNEES_11 + PNEES_12 + PNEES_13 + PNEES_15 + PNEES_16 + PNEES_18
IES.EFPR =~ IES_EFPR_3 + IES_EFPR_10 + IES_EFPR_16 + IES_EFPR_17 + IES_EFPR_23 + IES_EFPR_24 + IES_EFPR_25 + IES_EFPR_26
IES.RHSC =~ IES_RHSC_11 + IES_RHSC_12 + IES_RHSC_13 + IES_RHSC_35 + IES_RHSC_36 + IES_RHSC_37
IES.BFCC =~ IES_BFCC_31 + IES_BFCC_32 + IES_BFCC_33
SREBQ. =~ SREBQ_A + SREBQ_B + SREBQ_C + SREBQ_D + SREBQ_E
BES. =~ BES_1 + BES_2 + BES_3 + BES_4 + BES_5 + BES_6 + BES_7 + BES_8 + BES_9 + BES_10 + BES_11 + BES_12 + BES_13 + BES_14 + BES_15 + BES_16
PFS. =~ PFS_1 + PFS_2 + PFS_3 + PFS_4 + PFS_5 + PFS_6 + PFS_7 + PFS_8 + PFS_9 + PFS_10 + PFS_11 + PFS_12 + PFS_13 + PFS_14 + PFS_15
AEBQ.EOE =~ AEBQ_153 + AEBQ_155 + AEBQ_154 + AEBQ_156 + AEBQ_157
AEBQ.H =~ AEBQ_146 + AEBQ_145 + AEBQ_144 + AEBQ_147 + AEBQ_148
AEBQ.FR =~ AEBQ_149 + AEBQ_150 + AEBQ_151 + AEBQ_152
DEBQ.EX =~ DEBQ_11 + DEBQ_12 + DEBQ_13 + DEBQ_14 + DEBQ_15 + DEBQ_16 + DEBQ_17 + DEBQ_18 + DEBQ_19 + DEBQ_20
TFEQ.D =~ TFEQ_D_16 + TFEQ_D_25 + TFEQ_D_31 + TFEQ_D_1 + TFEQ_D_2 + TFEQ_D_7 + TFEQ_D_9 + TFEQ_D_11 + TFEQ_D_13 + TFEQ_D_15 + TFEQ_D_20 + TFEQ_D_27 + TFEQ_D_36 + TFEQ_D_45 + TFEQ_D_49 + TFEQ_D_51
TFEQ.H =~ TFEQ_H_3 + TFEQ_H_5 + TFEQ_H_8 + TFEQ_H_12 + TFEQ_H_17 + TFEQ_H_19 + TFEQ_H_22 + TFEQ_H_24 + TFEQ_H_26 + TFEQ_H_29 + TFEQ_H_34 + TFEQ_H_39 + TFEQ_H_41 + TFEQ_H_47
PNEES.N =~ PNEES_1 + PNEES_2 + PNEES_4 + PNEES_6 + PNEES_7 + PNEES_8 + PNEES_11 + PNEES_12 + PNEES_13 + PNEES_15 + PNEES_16 + PNEES_18
'
### calculate model
item.cfa.1 <- cfa(item.model1, data = item.dat, missing="pairwise", std.lv = TRUE, ordered =ALL)
summary(item.cfa.1, fit.measures=TRUE, standardized=TRUE)
When I run the code I get this error message:
In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, :
lavaan WARNING:
Could not compute standard errors! The information matrix could
not be inverted. This may be a symptom that the model is not
identified.
I understand this could be because my model is not identified. However, when I check the df's it says there are 7021 df's. I am also not sure how to test my model to see if it under identified. Any advice would be very helpful.

Paste not working for long strings? [closed]

