# Number of variables in Multiple Linear Regression in R

I'm working on Multiple Linear Regression in R with Yield and weekly weather data. While selecting variables, It raises hands after considering only 6 variables in the equation for 'Reg_brief' and says, 'length >1'.

1. How to increase the capability to consider 'n' number of variables (weeks)?
2. There is change in Regression Command and I don't know how to complete it so that it can run properly.

In case anybody has some code to help in this please!

You need to give us more context. Please post your code--as text not a picture. And maybe your data using `dput()`.

Dear startz,
Here is the code for testing.

# Grand-Mean Center Continuous Predictor (or Moderator) variables

Kota\$c_ARF25 <- Kota\$ARF25 - mean(Kota\$ARF25, na.rm=TRUE)

Kota\$c_ARF26 <- Kota\$ARF26 - mean(Kota\$ARF26, na.rm=TRUE)

Kota\$c_ARF27 <- Kota\$ARF27 - mean(Kota\$ARF27, na.rm=TRUE)

Kota\$c_ARF28 <- Kota\$ARF28 - mean(Kota\$ARF28, na.rm=TRUE)

Kota\$c_ARF29 <- Kota\$ARF29 - mean(Kota\$ARF29, na.rm=TRUE)

Kota\$c_ARF30 <- Kota\$ARF30 - mean(Kota\$ARF30, na.rm=TRUE)

Kota\$c_ARF31 <- Kota\$ARF31 - mean(Kota\$ARF31, na.rm=TRUE)

Kota\$c_ARF32 <- Kota\$ARF32 - mean(Kota\$ARF32, na.rm=TRUE)

Kota\$c_ARF33 <- Kota\$ARF33 - mean(Kota\$ARF33, na.rm=TRUE)

Kota\$c_ARF34 <- Kota\$ARF34 - mean(Kota\$ARF34, na.rm=TRUE)

Kota\$c_ARF35 <- Kota\$ARF35 - mean(Kota\$ARF35, na.rm=TRUE)

Kota\$c_ARF36 <- Kota\$ARF36 - mean(Kota\$ARF36, na.rm=TRUE)

Kota\$c_ARF37 <- Kota\$ARF37 - mean(Kota\$ARF37, na.rm=TRUE)

Kota\$c_ARF38 <- Kota\$ARF38 - mean(Kota\$ARF38, na.rm=TRUE)

Kota\$c_ARF39 <- Kota\$ARF39 - mean(Kota\$ARF39, na.rm=TRUE)

Kota\$c_ARF40 <- Kota\$ARF40 - mean(Kota\$ARF40, na.rm=TRUE)

Kota\$c_ARF41 <- Kota\$ARF41 - mean(Kota\$ARF41, na.rm=TRUE)

Kota\$c_ARF42 <- Kota\$ARF42 - mean(Kota\$ARF42, na.rm=TRUE)

Kota\$c_ARF43 <- Kota\$ARF43 - mean(Kota\$ARF43, na.rm=TRUE)

Kota\$c_Tmax25 <- Kota\$Tmax25 - mean(Kota\$Tmax25, na.rm=TRUE)

Kota\$c_Tmax26 <- Kota\$Tmax26 - mean(Kota\$Tmax26, na.rm=TRUE)

Kota\$c_Tmax27 <- Kota\$Tmax27 - mean(Kota\$Tmax27, na.rm=TRUE)

Kota\$c_Tmax28 <- Kota\$Tmax28 - mean(Kota\$Tmax28, na.rm=TRUE)

Kota\$c_Tmax29 <- Kota\$Tmax29 - mean(Kota\$Tmax29, na.rm=TRUE)

Kota\$c_Tmax30 <- Kota\$Tmax30 - mean(Kota\$Tmax30, na.rm=TRUE)

Kota\$c_Tmax31 <- Kota\$Tmax31 - mean(Kota\$Tmax31, na.rm=TRUE)

Kota\$c_Tmax32 <- Kota\$Tmax32 - mean(Kota\$Tmax32, na.rm=TRUE)

