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rpath
I Del Toro
I Del Toro

Reputation: 943

Path Analysis with plspm

I am attempting to build a Partial Least Squares Path Model using 'plspm'. After reading through the tutorial and formatting my data I am getting hung up on an error:

"Error in if (w.dif < tol || itermax == iter) break : missing value where TRUE/FALSE needed".

I assume that this error is the result of missing values for some of the latent variables (e.g. Soil_Displaced) has a lot of NAs because this variable was only measured in a subset of the replicates in the experiment. Is there a way to get around this error and work with variables with a lot of missing values. I am attaching my code and dateset here and the dataset can also be found in this dropbox file; https://www.dropbox.com/sh/51x08p4yf5qlbp5/-al2pwdCol

this is my code for now: 

# inner model matrix
warming = c(0,0,0,0,0,0)
Treatment=c(0,0,0,0,0,0)
Soil_Displaced = c(1,1,0,0,0,0)
Mass_Lost_10mm = c(1,1,0,0,0,0)
Mass_Lost_01mm = c(1,1,0,0,0,0)
Daily_CO2      = c(1,1,0,1,0,0)

Path_inner = rbind(warming, Treatment, Soil_Displaced, Mass_Lost_10mm, Mass_Lost_01mm,Daily_CO2 )

innerplot(Path_inner) 

#develop the outter model 
Path_outter = list (3, 4:5, 6, 7, 8, 9)

# modes
#designates the model as a reflective model 
Path_modes = rep("A", 6)                                                          

# Run it plspm(Data, inner matrix, outer list, modes)
Path_pls = plspm(data.2011, Path_inner, Path_outter, Path_modes)

Any input on this issue would be helpful. Thanks!

Upvotes: 1

Views: 2062

Answers (2)

Jannic Beck
Jannic Beck

Reputation: 2425

plspm does work limited with missing values, you have to set the scaling to numeric.

for your example the code looks as follows:

example_scaling = list(c("NUM"),
                       c("NUM", "NUM"),
                       c("NUM"),
                       c("NUM"),
                       c("NUM"),
                       c("NUM"))

Path_pls = plspm(data.2011, Path_inner, Path_outter, Path_modes, scaling = example_scaling)

But heres the limitation:

However if your dataset contains one observation where all indicators of a latent variable are missing values, this won't work.

First Case: F.e. the latent variable "Treatment" has 2 indicators, if one of them is NA, it works fine.

Second Case: But if there is just one observation where both indicators are NA, it won't work.

Since youre measuring the other 5 latent variables with just one indicator and you say your data contains lots of missing values, the second one will likely be the case.

Upvotes: 2

I Del Toro
I Del Toro

Reputation: 943

PLSPM will not work with missing values therefore I had to interpolate some of the missing values from known observations. When this was done the code above worked great!.

Upvotes: 0

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