Closed. This question is not reproducible or was caused by typos. It is not currently accepting answers.
This question was caused by a typo or a problem that can no longer be reproduced. While similar questions may be on-topic here, this one was resolved in a way less likely to help future readers.
Closed 5 years ago.
Improve this question
I cannot for the life of me figure out why paste with collapse="\n" won't work for me, for just this line (it works in other parts of the code).
Perhaps a character limit with the paste function?
(I have trimmed leading and lagging white space)
Below you will notice that paste does not in fact insert \n between the two long strings:
> MM
[1] "F1_all =~ target\nF2_all =~ target\nF3_all =~ target\nF4_all =~ target\nF5_all =~ target\nF6_all =~ target"
> regsflat
[1] "F1_all ~ 1*F1_0351 + 1*F1_0354 + 1*F1_0414 + 1*F1_0415 + 1*F1_0420 + 1*F1_0430 + 1*F1_0464 + 1*F1_0484 + 1*F1_0488 + 1*F1_0496 + 1*F1_0508 + 1*F1_0517 + 1*F1_0527 + 1*F1_0592 + 1*F1_0593 + 1*F1_0596 + 1*F1_0609 + 1*F1_0640 + 1*F1_0646 + 1*F1_0647 + 1*F1_0683 + 1*F1_0686 + 1*F1_0691 + 1*F1_0696 + 1*F1_0713 + 1*F1_0715 + 1*F1_0717 + 1*F1_0757 + 1*F1_0759 + 1*F1_0764 + 1*F1_0765 + 1*F1_0771 + 1*F1_0772 + 1*F1_0775 + 1*F1_0776 + 1*F1_0778 + 1*F1_0781 + 1*F1_0793 + 1*F1_0796 + 1*F1_0797 + 1*F1_0799 + 1*F1_0842 + 1*F1_0843 + 1*F1_0845 + 1*F1_0865 + 1*F1_0879 + 1*F1_0895 + 1*F1_0936 + 1*F1_1544 + 1*F1_1545 + 1*F1_1802 + 1*F1_1803 + 1*F1_1804 + 1*F1_1805 + 1*F1_1806 + 1*F1_1807 + 1*F1_1809 + 1*F1_1815 + 1*F1_2261 + 1*F1_2262 + 1*F1_2353 + 1*F1_2354 + 1*F1_2435 + 1*F1_BBRM1WA + 1*F1_BBRM2WA + 1*F1_BUSINESSBANKWA + 1*F1_CBWACENTRAL + 1*F1_CBWASOUTH + 1*F1_R&R-WESTCOAST\nF2_all ~ 1*F2_0351 + 1*F2_0354 + 1*F2_0414 + 1*F2_0415 + 1*F2_0420 + 1*F2_0430 + 1*F2_0464 + 1*F2_0484 + 1*F2_0488 + 1*F2_0496 + 1*F2_0508 + 1*F2_0517 + 1*F2_0527 + 1*F2_0592 + 1*F2_0593 + 1*F2_0596 + 1*F2_0609 + 1*F2_0640 + 1*F2_0646 + 1*F2_0647 + 1*F2_0683 + 1*F2_0686 + 1*F2_0691 + 1*F2_0696 + 1*F2_0713 + 1*F2_0715 + 1*F2_0717 + 1*F2_0757 + 1*F2_0759 + 1*F2_0764 + 1*F2_0765 + 1*F2_0771 + 1*F2_0772 + 1*F2_0775 + 1*F2_0776 + 1*F2_0778 + 1*F2_0781 + 1*F2_0793 + 1*F2_0796 + 1*F2_0797 + 1*F2_0799 + 1*F2_0842 + 1*F2_0843 + 1*F2_0845 + 1*F2_0865 + 1*F2_0879 + 1*F2_0895 + 1*F2_0936 + 1*F2_1544 + 1*F2_1545 + 1*F2_1802 + 1*F2_1803 + 1*F2_1804 + 1*F2_1805 + 1*F2_1806 + 1*F2_1807 + 1*F2_1809 + 1*F2_1815 + 1*F2_2261 + 1*F2_2262 + 1*F2_2353 + 1*F2_2354 + 1*F2_2435 + 1*F2_BBRM1WA + 1*F2_BBRM2WA + 1*F2_BUSINESSBANKWA + 1*F2_CBWACENTRAL + 1*F2_CBWASOUTH + 1*F2_R&R-WESTCOAST\nF3_all ~ 1*F3_0351 + 1*F3_0354 + 1*F3_0414 + 1*F3_0415 + 1*F3_0420 + 1*F3_0430 + 1*F3_0464 + 1*F3_0484 + 1*F3_0488 + 1*F3_0496 + 1*F3_0508 + 1*F3_0517 + 1*F3_0527 + 1*F3_0592 + 1*F3_0593 + 1*F3_0596 + 1*F3_0609 + 1*F3_0640 + 1*F3_0646 + 1*F3_0647 + 1*F3_0683 + 1*F3_0686 + 1*F3_0691 + 1*F3_0696 + 1*F3_0713 + 1*F3_0715 + 1*F3_0717 + 1*F3_0757 + 1*F3_0759 + 1*F3_0764 + 1*F3_0765 + 1*F3_0771 + 1*F3_0772 + 1*F3_0775 + 1*F3_0776 + 1*F3_0778 + 1*F3_0781 + 1*F3_0793 + 1*F3_0796 + 1*F3_0797 + 1*F3_0799 + 1*F3_0842 + 1*F3_0843 + 1*F3_0845 + 1*F3_0865 + 1*F3_0879 + 1*F3_0895 + 1*F3_0936 + 1*F3_1544 + 1*F3_1545 + 1*F3_1802 + 1*F3_1803 + 1*F3_1804 + 1*F3_1805 + 1*F3_1806 + 1*F3_1807 + 1*F3_1809 + 1*F3_1815 + 1*F3_2261 + 1*F3_2262 + 1*F3_2353 + 1*F3_2354 + 1*F3_2435 + 1*F3_BBRM1WA + 1*F3_BBRM2WA + 1*F3_BUSINESSBANKWA + 1*F3_CBWACENTRAL + 1*F3_CBWASOUTH + 1*F3_R&R-WESTCOAST\nF4_all ~ 1*F4_0351 + 1*F4_0354 + 1*F4_0414 + 1*F4_0415 + 1*F4_0420 + 1*F4_0430 + 1*F4_0464 + 1*F4_0484 + 1*F4_0488 + 1*F4_0496 + 1*F4_0508 + 1*F4_0517 + 1*F4_0527 + 1*F4_0592 + 1*F4_0593 + 1*F4_0596 + 1*F4_0609 + 1*F4_0640 + 1*F4_0646 + 1*F4_0647 + 1*F4_0683 + 1*F4_0686 + 1*F4_0691 + 1*F4_0696 + 1*F4_0713 + 1*F4_0715 + 1*F4_0717 + 1*F4_0757 + 1*F4_0759 + 1*F4_0764 + 1*F4_0765 + 1*F4_0771 + 1*F4_0772 + 1*F4_0775 + 1*F4_0776 + 1*F4_0778 + 1*F4_0781 + 1*F4_0793 + 1*F4_0796 + 1*F4_0797 + 1*F4_0799 + 1*F4_0842 + 1*F4_0843 + 1*F4_0845 + 1*F4_0865 + 1*F4_0879 + 1*F4_0895 + 1*F4_0936 + 1*F4_1544 + 1*F4_1545 + 1*F4_1802 + 1*F4_1803 + 1*F4_1804 + 1*F4_1805 + 1*F4_1806 + 1*F4_1807 + 1*F4_1809 + 1*F4_1815 + 1*F4_2261 + 1*F4_2262 + 1*F4_2353 + 1*F4_2354 + 1*F4_2435 + 1*F4_BBRM1WA + 1*F4_BBRM2WA + 1*F4_BUSINESSBANKWA + 1*F4_CBWACENTRAL + 1*F4_CBWASOUTH + 1*F4_R&R-WESTCOAST\nF5_all ~ 1*F5_0351 + 1*F5_0354 + 1*F5_0414 + 1*F5_0415 + 1*F5_0420 + 1*F5_0430 + 1*F5_0464 + 1*F5_0484 + 1*F5_0488 + 1*F5_0496 + 1*F5_0508 + 1*F5_0517 + 1*F5_0527 + 1*F5_0592 + 1*F5_0593 + 1*F5_0596 + 1*F5_0609 + 1*F5_0640 + 1*F5_0646 + 1*F5_0647 + 1*F5_0683 + 1*F5_0686 + 