Kota\$c_Tmax33 <- Kota\$Tmax33 - mean(Kota\$Tmax33, na.rm=TRUE)

Kota\$c_Tmax34 <- Kota\$Tmax34 - mean(Kota\$Tmax34, na.rm=TRUE)

Kota\$c_Tmax35 <- Kota\$Tmax35 - mean(Kota\$Tmax35, na.rm=TRUE)

Kota\$c_Tmax36 <- Kota\$Tmax36 - mean(Kota\$Tmax36, na.rm=TRUE)

Kota\$c_Tmax37 <- Kota\$Tmax37 - mean(Kota\$Tmax37, na.rm=TRUE)

Kota\$c_Tmax38 <- Kota\$Tmax38 - mean(Kota\$Tmax38, na.rm=TRUE)

Kota\$c_Tmax39 <- Kota\$Tmax39 - mean(Kota\$Tmax39, na.rm=TRUE)

Kota\$c_Tmax40 <- Kota\$Tmax40 - mean(Kota\$Tmax40, na.rm=TRUE)

Kota\$c_Tmax41 <- Kota\$Tmax41 - mean(Kota\$Tmax41, na.rm=TRUE)

Kota\$c_Tmax42 <- Kota\$Tmax42 - mean(Kota\$Tmax42, na.rm=TRUE)

Kota\$c_Tmax43 <- Kota\$Tmax43 - mean(Kota\$Tmax43, na.rm=TRUE)

Kota\$c_Tmin25 <- Kota\$Tmin25 - mean(Kota\$Tmin25, na.rm=TRUE)

Kota\$c_Tmin26 <- Kota\$Tmin26 - mean(Kota\$Tmin26, na.rm=TRUE)

Kota\$c_Tmin27 <- Kota\$Tmin27 - mean(Kota\$Tmin27, na.rm=TRUE)

Kota\$c_Tmin28 <- Kota\$Tmin28 - mean(Kota\$Tmin28, na.rm=TRUE)

Kota\$c_Tmin29 <- Kota\$Tmin29 - mean(Kota\$Tmin29, na.rm=TRUE)

Kota\$c_Tmin30 <- Kota\$Tmin30 - mean(Kota\$Tmin30, na.rm=TRUE)

Kota\$c_Tmin31 <- Kota\$Tmin31 - mean(Kota\$Tmin31, na.rm=TRUE)

Kota\$c_Tmin32 <- Kota\$Tmin32 - mean(Kota\$Tmin32, na.rm=TRUE)

Kota\$c_Tmin33 <- Kota\$Tmin33 - mean(Kota\$Tmin33, na.rm=TRUE)

Kota\$c_Tmin34 <- Kota\$Tmin34 - mean(Kota\$Tmin34, na.rm=TRUE)

Kota\$c_Tmin35 <- Kota\$Tmin35 - mean(Kota\$Tmin35, na.rm=TRUE)

Kota\$c_Tmin36 <- Kota\$Tmin36 - mean(Kota\$Tmin36, na.rm=TRUE)

Kota\$c_Tmin37 <- Kota\$Tmin37 - mean(Kota\$Tmin37, na.rm=TRUE)

Kota\$c_Tmin38 <- Kota\$Tmin38 - mean(Kota\$Tmin38, na.rm=TRUE)

Kota\$c_Tmin39 <- Kota\$Tmin39 - mean(Kota\$Tmin39, na.rm=TRUE)

Kota\$c_Tmin40 <- Kota\$Tmin40 - mean(Kota\$Tmin40, na.rm=TRUE)

Kota\$c_Tmin41 <- Kota\$Tmin41 - mean(Kota\$Tmin41, na.rm=TRUE)

Kota\$c_Tmin42 <- Kota\$Tmin42 - mean(Kota\$Tmin42, na.rm=TRUE)

Kota\$c_Tmin43 <- Kota\$Tmin43 - mean(Kota\$Tmin43, na.rm=TRUE)

Kota\$c_SSH25 <- Kota\$SSH25 - mean(Kota\$SSH25, na.rm=TRUE)

