Fit avg

require(olsrr)
year=2015
path="/media/phil/868D-F637/Work/Wife/"
df=read.csv(file = paste0(path, "SAM_quarteravg_",toString(year),".csv"))

Ts2cm_av = mean(df$Ts_2cm, na.rm = T )
Ts4cm_av = mean(df$Ts_4cm, na.rm = T )
Ts6cm_av = mean(df$Ts_6cm, na.rm = T )
Ts7cm_av = mean(df$Ts_7cm, na.rm = T )
Ts8cm_av = mean(df$Ts_8cm, na.rm = T )
Ta_av = mean(df$Ta, na.rm = T )
Ustar_av = mean(df$Ustar, na.rm = T )
Pa_av = mean(df$Pa, na.rm = T )

for (row in 1:nrow(df)){
  t7 = df[row,"Ts_7cm"]
  df[row, "dTs_7cm"] = (t7 - Ts7cm_av)/10
  t2 = df[row,"Ts_2cm"]
  df[row, "dTs_2cm"] = (t2 - Ts2cm_av)/10
  t4 = df[row,"Ts_4cm"]
  df[row, "dTs_4cm"] = (t4 - Ts4cm_av)/10
}
for (row in 1:nrow(df)){
  t6 = df[row,"Ts_6cm"]
  df[row, "dTs_6cm"] = 0 #(t6 - Ts6cm_av)/10
}
for (row in 1:nrow(df)){
  t8 = df[row,"Ts_8cm"]
  df[row, "dTs_8cm"] = (t8 - Ts8cm_av)/10
}
for (row in 1:nrow(df)){
  rh = df[row,"RH"]
  df[row, "rRH"] = rh / 100
  ust = df[row,"Ustar"]
  df[row, "dUstar"] = ust - Ustar_av
  ta = df[row,"Ta"]
  df[row, "dTa"] = (ta - Ta_av)/10
  pa = df[row,"Pa"]
  df[row, "dPa"] = (pa - Pa_av)
}
# fm = formula(log(FCH4MF0)~dTa+dPa+dTs_7cm+dUstar+dTs_2cm+dTs_4cm+dTs_8cm+log(rRH))

fm = formula(log(FCH4MF0)~dTa+dPa+dUstar+dTs_2cm+dTs_8cm+log(rRH))
model <- lm(fm,data=df)
ols = ols_step_both_aic(model, details = TRUE )
ols
#plot(model)
model$coefficients
mdf = df[c(ols$predictors, "FCH4MF0")]
# for (row in 1:nrow(df)){
# }
# for(i in ols$predictors){print(i)}
# mmps(model, terms = ~ dUstar+rRH)
warnings()
df$fittedCH4 = exp(model$fitted.values)