1*F5_0691 + 1*F5_0696 + 1*F5_0713 + 1*F5_0715 + 1*F5_0717 + 1*F5_0757 + 1*F5_0759 + 1*F5_0764 + 1*F5_0765 + 1*F5_0771 + 1*F5_0772 + 1*F5_0775 + 1*F5_0776 + 1*F5_0778 + 1*F5_0781 + 1*F5_0793 + 1*F5_0796 + 1*F5_0797 + 1*F5_0799 + 1*F5_0842 + 1*F5_0843 + 1*F5_0845 + 1*F5_0865 + 1*F5_0879 + 1*F5_0895 + 1*F5_0936 + 1*F5_1544 + 1*F5_1545 + 1*F5_1802 + 1*F5_1803 + 1*F5_1804 + 1*F5_1805 + 1*F5_1806 + 1*F5_1807 + 1*F5_1809 + 1*F5_1815 + 1*F5_2261 + 1*F5_2262 + 1*F5_2353 + 1*F5_2354 + 1*F5_2435 + 1*F5_BBRM1WA + 1*F5_BBRM2WA + 1*F5_BUSINESSBANKWA + 1*F5_CBWACENTRAL + 1*F5_CBWASOUTH + 1*F5_R&R-WESTCOAST\nF6_all ~ 1*F6_0351 + 1*F6_0354 + 1*F6_0414 + 1*F6_0415 + 1*F6_0420 + 1*F6_0430 + 1*F6_0464 + 1*F6_0484 + 1*F6_0488 + 1*F6_0496 + 1*F6_0508 + 1*F6_0517 + 1*F6_0527 + 1*F6_0592 + 1*F6_0593 + 1*F6_0596 + 1*F6_0609 + 1*F6_0640 + 1*F6_0646 + 1*F6_0647 + 1*F6_0683 + 1*F6_0686 + 1*F6_0691 + 1*F6_0696 + 1*F6_0713 + 1*F6_0715 + 1*F6_0717 + 1*F6_0757 + 1*F6_0759 + 1*F6_0764 + 1*F6_0765 + 1*F6_0771 + 1*F6_0772 + 1*F6_0775 + 1*F6_0776 + 1*F6_0778 + 1*F6_0781 + 1*F6_0793 + 1*F6_0796 + 1*F6_0797 + 1*F6_0799 + 1*F6_0842 + 1*F6_0843 + 1*F6_0845 + 1*F6_0865 + 1*F6_0879 + 1*F6_0895 + 1*F6_0936 + 1*F6_1544 + 1*F6_1545 + 1*F6_1802 + 1*F6_1803 + 1*F6_1804 + 1*F6_1805 + 1*F6_1806 + 1*F6_1807 + 1*F6_1809 + 1*F6_1815 + 1*F6_2261 + 1*F6_2262 + 1*F6_2353 + 1*F6_2354 + 1*F6_2435 + 1*F6_BBRM1WA + 1*F6_BBRM2WA + 1*F6_BUSINESSBANKWA + 1*F6_CBWACENTRAL + 1*F6_CBWASOUTH + 1*F6_R&R-WESTCOAST"
> paste(MM, regsflat, collapse="\n")
[1] "F1_all =~ target\nF2_all =~ target\nF3_all =~ target\nF4_all =~ target\nF5_all =~ target\nF6_all =~ target F1_all ~ 1*F1_0351 + 1*F1_0354 + 1*F1_0414 + 1*F1_0415 + 1*F1_0420 + 1*F1_0430 + 1*F1_0464 + 1*F1_0484 + 1*F1_0488 + 1*F1_0496 + 1*F1_0508 + 1*F1_0517 + 1*F1_0527 + 1*F1_0592 + 1*F1_0593 + 1*F1_0596 + 1*F1_0609 + 1*F1_0640 + 1*F1_0646 + 1*F1_0647 + 1*F1_0683 + 1*F1_0686 + 1*F1_0691 + 1*F1_0696 + 1*F1_0713 + 1*F1_0715 + 1*F1_0717 + 1*F1_0757 + 1*F1_0759 + 1*F1_0764 + 1*F1_0765 + 1*F1_0771 + 1*F1_0772 + 1*F1_0775 + 1*F1_0776 + 1*F1_0778 + 1*F1_0781 + 1*F1_0793 + 1*F1_0796 + 1*F1_0797 + 1*F1_0799 + 1*F1_0842 + 1*F1_0843 + 1*F1_0845 + 1*F1_0865 + 1*F1_0879 + 1*F1_0895 + 1*F1_0936 + 1*F1_1544 + 1*F1_1545 + 1*F1_1802 + 1*F1_1803 + 1*F1_1804 + 1*F1_1805 + 1*F1_1806 + 1*F1_1807 + 1*F1_1809 + 1*F1_1815 + 1*F1_2261 + 1*F1_2262 + 1*F1_2353 + 1*F1_2354 + 1*F1_2435 + 1*F1_BBRM1WA + 1*F1_BBRM2WA + 1*F1_BUSINESSBANKWA + 1*F1_CBWACENTRAL + 1*F1_CBWASOUTH + 1*F1_R&R-WESTCOAST\nF2_all ~ 1*F2_0351 + 1*F2_0354 + 1*F2_0414 + 1*F2_0415 + 1*F2_0420 + 1*F2_0430 + 1*F2_0464 + 1*F2_0484 + 1*F2_0488 + 1*F2_0496 + 1*F2_0508 + 1*F2_0517 + 1*F2_0527 + 1*F2_0592 + 1*F2_0593 + 1*F2_0596 + 1*F2_0609 + 1*F2_0640 + 1*F2_0646 + 1*F2_0647 + 1*F2_0683 + 1*F2_0686 + 1*F2_0691 + 1*F2_0696 + 1*F2_0713 + 1*F2_0715 + 1*F2_0717 + 1*F2_0757 + 1*F2_0759 + 1*F2_0764 + 1*F2_0765 + 1*F2_0771 + 1*F2_0772 + 1*F2_0775 + 1*F2_0776 + 1*F2_0778 + 1*F2_0781 + 1*F2_0793 + 1*F2_0796 + 1*F2_0797 + 1*F2_0799 + 1*F2_0842 + 1*F2_0843 + 1*F2_0845 + 1*F2_0865 + 1*F2_0879 + 1*F2_0895 + 1*F2_0936 + 1*F2_1544 + 1*F2_1545 + 1*F2_1802 + 1*F2_1803 + 1*F2_1804 + 1*F2_1805 + 1*F2_1806 + 1*F2_1807 + 1*F2_1809 + 1*F2_1815 + 1*F2_2261 + 1*F2_2262 + 1*F2_2353 + 1*F2_2354 + 1*F2_2435 + 1*F2_BBRM1WA + 1*F2_BBRM2WA + 1*F2_BUSINESSBANKWA + 1*F2_CBWACENTRAL + 1*F2_CBWASOUTH + 1*F2_R&R-WESTCOAST\nF3_all ~ 1*F3_0351 + 1*F3_0354 + 1*F3_0414 + 1*F3_0415 + 1*F3_0420 + 1*F3_0430 + 1*F3_0464 + 1*F3_0484 + 1*F3_0488 + 1*F3_0496 + 1*F3_0508 + 1*F3_0517 + 1*F3_0527 + 1*F3_0592 + 1*F3_0593 + 1*F3_0596 + 1*F3_0609 + 1*F3_0640 + 1*F3_0646 + 1*F3_0647 + 1*F3_0683 + 1*F3_0686 + 1*F3_0691 + 1*F3_0696 + 1*F3_0713 + 1*F3_0715 + 1*F3_0717 + 1*F3_0757 + 1*F3_0759 + 1*F3_0764 + 1*F3_0765 + 1*F3_0771 + 1*F3_0772 + 1*F3_0775 + 1*F3_0776 + 1*F3_0778 + 1*F3_0781 + 1*F3_0793 + 1*F3_0796 + 1*F3_0797 + 1*F3_0799 + 1*F3_0842 + 1*F3_0843 + 1*F3_0845 + 1*F3_0865 + 1*F3_0879 + 1*F3_0895 + 1*F3_0936 + 1*F3_1544 + 1*F3_1545 + 1*F3_1802 + 1*F3_1803 + 1*F3_1804 + 1*F3_1805 + 1*F3_1806 + 1*F3_1807 + 1*F3_1809 + 1*F3_1815 + 1*F3_2261 + 1*F3_2262 + 1*F3_2353 + 1*F3_2354 + 1*F3_2435 + 1*F3_BBRM1WA + 1*F3_BBRM2WA + 1*F3_BUSINESSBANKWA + 1*F3_CBWACENTRAL + 1*F3_CBWASOUTH + 1*F3_R&R-WESTCOAST\nF4_all ~ 1*F4_0351 + 1*F4_0354 + 1*F4_0414 + 1*F4_0415 + 1*F4_0420 + 1*F4_0430 + 1*F4_0464 + 1*F4_0484 + 1*F4_0488 + 1*F4_0496 + 1*F4_0508 + 1*F4_0517 + 1*F4_0527 + 1*F4_0592 + 1*F4_0593 + 1*F4_0596 + 1*F4_0609 + 1*F4_0640 + 1*F4_0646 + 1*F4_0647 + 1*F4_0683 + 1*F4_0686 + 1*F4_0691 + 1*F4_0696 + 1*F4_0713 + 1*F4_0715 + 1*F4_0717 + 