Kota\$c_SSH26 <- Kota\$SSH26 - mean(Kota\$SSH26, na.rm=TRUE)

Kota\$c_SSH27 <- Kota\$SSH27 - mean(Kota\$SSH27, na.rm=TRUE)

Kota\$c_SSH28 <- Kota\$SSH28 - mean(Kota\$SSH28, na.rm=TRUE)

Kota\$c_SSH29 <- Kota\$SSH29 - mean(Kota\$SSH29, na.rm=TRUE)

Kota\$c_SSH30 <- Kota\$SSH30 - mean(Kota\$SSH30, na.rm=TRUE)

Kota\$c_SSH31 <- Kota\$SSH31 - mean(Kota\$SSH31, na.rm=TRUE)

Kota\$c_SSH32 <- Kota\$SSH32 - mean(Kota\$SSH32, na.rm=TRUE)

Kota\$c_SSH33 <- Kota\$SSH33 - mean(Kota\$SSH33, na.rm=TRUE)

Kota\$c_SSH34 <- Kota\$SSH34 - mean(Kota\$SSH34, na.rm=TRUE)

Kota\$c_SSH35 <- Kota\$SSH35 - mean(Kota\$SSH35, na.rm=TRUE)

Kota\$c_SSH36 <- Kota\$SSH36 - mean(Kota\$SSH36, na.rm=TRUE)

Kota\$c_SSH37 <- Kota\$SSH37 - mean(Kota\$SSH37, na.rm=TRUE)

Kota\$c_SSH38 <- Kota\$SSH38 - mean(Kota\$SSH38, na.rm=TRUE)

Kota\$c_SSH39 <- Kota\$SSH39 - mean(Kota\$SSH39, na.rm=TRUE)

Kota\$c_SSH40 <- Kota\$SSH40 - mean(Kota\$SSH40, na.rm=TRUE)

Kota\$c_SSH41 <- Kota\$SSH41 - mean(Kota\$SSH41, na.rm=TRUE)

Kota\$c_SSH42 <- Kota\$SSH42 - mean(Kota\$SSH42, na.rm=TRUE)

Kota\$c_SSH43 <- Kota\$SSH43 - mean(Kota\$SSH43, na.rm=TRUE)

# 3) Slope Difefrences: Multiple Linear Regression Model (multiplicative)

#### Rainfall

install.packages("lessR")

library(lessR)

# Step1 : Criterion Related Variability

reg_brief(Evovled ~ c_ARF26 + c_ARF27 + c_ARF28 + c_ARF29 + c_ARF30 + c_ARF31, data=Kota)

reg_brief(Evovled ~ c_ARF32 + c_ARF33 + c_ARF34 + c_ARF35 + c_ARF36 + c_ARF37, data=Kota)

reg_brief(Evovled ~ c_ARF38 + c_ARF39 + c_ARF40 + c_ARF41 + c_ARF42 + c_ARF43, data=Kota)

reg_brief(Evovled ~ c_ARF27 + c_ARF28 + c_ARF31 + c_ARF32 + c_ARF33 + c_ARF34, data=Kota)

reg_brief(Evovled ~ c_ARF35 + c_ARF36 + c_ARF40 + c_ARF42 + c_ARF43, data=Kota)

Regression(Evovled ~ c_ARF31 + c_ARF32 + c_ARF40 + c_ARF42, data=Kota, brief = TRUE)

install.packages("interactions")
library(interactions)

# Intercept Differences

reg.brief(Yield ~ c_ARF31 + c_Tmax31 + c_Tmin31, data=Kota)
reg.brief(Yield ~ c_ARF31 + c_Tmax31 + c_Tmin31 + c_SSH31, data=Kota)

Int_model <- lm(Yield ~ c_rfwk1 + c_tmaxwk1, data= Kota)

probe_interaction(Int_model,
pred=c_ARF31,
modx=c_Tmax31,
x.label="Rainfall",
y.label="Predicted Yield")

# Slope Difefrences

reg_brief(Yield ~ c_ARF31 * c_Tmax31 * c_Tmin31, data=Kota)
Int_model <- lm(Yield ~ c_ARF31 * c_Tmax31 * c_Tmin31 * c_SSH31, data= Kota)

probe_interaction(slope_Model,
pred=c_ARF31,
modx=c_Tmax31,
x.label="Rainfall",
y.label="Predicted Yield")

Don't know how to share data file in csv format using dput(). Any other way please suggest.

