Error in Model.Frame.Default: Variable Lengths Differ

Error in model.frame.default: variable lengths differ

Joran suggested to first remove the NAs before running the model. Thus, I removed the NAs, run the model and obtained the residuals. When I updated model2 by inclusion of the lagged residuals, the error message did not appear again.

Remove NAs

df2<-df1[complete.cases(df1),]

Run the main model

model2<-gam(death ~ pm10 + s(trend,k=14*7)+ s(temp,k=5), data=df2, family=poisson)

Obtain residuals

resid2 <- residuals(model2,type="deviance")

Update model2 by including the lag 1 residuals

model2_1 <- update(model2,.~.+ Lag(resid2,1),  na.action=na.omit)

R - error variable lengths differ

This happens because in your first step you created a separate variable outside of your data frame, transLOT<-log(LengthofTimemin). When you remove a row from the data, transLOT is unchanged. Even worse than differing lengths, your data doesn't line up any more - if the different lengths were ignored, your rows of data would be "off by one" compared to the response after the row you removed.

The simple solution is to create your transLOT variable in the data frame. Then, whenever you do things to the data (like remove rows), the same thing is done to transLOT.

resdata$transLOT <- log(resdata$LengthofTimemin)

Note that I also use the resdata$LengthofTimemin rather than LengthofTimemin which you seem to have in your workspace. Did you use attach() at some point? You shouldn't use attach for exactly this reason. Keep variables in the data frame!

Error in model.frame.default: variable lengths differ, R predict function

I think this has happened because you have used ov_dev$futime and ov_dev$fustat in your model specification rather than just using futime and fustat. That means that when you come to predict, the model is using the ov_dev data for the dependent variable but ov_val for the independent variables, which are of different length (13 versus 14). Just remove the data frame prefix and trust the data parameter:

library(survival)
library(survminer)
library(dplyr)

# Create fake development dataset
ov_dev <- ovarian[1:13,]

# Create fake validation dataset
ov_val <- ovarian[13:26,]

# Run cox model
fit.coxph <- coxph(Surv(futime, fustat) ~ rx + resid.ds + age + ecog.ps, 
                   data = ov_dev)

p <- log(predict(fit.coxph, newdata = ov_val, type = "expected"))

p
#>  [1]  0.4272783 -0.1486577 -1.8988833 -1.1887086 -0.8849632 -1.3374428
#>  [7] -1.2294725 -1.5021708 -0.3264792  0.5633839 -3.0457613 -2.2476071
#> [13] -1.6754877 -3.0691996

^{Created on 2020-08-19 by the reprex package (v0.3.0)}

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