1*F4_0757 + 1*F4_0759 + 1*F4_0764 + 1*F4_0765 + 1*F4_0771 + 1*F4_0772 + 1*F4_0775 + 1*F4_0776 + 1*F4_0778 + 1*F4_0781 + 1*F4_0793 + 1*F4_0796 + 1*F4_0797 + 1*F4_0799 + 1*F4_0842 + 1*F4_0843 + 1*F4_0845 + 1*F4_0865 + 1*F4_0879 + 1*F4_0895 + 1*F4_0936 + 1*F4_1544 + 1*F4_1545 + 1*F4_1802 + 1*F4_1803 + 1*F4_1804 + 1*F4_1805 + 1*F4_1806 + 1*F4_1807 + 1*F4_1809 + 1*F4_1815 + 1*F4_2261 + 1*F4_2262 + 1*F4_2353 + 1*F4_2354 + 1*F4_2435 + 1*F4_BBRM1WA + 1*F4_BBRM2WA + 1*F4_BUSINESSBANKWA + 1*F4_CBWACENTRAL + 1*F4_CBWASOUTH + 1*F4_R&R-WESTCOAST\nF5_all ~ 1*F5_0351 + 1*F5_0354 + 1*F5_0414 + 1*F5_0415 + 1*F5_0420 + 1*F5_0430 + 1*F5_0464 + 1*F5_0484 + 1*F5_0488 + 1*F5_0496 + 1*F5_0508 + 1*F5_0517 + 1*F5_0527 + 1*F5_0592 + 1*F5_0593 + 1*F5_0596 + 1*F5_0609 + 1*F5_0640 + 1*F5_0646 + 1*F5_0647 + 1*F5_0683 + 1*F5_0686 + 1*F5_0691 + 1*F5_0696 + 1*F5_0713 + 1*F5_0715 + 1*F5_0717 + 1*F5_0757 + 1*F5_0759 + 1*F5_0764 + 1*F5_0765 + 1*F5_0771 + 1*F5_0772 + 1*F5_0775 + 1*F5_0776 + 1*F5_0778 + 1*F5_0781 + 1*F5_0793 + 1*F5_0796 + 1*F5_0797 + 1*F5_0799 + 1*F5_0842 + 1*F5_0843 + 1*F5_0845 + 1*F5_0865 + 1*F5_0879 + 1*F5_0895 + 1*F5_0936 + 1*F5_1544 + 1*F5_1545 + 1*F5_1802 + 1*F5_1803 + 1*F5_1804 + 1*F5_1805 + 1*F5_1806 + 1*F5_1807 + 1*F5_1809 + 1*F5_1815 + 1*F5_2261 + 1*F5_2262 + 1*F5_2353 + 1*F5_2354 + 1*F5_2435 + 1*F5_BBRM1WA + 1*F5_BBRM2WA + 1*F5_BUSINESSBANKWA + 1*F5_CBWACENTRAL + 1*F5_CBWASOUTH + 1*F5_R&R-WESTCOAST\nF6_all ~ 1*F6_0351 + 1*F6_0354 + 1*F6_0414 + 1*F6_0415 + 1*F6_0420 + 1*F6_0430 + 1*F6_0464 + 1*F6_0484 + 1*F6_0488 + 1*F6_0496 + 1*F6_0508 + 1*F6_0517 + 1*F6_0527 + 1*F6_0592 + 1*F6_0593 + 1*F6_0596 + 1*F6_0609 + 1*F6_0640 + 1*F6_0646 + 1*F6_0647 + 1*F6_0683 + 1*F6_0686 + 1*F6_0691 + 1*F6_0696 + 1*F6_0713 + 1*F6_0715 + 1*F6_0717 + 1*F6_0757 + 1*F6_0759 + 1*F6_0764 + 1*F6_0765 + 1*F6_0771 + 1*F6_0772 + 1*F6_0775 + 1*F6_0776 + 1*F6_0778 + 1*F6_0781 + 1*F6_0793 + 1*F6_0796 + 1*F6_0797 + 1*F6_0799 + 1*F6_0842 + 1*F6_0843 + 1*F6_0845 + 1*F6_0865 + 1*F6_0879 + 1*F6_0895 + 1*F6_0936 + 1*F6_1544 + 1*F6_1545 + 1*F6_1802 + 1*F6_1803 + 1*F6_1804 + 1*F6_1805 + 1*F6_1806 + 1*F6_1807 + 1*F6_1809 + 1*F6_1815 + 1*F6_2261 + 1*F6_2262 + 1*F6_2353 + 1*F6_2354 + 1*F6_2435 + 1*F6_BBRM1WA + 1*F6_BBRM2WA + 1*F6_BUSINESSBANKWA + 1*F6_CBWACENTRAL + 1*F6_CBWASOUTH + 1*F6_R&R-WESTCOAST"
>