You can post data by giving the command `dput(Kota)` and then copy-and-pasting here between sets of three backticks. If the data is too long, you might do `dput(head(Kota,20))` instead.

But maybe first, show us the error message you get.

I think we need to see your code and some sample data. See

A handy way to supply some sample data is the dput() function. In the case of a large dataset something like dput(head(mydata, 100)) should supply the data we need. Just do dput(mydata) where mydata is your data. Copy the output and paste it here between
```

```

reg_brief(Evovled ~ c_ARF26 + c_ARF27 + c_ARF28 + c_ARF29 + c_ARF30 + c_ARF31 + c_ARF32 + c_ARF33 + c_ARF34 + c_ARF35 + c_ARF36 + c_ARF37 + c_ARF38 + c_ARF39 + c_ARF40 + c_ARF41 + c_ARF42 + c_ARF43, data=karnal)

## Error:

Need more than 16 rows of data.
structure(list(Year = c(2016, 2017, 2018, 2019, 2020, 2021),
Yield = c(4.12, 4.05, 4.3, 3.3, 5.1, 3.8), Tmax25 = c(36.9,
34.3, 35.7, 36.1, 35.8, 35.6), Tmin25 = c(27.6, 25.3, 25.9,
24.6, 27, 25.9), ARF25 = c(1.6, 1.1, 5.1, 2.2, 7.1, 0), SSH25 = c(8,
9.6, 9.4, 8.9, 9.3, 10), RH25 = c(73.6, 76.7, 74.1, 80.3,
87.4, 72.1), Tmax26 = c(37, 32.6, 35.2, 37.6, 36.3, 38.1),
Tmin26 = c(27.7, 25.4, 26.1, 27.7, 27.7, 26.6), ARF26 = c(1.6,
30.4, 7.6, 0, 0.3, 2), SSH26 = c(3.1, 6.7, 7.9, 9.2, 7.9,
10.3), RH26 = c(83.1, 84.9, 79, 73.4, 86.7, 74.3), Tmax27 = c(33.4,
33.6, 34.5, 36.1, 35.4, 38.5), Tmin27 = c(26, 26.3, 26.2,
27.5, 26.1, 27.4), ARF27 = c(12.2, 1.1, 8.9, 2.3, 19.5, 0.5
), SSH27 = c(4.5, 8.8, 10.4, 6.7, 8.8, 9.8), RH27 = c(90,
83.7, 84.6, 81.4, 90, 76.4), Tmax28 = c(33.6, 33.8, 34.3,
32.7, 33.4, 34.1), Tmin28 = c(27.2, 26.6, 26.9, 25.7, 25.8,
26), ARF28 = c(1.7, 1.5, 5.9, 11.3, 14.1, 42.2), SSH28 = c(6.1,
9.4, 8.3, 2.4, 8.5, 5.9), RH28 = c(84.3, 81.6, 85.7, 94,
93.7, 89.7), Tmax29 = c(32.9, 34, 33.6, 30.1, 32.2, 32.1),
Tmin29 = c(26.5, 26.6, 25.7, 25.6, 26.4, 26.1), ARF29 = c(1.5,
0.4, 27.9, 17, 12.6, 14), SSH29 = c(6.3, 10.1, 4.2, 4.2,
5.1, 4.5), RH29 = c(88, 79.1, 87.9, 94.9, 96.4, 91.7), Tmax30 = c(33,
33.8, 31, 32.6, 33.3, 32.1), Tmin30 = c(26.2, 26.3, 25, 26.1,
26.8, 26.6), ARF30 = c(2.4, 2.1, 34.8, 4.3, 1.4, 38.7), SSH30 = c(2.7,
8.3, 3.6, 4.9, 7.7, 4), RH30 = c(89.9, 80.9, 94.3, 91.4,
94.4, 95), Tmax31 = c(32.3, 31.7, 33.4, 31.9, 33.3, 32.1),
Tmin31 = c(25.8, 25.6, 26.2, 26.5, 26.8, 26.8), ARF31 = c(8.5,
12.7, 2.3, 2, 16.4, 4.6), SSH31 = c(6.5, 1.8, 8.4, 4.8, 6,
3.8), RH31 = c(88.3, 92.1, 83.9, 94, 93.9, 92.9), Tmax32 = c(31.8,
33, 31.9, 33.1, 34.3, 33.3), Tmin32 = c(25.7, 26.5, 25.6,
26.4, 27.6, 26.8), ARF32 = c(15.1, 9.9, 9.6, 0, 15.3, 5.3
), SSH32 = c(4.3, 5, 7.5, 7.1, 6.6, 5.6), RH32 = c(92.4,
90.7, 92.4, 92.4, 94.7, 90), Tmax33 = c(31.5, 33.8, 33, 32.5,
32.1, 34.4), Tmin33 = c(25.2, 26.4, 26.1, 26.2, 26.6, 26.9
), ARF33 = c(12, 0, 0.1, 8.7, 11.1, 0), SSH33 = c(4.7, 9.8,
6.7, 3.3, 5.2, 9.8), RH33 = c(92, 75.8, 89.4, 93.9, 97.7,
89.1), Tmax34 = c(33.1, 33.7, 31.9, 32.9, 31.5, 31.3), Tmin34 = c(25.7,
25.6, 25.7, 25.6, 25.3, 25.9), ARF34 = c(8.5, 4, 5.9, 0,
8.3, 11), SSH34 = c(6.5, 6.7, 6.5, 7, 5.7, 4.1), RH34 = c(89,
89.6, 91, 97.1, 95.7, 95.9), Tmax35 = c(32, 32.4, 32.3, 34.2,
32.7, 32.3), Tmin35 = c(24.7, 24.8, 25.6, 26.1, 25.5, 25.2
), ARF35 = c(7.9, 18.7, 8.2, 3.8, 19, 1.4), SSH35 = c(4.9,
6.5, 3.4, 6.8, 8.1, 5.8), RH35 = c(93.3, 87.9, 93.6, 94.9,
93.7, 97.4), Tmax36 = c(32.6, 32.2, 31.6, 33.6, 34.1, 31.5
), Tmin36 = c(24.5, 23.7, 23.9, 26.1, 25.8, 25.2), ARF36 = c(0,
3.8, 4.7, 0.5, 0, 7.5), SSH36 = c(9.5, 9.7, 3.2, 5.7, 7.2,
4.9), RH36 = c(87.9, 90.9, 95, 96, 96, 96.4), Tmax37 = c(33.4,
33.3, 32.5, 34.5, 34.7, 30.7), Tmin37 = c(24.1, 24, 24.5,
26.3, 26.1, 24.6), ARF37 = c(0.3, 0, 0, 0, 0, 4), SSH37 = c(9.9,
8.5, 8.5, 6.5, 8.1, 4), RH37 = c(87.9, 90.3, 90.4, 97.3,
97.6, 96), Tmax38 = c(34, 32.7, 31.9, 32.8, 35, 32.1), Tmin38 = c(24,
22.2, 22.3, 23.6, 25.8, 24.7), ARF38 = c(0, 6.5, 6.4, 1.4,
0, 2.5), SSH38 = c(7, 5.4, 7.8, 7.5, 7.9, 6.3), RH38 = c(88,
91.4, 90.7, 93.7, 92.9, 93.3), Tmax39 = c(33.8, 31.5, 29.6,
31.4, 34.3, 32.6), Tmin39 = c(23.6, 22.2, 21.6, 23.7, 22.6,
24.4), ARF39 = c(0, 3.4, 25.9, 0.1, 0, 16.8), SSH39 = c(4,
6.8, 4.8, 6.4, 7.8, 6.8), RH39 = c(91, 93.4, 95.6, 96.1,
94.1, 92.7), Tmax40 = c(34.4, 33.9, 33.2, 30.9, 34.5, 33),
Tmin40 = c(24.2, 19.7, 19.8, 20.5, 18.7, 23.8), ARF40 = c(0,
0, 0, 0.3, 0, 6.4), SSH40 = c(3.5, 8.7, 9.1, 6.4, 8.6, 6.5
), RH40 = c(92.3, 90.6, 93.6, 97.9, 97.1, 96.7), Tmax41 = c(33.8,
34, 30.8, 31.9, 34.2, 33.9), Tmin41 = c(18.2, 19.6, 18.8,
18.2, 19.1, 20.1), ARF41 = c(0, 0, 0, 0, 0, 0), SSH41 = c(7.3,
5.5, 7.1, 6.9, 7, 8.1), RH41 = c(84.3, 92.6, 85.3, 98.4,
96.6, 92.4), Tmax42 = c(33.4, 33.9, 31.7, 32, 33.5, 30.7),
Tmin42 = c(15.4, 16.4, 16.1, 18, 15.3, 18.6), ARF42 = c(0,
0, 0, 0, 0, 1.2), SSH42 = c(7.8, 7.2, 7.7, 4.4, 7.5, 6.1),
RH42 = c(83.4, 92.6, 90, 99.1, 91.1, 90.9), Tmax43 = c(32.2,
31.2, 29.9, 30.5, 31.9, 29.6), Tmin43 = c(14.8, 15, 13.5,
16.6, 14, 15.8), ARF43 = c(0, 0, 0, 0, 0, 4.6), SSH43 = c(7.8,
6.3, 8, 6.8, 6.8, 7.7), RH43 = c(87.6, 91.1, 94.7, 96.6,
93.1, 86.9)), row.names = c(NA, -6L), class = c("tbl_df",
"tbl", "data. Frame"))