Try this:
paste(MM, regsflat, sep="\n")

Random Forest in R (multi-label-classification)

I'm fairly new to R, trying to implement Random Forest algorithm.
My training and test set have 60 features in the format:
Train: feature1,feature2 .. feature60,Label
Test: FileName,feature1,feature2 ... feature60
Train-sample
mov-mov,or-or,push-push,or-mov,sub-sub,mov-or,sub-mov,xor-or,call-sub,mul-imul,mov-push,push-mov,push-call,or-jz,mov-mul,cmp-or,mov-sub,sub-or,or-sub,or-push,jnz-or,jmp-sub,or-in,mov-call,retn-sub,mul-mul,or-jmp,imul-mul,pop-pop,nop-nop,nop-mul,sub-push,imul-mov,test-or,mul-mov,lea-push,std-mov,in-call,or-call,mov-std,mov-cmp,std-mul,call-or,jz-mov,push-or,pop-retn,add-mov,mov-add,mov-xor,in-inc,mov-pop,in-or,in-push,push-lea,lea-mov,mov-lea,sub-add,std-std,sub-cmp,or-cmp,Label
687,1346,1390,1337,750,2770,1518,418,1523,0,441,532,612,512,0,411,354,310,412,495,134,236,318,237,226,0,0,0,200,0,0,386,39,365,0,0,0,125,528,0,125,0,41,260,169,143,149,61,89,0,127,126,107,44,45,40,79,0,273,157,9
812,873,83,533,88,484,264,106,199,0,188,137,128,51,38,92,131,102,52,58,37,26,428,95,107,0,34,0,58,0,0,39,0,26,0,27,0,152,152,0,45,0,124,0,0,73,84,88,22,23,59,319,105,56,86,47,0,0,43,41,2
Test-sample
FileName,mov-mov,or-or,push-push,or-mov,sub-sub,mov-or,xor-or,sub-mov,call-sub,mul-imul,push-mov,mov-push,push-call,mov-mul,or-jz,cmp-or,mov-sub,sub-or,or-sub,or-push,jmp-sub,jnz-or,or-in,mul-mul,or-jmp,mov-call,retn-sub,imul-mul,nop-mul,pop-pop,nop-nop,imul-mov,sub-push,mul-mov,test-or,lea-push,std-mov,or-call,mov-std,in-call,std-mul,mov-cmp,call-or,push-or,jz-mov,pop-retn,in-or,add-mov,mov-add,in-inc,mov-xor,in-push,push-lea,mov-pop,lea-mov,mov-lea,mov-nop,or-cmp,sub-add,sub-cmp
Ig2DB5tSiEy1cJvV0zdw,166,360,291,194,41,201,62,61,41,18,85,56,121,18,15,0,57,131,113,123,0,9,54,0,0,18,15,0,0,15,0,8,25,0,0,11,0,70,0,43,0,0,63,37,0,14,51,43,56,36,26,0,20,14,17,14,0,9,18,0
k4HCwy5WRFXczJU6eQdT,3,88,106,23,104,0,12,43,59,0,65,87,99,0,2,2,47,22,4,53,1,5,0,0,0,0,46,0,0,0,0,0,4,0,0,6,0,44,0,21,0,0,0,0,0,0,0,2,1,1,3,0,1,2,9,2,0,0,44,2
So what I have so far in R is this,
library(randomForest);
dat <- read.csv("train-sample.csv", sep=",", h=T);
test <- read.csv("test-sample.csv", sep=",", h=T);
attach(dat);
#If I do this, I get Error: unexpected 'in' ...
rfmodel = randomForest (Label ~ mov-mov + or-or + push-push + or-mov + sub-sub + mov-or + sub-mov + xor-or + call-sub + mul-imul + mov-push + push-mov + push-call + or-jz + mov-mul + cmp-or + mov-sub + sub-or + or-sub + or-push + jnz-or + jmp-sub + or-in + mov-call + retn-sub + mul-mul + or-jmp + imul-mul + pop-pop + nop-nop + nop-mul + sub-push + imul-mov + test-or + mul-mov + lea-push + std-mov + in-call + or-call + mov-std + mov-cmp + std-mul + call-or + jz-mov + push-or + pop-retn + add-mov + mov-add + mov-xor + in-inc + mov-pop + in-or + in-push + push-lea + lea-mov + mov-lea + sub-add + std-std + sub-cmp + or-cmp, data=dat);
#If I do this, I get Error in terms.formula(formula, data = data) : invalid model formula in ExtractVars
rfmodel = randomForest (Label ~ 'mov-mov' + 'or-or' + 'push-push' + or-mov + sub-sub + mov-or + sub-mov + xor-or + call-sub + mul-imul + mov-push + push-mov + push-call + or-jz + mov-mul + cmp-or + mov-sub + sub-or + or-sub + or-push + jnz-or + jmp-sub + 'or-in' + mov-call + retn-sub + mul-mul + or-jmp + imul-mul + pop-pop + nop-nop + nop-mul + sub-push + imul-mov + test-or + mul-mov + lea-push + 'std-mov' + 'in-call' + 'or-call' + 'mov-std' + 'mov-cmp' + 'std-mul' + 'call-or' + 'jz-mov' + 'push-or' + 'pop-retn' + 'add-mov' + 'mov-add' + 'mov-xor' + 'in-inc' + 'mov-pop' + 'in-or' + 'in-push' + 'push-lea' + 'lea-mov' + 'mov-lea' + 'sub-add' + 'std-std' + 'sub-cmp' + 'or-cmp', data=dat);
#I even tried this and got Error in na.fail.default(list(Label = c(9L, 2L, 9L, 1L, 8L, 6L, 2L, 2L, : missing values in object
rfmodel <- randomForest(Label~., dat);
So I'm kinda stuck. I want to end up using something like,
predicted <- predict(rfmodel, test, type="response");
prop.table(table(test$FileName, predicted),1);
To get an output in form of:
FileName, Label1, Label2, Label3 .. Label9
name1, 0.98, 0, 0.02, 0, 0 .. 0
(basically the fileName with probabilities of each label)
Any help is appreciated. Thank you.