reg_brief(Evovled ~ c_ARF26 + c_ARF27 + c_ARF28 + c_ARF29 + c_ARF30 + c_ARF31 + c_ARF32 + c_ARF33 + c_ARF34 + c_ARF35 + c_ARF36 + c_ARF37 + c_ARF38 + c_ARF39 + c_ARF40 + c_ARF41 + c_ARF42 + c_ARF43, data=karnal)

## Error:

Need more than 16 rows of data.

``````structure(list(Year = c(2016, 2017, 2018, 2019, 2020, 2021),
Yield = c(4.12, 4.05, 4.3, 3.3, 5.1, 3.8), Tmax25 = c(36.9,
34.3, 35.7, 36.1, 35.8, 35.6), Tmin25 = c(27.6, 25.3, 25.9,
24.6, 27, 25.9), ARF25 = c(1.6, 1.1, 5.1, 2.2, 7.1, 0), SSH25 = c(8,
9.6, 9.4, 8.9, 9.3, 10), RH25 = c(73.6, 76.7, 74.1, 80.3,
87.4, 72.1), Tmax26 = c(37, 32.6, 35.2, 37.6, 36.3, 38.1),
Tmin26 = c(27.7, 25.4, 26.1, 27.7, 27.7, 26.6), ARF26 = c(1.6,
30.4, 7.6, 0, 0.3, 2), SSH26 = c(3.1, 6.7, 7.9, 9.2, 7.9,
10.3), RH26 = c(83.1, 84.9, 79, 73.4, 86.7, 74.3), Tmax27 = c(33.4,
33.6, 34.5, 36.1, 35.4, 38.5), Tmin27 = c(26, 26.3, 26.2,
27.5, 26.1, 27.4), ARF27 = c(12.2, 1.1, 8.9, 2.3, 19.5, 0.5
), SSH27 = c(4.5, 8.8, 10.4, 6.7, 8.8, 9.8), RH27 = c(90,
83.7, 84.6, 81.4, 90, 76.4), Tmax28 = c(33.6, 33.8, 34.3,
32.7, 33.4, 34.1), Tmin28 = c(27.2, 26.6, 26.9, 25.7, 25.8,
26), ARF28 = c(1.7, 1.5, 5.9, 11.3, 14.1, 42.2), SSH28 = c(6.1,
9.4, 8.3, 2.4, 8.5, 5.9), RH28 = c(84.3, 81.6, 85.7, 94,
93.7, 89.7), Tmax29 = c(32.9, 34, 33.6, 30.1, 32.2, 32.1),
Tmin29 = c(26.5, 26.6, 25.7, 25.6, 26.4, 26.1), ARF29 = c(1.5,
0.4, 27.9, 17, 12.6, 14), SSH29 = c(6.3, 10.1, 4.2, 4.2,
5.1, 4.5), RH29 = c(88, 79.1, 87.9, 94.9, 96.4, 91.7), Tmax30 = c(33,
33.8, 31, 32.6, 33.3, 32.1), Tmin30 = c(26.2, 26.3, 25, 26.1,
26.8, 26.6), ARF30 = c(2.4, 2.1, 34.8, 4.3, 1.4, 38.7), SSH30 = c(2.7,
8.3, 3.6, 4.9, 7.7, 4), RH30 = c(89.9, 80.9, 94.3, 91.4,
94.4, 95), Tmax31 = c(32.3, 31.7, 33.4, 31.9, 33.3, 32.1),
Tmin31 = c(25.8, 25.6, 26.2, 26.5, 26.8, 26.8), ARF31 = c(8.5,
12.7, 2.3, 2, 16.4, 4.6), SSH31 = c(6.5, 1.8, 8.4, 4.8, 6,
3.8), RH31 = c(88.3, 92.1, 83.9, 94, 93.9, 92.9), Tmax32 = c(31.8,
33, 31.9, 33.1, 34.3, 33.3), Tmin32 = c(25.7, 26.5, 25.6,
26.4, 27.6, 26.8), ARF32 = c(15.1, 9.9, 9.6, 0, 15.3, 5.3
), SSH32 = c(4.3, 5, 7.5, 7.1, 6.6, 5.6), RH32 = c(92.4,
90.7, 92.4, 92.4, 94.7, 90), Tmax33 = c(31.5, 33.8, 33, 32.5,
32.1, 34.4), Tmin33 = c(25.2, 26.4, 26.1, 26.2, 26.6, 26.9
), ARF33 = c(12, 0, 0.1, 8.7, 11.1, 0), SSH33 = c(4.7, 9.8,
6.7, 3.3, 5.2, 9.8), RH33 = c(92, 75.8, 89.4, 93.9, 97.7,
89.1), Tmax34 = c(33.1, 33.7, 31.9, 32.9, 31.5, 31.3), Tmin34 = c(25.7,
25.6, 25.7, 25.6, 25.3, 25.9), ARF34 = c(8.5, 4, 5.9, 0,
8.3, 11), SSH34 = c(6.5, 6.7, 6.5, 7, 5.7, 4.1), RH34 = c(89,
89.6, 91, 97.1, 95.7, 95.9), Tmax35 = c(32, 32.4, 32.3, 34.2,
32.7, 32.3), Tmin35 = c(24.7, 24.8, 25.6, 26.1, 25.5, 25.2
), ARF35 = c(7.9, 18.7, 8.2, 3.8, 19, 1.4), SSH35 = c(4.9,
6.5, 3.4, 6.8, 8.1, 5.8), RH35 = c(93.3, 87.9, 93.6, 94.9,
93.7, 97.4), Tmax36 = c(32.6, 32.2, 31.6, 33.6, 34.1, 31.5
), Tmin36 = c(24.5, 23.7, 23.9, 26.1, 25.8, 25.2), ARF36 = c(0,
3.8, 4.7, 0.5, 0, 7.5), SSH36 = c(9.5, 9.7, 3.2, 5.7, 7.2,
4.9), RH36 = c(87.9, 90.9, 95, 96, 96, 96.4), Tmax37 = c(33.4,
33.3, 32.5, 34.5, 34.7, 30.7), Tmin37 = c(24.1, 24, 24.5,
26.3, 26.1, 24.6), ARF37 = c(0.3, 0, 0, 0, 0, 4), SSH37 = c(9.9,
8.5, 8.5, 6.5, 8.1, 4), RH37 = c(87.9, 90.3, 90.4, 97.3,
97.6, 96), Tmax38 = c(34, 32.7, 31.9, 32.8, 35, 32.1), Tmin38 = c(24,
22.2, 22.3, 23.6, 25.8, 24.7), ARF38 = c(0, 6.5, 6.4, 1.4,
0, 2.5), SSH38 = c(7, 5.4, 7.8, 7.5, 7.9, 6.3), RH38 = c(88,
91.4, 90.7, 93.7, 92.9, 93.3), Tmax39 = c(33.8, 31.5, 29.6,
31.4, 34.3, 32.6), Tmin39 = c(23.6, 22.2, 21.6, 23.7, 22.6,
24.4), ARF39 = c(0, 3.4, 25.9, 0.1, 0, 16.8), SSH39 = c(4,
6.8, 4.8, 6.4, 7.8, 6.8), RH39 = c(91, 93.4, 95.6, 96.1,
94.1, 92.7), Tmax40 = c(34.4, 33.9, 33.2, 30.9, 34.5, 33),
Tmin40 = c(24.2, 19.7, 19.8, 20.5, 18.7, 23.8), ARF40 = c(0,
0, 0, 0.3, 0, 6.4), SSH40 = c(3.5, 8.7, 9.1, 6.4, 8.6, 6.5