How to convert R formula to text?

I have trouble working with formula as with text. What I'm trying to do is to concatenate the formula to the title of the graph. However, when I try to work with the formula as with text, I fail:
model <- lm(celkem ~ rok + mesic)
formula(model)
# celkem ~ rok + mesic
This is fine. Now I want to build string like "my text celkem ~ rok + mesic" - this is where the problem comes:
paste("my text", formula(model))
# [1] "my text ~" "my text celkem" "my text rok + mesic"
paste("my text", as.character(formula(model)))
# [1] "my text ~" "my text celkem" "my text rok + mesic"
paste("my text", toString(formula(model)))
# [1] "my text ~, celkem, rok + mesic"
Now I see there is a sprint function in package gtools, but I think this is such a basic thing that it deserves a solution within the default environment!!

A short solution from the package formula.tools, as a function as.character.formula:
frm <- celkem ~ rok + mesic
Reduce(paste, deparse(frm))
# [1] "celkem ~ rok + mesic"
library(formula.tools)
as.character(frm)
# [1] "celkem ~ rok + mesic"
Reduce might be useful in case of long formulas:
frm <- formula(paste("y ~ ", paste0("x", 1:12, collapse = " + ")))
deparse(frm)
# [1] "y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + "
# [2] " x12"
Reduce(paste, deparse(frm))
# [1] "y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + x12"
Which is because of width.cutoff = 60L in ?deparse.

Try format :
paste("my text", format(frm))
## [1] "my text celkem ~ rok + mesic"

Simplest solution covering everything:
f <- formula(model)
paste(deparse(f, width.cutoff = 500), collapse="")

R 4.0.0 (released 2020-04-24) introduced deparse1 which never splits the result into multiple strings:
f <- y ~ a + b + c + d + e + f + g + h + i + j + k + l + m + n + o +
p + q + r + s + t + u + v + w + x + y + z
deparse(f)
# [1] "y ~ a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + " " p + q + r + s + t + u + v + w + x + y + z"
deparse1(f)
# [1] "y ~ a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z"
However, it still has a width.cutoff argument (default (an maximum): 500) after which linebreaks are introduced but with lines separated by collapse (default: " ") not \n, leaving extra white whitespace (even with collapse = "") (use gsub to remove them if needed, see Ross D's answer):
> f <- rlang::parse_expr( paste0("y~", paste0(rep(letters, 20), collapse="+")))
> deparse1(f, collapse = "")
[1] "y ~ a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u + v + w + x + y + z"
To use it in R < 4.0.0 use backports (recommended)
or copy it's implementation:
# Part of the R package, https://www.R-project.org
#
# Copyright (C) 1995-2019 The R Core Team
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# A copy of the GNU General Public License is available at
# https://www.R-project.org/Licenses/
deparse1 <- function (expr, collapse = " ", width.cutoff = 500L, ...)
paste(deparse(expr, width.cutoff, ...), collapse = collapse)

or as an alternative to Julius's version (note: your code was not self-contained)
celkem = 1
rok = 1
mesic = 1
model <- lm(celkem ~ rok + mesic)
paste("my model ", deparse(formula(model)))

The easiest way is this:
f = formula(model)
paste(f[2],f[3],sep='~')
done!

Here a solution which use print.formula, it seems trick but it do the job in oneline and avoid the use of deparse and no need to use extra package. I just capture the output of the print formula, using capture.output
paste("my text",capture.output(print(formula(celkem ~ rok + mesic))))
[1] "my text celkem ~ rok + mesic"
In case of long formula:
ff <- formula(paste("y ~ ", paste0("x", 1:12, collapse = " + ")))
paste("my text",paste(capture.output(print(ff)), collapse= ' '))
"my text y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + x12"

Another deparse-based solution is rlang::expr_text() (and rlang::quo_text()):
f <- Y ~ 1 + a + b + c + d + e + f + g + h + i +j + k + l + m + n + o + p + q + r + s + t + u
rlang::quo_text(f)
#> [1] "Y ~ 1 + a + b + c + d + e + f + g + h + i + j + k + l + m + n + \n o + p + q + r + s + t + u"
They do have a width argument to avoid line breaks, but that is limited to 500 characters too. At least it's a single function that is most likely loaded already...

Then add gsub to remove white spaces
gsub(" ", "", paste(format(frm), collapse = ""))

Was optimizing some functions today. A few approaches that have not been mentioned so far.
f <- Y ~ 1 + a + b + c + d + e + f + g + h + i + j + k + l + m + n + o + p + q + r + s + t + u
bench::mark(
expression = as.character(as.expression(f)),
deparse = deparse(f, width.cutoff = 500L),
deparse1 = deparse1(f),
tools = formula.tools:::as.character.formula(f),
stringi = stringi::stri_c(f),
I = as.character(I(f)),
as = as(f, "character"),
txt = gettext(f),
txtf = gettextf(f),
sub = sub("", "", f),
chr = as.character(f),
str = substring(f, 1L),
paste = paste0(f),
)[c(1, 3, 5, 7)]
#> # A tibble: 13 x 3
#> expression median mem_alloc
#> <bch:expr> <bch:tm> <bch:byt>
#> 1 expression 15.4us 0B
#> 2 deparse 31us 0B
#> 3 deparse1 34us 0B
#> 4 tools 58.7us 1.74MB
#> 5 stringi 67us 3.09KB
#> 6 I 64.1us 0B
#> 7 as 100.5us 521.61KB
#> 8 txt 83.4us 0B
#> 9 txtf 85.8us 3.12KB
#> 10 sub 64.6us 0B
#> 11 chr 60us 0B
#> 12 str 62.8us 0B
#> 13 paste 63.5us 0B

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