), RH40 = c(92.3, 90.6, 93.6, 97.9, 97.1, 96.7), Tmax41 = c(33.8,
34, 30.8, 31.9, 34.2, 33.9), Tmin41 = c(18.2, 19.6, 18.8,
18.2, 19.1, 20.1), ARF41 = c(0, 0, 0, 0, 0, 0), SSH41 = c(7.3,
5.5, 7.1, 6.9, 7, 8.1), RH41 = c(84.3, 92.6, 85.3, 98.4,
96.6, 92.4), Tmax42 = c(33.4, 33.9, 31.7, 32, 33.5, 30.7),
Tmin42 = c(15.4, 16.4, 16.1, 18, 15.3, 18.6), ARF42 = c(0,
0, 0, 0, 0, 1.2), SSH42 = c(7.8, 7.2, 7.7, 4.4, 7.5, 6.1),
RH42 = c(83.4, 92.6, 90, 99.1, 91.1, 90.9), Tmax43 = c(32.2,
31.2, 29.9, 30.5, 31.9, 29.6), Tmin43 = c(14.8, 15, 13.5,
16.6, 14, 15.8), ARF43 = c(0, 0, 0, 0, 0, 4.6), SSH43 = c(7.8,
6.3, 8, 6.8, 6.8, 7.7), RH43 = c(87.6, 91.1, 94.7, 96.6,
93.1, 86.9)), row.names = c(NA, -6L), class = c("tbl_df",
"tbl", "data. Frame"))``````````

What you have shared (example data that is presumably to stand in for `karnal`) and a snippet of code `reg_brief(Evovled ~ c_ARF26 + c_AR etc....` using `karnal`
this cannot possibly work as unfortunately `karnal` as shared with us, has no variable called `Evolved` which the reg_brief code expects to see

changing the name that you called your dataframe wont change the columns in it.
You either provided us a data.frame with an Evovled column, or you didnt.

I have changed the column name from 'Evovled' to 'Yield' and have given the data using dput.

In a regression you can't have more right-hand side variables than you have data points.

I will give you some help leveraging hte library(tidyverse)
instead of many dozens of `Kota\$c_ARF25 <- Kota\$ARF25 - mean(Kota\$ARF25, na.rm=TRUE)`
type statements you can with one line, make a new dataframe from your old where there is a version of every numeric variable with its mean subtracted in the way you do and label c_nameofvariable
it looks like :

``````Kota2 <- mutate(
Kota,
across(where(is.numeric),
list("c" = \(x){x - mean(x, na.rm = TRUE)}),
.names = "{.fn}_{.col}"
)
)``````